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from __future__ import annotations |
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import asyncio |
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from collections import deque |
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import contextlib |
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from functools import partial |
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import shutil |
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import urllib.parse |
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from datetime import datetime |
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import uuid |
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from enum import Enum |
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from metagpt.logs import set_llm_stream_logfunc |
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import pathlib |
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from fastapi import FastAPI, Request, HTTPException |
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from fastapi.responses import StreamingResponse, RedirectResponse |
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from fastapi.staticfiles import StaticFiles |
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import fire |
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from pydantic import BaseModel, Field |
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import uvicorn |
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from typing import Any, Optional |
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from metagpt.schema import Message |
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from metagpt.actions.action import Action |
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from metagpt.actions.action_output import ActionOutput |
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from metagpt.config import CONFIG |
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from software_company import RoleRun, SoftwareCompany |
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class QueryAnswerType(Enum): |
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Query = "Q" |
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Answer = "A" |
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class SentenceType(Enum): |
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TEXT = "text" |
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HIHT = "hint" |
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ACTION = "action" |
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class MessageStatus(Enum): |
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COMPLETE = "complete" |
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class SentenceValue(BaseModel): |
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answer: str |
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class Sentence(BaseModel): |
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type: str |
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id: Optional[str] = None |
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value: SentenceValue |
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is_finished: Optional[bool] = None |
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class Sentences(BaseModel): |
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id: Optional[str] = None |
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action: Optional[str] = None |
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role: Optional[str] = None |
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skill: Optional[str] = None |
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description: Optional[str] = None |
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timestamp: str = datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f%z") |
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status: str |
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contents: list[dict] |
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class NewMsg(BaseModel): |
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"""Chat with MetaGPT""" |
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query: str = Field(description="Problem description") |
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config: dict[str, Any] = Field(description="Configuration information") |
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class ErrorInfo(BaseModel): |
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error: str = None |
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traceback: str = None |
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class ThinkActStep(BaseModel): |
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id: str |
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status: str |
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title: str |
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timestamp: str |
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description: str |
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content: Sentence = None |
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class ThinkActPrompt(BaseModel): |
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message_id: int = None |
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timestamp: str = datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f%z") |
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step: ThinkActStep = None |
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skill: Optional[str] = None |
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role: Optional[str] = None |
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def update_think(self, tc_id, action: Action): |
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self.step = ThinkActStep( |
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id=str(tc_id), |
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status="running", |
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title=action.desc, |
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timestamp=datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f%z"), |
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description=action.desc, |
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) |
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def update_act(self, message: ActionOutput | str, is_finished: bool = True): |
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if is_finished: |
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self.step.status = "finish" |
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self.step.content = Sentence( |
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type="text", |
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id=str(1), |
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value=SentenceValue(answer=message.content if is_finished else message), |
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is_finished=is_finished, |
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) |
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@staticmethod |
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def guid32(): |
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return str(uuid.uuid4()).replace("-", "")[0:32] |
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@property |
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def prompt(self): |
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v = self.json(exclude_unset=True) |
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return urllib.parse.quote(v) |
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class MessageJsonModel(BaseModel): |
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steps: list[Sentences] |
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qa_type: str |
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created_at: datetime = datetime.now() |
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query_time: datetime = datetime.now() |
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answer_time: datetime = datetime.now() |
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score: Optional[int] = None |
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feedback: Optional[str] = None |
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def add_think_act(self, think_act_prompt: ThinkActPrompt): |
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s = Sentences( |
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action=think_act_prompt.step.title, |
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skill=think_act_prompt.skill, |
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description=think_act_prompt.step.description, |
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timestamp=think_act_prompt.timestamp, |
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status=think_act_prompt.step.status, |
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contents=[think_act_prompt.step.content.dict()], |
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) |
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self.steps.append(s) |
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@property |
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def prompt(self): |
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v = self.json(exclude_unset=True) |
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return urllib.parse.quote(v) |
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async def create_message(req_model: NewMsg, request: Request): |
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""" |
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Session message stream |
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""" |
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try: |
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config = {k.upper(): v for k, v in req_model.config.items()} |
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set_context(config, uuid.uuid4().hex) |
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msg_queue = deque() |
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CONFIG.LLM_STREAM_LOG = lambda x: msg_queue.appendleft(x) if x else None |
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role = SoftwareCompany() |
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role.recv(message=Message(content=req_model.query)) |
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answer = MessageJsonModel( |
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steps=[ |
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Sentences( |
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contents=[ |
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Sentence(type=SentenceType.TEXT.value, value=SentenceValue(answer=req_model.query), is_finished=True) |
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], |
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status=MessageStatus.COMPLETE.value, |
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) |
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], |
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qa_type=QueryAnswerType.Answer.value, |
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) |
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tc_id = 0 |
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while True: |
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tc_id += 1 |
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if request and await request.is_disconnected(): |
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return |
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think_result: RoleRun = await role.think() |
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if not think_result: |
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break |
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think_act_prompt = ThinkActPrompt(role=think_result.role.profile) |
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think_act_prompt.update_think(tc_id, think_result) |
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yield think_act_prompt.prompt + "\n\n" |
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task = asyncio.create_task(role.act()) |
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while not await request.is_disconnected(): |
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if msg_queue: |
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think_act_prompt.update_act(msg_queue.pop(), False) |
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yield think_act_prompt.prompt + "\n\n" |
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continue |
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if task.done(): |
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break |
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await asyncio.sleep(0.5) |
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act_result = await task |
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think_act_prompt.update_act(act_result) |
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yield think_act_prompt.prompt + "\n\n" |
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answer.add_think_act(think_act_prompt) |
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yield answer.prompt + "\n\n" |
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finally: |
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shutil.rmtree(CONFIG.WORKSPACE_PATH) |
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default_llm_stream_log = partial(print, end="") |
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def llm_stream_log(msg): |
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with contextlib.suppress(): |
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CONFIG._get("LLM_STREAM_LOG", default_llm_stream_log)(msg) |
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def set_context(context, uid): |
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context["WORKSPACE_PATH"] = pathlib.Path("workspace", uid) |
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for old, new in (("DEPLOYMENT_ID", "DEPLOYMENT_NAME"), ("OPENAI_API_BASE", "OPENAI_BASE_URL")): |
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if old in context and new not in context: |
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context[new] = context[old] |
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CONFIG.set_context(context) |
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return context |
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class ChatHandler: |
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@staticmethod |
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async def create_message(req_model: NewMsg, request: Request): |
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"""Message stream, using SSE.""" |
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event = create_message(req_model, request) |
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headers = {"Cache-Control": "no-cache", "Connection": "keep-alive"} |
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return StreamingResponse(event, headers=headers, media_type="text/event-stream") |
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app = FastAPI() |
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app.mount( |
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"/storage", |
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StaticFiles(directory="./storage/"), |
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name="static", |
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) |
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app.add_api_route( |
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"/api/messages", |
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endpoint=ChatHandler.create_message, |
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methods=["post"], |
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summary="Session message sending (streaming response)", |
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) |
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app.mount( |
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"/", |
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StaticFiles(directory="./static/", html=True), |
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name="static", |
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) |
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set_llm_stream_logfunc(llm_stream_log) |
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def main(): |
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uvicorn.run(app="__main__:app", host="0.0.0.0", port=7860) |
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if __name__ == "__main__": |
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fire.Fire(main) |
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