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from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
# Load the FAISS index
index = faiss.read_index("database/pdf_sections_index.faiss")
# Load the embedding model
model = SentenceTransformer('all-MiniLM-L6-v2')
def search_faiss(query, k=3):
query_vector = model.encode([query])[0].astype('float32')
query_vector = np.expand_dims(query_vector, axis=0)
distances, indices = index.search(query_vector, k)
return distances, indices
# Example usage
query = "What is mental Health?"
distances, indices = search_faiss(query)
print(f"Query: {query}")
print(f"Distances: {distances}")
print(f"Indices: {indices}") |