A knowledge-centric workflow where RAG (Retrieval Augmented Generation) agents interact with vector databases to store and retrieve information efficiently, enabling sophisticated question-answering and information retrieval capabilities.

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Quick Start

1

Install Package

Install PraisonAI Agents with knowledge support:

pip install "praisonaiagents[knowledge]"
2

Set API Key

Set your OpenAI API key:

export OPENAI_API_KEY=xxxxx
3

Create Script

Create a new file app.py:

from praisonaiagents import Agent

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

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

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Data Indexing and Retrieval Agents

Indexing and Ingestion are relatively the same.

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

from praisonaiagents import Agent

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

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

​
Advanced Configuration

For more control over the knowledge base, you can specify a configuration:

from praisonaiagents import Agent

config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "praison",
            "path": ".praison",
        }
    }
}

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

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

​
Multi-Agent Knowledge System

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

from praisonaiagents import Agent, Task, PraisonAIAgents
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": "praison",
            "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=["sample.pdf"], # Indexing
    knowledge_config=config, # Configuration
    verbose=True
)

# 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 # Agent
)

# Create and start the agents
agents = PraisonAIAgents(
    agents=[knowledge_agent],
    tasks=[knowledge_task],
    process="sequential",
    user_id="user1" # User ID
)

# Start execution
result = agents.start() # Retrieval

​
Retrieval Agents

Retrieval is the process of querying the vector database for information. Considering there is data already in the Vector Database.

1

Install Package

First, install the PraisonAI Agents package:

pip install praisonaiagents
2

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

export OPENAI_API_KEY=your_api_key_here
3

Create a file

Create a new file rag_agent.py with the basic setup:

from praisonaiagents import Agent, Task, PraisonAIAgents

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

# Create an agent
rag_agent = Agent(
    name="RAG Agent",
    role="Information Specialist",
    goal="Retrieve knowledge efficiently",
    llm="gpt-4o-mini"
)

# Define a task for the agent
rag_task = Task(
    name="RAG Task",
    description="What is KAG?",
    expected_output="Answer to the question",
    agent=rag_agent,
    context=[config] # Retrieval : Vector Database provided as context
)

# Build Agents
agents = PraisonAIAgents(
    agents=[rag_agent],
    tasks=[rag_task],
    user_id="user1"
)

# Start Agents
agents.start()
4

Start Agents

Type this in your terminal to run your agents:

python rag_agent.py

Requirements

  • Python 3.10 or higher
  • OpenAI API key. Generate OpenAI API key here. Use Other models using this guide.
  • ChromaDB or other supported vector database

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Adding Knowledge to RAG Agents

from praisonaiagents import Agent

config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "praison",
            "path": ".praison",
        }
    }
}

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

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

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Understanding RAG Agents

What are RAG Agents?

RAG (Retrieval Augmented Generation) agents enable:

  • Efficient knowledge retrieval
  • Semantic search capabilities
  • Persistent knowledge storage
  • Context-aware responses

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Features

RAG Architecture

Store and manage vector embeddings efficiently.

Semantic Search

Find relevant information using semantic similarity.

Knowledge Integration

Seamlessly integrate with existing knowledge bases.

Context Management

Handle complex contextual queries and responses.

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Troubleshooting

RAG Issues

If RAG system isn’t working:

  • Check database configuration
  • Verify connection settings
  • Enable verbose mode for debugging

Query Issues

If queries aren’t returning expected results:

  • Check embedding quality
  • Verify search parameters
  • Monitor similarity thresholds

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Next Steps

AutoAgents

Learn about automatically created and managed AI agents

Mini Agents

Explore lightweight, focused AI agents

For optimal results, ensure your vector database is properly configured and indexed for your specific use case.

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