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Commit 4e4b07ce authored by takatost's avatar takatost
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Merge branch 'feat/workflow-backend' into deploy/dev

parents 8d4d0a29 5fe0d50c
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api/.env.example
View file @ 4e4b07ce
......@@ -135,4 +135,4 @@ BATCH_UPLOAD_LIMIT=10
# CODE EXECUTION CONFIGURATION
CODE_EXECUTION_ENDPOINT=
CODE_EXECUTINO_API_KEY=
CODE_EXECUTION_API_KEY=
api/core/app/apps/base_app_runner.py
View file @ 4e4b07ce
......@@ -23,7 +23,8 @@ from core.model_runtime.errors.invoke import InvokeBadRequestError
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.moderation.input_moderation import InputModeration
from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
from core.prompt.simple_prompt_transform import SimplePromptTransform
from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
from core.prompt.simple_prompt_transform import ModelMode, SimplePromptTransform
from models.model import App, AppMode, Message, MessageAnnotation
......@@ -155,13 +156,39 @@ class AppRunner:
model_config=model_config
)
else:
memory_config = MemoryConfig(
window=MemoryConfig.WindowConfig(
enabled=False
)
)
model_mode = ModelMode.value_of(model_config.mode)
if model_mode == ModelMode.COMPLETION:
advanced_completion_prompt_template = prompt_template_entity.advanced_completion_prompt_template
prompt_template = CompletionModelPromptTemplate(
text=advanced_completion_prompt_template.prompt
)
memory_config.role_prefix = MemoryConfig.RolePrefix(
user=advanced_completion_prompt_template.role_prefix.user,
assistant=advanced_completion_prompt_template.role_prefix.assistant
)
else:
prompt_template = []
for message in prompt_template_entity.advanced_chat_prompt_template.messages:
prompt_template.append(ChatModelMessage(
text=message.text,
role=message.role
))
prompt_transform = AdvancedPromptTransform()
prompt_messages = prompt_transform.get_prompt(
prompt_template_entity=prompt_template_entity,
prompt_template=prompt_template,
inputs=inputs,
query=query if query else '',
files=files,
context=context,
memory_config=memory_config,
memory=memory,
model_config=model_config
)
......
api/core/app/apps/easy_ui_based_generate_task_pipeline.py
View file @ 4e4b07ce
......@@ -30,17 +30,12 @@ from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotIni
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
from core.model_runtime.entities.message_entities import (
AssistantPromptMessage,
ImagePromptMessageContent,
PromptMessage,
PromptMessageContentType,
PromptMessageRole,
TextPromptMessageContent,
)
from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.model_runtime.utils.encoders import jsonable_encoder
from core.moderation.output_moderation import ModerationRule, OutputModeration
from core.prompt.simple_prompt_transform import ModelMode
from core.prompt.utils.prompt_message_util import PromptMessageUtil
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
from core.tools.tool_file_manager import ToolFileManager
from events.message_event import message_was_created
......@@ -438,7 +433,10 @@ class EasyUIBasedGenerateTaskPipeline:
self._message = db.session.query(Message).filter(Message.id == self._message.id).first()
self._conversation = db.session.query(Conversation).filter(Conversation.id == self._conversation.id).first()
self._message.message = self._prompt_messages_to_prompt_for_saving(self._task_state.llm_result.prompt_messages)
self._message.message = PromptMessageUtil.prompt_messages_to_prompt_for_saving(
self._model_config.mode,
self._task_state.llm_result.prompt_messages
)
self._message.message_tokens = usage.prompt_tokens
self._message.message_unit_price = usage.prompt_unit_price
self._message.message_price_unit = usage.prompt_price_unit
......@@ -582,77 +580,6 @@ class EasyUIBasedGenerateTaskPipeline:
"""
return "data: " + json.dumps(response) + "\n\n"
def _prompt_messages_to_prompt_for_saving(self, prompt_messages: list[PromptMessage]) -> list[dict]:
"""
Prompt messages to prompt for saving.
:param prompt_messages: prompt messages
:return:
"""
prompts = []
if self._model_config.mode == ModelMode.CHAT.value:
for prompt_message in prompt_messages:
if prompt_message.role == PromptMessageRole.USER:
role = 'user'
elif prompt_message.role == PromptMessageRole.ASSISTANT:
role = 'assistant'
elif prompt_message.role == PromptMessageRole.SYSTEM:
role = 'system'
else:
continue
text = ''
files = []
if isinstance(prompt_message.content, list):
for content in prompt_message.content:
if content.type == PromptMessageContentType.TEXT:
content = cast(TextPromptMessageContent, content)
text += content.data
else:
content = cast(ImagePromptMessageContent, content)
files.append({
"type": 'image',
"data": content.data[:10] + '...[TRUNCATED]...' + content.data[-10:],
"detail": content.detail.value
})
else:
text = prompt_message.content
prompts.append({
"role": role,
"text": text,
"files": files
})
else:
prompt_message = prompt_messages[0]
text = ''
files = []
if isinstance(prompt_message.content, list):
for content in prompt_message.content:
if content.type == PromptMessageContentType.TEXT:
content = cast(TextPromptMessageContent, content)
text += content.data
else:
content = cast(ImagePromptMessageContent, content)
files.append({
"type": 'image',
"data": content.data[:10] + '...[TRUNCATED]...' + content.data[-10:],
"detail": content.detail.value
})
else:
text = prompt_message.content
params = {
"role": 'user',
"text": text,
}
if files:
params['files'] = files
prompts.append(params)
return prompts
def _init_output_moderation(self) -> Optional[OutputModeration]:
"""
Init output moderation.
......
api/core/helper/code_executor/code_executor.py
View file @ 4e4b07ce
......@@ -5,6 +5,7 @@ from httpx import post
from pydantic import BaseModel
from yarl import URL
from core.helper.code_executor.javascript_transformer import NodeJsTemplateTransformer
from core.helper.code_executor.jina2_transformer import Jinja2TemplateTransformer
from core.helper.code_executor.python_transformer import PythonTemplateTransformer
......@@ -39,17 +40,20 @@ class CodeExecutor:
template_transformer = PythonTemplateTransformer
elif language == 'jinja2':
template_transformer = Jinja2TemplateTransformer
elif language == 'javascript':
template_transformer = NodeJsTemplateTransformer
else:
raise CodeExecutionException('Unsupported language')
runner = template_transformer.transform_caller(code, inputs)
url = URL(CODE_EXECUTION_ENDPOINT) / 'v1' / 'sandbox' / 'run'
headers = {
'X-Api-Key': CODE_EXECUTION_API_KEY
}
data = {
'language': language if language != 'jinja2' else 'python3',
'language': 'python3' if language == 'jinja2' else
'nodejs' if language == 'javascript' else
'python3' if language == 'python3' else None,
'code': runner,
}
......
api/core/helper/code_executor/javascript_transformer.py
View file @ 4e4b07ce
# TODO
\ No newline at end of file
import json
import re
from core.helper.code_executor.template_transformer import TemplateTransformer
NODEJS_RUNNER = """// declare main function here
{{code}}
// execute main function, and return the result
// inputs is a dict, unstructured inputs
output = main({{inputs}})
// convert output to json and print
output = JSON.stringify(output)
result = `<<RESULT>>${output}<<RESULT>>`
console.log(result)
"""
class NodeJsTemplateTransformer(TemplateTransformer):
@classmethod
def transform_caller(cls, code: str, inputs: dict) -> str:
"""
Transform code to python runner
:param code: code
:param inputs: inputs
:return:
"""
# transform inputs to json string
inputs_str = json.dumps(inputs, indent=4)
# replace code and inputs
runner = NODEJS_RUNNER.replace('{{code}}', code)
runner = runner.replace('{{inputs}}', inputs_str)
return runner
@classmethod
def transform_response(cls, response: str) -> dict:
"""
Transform response to dict
:param response: response
:return:
"""
# extract result
result = re.search(r'<<RESULT>>(.*)<<RESULT>>', response, re.DOTALL)
if not result:
raise ValueError('Failed to parse result')
result = result.group(1)
return json.loads(result)
api/core/model_manager.py
View file @ 4e4b07ce
......@@ -24,11 +24,11 @@ class ModelInstance:
"""
def __init__(self, provider_model_bundle: ProviderModelBundle, model: str) -> None:
self._provider_model_bundle = provider_model_bundle
self.provider_model_bundle = provider_model_bundle
self.model = model
self.provider = provider_model_bundle.configuration.provider.provider
self.credentials = self._fetch_credentials_from_bundle(provider_model_bundle, model)
self.model_type_instance = self._provider_model_bundle.model_type_instance
self.model_type_instance = self.provider_model_bundle.model_type_instance
def _fetch_credentials_from_bundle(self, provider_model_bundle: ProviderModelBundle, model: str) -> dict:
"""
......
api/core/prompt/advanced_prompt_transform.py
View file @ 4e4b07ce
from typing import Optional
from typing import Optional, Union
from core.app.app_config.entities import AdvancedCompletionPromptTemplateEntity, PromptTemplateEntity
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
from core.file.file_obj import FileObj
from core.memory.token_buffer_memory import TokenBufferMemory
......@@ -12,6 +11,7 @@ from core.model_runtime.entities.message_entities import (
TextPromptMessageContent,
UserPromptMessage,
)
from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
from core.prompt.prompt_transform import PromptTransform
from core.prompt.simple_prompt_transform import ModelMode
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
......@@ -22,11 +22,12 @@ class AdvancedPromptTransform(PromptTransform):
Advanced Prompt Transform for Workflow LLM Node.
"""
def get_prompt(self, prompt_template_entity: PromptTemplateEntity,
def get_prompt(self, prompt_template: Union[list[ChatModelMessage], CompletionModelPromptTemplate],
inputs: dict,
query: str,
files: list[FileObj],
context: Optional[str],
memory_config: Optional[MemoryConfig],
memory: Optional[TokenBufferMemory],
model_config: ModelConfigWithCredentialsEntity) -> list[PromptMessage]:
prompt_messages = []
......@@ -34,21 +35,23 @@ class AdvancedPromptTransform(PromptTransform):
model_mode = ModelMode.value_of(model_config.mode)
if model_mode == ModelMode.COMPLETION:
prompt_messages = self._get_completion_model_prompt_messages(
prompt_template_entity=prompt_template_entity,
prompt_template=prompt_template,
inputs=inputs,
query=query,
files=files,
context=context,
memory_config=memory_config,
memory=memory,
model_config=model_config
)
elif model_mode == ModelMode.CHAT:
prompt_messages = self._get_chat_model_prompt_messages(
prompt_template_entity=prompt_template_entity,
prompt_template=prompt_template,
inputs=inputs,
query=query,
files=files,
context=context,
memory_config=memory_config,
memory=memory,
model_config=model_config
)
......@@ -56,17 +59,18 @@ class AdvancedPromptTransform(PromptTransform):
return prompt_messages
def _get_completion_model_prompt_messages(self,
prompt_template_entity: PromptTemplateEntity,
prompt_template: CompletionModelPromptTemplate,
inputs: dict,
query: Optional[str],
files: list[FileObj],
context: Optional[str],
memory_config: Optional[MemoryConfig],
memory: Optional[TokenBufferMemory],
model_config: ModelConfigWithCredentialsEntity) -> list[PromptMessage]:
"""
Get completion model prompt messages.
"""
raw_prompt = prompt_template_entity.advanced_completion_prompt_template.prompt
raw_prompt = prompt_template.text
prompt_messages = []
......@@ -75,15 +79,17 @@ class AdvancedPromptTransform(PromptTransform):
prompt_inputs = self._set_context_variable(context, prompt_template, prompt_inputs)
role_prefix = prompt_template_entity.advanced_completion_prompt_template.role_prefix
prompt_inputs = self._set_histories_variable(
memory=memory,
raw_prompt=raw_prompt,
role_prefix=role_prefix,
prompt_template=prompt_template,
prompt_inputs=prompt_inputs,
model_config=model_config
)
if memory and memory_config:
role_prefix = memory_config.role_prefix
prompt_inputs = self._set_histories_variable(
memory=memory,
memory_config=memory_config,
raw_prompt=raw_prompt,
role_prefix=role_prefix,
prompt_template=prompt_template,
prompt_inputs=prompt_inputs,
model_config=model_config
)
if query:
prompt_inputs = self._set_query_variable(query, prompt_template, prompt_inputs)
......@@ -104,17 +110,18 @@ class AdvancedPromptTransform(PromptTransform):
return prompt_messages
def _get_chat_model_prompt_messages(self,
prompt_template_entity: PromptTemplateEntity,
prompt_template: list[ChatModelMessage],
inputs: dict,
query: Optional[str],
files: list[FileObj],
context: Optional[str],
memory_config: Optional[MemoryConfig],
memory: Optional[TokenBufferMemory],
model_config: ModelConfigWithCredentialsEntity) -> list[PromptMessage]:
"""
Get chat model prompt messages.
"""
raw_prompt_list = prompt_template_entity.advanced_chat_prompt_template.messages
raw_prompt_list = prompt_template
prompt_messages = []
......@@ -137,8 +144,8 @@ class AdvancedPromptTransform(PromptTransform):
elif prompt_item.role == PromptMessageRole.ASSISTANT:
prompt_messages.append(AssistantPromptMessage(content=prompt))
if memory:
prompt_messages = self._append_chat_histories(memory, prompt_messages, model_config)
if memory and memory_config:
prompt_messages = self._append_chat_histories(memory, memory_config, prompt_messages, model_config)
if files:
prompt_message_contents = [TextPromptMessageContent(data=query)]
......@@ -195,8 +202,9 @@ class AdvancedPromptTransform(PromptTransform):
return prompt_inputs
def _set_histories_variable(self, memory: TokenBufferMemory,
memory_config: MemoryConfig,
raw_prompt: str,
role_prefix: AdvancedCompletionPromptTemplateEntity.RolePrefixEntity,
role_prefix: MemoryConfig.RolePrefix,
prompt_template: PromptTemplateParser,
prompt_inputs: dict,
model_config: ModelConfigWithCredentialsEntity) -> dict:
......@@ -213,6 +221,7 @@ class AdvancedPromptTransform(PromptTransform):
histories = self._get_history_messages_from_memory(
memory=memory,
memory_config=memory_config,
max_token_limit=rest_tokens,
human_prefix=role_prefix.user,
ai_prefix=role_prefix.assistant
......
api/core/prompt/entities/advanced_prompt_entities.py 0 → 100644
View file @ 4e4b07ce
from typing import Optional
from pydantic import BaseModel
from core.model_runtime.entities.message_entities import PromptMessageRole
class ChatModelMessage(BaseModel):
"""
Chat Message.
"""
text: str
role: PromptMessageRole
class CompletionModelPromptTemplate(BaseModel):
"""
Completion Model Prompt Template.
"""
text: str
class MemoryConfig(BaseModel):
"""
Memory Config.
"""
class RolePrefix(BaseModel):
"""
Role Prefix.
"""
user: str
assistant: str
class WindowConfig(BaseModel):
"""
Window Config.
"""
enabled: bool
size: Optional[int] = None
role_prefix: Optional[RolePrefix] = None
window: WindowConfig
api/core/prompt/prompt_transform.py
View file @ 4e4b07ce
......@@ -5,19 +5,22 @@ from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_runtime.entities.message_entities import PromptMessage
from core.model_runtime.entities.model_entities import ModelPropertyKey
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.prompt.entities.advanced_prompt_entities import MemoryConfig
class PromptTransform:
def _append_chat_histories(self, memory: TokenBufferMemory,
memory_config: MemoryConfig,
prompt_messages: list[PromptMessage],
model_config: ModelConfigWithCredentialsEntity) -> list[PromptMessage]:
rest_tokens = self._calculate_rest_token(prompt_messages, model_config)
histories = self._get_history_messages_list_from_memory(memory, rest_tokens)
histories = self._get_history_messages_list_from_memory(memory, memory_config, rest_tokens)
prompt_messages.extend(histories)
return prompt_messages
def _calculate_rest_token(self, prompt_messages: list[PromptMessage], model_config: ModelConfigWithCredentialsEntity) -> int:
def _calculate_rest_token(self, prompt_messages: list[PromptMessage],
model_config: ModelConfigWithCredentialsEntity) -> int:
rest_tokens = 2000
model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
......@@ -44,6 +47,7 @@ class PromptTransform:
return rest_tokens
def _get_history_messages_from_memory(self, memory: TokenBufferMemory,
memory_config: MemoryConfig,
max_token_limit: int,
human_prefix: Optional[str] = None,
ai_prefix: Optional[str] = None) -> str:
......@@ -58,13 +62,22 @@ class PromptTransform:
if ai_prefix:
kwargs['ai_prefix'] = ai_prefix
if memory_config.window.enabled and memory_config.window.size is not None and memory_config.window.size > 0:
kwargs['message_limit'] = memory_config.window.size
return memory.get_history_prompt_text(
**kwargs
)
def _get_history_messages_list_from_memory(self, memory: TokenBufferMemory,
memory_config: MemoryConfig,
max_token_limit: int) -> list[PromptMessage]:
"""Get memory messages."""
return memory.get_history_prompt_messages(
max_token_limit=max_token_limit
max_token_limit=max_token_limit,
message_limit=memory_config.window.size
if (memory_config.window.enabled
and memory_config.window.size is not None
and memory_config.window.size > 0)
else 10
)
api/core/prompt/simple_prompt_transform.py
View file @ 4e4b07ce
......@@ -13,6 +13,7 @@ from core.model_runtime.entities.message_entities import (
TextPromptMessageContent,
UserPromptMessage,
)
from core.prompt.entities.advanced_prompt_entities import MemoryConfig
from core.prompt.prompt_transform import PromptTransform
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
from models.model import AppMode
......@@ -182,6 +183,11 @@ class SimplePromptTransform(PromptTransform):
if memory:
prompt_messages = self._append_chat_histories(
memory=memory,
memory_config=MemoryConfig(
window=MemoryConfig.WindowConfig(
enabled=False,
)
),
prompt_messages=prompt_messages,
model_config=model_config
)
......@@ -220,6 +226,11 @@ class SimplePromptTransform(PromptTransform):
rest_tokens = self._calculate_rest_token([tmp_human_message], model_config)
histories = self._get_history_messages_from_memory(
memory=memory,
memory_config=MemoryConfig(
window=MemoryConfig.WindowConfig(
enabled=False,
)
),
max_token_limit=rest_tokens,
ai_prefix=prompt_rules['human_prefix'] if 'human_prefix' in prompt_rules else 'Human',
human_prefix=prompt_rules['assistant_prefix'] if 'assistant_prefix' in prompt_rules else 'Assistant'
......
api/core/prompt/utils/prompt_message_util.py 0 → 100644
View file @ 4e4b07ce
from typing import cast
from core.model_runtime.entities.message_entities import (
ImagePromptMessageContent,
PromptMessage,
PromptMessageContentType,
PromptMessageRole,
TextPromptMessageContent,
)
from core.prompt.simple_prompt_transform import ModelMode
class PromptMessageUtil:
@staticmethod
def prompt_messages_to_prompt_for_saving(model_mode: str, prompt_messages: list[PromptMessage]) -> list[dict]:
"""
Prompt messages to prompt for saving.
:param model_mode: model mode
:param prompt_messages: prompt messages
:return:
"""
prompts = []
if model_mode == ModelMode.CHAT.value:
for prompt_message in prompt_messages:
if prompt_message.role == PromptMessageRole.USER:
role = 'user'
elif prompt_message.role == PromptMessageRole.ASSISTANT:
role = 'assistant'
elif prompt_message.role == PromptMessageRole.SYSTEM:
role = 'system'
else:
continue
text = ''
files = []
if isinstance(prompt_message.content, list):
for content in prompt_message.content:
if content.type == PromptMessageContentType.TEXT:
content = cast(TextPromptMessageContent, content)
text += content.data
else:
content = cast(ImagePromptMessageContent, content)
files.append({
"type": 'image',
"data": content.data[:10] + '...[TRUNCATED]...' + content.data[-10:],
"detail": content.detail.value
})
else:
text = prompt_message.content
prompts.append({
"role": role,
"text": text,
"files": files
})
else:
prompt_message = prompt_messages[0]
text = ''
files = []
if isinstance(prompt_message.content, list):
for content in prompt_message.content:
if content.type == PromptMessageContentType.TEXT:
content = cast(TextPromptMessageContent, content)
text += content.data
else:
content = cast(ImagePromptMessageContent, content)
files.append({
"type": 'image',
"data": content.data[:10] + '...[TRUNCATED]...' + content.data[-10:],
"detail": content.detail.value
})
else:
text = prompt_message.content
params = {
"role": 'user',
"text": text,
}
if files:
params['files'] = files
prompts.append(params)
return prompts
api/core/workflow/entities/node_entities.py
View file @ 4e4b07ce
......@@ -12,7 +12,7 @@ class NodeType(Enum):
"""
START = 'start'
END = 'end'
DIRECT_ANSWER = 'direct-answer'
ANSWER = 'answer'
LLM = 'llm'
KNOWLEDGE_RETRIEVAL = 'knowledge-retrieval'
IF_ELSE = 'if-else'
......
api/core/workflow/nodes/direct_answer/direct_answer_node.py → api/core/workflow/nodes/answer/answer_node.py
View file @ 4e4b07ce
......@@ -5,14 +5,14 @@ from core.prompt.utils.prompt_template_parser import PromptTemplateParser
from core.workflow.entities.base_node_data_entities import BaseNodeData
from core.workflow.entities.node_entities import NodeRunResult, NodeType
from core.workflow.entities.variable_pool import ValueType, VariablePool
from core.workflow.nodes.answer.entities import AnswerNodeData
from core.workflow.nodes.base_node import BaseNode
from core.workflow.nodes.direct_answer.entities import DirectAnswerNodeData
from models.workflow import WorkflowNodeExecutionStatus
class DirectAnswerNode(BaseNode):
_node_data_cls = DirectAnswerNodeData
node_type = NodeType.DIRECT_ANSWER
class AnswerNode(BaseNode):
_node_data_cls = AnswerNodeData
node_type = NodeType.ANSWER
def _run(self, variable_pool: VariablePool) -> NodeRunResult:
"""
......
api/core/workflow/nodes/direct_answer/entities.py → api/core/workflow/nodes/answer/entities.py
View file @ 4e4b07ce
......@@ -2,9 +2,9 @@ from core.workflow.entities.base_node_data_entities import BaseNodeData
from core.workflow.entities.variable_entities import VariableSelector
class DirectAnswerNodeData(BaseNodeData):
class AnswerNodeData(BaseNodeData):
"""
DirectAnswer Node Data.
Answer Node Data.
"""
variables: list[VariableSelector] = []
answer: str
api/core/workflow/nodes/code/code_node.py
View file @ 4e4b07ce
......@@ -15,6 +15,16 @@ MAX_STRING_LENGTH = 1000
MAX_STRING_ARRAY_LENGTH = 30
MAX_NUMBER_ARRAY_LENGTH = 1000
JAVASCRIPT_DEFAULT_CODE = """function main({args1, args2}) {
return {
result: args1 + args2
}
}"""
PYTHON_DEFAULT_CODE = """def main(args1: int, args2: int) -> dict:
return {
"result": args1 + args2,
}"""
class CodeNode(BaseNode):
_node_data_cls = CodeNodeData
......@@ -42,9 +52,7 @@ class CodeNode(BaseNode):
}
],
"code_language": "javascript",
"code": "async function main(arg1, arg2) {\n return new Promise((resolve, reject) => {"
"\n if (true) {\n resolve({\n \"result\": arg1 + arg2"
"\n });\n } else {\n reject(\"e\");\n }\n });\n}",
"code": JAVASCRIPT_DEFAULT_CODE,
"outputs": [
{
"variable": "result",
......@@ -68,8 +76,7 @@ class CodeNode(BaseNode):
}
],
"code_language": "python3",
"code": "def main(\n arg1: int,\n arg2: int,\n) -> int:\n return {\n \"result\": arg1 "
"+ arg2\n }",
"code": PYTHON_DEFAULT_CODE,
"outputs": [
{
"variable": "result",
......
api/core/workflow/nodes/code/entities.py
View file @ 4e4b07ce
......@@ -17,4 +17,4 @@ class CodeNodeData(BaseNodeData):
variables: list[VariableSelector]
code_language: Literal['python3', 'javascript']
code: str
outputs: dict[str, Output]
outputs: dict[str, Output]
\ No newline at end of file
api/core/workflow/nodes/llm/entities.py
View file @ 4e4b07ce
from typing import Any, Literal, Optional, Union
from pydantic import BaseModel
from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
from core.workflow.entities.base_node_data_entities import BaseNodeData
from core.workflow.entities.variable_entities import VariableSelector
class ModelConfig(BaseModel):
"""
Model Config.
"""
provider: str
name: str
mode: str
completion_params: dict[str, Any] = {}
class ContextConfig(BaseModel):
"""
Context Config.
"""
enabled: bool
variable_selector: Optional[list[str]] = None
class VisionConfig(BaseModel):
"""
Vision Config.
"""
class Configs(BaseModel):
"""
Configs.
"""
detail: Literal['low', 'high']
enabled: bool
configs: Optional[Configs] = None
class LLMNodeData(BaseNodeData):
"""
LLM Node Data.
"""
pass
model: ModelConfig
variables: list[VariableSelector] = []
prompt_template: Union[list[ChatModelMessage], CompletionModelPromptTemplate]
memory: Optional[MemoryConfig] = None
context: ContextConfig
vision: VisionConfig
api/core/workflow/nodes/llm/llm_node.py
View file @ 4e4b07ce
This diff is collapsed.
api/core/workflow/workflow_engine_manager.py
View file @ 4e4b07ce
......@@ -7,9 +7,9 @@ from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResu
from core.workflow.entities.variable_pool import VariablePool, VariableValue
from core.workflow.entities.workflow_entities import WorkflowNodeAndResult, WorkflowRunState
from core.workflow.errors import WorkflowNodeRunFailedError
from core.workflow.nodes.answer.answer_node import AnswerNode
from core.workflow.nodes.base_node import BaseNode, UserFrom
from core.workflow.nodes.code.code_node import CodeNode
from core.workflow.nodes.direct_answer.direct_answer_node import DirectAnswerNode
from core.workflow.nodes.end.end_node import EndNode
from core.workflow.nodes.http_request.http_request_node import HttpRequestNode
from core.workflow.nodes.if_else.if_else_node import IfElseNode
......@@ -24,13 +24,12 @@ from extensions.ext_database import db
from models.workflow import (
Workflow,
WorkflowNodeExecutionStatus,
WorkflowType,
)
node_classes = {
NodeType.START: StartNode,
NodeType.END: EndNode,
NodeType.DIRECT_ANSWER: DirectAnswerNode,
NodeType.ANSWER: AnswerNode,
NodeType.LLM: LLMNode,
NodeType.KNOWLEDGE_RETRIEVAL: KnowledgeRetrievalNode,
NodeType.IF_ELSE: IfElseNode,
......@@ -156,7 +155,7 @@ class WorkflowEngineManager:
callbacks=callbacks
)
if next_node.node_type == NodeType.END:
if next_node.node_type in [NodeType.END, NodeType.ANSWER]:
break
predecessor_node = next_node
......@@ -402,10 +401,16 @@ class WorkflowEngineManager:
# add to workflow_nodes_and_results
workflow_run_state.workflow_nodes_and_results.append(workflow_nodes_and_result)
# run node, result must have inputs, process_data, outputs, execution_metadata
node_run_result = node.run(
variable_pool=workflow_run_state.variable_pool
)
try:
# run node, result must have inputs, process_data, outputs, execution_metadata
node_run_result = node.run(
variable_pool=workflow_run_state.variable_pool
)
except Exception as e:
node_run_result = NodeRunResult(
status=WorkflowNodeExecutionStatus.FAILED,
error=str(e)
)
if node_run_result.status == WorkflowNodeExecutionStatus.FAILED:
# node run failed
......@@ -420,9 +425,6 @@ class WorkflowEngineManager:
raise ValueError(f"Node {node.node_data.title} run failed: {node_run_result.error}")
# set end node output if in chat
self._set_end_node_output_if_in_chat(workflow_run_state, node, node_run_result)
workflow_nodes_and_result.result = node_run_result
# node run success
......@@ -453,29 +455,6 @@ class WorkflowEngineManager:
db.session.close()
def _set_end_node_output_if_in_chat(self, workflow_run_state: WorkflowRunState,
node: BaseNode,
node_run_result: NodeRunResult) -> None:
"""
Set end node output if in chat
:param workflow_run_state: workflow run state
:param node: current node
:param node_run_result: node run result
:return:
"""
if workflow_run_state.workflow_type == WorkflowType.CHAT and node.node_type == NodeType.END:
workflow_nodes_and_result_before_end = workflow_run_state.workflow_nodes_and_results[-2]
if workflow_nodes_and_result_before_end:
if workflow_nodes_and_result_before_end.node.node_type == NodeType.LLM:
if not node_run_result.outputs:
node_run_result.outputs = {}
node_run_result.outputs['text'] = workflow_nodes_and_result_before_end.result.outputs.get('text')
elif workflow_nodes_and_result_before_end.node.node_type == NodeType.DIRECT_ANSWER:
if not node_run_result.outputs:
node_run_result.outputs = {}
node_run_result.outputs['text'] = workflow_nodes_and_result_before_end.result.outputs.get('answer')
def _append_variables_recursively(self, variable_pool: VariablePool,
node_id: str,
......
api/services/workflow_service.py
View file @ 4e4b07ce
......@@ -270,28 +270,48 @@ class WorkflowService:
return workflow_node_execution
# create workflow node execution
workflow_node_execution = WorkflowNodeExecution(
tenant_id=app_model.tenant_id,
app_id=app_model.id,
workflow_id=draft_workflow.id,
triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP.value,
index=1,
node_id=node_id,
node_type=node_instance.node_type.value,
title=node_instance.node_data.title,
inputs=json.dumps(node_run_result.inputs) if node_run_result.inputs else None,
process_data=json.dumps(node_run_result.process_data) if node_run_result.process_data else None,
outputs=json.dumps(node_run_result.outputs) if node_run_result.outputs else None,
execution_metadata=(json.dumps(jsonable_encoder(node_run_result.metadata))
if node_run_result.metadata else None),
status=WorkflowNodeExecutionStatus.SUCCEEDED.value,
elapsed_time=time.perf_counter() - start_at,
created_by_role=CreatedByRole.ACCOUNT.value,
created_by=account.id,
created_at=datetime.utcnow(),
finished_at=datetime.utcnow()
)
if node_run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED:
# create workflow node execution
workflow_node_execution = WorkflowNodeExecution(
tenant_id=app_model.tenant_id,
app_id=app_model.id,
workflow_id=draft_workflow.id,
triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP.value,
index=1,
node_id=node_id,
node_type=node_instance.node_type.value,
title=node_instance.node_data.title,
inputs=json.dumps(node_run_result.inputs) if node_run_result.inputs else None,
process_data=json.dumps(node_run_result.process_data) if node_run_result.process_data else None,
outputs=json.dumps(node_run_result.outputs) if node_run_result.outputs else None,
execution_metadata=(json.dumps(jsonable_encoder(node_run_result.metadata))
if node_run_result.metadata else None),
status=WorkflowNodeExecutionStatus.SUCCEEDED.value,
elapsed_time=time.perf_counter() - start_at,
created_by_role=CreatedByRole.ACCOUNT.value,
created_by=account.id,
created_at=datetime.utcnow(),
finished_at=datetime.utcnow()
)
else:
# create workflow node execution
workflow_node_execution = WorkflowNodeExecution(
tenant_id=app_model.tenant_id,
app_id=app_model.id,
workflow_id=draft_workflow.id,
triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP.value,
index=1,
node_id=node_id,
node_type=node_instance.node_type.value,
title=node_instance.node_data.title,
status=node_run_result.status.value,
error=node_run_result.error,
elapsed_time=time.perf_counter() - start_at,
created_by_role=CreatedByRole.ACCOUNT.value,
created_by=account.id,
created_at=datetime.utcnow(),
finished_at=datetime.utcnow()
)
db.session.add(workflow_node_execution)
db.session.commit()
......
api/tests/integration_tests/workflow/nodes/test_llm.py 0 → 100644
View file @ 4e4b07ce
import os
from unittest.mock import MagicMock
import pytest
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
from core.entities.provider_configuration import ProviderModelBundle, ProviderConfiguration
from core.entities.provider_entities import SystemConfiguration, CustomConfiguration, CustomProviderConfiguration
from core.model_manager import ModelInstance
from core.model_runtime.entities.model_entities import ModelType
from core.model_runtime.model_providers import ModelProviderFactory
from core.workflow.entities.node_entities import SystemVariable
from core.workflow.entities.variable_pool import VariablePool
from core.workflow.nodes.base_node import UserFrom
from core.workflow.nodes.llm.llm_node import LLMNode
from extensions.ext_database import db
from models.provider import ProviderType
from models.workflow import WorkflowNodeExecutionStatus
"""FOR MOCK FIXTURES, DO NOT REMOVE"""
from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock
@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
def test_execute_llm(setup_openai_mock):
node = LLMNode(
tenant_id='1',
app_id='1',
workflow_id='1',
user_id='1',
user_from=UserFrom.ACCOUNT,
config={
'id': 'llm',
'data': {
'title': '123',
'type': 'llm',
'model': {
'provider': 'openai',
'name': 'gpt-3.5.turbo',
'mode': 'chat',
'completion_params': {}
},
'variables': [
{
'variable': 'weather',
'value_selector': ['abc', 'output'],
},
{
'variable': 'query',
'value_selector': ['sys', 'query']
}
],
'prompt_template': [
{
'role': 'system',
'text': 'you are a helpful assistant.\ntoday\'s weather is {{weather}}.'
},
{
'role': 'user',
'text': '{{query}}'
}
],
'memory': {
'window': {
'enabled': True,
'size': 2
}
},
'context': {
'enabled': False
},
'vision': {
'enabled': False
}
}
}
)
# construct variable pool
pool = VariablePool(system_variables={
SystemVariable.QUERY: 'what\'s the weather today?',
SystemVariable.FILES: [],
SystemVariable.CONVERSATION: 'abababa'
}, user_inputs={})
pool.append_variable(node_id='abc', variable_key_list=['output'], value='sunny')
credentials = {
'openai_api_key': os.environ.get('OPENAI_API_KEY')
}
provider_instance = ModelProviderFactory().get_provider_instance('openai')
model_type_instance = provider_instance.get_model_instance(ModelType.LLM)
provider_model_bundle = ProviderModelBundle(
configuration=ProviderConfiguration(
tenant_id='1',
provider=provider_instance.get_provider_schema(),
preferred_provider_type=ProviderType.CUSTOM,
using_provider_type=ProviderType.CUSTOM,
system_configuration=SystemConfiguration(
enabled=False
),
custom_configuration=CustomConfiguration(
provider=CustomProviderConfiguration(
credentials=credentials
)
)
),
provider_instance=provider_instance,
model_type_instance=model_type_instance
)
model_instance = ModelInstance(provider_model_bundle=provider_model_bundle, model='gpt-3.5-turbo')
model_config = ModelConfigWithCredentialsEntity(
model='gpt-3.5-turbo',
provider='openai',
mode='chat',
credentials=credentials,
parameters={},
model_schema=model_type_instance.get_model_schema('gpt-3.5-turbo'),
provider_model_bundle=provider_model_bundle
)
# Mock db.session.close()
db.session.close = MagicMock()
node._fetch_model_config = MagicMock(return_value=tuple([model_instance, model_config]))
# execute node
result = node.run(pool)
assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
assert result.outputs['text'] is not None
assert result.outputs['usage']['total_tokens'] > 0
api/tests/integration_tests/workflow/nodes/test_template_transform.py
View file @ 4e4b07ce
import pytest
from core.app.entities.app_invoke_entities import InvokeFrom
from core.workflow.entities.variable_pool import VariablePool
from core.workflow.nodes.base_node import UserFrom
from core.workflow.nodes.template_transform.template_transform_node import TemplateTransformNode
from models.workflow import WorkflowNodeExecutionStatus
from tests.integration_tests.workflow.nodes.__mock.code_executor import setup_code_executor_mock
......@@ -14,7 +14,7 @@ def test_execute_code(setup_code_executor_mock):
app_id='1',
workflow_id='1',
user_id='1',
user_from=InvokeFrom.WEB_APP,
user_from=UserFrom.END_USER,
config={
'id': '1',
'data': {
......
api/tests/unit_tests/core/prompt/test_advanced_prompt_transform.py
View file @ 4e4b07ce
......@@ -2,12 +2,12 @@ from unittest.mock import MagicMock
import pytest
from core.app.app_config.entities import PromptTemplateEntity, AdvancedCompletionPromptTemplateEntity, \
ModelConfigEntity, AdvancedChatPromptTemplateEntity, AdvancedChatMessageEntity, FileUploadEntity
from core.app.app_config.entities import ModelConfigEntity, FileUploadEntity
from core.file.file_obj import FileObj, FileType, FileTransferMethod
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_runtime.entities.message_entities import UserPromptMessage, AssistantPromptMessage, PromptMessageRole
from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
from core.prompt.entities.advanced_prompt_entities import CompletionModelPromptTemplate, MemoryConfig, ChatModelMessage
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
from models.model import Conversation
......@@ -18,16 +18,20 @@ def test__get_completion_model_prompt_messages():
model_config_mock.model = 'gpt-3.5-turbo-instruct'
prompt_template = "Context:\n{{#context#}}\n\nHistories:\n{{#histories#}}\n\nyou are {{name}}."
prompt_template_entity = PromptTemplateEntity(
prompt_type=PromptTemplateEntity.PromptType.ADVANCED,
advanced_completion_prompt_template=AdvancedCompletionPromptTemplateEntity(
prompt=prompt_template,
role_prefix=AdvancedCompletionPromptTemplateEntity.RolePrefixEntity(
user="Human",
assistant="Assistant"
)
prompt_template_config = CompletionModelPromptTemplate(
text=prompt_template
)
memory_config = MemoryConfig(
role_prefix=MemoryConfig.RolePrefix(
user="Human",
assistant="Assistant"
),
window=MemoryConfig.WindowConfig(
enabled=False
)
)
inputs = {
"name": "John"
}
......@@ -48,11 +52,12 @@ def test__get_completion_model_prompt_messages():
prompt_transform = AdvancedPromptTransform()
prompt_transform._calculate_rest_token = MagicMock(return_value=2000)
prompt_messages = prompt_transform._get_completion_model_prompt_messages(
prompt_template_entity=prompt_template_entity,
prompt_template=prompt_template_config,
inputs=inputs,
query=None,
files=files,
context=context,
memory_config=memory_config,
memory=memory,
model_config=model_config_mock
)
......@@ -67,7 +72,7 @@ def test__get_completion_model_prompt_messages():
def test__get_chat_model_prompt_messages(get_chat_model_args):
model_config_mock, prompt_template_entity, inputs, context = get_chat_model_args
model_config_mock, memory_config, messages, inputs, context = get_chat_model_args
files = []
query = "Hi2."
......@@ -86,11 +91,12 @@ def test__get_chat_model_prompt_messages(get_chat_model_args):
prompt_transform = AdvancedPromptTransform()
prompt_transform._calculate_rest_token = MagicMock(return_value=2000)
prompt_messages = prompt_transform._get_chat_model_prompt_messages(
prompt_template_entity=prompt_template_entity,
prompt_template=messages,
inputs=inputs,
query=query,
files=files,
context=context,
memory_config=memory_config,
memory=memory,
model_config=model_config_mock
)
......@@ -98,24 +104,25 @@ def test__get_chat_model_prompt_messages(get_chat_model_args):
assert len(prompt_messages) == 6
assert prompt_messages[0].role == PromptMessageRole.SYSTEM
assert prompt_messages[0].content == PromptTemplateParser(
template=prompt_template_entity.advanced_chat_prompt_template.messages[0].text
template=messages[0].text
).format({**inputs, "#context#": context})
assert prompt_messages[5].content == query
def test__get_chat_model_prompt_messages_no_memory(get_chat_model_args):
model_config_mock, prompt_template_entity, inputs, context = get_chat_model_args
model_config_mock, _, messages, inputs, context = get_chat_model_args
files = []
prompt_transform = AdvancedPromptTransform()
prompt_transform._calculate_rest_token = MagicMock(return_value=2000)
prompt_messages = prompt_transform._get_chat_model_prompt_messages(
prompt_template_entity=prompt_template_entity,
prompt_template=messages,
inputs=inputs,
query=None,
files=files,
context=context,
memory_config=None,
memory=None,
model_config=model_config_mock
)
......@@ -123,12 +130,12 @@ def test__get_chat_model_prompt_messages_no_memory(get_chat_model_args):
assert len(prompt_messages) == 3
assert prompt_messages[0].role == PromptMessageRole.SYSTEM
assert prompt_messages[0].content == PromptTemplateParser(
template=prompt_template_entity.advanced_chat_prompt_template.messages[0].text
template=messages[0].text
).format({**inputs, "#context#": context})
def test__get_chat_model_prompt_messages_with_files_no_memory(get_chat_model_args):
model_config_mock, prompt_template_entity, inputs, context = get_chat_model_args
model_config_mock, _, messages, inputs, context = get_chat_model_args
files = [
FileObj(
......@@ -148,11 +155,12 @@ def test__get_chat_model_prompt_messages_with_files_no_memory(get_chat_model_arg
prompt_transform = AdvancedPromptTransform()
prompt_transform._calculate_rest_token = MagicMock(return_value=2000)
prompt_messages = prompt_transform._get_chat_model_prompt_messages(
prompt_template_entity=prompt_template_entity,
prompt_template=messages,
inputs=inputs,
query=None,
files=files,
context=context,
memory_config=None,
memory=None,
model_config=model_config_mock
)
......@@ -160,7 +168,7 @@ def test__get_chat_model_prompt_messages_with_files_no_memory(get_chat_model_arg
assert len(prompt_messages) == 4
assert prompt_messages[0].role == PromptMessageRole.SYSTEM
assert prompt_messages[0].content == PromptTemplateParser(
template=prompt_template_entity.advanced_chat_prompt_template.messages[0].text
template=messages[0].text
).format({**inputs, "#context#": context})
assert isinstance(prompt_messages[3].content, list)
assert len(prompt_messages[3].content) == 2
......@@ -173,22 +181,31 @@ def get_chat_model_args():
model_config_mock.provider = 'openai'
model_config_mock.model = 'gpt-4'
prompt_template_entity = PromptTemplateEntity(
prompt_type=PromptTemplateEntity.PromptType.ADVANCED,
advanced_chat_prompt_template=AdvancedChatPromptTemplateEntity(
messages=[
AdvancedChatMessageEntity(text="You are a helpful assistant named {{name}}.\n\nContext:\n{{#context#}}",
role=PromptMessageRole.SYSTEM),
AdvancedChatMessageEntity(text="Hi.", role=PromptMessageRole.USER),
AdvancedChatMessageEntity(text="Hello!", role=PromptMessageRole.ASSISTANT),
]
memory_config = MemoryConfig(
window=MemoryConfig.WindowConfig(
enabled=False
)
)
prompt_messages = [
ChatModelMessage(
text="You are a helpful assistant named {{name}}.\n\nContext:\n{{#context#}}",
role=PromptMessageRole.SYSTEM
),
ChatModelMessage(
text="Hi.",
role=PromptMessageRole.USER
),
ChatModelMessage(
text="Hello!",
role=PromptMessageRole.ASSISTANT
)
]
inputs = {
"name": "John"
}
context = "I am superman."
return model_config_mock, prompt_template_entity, inputs, context
return model_config_mock, memory_config, prompt_messages, inputs, context
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