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3b81c5d d216b1f 3b81c5d 52387a0 3b81c5d 52387a0 3b81c5d 52387a0 d216b1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | #manabUtils.py
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from huggingface_hub import hf_hub_download
import os
def retrieve_chunks(repo_id, embedding_model="sentence-transformers/all-MiniLM-L6-v2"):
"""
Retreive chunks from HF dataset repo FAISS index
"""
try:
# Step 1: Create embeddings (FIX: was missing)
embeddings = HuggingFaceEmbeddings(model_name=embedding_model)
# Step 2: Download FAISS files from HF Hub
faiss_path = hf_hub_download(
repo_id=repo_id,
filename="index.faiss",
repo_type="dataset"
)
pkl_path = hf_hub_download(
repo_id=repo_id,
filename="index.pkl",
repo_type="dataset"
)
# Step 3: Load FAISS vectorstore (FIX: pass embeddings object, not string)
folder_path = os.path.dirname(faiss_path)
vectorstore = FAISS.load_local(
folder_path=folder_path,
embeddings=embeddings, # FIXED: was 'embedding_model' string
allow_dangerous_deserialization=True
)
# Step 4: Create retriever
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
except Exception as e:
print(f"Error in generate_qa_chain: {e}")
return None
return retriever
def retrieve_chunks_GPC():
"""
Retreive chunks from HF dataset for GPC
"""
embedding_model="sentence-transformers/all-MiniLM-L6-v2"
repo_id="manabb/NRLGPC"
try:
# Step 1: Create embeddings (FIX: was missing)
embeddings = HuggingFaceEmbeddings(model_name=embedding_model)
# Step 2: Download FAISS files from HF Hub
faiss_path = hf_hub_download(
repo_id=repo_id,
filename="faiss_gpc_goods_merged/index.faiss",
repo_type="dataset"
)
pkl_path = hf_hub_download(
repo_id=repo_id,
filename="faiss_gpc_goods_merged/index.pkl",
repo_type="dataset"
)
# Step 3: Load FAISS vectorstore (FIX: pass embeddings object, not string)
folder_path = os.path.dirname(faiss_path)
vectorstore = FAISS.load_local(
folder_path=folder_path,
embeddings=embeddings, # FIXED: was 'embedding_model' string
allow_dangerous_deserialization=True
)
# Step 4: Create retriever
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
except Exception as e:
print(f"Error in generate_qa_chain: {e}")
return None
return retriever |