create_faiss_index.py

import faiss
import pickle
from sentence_transformers import SentenceTransformer
import chromadb

# Initialize SentenceTransformer model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Load documents or data that you want to index
documents = ['document 1 text', 'document 2 text', 'document 3 text']

# Generate embeddings for documents
embeddings = model.encode(documents)

# Create FAISS index
faiss_index = faiss.IndexFlatL2(embeddings.shape[1])  # Using L2 distance

# Add embeddings to FAISS index
faiss_index.add(embeddings)

# Save the FAISS index
with open('faiss_index.index', 'wb') as f:
    pickle.dump(faiss_index, f)