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import os, uvicorn
from fastapi import FastAPI
from pydantic import BaseModel
from langchain_google_genai import GoogleGenerativeAIEmbeddings
import google.generativeai as genai
from langchain_community.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
app = FastAPI()
class Question(BaseModel):
query: str
load_dotenv()
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
conversational_model = ChatGoogleGenerativeAI(model="gemini-2.5-pro", temperature=0.7)
def get_conversational_chain():
prompt_template = """
You are a helpful assistant tasked with extracting accurate answers **only from the given context**.
If the question is about matching (e.g., "Who is referred to as X?"), select the correct match from the context.
If the correct answer is **not present** in the context, respond exactly with:
"উত্তর প্রসঙ্গে নেই" (The answer is not in the context.)
---
প্রসঙ্গ (Context):
{context}
প্রশ্ন (Question):
{question}
উত্তর (Answer):
"""
prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
return load_qa_chain(conversational_model, chain_type="stuff", prompt=prompt)
@app.post("/ask")
async def create_items(que: Question):
try:
db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
docs_and_scores = db.similarity_search_with_score(que.query, k=100)
docs = [doc for doc, score in docs_and_scores]
chain = get_conversational_chain()
response = chain({"input_documents": docs, "question": que.query})
return {"answer": response["output_text"]}
except Exception as e:
return {"error": str(e)}
if __name__ == "__main__":
uvicorn.run(app, host="127.0.0.1", port=5656)
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