Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

llm metadata fix #1836

Merged
merged 1 commit into from
Feb 28, 2025
Merged

llm metadata fix #1836

merged 1 commit into from
Feb 28, 2025

Conversation

sfahad1414
Copy link
Collaborator

@sfahad1414 sfahad1414 commented Feb 26, 2025

Summary by CodeRabbit

  • Refactor
    • Improved language model metadata filtering so that only options with available models are included in responses, streamlining the presented data.
  • Tests
    • Updated test validations to ensure that unsupported metadata entries are excluded from the output, enhancing overall consistency and reliability.

Copy link

coderabbitai bot commented Feb 26, 2025

Walkthrough

The changes modify the fetch_llm_metadata function in the LLMProcessor class to compile metadata into a new dictionary named final_metadata. The function now only includes LLM types with a non-empty models list and returns this refined output. Additionally, the test for test_get_llm_metadata has been updated to assert that the "anthropic" key is absent, reflecting the new metadata structure.

Changes

File(s) Change Summary
kairon/shared/llm/processor.py Updated fetch_llm_metadata to initialize a new final_metadata dict; now adds LLM types only if their models list is non-empty and returns it.
tests/integration_test/services_test.py Modified test_get_llm_metadata assertions: removed checks for "anthropic" properties and added assertion to confirm its absence.

Sequence Diagram(s)

sequenceDiagram
    participant Client as Caller
    participant LLMProcessor as LLMProcessor
    Client->>LLMProcessor: Call fetch_llm_metadata(bot)
    LLMProcessor->>LLMProcessor: Initialize metadata & final_metadata
    LLMProcessor->>LLMProcessor: For each LLM type, check if models list is non-empty
    LLMProcessor->>LLMProcessor: Add valid LLM type info to final_metadata
    LLMProcessor-->>Client: Return final_metadata
Loading

Possibly related PRs

Poem

I'm a rabbit dancing through lines of code,
Hop by hop in metadata's abode.
Final values shine where models play,
"Anthropic" is gone, whiskers say hooray!
With every change, I bound with delight 🥕!

Tip

CodeRabbit's docstrings feature is now available as part of our Pro Plan! Simply use the command @coderabbitai generate docstrings to have CodeRabbit automatically generate docstrings for your pull request. We would love to hear your feedback on Discord.

✨ Finishing Touches
  • 📝 Generate Docstrings

Thank you for using CodeRabbit. We offer it for free to the OSS community and would appreciate your support in helping us grow. If you find it useful, would you consider giving us a shout-out on your favorite social media?

❤️ Share
🪧 Tips

Chat

There are 3 ways to chat with CodeRabbit:

  • Review comments: Directly reply to a review comment made by CodeRabbit. Example:
    • I pushed a fix in commit <commit_id>, please review it.
    • Generate unit testing code for this file.
    • Open a follow-up GitHub issue for this discussion.
  • Files and specific lines of code (under the "Files changed" tab): Tag @coderabbitai in a new review comment at the desired location with your query. Examples:
    • @coderabbitai generate unit testing code for this file.
    • @coderabbitai modularize this function.
  • PR comments: Tag @coderabbitai in a new PR comment to ask questions about the PR branch. For the best results, please provide a very specific query, as very limited context is provided in this mode. Examples:
    • @coderabbitai gather interesting stats about this repository and render them as a table. Additionally, render a pie chart showing the language distribution in the codebase.
    • @coderabbitai read src/utils.ts and generate unit testing code.
    • @coderabbitai read the files in the src/scheduler package and generate a class diagram using mermaid and a README in the markdown format.
    • @coderabbitai help me debug CodeRabbit configuration file.

Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments.

CodeRabbit Commands (Invoked using PR comments)

  • @coderabbitai pause to pause the reviews on a PR.
  • @coderabbitai resume to resume the paused reviews.
  • @coderabbitai review to trigger an incremental review. This is useful when automatic reviews are disabled for the repository.
  • @coderabbitai full review to do a full review from scratch and review all the files again.
  • @coderabbitai summary to regenerate the summary of the PR.
  • @coderabbitai generate docstrings to generate docstrings for this PR.
  • @coderabbitai resolve resolve all the CodeRabbit review comments.
  • @coderabbitai configuration to show the current CodeRabbit configuration for the repository.
  • @coderabbitai help to get help.

Other keywords and placeholders

  • Add @coderabbitai ignore anywhere in the PR description to prevent this PR from being reviewed.
  • Add @coderabbitai summary to generate the high-level summary at a specific location in the PR description.
  • Add @coderabbitai anywhere in the PR title to generate the title automatically.

CodeRabbit Configuration File (.coderabbit.yaml)

  • You can programmatically configure CodeRabbit by adding a .coderabbit.yaml file to the root of your repository.
  • Please see the configuration documentation for more information.
  • If your editor has YAML language server enabled, you can add the path at the top of this file to enable auto-completion and validation: # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json

Documentation and Community

  • Visit our Documentation for detailed information on how to use CodeRabbit.
  • Join our Discord Community to get help, request features, and share feedback.
  • Follow us on X/Twitter for updates and announcements.

Copy link

@coderabbitai coderabbitai bot left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Actionable comments posted: 0

🧹 Nitpick comments (2)
tests/integration_test/services_test.py (1)

1596-1598: Remove commented out code.

The new assertion correctly verifies that "anthropic" is not included in the metadata, which aligns with the changes made to fetch_llm_metadata. However, the commented-out assertions should be removed rather than left in the codebase to maintain cleanliness.

 assert "anthropic" not in actual["data"]
-#assert "model" in actual["data"]["anthropic"]["properties"]
-#assert actual["data"]["anthropic"]["properties"]["model"]["enum"] == []
kairon/shared/llm/processor.py (1)

412-417: Update the function's docstring to reflect the new behavior.

The docstring should be updated to clarify that the function now only returns metadata for LLM types that have associated models, not all LLM types.

    @staticmethod
    def fetch_llm_metadata(bot: str):
        """
        Fetches the llm_type and corresponding models for a particular bot.
        :param bot: bot id
-        :return: dictionary where each key is a llm_type and the value is a list of models.
+        :return: dictionary where each key is a llm_type with non-empty models list and the value is the LLM metadata.
        """
📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between f95252a and 93886ea.

📒 Files selected for processing (2)
  • kairon/shared/llm/processor.py (2 hunks)
  • tests/integration_test/services_test.py (1 hunks)
⏰ Context from checks skipped due to timeout of 90000ms (2)
  • GitHub Check: Python CI
  • GitHub Check: Analyze (python)
🔇 Additional comments (2)
kairon/shared/llm/processor.py (2)

420-434: Filtering out LLM types with empty models - good optimization!

This change ensures that only LLM types with available models are included in the returned metadata, which is a good optimization to avoid unnecessary data in the response.


435-435: Return value change looks good.

The change to return final_metadata instead of metadata is consistent with the optimization to only include LLM types with models.

Copy link
Collaborator

@sushantpatade sushantpatade left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Approved

@sushantpatade sushantpatade merged commit 56af53c into master Feb 28, 2025
7 of 8 checks passed
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

2 participants