[BUG] fix sparse autoembed queries on search api

[jairad26]

Description of changes

Summarize the changes made by this PR.

• Improvements & Bug fixes
  • There was a bug in the search api, where search queries were not auto-embedded. This PR fixes that and adds a test to ensure that search api explicitly works on autoembedding in both the read and write path. previously, there were tests on the underlying search class and manually calling the embed function to ensure the embedded results were valid. this test is e2e, ensuring that adds and queries return the desired result and the search api functions as intended
• New functionality
  • ...

Test plan

How are these changes tested?

• Tests pass locally with pytest for python, yarn test for js, cargo test for rust

Migration plan

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Observability plan

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Documentation Changes

Are all docstrings for user-facing APIs updated if required? Do we need to make documentation changes in the docs section?

[jairad26]

• [BUG] fix sparse autoembed queries on search api #5800 👈 (View in Graphite)
• main

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[github-actions]

Reviewer Checklist

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Testing, Bugs, Errors, Logs, Documentation

• Can you think of any use case in which the code does not behave as intended? Have they been tested?
• Can you think of any inputs or external events that could break the code? Is user input validated and safe? Have they been tested?
• If appropriate, are there adequate property based tests?
• If appropriate, are there adequate unit tests?
• Should any logging, debugging, tracing information be added or removed?
• Are error messages user-friendly?
• Have all documentation changes needed been made?
• Have all non-obvious changes been commented?

System Compatibility

• Are there any potential impacts on other parts of the system or backward compatibility?
• Does this change intersect with any items on our roadmap, and if so, is there a plan for fitting them together?

Quality

• Is this code of a unexpectedly high quality (Readability, Modularity, Intuitiveness)

[propel-code-bot]

Fix auto-embedding of string KNN queries in Collection.search() (sparse & dense)

This PR patches a bug where Collection.search() forwarded the original Search objects rather than their auto-embedded versions, causing string queries (especially sparse vector cases) to bypass the embedding function. The fix is a one-line change that passes embedded_searches (cast to List[Search]) into the client call, ensuring both dense and sparse auto-embedding paths work. A new end-to-end pytest adds 68 LOC to guard against regressions by inserting documents with a registered SparseEmbeddingFunction and asserting correct retrieval via string-based queries.

Key Changes

• Updated chromadb/api/models/Collection.py to pass embedded_searches via cast(List[Search], embedded_searches).
• Added extensive e2e test test_search_api_uses_embedded_searches_with_sparse_embeddings and helper TestSparseEmbeddingFunction in chromadb/test/api/test_schema_e2e.py (~68 LOC).

Affected Areas

• chromadb/api/models/Collection.search() implementation
• Test suite for schema/search behaviour

This summary was automatically generated by @propel-code-bot

[HammadB]

can we cut a ticket for this type error? It should not happen.

[jairad26]

yea ill cut it. it's weird, doesnt give red squiggly its only mypy