app.py

import os
from fastapi import FastAPI, HTTPException
from langchain.prompts import PromptTemplate
from pydantic import BaseModel
from typing import Optional
from dotenv import load_dotenv

from embeddings.embeddings import generate_embeddings
from elastic.retrieval import search_certification_chunks
from prompting.rewrite_question import classify_certification, initialize_llms, process_query

load_dotenv()

app = FastAPI(
    title="Hydrogen Certification RAG System",
    description="API for querying hydrogen certification documents using RAG",
    version="0.1.0"
)

# Initialize LLMs and Elasticsearch client
llms = initialize_llms()

# Request models
class QueryRequest(BaseModel):
    query: str


    
llm = initialize_llms()["rewrite_llm"]
    

# Endpoints
@app.post("/query")
async def handle_query(request: QueryRequest):
    """
    Process a query through the full RAG pipeline:
    1. Classify certification (if not provided)
    2. Optimize query based on specificity
    3. Search relevant chunks
    """
    try:
        # Step 1: Determine certification
        query = request.query
        certification = classify_certification(request.query, llms["rewrite_llm"])
        if "no certification mentioned" in certification :
                raise HTTPException(
                    status_code=400,
                    detail="No certification specified in query and none provided"
                )

        # Step 2: Process query
        processed_query = process_query(request.query, llms)
        question_vector = generate_embeddings(processed_query)

        # Step 3: Search
        results = search_certification_chunks(
            index_name="certif_index",
            certification_name=certification,
            text_query=processed_query,
            vector_query=question_vector,
        )
        
        results_ = search_certification_chunks(
            index_name="certification_index",
            certification_name=certification,
            text_query=processed_query,
            vector_query=question_vector,
        )
        
        results_merged = ". ".join([result["text"] for result in results])
        results_merged_ = ". ".join([result["text"] for result in results_])
        
        template = """
        You are an AI assistant tasked with providing answers based on the given context about a specific hydrogen certification. 

        Provide a clear, concise response that directly addresses the question without unnecessary information.

        Question: {question}
        Certification: {certification}
        Context: {context}

        Answer:
        """
        prompt = PromptTemplate(
        input_variables=["question", "certification", "context"],
        template=template
        )
    
        chain = prompt | llm
        answer = chain.invoke({"question": processed_query, "certification": certification, "context": results_merged}).content
        answer_ = chain.invoke({"question": processed_query, "certification": certification, "context": results_merged_}).content
        

        return {
            "certification": certification,
            "certif_index": answer,
            "certification_index": answer_,
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/certifications", response_model=list[str])
async def list_certifications():
    """List all available certifications"""
    try:
        certs_dir = "docs/processed"
        return [f for f in os.listdir(certs_dir) if os.path.isdir(os.path.join(certs_dir, f))]
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

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