techlist.io
KREN
line

ODW #5: Building a RAG System with a Vector DB and Agent Skills (opens in new tab)

ai-agentmcpragclaude-codemarkdownvector-dbswiftchromadb

The workshop demonstrated how a lightweight RAG system can make large collections of technical documentation easier for developers and AI agents to use. Using ChromaDB, Swift Evolution proposals were indexed locally and exposed to Claude Code through MCP. Agent skills then simplified searches by teaching the agent which collection, metadata, and query practices to use. The approach improves document discovery and can support code generation and review.

Why RAG Is Needed

  • Large application teams maintain extensive documentation and architectural guidelines.
  • Developers often spend significant time searching for information about:
    • Introducing dependencies
    • Resolving build errors
    • Following architectural rules
  • Asking experts can solve problems, but consumes time for both the questioner and the responder.
  • RAG provides AI agents with structured, searchable knowledge so they can answer questions more accurately using internal documents.

Building a RAG System with ChromaDB

  • The workshop used ChromaDB, an open-source local vector database with Python and JavaScript client libraries.
  • Swift Evolution proposals served as the sample dataset:
    • Approximately 500 Markdown documents
    • Consistent structure and proposal IDs such as SE-0400
    • Metadata including implementation status and authors
  • Participants indexed the documents locally and connected the database to Claude Code through an MCP tool.
  • This allowed the coding agent to retrieve and reference Swift language proposals during conversations.

Improving Search with Agent Skills

  • MCP exposes the available database tools, but the agent still needs to know:
    • Which collection contains the relevant data
    • Which metadata fields are useful
    • How to formulate effective queries
  • A dedicated searching-swift-evolution skill encoded this knowledge, including:
    • The swift-evolution collection name
    • Proposal ID formats such as SE-0255 and ST-0001
    • Metadata such as Status and Authors
    • A recommendation to query in English
  • With the skill, users could issue simple requests such as “Investigate SE-0500” without explaining the database structure or MCP workflow.
  • The workshop also covered skill mechanics, authoring best practices, and practical skill development.
  • Participants later indexed their own Markdown documents, created search skills, and learned how to deploy the database to LY Corporation’s internal Flava cloud for sharing.

Potential Applications

  • Natural-language document search can make internal technical knowledge significantly more accessible.
  • Coding agents can retrieve relevant documentation automatically before:
    • Generating code
    • Reviewing code
    • Checking compliance with architectural or implementation guidelines
  • Combining RAG with agent skills or Claude Code sub-agents can embed organizational knowledge directly into development workflows.

Workshop Design and Results

  • The online workshop used demonstrations by instructors and mock participants.
  • More than 1,000 people attended.
  • Its structure balanced lectures and hands-on exercises:
    • Lectures explained the core concepts concisely.
    • Practical demonstrations showed how to apply the system to real work documents.
  • This balance helped participants understand both the underlying ideas and their practical use.

Overall, the workshop showed that a local vector database plus MCP and well-designed agent skills can provide a simple, effective foundation for searchable engineering knowledge and AI-assisted development.