Create main.py

main.py

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+import os
+import zipfile
+import tempfile
+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_groq import ChatGroq
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+
+app = FastAPI()
+
+# === Globals ===
+llm = None
+embeddings = None
+vectorstore = None
+retriever = None
+chain = None
+
+
+class QueryRequest(BaseModel):
+    question: str
+
+
+def _unpack_faiss(src: str, dest_dir: str) -> str:
+    """
+    If src is a .zip, unzip it into dest_dir and return
+    the path to the extracted FAISS folder. Otherwise
+    assume src is already a folder and return it.
+    """
+    if zipfile.is_zipfile(src):
+        with zipfile.ZipFile(src, "r") as zf:
+            zf.extractall(dest_dir)
+        # if there’s exactly one subfolder, use it
+        items = os.listdir(dest_dir)
+        if len(items) == 1 and os.path.isdir(os.path.join(dest_dir, items[0])):
+            return os.path.join(dest_dir, items[0])
+        return dest_dir
+    else:
+        # src is already a directory
+        return src
+
+
+@app.on_event("startup")
+def load_components():
+    global llm, embeddings, vectorstore, retriever, chain
+
+    # --- 1) Initialize LLM & Embeddings ---
+    api_key = os.getenv("api_key")
+    llm = ChatGroq(
+        model="meta-llama/llama-4-scout-17b-16e-instruct",
+        temperature=0,
+        max_tokens=1024,
+        api_key=api_key,
+    )
+
+    embeddings = HuggingFaceEmbeddings(
+        model_name="intfloat/multilingual-e5-large",
+        model_kwargs={"device": "cpu"},
+        encode_kwargs={"normalize_embeddings": True},
+    )
+
+    # --- 2) Load & merge two FAISS indexes ---
+    # Paths to your two vectorstores (could be .zip or folders)
+    src1 = "faiss_index.zip"
+    src2 = "faiss_index_extra.zip"
+
+    # Temporary dirs for extraction
+    tmp1 = tempfile.mkdtemp()
+    tmp2 = tempfile.mkdtemp()
+
+    # Unpack and load each
+    path1 = _unpack_faiss(src1, tmp1)
+    vs1 = FAISS.load_local(path1, embeddings, allow_dangerous_deserialization=True)
+
+    path2 = _unpack_faiss(src2, tmp2)
+    vs2 = FAISS.load_local(path2, embeddings, allow_dangerous_deserialization=True)
+
+    # Merge vs2 into vs1
+    vs1.merge_from(vs2)
+
+    # Assign the merged store to our global
+    vectorstore = vs1
+
+    # --- 3) Build retriever & QA chain ---
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
+
+    prompt = PromptTemplate(
+        template="""
+You are an expert assistant on Islamic knowledge.  
+Use **only** the information in the “Retrieved context” to answer the user’s question.  
+Do **not** add any outside information, personal opinions, or conjecture—if the answer is not contained in the context, reply with “لا أعلم”.  
+Be concise, accurate, and directly address the user’s question.
+
+Retrieved context:
+{context}
+
+User’s question:
+{question}
+
+Your response:
+""",
+        input_variables=["context", "question"],
+    )
+
+    chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=retriever,
+        return_source_documents=False,
+        chain_type_kwargs={"prompt": prompt},
+    )
+
+    print("✅ Loaded and merged both FAISS indexes, QA chain is ready.")
+
+
+@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))