Skip to main content
Knowledge bases in Agno are architecturally designed for AI agent retrieval, with specialized components that bridge the gap between raw data storage and intelligent information access.

​
Knowledge Base Components

Knowledge bases consist of several interconnected layers that work together to optimize information for agent retrieval:

​
Storage Layer

Vector Database: Stores processed content as embeddings optimized for similarity search
  • PgVector for production scalability
  • LanceDB for development and testing
  • Pinecone for managed cloud deployments

​
Processing Layer

Content Pipeline: Transforms raw information into searchable format
  • Readers parse different file types
  • Chunking strategies break content into optimal pieces
  • Embedders convert text to vector representations

​
Access Layer

Search Interface: Enables intelligent information retrieval
  • Semantic similarity search
  • Hybrid search combining vector and keyword matching
  • Metadata filtering for precise results

​
How Agents Use Knowledge Bases

When you give an agent access to a knowledge base, several powerful capabilities emerge:

​
Automatic Information Retrieval

The agent doesn’t need to be told when to search - it automatically determines when additional information would help answer a question or complete a task. Although - explicitly instructing the agent to search a knowledge base is a perfectly fine and very common use case. 
# Agent automatically searches when needed
user: "What's our current return policy?"
# Agent searches knowledge base for return policy information
# Agent responds with current, accurate policy details

​
Contextual Understanding

The agent understands the context of questions and searches for the most relevant information, not just keyword matches. 
# Understands intent and context
user: "Can I send back this item I bought last month?"
# Searches for: return policies, time limits, return procedures
# Not just: "send back", "item", "bought", "month"

​
Source Attribution

Agents can provide references to where they found information, building trust and enabling verification. 
# Response includes source information
"Based on section 3.2 of our Return Policy document,
items can be returned within 30 days of purchase..."

​
Knowledge Base Architecture

Here’s how the different pieces work together:
1

Content Ingestion

Raw content is processed through readers that understand different file formats (PDF, websites, databases, etc.) and extract meaningful text.
2

Intelligent Chunking

Large documents are broken down into smaller, meaningful pieces using chunking strategies that preserve context while enabling precise retrieval.
3

Embedding Generation

Each chunk is converted into a vector embedding that captures its semantic meaning using embedders powered by language models.
4

Vector Storage

Embeddings are stored in vector databases optimized for similarity search, often with support for hybrid search combining semantic and keyword matching.
5

Intelligent Retrieval

When agents need information, they generate search queries, find similar embeddings, and retrieve the most relevant content chunks.

​
Benefits of Knowledge-Powered Agents

​
Accuracy and Reliability

  • Responses are grounded in your specific information, not generic training data
  • Reduced hallucinations because agents reference actual sources
  • Up-to-date information that reflects your current state

​
Scalability and Maintenance

  • Add new information without retraining or modifying code
  • Handle unlimited amounts of information without performance degradation
  • Easy updates by simply adding new content to the knowledge base

​
Context Awareness

  • Agents understand your specific domain, terminology, and processes
  • Responses are tailored to your organization’s context and needs
  • Consistent information across all agent interactions

​
Getting Started with Knowledge Bases

Ready to build your own knowledge base? The process is straightforward: 
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.lancedb import LanceDb

# Create a knowledge base
knowledge = Knowledge(
    vector_db=LanceDb(table_name="my_knowledge")
)

# Add your content
knowledge.add_content(path="documents/")

​
Learn More

Content Types

Explore different ways to add information to your knowledge base

Search & Retrieval

Learn how agents search and find relevant information

Vector Databases

Choose the right storage solution for your knowledge base

Performance Tips

Optimize your knowledge base for speed and accuracy
⌘I