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import os
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
# Path to your saved FAISS vectorstore
VECTORSTORE_DIR = "../vectorStore"
def query_faiss():
# Load embeddings
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
# Load FAISS vectorstore
db = FAISS.load_local(VECTORSTORE_DIR, embeddings, allow_dangerous_deserialization=True)
print("✅ FAISS vectorstore loaded successfully.")
print(f"Total chunks in DB: {len(db.docstore._dict)}")
while True:
query = input("\nEnter your query (or type 'exit' to quit): ").strip()
if query.lower() == "exit":
print("Exiting...")
break
# Perform similarity search
results = db.similarity_search(query, k=5) # top 5 results
if not results:
print("❌ No matching documents found.")
else:
print(f"\n🔹 Top {len(results)} matches:")
for i, doc in enumerate(results, 1):
# Print metadata + first 200 chars of content
content_preview = doc.page_content[:200].replace("\n", " ")
print(f"{i}. {content_preview}")
print(f" Metadata: {doc.metadata}\n")
if __name__ == "__main__":
query_faiss()
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