project

main.py

@@ -0,0 +1,94 @@
+import os
+import logging
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+
+from app.rag import RAGPipeline
+from app.llm import LLMClient
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+ALLOWED_ORIGINS = [
+    origin.strip()
+    for origin in os.getenv(
+        "ALLOWED_ORIGINS",
+        "http://localhost:3000,https://aprouhi.com"
+    ).split(",")
+    if origin.strip()
+]
+
+rag: RAGPipeline | None = None
+llm: LLMClient | None = None
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global rag, llm
+    logger.info("Initialising RAG pipeline and LLM client...")
+    rag = RAGPipeline()
+    llm = LLMClient()
+    logger.info("Ready.")
+    yield
+    logger.info("Shutting down.")
+
+
+app = FastAPI(
+    title="Parsa Rouhi — Chatbot API",
+    description="Ask anything about Parsa's skills, projects, and experience.",
+    version="1.0.0",
+    lifespan=lifespan,
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=ALLOWED_ORIGINS,
+    allow_credentials=True,
+    allow_methods=["GET", "POST", "OPTIONS"],
+    allow_headers=["*"],
+)
+
+class Message(BaseModel):
+    role: str = Field(..., pattern="^(user|assistant)$")
+    content: str
+
+class ChatRequest(BaseModel):
+    message: str = Field(..., min_length=1, max_length=1000)
+    history: list[Message] = Field(default_factory=list, max_length=20)
+
+class ChatResponse(BaseModel):
+    response: str
+    sources_retrieved: int
+
+
+@app.get("/health")
+async def health():
+    return {"status": "ok", "rag_ready": rag is not None, "llm_ready": llm is not None}
+
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(req: ChatRequest):
+    if rag is None or llm is None:
+        raise HTTPException(status_code=503, detail="Service is still initialising. Please retry.")
+
+    try:
+        # Retrieve relevant context
+        context = rag.retrieve(req.message)
+        chunks_count = context.count("---") + 1 if context else 0
+
+        # Generate response
+        history = [m.model_dump() for m in req.history]
+        answer = llm.generate(
+            user_message=req.message,
+            context=context,
+            history=history,
+        )
+
+        return ChatResponse(response=answer, sources_retrieved=chunks_count)
+
+    except Exception as e:
+        logger.exception("Error during chat generation")
+        raise HTTPException(status_code=500, detail=str(e))