70 lines
1.7 KiB
Python
70 lines
1.7 KiB
Python
from fastapi import APIRouter, Body, Depends
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from openai import OpenAI
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from pydantic import BaseModel
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from api.dependencies import get_llm_client
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router = APIRouter()
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class ChatRequest(BaseModel):
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message: str
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session_id: str | None = None
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def run_agent(client: OpenAI, message: str, session_id: str | None = None) -> str:
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response = client.chat.completions.create(
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model="deepseek-chat",
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messages=[
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{"role": "system", "content": "You are a helpful agent."},
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{"role": "user", "content": message},
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],
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)
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return response.choices[0].message.content
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@router.get("/")
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def root():
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return {"status": "ok"}
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@router.post("/chat")
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def chat(req: ChatRequest, client: OpenAI = Depends(get_llm_client)):
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response = run_agent(client, req.message, req.session_id)
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return {"response": response, "session_id": req.session_id}
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@router.get("/models")
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def list_models():
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return {
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"object": "list",
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"data": [
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{
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"id": "agent-model",
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"object": "model",
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"created": 0,
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"owned_by": "local-agent",
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}
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],
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}
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@router.post("/chat/completions")
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def chat_completions(
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payload: dict = Body(...),
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client: OpenAI = Depends(get_llm_client),
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):
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messages = payload["messages"]
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user_message = messages[-1]["content"]
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response = run_agent(client, user_message)
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return {
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"id": "chatcmpl-local",
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"object": "chat.completion",
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"choices": [
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{
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"index": 0,
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"message": {"role": "assistant", "content": response},
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}
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],
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} |