File size: 6,060 Bytes
3bb4c79 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import urllib.parse
import uuid
from datetime import datetime
from enum import Enum
from typing import Any, Optional
import fire
import uvicorn
from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import RedirectResponse, StreamingResponse
from fastapi.staticfiles import StaticFiles
from metagpt.actions.action import Action
from metagpt.actions.action_output import ActionOutput
from metagpt.config import CONFIG
from pydantic import BaseModel, Field
from agent.roles.software_company import RoleRun, SoftwareCompany
class QueryAnswerType(Enum):
Query = "Q"
Answer = "A"
class SentenceType(Enum):
TEXT = "text"
HIHT = "hint"
ACTION = "action"
class MessageStatus(Enum):
COMPLETE = "complete"
class SentenceValue(BaseModel):
answer: str
class Sentence(BaseModel):
type: str
id: Optional[str] = None
value: SentenceValue
is_finished: Optional[bool] = None
class Sentences(BaseModel):
id: Optional[str] = None
action: Optional[str] = None
role: Optional[str] = None
skill: Optional[str] = None
description: Optional[str] = None
timestamp: str = datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f%z")
status: str
contents: list[Sentence]
class NewMsg(BaseModel):
"""Chat with MetaGPT"""
query: str = Field(description="Problem description")
config: dict[str, Any] = Field(description="Configuration information")
class ErrorInfo(BaseModel):
error: str = None
traceback: str = None
class ThinkActStep(BaseModel):
id: str
status: str
title: str
timestamp: str
description: str
content: Sentence = None
class ThinkActPrompt(BaseModel):
message_id: int = None
timestamp: str = datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f%z")
step: ThinkActStep = None
skill: Optional[str] = None
role: Optional[str] = None
def update_think(self, tc_id, action: Action):
self.step = ThinkActStep(
id=str(tc_id),
status="running",
title=action.desc,
timestamp=datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f%z"),
description=action.desc,
)
def update_act(self, message: ActionOutput):
self.step.status = "finish"
self.step.content = Sentence(
type="text",
id=ThinkActPrompt.guid32(),
value=SentenceValue(answer=message.content),
is_finished=True,
)
@staticmethod
def guid32():
return str(uuid.uuid4()).replace("-", "")[0:32]
@property
def prompt(self):
v = self.model_dump_json()
return urllib.parse.quote(v)
class MessageJsonModel(BaseModel):
steps: list[Sentences]
qa_type: str
created_at: datetime = datetime.now()
query_time: datetime = datetime.now()
answer_time: datetime = datetime.now()
score: Optional[int] = None
feedback: Optional[str] = None
def add_think_act(self, think_act_prompt: ThinkActPrompt):
s = Sentences(
action=think_act_prompt.step.title,
skill=think_act_prompt.skill,
description=think_act_prompt.step.description,
timestamp=think_act_prompt.timestamp,
status=think_act_prompt.step.status,
contents=[think_act_prompt.step.content.model_dump()],
)
self.steps.append(s)
@property
def prompt(self):
v = self.model_dump_json(exclude_unset=True)
return urllib.parse.quote(v)
async def create_message(req_model: NewMsg, request: Request):
"""
Session message stream
"""
config = {k.upper(): v for k, v in req_model.config.items()}
CONFIG.set_context(config)
role = SoftwareCompany()
role.company.run_project(idea=req_model.query)
answer = MessageJsonModel(
steps=[
Sentences(
contents=[
Sentence(
type=SentenceType.TEXT.value, value=SentenceValue(answer=req_model.query), is_finished=True
)
],
status=MessageStatus.COMPLETE.value,
)
],
qa_type=QueryAnswerType.Answer.value,
)
tc_id = 0
while True:
tc_id += 1
if request and await request.is_disconnected():
return
think_result: RoleRun = await role.think()
if not think_result: # End of conversion
break
think_act_prompt = ThinkActPrompt(role=think_result.role.profile)
think_act_prompt.update_think(tc_id, think_result)
yield think_act_prompt.prompt + "\n\n"
act_result = await role.act()
think_act_prompt.update_act(act_result)
yield think_act_prompt.prompt + "\n\n"
answer.add_think_act(think_act_prompt)
yield answer.prompt + "\n\n" # Notify the front-end that the message is complete.
class ChatHandler:
@staticmethod
async def create_message(req_model: NewMsg, request: Request):
"""Message stream, using SSE."""
event = create_message(req_model, request)
headers = {"Cache-Control": "no-cache", "Connection": "keep-alive"}
return StreamingResponse(event, headers=headers, media_type="text/event-stream")
app = FastAPI()
app.mount(
"/static",
StaticFiles(directory="./agent/static/", check_dir=True),
name="static",
)
app.add_api_route(
"/api/messages",
endpoint=ChatHandler.create_message,
methods=["post"],
summary="Session message sending (streaming response)",
)
@app.get("/{catch_all:path}")
async def catch_all(request: Request):
if request.url.path == "/":
return RedirectResponse(url="/static/index.html")
if request.url.path.startswith("/api"):
raise HTTPException(status_code=404)
new_path = f"/static{request.url.path}"
return RedirectResponse(url=new_path)
def main():
uvicorn.run(app="__main__:app", host="0.0.0.0", port=7860)
if __name__ == "__main__":
fire.Fire(main)
|