What retrieval algorithm does SimpleVectorStore use for retrieving the top_k results?

[rahulkrprajapati]

What retrieval method does llama-index use when using their default VectorStoreIndex and no vector store is specified in storage context. the vector store defaults to an in memory SimpleVectorStore but what's the algorithm behind the top_k retrieval?

[dosubot[bot]]

🤖

Hey @rahulkrprajapati! Great to see you diving into the details again. Hope you're doing well!

The retrieval algorithm used by the SimpleVectorStore in LlamaIndex for retrieving the top_k results when using the default VectorStoreIndex and no vector store is specified in the storage context is not explicitly mentioned in the provided context. However, the retrieval process can be inferred from the methods in the VectorIndexRetriever class.

The retrieval process is as follows:

1. The _retrieve or _aretrieve method is called with a QueryBundle as an argument. If the vector store is an embedding query and the QueryBundle does not have an embedding but has embedding strings, the embedding is computed using the embed_model from the service context.

2. The _get_nodes_with_embeddings or _aget_nodes_with_embeddings method is called with the QueryBundle (now with embeddings). This method builds a VectorStoreQuery using the _build_vector_store_query method and then queries the vector store with this query.

3. The _build_vector_store_query method constructs a VectorStoreQuery object with the query embedding, similarity_top_k, node_ids, doc_ids, query_str, mode, alpha, filters, and sparse_top_k from the VectorIndexRetriever instance.

4. The vector store's query or aquery method is called with the VectorStoreQuery and any additional kwargs. The result is a VectorStoreQueryResult.

5. The _build_node_list_from_query_result method is called with the VectorStoreQueryResult. This method constructs a list of NodeWithScore objects from the query result. If the vector store does not keep text and returns node indices, the nodes are recovered from the docstore using the node ids. If the vector store keeps text and returns nodes, only image or index nodes are recovered from the docstore. The scores are taken from the similarities in the query result.

6. The list of NodeWithScore objects is returned.

The specific algorithm used to compute the top_k most similar vectors in the vector store is not specified in the provided context. You can find more details in the retriever.py file.

As for the specific algorithm used by the SimpleVectorStore in LlamaIndex to compute the top_k most similar vectors, it depends on the mode specified in the query. The modes and their corresponding algorithms are as follows:

1. SVM, LINEAR_REGRESSION, LOGISTIC_REGRESSION: These modes use the get_top_k_embeddings_learner function to compute the top_k most similar vectors.

2. MMR: This mode uses the get_top_k_mmr_embeddings function to compute the top_k most similar vectors.

3. DEFAULT: This mode uses the get_top_k_embeddings function to compute the top_k most similar vectors.

You can find more details in the simple.py file.

Unfortunately, the exact functionality of the get_top_k_embeddings_learner function in the SimpleVectorStore class and how it computes the top_k most similar vectors is not specified in the provided context. I wasn't able to find an answer within the repository, but the answer may be available elsewhere or I could have missed it. If you have more information about this function or if there are other parts of the code you're interested in, please let me know!

Sources

• llama_index/indices/vector_store/retrievers/retriever.py
• llama_index/vector_stores/simple.py

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