from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

# Load MiniLM embedder
embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

def embed_texts(texts):
    return embedder.encode(texts, convert_to_tensor=False)

def build_faiss_index(texts):
    embeddings = embed_texts(texts)
    index = faiss.IndexFlatL2(embeddings[0].shape[0])
    index.add(np.array(embeddings))
    return index, embeddings

def retrieve(query, index, docs, k=3):
    query_embedding = embed_texts([query])
    distances, indices = index.search(np.array(query_embedding), k)
    return [docs[i] for i in indices[0]]