Create main.py

main.py

@@ -0,0 +1,111 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_groq import ChatGroq
+import zipfile
+import os
+
+app = FastAPI()
+
+# === Startup config ===
+class QueryRequest(BaseModel):
+    question: str
+
+llm = None
+retriever = None
+chain = None
+
+@app.on_event("startup")
+def load_components():
+    global llm, retriever, chain
+
+    api_key = os.getenv('api_key')
+
+    # --- Load LLM ---
+    llm = ChatGroq(
+        model="meta-llama/llama-4-scout-17b-16e-instruct",
+        temperature=0,
+        max_tokens=1024,
+        api_key=api_key
+    )
+
+    # --- Load Embeddings ---
+    embeddings = HuggingFaceEmbeddings(
+        model_name="intfloat/multilingual-e5-large",
+        model_kwargs={"device": "cpu"},
+        encode_kwargs={"normalize_embeddings": True},
+    )
+
+    # --- Unzip Vectorstore if needed ---
+    zip_path = "faiss_index.zip"
+    extract_path = "faiss_index"
+    if not os.path.exists(extract_path):
+        with zipfile.ZipFile(zip_path, 'r') as z:
+            z.extractall(extract_path)
+        print("✅ Unzipped FAISS index.")
+
+    # --- Load FAISS Vectorstore & create retriever ---
+    vectorstore = FAISS.load_local(
+        extract_path,
+        embeddings,
+        allow_dangerous_deserialization=True
+    )
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
+    print("✅ FAISS index loaded.")
+
+    # --- Prepare prompt template ---
+    quiz_solving_prompt = """
+You are an Arabic Hadith Finder assistant.
+Your goal is to provide an accurate and concise answer extracted directly from the provided retrieved context.
+Your task is to output only the exact Arabic Hadith (as it appears in the context), removing any extraneous or irrelevant data.
+
+Instructions:
+1. Identify the segment in the retrieved context that directly answers the user's question.
+2. Output the Hadith exactly as it appears in Arabic in the context.
+3. Remove any information that does not pertain directly to the query.
+4. If the context does not contain sufficient information to answer the question, respond with "لا أعلم". Do not add or infer any extra information.
+5. Provide the complete reference of the Hadith (if available), including:
+   - Chapter Number and Name (Arabic and/or English)
+   - Section Number and Name
+   - Hadith Number
+   - Arabic Isnad and Matn
+   - Arabic Grade (if present)
+   - Hadith Book name
+
+Retrieved context:
+{context}
+
+User's question:
+{question}
+
+Your response:
+"""
+    prompt = PromptTemplate(
+        template=quiz_solving_prompt,
+        input_variables=["context", "question"]
+    )
+
+    # --- Assemble a stateless RetrievalQA chain (no memory) ---
+    chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=retriever,
+        return_source_documents=False,
+        chain_type_kwargs={"prompt": prompt},
+        verbose=False,
+    )
+
+@app.get("/")
+def root():
+    return {"message": "Arabic Hadith Finder API is up..."}
+
+@app.post("/query")
+def query(request: QueryRequest):
+    try:
+        result = chain.invoke({"query": request.question})
+        return {"answer": result["result"]}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))