mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-02-03 05:03:12 +08:00
make format
This commit is contained in:
glide-the
parent
2f1c9bfd11
commit
056b15b99b
@ -2,8 +2,11 @@ import typing
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from subprocess import Popen
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from typing import Optional
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from model_providers.core.bootstrap.openai_protocol import ChatCompletionStreamResponseChoice, \
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ChatCompletionStreamResponse, Finish
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from model_providers.core.bootstrap.openai_protocol import (
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ChatCompletionStreamResponse,
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ChatCompletionStreamResponseChoice,
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Finish,
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)
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from model_providers.core.utils.generic import jsonify
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if typing.TYPE_CHECKING:
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@ -11,12 +14,14 @@ if typing.TYPE_CHECKING:
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def create_stream_chunk(
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request_id: str,
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model: str,
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delta: "ChatCompletionMessage",
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index: Optional[int] = 0,
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finish_reason: Optional[Finish] = None,
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request_id: str,
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model: str,
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delta: "ChatCompletionMessage",
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index: Optional[int] = 0,
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finish_reason: Optional[Finish] = None,
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) -> str:
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choice = ChatCompletionStreamResponseChoice(index=index, delta=delta, finish_reason=finish_reason)
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choice = ChatCompletionStreamResponseChoice(
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index=index, delta=delta, finish_reason=finish_reason
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)
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chunk = ChatCompletionStreamResponse(id=request_id, model=model, choices=[choice])
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return jsonify(chunk)
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@ -5,7 +5,18 @@ import multiprocessing as mp
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import os
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import pprint
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import threading
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from typing import Any, Dict, Optional, Union, Tuple, Type, List, cast, Generator, AsyncGenerator
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from typing import (
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Any,
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AsyncGenerator,
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Dict,
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Generator,
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List,
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Optional,
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Tuple,
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Type,
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Union,
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cast,
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)
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from fastapi import APIRouter, FastAPI, HTTPException, Request, Response, status
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from fastapi.middleware.cors import CORSMiddleware
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@ -15,21 +26,36 @@ from uvicorn import Config, Server
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from model_providers.bootstrap_web.common import create_stream_chunk
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from model_providers.core.bootstrap import OpenAIBootstrapBaseWeb
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from model_providers.core.bootstrap.openai_protocol import (
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ChatCompletionMessage,
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ChatCompletionRequest,
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ChatCompletionResponse,
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ChatCompletionResponseChoice,
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ChatCompletionStreamResponse,
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ChatMessage,
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EmbeddingsRequest,
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EmbeddingsResponse,
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Finish,
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FunctionAvailable,
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ModelCard,
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ModelList, ChatMessage, ChatCompletionMessage, Role, Finish, ChatCompletionResponseChoice, UsageInfo,
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ModelList,
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Role,
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UsageInfo,
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)
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from model_providers.core.model_runtime.entities.llm_entities import (
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LLMResult,
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LLMResultChunk,
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)
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from model_providers.core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
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from model_providers.core.model_runtime.entities.message_entities import (
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UserPromptMessage, PromptMessage, AssistantPromptMessage, ToolPromptMessage, SystemPromptMessage,
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PromptMessageContent, PromptMessageContentType, TextPromptMessageContent, ImagePromptMessageContent,
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AssistantPromptMessage,
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ImagePromptMessageContent,
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PromptMessage,
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PromptMessageContent,
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PromptMessageContentType,
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PromptMessageTool,
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SystemPromptMessage,
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TextPromptMessageContent,
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ToolPromptMessage,
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UserPromptMessage,
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)
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from model_providers.core.model_runtime.entities.model_entities import (
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AIModelEntity,
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@ -81,7 +107,7 @@ def _convert_prompt_message_to_dict(message: PromptMessage) -> dict:
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def _create_template_from_message_type(
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message_type: str, template: Union[str, list]
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message_type: str, template: Union[str, list]
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) -> PromptMessage:
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"""Create a message prompt template from a message type and template string.
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@ -102,9 +128,7 @@ def _create_template_from_message_type(
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text: str = tmpl
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else:
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text = cast(dict, tmpl)["text"] # type: ignore[assignment] # noqa: E501
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content.append(
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TextPromptMessageContent(data=text)
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)
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content.append(TextPromptMessageContent(data=text))
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elif isinstance(tmpl, dict) and "image_url" in tmpl:
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img_template = cast(dict, tmpl)["image_url"]
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if isinstance(img_template, str):
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@ -142,7 +166,7 @@ def _create_template_from_message_type(
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def _convert_to_message(
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message: MessageLikeRepresentation,
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message: MessageLikeRepresentation,
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) -> Union[PromptMessage]:
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"""Instantiate a message from a variety of message formats.
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@ -161,7 +185,9 @@ def _convert_to_message(
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an instance of a message or a message template
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"""
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if isinstance(message, ChatMessage):
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_message = _create_template_from_message_type(message.role.to_origin_role(), message.content)
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_message = _create_template_from_message_type(
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message.role.to_origin_role(), message.content
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)
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elif isinstance(message, PromptMessage):
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_message = message
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@ -174,16 +200,16 @@ def _convert_to_message(
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if isinstance(message_type_str, str):
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_message = _create_template_from_message_type(message_type_str, template)
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else:
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raise ValueError(
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f"Expected message type string, got {message_type_str}"
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)
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raise ValueError(f"Expected message type string, got {message_type_str}")
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else:
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raise NotImplementedError(f"Unsupported message type: {type(message)}")
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return _message
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async def _stream_openai_chat_completion(response: Generator) -> AsyncGenerator[str, None]:
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async def _stream_openai_chat_completion(
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response: Generator,
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) -> AsyncGenerator[str, None]:
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request_id, model = None, None
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for chunk in response:
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if not isinstance(chunk, LLMResultChunk):
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@ -194,27 +220,41 @@ async def _stream_openai_chat_completion(response: Generator) -> AsyncGenerator[
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model = chunk.model
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if request_id is None:
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request_id = "request_id"
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yield create_stream_chunk(request_id, model, ChatCompletionMessage(role=Role.ASSISTANT, content=""))
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yield create_stream_chunk(
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request_id,
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model,
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ChatCompletionMessage(role=Role.ASSISTANT, content=""),
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)
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new_token = chunk.delta.message.content
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if new_token:
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delta = ChatCompletionMessage(role=Role.value_of(chunk.delta.message.role.to_origin_role()),
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content=new_token,
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tool_calls=chunk.delta.message.tool_calls)
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yield create_stream_chunk(request_id=request_id,
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model=model, delta=delta,
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index=chunk.delta.index,
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finish_reason=chunk.delta.finish_reason)
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delta = ChatCompletionMessage(
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role=Role.value_of(chunk.delta.message.role.to_origin_role()),
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content=new_token,
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tool_calls=chunk.delta.message.tool_calls,
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)
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yield create_stream_chunk(
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request_id=request_id,
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model=model,
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delta=delta,
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index=chunk.delta.index,
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finish_reason=chunk.delta.finish_reason,
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)
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yield create_stream_chunk(request_id, model, ChatCompletionMessage(), finish_reason=Finish.STOP)
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yield create_stream_chunk(
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request_id, model, ChatCompletionMessage(), finish_reason=Finish.STOP
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)
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yield "[DONE]"
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async def _openai_chat_completion(response: LLMResult) -> ChatCompletionResponse:
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choice = ChatCompletionResponseChoice(
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index=0, message=ChatCompletionMessage(**_convert_prompt_message_to_dict(message=response.message)),
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finish_reason=Finish.STOP
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index=0,
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message=ChatCompletionMessage(
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**_convert_prompt_message_to_dict(message=response.message)
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),
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finish_reason=Finish.STOP,
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)
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usage = UsageInfo(
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prompt_tokens=response.usage.prompt_tokens,
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@ -335,7 +375,7 @@ class RESTFulOpenAIBootstrapBaseWeb(OpenAIBootstrapBaseWeb):
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return ModelList(data=models_list)
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async def create_embeddings(
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self, provider: str, request: Request, embeddings_request: EmbeddingsRequest
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self, provider: str, request: Request, embeddings_request: EmbeddingsRequest
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):
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logger.info(
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f"Received create_embeddings request: {pprint.pformat(embeddings_request.dict())}"
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@ -345,7 +385,7 @@ class RESTFulOpenAIBootstrapBaseWeb(OpenAIBootstrapBaseWeb):
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return EmbeddingsResponse(**dictify(response))
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async def create_chat_completion(
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self, provider: str, request: Request, chat_request: ChatCompletionRequest
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self, provider: str, request: Request, chat_request: ChatCompletionRequest
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):
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logger.info(
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f"Received chat completion request: {pprint.pformat(chat_request.dict())}"
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@ -354,22 +394,29 @@ class RESTFulOpenAIBootstrapBaseWeb(OpenAIBootstrapBaseWeb):
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model_instance = self._provider_manager.get_model_instance(
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provider=provider, model_type=ModelType.LLM, model=chat_request.model
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)
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prompt_messages = [_convert_to_message(message) for message in chat_request.messages]
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prompt_messages = [
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_convert_to_message(message) for message in chat_request.messages
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]
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tools = [PromptMessageTool(name=f.function.name,
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description=f.function.description,
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parameters=f.function.parameters
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)
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for f in chat_request.tools]
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tools = [
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PromptMessageTool(
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name=f.function.name,
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description=f.function.description,
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parameters=f.function.parameters,
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)
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for f in chat_request.tools
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]
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if chat_request.functions:
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tools.extend([PromptMessageTool(name=f.name,
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description=f.description,
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parameters=f.parameters
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) for f in chat_request.functions])
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tools.extend(
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[
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PromptMessageTool(
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name=f.name, description=f.description, parameters=f.parameters
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)
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for f in chat_request.functions
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]
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)
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try:
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response = model_instance.invoke_llm(
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prompt_messages=prompt_messages,
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model_parameters={**chat_request.to_model_parameters_dict()},
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@ -380,21 +427,25 @@ class RESTFulOpenAIBootstrapBaseWeb(OpenAIBootstrapBaseWeb):
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)
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if chat_request.stream:
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return EventSourceResponse(_stream_openai_chat_completion(response), media_type="text/event-stream")
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return EventSourceResponse(
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_stream_openai_chat_completion(response),
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media_type="text/event-stream",
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)
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else:
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return await _openai_chat_completion(response)
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except ValueError as e:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(e))
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except InvokeError as e:
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raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e))
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e)
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)
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def run(
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cfg: Dict,
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logging_conf: Optional[dict] = None,
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started_event: mp.Event = None,
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cfg: Dict,
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logging_conf: Optional[dict] = None,
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started_event: mp.Event = None,
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):
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logging.config.dictConfig(logging_conf) # type: ignore
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try:
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