services/vector_store.py

from langchain_community.vectorstores import FAISS
# from langchain_community.embeddings import SentenceTransformerEmbeddings
# from langchain_community.embeddings.ollama import OllamaEmbeddings
from langchain_huggingface import HuggingFaceEmbeddings

def create_vector_store(splits):
    # embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
    # embeddings = OllamaEmbeddings(model="nomic-embed-text")
    return FAISS.from_documents(splits, embeddings)