Add support for resolving indicators

[keyurva]

• Added support for resolving NL queries to indicators (variables and topics)
• Updated resolve endpoint to support resolver and target arguments
• Added fetch_indicators convenience method
• Added match scores and entity types in response candidates
• Added unit tests and also did manual verification against custom dc on dc-dev with this script

[gemini-code-assist]

Summary of Changes

Hello @keyurva, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the datacommons_client by introducing the capability to resolve natural language queries into specific indicators like statistical variables and topics. It achieves this by extending the existing resolve endpoint with new optional parameters and providing a dedicated, user-friendly method for indicator fetching. Furthermore, the changes enrich the resolution response with additional metadata, offering more detailed insights into the resolved candidates.

Highlights

• Indicator Resolution Support: Added functionality to resolve natural language queries into indicators, such as statistical variables and topics.
• Enhanced Resolve Endpoint: The resolve endpoint now supports new resolver and target arguments, allowing for more specific resolution queries.
• New Convenience Method: A new fetch_indicators method has been introduced for easily fetching resolved indicators.
• Enriched Response Data: Resolution response candidates now include additional match scores and entity types in their metadata for better context.
• Comprehensive Testing: New unit tests have been added for the indicator resolution functionality, alongside regression tests to ensure existing features remain stable.

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[gemini-code-assist]

Code Review

This pull request effectively adds support for resolving natural language queries to indicators by updating the resolve endpoint, adding a new fetch_indicators convenience method, and updating the relevant data models and tests. The changes are logical and well-tested. My review includes one suggestion to add validation logic to the ResolveRequestPayload model to ensure the new arguments (resolver and target) are used correctly, which will improve the robustness of the client.

[gemini-code-assist]

To ensure that ResolveRequestPayload is always used correctly, it's a good practice to add validation directly to the Pydantic model. This will prevent invalid combinations of expression, resolver, and target regardless of how the payload is created.

You can add a model_validator to enforce that:

1. expression and resolver are mutually exclusive.
2. target is only provided when resolver is also present.

Here is a suggested implementation to add to the ResolveRequestPayload class:

  @model_validator(mode="after")
  def _validate_resolver_args(self):
    if self.expression is not None and self.resolver is not None:
      raise ValueError(
          "`expression` and `resolver` are mutually exclusive and cannot be used together."
      )
    if self.target is not None and self.resolver is None:
      raise ValueError("`target` can only be used with `resolver`.")
    return self