Local RAG FAQs

Question 1

How does Local RAG improve AI agent performance and context?

Accepted Answer

By indexing code, documentation, and real-time conversation histories, Local RAG provides AI agents with a dynamic and comprehensive understanding of the project. This allows agents to recall past decisions, find relevant information semantically, and even surface uncommitted changes, leading to more informed and accurate responses.

Question 2

What is Local RAG and its primary function?

Accepted Answer

Local RAG (Retrieval-Augmented Generation) is a software tool that provides semantic search capabilities across your codebase files and AI conversation histories. Its primary function is to enhance contextual awareness for AI agents and help developers efficiently navigate and understand complex projects.

Question 3

How does Local RAG help identify documentation gaps?

Accepted Answer

Local RAG includes built-in analytics that track user queries. It identifies documentation gaps by highlighting queries that return no results or low-relevance results, showing you exactly where your existing documentation is falling short and needs improvement.

Question 4

Can Local RAG assist with code refactoring and understanding project structure?

Accepted Answer

Yes, Local RAG offers a 'find_usages' feature to locate all call sites for specific symbols, helping understand the impact of changes. Additionally, it can generate Mermaid dependency graphs to visualize project structure and file relationships, making refactoring safer and project exploration easier.

Question 5

Is Local RAG a cloud-based service, or does it run locally?

Accepted Answer

Local RAG is designed to run entirely locally. It requires no API keys, cloud services, or Docker, ensuring your codebase and conversation data remain private and secure on your machine. Installation is simple, often just a `bunx` command.

Local RAG

Local RAG

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主要功能

使用案例