realtime-rag-pipeline / retriever /embed_documents.py
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from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
def embed_documents(documents):
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vector_store = FAISS.from_texts([doc['text'] for doc in documents], embedding_model)
return vector_store