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agno-knowledge

Provides document ingestion and retrieval for LLM-based QA applications.

4|Updated Feb 19, 2026
Search Tags:#semantic search#embeddings#rag#vector database#knowledge base#agno#document loading
One-click install
npx skills add https://github.com/ajshedivy/agno-cookbook --skill agno-knowledge
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agno-knowledge
Source: https://github.com/ajshedivy/agno-cookbook/tree/main/plugins/agno-framework/skills/agno-knowledge
Command: npx skills add https://github.com/ajshedivy/agno-cookbook --skill agno-knowledge

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agno[chromadb,google,openai], chromadb, pypdf, python-docx, lancedb, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables the creation and management of sophisticated knowledge bases for AI agents, allowing them to access and utilize vast amounts of information for more informed responses and actions.

Core Features & Use Cases

  • Vector Database Integration: Supports ChromaDB, PgVector, and LanceDB for efficient semantic search.
  • Diverse Data Loading: Load documents from URLs, local files (PDF, DOCX, TXT), and entire directories.
  • Customizable Embeddings & Chunking: Utilize various embedders (Google, OpenAI, Ollama) and chunking strategies for optimal data representation.
  • Use Case: An AI assistant needs to answer questions about a company's internal documentation. This Skill allows you to load all documentation into a vector database, enabling the agent to perform semantic searches and provide accurate, context-aware answers.

Quick Start

Use the agno-knowledge skill to insert the document from the URL 'https://docs.agno.com/introduction.md' into the knowledge base.

Frequently Asked Questions about agno-knowledge

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

FAQPage Schema
How do I build a searchable knowledge base for AI agents using RAG?▼

Build a searchable knowledge base for AI agents by loading documents from URLs or local files into a vector database like ChromaDB, generating embeddings for semantic search to enable RAG capabilities.

What document formats can I load into a vector database for semantic search?▼

Load PDF, DOCX, TXT files, and entire local directories into a vector database for semantic search, alongside fetching documents directly from URLs to build a comprehensive knowledge base.

Does the agno-knowledge skill support keyword and hybrid search in addition to semantic search?▼

Yes, the agno-knowledge skill supports semantic, keyword, and hybrid search methods, allowing AI agents to perform flexible retrieval from the vector database knowledge base.

Can I use local models like Ollama for generating embeddings with ChromaDB?▼

Yes, you can use Ollama, OpenAI, or Google embedders to generate vector embeddings for ChromaDB, allowing customizable data representation and chunking strategies for your knowledge base.

How do I integrate a ChromaDB vector database with Agno agents for document retrieval?▼

Integrate ChromaDB with Agno agents by loading your documents into the vector database and configuring the embedder, enabling the agent to query the knowledge base for context-aware responses.

What are the limitations of using PgVector versus LanceDB for agent knowledge bases?▼

PgVector and LanceDB offer different performance characteristics for vector storage and semantic search, with ChromaDB also supported; the choice depends on your existing database infrastructure and scaling requirements.

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