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