feat: universal chat in explore (#649)

Co-authored-by: StyleZhang <jasonapring2015@outlook.com>

.github/workflows/check_no_chinese_comments.py

@@ -19,7 +19,7 @@ def check_file_for_chinese_comments(file_path):
 
 def main():
     has_chinese = False
-    excluded_files = ["model_template.py", 'stopwords.py', 'commands.py', 'indexing_runner.py']
+    excluded_files = ["model_template.py", 'stopwords.py', 'commands.py', 'indexing_runner.py', 'web_reader_tool.py']
 
     for root, _, files in os.walk("."):
         for file in files:

api/app.py

@@ -22,7 +22,7 @@ from extensions.ext_database import db
 from extensions.ext_login import login_manager
 
 # DO NOT REMOVE BELOW
-from models import model, account, dataset, web, task, source
+from models import model, account, dataset, web, task, source, tool
 from events import event_handlers
 # DO NOT REMOVE ABOVE
 

api/controllers/console/__init__.py

@@ -18,7 +18,10 @@ from .auth import login, oauth, data_source_oauth, activate
 from .datasets import datasets, datasets_document, datasets_segments, file, hit_testing, data_source
 
 # Import workspace controllers
-from .workspace import workspace, members, providers, account
+from .workspace import workspace, members, model_providers, account, tool_providers
 
 # Import explore controllers
 from .explore import installed_app, recommended_app, completion, conversation, message, parameter, saved_message, audio
+
+# Import universal chat controllers
+from .universal_chat import chat, conversation, message, parameter, audio

api/controllers/console/app/app.py

@@ -24,6 +24,7 @@ model_config_fields = {
     'suggested_questions_after_answer': fields.Raw(attribute='suggested_questions_after_answer_dict'),
     'speech_to_text': fields.Raw(attribute='speech_to_text_dict'),
     'more_like_this': fields.Raw(attribute='more_like_this_dict'),
+    'sensitive_word_avoidance': fields.Raw(attribute='sensitive_word_avoidance_dict'),
     'model': fields.Raw(attribute='model_dict'),
     'user_input_form': fields.Raw(attribute='user_input_form_list'),
     'pre_prompt': fields.String,
@@ -96,7 +97,8 @@ class AppListApi(Resource):
         args = parser.parse_args()
 
         app_models = db.paginate(
-            db.select(App).where(App.tenant_id == current_user.current_tenant_id).order_by(App.created_at.desc()),
+            db.select(App).where(App.tenant_id == current_user.current_tenant_id,
+                                 App.is_universal == False).order_by(App.created_at.desc()),
             page=args['page'],
             per_page=args['limit'],
             error_out=False)
@@ -147,6 +149,7 @@ class AppListApi(Resource):
                 suggested_questions_after_answer=json.dumps(model_configuration['suggested_questions_after_answer']),
                 speech_to_text=json.dumps(model_configuration['speech_to_text']),
                 more_like_this=json.dumps(model_configuration['more_like_this']),
+                sensitive_word_avoidance=json.dumps(model_configuration['sensitive_word_avoidance']),
                 model=json.dumps(model_configuration['model']),
                 user_input_form=json.dumps(model_configuration['user_input_form']),
                 pre_prompt=model_configuration['pre_prompt'],
@@ -438,6 +441,7 @@ class AppCopy(Resource):
             suggested_questions_after_answer=app_config.suggested_questions_after_answer,
             speech_to_text=app_config.speech_to_text,
             more_like_this=app_config.more_like_this,
+            sensitive_word_avoidance=app_config.sensitive_word_avoidance,
             model=app_config.model,
             user_input_form=app_config.user_input_form,
             pre_prompt=app_config.pre_prompt,

api/controllers/console/app/conversation.py

@@ -163,7 +163,7 @@ class CompletionConversationApi(Resource):
 
         if args['end']:
             end_datetime = datetime.strptime(args['end'], '%Y-%m-%d %H:%M')
-            end_datetime = end_datetime.replace(second=0)
+            end_datetime = end_datetime.replace(second=59)
 
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
@@ -322,7 +322,7 @@ class ChatConversationApi(Resource):
 
         if args['end']:
             end_datetime = datetime.strptime(args['end'], '%Y-%m-%d %H:%M')
-            end_datetime = end_datetime.replace(second=0)
+            end_datetime = end_datetime.replace(second=59)
 
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)

api/controllers/console/app/model_config.py

@@ -43,6 +43,7 @@ class ModelConfigResource(Resource):
             suggested_questions_after_answer=json.dumps(model_configuration['suggested_questions_after_answer']),
             speech_to_text=json.dumps(model_configuration['speech_to_text']),
             more_like_this=json.dumps(model_configuration['more_like_this']),
+            sensitive_word_avoidance=json.dumps(model_configuration['sensitive_word_avoidance']),
             model=json.dumps(model_configuration['model']),
             user_input_form=json.dumps(model_configuration['user_input_form']),
             pre_prompt=model_configuration['pre_prompt'],

api/controllers/console/explore/conversation.py

@@ -65,7 +65,10 @@ class ConversationApi(InstalledAppResource):
             raise NotChatAppError()
 
         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

api/controllers/console/explore/parameter.py

@@ -4,6 +4,10 @@ from flask_restful import marshal_with, fields
 from controllers.console import api
 from controllers.console.explore.wraps import InstalledAppResource
 
+from core.llm.llm_builder import LLMBuilder
+from models.provider import ProviderName
+from models.model import InstalledApp
+
 
 class AppParameterApi(InstalledAppResource):
     """Resource for app variables."""
@@ -27,16 +31,17 @@ class AppParameterApi(InstalledAppResource):
     }
 
     @marshal_with(parameters_fields)
-    def get(self, installed_app):
+    def get(self, installed_app: InstalledApp):
         """Retrieve app parameters."""
         app_model = installed_app.app
         app_model_config = app_model.app_model_config
+        provider_name = LLMBuilder.get_default_provider(installed_app.tenant_id, 'whisper-1')
 
         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,
+            'speech_to_text': app_model_config.speech_to_text_dict if provider_name == ProviderName.OPENAI.value else { 'enabled': False },
             'more_like_this': app_model_config.more_like_this_dict,
             'user_input_form': app_model_config.user_input_form_list
         }

api/controllers/console/universal_chat/audio.py

+# -*- coding:utf-8 -*-
+import logging
+
+from flask import request
+from werkzeug.exceptions import InternalServerError
+
+import services
+from controllers.console import api
+from controllers.console.app.error import AppUnavailableError, ProviderNotInitializeError, \
+    ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError, \
+    NoAudioUploadedError, AudioTooLargeError, \
+    UnsupportedAudioTypeError, ProviderNotSupportSpeechToTextError
+from controllers.console.universal_chat.wraps import UniversalChatResource
+from core.llm.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \
+    LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
+from services.audio_service import AudioService
+from services.errors.audio import NoAudioUploadedServiceError, AudioTooLargeServiceError, \
+    UnsupportedAudioTypeServiceError, ProviderNotSupportSpeechToTextServiceError
+from models.model import AppModelConfig
+
+
+class UniversalChatAudioApi(UniversalChatResource):
+    def post(self, universal_app):
+        app_model = universal_app
+        app_model_config: AppModelConfig = app_model.app_model_config
+
+        if not app_model_config.speech_to_text_dict['enabled']:
+            raise AppUnavailableError()
+
+        file = request.files['file']
+
+        try:
+            response = AudioService.transcript(
+                tenant_id=app_model.tenant_id,
+                file=file,
+            )
+
+            return response
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except NoAudioUploadedServiceError:
+            raise NoAudioUploadedError()
+        except AudioTooLargeServiceError as e:
+            raise AudioTooLargeError(str(e))
+        except UnsupportedAudioTypeServiceError:
+            raise UnsupportedAudioTypeError()
+        except ProviderNotSupportSpeechToTextServiceError:
+            raise ProviderNotSupportSpeechToTextError()
+        except ProviderTokenNotInitError:
+            raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            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

+import json
+import logging
+from typing import Generator, Union
+
+from flask import Response, stream_with_context
+from flask_login import current_user
+from flask_restful import reqparse
+from werkzeug.exceptions import InternalServerError, NotFound
+
+import services
+from controllers.console import api
+from controllers.console.app.error import ConversationCompletedError, AppUnavailableError, ProviderNotInitializeError, \
+    ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError
+from controllers.console.universal_chat.wraps import UniversalChatResource
+from core.constant import llm_constant
+from core.conversation_message_task import PubHandler
+from core.llm.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError, \
+    LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, LLMRateLimitError, LLMAuthorizationError
+from libs.helper import uuid_value
+from services.completion_service import CompletionService
+
+
+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('conversation_id', type=uuid_value, location='json')
+        parser.add_argument('model', type=str, required=True, location='json')
+        parser.add_argument('tools', type=list, required=True, 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']
+
+        if not llm_constant.models[args['model']]:
+            raise ValueError("Model not exists.")
+
+        args['model_config']['model']['provider'] = llm_constant.models[args['model']]
+        args['model_config']['agent_mode']['tools'] = args['tools']
+
+        args['inputs'] = {}
+
+        del args['model']
+        del args['tools']
+
+        try:
+            response = CompletionService.completion(
+                app_model=app_model,
+                user=current_user,
+                args=args,
+                from_source='console',
+                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 (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        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):
+        PubHandler.stop(current_user, task_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:
+            try:
+                for chunk in response:
+                    yield chunk
+            except services.errors.conversation.ConversationNotExistsError:
+                yield "data: " + json.dumps(api.handle_error(NotFound("Conversation Not Exists.")).get_json()) + "\n\n"
+            except services.errors.conversation.ConversationCompletedError:
+                yield "data: " + json.dumps(api.handle_error(ConversationCompletedError()).get_json()) + "\n\n"
+            except services.errors.app_model_config.AppModelConfigBrokenError:
+                logging.exception("App model config broken.")
+                yield "data: " + json.dumps(api.handle_error(AppUnavailableError()).get_json()) + "\n\n"
+            except ProviderTokenNotInitError:
+                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError()).get_json()) + "\n\n"
+            except QuotaExceededError:
+                yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n"
+            except ModelCurrentlyNotSupportError:
+                yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
+            except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                    LLMRateLimitError, LLMAuthorizationError) as e:
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+            except ValueError as e:
+                yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
+            except Exception:
+                logging.exception("internal server error.")
+                yield "data: " + json.dumps(api.handle_error(InternalServerError()).get_json()) + "\n\n"
+
+        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

+# -*- coding:utf-8 -*-
+from flask_login import current_user
+from flask_restful import fields, reqparse, marshal_with
+from flask_restful.inputs import int_range
+from werkzeug.exceptions import NotFound
+
+from controllers.console import api
+from controllers.console.universal_chat.wraps import UniversalChatResource
+from libs.helper import TimestampField, uuid_value
+from services.conversation_service import ConversationService
+from services.errors.conversation import LastConversationNotExistsError, ConversationNotExistsError
+from services.web_conversation_service import WebConversationService
+
+conversation_fields = {
+    'id': fields.String,
+    'name': fields.String,
+    'inputs': fields.Raw,
+    'status': fields.String,
+    'introduction': fields.String,
+    'created_at': TimestampField,
+    'model_config': fields.Raw,
+}
+
+conversation_infinite_scroll_pagination_fields = {
+    'limit': fields.Integer,
+    'has_more': fields.Boolean,
+    'data': fields.List(fields.Nested(conversation_fields))
+}
+
+
+class UniversalChatConversationListApi(UniversalChatResource):
+
+    @marshal_with(conversation_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_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=True, location='json')
+        args = parser.parse_args()
+
+        try:
+            return ConversationService.rename(app_model, conversation_id, current_user, args['name'])
+        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

+# -*- coding:utf-8 -*-
+import logging
+
+from flask_login import current_user
+from flask_restful import reqparse, fields, marshal_with
+from flask_restful.inputs import int_range
+from werkzeug.exceptions import NotFound, InternalServerError
+
+import services
+from controllers.console import api
+from controllers.console.app.error import ProviderNotInitializeError, \
+    ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError
+from controllers.console.explore.error import AppSuggestedQuestionsAfterAnswerDisabledError
+from controllers.console.universal_chat.wraps import UniversalChatResource
+from core.llm.error import LLMRateLimitError, LLMBadRequestError, LLMAuthorizationError, LLMAPIConnectionError, \
+    ProviderTokenNotInitError, LLMAPIUnavailableError, QuotaExceededError, ModelCurrentlyNotSupportError
+from libs.helper import uuid_value, TimestampField
+from services.errors.conversation import ConversationNotExistsError
+from services.errors.message import MessageNotExistsError, SuggestedQuestionsAfterAnswerDisabledError
+from services.message_service import MessageService
+
+
+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
+    }
+
+    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),
+        '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 (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        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

+# -*- coding:utf-8 -*-
+from flask_restful import marshal_with, fields
+
+from controllers.console import api
+from controllers.console.universal_chat.wraps import UniversalChatResource
+
+from core.llm.llm_builder import LLMBuilder
+from models.provider import ProviderName
+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,
+    }
+
+    @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
+        provider_name = LLMBuilder.get_default_provider(universal_app.tenant_id, 'whisper-1')
+
+        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 if provider_name == ProviderName.OPENAI.value else { 'enabled': False },
+        }
+
+
+api.add_resource(UniversalChatParameterApi, '/universal-chat/parameters')

api/controllers/console/universal_chat/wraps.py

+import json
+from functools import wraps
+
+from flask_login import login_required, current_user
+from flask_restful import Resource
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from extensions.ext_database import db
+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}),
+                    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

+import json
+
+from flask_login import login_required, current_user
+from flask_restful import Resource, abort, reqparse
+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 models.tool import ToolProvider, ToolProviderName
+
+
+class ToolProviderListApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        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
+            }
+
+        tool_providers = db.session.query(ToolProvider).filter(ToolProvider.tenant_id == tenant_id).all()
+
+        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 list(tool_credential_dict.values())
+
+
+class ToolProviderCredentialsApi(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}')
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
+        args = parser.parse_args()
+
+        tenant_id = current_user.current_tenant_id
+
+        tool_provider_service = ToolProviderService(tenant_id, provider)
+
+        try:
+            tool_provider_service.credentials_validate(args['credentials'])
+        except ToolValidateFailedError as ex:
+            raise ValueError(str(ex))
+
+        encrypted_credentials = json.dumps(tool_provider_service.encrypt_credentials(args['credentials']))
+
+        tenant = current_user.current_tenant
+
+        tool_provider_model = db.session.query(ToolProvider).filter(
+                ToolProvider.tenant_id == tenant.id,
+                ToolProvider.tool_name == provider,
+            ).first()
+
+        # 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
+            )
+            db.session.add(tool_provider_model)
+
+        db.session.commit()
+
+        return {'result': 'success'}, 201
+
+
+class ToolProviderCredentialsValidateApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, provider):
+        if provider not in [p.value for p in ToolProviderName]:
+            abort(404)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
+        args = parser.parse_args()
+
+        result = True
+        error = None
+
+        tenant_id = current_user.current_tenant_id
+
+        tool_provider_service = ToolProviderService(tenant_id, provider)
+
+        try:
+            tool_provider_service.credentials_validate(args['credentials'])
+        except ToolValidateFailedError as ex:
+            result = False
+            error = str(ex)
+
+        response = {'result': 'success' if result else 'error'}
+
+        if not result:
+            response['error'] = error
+
+        return response
+
+
+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/controllers/service_api/app/app.py

@@ -4,6 +4,10 @@ from flask_restful import fields, marshal_with
 from controllers.service_api import api
 from controllers.service_api.wraps import AppApiResource
 
+from core.llm.llm_builder import LLMBuilder
+from models.provider import ProviderName
+from models.model import App
+
 
 class AppParameterApi(AppApiResource):
     """Resource for app variables."""
@@ -28,15 +32,16 @@ class AppParameterApi(AppApiResource):
     }
 
     @marshal_with(parameters_fields)
-    def get(self, app_model, end_user):
+    def get(self, app_model: App, end_user):
         """Retrieve app parameters."""
         app_model_config = app_model.app_model_config
+        provider_name = LLMBuilder.get_default_provider(app_model.tenant_id, 'whisper-1')
 
         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,
+            'speech_to_text': app_model_config.speech_to_text_dict if provider_name == ProviderName.OPENAI.value else { 'enabled': False },
             'more_like_this': app_model_config.more_like_this_dict,
             'user_input_form': app_model_config.user_input_form_list
         }

api/controllers/web/app.py

@@ -4,6 +4,10 @@ from flask_restful import marshal_with, fields
 from controllers.web import api
 from controllers.web.wraps import WebApiResource
 
+from core.llm.llm_builder import LLMBuilder
+from models.provider import ProviderName
+from models.model import App
+
 
 class AppParameterApi(WebApiResource):
     """Resource for app variables."""
@@ -27,15 +31,16 @@ class AppParameterApi(WebApiResource):
     }
 
     @marshal_with(parameters_fields)
-    def get(self, app_model, end_user):
+    def get(self, app_model: App, end_user):
         """Retrieve app parameters."""
         app_model_config = app_model.app_model_config
+        provider_name = LLMBuilder.get_default_provider(app_model.tenant_id, 'whisper-1')
 
         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,
+            'speech_to_text': app_model_config.speech_to_text_dict if provider_name == ProviderName.OPENAI.value else { 'enabled': False },
             'more_like_this': app_model_config.more_like_this_dict,
             'user_input_form': app_model_config.user_input_form_list
         }

api/controllers/web/conversation.py

@@ -62,7 +62,10 @@ class ConversationApi(WebApiResource):
             raise NotChatAppError()
 
         conversation_id = str(c_id)
+        try:
             ConversationService.delete(app_model, conversation_id, end_user)
+        except ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
         WebConversationService.unpin(app_model, conversation_id, end_user)
 
         return {"result": "success"}, 204

api/core/agent/agent/calc_token_mixin.py

+from typing import cast, List
+
+from langchain import OpenAI
+from langchain.base_language import BaseLanguageModel
+from langchain.chat_models.openai import ChatOpenAI
+from langchain.schema import BaseMessage
+
+from core.constant import llm_constant
+
+
+class CalcTokenMixin:
+
+    def get_num_tokens_from_messages(self, llm: BaseLanguageModel, messages: List[BaseMessage], **kwargs) -> int:
+        llm = cast(ChatOpenAI, llm)
+        return llm.get_num_tokens_from_messages(messages)
+
+    def get_message_rest_tokens(self, llm: BaseLanguageModel, messages: List[BaseMessage], **kwargs) -> int:
+        """
+        Got the rest tokens available for the model after excluding messages tokens and completion max tokens
+
+        :param llm:
+        :param messages:
+        :return:
+        """
+        llm = cast(ChatOpenAI, llm)
+        llm_max_tokens = llm_constant.max_context_token_length[llm.model_name]
+        completion_max_tokens = llm.max_tokens
+        used_tokens = self.get_num_tokens_from_messages(llm, messages, **kwargs)
+        rest_tokens = llm_max_tokens - completion_max_tokens - used_tokens
+
+        return rest_tokens
+
+
+class ExceededLLMTokensLimitError(Exception):
+    pass

api/core/agent/agent/multi_dataset_router_agent.py

+from typing import Tuple, List, Any, Union, Sequence, Optional, cast
+
+from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import Callbacks
+from langchain.prompts.chat import BaseMessagePromptTemplate
+from langchain.schema import AgentAction, AgentFinish, BaseLanguageModel, SystemMessage
+from langchain.tools import BaseTool
+
+from core.tool.dataset_retriever_tool import DatasetRetrieverTool
+
+
+class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
+    """
+    An Multi Dataset Retrieve Agent driven by Router.
+    """
+
+    def should_use_agent(self, query: str):
+        """
+        return should use agent
+
+        :param query:
+        :return:
+        """
+        return True
+
+    def plan(
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        callbacks: Callbacks = None,
+        **kwargs: Any,
+    ) -> Union[AgentAction, AgentFinish]:
+        """Given input, decided what to do.
+
+        Args:
+            intermediate_steps: Steps the LLM has taken to date, along with observations
+            **kwargs: User inputs.
+
+        Returns:
+            Action specifying what tool to use.
+        """
+        if len(self.tools) == 0:
+            return AgentFinish(return_values={"output": ''}, log='')
+        elif len(self.tools) == 1:
+            tool = next(iter(self.tools))
+            tool = cast(DatasetRetrieverTool, tool)
+            rst = tool.run(tool_input={'dataset_id': tool.dataset_id, 'query': kwargs['input']})
+            return AgentFinish(return_values={"output": rst}, log=rst)
+
+        if intermediate_steps:
+            _, observation = intermediate_steps[-1]
+            return AgentFinish(return_values={"output": observation}, log=observation)
+
+        return super().plan(intermediate_steps, callbacks, **kwargs)
+
+    async def aplan(
+            self,
+            intermediate_steps: List[Tuple[AgentAction, str]],
+            callbacks: Callbacks = None,
+            **kwargs: Any,
+    ) -> Union[AgentAction, AgentFinish]:
+        raise NotImplementedError()
+
+    @classmethod
+    def from_llm_and_tools(
+            cls,
+            llm: BaseLanguageModel,
+            tools: Sequence[BaseTool],
+            callback_manager: Optional[BaseCallbackManager] = None,
+            extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None,
+            system_message: Optional[SystemMessage] = SystemMessage(
+                content="You are a helpful AI assistant."
+            ),
+            **kwargs: Any,
+    ) -> BaseSingleActionAgent:
+        llm.model_name = 'gpt-3.5-turbo'
+        return super().from_llm_and_tools(
+            llm=llm,
+            tools=tools,
+            callback_manager=callback_manager,
+            extra_prompt_messages=extra_prompt_messages,
+            system_message=system_message,
+            **kwargs,
+        )

api/core/agent/agent/openai_function_call.py

+from datetime import datetime
+from typing import List, Tuple, Any, Union, Sequence, Optional
+
+import pytz
+from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent
+from langchain.agents.openai_functions_agent.base import _parse_ai_message, \
+    _format_intermediate_steps
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import Callbacks
+from langchain.prompts.chat import BaseMessagePromptTemplate
+from langchain.schema import AgentAction, AgentFinish, SystemMessage, BaseLanguageModel
+from langchain.tools import BaseTool
+
+from core.agent.agent.calc_token_mixin import ExceededLLMTokensLimitError
+from core.agent.agent.openai_function_call_summarize_mixin import OpenAIFunctionCallSummarizeMixin
+
+
+class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, OpenAIFunctionCallSummarizeMixin):
+
+    @classmethod
+    def from_llm_and_tools(
+            cls,
+            llm: BaseLanguageModel,
+            tools: Sequence[BaseTool],
+            callback_manager: Optional[BaseCallbackManager] = None,
+            extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None,
+            system_message: Optional[SystemMessage] = SystemMessage(
+                content="You are a helpful AI assistant."
+            ),
+            **kwargs: Any,
+    ) -> BaseSingleActionAgent:
+        return super().from_llm_and_tools(
+            llm=llm,
+            tools=tools,
+            callback_manager=callback_manager,
+            extra_prompt_messages=extra_prompt_messages,
+            system_message=cls.get_system_message(),
+            **kwargs,
+        )
+
+    def should_use_agent(self, query: str):
+        """
+        return should use agent
+
+        :param query:
+        :return:
+        """
+        original_max_tokens = self.llm.max_tokens
+        self.llm.max_tokens = 15
+
+        prompt = self.prompt.format_prompt(input=query, agent_scratchpad=[])
+        messages = prompt.to_messages()
+
+        predicted_message = self.llm.predict_messages(
+            messages, functions=self.functions, callbacks=None
+        )
+
+        function_call = predicted_message.additional_kwargs.get("function_call", {})
+
+        self.llm.max_tokens = original_max_tokens
+
+        return True if function_call else False
+
+    def plan(
+            self,
+            intermediate_steps: List[Tuple[AgentAction, str]],
+            callbacks: Callbacks = None,
+            **kwargs: Any,
+    ) -> Union[AgentAction, AgentFinish]:
+        """Given input, decided what to do.
+
+        Args:
+            intermediate_steps: Steps the LLM has taken to date, along with observations
+            **kwargs: User inputs.
+
+        Returns:
+            Action specifying what tool to use.
+        """
+        agent_scratchpad = _format_intermediate_steps(intermediate_steps)
+        selected_inputs = {
+            k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
+        }
+        full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
+        prompt = self.prompt.format_prompt(**full_inputs)
+        messages = prompt.to_messages()
+
+        # summarize messages if rest_tokens < 0
+        try:
+            messages = self.summarize_messages_if_needed(self.llm, messages, functions=self.functions)
+        except ExceededLLMTokensLimitError as e:
+            return AgentFinish(return_values={"output": str(e)}, log=str(e))
+
+        predicted_message = self.llm.predict_messages(
+            messages, functions=self.functions, callbacks=callbacks
+        )
+        agent_decision = _parse_ai_message(predicted_message)
+        return agent_decision
+
+    @classmethod
+    def get_system_message(cls):
+        # get current time
+        current_time = datetime.now()
+        current_timezone = pytz.timezone('UTC')
+        current_time = current_timezone.localize(current_time)
+
+        return SystemMessage(content="You are a helpful AI assistant.\n"
+                                     "Current time: {}\n"
+                                     "Respond directly if appropriate.".format(
+            current_time.strftime("%Y-%m-%d %H:%M:%S %Z%z")))
+
+    def return_stopped_response(
+            self,
+            early_stopping_method: str,
+            intermediate_steps: List[Tuple[AgentAction, str]],
+            **kwargs: Any,
+    ) -> AgentFinish:
+        try:
+            return super().return_stopped_response(early_stopping_method, intermediate_steps, **kwargs)
+        except ValueError:
+            return AgentFinish({"output": "I'm sorry, I don't know how to respond to that."}, "")

api/core/agent/agent/openai_function_call_summarize_mixin.py

+from typing import cast, List
+
+from langchain.chat_models import ChatOpenAI
+from langchain.chat_models.openai import _convert_message_to_dict
+from langchain.memory.summary import SummarizerMixin
+from langchain.schema import SystemMessage, HumanMessage, BaseMessage, AIMessage, BaseLanguageModel
+from pydantic import BaseModel
+
+from core.agent.agent.calc_token_mixin import ExceededLLMTokensLimitError, CalcTokenMixin
+
+
+class OpenAIFunctionCallSummarizeMixin(BaseModel, CalcTokenMixin):
+    moving_summary_buffer: str = ""
+    moving_summary_index: int = 0
+    summary_llm: BaseLanguageModel
+
+    def summarize_messages_if_needed(self, llm: BaseLanguageModel, messages: List[BaseMessage], **kwargs) -> List[BaseMessage]:
+        # calculate rest tokens and summarize previous function observation messages if rest_tokens < 0
+        rest_tokens = self.get_message_rest_tokens(llm, messages, **kwargs)
+        rest_tokens = rest_tokens - 20  # to deal with the inaccuracy of rest_tokens
+        if rest_tokens >= 0:
+            return messages
+
+        system_message = None
+        human_message = None
+        should_summary_messages = []
+        for message in messages:
+            if isinstance(message, SystemMessage):
+                system_message = message
+            elif isinstance(message, HumanMessage):
+                human_message = message
+            else:
+                should_summary_messages.append(message)
+
+        if len(should_summary_messages) > 2:
+            ai_message = should_summary_messages[-2]
+            function_message = should_summary_messages[-1]
+            should_summary_messages = should_summary_messages[self.moving_summary_index:-2]
+            self.moving_summary_index = len(should_summary_messages)
+        else:
+            error_msg = "Exceeded LLM tokens limit, stopped."
+            raise ExceededLLMTokensLimitError(error_msg)
+
+        new_messages = [system_message, human_message]
+
+        if self.moving_summary_index == 0:
+            should_summary_messages.insert(0, human_message)
+
+        summary_handler = SummarizerMixin(llm=self.summary_llm)
+        self.moving_summary_buffer = summary_handler.predict_new_summary(
+            messages=should_summary_messages,
+            existing_summary=self.moving_summary_buffer
+        )
+
+        new_messages.append(AIMessage(content=self.moving_summary_buffer))
+        new_messages.append(ai_message)
+        new_messages.append(function_message)
+
+        return new_messages
+
+    def get_num_tokens_from_messages(self, llm: BaseLanguageModel, messages: List[BaseMessage], **kwargs) -> int:
+        """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
+
+        Official documentation: https://github.com/openai/openai-cookbook/blob/
+        main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
+        llm = cast(ChatOpenAI, llm)
+        model, encoding = llm._get_encoding_model()
+        if model.startswith("gpt-3.5-turbo"):
+            # every message follows <im_start>{role/name}\n{content}<im_end>\n
+            tokens_per_message = 4
+            # if there's a name, the role is omitted
+            tokens_per_name = -1
+        elif model.startswith("gpt-4"):
+            tokens_per_message = 3
+            tokens_per_name = 1
+        else:
+            raise NotImplementedError(
+                f"get_num_tokens_from_messages() is not presently implemented "
+                f"for model {model}."
+                "See https://github.com/openai/openai-python/blob/main/chatml.md for "
+                "information on how messages are converted to tokens."
+            )
+        num_tokens = 0
+        for m in messages:
+            message = _convert_message_to_dict(m)
+            num_tokens += tokens_per_message
+            for key, value in message.items():
+                if key == "function_call":
+                    for f_key, f_value in value.items():
+                        num_tokens += len(encoding.encode(f_key))
+                        num_tokens += len(encoding.encode(f_value))
+                else:
+                    num_tokens += len(encoding.encode(value))
+
+                if key == "name":
+                    num_tokens += tokens_per_name
+        # every reply is primed with <im_start>assistant
+        num_tokens += 3
+
+        if kwargs.get('functions'):
+            for function in kwargs.get('functions'):
+                num_tokens += len(encoding.encode('name'))
+                num_tokens += len(encoding.encode(function.get("name")))
+                num_tokens += len(encoding.encode('description'))
+                num_tokens += len(encoding.encode(function.get("description")))
+                parameters = function.get("parameters")
+                num_tokens += len(encoding.encode('parameters'))
+                if 'title' in parameters:
+                    num_tokens += len(encoding.encode('title'))
+                    num_tokens += len(encoding.encode(parameters.get("title")))
+                num_tokens += len(encoding.encode('type'))
+                num_tokens += len(encoding.encode(parameters.get("type")))
+                if 'properties' in parameters:
+                    num_tokens += len(encoding.encode('properties'))
+                    for key, value in parameters.get('properties').items():
+                        num_tokens += len(encoding.encode(key))
+                        for field_key, field_value in value.items():
+                            num_tokens += len(encoding.encode(field_key))
+                            if field_key == 'enum':
+                                for enum_field in field_value:
+                                    num_tokens += 3
+                                    num_tokens += len(encoding.encode(enum_field))
+                            else:
+                                num_tokens += len(encoding.encode(field_key))
+                                num_tokens += len(encoding.encode(str(field_value)))
+                if 'required' in parameters:
+                    num_tokens += len(encoding.encode('required'))
+                    for required_field in parameters['required']:
+                        num_tokens += 3
+                        num_tokens += len(encoding.encode(required_field))
+
+        return num_tokens

api/core/agent/agent/openai_multi_function_call.py

+from datetime import datetime
+from typing import List, Tuple, Any, Union, Sequence, Optional
+
+import pytz
+from langchain.agents import BaseMultiActionAgent
+from langchain.agents.openai_functions_multi_agent.base import OpenAIMultiFunctionsAgent, _format_intermediate_steps, \
+    _parse_ai_message
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import Callbacks
+from langchain.prompts.chat import BaseMessagePromptTemplate
+from langchain.schema import AgentAction, AgentFinish, SystemMessage, BaseLanguageModel
+from langchain.tools import BaseTool
+
+from core.agent.agent.calc_token_mixin import ExceededLLMTokensLimitError
+from core.agent.agent.openai_function_call_summarize_mixin import OpenAIFunctionCallSummarizeMixin
+
+
+class AutoSummarizingOpenMultiAIFunctionCallAgent(OpenAIMultiFunctionsAgent, OpenAIFunctionCallSummarizeMixin):
+
+    @classmethod
+    def from_llm_and_tools(
+            cls,
+            llm: BaseLanguageModel,
+            tools: Sequence[BaseTool],
+            callback_manager: Optional[BaseCallbackManager] = None,
+            extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None,
+            system_message: Optional[SystemMessage] = SystemMessage(
+                content="You are a helpful AI assistant."
+            ),
+            **kwargs: Any,
+    ) -> BaseMultiActionAgent:
+        return super().from_llm_and_tools(
+            llm=llm,
+            tools=tools,
+            callback_manager=callback_manager,
+            extra_prompt_messages=extra_prompt_messages,
+            system_message=cls.get_system_message(),
+            **kwargs,
+        )
+
+    def should_use_agent(self, query: str):
+        """
+        return should use agent
+
+        :param query:
+        :return:
+        """
+        original_max_tokens = self.llm.max_tokens
+        self.llm.max_tokens = 15
+
+        prompt = self.prompt.format_prompt(input=query, agent_scratchpad=[])
+        messages = prompt.to_messages()
+
+        predicted_message = self.llm.predict_messages(
+            messages, functions=self.functions, callbacks=None
+        )
+
+        function_call = predicted_message.additional_kwargs.get("function_call", {})
+
+        self.llm.max_tokens = original_max_tokens
+
+        return True if function_call else False
+
+    def plan(
+            self,
+            intermediate_steps: List[Tuple[AgentAction, str]],
+            callbacks: Callbacks = None,
+            **kwargs: Any,
+    ) -> Union[AgentAction, AgentFinish]:
+        """Given input, decided what to do.
+
+        Args:
+            intermediate_steps: Steps the LLM has taken to date, along with observations
+            **kwargs: User inputs.
+
+        Returns:
+            Action specifying what tool to use.
+        """
+        agent_scratchpad = _format_intermediate_steps(intermediate_steps)
+        selected_inputs = {
+            k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
+        }
+        full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
+        prompt = self.prompt.format_prompt(**full_inputs)
+        messages = prompt.to_messages()
+
+        # summarize messages if rest_tokens < 0
+        try:
+            messages = self.summarize_messages_if_needed(self.llm, messages, functions=self.functions)
+        except ExceededLLMTokensLimitError as e:
+            return AgentFinish(return_values={"output": str(e)}, log=str(e))
+
+        predicted_message = self.llm.predict_messages(
+            messages, functions=self.functions, callbacks=callbacks
+        )
+        agent_decision = _parse_ai_message(predicted_message)
+        return agent_decision
+
+    @classmethod
+    def get_system_message(cls):
+        # get current time
+        current_time = datetime.now()
+        current_timezone = pytz.timezone('UTC')
+        current_time = current_timezone.localize(current_time)
+
+        return SystemMessage(content="You are a helpful AI assistant.\n"
+                                     "Current time: {}\n"
+                                     "Respond directly if appropriate.".format(
+            current_time.strftime("%Y-%m-%d %H:%M:%S %Z%z")))

api/core/agent/agent/output_parser/structured_chat.py

+import json
+import re
+from typing import Union
+
+from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser as LCStructuredChatOutputParser, \
+    logger
+from langchain.schema import AgentAction, AgentFinish, OutputParserException
+
+
+class StructuredChatOutputParser(LCStructuredChatOutputParser):
+    def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
+        try:
+            action_match = re.search(r"```(.*?)\n(.*?)```?", text, re.DOTALL)
+            if action_match is not None:
+                response = json.loads(action_match.group(2).strip(), strict=False)
+                if isinstance(response, list):
+                    # gpt turbo frequently ignores the directive to emit a single action
+                    logger.warning("Got multiple action responses: %s", response)
+                    response = response[0]
+                if response["action"] == "Final Answer":
+                    return AgentFinish({"output": response["action_input"]}, text)
+                else:
+                    return AgentAction(
+                        response["action"], response.get("action_input", {}), text
+                    )
+            else:
+                return AgentFinish({"output": text}, text)
+        except Exception as e:
+            raise OutputParserException(f"Could not parse LLM output: {text}") from e

api/core/agent/agent/structured_chat.py

+import re
+from typing import List, Tuple, Any, Union, Sequence, Optional
+
+from langchain import BasePromptTemplate
+from langchain.agents import StructuredChatAgent, AgentOutputParser, Agent
+from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import Callbacks
+from langchain.memory.summary import SummarizerMixin
+from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
+from langchain.schema import AgentAction, AgentFinish, AIMessage, HumanMessage
+from langchain.tools import BaseTool
+from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
+
+from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
+
+
+FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
+The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.
+Valid "action" values: "Final Answer" or {tool_names}
+
+Provide only ONE action per $JSON_BLOB, as shown:
+
+```
+{{{{
+  "action": $TOOL_NAME,
+  "action_input": $INPUT
+}}}}
+```
+
+Follow this format:
+
+Question: input question to answer
+Thought: consider previous and subsequent steps
+Action:
+```
+$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"
+}}}}
+```"""
+
+
+class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
+    moving_summary_buffer: str = ""
+    moving_summary_index: int = 0
+    summary_llm: BaseLanguageModel
+
+    def should_use_agent(self, query: str):
+        """
+        return should use agent
+        Using the ReACT mode to determine whether an agent is needed is costly,
+        so it's better to just use an Agent for reasoning, which is cheaper.
+
+        :param query:
+        :return:
+        """
+        return True
+
+    def plan(
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        callbacks: Callbacks = None,
+        **kwargs: Any,
+    ) -> Union[AgentAction, AgentFinish]:
+        """Given input, decided what to do.
+
+        Args:
+            intermediate_steps: Steps the LLM has taken to date,
+                along with observations
+            callbacks: Callbacks to run.
+            **kwargs: User inputs.
+
+        Returns:
+            Action specifying what tool to use.
+        """
+        full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
+
+        prompts, _ = self.llm_chain.prep_prompts(input_list=[self.llm_chain.prep_inputs(full_inputs)])
+        messages = []
+        if prompts:
+            messages = prompts[0].to_messages()
+
+        rest_tokens = self.get_message_rest_tokens(self.llm_chain.llm, messages)
+        if rest_tokens < 0:
+            full_inputs = self.summarize_messages(intermediate_steps, **kwargs)
+
+        full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
+        return self.output_parser.parse(full_output)
+
+    def summarize_messages(self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs):
+        if len(intermediate_steps) >= 2:
+            should_summary_intermediate_steps = intermediate_steps[self.moving_summary_index:-1]
+            should_summary_messages = [AIMessage(content=observation)
+                                       for _, observation in should_summary_intermediate_steps]
+            if self.moving_summary_index == 0:
+                should_summary_messages.insert(0, HumanMessage(content=kwargs.get("input")))
+
+            self.moving_summary_index = len(intermediate_steps)
+        else:
+            error_msg = "Exceeded LLM tokens limit, stopped."
+            raise ExceededLLMTokensLimitError(error_msg)
+
+        summary_handler = SummarizerMixin(llm=self.summary_llm)
+        if self.moving_summary_buffer and 'chat_history' in kwargs:
+            kwargs["chat_history"].pop()
+
+        self.moving_summary_buffer = summary_handler.predict_new_summary(
+            messages=should_summary_messages,
+            existing_summary=self.moving_summary_buffer
+        )
+
+        if 'chat_history' in kwargs:
+            kwargs["chat_history"].append(AIMessage(content=self.moving_summary_buffer))
+
+        return self.get_full_inputs([intermediate_steps[-1]], **kwargs)
+
+    @classmethod
+    def create_prompt(
+            cls,
+            tools: Sequence[BaseTool],
+            prefix: str = PREFIX,
+            suffix: str = SUFFIX,
+            human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
+            format_instructions: str = FORMAT_INSTRUCTIONS,
+            input_variables: Optional[List[str]] = None,
+            memory_prompts: Optional[List[BasePromptTemplate]] = None,
+    ) -> BasePromptTemplate:
+        tool_strings = []
+        for tool in tools:
+            args_schema = re.sub("}", "}}}}", re.sub("{", "{{{{", str(tool.args)))
+            tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
+        formatted_tools = "\n".join(tool_strings)
+        tool_names = ", ".join([('"' + tool.name + '"') for tool in tools])
+        format_instructions = format_instructions.format(tool_names=tool_names)
+        template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
+        if input_variables is None:
+            input_variables = ["input", "agent_scratchpad"]
+        _memory_prompts = memory_prompts or []
+        messages = [
+            SystemMessagePromptTemplate.from_template(template),
+            *_memory_prompts,
+            HumanMessagePromptTemplate.from_template(human_message_template),
+        ]
+        return ChatPromptTemplate(input_variables=input_variables, messages=messages)
+
+    @classmethod
+    def from_llm_and_tools(
+            cls,
+            llm: BaseLanguageModel,
+            tools: Sequence[BaseTool],
+            callback_manager: Optional[BaseCallbackManager] = None,
+            output_parser: Optional[AgentOutputParser] = None,
+            prefix: str = PREFIX,
+            suffix: str = SUFFIX,
+            human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
+            format_instructions: str = FORMAT_INSTRUCTIONS,
+            input_variables: Optional[List[str]] = None,
+            memory_prompts: Optional[List[BasePromptTemplate]] = None,
+            **kwargs: Any,
+    ) -> Agent:
+        return super().from_llm_and_tools(
+            llm=llm,
+            tools=tools,
+            callback_manager=callback_manager,
+            output_parser=output_parser,
+            prefix=prefix,
+            suffix=suffix,
+            human_message_template=human_message_template,
+            format_instructions=format_instructions,
+            input_variables=input_variables,
+            memory_prompts=memory_prompts,
+            **kwargs,
+        )

api/core/agent/agent_builder.py

-from typing import Optional
-
-from langchain import LLMChain
-from langchain.agents import ZeroShotAgent, AgentExecutor, ConversationalAgent
-from langchain.callbacks.manager import CallbackManager
-from langchain.memory.chat_memory import BaseChatMemory
-
-from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
-from core.callback_handler.dataset_tool_callback_handler import DatasetToolCallbackHandler
-from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
-from core.llm.llm_builder import LLMBuilder
-
-
-class AgentBuilder:
-    @classmethod
-    def to_agent_chain(cls, tenant_id: str, tools, memory: Optional[BaseChatMemory],
-                       dataset_tool_callback_handler: DatasetToolCallbackHandler,
-                       agent_loop_gather_callback_handler: AgentLoopGatherCallbackHandler):
-        llm = LLMBuilder.to_llm(
-            tenant_id=tenant_id,
-            model_name=agent_loop_gather_callback_handler.model_name,
-            temperature=0,
-            max_tokens=1024,
-            callbacks=[agent_loop_gather_callback_handler, DifyStdOutCallbackHandler()]
-        )
-
-        for tool in tools:
-            tool.callbacks = [
-                agent_loop_gather_callback_handler,
-                dataset_tool_callback_handler,
-                DifyStdOutCallbackHandler()
-            ]
-
-        prompt = cls.build_agent_prompt_template(
-            tools=tools,
-            memory=memory,
-        )
-
-        agent_llm_chain = LLMChain(
-            llm=llm,
-            prompt=prompt,
-        )
-
-        agent = cls.build_agent(agent_llm_chain=agent_llm_chain, memory=memory)
-
-        agent_callback_manager = CallbackManager(
-            [agent_loop_gather_callback_handler, DifyStdOutCallbackHandler()]
-        )
-
-        agent_chain = AgentExecutor.from_agent_and_tools(
-            tools=tools,
-            agent=agent,
-            memory=memory,
-            callbacks=agent_callback_manager,
-            max_iterations=6,
-            early_stopping_method="generate",
-            # `generate` will continue to complete the last inference after reaching the iteration limit or request time limit
-        )
-
-        return agent_chain
-
-    @classmethod
-    def build_agent_prompt_template(cls, tools, memory: Optional[BaseChatMemory]):
-        if memory:
-            prompt = ConversationalAgent.create_prompt(
-                tools=tools,
-            )
-        else:
-            prompt = ZeroShotAgent.create_prompt(
-                tools=tools,
-            )
-
-        return prompt
-
-    @classmethod
-    def build_agent(cls, agent_llm_chain: LLMChain, memory: Optional[BaseChatMemory]):
-        if memory:
-            agent = ConversationalAgent(
-                llm_chain=agent_llm_chain
-            )
-        else:
-            agent = ZeroShotAgent(
-                llm_chain=agent_llm_chain
-            )
-
-        return agent

api/core/agent/agent_executor.py

+import enum
+import logging
+from typing import Union, Optional
+
+from langchain.agents import BaseSingleActionAgent, BaseMultiActionAgent
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.manager import Callbacks
+from langchain.memory.chat_memory import BaseChatMemory
+from langchain.tools import BaseTool
+from pydantic import BaseModel, Extra
+
+from core.agent.agent.multi_dataset_router_agent import MultiDatasetRouterAgent
+from core.agent.agent.openai_function_call import AutoSummarizingOpenAIFunctionCallAgent
+from core.agent.agent.openai_multi_function_call import AutoSummarizingOpenMultiAIFunctionCallAgent
+from core.agent.agent.output_parser.structured_chat import StructuredChatOutputParser
+from core.agent.agent.structured_chat import AutoSummarizingStructuredChatAgent
+from langchain.agents import AgentExecutor as LCAgentExecutor
+
+from core.tool.dataset_retriever_tool import DatasetRetrieverTool
+
+
+class PlanningStrategy(str, enum.Enum):
+    ROUTER = 'router'
+    REACT = 'react'
+    FUNCTION_CALL = 'function_call'
+    MULTI_FUNCTION_CALL = 'multi_function_call'
+
+
+class AgentConfiguration(BaseModel):
+    strategy: PlanningStrategy
+    llm: BaseLanguageModel
+    tools: list[BaseTool]
+    summary_llm: BaseLanguageModel
+    memory: Optional[BaseChatMemory] = None
+    callbacks: Callbacks = None
+    max_iterations: int = 6
+    max_execution_time: Optional[float] = None
+    early_stopping_method: str = "generate"
+    # `generate` will continue to complete the last inference after reaching the iteration limit or request time limit
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+
+class AgentExecuteResult(BaseModel):
+    strategy: PlanningStrategy
+    output: Optional[str]
+    configuration: AgentConfiguration
+
+
+class AgentExecutor:
+    def __init__(self, configuration: AgentConfiguration):
+        self.configuration = configuration
+        self.agent = self._init_agent()
+
+    def _init_agent(self) -> Union[BaseSingleActionAgent | BaseMultiActionAgent]:
+        if self.configuration.strategy == PlanningStrategy.REACT:
+            agent = AutoSummarizingStructuredChatAgent.from_llm_and_tools(
+                llm=self.configuration.llm,
+                tools=self.configuration.tools,
+                output_parser=StructuredChatOutputParser(),
+                summary_llm=self.configuration.summary_llm,
+                verbose=True
+            )
+        elif self.configuration.strategy == PlanningStrategy.FUNCTION_CALL:
+            agent = AutoSummarizingOpenAIFunctionCallAgent.from_llm_and_tools(
+                llm=self.configuration.llm,
+                tools=self.configuration.tools,
+                extra_prompt_messages=self.configuration.memory.buffer if self.configuration.memory else None,  # used for read chat histories memory
+                summary_llm=self.configuration.summary_llm,
+                verbose=True
+            )
+        elif self.configuration.strategy == PlanningStrategy.MULTI_FUNCTION_CALL:
+            agent = AutoSummarizingOpenMultiAIFunctionCallAgent.from_llm_and_tools(
+                llm=self.configuration.llm,
+                tools=self.configuration.tools,
+                extra_prompt_messages=self.configuration.memory.buffer if self.configuration.memory else None,  # used for read chat histories memory
+                summary_llm=self.configuration.summary_llm,
+                verbose=True
+            )
+        elif self.configuration.strategy == PlanningStrategy.ROUTER:
+            self.configuration.tools = [t for t in self.configuration.tools if isinstance(t, DatasetRetrieverTool)]
+            agent = MultiDatasetRouterAgent.from_llm_and_tools(
+                llm=self.configuration.llm,
+                tools=self.configuration.tools,
+                extra_prompt_messages=self.configuration.memory.buffer if self.configuration.memory else None,
+                verbose=True
+            )
+        else:
+            raise NotImplementedError(f"Unknown Agent Strategy: {self.configuration.strategy}")
+
+        return agent
+
+    def should_use_agent(self, query: str) -> bool:
+        return self.agent.should_use_agent(query)
+
+    def run(self, query: str) -> AgentExecuteResult:
+        agent_executor = LCAgentExecutor.from_agent_and_tools(
+            agent=self.agent,
+            tools=self.configuration.tools,
+            memory=self.configuration.memory,
+            max_iterations=self.configuration.max_iterations,
+            max_execution_time=self.configuration.max_execution_time,
+            early_stopping_method=self.configuration.early_stopping_method,
+            callbacks=self.configuration.callbacks
+        )
+
+        try:
+            output = agent_executor.run(query)
+        except Exception:
+            logging.exception("agent_executor run failed")
+            output = None
+
+        return AgentExecuteResult(
+            output=output,
+            strategy=self.configuration.strategy,
+            configuration=self.configuration
+        )

api/core/callback_handler/agent_loop_gather_callback_handler.py

+import json
 import logging
 import time
 
 from typing import Any, Dict, List, Union, Optional
 
+from langchain.agents import openai_functions_agent, openai_functions_multi_agent
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema import AgentAction, AgentFinish, LLMResult
+from langchain.schema import AgentAction, AgentFinish, LLMResult, ChatGeneration
 
 from core.callback_handler.entity.agent_loop import AgentLoop
 from core.conversation_message_task import ConversationMessageTask
@@ -20,6 +22,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         self.conversation_message_task = conversation_message_task
         self._agent_loops = []
         self._current_loop = None
+        self._message_agent_thought = None
         self.current_chain = None
 
     @property
@@ -29,6 +32,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
     def clear_agent_loops(self) -> None:
         self._agent_loops = []
         self._current_loop = None
+        self._message_agent_thought = None
 
     @property
     def always_verbose(self) -> bool:
@@ -61,8 +65,20 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         # kwargs={}
         if self._current_loop and self._current_loop.status == 'llm_started':
             self._current_loop.status = 'llm_end'
+            if response.llm_output:
                 self._current_loop.prompt_tokens = response.llm_output['token_usage']['prompt_tokens']
+            completion_generation = response.generations[0][0]
+            if isinstance(completion_generation, ChatGeneration):
+                completion_message = completion_generation.message
+                if 'function_call' in completion_message.additional_kwargs:
+                    self._current_loop.completion \
+                        = json.dumps({'function_call': completion_message.additional_kwargs['function_call']})
+                else:
                     self._current_loop.completion = response.generations[0][0].text
+            else:
+                self._current_loop.completion = completion_generation.text
+
+            if response.llm_output:
                 self._current_loop.completion_tokens = response.llm_output['token_usage']['completion_tokens']
 
     def on_llm_error(
@@ -71,6 +87,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         logging.error(error)
         self._agent_loops = []
         self._current_loop = None
+        self._message_agent_thought = None
 
     def on_tool_start(
         self,
@@ -89,8 +106,15 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
     ) -> Any:
         """Run on agent action."""
         tool = action.tool
-        tool_input = action.tool_input
-        action_name_position = action.log.index("\nAction:") + 1 if action.log else -1
+        tool_input = json.dumps({"query": action.tool_input}
+                                if isinstance(action.tool_input, str) else action.tool_input)
+        completion = None
+        if isinstance(action, openai_functions_agent.base._FunctionsAgentAction) \
+                or isinstance(action, openai_functions_multi_agent.base._FunctionsAgentAction):
+            thought = action.log.strip()
+            completion = json.dumps({'function_call': action.message_log[0].additional_kwargs['function_call']})
+        else:
+            action_name_position = action.log.index("Action:") if action.log else -1
             thought = action.log[:action_name_position].strip() if action.log else ''
 
         if self._current_loop and self._current_loop.status == 'llm_end':
@@ -98,6 +122,13 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
             self._current_loop.thought = thought
             self._current_loop.tool_name = tool
             self._current_loop.tool_input = tool_input
+            if completion is not None:
+                self._current_loop.completion = completion
+
+            self._message_agent_thought = self.conversation_message_task.on_agent_start(
+                self.current_chain,
+                self._current_loop
+            )
 
     def on_tool_end(
         self,
@@ -120,10 +151,13 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
             self._current_loop.completed_at = time.perf_counter()
             self._current_loop.latency = self._current_loop.completed_at - self._current_loop.started_at
 
-            self.conversation_message_task.on_agent_end(self.current_chain, self.model_name, self._current_loop)
+            self.conversation_message_task.on_agent_end(
+                self._message_agent_thought, self.model_name, self._current_loop
+            )
 
             self._agent_loops.append(self._current_loop)
             self._current_loop = None
+            self._message_agent_thought = None
 
     def on_tool_error(
         self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
@@ -132,6 +166,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         logging.error(error)
         self._agent_loops = []
         self._current_loop = None
+        self._message_agent_thought = None
 
     def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
         """Run on agent end."""
@@ -141,10 +176,18 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
             self._current_loop.completed = True
             self._current_loop.completed_at = time.perf_counter()
             self._current_loop.latency = self._current_loop.completed_at - self._current_loop.started_at
+            self._current_loop.thought = '[DONE]'
+            self._message_agent_thought = self.conversation_message_task.on_agent_start(
+                self.current_chain,
+                self._current_loop
+            )
 
-            self.conversation_message_task.on_agent_end(self.current_chain, self.model_name, self._current_loop)
+            self.conversation_message_task.on_agent_end(
+                self._message_agent_thought, self.model_name, self._current_loop
+            )
 
             self._agent_loops.append(self._current_loop)
             self._current_loop = None
+            self._message_agent_thought = None
         elif not self._current_loop and self._agent_loops:
             self._agent_loops[-1].status = 'agent_finish'

api/core/callback_handler/dataset_tool_callback_handler.py

+import json
 import logging
 
 from typing import Any, Dict, List, Union, Optional
@@ -43,9 +44,11 @@ class DatasetToolCallbackHandler(BaseCallbackHandler):
         input_str: str,
         **kwargs: Any,
     ) -> None:
-        tool_name = serialized.get('name')
-        dataset_id = tool_name[len("dataset-"):]
-        self.conversation_message_task.on_dataset_query_end(DatasetQueryObj(dataset_id=dataset_id, query=input_str))
+        # tool_name = serialized.get('name')
+        input_dict = json.loads(input_str.replace("'", "\""))
+        dataset_id = input_dict.get('dataset_id')
+        query = input_dict.get('query')
+        self.conversation_message_task.on_dataset_query_end(DatasetQueryObj(dataset_id=dataset_id, query=query))
 
     def on_tool_end(
         self,

api/core/callback_handler/entity/agent_loop.py

@@ -10,9 +10,9 @@ class AgentLoop(BaseModel):
     tool_output: str = None
 
     prompt: str = None
-    prompt_tokens: int = None
+    prompt_tokens: int = 0
     completion: str = None
-    completion_tokens: int = None
+    completion_tokens: int = 0
 
     latency: float = None
 

api/core/callback_handler/llm_callback_handler.py

 import logging
 import time
-from typing import Any, Dict, List, Union, Optional
+from typing import Any, Dict, List, Union
 
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema import AgentAction, AgentFinish, LLMResult, HumanMessage, AIMessage, SystemMessage, BaseMessage
+from langchain.schema import LLMResult, BaseMessage, BaseLanguageModel
 
 from core.callback_handler.entity.llm_message import LLMMessage
 from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException
-from core.llm.streamable_chat_open_ai import StreamableChatOpenAI
-from core.llm.streamable_open_ai import StreamableOpenAI
 
 
 class LLMCallbackHandler(BaseCallbackHandler):
     raise_error: bool = True
 
-    def __init__(self, llm: Union[StreamableOpenAI, StreamableChatOpenAI],
+    def __init__(self, llm: BaseLanguageModel,
                  conversation_message_task: ConversationMessageTask):
         self.llm = llm
         self.llm_message = LLMMessage()

api/core/callback_handler/main_chain_gather_callback_handler.py

@@ -20,15 +20,13 @@ class MainChainGatherCallbackHandler(BaseCallbackHandler):
         self._current_chain_result = None
         self._current_chain_message = None
         self.conversation_message_task = conversation_message_task
-        self.agent_loop_gather_callback_handler = AgentLoopGatherCallbackHandler(
-            llm_constant.agent_model_name,
-            conversation_message_task
-        )
+        self.agent_callback = None
 
     def clear_chain_results(self) -> None:
         self._current_chain_result = None
         self._current_chain_message = None
-        self.agent_loop_gather_callback_handler.current_chain = None
+        if self.agent_callback:
+            self.agent_callback.current_chain = None
 
     @property
     def always_verbose(self) -> bool:
@@ -58,7 +56,8 @@ class MainChainGatherCallbackHandler(BaseCallbackHandler):
                     started_at=time.perf_counter()
                 )
                 self._current_chain_message = self.conversation_message_task.init_chain(self._current_chain_result)
-                self.agent_loop_gather_callback_handler.current_chain = self._current_chain_message
+                if self.agent_callback:
+                    self.agent_callback.current_chain = self._current_chain_message
 
     def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
         """Print out that we finished a chain."""

api/core/chain/chain_builder.py

-from typing import Optional
-
-from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
-from core.chain.sensitive_word_avoidance_chain import SensitiveWordAvoidanceChain
-from core.chain.tool_chain import ToolChain
-
-
-class ChainBuilder:
-    @classmethod
-    def to_tool_chain(cls, tool, **kwargs) -> ToolChain:
-        return ToolChain(
-            tool=tool,
-            input_key=kwargs.get('input_key', 'input'),
-            output_key=kwargs.get('output_key', 'tool_output'),
-            callbacks=[DifyStdOutCallbackHandler()]
-        )
-
-    @classmethod
-    def to_sensitive_word_avoidance_chain(cls, tool_config: dict, **kwargs) -> Optional[
-        SensitiveWordAvoidanceChain]:
-        sensitive_words = tool_config.get("words", "")
-        if tool_config.get("enabled", False) \
-                and sensitive_words:
-            return SensitiveWordAvoidanceChain(
-                sensitive_words=sensitive_words.split(","),
-                canned_response=tool_config.get("canned_response", ''),
-                output_key="sensitive_word_avoidance_output",
-                callbacks=[DifyStdOutCallbackHandler()],
-                **kwargs
-            )
-
-        return None

api/core/chain/llm_router_chain.py

-"""Base classes for LLM-powered router chains."""
-from __future__ import annotations
-
-from typing import Any, Dict, List, Optional, Type, cast, NamedTuple
-
-from langchain.base_language import BaseLanguageModel
-from langchain.callbacks.manager import CallbackManagerForChainRun
-from langchain.chains.base import Chain
-from pydantic import root_validator
-
-from langchain.chains import LLMChain
-from langchain.prompts import BasePromptTemplate
-from langchain.schema import BaseOutputParser, OutputParserException
-
-from libs.json_in_md_parser import parse_and_check_json_markdown
-
-
-class Route(NamedTuple):
-    destination: Optional[str]
-    next_inputs: Dict[str, Any]
-
-
-class LLMRouterChain(Chain):
-    """A router chain that uses an LLM chain to perform routing."""
-
-    llm_chain: LLMChain
-    """LLM chain used to perform routing"""
-
-    @root_validator()
-    def validate_prompt(cls, values: dict) -> dict:
-        prompt = values["llm_chain"].prompt
-        if prompt.output_parser is None:
-            raise ValueError(
-                "LLMRouterChain requires base llm_chain prompt to have an output"
-                " parser that converts LLM text output to a dictionary with keys"
-                " 'destination' and 'next_inputs'. Received a prompt with no output"
-                " parser."
-            )
-        return values
-
-    @property
-    def input_keys(self) -> List[str]:
-        """Will be whatever keys the LLM chain prompt expects.
-
-        :meta private:
-        """
-        return self.llm_chain.input_keys
-
-    def _validate_outputs(self, outputs: Dict[str, Any]) -> None:
-        super()._validate_outputs(outputs)
-        if not isinstance(outputs["next_inputs"], dict):
-            raise ValueError
-
-    def _call(
-            self,
-            inputs: Dict[str, Any],
-            run_manager: Optional[CallbackManagerForChainRun] = None,
-    ) -> Dict[str, Any]:
-        output = cast(
-            Dict[str, Any],
-            self.llm_chain.predict_and_parse(**inputs),
-        )
-        return output
-
-    @classmethod
-    def from_llm(
-        cls, llm: BaseLanguageModel, prompt: BasePromptTemplate, **kwargs: Any
-    ) -> LLMRouterChain:
-        """Convenience constructor."""
-        llm_chain = LLMChain(llm=llm, prompt=prompt)
-        return cls(llm_chain=llm_chain, **kwargs)
-
-    @property
-    def output_keys(self) -> List[str]:
-        return ["destination", "next_inputs"]
-
-    def route(self, inputs: Dict[str, Any]) -> Route:
-        result = self(inputs)
-        return Route(result["destination"], result["next_inputs"])
-
-
-class RouterOutputParser(BaseOutputParser[Dict[str, str]]):
-    """Parser for output of router chain int he multi-prompt chain."""
-
-    default_destination: str = "DEFAULT"
-    next_inputs_type: Type = str
-    next_inputs_inner_key: str = "input"
-
-    def parse(self, text: str) -> Dict[str, Any]:
-        try:
-            expected_keys = ["destination", "next_inputs"]
-            parsed = parse_and_check_json_markdown(text, expected_keys)
-            if not isinstance(parsed["destination"], str):
-                raise ValueError("Expected 'destination' to be a string.")
-            if not isinstance(parsed["next_inputs"], self.next_inputs_type):
-                raise ValueError(
-                    f"Expected 'next_inputs' to be {self.next_inputs_type}."
-                )
-            parsed["next_inputs"] = {self.next_inputs_inner_key: parsed["next_inputs"]}
-            if (
-                parsed["destination"].strip().lower()
-                == self.default_destination.lower()
-            ):
-                parsed["destination"] = None
-            else:
-                parsed["destination"] = parsed["destination"].strip()
-            return parsed
-        except Exception as e:
-            raise OutputParserException(
-                f"Parsing text\n{text}\n of llm router raised following error:\n{e}"
-            )

api/core/chain/main_chain_builder.py

-from typing import Optional, List, cast
-
-from langchain.chains import SequentialChain
-from langchain.chains.base import Chain
-from langchain.memory.chat_memory import BaseChatMemory
-
-from core.callback_handler.main_chain_gather_callback_handler import MainChainGatherCallbackHandler
-from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
-from core.chain.chain_builder import ChainBuilder
-from core.chain.multi_dataset_router_chain import MultiDatasetRouterChain
-from core.conversation_message_task import ConversationMessageTask
-from extensions.ext_database import db
-from models.dataset import Dataset
-
-
-class MainChainBuilder:
-    @classmethod
-    def to_langchain_components(cls, tenant_id: str, agent_mode: dict, memory: Optional[BaseChatMemory],
-                                rest_tokens: int,
-                                conversation_message_task: ConversationMessageTask):
-        first_input_key = "input"
-        final_output_key = "output"
-
-        chains = []
-
-        chain_callback_handler = MainChainGatherCallbackHandler(conversation_message_task)
-
-        # agent mode
-        tool_chains, chains_output_key = cls.get_agent_chains(
-            tenant_id=tenant_id,
-            agent_mode=agent_mode,
-            rest_tokens=rest_tokens,
-            memory=memory,
-            conversation_message_task=conversation_message_task
-        )
-        chains += tool_chains
-
-        if chains_output_key:
-            final_output_key = chains_output_key
-
-        if len(chains) == 0:
-            return None
-
-        for chain in chains:
-            chain = cast(Chain, chain)
-            chain.callbacks.append(chain_callback_handler)
-
-        # build main chain
-        overall_chain = SequentialChain(
-            chains=chains,
-            input_variables=[first_input_key],
-            output_variables=[final_output_key],
-            memory=memory,  # only for use the memory prompt input key
-        )
-
-        return overall_chain
-
-    @classmethod
-    def get_agent_chains(cls, tenant_id: str, agent_mode: dict,
-                         rest_tokens: int,
-                         memory: Optional[BaseChatMemory],
-                         conversation_message_task: ConversationMessageTask):
-        # agent mode
-        chains = []
-        if agent_mode and agent_mode.get('enabled'):
-            tools = agent_mode.get('tools', [])
-
-            pre_fixed_chains = []
-            # agent_tools = []
-            datasets = []
-            for tool in tools:
-                tool_type = list(tool.keys())[0]
-                tool_config = list(tool.values())[0]
-                if tool_type == 'sensitive-word-avoidance':
-                    chain = ChainBuilder.to_sensitive_word_avoidance_chain(tool_config)
-                    if chain:
-                        pre_fixed_chains.append(chain)
-                elif tool_type == "dataset":
-                    # get dataset from dataset id
-                    dataset = db.session.query(Dataset).filter(
-                        Dataset.tenant_id == tenant_id,
-                        Dataset.id == tool_config.get("id")
-                    ).first()
-
-                    if dataset:
-                        datasets.append(dataset)
-
-            # add pre-fixed chains
-            chains += pre_fixed_chains
-
-            if len(datasets) > 0:
-                # tool to chain
-                multi_dataset_router_chain = MultiDatasetRouterChain.from_datasets(
-                    tenant_id=tenant_id,
-                    datasets=datasets,
-                    conversation_message_task=conversation_message_task,
-                    rest_tokens=rest_tokens,
-                    callbacks=[DifyStdOutCallbackHandler()]
-                )
-                chains.append(multi_dataset_router_chain)
-
-        final_output_key = cls.get_chains_output_key(chains)
-
-        return chains, final_output_key
-
-    @classmethod
-    def get_chains_output_key(cls, chains: List[Chain]):
-        if len(chains) > 0:
-            return chains[-1].output_keys[0]
-        return None

api/core/chain/multi_dataset_router_chain.py

-import math
-import re
-from typing import Mapping, List, Dict, Any, Optional
-
-from langchain import PromptTemplate
-from langchain.callbacks.manager import CallbackManagerForChainRun
-from langchain.chains.base import Chain
-from pydantic import Extra
-
-from core.callback_handler.dataset_tool_callback_handler import DatasetToolCallbackHandler
-from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
-from core.chain.llm_router_chain import LLMRouterChain, RouterOutputParser
-from core.conversation_message_task import ConversationMessageTask
-from core.llm.llm_builder import LLMBuilder
-from core.tool.dataset_index_tool import DatasetTool
-from models.dataset import Dataset, DatasetProcessRule
-
-DEFAULT_K = 2
-CONTEXT_TOKENS_PERCENT = 0.3
-MULTI_PROMPT_ROUTER_TEMPLATE = """
-Given a raw text input to a language model select the model prompt best suited for \
-the input. You will be given the names of the available prompts and a description of \
-what the prompt is best suited for. You may also revise the original input if you \
-think that revising it will ultimately lead to a better response from the language \
-model.
-
-<< FORMATTING >>
-Return a markdown code snippet with a JSON object formatted to look like, \
-no any other string out of markdown code snippet:
-```json
-{{{{
-    "destination": string \\ name of the prompt to use or "DEFAULT"
-    "next_inputs": string \\ a potentially modified version of the original input
-}}}}
-```
-
-REMEMBER: "destination" MUST be one of the candidate prompt names specified below OR \
-it can be "DEFAULT" if the input is not well suited for any of the candidate prompts.
-REMEMBER: "next_inputs" can just be the original input if you don't think any \
-modifications are needed.
-
-<< CANDIDATE PROMPTS >>
-{destinations}
-
-<< INPUT >>
-{{input}}
-
-<< OUTPUT >>
-"""
-
-
-class MultiDatasetRouterChain(Chain):
-    """Use a single chain to route an input to one of multiple candidate chains."""
-
-    router_chain: LLMRouterChain
-    """Chain for deciding a destination chain and the input to it."""
-    dataset_tools: Mapping[str, DatasetTool]
-    """Map of name to candidate chains that inputs can be routed to."""
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.forbid
-        arbitrary_types_allowed = True
-
-    @property
-    def input_keys(self) -> List[str]:
-        """Will be whatever keys the router chain prompt expects.
-
-        :meta private:
-        """
-        return self.router_chain.input_keys
-
-    @property
-    def output_keys(self) -> List[str]:
-        return ["text"]
-
-    @classmethod
-    def from_datasets(
-            cls,
-            tenant_id: str,
-            datasets: List[Dataset],
-            conversation_message_task: ConversationMessageTask,
-            rest_tokens: int,
-            **kwargs: Any,
-    ):
-        """Convenience constructor for instantiating from destination prompts."""
-        llm = LLMBuilder.to_llm(
-            tenant_id=tenant_id,
-            model_name='gpt-3.5-turbo',
-            temperature=0,
-            max_tokens=1024,
-            callbacks=[DifyStdOutCallbackHandler()]
-        )
-
-        destinations = ["[[{}]]: {}".format(d.id, d.description.replace('\n', ' ') if d.description
-                        else ('useful for when you want to answer queries about the ' + d.name))
-                        for d in datasets]
-        destinations_str = "\n".join(destinations)
-        router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(
-            destinations=destinations_str
-        )
-
-        router_prompt = PromptTemplate(
-            template=router_template,
-            input_variables=["input"],
-            output_parser=RouterOutputParser(),
-        )
-
-        router_chain = LLMRouterChain.from_llm(llm, router_prompt)
-        dataset_tools = {}
-        for dataset in datasets:
-            # fulfill description when it is empty
-            if dataset.available_document_count == 0 or dataset.available_document_count == 0:
-                continue
-
-            description = dataset.description
-            if not description:
-                description = 'useful for when you want to answer queries about the ' + dataset.name
-
-            k = cls._dynamic_calc_retrieve_k(dataset, rest_tokens)
-            if k == 0:
-                continue
-
-            dataset_tool = DatasetTool(
-                name=f"dataset-{dataset.id}",
-                description=description,
-                k=k,
-                dataset=dataset,
-                callbacks=[DatasetToolCallbackHandler(conversation_message_task), DifyStdOutCallbackHandler()]
-            )
-
-            dataset_tools[str(dataset.id)] = dataset_tool
-
-        return cls(
-            router_chain=router_chain,
-            dataset_tools=dataset_tools,
-            **kwargs,
-        )
-
-    @classmethod
-    def _dynamic_calc_retrieve_k(cls, dataset: Dataset, rest_tokens: int) -> int:
-        processing_rule = dataset.latest_process_rule
-        if not processing_rule:
-            return DEFAULT_K
-
-        if processing_rule.mode == "custom":
-            rules = processing_rule.rules_dict
-            if not rules:
-                return DEFAULT_K
-
-            segmentation = rules["segmentation"]
-            segment_max_tokens = segmentation["max_tokens"]
-        else:
-            segment_max_tokens = DatasetProcessRule.AUTOMATIC_RULES['segmentation']['max_tokens']
-
-        # when rest_tokens is less than default context tokens
-        if rest_tokens < segment_max_tokens * DEFAULT_K:
-            return rest_tokens // segment_max_tokens
-
-        context_limit_tokens = math.floor(rest_tokens * CONTEXT_TOKENS_PERCENT)
-
-        # when context_limit_tokens is less than default context tokens, use default_k
-        if context_limit_tokens <= segment_max_tokens * DEFAULT_K:
-            return DEFAULT_K
-
-        # Expand the k value when there's still some room left in the 30% rest tokens space
-        return context_limit_tokens // segment_max_tokens
-
-    def _call(
-        self,
-        inputs: Dict[str, Any],
-        run_manager: Optional[CallbackManagerForChainRun] = None,
-    ) -> Dict[str, Any]:
-        if len(self.dataset_tools) == 0:
-            return {"text": ''}
-        elif len(self.dataset_tools) == 1:
-            return {"text": next(iter(self.dataset_tools.values())).run(inputs['input'])}
-
-        route = self.router_chain.route(inputs)
-
-        destination = ''
-        if route.destination:
-            pattern = r'\b[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}\b'
-            match = re.search(pattern, route.destination, re.IGNORECASE)
-            if match:
-                destination = match.group()
-
-        if not destination:
-            return {"text": ''}
-        elif destination in self.dataset_tools:
-            return {"text": self.dataset_tools[destination].run(
-                route.next_inputs['input']
-            )}
-        else:
-            raise ValueError(
-                f"Received invalid destination chain name '{destination}'"
-            )

api/core/chain/tool_chain.py

-from typing import List, Dict, Optional, Any
-
-from langchain.callbacks.manager import CallbackManagerForChainRun, AsyncCallbackManagerForChainRun
-from langchain.chains.base import Chain
-from langchain.tools import BaseTool
-
-
-class ToolChain(Chain):
-    input_key: str = "input"  #: :meta private:
-    output_key: str = "output"  #: :meta private:
-
-    tool: BaseTool
-
-    @property
-    def _chain_type(self) -> str:
-        return "tool_chain"
-
-    @property
-    def input_keys(self) -> List[str]:
-        """Expect input key.
-
-        :meta private:
-        """
-        return [self.input_key]
-
-    @property
-    def output_keys(self) -> List[str]:
-        """Return output key.
-
-        :meta private:
-        """
-        return [self.output_key]
-
-    def _call(
-            self,
-            inputs: Dict[str, Any],
-            run_manager: Optional[CallbackManagerForChainRun] = None,
-    ) -> Dict[str, Any]:
-        input = inputs[self.input_key]
-        output = self.tool.run(input, self.verbose)
-        return {self.output_key: output}
-
-    async def _acall(
-            self,
-            inputs: Dict[str, Any],
-            run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
-    ) -> Dict[str, Any]:
-        """Run the logic of this chain and return the output."""
-        input = inputs[self.input_key]
-        output = await self.tool.arun(input, self.verbose)
-        return {self.output_key: output}

api/core/conversation_message_task.py

@@ -52,7 +52,7 @@ class ConversationMessageTask:
             message=self.message,
             conversation=self.conversation,
             chain_pub=False,  # disabled currently
-            agent_thought_pub=False  # disabled currently
+            agent_thought_pub=True
         )
 
     def init(self):
@@ -69,6 +69,7 @@ class ConversationMessageTask:
                 "suggested_questions": self.app_model_config.suggested_questions_list,
                 "suggested_questions_after_answer": self.app_model_config.suggested_questions_after_answer_dict,
                 "more_like_this": self.app_model_config.more_like_this_dict,
+                "sensitive_word_avoidance": self.app_model_config.sensitive_word_avoidance_dict,
                 "user_input_form": self.app_model_config.user_input_form_list,
             }
 
@@ -207,7 +208,28 @@ class ConversationMessageTask:
 
         self._pub_handler.pub_chain(message_chain)
 
-    def on_agent_end(self, message_chain: MessageChain, agent_model_name: str,
+    def on_agent_start(self, message_chain: MessageChain, agent_loop: AgentLoop) -> MessageAgentThought:
+        message_agent_thought = MessageAgentThought(
+            message_id=self.message.id,
+            message_chain_id=message_chain.id,
+            position=agent_loop.position,
+            thought=agent_loop.thought,
+            tool=agent_loop.tool_name,
+            tool_input=agent_loop.tool_input,
+            message=agent_loop.prompt,
+            answer=agent_loop.completion,
+            created_by_role=('account' if isinstance(self.user, Account) else 'end_user'),
+            created_by=self.user.id
+        )
+
+        db.session.add(message_agent_thought)
+        db.session.flush()
+
+        self._pub_handler.pub_agent_thought(message_agent_thought)
+
+        return message_agent_thought
+
+    def on_agent_end(self, message_agent_thought: MessageAgentThought, agent_model_name: str,
                      agent_loop: AgentLoop):
         agent_message_unit_price = llm_constant.model_prices[agent_model_name]['prompt']
         agent_answer_unit_price = llm_constant.model_prices[agent_model_name]['completion']
@@ -222,34 +244,18 @@ class ConversationMessageTask:
             agent_answer_unit_price
         )
 
-        message_agent_loop = MessageAgentThought(
-            message_id=self.message.id,
-            message_chain_id=message_chain.id,
-            position=agent_loop.position,
-            thought=agent_loop.thought,
-            tool=agent_loop.tool_name,
-            tool_input=agent_loop.tool_input,
-            observation=agent_loop.tool_output,
-            tool_process_data='',  # currently not support
-            message=agent_loop.prompt,
-            message_token=loop_message_tokens,
-            message_unit_price=agent_message_unit_price,
-            answer=agent_loop.completion,
-            answer_token=loop_answer_tokens,
-            answer_unit_price=agent_answer_unit_price,
-            latency=agent_loop.latency,
-            tokens=agent_loop.prompt_tokens + agent_loop.completion_tokens,
-            total_price=loop_total_price,
-            currency=llm_constant.model_currency,
-            created_by_role=('account' if isinstance(self.user, Account) else 'end_user'),
-            created_by=self.user.id
-        )
-
-        db.session.add(message_agent_loop)
+        message_agent_thought.observation = agent_loop.tool_output
+        message_agent_thought.tool_process_data = ''  # currently not support
+        message_agent_thought.message_token = loop_message_tokens
+        message_agent_thought.message_unit_price = agent_message_unit_price
+        message_agent_thought.answer_token = loop_answer_tokens
+        message_agent_thought.answer_unit_price = agent_answer_unit_price
+        message_agent_thought.latency = agent_loop.latency
+        message_agent_thought.tokens = agent_loop.prompt_tokens + agent_loop.completion_tokens
+        message_agent_thought.total_price = loop_total_price
+        message_agent_thought.currency = llm_constant.model_currency
         db.session.flush()
 
-        self._pub_handler.pub_agent_thought(message_agent_loop)
-
     def on_dataset_query_end(self, dataset_query_obj: DatasetQueryObj):
         dataset_query = DatasetQuery(
             dataset_id=dataset_query_obj.dataset_id,
@@ -346,16 +352,14 @@ class PubHandler:
             content = {
                 'event': 'agent_thought',
                 'data': {
+                    'id': message_agent_thought.id,
                     'task_id': self._task_id,
                     'message_id': self._message.id,
                     'chain_id': message_agent_thought.message_chain_id,
-                    'agent_thought_id': message_agent_thought.id,
                     'position': message_agent_thought.position,
                     'thought': message_agent_thought.thought,
                     'tool': message_agent_thought.tool,
                     'tool_input': message_agent_thought.tool_input,
-                    'observation': message_agent_thought.observation,
-                    'answer': message_agent_thought.answer,
                     'mode': self._conversation.mode,
                     'conversation_id': self._conversation.id
                 }
@@ -388,6 +392,15 @@ class PubHandler:
     def _is_stopped(self):
         return redis_client.get(self._stopped_cache_key) is not None
 
+    @classmethod
+    def ping(cls, user: Union[Account | EndUser], task_id: str):
+        content = {
+            'event': 'ping'
+        }
+
+        channel = cls.generate_channel_name(user, task_id)
+        redis_client.publish(channel, json.dumps(content))
+
     @classmethod
     def stop(cls, user: Union[Account | EndUser], task_id: str):
         stopped_cache_key = cls.generate_stopped_cache_key(user, task_id)

api/core/data_loader/file_extractor.py

 import tempfile
 from pathlib import Path
-from typing import List, Union
+from typing import List, Union, Optional
 
+import requests
 from langchain.document_loaders import TextLoader, Docx2txtLoader
 from langchain.schema import Document
 
@@ -13,6 +14,9 @@ from core.data_loader.loader.pdf import PdfLoader
 from extensions.ext_storage import storage
 from models.model import UploadFile
 
+SUPPORT_URL_CONTENT_TYPES = ['application/pdf', 'text/plain']
+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"
+
 
 class FileExtractor:
     @classmethod
@@ -22,6 +26,25 @@ class FileExtractor:
             file_path = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}"
             storage.download(upload_file.key, file_path)
 
+            return cls.load_from_file(file_path, return_text, upload_file)
+
+    @classmethod
+    def load_from_url(cls, url: str, return_text: bool = False) -> Union[List[Document] | str]:
+        response = requests.get(url, headers={
+            "User-Agent": USER_AGENT
+        })
+
+        with tempfile.TemporaryDirectory() as temp_dir:
+            suffix = Path(url).suffix
+            file_path = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}"
+            with open(file_path, 'wb') as file:
+                file.write(response.content)
+
+            return cls.load_from_file(file_path, return_text)
+
+    @classmethod
+    def load_from_file(cls, file_path: str, return_text: bool = False,
+                       upload_file: Optional[UploadFile] = None) -> Union[List[Document] | str]:
         input_file = Path(file_path)
         delimiter = '\n'
         if input_file.suffix == '.xlsx':

api/core/llm/fake.py

+import time
+from typing import List, Optional, Any, Mapping
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.chat_models.base import SimpleChatModel
+from langchain.schema import BaseMessage, ChatResult, AIMessage, ChatGeneration, BaseLanguageModel
+
+
+class FakeLLM(SimpleChatModel):
+    """Fake ChatModel for testing purposes."""
+
+    streaming: bool = False
+    """Whether to stream the results or not."""
+    response: str
+    origin_llm: Optional[BaseLanguageModel] = None
+
+    @property
+    def _llm_type(self) -> str:
+        return "fake-chat-model"
+
+    def _call(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """First try to lookup in queries, else return 'foo' or 'bar'."""
+        return self.response
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        return {"response": self.response}
+
+    def get_num_tokens(self, text: str) -> int:
+        return self.origin_llm.get_num_tokens(text) if self.origin_llm else 0
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        output_str = self._call(messages, stop=stop, run_manager=run_manager, **kwargs)
+        if self.streaming:
+            for token in output_str:
+                if run_manager:
+                    run_manager.on_llm_new_token(token)
+                    time.sleep(0.01)
+
+        message = AIMessage(content=output_str)
+        generation = ChatGeneration(message=message)
+        llm_output = {"token_usage": {
+            'prompt_tokens': 0,
+            'completion_tokens': 0,
+            'total_tokens': 0,
+        }}
+        return ChatResult(generations=[generation], llm_output=llm_output)

api/core/llm/provider/azure_provider.py

@@ -10,6 +10,9 @@ from core.llm.provider.errors import ValidateFailedError
 from models.provider import ProviderName
 
 
+AZURE_OPENAI_API_VERSION = '2023-07-01-preview'
+
+
 class AzureProvider(BaseProvider):
     def get_models(self, model_id: Optional[str] = None, credentials: Optional[dict] = None) -> list[dict]:
         return []
@@ -50,9 +53,10 @@ class AzureProvider(BaseProvider):
         """
         config = self.get_provider_api_key(model_id=model_id)
         config['openai_api_type'] = 'azure'
+        config['openai_api_version'] = AZURE_OPENAI_API_VERSION
         if model_id == 'text-embedding-ada-002':
             config['deployment'] = model_id.replace('.', '') if model_id else None
-            config['chunk_size'] = 1
+            config['chunk_size'] = 16
         else:
             config['deployment_name'] = model_id.replace('.', '') if model_id else None
         return config
@@ -69,7 +73,7 @@ class AzureProvider(BaseProvider):
         except:
             config = {
                 'openai_api_type': 'azure',
-                'openai_api_version': '2023-03-15-preview',
+                'openai_api_version': AZURE_OPENAI_API_VERSION,
                 'openai_api_base': '',
                 'openai_api_key': ''
             }
@@ -78,7 +82,7 @@ class AzureProvider(BaseProvider):
             if not config.get('openai_api_key'):
                 config = {
                     'openai_api_type': 'azure',
-                    'openai_api_version': '2023-03-15-preview',
+                    'openai_api_version': AZURE_OPENAI_API_VERSION,
                     'openai_api_base': '',
                     'openai_api_key': ''
                 }
@@ -100,7 +104,7 @@ class AzureProvider(BaseProvider):
                 raise ValueError('Config must be a object.')
 
             if 'openai_api_version' not in config:
-                config['openai_api_version'] = '2023-03-15-preview'
+                config['openai_api_version'] = AZURE_OPENAI_API_VERSION
 
             self.check_embedding_model(credentials=config)
         except ValidateFailedError as e:
@@ -119,7 +123,7 @@ class AzureProvider(BaseProvider):
         """
         return json.dumps({
             'openai_api_type': 'azure',
-            'openai_api_version': '2023-03-15-preview',
+            'openai_api_version': AZURE_OPENAI_API_VERSION,
             'openai_api_base': config['openai_api_base'],
             'openai_api_key': self.encrypt_token(config['openai_api_key'])
         })

api/core/llm/streamable_azure_chat_open_ai.py

-from langchain.callbacks.manager import Callbacks
-from langchain.schema import BaseMessage, LLMResult
+from langchain.callbacks.manager import Callbacks, CallbackManagerForLLMRun
+from langchain.chat_models.openai import _convert_dict_to_message
+from langchain.schema import BaseMessage, LLMResult, ChatResult, ChatGeneration
 from langchain.chat_models import AzureChatOpenAI
-from typing import Optional, List, Dict, Any
+from typing import Optional, List, Dict, Any, Tuple, Union
 
 from pydantic import root_validator
 
@@ -9,6 +10,11 @@ from core.llm.wrappers.openai_wrapper import handle_openai_exceptions
 
 
 class StreamableAzureChatOpenAI(AzureChatOpenAI):
+    request_timeout: Optional[Union[float, Tuple[float, float]]] = (5.0, 300.0)
+    """Timeout for requests to OpenAI completion API. Default is 600 seconds."""
+    max_retries: int = 1
+    """Maximum number of retries to make when generating."""
+
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
@@ -71,3 +77,43 @@ class StreamableAzureChatOpenAI(AzureChatOpenAI):
         params['model_kwargs'] = model_kwargs
 
         return params
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        message_dicts, params = self._create_message_dicts(messages, stop)
+        params = {**params, **kwargs}
+        if self.streaming:
+            inner_completion = ""
+            role = "assistant"
+            params["stream"] = True
+            function_call: Optional[dict] = None
+            for stream_resp in self.completion_with_retry(
+                messages=message_dicts, **params
+            ):
+                if len(stream_resp["choices"]) > 0:
+                    role = stream_resp["choices"][0]["delta"].get("role", role)
+                    token = stream_resp["choices"][0]["delta"].get("content") or ""
+                    inner_completion += token
+                    _function_call = stream_resp["choices"][0]["delta"].get("function_call")
+                    if _function_call:
+                        if function_call is None:
+                            function_call = _function_call
+                        else:
+                            function_call["arguments"] += _function_call["arguments"]
+                    if run_manager:
+                        run_manager.on_llm_new_token(token)
+            message = _convert_dict_to_message(
+                {
+                    "content": inner_completion,
+                    "role": role,
+                    "function_call": function_call,
+                }
+            )
+            return ChatResult(generations=[ChatGeneration(message=message)])
+        response = self.completion_with_retry(messages=message_dicts, **params)
+        return self._create_chat_result(response)

api/core/llm/streamable_azure_open_ai.py

 from langchain.callbacks.manager import Callbacks
 from langchain.llms import AzureOpenAI
 from langchain.schema import LLMResult
-from typing import Optional, List, Dict, Mapping, Any
+from typing import Optional, List, Dict, Mapping, Any, Union, Tuple
 
 from pydantic import root_validator
 
@@ -11,6 +11,10 @@ from core.llm.wrappers.openai_wrapper import handle_openai_exceptions
 class StreamableAzureOpenAI(AzureOpenAI):
     openai_api_type: str = "azure"
     openai_api_version: str = ""
+    request_timeout: Optional[Union[float, Tuple[float, float]]] = (5.0, 300.0)
+    """Timeout for requests to OpenAI completion API. Default is 600 seconds."""
+    max_retries: int = 1
+    """Maximum number of retries to make when generating."""
 
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:

api/core/llm/streamable_chat_anthropic.py

 from typing import List, Optional, Any, Dict
 
+from httpx import Timeout
 from langchain.callbacks.manager import Callbacks
 from langchain.chat_models import ChatAnthropic
-from langchain.schema import BaseMessage, LLMResult
+from langchain.schema import BaseMessage, LLMResult, SystemMessage, AIMessage, HumanMessage, ChatMessage
+from pydantic import root_validator
 
 from core.llm.wrappers.anthropic_wrapper import handle_anthropic_exceptions
 
@@ -12,6 +14,14 @@ class StreamableChatAnthropic(ChatAnthropic):
     Wrapper around Anthropic's large language model.
     """
 
+    default_request_timeout: Optional[float] = Timeout(timeout=300.0, connect=5.0)
+
+    @root_validator()
+    def prepare_params(cls, values: Dict) -> Dict:
+        values['model_name'] = values.get('model')
+        values['max_tokens'] = values.get('max_tokens_to_sample')
+        return values
+
     @handle_anthropic_exceptions
     def generate(
             self,
@@ -37,3 +47,16 @@ class StreamableChatAnthropic(ChatAnthropic):
         del params['presence_penalty']
 
         return params
+
+    def _convert_one_message_to_text(self, message: BaseMessage) -> str:
+        if isinstance(message, ChatMessage):
+            message_text = f"\n\n{message.role.capitalize()}: {message.content}"
+        elif isinstance(message, HumanMessage):
+            message_text = f"{self.HUMAN_PROMPT} {message.content}"
+        elif isinstance(message, AIMessage):
+            message_text = f"{self.AI_PROMPT} {message.content}"
+        elif isinstance(message, SystemMessage):
+            message_text = f"<admin>{message.content}</admin>"
+        else:
+            raise ValueError(f"Got unknown type {message}")
+        return message_text
\ No newline at end of file

api/core/llm/streamable_chat_open_ai.py

@@ -3,7 +3,7 @@ import os
 from langchain.callbacks.manager import Callbacks
 from langchain.schema import BaseMessage, LLMResult
 from langchain.chat_models import ChatOpenAI
-from typing import Optional, List, Dict, Any
+from typing import Optional, List, Dict, Any, Union, Tuple
 
 from pydantic import root_validator
 
@@ -11,6 +11,10 @@ from core.llm.wrappers.openai_wrapper import handle_openai_exceptions
 
 
 class StreamableChatOpenAI(ChatOpenAI):
+    request_timeout: Optional[Union[float, Tuple[float, float]]] = (5.0, 300.0)
+    """Timeout for requests to OpenAI completion API. Default is 600 seconds."""
+    max_retries: int = 1
+    """Maximum number of retries to make when generating."""
 
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:

api/core/llm/streamable_open_ai.py

@@ -2,7 +2,7 @@ import os
 
 from langchain.callbacks.manager import Callbacks
 from langchain.schema import LLMResult
-from typing import Optional, List, Dict, Any, Mapping
+from typing import Optional, List, Dict, Any, Mapping, Union, Tuple
 from langchain import OpenAI
 from pydantic import root_validator
 
@@ -10,6 +10,10 @@ from core.llm.wrappers.openai_wrapper import handle_openai_exceptions
 
 
 class StreamableOpenAI(OpenAI):
+    request_timeout: Optional[Union[float, Tuple[float, float]]] = (5.0, 300.0)
+    """Timeout for requests to OpenAI completion API. Default is 600 seconds."""
+    max_retries: int = 1
+    """Maximum number of retries to make when generating."""
 
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:

api/core/tool/dataset_index_tool.py → api/core/tool/dataset_retriever_tool.py

+import re
+from typing import Type
+
 from flask import current_app
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.tools import BaseTool
+from pydantic import Field, BaseModel
 
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
 from core.embedding.cached_embedding import CacheEmbedding
 from core.index.keyword_table_index.keyword_table_index import KeywordTableIndex, KeywordTableConfig
 from core.index.vector_index.vector_index import VectorIndex
 from core.llm.llm_builder import LLMBuilder
+from extensions.ext_database import db
 from models.dataset import Dataset
 
 
-class DatasetTool(BaseTool):
+class DatasetRetrieverToolInput(BaseModel):
+    dataset_id: str = Field(..., description="ID of dataset to be queried. MUST be UUID format.")
+    query: str = Field(..., description="Query for the dataset to be used to retrieve the dataset.")
+
+
+class DatasetRetrieverTool(BaseTool):
     """Tool for querying a Dataset."""
+    name: str = "dataset"
+    args_schema: Type[BaseModel] = DatasetRetrieverToolInput
+    description: str = "use this to retrieve a dataset. "
+
+    tenant_id: str
+    dataset_id: str
+    k: int = 3
+
+    @classmethod
+    def from_dataset(cls, dataset: Dataset, **kwargs):
+        description = dataset.description.replace('\n', '').replace('\r', '')
+        if not description:
+            description = 'useful for when you want to answer queries about the ' + dataset.name
+
+        description += '\nID of dataset MUST be ' + dataset.id
+        return cls(
+            tenant_id=dataset.tenant_id,
+            dataset_id=dataset.id,
+            description=description,
+            **kwargs
+        )
 
-    dataset: Dataset
-    k: int = 2
+    def _run(self, dataset_id: str, query: str) -> str:
+        pattern = r'\b[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}\b'
+        match = re.search(pattern, dataset_id, re.IGNORECASE)
+        if match:
+            dataset_id = match.group()
 
-    def _run(self, tool_input: str) -> str:
-        if self.dataset.indexing_technique == "economy":
+        dataset = db.session.query(Dataset).filter(
+            Dataset.tenant_id == self.tenant_id,
+            Dataset.id == dataset_id
+        ).first()
+
+        if not dataset:
+            return f'[{self.name} failed to find dataset with id {dataset_id}.]'
+
+        if dataset.indexing_technique == "economy":
             # use keyword table query
             kw_table_index = KeywordTableIndex(
-                dataset=self.dataset,
+                dataset=dataset,
                 config=KeywordTableConfig(
                     max_keywords_per_chunk=5
                 )
             )
 
-            documents = kw_table_index.search(tool_input, search_kwargs={'k': self.k})
+            documents = kw_table_index.search(query, search_kwargs={'k': self.k})
         else:
             model_credentials = LLMBuilder.get_model_credentials(
-                tenant_id=self.dataset.tenant_id,
-                model_provider=LLMBuilder.get_default_provider(self.dataset.tenant_id, 'text-embedding-ada-002'),
+                tenant_id=dataset.tenant_id,
+                model_provider=LLMBuilder.get_default_provider(dataset.tenant_id, 'text-embedding-ada-002'),
                 model_name='text-embedding-ada-002'
             )
 
@@ -39,49 +80,26 @@ class DatasetTool(BaseTool):
             ))
 
             vector_index = VectorIndex(
-                dataset=self.dataset,
+                dataset=dataset,
                 config=current_app.config,
                 embeddings=embeddings
             )
 
+            if self.k > 0:
                 documents = vector_index.search(
-                tool_input,
+                    query,
                     search_type='similarity',
                     search_kwargs={
                         'k': self.k
                     }
                 )
+            else:
+                documents = []
 
-            hit_callback = DatasetIndexToolCallbackHandler(self.dataset.id)
+            hit_callback = DatasetIndexToolCallbackHandler(dataset.id)
             hit_callback.on_tool_end(documents)
 
         return str("\n".join([document.page_content for document in documents]))
 
     async def _arun(self, tool_input: str) -> str:
-        model_credentials = LLMBuilder.get_model_credentials(
-            tenant_id=self.dataset.tenant_id,
-            model_provider=LLMBuilder.get_default_provider(self.dataset.tenant_id, 'text-embedding-ada-002'),
-            model_name='text-embedding-ada-002'
-        )
-
-        embeddings = CacheEmbedding(OpenAIEmbeddings(
-            **model_credentials
-        ))
-
-        vector_index = VectorIndex(
-            dataset=self.dataset,
-            config=current_app.config,
-            embeddings=embeddings
-        )
-
-        documents = await vector_index.asearch(
-            tool_input,
-            search_type='similarity',
-            search_kwargs={
-                'k': 10
-            }
-        )
-
-        hit_callback = DatasetIndexToolCallbackHandler(self.dataset.id)
-        hit_callback.on_tool_end(documents)
-        return str("\n".join([document.page_content for document in documents]))
+        raise NotImplementedError()

api/core/tool/provider/base.py

+import base64
+from abc import ABC, abstractmethod
+from typing import Optional
+
+from extensions.ext_database import db
+from libs import rsa
+from models.account import Tenant
+from models.tool import ToolProvider, ToolProviderName
+
+
+class BaseToolProvider(ABC):
+    def __init__(self, tenant_id: str):
+        self.tenant_id = tenant_id
+
+    @abstractmethod
+    def get_provider_name(self) -> ToolProviderName:
+        raise NotImplementedError
+
+    @abstractmethod
+    def encrypt_credentials(self, credentials: dict) -> Optional[dict]:
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_credentials(self, obfuscated: bool = False) -> Optional[dict]:
+        raise NotImplementedError
+
+    @abstractmethod
+    def credentials_to_func_kwargs(self) -> Optional[dict]:
+        raise NotImplementedError
+
+    @abstractmethod
+    def credentials_validate(self, credentials: dict):
+        raise NotImplementedError
+
+    def get_provider(self, must_enabled: bool = False) -> Optional[ToolProvider]:
+        """
+        Returns the Provider instance for the given tenant_id and tool_name.
+        """
+        query = db.session.query(ToolProvider).filter(
+            ToolProvider.tenant_id == self.tenant_id,
+            ToolProvider.tool_name == self.get_provider_name().value
+        )
+
+        if must_enabled:
+            query = query.filter(ToolProvider.is_enabled == True)
+
+        return query.first()
+
+    def encrypt_token(self, token) -> str:
+        tenant = db.session.query(Tenant).filter(Tenant.id == self.tenant_id).first()
+        encrypted_token = rsa.encrypt(token, tenant.encrypt_public_key)
+        return base64.b64encode(encrypted_token).decode()
+
+    def decrypt_token(self, token: str, obfuscated: bool = False) -> str:
+        token = rsa.decrypt(base64.b64decode(token), self.tenant_id)
+
+        if obfuscated:
+            return self._obfuscated_token(token)
+
+        return token
+
+    def _obfuscated_token(self, token: str) -> str:
+        return token[:6] + '*' * (len(token) - 8) + token[-2:]

api/core/tool/provider/errors.py

+class ToolValidateFailedError(Exception):
+    description = "Tool Provider Validate failed"

api/core/tool/provider/serpapi_provider.py

+from typing import Optional
+
+from core.tool.provider.base import BaseToolProvider
+from core.tool.provider.errors import ToolValidateFailedError
+from core.tool.serpapi_wrapper import OptimizedSerpAPIWrapper
+from models.tool import ToolProviderName
+
+
+class SerpAPIToolProvider(BaseToolProvider):
+    def get_provider_name(self) -> ToolProviderName:
+        """
+        Returns the name of the provider.
+
+        :return:
+        """
+        return ToolProviderName.SERPAPI
+
+    def get_credentials(self, obfuscated: bool = False) -> Optional[dict]:
+        """
+        Returns the credentials for SerpAPI as a dictionary.
+
+        :param obfuscated: obfuscate credentials if True
+        :return:
+        """
+        tool_provider = self.get_provider(must_enabled=True)
+        if not tool_provider:
+            return None
+
+        credentials = tool_provider.credentials
+        if not credentials:
+            return None
+
+        if credentials.get('api_key'):
+            credentials['api_key'] = self.decrypt_token(credentials.get('api_key'), obfuscated)
+
+        return credentials
+
+    def credentials_to_func_kwargs(self) -> Optional[dict]:
+        """
+        Returns the credentials function kwargs as a dictionary.
+
+        :return:
+        """
+        credentials = self.get_credentials()
+        if not credentials:
+            return None
+
+        return {
+            'serpapi_api_key': credentials.get('api_key')
+        }
+
+    def credentials_validate(self, credentials: dict):
+        """
+        Validates the given credentials.
+
+        :param credentials:
+        :return:
+        """
+        if 'api_key' not in credentials or not credentials.get('api_key'):
+            raise ToolValidateFailedError("SerpAPI api_key is required.")
+
+        api_key = credentials.get('api_key')
+
+        try:
+            OptimizedSerpAPIWrapper(serpapi_api_key=api_key).run(query='test')
+        except Exception as e:
+            raise ToolValidateFailedError("SerpAPI api_key is invalid. {}".format(e))
+
+    def encrypt_credentials(self, credentials: dict) -> Optional[dict]:
+        """
+        Encrypts the given credentials.
+
+        :param credentials:
+        :return:
+        """
+        credentials['api_key'] = self.encrypt_token(credentials.get('api_key'))
+        return credentials

api/core/tool/provider/tool_provider_service.py

+from typing import Optional
+
+from core.tool.provider.base import BaseToolProvider
+from core.tool.provider.serpapi_provider import SerpAPIToolProvider
+
+
+class ToolProviderService:
+
+    def __init__(self, tenant_id: str, provider_name: str):
+        self.provider = self._init_provider(tenant_id, provider_name)
+
+    def _init_provider(self, tenant_id: str, provider_name: str) -> BaseToolProvider:
+        if provider_name == 'serpapi':
+            return SerpAPIToolProvider(tenant_id)
+        else:
+            raise Exception('tool provider {} not found'.format(provider_name))
+
+    def get_credentials(self, obfuscated: bool = False) -> Optional[dict]:
+        """
+        Returns the credentials for Tool as a dictionary.
+
+        :param obfuscated:
+        :return:
+        """
+        return self.provider.get_credentials(obfuscated)
+
+    def credentials_validate(self, credentials: dict):
+        """
+        Validates the given credentials.
+
+        :param credentials:
+        :raises: ValidateFailedError
+        """
+        return self.provider.credentials_validate(credentials)
+
+    def encrypt_credentials(self, credentials: dict):
+        """
+        Encrypts the given credentials.
+
+        :param credentials:
+        :return:
+        """
+        return self.provider.encrypt_credentials(credentials)

api/core/tool/serpapi_wrapper.py

+from langchain import SerpAPIWrapper
+from pydantic import Field, BaseModel
+
+
+class OptimizedSerpAPIInput(BaseModel):
+    query: str = Field(..., description="search query.")
+
+
+class OptimizedSerpAPIWrapper(SerpAPIWrapper):
+
+    @staticmethod
+    def _process_response(res: dict, num_results: int = 5) -> str:
+        """Process response from SerpAPI."""
+        if "error" in res.keys():
+            raise ValueError(f"Got error from SerpAPI: {res['error']}")
+        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 'organic_results' in res.keys() and len(res['organic_results']) > 0:
+            toret = ""
+            for result in res["organic_results"][:num_results]:
+                if "link" in result:
+                    toret += "----------------\nlink: " + result["link"] + "\n"
+                if "snippet" in result:
+                    toret += "snippet: " + result["snippet"] + "\n"
+        else:
+            toret = "No good search result found"
+        return "search result:\n" + toret

api/migrations/versions/2beac44e5f5f_add_is_universal_in_apps.py

+"""add is_universal in apps
+
+Revision ID: 2beac44e5f5f
+Revises: d3d503a3471c
+Create Date: 2023-07-07 12:11:29.156057
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = '2beac44e5f5f'
+down_revision = 'a5b56fb053ef'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('apps', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('is_universal', sa.Boolean(), server_default=sa.text('false'), nullable=False))
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('apps', schema=None) as batch_op:
+        batch_op.drop_column('is_universal')
+
+    # ### end Alembic commands ###

api/migrations/versions/7ce5a52e4eee_add_tool_providers.py

+"""add tool providers
+
+Revision ID: 7ce5a52e4eee
+Revises: 2beac44e5f5f
+Create Date: 2023-07-10 10:26:50.074515
+
+"""
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = '7ce5a52e4eee'
+down_revision = '2beac44e5f5f'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('tool_providers',
+    sa.Column('id', postgresql.UUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
+    sa.Column('tenant_id', postgresql.UUID(), nullable=False),
+    sa.Column('tool_name', sa.String(length=40), nullable=False),
+    sa.Column('encrypted_credentials', sa.Text(), nullable=True),
+    sa.Column('is_enabled', sa.Boolean(), server_default=sa.text('false'), 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_provider_pkey'),
+    sa.UniqueConstraint('tenant_id', 'tool_name', name='unique_tool_provider_tool_name')
+    )
+    with op.batch_alter_table('app_model_configs', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('sensitive_word_avoidance', sa.Text(), nullable=True))
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('app_model_configs', schema=None) as batch_op:
+        batch_op.drop_column('sensitive_word_avoidance')
+
+    op.drop_table('tool_providers')
+    # ### end Alembic commands ###

api/models/model.py

@@ -40,6 +40,7 @@ class App(db.Model):
     api_rph = db.Column(db.Integer, nullable=False)
     is_demo = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
     is_public = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
+    is_universal = db.Column(db.Boolean, nullable=False, server_default=db.text('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)'))
 
@@ -88,6 +89,7 @@ class AppModelConfig(db.Model):
     user_input_form = db.Column(db.Text)
     pre_prompt = db.Column(db.Text)
     agent_mode = db.Column(db.Text)
+    sensitive_word_avoidance = db.Column(db.Text)
 
     @property
     def app(self):
@@ -116,14 +118,35 @@ class AppModelConfig(db.Model):
     def more_like_this_dict(self) -> dict:
         return json.loads(self.more_like_this) if self.more_like_this else {"enabled": False}
 
+    @property
+    def sensitive_word_avoidance_dict(self) -> dict:
+        return json.loads(self.sensitive_word_avoidance) if self.sensitive_word_avoidance \
+            else {"enabled": False, "words": [], "canned_response": []}
+
     @property
     def user_input_form_list(self) -> dict:
         return json.loads(self.user_input_form) if self.user_input_form else []
 
     @property
     def agent_mode_dict(self) -> dict:
-        return json.loads(self.agent_mode) if self.agent_mode else {"enabled": False, "tools": []}
-
+        return json.loads(self.agent_mode) if self.agent_mode else {"enabled": False, "strategy": None, "tools": []}
+
+    def to_dict(self) -> dict:
+        return {
+            "provider": "",
+            "model_id": "",
+            "configs": {},
+            "opening_statement": self.opening_statement,
+            "suggested_questions": self.suggested_questions_list,
+            "suggested_questions_after_answer": self.suggested_questions_after_answer_dict,
+            "speech_to_text": self.speech_to_text_dict,
+            "more_like_this": self.more_like_this_dict,
+            "sensitive_word_avoidance": self.sensitive_word_avoidance_dict,
+            "model": self.model_dict,
+            "user_input_form": self.user_input_form_list,
+            "pre_prompt": self.pre_prompt,
+            "agent_mode": self.agent_mode_dict
+        }
 
 class RecommendedApp(db.Model):
     __tablename__ = 'recommended_apps'
@@ -237,6 +260,9 @@ class Conversation(db.Model):
                     if 'speech_to_text' in override_model_configs else {"enabled": False}
                 model_config['more_like_this'] = override_model_configs['more_like_this'] \
                     if 'more_like_this' in override_model_configs else {"enabled": False}
+                model_config['sensitive_word_avoidance'] = override_model_configs['sensitive_word_avoidance'] \
+                    if 'sensitive_word_avoidance' in override_model_configs \
+                    else {"enabled": False, "words": [], "canned_response": []}
                 model_config['user_input_form'] = override_model_configs['user_input_form']
             else:
                 model_config['configs'] = override_model_configs
@@ -253,6 +279,7 @@ class Conversation(db.Model):
             model_config['suggested_questions_after_answer'] = app_model_config.suggested_questions_after_answer_dict
             model_config['speech_to_text'] = app_model_config.speech_to_text_dict
             model_config['more_like_this'] = app_model_config.more_like_this_dict
+            model_config['sensitive_word_avoidance'] = app_model_config.sensitive_word_avoidance_dict
             model_config['user_input_form'] = app_model_config.user_input_form_list
 
         model_config['model_id'] = self.model_id
@@ -393,6 +420,11 @@ class Message(db.Model):
     def in_debug_mode(self):
         return self.override_model_configs is not None
 
+    @property
+    def agent_thoughts(self):
+        return db.session.query(MessageAgentThought).filter(MessageAgentThought.message_id == self.id)\
+            .order_by(MessageAgentThought.position.asc()).all()
+
 
 class MessageFeedback(db.Model):
     __tablename__ = 'message_feedbacks'

api/models/tool.py

+import json
+from enum import Enum
+
+from sqlalchemy.dialects.postgresql import UUID
+
+from extensions.ext_database import db
+
+
+class ToolProviderName(Enum):
+    SERPAPI = 'serpapi'
+
+    @staticmethod
+    def value_of(value):
+        for member in ToolProviderName:
+            if member.value == value:
+                return member
+        raise ValueError(f"No matching enum found for value '{value}'")
+
+
+class ToolProvider(db.Model):
+    __tablename__ = 'tool_providers'
+    __table_args__ = (
+        db.PrimaryKeyConstraint('id', name='tool_provider_pkey'),
+        db.UniqueConstraint('tenant_id', 'tool_name', name='unique_tool_provider_tool_name')
+    )
+
+    id = db.Column(UUID, server_default=db.text('uuid_generate_v4()'))
+    tenant_id = db.Column(UUID, nullable=False)
+    tool_name = db.Column(db.String(40), nullable=False)
+    encrypted_credentials = db.Column(db.Text, nullable=True)
+    is_enabled = db.Column(db.Boolean, nullable=False, server_default=db.text('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 credentials_is_set(self):
+        """
+         Returns True if the encrypted_config is not None, indicating that the token is set.
+         """
+        return self.encrypted_credentials is not None
+
+    @property
+    def credentials(self):
+        """
+        Returns the decrypted config.
+        """
+        return json.loads(self.encrypted_credentials) if self.encrypted_credentials is not None else None

api/requirements.txt

@@ -10,8 +10,8 @@ flask-session2==1.3.1
 flask-cors==3.0.10
 gunicorn~=20.1.0
 gevent~=22.10.2
-langchain==0.0.230
-openai~=0.27.5
+langchain==0.0.239
+openai~=0.27.8
 psycopg2-binary~=2.9.6
 pycryptodome==3.17
 python-dotenv==1.0.0
@@ -36,3 +36,8 @@ pypdfium2==4.16.0
 resend~=0.5.1
 pyjwt~=2.6.0
 anthropic~=0.3.4
+newspaper3k==0.2.8
+google-api-python-client==2.90.0
+wikipedia==1.4.0
+readabilipy==0.2.0
+google-search-results==2.4.2
\ No newline at end of file

api/services/app_model_config_service.py

 import re
 import uuid
 
+from core.agent.agent_executor import PlanningStrategy
 from core.constant import llm_constant
 from models.account import Account
 from services.dataset_service import DatasetService
@@ -31,6 +32,16 @@ MODELS_BY_APP_MODE = {
     ]
 }
 
+SUPPORT_AGENT_MODELS = [
+    "gpt-4",
+    "gpt-4-32k",
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-16k",
+]
+
+SUPPORT_TOOLS = ["dataset", "google_search", "web_reader", "wikipedia"]
+
+
 class AppModelConfigService:
     @staticmethod
     def is_dataset_exists(account: Account, dataset_id: str) -> bool:
@@ -58,7 +69,8 @@ class AppModelConfigService:
         if not isinstance(cp["max_tokens"], int) or cp["max_tokens"] <= 0 or cp["max_tokens"] > \
                 llm_constant.max_context_token_length[model_name]:
             raise ValueError(
-                "max_tokens must be an integer greater than 0 and not exceeding the maximum value of the corresponding model")
+                "max_tokens must be an integer greater than 0 "
+                "and not exceeding the maximum value of the corresponding model")
 
         # temperature
         if 'temperature' not in cp:
@@ -149,11 +161,6 @@ class AppModelConfigService:
         if not isinstance(config["speech_to_text"]["enabled"], bool):
             raise ValueError("enabled in speech_to_text must be of boolean type")
 
-        provider_name = LLMBuilder.get_default_provider(account.current_tenant_id, 'whisper-1')
-
-        if config["speech_to_text"]["enabled"] and provider_name != 'openai':
-            raise ValueError("provider not support speech to text")
-
         # more_like_this
         if 'more_like_this' not in config or not config["more_like_this"]:
             config["more_like_this"] = {
@@ -169,6 +176,33 @@ class AppModelConfigService:
         if not isinstance(config["more_like_this"]["enabled"], bool):
             raise ValueError("enabled in more_like_this must be of boolean type")
 
+        # sensitive_word_avoidance
+        if 'sensitive_word_avoidance' not in config or not config["sensitive_word_avoidance"]:
+            config["sensitive_word_avoidance"] = {
+                "enabled": False
+            }
+
+        if not isinstance(config["sensitive_word_avoidance"], dict):
+            raise ValueError("sensitive_word_avoidance must be of dict type")
+
+        if "enabled" not in config["sensitive_word_avoidance"] or not config["sensitive_word_avoidance"]["enabled"]:
+            config["sensitive_word_avoidance"]["enabled"] = False
+
+        if not isinstance(config["sensitive_word_avoidance"]["enabled"], bool):
+            raise ValueError("enabled in sensitive_word_avoidance must be of boolean type")
+
+        if "words" not in config["sensitive_word_avoidance"] or not config["sensitive_word_avoidance"]["words"]:
+            config["sensitive_word_avoidance"]["words"] = ""
+
+        if not isinstance(config["sensitive_word_avoidance"]["words"], str):
+            raise ValueError("words in sensitive_word_avoidance must be of string type")
+
+        if "canned_response" not in config["sensitive_word_avoidance"] or not config["sensitive_word_avoidance"]["canned_response"]:
+            config["sensitive_word_avoidance"]["canned_response"] = ""
+
+        if not isinstance(config["sensitive_word_avoidance"]["canned_response"], str):
+            raise ValueError("canned_response in sensitive_word_avoidance must be of string type")
+
         # model
         if 'model' not in config:
             raise ValueError("model is required")
@@ -274,6 +308,12 @@ class AppModelConfigService:
         if not isinstance(config["agent_mode"]["enabled"], bool):
             raise ValueError("enabled in agent_mode must be of boolean type")
 
+        if "strategy" not in config["agent_mode"] or not config["agent_mode"]["strategy"]:
+            config["agent_mode"]["strategy"] = PlanningStrategy.ROUTER.value
+
+        if config["agent_mode"]["strategy"] not in [member.value for member in list(PlanningStrategy.__members__.values())]:
+            raise ValueError("strategy in agent_mode must be in the specified strategy list")
+
         if "tools" not in config["agent_mode"] or not config["agent_mode"]["tools"]:
             config["agent_mode"]["tools"] = []
 
@@ -282,8 +322,8 @@ class AppModelConfigService:
 
         for tool in config["agent_mode"]["tools"]:
             key = list(tool.keys())[0]
-            if key not in ["sensitive-word-avoidance", "dataset"]:
-                raise ValueError("Keys in agent_mode.tools list can only be 'sensitive-word-avoidance' or 'dataset'")
+            if key not in SUPPORT_TOOLS:
+                raise ValueError("Keys in agent_mode.tools must be in the specified tool list")
 
             tool_item = tool[key]
 
@@ -293,19 +333,7 @@ class AppModelConfigService:
             if not isinstance(tool_item["enabled"], bool):
                 raise ValueError("enabled in agent_mode.tools must be of boolean type")
 
-            if key == "sensitive-word-avoidance":
-                if "words" not in tool_item or not tool_item["words"]:
-                    tool_item["words"] = ""
-
-                if not isinstance(tool_item["words"], str):
-                    raise ValueError("words in sensitive-word-avoidance must be of string type")
-
-                if "canned_response" not in tool_item or not tool_item["canned_response"]:
-                    tool_item["canned_response"] = ""
-
-                if not isinstance(tool_item["canned_response"], str):
-                    raise ValueError("canned_response in sensitive-word-avoidance must be of string type")
-            elif key == "dataset":
+            if key == "dataset":
                 if 'id' not in tool_item:
                     raise ValueError("id is required in dataset")
 
@@ -324,6 +352,7 @@ class AppModelConfigService:
             "suggested_questions_after_answer": config["suggested_questions_after_answer"],
             "speech_to_text": config["speech_to_text"],
             "more_like_this": config["more_like_this"],
+            "sensitive_word_avoidance": config["sensitive_word_avoidance"],
             "model": {
                 "provider": config["model"]["provider"],
                 "name": config["model"]["name"],

api/services/completion_service.py

@@ -37,6 +37,8 @@ class CompletionService:
         if not query:
             raise ValueError('query is required')
 
+        query = query.replace('\x00', '')
+
         conversation_id = args['conversation_id'] if 'conversation_id' in args else None
 
         conversation = None
@@ -140,6 +142,7 @@ class CompletionService:
                     suggested_questions=json.dumps(model_config['suggested_questions']),
                     suggested_questions_after_answer=json.dumps(model_config['suggested_questions_after_answer']),
                     more_like_this=json.dumps(model_config['more_like_this']),
+                    sensitive_word_avoidance=json.dumps(model_config['sensitive_word_avoidance']),
                     model=json.dumps(model_config['model']),
                     user_input_form=json.dumps(model_config['user_input_form']),
                     pre_prompt=model_config['pre_prompt'],
@@ -171,7 +174,7 @@ class CompletionService:
 
         generate_worker_thread.start()
 
-        # wait for 5 minutes to close the thread
+        # wait for 10 minutes to close the thread
         cls.countdown_and_close(generate_worker_thread, pubsub, user, generate_task_id)
 
         return cls.compact_response(pubsub, streaming)
@@ -179,9 +182,9 @@ class CompletionService:
     @classmethod
     def get_real_user_instead_of_proxy_obj(cls, user: Union[Account, EndUser]):
         if isinstance(user, Account):
-            user = db.session.query(Account).get(user.id)
+            user = db.session.query(Account).filter(Account.id == user.id).first()
         elif isinstance(user, EndUser):
-            user = db.session.query(EndUser).get(user.id)
+            user = db.session.query(EndUser).filter(EndUser.id == user.id).first()
         else:
             raise Exception("Unknown user type")
 
@@ -226,12 +229,15 @@ class CompletionService:
 
     @classmethod
     def countdown_and_close(cls, worker_thread, pubsub, user, generate_task_id) -> threading.Thread:
-        # wait for 5 minutes to close the thread
-        timeout = 300
+        # wait for 10 minutes to close the thread
+        timeout = 600
 
         def close_pubsub():
             sleep_iterations = 0
             while sleep_iterations < timeout and worker_thread.is_alive():
+                if sleep_iterations > 0 and sleep_iterations % 10 == 0:
+                    PubHandler.ping(user, generate_task_id)
+
                 time.sleep(1)
                 sleep_iterations += 1
 
@@ -369,7 +375,7 @@ class CompletionService:
                     if len(value) > max_length:
                         raise ValueError(f'{variable} in input form must be less than {max_length} characters')
 
-            filtered_inputs[variable] = value
+            filtered_inputs[variable] = value.replace('\x00', '') if value else None
 
         return filtered_inputs
 
@@ -418,6 +424,10 @@ class CompletionService:
                                 yield "data: " + json.dumps(cls.get_chain_response_data(result.get('data'))) + "\n\n"
                             elif event == 'agent_thought':
                                 yield "data: " + json.dumps(cls.get_agent_thought_response_data(result.get('data'))) + "\n\n"
+                            elif event == 'ping':
+                                yield "event: ping\n\n"
+                            else:
+                                yield "data: " + json.dumps(result) + "\n\n"
                 except ValueError as e:
                     if e.args[0] != "I/O operation on closed file.":  # ignore this error
                         logging.exception(e)
@@ -467,16 +477,14 @@ class CompletionService:
     def get_agent_thought_response_data(cls, data: dict):
         response_data = {
             'event': 'agent_thought',
-            'id': data.get('agent_thought_id'),
+            'id': data.get('id'),
             'chain_id': data.get('chain_id'),
             'task_id': data.get('task_id'),
             'message_id': data.get('message_id'),
             'position': data.get('position'),
             'thought': data.get('thought'),
-            'tool': data.get('tool'),  # todo use real dataset obj replace it
+            'tool': data.get('tool'),
             'tool_input': data.get('tool_input'),
-            'observation': data.get('observation'),
-            'answer': data.get('answer') if not data.get('thought') else '',
             'created_at': int(time.time())
         }