app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from langchain_groq import ChatGroq
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
import os

# Load environment variables from .env file if it exists
try:
    load_dotenv()
except:
    pass  # If .env doesn't exist (like in Vercel), continue without it

# Initialize FastAPI app
app = FastAPI()

# Create a request model
class SearchQuery(BaseModel):
    query: str

# Check if API key exists
if not os.getenv("GROQ_API_KEY"):
    raise ValueError("GROQ_API_KEY environment variable is not set")

# Initialize LangChain with Groq
llm = ChatGroq(
    temperature=0.7,
    model_name="mixtral-8x7b-32768",  # You can also use "llama2-70b-4096"
)

prompt_template = PromptTemplate(
    input_variables=["query"],
    template="Please provide a detailed response to the following query: {query}"
)
chain = LLMChain(llm=llm, prompt=prompt_template)

@app.post("/search")
async def process_search(search_query: SearchQuery):
    try:
        # Process the query using LangChain
        response = chain.run(query=search_query.query)
        
        return {
            "status": "success",
            "response": response
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/")
async def root():
    return {"message": "Search API is running"}