Create create_faiss_index.py

create_faiss_index.py

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+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)