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from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import hashlib
# Load model once
embedder = SentenceTransformer('all-MiniLM-L6-v2')
class VectorStore:
def __init__(self):
self.texts = []
self.embeddings = []
self.index = None
self.text_hashes = set()
def add_texts(self, texts):
"""Add list of texts to the store, avoiding duplicates"""
new_texts = []
for text in texts:
text_hash = hashlib.md5(text.encode()).hexdigest()
if text_hash not in self.text_hashes:
new_texts.append(text)
self.text_hashes.add(text_hash)
if not new_texts:
return
# Encode new texts
new_embeds = embedder.encode(new_texts)
self.texts.extend(new_texts)
self.embeddings.extend(new_embeds)
# Update FAISS index
if self.index is None:
self.index = faiss.IndexFlatL2(new_embeds[0].shape[0])
# Convert to numpy array and add to index
embeds_array = np.array(self.embeddings).astype('float32')
self.index.reset()
self.index.add(embeds_array)
def retrieve(self, query, top_k=3):
"""Return top-k relevant texts and their indices"""
if not self.index or not self.texts:
return [], []
# Encode query
query_embed = embedder.encode([query])
query_array = np.array(query_embed).astype('float32')
# Search
distances, indices = self.index.search(query_array, k=min(top_k, len(self.texts)))
# Return texts and indices
return [self.texts[i] for i in indices[0]], indices[0].tolist()
def clear(self):
"""Clear the vector store"""
self.texts = []
self.embeddings = []
self.index = None
self.text_hashes = set() |