[DOC] Add notebook to show using query with id filter

[jairad26]

Description of changes

This PR adds a python notebook showing how to use the id filter in query to help in decreasing the search space. The notebook goes through an ecommerce example, where the user has categories and products stored in a sqlite3 db, with only the text data stored in chroma. It then uses normal sql queries to find the subset of IDs it wants to search across semantically, and passes that to chroma.

Test plan

How are these changes tested?

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

Documentation Changes

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

[jairad26]

• [DOC] Add notebook to show using query with id filter #4521 👈 (View in Graphite)
• main

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[github-actions]

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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?
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• 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?
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System Compatibility

• Are there any potential impacts on other parts of the system or backward compatibility?
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Quality

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

[propel-code-bot]

[CriticalError]

There's a mismatch between product descriptions and their actual associations in the demo output. For example, in scenario 2 results, the CloudRest Memory Foam Mattress shows description about denim jeans, and CrispWave Air Fryer shows description about t-shirts. This happens because the setup logic assigns descriptions from one category to products from another.

In the setup_databases function, when fetching descriptions for each product, try ensuring that descriptions match the correct category and product index:

[propel-code-bot]

Add Example Notebook: Using Query with ID Filter in Chroma

This PR introduces a new Python Jupyter notebook that demonstrates how to use Chroma's query functionality with an ID filter to reduce search space, using a legal cases dataset as an illustrative example. The notebook walks through creating and populating SQLite tables, ingesting text data into Chroma, and querying using filtered IDs derived from structured SQL queries, thereby showcasing best practices for combining structured and semantic search.

Key Changes:
• Added 'query_with_id_filter.ipynb' under examples/basic_functionality/
• Notebook builds a synthetic legal cases dataset with jurisdictions and case types in SQLite and indexed text in Chroma.
• End-to-end demonstration of fetching candidate document IDs via SQL, then using those IDs to semantically search with Chroma's query() API.
• Notebook code generates sample scenario, database setup logic, and semantic search queries.

Affected Areas:
• examples/basic_functionality/query_with_id_filter.ipynb

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