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| from langchain_community.vectorstores import FAISS
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| from langchain_community.embeddings import HuggingFaceEmbeddings
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| import config
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| def get_embeddings_model(model_name=config.EMBEDDING_MODEL_NAME):
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| """Loads and returns the embedding model."""
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| print(f"Loading embedding model: {model_name}...")
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| return HuggingFaceEmbeddings(model_name=model_name)
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| def create_and_save_store(documents, embeddings, save_path=config.VECTOR_STORE_PATH):
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| """
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| Creates a FAISS vector database from the given documents and saves it to disk.
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| """
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| print("Creating and saving the FAISS vector store...")
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| vector_store = FAISS.from_documents(documents, embeddings)
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| vector_store.save_local(save_path)
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| print(f"✅ Vector store successfully saved to '{save_path}'.")
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| def load_store(embeddings, load_path=config.VECTOR_STORE_PATH):
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| """
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| Loads the FAISS vector database from a local path.
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| """
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| print(f"Loading vector store from: {load_path}...")
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| return FAISS.load_local(load_path, embeddings, allow_dangerous_deserialization=True)
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