query mode "embedding" not supported by GPTSimpleVectorIndex?

[adamsussman]

According to the documentation, GPTSimpleVectorIndex may be queried with "embedding" mode (last example of https://gpt-index.readthedocs.io/en/latest/how_to/embeddings.html). However when I do so, I get an error.

embed_model = LangchainEmbedding(HuggingFaceEmbeddings())
index = GPTSimpleVectorIndex.load_from_disk(
    save_path="index.json",
    embed_model=embed_model,
)

response = index.query(
    query_string,     
    mode="embedding",
)

error:

  File "/usr/local/lib/python3.10/site-packages/gpt_index/indices/base.py", line 334, in query
    return query_runner.query(query_str, self._index_struct)
  File "/usr/local/lib/python3.10/site-packages/gpt_index/indices/query/query_runner.py", line 100, in query
    query_cls = get_query_cls(index_struct_type, mode)
  File "/usr/local/lib/python3.10/site-packages/gpt_index/indices/query/query_map.py", line 79, in get_query_cls
    return MODE_TO_QUERY_MAP_SIMPLE[mode]

Is this an actual error or is it not possible/recommended to use embeddings with GPTSimpleVectorIndex? If I use Tree or List indices, then when I save them to disk, there are no actual embeddings, so I am confused on the correct set of classes to use for top-k embedding based queries.

[jerryjliu]

oh the mode="default" already does embedding-based lookup for GPTSimpleVectorIndex (since the entire index is centered around storing embeddings for text chunks and retrieving them). I can make this more clear in the docstring if that helps?

[adamsussman]

That would definitely help. Or just accept both silently? Either way, its good to know that "default" means different things for different indices.

[adamsussman]

But, also please correct the documentation, since the example does not work as written.

[jerryjliu]

yes will do!