convex-agents-rag_skill

Overview

This skill implements Retrieval-Augmented Generation (RAG) patterns to enhance agents with searchable, custom knowledge bases. It helps agents ground responses in specific documents and reduce hallucinations by combining semantic search with LLM generation. Use it to add contextual retrieval and namespace isolation for user- or team-specific data.

How this skill works

The skill ingests documents into namespaced stores and creates vector and text indexes for hybrid search. Agents query the RAG component to retrieve relevant passages, then augment prompts with retrieved context before generating a final response. Filtering, limits, and namespace isolation let you control scope and precision of retrieved content.

When to use it

• When agents must reference product docs, policies, or any specific corpus to answer accurately.
• To build support systems, FAQs, or documentation assistants that require grounded answers.
• When you need semantic search across custom content rather than keyword matching.
• To reduce hallucinations by constraining generation to known information.
• When managing multi-tenant data using isolated namespaces for users or teams.

Best practices

• Organize content into namespaces like user:userId or team:teamId to prevent cross-tenant leaks.
• Combine vector search with text filters (hybrid search) to balance relevance and precision.
• Limit retrieved passages and preformat context to keep prompts concise and focused.
• Apply filterNames or metadata filters to target specific documents or file types.
• Periodically re-ingest or re-index updated documents to keep results current.

Example use cases

• Answering customer support queries by retrieving product manual excerpts and generating step-by-step guidance.
• Generating policy-compliant responses by grounding agent replies in an organization’s policy documents.
• Creating a searchable internal knowledge assistant that returns concise, sourced answers from team docs.
• Powering an FAQ bot that combines semantic search with generation to handle follow-up questions.
• Providing personalized assistant behavior by storing and retrieving user-specific notes or preferences.