Update main.py
Browse files
main.py
CHANGED
@@ -1,7 +1,6 @@
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import os
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import zipfile
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import tempfile
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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@@ -25,27 +24,37 @@ class QueryRequest(BaseModel):
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question: str
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def _unpack_faiss(src_path: str
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"""
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If src_path is a
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If src_path is already a directory, return it directly.
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"""
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# 1) True ZIP file?
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if zipfile.is_zipfile(src_path):
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with zipfile.ZipFile(src_path, "r") as zf:
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zf.extractall(
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for root, _, files in os.walk(extract_to):
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if any(f.endswith(".faiss") for f in files):
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return root
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raise RuntimeError(f"No .faiss index found inside ZIP: {src_path}")
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# 2) Already a folder?
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if os.path.isdir(src_path):
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return src_path
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@app.on_event("startup")
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@@ -57,7 +66,7 @@ def load_components():
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model="meta-llama/llama-4-scout-17b-16e-instruct",
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temperature=0,
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max_tokens=1024,
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api_key=os.getenv("
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)
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embeddings = HuggingFaceEmbeddings(
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model_name="intfloat/multilingual-e5-large",
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@@ -66,29 +75,17 @@ def load_components():
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)
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# --- 2) Load & merge two FAISS indexes ---
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tmp1 = tempfile.TemporaryDirectory()
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tmp2 = tempfile.TemporaryDirectory()
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dir1 = _unpack_faiss(src1, tmp1.name)
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dir2 = _unpack_faiss(src2, tmp2.name)
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vs1 = FAISS.load_local(dir1, embeddings, allow_dangerous_deserialization=True)
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vs2 = FAISS.load_local(dir2, embeddings, allow_dangerous_deserialization=True)
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vs1.merge_from(vs2)
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vectorstore = vs1
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# --- 3) Build retriever & QA chain ---
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retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
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prompt = PromptTemplate(
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template="""
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You are an expert assistant on Islamic knowledge.
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Use **only** the information in the “Retrieved context” to answer the user’s question.
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Do **not** add any outside information, personal opinions, or conjecture—if the answer is not contained in the context, reply with “لا أعلم”.
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Be concise, accurate, and directly address the user’s question.
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Retrieved context:
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@@ -114,7 +111,7 @@ Your response:
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@app.get("/")
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def root():
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return {"message": "Arabic Hadith Finder API is up
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@app.post("/query")
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import os
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import zipfile
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import tempfile
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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question: str
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def _unpack_faiss(src_path: str) -> str:
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"""
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If src_path is a ZIP, unzip it into a temp dir and return the folder
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containing the .faiss files; if it’s already a folder, return it.
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"""
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if zipfile.is_zipfile(src_path):
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tmp = tempfile.TemporaryDirectory()
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with zipfile.ZipFile(src_path, "r") as zf:
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zf.extractall(tmp.name)
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for root, _, files in os.walk(tmp.name):
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if any(f.endswith(".faiss") for f in files):
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return root
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raise RuntimeError(f"No .faiss index found inside ZIP: {src_path}")
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elif os.path.isdir(src_path):
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return src_path
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else:
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raise RuntimeError(f"Path is neither a valid ZIP nor a directory: {src_path}")
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def load_and_merge_faiss(path1: str, path2: str, embeddings: HuggingFaceEmbeddings) -> FAISS:
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"""
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Load two FAISS indexes (either zip files or folders), merge them,
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and return the combined FAISS vectorstore.
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"""
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dir1 = _unpack_faiss(path1)
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dir2 = _unpack_faiss(path2)
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vs1 = FAISS.load_local(dir1, embeddings, allow_dangerous_deserialization=True)
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vs2 = FAISS.load_local(dir2, embeddings, allow_dangerous_deserialization=True)
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vs1.merge_from(vs2)
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return vs1
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@app.on_event("startup")
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model="meta-llama/llama-4-scout-17b-16e-instruct",
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temperature=0,
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max_tokens=1024,
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api_key=os.getenv("API_KEY"),
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)
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embeddings = HuggingFaceEmbeddings(
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model_name="intfloat/multilingual-e5-large",
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)
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# --- 2) Load & merge two FAISS indexes ---
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src1 = os.getenv("FAISS_INDEX_PATH_1", "faiss_index.zip")
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src2 = os.getenv("FAISS_INDEX_PATH_2", "faiss_index_extra.zip")
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vectorstore = load_and_merge_faiss(src1, src2, embeddings)
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# --- 3) Build retriever & QA chain ---
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retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
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prompt = PromptTemplate(
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template="""
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You are an expert assistant on Islamic knowledge.
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Use **only** the information in the “Retrieved context” to answer the user’s question.
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Do **not** add any outside information, personal opinions, or conjecture—if the answer is not contained in the context, reply with “لا أعلم”.
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Be concise, accurate, and directly address the user’s question.
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Retrieved context:
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@app.get("/")
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def root():
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return {"message": "Arabic Hadith Finder API is up and running!"}
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@app.post("/query")
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