Convex Agents RAG

Name: Convex Agents RAG
Availability: InStock
Author: sstobo

Enable retrieval-augmented generation for Convex Agents with namespace-based knowledge bases.

25|4|Updated Oct 18, 2025

Search Tags:#rag #agents #convex #retrieval-augmented generation #semantic-search #knowledge-base

Ready to run

npx skills add https://github.com/Sstobo/convex-skills --skill convex-agents-rag

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Convex Agents RAG
Source: https://github.com/Sstobo/convex-skills/tree/main/convex-agents-rag
Command: npx skills add https://github.com/Sstobo/convex-skills --skill convex-agents-rag

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Agents often need to provide accurate, context-grounded answers by searching through documents and knowledge bases. This Skill enables retrieval-augmented generation for AI agents.

Core Features & Use Cases

RAG integration with Convex Agents: semantic search over namespace content, context grounding, and knowledge-base querying.
Use Cases: support docs, policies, product FAQs; chat agents that answer with sourced context.

Quick Start

Ingest documents into a namespace and configure your agent to leverage rag for contextual responses.

Frequently Asked Questions about Convex Agents RAG

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I add retrieval-augmented generation to Convex Agents?▼

To add retrieval-augmented generation to Convex Agents, integrate the rag module in convex.config.ts and ingest your documents into separate namespaces for semantic search. This grounds agent responses with knowledge-base context.

What is semantic search over namespaces for AI agents?▼

Semantic search over namespaces is a retrieval mechanism that allows AI agents to query specific document sets, such as support docs or policies, to provide accurate and context-grounded answers.

How do I ingest documents into a knowledge base for contextual chat responses?▼

Ingest documents into a configured namespace to populate the knowledge base. Once ingested, your Convex Agent can leverage the rag module to query this content and answer with sourced context.

Can I use separate namespaces for different policy documents in Convex?▼

Yes, you can ingest content into separate namespaces. This allows your agents to perform semantic search over isolated document sets, ensuring responses are grounded in the correct policy or product data context.

Does Convex Agents RAG support searching product FAQs and support docs?▼

Yes, retrieval-augmented generation supports searching product FAQs and support docs. The skill grounds agent responses by applying semantic search over your ingested knowledge base content.

Why are my AI agent responses lacking context from my knowledge base?▼

Agent responses lack knowledge base context if documents are not properly ingested into namespaces or the rag module is not configured in convex.config.ts. Grounding requires semantic search integration to source relevant information.