Knowledge Base System

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

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

Quick Start

from praisonaiagents import Agent

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

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

from praisonaiagents import Knowledge

# Initialise knowledge base
kb = Knowledge({
    "vector_store": {
        "provider": "chroma",
        "config": {"collection_name": "my_knowledge"}
    }
})

# Add documents
kb.add("document.pdf", user_id="user123")
kb.add("https://example.com/article", user_id="user123")

# Store text directly
kb.store("Important fact about AI", user_id="user123")

# Search
results = kb.search("AI applications", user_id="user123")

# Search with reranking
results = kb.search(
    query="machine learning applications",
    user_id="user123",
    rerank=True,
    top_k=10
)

# Filter by metadata
results = kb.search(
    query="technical specs",
    user_id="user123",
    filters={"category": "engineering"}
)

Configuration Options

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"
        }
    }
}

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
    }
}

Chunking Strategies

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

Best for: Consistent chunk sizes, token-limited models

kb = Knowledge({
    "chunker": {
        "name": "sentence",
        "chunk_size": 10,  # sentences per chunk
        "min_chunk_size": 3
    }
})

Best for: Natural text boundaries, Q&A systems

kb = Knowledge({
    "chunker": {
        "name": "semantic",
        "threshold": 0.7,
        "min_chunk_size": 100
    }
})

Best for: Topic-based segmentation, research papers

kb = Knowledge({
    "chunker": {
        "name": "sdpm",
        "max_chunk_size": 1000
    }
})

Best for: Document structure preservation

Document Processing

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

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")

Search Features

Basic Search

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

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

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
)

# Combine keyword and semantic search
results = kb.search(
    query="transformer architecture",
    user_id="user123",
    keyword_search=True,  # Better recall
    filter_memories=True  # Better precision
)

# Filter by metadata
results = kb.search(
    query="implementation details",
    user_id="user123",
    filters={
        "category": "technical",
        "year": {"$gte": 2023}
    }
)

Memory Integration

When used with agents, knowledge automatically integrates with memory:

agent = Agent(
    name="Research Assistant",
    knowledge={
        "sources": ["papers/"],
        "vector_store": {
            "provider": "chroma",
            "config": {"collection_name": "research_docs"}
        }
    },
    memory=True  # Enable memory integration
)

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

Graph Store Features

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

Configuration

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

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"
)

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

Example: Research Assistant

from praisonaiagents import Agent
from praisonaiagents 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={
        "sources": ["research_papers"],
        **knowledge_config
    }
)

# 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')}")

Next Steps

Learn about memory integration

Build RAG applications

Knowledge Storage

Knowledge Backends

​Knowledge Base System

​Key Features

​Quick Start

​Configuration Options

​Basic Configuration

​Advanced Configuration with Graph Store

​Chunking Strategies

​Document Processing

​Supported File Types

​Processing Options

​Search Features

​Basic Search

​Advanced Search Options

​Memory Integration

​Graph Store Features

​Configuration

​Relationship Queries

​Best Practices