feat: Updating retrieve online documents v2 to work for other fields for sq…

[franciscojavierarceo]

What this PR does / why we need it:

This PR enables full text search for the retrieve_online_documents/ endpoint for SQLite Vec. It also establishes a new parameter in the SDK method called query_string that can be passed to use key word search. There are a number of limitations with this approach as the top_k parameter can be misleading (as evident by the example). This offers a good start for keyword search that leverages the existing vector retrieval endpoint. As a next step, enabling hybrid search would be beneficial.

It makes keyword search as simple as:

results = store.retrieve_online_documents_v2(
    features=[
        "document_embeddings:Embeddings",
        "document_embeddings:content",
        "document_embeddings:title",
    ],
    query=query_embedding,
    query_string="(content: 5) OR (title: 1) OR (title: 3)",
    top_k=3,
).to_dict()
print(results)

• feature_server.py:
  • Added optional query_string parameter to the GetOnlineFeaturesRequest class.
  • Updated retrieve_online_documents to support the query_string parameter.
• feature_store.py:
  • Added optional query_string parameter to retrieve_online_documents_v2.
  • Updated related methods to handle query_string.
• feature_view.py:
  • Added an assertion to ensure only one vector feature per feature view.
• milvus.py:
  • Added optional query_string parameter to retrieve_online_documents_v2.
• online_store.py:
  • Added optional query_string parameter to retrieve_online_documents_v2.
• sqlite.py:
  • Extensive changes to support text search with BM25, including adding text_search_enabled configuration and handling query_string.
  • Updated SQL operations to support the new functionalities.
• passthrough_provider.py and provider.py:
  • Updated retrieve_online_documents_v2 to support the query_string.
• types.py:
  • Added FEAST_VECTOR_TYPES list for handling vector types.
• example_feature_repo_1.py:
  • Added content and title fields to an example feature view.

Which issue(s) this PR fixes:

#5081
#5073

Misc

[franciscojavierarceo]

@HaoXuAI

[HaoXuAI]

wonder if this is used somewhere? :)

[franciscojavierarceo]

https://github.com/feast-dev/feast/pull/5082/files#r1972213724

[franciscojavierarceo]

@HaoXuAI see here!

[franciscojavierarceo]

Technically there's a flaw here I need to resolve because you have to pass in a query embedding for just pure text search, which is silly.