Feat/result filtering

[armaan-71]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @armaan-71, 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 introduces a significant enhancement to the lead generation process by implementing an LLM-powered filtering mechanism for search results. The primary goal is to improve the quality and relevance of generated leads by intelligently sifting through initial search outcomes, ensuring that only legitimate company websites are considered. This change streamlines the subsequent lead mapping by providing a cleaner, more focused dataset.

Highlights

• LLM-based Result Filtering: Added a new filter_results function that utilizes a Large Language Model (LLM) to intelligently filter search results.
• Improved Lead Quality: Implemented logic to identify and retain only actual company homepages or about pages, rejecting irrelevant content such as blog posts, social media profiles, and directory listings.
• Integration into Handler: Integrated the new filter_results function into the main handler logic, ensuring that only high-quality, filtered results are passed on for lead generation.

🧠 New Feature in Public Preview: You can now enable Memory to help Gemini Code Assist learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. Click here to enable Memory in your admin console.

Changelog

• backend/python/src/handlers/process_run.py
  • Added a new filter_results function to apply LLM-based filtering to search results.
  • Modified the handler function to utilize the new filter_results function before mapping results to leads.

Activity

• No human activity has been recorded on this pull request yet.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request introduces a new feature to filter search results using an LLM, aiming to improve the quality of leads by identifying actual company websites. The security review agent was unable to process the original model response, so no specific security vulnerabilities were identified. The implementation of the filter_results function includes sensible fallbacks, and a suggestion has been made to improve code conciseness and idiomatic style.

[gemini-code-assist]

For conciseness and to follow Python best practices, this for loop for building condensed_results can be refactored into a more idiomatic list comprehension. This change will make the code more compact and readable.

    condensed_results = [
        {
            "index": i,
            "title": r.get("title", ""),
            "snippet": r.get("snippet", r.get("description", "")),
            "domain": parse_domain(r.get("link", r.get("website", ""))),
        }
        for i, r in enumerate(results)
    ]