Embeddings turn text into numerical representations (vectors), which lets us measure the semantic meaning of that text. Vector Stores (e.g., Chroma, FAISS) store these embedding vectors and index them so they can be searched quickly.
python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
# Assume 'documents' are loaded and split
# Initialize the embeddings model
embeddings = OpenAIEmbeddings()
# Create a vector store from the documents
vectorstore = Chroma.from_documents(documents, embeddings)You should see
(The documents' text gets converted into vectors and stored in the Chroma vector store)