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Knowledge Base System

The knowledge system provides sophisticated document processing and semantic search capabilities, enabling agents to access and utilise information from various sources.

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Key Features

Process PDFs, documents, spreadsheets, images, and web content

Multiple strategies for optimal text segmentation

Vector-based search with optional reranking

User, agent, and run-specific knowledge scoping

Optional relationship extraction and storage

Automatic quality assessment for stored knowledge

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Quick Start

from praisonaiagents import Agent

agent = Agent(
    name="Research Assistant",
    instructions="Answer questions using the knowledge base.",
    knowledge=["research_paper.pdf", "data.txt"],
    knowledge_config={
        "vector_store": {
            "provider": "chroma",
            "config": {
                "collection_name": "research_docs",
                "path": ".praison"
            }
        }
    }
)

response = agent.start("What are the key findings?")

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Configuration Options

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Basic Configuration

knowledge_config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "knowledge_base",
            "path": ".praison",
            "distance_metric": "cosine"
        }
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "model": "text-embedding-3-small"
        }
    }
}

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Advanced Configuration with Graph Store

knowledge_config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "knowledge_base",
            "path": ".praison"
        }
    },
    "graph_store": {
        "provider": "neo4j",
        "config": {
            "url": "bolt://localhost:7687",
            "username": "neo4j",
            "password": "password"
        }
    },
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4o-mini",
            "temperature": 0
        }
    },
    "reranker": {
        "enabled": True,
        "default_rerank": False
    }
}

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Chunking Strategies

kb = Knowledge({
    "chunker": {
        "name": "token",
        "chunk_size": 500,
        "chunk_overlap": 50
    }
})

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Document Processing

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Supported File Types

  • PDF (.pdf)
  • Word (.doc, .docx)
  • Text (.txt)
  • Markdown (.md)
  • RTF (.rtf)

  • Excel (.xls, .xlsx)
  • CSV (.csv)
  • JSON (.json)
  • XML (.xml)

  • Images (OCR)
  • HTML pages
  • Web URLs
  • YouTube videos

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Processing Options

# Add with metadata
kb.add(
    "research.pdf",
    user_id="user123",
    metadata={
        "category": "AI Research",
        "year": 2024,
        "author": "Dr. Smith"
    }
)

# Batch processing
documents = ["doc1.pdf", "doc2.txt", "doc3.md"]
for doc in documents:
    kb.add(doc, user_id="user123")

# URL processing
kb.add("https://arxiv.org/pdf/2301.00000.pdf", user_id="user123")

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Search Features

# Simple search
results = kb.search("artificial intelligence", limit=5)

# User-scoped search
results = kb.search(
    query="machine learning",
    user_id="user123",
    limit=10
)

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Advanced Search Options

# Enable Mem0 reranking for better relevance
results = kb.search(
    query="neural networks",
    user_id="user123",
    rerank=True,
    top_k=20  # Retrieve more before reranking
)

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Memory Integration

When used with agents, knowledge automatically integrates with memory: 
agent = Agent(
    name="Research Assistant",
    knowledge=["papers/"],  # Directory of papers
    knowledge_config=config,
    memory=True  # Enable memory integration
)

# Knowledge is automatically searched during conversations
response = agent.chat("What does the research say about transformers?")

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Graph Store Features

Graph stores enable relationship extraction and complex queries beyond simple semantic search.

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Configuration

knowledge_config = {
    "graph_store": {
        "provider": "neo4j",  # or "memgraph"
        "config": {
            "url": "bolt://localhost:7687",
            "username": "neo4j",
            "password": "password"
        }
    },
    "extract_relationships": True
}

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Relationship Queries

# Find related concepts
results = kb.search_graph(
    "What concepts are related to transformers?",
    user_id="user123"
)

# Explore connections
results = kb.search_graph(
    "How is attention mechanism connected to BERT?",
    user_id="user123"
)

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Best Practices

Chunking Strategy

  • Smaller chunks (100-200 tokens): Better precision
  • Larger chunks (500-1000 tokens): Better context
  • Match chunk size to query complexity

Organisation

  • Separate collections by domain
  • Use metadata for filtering
  • Regular cleanup of outdated content

Performance

  • Enable caching for repeated queries
  • Use appropriate embedding models
  • Batch document processing

Quality

  • Verify document processing
  • Monitor search relevance
  • Regular reindexing if needed

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Example: Research Assistant

from praisonaiagents import Agent
from praisonaiagents.knowledge import Knowledge

# Configure knowledge base
knowledge_config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "research_papers",
            "path": "./knowledge_db"
        }
    },
    "chunker": {
        "name": "semantic",
        "threshold": 0.7
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "model": "text-embedding-3-small"
        }
    },
    "reranker": {
        "enabled": True
    }
}

# Create research assistant
research_agent = Agent(
    name="Research Assistant",
    instructions="""You are an expert research assistant.
    Use the knowledge base to provide accurate, well-sourced answers.
    Always cite the specific documents you reference.""",
    knowledge=[
        "papers/ai_safety.pdf",
        "papers/llm_alignment.pdf",
        "https://arxiv.org/pdf/2301.00234.pdf"
    ],
    knowledge_config=knowledge_config,
    knowledge_sources=["research_papers"],  # Named source
    markdown=True
)

# Use the assistant
response = research_agent.chat(
    "What are the main approaches to AI alignment?"
)

# Direct knowledge queries
kb = research_agent.knowledge_instance
papers = kb.search("alignment techniques", limit=5)
for paper in papers:
    print(f"- {paper['text'][:100]}...")
    print(f"  Source: {paper.get('metadata', {}).get('source')}")

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Next Steps

Learn about memory integration

Build RAG applications