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dify
Unverified Commit 86286e1a authored by Yeuoly's avatar Yeuoly Committed by GitHub
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Feat/assistant app (#2086) 

Co-authored-by: 's avatarchenhe <guchenhe@gmail.com>
Co-authored-by: 's avatarPascal M <11357019+perzeuss@users.noreply.github.com>
parent 7bbe12b2
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.github/workflows/tool-tests.yaml 0 → 100644
View file @ 86286e1a
name: Run Tool Pytest
on:
pull_request:
branches:
- main
jobs:
test:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.10'
cache: 'pip'
cache-dependency-path: ./api/requirements.txt
- name: Install dependencies
run: pip install -r ./api/requirements.txt
- name: Run pytest
run: pytest ./api/tests/integration_tests/tools/test_all_provider.py
CONTRIBUTING.md
View file @ 86286e1a
......@@ -91,6 +91,8 @@ To validate your set up, head over to [http://localhost:3000](http://localhost:3
If you are adding a model provider, [this guide](https://github.com/langgenius/dify/blob/main/api/core/model_runtime/README.md) is for you.
If you are adding a tool provider to Agent or Workflow, [this guide](./api/core/tools/README.md) is for you.
To help you quickly navigate where your contribution fits, a brief, annotated outline of Dify's backend & frontend is as follows:
### Backend
......
api/app.py
View file @ 86286e1a
......@@ -30,7 +30,7 @@ from flask import Flask, Response, request
from flask_cors import CORS
from libs.passport import PassportService
# DO NOT REMOVE BELOW
from models import account, dataset, model, source, task, tool, web
from models import account, dataset, model, source, task, tool, web, tools
from services.account_service import AccountService
# DO NOT REMOVE ABOVE
......
api/commands.py
View file @ 86286e1a
......@@ -22,7 +22,7 @@ from libs.password import hash_password, password_pattern, valid_password
from libs.rsa import generate_key_pair
from models.account import InvitationCode, Tenant, TenantAccountJoin
from models.dataset import Dataset, DatasetCollectionBinding, DatasetQuery, Document
from models.model import Account, App, AppModelConfig, Message, MessageAnnotation
from models.model import Account, App, AppModelConfig, Message, MessageAnnotation, InstalledApp
from models.provider import Provider, ProviderModel, ProviderQuotaType, ProviderType
from qdrant_client.http.models import TextIndexParams, TextIndexType, TokenizerType
from tqdm import tqdm
......@@ -775,6 +775,66 @@ def add_annotation_question_field_value():
click.echo(
click.style(f'Congratulations! add annotation question value successful. Deal count {message_annotation_deal_count}', fg='green'))
@click.command('migrate-universal-chat-to-installed-app', help='Migrate universal chat to installed app.')
@click.option("--batch-size", default=500, help="Number of records to migrate in each batch.")
def migrate_universal_chat_to_installed_app(batch_size):
total_records = db.session.query(App).filter(
App.is_universal == True
).count()
if total_records == 0:
click.secho("No data to migrate.", fg='green')
return
num_batches = (total_records + batch_size - 1) // batch_size
with tqdm(total=total_records, desc="Migrating Data") as pbar:
for i in range(num_batches):
offset = i * batch_size
limit = min(batch_size, total_records - offset)
click.secho(f"Fetching batch {i + 1}/{num_batches} from source database...", fg='green')
data_batch: list[App] = db.session.query(App) \
.filter(App.is_universal == True) \
.order_by(App.created_at) \
.offset(offset).limit(limit).all()
if not data_batch:
click.secho("No more data to migrate.", fg='green')
break
try:
click.secho(f"Migrating {len(data_batch)} records...", fg='green')
for data in data_batch:
# check if the app is already installed
installed_app = db.session.query(InstalledApp).filter(
InstalledApp.app_id == data.id
).first()
if installed_app:
continue
# insert installed app
installed_app = InstalledApp(
app_id=data.id,
tenant_id=data.tenant_id,
position=0,
app_owner_tenant_id=data.tenant_id,
is_pinned=True,
last_used_at=datetime.datetime.utcnow(),
)
db.session.add(installed_app)
db.session.commit()
except Exception as e:
click.secho(f"Error while migrating data: {e}, app_id: {data.id}", fg='red')
continue
click.secho(f"Successfully migrated batch {i + 1}/{num_batches}.", fg='green')
pbar.update(len(data_batch))
def register_commands(app):
app.cli.add_command(reset_password)
......@@ -791,3 +851,4 @@ def register_commands(app):
app.cli.add_command(migrate_default_input_to_dataset_query_variable)
app.cli.add_command(add_qdrant_full_text_index)
app.cli.add_command(add_annotation_question_field_value)
app.cli.add_command(migrate_universal_chat_to_installed_app)
api/controllers/console/__init__.py
View file @ 86286e1a
......@@ -16,7 +16,5 @@ from .billing import billing
from .datasets import data_source, datasets, datasets_document, datasets_segments, file, hit_testing
# Import explore controllers
from .explore import audio, completion, conversation, installed_app, message, parameter, recommended_app, saved_message
# Import universal chat controllers
from .universal_chat import audio, chat, conversation, message, parameter
# Import workspace controllers
from .workspace import account, members, model_providers, models, tool_providers, workspace
api/controllers/console/app/app.py
View file @ 86286e1a
......@@ -16,14 +16,15 @@ from events.app_event import app_was_created, app_was_deleted
from extensions.ext_database import db
from fields.app_fields import (app_detail_fields, app_detail_fields_with_site, app_pagination_fields,
template_list_fields)
from flask import current_app
from flask_login import current_user
from flask_restful import Resource, abort, inputs, marshal_with, reqparse
from libs.login import login_required
from models.model import App, AppModelConfig, Site
from models.tools import ApiToolProvider
from services.app_model_config_service import AppModelConfigService
from werkzeug.exceptions import Forbidden
def _get_app(app_id, tenant_id):
app = db.session.query(App).filter(App.id == app_id, App.tenant_id == tenant_id).first()
if not app:
......@@ -42,14 +43,30 @@ class AppListApi(Resource):
parser = reqparse.RequestParser()
parser.add_argument('page', type=inputs.int_range(1, 99999), required=False, default=1, location='args')
parser.add_argument('limit', type=inputs.int_range(1, 100), required=False, default=20, location='args')
parser.add_argument('mode', type=str, choices=['chat', 'completion', 'all'], default='all', location='args', required=False)
parser.add_argument('name', type=str, location='args', required=False)
args = parser.parse_args()
filters = [
App.tenant_id == current_user.current_tenant_id,
]
if args['mode'] == 'completion':
filters.append(App.mode == 'completion')
elif args['mode'] == 'chat':
filters.append(App.mode == 'chat')
else:
pass
if 'name' in args and args['name']:
filters.append(App.name.ilike(f'%{args["name"]}%'))
app_models = db.paginate(
db.select(App).where(App.tenant_id == current_user.current_tenant_id,
App.is_universal == False).order_by(App.created_at.desc()),
db.select(App).where(*filters).order_by(App.created_at.desc()),
page=args['page'],
per_page=args['limit'],
error_out=False)
error_out=False
)
return app_models
......@@ -62,7 +79,7 @@ class AppListApi(Resource):
"""Create app"""
parser = reqparse.RequestParser()
parser.add_argument('name', type=str, required=True, location='json')
parser.add_argument('mode', type=str, choices=['completion', 'chat'], location='json')
parser.add_argument('mode', type=str, choices=['completion', 'chat', 'assistant'], location='json')
parser.add_argument('icon', type=str, location='json')
parser.add_argument('icon_background', type=str, location='json')
parser.add_argument('model_config', type=dict, location='json')
......
api/controllers/console/explore/installed_app.py
View file @ 86286e1a
......@@ -33,8 +33,9 @@ class InstalledAppsListApi(Resource):
'app_owner_tenant_id': installed_app.app_owner_tenant_id,
'is_pinned': installed_app.is_pinned,
'last_used_at': installed_app.last_used_at,
"editable": current_user.role in ["owner", "admin"],
"uninstallable": current_tenant_id == installed_app.app_owner_tenant_id
'editable': current_user.role in ["owner", "admin"],
'uninstallable': current_tenant_id == installed_app.app_owner_tenant_id,
'is_agent': installed_app.is_agent
}
for installed_app in installed_apps
]
......
api/controllers/console/explore/message.py
View file @ 86286e1a
......@@ -17,9 +17,9 @@ from core.model_runtime.errors.invoke import InvokeError
from fields.message_fields import message_infinite_scroll_pagination_fields
from flask import Response, stream_with_context
from flask_login import current_user
from flask_restful import marshal_with, reqparse
from flask_restful import marshal_with, reqparse, fields
from flask_restful.inputs import int_range
from libs.helper import uuid_value
from libs.helper import uuid_value, TimestampField
from services.completion_service import CompletionService
from services.errors.app import MoreLikeThisDisabledError
from services.errors.conversation import ConversationNotExistsError
......@@ -29,7 +29,6 @@ from werkzeug.exceptions import InternalServerError, NotFound
class MessageListApi(InstalledAppResource):
@marshal_with(message_infinite_scroll_pagination_fields)
def get(self, installed_app):
app_model = installed_app.app
......@@ -51,7 +50,6 @@ class MessageListApi(InstalledAppResource):
except services.errors.message.FirstMessageNotExistsError:
raise NotFound("First Message Not Exists.")
class MessageFeedbackApi(InstalledAppResource):
def post(self, installed_app, message_id):
app_model = installed_app.app
......
api/controllers/console/explore/parameter.py
View file @ 86286e1a
# -*- coding:utf-8 -*-
import json
from controllers.console import api
from controllers.console.explore.wraps import InstalledAppResource
from flask import current_app
from flask_restful import fields, marshal_with
from models.model import InstalledApp
from models.model import InstalledApp, AppModelConfig
from models.tools import ApiToolProvider
from extensions.ext_database import db
class AppParameterApi(InstalledAppResource):
"""Resource for app variables."""
......@@ -58,5 +62,42 @@ class AppParameterApi(InstalledAppResource):
}
}
class ExploreAppMetaApi(InstalledAppResource):
def get(self, installed_app: InstalledApp):
"""Get app meta"""
app_model_config: AppModelConfig = installed_app.app.app_model_config
agent_config = app_model_config.agent_mode_dict or {}
meta = {
'tool_icons': {}
}
# get all tools
tools = agent_config.get('tools', [])
url_prefix = (current_app.config.get("CONSOLE_API_URL")
+ f"/console/api/workspaces/current/tool-provider/builtin/")
for tool in tools:
keys = list(tool.keys())
if len(keys) >= 4:
# current tool standard
provider_type = tool.get('provider_type')
provider_id = tool.get('provider_id')
tool_name = tool.get('tool_name')
if provider_type == 'builtin':
meta['tool_icons'][tool_name] = url_prefix + provider_id + '/icon'
elif provider_type == 'api':
try:
provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
ApiToolProvider.id == provider_id
)
meta['tool_icons'][tool_name] = json.loads(provider.icon)
except:
meta['tool_icons'][tool_name] = {
"background": "#252525",
"content": "\ud83d\ude01"
}
return meta
api.add_resource(AppParameterApi, '/installed-apps/<uuid:installed_app_id>/parameters', endpoint='installed_app_parameters')
api.add_resource(ExploreAppMetaApi, '/installed-apps/<uuid:installed_app_id>/meta', endpoint='installed_app_meta')
api/controllers/console/explore/recommended_app.py
View file @ 86286e1a
......@@ -29,7 +29,8 @@ recommended_app_fields = {
'is_listed': fields.Boolean,
'install_count': fields.Integer,
'installed': fields.Boolean,
'editable': fields.Boolean
'editable': fields.Boolean,
'is_agent': fields.Boolean
}
recommended_app_list_fields = {
......@@ -82,6 +83,7 @@ class RecommendedAppListApi(Resource):
'install_count': recommended_app.install_count,
'installed': installed,
'editable': current_user.role in ['owner', 'admin'],
"is_agent": app.is_agent
}
recommended_apps_result.append(recommended_app_result)
......
api/controllers/console/universal_chat/audio.py
View file @ 86286e1a
......@@ -60,5 +60,3 @@ class UniversalChatAudioApi(UniversalChatResource):
logging.exception("internal server error.")
raise InternalServerError()
api.add_resource(UniversalChatAudioApi, '/universal-chat/audio-to-text')
\ No newline at end of file
api/controllers/console/universal_chat/chat.py
View file @ 86286e1a
import json
import logging
from typing import Generator, Union
import services
from controllers.console import api
from controllers.console.app.error import (AppUnavailableError, CompletionRequestError, ConversationCompletedError,
ProviderModelCurrentlyNotSupportError, ProviderNotInitializeError,
ProviderQuotaExceededError)
from controllers.console.universal_chat.wraps import UniversalChatResource
from core.application_queue_manager import ApplicationQueueManager
from core.entities.application_entities import InvokeFrom
from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
from core.model_runtime.errors.invoke import InvokeError
from flask import Response, stream_with_context
from flask_login import current_user
from flask_restful import reqparse
from libs.helper import uuid_value
from services.completion_service import CompletionService
from werkzeug.exceptions import InternalServerError, NotFound
class UniversalChatApi(UniversalChatResource):
def post(self, universal_app):
app_model = universal_app
parser = reqparse.RequestParser()
parser.add_argument('query', type=str, required=True, location='json')
parser.add_argument('files', type=list, required=False, location='json')
parser.add_argument('conversation_id', type=uuid_value, location='json')
parser.add_argument('provider', type=str, required=True, location='json')
parser.add_argument('model', type=str, required=True, location='json')
parser.add_argument('tools', type=list, required=True, location='json')
parser.add_argument('retriever_from', type=str, required=False, default='universal_app', location='json')
args = parser.parse_args()
app_model_config = app_model.app_model_config
# update app model config
args['model_config'] = app_model_config.to_dict()
args['model_config']['model']['name'] = args['model']
args['model_config']['model']['provider'] = args['provider']
args['model_config']['agent_mode']['tools'] = args['tools']
if not args['model_config']['agent_mode']['tools']:
args['model_config']['agent_mode']['tools'] = [
{
"current_datetime": {
"enabled": True
}
}
]
else:
args['model_config']['agent_mode']['tools'].append({
"current_datetime": {
"enabled": True
}
})
args['inputs'] = {}
del args['model']
del args['tools']
args['auto_generate_name'] = False
try:
response = CompletionService.completion(
app_model=app_model,
user=current_user,
args=args,
invoke_from=InvokeFrom.EXPLORE,
streaming=True,
is_model_config_override=True,
)
return compact_response(response)
except services.errors.conversation.ConversationNotExistsError:
raise NotFound("Conversation Not Exists.")
except services.errors.conversation.ConversationCompletedError:
raise ConversationCompletedError()
except services.errors.app_model_config.AppModelConfigBrokenError:
logging.exception("App model config broken.")
raise AppUnavailableError()
except ProviderTokenNotInitError:
raise ProviderNotInitializeError()
except QuotaExceededError:
raise ProviderQuotaExceededError()
except ModelCurrentlyNotSupportError:
raise ProviderModelCurrentlyNotSupportError()
except InvokeError as e:
raise CompletionRequestError(e.description)
except ValueError as e:
raise e
except Exception as e:
logging.exception("internal server error.")
raise InternalServerError()
class UniversalChatStopApi(UniversalChatResource):
def post(self, universal_app, task_id):
ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.EXPLORE, current_user.id)
return {'result': 'success'}, 200
def compact_response(response: Union[dict, Generator]) -> Response:
if isinstance(response, dict):
return Response(response=json.dumps(response), status=200, mimetype='application/json')
else:
def generate() -> Generator:
for chunk in response:
yield chunk
return Response(stream_with_context(generate()), status=200,
mimetype='text/event-stream')
api.add_resource(UniversalChatApi, '/universal-chat/messages')
api.add_resource(UniversalChatStopApi, '/universal-chat/messages/<string:task_id>/stop')
api/controllers/console/universal_chat/conversation.py deleted 100644 → 0
View file @ 7bbe12b2
# -*- coding:utf-8 -*-
from controllers.console import api
from controllers.console.universal_chat.wraps import UniversalChatResource
from fields.conversation_fields import (conversation_with_model_config_fields,
conversation_with_model_config_infinite_scroll_pagination_fields)
from flask_login import current_user
from flask_restful import fields, marshal_with, reqparse
from flask_restful.inputs import int_range
from libs.helper import TimestampField, uuid_value
from services.conversation_service import ConversationService
from services.errors.conversation import ConversationNotExistsError, LastConversationNotExistsError
from services.web_conversation_service import WebConversationService
from werkzeug.exceptions import NotFound
class UniversalChatConversationListApi(UniversalChatResource):
@marshal_with(conversation_with_model_config_infinite_scroll_pagination_fields)
def get(self, universal_app):
app_model = universal_app
parser = reqparse.RequestParser()
parser.add_argument('last_id', type=uuid_value, location='args')
parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args')
parser.add_argument('pinned', type=str, choices=['true', 'false', None], location='args')
args = parser.parse_args()
pinned = None
if 'pinned' in args and args['pinned'] is not None:
pinned = True if args['pinned'] == 'true' else False
try:
return WebConversationService.pagination_by_last_id(
app_model=app_model,
user=current_user,
last_id=args['last_id'],
limit=args['limit'],
pinned=pinned
)
except LastConversationNotExistsError:
raise NotFound("Last Conversation Not Exists.")
class UniversalChatConversationApi(UniversalChatResource):
def delete(self, universal_app, c_id):
app_model = universal_app
conversation_id = str(c_id)
try:
ConversationService.delete(app_model, conversation_id, current_user)
except ConversationNotExistsError:
raise NotFound("Conversation Not Exists.")
WebConversationService.unpin(app_model, conversation_id, current_user)
return {"result": "success"}, 204
class UniversalChatConversationRenameApi(UniversalChatResource):
@marshal_with(conversation_with_model_config_fields)
def post(self, universal_app, c_id):
app_model = universal_app
conversation_id = str(c_id)
parser = reqparse.RequestParser()
parser.add_argument('name', type=str, required=False, location='json')
parser.add_argument('auto_generate', type=bool, required=False, default=False, location='json')
args = parser.parse_args()
try:
return ConversationService.rename(
app_model,
conversation_id,
current_user,
args['name'],
args['auto_generate']
)
except ConversationNotExistsError:
raise NotFound("Conversation Not Exists.")
class UniversalChatConversationPinApi(UniversalChatResource):
def patch(self, universal_app, c_id):
app_model = universal_app
conversation_id = str(c_id)
try:
WebConversationService.pin(app_model, conversation_id, current_user)
except ConversationNotExistsError:
raise NotFound("Conversation Not Exists.")
return {"result": "success"}
class UniversalChatConversationUnPinApi(UniversalChatResource):
def patch(self, universal_app, c_id):
app_model = universal_app
conversation_id = str(c_id)
WebConversationService.unpin(app_model, conversation_id, current_user)
return {"result": "success"}
api.add_resource(UniversalChatConversationRenameApi, '/universal-chat/conversations/<uuid:c_id>/name')
api.add_resource(UniversalChatConversationListApi, '/universal-chat/conversations')
api.add_resource(UniversalChatConversationApi, '/universal-chat/conversations/<uuid:c_id>')
api.add_resource(UniversalChatConversationPinApi, '/universal-chat/conversations/<uuid:c_id>/pin')
api.add_resource(UniversalChatConversationUnPinApi, '/universal-chat/conversations/<uuid:c_id>/unpin')
api/controllers/console/universal_chat/message.py deleted 100644 → 0
View file @ 7bbe12b2
# -*- coding:utf-8 -*-
import logging
import services
from controllers.console import api
from controllers.console.app.error import (CompletionRequestError, ProviderModelCurrentlyNotSupportError,
ProviderNotInitializeError, ProviderQuotaExceededError)
from controllers.console.explore.error import AppSuggestedQuestionsAfterAnswerDisabledError
from controllers.console.universal_chat.wraps import UniversalChatResource
from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
from core.model_runtime.errors.invoke import InvokeError
from flask_login import current_user
from flask_restful import fields, marshal_with, reqparse
from flask_restful.inputs import int_range
from libs.helper import TimestampField, uuid_value
from services.errors.conversation import ConversationNotExistsError
from services.errors.message import MessageNotExistsError, SuggestedQuestionsAfterAnswerDisabledError
from services.message_service import MessageService
from werkzeug.exceptions import InternalServerError, NotFound
class UniversalChatMessageListApi(UniversalChatResource):
feedback_fields = {
'rating': fields.String
}
agent_thought_fields = {
'id': fields.String,
'chain_id': fields.String,
'message_id': fields.String,
'position': fields.Integer,
'thought': fields.String,
'tool': fields.String,
'tool_input': fields.String,
'created_at': TimestampField
}
retriever_resource_fields = {
'id': fields.String,
'message_id': fields.String,
'position': fields.Integer,
'dataset_id': fields.String,
'dataset_name': fields.String,
'document_id': fields.String,
'document_name': fields.String,
'data_source_type': fields.String,
'segment_id': fields.String,
'score': fields.Float,
'hit_count': fields.Integer,
'word_count': fields.Integer,
'segment_position': fields.Integer,
'index_node_hash': fields.String,
'content': fields.String,
'created_at': TimestampField
}
message_fields = {
'id': fields.String,
'conversation_id': fields.String,
'inputs': fields.Raw,
'query': fields.String,
'answer': fields.String,
'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
'created_at': TimestampField,
'agent_thoughts': fields.List(fields.Nested(agent_thought_fields))
}
message_infinite_scroll_pagination_fields = {
'limit': fields.Integer,
'has_more': fields.Boolean,
'data': fields.List(fields.Nested(message_fields))
}
@marshal_with(message_infinite_scroll_pagination_fields)
def get(self, universal_app):
app_model = universal_app
parser = reqparse.RequestParser()
parser.add_argument('conversation_id', required=True, type=uuid_value, location='args')
parser.add_argument('first_id', type=uuid_value, location='args')
parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args')
args = parser.parse_args()
try:
return MessageService.pagination_by_first_id(app_model, current_user,
args['conversation_id'], args['first_id'], args['limit'])
except services.errors.conversation.ConversationNotExistsError:
raise NotFound("Conversation Not Exists.")
except services.errors.message.FirstMessageNotExistsError:
raise NotFound("First Message Not Exists.")
class UniversalChatMessageFeedbackApi(UniversalChatResource):
def post(self, universal_app, message_id):
app_model = universal_app
message_id = str(message_id)
parser = reqparse.RequestParser()
parser.add_argument('rating', type=str, choices=['like', 'dislike', None], location='json')
args = parser.parse_args()
try:
MessageService.create_feedback(app_model, message_id, current_user, args['rating'])
except services.errors.message.MessageNotExistsError:
raise NotFound("Message Not Exists.")
return {'result': 'success'}
class UniversalChatMessageSuggestedQuestionApi(UniversalChatResource):
def get(self, universal_app, message_id):
app_model = universal_app
message_id = str(message_id)
try:
questions = MessageService.get_suggested_questions_after_answer(
app_model=app_model,
user=current_user,
message_id=message_id
)
except MessageNotExistsError:
raise NotFound("Message not found")
except ConversationNotExistsError:
raise NotFound("Conversation not found")
except SuggestedQuestionsAfterAnswerDisabledError:
raise AppSuggestedQuestionsAfterAnswerDisabledError()
except ProviderTokenNotInitError:
raise ProviderNotInitializeError()
except QuotaExceededError:
raise ProviderQuotaExceededError()
except ModelCurrentlyNotSupportError:
raise ProviderModelCurrentlyNotSupportError()
except InvokeError as e:
raise CompletionRequestError(e.description)
except Exception:
logging.exception("internal server error.")
raise InternalServerError()
return {'data': questions}
api.add_resource(UniversalChatMessageListApi, '/universal-chat/messages')
api.add_resource(UniversalChatMessageFeedbackApi, '/universal-chat/messages/<uuid:message_id>/feedbacks')
api.add_resource(UniversalChatMessageSuggestedQuestionApi, '/universal-chat/messages/<uuid:message_id>/suggested-questions')
api/controllers/console/universal_chat/parameter.py deleted 100644 → 0
View file @ 7bbe12b2
# -*- coding:utf-8 -*-
import json
from controllers.console import api
from controllers.console.universal_chat.wraps import UniversalChatResource
from flask_restful import fields, marshal_with
from models.model import App
class UniversalChatParameterApi(UniversalChatResource):
"""Resource for app variables."""
parameters_fields = {
'opening_statement': fields.String,
'suggested_questions': fields.Raw,
'suggested_questions_after_answer': fields.Raw,
'speech_to_text': fields.Raw,
'retriever_resource': fields.Raw,
'annotation_reply': fields.Raw
}
@marshal_with(parameters_fields)
def get(self, universal_app: App):
"""Retrieve app parameters."""
app_model = universal_app
app_model_config = app_model.app_model_config
app_model_config.retriever_resource = json.dumps({'enabled': True})
return {
'opening_statement': app_model_config.opening_statement,
'suggested_questions': app_model_config.suggested_questions_list,
'suggested_questions_after_answer': app_model_config.suggested_questions_after_answer_dict,
'speech_to_text': app_model_config.speech_to_text_dict,
'retriever_resource': app_model_config.retriever_resource_dict,
'annotation_reply': app_model_config.annotation_reply_dict,
}
api.add_resource(UniversalChatParameterApi, '/universal-chat/parameters')
api/controllers/console/universal_chat/wraps.py deleted 100644 → 0
View file @ 7bbe12b2
import json
from functools import wraps
from controllers.console.setup import setup_required
from controllers.console.wraps import account_initialization_required
from extensions.ext_database import db
from flask_login import current_user
from flask_restful import Resource
from libs.login import login_required
from models.model import App, AppModelConfig
def universal_chat_app_required(view=None):
def decorator(view):
@wraps(view)
def decorated(*args, **kwargs):
# get universal chat app
universal_app = db.session.query(App).filter(
App.tenant_id == current_user.current_tenant_id,
App.is_universal == True
).first()
if universal_app is None:
# create universal app if not exists
universal_app = App(
tenant_id=current_user.current_tenant_id,
name='Universal Chat',
mode='chat',
is_universal=True,
icon='',
icon_background='',
api_rpm=0,
api_rph=0,
enable_site=False,
enable_api=False,
status='normal'
)
db.session.add(universal_app)
db.session.flush()
app_model_config = AppModelConfig(
provider="",
model_id="",
configs={},
opening_statement='',
suggested_questions=json.dumps([]),
suggested_questions_after_answer=json.dumps({'enabled': True}),
speech_to_text=json.dumps({'enabled': True}),
retriever_resource=json.dumps({'enabled': True}),
more_like_this=None,
sensitive_word_avoidance=None,
model=json.dumps({
"provider": "openai",
"name": "gpt-3.5-turbo-16k",
"completion_params": {
"max_tokens": 800,
"temperature": 0.8,
"top_p": 1,
"presence_penalty": 0,
"frequency_penalty": 0
}
}),
user_input_form=json.dumps([]),
pre_prompt='',
agent_mode=json.dumps({"enabled": True, "strategy": "function_call", "tools": []}),
)
app_model_config.app_id = universal_app.id
db.session.add(app_model_config)
db.session.flush()
universal_app.app_model_config_id = app_model_config.id
db.session.commit()
return view(universal_app, *args, **kwargs)
return decorated
if view:
return decorator(view)
return decorator
class UniversalChatResource(Resource):
# must be reversed if there are multiple decorators
method_decorators = [universal_chat_app_required, account_initialization_required, login_required, setup_required]
api/controllers/console/workspace/tool_providers.py
View file @ 86286e1a
import json
from libs.login import login_required
from flask_login import current_user
from flask_restful import Resource, reqparse
from flask import send_file
from werkzeug.exceptions import Forbidden
from controllers.console import api
from controllers.console.setup import setup_required
from controllers.console.wraps import account_initialization_required
from core.tool.provider.errors import ToolValidateFailedError
from core.tool.provider.tool_provider_service import ToolProviderService
from extensions.ext_database import db
from flask_login import current_user
from flask_restful import Resource, abort, reqparse
from libs.login import login_required
from models.tool import ToolProvider, ToolProviderName
from werkzeug.exceptions import Forbidden
from services.tools_manage_service import ToolManageService
class ToolProviderListApi(Resource):
import io
class ToolProviderListApi(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self):
user_id = current_user.id
tenant_id = current_user.current_tenant_id
tool_credential_dict = {}
for tool_name in ToolProviderName:
tool_credential_dict[tool_name.value] = {
'tool_name': tool_name.value,
'is_enabled': False,
'credentials': None
}
return ToolManageService.list_tool_providers(user_id, tenant_id)
tool_providers = db.session.query(ToolProvider).filter(ToolProvider.tenant_id == tenant_id).all()
class ToolBuiltinProviderListToolsApi(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self, provider):
user_id = current_user.id
tenant_id = current_user.current_tenant_id
for p in tool_providers:
if p.is_enabled:
tool_credential_dict[p.tool_name] = {
'tool_name': p.tool_name,
'is_enabled': p.is_enabled,
'credentials': ToolProviderService(tenant_id, p.tool_name).get_credentials(obfuscated=True)
}
return ToolManageService.list_builtin_tool_provider_tools(
user_id,
tenant_id,
provider,
)
return list(tool_credential_dict.values())
class ToolBuiltinProviderDeleteApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self, provider):
if current_user.current_tenant.current_role not in ['admin', 'owner']:
raise Forbidden()
user_id = current_user.id
tenant_id = current_user.current_tenant_id
class ToolProviderCredentialsApi(Resource):
return ToolManageService.delete_builtin_tool_provider(
user_id,
tenant_id,
provider,
)
class ToolBuiltinProviderUpdateApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self, provider):
if provider not in [p.value for p in ToolProviderName]:
abort(404)
# The role of the current user in the ta table must be admin or owner
if current_user.current_tenant.current_role not in ['admin', 'owner']:
raise Forbidden(f'User {current_user.id} is not authorized to update provider token, '
f'current_role is {current_user.current_tenant.current_role}')
raise Forbidden()
user_id = current_user.id
tenant_id = current_user.current_tenant_id
parser = reqparse.RequestParser()
parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
args = parser.parse_args()
return ToolManageService.update_builtin_tool_provider(
user_id,
tenant_id,
provider,
args['credentials'],
)
class ToolBuiltinProviderIconApi(Resource):
@setup_required
def get(self, provider):
icon_bytes, minetype = ToolManageService.get_builtin_tool_provider_icon(provider)
return send_file(io.BytesIO(icon_bytes), mimetype=minetype)
class ToolApiProviderAddApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self):
if current_user.current_tenant.current_role not in ['admin', 'owner']:
raise Forbidden()
user_id = current_user.id
tenant_id = current_user.current_tenant_id
tool_provider_service = ToolProviderService(tenant_id, provider)
parser = reqparse.RequestParser()
parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
parser.add_argument('schema_type', type=str, required=True, nullable=False, location='json')
parser.add_argument('schema', type=str, required=True, nullable=False, location='json')
parser.add_argument('provider', type=str, required=True, nullable=False, location='json')
parser.add_argument('icon', type=dict, required=True, nullable=False, location='json')
parser.add_argument('privacy_policy', type=str, required=False, nullable=True, location='json')
args = parser.parse_args()
try:
tool_provider_service.credentials_validate(args['credentials'])
except ToolValidateFailedError as ex:
raise ValueError(str(ex))
return ToolManageService.create_api_tool_provider(
user_id,
tenant_id,
args['provider'],
args['icon'],
args['credentials'],
args['schema_type'],
args['schema'],
args.get('privacy_policy', ''),
)
encrypted_credentials = json.dumps(tool_provider_service.encrypt_credentials(args['credentials']))
class ToolApiProviderGetRemoteSchemaApi(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self):
parser = reqparse.RequestParser()
tenant = current_user.current_tenant
parser.add_argument('url', type=str, required=True, nullable=False, location='args')
tool_provider_model = db.session.query(ToolProvider).filter(
ToolProvider.tenant_id == tenant.id,
ToolProvider.tool_name == provider,
).first()
args = parser.parse_args()
# Only allow updating token for CUSTOM provider type
if tool_provider_model:
tool_provider_model.encrypted_credentials = encrypted_credentials
tool_provider_model.is_enabled = True
else:
tool_provider_model = ToolProvider(
tenant_id=tenant.id,
tool_name=provider,
encrypted_credentials=encrypted_credentials,
is_enabled=True
return ToolManageService.get_api_tool_provider_remote_schema(
current_user.id,
current_user.current_tenant_id,
args['url'],
)
db.session.add(tool_provider_model)
db.session.commit()
class ToolApiProviderListToolsApi(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self):
user_id = current_user.id
tenant_id = current_user.current_tenant_id
return {'result': 'success'}, 201
parser = reqparse.RequestParser()
parser.add_argument('provider', type=str, required=True, nullable=False, location='args')
class ToolProviderCredentialsValidateApi(Resource):
args = parser.parse_args()
return ToolManageService.list_api_tool_provider_tools(
user_id,
tenant_id,
args['provider'],
)
class ToolApiProviderUpdateApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self, provider):
if provider not in [p.value for p in ToolProviderName]:
abort(404)
def post(self):
if current_user.current_tenant.current_role not in ['admin', 'owner']:
raise Forbidden()
user_id = current_user.id
tenant_id = current_user.current_tenant_id
parser = reqparse.RequestParser()
parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
parser.add_argument('schema_type', type=str, required=True, nullable=False, location='json')
parser.add_argument('schema', type=str, required=True, nullable=False, location='json')
parser.add_argument('provider', type=str, required=True, nullable=False, location='json')
parser.add_argument('original_provider', type=str, required=True, nullable=False, location='json')
parser.add_argument('icon', type=str, required=True, nullable=False, location='json')
parser.add_argument('privacy_policy', type=str, required=True, nullable=False, location='json')
args = parser.parse_args()
return ToolManageService.update_api_tool_provider(
user_id,
tenant_id,
args['provider'],
args['original_provider'],
args['icon'],
args['credentials'],
args['schema_type'],
args['schema'],
args['privacy_policy'],
)
class ToolApiProviderDeleteApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self):
if current_user.current_tenant.current_role not in ['admin', 'owner']:
raise Forbidden()
user_id = current_user.id
tenant_id = current_user.current_tenant_id
parser = reqparse.RequestParser()
parser.add_argument('provider', type=str, required=True, nullable=False, location='json')
args = parser.parse_args()
result = True
error = None
return ToolManageService.delete_api_tool_provider(
user_id,
tenant_id,
args['provider'],
)
class ToolApiProviderGetApi(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self):
user_id = current_user.id
tenant_id = current_user.current_tenant_id
tool_provider_service = ToolProviderService(tenant_id, provider)
parser = reqparse.RequestParser()
parser.add_argument('provider', type=str, required=True, nullable=False, location='args')
args = parser.parse_args()
return ToolManageService.get_api_tool_provider(
user_id,
tenant_id,
args['provider'],
)
class ToolBuiltinProviderCredentialsSchemaApi(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self, provider):
return ToolManageService.list_builtin_provider_credentials_schema(provider)
class ToolApiProviderSchemaApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self):
parser = reqparse.RequestParser()
try:
tool_provider_service.credentials_validate(args['credentials'])
except ToolValidateFailedError as ex:
result = False
error = str(ex)
parser.add_argument('schema', type=str, required=True, nullable=False, location='json')
response = {'result': 'success' if result else 'error'}
args = parser.parse_args()
if not result:
response['error'] = error
return ToolManageService.parser_api_schema(
schema=args['schema'],
)
return response
class ToolApiProviderPreviousTestApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self):
parser = reqparse.RequestParser()
parser.add_argument('tool_name', type=str, required=True, nullable=False, location='json')
parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
parser.add_argument('parameters', type=dict, required=True, nullable=False, location='json')
parser.add_argument('schema_type', type=str, required=True, nullable=False, location='json')
parser.add_argument('schema', type=str, required=True, nullable=False, location='json')
args = parser.parse_args()
return ToolManageService.test_api_tool_preview(
current_user.current_tenant_id,
args['tool_name'],
args['credentials'],
args['parameters'],
args['schema_type'],
args['schema'],
)
api.add_resource(ToolProviderListApi, '/workspaces/current/tool-providers')
api.add_resource(ToolProviderCredentialsApi, '/workspaces/current/tool-providers/<provider>/credentials')
api.add_resource(ToolProviderCredentialsValidateApi,
'/workspaces/current/tool-providers/<provider>/credentials-validate')
api.add_resource(ToolBuiltinProviderListToolsApi, '/workspaces/current/tool-provider/builtin/<provider>/tools')
api.add_resource(ToolBuiltinProviderDeleteApi, '/workspaces/current/tool-provider/builtin/<provider>/delete')
api.add_resource(ToolBuiltinProviderUpdateApi, '/workspaces/current/tool-provider/builtin/<provider>/update')
api.add_resource(ToolBuiltinProviderCredentialsSchemaApi, '/workspaces/current/tool-provider/builtin/<provider>/credentials_schema')
api.add_resource(ToolBuiltinProviderIconApi, '/workspaces/current/tool-provider/builtin/<provider>/icon')
api.add_resource(ToolApiProviderAddApi, '/workspaces/current/tool-provider/api/add')
api.add_resource(ToolApiProviderGetRemoteSchemaApi, '/workspaces/current/tool-provider/api/remote')
api.add_resource(ToolApiProviderListToolsApi, '/workspaces/current/tool-provider/api/tools')
api.add_resource(ToolApiProviderUpdateApi, '/workspaces/current/tool-provider/api/update')
api.add_resource(ToolApiProviderDeleteApi, '/workspaces/current/tool-provider/api/delete')
api.add_resource(ToolApiProviderGetApi, '/workspaces/current/tool-provider/api/get')
api.add_resource(ToolApiProviderSchemaApi, '/workspaces/current/tool-provider/api/schema')
api.add_resource(ToolApiProviderPreviousTestApi, '/workspaces/current/tool-provider/api/test/pre')
api/controllers/files/__init__.py
View file @ 86286e1a
......@@ -7,3 +7,4 @@ api = ExternalApi(bp)
from . import image_preview
from . import tool_files
\ No newline at end of file
api/controllers/files/tool_files.py 0 → 100644
View file @ 86286e1a
from controllers.files import api
from flask import Response
from flask_restful import Resource, reqparse
from libs.exception import BaseHTTPException
from werkzeug.exceptions import NotFound, Forbidden
from core.tools.tool_file_manager import ToolFileManager
class ToolFilePreviewApi(Resource):
def get(self, file_id, extension):
file_id = str(file_id)
parser = reqparse.RequestParser()
parser.add_argument('timestamp', type=str, required=True, location='args')
parser.add_argument('nonce', type=str, required=True, location='args')
parser.add_argument('sign', type=str, required=True, location='args')
args = parser.parse_args()
if not ToolFileManager.verify_file(file_id=file_id,
timestamp=args['timestamp'],
nonce=args['nonce'],
sign=args['sign'],
):
raise Forbidden('Invalid request.')
try:
result = ToolFileManager.get_file_generator_by_message_file_id(
file_id,
)
if not result:
raise NotFound(f'file is not found')
generator, mimetype = result
except Exception:
raise UnsupportedFileTypeError()
return Response(generator, mimetype=mimetype)
api.add_resource(ToolFilePreviewApi, '/files/tools/<uuid:file_id>.<string:extension>')
class UnsupportedFileTypeError(BaseHTTPException):
error_code = 'unsupported_file_type'
description = "File type not allowed."
code = 415
api/controllers/service_api/app/app.py
View file @ 86286e1a
......@@ -3,7 +3,12 @@ from controllers.service_api import api
from controllers.service_api.wraps import AppApiResource
from flask import current_app
from flask_restful import fields, marshal_with
from models.model import App
from models.model import App, AppModelConfig
from models.tools import ApiToolProvider
import json
from extensions.ext_database import db
class AppParameterApi(AppApiResource):
......@@ -58,5 +63,42 @@ class AppParameterApi(AppApiResource):
}
}
class AppMetaApi(AppApiResource):
def get(self, app_model: App, end_user):
"""Get app meta"""
app_model_config: AppModelConfig = app_model.app_model_config
agent_config = app_model_config.agent_mode_dict or {}
meta = {
'tool_icons': {}
}
# get all tools
tools = agent_config.get('tools', [])
url_prefix = (current_app.config.get("CONSOLE_API_URL")
+ f"/console/api/workspaces/current/tool-provider/builtin/")
for tool in tools:
keys = list(tool.keys())
if len(keys) >= 4:
# current tool standard
provider_type = tool.get('provider_type')
provider_id = tool.get('provider_id')
tool_name = tool.get('tool_name')
if provider_type == 'builtin':
meta['tool_icons'][tool_name] = url_prefix + provider_id + '/icon'
elif provider_type == 'api':
try:
provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
ApiToolProvider.id == provider_id
)
meta['tool_icons'][tool_name] = json.loads(provider.icon)
except:
meta['tool_icons'][tool_name] = {
"background": "#252525",
"content": "\ud83d\ude01"
}
return meta
api.add_resource(AppParameterApi, '/parameters')
api.add_resource(AppMetaApi, '/meta')
api/controllers/service_api/app/message.py
View file @ 86286e1a
......@@ -37,6 +37,19 @@ class MessageListApi(AppApiResource):
'created_at': TimestampField
}
agent_thought_fields = {
'id': fields.String,
'chain_id': fields.String,
'message_id': fields.String,
'position': fields.Integer,
'thought': fields.String,
'tool': fields.String,
'tool_input': fields.String,
'created_at': TimestampField,
'observation': fields.String,
'message_files': fields.List(fields.String, attribute='files')
}
message_fields = {
'id': fields.String,
'conversation_id': fields.String,
......@@ -46,7 +59,8 @@ class MessageListApi(AppApiResource):
'message_files': fields.List(fields.Nested(message_file_fields), attribute='files'),
'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
'created_at': TimestampField
'created_at': TimestampField,
'agent_thoughts': fields.List(fields.Nested(agent_thought_fields))
}
message_infinite_scroll_pagination_fields = {
......
api/controllers/web/app.py
View file @ 86286e1a
......@@ -3,7 +3,12 @@ from controllers.web import api
from controllers.web.wraps import WebApiResource
from flask import current_app
from flask_restful import fields, marshal_with
from models.model import App
from models.model import App, AppModelConfig
from models.tools import ApiToolProvider
from extensions.ext_database import db
import json
class AppParameterApi(WebApiResource):
......@@ -57,5 +62,42 @@ class AppParameterApi(WebApiResource):
}
}
class AppMeta(WebApiResource):
def get(self, app_model: App, end_user):
"""Get app meta"""
app_model_config: AppModelConfig = app_model.app_model_config
agent_config = app_model_config.agent_mode_dict or {}
meta = {
'tool_icons': {}
}
# get all tools
tools = agent_config.get('tools', [])
url_prefix = (current_app.config.get("CONSOLE_API_URL")
+ f"/console/api/workspaces/current/tool-provider/builtin/")
for tool in tools:
keys = list(tool.keys())
if len(keys) >= 4:
# current tool standard
provider_type = tool.get('provider_type')
provider_id = tool.get('provider_id')
tool_name = tool.get('tool_name')
if provider_type == 'builtin':
meta['tool_icons'][tool_name] = url_prefix + provider_id + '/icon'
elif provider_type == 'api':
try:
provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
ApiToolProvider.id == provider_id
)
meta['tool_icons'][tool_name] = json.loads(provider.icon)
except:
meta['tool_icons'][tool_name] = {
"background": "#252525",
"content": "\ud83d\ude01"
}
return meta
api.add_resource(AppParameterApi, '/parameters')
api.add_resource(AppMeta, '/meta')
\ No newline at end of file
api/controllers/web/message.py
View file @ 86286e1a
......@@ -14,6 +14,7 @@ from core.entities.application_entities import InvokeFrom
from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
from core.model_runtime.errors.invoke import InvokeError
from fields.conversation_fields import message_file_fields
from fields.message_fields import agent_thought_fields
from flask import Response, stream_with_context
from flask_restful import fields, marshal_with, reqparse
from flask_restful.inputs import int_range
......@@ -59,7 +60,8 @@ class MessageListApi(WebApiResource):
'message_files': fields.List(fields.Nested(message_file_fields), attribute='files'),
'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
'created_at': TimestampField
'created_at': TimestampField,
'agent_thoughts': fields.List(fields.Nested(agent_thought_fields))
}
message_infinite_scroll_pagination_fields = {
......
api/core/agent/agent_executor.py
View file @ 86286e1a
......@@ -13,8 +13,8 @@ from core.entities.message_entities import prompt_messages_to_lc_messages
from core.helper import moderation
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_runtime.errors.invoke import InvokeError
from core.tool.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
from core.tool.dataset_retriever_tool import DatasetRetrieverTool
from core.tools.tool.dataset_retriever.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
from langchain.agents import AgentExecutor as LCAgentExecutor
from langchain.agents import BaseMultiActionAgent, BaseSingleActionAgent
from langchain.callbacks.manager import Callbacks
......
api/core/app_runner/app_runner.py
View file @ 86286e1a
......@@ -2,7 +2,8 @@ import time
from typing import Generator, List, Optional, Tuple, Union, cast
from core.application_queue_manager import ApplicationQueueManager, PublishFrom
from core.entities.application_entities import AppOrchestrationConfigEntity, ModelConfigEntity, PromptTemplateEntity
from core.entities.application_entities import AppOrchestrationConfigEntity, ModelConfigEntity, \
PromptTemplateEntity, ExternalDataVariableEntity, ApplicationGenerateEntity, InvokeFrom
from core.file.file_obj import FileObj
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
......@@ -10,9 +11,12 @@ from core.model_runtime.entities.message_entities import AssistantPromptMessage,
from core.model_runtime.entities.model_entities import ModelPropertyKey
from core.model_runtime.errors.invoke import InvokeBadRequestError
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.features.hosting_moderation import HostingModerationFeature
from core.features.moderation import ModerationFeature
from core.features.external_data_fetch import ExternalDataFetchFeature
from core.features.annotation_reply import AnnotationReplyFeature
from core.prompt.prompt_transform import PromptTransform
from models.model import App
from models.model import App, MessageAnnotation, Message
class AppRunner:
def get_pre_calculate_rest_tokens(self, app_record: App,
......@@ -199,7 +203,8 @@ class AppRunner:
def _handle_invoke_result(self, invoke_result: Union[LLMResult, Generator],
queue_manager: ApplicationQueueManager,
stream: bool) -> None:
stream: bool,
agent: bool = False) -> None:
"""
Handle invoke result
:param invoke_result: invoke result
......@@ -210,16 +215,19 @@ class AppRunner:
if not stream:
self._handle_invoke_result_direct(
invoke_result=invoke_result,
queue_manager=queue_manager
queue_manager=queue_manager,
agent=agent
)
else:
self._handle_invoke_result_stream(
invoke_result=invoke_result,
queue_manager=queue_manager
queue_manager=queue_manager,
agent=agent
)
def _handle_invoke_result_direct(self, invoke_result: LLMResult,
queue_manager: ApplicationQueueManager) -> None:
queue_manager: ApplicationQueueManager,
agent: bool) -> None:
"""
Handle invoke result direct
:param invoke_result: invoke result
......@@ -232,7 +240,8 @@ class AppRunner:
)
def _handle_invoke_result_stream(self, invoke_result: Generator,
queue_manager: ApplicationQueueManager) -> None:
queue_manager: ApplicationQueueManager,
agent: bool) -> None:
"""
Handle invoke result
:param invoke_result: invoke result
......@@ -244,7 +253,10 @@ class AppRunner:
text = ''
usage = None
for result in invoke_result:
if not agent:
queue_manager.publish_chunk_message(result, PublishFrom.APPLICATION_MANAGER)
else:
queue_manager.publish_agent_chunk_message(result, PublishFrom.APPLICATION_MANAGER)
text += result.delta.message.content
......@@ -271,3 +283,101 @@ class AppRunner:
llm_result=llm_result,
pub_from=PublishFrom.APPLICATION_MANAGER
)
def moderation_for_inputs(self, app_id: str,
tenant_id: str,
app_orchestration_config_entity: AppOrchestrationConfigEntity,
inputs: dict,
query: str) -> Tuple[bool, dict, str]:
"""
Process sensitive_word_avoidance.
:param app_id: app id
:param tenant_id: tenant id
:param app_orchestration_config_entity: app orchestration config entity
:param inputs: inputs
:param query: query
:return:
"""
moderation_feature = ModerationFeature()
return moderation_feature.check(
app_id=app_id,
tenant_id=tenant_id,
app_orchestration_config_entity=app_orchestration_config_entity,
inputs=inputs,
query=query,
)
def check_hosting_moderation(self, application_generate_entity: ApplicationGenerateEntity,
queue_manager: ApplicationQueueManager,
prompt_messages: list[PromptMessage]) -> bool:
"""
Check hosting moderation
:param application_generate_entity: application generate entity
:param queue_manager: queue manager
:param prompt_messages: prompt messages
:return:
"""
hosting_moderation_feature = HostingModerationFeature()
moderation_result = hosting_moderation_feature.check(
application_generate_entity=application_generate_entity,
prompt_messages=prompt_messages
)
if moderation_result:
self.direct_output(
queue_manager=queue_manager,
app_orchestration_config=application_generate_entity.app_orchestration_config_entity,
prompt_messages=prompt_messages,
text="I apologize for any confusion, " \
"but I'm an AI assistant to be helpful, harmless, and honest.",
stream=application_generate_entity.stream
)
return moderation_result
def fill_in_inputs_from_external_data_tools(self, tenant_id: str,
app_id: str,
external_data_tools: list[ExternalDataVariableEntity],
inputs: dict,
query: str) -> dict:
"""
Fill in variable inputs from external data tools if exists.
:param tenant_id: workspace id
:param app_id: app id
:param external_data_tools: external data tools configs
:param inputs: the inputs
:param query: the query
:return: the filled inputs
"""
external_data_fetch_feature = ExternalDataFetchFeature()
return external_data_fetch_feature.fetch(
tenant_id=tenant_id,
app_id=app_id,
external_data_tools=external_data_tools,
inputs=inputs,
query=query
)
def query_app_annotations_to_reply(self, app_record: App,
message: Message,
query: str,
user_id: str,
invoke_from: InvokeFrom) -> Optional[MessageAnnotation]:
"""
Query app annotations to reply
:param app_record: app record
:param message: message
:param query: query
:param user_id: user id
:param invoke_from: invoke from
:return:
"""
annotation_reply_feature = AnnotationReplyFeature()
return annotation_reply_feature.query(
app_record=app_record,
message=message,
query=query,
user_id=user_id,
invoke_from=invoke_from
)
\ No newline at end of file
api/core/app_runner/agent_app_runner.py → api/core/app_runner/assistant_app_runner.py
View file @ 86286e1a
......@@ -2,33 +2,34 @@ import json
import logging
from typing import cast
from core.agent.agent.agent_llm_callback import AgentLLMCallback
from core.app_runner.app_runner import AppRunner
from core.application_queue_manager import ApplicationQueueManager
from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
from core.entities.application_entities import ApplicationGenerateEntity, ModelConfigEntity, PromptTemplateEntity
from core.features.agent_runner import AgentRunnerFeature
from core.features.assistant_cot_runner import AssistantCotApplicationRunner
from core.features.assistant_fc_runner import AssistantFunctionCallApplicationRunner
from core.entities.application_entities import ApplicationGenerateEntity, ModelConfigEntity, \
AgentEntity
from core.application_queue_manager import ApplicationQueueManager, PublishFrom
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_manager import ModelInstance
from core.model_runtime.entities.llm_entities import LLMUsage
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.moderation.base import ModerationException
from core.tools.entities.tool_entities import ToolRuntimeVariablePool
from extensions.ext_database import db
from models.model import App, Conversation, Message, MessageAgentThought, MessageChain
from models.model import Conversation, Message, App, MessageChain, MessageAgentThought
from models.tools import ToolConversationVariables
logger = logging.getLogger(__name__)
class AgentApplicationRunner(AppRunner):
class AssistantApplicationRunner(AppRunner):
"""
Agent Application Runner
Assistant Application Runner
"""
def run(self, application_generate_entity: ApplicationGenerateEntity,
queue_manager: ApplicationQueueManager,
conversation: Conversation,
message: Message) -> None:
"""
Run agent application
Run assistant application
:param application_generate_entity: application generate entity
:param queue_manager: application queue manager
:param conversation: conversation
......@@ -72,125 +73,215 @@ class AgentApplicationRunner(AppRunner):
model_instance=model_instance
)
# reorganize all inputs and template to prompt messages
# organize all inputs and template to prompt messages
# Include: prompt template, inputs, query(optional), files(optional)
# memory(optional)
prompt_messages, stop = self.organize_prompt_messages(
prompt_messages, _ = self.organize_prompt_messages(
app_record=app_record,
model_config=app_orchestration_config.model_config,
prompt_template_entity=app_orchestration_config.prompt_template,
inputs=inputs,
files=files,
query=query,
context=None,
memory=memory
)
# Create MessageChain
message_chain = self._init_message_chain(
message=message,
query=query
# moderation
try:
# process sensitive_word_avoidance
_, inputs, query = self.moderation_for_inputs(
app_id=app_record.id,
tenant_id=application_generate_entity.tenant_id,
app_orchestration_config_entity=app_orchestration_config,
inputs=inputs,
query=query,
)
# add agent callback to record agent thoughts
agent_callback = AgentLoopGatherCallbackHandler(
model_config=app_orchestration_config.model_config,
message=message,
except ModerationException as e:
self.direct_output(
queue_manager=queue_manager,
message_chain=message_chain
)
# init LLM Callback
agent_llm_callback = AgentLLMCallback(
agent_callback=agent_callback
)
agent_runner = AgentRunnerFeature(
tenant_id=application_generate_entity.tenant_id,
app_orchestration_config=app_orchestration_config,
model_config=app_orchestration_config.model_config,
config=app_orchestration_config.agent,
queue_manager=queue_manager,
message=message,
user_id=application_generate_entity.user_id,
agent_llm_callback=agent_llm_callback,
callback=agent_callback,
memory=memory
prompt_messages=prompt_messages,
text=str(e),
stream=application_generate_entity.stream
)
return
# agent run
result = agent_runner.run(
if query:
# annotation reply
annotation_reply = self.query_app_annotations_to_reply(
app_record=app_record,
message=message,
query=query,
user_id=application_generate_entity.user_id,
invoke_from=application_generate_entity.invoke_from
)
if result:
self._save_message_chain(
message_chain=message_chain,
output_text=result
if annotation_reply:
queue_manager.publish_annotation_reply(
message_annotation_id=annotation_reply.id,
pub_from=PublishFrom.APPLICATION_MANAGER
)
if (result
and app_orchestration_config.prompt_template.prompt_type == PromptTemplateEntity.PromptType.SIMPLE
and app_orchestration_config.prompt_template.simple_prompt_template
):
# Direct output if agent result exists and has pre prompt
self.direct_output(
queue_manager=queue_manager,
app_orchestration_config=app_orchestration_config,
prompt_messages=prompt_messages,
stream=application_generate_entity.stream,
text=result,
usage=self._get_usage_of_all_agent_thoughts(
model_config=app_orchestration_config.model_config,
message=message
text=annotation_reply.content,
stream=application_generate_entity.stream
)
return
# fill in variable inputs from external data tools if exists
external_data_tools = app_orchestration_config.external_data_variables
if external_data_tools:
inputs = self.fill_in_inputs_from_external_data_tools(
tenant_id=app_record.tenant_id,
app_id=app_record.id,
external_data_tools=external_data_tools,
inputs=inputs,
query=query
)
else:
# As normal LLM run, agent result as context
context = result
# reorganize all inputs and template to prompt messages
# Include: prompt template, inputs, query(optional), files(optional)
# memory(optional), external data, dataset context(optional)
prompt_messages, stop = self.organize_prompt_messages(
prompt_messages, _ = self.organize_prompt_messages(
app_record=app_record,
model_config=app_orchestration_config.model_config,
prompt_template_entity=app_orchestration_config.prompt_template,
inputs=inputs,
files=files,
query=query,
context=context,
memory=memory
)
# Re-calculate the max tokens if sum(prompt_token + max_tokens) over model token limit
self.recale_llm_max_tokens(
model_config=app_orchestration_config.model_config,
# check hosting moderation
hosting_moderation_result = self.check_hosting_moderation(
application_generate_entity=application_generate_entity,
queue_manager=queue_manager,
prompt_messages=prompt_messages
)
# Invoke model
if hosting_moderation_result:
return
agent_entity = app_orchestration_config.agent
# load tool variables
tool_conversation_variables = self._load_tool_variables(conversation_id=conversation.id,
user_id=application_generate_entity.user_id,
tanent_id=application_generate_entity.tenant_id)
# convert db variables to tool variables
tool_variables = self._convert_db_variables_to_tool_variables(tool_conversation_variables)
message_chain = self._init_message_chain(
message=message,
query=query
)
# init model instance
model_instance = ModelInstance(
provider_model_bundle=app_orchestration_config.model_config.provider_model_bundle,
model=app_orchestration_config.model_config.model
)
prompt_message, _ = self.organize_prompt_messages(
app_record=app_record,
model_config=app_orchestration_config.model_config,
prompt_template_entity=app_orchestration_config.prompt_template,
inputs=inputs,
files=files,
query=query,
memory=memory,
)
invoke_result = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters=app_orchestration_config.model_config.parameters,
stop=stop,
stream=application_generate_entity.stream,
user=application_generate_entity.user_id,
# start agent runner
if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
assistant_cot_runner = AssistantCotApplicationRunner(
tenant_id=application_generate_entity.tenant_id,
application_generate_entity=application_generate_entity,
app_orchestration_config=app_orchestration_config,
model_config=app_orchestration_config.model_config,
config=agent_entity,
queue_manager=queue_manager,
message=message,
user_id=application_generate_entity.user_id,
memory=memory,
prompt_messages=prompt_message,
variables_pool=tool_variables,
db_variables=tool_conversation_variables,
)
invoke_result = assistant_cot_runner.run(
model_instance=model_instance,
conversation=conversation,
message=message,
query=query,
)
elif agent_entity.strategy == AgentEntity.Strategy.FUNCTION_CALLING:
assistant_cot_runner = AssistantFunctionCallApplicationRunner(
tenant_id=application_generate_entity.tenant_id,
application_generate_entity=application_generate_entity,
app_orchestration_config=app_orchestration_config,
model_config=app_orchestration_config.model_config,
config=agent_entity,
queue_manager=queue_manager,
message=message,
user_id=application_generate_entity.user_id,
memory=memory,
prompt_messages=prompt_message,
variables_pool=tool_variables,
db_variables=tool_conversation_variables
)
invoke_result = assistant_cot_runner.run(
model_instance=model_instance,
conversation=conversation,
message=message,
query=query,
)
# handle invoke result
self._handle_invoke_result(
invoke_result=invoke_result,
queue_manager=queue_manager,
stream=application_generate_entity.stream
stream=application_generate_entity.stream,
agent=True
)
def _load_tool_variables(self, conversation_id: str, user_id: str, tanent_id: str) -> ToolConversationVariables:
"""
load tool variables from database
"""
tool_variables: ToolConversationVariables = db.session.query(ToolConversationVariables).filter(
ToolConversationVariables.conversation_id == conversation_id,
ToolConversationVariables.tenant_id == tanent_id
).first()
if tool_variables:
# save tool variables to session, so that we can update it later
db.session.add(tool_variables)
else:
# create new tool variables
tool_variables = ToolConversationVariables(
conversation_id=conversation_id,
user_id=user_id,
tenant_id=tanent_id,
variables_str='[]',
)
db.session.add(tool_variables)
db.session.commit()
return tool_variables
def _convert_db_variables_to_tool_variables(self, db_variables: ToolConversationVariables) -> ToolRuntimeVariablePool:
"""
convert db variables to tool variables
"""
return ToolRuntimeVariablePool(**{
'conversation_id': db_variables.conversation_id,
'user_id': db_variables.user_id,
'tenant_id': db_variables.tenant_id,
'pool': db_variables.variables
})
def _init_message_chain(self, message: Message, query: str) -> MessageChain:
"""
Init MessageChain
......@@ -237,8 +328,8 @@ class AgentApplicationRunner(AppRunner):
all_message_tokens = 0
all_answer_tokens = 0
for agent_thought in agent_thoughts:
all_message_tokens += agent_thought.message_token
all_answer_tokens += agent_thought.answer_token
all_message_tokens += agent_thought.message_tokens
all_answer_tokens += agent_thought.answer_tokens
model_type_instance = model_config.provider_model_bundle.model_type_instance
model_type_instance = cast(LargeLanguageModel, model_type_instance)
......
api/core/app_runner/basic_app_runner.py
View file @ 86286e1a
import logging
from typing import Optional, Tuple
from typing import Optional
from core.app_runner.app_runner import AppRunner
from core.application_queue_manager import ApplicationQueueManager, PublishFrom
from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
from core.entities.application_entities import (ApplicationGenerateEntity, AppOrchestrationConfigEntity, DatasetEntity,
ExternalDataVariableEntity, InvokeFrom, ModelConfigEntity)
from core.features.annotation_reply import AnnotationReplyFeature
from core.entities.application_entities import (ApplicationGenerateEntity, DatasetEntity,
InvokeFrom, ModelConfigEntity)
from core.features.dataset_retrieval import DatasetRetrievalFeature
from core.features.external_data_fetch import ExternalDataFetchFeature
from core.features.hosting_moderation import HostingModerationFeature
from core.features.moderation import ModerationFeature
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_manager import ModelInstance
from core.model_runtime.entities.message_entities import PromptMessage
from core.moderation.base import ModerationException
from core.prompt.prompt_transform import AppMode
from extensions.ext_database import db
from models.model import App, Conversation, Message, MessageAnnotation
from models.model import App, Conversation, Message
logger = logging.getLogger(__name__)
......@@ -213,76 +208,6 @@ class BasicApplicationRunner(AppRunner):
stream=application_generate_entity.stream
)
def moderation_for_inputs(self, app_id: str,
tenant_id: str,
app_orchestration_config_entity: AppOrchestrationConfigEntity,
inputs: dict,
query: str) -> Tuple[bool, dict, str]:
"""
Process sensitive_word_avoidance.
:param app_id: app id
:param tenant_id: tenant id
:param app_orchestration_config_entity: app orchestration config entity
:param inputs: inputs
:param query: query
:return:
"""
moderation_feature = ModerationFeature()
return moderation_feature.check(
app_id=app_id,
tenant_id=tenant_id,
app_orchestration_config_entity=app_orchestration_config_entity,
inputs=inputs,
query=query,
)
def query_app_annotations_to_reply(self, app_record: App,
message: Message,
query: str,
user_id: str,
invoke_from: InvokeFrom) -> Optional[MessageAnnotation]:
"""
Query app annotations to reply
:param app_record: app record
:param message: message
:param query: query
:param user_id: user id
:param invoke_from: invoke from
:return:
"""
annotation_reply_feature = AnnotationReplyFeature()
return annotation_reply_feature.query(
app_record=app_record,
message=message,
query=query,
user_id=user_id,
invoke_from=invoke_from
)
def fill_in_inputs_from_external_data_tools(self, tenant_id: str,
app_id: str,
external_data_tools: list[ExternalDataVariableEntity],
inputs: dict,
query: str) -> dict:
"""
Fill in variable inputs from external data tools if exists.
:param tenant_id: workspace id
:param app_id: app id
:param external_data_tools: external data tools configs
:param inputs: the inputs
:param query: the query
:return: the filled inputs
"""
external_data_fetch_feature = ExternalDataFetchFeature()
return external_data_fetch_feature.fetch(
tenant_id=tenant_id,
app_id=app_id,
external_data_tools=external_data_tools,
inputs=inputs,
query=query
)
def retrieve_dataset_context(self, tenant_id: str,
app_record: App,
queue_manager: ApplicationQueueManager,
......@@ -335,30 +260,3 @@ class BasicApplicationRunner(AppRunner):
memory=memory
)
\ No newline at end of file
def check_hosting_moderation(self, application_generate_entity: ApplicationGenerateEntity,
queue_manager: ApplicationQueueManager,
prompt_messages: list[PromptMessage]) -> bool:
"""
Check hosting moderation
:param application_generate_entity: application generate entity
:param queue_manager: queue manager
:param prompt_messages: prompt messages
:return:
"""
hosting_moderation_feature = HostingModerationFeature()
moderation_result = hosting_moderation_feature.check(
application_generate_entity=application_generate_entity,
prompt_messages=prompt_messages
)
if moderation_result:
self.direct_output(
queue_manager=queue_manager,
app_orchestration_config=application_generate_entity.app_orchestration_config_entity,
prompt_messages=prompt_messages,
text="I apologize for any confusion, " \
"but I'm an AI assistant to be helpful, harmless, and honest.",
stream=application_generate_entity.stream
)
return moderation_result
api/core/app_runner/generate_task_pipeline.py
View file @ 86286e1a
......@@ -8,7 +8,8 @@ from core.application_queue_manager import ApplicationQueueManager, PublishFrom
from core.entities.application_entities import ApplicationGenerateEntity, InvokeFrom
from core.entities.queue_entities import (AnnotationReplyEvent, QueueAgentThoughtEvent, QueueErrorEvent,
QueueMessageEndEvent, QueueMessageEvent, QueueMessageReplaceEvent,
QueuePingEvent, QueueRetrieverResourcesEvent, QueueStopEvent)
QueuePingEvent, QueueRetrieverResourcesEvent, QueueStopEvent,
QueueMessageFileEvent, QueueAgentMessageEvent)
from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
from core.model_runtime.entities.message_entities import (AssistantPromptMessage, ImagePromptMessageContent,
......@@ -16,11 +17,12 @@ from core.model_runtime.entities.message_entities import (AssistantPromptMessage
TextPromptMessageContent)
from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.tools.tool_file_manager import ToolFileManager
from core.model_runtime.utils.encoders import jsonable_encoder
from core.prompt.prompt_template import PromptTemplateParser
from events.message_event import message_was_created
from extensions.ext_database import db
from models.model import Conversation, Message, MessageAgentThought
from models.model import Conversation, Message, MessageAgentThought, MessageFile
from pydantic import BaseModel
from services.annotation_service import AppAnnotationService
......@@ -284,6 +286,7 @@ class GenerateTaskPipeline:
.filter(MessageAgentThought.id == event.agent_thought_id)
.first()
)
db.session.refresh(agent_thought)
if agent_thought:
response = {
......@@ -293,16 +296,48 @@ class GenerateTaskPipeline:
'message_id': self._message.id,
'position': agent_thought.position,
'thought': agent_thought.thought,
'observation': agent_thought.observation,
'tool': agent_thought.tool,
'tool_input': agent_thought.tool_input,
'created_at': int(self._message.created_at.timestamp())
'created_at': int(self._message.created_at.timestamp()),
'message_files': agent_thought.files
}
if self._conversation.mode == 'chat':
response['conversation_id'] = self._conversation.id
yield self._yield_response(response)
elif isinstance(event, QueueMessageFileEvent):
message_file: MessageFile = (
db.session.query(MessageFile)
.filter(MessageFile.id == event.message_file_id)
.first()
)
# get extension
if '.' in message_file.url:
extension = f'.{message_file.url.split(".")[-1]}'
if len(extension) > 10:
extension = '.bin'
else:
extension = '.bin'
# add sign url
url = ToolFileManager.sign_file(file_id=message_file.id, extension=extension)
if message_file:
response = {
'event': 'message_file',
'id': message_file.id,
'type': message_file.type,
'belongs_to': message_file.belongs_to or 'user',
'url': url
}
if self._conversation.mode == 'chat':
response['conversation_id'] = self._conversation.id
yield self._yield_response(response)
elif isinstance(event, QueueMessageEvent):
elif isinstance(event, (QueueMessageEvent, QueueAgentMessageEvent)):
chunk = event.chunk
delta_text = chunk.delta.message.content
if delta_text is None:
......@@ -332,7 +367,7 @@ class GenerateTaskPipeline:
self._output_moderation_handler.append_new_token(delta_text)
self._task_state.llm_result.message.content += delta_text
response = self._handle_chunk(delta_text)
response = self._handle_chunk(delta_text, agent=isinstance(event, QueueAgentMessageEvent))
yield self._yield_response(response)
elif isinstance(event, QueueMessageReplaceEvent):
response = {
......@@ -384,14 +419,14 @@ class GenerateTaskPipeline:
extras=self._application_generate_entity.extras
)
def _handle_chunk(self, text: str) -> dict:
def _handle_chunk(self, text: str, agent: bool = False) -> dict:
"""
Handle completed event.
:param text: text
:return:
"""
response = {
'event': 'message',
'event': 'message' if not agent else 'agent_message',
'id': self._message.id,
'task_id': self._application_generate_entity.task_id,
'message_id': self._message.id,
......
api/core/application_manager.py
View file @ 86286e1a
......@@ -4,7 +4,7 @@ import threading
import uuid
from typing import Any, Generator, Optional, Tuple, Union, cast
from core.app_runner.agent_app_runner import AgentApplicationRunner
from core.app_runner.assistant_app_runner import AssistantApplicationRunner
from core.app_runner.basic_app_runner import BasicApplicationRunner
from core.app_runner.generate_task_pipeline import GenerateTaskPipeline
from core.application_queue_manager import ApplicationQueueManager, ConversationTaskStoppedException, PublishFrom
......@@ -13,7 +13,7 @@ from core.entities.application_entities import (AdvancedChatPromptTemplateEntity
ApplicationGenerateEntity, AppOrchestrationConfigEntity, DatasetEntity,
DatasetRetrieveConfigEntity, ExternalDataVariableEntity,
FileUploadEntity, InvokeFrom, ModelConfigEntity, PromptTemplateEntity,
SensitiveWordAvoidanceEntity)
SensitiveWordAvoidanceEntity, AgentPromptEntity)
from core.entities.model_entities import ModelStatus
from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
from core.file.file_obj import FileObj
......@@ -23,6 +23,7 @@ from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeErr
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.prompt.prompt_template import PromptTemplateParser
from core.provider_manager import ProviderManager
from core.tools.prompt.template import REACT_PROMPT_TEMPLATES
from extensions.ext_database import db
from flask import Flask, current_app
from models.account import Account
......@@ -93,6 +94,9 @@ class ApplicationManager:
extras=extras
)
if not stream and application_generate_entity.app_orchestration_config_entity.agent:
raise ValueError("Agent app is not supported in blocking mode.")
# init generate records
(
conversation,
......@@ -151,7 +155,7 @@ class ApplicationManager:
if application_generate_entity.app_orchestration_config_entity.agent:
# agent app
runner = AgentApplicationRunner()
runner = AssistantApplicationRunner()
runner.run(
application_generate_entity=application_generate_entity,
queue_manager=queue_manager,
......@@ -354,6 +358,8 @@ class ApplicationManager:
# external data variables
properties['external_data_variables'] = []
# old external_data_tools
external_data_tools = copy_app_model_config_dict.get('external_data_tools', [])
for external_data_tool in external_data_tools:
if 'enabled' not in external_data_tool or not external_data_tool['enabled']:
......@@ -367,6 +373,19 @@ class ApplicationManager:
)
)
# current external_data_tools
for variable in copy_app_model_config_dict.get('user_input_form', []):
typ = list(variable.keys())[0]
if typ == 'external_data_tool':
val = variable[typ]
properties['external_data_variables'].append(
ExternalDataVariableEntity(
variable=val['variable'],
type=val['type'],
config=val['config']
)
)
# show retrieve source
show_retrieve_source = False
retriever_resource_dict = copy_app_model_config_dict.get('retriever_resource')
......@@ -376,14 +395,63 @@ class ApplicationManager:
properties['show_retrieve_source'] = show_retrieve_source
dataset_ids = []
if 'datasets' in copy_app_model_config_dict.get('dataset_configs', {}):
datasets = copy_app_model_config_dict.get('dataset_configs', {}).get('datasets', {
'strategy': 'router',
'datasets': []
})
for dataset in datasets.get('datasets', []):
keys = list(dataset.keys())
if len(keys) == 0 or keys[0] != 'dataset':
continue
dataset = dataset['dataset']
if 'enabled' not in dataset or not dataset['enabled']:
continue
dataset_id = dataset.get('id', None)
if dataset_id:
dataset_ids.append(dataset_id)
else:
datasets = {'strategy': 'router', 'datasets': []}
if 'agent_mode' in copy_app_model_config_dict and copy_app_model_config_dict['agent_mode'] \
and 'enabled' in copy_app_model_config_dict['agent_mode'] and copy_app_model_config_dict['agent_mode'][
'enabled']:
agent_dict = copy_app_model_config_dict.get('agent_mode')
agent_strategy = agent_dict.get('strategy', 'router')
if agent_strategy in ['router', 'react_router']:
dataset_ids = []
agent_dict = copy_app_model_config_dict.get('agent_mode', {})
agent_strategy = agent_dict.get('strategy', 'cot')
if agent_strategy == 'function_call':
strategy = AgentEntity.Strategy.FUNCTION_CALLING
elif agent_strategy == 'cot' or agent_strategy == 'react':
strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
else:
# old configs, try to detect default strategy
if copy_app_model_config_dict['model']['provider'] == 'openai':
strategy = AgentEntity.Strategy.FUNCTION_CALLING
else:
strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
agent_tools = []
for tool in agent_dict.get('tools', []):
keys = tool.keys()
if len(keys) >= 4:
if "enabled" not in tool or not tool["enabled"]:
continue
agent_tool_properties = {
'provider_type': tool['provider_type'],
'provider_id': tool['provider_id'],
'tool_name': tool['tool_name'],
'tool_parameters': tool['tool_parameters'] if 'tool_parameters' in tool else {}
}
agent_tools.append(AgentToolEntity(**agent_tool_properties))
elif len(keys) == 1:
# old standard
key = list(tool.keys())[0]
if key != 'dataset':
......@@ -397,8 +465,34 @@ class ApplicationManager:
dataset_id = tool_item['id']
dataset_ids.append(dataset_id)
agent_prompt = agent_dict.get('prompt', None) or {}
# check model mode
model_mode = copy_app_model_config_dict.get('model', {}).get('mode', 'completion')
if model_mode == 'completion':
agent_prompt_entity = AgentPromptEntity(
first_prompt=agent_prompt.get('first_prompt', REACT_PROMPT_TEMPLATES['english']['completion']['prompt']),
next_iteration=agent_prompt.get('next_iteration', REACT_PROMPT_TEMPLATES['english']['completion']['agent_scratchpad']),
)
else:
agent_prompt_entity = AgentPromptEntity(
first_prompt=agent_prompt.get('first_prompt', REACT_PROMPT_TEMPLATES['english']['chat']['prompt']),
next_iteration=agent_prompt.get('next_iteration', REACT_PROMPT_TEMPLATES['english']['chat']['agent_scratchpad']),
)
properties['agent'] = AgentEntity(
provider=properties['model_config'].provider,
model=properties['model_config'].model,
strategy=strategy,
prompt=agent_prompt_entity,
tools=agent_tools,
max_iteration=agent_dict.get('max_iteration', 5)
)
if len(dataset_ids) > 0:
# dataset configs
dataset_configs = copy_app_model_config_dict.get('dataset_configs', {'retrieval_model': 'single'})
query_variable = copy_app_model_config_dict.get('dataset_query_variable')
if dataset_configs['retrieval_model'] == 'single':
properties['dataset'] = DatasetEntity(
dataset_ids=dataset_ids,
......@@ -407,7 +501,7 @@ class ApplicationManager:
retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of(
dataset_configs['retrieval_model']
),
single_strategy=agent_strategy
single_strategy=datasets.get('strategy', 'router')
)
)
else:
......@@ -423,33 +517,6 @@ class ApplicationManager:
reranking_model=dataset_configs.get('reranking_model')
)
)
else:
if agent_strategy == 'react':
strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
else:
strategy = AgentEntity.Strategy.FUNCTION_CALLING
agent_tools = []
for tool in agent_dict.get('tools', []):
key = list(tool.keys())[0]
tool_item = tool[key]
agent_tool_properties = {
"tool_id": key
}
if "enabled" not in tool_item or not tool_item["enabled"]:
continue
agent_tool_properties["config"] = tool_item
agent_tools.append(AgentToolEntity(**agent_tool_properties))
properties['agent'] = AgentEntity(
provider=properties['model_config'].provider,
model=properties['model_config'].model,
strategy=strategy,
tools=agent_tools
)
# file upload
file_upload_dict = copy_app_model_config_dict.get('file_upload')
......@@ -601,6 +668,7 @@ class ApplicationManager:
message_id=message.id,
type=file.type.value,
transfer_method=file.transfer_method.value,
belongs_to='user',
url=file.url,
upload_file_id=file.upload_file_id,
created_by_role=('account' if account_id else 'end_user'),
......
api/core/application_queue_manager.py
View file @ 86286e1a
......@@ -7,10 +7,10 @@ from core.entities.application_entities import InvokeFrom
from core.entities.queue_entities import (AnnotationReplyEvent, AppQueueEvent, QueueAgentThoughtEvent, QueueErrorEvent,
QueueMessage, QueueMessageEndEvent, QueueMessageEvent,
QueueMessageReplaceEvent, QueuePingEvent, QueueRetrieverResourcesEvent,
QueueStopEvent)
QueueStopEvent, QueueMessageFileEvent, QueueAgentMessageEvent)
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
from extensions.ext_redis import redis_client
from models.model import MessageAgentThought
from models.model import MessageAgentThought, MessageFile
from sqlalchemy.orm import DeclarativeMeta
......@@ -96,6 +96,18 @@ class ApplicationQueueManager:
chunk=chunk
), pub_from)
def publish_agent_chunk_message(self, chunk: LLMResultChunk, pub_from: PublishFrom) -> None:
"""
Publish agent chunk message to channel
:param chunk: chunk
:param pub_from: publish from
:return:
"""
self.publish(QueueAgentMessageEvent(
chunk=chunk
), pub_from)
def publish_message_replace(self, text: str, pub_from: PublishFrom) -> None:
"""
Publish message replace
......@@ -144,6 +156,17 @@ class ApplicationQueueManager:
agent_thought_id=message_agent_thought.id
), pub_from)
def publish_message_file(self, message_file: MessageFile, pub_from: PublishFrom) -> None:
"""
Publish agent thought
:param message_file: message file
:param pub_from: publish from
:return:
"""
self.publish(QueueMessageFileEvent(
message_file_id=message_file.id
), pub_from)
def publish_error(self, e, pub_from: PublishFrom) -> None:
"""
Publish error
......
api/core/callback_handler/agent_tool_callback_handler.py 0 → 100644
View file @ 86286e1a
import os
from typing import Any, Dict, Optional, Union
from pydantic import BaseModel
from langchain.callbacks.base import BaseCallbackHandler
from langchain.input import print_text
class DifyAgentCallbackHandler(BaseCallbackHandler, BaseModel):
"""Callback Handler that prints to std out."""
color: Optional[str] = ''
current_loop = 1
def __init__(self, color: Optional[str] = None) -> None:
super().__init__()
"""Initialize callback handler."""
# use a specific color is not specified
self.color = color or 'green'
self.current_loop = 1
def on_tool_start(
self,
tool_name: str,
tool_inputs: Dict[str, Any],
) -> None:
"""Do nothing."""
print_text("\n[on_tool_start] ToolCall:" + tool_name + "\n" + str(tool_inputs) + "\n", color=self.color)
def on_tool_end(
self,
tool_name: str,
tool_inputs: Dict[str, Any],
tool_outputs: str,
) -> None:
"""If not the final action, print out observation."""
print_text("\n[on_tool_end]\n", color=self.color)
print_text("Tool: " + tool_name + "\n", color=self.color)
print_text("Inputs: " + str(tool_inputs) + "\n", color=self.color)
print_text("Outputs: " + str(tool_outputs) + "\n", color=self.color)
print_text("\n")
def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing."""
print_text("\n[on_tool_error] Error: " + str(error) + "\n", color='red')
def on_agent_start(
self, thought: str
) -> None:
"""Run on agent start."""
if thought:
print_text("\n[on_agent_start] \nCurrent Loop: " + \
str(self.current_loop) + \
"\nThought: " + thought + "\n", color=self.color)
else:
print_text("\n[on_agent_start] \nCurrent Loop: " + str(self.current_loop) + "\n", color=self.color)
def on_agent_finish(
self, color: Optional[str] = None, **kwargs: Any
) -> None:
"""Run on agent end."""
print_text("\n[on_agent_finish]\n Loop: " + str(self.current_loop) + "\n", color=self.color)
self.current_loop += 1
@property
def ignore_agent(self) -> bool:
"""Whether to ignore agent callbacks."""
return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
@property
def ignore_chat_model(self) -> bool:
"""Whether to ignore chat model callbacks."""
return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
api/core/entities/application_entities.py
View file @ 86286e1a
from enum import Enum
from typing import Any, Optional, cast
from typing import Optional, Any, cast, Literal, Union
from pydantic import BaseModel
from core.entities.provider_configuration import ProviderModelBundle
from core.file.file_obj import FileObj
from core.model_runtime.entities.message_entities import PromptMessageRole
from core.model_runtime.entities.model_entities import AIModelEntity
from pydantic import BaseModel
class ModelConfigEntity(BaseModel):
......@@ -153,9 +154,35 @@ class AgentToolEntity(BaseModel):
"""
Agent Tool Entity.
"""
tool_id: str
config: dict[str, Any] = {}
provider_type: Literal["builtin", "api"]
provider_id: str
tool_name: str
tool_parameters: dict[str, Any] = {}
class AgentPromptEntity(BaseModel):
"""
Agent Prompt Entity.
"""
first_prompt: str
next_iteration: str
class AgentScratchpadUnit(BaseModel):
"""
Agent First Prompt Entity.
"""
class Action(BaseModel):
"""
Action Entity.
"""
action_name: str
action_input: Union[dict, str]
agent_response: Optional[str] = None
thought: Optional[str] = None
action_str: Optional[str] = None
observation: Optional[str] = None
action: Optional[Action] = None
class AgentEntity(BaseModel):
"""
......@@ -171,8 +198,9 @@ class AgentEntity(BaseModel):
provider: str
model: str
strategy: Strategy
tools: list[AgentToolEntity] = []
prompt: Optional[AgentPromptEntity] = None
tools: list[AgentToolEntity] = None
max_iteration: int = 5
class AppOrchestrationConfigEntity(BaseModel):
"""
......
api/core/entities/queue_entities.py
View file @ 86286e1a
......@@ -10,11 +10,13 @@ class QueueEvent(Enum):
QueueEvent enum
"""
MESSAGE = "message"
AGENT_MESSAGE = "agent_message"
MESSAGE_REPLACE = "message-replace"
MESSAGE_END = "message-end"
RETRIEVER_RESOURCES = "retriever-resources"
ANNOTATION_REPLY = "annotation-reply"
AGENT_THOUGHT = "agent-thought"
MESSAGE_FILE = "message-file"
ERROR = "error"
PING = "ping"
STOP = "stop"
......@@ -34,6 +36,13 @@ class QueueMessageEvent(AppQueueEvent):
event = QueueEvent.MESSAGE
chunk: LLMResultChunk
class QueueAgentMessageEvent(AppQueueEvent):
"""
QueueMessageEvent entity
"""
event = QueueEvent.AGENT_MESSAGE
chunk: LLMResultChunk
class QueueMessageReplaceEvent(AppQueueEvent):
"""
......@@ -74,6 +83,12 @@ class QueueAgentThoughtEvent(AppQueueEvent):
event = QueueEvent.AGENT_THOUGHT
agent_thought_id: str
class QueueMessageFileEvent(AppQueueEvent):
"""
QueueAgentThoughtEvent entity
"""
event = QueueEvent.MESSAGE_FILE
message_file_id: str
class QueueErrorEvent(AppQueueEvent):
"""
......
api/core/features/agent_runner.py
View file @ 86286e1a
import logging
from typing import List, Optional, cast
from typing import cast, Optional, List
from langchain import WikipediaAPIWrapper
from langchain.callbacks.base import BaseCallbackHandler
from langchain.tools import BaseTool, WikipediaQueryRun, Tool
from pydantic import BaseModel, Field
from core.agent.agent.agent_llm_callback import AgentLLMCallback
from core.agent.agent_executor import AgentConfiguration, AgentExecutor, PlanningStrategy
from core.agent.agent_executor import PlanningStrategy, AgentConfiguration, AgentExecutor
from core.application_queue_manager import ApplicationQueueManager
from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
from core.entities.application_entities import (AgentEntity, AgentToolEntity, AppOrchestrationConfigEntity, InvokeFrom,
ModelConfigEntity)
from core.entities.application_entities import ModelConfigEntity, InvokeFrom, \
AgentEntity, AgentToolEntity, AppOrchestrationConfigEntity
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_runtime.entities.model_entities import ModelFeature, ModelType
from core.model_runtime.model_providers import model_provider_factory
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.tool.current_datetime_tool import DatetimeTool
from core.tool.dataset_retriever_tool import DatasetRetrieverTool
from core.tool.provider.serpapi_provider import SerpAPIToolProvider
from core.tool.serpapi_wrapper import OptimizedSerpAPIInput, OptimizedSerpAPIWrapper
from core.tool.web_reader_tool import WebReaderTool
from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
from extensions.ext_database import db
from langchain import WikipediaAPIWrapper
from langchain.callbacks.base import BaseCallbackHandler
from langchain.tools import BaseTool, Tool, WikipediaQueryRun
from models.dataset import Dataset
from models.model import Message
from pydantic import BaseModel, Field
logger = logging.getLogger(__name__)
......@@ -132,55 +129,6 @@ class AgentRunnerFeature:
logger.exception("agent_executor run failed")
return None
def to_tools(self, tool_configs: list[AgentToolEntity],
invoke_from: InvokeFrom,
callbacks: list[BaseCallbackHandler]) \
-> Optional[List[BaseTool]]:
"""
Convert tool configs to tools
:param tool_configs: tool configs
:param invoke_from: invoke from
:param callbacks: callbacks
"""
tools = []
for tool_config in tool_configs:
tool = None
if tool_config.tool_id == "dataset":
tool = self.to_dataset_retriever_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
elif tool_config.tool_id == "web_reader":
tool = self.to_web_reader_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
elif tool_config.tool_id == "google_search":
tool = self.to_google_search_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
elif tool_config.tool_id == "wikipedia":
tool = self.to_wikipedia_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
elif tool_config.tool_id == "current_datetime":
tool = self.to_current_datetime_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
if tool:
if tool.callbacks is not None:
tool.callbacks.extend(callbacks)
else:
tool.callbacks = callbacks
tools.append(tool)
return tools
def to_dataset_retriever_tool(self, tool_config: dict,
invoke_from: InvokeFrom) \
-> Optional[BaseTool]:
......@@ -248,77 +196,3 @@ class AgentRunnerFeature:
)
return tool
\ No newline at end of file
def to_web_reader_tool(self, tool_config: dict,
invoke_from: InvokeFrom) -> Optional[BaseTool]:
"""
A tool for reading web pages
:param tool_config: tool config
:param invoke_from: invoke from
:return:
"""
model_parameters = {
"temperature": 0,
"max_tokens": 500
}
tool = WebReaderTool(
model_config=self.model_config,
model_parameters=model_parameters,
max_chunk_length=4000,
continue_reading=True
)
return tool
def to_google_search_tool(self, tool_config: dict,
invoke_from: InvokeFrom) -> Optional[BaseTool]:
"""
A tool for performing a Google search and extracting snippets and webpages
:param tool_config: tool config
:param invoke_from: invoke from
:return:
"""
tool_provider = SerpAPIToolProvider(tenant_id=self.tenant_id)
func_kwargs = tool_provider.credentials_to_func_kwargs()
if not func_kwargs:
return None
tool = Tool(
name="google_search",
description="A tool for performing a Google search and extracting snippets and webpages "
"when you need to search for something you don't know or when your information "
"is not up to date. "
"Input should be a search query.",
func=OptimizedSerpAPIWrapper(**func_kwargs).run,
args_schema=OptimizedSerpAPIInput
)
return tool
def to_current_datetime_tool(self, tool_config: dict,
invoke_from: InvokeFrom) -> Optional[BaseTool]:
"""
A tool for getting the current date and time
:param tool_config: tool config
:param invoke_from: invoke from
:return:
"""
return DatetimeTool()
def to_wikipedia_tool(self, tool_config: dict,
invoke_from: InvokeFrom) -> Optional[BaseTool]:
"""
A tool for searching Wikipedia
:param tool_config: tool config
:param invoke_from: invoke from
:return:
"""
class WikipediaInput(BaseModel):
query: str = Field(..., description="search query.")
return WikipediaQueryRun(
name="wikipedia",
api_wrapper=WikipediaAPIWrapper(doc_content_chars_max=4000),
args_schema=WikipediaInput
)
api/core/features/assistant_base_runner.py 0 → 100644
View file @ 86286e1a
import logging
import json
from typing import Optional, List, Tuple, Union
from datetime import datetime
from mimetypes import guess_extension
from core.app_runner.app_runner import AppRunner
from extensions.ext_database import db
from models.model import MessageAgentThought, Message, MessageFile
from models.tools import ToolConversationVariables
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolInvokeMessageBinary, \
ToolRuntimeVariablePool, ToolParamter
from core.tools.tool.tool import Tool
from core.tools.tool_manager import ToolManager
from core.tools.tool_file_manager import ToolFileManager
from core.tools.tool.dataset_retriever_tool import DatasetRetrieverTool
from core.app_runner.app_runner import AppRunner
from core.callback_handler.agent_tool_callback_handler import DifyAgentCallbackHandler
from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
from core.entities.application_entities import ModelConfigEntity, AgentEntity, AgentToolEntity
from core.application_queue_manager import ApplicationQueueManager
from core.memory.token_buffer_memory import TokenBufferMemory
from core.entities.application_entities import ModelConfigEntity, \
AgentEntity, AppOrchestrationConfigEntity, ApplicationGenerateEntity, InvokeFrom
from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
from core.model_runtime.entities.llm_entities import LLMUsage
from core.model_runtime.utils.encoders import jsonable_encoder
from core.file.message_file_parser import FileTransferMethod
logger = logging.getLogger(__name__)
class BaseAssistantApplicationRunner(AppRunner):
def __init__(self, tenant_id: str,
application_generate_entity: ApplicationGenerateEntity,
app_orchestration_config: AppOrchestrationConfigEntity,
model_config: ModelConfigEntity,
config: AgentEntity,
queue_manager: ApplicationQueueManager,
message: Message,
user_id: str,
memory: Optional[TokenBufferMemory] = None,
prompt_messages: Optional[List[PromptMessage]] = None,
variables_pool: Optional[ToolRuntimeVariablePool] = None,
db_variables: Optional[ToolConversationVariables] = None,
) -> None:
"""
Agent runner
:param tenant_id: tenant id
:param app_orchestration_config: app orchestration config
:param model_config: model config
:param config: dataset config
:param queue_manager: queue manager
:param message: message
:param user_id: user id
:param agent_llm_callback: agent llm callback
:param callback: callback
:param memory: memory
"""
self.tenant_id = tenant_id
self.application_generate_entity = application_generate_entity
self.app_orchestration_config = app_orchestration_config
self.model_config = model_config
self.config = config
self.queue_manager = queue_manager
self.message = message
self.user_id = user_id
self.memory = memory
self.history_prompt_messages = prompt_messages
self.variables_pool = variables_pool
self.db_variables_pool = db_variables
# init callback
self.agent_callback = DifyAgentCallbackHandler()
# init dataset tools
hit_callback = DatasetIndexToolCallbackHandler(
queue_manager=queue_manager,
app_id=self.application_generate_entity.app_id,
message_id=message.id,
user_id=user_id,
invoke_from=self.application_generate_entity.invoke_from,
)
self.dataset_tools = DatasetRetrieverTool.get_dataset_tools(
tenant_id=tenant_id,
dataset_ids=app_orchestration_config.dataset.dataset_ids if app_orchestration_config.dataset else [],
retrieve_config=app_orchestration_config.dataset.retrieve_config if app_orchestration_config.dataset else None,
return_resource=app_orchestration_config.show_retrieve_source,
invoke_from=application_generate_entity.invoke_from,
hit_callback=hit_callback
)
# get how many agent thoughts have been created
self.agent_thought_count = db.session.query(MessageAgentThought).filter(
MessageAgentThought.message_id == self.message.id,
).count()
def _repacket_app_orchestration_config(self, app_orchestration_config: AppOrchestrationConfigEntity) -> AppOrchestrationConfigEntity:
"""
Repacket app orchestration config
"""
if app_orchestration_config.prompt_template.simple_prompt_template is None:
app_orchestration_config.prompt_template.simple_prompt_template = ''
return app_orchestration_config
def _convert_tool_response_to_str(self, tool_response: List[ToolInvokeMessage]) -> str:
"""
Handle tool response
"""
result = ''
for response in tool_response:
if response.type == ToolInvokeMessage.MessageType.TEXT:
result += response.message
elif response.type == ToolInvokeMessage.MessageType.LINK:
result += f"result link: {response.message}. please dirct user to check it."
elif response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
response.type == ToolInvokeMessage.MessageType.IMAGE:
result += f"image has been created and sent to user already, you should tell user to check it now."
else:
result += f"tool response: {response.message}."
return result
def _convert_tool_to_prompt_message_tool(self, tool: AgentToolEntity) -> Tuple[PromptMessageTool, Tool]:
"""
convert tool to prompt message tool
"""
tool_entity = ToolManager.get_tool_runtime(
provider_type=tool.provider_type, provider_name=tool.provider_id, tool_name=tool.tool_name,
tanent_id=self.application_generate_entity.tenant_id,
agent_callback=self.agent_callback
)
tool_entity.load_variables(self.variables_pool)
message_tool = PromptMessageTool(
name=tool.tool_name,
description=tool_entity.description.llm,
parameters={
"type": "object",
"properties": {},
"required": [],
}
)
runtime_parameters = {}
parameters = tool_entity.parameters or []
user_parameters = tool_entity.get_runtime_parameters() or []
# override parameters
for parameter in user_parameters:
# check if parameter in tool parameters
found = False
for tool_parameter in parameters:
if tool_parameter.name == parameter.name:
found = True
break
if found:
# override parameter
tool_parameter.type = parameter.type
tool_parameter.form = parameter.form
tool_parameter.required = parameter.required
tool_parameter.default = parameter.default
tool_parameter.options = parameter.options
tool_parameter.llm_description = parameter.llm_description
else:
# add new parameter
parameters.append(parameter)
for parameter in parameters:
parameter_type = 'string'
enum = []
if parameter.type == ToolParamter.ToolParameterType.STRING:
parameter_type = 'string'
elif parameter.type == ToolParamter.ToolParameterType.BOOLEAN:
parameter_type = 'boolean'
elif parameter.type == ToolParamter.ToolParameterType.NUMBER:
parameter_type = 'number'
elif parameter.type == ToolParamter.ToolParameterType.SELECT:
for option in parameter.options:
enum.append(option.value)
parameter_type = 'string'
else:
raise ValueError(f"parameter type {parameter.type} is not supported")
if parameter.form == ToolParamter.ToolParameterForm.FORM:
# get tool parameter from form
tool_parameter_config = tool.tool_parameters.get(parameter.name)
if not tool_parameter_config:
# get default value
tool_parameter_config = parameter.default
if not tool_parameter_config and parameter.required:
raise ValueError(f"tool parameter {parameter.name} not found in tool config")
if parameter.type == ToolParamter.ToolParameterType.SELECT:
# check if tool_parameter_config in options
options = list(map(lambda x: x.value, parameter.options))
if tool_parameter_config not in options:
raise ValueError(f"tool parameter {parameter.name} value {tool_parameter_config} not in options {options}")
# convert tool parameter config to correct type
try:
if parameter.type == ToolParamter.ToolParameterType.NUMBER:
# check if tool parameter is integer
if isinstance(tool_parameter_config, int):
tool_parameter_config = tool_parameter_config
elif isinstance(tool_parameter_config, float):
tool_parameter_config = tool_parameter_config
elif isinstance(tool_parameter_config, str):
if '.' in tool_parameter_config:
tool_parameter_config = float(tool_parameter_config)
else:
tool_parameter_config = int(tool_parameter_config)
elif parameter.type == ToolParamter.ToolParameterType.BOOLEAN:
tool_parameter_config = bool(tool_parameter_config)
elif parameter.type not in [ToolParamter.ToolParameterType.SELECT, ToolParamter.ToolParameterType.STRING]:
tool_parameter_config = str(tool_parameter_config)
elif parameter.type == ToolParamter.ToolParameterType:
tool_parameter_config = str(tool_parameter_config)
except Exception as e:
raise ValueError(f"tool parameter {parameter.name} value {tool_parameter_config} is not correct type")
# save tool parameter to tool entity memory
runtime_parameters[parameter.name] = tool_parameter_config
elif parameter.form == ToolParamter.ToolParameterForm.LLM:
message_tool.parameters['properties'][parameter.name] = {
"type": parameter_type,
"description": parameter.llm_description or '',
}
if len(enum) > 0:
message_tool.parameters['properties'][parameter.name]['enum'] = enum
if parameter.required:
message_tool.parameters['required'].append(parameter.name)
tool_entity.runtime.runtime_parameters.update(runtime_parameters)
return message_tool, tool_entity
def _convert_dataset_retriever_tool_to_prompt_message_tool(self, tool: DatasetRetrieverTool) -> PromptMessageTool:
"""
convert dataset retriever tool to prompt message tool
"""
prompt_tool = PromptMessageTool(
name=tool.identity.name,
description=tool.description.llm,
parameters={
"type": "object",
"properties": {},
"required": [],
}
)
for parameter in tool.get_runtime_parameters():
parameter_type = 'string'
prompt_tool.parameters['properties'][parameter.name] = {
"type": parameter_type,
"description": parameter.llm_description or '',
}
if parameter.required:
if parameter.name not in prompt_tool.parameters['required']:
prompt_tool.parameters['required'].append(parameter.name)
return prompt_tool
def update_prompt_message_tool(self, tool: Tool, prompt_tool: PromptMessageTool) -> PromptMessageTool:
"""
update prompt message tool
"""
# try to get tool runtime parameters
tool_runtime_parameters = tool.get_runtime_parameters() or []
for parameter in tool_runtime_parameters:
parameter_type = 'string'
enum = []
if parameter.type == ToolParamter.ToolParameterType.STRING:
parameter_type = 'string'
elif parameter.type == ToolParamter.ToolParameterType.BOOLEAN:
parameter_type = 'boolean'
elif parameter.type == ToolParamter.ToolParameterType.NUMBER:
parameter_type = 'number'
elif parameter.type == ToolParamter.ToolParameterType.SELECT:
for option in parameter.options:
enum.append(option.value)
parameter_type = 'string'
else:
raise ValueError(f"parameter type {parameter.type} is not supported")
if parameter.form == ToolParamter.ToolParameterForm.LLM:
prompt_tool.parameters['properties'][parameter.name] = {
"type": parameter_type,
"description": parameter.llm_description or '',
}
if len(enum) > 0:
prompt_tool.parameters['properties'][parameter.name]['enum'] = enum
if parameter.required:
if parameter.name not in prompt_tool.parameters['required']:
prompt_tool.parameters['required'].append(parameter.name)
return prompt_tool
def extract_tool_response_binary(self, tool_response: List[ToolInvokeMessage]) -> List[ToolInvokeMessageBinary]:
"""
Extract tool response binary
"""
result = []
for response in tool_response:
if response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
response.type == ToolInvokeMessage.MessageType.IMAGE:
result.append(ToolInvokeMessageBinary(
mimetype=response.meta.get('mime_type', 'octet/stream'),
url=response.message,
save_as=response.save_as,
))
elif response.type == ToolInvokeMessage.MessageType.BLOB:
result.append(ToolInvokeMessageBinary(
mimetype=response.meta.get('mime_type', 'octet/stream'),
url=response.message,
save_as=response.save_as,
))
elif response.type == ToolInvokeMessage.MessageType.LINK:
# check if there is a mime type in meta
if response.meta and 'mime_type' in response.meta:
result.append(ToolInvokeMessageBinary(
mimetype=response.meta.get('mime_type', 'octet/stream') if response.meta else 'octet/stream',
url=response.message,
save_as=response.save_as,
))
return result
def create_message_files(self, messages: List[ToolInvokeMessageBinary]) -> List[Tuple[MessageFile, bool]]:
"""
Create message file
:param messages: messages
:return: message files, should save as variable
"""
result = []
for message in messages:
file_type = 'bin'
if 'image' in message.mimetype:
file_type = 'image'
elif 'video' in message.mimetype:
file_type = 'video'
elif 'audio' in message.mimetype:
file_type = 'audio'
elif 'text' in message.mimetype:
file_type = 'text'
elif 'pdf' in message.mimetype:
file_type = 'pdf'
elif 'zip' in message.mimetype:
file_type = 'archive'
# ...
invoke_from = self.application_generate_entity.invoke_from
message_file = MessageFile(
message_id=self.message.id,
type=file_type,
transfer_method=FileTransferMethod.TOOL_FILE.value,
belongs_to='assistant',
url=message.url,
upload_file_id=None,
created_by_role=('account'if invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end_user'),
created_by=self.user_id,
)
db.session.add(message_file)
result.append((
message_file,
message.save_as
))
db.session.commit()
return result
def create_agent_thought(self, message_id: str, message: str,
tool_name: str, tool_input: str, messages_ids: List[str]
) -> MessageAgentThought:
"""
Create agent thought
"""
thought = MessageAgentThought(
message_id=message_id,
message_chain_id=None,
thought='',
tool=tool_name,
tool_input=tool_input,
message=message,
message_token=0,
message_unit_price=0,
message_price_unit=0,
message_files=json.dumps(messages_ids) if messages_ids else '',
answer='',
observation='',
answer_token=0,
answer_unit_price=0,
answer_price_unit=0,
tokens=0,
total_price=0,
position=self.agent_thought_count + 1,
currency='USD',
latency=0,
created_by_role='account',
created_by=self.user_id,
)
db.session.add(thought)
db.session.commit()
self.agent_thought_count += 1
return thought
def save_agent_thought(self,
agent_thought: MessageAgentThought,
tool_name: str,
tool_input: Union[str, dict],
thought: str,
observation: str,
answer: str,
messages_ids: List[str],
llm_usage: LLMUsage = None) -> MessageAgentThought:
"""
Save agent thought
"""
if thought is not None:
agent_thought.thought = thought
if tool_name is not None:
agent_thought.tool = tool_name
if tool_input is not None:
if isinstance(tool_input, dict):
try:
tool_input = json.dumps(tool_input, ensure_ascii=False)
except Exception as e:
tool_input = json.dumps(tool_input)
agent_thought.tool_input = tool_input
if observation is not None:
agent_thought.observation = observation
if answer is not None:
agent_thought.answer = answer
if messages_ids is not None and len(messages_ids) > 0:
agent_thought.message_files = json.dumps(messages_ids)
if llm_usage:
agent_thought.message_token = llm_usage.prompt_tokens
agent_thought.message_price_unit = llm_usage.prompt_price_unit
agent_thought.message_unit_price = llm_usage.prompt_unit_price
agent_thought.answer_token = llm_usage.completion_tokens
agent_thought.answer_price_unit = llm_usage.completion_price_unit
agent_thought.answer_unit_price = llm_usage.completion_unit_price
agent_thought.tokens = llm_usage.total_tokens
agent_thought.total_price = llm_usage.total_price
db.session.commit()
def get_history_prompt_messages(self) -> List[PromptMessage]:
"""
Get history prompt messages
"""
if self.history_prompt_messages is None:
self.history_prompt_messages = db.session.query(PromptMessage).filter(
PromptMessage.message_id == self.message.id,
).order_by(PromptMessage.position.asc()).all()
return self.history_prompt_messages
def transform_tool_invoke_messages(self, messages: List[ToolInvokeMessage]) -> List[ToolInvokeMessage]:
"""
Transform tool message into agent thought
"""
result = []
for message in messages:
if message.type == ToolInvokeMessage.MessageType.TEXT:
result.append(message)
elif message.type == ToolInvokeMessage.MessageType.LINK:
result.append(message)
elif message.type == ToolInvokeMessage.MessageType.IMAGE:
# try to download image
try:
file = ToolFileManager.create_file_by_url(user_id=self.user_id, tenant_id=self.tenant_id,
conversation_id=self.message.conversation_id,
file_url=message.message)
url = f'/files/tools/{file.id}{guess_extension(file.mimetype) or ".png"}'
result.append(ToolInvokeMessage(
type=ToolInvokeMessage.MessageType.IMAGE_LINK,
message=url,
save_as=message.save_as,
meta=message.meta.copy() if message.meta is not None else {},
))
except Exception as e:
logger.exception(e)
result.append(ToolInvokeMessage(
type=ToolInvokeMessage.MessageType.TEXT,
message=f"Failed to download image: {message.message}, you can try to download it yourself.",
meta=message.meta.copy() if message.meta is not None else {},
save_as=message.save_as,
))
elif message.type == ToolInvokeMessage.MessageType.BLOB:
# get mime type and save blob to storage
mimetype = message.meta.get('mime_type', 'octet/stream')
# if message is str, encode it to bytes
if isinstance(message.message, str):
message.message = message.message.encode('utf-8')
file = ToolFileManager.create_file_by_raw(user_id=self.user_id, tenant_id=self.tenant_id,
conversation_id=self.message.conversation_id,
file_binary=message.message,
mimetype=mimetype)
url = f'/files/tools/{file.id}{guess_extension(file.mimetype) or ".bin"}'
# check if file is image
if 'image' in mimetype:
result.append(ToolInvokeMessage(
type=ToolInvokeMessage.MessageType.IMAGE_LINK,
message=url,
save_as=message.save_as,
meta=message.meta.copy() if message.meta is not None else {},
))
else:
result.append(ToolInvokeMessage(
type=ToolInvokeMessage.MessageType.LINK,
message=url,
save_as=message.save_as,
meta=message.meta.copy() if message.meta is not None else {},
))
else:
result.append(message)
return result
def update_db_variables(self, tool_variables: ToolRuntimeVariablePool, db_variables: ToolConversationVariables):
"""
convert tool variables to db variables
"""
db_variables.updated_at = datetime.utcnow()
db_variables.variables_str = json.dumps(jsonable_encoder(tool_variables.pool))
db.session.commit()
\ No newline at end of file
api/core/features/assistant_cot_runner.py 0 → 100644
View file @ 86286e1a
import json
import logging
import re
from typing import Literal, Union, Generator, Dict, List
from core.entities.application_entities import AgentPromptEntity, AgentScratchpadUnit
from core.application_queue_manager import PublishFrom
from core.model_runtime.utils.encoders import jsonable_encoder
from core.model_runtime.entities.message_entities import PromptMessageTool, PromptMessage, \
UserPromptMessage, SystemPromptMessage, AssistantPromptMessage
from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage, LLMResultChunk, LLMResultChunkDelta
from core.model_manager import ModelInstance
from core.tools.errors import ToolInvokeError, ToolNotFoundError, \
ToolNotSupportedError, ToolProviderNotFoundError, ToolParamterValidationError, \
ToolProviderCredentialValidationError
from core.features.assistant_base_runner import BaseAssistantApplicationRunner
from models.model import Conversation, Message
logger = logging.getLogger(__name__)
class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
def run(self, model_instance: ModelInstance,
conversation: Conversation,
message: Message,
query: str,
) -> Union[Generator, LLMResult]:
"""
Run Cot agent application
"""
app_orchestration_config = self.app_orchestration_config
self._repacket_app_orchestration_config(app_orchestration_config)
agent_scratchpad: List[AgentScratchpadUnit] = []
# check model mode
if self.app_orchestration_config.model_config.mode == "completion":
# TODO: stop words
if 'Observation' not in app_orchestration_config.model_config.stop:
app_orchestration_config.model_config.stop.append('Observation')
iteration_step = 1
max_iteration_steps = min(self.app_orchestration_config.agent.max_iteration, 5) + 1
prompt_messages = self.history_prompt_messages
# convert tools into ModelRuntime Tool format
prompt_messages_tools: List[PromptMessageTool] = []
tool_instances = {}
for tool in self.app_orchestration_config.agent.tools if self.app_orchestration_config.agent else []:
try:
prompt_tool, tool_entity = self._convert_tool_to_prompt_message_tool(tool)
except Exception:
# api tool may be deleted
continue
# save tool entity
tool_instances[tool.tool_name] = tool_entity
# save prompt tool
prompt_messages_tools.append(prompt_tool)
# convert dataset tools into ModelRuntime Tool format
for dataset_tool in self.dataset_tools:
prompt_tool = self._convert_dataset_retriever_tool_to_prompt_message_tool(dataset_tool)
# save prompt tool
prompt_messages_tools.append(prompt_tool)
# save tool entity
tool_instances[dataset_tool.identity.name] = dataset_tool
function_call_state = True
llm_usage = {
'usage': None
}
final_answer = ''
def increse_usage(final_llm_usage_dict: Dict[str, LLMUsage], usage: LLMUsage):
if not final_llm_usage_dict['usage']:
final_llm_usage_dict['usage'] = usage
else:
llm_usage = final_llm_usage_dict['usage']
llm_usage.prompt_tokens += usage.prompt_tokens
llm_usage.completion_tokens += usage.completion_tokens
llm_usage.prompt_price += usage.prompt_price
llm_usage.completion_price += usage.completion_price
while function_call_state and iteration_step <= max_iteration_steps:
# continue to run until there is not any tool call
function_call_state = False
if iteration_step == max_iteration_steps:
# the last iteration, remove all tools
prompt_messages_tools = []
message_file_ids = []
agent_thought = self.create_agent_thought(
message_id=message.id,
message='',
tool_name='',
tool_input='',
messages_ids=message_file_ids
)
self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
# update prompt messages
prompt_messages = self._originze_cot_prompt_messages(
mode=app_orchestration_config.model_config.mode,
prompt_messages=prompt_messages,
tools=prompt_messages_tools,
agent_scratchpad=agent_scratchpad,
agent_prompt_message=app_orchestration_config.agent.prompt,
instruction=app_orchestration_config.prompt_template.simple_prompt_template,
input=query
)
# recale llm max tokens
self.recale_llm_max_tokens(self.model_config, prompt_messages)
# invoke model
llm_result: LLMResult = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters=app_orchestration_config.model_config.parameters,
tools=[],
stop=app_orchestration_config.model_config.stop,
stream=False,
user=self.user_id,
callbacks=[],
)
# check llm result
if not llm_result:
raise ValueError("failed to invoke llm")
# get scratchpad
scratchpad = self._extract_response_scratchpad(llm_result.message.content)
agent_scratchpad.append(scratchpad)
# get llm usage
if llm_result.usage:
increse_usage(llm_usage, llm_result.usage)
self.save_agent_thought(agent_thought=agent_thought,
tool_name=scratchpad.action.action_name if scratchpad.action else '',
tool_input=scratchpad.action.action_input if scratchpad.action else '',
thought=scratchpad.thought,
observation='',
answer=llm_result.message.content,
messages_ids=[],
llm_usage=llm_result.usage)
if scratchpad.action and scratchpad.action.action_name.lower() != "final answer":
self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
# publish agent thought if it's not empty and there is a action
if scratchpad.thought and scratchpad.action:
# check if final answer
if not scratchpad.action.action_name.lower() == "final answer":
yield LLMResultChunk(
model=model_instance.model,
prompt_messages=prompt_messages,
delta=LLMResultChunkDelta(
index=0,
message=AssistantPromptMessage(
content=scratchpad.thought
),
usage=llm_result.usage,
),
system_fingerprint=''
)
if not scratchpad.action:
# failed to extract action, return final answer directly
final_answer = scratchpad.agent_response or ''
else:
if scratchpad.action.action_name.lower() == "final answer":
# action is final answer, return final answer directly
try:
final_answer = scratchpad.action.action_input if \
isinstance(scratchpad.action.action_input, str) else \
json.dumps(scratchpad.action.action_input)
except json.JSONDecodeError:
final_answer = f'{scratchpad.action.action_input}'
else:
function_call_state = True
# action is tool call, invoke tool
tool_call_name = scratchpad.action.action_name
tool_call_args = scratchpad.action.action_input
tool_instance = tool_instances.get(tool_call_name)
if not tool_instance:
logger.error(f"failed to find tool instance: {tool_call_name}")
answer = f"there is not a tool named {tool_call_name}"
self.save_agent_thought(agent_thought=agent_thought,
tool_name='',
tool_input='',
thought=None,
observation=answer,
answer=answer,
messages_ids=[])
self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
else:
# invoke tool
error_response = None
try:
tool_response = tool_instance.invoke(
user_id=self.user_id,
tool_paramters=tool_call_args if isinstance(tool_call_args, dict) else json.loads(tool_call_args)
)
# transform tool response to llm friendly response
tool_response = self.transform_tool_invoke_messages(tool_response)
# extract binary data from tool invoke message
binary_files = self.extract_tool_response_binary(tool_response)
# create message file
message_files = self.create_message_files(binary_files)
# publish files
for message_file, save_as in message_files:
if save_as:
self.variables_pool.set_file(tool_name=tool_call_name,
value=message_file.id,
name=save_as)
self.queue_manager.publish_message_file(message_file, PublishFrom.APPLICATION_MANAGER)
message_file_ids = [message_file.id for message_file, _ in message_files]
except ToolProviderCredentialValidationError as e:
error_response = f"Plese check your tool provider credentials"
except (
ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError
) as e:
error_response = f"there is not a tool named {tool_call_name}"
except (
ToolParamterValidationError
) as e:
error_response = f"tool paramters validation error: {e}, please check your tool paramters"
except ToolInvokeError as e:
error_response = f"tool invoke error: {e}"
except Exception as e:
error_response = f"unknown error: {e}"
if error_response:
observation = error_response
logger.error(error_response)
else:
observation = self._convert_tool_response_to_str(tool_response)
# save scratchpad
scratchpad.observation = observation
scratchpad.agent_response = llm_result.message.content
# save agent thought
self.save_agent_thought(
agent_thought=agent_thought,
tool_name=tool_call_name,
tool_input=tool_call_args,
thought=None,
observation=observation,
answer=llm_result.message.content,
messages_ids=message_file_ids,
)
self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
# update prompt tool message
for prompt_tool in prompt_messages_tools:
self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
iteration_step += 1
yield LLMResultChunk(
model=model_instance.model,
prompt_messages=prompt_messages,
delta=LLMResultChunkDelta(
index=0,
message=AssistantPromptMessage(
content=final_answer
),
usage=llm_usage['usage']
),
system_fingerprint=''
)
# save agent thought
self.save_agent_thought(
agent_thought=agent_thought,
tool_name='',
tool_input='',
thought=final_answer,
observation='',
answer=final_answer,
messages_ids=[]
)
self.update_db_variables(self.variables_pool, self.db_variables_pool)
# publish end event
self.queue_manager.publish_message_end(LLMResult(
model=model_instance.model,
prompt_messages=prompt_messages,
message=AssistantPromptMessage(
content=final_answer
),
usage=llm_usage['usage'],
system_fingerprint=''
), PublishFrom.APPLICATION_MANAGER)
def _extract_response_scratchpad(self, content: str) -> AgentScratchpadUnit:
"""
extract response from llm response
"""
def extra_quotes() -> AgentScratchpadUnit:
agent_response = content
# try to extract all quotes
pattern = re.compile(r'```(.*?)```', re.DOTALL)
quotes = pattern.findall(content)
# try to extract action from end to start
for i in range(len(quotes) - 1, 0, -1):
"""
1. use json load to parse action
2. use plain text `Action: xxx` to parse action
"""
try:
action = json.loads(quotes[i].replace('```', ''))
action_name = action.get("action")
action_input = action.get("action_input")
agent_thought = agent_response.replace(quotes[i], '')
if action_name and action_input:
return AgentScratchpadUnit(
agent_response=content,
thought=agent_thought,
action_str=quotes[i],
action=AgentScratchpadUnit.Action(
action_name=action_name,
action_input=action_input,
)
)
except:
# try to parse action from plain text
action_name = re.findall(r'action: (.*)', quotes[i], re.IGNORECASE)
action_input = re.findall(r'action input: (.*)', quotes[i], re.IGNORECASE)
# delete action from agent response
agent_thought = agent_response.replace(quotes[i], '')
# remove extra quotes
agent_thought = re.sub(r'```(json)*\n*```', '', agent_thought, flags=re.DOTALL)
# remove Action: xxx from agent thought
agent_thought = re.sub(r'Action:.*', '', agent_thought, flags=re.IGNORECASE)
if action_name and action_input:
return AgentScratchpadUnit(
agent_response=content,
thought=agent_thought,
action_str=quotes[i],
action=AgentScratchpadUnit.Action(
action_name=action_name[0],
action_input=action_input[0],
)
)
def extra_json():
agent_response = content
# try to extract all json
structures, pair_match_stack = [], []
started_at, end_at = 0, 0
for i in range(len(content)):
if content[i] == '{':
pair_match_stack.append(i)
if len(pair_match_stack) == 1:
started_at = i
elif content[i] == '}':
begin = pair_match_stack.pop()
if not pair_match_stack:
end_at = i + 1
structures.append((content[begin:i+1], (started_at, end_at)))
# handle the last character
if pair_match_stack:
end_at = len(content)
structures.append((content[pair_match_stack[0]:], (started_at, end_at)))
for i in range(len(structures), 0, -1):
try:
json_content, (started_at, end_at) = structures[i - 1]
action = json.loads(json_content)
action_name = action.get("action")
action_input = action.get("action_input")
# delete json content from agent response
agent_thought = agent_response[:started_at] + agent_response[end_at:]
# remove extra quotes like ```(json)*\n\n```
agent_thought = re.sub(r'```(json)*\n*```', '', agent_thought, flags=re.DOTALL)
# remove Action: xxx from agent thought
agent_thought = re.sub(r'Action:.*', '', agent_thought, flags=re.IGNORECASE)
if action_name and action_input:
return AgentScratchpadUnit(
agent_response=content,
thought=agent_thought,
action_str=json_content,
action=AgentScratchpadUnit.Action(
action_name=action_name,
action_input=action_input,
)
)
except:
pass
agent_scratchpad = extra_quotes()
if agent_scratchpad:
return agent_scratchpad
agent_scratchpad = extra_json()
if agent_scratchpad:
return agent_scratchpad
return AgentScratchpadUnit(
agent_response=content,
thought=content,
action_str='',
action=None
)
def _check_cot_prompt_messages(self, mode: Literal["completion", "chat"],
agent_prompt_message: AgentPromptEntity,
):
"""
check chain of thought prompt messages, a standard prompt message is like:
Respond to the human as helpfully and accurately as possible.
{{instruction}}
You have access to the following tools:
{{tools}}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid action values: "Final Answer" or {{tool_names}}
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $ACTION_INPUT
}
```
"""
# parse agent prompt message
first_prompt = agent_prompt_message.first_prompt
next_iteration = agent_prompt_message.next_iteration
if not isinstance(first_prompt, str) or not isinstance(next_iteration, str):
raise ValueError(f"first_prompt or next_iteration is required in CoT agent mode")
# check instruction, tools, and tool_names slots
if not first_prompt.find("{{instruction}}") >= 0:
raise ValueError("{{instruction}} is required in first_prompt")
if not first_prompt.find("{{tools}}") >= 0:
raise ValueError("{{tools}} is required in first_prompt")
if not first_prompt.find("{{tool_names}}") >= 0:
raise ValueError("{{tool_names}} is required in first_prompt")
if mode == "completion":
if not first_prompt.find("{{query}}") >= 0:
raise ValueError("{{query}} is required in first_prompt")
if not first_prompt.find("{{agent_scratchpad}}") >= 0:
raise ValueError("{{agent_scratchpad}} is required in first_prompt")
if mode == "completion":
if not next_iteration.find("{{observation}}") >= 0:
raise ValueError("{{observation}} is required in next_iteration")
def _convert_strachpad_list_to_str(self, agent_scratchpad: List[AgentScratchpadUnit]) -> str:
"""
convert agent scratchpad list to str
"""
next_iteration = self.app_orchestration_config.agent.prompt.next_iteration
result = ''
for scratchpad in agent_scratchpad:
result += scratchpad.thought + next_iteration.replace("{{observation}}", scratchpad.observation) + "\n"
return result
def _originze_cot_prompt_messages(self, mode: Literal["completion", "chat"],
prompt_messages: List[PromptMessage],
tools: List[PromptMessageTool],
agent_scratchpad: List[AgentScratchpadUnit],
agent_prompt_message: AgentPromptEntity,
instruction: str,
input: str,
) -> List[PromptMessage]:
"""
originze chain of thought prompt messages, a standard prompt message is like:
Respond to the human as helpfully and accurately as possible.
{{instruction}}
You have access to the following tools:
{{tools}}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid action values: "Final Answer" or {{tool_names}}
Provide only ONE action per $JSON_BLOB, as shown:
```
{{{{
"action": $TOOL_NAME,
"action_input": $ACTION_INPUT
}}}}
```
"""
self._check_cot_prompt_messages(mode, agent_prompt_message)
# parse agent prompt message
first_prompt = agent_prompt_message.first_prompt
# parse tools
tools_str = self._jsonify_tool_prompt_messages(tools)
# parse tools name
tool_names = '"' + '","'.join([tool.name for tool in tools]) + '"'
# get system message
system_message = first_prompt.replace("{{instruction}}", instruction) \
.replace("{{tools}}", tools_str) \
.replace("{{tool_names}}", tool_names)
# originze prompt messages
if mode == "chat":
# override system message
overrided = False
prompt_messages = prompt_messages.copy()
for prompt_message in prompt_messages:
if isinstance(prompt_message, SystemPromptMessage):
prompt_message.content = system_message
overrided = True
break
if not overrided:
prompt_messages.insert(0, SystemPromptMessage(
content=system_message,
))
# add assistant message
if len(agent_scratchpad) > 0:
prompt_messages.append(AssistantPromptMessage(
content=agent_scratchpad[-1].thought + "\n" + agent_scratchpad[-1].observation
))
# add user message
if len(agent_scratchpad) > 0:
prompt_messages.append(UserPromptMessage(
content=input,
))
return prompt_messages
elif mode == "completion":
# parse agent scratchpad
agent_scratchpad_str = self._convert_strachpad_list_to_str(agent_scratchpad)
# parse prompt messages
return [UserPromptMessage(
content=first_prompt.replace("{{instruction}}", instruction)
.replace("{{tools}}", tools_str)
.replace("{{tool_names}}", tool_names)
.replace("{{query}}", input)
.replace("{{agent_scratchpad}}", agent_scratchpad_str),
)]
else:
raise ValueError(f"mode {mode} is not supported")
def _jsonify_tool_prompt_messages(self, tools: list[PromptMessageTool]) -> str:
"""
jsonify tool prompt messages
"""
tools = jsonable_encoder(tools)
try:
return json.dumps(tools, ensure_ascii=False)
except json.JSONDecodeError:
return json.dumps(tools)
\ No newline at end of file
api/core/features/assistant_fc_runner.py 0 → 100644
View file @ 86286e1a
import json
import logging
from typing import Union, Generator, Dict, Any, Tuple, List
from core.model_runtime.entities.message_entities import PromptMessage, UserPromptMessage,\
SystemPromptMessage, AssistantPromptMessage, ToolPromptMessage, PromptMessageTool
from core.model_runtime.entities.llm_entities import LLMResultChunk, LLMResult, LLMUsage
from core.model_manager import ModelInstance
from core.application_queue_manager import PublishFrom
from core.tools.errors import ToolInvokeError, ToolNotFoundError, \
ToolNotSupportedError, ToolProviderNotFoundError, ToolParamterValidationError, \
ToolProviderCredentialValidationError
from core.features.assistant_base_runner import BaseAssistantApplicationRunner
from models.model import Conversation, Message, MessageAgentThought
logger = logging.getLogger(__name__)
class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
def run(self, model_instance: ModelInstance,
conversation: Conversation,
message: Message,
query: str,
) -> Generator[LLMResultChunk, None, None]:
"""
Run FunctionCall agent application
"""
app_orchestration_config = self.app_orchestration_config
prompt_template = self.app_orchestration_config.prompt_template.simple_prompt_template or ''
prompt_messages = self.history_prompt_messages
prompt_messages = self.organize_prompt_messages(
prompt_template=prompt_template,
query=query,
prompt_messages=prompt_messages
)
# convert tools into ModelRuntime Tool format
prompt_messages_tools: List[PromptMessageTool] = []
tool_instances = {}
for tool in self.app_orchestration_config.agent.tools if self.app_orchestration_config.agent else []:
try:
prompt_tool, tool_entity = self._convert_tool_to_prompt_message_tool(tool)
except Exception:
# api tool may be deleted
continue
# save tool entity
tool_instances[tool.tool_name] = tool_entity
# save prompt tool
prompt_messages_tools.append(prompt_tool)
# convert dataset tools into ModelRuntime Tool format
for dataset_tool in self.dataset_tools:
prompt_tool = self._convert_dataset_retriever_tool_to_prompt_message_tool(dataset_tool)
# save prompt tool
prompt_messages_tools.append(prompt_tool)
# save tool entity
tool_instances[dataset_tool.identity.name] = dataset_tool
iteration_step = 1
max_iteration_steps = min(app_orchestration_config.agent.max_iteration, 5) + 1
# continue to run until there is not any tool call
function_call_state = True
agent_thoughts: List[MessageAgentThought] = []
llm_usage = {
'usage': None
}
final_answer = ''
def increase_usage(final_llm_usage_dict: Dict[str, LLMUsage], usage: LLMUsage):
if not final_llm_usage_dict['usage']:
final_llm_usage_dict['usage'] = usage
else:
llm_usage = final_llm_usage_dict['usage']
llm_usage.prompt_tokens += usage.prompt_tokens
llm_usage.completion_tokens += usage.completion_tokens
llm_usage.prompt_price += usage.prompt_price
llm_usage.completion_price += usage.completion_price
while function_call_state and iteration_step <= max_iteration_steps:
function_call_state = False
if iteration_step == max_iteration_steps:
# the last iteration, remove all tools
prompt_messages_tools = []
message_file_ids = []
agent_thought = self.create_agent_thought(
message_id=message.id,
message='',
tool_name='',
tool_input='',
messages_ids=message_file_ids
)
self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
# recale llm max tokens
self.recale_llm_max_tokens(self.model_config, prompt_messages)
# invoke model
chunks: Generator[LLMResultChunk, None, None] = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters=app_orchestration_config.model_config.parameters,
tools=prompt_messages_tools,
stop=app_orchestration_config.model_config.stop,
stream=True,
user=self.user_id,
callbacks=[],
)
tool_calls: List[Tuple[str, str, Dict[str, Any]]] = []
# save full response
response = ''
# save tool call names and inputs
tool_call_names = ''
tool_call_inputs = ''
current_llm_usage = None
for chunk in chunks:
# check if there is any tool call
if self.check_tool_calls(chunk):
function_call_state = True
tool_calls.extend(self.extract_tool_calls(chunk))
tool_call_names = ';'.join([tool_call[1] for tool_call in tool_calls])
try:
tool_call_inputs = json.dumps({
tool_call[1]: tool_call[2] for tool_call in tool_calls
}, ensure_ascii=False)
except json.JSONDecodeError as e:
# ensure ascii to avoid encoding error
tool_call_inputs = json.dumps({
tool_call[1]: tool_call[2] for tool_call in tool_calls
})
if chunk.delta.message and chunk.delta.message.content:
if isinstance(chunk.delta.message.content, list):
for content in chunk.delta.message.content:
response += content.data
else:
response += chunk.delta.message.content
if chunk.delta.usage:
increase_usage(llm_usage, chunk.delta.usage)
current_llm_usage = chunk.delta.usage
yield chunk
# save thought
self.save_agent_thought(
agent_thought=agent_thought,
tool_name=tool_call_names,
tool_input=tool_call_inputs,
thought=response,
observation=None,
answer=response,
messages_ids=[],
llm_usage=current_llm_usage
)
self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
final_answer += response + '\n'
# call tools
tool_responses = []
for tool_call_id, tool_call_name, tool_call_args in tool_calls:
tool_instance = tool_instances.get(tool_call_name)
if not tool_instance:
logger.error(f"failed to find tool instance: {tool_call_name}")
tool_response = {
"tool_call_id": tool_call_id,
"tool_call_name": tool_call_name,
"tool_response": f"there is not a tool named {tool_call_name}"
}
tool_responses.append(tool_response)
else:
# invoke tool
error_response = None
try:
tool_invoke_message = tool_instance.invoke(
user_id=self.user_id,
tool_paramters=tool_call_args,
)
# transform tool invoke message to get LLM friendly message
tool_invoke_message = self.transform_tool_invoke_messages(tool_invoke_message)
# extract binary data from tool invoke message
binary_files = self.extract_tool_response_binary(tool_invoke_message)
# create message file
message_files = self.create_message_files(binary_files)
# publish files
for message_file, save_as in message_files:
if save_as:
self.variables_pool.set_file(tool_name=tool_call_name, value=message_file.id, name=save_as)
# publish message file
self.queue_manager.publish_message_file(message_file, PublishFrom.APPLICATION_MANAGER)
# add message file ids
message_file_ids.append(message_file.id)
except ToolProviderCredentialValidationError as e:
error_response = f"Plese check your tool provider credentials"
except (
ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError
) as e:
error_response = f"there is not a tool named {tool_call_name}"
except (
ToolParamterValidationError
) as e:
error_response = f"tool paramters validation error: {e}, please check your tool paramters"
except ToolInvokeError as e:
error_response = f"tool invoke error: {e}"
except Exception as e:
error_response = f"unknown error: {e}"
if error_response:
observation = error_response
logger.error(error_response)
tool_response = {
"tool_call_id": tool_call_id,
"tool_call_name": tool_call_name,
"tool_response": error_response
}
tool_responses.append(tool_response)
else:
observation = self._convert_tool_response_to_str(tool_invoke_message)
tool_response = {
"tool_call_id": tool_call_id,
"tool_call_name": tool_call_name,
"tool_response": observation
}
tool_responses.append(tool_response)
prompt_messages = self.organize_prompt_messages(
prompt_template=prompt_template,
query=None,
tool_call_id=tool_call_id,
tool_call_name=tool_call_name,
tool_response=tool_response['tool_response'],
prompt_messages=prompt_messages,
)
if len(tool_responses) > 0:
# save agent thought
self.save_agent_thought(
agent_thought=agent_thought,
tool_name=None,
tool_input=None,
thought=None,
observation=tool_response['tool_response'],
answer=None,
messages_ids=message_file_ids
)
self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
# update prompt messages
if response.strip():
prompt_messages.append(AssistantPromptMessage(
content=response,
))
# update prompt tool
for prompt_tool in prompt_messages_tools:
self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
iteration_step += 1
self.update_db_variables(self.variables_pool, self.db_variables_pool)
# publish end event
self.queue_manager.publish_message_end(LLMResult(
model=model_instance.model,
prompt_messages=prompt_messages,
message=AssistantPromptMessage(
content=final_answer,
),
usage=llm_usage['usage'],
system_fingerprint=''
), PublishFrom.APPLICATION_MANAGER)
def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
"""
Check if there is any tool call in llm result chunk
"""
if llm_result_chunk.delta.message.tool_calls:
return True
return False
def extract_tool_calls(self, llm_result_chunk: LLMResultChunk) -> Union[None, List[Tuple[str, str, Dict[str, Any]]]]:
"""
Extract tool calls from llm result chunk
Returns:
List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
"""
tool_calls = []
for prompt_message in llm_result_chunk.delta.message.tool_calls:
tool_calls.append((
prompt_message.id,
prompt_message.function.name,
json.loads(prompt_message.function.arguments),
))
return tool_calls
def organize_prompt_messages(self, prompt_template: str,
query: str = None,
tool_call_id: str = None, tool_call_name: str = None, tool_response: str = None,
prompt_messages: list[PromptMessage] = None
) -> list[PromptMessage]:
"""
Organize prompt messages
"""
if not prompt_messages:
prompt_messages = [
SystemPromptMessage(content=prompt_template),
UserPromptMessage(content=query),
]
else:
if tool_response:
prompt_messages = prompt_messages.copy()
prompt_messages.append(
ToolPromptMessage(
content=tool_response,
tool_call_id=tool_call_id,
name=tool_call_name,
)
)
return prompt_messages
\ No newline at end of file
api/core/features/dataset_retrieval.py
View file @ 86286e1a
......@@ -6,8 +6,8 @@ from core.entities.application_entities import DatasetEntity, DatasetRetrieveCon
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_runtime.entities.model_entities import ModelFeature
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.tool.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
from core.tool.dataset_retriever_tool import DatasetRetrieverTool
from core.tools.tool.dataset_retriever.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
from extensions.ext_database import db
from langchain.tools import BaseTool
from models.dataset import Dataset
......
api/core/file/file_obj.py
View file @ 86286e1a
......@@ -22,6 +22,7 @@ class FileType(enum.Enum):
class FileTransferMethod(enum.Enum):
REMOTE_URL = 'remote_url'
LOCAL_FILE = 'local_file'
TOOL_FILE = 'tool_file'
@staticmethod
def value_of(value):
......@@ -30,6 +31,16 @@ class FileTransferMethod(enum.Enum):
return member
raise ValueError(f"No matching enum found for value '{value}'")
class FileBelongsTo(enum.Enum):
USER = 'user'
ASSISTANT = 'assistant'
@staticmethod
def value_of(value):
for member in FileBelongsTo:
if member.value == value:
return member
raise ValueError(f"No matching enum found for value '{value}'")
class FileObj(BaseModel):
id: Optional[str]
......
api/core/file/message_file_parser.py
View file @ 86286e1a
from typing import Dict, List, Optional, Union
import requests
from core.file.file_obj import FileObj, FileTransferMethod, FileType
from core.file.file_obj import FileObj, FileTransferMethod, FileType, FileBelongsTo
from services.file_service import IMAGE_EXTENSIONS
from extensions.ext_database import db
from models.account import Account
......@@ -128,6 +128,9 @@ class MessageFileParser:
# group by file type and convert file args or message files to FileObj
for file in files:
if file.belongs_to == FileBelongsTo.ASSISTANT.value:
continue
file_obj = self._to_file_obj(file, file_upload_config)
if file_obj.type not in type_file_objs:
continue
......
api/core/file/tool_file_parser.py 0 → 100644
View file @ 86286e1a
tool_file_manager = {
'manager': None
}
class ToolFileParser:
@staticmethod
def get_tool_file_manager() -> 'ToolFileManager':
return tool_file_manager['manager']
\ No newline at end of file
api/core/model_runtime/model_providers/openai/llm/llm.py
View file @ 86286e1a
......@@ -485,19 +485,37 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
:return: llm response chunk generator
"""
full_assistant_content = ''
delta_assistant_message_function_call_storage: ChoiceDeltaFunctionCall = None
for chunk in response:
if len(chunk.choices) == 0:
continue
delta = chunk.choices[0]
if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ''):
if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == '') and \
delta.delta.function_call is None:
continue
# assistant_message_tool_calls = delta.delta.tool_calls
assistant_message_function_call = delta.delta.function_call
# extract tool calls from response
if delta_assistant_message_function_call_storage is not None:
# handle process of stream function call
if assistant_message_function_call:
# message has not ended ever
delta_assistant_message_function_call_storage.arguments += assistant_message_function_call.arguments
continue
else:
# message has ended
assistant_message_function_call = delta_assistant_message_function_call_storage
delta_assistant_message_function_call_storage = None
else:
if assistant_message_function_call:
# start of stream function call
delta_assistant_message_function_call_storage = assistant_message_function_call
continue
# tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
function_call = self._extract_response_function_call(assistant_message_function_call)
tool_calls = [function_call] if function_call else []
......
api/core/tools/README.md 0 → 100644
View file @ 86286e1a
# Tools
This module implements built-in tools used in Agent Assistants and Workflows within Dify. You could define and display your own tools in this module, without modifying the frontend logic. This decoupling allows for easier horizontal scaling of Dify's capabilities.
## Feature Introduction
The tools provided for Agents and Workflows are currently divided into two categories:
- `Built-in Tools` are internally implemented within our product and are hardcoded for use in Agents and Workflows.
- `Api-Based Tools` leverage third-party APIs for implementation. You don't need to code to integrate these -- simply provide interface definitions in formats like `OpenAPI` , `Swagger`, or the `OpenAI-plugin` on the front-end.
### Built-in Tool Providers
![Alt text](docs/zh_Hans/images/index/image.png)
### API Tool Providers
![Alt text](docs/zh_Hans/images/index/image-1.png)
## Tool Integration
To enable developers to build flexible and powerful tools, we provide two guides:
### [Quick Integration 👈🏻](./docs/en_US/tool_scale_out.md)
Quick integration aims at quickly getting you up to speed with tool integration by walking over an example Google Search tool.
### [Advanced Integration 👈🏻](./docs/en_US/advanced_scale_out.md)
Advanced integration will offer a deeper dive into the module interfaces, and explain how to implement more complex capabilities, such as generating images, combining multiple tools, and managing the flow of parameters, images, and files between different tools.
\ No newline at end of file
api/core/tools/README_CN.md 0 → 100644
View file @ 86286e1a
# Tools
该模块提供了各Agent和Workflow中会使用的内置工具的调用、鉴权接口，并为 Dify 提供了统一的工具供应商的信息和凭据表单规则。
- 一方面将工具和业务代码解耦，方便开发者对模型横向扩展，
- 另一方面提供了只需在后端定义供应商和工具，即可在前端页面直接展示，无需修改前端逻辑。
## 功能介绍
对于给Agent和Workflow提供的工具，我们当前将其分为两类：
- `Built-in Tools` 内置工具，即Dify内部实现的工具，通过硬编码的方式提供给Agent和Workflow使用。
- `Api-Based Tools` 基于API的工具，即通过调用第三方API实现的工具，`Api-Based Tool`不需要再额外定义，只需提供`OpenAPI` `Swagger` `OpenAI plugin`等接口文档即可。
### 内置工具供应商
![Alt text](docs/zh_Hans/images/index/image.png)
### API工具供应商
![Alt text](docs/zh_Hans/images/index/image-1.png)
## 工具接入
为了实现更灵活更强大的功能，Tools提供了一系列的接口，帮助开发者快速构建想要的工具，本文作为开发者的入门指南，将会以[快速接入](./docs/zh_Hans/tool_scale_out.md)和[高级接入](./docs/zh_Hans/advanced_scale_out.md)两部分介绍如何接入工具。
### [快速接入 👈🏻](./docs/zh_Hans/tool_scale_out.md)
快速接入可以帮助你在10~20分钟内完成工具的接入，但是这种接入方式只能实现简单的功能，如果你想要实现更复杂的功能，可以参考下面的高级接入。
### [高级接入 👈🏻](./docs/zh_Hans/advanced_scale_out.md)
高级接入将介绍如何实现更复杂的功能配置，包括实现图生图、实现多个工具的组合、实现参数、图片、文件在多个工具之间的流转。
\ No newline at end of file
api/core/tools/docs/en_US/advanced_scale_out.md 0 → 100644
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# Advanced Tool Integration
Before starting with this advanced guide, please make sure you have a basic understanding of the tool integration process in Dify. Check out [Quick Integration](./tool_scale_out.md) for a quick runthrough.
## Tool Interface
We have defined a series of helper methods in the `Tool` class to help developers quickly build more complex tools.
### Message Return
Dify supports various message types such as `text`, `link`, `image`, and `file BLOB`. You can return different types of messages to the LLM and users through the following interfaces.
Please note, some parameters in the following interfaces will be introduced in later sections.
#### Image URL
You only need to pass the URL of the image, and Dify will automatically download the image and return it to the user.
```python
def create_image_message(self, image: str, save_as: str = '') -> ToolInvokeMessage:
"""
create an image message
:param image: the url of the image
:return: the image message
"""
```
#### Link
If you need to return a link, you can use the following interface.
```python
def create_link_message(self, link: str, save_as: str = '') -> ToolInvokeMessage:
"""
create a link message
:param link: the url of the link
:return: the link message
"""
```
#### Text
If you need to return a text message, you can use the following interface.
```python
def create_text_message(self, text: str, save_as: str = '') -> ToolInvokeMessage:
"""
create a text message
:param text: the text of the message
:return: the text message
"""
```
#### File BLOB
If you need to return the raw data of a file, such as images, audio, video, PPT, Word, Excel, etc., you can use the following interface.
- `blob` The raw data of the file, of bytes type
- `meta` The metadata of the file, if you know the type of the file, it is best to pass a `mime_type`, otherwise Dify will use `octet/stream` as the default type
```python
def create_blob_message(self, blob: bytes, meta: dict = None, save_as: str = '') -> ToolInvokeMessage:
"""
create a blob message
:param blob: the blob
:return: the blob message
"""
```
### Shortcut Tools
In large model applications, we have two common needs:
- First, summarize a long text in advance, and then pass the summary content to the LLM to prevent the original text from being too long for the LLM to handle
- The content obtained by the tool is a link, and the web page information needs to be crawled before it can be returned to the LLM
To help developers quickly implement these two needs, we provide the following two shortcut tools.
#### Text Summary Tool
This tool takes in an user_id and the text to be summarized, and returns the summarized text. Dify will use the default model of the current workspace to summarize the long text.
```python
def summary(self, user_id: str, content: str) -> str:
"""
summary the content
:param user_id: the user id
:param content: the content
:return: the summary
"""
```
#### Web Page Crawling Tool
This tool takes in web page link to be crawled and a user_agent (which can be empty), and returns a string containing the information of the web page. The `user_agent` is an optional parameter that can be used to identify the tool. If not passed, Dify will use the default `user_agent`.
```python
def get_url(self, url: str, user_agent: str = None) -> str:
"""
get url
""" the crawled result
```
### Variable Pool
We have introduced a variable pool in `Tool` to store variables, files, etc. generated during the tool's operation. These variables can be used by other tools during the tool's operation.
Next, we will use `DallE3` and `Vectorizer.AI` as examples to introduce how to use the variable pool.
- `DallE3` is an image generation tool that can generate images based on text. Here, we will let `DallE3` generate a logo for a coffee shop
- `Vectorizer.AI` is a vector image conversion tool that can convert images into vector images, so that the images can be infinitely enlarged without distortion. Here, we will convert the PNG icon generated by `DallE3` into a vector image, so that it can be truly used by designers.
#### DallE3
First, we use DallE3. After creating the image, we save the image to the variable pool. The code is as follows:
```python
from typing import Any, Dict, List, Union
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.tool.builtin_tool import BuiltinTool
from base64 import b64decode
from openai import OpenAI
class DallE3Tool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
client = OpenAI(
api_key=self.runtime.credentials['openai_api_key'],
)
# prompt
prompt = tool_paramters.get('prompt', '')
if not prompt:
return self.create_text_message('Please input prompt')
# call openapi dalle3
response = client.images.generate(
prompt=prompt, model='dall-e-3',
size='1024x1024', n=1, style='vivid', quality='standard',
response_format='b64_json'
)
result = []
for image in response.data:
# Save all images to the variable pool through the save_as parameter. The variable name is self.VARIABLE_KEY.IMAGE.value. If new images are generated later, they will overwrite the previous images.
result.append(self.create_blob_message(blob=b64decode(image.b64_json),
meta={ 'mime_type': 'image/png' },
save_as=self.VARIABLE_KEY.IMAGE.value))
return result
```
Note that we used `self.VARIABLE_KEY.IMAGE.value` as the variable name of the image. In order for developers' tools to cooperate with each other, we defined this `KEY`. You can use it freely, or you can choose not to use this `KEY`. Passing a custom KEY is also acceptable.
#### Vectorizer.AI
Next, we use Vectorizer.AI to convert the PNG icon generated by DallE3 into a vector image. Let's go through the functions we defined here. The code is as follows:
```python
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from base64 import b64decode
class VectorizerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
Tool invocation, the image variable name needs to be passed in from here, so that we can get the image from the variable pool
"""
def get_runtime_parameters(self) -> List[ToolParamter]:
"""
Override the tool parameter list, we can dynamically generate the parameter list based on the actual situation in the current variable pool, so that the LLM can generate the form based on the parameter list
"""
def is_tool_avaliable(self) -> bool:
"""
Whether the current tool is available, if there is no image in the current variable pool, then we don't need to display this tool, just return False here
"""
```
Next, let's implement these three functions
```python
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from base64 import b64decode
class VectorizerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
api_key_name = self.runtime.credentials.get('api_key_name', None)
api_key_value = self.runtime.credentials.get('api_key_value', None)
if not api_key_name or not api_key_value:
raise ToolProviderCredentialValidationError('Please input api key name and value')
# Get image_id, the definition of image_id can be found in get_runtime_parameters
image_id = tool_paramters.get('image_id', '')
if not image_id:
return self.create_text_message('Please input image id')
# Get the image generated by DallE from the variable pool
image_binary = self.get_variable_file(self.VARIABLE_KEY.IMAGE)
if not image_binary:
return self.create_text_message('Image not found, please request user to generate image firstly.')
# Generate vector image
response = post(
'https://vectorizer.ai/api/v1/vectorize',
files={ 'image': image_binary },
data={ 'mode': 'test' },
auth=(api_key_name, api_key_value),
timeout=30
)
if response.status_code != 200:
raise Exception(response.text)
return [
self.create_text_message('the vectorized svg is saved as an image.'),
self.create_blob_message(blob=response.content,
meta={'mime_type': 'image/svg+xml'})
]
def get_runtime_parameters(self) -> List[ToolParamter]:
"""
override the runtime parameters
"""
# Here, we override the tool parameter list, define the image_id, and set its option list to all images in the current variable pool. The configuration here is consistent with the configuration in yaml.
return [
ToolParamter.get_simple_instance(
name='image_id',
llm_description=f'the image id that you want to vectorize, \
and the image id should be specified in \
{[i.name for i in self.list_default_image_variables()]}',
type=ToolParamter.ToolParameterType.SELECT,
required=True,
options=[i.name for i in self.list_default_image_variables()]
)
]
def is_tool_avaliable(self) -> bool:
# Only when there are images in the variable pool, the LLM needs to use this tool
return len(self.list_default_image_variables()) > 0
```
It's worth noting that we didn't actually use `image_id` here. We assumed that there must be an image in the default variable pool when calling this tool, so we directly used `image_binary = self.get_variable_file(self.VARIABLE_KEY.IMAGE)` to get the image. In cases where the model's capabilities are weak, we recommend developers to do the same, which can effectively improve fault tolerance and avoid the model passing incorrect parameters.
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# Quick Tool Integration
Here, we will use GoogleSearch as an example to demonstrate how to quickly integrate a tool.
## 1. Prepare the Tool Provider yaml
### Introduction
This yaml declares a new tool provider, and includes information like the provider's name, icon, author, and other details that are fetched by the frontend for display.
### Example
We need to create a `google` module (folder) under `core/tools/provider/builtin`, and create `google.yaml`. The name must be consistent with the module name.
Subsequently, all operations related to this tool will be carried out under this module.
```yaml
identity: # Basic information of the tool provider
author: Dify # Author
name: google # Name, unique, no duplication with other providers
label: # Label for frontend display
en_US: Google # English label
zh_Hans: Google # Chinese label
description: # Description for frontend display
en_US: Google # English description
zh_Hans: Google # Chinese description
icon: icon.svg # Icon, needs to be placed in the _assets folder of the current module
```
- The `identity` field is mandatory, it contains the basic information of the tool provider, including author, name, label, description, icon, etc.
- The icon needs to be placed in the `_assets` folder of the current module, you can refer to [here](../../provider/builtin/google/_assets/icon.svg).
## 2. Prepare Provider Credentials
Google, as a third-party tool, uses the API provided by SerpApi, which requires an API Key to use. This means that this tool needs a credential to use. For tools like `wikipedia`, there is no need to fill in the credential field, you can refer to [here](../../provider/builtin/wikipedia/wikipedia.yaml).
After configuring the credential field, the effect is as follows:
```yaml
identity:
author: Dify
name: google
label:
en_US: Google
zh_Hans: Google
description:
en_US: Google
zh_Hans: Google
icon: icon.svg
credentails_for_provider: # Credential field
serpapi_api_key: # Credential field name
type: secret-input # Credential field type
required: true # Required or not
label: # Credential field label
en_US: SerpApi API key # English label
zh_Hans: SerpApi API key # Chinese label
placeholder: # Credential field placeholder
en_US: Please input your SerpApi API key # English placeholder
zh_Hans: 请输入你的 SerpApi API key # Chinese placeholder
help: # Credential field help text
en_US: Get your SerpApi API key from SerpApi # English help text
zh_Hans: 从 SerpApi 获取您的 SerpApi API key # Chinese help text
url: https://serpapi.com/manage-api-key # Credential field help link
```
- `type`: Credential field type, currently can be either `secret-input`, `text-input`, or `select` , corresponding to password input box, text input box, and drop-down box, respectively. If set to `secret-input`, it will mask the input content on the frontend, and the backend will encrypt the input content.
## 3. Prepare Tool yaml
A provider can have multiple tools, each tool needs a yaml file to describe, this file contains the basic information, parameters, output, etc. of the tool.
Still taking GoogleSearch as an example, we need to create a `tools` module under the `google` module, and create `tools/google_search.yaml`, the content is as follows.
```yaml
identity: # Basic information of the tool
name: google_search # Tool name, unique, no duplication with other tools
author: Dify # Author
label: # Label for frontend display
en_US: GoogleSearch # English label
zh_Hans: 谷歌搜索 # Chinese label
description: # Description for frontend display
human: # Introduction for frontend display, supports multiple languages
en_US: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query.
zh_Hans: 一个用于执行 Google SERP 搜索并提取片段和网页的工具。输入应该是一个搜索查询。
llm: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query. # Introduction passed to LLM, in order to make LLM better understand this tool, we suggest to write as detailed information about this tool as possible here, so that LLM can understand and use this tool
parameters: # Parameter list
- name: query # Parameter name
type: string # Parameter type
required: true # Required or not
label: # Parameter label
en_US: Query string # English label
zh_Hans: 查询语句 # Chinese label
human_description: # Introduction for frontend display, supports multiple languages
en_US: used for searching
zh_Hans: 用于搜索网页内容
llm_description: key words for searching # Introduction passed to LLM, similarly, in order to make LLM better understand this parameter, we suggest to write as detailed information about this parameter as possible here, so that LLM can understand this parameter
form: llm # Form type, llm means this parameter needs to be inferred by Agent, the frontend will not display this parameter
- name: result_type
type: select # Parameter type
required: true
options: # Drop-down box options
- value: text
label:
en_US: text
zh_Hans: 文本
- value: link
label:
en_US: link
zh_Hans: 链接
default: link
label:
en_US: Result type
zh_Hans: 结果类型
human_description:
en_US: used for selecting the result type, text or link
zh_Hans: 用于选择结果类型，使用文本还是链接进行展示
form: form # Form type, form means this parameter needs to be filled in by the user on the frontend before the conversation starts
```
- The `identity` field is mandatory, it contains the basic information of the tool, including name, author, label, description, etc.
- `parameters` Parameter list
- `name` Parameter name, unique, no duplication with other parameters
- `type` Parameter type, currently supports `string`, `number`, `boolean`, `select` four types, corresponding to string, number, boolean, drop-down box
- `required` Required or not
- In `llm` mode, if the parameter is required, the Agent is required to infer this parameter
- In `form` mode, if the parameter is required, the user is required to fill in this parameter on the frontend before the conversation starts
- `options` Parameter options
- In `llm` mode, Dify will pass all options to LLM, LLM can infer based on these options
- In `form` mode, when `type` is `select`, the frontend will display these options
- `default` Default value
- `label` Parameter label, for frontend display
- `human_description` Introduction for frontend display, supports multiple languages
- `llm_description` Introduction passed to LLM, in order to make LLM better understand this parameter, we suggest to write as detailed information about this parameter as possible here, so that LLM can understand this parameter
- `form` Form type, currently supports `llm`, `form` two types, corresponding to Agent self-inference and frontend filling
## 4. Add Tool Logic
After completing the tool configuration, we can start writing the tool code that defines how it is invoked.
Create `google_search.py` under the `google/tools` module, the content is as follows.
```python
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from typing import Any, Dict, List, Union
class GoogleSearchTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
query = tool_paramters['query']
result_type = tool_paramters['result_type']
api_key = self.runtime.credentials['serpapi_api_key']
# TODO: search with serpapi
result = SerpAPI(api_key).run(query, result_type=result_type)
if result_type == 'text':
return self.create_text_message(text=result)
return self.create_link_message(link=result)
```
### Parameters
The overall logic of the tool is in the `_invoke` method, this method accepts two parameters: `user_id` and `tool_paramters`, which represent the user ID and tool parameters respectively
### Return Data
When the tool returns, you can choose to return one message or multiple messages, here we return one message, using `create_text_message` and `create_link_message` can create a text message or a link message.
## 5. Add Provider Code
Finally, we need to create a provider class under the provider module to implement the provider's credential verification logic. If the credential verification fails, it will throw a `ToolProviderCredentialValidationError` exception.
Create `google.py` under the `google` module, the content is as follows.
```python
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolProviderType
from core.tools.tool.tool import Tool
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.google.tools.google_search import GoogleSearchTool
from typing import Any, Dict
class GoogleProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
# 1. Here you need to instantiate a GoogleSearchTool with GoogleSearchTool(), it will automatically load the yaml configuration of GoogleSearchTool, but at this time it does not have credential information inside
# 2. Then you need to use the fork_tool_runtime method to pass the current credential information to GoogleSearchTool
# 3. Finally, invoke it, the parameters need to be passed according to the parameter rules configured in the yaml of GoogleSearchTool
GoogleSearchTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"query": "test",
"result_type": "link"
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
```
## Completion
After the above steps are completed, we can see this tool on the frontend, and it can be used in the Agent.
Of course, because google_search needs a credential, before using it, you also need to input your credentials on the frontend.
![Alt text](../zh_Hans/images/index/image-2.png)
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# 高级接入Tool
在开始高级接入之前，请确保你已经阅读过[快速接入](./tool_scale_out.md)，并对Dify的工具接入流程有了基本的了解。
## 工具接口
我们在`Tool`类中定义了一系列快捷方法，用于帮助开发者快速构较为复杂的工具
### 消息返回
Dify支持`文本` `链接` `图片` `文件BLOB` 等多种消息类型，你可以通过以下几个接口返回不同类型的消息给LLM和用户。
注意，在下面的接口中的部分参数将在后面的章节中介绍。
#### 图片URL
只需要传递图片的URL即可，Dify会自动下载图片并返回给用户。
```python
def create_image_message(self, image: str, save_as: str = '') -> ToolInvokeMessage:
"""
create an image message
:param image: the url of the image
:return: the image message
"""
```
#### 链接
如果你需要返回一个链接，可以使用以下接口。
```python
def create_link_message(self, link: str, save_as: str = '') -> ToolInvokeMessage:
"""
create a link message
:param link: the url of the link
:return: the link message
"""
```
#### 文本
如果你需要返回一个文本消息，可以使用以下接口。
```python
def create_text_message(self, text: str, save_as: str = '') -> ToolInvokeMessage:
"""
create a text message
:param text: the text of the message
:return: the text message
"""
```
#### 文件BLOB
如果你需要返回文件的原始数据，如图片、音频、视频、PPT、Word、Excel等，可以使用以下接口。
- `blob` 文件的原始数据，bytes类型
- `meta` 文件的元数据，如果你知道该文件的类型，最好传递一个`mime_type`，否则Dify将使用`octet/stream`作为默认类型
```python
def create_blob_message(self, blob: bytes, meta: dict = None, save_as: str = '') -> ToolInvokeMessage:
"""
create a blob message
:param blob: the blob
:return: the blob message
"""
```
### 快捷工具
在大模型应用中，我们有两种常见的需求：
- 先将很长的文本进行提前总结，然后再将总结内容传递给LLM，以防止原文本过长导致LLM无法处理
- 工具获取到的内容是一个链接，需要爬取网页信息后再返回给LLM
为了帮助开发者快速实现这两种需求，我们提供了以下两个快捷工具。
#### 文本总结工具
该工具需要传入user_id和需要进行总结的文本，返回一个总结后的文本，Dify会使用当前工作空间的默认模型对长文本进行总结。
```python
def summary(self, user_id: str, content: str) -> str:
"""
summary the content
:param user_id: the user id
:param content: the content
:return: the summary
"""
```
#### 网页爬取工具
该工具需要传入需要爬取的网页链接和一个user_agent（可为空），返回一个包含该网页信息的字符串，其中`user_agent`是可选参数，可以用来识别工具，如果不传递，Dify将使用默认的`user_agent`。
```python
def get_url(self, url: str, user_agent: str = None) -> str:
"""
get url
""" the crawled result
```
### 变量池
我们在`Tool`中引入了一个变量池，用于存储工具运行过程中产生的变量、文件等，这些变量可以在工具运行过程中被其他工具使用。
下面，我们以`DallE3`和`Vectorizer.AI`为例，介绍如何使用变量池。
- `DallE3`是一个图片生成工具，它可以根据文本生成图片，在这里，我们将让`DallE3`生成一个咖啡厅的Logo
- `Vectorizer.AI`是一个矢量图转换工具，它可以将图片转换为矢量图，使得图片可以无限放大而不失真，在这里，我们将`DallE3`生成的PNG图标转换为矢量图，从而可以真正被设计师使用。
#### DallE3
首先我们使用DallE3，在创建完图片以后，我们将图片保存到变量池中，代码如下
```python
from typing import Any, Dict, List, Union
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.tool.builtin_tool import BuiltinTool
from base64 import b64decode
from openai import OpenAI
class DallE3Tool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
client = OpenAI(
api_key=self.runtime.credentials['openai_api_key'],
)
# prompt
prompt = tool_paramters.get('prompt', '')
if not prompt:
return self.create_text_message('Please input prompt')
# call openapi dalle3
response = client.images.generate(
prompt=prompt, model='dall-e-3',
size='1024x1024', n=1, style='vivid', quality='standard',
response_format='b64_json'
)
result = []
for image in response.data:
# 将所有图片通过save_as参数保存到变量池中，变量名为self.VARIABLE_KEY.IMAGE.value，如果如果后续有新的图片生成，那么将会覆盖之前的图片
result.append(self.create_blob_message(blob=b64decode(image.b64_json),
meta={ 'mime_type': 'image/png' },
save_as=self.VARIABLE_KEY.IMAGE.value))
return result
```
我们可以注意到这里我们使用了`self.VARIABLE_KEY.IMAGE.value`作为图片的变量名，为了便于开发者们的工具能够互相配合，我们定义了这个`KEY`，大家可以自由使用，也可以不使用这个`KEY`，传递一个自定义的KEY也是可以的。
#### Vectorizer.AI
接下来我们使用Vectorizer.AI，将DallE3生成的PNG图标转换为矢量图，我们先来过一遍我们在这里定义的函数，代码如下
```python
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from base64 import b64decode
class VectorizerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
工具调用，图片变量名需要从这里传递进来，从而我们就可以从变量池中获取到图片
"""
def get_runtime_parameters(self) -> List[ToolParamter]:
"""
重写工具参数列表，我们可以根据当前变量池里的实际情况来动态生成参数列表，从而LLM可以根据参数列表来生成表单
"""
def is_tool_avaliable(self) -> bool:
"""
当前工具是否可用，如果当前变量池中没有图片，那么我们就不需要展示这个工具，这里返回False即可
"""
```
接下来我们来实现这三个函数
```python
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from base64 import b64decode
class VectorizerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
api_key_name = self.runtime.credentials.get('api_key_name', None)
api_key_value = self.runtime.credentials.get('api_key_value', None)
if not api_key_name or not api_key_value:
raise ToolProviderCredentialValidationError('Please input api key name and value')
# 获取image_id，image_id的定义可以在get_runtime_parameters中找到
image_id = tool_paramters.get('image_id', '')
if not image_id:
return self.create_text_message('Please input image id')
# 从变量池中获取到之前DallE生成的图片
image_binary = self.get_variable_file(self.VARIABLE_KEY.IMAGE)
if not image_binary:
return self.create_text_message('Image not found, please request user to generate image firstly.')
# 生成矢量图
response = post(
'https://vectorizer.ai/api/v1/vectorize',
files={ 'image': image_binary },
data={ 'mode': 'test' },
auth=(api_key_name, api_key_value),
timeout=30
)
if response.status_code != 200:
raise Exception(response.text)
return [
self.create_text_message('the vectorized svg is saved as an image.'),
self.create_blob_message(blob=response.content,
meta={'mime_type': 'image/svg+xml'})
]
def get_runtime_parameters(self) -> List[ToolParamter]:
"""
override the runtime parameters
"""
# 这里，我们重写了工具参数列表，定义了image_id，并设置了它的选项列表为当前变量池中的所有图片，这里的配置与yaml中的配置是一致的
return [
ToolParamter.get_simple_instance(
name='image_id',
llm_description=f'the image id that you want to vectorize, \
and the image id should be specified in \
{[i.name for i in self.list_default_image_variables()]}',
type=ToolParamter.ToolParameterType.SELECT,
required=True,
options=[i.name for i in self.list_default_image_variables()]
)
]
def is_tool_avaliable(self) -> bool:
# 只有当变量池中有图片时，LLM才需要使用这个工具
return len(self.list_default_image_variables()) > 0
```
可以注意到的是，我们这里其实并没有使用到`image_id`，我们已经假设了调用这个工具的时候一定有一张图片在默认的变量池中，所以直接使用了`image_binary = self.get_variable_file(self.VARIABLE_KEY.IMAGE)`来获取图片，在模型能力较弱的情况下，我们建议开发者们也这样做，可以有效提升容错率，避免模型传递错误的参数。
\ No newline at end of file
api/core/tools/docs/zh_Hans/tool_scale_out.md 0 → 100644
View file @ 86286e1a
# 快速接入Tool
这里我们以GoogleSearch为例，介绍如何快速接入一个工具。
## 1. 准备工具供应商yaml
### 介绍
这个yaml将包含工具供应商的信息，包括供应商名称、图标、作者等详细信息，以帮助前端灵活展示。
### 示例
我们需要在 `core/tools/provider/builtin`下创建一个`google`模块（文件夹），并创建`google.yaml`，名称必须与模块名称一致。
后续，我们关于这个工具的所有操作都将在这个模块下进行。
```yaml
identity: # 工具供应商的基本信息
author: Dify # 作者
name: google # 名称，唯一，不允许和其他供应商重名
label: # 标签，用于前端展示
en_US: Google # 英文标签
zh_Hans: Google # 中文标签
description: # 描述，用于前端展示
en_US: Google # 英文描述
zh_Hans: Google # 中文描述
icon: icon.svg # 图标，需要放置在当前模块的_assets文件夹下
```
- `identity` 字段是必须的，它包含了工具供应商的基本信息，包括作者、名称、标签、描述、图标等
- 图标需要放置在当前模块的`_assets`文件夹下，可以参考[这里](../../provider/builtin/google/_assets/icon.svg)。
## 2. 准备供应商凭据
Google作为一个第三方工具，使用了SerpApi提供的API，而SerpApi需要一个API Key才能使用，那么就意味着这个工具需要一个凭据才可以使用，而像`wikipedia`这样的工具，就不需要填写凭据字段，可以参考[这里](../../provider/builtin/wikipedia/wikipedia.yaml)。
配置好凭据字段后效果如下：
```yaml
identity:
author: Dify
name: google
label:
en_US: Google
zh_Hans: Google
description:
en_US: Google
zh_Hans: Google
icon: icon.svg
credentails_for_provider: # 凭据字段
serpapi_api_key: # 凭据字段名称
type: secret-input # 凭据字段类型
required: true # 是否必填
label: # 凭据字段标签
en_US: SerpApi API key # 英文标签
zh_Hans: SerpApi API key # 中文标签
placeholder: # 凭据字段占位符
en_US: Please input your SerpApi API key # 英文占位符
zh_Hans: 请输入你的 SerpApi API key # 中文占位符
help: # 凭据字段帮助文本
en_US: Get your SerpApi API key from SerpApi # 英文帮助文本
zh_Hans: 从 SerpApi 获取您的 SerpApi API key # 中文帮助文本
url: https://serpapi.com/manage-api-key # 凭据字段帮助链接
```
- `type`：凭据字段类型，目前支持`secret-input`、`text-input`、`select` 三种类型，分别对应密码输入框、文本输入框、下拉框，如果为`secret-input`，则会在前端隐藏输入内容，并且后端会对输入内容进行加密。
## 3. 准备工具yaml
一个供应商底下可以有多个工具，每个工具都需要一个yaml文件来描述，这个文件包含了工具的基本信息、参数、输出等。
仍然以GoogleSearch为例，我们需要在`google`模块下创建一个`tools`模块，并创建`tools/google_search.yaml`，内容如下。
```yaml
identity: # 工具的基本信息
name: google_search # 工具名称，唯一，不允许和其他工具重名
author: Dify # 作者
label: # 标签，用于前端展示
en_US: GoogleSearch # 英文标签
zh_Hans: 谷歌搜索 # 中文标签
description: # 描述，用于前端展示
human: # 用于前端展示的介绍，支持多语言
en_US: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query.
zh_Hans: 一个用于执行 Google SERP 搜索并提取片段和网页的工具。输入应该是一个搜索查询。
llm: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query. # 传递给LLM的介绍，为了使得LLM更好理解这个工具，我们建议在这里写上关于这个工具尽可能详细的信息，让LLM能够理解并使用这个工具
parameters: # 参数列表
- name: query # 参数名称
type: string # 参数类型
required: true # 是否必填
label: # 参数标签
en_US: Query string # 英文标签
zh_Hans: 查询语句 # 中文标签
human_description: # 用于前端展示的介绍，支持多语言
en_US: used for searching
zh_Hans: 用于搜索网页内容
llm_description: key words for searching # 传递给LLM的介绍，同上，为了使得LLM更好理解这个参数，我们建议在这里写上关于这个参数尽可能详细的信息，让LLM能够理解这个参数
form: llm # 表单类型，llm表示这个参数需要由Agent自行推理出来，前端将不会展示这个参数
- name: result_type
type: select # 参数类型
required: true
options: # 下拉框选项
- value: text
label:
en_US: text
zh_Hans: 文本
- value: link
label:
en_US: link
zh_Hans: 链接
default: link
label:
en_US: Result type
zh_Hans: 结果类型
human_description:
en_US: used for selecting the result type, text or link
zh_Hans: 用于选择结果类型，使用文本还是链接进行展示
form: form # 表单类型，form表示这个参数需要由用户在对话开始前在前端填写
```
- `identity` 字段是必须的，它包含了工具的基本信息，包括名称、作者、标签、描述等
- `parameters` 参数列表
- `name` 参数名称，唯一，不允许和其他参数重名
- `type` 参数类型，目前支持`string`、`number`、`boolean`、`select` 四种类型，分别对应字符串、数字、布尔值、下拉框
- `required` 是否必填
- 在`llm`模式下，如果参数为必填，则会要求Agent必须要推理出这个参数
- 在`form`模式下，如果参数为必填，则会要求用户在对话开始前在前端填写这个参数
- `options` 参数选项
- 在`llm`模式下，Dify会将所有选项传递给LLM，LLM可以根据这些选项进行推理
- 在`form`模式下，`type`为`select`时，前端会展示这些选项
- `default` 默认值
- `label` 参数标签，用于前端展示
- `human_description` 用于前端展示的介绍，支持多语言
- `llm_description` 传递给LLM的介绍，为了使得LLM更好理解这个参数，我们建议在这里写上关于这个参数尽可能详细的信息，让LLM能够理解这个参数
- `form` 表单类型，目前支持`llm`、`form`两种类型，分别对应Agent自行推理和前端填写
## 4. 准备工具代码
当完成工具的配置以后，我们就可以开始编写工具代码了，主要用于实现工具的逻辑。
在`google/tools`模块下创建`google_search.py`，内容如下。
```python
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from typing import Any, Dict, List, Union
class GoogleSearchTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
query = tool_paramters['query']
result_type = tool_paramters['result_type']
api_key = self.runtime.credentials['serpapi_api_key']
# TODO: search with serpapi
result = SerpAPI(api_key).run(query, result_type=result_type)
if result_type == 'text':
return self.create_text_message(text=result)
return self.create_link_message(link=result)
```
### 参数
工具的整体逻辑都在`_invoke`方法中，这个方法接收两个参数：`user_id`和`tool_paramters`，分别表示用户ID和工具参数
### 返回数据
在工具返回时，你可以选择返回一个消息或者多个消息，这里我们返回一个消息，使用`create_text_message`和`create_link_message`可以创建一个文本消息或者一个链接消息。
## 5. 准备供应商代码
最后，我们需要在供应商模块下创建一个供应商类，用于实现供应商的凭据验证逻辑，如果凭据验证失败，将会抛出`ToolProviderCredentialValidationError`异常。
在`google`模块下创建`google.py`，内容如下。
```python
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolProviderType
from core.tools.tool.tool import Tool
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.google.tools.google_search import GoogleSearchTool
from typing import Any, Dict
class GoogleProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
# 1. 此处需要使用GoogleSearchTool()实例化一个GoogleSearchTool，它会自动加载GoogleSearchTool的yaml配置，但是此时它内部没有凭据信息
# 2. 随后需要使用fork_tool_runtime方法，将当前的凭据信息传递给GoogleSearchTool
# 3. 最后invoke即可，参数需要根据GoogleSearchTool的yaml中配置的参数规则进行传递
GoogleSearchTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"query": "test",
"result_type": "link"
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
```
## 完成
当上述步骤完成以后，我们就可以在前端看到这个工具了，并且可以在Agent中使用这个工具。
当然，因为google_search需要一个凭据，在使用之前，还需要在前端配置它的凭据。
![Alt text](images/index/image-2.png)
api/core/tools/entities/common_entities.py 0 → 100644
View file @ 86286e1a
from typing import Optional
from pydantic import BaseModel
class I18nObject(BaseModel):
"""
Model class for i18n object.
"""
zh_Hans: Optional[str] = None
en_US: str
def __init__(self, **data):
super().__init__(**data)
if not self.zh_Hans:
self.zh_Hans = self.en_US
def to_dict(self) -> dict:
return {
'zh_Hans': self.zh_Hans,
'en_US': self.en_US,
}
\ No newline at end of file
api/core/tools/entities/constant.py 0 → 100644
View file @ 86286e1a
class DEFAULT_PROVIDERS:
API_BASED = '__api_based'
APP_BASED = '__app_based'
\ No newline at end of file
api/core/tools/entities/tool_bundle.py 0 → 100644
View file @ 86286e1a
from pydantic import BaseModel
from typing import Dict, Optional, Any, List
from core.tools.entities.tool_entities import ToolProviderType, ToolParamter
class ApiBasedToolBundle(BaseModel):
"""
This class is used to store the schema information of an api based tool. such as the url, the method, the parameters, etc.
"""
# server_url
server_url: str
# method
method: str
# summary
summary: Optional[str] = None
# operation_id
operation_id: str = None
# parameters
parameters: Optional[List[ToolParamter]] = None
# author
author: str
# icon
icon: Optional[str] = None
# openapi operation
openapi: dict
class AppToolBundle(BaseModel):
"""
This class is used to store the schema information of an tool for an app.
"""
type: ToolProviderType
credential: Optional[Dict[str, Any]] = None
provider_id: str
tool_name: str
\ No newline at end of file
api/core/tools/entities/tool_entities.py 0 → 100644
View file @ 86286e1a
from pydantic import BaseModel, Field
from enum import Enum
from typing import Optional, List, Dict, Any, Union, cast
from core.tools.entities.common_entities import I18nObject
class ToolProviderType(Enum):
"""
Enum class for tool provider
"""
BUILT_IN = "built-in"
APP_BASED = "app-based"
API_BASED = "api-based"
@classmethod
def value_of(cls, value: str) -> 'ToolProviderType':
"""
Get value of given mode.
:param value: mode value
:return: mode
"""
for mode in cls:
if mode.value == value:
return mode
raise ValueError(f'invalid mode value {value}')
class ApiProviderSchemaType(Enum):
"""
Enum class for api provider schema type.
"""
OPENAPI = "openapi"
SWAGGER = "swagger"
OPENAI_PLUGIN = "openai_plugin"
OPENAI_ACTIONS = "openai_actions"
@classmethod
def value_of(cls, value: str) -> 'ApiProviderSchemaType':
"""
Get value of given mode.
:param value: mode value
:return: mode
"""
for mode in cls:
if mode.value == value:
return mode
raise ValueError(f'invalid mode value {value}')
class ApiProviderAuthType(Enum):
"""
Enum class for api provider auth type.
"""
NONE = "none"
API_KEY = "api_key"
@classmethod
def value_of(cls, value: str) -> 'ApiProviderAuthType':
"""
Get value of given mode.
:param value: mode value
:return: mode
"""
for mode in cls:
if mode.value == value:
return mode
raise ValueError(f'invalid mode value {value}')
class ToolInvokeMessage(BaseModel):
class MessageType(Enum):
TEXT = "text"
IMAGE = "image"
LINK = "link"
BLOB = "blob"
IMAGE_LINK = "image_link"
type: MessageType = MessageType.TEXT
"""
plain text, image url or link url
"""
message: Union[str, bytes] = None
meta: Dict[str, Any] = None
save_as: str = ''
class ToolInvokeMessageBinary(BaseModel):
mimetype: str = Field(..., description="The mimetype of the binary")
url: str = Field(..., description="The url of the binary")
save_as: str = ''
class ToolParamterOption(BaseModel):
value: str = Field(..., description="The value of the option")
label: I18nObject = Field(..., description="The label of the option")
class ToolParamter(BaseModel):
class ToolParameterType(Enum):
STRING = "string"
NUMBER = "number"
BOOLEAN = "boolean"
SELECT = "select"
class ToolParameterForm(Enum):
SCHEMA = "schema" # should be set while adding tool
FORM = "form" # should be set before invoking tool
LLM = "llm" # will be set by LLM
name: str = Field(..., description="The name of the parameter")
label: I18nObject = Field(..., description="The label presented to the user")
human_description: I18nObject = Field(..., description="The description presented to the user")
type: ToolParameterType = Field(..., description="The type of the parameter")
form: ToolParameterForm = Field(..., description="The form of the parameter, schema/form/llm")
llm_description: Optional[str] = None
required: Optional[bool] = False
default: Optional[str] = None
min: Optional[Union[float, int]] = None
max: Optional[Union[float, int]] = None
options: Optional[List[ToolParamterOption]] = None
@classmethod
def get_simple_instance(cls,
name: str, llm_description: str, type: ToolParameterType,
required: bool, options: Optional[List[str]] = None) -> 'ToolParamter':
"""
get a simple tool parameter
:param name: the name of the parameter
:param llm_description: the description presented to the LLM
:param type: the type of the parameter
:param required: if the parameter is required
:param options: the options of the parameter
"""
# convert options to ToolParamterOption
if options:
options = [ToolParamterOption(value=option, label=I18nObject(en_US=option, zh_Hans=option)) for option in options]
return cls(
name=name,
label=I18nObject(en_US='', zh_Hans=''),
human_description=I18nObject(en_US='', zh_Hans=''),
type=type,
form=cls.ToolParameterForm.LLM,
llm_description=llm_description,
required=required,
options=options,
)
class ToolProviderIdentity(BaseModel):
author: str = Field(..., description="The author of the tool")
name: str = Field(..., description="The name of the tool")
description: I18nObject = Field(..., description="The description of the tool")
icon: str = Field(..., description="The icon of the tool")
label: I18nObject = Field(..., description="The label of the tool")
class ToolDescription(BaseModel):
human: I18nObject = Field(..., description="The description presented to the user")
llm: str = Field(..., description="The description presented to the LLM")
class ToolIdentity(BaseModel):
author: str = Field(..., description="The author of the tool")
name: str = Field(..., description="The name of the tool")
label: I18nObject = Field(..., description="The label of the tool")
class ToolCredentialsOption(BaseModel):
value: str = Field(..., description="The value of the option")
label: I18nObject = Field(..., description="The label of the option")
class ToolProviderCredentials(BaseModel):
class CredentialsType(Enum):
SECRET_INPUT = "secret-input"
TEXT_INPUT = "text-input"
SELECT = "select"
@classmethod
def value_of(cls, value: str) -> "ToolProviderCredentials.CredentialsType":
"""
Get value of given mode.
:param value: mode value
:return: mode
"""
for mode in cls:
if mode.value == value:
return mode
raise ValueError(f'invalid mode value {value}')
@staticmethod
def defaut(value: str) -> str:
return ""
name: str = Field(..., description="The name of the credentials")
type: CredentialsType = Field(..., description="The type of the credentials")
required: bool = False
default: Optional[str] = None
options: Optional[List[ToolCredentialsOption]] = None
label: Optional[I18nObject] = None
help: Optional[I18nObject] = None
url: Optional[str] = None
placeholder: Optional[I18nObject] = None
def to_dict(self) -> dict:
return {
'name': self.name,
'type': self.type.value,
'required': self.required,
'default': self.default,
'options': self.options,
'help': self.help.to_dict() if self.help else None,
'label': self.label.to_dict(),
'url': self.url,
'placeholder': self.placeholder.to_dict() if self.placeholder else None,
}
class ToolRuntimeVariableType(Enum):
TEXT = "text"
IMAGE = "image"
class ToolRuntimeVariable(BaseModel):
type: ToolRuntimeVariableType = Field(..., description="The type of the variable")
name: str = Field(..., description="The name of the variable")
position: int = Field(..., description="The position of the variable")
tool_name: str = Field(..., description="The name of the tool")
class ToolRuntimeTextVariable(ToolRuntimeVariable):
value: str = Field(..., description="The value of the variable")
class ToolRuntimeImageVariable(ToolRuntimeVariable):
value: str = Field(..., description="The path of the image")
class ToolRuntimeVariablePool(BaseModel):
conversation_id: str = Field(..., description="The conversation id")
user_id: str = Field(..., description="The user id")
tenant_id: str = Field(..., description="The tenant id of assistant")
pool: List[ToolRuntimeVariable] = Field(..., description="The pool of variables")
def __init__(self, **data: Any):
pool = data.get('pool', [])
# convert pool into correct type
for index, variable in enumerate(pool):
if variable['type'] == ToolRuntimeVariableType.TEXT.value:
pool[index] = ToolRuntimeTextVariable(**variable)
elif variable['type'] == ToolRuntimeVariableType.IMAGE.value:
pool[index] = ToolRuntimeImageVariable(**variable)
super().__init__(**data)
def dict(self) -> dict:
return {
'conversation_id': self.conversation_id,
'user_id': self.user_id,
'tenant_id': self.tenant_id,
'pool': [variable.dict() for variable in self.pool],
}
def set_text(self, tool_name: str, name: str, value: str) -> None:
"""
set a text variable
"""
for variable in self.pool:
if variable.name == name:
if variable.type == ToolRuntimeVariableType.TEXT:
variable = cast(ToolRuntimeTextVariable, variable)
variable.value = value
return
variable = ToolRuntimeTextVariable(
type=ToolRuntimeVariableType.TEXT,
name=name,
position=len(self.pool),
tool_name=tool_name,
value=value,
)
self.pool.append(variable)
def set_file(self, tool_name: str, value: str, name: str = None) -> None:
"""
set an image variable
:param tool_name: the name of the tool
:param value: the id of the file
"""
# check how many image variables are there
image_variable_count = 0
for variable in self.pool:
if variable.type == ToolRuntimeVariableType.IMAGE:
image_variable_count += 1
if name is None:
name = f"file_{image_variable_count}"
for variable in self.pool:
if variable.name == name:
if variable.type == ToolRuntimeVariableType.IMAGE:
variable = cast(ToolRuntimeImageVariable, variable)
variable.value = value
return
variable = ToolRuntimeImageVariable(
type=ToolRuntimeVariableType.IMAGE,
name=name,
position=len(self.pool),
tool_name=tool_name,
value=value,
)
self.pool.append(variable)
\ No newline at end of file
api/core/tools/entities/user_entities.py 0 → 100644
View file @ 86286e1a
from pydantic import BaseModel
from enum import Enum
from typing import List, Dict, Optional
from core.tools.entities.common_entities import I18nObject
from core.tools.entities.tool_entities import ToolProviderCredentials
from core.tools.tool.tool import ToolParamter
class UserToolProvider(BaseModel):
class ProviderType(Enum):
BUILTIN = "builtin"
APP = "app"
API = "api"
id: str
author: str
name: str # identifier
description: I18nObject
icon: str
label: I18nObject # label
type: ProviderType
team_credentials: dict = None
is_team_authorization: bool = False
allow_delete: bool = True
def to_dict(self) -> dict:
return {
'id': self.id,
'author': self.author,
'name': self.name,
'description': self.description.to_dict(),
'icon': self.icon,
'label': self.label.to_dict(),
'type': self.type.value,
'team_credentials': self.team_credentials,
'is_team_authorization': self.is_team_authorization,
'allow_delete': self.allow_delete
}
class UserToolProviderCredentials(BaseModel):
credentails: Dict[str, ToolProviderCredentials]
class UserTool(BaseModel):
author: str
name: str # identifier
label: I18nObject # label
description: I18nObject
parameters: Optional[List[ToolParamter]]
\ No newline at end of file
api/core/tools/errors.py 0 → 100644
View file @ 86286e1a
class ToolProviderNotFoundError(ValueError):
pass
class ToolNotFoundError(ValueError):
pass
class ToolParamterValidationError(ValueError):
pass
class ToolProviderCredentialValidationError(ValueError):
pass
class ToolNotSupportedError(ValueError):
pass
class ToolInvokeError(ValueError):
pass
class ToolApiSchemaError(ValueError):
pass
\ No newline at end of file
api/core/tools/model/errors.py 0 → 100644
View file @ 86286e1a
class InvokeModelError(Exception):
pass
\ No newline at end of file
api/core/tools/model/tool_model_manager.py 0 → 100644
View file @ 86286e1a
"""
For some reason, model will be used in tools like WebScraperTool, WikipediaSearchTool etc.
Therefore, a model manager is needed to list/invoke/validate models.
"""
from core.model_runtime.entities.message_entities import PromptMessage
from core.model_runtime.entities.llm_entities import LLMResult
from core.model_runtime.entities.model_entities import ModelType
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel, ModelPropertyKey
from core.model_runtime.errors.invoke import InvokeRateLimitError, InvokeBadRequestError, \
InvokeConnectionError, InvokeAuthorizationError, InvokeServerUnavailableError
from core.model_runtime.utils.encoders import jsonable_encoder
from core.model_manager import ModelManager
from core.tools.model.errors import InvokeModelError
from extensions.ext_database import db
from models.tools import ToolModelInvoke
from typing import List, cast
import json
class ToolModelManager:
@staticmethod
def get_max_llm_context_tokens(
tenant_id: str,
) -> int:
"""
get max llm context tokens of the model
"""
model_manager = ModelManager()
model_instance = model_manager.get_default_model_instance(
tenant_id=tenant_id, model_type=ModelType.LLM,
)
if not model_instance:
raise InvokeModelError(f'Model not found')
llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
schema = llm_model.get_model_schema(model_instance.model, model_instance.credentials)
if not schema:
raise InvokeModelError(f'No model schema found')
max_tokens = schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE, None)
if max_tokens is None:
return 2048
return max_tokens
@staticmethod
def calculate_tokens(
tenant_id: str,
prompt_messages: List[PromptMessage]
) -> int:
"""
calculate tokens from prompt messages and model parameters
"""
# get model instance
model_manager = ModelManager()
model_instance = model_manager.get_default_model_instance(
tenant_id=tenant_id, model_type=ModelType.LLM
)
if not model_instance:
raise InvokeModelError(f'Model not found')
llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
# get tokens
tokens = llm_model.get_num_tokens(model_instance.model, model_instance.credentials, prompt_messages)
return tokens
@staticmethod
def invoke(
user_id: str, tenant_id: str,
tool_type: str, tool_name: str,
prompt_messages: List[PromptMessage]
) -> LLMResult:
"""
invoke model with parameters in user's own context
:param user_id: user id
:param tenant_id: tenant id, the tenant id of the creator of the tool
:param tool_provider: tool provider
:param tool_id: tool id
:param tool_name: tool name
:param provider: model provider
:param model: model name
:param model_parameters: model parameters
:param prompt_messages: prompt messages
:return: AssistantPromptMessage
"""
# get model manager
model_manager = ModelManager()
# get model instance
model_instance = model_manager.get_default_model_instance(
tenant_id=tenant_id, model_type=ModelType.LLM,
)
llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
# get model credentials
model_credentials = model_instance.credentials
# get prompt tokens
prompt_tokens = llm_model.get_num_tokens(model_instance.model, model_credentials, prompt_messages)
model_parameters = {
'temperature': 0.8,
'top_p': 0.8,
}
# create tool model invoke
tool_model_invoke = ToolModelInvoke(
user_id=user_id,
tenant_id=tenant_id,
provider=model_instance.provider,
tool_type=tool_type,
tool_name=tool_name,
model_parameters=json.dumps(model_parameters),
prompt_messages=json.dumps(jsonable_encoder(prompt_messages)),
model_response='',
prompt_tokens=prompt_tokens,
answer_tokens=0,
answer_unit_price=0,
answer_price_unit=0,
provider_response_latency=0,
total_price=0,
currency='USD',
)
db.session.add(tool_model_invoke)
db.session.commit()
try:
response: LLMResult = llm_model.invoke(
model=model_instance.model,
credentials=model_credentials,
prompt_messages=prompt_messages,
model_parameters=model_parameters,
tools=[], stop=[], stream=False, user=user_id, callbacks=[]
)
except InvokeRateLimitError as e:
raise InvokeModelError(f'Invoke rate limit error: {e}')
except InvokeBadRequestError as e:
raise InvokeModelError(f'Invoke bad request error: {e}')
except InvokeConnectionError as e:
raise InvokeModelError(f'Invoke connection error: {e}')
except InvokeAuthorizationError as e:
raise InvokeModelError(f'Invoke authorization error')
except InvokeServerUnavailableError as e:
raise InvokeModelError(f'Invoke server unavailable error: {e}')
except Exception as e:
raise InvokeModelError(f'Invoke error: {e}')
# update tool model invoke
tool_model_invoke.model_response = response.message.content
if response.usage:
tool_model_invoke.answer_tokens = response.usage.completion_tokens
tool_model_invoke.answer_unit_price = response.usage.completion_unit_price
tool_model_invoke.answer_price_unit = response.usage.completion_price_unit
tool_model_invoke.provider_response_latency = response.usage.latency
tool_model_invoke.total_price = response.usage.total_price
tool_model_invoke.currency = response.usage.currency
db.session.commit()
return response
\ No newline at end of file
api/core/tools/prompt/template.py 0 → 100644
View file @ 86286e1a
ENGLISH_REACT_COMPLETION_PROMPT_TEMPLATES = """Respond to the human as helpfully and accurately as possible.
{{instruction}}
You have access to the following tools:
{{tools}}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid "action" values: "Final Answer" or {{tool_names}}
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $ACTION_INPUT
}
```
Follow this format:
Question: input question to answer
Thought: consider previous and subsequent steps
Action:
```
$JSON_BLOB
```
Observation: action result
... (repeat Thought/Action/Observation N times)
Thought: I know what to respond
Action:
```
{
"action": "Final Answer",
"action_input": "Final response to human"
}
```
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
Question: {{query}}
Thought: {{agent_scratchpad}}"""
ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES = """Observation: {{observation}}
Thought:"""
ENGLISH_REACT_CHAT_PROMPT_TEMPLATES = """Respond to the human as helpfully and accurately as possible.
{{instruction}}
You have access to the following tools:
{{tools}}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid "action" values: "Final Answer" or {{tool_names}}
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $ACTION_INPUT
}
```
Follow this format:
Question: input question to answer
Thought: consider previous and subsequent steps
Action:
```
$JSON_BLOB
```
Observation: action result
... (repeat Thought/Action/Observation N times)
Thought: I know what to respond
Action:
```
{
"action": "Final Answer",
"action_input": "Final response to human"
}
```
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
"""
ENGLISH_REACT_CHAT_AGENT_SCRATCHPAD_TEMPLATES = ""
REACT_PROMPT_TEMPLATES = {
'english': {
'chat': {
'prompt': ENGLISH_REACT_CHAT_PROMPT_TEMPLATES,
'agent_scratchpad': ENGLISH_REACT_CHAT_AGENT_SCRATCHPAD_TEMPLATES
},
'completion': {
'prompt': ENGLISH_REACT_COMPLETION_PROMPT_TEMPLATES,
'agent_scratchpad': ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES
}
}
}
\ No newline at end of file
api/core/tools/provider/api_tool_provider.py 0 → 100644
View file @ 86286e1a
from typing import Any, Dict, List
from core.tools.entities.tool_entities import ToolProviderType, ApiProviderAuthType, ToolProviderCredentials, ToolCredentialsOption
from core.tools.entities.common_entities import I18nObject
from core.tools.entities.tool_bundle import ApiBasedToolBundle
from core.tools.tool.tool import Tool
from core.tools.tool.api_tool import ApiTool
from core.tools.provider.tool_provider import ToolProviderController
from extensions.ext_database import db
from models.tools import ApiToolProvider
class ApiBasedToolProviderController(ToolProviderController):
@staticmethod
def from_db(db_provider: ApiToolProvider, auth_type: ApiProviderAuthType) -> 'ApiBasedToolProviderController':
credentials_schema = {
'auth_type': ToolProviderCredentials(
name='auth_type',
required=True,
type=ToolProviderCredentials.CredentialsType.SELECT,
options=[
ToolCredentialsOption(value='none', label=I18nObject(en_US='None', zh_Hans='无')),
ToolCredentialsOption(value='api_key', label=I18nObject(en_US='api_key', zh_Hans='api_key'))
],
default='none',
help=I18nObject(
en_US='The auth type of the api provider',
zh_Hans='api provider 的认证类型'
)
)
}
if auth_type == ApiProviderAuthType.API_KEY:
credentials_schema = {
**credentials_schema,
'api_key_header': ToolProviderCredentials(
name='api_key_header',
required=False,
default='api_key',
type=ToolProviderCredentials.CredentialsType.TEXT_INPUT,
help=I18nObject(
en_US='The header name of the api key',
zh_Hans='携带 api key 的 header 名称'
)
),
'api_key_value': ToolProviderCredentials(
name='api_key_value',
required=True,
type=ToolProviderCredentials.CredentialsType.SECRET_INPUT,
help=I18nObject(
en_US='The api key',
zh_Hans='api key的值'
)
)
}
elif auth_type == ApiProviderAuthType.NONE:
pass
else:
raise ValueError(f'invalid auth type {auth_type}')
return ApiBasedToolProviderController(**{
'identity': {
'author': db_provider.user.name if db_provider.user_id and db_provider.user else '',
'name': db_provider.name,
'label': {
'en_US': db_provider.name,
'zh_Hans': db_provider.name
},
'description': {
'en_US': db_provider.description,
'zh_Hans': db_provider.description
},
'icon': db_provider.icon
},
'credentials_schema': credentials_schema
})
@property
def app_type(self) -> ToolProviderType:
return ToolProviderType.API_BASED
def _validate_credentials(self, tool_name: str, credentials: Dict[str, Any]) -> None:
pass
def validate_parameters(self, tool_name: str, tool_parameters: Dict[str, Any]) -> None:
pass
def _parse_tool_bundle(self, tool_bundle: ApiBasedToolBundle) -> ApiTool:
"""
parse tool bundle to tool
:param tool_bundle: the tool bundle
:return: the tool
"""
return ApiTool(**{
'api_bundle': tool_bundle,
'identity' : {
'author': tool_bundle.author,
'name': tool_bundle.operation_id,
'label': {
'en_US': tool_bundle.operation_id,
'zh_Hans': tool_bundle.operation_id
},
'icon': tool_bundle.icon if tool_bundle.icon else ''
},
'description': {
'human': {
'en_US': tool_bundle.summary or '',
'zh_Hans': tool_bundle.summary or ''
},
'llm': tool_bundle.summary or ''
},
'parameters' : tool_bundle.parameters if tool_bundle.parameters else [],
})
def load_bundled_tools(self, tools: List[ApiBasedToolBundle]) -> List[ApiTool]:
"""
load bundled tools
:param tools: the bundled tools
:return: the tools
"""
self.tools = [self._parse_tool_bundle(tool) for tool in tools]
return self.tools
def get_tools(self, user_id: str, tanent_id: str) -> List[ApiTool]:
"""
fetch tools from database
:param user_id: the user id
:param tanent_id: the tanent id
:return: the tools
"""
if self.tools is not None:
return self.tools
tools: List[Tool] = []
# get tanent api providers
db_providers: List[ApiToolProvider] = db.session.query(ApiToolProvider).filter(
ApiToolProvider.tenant_id == tanent_id,
ApiToolProvider.name == self.identity.name
).all()
if db_providers and len(db_providers) != 0:
for db_provider in db_providers:
for tool in db_provider.tools:
assistant_tool = self._parse_tool_bundle(tool)
assistant_tool.is_team_authorization = True
tools.append(assistant_tool)
self.tools = tools
return tools
def get_tool(self, tool_name: str) -> ApiTool:
"""
get tool by name
:param tool_name: the name of the tool
:return: the tool
"""
if self.tools is None:
self.get_tools()
for tool in self.tools:
if tool.identity.name == tool_name:
return tool
raise ValueError(f'tool {tool_name} not found')
\ No newline at end of file
api/core/tools/provider/app_tool_provider.py 0 → 100644
View file @ 86286e1a
from typing import Any, Dict, List
from core.tools.entities.tool_entities import ToolProviderType, ToolParamter, ToolParamterOption
from core.tools.tool.tool import Tool
from core.tools.entities.common_entities import I18nObject
from core.tools.provider.tool_provider import ToolProviderController
from extensions.ext_database import db
from models.tools import PublishedAppTool
from models.model import App, AppModelConfig
import logging
logger = logging.getLogger(__name__)
class AppBasedToolProviderEntity(ToolProviderController):
@property
def app_type(self) -> ToolProviderType:
return ToolProviderType.APP_BASED
def _validate_credentials(self, tool_name: str, credentials: Dict[str, Any]) -> None:
pass
def validate_parameters(self, tool_name: str, tool_parameters: Dict[str, Any]) -> None:
pass
def get_tools(self, user_id: str) -> List[Tool]:
db_tools: List[PublishedAppTool] = db.session.query(PublishedAppTool).filter(
PublishedAppTool.user_id == user_id,
).all()
if not db_tools or len(db_tools) == 0:
return []
tools: List[Tool] = []
for db_tool in db_tools:
tool = {
'identity': {
'author': db_tool.author,
'name': db_tool.tool_name,
'label': {
'en_US': db_tool.tool_name,
'zh_Hans': db_tool.tool_name
},
'icon': ''
},
'description': {
'human': {
'en_US': db_tool.description_i18n.en_US,
'zh_Hans': db_tool.description_i18n.zh_Hans
},
'llm': db_tool.llm_description
},
'parameters': []
}
# get app from db
app: App = db_tool.app
if not app:
logger.error(f"app {db_tool.app_id} not found")
continue
app_model_config: AppModelConfig = app.app_model_config
user_input_form_list = app_model_config.user_input_form_list
for input_form in user_input_form_list:
# get type
form_type = input_form.keys()[0]
default = input_form[form_type]['default']
required = input_form[form_type]['required']
label = input_form[form_type]['label']
variable_name = input_form[form_type]['variable_name']
options = input_form[form_type].get('options', [])
if form_type == 'paragraph' or form_type == 'text-input':
tool['parameters'].append(ToolParamter(
name=variable_name,
label=I18nObject(
en_US=label,
zh_Hans=label
),
human_description=I18nObject(
en_US=label,
zh_Hans=label
),
llm_description=label,
form=ToolParamter.ToolParameterForm.FORM,
type=ToolParamter.ToolParameterType.STRING,
required=required,
default=default
))
elif form_type == 'select':
tool['parameters'].append(ToolParamter(
name=variable_name,
label=I18nObject(
en_US=label,
zh_Hans=label
),
human_description=I18nObject(
en_US=label,
zh_Hans=label
),
llm_description=label,
form=ToolParamter.ToolParameterForm.FORM,
type=ToolParamter.ToolParameterType.SELECT,
required=required,
default=default,
options=[ToolParamterOption(
value=option,
label=I18nObject(
en_US=option,
zh_Hans=option
)
) for option in options]
))
tools.append(Tool(**tool))
return tools
\ No newline at end of file
api/core/tools/provider/builtin/_positions.py 0 → 100644
View file @ 86286e1a
from core.tools.entities.user_entities import UserToolProvider
from typing import List
position = {
'google': 1,
'wikipedia': 2,
'dalle': 3,
'webscraper': 4,
'wolframalpha': 5,
'chart': 6,
'time': 7,
'yahoo': 8,
'stablediffusion': 9,
'vectorizer': 10,
'youtube': 11,
}
class BuiltinToolProviderSort:
@staticmethod
def sort(providers: List[UserToolProvider]) -> List[UserToolProvider]:
def sort_compare(provider: UserToolProvider) -> int:
return position.get(provider.name, 10000)
sorted_providers = sorted(providers, key=sort_compare)
return sorted_providers
\ No newline at end of file
api/core/tools/provider/builtin/chart/chart.py 0 → 100644
View file @ 86286e1a
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.chart.tools.line import LinearChartTool
import matplotlib.pyplot as plt
# use a business theme
plt.style.use('seaborn-v0_8-darkgrid')
class ChartProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: dict) -> None:
try:
LinearChartTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"data": "1,3,5,7,9,2,4,6,8,10",
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
api/core/tools/provider/builtin/chart/chart.yaml 0 → 100644
View file @ 86286e1a
identity:
author: Dify
name: chart
label:
en_US: ChartGenerator
zh_Hans: 图表生成
description:
en_US: Chart Generator is a tool for generating statistical charts like bar chart, line chart, pie chart, etc.
zh_Hans: 图表生成是一个用于生成可视化图表的工具，你可以通过它来生成柱状图、折线图、饼图等各类图表
icon: icon.png
credentails_for_provider:
api/core/tools/provider/builtin/chart/tools/bar.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
import matplotlib.pyplot as plt
import io
from typing import Any, Dict, List, Union
class BarChartTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
data = tool_paramters.get('data', '')
if not data:
return self.create_text_message('Please input data')
data = data.split(';')
# if all data is int, convert to int
if all([i.isdigit() for i in data]):
data = [int(i) for i in data]
else:
data = [float(i) for i in data]
axis = tool_paramters.get('x_axis', None) or None
if axis:
axis = axis.split(';')
if len(axis) != len(data):
axis = None
flg, ax = plt.subplots(figsize=(10, 8))
if axis:
axis = [label[:10] + '...' if len(label) > 10 else label for label in axis]
ax.set_xticklabels(axis, rotation=45, ha='right')
ax.bar(axis, data)
else:
ax.bar(range(len(data)), data)
buf = io.BytesIO()
flg.savefig(buf, format='png')
buf.seek(0)
plt.close(flg)
return [
self.create_text_message('the bar chart is saved as an image.'),
self.create_blob_message(blob=buf.read(),
meta={'mime_type': 'image/png'})
]
\ No newline at end of file
api/core/tools/provider/builtin/chart/tools/bar.yaml 0 → 100644
View file @ 86286e1a
identity:
name: bar_chart
author: Dify
label:
en_US: Bar Chart
zh_Hans: 柱状图
icon: icon.svg
description:
human:
en_US: Bar chart
zh_Hans: 柱状图
llm: generate a bar chart with input data
parameters:
- name: data
type: string
required: true
label:
en_US: data
zh_Hans: 数据
human_description:
en_US: data for generating bar chart
zh_Hans: 用于生成柱状图的数据
llm_description: data for generating bar chart, data should be a string contains a list of numbers like "1;2;3;4;5"
form: llm
- name: x_axis
type: string
required: false
label:
en_US: X Axis
zh_Hans: x 轴
human_description:
en_US: X axis for bar chart
zh_Hans: 柱状图的 x 轴
llm_description: x axis for bar chart, x axis should be a string contains a list of texts like "a;b;c;1;2" in order to match the data
form: llm
api/core/tools/provider/builtin/chart/tools/line.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
import matplotlib.pyplot as plt
import io
from typing import Any, Dict, List, Union
class LinearChartTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
data = tool_paramters.get('data', '')
if not data:
return self.create_text_message('Please input data')
data = data.split(';')
axis = tool_paramters.get('x_axis', None) or None
if axis:
axis = axis.split(';')
if len(axis) != len(data):
axis = None
# if all data is int, convert to int
if all([i.isdigit() for i in data]):
data = [int(i) for i in data]
else:
data = [float(i) for i in data]
flg, ax = plt.subplots(figsize=(10, 8))
if axis:
axis = [label[:10] + '...' if len(label) > 10 else label for label in axis]
ax.set_xticklabels(axis, rotation=45, ha='right')
ax.plot(axis, data)
else:
ax.plot(data)
buf = io.BytesIO()
flg.savefig(buf, format='png')
buf.seek(0)
plt.close(flg)
return [
self.create_text_message('the linear chart is saved as an image.'),
self.create_blob_message(blob=buf.read(),
meta={'mime_type': 'image/png'})
]
\ No newline at end of file
api/core/tools/provider/builtin/chart/tools/line.yaml 0 → 100644
View file @ 86286e1a
identity:
name: line_chart
author: Dify
label:
en_US: Linear Chart
zh_Hans: 线性图表
icon: icon.svg
description:
human:
en_US: linear chart
zh_Hans: 线性图表
llm: generate a linear chart with input data
parameters:
- name: data
type: string
required: true
label:
en_US: data
zh_Hans: 数据
human_description:
en_US: data for generating linear chart
zh_Hans: 用于生成线性图表的数据
llm_description: data for generating linear chart, data should be a string contains a list of numbers like "1;2;3;4;5"
form: llm
- name: x_axis
type: string
required: false
label:
en_US: X Axis
zh_Hans: x 轴
human_description:
en_US: X axis for linear chart
zh_Hans: 线性图表的 x 轴
llm_description: x axis for linear chart, x axis should be a string contains a list of texts like "a;b;c;1;2" in order to match the data
form: llm
api/core/tools/provider/builtin/chart/tools/pie.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
import matplotlib.pyplot as plt
import io
from typing import Any, Dict, List, Union
class PieChartTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
data = tool_paramters.get('data', '')
if not data:
return self.create_text_message('Please input data')
data = data.split(';')
categories = tool_paramters.get('categories', None) or None
# if all data is int, convert to int
if all([i.isdigit() for i in data]):
data = [int(i) for i in data]
else:
data = [float(i) for i in data]
flg, ax = plt.subplots()
if categories:
categories = categories.split(';')
if len(categories) != len(data):
categories = None
if categories:
ax.pie(data, labels=categories)
else:
ax.pie(data)
buf = io.BytesIO()
flg.savefig(buf, format='png')
buf.seek(0)
plt.close(flg)
return [
self.create_text_message('the pie chart is saved as an image.'),
self.create_blob_message(blob=buf.read(),
meta={'mime_type': 'image/png'})
]
\ No newline at end of file
api/core/tools/provider/builtin/chart/tools/pie.yaml 0 → 100644
View file @ 86286e1a
identity:
name: pie_chart
author: Dify
label:
en_US: Pie Chart
zh_Hans: 饼图
icon: icon.svg
description:
human:
en_US: Pie chart
zh_Hans: 饼图
llm: generate a pie chart with input data
parameters:
- name: data
type: string
required: true
label:
en_US: data
zh_Hans: 数据
human_description:
en_US: data for generating pie chart
zh_Hans: 用于生成饼图的数据
llm_description: data for generating pie chart, data should be a string contains a list of numbers like "1;2;3;4;5"
form: llm
- name: categories
type: string
required: true
label:
en_US: Categories
zh_Hans: 分类
human_description:
en_US: Categories for pie chart
zh_Hans: 饼图的分类
llm_description: categories for pie chart, categories should be a string contains a list of texts like "a;b;c;1;2" in order to match the data, each category should be split by ";"
form: llm
api/core/tools/provider/builtin/dalle/dalle.py 0 → 100644
View file @ 86286e1a
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.provider.builtin.dalle.tools.dalle2 import DallE2Tool
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict
class DALLEProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
DallE2Tool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"prompt": "cute girl, blue eyes, white hair, anime style",
"size": "small",
"n": 1
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
api/core/tools/provider/builtin/dalle/dalle.yaml 0 → 100644
View file @ 86286e1a
identity:
author: Dify
name: dalle
label:
en_US: DALL-E
zh_Hans: DALL-E 绘画
description:
en_US: DALL-E art
zh_Hans: DALL-E 绘画
icon: icon.png
credentails_for_provider:
openai_api_key:
type: secret-input
required: true
label:
en_US: OpenAI API key
zh_Hans: OpenAI API key
help:
en_US: Please input your OpenAI API key
zh_Hans: 请输入你的 OpenAI API key
placeholder:
en_US: Please input your OpenAI API key
zh_Hans: 请输入你的 OpenAI API key
openai_organizaion_id:
type: text-input
required: false
label:
en_US: OpenAI organization ID
zh_Hans: OpenAI organization ID
help:
en_US: Please input your OpenAI organization ID
zh_Hans: 请输入你的 OpenAI organization ID
placeholder:
en_US: Please input your OpenAI organization ID
zh_Hans: 请输入你的 OpenAI organization ID
openai_base_url:
type: text-input
required: false
label:
en_US: OpenAI base URL
zh_Hans: OpenAI base URL
help:
en_US: Please input your OpenAI base URL
zh_Hans: 请输入你的 OpenAI base URL
placeholder:
en_US: Please input your OpenAI base URL
zh_Hans: 请输入你的 OpenAI base URL
api/core/tools/provider/builtin/dalle/tools/dalle2.py 0 → 100644
View file @ 86286e1a
from typing import Any, Dict, List, Union
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.tool.builtin_tool import BuiltinTool
from base64 import b64decode
from os.path import join
from openai import OpenAI
class DallE2Tool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
openai_organization = self.runtime.credentials.get('openai_organizaion_id', None)
if not openai_organization:
openai_organization = None
openai_base_url = self.runtime.credentials.get('openai_base_url', None)
if not openai_base_url:
openai_base_url = None
else:
openai_base_url = join(openai_base_url, 'v1')
client = OpenAI(
api_key=self.runtime.credentials['openai_api_key'],
base_url=openai_base_url,
organization=openai_organization
)
SIZE_MAPPING = {
'small': '256x256',
'medium': '512x512',
'large': '1024x1024',
}
# prompt
prompt = tool_paramters.get('prompt', '')
if not prompt:
return self.create_text_message('Please input prompt')
# get size
size = SIZE_MAPPING[tool_paramters.get('size', 'large')]
# get n
n = tool_paramters.get('n', 1)
# call openapi dalle2
response = client.images.generate(
prompt=prompt,
model='dall-e-2',
size=size,
n=n,
response_format='b64_json'
)
result = []
for image in response.data:
result.append(self.create_blob_message(blob=b64decode(image.b64_json),
meta={ 'mime_type': 'image/png' },
save_as=self.VARIABLE_KEY.IMAGE.value))
return result
api/core/tools/provider/builtin/dalle/tools/dalle2.yaml 0 → 100644
View file @ 86286e1a
identity:
name: dalle2
author: Dify
label:
en_US: DALL-E 2
zh_Hans: DALL-E 2 绘画
description:
en_US: DALL-E 2 is a powerful drawing tool that can draw the image you want based on your prompt
zh_Hans: DALL-E 2 是一个强大的绘画工具，它可以根据您的提示词绘制出您想要的图像
description:
human:
en_US: DALL-E is a text to image tool
zh_Hans: DALL-E 是一个文本到图像的工具
llm: DALL-E is a tool used to generate images from text
parameters:
- name: prompt
type: string
required: true
label:
en_US: Prompt
zh_Hans: 提示词
human_description:
en_US: Image prompt, you can check the official documentation of DallE 2
zh_Hans: 图像提示词，您可以查看DallE 2 的官方文档
llm_description: Image prompt of DallE 2, you should describe the image you want to generate as a list of words as possible as detailed
form: llm
- name: size
type: select
required: true
human_description:
en_US: used for selecting the image size
zh_Hans: 用于选择图像大小
label:
en_US: Image size
zh_Hans: 图像大小
form: form
options:
- value: small
label:
en_US: Small(256x256)
zh_Hans: 小(256x256)
- value: medium
label:
en_US: Medium(512x512)
zh_Hans: 中(512x512)
- value: large
label:
en_US: Large(1024x1024)
zh_Hans: 大(1024x1024)
default: large
- name: n
type: number
required: true
human_description:
en_US: used for selecting the number of images
zh_Hans: 用于选择图像数量
label:
en_US: Number of images
zh_Hans: 图像数量
form: form
default: 1
min: 1
max: 10
api/core/tools/provider/builtin/dalle/tools/dalle3.py 0 → 100644
View file @ 86286e1a
from typing import Any, Dict, List, Union
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.tool.builtin_tool import BuiltinTool
from base64 import b64decode
from os.path import join
from openai import OpenAI
class DallE3Tool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
openai_organization = self.runtime.credentials.get('openai_organizaion_id', None)
if not openai_organization:
openai_organization = None
openai_base_url = self.runtime.credentials.get('openai_base_url', None)
if not openai_base_url:
openai_base_url = None
else:
openai_base_url = join(openai_base_url, 'v1')
client = OpenAI(
api_key=self.runtime.credentials['openai_api_key'],
base_url=openai_base_url,
organization=openai_organization
)
SIZE_MAPPING = {
'square': '1024x1024',
'vertical': '1024x1792',
'horizontal': '1792x1024',
}
# prompt
prompt = tool_paramters.get('prompt', '')
if not prompt:
return self.create_text_message('Please input prompt')
# get size
size = SIZE_MAPPING[tool_paramters.get('size', 'square')]
# get n
n = tool_paramters.get('n', 1)
# get quality
quality = tool_paramters.get('quality', 'standard')
if quality not in ['standard', 'hd']:
return self.create_text_message('Invalid quality')
# get style
style = tool_paramters.get('style', 'vivid')
if style not in ['natural', 'vivid']:
return self.create_text_message('Invalid style')
# call openapi dalle3
response = client.images.generate(
prompt=prompt,
model='dall-e-3',
size=size,
n=n,
style=style,
quality=quality,
response_format='b64_json'
)
result = []
for image in response.data:
result.append(self.create_blob_message(blob=b64decode(image.b64_json),
meta={ 'mime_type': 'image/png' },
save_as=self.VARIABLE_KEY.IMAGE.value))
return result
api/core/tools/provider/builtin/dalle/tools/dalle3.yaml 0 → 100644
View file @ 86286e1a
identity:
name: dalle3
author: Dify
label:
en_US: DALL-E 3
zh_Hans: DALL-E 3 绘画
description:
en_US: DALL-E 3 is a powerful drawing tool that can draw the image you want based on your prompt, compared to DallE 2, DallE 3 has stronger drawing ability, but it will consume more resources
zh_Hans: DALL-E 3 是一个强大的绘画工具，它可以根据您的提示词绘制出您想要的图像，相比于DallE 2， DallE 3拥有更强的绘画能力，但会消耗更多的资源
description:
human:
en_US: DALL-E is a text to image tool
zh_Hans: DALL-E 是一个文本到图像的工具
llm: DALL-E is a tool used to generate images from text
parameters:
- name: prompt
type: string
required: true
label:
en_US: Prompt
zh_Hans: 提示词
human_description:
en_US: Image prompt, you can check the official documentation of DallE 3
zh_Hans: 图像提示词，您可以查看DallE 3 的官方文档
llm_description: Image prompt of DallE 3, you should describe the image you want to generate as a list of words as possible as detailed
form: llm
- name: size
type: select
required: true
human_description:
en_US: selecting the image size
zh_Hans: 选择图像大小
label:
en_US: Image size
zh_Hans: 图像大小
form: form
options:
- value: square
label:
en_US: Squre(1024x1024)
zh_Hans: 方(1024x1024)
- value: vertical
label:
en_US: Vertical(1024x1792)
zh_Hans: 竖屏(1024x1792)
- value: horizontal
label:
en_US: Horizontal(1792x1024)
zh_Hans: 横屏(1792x1024)
default: square
- name: n
type: number
required: true
human_description:
en_US: selecting the number of images
zh_Hans: 选择图像数量
label:
en_US: Number of images
zh_Hans: 图像数量
form: form
min: 1
max: 1
default: 1
- name: quality
type: select
required: true
human_description:
en_US: selecting the image quality
zh_Hans: 选择图像质量
label:
en_US: Image quality
zh_Hans: 图像质量
form: form
options:
- value: standard
label:
en_US: Standard
zh_Hans: 标准
- value: hd
label:
en_US: HD
zh_Hans: 高清
default: standard
- name: style
type: select
required: true
human_description:
en_US: selecting the image style
zh_Hans: 选择图像风格
label:
en_US: Image style
zh_Hans: 图像风格
form: form
options:
- value: vivid
label:
en_US: Vivid
zh_Hans: 生动
- value: natural
label:
en_US: Natural
zh_Hans: 自然
default: vivid
api/core/tools/provider/builtin/google/_assets/icon.svg 0 → 100644
View file @ 86286e1a
<svg xmlns="http://www.w3.org/2000/svg" width="24" height="25" viewBox="0 0 24 25" fill="none">
<path d="M22.501 12.7332C22.501 11.8699 22.4296 11.2399 22.2748 10.5865H12.2153V14.4832H18.12C18.001 15.4515 17.3582 16.9099 15.9296 17.8898L15.9096 18.0203L19.0902 20.435L19.3106 20.4565C21.3343 18.6249 22.501 15.9298 22.501 12.7332Z" fill="#4285F4"/>
<path d="M12.214 23C15.1068 23 17.5353 22.0666 19.3092 20.4567L15.9282 17.8899C15.0235 18.5083 13.8092 18.9399 12.214 18.9399C9.38069 18.9399 6.97596 17.1083 6.11874 14.5766L5.99309 14.5871L2.68583 17.0954L2.64258 17.2132C4.40446 20.6433 8.0235 23 12.214 23Z" fill="#34A853"/>
<path d="M6.12046 14.5766C5.89428 13.9233 5.76337 13.2233 5.76337 12.5C5.76337 11.7766 5.89428 11.0766 6.10856 10.4233L6.10257 10.2841L2.75386 7.7355L2.64429 7.78658C1.91814 9.20993 1.50146 10.8083 1.50146 12.5C1.50146 14.1916 1.91814 15.7899 2.64429 17.2132L6.12046 14.5766Z" fill="#FBBC05"/>
<path d="M12.2141 6.05997C14.2259 6.05997 15.583 6.91163 16.3569 7.62335L19.3807 4.73C17.5236 3.03834 15.1069 2 12.2141 2C8.02353 2 4.40447 4.35665 2.64258 7.78662L6.10686 10.4233C6.97598 7.89166 9.38073 6.05997 12.2141 6.05997Z" fill="#EB4335"/>
</svg>
\ No newline at end of file
api/core/tools/provider/builtin/google/google.py 0 → 100644
View file @ 86286e1a
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.google.tools.google_search import GoogleSearchTool
from typing import Any, Dict, List
class GoogleProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
GoogleSearchTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"query": "test",
"result_type": "link"
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
api/core/tools/provider/builtin/google/google.yaml 0 → 100644
View file @ 86286e1a
identity:
author: Dify
name: google
label:
en_US: Google
zh_Hans: Google
description:
en_US: Google
zh_Hans: GoogleSearch
icon: icon.svg
credentails_for_provider:
serpapi_api_key:
type: secret-input
required: true
label:
en_US: SerpApi API key
zh_Hans: SerpApi API key
placeholder:
en_US: Please input your SerpApi API key
zh_Hans: 请输入你的 SerpApi API key
help:
en_US: Get your SerpApi API key from SerpApi
zh_Hans: 从 SerpApi 获取您的 SerpApi API key
url: https://serpapi.com/manage-api-key
api/core/tools/provider/builtin/google/tools/google_search.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from typing import Any, Dict, List, Union
import os
import sys
from serpapi import GoogleSearch
class HiddenPrints:
"""Context manager to hide prints."""
def __enter__(self) -> None:
"""Open file to pipe stdout to."""
self._original_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
def __exit__(self, *_: Any) -> None:
"""Close file that stdout was piped to."""
sys.stdout.close()
sys.stdout = self._original_stdout
class SerpAPI:
"""
SerpAPI tool provider.
"""
search_engine: Any #: :meta private:
serpapi_api_key: str = None
def __init__(self, api_key: str) -> None:
"""Initialize SerpAPI tool provider."""
self.serpapi_api_key = api_key
self.search_engine = GoogleSearch
def run(self, query: str, **kwargs: Any) -> str:
"""Run query through SerpAPI and parse result."""
typ = kwargs.get("result_type", "text")
return self._process_response(self.results(query), typ=typ)
def results(self, query: str) -> dict:
"""Run query through SerpAPI and return the raw result."""
params = self.get_params(query)
with HiddenPrints():
search = self.search_engine(params)
res = search.get_dict()
return res
def get_params(self, query: str) -> Dict[str, str]:
"""Get parameters for SerpAPI."""
_params = {
"api_key": self.serpapi_api_key,
"q": query,
}
params = {
"engine": "google",
"google_domain": "google.com",
"gl": "us",
"hl": "en",
**_params
}
return params
@staticmethod
def _process_response(res: dict, typ: str) -> str:
"""Process response from SerpAPI."""
if "error" in res.keys():
raise ValueError(f"Got error from SerpAPI: {res['error']}")
if typ == "text":
if "answer_box" in res.keys() and type(res["answer_box"]) == list:
res["answer_box"] = res["answer_box"][0]
if "answer_box" in res.keys() and "answer" in res["answer_box"].keys():
toret = res["answer_box"]["answer"]
elif "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
toret = res["answer_box"]["snippet"]
elif (
"answer_box" in res.keys()
and "snippet_highlighted_words" in res["answer_box"].keys()
):
toret = res["answer_box"]["snippet_highlighted_words"][0]
elif (
"sports_results" in res.keys()
and "game_spotlight" in res["sports_results"].keys()
):
toret = res["sports_results"]["game_spotlight"]
elif (
"shopping_results" in res.keys()
and "title" in res["shopping_results"][0].keys()
):
toret = res["shopping_results"][:3]
elif (
"knowledge_graph" in res.keys()
and "description" in res["knowledge_graph"].keys()
):
toret = res["knowledge_graph"]["description"]
elif "snippet" in res["organic_results"][0].keys():
toret = res["organic_results"][0]["snippet"]
elif "link" in res["organic_results"][0].keys():
toret = res["organic_results"][0]["link"]
elif (
"images_results" in res.keys()
and "thumbnail" in res["images_results"][0].keys()
):
thumbnails = [item["thumbnail"] for item in res["images_results"][:10]]
toret = thumbnails
else:
toret = "No good search result found"
elif typ == "link":
if "knowledge_graph" in res.keys() and "title" in res["knowledge_graph"].keys() \
and "description_link" in res["knowledge_graph"].keys():
toret = res["knowledge_graph"]["description_link"]
elif "knowledge_graph" in res.keys() and "see_results_about" in res["knowledge_graph"].keys() \
and len(res["knowledge_graph"]["see_results_about"]) > 0:
see_result_about = res["knowledge_graph"]["see_results_about"]
toret = ""
for item in see_result_about:
if "name" not in item.keys() or "link" not in item.keys():
continue
toret += f"[{item['name']}]({item['link']})\n"
elif "organic_results" in res.keys() and len(res["organic_results"]) > 0:
organic_results = res["organic_results"]
toret = ""
for item in organic_results:
if "title" not in item.keys() or "link" not in item.keys():
continue
toret += f"[{item['title']}]({item['link']})\n"
elif "related_questions" in res.keys() and len(res["related_questions"]) > 0:
related_questions = res["related_questions"]
toret = ""
for item in related_questions:
if "question" not in item.keys() or "link" not in item.keys():
continue
toret += f"[{item['question']}]({item['link']})\n"
elif "related_searches" in res.keys() and len(res["related_searches"]) > 0:
related_searches = res["related_searches"]
toret = ""
for item in related_searches:
if "query" not in item.keys() or "link" not in item.keys():
continue
toret += f"[{item['query']}]({item['link']})\n"
else:
toret = "No good search result found"
return toret
class GoogleSearchTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
query = tool_paramters['query']
result_type = tool_paramters['result_type']
api_key = self.runtime.credentials['serpapi_api_key']
result = SerpAPI(api_key).run(query, result_type=result_type)
if result_type == 'text':
return self.create_text_message(text=result)
return self.create_link_message(link=result)
\ No newline at end of file
api/core/tools/provider/builtin/google/tools/google_search.yaml 0 → 100644
View file @ 86286e1a
identity:
name: google_search
author: Dify
label:
en_US: GoogleSearch
zh_Hans: 谷歌搜索
description:
human:
en_US: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query.
zh_Hans: 一个用于执行 Google SERP 搜索并提取片段和网页的工具。输入应该是一个搜索查询。
llm: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query.
parameters:
- name: query
type: string
required: true
label:
en_US: Query string
zh_Hans: 查询语句
human_description:
en_US: used for searching
zh_Hans: 用于搜索网页内容
llm_description: key words for searching
form: llm
- name: result_type
type: select
required: true
options:
- value: text
label:
en_US: text
zh_Hans: 文本
- value: link
label:
en_US: link
zh_Hans: 链接
default: link
label:
en_US: Result type
zh_Hans: 结果类型
human_description:
en_US: used for selecting the result type, text or link
zh_Hans: 用于选择结果类型，使用文本还是链接进行展示
form: form
api/core/tools/provider/builtin/stablediffusion/stablediffusion.py 0 → 100644
View file @ 86286e1a
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.stablediffusion.tools.stable_diffusion import StableDiffusionTool
from typing import Any, Dict
class StableDiffusionProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
StableDiffusionTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"prompt": "cat",
"lora": "",
"steps": 1,
"width": 512,
"height": 512,
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
api/core/tools/provider/builtin/stablediffusion/stablediffusion.yaml 0 → 100644
View file @ 86286e1a
identity:
author: Dify
name: stablediffusion
label:
en_US: Stable Diffusion
zh_Hans: Stable Diffusion
description:
en_US: Stable Diffusion is a tool for generating images which can be deployed locally.
zh_Hans: Stable Diffusion 是一个可以在本地部署的图片生成的工具。
icon: icon.png
credentails_for_provider:
base_url:
type: secret-input
required: true
label:
en_US: Base URL
zh_Hans: StableDiffusion服务器的Base URL
placeholder:
en_US: Please input your StableDiffusion server's Base URL
zh_Hans: 请输入你的 StableDiffusion 服务器的 Base URL
model:
type: text-input
required: true
label:
en_US: Model
zh_Hans: 模型
placeholder:
en_US: Please input your model
zh_Hans: 请输入你的模型名称
api/core/tools/provider/builtin/stablediffusion/tools/stable_diffusion.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter, ToolParamterOption
from core.tools.entities.common_entities import I18nObject
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from os.path import join
from base64 import b64decode, b64encode
from PIL import Image
import json
import io
from copy import deepcopy
DRAW_TEXT_OPTIONS = {
"prompt": "",
"negative_prompt": "",
"seed": -1,
"subseed": -1,
"subseed_strength": 0,
"seed_resize_from_h": -1,
'sampler_index': 'DPM++ SDE Karras',
"seed_resize_from_w": -1,
"batch_size": 1,
"n_iter": 1,
"steps": 10,
"cfg_scale": 7,
"width": 1024,
"height": 1024,
"restore_faces": False,
"do_not_save_samples": False,
"do_not_save_grid": False,
"eta": 0,
"denoising_strength": 0,
"s_min_uncond": 0,
"s_churn": 0,
"s_tmax": 0,
"s_tmin": 0,
"s_noise": 0,
"override_settings": {},
"override_settings_restore_afterwards": True,
"refiner_switch_at": 0,
"disable_extra_networks": False,
"comments": {},
"enable_hr": False,
"firstphase_width": 0,
"firstphase_height": 0,
"hr_scale": 2,
"hr_second_pass_steps": 0,
"hr_resize_x": 0,
"hr_resize_y": 0,
"hr_prompt": "",
"hr_negative_prompt": "",
"script_args": [],
"send_images": True,
"save_images": False,
"alwayson_scripts": {}
}
class StableDiffusionTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
# base url
base_url = self.runtime.credentials.get('base_url', None)
if not base_url:
return self.create_text_message('Please input base_url')
model = self.runtime.credentials.get('model', None)
if not model:
return self.create_text_message('Please input model')
# set model
try:
url = join(base_url, 'sdapi/v1/options')
response = post(url, data=json.dumps({
'sd_model_checkpoint': model
}))
if response.status_code != 200:
raise ToolProviderCredentialValidationError('Failed to set model, please tell user to set model')
except Exception as e:
raise ToolProviderCredentialValidationError('Failed to set model, please tell user to set model')
# prompt
prompt = tool_paramters.get('prompt', '')
if not prompt:
return self.create_text_message('Please input prompt')
# get negative prompt
negative_prompt = tool_paramters.get('negative_prompt', '')
# get size
width = tool_paramters.get('width', 1024)
height = tool_paramters.get('height', 1024)
# get steps
steps = tool_paramters.get('steps', 1)
# get lora
lora = tool_paramters.get('lora', '')
# get image id
image_id = tool_paramters.get('image_id', '')
if image_id.strip():
image_variable = self.get_default_image_variable()
if image_variable:
image_binary = self.get_variable_file(image_variable.name)
if not image_binary:
return self.create_text_message('Image not found, please request user to generate image firstly.')
# convert image to RGB
image = Image.open(io.BytesIO(image_binary))
image = image.convert("RGB")
buffer = io.BytesIO()
image.save(buffer, format="PNG")
image_binary = buffer.getvalue()
image.close()
return self.img2img(base_url=base_url,
lora=lora,
image_binary=image_binary,
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
steps=steps)
return self.text2img(base_url=base_url,
lora=lora,
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
steps=steps)
def img2img(self, base_url: str, lora: str, image_binary: bytes,
prompt: str, negative_prompt: str,
width: int, height: int, steps: int) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
generate image
"""
draw_options = {
"init_images": [b64encode(image_binary).decode('utf-8')],
"prompt": "",
"negative_prompt": negative_prompt,
"denoising_strength": 0.9,
"width": width,
"height": height,
"cfg_scale": 7,
"sampler_name": "Euler a",
"restore_faces": False,
"steps": steps,
"script_args": ["outpainting mk2"]
}
if lora:
draw_options['prompt'] = f'{lora},{prompt}'
else:
draw_options['prompt'] = prompt
try:
url = join(base_url, 'sdapi/v1/img2img')
response = post(url, data=json.dumps(draw_options), timeout=120)
if response.status_code != 200:
return self.create_text_message('Failed to generate image')
image = response.json()['images'][0]
return self.create_blob_message(blob=b64decode(image),
meta={ 'mime_type': 'image/png' },
save_as=self.VARIABLE_KEY.IMAGE.value)
except Exception as e:
return self.create_text_message('Failed to generate image')
def text2img(self, base_url: str, lora: str, prompt: str, negative_prompt: str, width: int, height: int, steps: int) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
generate image
"""
# copy draw options
draw_options = deepcopy(DRAW_TEXT_OPTIONS)
if lora:
draw_options['prompt'] = f'{lora},{prompt}'
draw_options['width'] = width
draw_options['height'] = height
draw_options['steps'] = steps
draw_options['negative_prompt'] = negative_prompt
try:
url = join(base_url, 'sdapi/v1/txt2img')
response = post(url, data=json.dumps(draw_options), timeout=120)
if response.status_code != 200:
return self.create_text_message('Failed to generate image')
image = response.json()['images'][0]
return self.create_blob_message(blob=b64decode(image),
meta={ 'mime_type': 'image/png' },
save_as=self.VARIABLE_KEY.IMAGE.value)
except Exception as e:
return self.create_text_message('Failed to generate image')
def get_runtime_parameters(self) -> List[ToolParamter]:
parameters = [
ToolParamter(name='prompt',
label=I18nObject(en_US='Prompt', zh_Hans='Prompt'),
human_description=I18nObject(
en_US='Image prompt, you can check the official documentation of Stable Diffusion',
zh_Hans='图像提示词，您可以查看 Stable Diffusion 的官方文档',
),
type=ToolParamter.ToolParameterType.STRING,
form=ToolParamter.ToolParameterForm.LLM,
llm_description='Image prompt of Stable Diffusion, you should describe the image you want to generate as a list of words as possible as detailed, the prompt must be written in English.',
required=True),
]
if len(self.list_default_image_variables()) != 0:
parameters.append(
ToolParamter(name='image_id',
label=I18nObject(en_US='image_id', zh_Hans='image_id'),
human_description=I18nObject(
en_US='Image id of the image you want to generate based on, if you want to generate image based on the default image, you can leave this field empty.',
zh_Hans='您想要生成的图像的图像 ID，如果您想要基于默认图像生成图像，则可以将此字段留空。',
),
type=ToolParamter.ToolParameterType.STRING,
form=ToolParamter.ToolParameterForm.LLM,
llm_description='Image id of the original image, you can leave this field empty if you want to generate a new image.',
required=True,
options=[ToolParamterOption(
value=i.name,
label=I18nObject(en_US=i.name, zh_Hans=i.name)
) for i in self.list_default_image_variables()])
)
return parameters
api/core/tools/provider/builtin/stablediffusion/tools/stable_diffusion.yaml 0 → 100644
View file @ 86286e1a
identity:
name: stable_diffusion
author: Dify
label:
en_US: Stable Diffusion WebUI
zh_Hans: Stable Diffusion WebUI
description:
human:
en_US: A tool for generating images which can be deployed locally, you can use stable-diffusion-webui to deploy it.
zh_Hans: 一个可以在本地部署的图片生成的工具，您可以使用 stable-diffusion-webui 来部署它。
llm: draw the image you want based on your prompt.
parameters:
- name: prompt
type: string
required: true
label:
en_US: Prompt
zh_Hans: 提示词
human_description:
en_US: Image prompt, you can check the official documentation of Stable Diffusion
zh_Hans: 图像提示词，您可以查看 Stable Diffusion 的官方文档
llm_description: Image prompt of Stable Diffusion, you should describe the image you want to generate as a list of words as possible as detailed, the prompt must be written in English.
form: llm
- name: lora
type: string
required: false
label:
en_US: Lora
zh_Hans: Lora
human_description:
en_US: Lora
zh_Hans: Lora
form: form
- name: steps
type: number
required: false
label:
en_US: Steps
zh_Hans: Steps
human_description:
en_US: Steps
zh_Hans: Steps
form: form
default: 10
- name: width
type: number
required: false
label:
en_US: Width
zh_Hans: Width
human_description:
en_US: Width
zh_Hans: Width
form: form
default: 1024
- name: height
type: number
required: false
label:
en_US: Height
zh_Hans: Height
human_description:
en_US: Height
zh_Hans: Height
form: form
default: 1024
- name: negative_prompt
type: string
required: false
label:
en_US: Negative prompt
zh_Hans: Negative prompt
human_description:
en_US: Negative prompt
zh_Hans: Negative prompt
form: form
default: bad art, ugly, deformed, watermark, duplicated, discontinuous lines
api/core/tools/provider/builtin/time/_assets/icon.svg 0 → 100644
View file @ 86286e1a
<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
<path fill-rule="evenodd" clip-rule="evenodd" d="M0.666992 8.00008C0.666992 3.94999 3.95024 0.666748 8.00033 0.666748C12.0504 0.666748 15.3337 3.94999 15.3337 8.00008C15.3337 12.0502 12.0504 15.3334 8.00033 15.3334C3.95024 15.3334 0.666992 12.0502 0.666992 8.00008ZM8.66699 4.00008C8.66699 3.63189 8.36852 3.33341 8.00033 3.33341C7.63213 3.33341 7.33366 3.63189 7.33366 4.00008V8.00008C7.33366 8.2526 7.47633 8.48344 7.70218 8.59637L10.3688 9.9297C10.6982 10.0944 11.0986 9.96088 11.2633 9.63156C11.4279 9.30224 11.2945 8.90179 10.9651 8.73713L8.66699 7.58806V4.00008Z" fill="#EC4A0A"/>
</svg>
\ No newline at end of file
api/core/tools/provider/builtin/time/time.py 0 → 100644
View file @ 86286e1a
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.time.tools.current_time import CurrentTimeTool
from typing import Any, Dict
class WikiPediaProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
CurrentTimeTool().invoke(
user_id='',
tool_paramters={},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
api/core/tools/provider/builtin/time/time.yaml 0 → 100644
View file @ 86286e1a
identity:
author: Dify
name: time
label:
en_US: CurrentTime
zh_Hans: 时间
description:
en_US: A tool for getting the current time.
zh_Hans: 一个用于获取当前时间的工具。
icon: icon.svg
credentails_for_provider:
api/core/tools/provider/builtin/time/tools/current_time.py 0 → 100644
View file @ 86286e1a
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.tool.builtin_tool import BuiltinTool
from typing import Any, Dict, List, Union
from datetime import datetime, timezone
class CurrentTimeTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
return self.create_text_message(f'{datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S %Z")}')
\ No newline at end of file
api/core/tools/provider/builtin/time/tools/current_time.yaml 0 → 100644
View file @ 86286e1a
identity:
name: current_time
author: Dify
label:
en_US: Current Time
zh_Hans: 获取当前时间
description:
human:
en_US: A tool for getting the current time.
zh_Hans: 一个用于获取当前时间的工具。
llm: A tool for getting the current time.
parameters:
api/core/tools/provider/builtin/vectorizer/tools/test_data.py 0 → 100644
View file @ 86286e1a
VECTORIZER_ICON_PNG = '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'
\ No newline at end of file
api/core/tools/provider/builtin/vectorizer/tools/vectorizer.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter
from core.tools.provider.builtin.vectorizer.tools.test_data import VECTORIZER_ICON_PNG
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from base64 import b64decode
class VectorizerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
api_key_name = self.runtime.credentials.get('api_key_name', None)
api_key_value = self.runtime.credentials.get('api_key_value', None)
mode = tool_paramters.get('mode', 'test')
if mode == 'production':
mode = 'preview'
if not api_key_name or not api_key_value:
raise ToolProviderCredentialValidationError('Please input api key name and value')
image_id = tool_paramters.get('image_id', '')
if not image_id:
return self.create_text_message('Please input image id')
if image_id.startswith('__test_'):
image_binary = b64decode(VECTORIZER_ICON_PNG)
else:
image_binary = self.get_variable_file(self.VARIABLE_KEY.IMAGE)
if not image_binary:
return self.create_text_message('Image not found, please request user to generate image firstly.')
response = post(
'https://vectorizer.ai/api/v1/vectorize',
files={
'image': image_binary
},
data={
'mode': mode
} if mode == 'test' else {},
auth=(api_key_name, api_key_value),
timeout=30
)
if response.status_code != 200:
raise Exception(response.text)
return [
self.create_text_message('the vectorized svg is saved as an image.'),
self.create_blob_message(blob=response.content,
meta={'mime_type': 'image/svg+xml'})
]
def get_runtime_parameters(self) -> List[ToolParamter]:
"""
override the runtime parameters
"""
return [
ToolParamter.get_simple_instance(
name='image_id',
llm_description=f'the image id that you want to vectorize, \
and the image id should be specified in \
{[i.name for i in self.list_default_image_variables()]}',
type=ToolParamter.ToolParameterType.SELECT,
required=True,
options=[i.name for i in self.list_default_image_variables()]
)
]
def is_tool_avaliable(self) -> bool:
return len(self.list_default_image_variables()) > 0
\ No newline at end of file
api/core/tools/provider/builtin/vectorizer/tools/vectorizer.yaml 0 → 100644
View file @ 86286e1a
identity:
name: vectorizer
author: Dify
label:
en_US: Vectorizer.AI
zh_Hans: Vectorizer.AI
description:
human:
en_US: Convert your PNG and JPG images to SVG vectors quickly and easily. Fully automatically. Using AI.
zh_Hans: 一个将 PNG 和 JPG 图像快速轻松地转换为 SVG 矢量图的工具。
llm: A tool for converting images to SVG vectors. you should input the image id as the input of this tool. the image id can be got from parameters.
parameters:
- name: mode
type: select
required: true
options:
- value: production
label:
en_US: production
zh_Hans: 生产模式
- value: test
label:
en_US: test
zh_Hans: 测试模式
default: test
label:
en_US: Mode
zh_Hans: 模式
human_description:
en_US: It is free to integrate with and test out the API in test mode, no subscription required.
zh_Hans: 在测试模式下，可以免费测试API。
form: form
api/core/tools/provider/builtin/vectorizer/vectorizer.py 0 → 100644
View file @ 86286e1a
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.vectorizer.tools.vectorizer import VectorizerTool
from typing import Any, Dict
class VectorizerProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
VectorizerTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"mode": "test",
"image_id": "__test_123"
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
api/core/tools/provider/builtin/vectorizer/vectorizer.yaml 0 → 100644
View file @ 86286e1a
identity:
author: Dify
name: vectorizer
label:
en_US: Vectorizer.AI
zh_Hans: Vectorizer.AI
description:
en_US: Convert your PNG and JPG images to SVG vectors quickly and easily. Fully automatically. Using AI.
zh_Hans: 一个将 PNG 和 JPG 图像快速轻松地转换为 SVG 矢量图的工具。
icon: icon.png
credentails_for_provider:
api_key_name:
type: secret-input
required: true
label:
en_US: Vectorizer.AI API Key name
zh_Hans: Vectorizer.AI API Key name
placeholder:
en_US: Please input your Vectorizer.AI ApiKey name
zh_Hans: 请输入你的 Vectorizer.AI ApiKey name
help:
en_US: Get your Vectorizer.AI API Key from Vectorizer.AI.
zh_Hans: 从 Vectorizer.AI 获取您的 Vectorizer.AI API Key。
url: https://vectorizer.ai/api
api_key_value:
type: secret-input
required: true
label:
en_US: Vectorizer.AI API Key
zh_Hans: Vectorizer.AI API Key
placeholder:
en_US: Please input your Vectorizer.AI ApiKey
zh_Hans: 请输入你的 Vectorizer.AI ApiKey
help:
en_US: Get your Vectorizer.AI API Key from Vectorizer.AI.
zh_Hans: 从 Vectorizer.AI 获取您的 Vectorizer.AI API Key。
api/core/tools/provider/builtin/webscraper/_assets/icon.svg 0 → 100644
View file @ 86286e1a
<svg xmlns="http://www.w3.org/2000/svg" width="16" height="17" viewBox="0 0 16 17" fill="none">
<path fill-rule="evenodd" clip-rule="evenodd" d="M2.6665 1.16667C1.56193 1.16667 0.666504 2.0621 0.666504 3.16667C0.666504 4.27124 1.56193 5.16667 2.6665 5.16667C2.79161 5.16667 2.91403 5.15519 3.03277 5.13321C2.3808 6.09319 1.99984 7.25211 1.99984 8.5C1.99984 9.7479 2.3808 10.9068 3.03277 11.8668C2.91403 11.8448 2.79161 11.8333 2.6665 11.8333C1.56193 11.8333 0.666504 12.7288 0.666504 13.8333C0.666504 14.9379 1.56193 15.8333 2.6665 15.8333C3.77107 15.8333 4.6665 14.9379 4.6665 13.8333C4.6665 13.7082 4.65502 13.5858 4.63304 13.4671C5.59302 14.119 6.75194 14.5 7.99984 14.5C9.24773 14.5 10.4066 14.119 11.3666 13.4671C11.3447 13.5858 11.3332 13.7082 11.3332 13.8333C11.3332 14.9379 12.2286 15.8333 13.3332 15.8333C14.4377 15.8333 15.3332 14.9379 15.3332 13.8333C15.3332 12.7288 14.4377 11.8333 13.3332 11.8333C13.2081 11.8333 13.0856 11.8448 12.9669 11.8668C13.6189 10.9068 13.9998 9.7479 13.9998 8.5C13.9998 7.25211 13.6189 6.09319 12.9669 5.13321C13.0856 5.15519 13.2081 5.16667 13.3332 5.16667C14.4377 5.16667 15.3332 4.27124 15.3332 3.16667C15.3332 2.0621 14.4377 1.16667 13.3332 1.16667C12.2286 1.16667 11.3332 2.0621 11.3332 3.16667C11.3332 3.29177 11.3447 3.41419 11.3666 3.53293C10.4066 2.88097 9.24773 2.50001 7.99984 2.50001C6.75194 2.50001 5.59302 2.88097 4.63304 3.53293C4.65502 3.41419 4.6665 3.29177 4.6665 3.16667C4.6665 2.0621 3.77107 1.16667 2.6665 1.16667ZM3.38043 7.83334C3.63081 6.08287 4.85262 4.64578 6.48223 4.08565C5.79223 5.22099 5.36488 6.50185 5.23815 7.83334H3.38043ZM6.48228 12.9144C4.85264 12.3543 3.63082 10.9172 3.38043 9.16667H5.23815C5.3649 10.4982 5.79226 11.779 6.48228 12.9144ZM12.6192 9.16667C12.3689 10.9168 11.1475 12.3537 9.5183 12.9141C10.2082 11.7788 10.6355 10.498 10.7622 9.16667H12.6192ZM9.51834 4.08596C11.1475 4.64631 12.3689 6.0832 12.6192 7.83334H10.7622C10.6355 6.50197 10.2082 5.22123 9.51834 4.08596ZM9.4218 7.83334C9.27457 6.52262 8.78381 5.27411 8.00019 4.2145C7.21658 5.27411 6.72582 6.52262 6.57859 7.83334H9.4218ZM6.5786 9.16667C6.72583 10.4774 7.21659 11.7259 8.00019 12.7855C8.7838 11.7259 9.27456 10.4774 9.42179 9.16667H6.5786Z" fill="#DD2590"/>
</svg>
\ No newline at end of file
api/core/tools/provider/builtin/webscraper/tools/webscraper.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.errors import ToolInvokeError
from typing import Any, Dict, List, Union
class WebscraperTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
try:
url = tool_paramters.get('url', '')
user_agent = tool_paramters.get('user_agent', '')
if not url:
return self.create_text_message('Please input url')
# get webpage
result = self.get_url(url, user_agent=user_agent)
# summarize and return
return self.create_text_message(self.summary(user_id=user_id, content=result))
except Exception as e:
raise ToolInvokeError(str(e))
\ No newline at end of file
api/core/tools/provider/builtin/webscraper/tools/webscraper.yaml 0 → 100644
View file @ 86286e1a
identity:
name: webscraper
author: Dify
label:
en_US: Web Scraper
zh_Hans: 网页爬虫
description:
human:
en_US: A tool for scraping webpages.
zh_Hans: 一个用于爬取网页的工具。
llm: A tool for scraping webpages. Input should be a URL.
parameters:
- name: url
type: string
required: true
label:
en_US: URL
zh_Hans: 网页链接
human_description:
en_US: used for linking to webpages
zh_Hans: 用于链接到网页
llm_description: url for scraping
form: llm
- name: user_agent
type: string
required: false
label:
en_US: User Agent
zh_Hans: User Agent
human_description:
en_US: used for identifying the browser.
zh_Hans: 用于识别浏览器。
form: form
default: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.1000.0 Safari/537.36
api/core/tools/provider/builtin/webscraper/webscraper.py 0 → 100644
View file @ 86286e1a
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.webscraper.tools.webscraper import WebscraperTool
from typing import Any, Dict, List
class WebscraperProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
WebscraperTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
'url': 'https://www.google.com',
'user_agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 '
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
api/core/tools/provider/builtin/webscraper/webscraper.yaml 0 → 100644
View file @ 86286e1a
identity:
author: Dify
name: webscraper
label:
en_US: WebScraper
zh_Hans: 网页抓取
description:
en_US: Web Scrapper tool kit is used to scrape web
zh_Hans: 一个用于抓取网页的工具。
icon: icon.svg
credentails_for_provider:
api/core/tools/provider/builtin/wikipedia/_assets/icon.svg 0 → 100644
View file @ 86286e1a
<svg xmlns="http://www.w3.org/2000/svg" width="24" height="25" viewBox="0 0 24 25" fill="none">
<path d="M22.3627 6.50009H18.3156H18.1783V6.63743V7.07751V7.21484H18.3156H18.5969C18.924 7.21484 19.2189 7.38272 19.386 7.66394C19.553 7.94516 19.5593 8.28448 19.4028 8.57169L14.9027 16.8317L12.8532 11.9459L14.7837 8.40336C15.1832 7.67026 15.95 7.21484 16.7849 7.21484H16.8761H17.0134V7.07751V6.63743V6.50009H16.8761H12.829H12.6917V6.63743V7.07751V7.21484H12.829H13.1102C13.4373 7.21484 13.7323 7.38272 13.8993 7.66394C14.0663 7.94516 14.0726 8.28448 13.9162 8.57169L12.5159 11.1419L11.268 8.16696C11.1776 7.95134 11.1999 7.71594 11.3294 7.52124C11.4589 7.32654 11.6673 7.21484 11.9011 7.21484H12.221H12.3583V7.07751V6.63743V6.50009H12.221H7.3808H7.24347V6.63743V7.07751V7.21484H7.3808H7.44737C8.40218 7.21484 9.25775 7.78379 9.62715 8.66426L11.471 13.0599L9.4161 16.8317L5.78141 8.16696C5.69095 7.95134 5.71334 7.71594 5.8428 7.52124C5.97227 7.32654 6.18065 7.21484 6.41449 7.21484H6.90603H7.04337V7.07751V6.63743V6.50009H6.90603H1.63734H1.5V6.63743V7.07751V7.21484H1.63734H1.96072C2.91554 7.21484 3.77116 7.78379 4.1405 8.66426L8.33049 18.6529C8.40379 18.8276 8.57372 18.9405 8.76347 18.9405C8.93762 18.9405 9.09139 18.849 9.17485 18.6958L9.72141 17.6928L11.8081 13.8635L13.8171 18.6528C13.8904 18.8275 14.0603 18.9404 14.2501 18.9404C14.4242 18.9404 14.578 18.849 14.6614 18.6958L15.208 17.6928L20.2703 8.40327C20.6698 7.67016 21.4366 7.21475 22.2715 7.21475H22.3627H22.5V7.07741V6.63734V6.5H22.3627V6.50009Z" fill="#222A30"/>
</svg>
\ No newline at end of file
api/core/tools/provider/builtin/wikipedia/tools/wikipedia_search.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from pydantic import BaseModel, Field
from typing import Any, Dict, List, Union
from langchain import WikipediaAPIWrapper
from langchain.tools import WikipediaQueryRun
class WikipediaInput(BaseModel):
query: str = Field(..., description="search query.")
class WikiPediaSearchTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
query = tool_paramters.get('query', '')
if not query:
return self.create_text_message('Please input query')
tool = WikipediaQueryRun(
name="wikipedia",
api_wrapper=WikipediaAPIWrapper(doc_content_chars_max=4000),
args_schema=WikipediaInput
)
result = tool.run(tool_input={
'query': query
})
return self.create_text_message(self.summary(user_id=user_id,content=result))
\ No newline at end of file
api/core/tools/provider/builtin/wikipedia/tools/wikipedia_search.yaml 0 → 100644
View file @ 86286e1a
identity:
name: wikipedia_search
author: Dify
label:
en_US: WikipediaSearch
zh_Hans: 维基百科搜索
icon: icon.svg
description:
human:
en_US: A tool for performing a Wikipedia search and extracting snippets and webpages.
zh_Hans: 一个用于执行维基百科搜索并提取片段和网页的工具。
llm: A tool for performing a Wikipedia search and extracting snippets and webpages. Input should be a search query.
parameters:
- name: query
type: string
required: true
label:
en_US: Query string
zh_Hans: 查询语句
human_description:
en_US: key words for searching
zh_Hans: 查询关键词
llm_description: key words for searching
form: llm
api/core/tools/provider/builtin/wikipedia/wikipedia.py 0 → 100644
View file @ 86286e1a
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.wikipedia.tools.wikipedia_search import WikiPediaSearchTool
class WikiPediaProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: dict) -> None:
try:
WikiPediaSearchTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"query": "misaka mikoto",
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
api/core/tools/provider/builtin/wikipedia/wikipedia.yaml 0 → 100644
View file @ 86286e1a
identity:
author: Dify
name: wikipedia
label:
en_US: Wikipedia
zh_Hans: 维基百科
description:
en_US: Wikipedia is a free online encyclopedia, created and edited by volunteers around the world.
zh_Hans: 维基百科是一个由全世界的志愿者创建和编辑的免费在线百科全书。
icon: icon.svg
credentails_for_provider:
api/core/tools/provider/builtin/wolframalpha/_assets/icon.svg 0 → 100644
View file @ 86286e1a
<svg xmlns="http://www.w3.org/2000/svg" width="22" height="23" viewBox="0 0 22 23" fill="none">
<path d="M21.4992 14.615L17.0326 15.5683L17.4865 20.0065L13.3037 18.2132L10.9994 22.0669L8.69549 18.2132L4.51225 20.0065L4.96656 15.5678L0.5 14.615L3.542 11.2832L0.5 7.9519L4.96656 6.99854L4.51225 2.55981L8.69549 4.35369L10.9994 0.5L13.3037 4.35369L17.4865 2.56031L17.0326 7.00401L21.4992 7.9519L18.4572 11.2832L21.4992 14.615Z" fill="#F16850"/>
<path d="M10.9993 7.23111L8.69495 4.35315L4.51221 2.56026L7.00396 5.84084L10.9993 7.23111Z" fill="#FD694F"/>
<path d="M4.96656 6.99847L0.5 7.95183L3.542 11.2831L7.11734 9.9838L4.96656 6.99847Z" fill="#FF3413"/>
<path d="M7.00346 5.84037L4.51221 2.55978L4.96602 6.99851L7.11729 9.98384L7.00346 5.84037Z" fill="#DC1D23"/>
<path d="M13.3031 4.35369L10.9987 0.5L8.69434 4.35369L10.9987 7.23116L13.3031 4.35369Z" fill="#FF9281"/>
<path d="M18.4577 11.2831L21.4997 7.95183L17.0331 6.99847L14.8818 9.9838L18.4577 11.2831Z" fill="#FF8B79"/>
<path d="M14.8823 9.98384L17.0331 6.99851L17.4929 2.55978L14.9957 5.84037L14.8818 9.98384H14.8823Z" fill="#FD694F"/>
<path d="M14.9954 5.84034L17.4926 2.55975L13.3044 4.35364L11 7.23111L14.9954 5.84034Z" fill="#EF5240"/>
<path d="M17.47 13.2694L21.4997 14.6149L18.4577 11.2831L14.8818 9.98383L17.47 13.2694Z" fill="#FF482C"/>
<path d="M7.11783 9.98383L3.542 11.2831L0.5 14.6149L4.52965 13.2699L7.11783 9.98383Z" fill="#EC2101"/>
<path d="M11 17.8612V22.0664L13.3044 18.2132L13.4008 14.439L11 17.8612Z" fill="#D21C22"/>
<path d="M17.4703 13.2693L13.4009 14.4389L17.0334 15.5682L21.4999 14.6149L17.4703 13.2693Z" fill="#C90901"/>
<path d="M13.3042 18.2132L17.4874 20.0065L17.0331 15.5683L13.4011 14.439L13.3042 18.2132Z" fill="#EC2101"/>
<path d="M4.52965 13.2693L0.5 14.6154L4.96656 15.5632L8.59906 14.4394L4.52965 13.2703V13.2693Z" fill="#B6171E"/>
<path d="M8.59912 14.439L8.69555 18.2132L10.9999 22.0669V17.8612L8.59912 14.439Z" fill="#B4151B"/>
<path d="M4.96602 15.5623L4.51221 20.006L8.69495 18.2131L8.59852 14.439L4.96602 15.5623Z" fill="#D21C22"/>
<path d="M14.882 9.98384L14.9954 5.84036L11 7.23113V11.2608L14.882 9.98384Z" fill="#E63320"/>
<path d="M11.0003 7.23113L7.00391 5.84036L7.11773 9.98384L10.9998 11.2608L11.0003 7.23113Z" fill="#FF4527"/>
<path d="M8.59912 14.439L10.9999 17.8613L13.4007 14.439L10.9999 11.2608L8.59912 14.439Z" fill="#FF9281"/>
<path d="M11 11.2608L13.4008 14.439L17.4702 13.2699L14.882 9.98386L11 11.2608Z" fill="#FD684D"/>
<path d="M7.1165 9.9839L4.52832 13.2694L8.59773 14.439L10.9985 11.2608L7.1165 9.9839Z" fill="#FD745C"/>
</svg>
\ No newline at end of file
api/core/tools/provider/builtin/wolframalpha/tools/wolframalpha.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.errors import ToolProviderCredentialValidationError, ToolInvokeError
from typing import Any, Dict, List, Union
from httpx import get
class WolframAlphaTool(BuiltinTool):
_base_url = 'https://api.wolframalpha.com/v2/query'
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
query = tool_paramters.get('query', '')
if not query:
return self.create_text_message('Please input query')
appid = self.runtime.credentials.get('appid', '')
if not appid:
raise ToolProviderCredentialValidationError('Please input appid')
params = {
'appid': appid,
'input': query,
'includepodid': 'Result',
'format': 'plaintext',
'output': 'json'
}
finished = False
result = None
# try 3 times at most
counter = 0
while not finished and counter < 3:
counter += 1
try:
response = get(self._base_url, params=params, timeout=20)
response.raise_for_status()
response_data = response.json()
except Exception as e:
raise ToolInvokeError(str(e))
if 'success' not in response_data['queryresult'] or response_data['queryresult']['success'] != True:
query_result = response_data.get('queryresult', {})
if 'error' in query_result and query_result['error']:
if 'msg' in query_result['error']:
if query_result['error']['msg'] == 'Invalid appid':
raise ToolProviderCredentialValidationError('Invalid appid')
raise ToolInvokeError('Failed to invoke tool')
if 'didyoumeans' in response_data['queryresult']:
# get the most likely interpretation
query = ''
max_score = 0
for didyoumean in response_data['queryresult']['didyoumeans']:
if float(didyoumean['score']) > max_score:
query = didyoumean['val']
max_score = float(didyoumean['score'])
params['input'] = query
else:
finished = True
if 'souces' in response_data['queryresult']:
return self.create_link_message(response_data['queryresult']['sources']['url'])
elif 'pods' in response_data['queryresult']:
result = response_data['queryresult']['pods'][0]['subpods'][0]['plaintext']
if not finished or not result:
return self.create_text_message('No result found')
return self.create_text_message(result)
\ No newline at end of file
api/core/tools/provider/builtin/wolframalpha/tools/wolframalpha.yaml 0 → 100644
View file @ 86286e1a
identity:
name: wolframalpha
author: Dify
label:
en_US: WolframAlpha
zh_Hans: WolframAlpha
description:
human:
en_US: WolframAlpha is a powerful computational knowledge engine.
zh_Hans: WolframAlpha 是一个强大的计算知识引擎。
llm: WolframAlpha is a powerful computational knowledge engine. one single query can get the answer of a question.
parameters:
- name: query
type: string
required: true
label:
en_US: Query string
zh_Hans: 计算语句
human_description:
en_US: used for calculating
zh_Hans: 用于计算最终结果
llm_description: a single query for calculating
form: llm
api/core/tools/provider/builtin/wolframalpha/wolframalpha.py 0 → 100644
View file @ 86286e1a
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolProviderType
from core.tools.tool.tool import Tool
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.wolframalpha.tools.wolframalpha import WolframAlphaTool
from typing import Any, Dict, List
class GoogleProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
WolframAlphaTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"query": "1+2+....+111",
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
api/core/tools/provider/builtin/wolframalpha/wolframalpha.yaml 0 → 100644
View file @ 86286e1a
identity:
author: Dify
name: wolframalpha
label:
en_US: WolframAlpha
zh_Hans: WolframAlpha
description:
en_US: WolframAlpha is a powerful computational knowledge engine.
zh_Hans: WolframAlpha 是一个强大的计算知识引擎。
icon: icon.svg
credentails_for_provider:
appid:
type: secret-input
required: true
label:
en_US: WolframAlpha AppID
zh_Hans: WolframAlpha AppID
placeholder:
en_US: Please input your WolframAlpha AppID
zh_Hans: 请输入你的 WolframAlpha AppID
help:
en_US: Get your WolframAlpha AppID from WolframAlpha, please use "full results" api access.
zh_Hans: 从 WolframAlpha 获取您的 WolframAlpha AppID，请使用 "full results" API。
url: https://products.wolframalpha.com/api
api/core/tools/provider/builtin/yahoo/tools/analytics.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from typing import Any, Dict, List, Union
from requests.exceptions import HTTPError, ReadTimeout
from datetime import datetime
from yfinance import download
import pandas as pd
class YahooFinanceAnalyticsTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
symbol = tool_paramters.get('symbol', '')
if not symbol:
return self.create_text_message('Please input symbol')
time_range = [None, None]
start_date = tool_paramters.get('start_date', '')
if start_date:
time_range[0] = start_date
else:
time_range[0] = '1800-01-01'
end_date = tool_paramters.get('end_date', '')
if end_date:
time_range[1] = end_date
else:
time_range[1] = datetime.now().strftime('%Y-%m-%d')
stock_data = download(symbol, start=time_range[0], end=time_range[1])
max_segments = min(15, len(stock_data))
rows_per_segment = len(stock_data) // max_segments
summary_data = []
for i in range(max_segments):
start_idx = i * rows_per_segment
end_idx = (i + 1) * rows_per_segment if i < max_segments - 1 else len(stock_data)
segment_data = stock_data.iloc[start_idx:end_idx]
segment_summary = {
'Start Date': segment_data.index[0],
'End Date': segment_data.index[-1],
'Average Close': segment_data['Close'].mean(),
'Average Volume': segment_data['Volume'].mean(),
'Average Open': segment_data['Open'].mean(),
'Average High': segment_data['High'].mean(),
'Average Low': segment_data['Low'].mean(),
'Average Adj Close': segment_data['Adj Close'].mean(),
'Max Close': segment_data['Close'].max(),
'Min Close': segment_data['Close'].min(),
'Max Volume': segment_data['Volume'].max(),
'Min Volume': segment_data['Volume'].min(),
'Max Open': segment_data['Open'].max(),
'Min Open': segment_data['Open'].min(),
'Max High': segment_data['High'].max(),
'Min High': segment_data['High'].min(),
}
summary_data.append(segment_summary)
summary_df = pd.DataFrame(summary_data)
try:
return self.create_text_message(str(summary_df.to_dict()))
except (HTTPError, ReadTimeout):
return self.create_text_message(f'There is a internet connection problem. Please try again later.')
\ No newline at end of file
api/core/tools/provider/builtin/yahoo/tools/analytics.yaml 0 → 100644
View file @ 86286e1a
identity:
name: yahoo_finance_analytics
author: Dify
label:
en_US: Analytics
zh_Hans: 分析
icon: icon.svg
description:
human:
en_US: A tool for get analytics about a ticker from Yahoo Finance.
zh_Hans: 一个用于从雅虎财经获取分析数据的工具。
llm: A tool for get analytics from Yahoo Finance. Input should be the ticker symbol like AAPL.
parameters:
- name: symbol
type: string
required: true
label:
en_US: Ticker symbol
zh_Hans: 股票代码
human_description:
en_US: The ticker symbol of the company you want to analyze.
zh_Hans: 你想要搜索的公司的股票代码。
llm_description: The ticker symbol of the company you want to analyze.
form: llm
- name: start_date
type: string
required: false
label:
en_US: Start date
zh_Hans: 开始日期
human_description:
en_US: The start date of the analytics.
zh_Hans: 分析的开始日期。
llm_description: The start date of the analytics, the format of the date must be YYYY-MM-DD like 2020-01-01.
form: llm
- name: end_date
type: string
required: false
label:
en_US: End date
zh_Hans: 结束日期
human_description:
en_US: The end date of the analytics.
zh_Hans: 分析的结束日期。
llm_description: The end date of the analytics, the format of the date must be YYYY-MM-DD like 2024-01-01.
form: llm
api/core/tools/provider/builtin/yahoo/tools/news.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from typing import Any, Dict, List, Union
from requests.exceptions import HTTPError, ReadTimeout
import yfinance
class YahooFinanceSearchTickerTool(BuiltinTool):
def _invoke(self,user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
'''
invoke tools
'''
query = tool_paramters.get('symbol', '')
if not query:
return self.create_text_message('Please input symbol')
try:
return self.run(ticker=query, user_id=user_id)
except (HTTPError, ReadTimeout):
return self.create_text_message(f'There is a internet connection problem. Please try again later.')
def run(self, ticker: str, user_id: str) -> ToolInvokeMessage:
company = yfinance.Ticker(ticker)
try:
if company.isin is None:
return self.create_text_message(f'Company ticker {ticker} not found.')
except (HTTPError, ReadTimeout, ConnectionError):
return self.create_text_message(f'Company ticker {ticker} not found.')
links = []
try:
links = [n['link'] for n in company.news if n['type'] == 'STORY']
except (HTTPError, ReadTimeout, ConnectionError):
if not links:
return self.create_text_message(f'There is nothing about {ticker} ticker')
if not links:
return self.create_text_message(f'No news found for company that searched with {ticker} ticker.')
result = '\n\n'.join([
self.get_url(link) for link in links
])
return self.create_text_message(self.summary(user_id=user_id, content=result))
api/core/tools/provider/builtin/yahoo/tools/news.yaml 0 → 100644
View file @ 86286e1a
identity:
name: yahoo_finance_news
author: Dify
label:
en_US: News
zh_Hans: 新闻
icon: icon.svg
description:
human:
en_US: A tool for get news about a ticker from Yahoo Finance.
zh_Hans: 一个用于从雅虎财经获取新闻的工具。
llm: A tool for get news from Yahoo Finance. Input should be the ticker symbol like AAPL.
parameters:
- name: symbol
type: string
required: true
label:
en_US: Ticker symbol
zh_Hans: 股票代码
human_description:
en_US: The ticker symbol of the company you want to search.
zh_Hans: 你想要搜索的公司的股票代码。
llm_description: The ticker symbol of the company you want to search.
form: llm
api/core/tools/provider/builtin/yahoo/tools/ticker.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from typing import Any, Dict, List, Union
from requests.exceptions import HTTPError, ReadTimeout
from yfinance import Ticker
class YahooFinanceSearchTickerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
query = tool_paramters.get('symbol', '')
if not query:
return self.create_text_message('Please input symbol')
try:
return self.create_text_message(self.run(ticker=query))
except (HTTPError, ReadTimeout):
return self.create_text_message(f'There is a internet connection problem. Please try again later.')
def run(self, ticker: str) -> str:
return str(Ticker(ticker).info)
\ No newline at end of file
api/core/tools/provider/builtin/yahoo/tools/ticker.yaml 0 → 100644
View file @ 86286e1a
identity:
name: yahoo_finance_ticker
author: Dify
label:
en_US: Ticker
zh_Hans: 股票信息
icon: icon.svg
description:
human:
en_US: A tool for search ticker information from Yahoo Finance.
zh_Hans: 一个用于从雅虎财经搜索股票信息的工具。
llm: A tool for search ticker information from Yahoo Finance. Input should be the ticker symbol like AAPL.
parameters:
- name: symbol
type: string
required: true
label:
en_US: Ticker symbol
zh_Hans: 股票代码
human_description:
en_US: The ticker symbol of the company you want to search.
zh_Hans: 你想要搜索的公司的股票代码。
llm_description: The ticker symbol of the company you want to search.
form: llm
api/core/tools/provider/builtin/yahoo/yahoo.py 0 → 100644
View file @ 86286e1a
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.yahoo.tools.ticker import YahooFinanceSearchTickerTool
class YahooFinanceProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: dict) -> None:
try:
YahooFinanceSearchTickerTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"ticker": "MSFT",
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
api/core/tools/provider/builtin/yahoo/yahoo.yaml 0 → 100644
View file @ 86286e1a
identity:
author: Dify
name: yahoo
label:
en_US: YahooFinance
zh_Hans: 雅虎财经
description:
en_US: Finance, and Yahoo! get the latest news, stock quotes, and interactive chart with Yahoo!
zh_Hans: 雅虎财经，获取并整理出最新的新闻、股票报价等一切你想要的财经信息。
icon: icon.png
credentails_for_provider:
api/core/tools/provider/builtin/youtube/tools/videos.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from typing import Any, Dict, List, Union
from datetime import datetime
from googleapiclient.discovery import build
class YoutubeVideosAnalyticsTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
channel = tool_paramters.get('channel', '')
if not channel:
return self.create_text_message('Please input symbol')
time_range = [None, None]
start_date = tool_paramters.get('start_date', '')
if start_date:
time_range[0] = start_date
else:
time_range[0] = '1800-01-01'
end_date = tool_paramters.get('end_date', '')
if end_date:
time_range[1] = end_date
else:
time_range[1] = datetime.now().strftime('%Y-%m-%d')
if 'google_api_key' not in self.runtime.credentials or not self.runtime.credentials['google_api_key']:
return self.create_text_message('Please input api key')
youtube = build('youtube', 'v3', developerKey=self.runtime.credentials['google_api_key'])
# try to get channel id
search_results = youtube.search().list(q='mrbeast', type='channel', order='relevance', part='id').execute()
channel_id = search_results['items'][0]['id']['channelId']
start_date, end_date = time_range
start_date = datetime.strptime(start_date, '%Y-%m-%d').strftime('%Y-%m-%dT%H:%M:%SZ')
end_date = datetime.strptime(end_date, '%Y-%m-%d').strftime('%Y-%m-%dT%H:%M:%SZ')
# get videos
time_range_videos = youtube.search().list(
part='snippet', channelId=channel_id, order='date', type='video',
publishedAfter=start_date,
publishedBefore=end_date
).execute()
def extract_video_data(video_list):
data = []
for video in video_list['items']:
video_id = video['id']['videoId']
video_info = youtube.videos().list(part='snippet,statistics', id=video_id).execute()
title = video_info['items'][0]['snippet']['title']
views = video_info['items'][0]['statistics']['viewCount']
data.append({'Title': title, 'Views': views})
return data
summary = extract_video_data(time_range_videos)
return self.create_text_message(str(summary))
\ No newline at end of file
api/core/tools/provider/builtin/youtube/tools/videos.yaml 0 → 100644
View file @ 86286e1a
identity:
name: youtube_video_statistics
author: Dify
label:
en_US: Video statistics
zh_Hans: 视频统计
icon: icon.svg
description:
human:
en_US: A tool for get statistics about a channel's videos.
zh_Hans: 一个用于获取油管频道视频统计数据的工具。
llm: A tool for get statistics about a channel's videos. Input should be the name of the channel like PewDiePie.
parameters:
- name: channel
type: string
required: true
label:
en_US: Channel name
zh_Hans: 频道名
human_description:
en_US: The name of the channel you want to search.
zh_Hans: 你想要搜索的油管频道名。
llm_description: The name of the channel you want to search.
form: llm
- name: start_date
type: string
required: false
label:
en_US: Start date
zh_Hans: 开始日期
human_description:
en_US: The start date of the analytics.
zh_Hans: 分析的开始日期。
llm_description: The start date of the analytics, the format of the date must be YYYY-MM-DD like 2020-01-01.
form: llm
- name: end_date
type: string
required: false
label:
en_US: End date
zh_Hans: 结束日期
human_description:
en_US: The end date of the analytics.
zh_Hans: 分析的结束日期。
llm_description: The end date of the analytics, the format of the date must be YYYY-MM-DD like 2024-01-01.
form: llm
api/core/tools/provider/builtin/youtube/youtube.py 0 → 100644
View file @ 86286e1a
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.youtube.tools.videos import YoutubeVideosAnalyticsTool
class YahooFinanceProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: dict) -> None:
try:
YoutubeVideosAnalyticsTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"channel": "TOKYO GIRLS COLLECTION",
"start_date": "2020-01-01",
"end_date": "2024-12-31",
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
api/core/tools/provider/builtin/youtube/youtube.yaml 0 → 100644
View file @ 86286e1a
identity:
author: Dify
name: youtube
label:
en_US: Youtube
zh_Hans: Youtube
description:
en_US: Youtube
zh_Hans: Youtube（油管）是全球最大的视频分享网站，用户可以在上面上传、观看和分享视频。
icon: icon.png
credentails_for_provider:
google_api_key:
type: secret-input
required: true
label:
en_US: Google API key
zh_Hans: Google API key
placeholder:
en_US: Please input your Google API key
zh_Hans: 请输入你的 Google API key
help:
en_US: Get your Google API key from Google
zh_Hans: 从 Google 获取您的 Google API key
url: https://console.developers.google.com/apis/credentials
api/core/tools/provider/builtin_tool_provider.py 0 → 100644
View file @ 86286e1a
from abc import abstractmethod
from typing import List, Dict, Any
from os import path, listdir
from yaml import load, FullLoader
from core.tools.entities.tool_entities import ToolProviderType, \
ToolParamter, ToolProviderCredentials
from core.tools.tool.tool import Tool
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.provider.tool_provider import ToolProviderController
from core.tools.entities.user_entities import UserToolProviderCredentials
from core.tools.errors import ToolNotFoundError, ToolProviderNotFoundError, \
ToolParamterValidationError, ToolProviderCredentialValidationError
import importlib
class BuiltinToolProviderController(ToolProviderController):
def __init__(self, **data: Any) -> None:
if self.app_type == ToolProviderType.API_BASED or self.app_type == ToolProviderType.APP_BASED:
super().__init__(**data)
return
# load provider yaml
provider = self.__class__.__module__.split('.')[-1]
yaml_path = path.join(path.dirname(path.realpath(__file__)), 'builtin', provider, f'{provider}.yaml')
try:
with open(yaml_path, 'r') as f:
provider_yaml = load(f.read(), FullLoader)
except:
raise ToolProviderNotFoundError(f'can not load provider yaml for {provider}')
if 'credentails_for_provider' in provider_yaml and provider_yaml['credentails_for_provider'] is not None:
# set credentials name
for credential_name in provider_yaml['credentails_for_provider']:
provider_yaml['credentails_for_provider'][credential_name]['name'] = credential_name
super().__init__(**{
'identity': provider_yaml['identity'],
'credentials_schema': provider_yaml['credentails_for_provider'] if 'credentails_for_provider' in provider_yaml else None,
})
def _get_bulitin_tools(self) -> List[Tool]:
"""
returns a list of tools that the provider can provide
:return: list of tools
"""
if self.tools:
return self.tools
provider = self.identity.name
tool_path = path.join(path.dirname(path.realpath(__file__)), "builtin", provider, "tools")
# get all the yaml files in the tool path
tool_files = list(filter(lambda x: x.endswith(".yaml") and not x.startswith("__"), listdir(tool_path)))
tools = []
for tool_file in tool_files:
with open(path.join(tool_path, tool_file), "r") as f:
# get tool name
tool_name = tool_file.split(".")[0]
tool = load(f.read(), FullLoader)
# get tool class, import the module
py_path = path.join(path.dirname(path.realpath(__file__)), 'builtin', provider, 'tools', f'{tool_name}.py')
spec = importlib.util.spec_from_file_location(f'core.tools.provider.builtin.{provider}.tools.{tool_name}', py_path)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)
# get all the classes in the module
classes = [x for _, x in vars(mod).items()
if isinstance(x, type) and x not in [BuiltinTool, Tool] and issubclass(x, BuiltinTool)
]
assistant_tool_class = classes[0]
tools.append(assistant_tool_class(**tool))
self.tools = tools
return tools
def get_credentails_schema(self) -> Dict[str, ToolProviderCredentials]:
"""
returns the credentials schema of the provider
:return: the credentials schema
"""
if not self.credentials_schema:
return {}
return self.credentials_schema.copy()
def user_get_credentails_schema(self) -> UserToolProviderCredentials:
"""
returns the credentials schema of the provider, this method is used for user
:return: the credentials schema
"""
credentials = self.credentials_schema.copy()
return UserToolProviderCredentials(credentails=credentials)
def get_tools(self) -> List[Tool]:
"""
returns a list of tools that the provider can provide
:return: list of tools
"""
return self._get_bulitin_tools()
def get_tool(self, tool_name: str) -> Tool:
"""
returns the tool that the provider can provide
"""
return next(filter(lambda x: x.identity.name == tool_name, self.get_tools()), None)
def get_parameters(self, tool_name: str) -> List[ToolParamter]:
"""
returns the parameters of the tool
:param tool_name: the name of the tool, defined in `get_tools`
:return: list of parameters
"""
tool = next(filter(lambda x: x.identity.name == tool_name, self.get_tools()), None)
if tool is None:
raise ToolNotFoundError(f'tool {tool_name} not found')
return tool.parameters
@property
def need_credentials(self) -> bool:
"""
returns whether the provider needs credentials
:return: whether the provider needs credentials
"""
return self.credentials_schema is not None and len(self.credentials_schema) != 0
@property
def app_type(self) -> ToolProviderType:
"""
returns the type of the provider
:return: type of the provider
"""
return ToolProviderType.BUILT_IN
def validate_parameters(self, tool_id: int, tool_name: str, tool_parameters: Dict[str, Any]) -> None:
"""
validate the parameters of the tool and set the default value if needed
:param tool_name: the name of the tool, defined in `get_tools`
:param tool_parameters: the parameters of the tool
"""
tool_parameters_schema = self.get_parameters(tool_name)
tool_parameters_need_to_validate: Dict[str, ToolParamter] = {}
for parameter in tool_parameters_schema:
tool_parameters_need_to_validate[parameter.name] = parameter
for parameter in tool_parameters:
if parameter not in tool_parameters_need_to_validate:
raise ToolParamterValidationError(f'parameter {parameter} not found in tool {tool_name}')
# check type
parameter_schema = tool_parameters_need_to_validate[parameter]
if parameter_schema.type == ToolParamter.ToolParameterType.STRING:
if not isinstance(tool_parameters[parameter], str):
raise ToolParamterValidationError(f'parameter {parameter} should be string')
elif parameter_schema.type == ToolParamter.ToolParameterType.NUMBER:
if not isinstance(tool_parameters[parameter], (int, float)):
raise ToolParamterValidationError(f'parameter {parameter} should be number')
if parameter_schema.min is not None and tool_parameters[parameter] < parameter_schema.min:
raise ToolParamterValidationError(f'parameter {parameter} should be greater than {parameter_schema.min}')
if parameter_schema.max is not None and tool_parameters[parameter] > parameter_schema.max:
raise ToolParamterValidationError(f'parameter {parameter} should be less than {parameter_schema.max}')
elif parameter_schema.type == ToolParamter.ToolParameterType.BOOLEAN:
if not isinstance(tool_parameters[parameter], bool):
raise ToolParamterValidationError(f'parameter {parameter} should be boolean')
elif parameter_schema.type == ToolParamter.ToolParameterType.SELECT:
if not isinstance(tool_parameters[parameter], str):
raise ToolParamterValidationError(f'parameter {parameter} should be string')
options = parameter_schema.options
if not isinstance(options, list):
raise ToolParamterValidationError(f'parameter {parameter} options should be list')
if tool_parameters[parameter] not in [x.value for x in options]:
raise ToolParamterValidationError(f'parameter {parameter} should be one of {options}')
tool_parameters_need_to_validate.pop(parameter)
for parameter in tool_parameters_need_to_validate:
parameter_schema = tool_parameters_need_to_validate[parameter]
if parameter_schema.required:
raise ToolParamterValidationError(f'parameter {parameter} is required')
# the parameter is not set currently, set the default value if needed
if parameter_schema.default is not None:
default_value = parameter_schema.default
# parse default value into the correct type
if parameter_schema.type == ToolParamter.ToolParameterType.STRING or \
parameter_schema.type == ToolParamter.ToolParameterType.SELECT:
default_value = str(default_value)
elif parameter_schema.type == ToolParamter.ToolParameterType.NUMBER:
default_value = float(default_value)
elif parameter_schema.type == ToolParamter.ToolParameterType.BOOLEAN:
default_value = bool(default_value)
tool_parameters[parameter] = default_value
def validate_credentials_format(self, credentials: Dict[str, Any]) -> None:
"""
validate the format of the credentials of the provider and set the default value if needed
:param credentials: the credentials of the tool
"""
credentials_schema = self.credentials_schema
if credentials_schema is None:
return
credentials_need_to_validate: Dict[str, ToolProviderCredentials] = {}
for credential_name in credentials_schema:
credentials_need_to_validate[credential_name] = credentials_schema[credential_name]
for credential_name in credentials:
if credential_name not in credentials_need_to_validate:
raise ToolProviderCredentialValidationError(f'credential {credential_name} not found in provider {self.identity.name}')
# check type
credential_schema = credentials_need_to_validate[credential_name]
if credential_schema == ToolProviderCredentials.CredentialsType.SECRET_INPUT or \
credential_schema == ToolProviderCredentials.CredentialsType.TEXT_INPUT:
if not isinstance(credentials[credential_name], str):
raise ToolProviderCredentialValidationError(f'credential {credential_name} should be string')
elif credential_schema.type == ToolProviderCredentials.CredentialsType.SELECT:
if not isinstance(credentials[credential_name], str):
raise ToolProviderCredentialValidationError(f'credential {credential_name} should be string')
options = credential_schema.options
if not isinstance(options, list):
raise ToolProviderCredentialValidationError(f'credential {credential_name} options should be list')
if credentials[credential_name] not in [x.value for x in options]:
raise ToolProviderCredentialValidationError(f'credential {credential_name} should be one of {options}')
credentials_need_to_validate.pop(credential_name)
for credential_name in credentials_need_to_validate:
credential_schema = credentials_need_to_validate[credential_name]
if credential_schema.required:
raise ToolProviderCredentialValidationError(f'credential {credential_name} is required')
# the credential is not set currently, set the default value if needed
if credential_schema.default is not None:
default_value = credential_schema.default
# parse default value into the correct type
if credential_schema.type == ToolProviderCredentials.CredentialsType.SECRET_INPUT or \
credential_schema.type == ToolProviderCredentials.CredentialsType.TEXT_INPUT or \
credential_schema.type == ToolProviderCredentials.CredentialsType.SELECT:
default_value = str(default_value)
credentials[credential_name] = default_value
def validate_credentials(self, credentials: Dict[str, Any]) -> None:
"""
validate the credentials of the provider
:param tool_name: the name of the tool, defined in `get_tools`
:param credentials: the credentials of the tool
"""
# validate credentials format
self.validate_credentials_format(credentials)
# validate credentials
self._validate_credentials(credentials)
@abstractmethod
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
"""
validate the credentials of the provider
:param tool_name: the name of the tool, defined in `get_tools`
:param credentials: the credentials of the tool
"""
pass
\ No newline at end of file
api/core/tools/provider/tool_provider.py 0 → 100644
View file @ 86286e1a
from abc import ABC, abstractmethod
from typing import List, Dict, Any, Optional
from pydantic import BaseModel
from core.tools.entities.tool_entities import ToolProviderType, \
ToolProviderIdentity, ToolParamter, ToolProviderCredentials
from core.tools.tool.tool import Tool
from core.tools.entities.user_entities import UserToolProviderCredentials
from core.tools.errors import ToolNotFoundError, \
ToolParamterValidationError, ToolProviderCredentialValidationError
class ToolProviderController(BaseModel, ABC):
identity: Optional[ToolProviderIdentity] = None
tools: Optional[List[Tool]] = None
credentials_schema: Optional[Dict[str, ToolProviderCredentials]] = None
def get_credentails_schema(self) -> Dict[str, ToolProviderCredentials]:
"""
returns the credentials schema of the provider
:return: the credentials schema
"""
return self.credentials_schema.copy()
def user_get_credentails_schema(self) -> UserToolProviderCredentials:
"""
returns the credentials schema of the provider, this method is used for user
:return: the credentials schema
"""
credentials = self.credentials_schema.copy()
return UserToolProviderCredentials(credentails=credentials)
@abstractmethod
def get_tools(self) -> List[Tool]:
"""
returns a list of tools that the provider can provide
:return: list of tools
"""
pass
@abstractmethod
def get_tool(self, tool_name: str) -> Tool:
"""
returns a tool that the provider can provide
:return: tool
"""
pass
def get_parameters(self, tool_name: str) -> List[ToolParamter]:
"""
returns the parameters of the tool
:param tool_name: the name of the tool, defined in `get_tools`
:return: list of parameters
"""
tool = next(filter(lambda x: x.identity.name == tool_name, self.get_tools()), None)
if tool is None:
raise ToolNotFoundError(f'tool {tool_name} not found')
return tool.parameters
@property
def app_type(self) -> ToolProviderType:
"""
returns the type of the provider
:return: type of the provider
"""
return ToolProviderType.BUILT_IN
def validate_parameters(self, tool_id: int, tool_name: str, tool_parameters: Dict[str, Any]) -> None:
"""
validate the parameters of the tool and set the default value if needed
:param tool_name: the name of the tool, defined in `get_tools`
:param tool_parameters: the parameters of the tool
"""
tool_parameters_schema = self.get_parameters(tool_name)
tool_parameters_need_to_validate: Dict[str, ToolParamter] = {}
for parameter in tool_parameters_schema:
tool_parameters_need_to_validate[parameter.name] = parameter
for parameter in tool_parameters:
if parameter not in tool_parameters_need_to_validate:
raise ToolParamterValidationError(f'parameter {parameter} not found in tool {tool_name}')
# check type
parameter_schema = tool_parameters_need_to_validate[parameter]
if parameter_schema.type == ToolParamter.ToolParameterType.STRING:
if not isinstance(tool_parameters[parameter], str):
raise ToolParamterValidationError(f'parameter {parameter} should be string')
elif parameter_schema.type == ToolParamter.ToolParameterType.NUMBER:
if not isinstance(tool_parameters[parameter], (int, float)):
raise ToolParamterValidationError(f'parameter {parameter} should be number')
if parameter_schema.min is not None and tool_parameters[parameter] < parameter_schema.min:
raise ToolParamterValidationError(f'parameter {parameter} should be greater than {parameter_schema.min}')
if parameter_schema.max is not None and tool_parameters[parameter] > parameter_schema.max:
raise ToolParamterValidationError(f'parameter {parameter} should be less than {parameter_schema.max}')
elif parameter_schema.type == ToolParamter.ToolParameterType.BOOLEAN:
if not isinstance(tool_parameters[parameter], bool):
raise ToolParamterValidationError(f'parameter {parameter} should be boolean')
elif parameter_schema.type == ToolParamter.ToolParameterType.SELECT:
if not isinstance(tool_parameters[parameter], str):
raise ToolParamterValidationError(f'parameter {parameter} should be string')
options = parameter_schema.options
if not isinstance(options, list):
raise ToolParamterValidationError(f'parameter {parameter} options should be list')
if tool_parameters[parameter] not in [x.value for x in options]:
raise ToolParamterValidationError(f'parameter {parameter} should be one of {options}')
tool_parameters_need_to_validate.pop(parameter)
for parameter in tool_parameters_need_to_validate:
parameter_schema = tool_parameters_need_to_validate[parameter]
if parameter_schema.required:
raise ToolParamterValidationError(f'parameter {parameter} is required')
# the parameter is not set currently, set the default value if needed
if parameter_schema.default is not None:
default_value = parameter_schema.default
# parse default value into the correct type
if parameter_schema.type == ToolParamter.ToolParameterType.STRING or \
parameter_schema.type == ToolParamter.ToolParameterType.SELECT:
default_value = str(default_value)
elif parameter_schema.type == ToolParamter.ToolParameterType.NUMBER:
default_value = float(default_value)
elif parameter_schema.type == ToolParamter.ToolParameterType.BOOLEAN:
default_value = bool(default_value)
tool_parameters[parameter] = default_value
def validate_credentials_format(self, credentials: Dict[str, Any]) -> None:
"""
validate the format of the credentials of the provider and set the default value if needed
:param credentials: the credentials of the tool
"""
credentials_schema = self.credentials_schema
if credentials_schema is None:
return
credentials_need_to_validate: Dict[str, ToolProviderCredentials] = {}
for credential_name in credentials_schema:
credentials_need_to_validate[credential_name] = credentials_schema[credential_name]
for credential_name in credentials:
if credential_name not in credentials_need_to_validate:
raise ToolProviderCredentialValidationError(f'credential {credential_name} not found in provider {self.identity.name}')
# check type
credential_schema = credentials_need_to_validate[credential_name]
if credential_schema == ToolProviderCredentials.CredentialsType.SECRET_INPUT or \
credential_schema == ToolProviderCredentials.CredentialsType.TEXT_INPUT:
if not isinstance(credentials[credential_name], str):
raise ToolProviderCredentialValidationError(f'credential {credential_name} should be string')
elif credential_schema.type == ToolProviderCredentials.CredentialsType.SELECT:
if not isinstance(credentials[credential_name], str):
raise ToolProviderCredentialValidationError(f'credential {credential_name} should be string')
options = credential_schema.options
if not isinstance(options, list):
raise ToolProviderCredentialValidationError(f'credential {credential_name} options should be list')
if credentials[credential_name] not in [x.value for x in options]:
raise ToolProviderCredentialValidationError(f'credential {credential_name} should be one of {options}')
credentials_need_to_validate.pop(credential_name)
for credential_name in credentials_need_to_validate:
credential_schema = credentials_need_to_validate[credential_name]
if credential_schema.required:
raise ToolProviderCredentialValidationError(f'credential {credential_name} is required')
# the credential is not set currently, set the default value if needed
if credential_schema.default is not None:
default_value = credential_schema.default
# parse default value into the correct type
if credential_schema.type == ToolProviderCredentials.CredentialsType.SECRET_INPUT or \
credential_schema.type == ToolProviderCredentials.CredentialsType.TEXT_INPUT or \
credential_schema.type == ToolProviderCredentials.CredentialsType.SELECT:
default_value = str(default_value)
credentials[credential_name] = default_value
def validate_credentials(self, credentials: Dict[str, Any]) -> None:
"""
validate the credentials of the provider
:param tool_name: the name of the tool, defined in `get_tools`
:param credentials: the credentials of the tool
"""
# validate credentials format
self.validate_credentials_format(credentials)
# validate credentials
self._validate_credentials(credentials)
@abstractmethod
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
"""
validate the credentials of the provider
:param tool_name: the name of the tool, defined in `get_tools`
:param credentials: the credentials of the tool
"""
pass
\ No newline at end of file
api/core/tools/tool/api_tool.py 0 → 100644
View file @ 86286e1a
from typing import Any, Dict, List, Union
from json import dumps
from core.tools.entities.tool_bundle import ApiBasedToolBundle
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.tool.tool import Tool
from core.tools.errors import ToolProviderCredentialValidationError
import httpx
import requests
class ApiTool(Tool):
api_bundle: ApiBasedToolBundle
"""
Api tool
"""
def fork_tool_runtime(self, meta: Dict[str, Any]) -> 'Tool':
"""
fork a new tool with meta data
:param meta: the meta data of a tool call processing, tenant_id is required
:return: the new tool
"""
return self.__class__(
identity=self.identity.copy() if self.identity else None,
parameters=self.parameters.copy() if self.parameters else None,
description=self.description.copy() if self.description else None,
api_bundle=self.api_bundle.copy() if self.api_bundle else None,
runtime=Tool.Runtime(**meta)
)
def validate_credentials(self, credentails: Dict[str, Any], parameters: Dict[str, Any], format_only: bool = False) -> None:
"""
validate the credentials for Api tool
"""
# assemble validate request and request parameters
headers = self.assembling_request(parameters)
if format_only:
return
response = self.do_http_request(self.api_bundle.server_url, self.api_bundle.method, headers, parameters)
# validate response
self.validate_and_parse_response(response)
def assembling_request(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
headers = {}
credentials = self.runtime.credentials or {}
if 'auth_type' not in credentials:
raise ToolProviderCredentialValidationError('Missing auth_type')
if credentials['auth_type'] == 'api_key':
api_key_header = 'api_key'
if 'api_key_header' in credentials:
api_key_header = credentials['api_key_header']
if 'api_key_value' not in credentials:
raise ToolProviderCredentialValidationError('Missing api_key_value')
headers[api_key_header] = credentials['api_key_value']
needed_parameters = [parameter for parameter in self.api_bundle.parameters if parameter.required]
for parameter in needed_parameters:
if parameter.required and parameter.name not in parameters:
raise ToolProviderCredentialValidationError(f"Missing required parameter {parameter.name}")
if parameter.default is not None and parameter.name not in parameters:
parameters[parameter.name] = parameter.default
return headers
def validate_and_parse_response(self, response: Union[httpx.Response, requests.Response]) -> str:
"""
validate the response
"""
if isinstance(response, httpx.Response):
if response.status_code >= 400:
raise ToolProviderCredentialValidationError(f"Request failed with status code {response.status_code}")
return response.text
elif isinstance(response, requests.Response):
if not response.ok:
raise ToolProviderCredentialValidationError(f"Request failed with status code {response.status_code}")
return response.text
else:
raise ValueError(f'Invalid response type {type(response)}')
def do_http_request(self, url: str, method: str, headers: Dict[str, Any], parameters: Dict[str, Any]) -> httpx.Response:
"""
do http request depending on api bundle
"""
method = method.lower()
params = {}
path_params = {}
body = {}
cookies = {}
# check parameters
for parameter in self.api_bundle.openapi.get('parameters', []):
if parameter['in'] == 'path':
value = ''
if parameter['name'] in parameters:
value = parameters[parameter['name']]
elif parameter['required']:
raise ToolProviderCredentialValidationError(f"Missing required parameter {parameter['name']}")
path_params[parameter['name']] = value
elif parameter['in'] == 'query':
value = ''
if parameter['name'] in parameters:
value = parameters[parameter['name']]
elif parameter['required']:
raise ToolProviderCredentialValidationError(f"Missing required parameter {parameter['name']}")
params[parameter['name']] = value
elif parameter['in'] == 'cookie':
value = ''
if parameter['name'] in parameters:
value = parameters[parameter['name']]
elif parameter['required']:
raise ToolProviderCredentialValidationError(f"Missing required parameter {parameter['name']}")
cookies[parameter['name']] = value
elif parameter['in'] == 'header':
value = ''
if parameter['name'] in parameters:
value = parameters[parameter['name']]
elif parameter['required']:
raise ToolProviderCredentialValidationError(f"Missing required parameter {parameter['name']}")
headers[parameter['name']] = value
# check if there is a request body and handle it
if 'requestBody' in self.api_bundle.openapi and self.api_bundle.openapi['requestBody'] is not None:
# handle json request body
if 'content' in self.api_bundle.openapi['requestBody']:
for content_type in self.api_bundle.openapi['requestBody']['content']:
headers['Content-Type'] = content_type
body_schema = self.api_bundle.openapi['requestBody']['content'][content_type]['schema']
required = body_schema['required'] if 'required' in body_schema else []
properties = body_schema['properties'] if 'properties' in body_schema else {}
for name, property in properties.items():
if name in parameters:
# convert type
try:
value = parameters[name]
if property['type'] == 'integer':
value = int(value)
elif property['type'] == 'number':
# check if it is a float
if '.' in value:
value = float(value)
else:
value = int(value)
elif property['type'] == 'boolean':
value = bool(value)
body[name] = value
except ValueError as e:
body[name] = parameters[name]
elif name in required:
raise ToolProviderCredentialValidationError(
f"Missing required parameter {name} in operation {self.api_bundle.operation_id}"
)
elif 'default' in property:
body[name] = property['default']
else:
body[name] = None
break
# replace path parameters
for name, value in path_params.items():
url = url.replace(f'{{{name}}}', value)
# parse http body data if needed, for GET/HEAD/OPTIONS/TRACE, the body is ignored
if 'Content-Type' in headers:
if headers['Content-Type'] == 'application/json':
body = dumps(body)
else:
body = body
# do http request
if method == 'get':
response = httpx.get(url, params=params, headers=headers, cookies=cookies, timeout=10, follow_redirects=True)
elif method == 'post':
response = httpx.post(url, params=params, headers=headers, cookies=cookies, data=body, timeout=10, follow_redirects=True)
elif method == 'put':
response = httpx.put(url, params=params, headers=headers, cookies=cookies, data=body, timeout=10, follow_redirects=True)
elif method == 'delete':
"""
request body data is unsupported for DELETE method in standard http protocol
however, OpenAPI 3.0 supports request body data for DELETE method, so we support it here by using requests
"""
response = requests.delete(url, params=params, headers=headers, cookies=cookies, data=body, timeout=10, allow_redirects=True)
elif method == 'patch':
response = httpx.patch(url, params=params, headers=headers, cookies=cookies, data=body, timeout=10, follow_redirects=True)
elif method == 'head':
response = httpx.head(url, params=params, headers=headers, cookies=cookies, timeout=10, follow_redirects=True)
elif method == 'options':
response = httpx.options(url, params=params, headers=headers, cookies=cookies, timeout=10, follow_redirects=True)
else:
raise ValueError(f'Invalid http method {method}')
return response
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) -> ToolInvokeMessage | List[ToolInvokeMessage]:
"""
invoke http request
"""
# assemble request
headers = self.assembling_request(tool_paramters)
# do http request
response = self.do_http_request(self.api_bundle.server_url, self.api_bundle.method, headers, tool_paramters)
# validate response
response = self.validate_and_parse_response(response)
# assemble invoke message
return self.create_text_message(response)
\ No newline at end of file
api/core/tools/tool/builtin_tool.py 0 → 100644
View file @ 86286e1a
from core.tools.tool.tool import Tool
from core.tools.model.tool_model_manager import ToolModelManager
from core.model_runtime.entities.message_entities import PromptMessage
from core.model_runtime.entities.llm_entities import LLMResult
from core.model_runtime.entities.message_entities import SystemPromptMessage, UserPromptMessage
from core.tools.utils.web_reader_tool import get_url
from typing import List
from enum import Enum
_SUMMARY_PROMPT = """You are a professional language researcher, you are interested in the language
and you can quickly aimed at the main point of an webpage and reproduce it in your own words but
retain the original meaning and keep the key points.
however, the text you got is too long, what you got is possible a part of the text.
Please summarize the text you got.
"""
class BuiltinTool(Tool):
"""
Builtin tool
:param meta: the meta data of a tool call processing
"""
def invoke_model(
self, user_id: str, prompt_messages: List[PromptMessage], stop: List[str]
) -> LLMResult:
"""
invoke model
:param model_config: the model config
:param prompt_messages: the prompt messages
:param stop: the stop words
:return: the model result
"""
# invoke model
return ToolModelManager.invoke(
user_id=user_id,
tenant_id=self.runtime.tenant_id,
tool_type='builtin',
tool_name=self.identity.name,
prompt_messages=prompt_messages,
)
def get_max_tokens(self) -> int:
"""
get max tokens
:param model_config: the model config
:return: the max tokens
"""
return ToolModelManager.get_max_llm_context_tokens(
tenant_id=self.runtime.tenant_id,
)
def get_prompt_tokens(self, prompt_messages: List[PromptMessage]) -> int:
"""
get prompt tokens
:param prompt_messages: the prompt messages
:return: the tokens
"""
return ToolModelManager.calculate_tokens(
tenant_id=self.runtime.tenant_id,
prompt_messages=prompt_messages
)
def summary(self, user_id: str, content: str) -> str:
max_tokens = self.get_max_tokens()
if self.get_prompt_tokens(prompt_messages=[
UserPromptMessage(content=content)
]) < max_tokens * 0.6:
return content
def get_prompt_tokens(content: str) -> int:
return self.get_prompt_tokens(prompt_messages=[
SystemPromptMessage(content=_SUMMARY_PROMPT),
UserPromptMessage(content=content)
])
def summarize(content: str) -> str:
summary = self.invoke_model(user_id=user_id, prompt_messages=[
SystemPromptMessage(content=_SUMMARY_PROMPT),
UserPromptMessage(content=content)
], stop=[])
return summary.message.content
lines = content.split('\n')
new_lines = []
# split long line into multiple lines
for i in range(len(lines)):
line = lines[i]
if not line.strip():
continue
if len(line) < max_tokens * 0.5:
new_lines.append(line)
elif get_prompt_tokens(line) > max_tokens * 0.7:
while get_prompt_tokens(line) > max_tokens * 0.7:
new_lines.append(line[:int(max_tokens * 0.5)])
line = line[int(max_tokens * 0.5):]
new_lines.append(line)
else:
new_lines.append(line)
# merge lines into messages with max tokens
messages: List[str] = []
for i in new_lines:
if len(messages) == 0:
messages.append(i)
else:
if len(messages[-1]) + len(i) < max_tokens * 0.5:
messages[-1] += i
if get_prompt_tokens(messages[-1] + i) > max_tokens * 0.7:
messages.append(i)
else:
messages[-1] += i
summaries = []
for i in range(len(messages)):
message = messages[i]
summary = summarize(message)
summaries.append(summary)
result = '\n'.join(summaries)
if self.get_prompt_tokens(prompt_messages=[
UserPromptMessage(content=result)
]) > max_tokens * 0.7:
return self.summary(user_id=user_id, content=result)
return result
def get_url(self, url: str, user_agent: str = None) -> str:
"""
get url
"""
return get_url(url, user_agent=user_agent)
\ No newline at end of file
api/core/tools/tool/dataset_retriever_tool.py 0 → 100644
View file @ 86286e1a
from typing import Any, Dict, List, Union
from core.features.dataset_retrieval import DatasetRetrievalFeature
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter, ToolIdentity, ToolDescription
from core.tools.tool.tool import Tool
from core.tools.entities.common_entities import I18nObject
from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
from core.entities.application_entities import DatasetRetrieveConfigEntity, InvokeFrom
from langchain.tools import BaseTool
class DatasetRetrieverTool(Tool):
langchain_tool: BaseTool
@staticmethod
def get_dataset_tools(tenant_id: str,
dataset_ids: list[str],
retrieve_config: DatasetRetrieveConfigEntity,
return_resource: bool,
invoke_from: InvokeFrom,
hit_callback: DatasetIndexToolCallbackHandler
) -> List['DatasetRetrieverTool']:
"""
get dataset tool
"""
# check if retrieve_config is valid
if dataset_ids is None or len(dataset_ids) == 0:
return []
if retrieve_config is None:
return []
feature = DatasetRetrievalFeature()
# save original retrieve strategy, and set retrieve strategy to SINGLE
# Agent only support SINGLE mode
original_retriever_mode = retrieve_config.retrieve_strategy
retrieve_config.retrieve_strategy = DatasetRetrieveConfigEntity.RetrieveStrategy.SINGLE
langchain_tools = feature.to_dataset_retriever_tool(
tenant_id=tenant_id,
dataset_ids=dataset_ids,
retrieve_config=retrieve_config,
return_resource=return_resource,
invoke_from=invoke_from,
hit_callback=hit_callback
)
# restore retrieve strategy
retrieve_config.retrieve_strategy = original_retriever_mode
# convert langchain tools to Tools
tools = []
for langchain_tool in langchain_tools:
tool = DatasetRetrieverTool(
langchain_tool=langchain_tool,
identity=ToolIdentity(author='', name=langchain_tool.name, label=I18nObject(en_US='', zh_Hans='')),
parameters=[],
is_team_authorization=True,
description=ToolDescription(
human=I18nObject(en_US='', zh_Hans=''),
llm=langchain_tool.description),
runtime=DatasetRetrieverTool.Runtime()
)
tools.append(tool)
return tools
def get_runtime_parameters(self) -> List[ToolParamter]:
return [
ToolParamter(name='query',
label=I18nObject(en_US='', zh_Hans=''),
human_description=I18nObject(en_US='', zh_Hans=''),
type=ToolParamter.ToolParameterType.STRING,
form=ToolParamter.ToolParameterForm.LLM,
llm_description='Query for the dataset to be used to retrieve the dataset.',
required=True,
default=''),
]
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) -> ToolInvokeMessage | List[ToolInvokeMessage]:
"""
invoke dataset retriever tool
"""
query = tool_paramters.get('query', None)
if not query:
return self.create_text_message(text='please input query')
# invoke dataset retriever tool
result = self.langchain_tool._run(query=query)
return self.create_text_message(text=result)
def validate_credentials(self, credentails: Dict[str, Any], parameters: Dict[str, Any]) -> None:
"""
validate the credentials for dataset retriever tool
"""
pass
\ No newline at end of file
api/core/tools/tool/tool.py 0 → 100644
View file @ 86286e1a
from pydantic import BaseModel
from typing import List, Dict, Any, Union, Optional
from abc import abstractmethod, ABC
from enum import Enum
from core.tools.entities.tool_entities import ToolIdentity, ToolInvokeMessage,\
ToolParamter, ToolDescription, ToolRuntimeVariablePool, ToolRuntimeVariable, ToolRuntimeImageVariable
from core.tools.tool_file_manager import ToolFileManager
from core.callback_handler.agent_tool_callback_handler import DifyAgentCallbackHandler
class Tool(BaseModel, ABC):
identity: ToolIdentity = None
parameters: Optional[List[ToolParamter]] = None
description: ToolDescription = None
is_team_authorization: bool = False
agent_callback: Optional[DifyAgentCallbackHandler] = None
use_callback: bool = False
class Runtime(BaseModel):
"""
Meta data of a tool call processing
"""
def __init__(self, **data: Any):
super().__init__(**data)
if not self.runtime_parameters:
self.runtime_parameters = {}
tenant_id: str = None
tool_id: str = None
credentials: Dict[str, Any] = None
runtime_parameters: Dict[str, Any] = None
runtime: Runtime = None
variables: ToolRuntimeVariablePool = None
def __init__(self, **data: Any):
super().__init__(**data)
if not self.agent_callback:
self.use_callback = False
else:
self.use_callback = True
class VARIABLE_KEY(Enum):
IMAGE = 'image'
def fork_tool_runtime(self, meta: Dict[str, Any], agent_callback: DifyAgentCallbackHandler = None) -> 'Tool':
"""
fork a new tool with meta data
:param meta: the meta data of a tool call processing, tenant_id is required
:return: the new tool
"""
return self.__class__(
identity=self.identity.copy() if self.identity else None,
parameters=self.parameters.copy() if self.parameters else None,
description=self.description.copy() if self.description else None,
runtime=Tool.Runtime(**meta),
agent_callback=agent_callback
)
def load_variables(self, variables: ToolRuntimeVariablePool):
"""
load variables from database
:param conversation_id: the conversation id
"""
self.variables = variables
def set_image_variable(self, variable_name: str, image_key: str) -> None:
"""
set an image variable
"""
if not self.variables:
return
self.variables.set_file(self.identity.name, variable_name, image_key)
def set_text_variable(self, variable_name: str, text: str) -> None:
"""
set a text variable
"""
if not self.variables:
return
self.variables.set_text(self.identity.name, variable_name, text)
def get_variable(self, name: Union[str, Enum]) -> Optional[ToolRuntimeVariable]:
"""
get a variable
:param name: the name of the variable
:return: the variable
"""
if not self.variables:
return None
if isinstance(name, Enum):
name = name.value
for variable in self.variables.pool:
if variable.name == name:
return variable
return None
def get_default_image_variable(self) -> Optional[ToolRuntimeVariable]:
"""
get the default image variable
:return: the image variable
"""
if not self.variables:
return None
return self.get_variable(self.VARIABLE_KEY.IMAGE)
def get_variable_file(self, name: Union[str, Enum]) -> Optional[bytes]:
"""
get a variable file
:param name: the name of the variable
:return: the variable file
"""
variable = self.get_variable(name)
if not variable:
return None
if not isinstance(variable, ToolRuntimeImageVariable):
return None
message_file_id = variable.value
# get file binary
file_binary = ToolFileManager.get_file_binary_by_message_file_id(message_file_id)
if not file_binary:
return None
return file_binary[0]
def list_variables(self) -> List[ToolRuntimeVariable]:
"""
list all variables
:return: the variables
"""
if not self.variables:
return []
return self.variables.pool
def list_default_image_variables(self) -> List[ToolRuntimeVariable]:
"""
list all image variables
:return: the image variables
"""
if not self.variables:
return []
result = []
for variable in self.variables.pool:
if variable.name.startswith(self.VARIABLE_KEY.IMAGE.value):
result.append(variable)
return result
def invoke(self, user_id: str, tool_paramters: Dict[str, Any]) -> List[ToolInvokeMessage]:
# update tool_paramters
if self.runtime.runtime_parameters:
tool_paramters.update(self.runtime.runtime_parameters)
# hit callback
if self.use_callback:
self.agent_callback.on_tool_start(
tool_name=self.identity.name,
tool_inputs=tool_paramters
)
try:
result = self._invoke(
user_id=user_id,
tool_paramters=tool_paramters,
)
except Exception as e:
if self.use_callback:
self.agent_callback.on_tool_error(e)
raise e
if not isinstance(result, list):
result = [result]
# hit callback
if self.use_callback:
self.agent_callback.on_tool_end(
tool_name=self.identity.name,
tool_inputs=tool_paramters,
tool_outputs=self._convert_tool_response_to_str(result)
)
return result
def _convert_tool_response_to_str(self, tool_response: List[ToolInvokeMessage]) -> str:
"""
Handle tool response
"""
result = ''
for response in tool_response:
if response.type == ToolInvokeMessage.MessageType.TEXT:
result += response.message
elif response.type == ToolInvokeMessage.MessageType.LINK:
result += f"result link: {response.message}. please dirct user to check it."
elif response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
response.type == ToolInvokeMessage.MessageType.IMAGE:
result += f"image has been created and sent to user already, you should tell user to check it now."
elif response.type == ToolInvokeMessage.MessageType.BLOB:
if len(response.message) > 114:
result += str(response.message[:114]) + '...'
else:
result += str(response.message)
else:
result += f"tool response: {response.message}."
return result
@abstractmethod
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
pass
def validate_credentials(self, credentails: Dict[str, Any], parameters: Dict[str, Any]) -> None:
"""
validate the credentials
:param credentails: the credentials
:param parameters: the parameters
"""
pass
def get_runtime_parameters(self) -> List[ToolParamter]:
"""
get the runtime parameters
interface for developer to dynamic change the parameters of a tool depends on the variables pool
:return: the runtime parameters
"""
return self.parameters
def is_tool_avaliable(self) -> bool:
"""
check if the tool is avaliable
:return: if the tool is avaliable
"""
return True
def create_image_message(self, image: str, save_as: str = '') -> ToolInvokeMessage:
"""
create an image message
:param image: the url of the image
:return: the image message
"""
return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.IMAGE,
message=image,
save_as=save_as)
def create_link_message(self, link: str, save_as: str = '') -> ToolInvokeMessage:
"""
create a link message
:param link: the url of the link
:return: the link message
"""
return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.LINK,
message=link,
save_as=save_as)
def create_text_message(self, text: str, save_as: str = '') -> ToolInvokeMessage:
"""
create a text message
:param text: the text
:return: the text message
"""
return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.TEXT,
message=text,
save_as=save_as
)
def create_blob_message(self, blob: bytes, meta: dict = None, save_as: str = '') -> ToolInvokeMessage:
"""
create a blob message
:param blob: the blob
:return: the blob message
"""
return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.BLOB,
message=blob, meta=meta,
save_as=save_as
)
\ No newline at end of file
api/core/tools/tool_file_manager.py 0 → 100644
View file @ 86286e1a
import logging
import time
import os
import hmac
import base64
import hashlib
from typing import Union, Tuple, Generator
from uuid import uuid4
from mimetypes import guess_extension, guess_type
from httpx import get
from flask import current_app
from models.tools import ToolFile
from models.model import MessageFile
from extensions.ext_database import db
from extensions.ext_storage import storage
logger = logging.getLogger(__name__)
class ToolFileManager:
@staticmethod
def sign_file(file_id: str, extension: str) -> str:
"""
sign file to get a temporary url
"""
base_url = current_app.config.get('FILES_URL')
file_preview_url = f'{base_url}/files/tools/{file_id}{extension}'
timestamp = str(int(time.time()))
nonce = os.urandom(16).hex()
data_to_sign = f"file-preview|{file_id}|{timestamp}|{nonce}"
secret_key = current_app.config['SECRET_KEY'].encode()
sign = hmac.new(secret_key, data_to_sign.encode(), hashlib.sha256).digest()
encoded_sign = base64.urlsafe_b64encode(sign).decode()
return f"{file_preview_url}?timestamp={timestamp}&nonce={nonce}&sign={encoded_sign}"
@staticmethod
def verify_file(file_id: str, timestamp: str, nonce: str, sign: str) -> bool:
"""
verify signature
"""
data_to_sign = f"file-preview|{file_id}|{timestamp}|{nonce}"
secret_key = current_app.config['SECRET_KEY'].encode()
recalculated_sign = hmac.new(secret_key, data_to_sign.encode(), hashlib.sha256).digest()
recalculated_encoded_sign = base64.urlsafe_b64encode(recalculated_sign).decode()
# verify signature
if sign != recalculated_encoded_sign:
return False
current_time = int(time.time())
return current_time - int(timestamp) <= 300 # expired after 5 minutes
@staticmethod
def create_file_by_raw(user_id: str, tenant_id: str,
conversation_id: str, file_binary: bytes,
mimetype: str
) -> ToolFile:
"""
create file
"""
extension = guess_extension(mimetype) or '.bin'
unique_name = uuid4().hex
filename = f"/tools/{tenant_id}/{unique_name}{extension}"
storage.save(filename, file_binary)
tool_file = ToolFile(user_id=user_id, tenant_id=tenant_id,
conversation_id=conversation_id, file_key=filename, mimetype=mimetype)
db.session.add(tool_file)
db.session.commit()
return tool_file
@staticmethod
def create_file_by_url(user_id: str, tenant_id: str,
conversation_id: str, file_url: str,
) -> ToolFile:
"""
create file
"""
# try to download image
response = get(file_url)
response.raise_for_status()
blob = response.content
mimetype = guess_type(file_url)[0] or 'octet/stream'
extension = guess_extension(mimetype) or '.bin'
unique_name = uuid4().hex
filename = f"/tools/{tenant_id}/{unique_name}{extension}"
storage.save(filename, blob)
tool_file = ToolFile(user_id=user_id, tenant_id=tenant_id,
conversation_id=conversation_id, file_key=filename,
mimetype=mimetype, original_url=file_url)
db.session.add(tool_file)
db.session.commit()
return tool_file
@staticmethod
def create_file_by_key(user_id: str, tenant_id: str,
conversation_id: str, file_key: str,
mimetype: str
) -> ToolFile:
"""
create file
"""
tool_file = ToolFile(user_id=user_id, tenant_id=tenant_id,
conversation_id=conversation_id, file_key=file_key, mimetype=mimetype)
return tool_file
@staticmethod
def get_file_binary(id: str) -> Union[Tuple[bytes, str], None]:
"""
get file binary
:param id: the id of the file
:return: the binary of the file, mime type
"""
tool_file: ToolFile = db.session.query(ToolFile).filter(
ToolFile.id == id,
).first()
if not tool_file:
return None
blob = storage.load_once(tool_file.file_key)
return blob, tool_file.mimetype
@staticmethod
def get_file_binary_by_message_file_id(id: str) -> Union[Tuple[bytes, str], None]:
"""
get file binary
:param id: the id of the file
:return: the binary of the file, mime type
"""
message_file: MessageFile = db.session.query(MessageFile).filter(
MessageFile.id == id,
).first()
# get tool file id
tool_file_id = message_file.url.split('/')[-1]
# trim extension
tool_file_id = tool_file_id.split('.')[0]
tool_file: ToolFile = db.session.query(ToolFile).filter(
ToolFile.id == tool_file_id,
).first()
if not tool_file:
return None
blob = storage.load_once(tool_file.file_key)
return blob, tool_file.mimetype
@staticmethod
def get_file_generator_by_message_file_id(id: str) -> Union[Tuple[Generator, str], None]:
"""
get file binary
:param id: the id of the file
:return: the binary of the file, mime type
"""
message_file: MessageFile = db.session.query(MessageFile).filter(
MessageFile.id == id,
).first()
# get tool file id
tool_file_id = message_file.url.split('/')[-1]
# trim extension
tool_file_id = tool_file_id.split('.')[0]
tool_file: ToolFile = db.session.query(ToolFile).filter(
ToolFile.id == tool_file_id,
).first()
if not tool_file:
return None
generator = storage.load_stream(tool_file.file_key)
return generator, tool_file.mimetype
# init tool_file_parser
from core.file.tool_file_parser import tool_file_manager
tool_file_manager['manager'] = ToolFileManager
api/core/tools/tool_manager.py 0 → 100644
View file @ 86286e1a
from typing import List, Dict, Any, Tuple, Union
from os import listdir, path
from core.tools.entities.tool_entities import ToolInvokeMessage, ApiProviderAuthType, ToolProviderCredentials
from core.tools.provider.tool_provider import ToolProviderController
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.tool.api_tool import ApiTool
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.entities.constant import DEFAULT_PROVIDERS
from core.tools.entities.common_entities import I18nObject
from core.tools.errors import ToolProviderNotFoundError
from core.tools.provider.api_tool_provider import ApiBasedToolProviderController
from core.tools.provider.app_tool_provider import AppBasedToolProviderEntity
from core.tools.entities.user_entities import UserToolProvider
from core.tools.utils.configration import ToolConfiguration
from core.tools.utils.encoder import serialize_base_model_dict
from core.tools.provider.builtin._positions import BuiltinToolProviderSort
from core.model_runtime.entities.message_entities import PromptMessage
from core.callback_handler.agent_tool_callback_handler import DifyAgentCallbackHandler
from extensions.ext_database import db
from models.tools import ApiToolProvider, BuiltinToolProvider
import importlib
import logging
import json
import mimetypes
logger = logging.getLogger(__name__)
_builtin_providers = {}
class ToolManager:
@staticmethod
def invoke(
provider: str,
tool_id: str,
tool_name: str,
tool_parameters: Dict[str, Any],
credentials: Dict[str, Any],
prompt_messages: List[PromptMessage],
) -> List[ToolInvokeMessage]:
"""
invoke the assistant
:param provider: the name of the provider
:param tool_id: the id of the tool
:param tool_name: the name of the tool, defined in `get_tools`
:param tool_parameters: the parameters of the tool
:param credentials: the credentials of the tool
:param prompt_messages: the prompt messages that the tool can use
:return: the messages that the tool wants to send to the user
"""
provider_entity: ToolProviderController = None
if provider == DEFAULT_PROVIDERS.API_BASED:
provider_entity = ApiBasedToolProviderController()
elif provider == DEFAULT_PROVIDERS.APP_BASED:
provider_entity = AppBasedToolProviderEntity()
if provider_entity is None:
# fetch the provider from .provider.builtin
py_path = path.join(path.dirname(path.realpath(__file__)), 'builtin', provider, f'{provider}.py')
spec = importlib.util.spec_from_file_location(f'core.tools.provider.builtin.{provider}.{provider}', py_path)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)
# get all the classes in the module
classes = [ x for _, x in vars(mod).items()
if isinstance(x, type) and x != ToolProviderController and issubclass(x, ToolProviderController)
]
if len(classes) == 0:
raise ToolProviderNotFoundError(f'provider {provider} not found')
if len(classes) > 1:
raise ToolProviderNotFoundError(f'multiple providers found for {provider}')
provider_entity = classes[0]()
return provider_entity.invoke(tool_id, tool_name, tool_parameters, credentials, prompt_messages)
@staticmethod
def get_builtin_provider(provider: str) -> BuiltinToolProviderController:
global _builtin_providers
"""
get the builtin provider
:param provider: the name of the provider
:return: the provider
"""
if len(_builtin_providers) == 0:
# init the builtin providers
ToolManager.list_builtin_providers()
if provider not in _builtin_providers:
raise ToolProviderNotFoundError(f'builtin provider {provider} not found')
return _builtin_providers[provider]
@staticmethod
def get_builtin_tool(provider: str, tool_name: str) -> BuiltinTool:
"""
get the builtin tool
:param provider: the name of the provider
:param tool_name: the name of the tool
:return: the provider, the tool
"""
provider_controller = ToolManager.get_builtin_provider(provider)
tool = provider_controller.get_tool(tool_name)
return tool
@staticmethod
def get_tool(provider_type: str, provider_id: str, tool_name: str, tanent_id: str = None) \
-> Union[BuiltinTool, ApiTool]:
"""
get the tool
:param provider_type: the type of the provider
:param provider_name: the name of the provider
:param tool_name: the name of the tool
:return: the tool
"""
if provider_type == 'builtin':
return ToolManager.get_builtin_tool(provider_id, tool_name)
elif provider_type == 'api':
if tanent_id is None:
raise ValueError('tanent id is required for api provider')
api_provider, _ = ToolManager.get_api_provider_controller(tanent_id, provider_id)
return api_provider.get_tool(tool_name)
elif provider_type == 'app':
raise NotImplementedError('app provider not implemented')
else:
raise ToolProviderNotFoundError(f'provider type {provider_type} not found')
@staticmethod
def get_tool_runtime(provider_type: str, provider_name: str, tool_name: str, tanent_id,
agent_callback: DifyAgentCallbackHandler = None) \
-> Union[BuiltinTool, ApiTool]:
"""
get the tool runtime
:param provider_type: the type of the provider
:param provider_name: the name of the provider
:param tool_name: the name of the tool
:return: the tool
"""
if provider_type == 'builtin':
builtin_tool = ToolManager.get_builtin_tool(provider_name, tool_name)
# check if the builtin tool need credentials
provider_controller = ToolManager.get_builtin_provider(provider_name)
if not provider_controller.need_credentials:
return builtin_tool.fork_tool_runtime(meta={
'tenant_id': tanent_id,
'credentials': {},
}, agent_callback=agent_callback)
# get credentials
builtin_provider: BuiltinToolProvider = db.session.query(BuiltinToolProvider).filter(
BuiltinToolProvider.tenant_id == tanent_id,
BuiltinToolProvider.provider == provider_name,
).first()
if builtin_provider is None:
raise ToolProviderNotFoundError(f'builtin provider {provider_name} not found')
# decrypt the credentials
credentials = builtin_provider.credentials
controller = ToolManager.get_builtin_provider(provider_name)
tool_configuration = ToolConfiguration(tenant_id=tanent_id, provider_controller=controller)
decrypted_credentails = tool_configuration.decrypt_tool_credentials(credentials)
return builtin_tool.fork_tool_runtime(meta={
'tenant_id': tanent_id,
'credentials': decrypted_credentails,
'runtime_parameters': {}
}, agent_callback=agent_callback)
elif provider_type == 'api':
if tanent_id is None:
raise ValueError('tanent id is required for api provider')
api_provider, credentials = ToolManager.get_api_provider_controller(tanent_id, provider_name)
# decrypt the credentials
tool_configuration = ToolConfiguration(tenant_id=tanent_id, provider_controller=api_provider)
decrypted_credentails = tool_configuration.decrypt_tool_credentials(credentials)
return api_provider.get_tool(tool_name).fork_tool_runtime(meta={
'tenant_id': tanent_id,
'credentials': decrypted_credentails,
})
elif provider_type == 'app':
raise NotImplementedError('app provider not implemented')
else:
raise ToolProviderNotFoundError(f'provider type {provider_type} not found')
@staticmethod
def get_builtin_provider_icon(provider: str) -> Tuple[str, str]:
"""
get the absolute path of the icon of the builtin provider
:param provider: the name of the provider
:return: the absolute path of the icon, the mime type of the icon
"""
# get provider
provider_controller = ToolManager.get_builtin_provider(provider)
absolute_path = path.join(path.dirname(path.realpath(__file__)), 'provider', 'builtin', provider, '_assets', provider_controller.identity.icon)
# check if the icon exists
if not path.exists(absolute_path):
raise ToolProviderNotFoundError(f'builtin provider {provider} icon not found')
# get the mime type
mime_type, _ = mimetypes.guess_type(absolute_path)
mime_type = mime_type or 'application/octet-stream'
return absolute_path, mime_type
@staticmethod
def list_builtin_providers() -> List[BuiltinToolProviderController]:
global _builtin_providers
# use cache first
if len(_builtin_providers) > 0:
return list(_builtin_providers.values())
builtin_providers = []
for provider in listdir(path.join(path.dirname(path.realpath(__file__)), 'provider', 'builtin')):
if provider.startswith('__'):
continue
if path.isdir(path.join(path.dirname(path.realpath(__file__)), 'provider', 'builtin', provider)):
if provider.startswith('__'):
continue
py_path = path.join(path.dirname(path.realpath(__file__)), 'provider', 'builtin', provider, f'{provider}.py')
spec = importlib.util.spec_from_file_location(f'core.tools.provider.builtin.{provider}.{provider}', py_path)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)
# load all classes
classes = [
obj for name, obj in vars(mod).items()
if isinstance(obj, type) and obj != BuiltinToolProviderController and issubclass(obj, BuiltinToolProviderController)
]
if len(classes) == 0:
raise ToolProviderNotFoundError(f'provider {provider} not found')
if len(classes) > 1:
raise ToolProviderNotFoundError(f'multiple providers found for {provider}')
# init provider
provider_class = classes[0]
builtin_providers.append(provider_class())
# cache the builtin providers
for provider in builtin_providers:
_builtin_providers[provider.identity.name] = provider
return builtin_providers
@staticmethod
def user_list_providers(
user_id: str,
tenant_id: str,
) -> List[UserToolProvider]:
result_providers: Dict[str, UserToolProvider] = {}
# get builtin providers
builtin_providers = ToolManager.list_builtin_providers()
# append builtin providers
for provider in builtin_providers:
result_providers[provider.identity.name] = UserToolProvider(
id=provider.identity.name,
author=provider.identity.author,
name=provider.identity.name,
description=I18nObject(
en_US=provider.identity.description.en_US,
zh_Hans=provider.identity.description.zh_Hans,
),
icon=provider.identity.icon,
label=I18nObject(
en_US=provider.identity.label.en_US,
zh_Hans=provider.identity.label.zh_Hans,
),
type=UserToolProvider.ProviderType.BUILTIN,
team_credentials={},
is_team_authorization=False,
)
# get credentials schema
schema = provider.get_credentails_schema()
for name, value in schema.items():
result_providers[provider.identity.name].team_credentials[name] = \
ToolProviderCredentials.CredentialsType.defaut(value.type)
# check if the provider need credentials
if not provider.need_credentials:
result_providers[provider.identity.name].is_team_authorization = True
result_providers[provider.identity.name].allow_delete = False
# get db builtin providers
db_builtin_providers: List[BuiltinToolProvider] = db.session.query(BuiltinToolProvider). \
filter(BuiltinToolProvider.tenant_id == tenant_id).all()
for db_builtin_provider in db_builtin_providers:
# add provider into providers
credentails = db_builtin_provider.credentials
provider_name = db_builtin_provider.provider
result_providers[provider_name].is_team_authorization = True
# package builtin tool provider controller
controller = ToolManager.get_builtin_provider(provider_name)
# init tool configuration
tool_configuration = ToolConfiguration(tenant_id=tenant_id, provider_controller=controller)
# decrypt the credentials and mask the credentials
decrypted_credentails = tool_configuration.decrypt_tool_credentials(credentials=credentails)
masked_credentials = tool_configuration.mask_tool_credentials(credentials=decrypted_credentails)
result_providers[provider_name].team_credentials = masked_credentials
# get db api providers
db_api_providers: List[ApiToolProvider] = db.session.query(ApiToolProvider). \
filter(ApiToolProvider.tenant_id == tenant_id).all()
for db_api_provider in db_api_providers:
username = 'Anonymous'
try:
username = db_api_provider.user.name
except Exception as e:
logger.error(f'failed to get user name for api provider {db_api_provider.id}: {str(e)}')
# add provider into providers
credentails = db_api_provider.credentials
provider_name = db_api_provider.name
result_providers[provider_name] = UserToolProvider(
id=db_api_provider.id,
author=username,
name=db_api_provider.name,
description=I18nObject(
en_US=db_api_provider.description,
zh_Hans=db_api_provider.description,
),
icon=db_api_provider.icon,
label=I18nObject(
en_US=db_api_provider.name,
zh_Hans=db_api_provider.name,
),
type=UserToolProvider.ProviderType.API,
team_credentials={},
is_team_authorization=True,
)
# package tool provider controller
controller = ApiBasedToolProviderController.from_db(
db_provider=db_api_provider,
auth_type=ApiProviderAuthType.API_KEY if db_api_provider.credentials['auth_type'] == 'api_key' else ApiProviderAuthType.NONE
)
# init tool configuration
tool_configuration = ToolConfiguration(tenant_id=tenant_id, provider_controller=controller)
# decrypt the credentials and mask the credentials
decrypted_credentails = tool_configuration.decrypt_tool_credentials(credentials=credentails)
masked_credentials = tool_configuration.mask_tool_credentials(credentials=decrypted_credentails)
result_providers[provider_name].team_credentials = masked_credentials
return BuiltinToolProviderSort.sort(list(result_providers.values()))
@staticmethod
def get_api_provider_controller(tanent_id: str, provider_id: str) -> Tuple[ApiBasedToolProviderController, Dict[str, Any]]:
"""
get the api provider
:param provider_name: the name of the provider
:return: the provider controller, the credentials
"""
provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
ApiToolProvider.id == provider_id,
ApiToolProvider.tenant_id == tanent_id,
).first()
if provider is None:
raise ToolProviderNotFoundError(f'api provider {provider_id} not found')
controller = ApiBasedToolProviderController.from_db(
provider, ApiProviderAuthType.API_KEY if provider.credentials['auth_type'] == 'api_key' else ApiProviderAuthType.NONE
)
controller.load_bundled_tools(provider.tools)
return controller, provider.credentials
@staticmethod
def user_get_api_provider(provider: str, tenant_id: str) -> dict:
"""
get api provider
"""
"""
get tool provider
"""
provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
ApiToolProvider.tenant_id == tenant_id,
ApiToolProvider.name == provider,
).first()
if provider is None:
raise ValueError(f'yout have not added provider {provider}')
try:
credentials = json.loads(provider.credentials_str) or {}
except:
credentials = {}
# package tool provider controller
controller = ApiBasedToolProviderController.from_db(
provider, ApiProviderAuthType.API_KEY if credentials['auth_type'] == 'api_key' else ApiProviderAuthType.NONE
)
# init tool configuration
tool_configuration = ToolConfiguration(tenant_id=tenant_id, provider_controller=controller)
decrypted_credentails = tool_configuration.decrypt_tool_credentials(credentials)
masked_credentials = tool_configuration.mask_tool_credentials(decrypted_credentails)
try:
icon = json.loads(provider.icon)
except:
icon = {
"background": "#252525",
"content": "\ud83d\ude01"
}
return json.loads(serialize_base_model_dict({
'schema_type': provider.schema_type,
'schema': provider.schema,
'tools': provider.tools,
'icon': icon,
'description': provider.description,
'credentials': masked_credentials,
'privacy_policy': provider.privacy_policy
}))
\ No newline at end of file
api/core/tools/utils/configration.py 0 → 100644
View file @ 86286e1a
from typing import Dict, Any
from pydantic import BaseModel
from core.tools.entities.tool_entities import ToolProviderCredentials
from core.tools.provider.tool_provider import ToolProviderController
from core.helper import encrypter
class ToolConfiguration(BaseModel):
tenant_id: str
provider_controller: ToolProviderController
def _deep_copy(self, credentails: Dict[str, str]) -> Dict[str, str]:
"""
deep copy credentials
"""
return {key: value for key, value in credentails.items()}
def encrypt_tool_credentials(self, credentails: Dict[str, str]) -> Dict[str, str]:
"""
encrypt tool credentials with tanent id
return a deep copy of credentials with encrypted values
"""
credentials = self._deep_copy(credentails)
# get fields need to be decrypted
fields = self.provider_controller.get_credentails_schema()
for field_name, field in fields.items():
if field.type == ToolProviderCredentials.CredentialsType.SECRET_INPUT:
if field_name in credentials:
encrypted = encrypter.encrypt_token(self.tenant_id, credentials[field_name])
credentials[field_name] = encrypted
return credentials
def mask_tool_credentials(self, credentials: Dict[str, Any]) -> Dict[str, Any]:
"""
mask tool credentials
return a deep copy of credentials with masked values
"""
credentials = self._deep_copy(credentials)
# get fields need to be decrypted
fields = self.provider_controller.get_credentails_schema()
for field_name, field in fields.items():
if field.type == ToolProviderCredentials.CredentialsType.SECRET_INPUT:
if field_name in credentials:
if len(credentials[field_name]) > 6:
credentials[field_name] = \
credentials[field_name][:2] + \
'*' * (len(credentials[field_name]) - 4) +\
credentials[field_name][-2:]
else:
credentials[field_name] = '*' * len(credentials[field_name])
return credentials
def decrypt_tool_credentials(self, credentials: Dict[str, str]) -> Dict[str, str]:
"""
decrypt tool credentials with tanent id
return a deep copy of credentials with decrypted values
"""
credentials = self._deep_copy(credentials)
# get fields need to be decrypted
fields = self.provider_controller.get_credentails_schema()
for field_name, field in fields.items():
if field.type == ToolProviderCredentials.CredentialsType.SECRET_INPUT:
if field_name in credentials:
try:
credentials[field_name] = encrypter.decrypt_token(self.tenant_id, credentials[field_name])
except:
pass
return credentials
\ No newline at end of file
api/core/tools/utils/encoder.py 0 → 100644
View file @ 86286e1a
from pydantic import BaseModel
from enum import Enum
from typing import List
def serialize_base_model_array(l: List[BaseModel]) -> str:
class _BaseModel(BaseModel):
__root__: List[BaseModel]
"""
{"__root__": [BaseModel, BaseModel, ...]}
"""
return _BaseModel(__root__=l).json()
def serialize_base_model_dict(b: dict) -> str:
class _BaseModel(BaseModel):
__root__: dict
"""
{"__root__": {BaseModel}}
"""
return _BaseModel(__root__=b).json()
api/core/tools/utils/parser.py 0 → 100644
View file @ 86286e1a
from core.tools.entities.tool_bundle import ApiBasedToolBundle
from core.tools.entities.tool_entities import ToolParamter, ToolParamterOption, ApiProviderSchemaType
from core.tools.entities.common_entities import I18nObject
from core.tools.errors import ToolProviderNotFoundError, ToolNotSupportedError, \
ToolApiSchemaError
from typing import List, Tuple
from yaml import FullLoader, load
from json import loads as json_loads, dumps as json_dumps
from requests import get
class ApiBasedToolSchemaParser:
@staticmethod
def parse_openapi_to_tool_bundle(openapi: dict, extra_info: dict = None, warning: dict = None) -> List[ApiBasedToolBundle]:
warning = warning if warning is not None else {}
extra_info = extra_info if extra_info is not None else {}
# set description to extra_info
if 'description' in openapi['info']:
extra_info['description'] = openapi['info']['description']
else:
extra_info['description'] = ''
if len(openapi['servers']) == 0:
raise ToolProviderNotFoundError('No server found in the openapi yaml.')
server_url = openapi['servers'][0]['url']
# list all interfaces
interfaces = []
for path, path_item in openapi['paths'].items():
methods = ['get', 'post', 'put', 'delete', 'patch', 'head', 'options', 'trace']
for method in methods:
if method in path_item:
interfaces.append({
'path': path,
'method': method,
'operation': path_item[method],
})
# get all parameters
bundles = []
for interface in interfaces:
# convert parameters
parameters = []
if 'parameters' in interface['operation']:
for parameter in interface['operation']['parameters']:
parameters.append(ToolParamter(
name=parameter['name'],
label=I18nObject(
en_US=parameter['name'],
zh_Hans=parameter['name']
),
human_description=I18nObject(
en_US=parameter.get('description', ''),
zh_Hans=parameter.get('description', '')
),
type=ToolParamter.ToolParameterType.STRING,
required=parameter.get('required', False),
form=ToolParamter.ToolParameterForm.LLM,
llm_description=parameter.get('description'),
default=parameter['default'] if 'default' in parameter else None,
))
# create tool bundle
# check if there is a request body
if 'requestBody' in interface['operation']:
request_body = interface['operation']['requestBody']
if 'content' in request_body:
for content_type, content in request_body['content'].items():
# if there is a reference, get the reference and overwrite the content
if 'schema' not in content:
content
if '$ref' in content['schema']:
# get the reference
root = openapi
reference = content['schema']['$ref'].split('/')[1:]
for ref in reference:
root = root[ref]
# overwrite the content
interface['operation']['requestBody']['content'][content_type]['schema'] = root
# parse body parameters
if 'schema' in interface['operation']['requestBody']['content'][content_type]:
body_schema = interface['operation']['requestBody']['content'][content_type]['schema']
required = body_schema['required'] if 'required' in body_schema else []
properties = body_schema['properties'] if 'properties' in body_schema else {}
for name, property in properties.items():
parameters.append(ToolParamter(
name=name,
label=I18nObject(
en_US=name,
zh_Hans=name
),
human_description=I18nObject(
en_US=property['description'] if 'description' in property else '',
zh_Hans=property['description'] if 'description' in property else ''
),
type=ToolParamter.ToolParameterType.STRING,
required=name in required,
form=ToolParamter.ToolParameterForm.LLM,
llm_description=property['description'] if 'description' in property else '',
default=property['default'] if 'default' in property else None,
))
# check if parameters is duplicated
parameters_count = {}
for parameter in parameters:
if parameter.name not in parameters_count:
parameters_count[parameter.name] = 0
parameters_count[parameter.name] += 1
for name, count in parameters_count.items():
if count > 1:
warning['duplicated_parameter'] = f'Parameter {name} is duplicated.'
bundles.append(ApiBasedToolBundle(
server_url=server_url + interface['path'],
method=interface['method'],
summary=interface['operation']['summary'] if 'summary' in interface['operation'] else None,
operation_id=interface['operation']['operationId'],
parameters=parameters,
author='',
icon=None,
openapi=interface['operation'],
))
return bundles
@staticmethod
def parse_openapi_yaml_to_tool_bundle(yaml: str, extra_info: dict = None, warning: dict = None) -> List[ApiBasedToolBundle]:
"""
parse openapi yaml to tool bundle
:param yaml: the yaml string
:return: the tool bundle
"""
warning = warning if warning is not None else {}
extra_info = extra_info if extra_info is not None else {}
openapi: dict = load(yaml, Loader=FullLoader)
if openapi is None:
raise ToolApiSchemaError('Invalid openapi yaml.')
return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(openapi, extra_info=extra_info, warning=warning)
@staticmethod
def parse_openapi_json_to_tool_bundle(json: str, extra_info: dict = None, warning: dict = None) -> List[ApiBasedToolBundle]:
"""
parse openapi yaml to tool bundle
:param yaml: the yaml string
:return: the tool bundle
"""
warning = warning if warning is not None else {}
extra_info = extra_info if extra_info is not None else {}
openapi: dict = json_loads(json)
if openapi is None:
raise ToolApiSchemaError('Invalid openapi json.')
return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(openapi, extra_info=extra_info, warning=warning)
@staticmethod
def parse_swagger_to_openapi(swagger: dict, extra_info: dict = None, warning: dict = None) -> dict:
"""
parse swagger to openapi
:param swagger: the swagger dict
:return: the openapi dict
"""
# convert swagger to openapi
info = swagger.get('info', {
'title': 'Swagger',
'description': 'Swagger',
'version': '1.0.0'
})
servers = swagger.get('servers', [])
if len(servers) == 0:
raise ToolApiSchemaError('No server found in the swagger yaml.')
openapi = {
'openapi': '3.0.0',
'info': {
'title': info.get('title', 'Swagger'),
'description': info.get('description', 'Swagger'),
'version': info.get('version', '1.0.0')
},
'servers': swagger['servers'],
'paths': {},
'components': {
'schemas': {}
}
}
# check paths
if 'paths' not in swagger or len(swagger['paths']) == 0:
raise ToolApiSchemaError('No paths found in the swagger yaml.')
# convert paths
for path, path_item in swagger['paths'].items():
openapi['paths'][path] = {}
for method, operation in path_item.items():
if 'operationId' not in operation:
raise ToolApiSchemaError(f'No operationId found in operation {method} {path}.')
if 'summary' not in operation or len(operation['summary']) == 0:
warning['missing_summary'] = f'No summary found in operation {method} {path}.'
if 'description' not in operation or len(operation['description']) == 0:
warning['missing_description'] = f'No description found in operation {method} {path}.'
openapi['paths'][path][method] = {
'operationId': operation['operationId'],
'summary': operation.get('summary', ''),
'description': operation.get('description', ''),
'parameters': operation.get('parameters', []),
'responses': operation.get('responses', {}),
}
if 'requestBody' in operation:
openapi['paths'][path][method]['requestBody'] = operation['requestBody']
# convert definitions
for name, definition in swagger['definitions'].items():
openapi['components']['schemas'][name] = definition
return openapi
@staticmethod
def parse_swagger_yaml_to_tool_bundle(yaml: str, extra_info: dict = None, warning: dict = None) -> List[ApiBasedToolBundle]:
"""
parse swagger yaml to tool bundle
:param yaml: the yaml string
:return: the tool bundle
"""
warning = warning if warning is not None else {}
extra_info = extra_info if extra_info is not None else {}
swagger: dict = load(yaml, Loader=FullLoader)
openapi = ApiBasedToolSchemaParser.parse_swagger_to_openapi(swagger, extra_info=extra_info, warning=warning)
return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(openapi, extra_info=extra_info, warning=warning)
@staticmethod
def parse_swagger_json_to_tool_bundle(json: str, extra_info: dict = None, warning: dict = None) -> List[ApiBasedToolBundle]:
"""
parse swagger yaml to tool bundle
:param yaml: the yaml string
:return: the tool bundle
"""
warning = warning if warning is not None else {}
extra_info = extra_info if extra_info is not None else {}
swagger: dict = json_loads(json)
openapi = ApiBasedToolSchemaParser.parse_swagger_to_openapi(swagger, extra_info=extra_info, warning=warning)
return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(openapi, extra_info=extra_info, warning=warning)
@staticmethod
def parse_openai_plugin_json_to_tool_bundle(json: str, extra_info: dict = None, warning: dict = None) -> List[ApiBasedToolBundle]:
"""
parse openapi plugin yaml to tool bundle
:param json: the json string
:return: the tool bundle
"""
warning = warning if warning is not None else {}
extra_info = extra_info if extra_info is not None else {}
try:
openai_plugin = json_loads(json)
api = openai_plugin['api']
api_url = api['url']
api_type = api['type']
except:
raise ToolProviderNotFoundError('Invalid openai plugin json.')
if api_type != 'openapi':
raise ToolNotSupportedError('Only openapi is supported now.')
# get openapi yaml
response = get(api_url, headers={
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) '
}, timeout=5)
if response.status_code != 200:
raise ToolProviderNotFoundError('cannot get openapi yaml from url.')
return ApiBasedToolSchemaParser.parse_openapi_yaml_to_tool_bundle(response.text, extra_info=extra_info, warning=warning)
@staticmethod
def auto_parse_to_tool_bundle(content: str, extra_info: dict = None, warning: dict = None) -> Tuple[List[ApiBasedToolBundle], str]:
"""
auto parse to tool bundle
:param content: the content
:return: tools bundle, schema_type
"""
warning = warning if warning is not None else {}
extra_info = extra_info if extra_info is not None else {}
json_possible = False
content = content.strip()
if content.startswith('{') and content.endswith('}'):
json_possible = True
if json_possible:
try:
return ApiBasedToolSchemaParser.parse_openapi_json_to_tool_bundle(content, extra_info=extra_info, warning=warning), \
ApiProviderSchemaType.OPENAPI.value
except:
pass
try:
return ApiBasedToolSchemaParser.parse_swagger_json_to_tool_bundle(content, extra_info=extra_info, warning=warning), \
ApiProviderSchemaType.SWAGGER.value
except:
pass
try:
return ApiBasedToolSchemaParser.parse_openai_plugin_json_to_tool_bundle(content, extra_info=extra_info, warning=warning), \
ApiProviderSchemaType.OPENAI_PLUGIN.value
except:
pass
else:
try:
return ApiBasedToolSchemaParser.parse_openapi_yaml_to_tool_bundle(content, extra_info=extra_info, warning=warning), \
ApiProviderSchemaType.OPENAPI.value
except:
pass
try:
return ApiBasedToolSchemaParser.parse_swagger_yaml_to_tool_bundle(content, extra_info=extra_info, warning=warning), \
ApiProviderSchemaType.SWAGGER.value
except:
pass
raise ToolApiSchemaError('Invalid api schema.')
\ No newline at end of file
api/core/tools/utils/web_reader_tool.py 0 → 100644
View file @ 86286e1a
import hashlib
import json
import os
import re
import site
import subprocess
import tempfile
import unicodedata
from contextlib import contextmanager
from typing import Type, Any
import requests
from bs4 import BeautifulSoup, NavigableString, Comment, CData
from langchain.chains import RefineDocumentsChain
from langchain.chains.summarize import refine_prompts
from langchain.schema import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.tools.base import BaseTool
from newspaper import Article
from pydantic import BaseModel, Field
from regex import regex
from core.chain.llm_chain import LLMChain
from core.data_loader import file_extractor
from core.data_loader.file_extractor import FileExtractor
from core.entities.application_entities import ModelConfigEntity
FULL_TEMPLATE = """
TITLE: {title}
AUTHORS: {authors}
PUBLISH DATE: {publish_date}
TOP_IMAGE_URL: {top_image}
TEXT:
{text}
"""
class WebReaderToolInput(BaseModel):
url: str = Field(..., description="URL of the website to read")
summary: bool = Field(
default=False,
description="When the user's question requires extracting the summarizing content of the webpage, "
"set it to true."
)
cursor: int = Field(
default=0,
description="Start reading from this character."
"Use when the first response was truncated"
"and you want to continue reading the page."
"The value cannot exceed 24000.",
)
class WebReaderTool(BaseTool):
"""Reader tool for getting website title and contents. Gives more control than SimpleReaderTool."""
name: str = "web_reader"
args_schema: Type[BaseModel] = WebReaderToolInput
description: str = "use this to read a website. " \
"If you can answer the question based on the information provided, " \
"there is no need to use."
page_contents: str = None
url: str = None
max_chunk_length: int = 4000
summary_chunk_tokens: int = 4000
summary_chunk_overlap: int = 0
summary_separators: list[str] = ["\n\n", "。", ".", " ", ""]
continue_reading: bool = True
model_config: ModelConfigEntity
model_parameters: dict[str, Any]
def _run(self, url: str, summary: bool = False, cursor: int = 0) -> str:
try:
if not self.page_contents or self.url != url:
page_contents = get_url(url)
self.page_contents = page_contents
self.url = url
else:
page_contents = self.page_contents
except Exception as e:
return f'Read this website failed, caused by: {str(e)}.'
if summary:
character_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
chunk_size=self.summary_chunk_tokens,
chunk_overlap=self.summary_chunk_overlap,
separators=self.summary_separators
)
texts = character_splitter.split_text(page_contents)
docs = [Document(page_content=t) for t in texts]
if len(docs) == 0 or docs[0].page_content.endswith('TEXT:'):
return "No content found."
# only use first 5 docs
if len(docs) > 5:
docs = docs[:5]
chain = self.get_summary_chain()
try:
page_contents = chain.run(docs)
except Exception as e:
return f'Read this website failed, caused by: {str(e)}.'
else:
page_contents = page_result(page_contents, cursor, self.max_chunk_length)
if self.continue_reading and len(page_contents) >= self.max_chunk_length:
page_contents += f"\nPAGE WAS TRUNCATED. IF YOU FIND INFORMATION THAT CAN ANSWER QUESTION " \
f"THEN DIRECT ANSWER AND STOP INVOKING web_reader TOOL, OTHERWISE USE " \
f"CURSOR={cursor+len(page_contents)} TO CONTINUE READING."
return page_contents
async def _arun(self, url: str) -> str:
raise NotImplementedError
def get_summary_chain(self) -> RefineDocumentsChain:
initial_chain = LLMChain(
model_config=self.model_config,
prompt=refine_prompts.PROMPT,
parameters=self.model_parameters
)
refine_chain = LLMChain(
model_config=self.model_config,
prompt=refine_prompts.REFINE_PROMPT,
parameters=self.model_parameters
)
return RefineDocumentsChain(
initial_llm_chain=initial_chain,
refine_llm_chain=refine_chain,
document_variable_name="text",
initial_response_name="existing_answer",
callbacks=self.callbacks
)
def page_result(text: str, cursor: int, max_length: int) -> str:
"""Page through `text` and return a substring of `max_length` characters starting from `cursor`."""
return text[cursor: cursor + max_length]
def get_url(url: str, user_agent: str = None) -> str:
"""Fetch URL and return the contents as a string."""
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
}
if user_agent:
headers["User-Agent"] = user_agent
supported_content_types = file_extractor.SUPPORT_URL_CONTENT_TYPES + ["text/html"]
head_response = requests.head(url, headers=headers, allow_redirects=True, timeout=(5, 10))
if head_response.status_code != 200:
return "URL returned status code {}.".format(head_response.status_code)
# check content-type
main_content_type = head_response.headers.get('Content-Type').split(';')[0].strip()
if main_content_type not in supported_content_types:
return "Unsupported content-type [{}] of URL.".format(main_content_type)
if main_content_type in file_extractor.SUPPORT_URL_CONTENT_TYPES:
return FileExtractor.load_from_url(url, return_text=True)
response = requests.get(url, headers=headers, allow_redirects=True, timeout=(5, 30))
a = extract_using_readabilipy(response.text)
if not a['plain_text'] or not a['plain_text'].strip():
return get_url_from_newspaper3k(url)
res = FULL_TEMPLATE.format(
title=a['title'],
authors=a['byline'],
publish_date=a['date'],
top_image="",
text=a['plain_text'] if a['plain_text'] else "",
)
return res
def get_url_from_newspaper3k(url: str) -> str:
a = Article(url)
a.download()
a.parse()
res = FULL_TEMPLATE.format(
title=a.title,
authors=a.authors,
publish_date=a.publish_date,
top_image=a.top_image,
text=a.text,
)
return res
def extract_using_readabilipy(html):
with tempfile.NamedTemporaryFile(delete=False, mode='w+') as f_html:
f_html.write(html)
f_html.close()
html_path = f_html.name
# Call Mozilla's Readability.js Readability.parse() function via node, writing output to a temporary file
article_json_path = html_path + ".json"
jsdir = os.path.join(find_module_path('readabilipy'), 'javascript')
with chdir(jsdir):
subprocess.check_call(["node", "ExtractArticle.js", "-i", html_path, "-o", article_json_path])
# Read output of call to Readability.parse() from JSON file and return as Python dictionary
with open(article_json_path, "r", encoding="utf-8") as json_file:
input_json = json.loads(json_file.read())
# Deleting files after processing
os.unlink(article_json_path)
os.unlink(html_path)
article_json = {
"title": None,
"byline": None,
"date": None,
"content": None,
"plain_content": None,
"plain_text": None
}
# Populate article fields from readability fields where present
if input_json:
if "title" in input_json and input_json["title"]:
article_json["title"] = input_json["title"]
if "byline" in input_json and input_json["byline"]:
article_json["byline"] = input_json["byline"]
if "date" in input_json and input_json["date"]:
article_json["date"] = input_json["date"]
if "content" in input_json and input_json["content"]:
article_json["content"] = input_json["content"]
article_json["plain_content"] = plain_content(article_json["content"], False, False)
article_json["plain_text"] = extract_text_blocks_as_plain_text(article_json["plain_content"])
if "textContent" in input_json and input_json["textContent"]:
article_json["plain_text"] = input_json["textContent"]
article_json["plain_text"] = re.sub(r'\n\s*\n', '\n', article_json["plain_text"])
return article_json
def find_module_path(module_name):
for package_path in site.getsitepackages():
potential_path = os.path.join(package_path, module_name)
if os.path.exists(potential_path):
return potential_path
return None
@contextmanager
def chdir(path):
"""Change directory in context and return to original on exit"""
# From https://stackoverflow.com/a/37996581, couldn't find a built-in
original_path = os.getcwd()
os.chdir(path)
try:
yield
finally:
os.chdir(original_path)
def extract_text_blocks_as_plain_text(paragraph_html):
# Load article as DOM
soup = BeautifulSoup(paragraph_html, 'html.parser')
# Select all lists
list_elements = soup.find_all(['ul', 'ol'])
# Prefix text in all list items with "* " and make lists paragraphs
for list_element in list_elements:
plain_items = "".join(list(filter(None, [plain_text_leaf_node(li)["text"] for li in list_element.find_all('li')])))
list_element.string = plain_items
list_element.name = "p"
# Select all text blocks
text_blocks = [s.parent for s in soup.find_all(string=True)]
text_blocks = [plain_text_leaf_node(block) for block in text_blocks]
# Drop empty paragraphs
text_blocks = list(filter(lambda p: p["text"] is not None, text_blocks))
return text_blocks
def plain_text_leaf_node(element):
# Extract all text, stripped of any child HTML elements and normalise it
plain_text = normalise_text(element.get_text())
if plain_text != "" and element.name == "li":
plain_text = "* {}, ".format(plain_text)
if plain_text == "":
plain_text = None
if "data-node-index" in element.attrs:
plain = {"node_index": element["data-node-index"], "text": plain_text}
else:
plain = {"text": plain_text}
return plain
def plain_content(readability_content, content_digests, node_indexes):
# Load article as DOM
soup = BeautifulSoup(readability_content, 'html.parser')
# Make all elements plain
elements = plain_elements(soup.contents, content_digests, node_indexes)
if node_indexes:
# Add node index attributes to nodes
elements = [add_node_indexes(element) for element in elements]
# Replace article contents with plain elements
soup.contents = elements
return str(soup)
def plain_elements(elements, content_digests, node_indexes):
# Get plain content versions of all elements
elements = [plain_element(element, content_digests, node_indexes)
for element in elements]
if content_digests:
# Add content digest attribute to nodes
elements = [add_content_digest(element) for element in elements]
return elements
def plain_element(element, content_digests, node_indexes):
# For lists, we make each item plain text
if is_leaf(element):
# For leaf node elements, extract the text content, discarding any HTML tags
# 1. Get element contents as text
plain_text = element.get_text()
# 2. Normalise the extracted text string to a canonical representation
plain_text = normalise_text(plain_text)
# 3. Update element content to be plain text
element.string = plain_text
elif is_text(element):
if is_non_printing(element):
# The simplified HTML may have come from Readability.js so might
# have non-printing text (e.g. Comment or CData). In this case, we
# keep the structure, but ensure that the string is empty.
element = type(element)("")
else:
plain_text = element.string
plain_text = normalise_text(plain_text)
element = type(element)(plain_text)
else:
# If not a leaf node or leaf type call recursively on child nodes, replacing
element.contents = plain_elements(element.contents, content_digests, node_indexes)
return element
def add_node_indexes(element, node_index="0"):
# Can't add attributes to string types
if is_text(element):
return element
# Add index to current element
element["data-node-index"] = node_index
# Add index to child elements
for local_idx, child in enumerate(
[c for c in element.contents if not is_text(c)], start=1):
# Can't add attributes to leaf string types
child_index = "{stem}.{local}".format(
stem=node_index, local=local_idx)
add_node_indexes(child, node_index=child_index)
return element
def normalise_text(text):
"""Normalise unicode and whitespace."""
# Normalise unicode first to try and standardise whitespace characters as much as possible before normalising them
text = strip_control_characters(text)
text = normalise_unicode(text)
text = normalise_whitespace(text)
return text
def strip_control_characters(text):
"""Strip out unicode control characters which might break the parsing."""
# Unicode control characters
# [Cc]: Other, Control [includes new lines]
# [Cf]: Other, Format
# [Cn]: Other, Not Assigned
# [Co]: Other, Private Use
# [Cs]: Other, Surrogate
control_chars = set(['Cc', 'Cf', 'Cn', 'Co', 'Cs'])
retained_chars = ['\t', '\n', '\r', '\f']
# Remove non-printing control characters
return "".join(["" if (unicodedata.category(char) in control_chars) and (char not in retained_chars) else char for char in text])
def normalise_unicode(text):
"""Normalise unicode such that things that are visually equivalent map to the same unicode string where possible."""
normal_form = "NFKC"
text = unicodedata.normalize(normal_form, text)
return text
def normalise_whitespace(text):
"""Replace runs of whitespace characters with a single space as this is what happens when HTML text is displayed."""
text = regex.sub(r"\s+", " ", text)
# Remove leading and trailing whitespace
text = text.strip()
return text
def is_leaf(element):
return (element.name in ['p', 'li'])
def is_text(element):
return isinstance(element, NavigableString)
def is_non_printing(element):
return any(isinstance(element, _e) for _e in [Comment, CData])
def add_content_digest(element):
if not is_text(element):
element["data-content-digest"] = content_digest(element)
return element
def content_digest(element):
if is_text(element):
# Hash
trimmed_string = element.string.strip()
if trimmed_string == "":
digest = ""
else:
digest = hashlib.sha256(trimmed_string.encode('utf-8')).hexdigest()
else:
contents = element.contents
num_contents = len(contents)
if num_contents == 0:
# No hash when no child elements exist
digest = ""
elif num_contents == 1:
# If single child, use digest of child
digest = content_digest(contents[0])
else:
# Build content digest from the "non-empty" digests of child nodes
digest = hashlib.sha256()
child_digests = list(
filter(lambda x: x != "", [content_digest(content) for content in contents]))
for child in child_digests:
digest.update(child.encode('utf-8'))
digest = digest.hexdigest()
return digest
api/events/event_handlers/update_app_dataset_join_when_app_model_config_updated.py
View file @ 86286e1a
......@@ -50,17 +50,24 @@ def get_dataset_ids_from_model_config(app_model_config: AppModelConfig) -> set:
return dataset_ids
agent_mode = app_model_config.agent_mode_dict
if agent_mode.get('enabled') is False:
return dataset_ids
if not agent_mode.get('tools'):
return dataset_ids
tools = agent_mode.get('tools')
tools = agent_mode.get('tools', []) or []
for tool in tools:
if len(list(tool.keys())) != 1:
continue
tool_type = list(tool.keys())[0]
tool_config = list(tool.values())[0]
if tool_type == "dataset":
dataset_ids.add(tool_config.get("id"))
# get dataset from dataset_configs
dataset_configs = app_model_config.dataset_configs_dict
datasets = dataset_configs.get('datasets', {}) or {}
for dataset in datasets.get('datasets', []) or []:
keys = list(dataset.keys())
if len(keys) == 1 and keys[0] == 'dataset':
if dataset['dataset'].get('id'):
dataset_ids.add(dataset['dataset'].get('id'))
return dataset_ids
api/fields/app_fields.py
View file @ 86286e1a
......@@ -40,6 +40,7 @@ app_detail_fields = {
'id': fields.String,
'name': fields.String,
'mode': fields.String,
'is_agent': fields.Boolean,
'icon': fields.String,
'icon_background': fields.String,
'enable_site': fields.Boolean,
......@@ -64,6 +65,7 @@ app_partial_fields = {
'id': fields.String,
'name': fields.String,
'mode': fields.String,
'is_agent': fields.Boolean,
'icon': fields.String,
'icon_background': fields.String,
'enable_site': fields.Boolean,
......@@ -120,11 +122,13 @@ app_detail_fields_with_site = {
'enable_api': fields.Boolean,
'api_rpm': fields.Integer,
'api_rph': fields.Integer,
'is_agent': fields.Boolean,
'is_demo': fields.Boolean,
'model_config': fields.Nested(model_config_fields, attribute='app_model_config'),
'site': fields.Nested(site_fields),
'api_base_url': fields.String,
'created_at': TimestampField
'created_at': TimestampField,
'deleted_tools': fields.List(fields.String),
}
app_site_fields = {
......
api/fields/conversation_fields.py
View file @ 86286e1a
......@@ -39,6 +39,20 @@ message_file_fields = {
'id': fields.String,
'type': fields.String,
'url': fields.String,
'belongs_to': fields.String(default='user'),
}
agent_thought_fields = {
'id': fields.String,
'chain_id': fields.String,
'message_id': fields.String,
'position': fields.Integer,
'thought': fields.String,
'tool': fields.String,
'tool_input': fields.String,
'created_at': TimestampField,
'observation': fields.String,
'files': fields.List(fields.String)
}
message_detail_fields = {
......@@ -58,6 +72,7 @@ message_detail_fields = {
'annotation': fields.Nested(annotation_fields, allow_null=True),
'annotation_hit_history': fields.Nested(annotation_hit_history_fields, allow_null=True),
'created_at': TimestampField,
'agent_thoughts': fields.List(fields.Nested(agent_thought_fields)),
'message_files': fields.List(fields.Nested(message_file_fields), attribute='files'),
}
......
api/fields/installed_app_fields.py
View file @ 86286e1a
......@@ -17,6 +17,7 @@ installed_app_fields = {
'last_used_at': TimestampField,
'editable': fields.Boolean,
'uninstallable': fields.Boolean,
'is_agent': fields.Boolean,
}
installed_app_list_fields = {
......
api/fields/message_fields.py
View file @ 86286e1a
......@@ -25,16 +25,53 @@ retriever_resource_fields = {
'created_at': TimestampField
}
feedback_fields = {
'rating': fields.String
}
agent_thought_fields = {
'id': fields.String,
'chain_id': fields.String,
'message_id': fields.String,
'position': fields.Integer,
'thought': fields.String,
'tool': fields.String,
'tool_input': fields.String,
'created_at': TimestampField,
'observation': fields.String,
'files': fields.List(fields.String)
}
retriever_resource_fields = {
'id': fields.String,
'message_id': fields.String,
'position': fields.Integer,
'dataset_id': fields.String,
'dataset_name': fields.String,
'document_id': fields.String,
'document_name': fields.String,
'data_source_type': fields.String,
'segment_id': fields.String,
'score': fields.Float,
'hit_count': fields.Integer,
'word_count': fields.Integer,
'segment_position': fields.Integer,
'index_node_hash': fields.String,
'content': fields.String,
'created_at': TimestampField
}
message_fields = {
'id': fields.String,
'conversation_id': fields.String,
'inputs': fields.Raw,
'query': fields.String,
'answer': fields.String,
'message_files': fields.List(fields.Nested(message_file_fields), attribute='files'),
'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
'created_at': TimestampField
'created_at': TimestampField,
'agent_thoughts': fields.List(fields.Nested(agent_thought_fields)),
'message_files': fields.List(fields.Nested(message_file_fields), attribute='files')
}
message_infinite_scroll_pagination_fields = {
......
api/migrations/versions/00bacef91f18_rename_api_provider_description.py 0 → 100644
View file @ 86286e1a
"""rename api provider description
Revision ID: 00bacef91f18
Revises: 8ec536f3c800
Create Date: 2024-01-07 04:07:34.482983
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '00bacef91f18'
down_revision = '8ec536f3c800'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_api_providers', schema=None) as batch_op:
batch_op.add_column(sa.Column('description', sa.Text(), nullable=False))
batch_op.drop_column('description_str')
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_api_providers', schema=None) as batch_op:
batch_op.add_column(sa.Column('description_str', sa.TEXT(), autoincrement=False, nullable=False))
batch_op.drop_column('description')
# ### end Alembic commands ###
api/migrations/versions/053da0c1d756_add_api_tool_privacy.py 0 → 100644
View file @ 86286e1a
"""add api tool privacy
Revision ID: 053da0c1d756
Revises: 4829e54d2fee
Create Date: 2024-01-12 06:47:21.656262
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision = '053da0c1d756'
down_revision = '4829e54d2fee'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('tool_conversation_variables',
sa.Column('id', postgresql.UUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
sa.Column('user_id', postgresql.UUID(), nullable=False),
sa.Column('tenant_id', postgresql.UUID(), nullable=False),
sa.Column('conversation_id', postgresql.UUID(), nullable=False),
sa.Column('variables_str', sa.Text(), nullable=False),
sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP(0)'), nullable=False),
sa.Column('updated_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP(0)'), nullable=False),
sa.PrimaryKeyConstraint('id', name='tool_conversation_variables_pkey')
)
with op.batch_alter_table('tool_api_providers', schema=None) as batch_op:
batch_op.add_column(sa.Column('privacy_policy', sa.String(length=255), nullable=True))
batch_op.alter_column('icon',
existing_type=sa.VARCHAR(length=256),
type_=sa.String(length=255),
existing_nullable=False)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_api_providers', schema=None) as batch_op:
batch_op.alter_column('icon',
existing_type=sa.String(length=255),
type_=sa.VARCHAR(length=256),
existing_nullable=False)
batch_op.drop_column('privacy_policy')
op.drop_table('tool_conversation_variables')
# ### end Alembic commands ###
api/migrations/versions/114eed84c228_remove_tool_id_from_model_invoke.py 0 → 100644
View file @ 86286e1a
"""remove tool id from model invoke
Revision ID: 114eed84c228
Revises: c71211c8f604
Create Date: 2024-01-10 04:40:57.257824
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision = '114eed84c228'
down_revision = 'c71211c8f604'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_model_invokes', schema=None) as batch_op:
batch_op.drop_column('tool_id')
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_model_invokes', schema=None) as batch_op:
batch_op.add_column(sa.Column('tool_id', postgresql.UUID(), autoincrement=False, nullable=False))
# ### end Alembic commands ###
api/migrations/versions/23db93619b9d_add_message_files_into_agent_thought.py 0 → 100644
View file @ 86286e1a
"""add message files into agent thought
Revision ID: 23db93619b9d
Revises: 8ae9bc661daa
Create Date: 2024-01-18 08:46:37.302657
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '23db93619b9d'
down_revision = '8ae9bc661daa'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('message_agent_thoughts', schema=None) as batch_op:
batch_op.add_column(sa.Column('message_files', sa.Text(), nullable=True))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('message_agent_thoughts', schema=None) as batch_op:
batch_op.drop_column('message_files')
# ### end Alembic commands ###
api/migrations/versions/3ef9b2b6bee6_add_assistant_app.py 0 → 100644
View file @ 86286e1a
"""add_assistant_app
Revision ID: 3ef9b2b6bee6
Revises: 89c7899ca936
Create Date: 2024-01-05 15:26:25.117551
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision = '3ef9b2b6bee6'
down_revision = '89c7899ca936'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('tool_api_providers',
sa.Column('id', postgresql.UUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
sa.Column('name', sa.String(length=40), nullable=False),
sa.Column('schema', sa.Text(), nullable=False),
sa.Column('schema_type_str', sa.String(length=40), nullable=False),
sa.Column('user_id', postgresql.UUID(), nullable=False),
sa.Column('tenant_id', postgresql.UUID(), nullable=False),
sa.Column('description_str', sa.Text(), nullable=False),
sa.Column('tools_str', sa.Text(), nullable=False),
sa.PrimaryKeyConstraint('id', name='tool_api_provider_pkey')
)
op.create_table('tool_builtin_providers',
sa.Column('id', postgresql.UUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
sa.Column('tenant_id', postgresql.UUID(), nullable=True),
sa.Column('user_id', postgresql.UUID(), nullable=False),
sa.Column('provider', sa.String(length=40), nullable=False),
sa.Column('encrypted_credentials', sa.Text(), nullable=True),
sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP(0)'), nullable=False),
sa.Column('updated_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP(0)'), nullable=False),
sa.PrimaryKeyConstraint('id', name='tool_builtin_provider_pkey'),
sa.UniqueConstraint('tenant_id', 'provider', name='unique_builtin_tool_provider')
)
op.create_table('tool_published_apps',
sa.Column('id', postgresql.UUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
sa.Column('app_id', postgresql.UUID(), nullable=False),
sa.Column('user_id', postgresql.UUID(), nullable=False),
sa.Column('description', sa.Text(), nullable=False),
sa.Column('llm_description', sa.Text(), nullable=False),
sa.Column('query_description', sa.Text(), nullable=False),
sa.Column('query_name', sa.String(length=40), nullable=False),
sa.Column('tool_name', sa.String(length=40), nullable=False),
sa.Column('author', sa.String(length=40), nullable=False),
sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP(0)'), nullable=False),
sa.Column('updated_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP(0)'), nullable=False),
sa.ForeignKeyConstraint(['app_id'], ['apps.id'], ),
sa.PrimaryKeyConstraint('id', name='published_app_tool_pkey'),
sa.UniqueConstraint('app_id', 'user_id', name='unique_published_app_tool')
)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('tool_published_apps')
op.drop_table('tool_builtin_providers')
op.drop_table('tool_api_providers')
# ### end Alembic commands ###
api/migrations/versions/4823da1d26cf_add_tool_file.py 0 → 100644
View file @ 86286e1a
"""add tool file
Revision ID: 4823da1d26cf
Revises: 053da0c1d756
Create Date: 2024-01-15 11:37:16.782718
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision = '4823da1d26cf'
down_revision = '053da0c1d756'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('tool_files',
sa.Column('id', postgresql.UUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
sa.Column('user_id', postgresql.UUID(), nullable=False),
sa.Column('tenant_id', postgresql.UUID(), nullable=False),
sa.Column('conversation_id', postgresql.UUID(), nullable=False),
sa.Column('file_key', sa.String(length=255), nullable=False),
sa.Column('mimetype', sa.String(length=255), nullable=False),
sa.Column('original_url', sa.String(length=255), nullable=True),
sa.PrimaryKeyConstraint('id', name='tool_file_pkey')
)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('tool_files')
# ### end Alembic commands ###
api/migrations/versions/4829e54d2fee_change_message_chain_id_to_nullable.py 0 → 100644
View file @ 86286e1a
"""change message chain id to nullable
Revision ID: 4829e54d2fee
Revises: 114eed84c228
Create Date: 2024-01-12 03:42:27.362415
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision = '4829e54d2fee'
down_revision = '114eed84c228'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('message_agent_thoughts', schema=None) as batch_op:
batch_op.alter_column('message_chain_id',
existing_type=postgresql.UUID(),
nullable=True)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('message_agent_thoughts', schema=None) as batch_op:
batch_op.alter_column('message_chain_id',
existing_type=postgresql.UUID(),
nullable=False)
# ### end Alembic commands ###
api/migrations/versions/8ae9bc661daa_add_tool_conversation_variables_idx.py 0 → 100644
View file @ 86286e1a
"""add tool conversation variables idx
Revision ID: 8ae9bc661daa
Revises: 9fafbd60eca1
Create Date: 2024-01-15 14:22:03.597692
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '8ae9bc661daa'
down_revision = '9fafbd60eca1'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_conversation_variables', schema=None) as batch_op:
batch_op.create_index('conversation_id_idx', ['conversation_id'], unique=False)
batch_op.create_index('user_id_idx', ['user_id'], unique=False)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_conversation_variables', schema=None) as batch_op:
batch_op.drop_index('user_id_idx')
batch_op.drop_index('conversation_id_idx')
# ### end Alembic commands ###
api/migrations/versions/8ec536f3c800_rename_api_provider_credentails.py 0 → 100644
View file @ 86286e1a
"""rename api provider credentails
Revision ID: 8ec536f3c800
Revises: ad472b61a054
Create Date: 2024-01-07 03:57:35.257545
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '8ec536f3c800'
down_revision = 'ad472b61a054'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_api_providers', schema=None) as batch_op:
batch_op.add_column(sa.Column('credentials_str', sa.Text(), nullable=False))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_api_providers', schema=None) as batch_op:
batch_op.drop_column('credentials_str')
# ### end Alembic commands ###
api/migrations/versions/9fafbd60eca1_add_message_file_belongs_to.py 0 → 100644
View file @ 86286e1a
"""add message file belongs to
Revision ID: 9fafbd60eca1
Revises: 4823da1d26cf
Create Date: 2024-01-15 13:07:20.340896
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '9fafbd60eca1'
down_revision = '4823da1d26cf'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('message_files', schema=None) as batch_op:
batch_op.add_column(sa.Column('belongs_to', sa.String(length=255), nullable=True))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('message_files', schema=None) as batch_op:
batch_op.drop_column('belongs_to')
# ### end Alembic commands ###
api/migrations/versions/ad472b61a054_add_api_provider_icon.py 0 → 100644
View file @ 86286e1a
"""add api provider icon
Revision ID: ad472b61a054
Revises: 3ef9b2b6bee6
Create Date: 2024-01-07 02:21:23.114790
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = 'ad472b61a054'
down_revision = '3ef9b2b6bee6'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_api_providers', schema=None) as batch_op:
batch_op.add_column(sa.Column('icon', sa.String(length=256), nullable=False))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_api_providers', schema=None) as batch_op:
batch_op.drop_column('icon')
# ### end Alembic commands ###
api/migrations/versions/c71211c8f604_add_tool_invoke_model_log.py 0 → 100644
View file @ 86286e1a
"""add tool_invoke_model_log
Revision ID: c71211c8f604
Revises: f25003750af4
Create Date: 2024-01-09 11:42:50.664797
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision = 'c71211c8f604'
down_revision = 'f25003750af4'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('tool_model_invokes',
sa.Column('id', postgresql.UUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
sa.Column('user_id', postgresql.UUID(), nullable=False),
sa.Column('tenant_id', postgresql.UUID(), nullable=False),
sa.Column('provider', sa.String(length=40), nullable=False),
sa.Column('tool_type', sa.String(length=40), nullable=False),
sa.Column('tool_name', sa.String(length=40), nullable=False),
sa.Column('tool_id', postgresql.UUID(), nullable=False),
sa.Column('model_parameters', sa.Text(), nullable=False),
sa.Column('prompt_messages', sa.Text(), nullable=False),
sa.Column('model_response', sa.Text(), nullable=False),
sa.Column('prompt_tokens', sa.Integer(), server_default=sa.text('0'), nullable=False),
sa.Column('answer_tokens', sa.Integer(), server_default=sa.text('0'), nullable=False),
sa.Column('answer_unit_price', sa.Numeric(precision=10, scale=4), nullable=False),
sa.Column('answer_price_unit', sa.Numeric(precision=10, scale=7), server_default=sa.text('0.001'), nullable=False),
sa.Column('provider_response_latency', sa.Float(), server_default=sa.text('0'), nullable=False),
sa.Column('total_price', sa.Numeric(precision=10, scale=7), nullable=True),
sa.Column('currency', sa.String(length=255), nullable=False),
sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP(0)'), nullable=False),
sa.Column('updated_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP(0)'), nullable=False),
sa.PrimaryKeyConstraint('id', name='tool_model_invoke_pkey')
)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('tool_model_invokes')
# ### end Alembic commands ###
api/migrations/versions/de95f5c77138_migration_serpapi_api_key.py 0 → 100644
View file @ 86286e1a
"""migration serpapi_api_key
Revision ID: de95f5c77138
Revises: 23db93619b9d
Create Date: 2024-01-21 12:09:04.651394
"""
from alembic import op
import sqlalchemy as sa
from json import dumps, loads
# revision identifiers, used by Alembic.
revision = 'de95f5c77138'
down_revision = '23db93619b9d'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
"""
1. select all tool_providers
2. insert api_key to tool_provider_configs
tool_providers
- id
- tenant_id
- tool_name
- encrypted_credentials
{"api_key": "$KEY"}
- created_at
- updated_at
tool_builtin_providers
- id <- tool_providers.id
- tenant_id <- tool_providers.tenant_id
- user_id <- tenant_account_joins.account_id (tenant_account_joins.tenant_id = tool_providers.tenant_id and tenant_account_joins.role = 'owner')
- encrypted_credentials <- tool_providers.encrypted_credentials
{"serpapi_api_key": "$KEY"}
- created_at <- tool_providers.created_at
- updated_at <- tool_providers.updated_at
"""
# select all tool_providers
tool_providers = op.get_bind().execute(
sa.text(
"SELECT * FROM tool_providers WHERE tool_name = 'serpapi'"
)
).fetchall()
# insert api_key to tool_provider_configs
for tool_provider in tool_providers:
id = tool_provider['id']
tenant_id = tool_provider['tenant_id']
encrypted_credentials = tool_provider['encrypted_credentials']
try:
credentials = loads(encrypted_credentials)
api_key = credentials['api_key']
credentials['serpapi_api_key'] = api_key
credentials.pop('api_key')
encrypted_credentials = dumps(credentials)
except Exception as e:
print(e)
continue
# get user_id
user_id = op.get_bind().execute(
sa.text(
"SELECT account_id FROM tenant_account_joins WHERE tenant_id = :tenant_id AND role = 'owner'"
),
tenant_id=tenant_id
).fetchone()['account_id']
created_at = tool_provider['created_at']
updated_at = tool_provider['updated_at']
# insert to tool_builtin_providers
# check if exists
exists = op.get_bind().execute(
sa.text(
"SELECT * FROM tool_builtin_providers WHERE tenant_id = :tenant_id AND provider = 'google'"
),
tenant_id=tenant_id
).fetchone()
if exists:
continue
op.get_bind().execute(
sa.text(
"INSERT INTO tool_builtin_providers (id, tenant_id, user_id, provider, encrypted_credentials, created_at, updated_at) VALUES (:id, :tenant_id, :user_id, :provider, :encrypted_credentials, :created_at, :updated_at)"
),
id=id,
tenant_id=tenant_id,
user_id=user_id,
provider='google',
encrypted_credentials=encrypted_credentials,
created_at=created_at,
updated_at=updated_at
)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
pass
# ### end Alembic commands ###
api/migrations/versions/f25003750af4_add_created_updated_at.py 0 → 100644
View file @ 86286e1a
"""add created/updated at
Revision ID: f25003750af4
Revises: 00bacef91f18
Create Date: 2024-01-07 04:53:24.441861
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = 'f25003750af4'
down_revision = '00bacef91f18'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_api_providers', schema=None) as batch_op:
batch_op.add_column(sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP(0)'), nullable=False))
batch_op.add_column(sa.Column('updated_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP(0)'), nullable=False))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('tool_api_providers', schema=None) as batch_op:
batch_op.drop_column('updated_at')
batch_op.drop_column('created_at')
# ### end Alembic commands ###
api/models/model.py
View file @ 86286e1a
import json
import uuid
from core.file.upload_file_parser import UploadFileParser
from core.file.tool_file_parser import ToolFileParser
from extensions.ext_database import db
from flask import current_app, request
from flask_login import UserMixin
from libs.helper import generate_string
from sqlalchemy import Float
from sqlalchemy import Float, text
from sqlalchemy.dialects.postgresql import UUID
from .account import Account, Tenant
......@@ -67,6 +69,64 @@ class App(db.Model):
tenant = db.session.query(Tenant).filter(Tenant.id == self.tenant_id).first()
return tenant
@property
def is_agent(self) -> bool:
app_model_config = self.app_model_config
if not app_model_config:
return False
if not app_model_config.agent_mode:
return False
if self.app_model_config.agent_mode_dict.get('enabled', False) \
and self.app_model_config.agent_mode_dict.get('strategy', '') in ['function_call', 'react']:
return True
return False
@property
def deleted_tools(self) -> list:
# get agent mode tools
app_model_config = self.app_model_config
if not app_model_config:
return []
if not app_model_config.agent_mode:
return []
agent_mode = app_model_config.agent_mode_dict
tools = agent_mode.get('tools', [])
provider_ids = []
for tool in tools:
keys = list(tool.keys())
if len(keys) >= 4:
provider_type = tool.get('provider_type', '')
provider_id = tool.get('provider_id', '')
if provider_type == 'api':
# check if provider id is a uuid string, if not, skip
try:
uuid.UUID(provider_id)
except Exception:
continue
provider_ids.append(provider_id)
if not provider_ids:
return []
api_providers = db.session.execute(
text('SELECT id FROM tool_api_providers WHERE id IN :provider_ids'),
{'provider_ids': tuple(provider_ids)}
).fetchall()
deleted_tools = []
current_api_provider_ids = [str(api_provider.id) for api_provider in api_providers]
for tool in tools:
keys = list(tool.keys())
if len(keys) >= 4:
provider_type = tool.get('provider_type', '')
provider_id = tool.get('provider_id', '')
if provider_type == 'api' and provider_id not in current_api_provider_ids:
deleted_tools.append(tool['tool_name'])
return deleted_tools
class AppModelConfig(db.Model):
__tablename__ = 'app_model_configs'
......@@ -168,7 +228,7 @@ class AppModelConfig(db.Model):
@property
def agent_mode_dict(self) -> dict:
return json.loads(self.agent_mode) if self.agent_mode else {"enabled": False, "strategy": None, "tools": []}
return json.loads(self.agent_mode) if self.agent_mode else {"enabled": False, "strategy": None, "tools": [], "prompt": None}
@property
def chat_prompt_config_dict(self) -> dict:
......@@ -337,6 +397,12 @@ class InstalledApp(db.Model):
tenant = db.session.query(Tenant).filter(Tenant.id == self.tenant_id).first()
return tenant
@property
def is_agent(self) -> bool:
app = self.app
if not app:
return False
return app.is_agent
class Conversation(db.Model):
__tablename__ = 'conversations'
......@@ -582,11 +648,22 @@ class Message(db.Model):
upload_file=upload_file,
force_url=True
)
if message_file.transfer_method == 'tool_file':
# get extension
if '.' in message_file.url:
extension = f'.{message_file.url.split(".")[-1]}'
if len(extension) > 10:
extension = '.bin'
else:
extension = '.bin'
# add sign url
url = ToolFileParser.get_tool_file_manager().sign_file(file_id=message_file.id, extension=extension)
files.append({
'id': message_file.id,
'type': message_file.type,
'url': url
'url': url,
'belongs_to': message_file.belongs_to if message_file.belongs_to else 'user'
})
return files
......@@ -632,12 +709,12 @@ class MessageFile(db.Model):
type = db.Column(db.String(255), nullable=False)
transfer_method = db.Column(db.String(255), nullable=False)
url = db.Column(db.Text, nullable=True)
belongs_to = db.Column(db.String(255), nullable=True)
upload_file_id = db.Column(UUID, nullable=True)
created_by_role = db.Column(db.String(255), nullable=False)
created_by = db.Column(UUID, nullable=False)
created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
class MessageAnnotation(db.Model):
__tablename__ = 'message_annotations'
__table_args__ = (
......@@ -912,7 +989,7 @@ class MessageAgentThought(db.Model):
id = db.Column(UUID, nullable=False, server_default=db.text('uuid_generate_v4()'))
message_id = db.Column(UUID, nullable=False)
message_chain_id = db.Column(UUID, nullable=False)
message_chain_id = db.Column(UUID, nullable=True)
position = db.Column(db.Integer, nullable=False)
thought = db.Column(db.Text, nullable=True)
tool = db.Column(db.Text, nullable=True)
......@@ -924,6 +1001,7 @@ class MessageAgentThought(db.Model):
message_token = db.Column(db.Integer, nullable=True)
message_unit_price = db.Column(db.Numeric, nullable=True)
message_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text('0.001'))
message_files = db.Column(db.Text, nullable=True)
answer = db.Column(db.Text, nullable=True)
answer_token = db.Column(db.Integer, nullable=True)
answer_unit_price = db.Column(db.Numeric, nullable=True)
......@@ -936,6 +1014,12 @@ class MessageAgentThought(db.Model):
created_by = db.Column(UUID, nullable=False)
created_at = db.Column(db.DateTime, nullable=False, server_default=db.func.current_timestamp())
@property
def files(self) -> list:
if self.message_files:
return json.loads(self.message_files)
else:
return []
class DatasetRetrieverResource(db.Model):
__tablename__ = 'dataset_retriever_resources'
......
api/models/tools.py 0 → 100644
View file @ 86286e1a
import json
from enum import Enum
from typing import List
from sqlalchemy.dialects.postgresql import UUID
from sqlalchemy import ForeignKey
from extensions.ext_database import db
from core.tools.entities.tool_bundle import ApiBasedToolBundle
from core.tools.entities.common_entities import I18nObject
from core.tools.entities.tool_entities import ApiProviderSchemaType, ToolRuntimeVariablePool
from models.model import Tenant, Account, App
class BuiltinToolProvider(db.Model):
"""
This table stores the tool provider information for built-in tools for each tenant.
"""
__tablename__ = 'tool_builtin_providers'
__table_args__ = (
db.PrimaryKeyConstraint('id', name='tool_builtin_provider_pkey'),
# one tenant can only have one tool provider with the same name
db.UniqueConstraint('tenant_id', 'provider', name='unique_builtin_tool_provider')
)
# id of the tool provider
id = db.Column(UUID, server_default=db.text('uuid_generate_v4()'))
# id of the tenant
tenant_id = db.Column(UUID, nullable=True)
# who created this tool provider
user_id = db.Column(UUID, nullable=False)
# name of the tool provider
provider = db.Column(db.String(40), nullable=False)
# credential of the tool provider
encrypted_credentials = db.Column(db.Text, nullable=True)
created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
@property
def credentials(self) -> dict:
return json.loads(self.encrypted_credentials)
class PublishedAppTool(db.Model):
"""
The table stores the apps published as a tool for each person.
"""
__tablename__ = 'tool_published_apps'
__table_args__ = (
db.PrimaryKeyConstraint('id', name='published_app_tool_pkey'),
db.UniqueConstraint('app_id', 'user_id', name='unique_published_app_tool')
)
# id of the tool provider
id = db.Column(UUID, server_default=db.text('uuid_generate_v4()'))
# id of the app
app_id = db.Column(UUID, ForeignKey('apps.id'), nullable=False)
# who published this tool
user_id = db.Column(UUID, nullable=False)
# description of the tool, stored in i18n format, for human
description = db.Column(db.Text, nullable=False)
# llm_description of the tool, for LLM
llm_description = db.Column(db.Text, nullable=False)
# query decription, query will be seem as a parameter of the tool, to describe this parameter to llm, we need this field
query_description = db.Column(db.Text, nullable=False)
# query name, the name of the query parameter
query_name = db.Column(db.String(40), nullable=False)
# name of the tool provider
tool_name = db.Column(db.String(40), nullable=False)
# author
author = db.Column(db.String(40), nullable=False)
created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
@property
def description_i18n(self) -> I18nObject:
return I18nObject(**json.loads(self.description))
@property
def app(self) -> App:
return db.session.query(App).filter(App.id == self.app_id).first()
class ApiToolProvider(db.Model):
"""
The table stores the api providers.
"""
__tablename__ = 'tool_api_providers'
__table_args__ = (
db.PrimaryKeyConstraint('id', name='tool_api_provider_pkey'),
)
id = db.Column(UUID, server_default=db.text('uuid_generate_v4()'))
# name of the api provider
name = db.Column(db.String(40), nullable=False)
# icon
icon = db.Column(db.String(255), nullable=False)
# original schema
schema = db.Column(db.Text, nullable=False)
schema_type_str = db.Column(db.String(40), nullable=False)
# who created this tool
user_id = db.Column(UUID, nullable=False)
# tanent id
tenant_id = db.Column(UUID, nullable=False)
# description of the provider
description = db.Column(db.Text, nullable=False)
# json format tools
tools_str = db.Column(db.Text, nullable=False)
# json format credentials
credentials_str = db.Column(db.Text, nullable=False)
# privacy policy
privacy_policy = db.Column(db.String(255), nullable=True)
created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
@property
def schema_type(self) -> ApiProviderSchemaType:
return ApiProviderSchemaType.value_of(self.schema_type_str)
@property
def tools(self) -> List[ApiBasedToolBundle]:
return [ApiBasedToolBundle(**tool) for tool in json.loads(self.tools_str)]
@property
def credentials(self) -> dict:
return json.loads(self.credentials_str)
@property
def is_taned(self) -> bool:
return self.tenant_id is not None
@property
def user(self) -> Account:
return db.session.query(Account).filter(Account.id == self.user_id).first()
@property
def tanent(self) -> Tenant:
return db.session.query(Tenant).filter(Tenant.id == self.tenant_id).first()
class ToolModelInvoke(db.Model):
"""
store the invoke logs from tool invoke
"""
__tablename__ = "tool_model_invokes"
__table_args__ = (
db.PrimaryKeyConstraint('id', name='tool_model_invoke_pkey'),
)
id = db.Column(UUID, server_default=db.text('uuid_generate_v4()'))
# who invoke this tool
user_id = db.Column(UUID, nullable=False)
# tanent id
tenant_id = db.Column(UUID, nullable=False)
# provider
provider = db.Column(db.String(40), nullable=False)
# type
tool_type = db.Column(db.String(40), nullable=False)
# tool name
tool_name = db.Column(db.String(40), nullable=False)
# invoke parameters
model_parameters = db.Column(db.Text, nullable=False)
# prompt messages
prompt_messages = db.Column(db.Text, nullable=False)
# invoke response
model_response = db.Column(db.Text, nullable=False)
prompt_tokens = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
answer_tokens = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
answer_unit_price = db.Column(db.Numeric(10, 4), nullable=False)
answer_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text('0.001'))
provider_response_latency = db.Column(db.Float, nullable=False, server_default=db.text('0'))
total_price = db.Column(db.Numeric(10, 7))
currency = db.Column(db.String(255), nullable=False)
created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
class ToolConversationVariables(db.Model):
"""
store the conversation variables from tool invoke
"""
__tablename__ = "tool_conversation_variables"
__table_args__ = (
db.PrimaryKeyConstraint('id', name='tool_conversation_variables_pkey'),
# add index for user_id and conversation_id
db.Index('user_id_idx', 'user_id'),
db.Index('conversation_id_idx', 'conversation_id'),
)
id = db.Column(UUID, server_default=db.text('uuid_generate_v4()'))
# conversation user id
user_id = db.Column(UUID, nullable=False)
# tanent id
tenant_id = db.Column(UUID, nullable=False)
# conversation id
conversation_id = db.Column(UUID, nullable=False)
# variables pool
variables_str = db.Column(db.Text, nullable=False)
created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
@property
def variables(self) -> dict:
return json.loads(self.variables_str)
class ToolFile(db.Model):
"""
store the file created by agent
"""
__tablename__ = "tool_files"
__table_args__ = (
db.PrimaryKeyConstraint('id', name='tool_file_pkey'),
)
id = db.Column(UUID, server_default=db.text('uuid_generate_v4()'))
# conversation user id
user_id = db.Column(UUID, nullable=False)
# tanent id
tenant_id = db.Column(UUID, nullable=False)
# conversation id
conversation_id = db.Column(UUID, nullable=False)
# file key
file_key = db.Column(db.String(255), nullable=False)
# mime type
mimetype = db.Column(db.String(255), nullable=False)
# original url
original_url = db.Column(db.String(255), nullable=True)
\ No newline at end of file
api/requirements.txt
View file @ 86286e1a
......@@ -62,3 +62,6 @@ bs4~=0.0.1
markdown~=3.5.1
google-generativeai~=0.3.2
httpx[socks]~=0.24.1
pydub~=0.25.1
matplotlib~=3.8.2
yfinance~=0.2.35
api/services/app_model_config_service.py
View file @ 86286e1a
......@@ -185,7 +185,7 @@ class AppModelConfigService:
variables = []
for item in config["user_input_form"]:
key = list(item.keys())[0]
if key not in ["text-input", "select", "paragraph"]:
if key not in ["text-input", "select", "paragraph", "external_data_tool"]:
raise ValueError("Keys in user_input_form list can only be 'text-input', 'paragraph' or 'select'")
form_item = item[key]
......@@ -262,9 +262,8 @@ class AppModelConfigService:
for tool in config["agent_mode"]["tools"]:
key = list(tool.keys())[0]
if key not in SUPPORT_TOOLS:
raise ValueError("Keys in agent_mode.tools must be in the specified tool list")
if key in SUPPORT_TOOLS:
# old style, use tool name as key
tool_item = tool[key]
if "enabled" not in tool_item or not tool_item["enabled"]:
......@@ -284,6 +283,18 @@ class AppModelConfigService:
if not cls.is_dataset_exists(account, tool_item["id"]):
raise ValueError("Dataset ID does not exist, please check your permission.")
else:
# latest style, use key-value pair
if "enabled" not in tool or not tool["enabled"]:
tool["enabled"] = False
if "provider_type" not in tool:
raise ValueError("provider_type is required in agent_mode.tools")
if "provider_id" not in tool:
raise ValueError("provider_id is required in agent_mode.tools")
if "tool_name" not in tool:
raise ValueError("tool_name is required in agent_mode.tools")
if "tool_parameters" not in tool:
raise ValueError("tool_parameters is required in agent_mode.tools")
# dataset_query_variable
cls.is_dataset_query_variable_valid(config, app_mode)
......@@ -454,6 +465,12 @@ class AppModelConfigService:
if 'dataset_configs' not in config or not config["dataset_configs"]:
config["dataset_configs"] = {'retrieval_model': 'single'}
if 'datasets' not in config["dataset_configs"] or not config["dataset_configs"]["datasets"]:
config["dataset_configs"]["datasets"] = {
"strategy": "router",
"datasets": []
}
if not isinstance(config["dataset_configs"], dict):
raise ValueError("dataset_configs must be of object type")
......
api/services/completion_service.py
View file @ 86286e1a
......@@ -227,6 +227,8 @@ class CompletionService:
input_type = list(config.keys())[0]
if variable not in user_inputs or not user_inputs[variable]:
if input_type == "external_data_tool":
continue
if "required" in input_config and input_config["required"]:
raise ValueError(f"{variable} is required in input form")
else:
......
api/services/tools_manage_service.py 0 → 100644
View file @ 86286e1a
from typing import List, Tuple
from flask import current_app
from core.tools.tool_manager import ToolManager
from core.tools.entities.user_entities import UserToolProvider, UserTool
from core.tools.entities.tool_entities import ApiProviderSchemaType, ApiProviderAuthType, ToolProviderCredentials, \
ToolCredentialsOption
from core.tools.entities.common_entities import I18nObject
from core.tools.entities.tool_bundle import ApiBasedToolBundle
from core.tools.provider.tool_provider import ToolProviderController
from core.tools.provider.api_tool_provider import ApiBasedToolProviderController
from core.tools.utils.parser import ApiBasedToolSchemaParser
from core.tools.utils.encoder import serialize_base_model_array, serialize_base_model_dict
from core.tools.utils.configration import ToolConfiguration
from core.tools.errors import ToolProviderCredentialValidationError, ToolProviderNotFoundError, ToolNotFoundError
from extensions.ext_database import db
from models.tools import BuiltinToolProvider, ApiToolProvider
from httpx import get
import json
class ToolManageService:
@staticmethod
def list_tool_providers(user_id: str, tanent_id: str):
"""
list tool providers
:return: the list of tool providers
"""
result = [provider.to_dict() for provider in ToolManager.user_list_providers(
user_id, tanent_id
)]
# add icon url prefix
for provider in result:
ToolManageService.repacket_provider(provider)
return result
@staticmethod
def repacket_provider(provider: dict):
"""
repacket provider
:param provider: the provider dict
"""
url_prefix = (current_app.config.get("CONSOLE_API_URL")
+ f"/console/api/workspaces/current/tool-provider/builtin/")
if 'icon' in provider:
if provider['type'] == UserToolProvider.ProviderType.BUILTIN.value:
provider['icon'] = url_prefix + provider['name'] + '/icon'
elif provider['type'] == UserToolProvider.ProviderType.API.value:
try:
provider['icon'] = json.loads(provider['icon'])
except:
provider['icon'] = {
"background": "#252525",
"content": "\ud83d\ude01"
}
@staticmethod
def list_builtin_tool_provider_tools(
user_id: str, tenant_id: str, provider: str
):
"""
list builtin tool provider tools
"""
provider_controller: ToolProviderController = ToolManager.get_builtin_provider(provider)
tools = provider_controller.get_tools()
result = [
UserTool(
author=tool.identity.author,
name=tool.identity.name,
label=tool.identity.label,
description=tool.description.human,
parameters=tool.parameters or []
) for tool in tools
]
return json.loads(
serialize_base_model_array(result)
)
@staticmethod
def list_builtin_provider_credentials_schema(
provider_name
):
"""
list builtin provider credentials schema
:return: the list of tool providers
"""
provider = ToolManager.get_builtin_provider(provider_name)
return [
v.to_dict() for _, v in (provider.credentials_schema or {}).items()
]
@staticmethod
def parser_api_schema(schema: str) -> List[ApiBasedToolBundle]:
"""
parse api schema to tool bundle
"""
try:
warnings = {}
try:
tool_bundles, schema_type = ApiBasedToolSchemaParser.auto_parse_to_tool_bundle(schema, warning=warnings)
except Exception as e:
raise ValueError(f'invalid schema: {str(e)}')
credentails_schema = [
ToolProviderCredentials(
name='auth_type',
type=ToolProviderCredentials.CredentialsType.SELECT,
required=True,
default='none',
options=[
ToolCredentialsOption(value='none', label=I18nObject(
en_US='None',
zh_Hans='无'
)),
ToolCredentialsOption(value='api_key', label=I18nObject(
en_US='Api Key',
zh_Hans='Api Key'
)),
],
placeholder=I18nObject(
en_US='Select auth type',
zh_Hans='选择认证方式'
)
),
ToolProviderCredentials(
name='api_key_header',
type=ToolProviderCredentials.CredentialsType.TEXT_INPUT,
required=False,
placeholder=I18nObject(
en_US='Enter api key header',
zh_Hans='输入 api key header，如：X-API-KEY'
),
default='api_key',
help=I18nObject(
en_US='HTTP header name for api key',
zh_Hans='HTTP 头部字段名，用于传递 api key'
)
),
ToolProviderCredentials(
name='api_key_value',
type=ToolProviderCredentials.CredentialsType.TEXT_INPUT,
required=False,
placeholder=I18nObject(
en_US='Enter api key',
zh_Hans='输入 api key'
),
default=''
),
]
return json.loads(serialize_base_model_dict(
{
'schema_type': schema_type,
'parameters_schema': tool_bundles,
'credentials_schema': credentails_schema,
'warning': warnings
}
))
except Exception as e:
raise ValueError(f'invalid schema: {str(e)}')
@staticmethod
def convert_schema_to_tool_bundles(schema: str, extra_info: dict = None) -> List[ApiBasedToolBundle]:
"""
convert schema to tool bundles
:return: the list of tool bundles, description
"""
try:
tool_bundles = ApiBasedToolSchemaParser.auto_parse_to_tool_bundle(schema, extra_info=extra_info)
return tool_bundles
except Exception as e:
raise ValueError(f'invalid schema: {str(e)}')
@staticmethod
def create_api_tool_provider(
user_id: str, tenant_id: str, provider_name: str, icon: dict, credentials: dict,
schema_type: str, schema: str, privacy_policy: str
):
"""
create api tool provider
"""
if schema_type not in [member.value for member in ApiProviderSchemaType]:
raise ValueError(f'invalid schema type {schema}')
# check if the provider exists
provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
ApiToolProvider.tenant_id == tenant_id,
ApiToolProvider.name == provider_name,
).first()
if provider is not None:
raise ValueError(f'provider {provider_name} already exists')
# parse openapi to tool bundle
extra_info = {}
# extra info like description will be set here
tool_bundles, schema_type = ToolManageService.convert_schema_to_tool_bundles(schema, extra_info)
if len(tool_bundles) > 10:
raise ValueError(f'the number of apis should be less than 10')
# create db provider
db_provider = ApiToolProvider(
tenant_id=tenant_id,
user_id=user_id,
name=provider_name,
icon=json.dumps(icon),
schema=schema,
description=extra_info.get('description', ''),
schema_type_str=schema_type,
tools_str=serialize_base_model_array(tool_bundles),
credentials_str={},
privacy_policy=privacy_policy
)
if 'auth_type' not in credentials:
raise ValueError('auth_type is required')
# get auth type, none or api key
auth_type = ApiProviderAuthType.value_of(credentials['auth_type'])
# create provider entity
provider_controller = ApiBasedToolProviderController.from_db(db_provider, auth_type)
# load tools into provider entity
provider_controller.load_bundled_tools(tool_bundles)
# encrypt credentials
tool_configuration = ToolConfiguration(tenant_id=tenant_id, provider_controller=provider_controller)
encrypted_credentials = tool_configuration.encrypt_tool_credentials(credentials)
db_provider.credentials_str = json.dumps(encrypted_credentials)
db.session.add(db_provider)
db.session.commit()
return { 'result': 'success' }
@staticmethod
def get_api_tool_provider_remote_schema(
user_id: str, tenant_id: str, url: str
):
"""
get api tool provider remote schema
"""
headers = {
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36 Edg/120.0.0.0",
"Accept": "*/*",
}
try:
response = get(url, headers=headers, timeout=10)
if response.status_code != 200:
raise ValueError(f'Got status code {response.status_code}')
schema = response.text
# try to parse schema, avoid SSRF attack
ToolManageService.parser_api_schema(schema)
except Exception as e:
raise ValueError(f'invalid schema, please check the url you provided')
return {
'schema': schema
}
@staticmethod
def list_api_tool_provider_tools(
user_id: str, tenant_id: str, provider: str
):
"""
list api tool provider tools
"""
provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
ApiToolProvider.tenant_id == tenant_id,
ApiToolProvider.name == provider,
).first()
if provider is None:
raise ValueError(f'yout have not added provider {provider}')
return json.loads(
serialize_base_model_array([
UserTool(
author=tool_bundle.author,
name=tool_bundle.operation_id,
label=I18nObject(
en_US=tool_bundle.operation_id,
zh_Hans=tool_bundle.operation_id
),
description=I18nObject(
en_US=tool_bundle.summary or '',
zh_Hans=tool_bundle.summary or ''
),
parameters=tool_bundle.parameters
) for tool_bundle in provider.tools
])
)
@staticmethod
def update_builtin_tool_provider(
user_id: str, tenant_id: str, provider_name: str, credentials: dict
):
"""
update builtin tool provider
"""
try:
# get provider
provider_controller = ToolManager.get_builtin_provider(provider_name)
if not provider_controller.need_credentials:
raise ValueError(f'provider {provider_name} does not need credentials')
# validate credentials
provider_controller.validate_credentials(credentials)
# encrypt credentials
tool_configuration = ToolConfiguration(tenant_id=tenant_id, provider_controller=provider_controller)
credentials = tool_configuration.encrypt_tool_credentials(credentials)
except (ToolProviderNotFoundError, ToolNotFoundError, ToolProviderCredentialValidationError) as e:
raise ValueError(str(e))
# get if the provider exists
provider: BuiltinToolProvider = db.session.query(BuiltinToolProvider).filter(
BuiltinToolProvider.tenant_id == tenant_id,
BuiltinToolProvider.provider == provider_name,
).first()
if provider is None:
# create provider
provider = BuiltinToolProvider(
tenant_id=tenant_id,
user_id=user_id,
provider=provider_name,
encrypted_credentials=json.dumps(credentials),
)
db.session.add(provider)
db.session.commit()
else:
provider.encrypted_credentials = json.dumps(credentials)
db.session.add(provider)
db.session.commit()
return { 'result': 'success' }
@staticmethod
def update_api_tool_provider(
user_id: str, tenant_id: str, provider_name: str, original_provider: str, icon: str, credentials: dict,
schema_type: str, schema: str, privacy_policy: str
):
"""
update api tool provider
"""
if schema_type not in [member.value for member in ApiProviderSchemaType]:
raise ValueError(f'invalid schema type {schema}')
# check if the provider exists
provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
ApiToolProvider.tenant_id == tenant_id,
ApiToolProvider.name == original_provider,
).first()
if provider is None:
raise ValueError(f'api provider {provider_name} does not exists')
# parse openapi to tool bundle
extra_info = {}
# extra info like description will be set here
tool_bundles, schema_type = ToolManageService.convert_schema_to_tool_bundles(schema, extra_info)
# update db provider
provider.name = provider_name
provider.icon = icon
provider.schema = schema
provider.description = extra_info.get('description', '')
provider.schema_type_str = ApiProviderSchemaType.OPENAPI.value
provider.tools_str = serialize_base_model_array(tool_bundles)
provider.credentials_str = json.dumps(credentials)
provider.privacy_policy = privacy_policy
if 'auth_type' not in credentials:
raise ValueError('auth_type is required')
# get auth type, none or api key
auth_type = ApiProviderAuthType.value_of(credentials['auth_type'])
# create provider entity
provider_entity = ApiBasedToolProviderController.from_db(provider, auth_type)
# load tools into provider entity
provider_entity.load_bundled_tools(tool_bundles)
db.session.add(provider)
db.session.commit()
return { 'result': 'success' }
@staticmethod
def delete_builtin_tool_provider(
user_id: str, tenant_id: str, provider: str
):
"""
delete tool provider
"""
provider: BuiltinToolProvider = db.session.query(BuiltinToolProvider).filter(
BuiltinToolProvider.tenant_id == tenant_id,
BuiltinToolProvider.provider == provider,
).first()
if provider is None:
raise ValueError(f'yout have not added provider {provider}')
db.session.delete(provider)
db.session.commit()
return { 'result': 'success' }
@staticmethod
def get_builtin_tool_provider_icon(
provider: str
):
"""
get tool provider icon and it's minetype
"""
icon_path, mime_type = ToolManager.get_builtin_provider_icon(provider)
with open(icon_path, 'rb') as f:
icon_bytes = f.read()
return icon_bytes, mime_type
@staticmethod
def delete_api_tool_provider(
user_id: str, tenant_id: str, provider: str
):
"""
delete tool provider
"""
provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
ApiToolProvider.tenant_id == tenant_id,
ApiToolProvider.name == provider,
).first()
if provider is None:
raise ValueError(f'yout have not added provider {provider}')
db.session.delete(provider)
db.session.commit()
return { 'result': 'success' }
@staticmethod
def get_api_tool_provider(
user_id: str, tenant_id: str, provider: str
):
"""
get api tool provider
"""
return ToolManager.user_get_api_provider(provider=provider, tenant_id=tenant_id)
@staticmethod
def test_api_tool_preview(
tenant_id: str, tool_name: str, credentials: dict, parameters: dict, schema_type: str, schema: str
):
"""
test api tool before adding api tool provider
1. parse schema into tool bundle
"""
if schema_type not in [member.value for member in ApiProviderSchemaType]:
raise ValueError(f'invalid schema type {schema_type}')
if schema_type == ApiProviderSchemaType.OPENAPI.value:
tool_bundles = ApiBasedToolSchemaParser.parse_openapi_yaml_to_tool_bundle(schema)
else:
raise ValueError(f'invalid schema type {schema_type}')
# get tool bundle
tool_bundle = next(filter(lambda tb: tb.operation_id == tool_name, tool_bundles), None)
if tool_bundle is None:
raise ValueError(f'invalid tool name {tool_name}')
# create a fake db provider
db_provider = ApiToolProvider(
tenant_id='', user_id='', name='', icon='',
schema=schema,
description='',
schema_type_str=ApiProviderSchemaType.OPENAPI.value,
tools_str=serialize_base_model_array(tool_bundles),
credentials_str=json.dumps(credentials),
)
if 'auth_type' not in credentials:
raise ValueError('auth_type is required')
# get auth type, none or api key
auth_type = ApiProviderAuthType.value_of(credentials['auth_type'])
# create provider entity
provider_controller = ApiBasedToolProviderController.from_db(db_provider, auth_type)
# load tools into provider entity
provider_controller.load_bundled_tools(tool_bundles)
try:
provider_controller.validate_credentials_format(credentials)
# get tool
tool = provider_controller.get_tool(tool_name)
tool = tool.fork_tool_runtime(meta={
'credentials': credentials,
'tenant_id': tenant_id,
})
tool.validate_credentials(credentials, parameters)
except Exception as e:
return { 'error': str(e) }
return { 'result': 'success' }
\ No newline at end of file
api/tests/integration_tests/.gitignore 0 → 100644
View file @ 86286e1a
.env.test
\ No newline at end of file
api/tests/integration_tests/tools/__mock_server/openapi_todo.py 0 → 100644
View file @ 86286e1a
from flask import Flask, request
from flask_restful import Api, Resource
app = Flask(__name__)
api = Api(app)
# Mock data
todos_data = {
"global": ["Buy groceries", "Finish project"],
"user1": ["Go for a run", "Read a book"],
}
class TodosResource(Resource):
def get(self, username):
todos = todos_data.get(username, [])
return {"todos": todos}
def post(self, username):
data = request.get_json()
new_todo = data.get("todo")
todos_data.setdefault(username, []).append(new_todo)
return {"message": "Todo added successfully"}
def delete(self, username):
data = request.get_json()
todo_idx = data.get("todo_idx")
todos = todos_data.get(username, [])
if 0 <= todo_idx < len(todos):
del todos[todo_idx]
return {"message": "Todo deleted successfully"}
return {"error": "Invalid todo index"}, 400
api.add_resource(TodosResource, '/todos/<string:username>')
if __name__ == '__main__':
app.run(port=5003, debug=True)
api/tests/integration_tests/tools/test_all_provider.py 0 → 100644
View file @ 86286e1a
from core.tools.tool_manager import ToolManager
def test_tool_providers():
"""
Test that all tool providers can be loaded
"""
providers = ToolManager.list_builtin_providers()
for provider in providers:
provider.get_tools()
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