Inconsistency in Node Usage Between ElasticSearch and ChromaDB for VectorStoreIndex in Dense_x Retrieval

[LikhithRishi]

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Question

I encountered an issue with the usage of nodes in VectorStoreIndex when working with ElasticSearch embeddings and ChromaDB embeddings.

Context:

In the case of dense_x retrieval using ElasticSearch embeddings, we use all_nodes (nodes + sub_nodes) in the VectorStoreIndex:

  self.vector_index = VectorStoreIndex(
            all_nodes, service_context=service_context, show_progress=True
        )

This approach works perfectly; we can reuse embeddings and get the expected responses.

However, when using ChromaDB embeddings, if we use only nodes in the VectorStoreIndex, it works fine:

 self._index = VectorStoreIndex(
                nodes, storage_context=self._storage_context, **kwargs
            )

But, if we attempt to use all_nodes (nodes + sub_nodes) as we do with ElasticSearch, it only works for the first time (i.e., during the creation of embeddings). When we try to reuse those embeddings to fetch a response, we encounter the following error: ValueError: Query id d28e3de4-1c4f-420a-9184-97bf8556b11b not found in either retriever_dict or query_engine_dict.

Questions:

1. Why does the approach of using all_nodes (nodes + sub_nodes) work for ElasticSearch embeddings but not for ChromaDB embeddings?
2. Why do we get a query ID error when attempting to reuse embeddings with all_nodes in ChromaDB?
3. Is there a specific reason we cannot use all_nodes with ChromaDB, and if so, could you provide guidance on how to properly manage this?

[dosubot[bot]]

The approach of using all_nodes (nodes + sub_nodes) works for ElasticSearch embeddings but not for ChromaDB embeddings due to differences in how these systems handle the addition and querying of embeddings.

1. Why does the approach of using all_nodes (nodes + sub_nodes) work for ElasticSearch embeddings but not for ChromaDB embeddings?

   • ElasticSearch and ChromaDB have different internal mechanisms for handling embeddings. ElasticSearch can manage the combined set of nodes and sub-nodes without issues, while ChromaDB requires specific handling for asynchronous operations and embedding formats. This difference in handling can lead to inconsistencies when using all_nodes with ChromaDB.

2. Why do we get a query ID error when attempting to reuse embeddings with all_nodes in ChromaDB?

   • The query ID error occurs because ChromaDB has limitations or constraints in handling large datasets or sub-nodes. Specifically, ChromaDB has a MAX_CHUNK_SIZE of 41665, and exceeding this limit can lead to errors. Additionally, if the _collection attribute is not properly initialized or if there are issues with metadata filters, it can result in query ID errors ^[1][2].

3. Is there a specific reason we cannot use all_nodes with ChromaDB, and if so, could you provide guidance on how to properly manage this?

   • To properly manage this issue with ChromaDB, ensure that the embeddings are correctly formatted and that asynchronous operations are handled appropriately. Here is an example of how to add and query embeddings in both synchronous and asynchronous manners:

   @pytest.mark.asyncio()
   @pytest.mark.parametrize("use_async", [True, False])
   async def test_add_to_chromadb_and_query(
      vector_store: ChromaVectorStore,
      node_embeddings: List[TextNode],
      use_async: bool,
   ) -> None:
      if use_async:
          await vector_store.async_add(node_embeddings)
          res = await vector_store.aquery(
              VectorStoreQuery(query_embedding=[1.0, 0.0, 0.0], similarity_top_k=1)
          )
      else:
          vector_store.add(node_embeddings)
          res = vector_store.query(
              VectorStoreQuery(query_embedding=[1.0, 0.0, 0.0], similarity_top_k=1)
          )
      assert res.nodes
      assert res.nodes[0].get_content() == "lorem ipsum"

   Additionally, ensure that the ChromaVectorStore is correctly initialized with the necessary parameters and that the MAX_CHUNK_SIZE is not exceeded ^[3][4].

By following these guidelines, you can manage the embeddings correctly and avoid the query ID error with ChromaDB.

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