(Example w/ Faiss)

1. Preparation of Source/Training Data:

   • Data Collection: Gather the source or training data, which could be text documents, images, or other forms of data.
   • Embedding Generation: Use an embedding model (e.g., BERT, Sentence Transformers, etc.) to convert the source data into dense vector representations (embeddings). This step is essential and happens before the vector search tool comes into play.

   pythonCopy code
   from sentence_transformers import SentenceTransformer
   
   model = SentenceTransformer('all-MiniLM-L6-v2')
   documents = ["Document 1 text", "Document 2 text", ...]
   document_embeddings = model.encode(documents)
   
   

2. Indexing with a Vector Search Tool:

   • Vector Store Creation: Use the vector search tool (e.g., Faiss, Annoy, HNSWlib, etc.) to create a vector index from the embeddings of the source data. This step involves storing the embeddings in a way that allows efficient similarity search.

   pythonCopy code
   import faiss
   index = faiss.IndexFlatL2(document_embeddings.shape[1])  # Using L2 distance
   index.add(document_embeddings)
   
   

3. Query Embedding Generation:

   • Query Embedding: When a new query is received, convert it into an embedding using the same embedding model used for the source data.

   pythonCopy code
   query = "Sample query text"
   query_embedding = model.encode([query])
   
   

4. Retrieval with a Vector Search Tool:

   • Similarity Search: Use the vector search tool to perform a similarity search with the query embedding against the indexed document embeddings. The tool returns the most similar documents.

   pythonCopy code
   D, I = index.search(query_embedding, k)  # k is the number of top results to retrieve
   
   

5. Response Generation:

   • Contextual Retrieval: Retrieve the top-k similar documents based on the indices returned by the vector search tool.
   • Generative Model: Use the retrieved documents as context for a generative model (e.g., GPT) to generate the final response.

   pythonCopy code
   relevant_documents = [documents[i] for i in I[0]]
   response = generative_model.generate_response(query, relevant_documents)
   
   

Summary

• Before Querying: The vector search tool is used to index the embeddings of the source or training data after they have been generated by an embedding model. This involves adding the embeddings to the index created by the tool.
• During Querying: The vector search tool performs the similarity search to retrieve relevant documents based on the query embedding.

Generalization Across Vector Search Tools

• Faiss: Efficient similarity search and clustering of dense vectors.
• Annoy (Approximate Nearest Neighbors Oh Yeah): Memory-efficient and fast nearest neighbor search.
• HNSWlib (Hierarchical Navigable Small World): High-performance approximate nearest neighbor search.
• ScaNN (Scalable Nearest Neighbors): Scalable and efficient similarity search.

All these tools fit into the RAG architecture by providing efficient mechanisms for indexing and retrieving vector representations of data, thereby enhancing the retrieval component of the system. The embedding generation is handled by separate models, and the vector search tools optimize the storage and search processes to enable quick and relevant document retrieval, which is then used to generate contextually informed responses.