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feat(awel): Support simple templates

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This commit is contained in:
Fangyin Cheng 2025-03-14 10:44:50 +08:00
parent 676d8ec70f
commit 8ccd4090b9
73 changed files with 1623 additions and 89 deletions
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21
packages/dbgpt-app/src/dbgpt_app/operators/llm.py
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@ -4,10 +4,13 @@ from dbgpt._private.pydantic import BaseModel, Field
from dbgpt.core import (
BaseMessage,
ChatPromptTemplate,
HumanPromptTemplate,
LLMClient,
MessagesPlaceholder,
ModelOutput,
ModelRequest,
StorageConversation,
SystemPromptTemplate,
)
from dbgpt.core.awel import (
DAG,
@ -55,6 +58,15 @@ class HOContextBody(BaseModel):
)
_DEFAULT_PROMPT_TEMPLATE = ChatPromptTemplate(
messages=[
SystemPromptTemplate.from_template(_("You are a helpful AI assistant.")),
MessagesPlaceholder(variable_name="chat_history"),
HumanPromptTemplate.from_template("{user_input}"),
]
)
class BaseHOLLMOperator(
BaseConversationOperator,
JoinOperator[ModelRequest],
@ -65,7 +77,7 @@ class BaseHOLLMOperator(
def __init__(
self,
prompt_template: ChatPromptTemplate,
prompt_template: Optional[ChatPromptTemplate] = None,
model: str = None,
llm_client: Optional[LLMClient] = None,
history_merge_mode: Literal["none", "window", "token"] = "window",
@ -84,7 +96,7 @@ class BaseHOLLMOperator(
self._history_merge_mode = history_merge_mode
self._user_message_key = user_message_key
self._has_history = history_merge_mode != "none"
self._prompt_template = prompt_template
self._prompt_template = prompt_template or _DEFAULT_PROMPT_TEMPLATE
self._model = model
self._history_key = history_key
self._str_history = False
@ -147,6 +159,9 @@ class BaseHOLLMOperator(
await self.current_dag_context.save_to_share_data(
self.SHARE_DATA_KEY_STORAGE_CONVERSATION, storage_conv
)
await self.current_dag_context.save_to_share_data(
self.SHARE_DATA_KEY_CONV_MODEL_NAME, req.model, overwrite=True
)
user_input = (
req.messages[-1] if isinstance(req.messages, list) else req.messages
@ -269,6 +284,8 @@ _PARAMETER_PROMPT_TEMPLATE = Parameter.build_from(
"prompt_template",
ChatPromptTemplate,
description=_("The prompt template for the conversation."),
optional=True,
default=None,
)
_PARAMETER_MODEL = Parameter.build_from(
_("Model Name"),

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packages/dbgpt-app/src/dbgpt_app/static/web/404.html
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<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="next-head-count" content="2"/><link rel="icon" href="/favicon.ico"/><meta name="description" content="Revolutionizing Database Interactions with Private LLM Technology"/><meta property="og:description" content="eosphoros-ai"/><meta property="og:title" content="DB-GPT"/><link rel="preload" href="/_next/static/css/11e24a325e03c71b.css" as="style"/><link rel="stylesheet" href="/_next/static/css/11e24a325e03c71b.css" data-n-g=""/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-2222915a8e2f2ed4.js" defer=""></script><script src="/_next/static/chunks/framework-8b06d32cbb857e0e.js" defer=""></script><script src="/_next/static/chunks/main-6c4c7f5b8c9b1320.js" defer=""></script><script src="/_next/static/chunks/pages/_app-04732f15c319fe0e.js" defer=""></script><script src="/_next/static/chunks/pages/_error-8095ba9e1bf12f30.js" defer=""></script><script src="/_next/static/jxCBaIEpTbVzOpLk40ABR/_buildManifest.js" defer=""></script><script src="/_next/static/jxCBaIEpTbVzOpLk40ABR/_ssgManifest.js" defer=""></script><link rel="stylesheet" href="/_next/static/css/antd-output/antd.min.7d5365b5.css"/></head><body><div id="__next"><div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"jxCBaIEpTbVzOpLk40ABR","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>
<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="next-head-count" content="2"/><link rel="icon" href="/favicon.ico"/><meta name="description" content="Revolutionizing Database Interactions with Private LLM Technology"/><meta property="og:description" content="eosphoros-ai"/><meta property="og:title" content="DB-GPT"/><link rel="preload" href="/_next/static/css/86868db7029b41db.css" as="style"/><link rel="stylesheet" href="/_next/static/css/86868db7029b41db.css" data-n-g=""/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-3c36ab6a3ff16f83.js" defer=""></script><script src="/_next/static/chunks/framework-8b06d32cbb857e0e.js" defer=""></script><script src="/_next/static/chunks/main-6c4c7f5b8c9b1320.js" defer=""></script><script src="/_next/static/chunks/pages/_app-e3f703f2ead29f03.js" defer=""></script><script src="/_next/static/chunks/pages/_error-8095ba9e1bf12f30.js" defer=""></script><script src="/_next/static/jzcGchJAn_wrQFWWcN3t0/_buildManifest.js" defer=""></script><script src="/_next/static/jzcGchJAn_wrQFWWcN3t0/_ssgManifest.js" defer=""></script><link rel="stylesheet" href="/_next/static/css/antd-output/antd.min.7d5365b5.css"/></head><body><div id="__next"><div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"jzcGchJAn_wrQFWWcN3t0","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>

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packages/dbgpt-app/src/dbgpt_app/static/web/404/index.html
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<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="next-head-count" content="2"/><link rel="icon" href="/favicon.ico"/><meta name="description" content="Revolutionizing Database Interactions with Private LLM Technology"/><meta property="og:description" content="eosphoros-ai"/><meta property="og:title" content="DB-GPT"/><link rel="preload" href="/_next/static/css/11e24a325e03c71b.css" as="style"/><link rel="stylesheet" href="/_next/static/css/11e24a325e03c71b.css" data-n-g=""/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-2222915a8e2f2ed4.js" defer=""></script><script src="/_next/static/chunks/framework-8b06d32cbb857e0e.js" defer=""></script><script src="/_next/static/chunks/main-6c4c7f5b8c9b1320.js" defer=""></script><script src="/_next/static/chunks/pages/_app-04732f15c319fe0e.js" defer=""></script><script src="/_next/static/chunks/pages/_error-8095ba9e1bf12f30.js" defer=""></script><script src="/_next/static/jxCBaIEpTbVzOpLk40ABR/_buildManifest.js" defer=""></script><script src="/_next/static/jxCBaIEpTbVzOpLk40ABR/_ssgManifest.js" defer=""></script><link rel="stylesheet" href="/_next/static/css/antd-output/antd.min.7d5365b5.css"/></head><body><div id="__next"><div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"jxCBaIEpTbVzOpLk40ABR","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>
<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="next-head-count" content="2"/><link rel="icon" href="/favicon.ico"/><meta name="description" content="Revolutionizing Database Interactions with Private LLM Technology"/><meta property="og:description" content="eosphoros-ai"/><meta property="og:title" content="DB-GPT"/><link rel="preload" href="/_next/static/css/86868db7029b41db.css" as="style"/><link rel="stylesheet" href="/_next/static/css/86868db7029b41db.css" data-n-g=""/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-3c36ab6a3ff16f83.js" defer=""></script><script src="/_next/static/chunks/framework-8b06d32cbb857e0e.js" defer=""></script><script src="/_next/static/chunks/main-6c4c7f5b8c9b1320.js" defer=""></script><script src="/_next/static/chunks/pages/_app-e3f703f2ead29f03.js" defer=""></script><script src="/_next/static/chunks/pages/_error-8095ba9e1bf12f30.js" defer=""></script><script src="/_next/static/jzcGchJAn_wrQFWWcN3t0/_buildManifest.js" defer=""></script><script src="/_next/static/jzcGchJAn_wrQFWWcN3t0/_ssgManifest.js" defer=""></script><link rel="stylesheet" href="/_next/static/css/antd-output/antd.min.7d5365b5.css"/></head><body><div id="__next"><div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"jzcGchJAn_wrQFWWcN3t0","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>

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packages/dbgpt-app/src/dbgpt_app/static/web/chat/index.html
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<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="next-head-count" content="2"/><link rel="icon" href="/favicon.ico"/><meta name="description" content="Revolutionizing Database Interactions with Private LLM Technology"/><meta property="og:description" content="eosphoros-ai"/><meta property="og:title" content="DB-GPT"/><link rel="preload" href="/_next/static/css/11e24a325e03c71b.css" as="style"/><link rel="stylesheet" href="/_next/static/css/11e24a325e03c71b.css" data-n-g=""/><link rel="preload" href="/_next/static/css/9b601b4de5d78ac2.css" as="style"/><link rel="stylesheet" href="/_next/static/css/9b601b4de5d78ac2.css" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-2222915a8e2f2ed4.js" defer=""></script><script src="/_next/static/chunks/framework-8b06d32cbb857e0e.js" defer=""></script><script src="/_next/static/chunks/main-6c4c7f5b8c9b1320.js" defer=""></script><script src="/_next/static/chunks/pages/_app-04732f15c319fe0e.js" defer=""></script><script src="/_next/static/chunks/2913-315ad705b1306902.js" defer=""></script><script src="/_next/static/chunks/3791-58df908ca3784958.js" defer=""></script><script src="/_next/static/chunks/5278-36ac2f07bcb92504.js" defer=""></script><script src="/_next/static/chunks/8791-d36492edb39795c5.js" defer=""></script><script src="/_next/static/chunks/4330-a1b5cee9f3b8b8f7.js" defer=""></script><script src="/_next/static/chunks/7611-eb56b3e5a9b8793d.js" defer=""></script><script src="/_next/static/chunks/1049-b2925c4c7e1e37be.js" defer=""></script><script src="/_next/static/chunks/5030-ed144c49e325cd61.js" defer=""></script><script src="/_next/static/chunks/2783-67b811a852a75cad.js" defer=""></script><script src="/_next/static/chunks/3457-105f31ebfbb8ea1c.js" defer=""></script><script src="/_next/static/chunks/2622-298871edd9c51e45.js" defer=""></script><script src="/_next/static/chunks/5914-4fb2d5af45fa1932.js" defer=""></script><script src="/_next/static/chunks/7124-9f5b0f08bb4ccef6.js" defer=""></script><script src="/_next/static/chunks/4633-9979f9d68a1b2d29.js" defer=""></script><script src="/_next/static/chunks/6231-082aa9c179c552ae.js" defer=""></script><script src="/_next/static/chunks/8424-b850b868a3630873.js" defer=""></script><script src="/_next/static/chunks/7249-b7cf7c212aad9e29.js" defer=""></script><script src="/_next/static/chunks/pages/chat-f781fd5484f1d36d.js" defer=""></script><script src="/_next/static/jxCBaIEpTbVzOpLk40ABR/_buildManifest.js" defer=""></script><script src="/_next/static/jxCBaIEpTbVzOpLk40ABR/_ssgManifest.js" defer=""></script><link rel="stylesheet" href="/_next/static/css/antd-output/antd.min.7d5365b5.css"/></head><body><div id="__next"><div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{}},"page":"/chat","query":{},"buildId":"jxCBaIEpTbVzOpLk40ABR","nextExport":true,"autoExport":true,"isFallback":false,"scriptLoader":[]}</script></body></html>
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packages/dbgpt-app/src/dbgpt_app/static/web/index.html
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packages/dbgpt-app/src/dbgpt_app/static/web/knowledge/graph/index.html
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packages/dbgpt-app/src/dbgpt_app/static/web/mobile/chat/components/ChatDialog/index.html
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packages/dbgpt-app/src/dbgpt_app/static/web/mobile/chat/components/Content/index.html
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packages/dbgpt-app/src/dbgpt_app/static/web/mobile/chat/components/DislikeDrawer/index.html
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packages/dbgpt-app/src/dbgpt_app/static/web/mobile/chat/components/Header/index.html
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packages/dbgpt-app/src/dbgpt_app/static/web/mobile/chat/components/ModelSelector/index.html
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packages/dbgpt-app/src/dbgpt_app/static/web/mobile/chat/components/OptionIcon/index.html
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packages/dbgpt-app/src/dbgpt_app/static/web/mobile/chat/components/Resource/index.html
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2
packages/dbgpt-app/src/dbgpt_app/static/web/mobile/chat/components/Thermometer/index.html
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@ -1 +1 @@
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2
packages/dbgpt-app/src/dbgpt_app/static/web/mobile/chat/index.html
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@ -1 +1 @@
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9
packages/dbgpt-core/src/dbgpt/core/awel/dag/base.py
View File

@ -736,8 +736,13 @@ class DAGContext:
async with self._share_data_lock:
if key in self._share_data and not overwrite:
raise ValueError(f"Share data key {key} already exists")
logger.debug(f"Save share data by key {key} to {id(self._share_data)}")
self._share_data[key] = data
if overwrite and key in self._share_data and data is None:
logger.warning(
f"Overwrite share data key {key} with None, nothing to do"
)
else:
logger.debug(f"Save share data by key {key} to {id(self._share_data)}")
self._share_data[key] = data
async def get_task_share_data(self, task_name: str, key: str) -> Any:
"""Get share data by task name and key.

22
packages/dbgpt-core/src/dbgpt/core/awel/flow/base.py
View File

@ -1290,17 +1290,17 @@ class ViewMetadata(BaseMetadata):
for field in self.inputs:
if field.mappers:
raise ValueError("Input field can't have mappers.")
dyn_cnt, is_last_field_dynamic = 0, False
for field in self.inputs:
if field.dynamic:
dyn_cnt += 1
is_last_field_dynamic = True
else:
if is_last_field_dynamic:
raise ValueError("Dynamic field input must be the last field.")
is_last_field_dynamic = False
if dyn_cnt > 1:
raise ValueError("Only one dynamic input field is allowed.")
# dyn_cnt, is_last_field_dynamic = 0, False
# for field in self.inputs:
# if field.dynamic:
# dyn_cnt += 1
# is_last_field_dynamic = True
# else:
# if is_last_field_dynamic:
# raise ValueError("Dynamic field input must be the last field.") # noqa
# is_last_field_dynamic = False
# if dyn_cnt > 1:
# raise ValueError("Only one dynamic input field is allowed.")
if self.outputs:
dyn_cnt, is_last_field_dynamic = 0, False
for field in self.outputs:

4
packages/dbgpt-core/src/dbgpt/core/awel/flow/flow_factory.py
View File

@ -1112,10 +1112,14 @@ def fill_flow_panel(
else:
raise
param.type_cls = new_param.type_cls
param.optional = new_param.optional
param.default = new_param.default
param.placeholder = new_param.placeholder
param.alias = new_param.alias
param.ui = new_param.ui
param.is_list = new_param.is_list
param.dynamic = new_param.dynamic
param.dynamic_minimum = new_param.dynamic_minimum
except (FlowException, ValueError) as e:
logger.warning(f"Unable to fill the flow panel: {e}")

4
packages/dbgpt-core/src/dbgpt/core/interface/operators/llm_operator.py
View File

@ -303,7 +303,7 @@ class BaseLLMOperator(BaseLLM, MapOperator[ModelRequest, ModelOutput], ABC):
ModelOutput: The model output.
"""
await self.current_dag_context.save_to_share_data(
self.SHARE_DATA_KEY_MODEL_NAME, request.model
self.SHARE_DATA_KEY_MODEL_NAME, request.model, overwrite=True
)
model_output = await self.llm_client.generate(request)
await self.save_model_output(self.current_dag_context, model_output)
@ -341,7 +341,7 @@ class BaseStreamingLLMOperator(
) -> AsyncIterator[ModelOutput]: # type: ignore
"""Streamify the request."""
await self.current_dag_context.save_to_share_data(
self.SHARE_DATA_KEY_MODEL_NAME, request.model
self.SHARE_DATA_KEY_MODEL_NAME, request.model, overwrite=True
)
model_output = None
async for output in self.llm_client.generate_stream(request): # type: ignore

10
packages/dbgpt-core/src/dbgpt/core/interface/operators/message_operator.py
View File

@ -32,6 +32,7 @@ logger = logging.getLogger(__name__)
class BaseConversationOperator(BaseOperator, ABC):
"""Base class for conversation operators."""
SHARE_DATA_KEY_CONV_MODEL_NAME = "conv_share_data_key_model_name"
SHARE_DATA_KEY_STORAGE_CONVERSATION = "share_data_key_storage_conversation"
SHARE_DATA_KEY_MODEL_REQUEST = "share_data_key_model_request"
SHARE_DATA_KEY_MODEL_REQUEST_CONTEXT = "share_data_key_model_request_context"
@ -561,8 +562,13 @@ class TokenBufferedConversationMapperOperator(ConversationMapperOperator):
eviction_policy = self._eviction_policy or self.eviction_policy
messages_by_round: List[List[BaseMessage]] = _split_messages_by_round(messages)
messages_str = _messages_to_str(_merge_multi_round_messages(messages_by_round))
model_name = self._model
if not model_name:
model_name = await self.current_dag_context.get_from_share_data(
self.SHARE_DATA_KEY_CONV_MODEL_NAME
)
# Fist time, we count the token of the messages
current_tokens = await self._llm_client.count_token(self._model, messages_str)
current_tokens = await self._llm_client.count_token(model_name, messages_str)
while current_tokens > self._max_token_limit:
# Evict the messages by round after all tokens are not greater than the max
@ -573,7 +579,7 @@ class TokenBufferedConversationMapperOperator(ConversationMapperOperator):
_merge_multi_round_messages(messages_by_round)
)
current_tokens = await self._llm_client.count_token(
self._model, messages_str
model_name, messages_str
)
message_mapper = self._message_mapper or self.map_multi_round_messages
return message_mapper(messages_by_round)

9
packages/dbgpt-core/src/dbgpt/model/utils/chatgpt_utils.py
View File

@ -173,9 +173,10 @@ class OpenAIStreamingOutputOperator(TransformStreamAbsOperator[ModelOutput, str]
before parent operator(Streaming Operator is trigger by downstream
Operator).
"""
return await self.current_dag_context.get_from_share_data(
model_name = await self.current_dag_context.get_from_share_data(
BaseLLM.SHARE_DATA_KEY_MODEL_NAME
)
return model_name
async for output in _to_openai_stream(model_output, None, model_caller):
yield output
@ -221,7 +222,7 @@ async def _to_openai_stream(
previous_thinking_text = ""
finish_stream_events = []
async for model_output in output_iter:
if model_caller is not None:
if model is None and model_caller is not None:
if asyncio.iscoroutinefunction(model_caller):
model = await model_caller()
else:
@ -257,7 +258,9 @@ async def _to_openai_stream(
delta=DeltaMessage(content=delta_text, reasoning_content=thinking_text),
finish_reason=model_output.finish_reason,
)
chunk = ChatCompletionStreamResponse(id=id, choices=[choice_data], model=model)
chunk = ChatCompletionStreamResponse(
id=id, choices=[choice_data], model=model or ""
)
if delta_text is None and thinking_text is None:
if model_output.finish_reason is not None:
finish_stream_events.append(chunk)

7
packages/dbgpt-serve/src/dbgpt_serve/flow/api/endpoints.py
View File

@ -469,7 +469,9 @@ async def export_flow(
else:
# Return the json file
return StreamingResponse(
io.BytesIO(json.dumps(flow_dict, ensure_ascii=False).encode("utf-8")),
io.BytesIO(
json.dumps(flow_dict, ensure_ascii=False, indent=4).encode("utf-8")
),
media_type="application/file",
headers={
"Content-Disposition": f"attachment;filename={file_name}.json"
@ -538,7 +540,6 @@ async def import_flow(
@router.get(
"/flow/templates",
response_model=Result[PaginationResult[ServerResponse]],
dependencies=[Depends(check_api_key)],
)
async def query_flow_templates(
@ -547,7 +548,7 @@ async def query_flow_templates(
page: int = Query(default=1, description="current page"),
page_size: int = Query(default=20, description="page size"),
service: Service = Depends(get_service),
) -> Result[PaginationResult[ServerResponse]]:
):
"""Query Flow templates."""
res = await blocking_func_to_async(

6
packages/dbgpt-serve/src/dbgpt_serve/flow/service/service.py
View File

@ -1,7 +1,7 @@
import json
import logging
import os
from typing import AsyncIterator, List, Optional, Tuple, cast
from typing import AsyncIterator, Dict, List, Optional, Tuple, cast
import schedule
from fastapi import HTTPException
@ -413,7 +413,7 @@ class Service(BaseService[ServeEntity, ServeRequest, ServerResponse]):
sys_code: Optional[str] = None,
page: int = 1,
page_size: int = 20,
) -> PaginationResult[ServerResponse]:
) -> PaginationResult[Dict]:
"""Get a list of Flow templates
Args:
@ -429,7 +429,7 @@ class Service(BaseService[ServeEntity, ServeRequest, ServerResponse]):
templates = []
for _, t in local_file_templates:
fill_flow_panel(t)
templates.append(t)
templates.append(t.to_dict())
return PaginationResult.build_from_all(templates, page, page_size)
async def chat_stream_flow_str(

573
packages/dbgpt-serve/src/dbgpt_serve/flow/templates/en/simple_streaming_chat_flow.json Normal file
View File

@ -0,0 +1,573 @@
{
"flow": {
"uid": "7a10472e-2f1c-48e1-a8f2-6a09c73a3035",
"label": "Simple Streaming Chat Workflow",
"name": "simple_streaming_chat_workflow",
"flow_category": "chat_flow",
"description": "Simple streaming conversation workflow with memory support.",
"state": "running",
"error_message": "",
"source": "DBGPT-WEB",
"source_url": null,
"version": "0.1.1",
"define_type": "json",
"editable": true,
"user_name": null,
"sys_code": null,
"dag_id": "flow_dag_simple_streaming_chat_workflow_7a10472e-2f1c-48e1-a8f2-6a09c73a3035",
"gmt_created": "2025-03-14 09:29:18",
"gmt_modified": "2025-03-14 09:29:18",
"metadata": {
"sse_output": true,
"streaming_output": true,
"tags": {},
"triggers": [
{
"trigger_type": "http",
"path": "/api/v1/awel/trigger/example/flow_dag_simple_streaming_chat_workflow_7a10472e-2f1c-48e1-a8f2-6a09c73a3035",
"methods": [
"POST"
],
"trigger_mode": "chat"
}
]
},
"variables": null,
"authors": null,
"flow_data": {
"edges": [
{
"source": "operator_common_llm_http_trigger___$$___trigger___$$___v1_0",
"source_order": 0,
"target": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0",
"target_order": 0,
"id": "operator_common_llm_http_trigger___$$___trigger___$$___v1_0|operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0",
"source_handle": "operator_common_llm_http_trigger___$$___trigger___$$___v1_0|outputs|0",
"target_handle": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0|inputs|0",
"type": "buttonedge"
},
{
"source": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0",
"source_order": 0,
"target": "operator_openai_streaming_output_operator___$$___output_parser___$$___v1_0",
"target_order": 0,
"id": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0|operator_openai_streaming_output_operator___$$___output_parser___$$___v1_0",
"source_handle": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0|outputs|0",
"target_handle": "operator_openai_streaming_output_operator___$$___output_parser___$$___v1_0|inputs|0",
"type": "buttonedge"
}
],
"viewport": {
"x": 982.8465398221631,
"y": 199.4747855714408,
"zoom": 1.286648702836454
},
"nodes": [
{
"width": 320,
"height": 632,
"id": "operator_common_llm_http_trigger___$$___trigger___$$___v1_0",
"position": {
"x": -634.976496786702,
"y": -28.51069702810255,
"zoom": 0.0
},
"type": "customNode",
"position_absolute": {
"x": -634.976496786702,
"y": -28.51069702810255,
"zoom": 0.0
},
"data": {
"label": "Common LLM Http Trigger",
"custom_label": null,
"name": "common_llm_http_trigger",
"description": "Trigger your workflow by http request, and parse the request body as a common LLM http body",
"category": "trigger",
"category_label": "Trigger",
"flow_type": "operator",
"icon": null,
"documentation_url": null,
"id": "operator_common_llm_http_trigger___$$___trigger___$$___v1_0",
"tags": {
"order": "higher-order",
"ui_version": "flow2.0"
},
"operator_type": "input",
"inputs": [],
"outputs": [
{
"type_name": "CommonLLMHttpRequestBody",
"type_cls": "dbgpt.core.awel.trigger.http_trigger.CommonLLMHttpRequestBody",
"label": "Request Body",
"custom_label": null,
"name": "request_body",
"description": "The request body of the API endpoint, parse as a common LLM http body",
"dynamic": false,
"dynamic_minimum": 0,
"is_list": false,
"mappers": null
},
{
"type_name": "str",
"type_cls": "builtins.str",
"label": "Request String Messages",
"custom_label": null,
"name": "request_string_messages",
"description": "The request string messages of the API endpoint, parsed from 'messages' field of the request body",
"dynamic": false,
"dynamic_minimum": 0,
"is_list": false,
"mappers": [
"dbgpt.core.awel.trigger.http_trigger.CommonLLMHttpTrigger.MessagesOutputMapper"
]
}
],
"version": "v1",
"type_name": "CommonLLMHttpTrigger",
"type_cls": "dbgpt.core.awel.trigger.http_trigger.CommonLLMHttpTrigger",
"parameters": [
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "API Endpoint",
"name": "endpoint",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": "/example/{dag_id}",
"placeholder": null,
"description": "The API endpoint",
"value": null,
"options": null
},
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "Http Methods",
"name": "methods",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": "POST",
"placeholder": null,
"description": "The methods of the API endpoint",
"value": null,
"options": [
{
"label": "HTTP Method PUT",
"name": "http_put",
"value": "PUT",
"children": null
},
{
"label": "HTTP Method POST",
"name": "http_post",
"value": "POST",
"children": null
}
]
},
{
"type_name": "bool",
"type_cls": "builtins.bool",
"dynamic": false,
"dynamic_minimum": 0,
"label": "Streaming Response",
"name": "streaming_response",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": false,
"placeholder": null,
"description": "Whether the response is streaming",
"value": false,
"options": null
},
{
"type_name": "BaseHttpBody",
"type_cls": "dbgpt.core.awel.trigger.http_trigger.BaseHttpBody",
"dynamic": false,
"dynamic_minimum": 0,
"label": "Http Response Body",
"name": "http_response_body",
"is_list": false,
"category": "resource",
"resource_type": "class",
"optional": true,
"default": null,
"placeholder": null,
"description": "The response body of the API endpoint",
"value": null,
"options": null
},
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "Response Media Type",
"name": "response_media_type",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "The response media type",
"value": null,
"options": null
},
{
"type_name": "int",
"type_cls": "builtins.int",
"dynamic": false,
"dynamic_minimum": 0,
"label": "Http Status Code",
"name": "status_code",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": 200,
"placeholder": null,
"description": "The http status code",
"value": null,
"options": null
}
]
}
},
{
"width": 320,
"height": 909,
"id": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0",
"position": {
"x": -138.14659213348716,
"y": -103.15617900792515,
"zoom": 0.0
},
"type": "customNode",
"position_absolute": {
"x": -138.14659213348716,
"y": -103.15617900792515,
"zoom": 0.0
},
"data": {
"label": "Streaming LLM Operator",
"custom_label": null,
"name": "higher_order_streaming_llm_operator",
"description": "High-level streaming LLM operator, supports multi-round conversation (conversation window, token length and no multi-round).",
"category": "llm",
"category_label": "LLM",
"flow_type": "operator",
"icon": null,
"documentation_url": null,
"id": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0",
"tags": {
"order": "higher-order",
"ui_version": "flow2.0"
},
"operator_type": "map",
"inputs": [
{
"type_name": "CommonLLMHttpRequestBody",
"type_cls": "dbgpt.core.awel.trigger.http_trigger.CommonLLMHttpRequestBody",
"label": "Common LLM Request Body",
"custom_label": null,
"name": "common_llm_request_body",
"description": "The common LLM request body.",
"dynamic": false,
"dynamic_minimum": 0,
"is_list": false,
"mappers": null
},
{
"type_name": "HOContextBody",
"type_cls": "dbgpt_app.operators.llm.HOContextBody",
"label": "Extra Context",
"custom_label": null,
"name": "extra_context",
"description": "Extra context for building prompt(Knowledge context, database schema, etc), you can add multiple context.",
"dynamic": true,
"dynamic_minimum": 0,
"is_list": false,
"mappers": null
}
],
"outputs": [
{
"type_name": "ModelOutput",
"type_cls": "dbgpt.core.interface.llm.ModelOutput",
"label": "Streaming Model Output",
"custom_label": null,
"name": "streaming_model_output",
"description": "The streaming model output.",
"dynamic": false,
"dynamic_minimum": 0,
"is_list": true,
"mappers": null
}
],
"version": "v1",
"type_name": "HOStreamingLLMOperator",
"type_cls": "dbgpt_app.operators.llm.HOStreamingLLMOperator",
"parameters": [
{
"type_name": "ChatPromptTemplate",
"type_cls": "dbgpt.core.interface.prompt.ChatPromptTemplate",
"dynamic": false,
"dynamic_minimum": 0,
"label": "Prompt Template",
"name": "prompt_template",
"is_list": false,
"category": "resource",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "The prompt template for the conversation.",
"value": null,
"options": null
},
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "Model Name",
"name": "model",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "The model name.",
"value": null,
"options": null
},
{
"type_name": "LLMClient",
"type_cls": "dbgpt.core.interface.llm.LLMClient",
"dynamic": false,
"dynamic_minimum": 0,
"label": "LLM Client",
"name": "llm_client",
"is_list": false,
"category": "resource",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "The LLM Client, how to connect to the LLM model, if not provided, it will use the default client deployed by DB-GPT.",
"value": null,
"options": null
},
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "History Message Merge Mode",
"name": "history_merge_mode",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": "none",
"placeholder": null,
"description": "The history merge mode, supports 'none', 'window' and 'token'. 'none': no history merge, 'window': merge by conversation window, 'token': merge by token length.",
"value": "window",
"options": [
{
"label": "No History",
"name": "none",
"value": "none",
"children": null
},
{
"label": "Message Window",
"name": "window",
"value": "window",
"children": null
},
{
"label": "Token Length",
"name": "token",
"value": "token",
"children": null
}
],
"ui": {
"refresh": false,
"refresh_depends": null,
"ui_type": "select",
"size": null,
"attr": null
}
},
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "User Message Key",
"name": "user_message_key",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": "user_input",
"placeholder": null,
"description": "The key of the user message in your prompt, default is 'user_input'.",
"value": null,
"options": null
},
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "History Key",
"name": "history_key",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "The chat history key, with chat history message pass to prompt template, if not provided, it will parse the prompt template to get the key.",
"value": null,
"options": null
},
{
"type_name": "int",
"type_cls": "builtins.int",
"dynamic": false,
"dynamic_minimum": 0,
"label": "Keep Start Rounds",
"name": "keep_start_rounds",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "The start rounds to keep in the chat history.",
"value": null,
"options": null
},
{
"type_name": "int",
"type_cls": "builtins.int",
"dynamic": false,
"dynamic_minimum": 0,
"label": "Keep End Rounds",
"name": "keep_end_rounds",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "The end rounds to keep in the chat history.",
"value": 10,
"options": null
},
{
"type_name": "int",
"type_cls": "builtins.int",
"dynamic": false,
"dynamic_minimum": 0,
"label": "Max Token Limit",
"name": "max_token_limit",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": 2048,
"placeholder": null,
"description": "The max token limit to keep in the chat history.",
"value": 8196,
"options": null
}
]
}
},
{
"width": 320,
"height": 235,
"id": "operator_openai_streaming_output_operator___$$___output_parser___$$___v1_0",
"position": {
"x": 302.45509852062725,
"y": 272.4327267076214,
"zoom": 0.0
},
"type": "customNode",
"position_absolute": {
"x": 302.45509852062725,
"y": 272.4327267076214,
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},
"data": {
"label": "OpenAI Streaming Output Operator",
"custom_label": null,
"name": "openai_streaming_output_operator",
"description": "The OpenAI streaming LLM operator.",
"category": "output_parser",
"category_label": "Output Parser",
"flow_type": "operator",
"icon": null,
"documentation_url": null,
"id": "operator_openai_streaming_output_operator___$$___output_parser___$$___v1_0",
"tags": {
"order": "higher-order",
"ui_version": "flow2.0"
},
"operator_type": "transform_stream",
"inputs": [
{
"type_name": "ModelOutput",
"type_cls": "dbgpt.core.interface.llm.ModelOutput",
"label": "Upstream Model Output",
"custom_label": null,
"name": "model_output",
"description": "The model output of upstream.",
"dynamic": false,
"dynamic_minimum": 0,
"is_list": true,
"mappers": null
}
],
"outputs": [
{
"type_name": "str",
"type_cls": "builtins.str",
"label": "Model Output",
"custom_label": null,
"name": "model_output",
"description": "The model output after transformed to openai stream format.",
"dynamic": false,
"dynamic_minimum": 0,
"is_list": true,
"mappers": null
}
],
"version": "v1",
"type_name": "OpenAIStreamingOutputOperator",
"type_cls": "dbgpt.model.utils.chatgpt_utils.OpenAIStreamingOutputOperator",
"parameters": []
}
}
]
}
}
}

573
packages/dbgpt-serve/src/dbgpt_serve/flow/templates/zh/simple_streaming_chat_flow.json Normal file
View File

@ -0,0 +1,573 @@
{
"flow": {
"uid": "71d2401f-4703-48f8-9cbc-35502a565c1e",
"label": "简单流式对话工作流",
"name": "simple_streaming_chat_workflow",
"flow_category": "chat_flow",
"description": "简单的流式对话工作流，支持记忆。",
"state": "running",
"error_message": "",
"source": "DBGPT-WEB",
"source_url": null,
"version": "0.1.1",
"define_type": "json",
"editable": true,
"user_name": null,
"sys_code": null,
"dag_id": "flow_dag_simple_streaming_chat_workflow_71d2401f-4703-48f8-9cbc-35502a565c1e",
"gmt_created": "2025-03-14 09:20:38",
"gmt_modified": "2025-03-14 09:20:38",
"metadata": {
"sse_output": true,
"streaming_output": true,
"tags": {},
"triggers": [
{
"trigger_type": "http",
"path": "/api/v1/awel/trigger/example/flow_dag_simple_streaming_chat_workflow_71d2401f-4703-48f8-9cbc-35502a565c1e",
"methods": [
"POST"
],
"trigger_mode": "chat"
}
]
},
"variables": null,
"authors": null,
"flow_data": {
"edges": [
{
"source": "operator_common_llm_http_trigger___$$___trigger___$$___v1_0",
"source_order": 0,
"target": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0",
"target_order": 0,
"id": "operator_common_llm_http_trigger___$$___trigger___$$___v1_0|operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0",
"source_handle": "operator_common_llm_http_trigger___$$___trigger___$$___v1_0|outputs|0",
"target_handle": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0|inputs|0",
"type": "buttonedge"
},
{
"source": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0",
"source_order": 0,
"target": "operator_openai_streaming_output_operator___$$___output_parser___$$___v1_0",
"target_order": 0,
"id": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0|operator_openai_streaming_output_operator___$$___output_parser___$$___v1_0",
"source_handle": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0|outputs|0",
"target_handle": "operator_openai_streaming_output_operator___$$___output_parser___$$___v1_0|inputs|0",
"type": "buttonedge"
}
],
"viewport": {
"x": 719.3238528343023,
"y": 132.13267383901623,
"zoom": 1.0443554904294883
},
"nodes": [
{
"width": 320,
"height": 632,
"id": "operator_common_llm_http_trigger___$$___trigger___$$___v1_0",
"position": {
"x": -437.17668641409807,
"y": -59.481076916763314,
"zoom": 0.0
},
"type": "customNode",
"position_absolute": {
"x": -437.17668641409807,
"y": -59.481076916763314,
"zoom": 0.0
},
"data": {
"label": "通用 LLM HTTP 触发器",
"custom_label": null,
"name": "common_llm_http_trigger",
"description": "通过 HTTP 请求触发工作流，并将请求体解析为通用 LLM HTTP 请求体",
"category": "trigger",
"category_label": "Trigger",
"flow_type": "operator",
"icon": null,
"documentation_url": null,
"id": "operator_common_llm_http_trigger___$$___trigger___$$___v1_0",
"tags": {
"order": "higher-order",
"ui_version": "flow2.0"
},
"operator_type": "input",
"inputs": [],
"outputs": [
{
"type_name": "CommonLLMHttpRequestBody",
"type_cls": "dbgpt.core.awel.trigger.http_trigger.CommonLLMHttpRequestBody",
"label": "Request Body",
"custom_label": null,
"name": "request_body",
"description": "The request body of the API endpoint, parse as a common LLM http body",
"dynamic": false,
"dynamic_minimum": 0,
"is_list": false,
"mappers": null
},
{
"type_name": "str",
"type_cls": "builtins.str",
"label": "Request String Messages",
"custom_label": null,
"name": "request_string_messages",
"description": "The request string messages of the API endpoint, parsed from 'messages' field of the request body",
"dynamic": false,
"dynamic_minimum": 0,
"is_list": false,
"mappers": [
"dbgpt.core.awel.trigger.http_trigger.CommonLLMHttpTrigger.MessagesOutputMapper"
]
}
],
"version": "v1",
"type_name": "CommonLLMHttpTrigger",
"type_cls": "dbgpt.core.awel.trigger.http_trigger.CommonLLMHttpTrigger",
"parameters": [
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "API 端点",
"name": "endpoint",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": "/example/{dag_id}",
"placeholder": null,
"description": "API 端点",
"value": null,
"options": null
},
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "HTTP 方法",
"name": "methods",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": "POST",
"placeholder": null,
"description": "API 端点的方法",
"value": null,
"options": [
{
"label": "HTTP Method PUT",
"name": "http_put",
"value": "PUT",
"children": null
},
{
"label": "HTTP Method POST",
"name": "http_post",
"value": "POST",
"children": null
}
]
},
{
"type_name": "bool",
"type_cls": "builtins.bool",
"dynamic": false,
"dynamic_minimum": 0,
"label": "流式响应",
"name": "streaming_response",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": false,
"placeholder": null,
"description": "响应是否为流式",
"value": false,
"options": null
},
{
"type_name": "BaseHttpBody",
"type_cls": "dbgpt.core.awel.trigger.http_trigger.BaseHttpBody",
"dynamic": false,
"dynamic_minimum": 0,
"label": "HTTP 响应体",
"name": "http_response_body",
"is_list": false,
"category": "resource",
"resource_type": "class",
"optional": true,
"default": null,
"placeholder": null,
"description": "API 端点的响应体",
"value": null,
"options": null
},
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "响应媒体类型",
"name": "response_media_type",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "响应媒体类型",
"value": null,
"options": null
},
{
"type_name": "int",
"type_cls": "builtins.int",
"dynamic": false,
"dynamic_minimum": 0,
"label": "HTTP 状态码",
"name": "status_code",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": 200,
"placeholder": null,
"description": "HTTP 状态码",
"value": null,
"options": null
}
]
}
},
{
"width": 320,
"height": 909,
"id": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0",
"position": {
"x": 170.06343404314669,
"y": -30.119628304764014,
"zoom": 0.0
},
"type": "customNode",
"position_absolute": {
"x": 170.06343404314669,
"y": -30.119628304764014,
"zoom": 0.0
},
"data": {
"label": "流式 LLM 算子",
"custom_label": null,
"name": "higher_order_streaming_llm_operator",
"description": "高级流式 LLM 算子，支持多轮对话（对话窗口、Token 长度和无多轮）。",
"category": "llm",
"category_label": "LLM",
"flow_type": "operator",
"icon": null,
"documentation_url": null,
"id": "operator_higher_order_streaming_llm_operator___$$___llm___$$___v1_0",
"tags": {
"order": "higher-order",
"ui_version": "flow2.0"
},
"operator_type": "map",
"inputs": [
{
"type_name": "CommonLLMHttpRequestBody",
"type_cls": "dbgpt.core.awel.trigger.http_trigger.CommonLLMHttpRequestBody",
"label": "Common LLM Request Body",
"custom_label": null,
"name": "common_llm_request_body",
"description": "The common LLM request body.",
"dynamic": false,
"dynamic_minimum": 0,
"is_list": false,
"mappers": null
},
{
"type_name": "HOContextBody",
"type_cls": "dbgpt_app.operators.llm.HOContextBody",
"label": "Extra Context",
"custom_label": null,
"name": "extra_context",
"description": "Extra context for building prompt(Knowledge context, database schema, etc), you can add multiple context.",
"dynamic": true,
"dynamic_minimum": 0,
"is_list": false,
"mappers": null
}
],
"outputs": [
{
"type_name": "ModelOutput",
"type_cls": "dbgpt.core.interface.llm.ModelOutput",
"label": "Streaming Model Output",
"custom_label": null,
"name": "streaming_model_output",
"description": "The streaming model output.",
"dynamic": false,
"dynamic_minimum": 0,
"is_list": true,
"mappers": null
}
],
"version": "v1",
"type_name": "HOStreamingLLMOperator",
"type_cls": "dbgpt_app.operators.llm.HOStreamingLLMOperator",
"parameters": [
{
"type_name": "ChatPromptTemplate",
"type_cls": "dbgpt.core.interface.prompt.ChatPromptTemplate",
"dynamic": false,
"dynamic_minimum": 0,
"label": "提示模板",
"name": "prompt_template",
"is_list": false,
"category": "resource",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "对话的提示模板。",
"value": null,
"options": null
},
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "模型名称",
"name": "model",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "模型名称。",
"value": null,
"options": null
},
{
"type_name": "LLMClient",
"type_cls": "dbgpt.core.interface.llm.LLMClient",
"dynamic": false,
"dynamic_minimum": 0,
"label": "LLM 客户端",
"name": "llm_client",
"is_list": false,
"category": "resource",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "LLM 客户端，如何连接到 LLM 模型，如果未提供，则使用由 DB-GPT 部署的默认客户端。",
"value": null,
"options": null
},
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "历史消息合并模式",
"name": "history_merge_mode",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": "none",
"placeholder": null,
"description": "历史合并模式，支持 'none'、'window' 和 'token'。'none'：不合并历史，'window'：按对话窗口合并，'token'：按 Token 长度合并。",
"value": "window",
"options": [
{
"label": "No History",
"name": "none",
"value": "none",
"children": null
},
{
"label": "Message Window",
"name": "window",
"value": "window",
"children": null
},
{
"label": "Token Length",
"name": "token",
"value": "token",
"children": null
}
],
"ui": {
"refresh": false,
"refresh_depends": null,
"ui_type": "select",
"size": null,
"attr": null
}
},
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "用户消息键",
"name": "user_message_key",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": "user_input",
"placeholder": null,
"description": "提示词中用户消息的键，默认为 'user_input'。",
"value": null,
"options": null
},
{
"type_name": "str",
"type_cls": "builtins.str",
"dynamic": false,
"dynamic_minimum": 0,
"label": "历史键",
"name": "history_key",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "聊天历史键，用于将聊天历史消息传递给提示模板，如果未提供，它将解析提示模板以获取键。",
"value": null,
"options": null
},
{
"type_name": "int",
"type_cls": "builtins.int",
"dynamic": false,
"dynamic_minimum": 0,
"label": "保留起始轮次",
"name": "keep_start_rounds",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "在聊天历史中保留的起始轮次。",
"value": null,
"options": null
},
{
"type_name": "int",
"type_cls": "builtins.int",
"dynamic": false,
"dynamic_minimum": 0,
"label": "保留结束轮次",
"name": "keep_end_rounds",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": null,
"placeholder": null,
"description": "在聊天历史中保留的结束轮次。",
"value": 10,
"options": null
},
{
"type_name": "int",
"type_cls": "builtins.int",
"dynamic": false,
"dynamic_minimum": 0,
"label": "最大 Token 限制",
"name": "max_token_limit",
"is_list": false,
"category": "common",
"resource_type": "instance",
"optional": true,
"default": 2048,
"placeholder": null,
"description": "在聊天历史中保留的最大 Token 数量。",
"value": 8196,
"options": null
}
]
}
},
{
"width": 320,
"height": 235,
"id": "operator_openai_streaming_output_operator___$$___output_parser___$$___v1_0",
"position": {
"x": 641.7322082533901,
"y": 237.60419500639273,
"zoom": 0.0
},
"type": "customNode",
"position_absolute": {
"x": 641.7322082533901,
"y": 237.60419500639273,
"zoom": 0.0
},
"data": {
"label": "OpenAI 流式输出算子",
"custom_label": null,
"name": "openai_streaming_output_operator",
"description": "OpenAI 流式大语言模型算子。",
"category": "output_parser",
"category_label": "Output Parser",
"flow_type": "operator",
"icon": null,
"documentation_url": null,
"id": "operator_openai_streaming_output_operator___$$___output_parser___$$___v1_0",
"tags": {
"order": "higher-order",
"ui_version": "flow2.0"
},
"operator_type": "transform_stream",
"inputs": [
{
"type_name": "ModelOutput",
"type_cls": "dbgpt.core.interface.llm.ModelOutput",
"label": "Upstream Model Output",
"custom_label": null,
"name": "model_output",
"description": "The model output of upstream.",
"dynamic": false,
"dynamic_minimum": 0,
"is_list": true,
"mappers": null
}
],
"outputs": [
{
"type_name": "str",
"type_cls": "builtins.str",
"label": "Model Output",
"custom_label": null,
"name": "model_output",
"description": "The model output after transformed to openai stream format.",
"dynamic": false,
"dynamic_minimum": 0,
"is_list": true,
"mappers": null
}
],
"version": "v1",
"type_name": "OpenAIStreamingOutputOperator",
"type_cls": "dbgpt.model.utils.chatgpt_utils.OpenAIStreamingOutputOperator",
"parameters": []
}
}
]
}
}
}

14
web/components/flow/canvas-modal/save-flow-modal.tsx
View File

@ -39,12 +39,20 @@ export const SaveFlowModal: React.FC<Props> = ({
setId(router.query.id || '');
}, [router.query.id]);
// function onLabelChange(e: React.ChangeEvent<HTMLInputElement>) {
// const label = e.target.value;
// // replace spaces with underscores, convert uppercase letters to lowercase, remove characters other than digits, letters, _, and -.
// const result = label
// .replace(/\s+/g, '_')
// .replace(/[^a-z0-9_-]/g, '')
// .toLowerCase();
// form.setFieldsValue({ name: result });
// }
function onLabelChange(e: React.ChangeEvent<HTMLInputElement>) {
const label = e.target.value;
// replace spaces with underscores, convert uppercase letters to lowercase, remove characters other than digits, letters, _, and -.
// replace spaces with underscores, convert letters to lowercase, but preserve Chinese and other characters
const result = label
.replace(/\s+/g, '_')
.replace(/[^a-z0-9_-]/g, '')
.replace(/\s+/g, '_') // replace spaces with underscores
.toLowerCase();
form.setFieldsValue({ name: result });
}

3
web/components/flow/canvas-node.tsx
View File

@ -140,7 +140,7 @@ const CanvasNode: React.FC<CanvasNodeProps> = ({ data }) => {
<TypeLabel label='Outputs' />
{outputs?.map((output, index) => (
<NodeHandler
key={`${data.id}_input_${index}`}
key={`${data.id}_output_${index}`}
node={data}
data={output}
type='source'
@ -159,6 +159,7 @@ const CanvasNode: React.FC<CanvasNodeProps> = ({ data }) => {
</div>
);
}
return null;
}
return (

194
web/components/flow/node-handler.tsx
View File

@ -1,7 +1,7 @@
import { IFlowNode, IFlowNodeInput, IFlowNodeOutput, IFlowNodeParameter } from '@/types/flow';
import { FLOW_NODES_KEY } from '@/utils';
import { InfoCircleOutlined, PlusOutlined } from '@ant-design/icons';
import { Popconfirm, Tooltip, Typography, message } from 'antd';
import { InfoCircleOutlined, MinusCircleOutlined, PlusOutlined } from '@ant-design/icons';
import { Button, Popconfirm, Tooltip, Typography, message } from 'antd';
import classNames from 'classnames';
import React from 'react';
import { useTranslation } from 'react-i18next';
@ -23,6 +23,20 @@ const NodeHandler: React.FC<NodeHandlerProps> = ({ node, data, type, label, inde
const reactflow = useReactFlow();
const [relatedNodes, setRelatedNodes] = React.useState<IFlowNode[]>([]);
const dynamic = data.dynamic || false;
const dynamicMinimum = data.dynamic_minimum || 0;
// Determine if input is optional based on dynamic and dynamicMinimum
const isOptional = () => {
if (dynamic) {
// When dynamic is true, it's optional if dynamicMinimum is 0
return dynamicMinimum === 0;
} else {
// When dynamic is false, use the original logic
return data.optional;
}
};
function isValidConnection(connection: Connection) {
const { sourceHandle, targetHandle, source, target } = connection;
const sourceNode = reactflow.getNode(source!);
@ -49,15 +63,20 @@ const NodeHandler: React.FC<NodeHandlerProps> = ({ node, data, type, label, inde
return false;
}
function showRelatedNodes() {
function showRelatedNodes(e: React.MouseEvent) {
e.stopPropagation();
e.preventDefault();
// find all nodes that can be connected to this node
const cache = localStorage.getItem(FLOW_NODES_KEY);
if (!cache) {
return;
}
const staticNodes = JSON.parse(cache);
const typeCls = data.type_cls;
let nodes: IFlowNode[] = [];
if (label === 'inputs') {
// find other operators and outputs matching this input type_cls
nodes = staticNodes
@ -89,9 +108,115 @@ const NodeHandler: React.FC<NodeHandlerProps> = ({ node, data, type, label, inde
);
}
}
setRelatedNodes(nodes);
}
// Add new dynamic field
function addDynamicField(e: React.MouseEvent) {
console.log('addDynamicField clicked', e);
e.stopPropagation();
e.preventDefault();
console.log(`Adding dynamic field for node ${node.id}, label=${label}, current field name=${data.name}`);
// Get current IO array
const ioArray = [...node[label]];
// Get the original field template
const fieldTemplate = { ...data };
// CHECK: How many dynamic fields of this type already exist
const dynamicFieldsCount = ioArray.filter(
item => item.type_cls === data.type_cls && item.name.startsWith(data.name),
).length;
// Create a new field based on the template
const newField = {
...fieldTemplate,
name: `${data.name}_${dynamicFieldsCount}`,
// keep the dynamic flag but reset the value
value: null,
};
// Push the new field to the array
ioArray.push(newField);
// Update the nodes in the flow
reactflow.setNodes(nodes => {
return nodes.map(n => {
if (n.id === node.id) {
return {
...n,
data: {
...n.data,
[label]: ioArray,
},
};
}
return n;
});
});
}
// Remove dynamic field
function removeDynamicField(e: React.MouseEvent) {
e.stopPropagation();
e.preventDefault();
// Get the count of dynamic fields of this type
const ioArray = [...node[label]];
const dynamicFields = ioArray.filter(item => item.type_cls === data.type_cls && item.name.startsWith(data.name));
console.log(
`Removing dynamic field at index ${index}, total count: ${dynamicFields.length}, minimum: ${dynamicMinimum}`,
);
// Make sure we don't go below the minimum
if (dynamicFields.length <= dynamicMinimum) {
console.log(`Cannot remove: already at minimum (${dynamicMinimum})`);
message.warning(t('minimum_dynamic_fields_warning', { count: dynamicMinimum }));
return;
}
// Remove the field at the current index
const updatedArray = ioArray.filter((_, idx) => idx !== index);
// Update the node data in the flow
reactflow.setNodes(nodes => {
return nodes.map(n => {
if (n.id === node.id) {
return {
...n,
data: {
...n.data,
[label]: updatedArray,
},
};
}
return n;
});
});
// Update the edges connected to this handle
const handleId = `${node.id}|${label}|${index}`;
reactflow.setEdges(edges => {
return edges.filter(
edge =>
(type === 'source' && edge.sourceHandle !== handleId) ||
(type === 'target' && edge.targetHandle !== handleId),
);
});
}
// Check if this field is the last one of this type (for dynamic fields)
const isLastDynamicField = () => {
if (!dynamic) return false;
const ioArray = node[label];
const dynamicFields = ioArray.filter(item => item.type_cls === data.type_cls && item.name.startsWith(data.name));
return index === ioArray.indexOf(dynamicFields[dynamicFields.length - 1]);
};
return (
<div
className={classNames('relative flex items-center', {
@ -124,16 +249,36 @@ const NodeHandler: React.FC<NodeHandlerProps> = ({ node, data, type, label, inde
}
>
{['inputs', 'parameters'].includes(label) && (
<PlusOutlined className='cursor-pointer' onClick={showRelatedNodes} />
<PlusOutlined
className='cursor-pointer mr-1'
onClick={e => {
e.stopPropagation();
e.preventDefault();
showRelatedNodes(e);
}}
/>
)}
</Popconfirm>
{label !== 'outputs' && <RequiredIcon optional={data.optional} />}
{['inputs', 'parameters'].includes(label) && dynamic && index >= dynamicMinimum && (
<MinusCircleOutlined
className='cursor-pointer text-red-500 mr-1'
onClick={e => {
e.stopPropagation();
e.preventDefault();
removeDynamicField(e);
}}
/>
)}
{label !== 'outputs' && <RequiredIcon optional={isOptional()} />}
{data.type_name}
{data.description && (
<Tooltip title={data.description}>
<InfoCircleOutlined className='ml-2 cursor-pointer' />
</Tooltip>
)}
<Popconfirm
placement='right'
icon={null}
@ -146,8 +291,45 @@ const NodeHandler: React.FC<NodeHandlerProps> = ({ node, data, type, label, inde
</div>
}
>
{['outputs'].includes(label) && <PlusOutlined className='ml-2 cursor-pointer' onClick={showRelatedNodes} />}
{['outputs'].includes(label) && (
<PlusOutlined
className='ml-2 cursor-pointer'
onClick={e => {
e.stopPropagation();
e.preventDefault();
showRelatedNodes(e);
}}
/>
)}
</Popconfirm>
{['outputs'].includes(label) && dynamic && index >= dynamicMinimum && (
<MinusCircleOutlined
className='ml-2 cursor-pointer text-red-500'
onClick={e => {
e.stopPropagation();
e.preventDefault();
removeDynamicField(e);
}}
/>
)}
{/* Add dynamic field button */}
{dynamic && isLastDynamicField() && (
<Button
type='primary'
size='small'
className='ml-2'
onClick={e => {
e.stopPropagation();
e.preventDefault();
addDynamicField(e);
}}
style={{ float: label === 'outputs' ? 'left' : 'right' }}
>
add
</Button>
)}
</Typography>
</div>
);

1
web/locales/en/flow.ts
View File

@ -26,4 +26,5 @@ export const FlowEn = {
Template_Name: 'Template Name',
Template_Label: 'Label',
Template_Action: 'Action',
minimum_dynamic_fields_warning: 'Please keep more than one dynamic field',
};

1
web/locales/zh/flow.ts
View File

@ -26,4 +26,5 @@ export const FlowZn = {
Template_Name: '模版名称',
Template_Label: '标签',
Template_Action: '操作',
minimum_dynamic_fields_warning: '请保留一个以上的动态字段',
};

6
web/types/flow.ts
View File

@ -70,6 +70,8 @@ export type IFlowNodeParameter = {
options?: any;
value: any;
is_list?: boolean;
dynamic?: boolean;
dynamic_minimum?: number;
ui: IFlowNodeParameterUI;
};
@ -101,6 +103,8 @@ export type IFlowNodeInput = {
optional?: boolean | undefined;
value: any;
is_list?: boolean;
dynamic?: boolean;
dynamic_minimum?: number;
};
export type IFlowNodeOutput = {
@ -112,6 +116,8 @@ export type IFlowNodeOutput = {
id: string;
optional?: boolean | undefined;
is_list?: boolean;
dynamic?: boolean;
dynamic_minimum?: number;
};
export type IFlowNode = Node & {

157
web/utils/flow.ts
View File

@ -76,23 +76,157 @@ export const mapUnderlineToHump = (flowData: IFlowData) => {
};
};
// Helper function to check if a dynamic input/output has enough connections
const checkDynamicConnections = (
nodeId: string,
fieldType: string,
// @typescript-eslint/no-unused-vars
fieldIndex: number,
edges: any[],
dynamicMinimum: number,
): boolean => {
// Count connections for this specific field type
const handlePrefix = `${nodeId}|${fieldType}|`;
const connectionCount = edges.filter(edge => {
// For inputs, check targetHandle; for outputs, check sourceHandle
const handle = fieldType === 'inputs' ? edge.targetHandle : edge.sourceHandle;
if (!handle) return false;
// Check if the handle belongs to this node and field type
return handle.startsWith(handlePrefix);
}).length;
// Return true if we have at least the minimum required connections
return connectionCount >= dynamicMinimum;
};
// Helper function to identify dynamic field groups
const getDynamicFieldGroups = (fields: any[]) => {
const groups: Record<string, any[]> = {};
fields.forEach(field => {
if (field.dynamic) {
// Extract base name (remove _X suffix if present)
const baseName = field.name.replace(/_\d+$/, '');
if (!groups[baseName]) {
groups[baseName] = [];
}
groups[baseName].push(field);
}
});
return groups;
};
// Helper function to validate dynamic parameters
const validateDynamicParameters = (node: IFlowDataNode): [boolean, string] => {
if (!node.data.parameters || node.data.parameters.length === 0) {
return [true, ''];
}
// Find all dynamic parameter groups
const dynamicParamGroups = getDynamicFieldGroups(node.data.parameters);
// Check each group
for (const [baseName, fields] of Object.entries(dynamicParamGroups)) {
const minimumRequired = fields[0].dynamic_minimum || 0;
// Skip if minimum is 0
if (minimumRequired === 0) continue;
// For dynamic parameters, we check if we have at least the minimum number
if (fields.length < minimumRequired) {
return [
false,
`The dynamic parameter ${baseName} of node ${node.data.label} requires at least ${minimumRequired} parameters`,
];
}
// Check if any required parameters are missing values
const requiredFields = fields.filter(field => !field.optional);
for (const field of requiredFields) {
if (field.value === undefined || field.value === null) {
return [false, `The parameter ${field.name} of node ${node.data.label} is required`];
}
}
}
return [true, ''];
};
export const checkFlowDataRequied = (flowData: IFlowData) => {
const { nodes, edges } = flowData;
// check the input, parameters that are required
let result: [boolean, IFlowDataNode, string] = [true, nodes[0], ''];
outerLoop: for (let i = 0; i < nodes.length; i++) {
const node = nodes[i].data;
const { inputs = [], parameters = [] } = node;
// check inputs
// Check dynamic input groups first
const dynamicInputGroups = getDynamicFieldGroups(inputs);
for (const [baseName, fields] of Object.entries(dynamicInputGroups)) {
const minimumRequired = fields[0].dynamic_minimum || 0;
if (minimumRequired > 0) {
// For dynamic fields, we check connections across all fields of this type
const hasEnoughConnections = checkDynamicConnections(nodes[i].id, 'inputs', 0, edges, minimumRequired);
if (!hasEnoughConnections) {
result = [
false,
nodes[i],
`The dynamic input ${baseName} of node ${node.label} requires at least ${minimumRequired} connections`,
];
break outerLoop;
}
}
}
// Check individual inputs
for (let j = 0; j < inputs.length; j++) {
if (!edges.some(edge => edge.targetHandle === `${nodes[i].id}|inputs|${j}`)) {
const input = inputs[j];
// Skip dynamic inputs that were checked above
if (input.dynamic) continue;
const isRequired = !input.optional;
if (isRequired && !edges.some(edge => edge.targetHandle === `${nodes[i].id}|inputs|${j}`)) {
result = [false, nodes[i], `The input ${inputs[j].type_name} of node ${node.label} is required`];
break outerLoop;
}
}
// Validate dynamic parameters
const [paramsValid, errorMessage] = validateDynamicParameters(nodes[i]);
if (!paramsValid) {
result = [false, nodes[i], errorMessage];
break outerLoop;
}
// Check dynamic parameter groups
const dynamicParamGroups = getDynamicFieldGroups(parameters);
for (const [baseName, fields] of Object.entries(dynamicParamGroups)) {
const minimumRequired = fields[0].dynamic_minimum || 0;
if (minimumRequired > 0 && fields[0].category === 'resource') {
// For dynamic params, check connections across all params of this type
const hasEnoughConnections = checkDynamicConnections(nodes[i].id, 'parameters', 0, edges, minimumRequired);
if (!hasEnoughConnections) {
result = [
false,
nodes[i],
`The dynamic parameter ${baseName} of node ${node.label} requires at least ${minimumRequired} connections`,
];
break outerLoop;
}
}
}
// check parameters
for (let k = 0; k < parameters.length; k++) {
const parameter = parameters[k];
// Skip dynamic parameters that were checked above
if (parameter.dynamic) continue;
if (
!parameter.optional &&
parameter.category === 'resource' &&
@ -109,7 +243,26 @@ export const checkFlowDataRequied = (flowData: IFlowData) => {
break outerLoop;
}
}
// Check dynamic output groups
const dynamicOutputGroups = getDynamicFieldGroups(node.outputs || []);
for (const [baseName, fields] of Object.entries(dynamicOutputGroups)) {
const minimumRequired = fields[0].dynamic_minimum || 0;
if (minimumRequired > 0) {
// For dynamic outputs, check connections across all outputs of this type
const hasEnoughConnections = checkDynamicConnections(nodes[i].id, 'outputs', 0, edges, minimumRequired);
if (!hasEnoughConnections) {
result = [
false,
nodes[i],
`The dynamic output ${baseName} of node ${node.label} requires at least ${minimumRequired} connections`,
];
break outerLoop;
}
}
}
}
return result;
};