| 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
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| from langchain_google_genai import ChatGoogleGenerativeAI
|
| from langchain.chains.question_answering import load_qa_chain
|
| from langchain.prompts import PromptTemplate
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| from dotenv import load_dotenv
|
|
|
| app = FastAPI()
|
| class Question(BaseModel):
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| query: str
|
|
|
| load_dotenv()
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| 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 = """
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| 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):
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| {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:
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| 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()
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| 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)
|
|
|
