CAPE issues RAG chatbot

[doomedraven]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @doomedraven, 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 sophisticated Retrieval-Augmented Generation (RAG) chatbot designed to automatically answer newly opened GitHub issues. By leveraging a knowledge base constructed from project documentation and historical issue data, the bot can semantically search for relevant information and use a large language model (Gemini) to formulate helpful, context-aware responses, significantly streamlining issue support and reducing response times.

Highlights

• Automated Issue Answering: A new GitHub Actions workflow (auto_answer.yml) has been introduced to automatically trigger a Retrieval-Augmented Generation (RAG) chatbot whenever a new issue is opened in the repository.
• RAG Chatbot Implementation: The auto_answer_bot.py script implements the core RAG logic, utilizing SentenceTransformer for creating embeddings, FAISS for efficient semantic search within the knowledge base, and Google's Gemini LLM for generating context-aware answers.
• Knowledge Base Management: A dedicated build_knowledge_base.py script is included to construct and update the bot's knowledge base. This script processes project documentation (RST files) and existing GitHub issues, converting them into searchable embeddings and metadata.
• Comprehensive Documentation: A detailed readme.md file provides step-by-step instructions for setting up the local knowledge base, deploying the bot to GitHub, and outlining the maintenance process to keep the bot's information current.

Ignored Files

• Ignored by pattern: .github/workflows/** (1)
  • .github/workflows/auto_answer.yml

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[gemini-code-assist]

Code Review

This pull request introduces a RAG-based chatbot to automatically answer GitHub issues, which is a fantastic addition. The implementation includes a GitHub Action, scripts for building the knowledge base and generating answers, and associated documentation. However, my review has identified several critical issues that need to be addressed before merging. The most significant is a security vulnerability related to the use of pickle for data serialization, which could allow for remote code execution. There are also critical correctness bugs in the knowledge base creation script that cause data duplication, as well as other issues concerning error handling, code clarity, and dependency management. I have provided detailed comments and suggestions for each point below.