AI Agents with Knowledge

Learn how to create AI agents with custom knowledge bases

Diagram

Feature	Knowledge	Memory
When Used	Pre-loaded before agent execution	Created and updated during runtime
Purpose	Provide static reference information	Store dynamic context and interactions
Storage	Read-only knowledge base	Read-write memory store
Persistence	Permanent until explicitly changed	Can be temporary (STM) or persistent (LTM)
Updates	Manual updates through knowledge files	Automatic updates during agent execution

Quick Start

Install Package
Install PraisonAI Agents with knowledge support:
bash
```
pip install "praisonaiagents[knowledge]"
```
Set API Key
Set your OpenAI API key:
bash
```
export OPENAI_API_KEY=xxxxx
```

Create Script

Create a new file app.py:

python

from praisonaiagents import Agent

agent = Agent(
    name="Knowledge Agent",
    instructions="You answer questions based on the provided knowledge.",
    knowledge=["small.pdf"]
)

agent.start("What is KAG in one line?")

Basic Usage

The simplest way to create a knowledge-based agent is without any configuration:

python

from praisonaiagents import Agent

agent = Agent(
    name="Knowledge Agent",
    instructions="You answer questions based on the provided knowledge.",
    knowledge=["small.pdf"]
)

agent.start("What is KAG in one line?")

Advanced Configuration

For more control over retrieval behavior, use the knowledge dict:

python

from praisonaiagents import Agent

agent = Agent(
    name="Knowledge Agent",
    instructions="You answer questions based on the provided knowledge.",
    knowledge={
        "sources": ["small.pdf"],
        "retrieval_k": 5,                 # Number of chunks to retrieve
        "rerank": True,                   # Enable reranking for better relevance
    }
)

agent.start("What is KAG in one line?")

Getting Answers with Citations

Use agent.query() for structured answers with source citations:

python

from praisonaiagents import Agent

agent = Agent(
    name="Research Agent",
    instructions="You answer questions based on the provided knowledge.",
    knowledge={
        "sources": ["research_paper.pdf"],
        "retrieval_k": 5,
    }
)

# Get structured result with citations
result = agent.query("What are the main findings?")

print(result.answer)
for citation in result.citations:
    print(f"  [{citation.id}] {citation.source}")

Retrieval-Only (No LLM Generation)

Use agent.retrieve() to get context without LLM generation:

python

# Get context pack for manual processing
context = agent.retrieve("What are the key findings?")

print(f"Found {len(context.citations)} sources")
print(context.context)

# Use with chat_with_context for custom workflows
response = agent.chat_with_context("Summarize these findings", context)

Multi-Agent Knowledge System

For more complex scenarios, you can create a knowledge-based system with multiple agents:

python

from praisonaiagents import Agent, Task, AgentTeam
import logging
import os

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Define the configuration for the Knowledge instance
config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "knowledge_test",
            "path": ".praison",
        }
    }
}

# Create an agent with knowledge capabilities
knowledge_agent = Agent(
    name="KnowledgeAgent",
    role="Information Specialist",
    goal="Store and retrieve knowledge efficiently",
    backstory="Expert in managing and utilizing stored knowledge",
    knowledge={
        "sources": ["sample.pdf"],
        "vector_store": {
            "provider": "chroma",
            "config": {
                "collection_name": "knowledge_test",
                "path": ".praison",
            }
        }
    },
)

# Define a task for the agent
knowledge_task = Task(
    name="knowledge_task",
    description="Who is Mervin Praison?",
    expected_output="Answer to the question",
    agent=knowledge_agent
)

# Create and start the agents
agents = AgentTeam(
    agents=[knowledge_agent],
    tasks=[knowledge_task],
    process="sequential",
)

# Start execution
result = agents.start()

Understanding Knowledge Configuration

Vector Store Options

Provider: Choose between different vector store backends (e.g., “chroma”)
Collection Name: Name for your knowledge collection
Path: Location to store the vector database

Supported File Types

PDF documents (*.pdf)
Text files (*.txt)
Markdown files (.md, .mdx)
And more…

Multi-Agent Setup

Role: Define specialized roles for knowledge agents
Goal: Set specific knowledge management objectives
Process: Choose between sequential or parallel execution

Features

Custom Knowledge

Import your own documents and data as knowledge sources

Vector Storage

Efficient storage and retrieval of knowledge embeddings

Multiple Sources

Combine multiple documents and file types

Persistent Storage

Save and reuse knowledge bases across sessions

Best Practices

Document Preparation
Clean and well-formatted documents work best
Break large documents into smaller chunks
Use consistent formatting

Knowledge Organization
Group related documents together
Use meaningful file names
Keep knowledge bases focused and relevant

Performance Optimization
Monitor vector store size
Clean up unused collections
Use appropriate chunk sizes

Multi-Agent Coordination
Define clear roles and responsibilities
Set appropriate logging levels for debugging
Use unique collection names for different agent groups

Next Steps

Learn about Memory Management for long-term recall
Explore Tool Integration for enhanced capabilities
Check out Examples for implementation ideas