Agents use knowledge to supplement their training data with domain expertise.

Knowledge is stored in a vector database and provides agents with business context at query time, helping them respond in a context-aware manner. The general syntax is:

from agno.agent import Agent, AgentKnowledge

# Create a knowledge base for the Agent
knowledge_base = AgentKnowledge(vector_db=...)

# Add information to the knowledge base
knowledge_base.load_text("The sky is blue")

# Add the knowledge base to the Agent and
# give it a tool to search the knowledge base as needed
agent = Agent(knowledge=knowledge_base, search_knowledge=True)

You can give your agent access to your knowledge base in the following ways:

  • You can set search_knowledge=True to provide a search_knowledge_base() tool to your agent. This is automatically added if you provide a knowledgebase.
  • You can set add_references=True to automatically add references from the knowledge base. Optionally pass your own retriever callable with the following signature:
def retriever(agent: Agent, query: str, num_documents: Optional[int], **kwargs) -> Optional[list[dict]]:
  ...
  • You can set update_knowledge=True to provide a add_to_knowledge() tool to your agent allowing it to update the knowledgebase.

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Vector Databases

While any type of storage can act as a knowledge base, vector databases offer the best solution for retrieving relevant results from dense information quickly. Here’s how vector databases are used with Agents:

1

Chunk the information

Break down the knowledge into smaller chunks to ensure our search query returns only relevant results.

2

Load the knowledge base

Convert the chunks into embedding vectors and store them in a vector database.

3

Search the knowledge base

When the user sends a message, we convert the input message into an embedding and “search” for nearest neighbors in the vector database.

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Example: RAG Agent with a PDF Knowledge Base

Let’s build a RAG Agent that answers questions from a PDF.

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Step 1: Run PgVector

Let’s use PgVector as our vector db as it can also provide storage for our Agents.

Install docker desktop and run PgVector on port 5532 using:

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  agno/pgvector:16

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Step 2: Traditional RAG

Retrieval Augmented Generation (RAG) means “stuffing the prompt with relevant information” to improve the model’s response. This is a 2 step process:

  1. Retrieve relevant information from the knowledge base.
  2. Augment the prompt to provide context to the model.

Let’s build a traditional RAG Agent that answers questions from a PDF of recipes.

1

Install libraries

Install the required libraries using pip

2

Create a Traditional RAG Agent

Create a file traditional_rag.py with the following contents

traditional_rag.py
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
knowledge_base = PDFUrlKnowledgeBase(
    # Read PDF from this URL
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    # Store embeddings in the `ai.recipes` table
    vector_db=PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.hybrid),
)
# Load the knowledge base: Comment after first run
knowledge_base.load(upsert=True)

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    knowledge=knowledge_base,
    # Enable RAG by adding references from AgentKnowledge to the user prompt.
    add_references=True,
    # Set as False because Agents default to `search_knowledge=True`
    search_knowledge=False,
    markdown=True,
    # debug_mode=True,
)
agent.print_response("How do I make chicken and galangal in coconut milk soup")
3

Run the agent

Run the agent (it takes a few seconds to load the knowledge base).


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Step 3: Agentic RAG

With traditional RAG above, add_references=True always adds information from the knowledge base to the prompt, regardless of whether it is relevant to the question or helpful.

With Agentic RAG, we let the Agent decide if it needs to access the knowledge base and what search parameters it needs to query the knowledge base.

Set search_knowledge=True and read_chat_history=True, giving the Agent tools to search its knowledge and chat history on demand.

1

Create an Agentic RAG Agent

Create a file agentic_rag.py with the following contents

agentic_rag.py
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.hybrid),
)
# Load the knowledge base: Comment out after first run
knowledge_base.load(upsert=True)

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    knowledge=knowledge_base,
    # Add a tool to search the knowledge base which enables agentic RAG.
    search_knowledge=True,
    # Add a tool to read chat history.
    read_chat_history=True,
    show_tool_calls=True,
    markdown=True,
    # debug_mode=True,
)
agent.print_response("How do I make chicken and galangal in coconut milk soup", stream=True)
agent.print_response("What was my last question?", markdown=True)
2

Run the agent

Run the agent

Notice how it searches the knowledge base and chat history when needed

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Attributes

ParameterTypeDefaultDescription
knowledgeAgentKnowledgeNoneProvides the knowledge base used by the agent.
search_knowledgeboolTrueAdds a tool that allows the Model to search the knowledge base (aka Agentic RAG). Enabled by default when knowledge is provided.
add_referencesboolFalseEnable RAG by adding references from AgentKnowledge to the user prompt.
retrieverCallable[..., Optional[list[dict]]]NoneFunction to get context to add to the user message. This function is called when add_references is True.
context_formatLiteral['json', 'yaml']jsonSpecifies the format for RAG, either “json” or “yaml”.
add_context_instructionsboolFalseIf True, add instructions for using the context to the system prompt (if knowledge is also provided). For example: add an instruction to prefer information from the knowledge base over its training data.

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Developer Resources

ToolsMemory