main.py

import os
import time
from typing import List

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage
from mistralai.exceptions import MistralAPIException
from datasets import load_dataset
from pinecone import Pinecone, ServerlessSpec

# Environment variables
MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY")
PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
INDEX_NAME = os.getenv("PINECONE_INDEX_NAME", "mistral-rag")

if not MISTRAL_API_KEY or not PINECONE_API_KEY:
    raise RuntimeError("Environment variables MISTRAL_API_KEY and PINECONE_API_KEY must be set.")

# Initialize clients
mistral = MistralClient(api_key=MISTRAL_API_KEY)
pc = Pinecone(api_key=PINECONE_API_KEY)

# Prepare or connect to Pinecone index
def init_index(dimensions: int):
    spec = ServerlessSpec(cloud="aws", region="us-east-1")
    existing = [idx["name"] for idx in pc.list_indexes()]
    if INDEX_NAME not in existing:
        npc.create_index(
            INDEX_NAME,
            dimension=dimensions,
            metric="dotproduct",
            spec=spec
        )
        # wait until ready
        while not pc.describe_index(INDEX_NAME).status["ready"]:
            time.sleep(1)
    return pc.Index(INDEX_NAME)

# Embedding model
EMBED_MODEL = "mistral-embed"

# Dynamic index client (populated on startup)
index = None

app = FastAPI(title="Mistral RAG API")

# Pydantic models
def chunk_to_upsert(metadata: List[dict]):
    """Helper to batch-embed and upsert metadata items into Pinecone."""
    # Perform adaptive batching
    batch_size = len(metadata)
    last_exc = None
    while batch_size >= 1:
        try:
            embeds = []
            for i in range(0, len(metadata), batch_size):
                batch = metadata[i : i + batch_size]
                texts = [f"{m['title']}\n{m['content']}" for m in batch]
                resp = mistral.embeddings(input=texts, model=EMBED_MODEL)
                embeds.extend([d.embedding for d in resp.data])
            # Prepare upsert list
            # Each metadata item must include 'id', 'title', 'content'
            to_upsert = [
                (m["id"], embeds[idx], {"title": m["title"], "content": m["content"]})
                for idx, m in enumerate(metadata)
            ]
            index.upsert(vectors=to_upsert)
            return
        except MistralAPIException as e:
            last_exc = e
            batch_size = max(1, batch_size // 2)
    raise last_exc

class Chunk(BaseModel):
    id: str
    title: str
    content: str

class IngestRequest(BaseModel):
    chunks: List[Chunk]

class QueryRequest(BaseModel):
    query: str
    top_k: int = 5

@app.on_event("startup")
async def startup_event():
    global index
    # Sanity check embed dimension from a sample
    sample_embed = mistral.embeddings(input=["test"], model=EMBED_MODEL)
    dims = len(sample_embed.data[0].embedding)
    index = init_index(dimensions=dims)

@app.post("/ingest")
async def ingest(req: IngestRequest):
    if not req.chunks:
        raise HTTPException(status_code=400, detail="No chunks provided to ingest.")
    metadata = [chunk.dict() for chunk in req.chunks]
    try:
        chunk_to_upsert(metadata)
        return {"status": "success", "ingested": len(req.chunks)}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/query")
async def query(req: QueryRequest):
    # Embed the query
    embed_resp = mistral.embeddings(input=[req.query], model=EMBED_MODEL)
    xq = embed_resp.data[0].embedding
    # Query Pinecone
    res = index.query(vector=xq, top_k=req.top_k, include_metadata=True)
    # Retrieve docs
    docs = [match['metadata']['content'] for match in res['matches']]
    # Construct system message
    system_content = "You are a helpful assistant that answers questions about AI using the context below.\n\nCONTEXT:\n" + "\n---\n".join(docs)
    messages = [
        ChatMessage(role="system", content=system_content),
        ChatMessage(role="user", content=req.query)
    ]
    chat_resp = mistral.chat(model="mistral-large-latest", messages=messages)
    answer = chat_resp.choices[0].message.content
    return {"query": req.query, "answers": answer, "docs": docs}

@app.get("/health")
async def health():
    return {"status": "ok"}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)