> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# KnowledgeConfig

> Configure RAG and knowledge retrieval for agents

Configure knowledge sources, embedding, chunking, and retrieval settings for RAG-powered agents.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Knowledge Pipeline"
        A[📄 Sources] --> B[✂️ Chunk]
        B --> C[🔢 Embed]
        C --> D[🔍 Retrieve]
        D --> E[📝 Augment]
    end
    
    classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
    classDef process fill:#F59E0B,stroke:#7C90A0,color:#fff
    classDef embed fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef retrieve fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef output fill:#10B981,stroke:#7C90A0,color:#fff
    
    class A input
    class B process
    class C embed
    class D retrieve
    class E output
```

## Quick Start

<Steps>
  <Step title="Simple Usage">
    Add knowledge sources directly:

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonaiagents import Agent

    agent = Agent(
        name="Knowledge Agent",
        instructions="Answer questions from the documentation",
        knowledge=["docs/", "data.pdf"]
    )
    ```
  </Step>

  <Step title="With Configuration">
    Configure retrieval settings:

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonaiagents import Agent
    from praisonaiagents.config import KnowledgeConfig

    agent = Agent(
        name="Knowledge Agent",
        instructions="Answer questions from the documentation",
        knowledge=KnowledgeConfig(
            sources=["docs/"],
            embedder="openai",
            chunking_strategy="semantic",
            retrieval_k=5,
            rerank=True
        )
    )
    ```
  </Step>
</Steps>

***

## Configuration Options

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents.config import KnowledgeConfig, ChunkingStrategy

config = KnowledgeConfig(
    # Knowledge sources
    sources=["docs/", "data.pdf"],
    
    # Embedder configuration
    embedder="openai",
    embedder_config=None,
    
    # Chunking settings
    chunking_strategy=ChunkingStrategy.SEMANTIC,
    chunk_size=1000,
    chunk_overlap=200,
    chunker=None,  # Alternative dict config
    
    # Retrieval settings
    retrieval_k=5,
    retrieval_threshold=0.0,
    
    # Reranking
    rerank=False,
    rerank_model=None,
    
    # Auto-retrieval
    auto_retrieve=True,
    
    # Vector store
    vector_store=None,
    
    # Advanced config
    config=None
)
```

| Parameter             | Type                      | Default      | Description                                                    |
| --------------------- | ------------------------- | ------------ | -------------------------------------------------------------- |
| `sources`             | `List[str]`               | `[]`         | Files, directories, or URLs                                    |
| `embedder`            | `str`                     | `"openai"`   | Embedding provider                                             |
| `embedder_config`     | `Dict \| None`            | `None`       | Embedder-specific settings                                     |
| `chunking_strategy`   | `str \| ChunkingStrategy` | `"semantic"` | Chunking method (`fixed`, `semantic`, `sentence`, `paragraph`) |
| `chunk_size`          | `int`                     | `1000`       | Target chunk size in tokens                                    |
| `chunk_overlap`       | `int`                     | `200`        | Overlap between chunks                                         |
| `chunker`             | `Dict \| None`            | `None`       | Alternative chunker config dict                                |
| `retrieval_k`         | `int`                     | `5`          | Number of chunks to retrieve                                   |
| `retrieval_threshold` | `float`                   | `0.0`        | Minimum similarity threshold                                   |
| `rerank`              | `bool`                    | `False`      | Enable result reranking                                        |
| `rerank_model`        | `str \| None`             | `None`       | Model for reranking                                            |
| `auto_retrieve`       | `bool`                    | `True`       | Auto-inject relevant context                                   |
| `vector_store`        | `Dict \| None`            | `None`       | Vector database settings                                       |
| `config`              | `Dict \| None`            | `None`       | Advanced configuration dict                                    |

***

## Common Patterns

### Pattern 1: High-Precision Retrieval

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents.config import KnowledgeConfig

agent = Agent(
    name="Precise Agent",
    instructions="Provide accurate answers",
    knowledge=KnowledgeConfig(
        sources=["docs/"],
        retrieval_k=10,
        rerank=True,
        retrieval_threshold=0.7
    )
)
```

### Pattern 2: Custom Chunking

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents.config import KnowledgeConfig

agent = Agent(
    name="Custom Chunk Agent",
    instructions="Process large documents",
    knowledge=KnowledgeConfig(
        sources=["large_docs/"],
        chunker={
            "type": "semantic",
            "chunk_size": 512,
            "chunk_overlap": 100
        }
    )
)
```

### Pattern 3: External Vector Store

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents.config import KnowledgeConfig

agent = Agent(
    name="Qdrant Agent",
    instructions="Use external vector database",
    knowledge=KnowledgeConfig(
        sources=["docs/"],
        vector_store={
            "provider": "qdrant",
            "url": "http://localhost:6333",
            "collection": "my_docs"
        }
    )
)
```

***

## Best Practices

<AccordionGroup>
  <Accordion title="Use Semantic Chunking for Documents">
    Semantic chunking preserves context better than fixed-size chunks, especially for technical documentation.
  </Accordion>

  <Accordion title="Enable Reranking for Accuracy">
    Reranking improves retrieval precision by using a cross-encoder to reorder results.
  </Accordion>

  <Accordion title="Set Retrieval Threshold">
    Use `retrieval_threshold` to filter low-quality matches and prevent hallucination.
  </Accordion>

  <Accordion title="Auto-Retrieve by Default">
    Keep `auto_retrieve=True` to automatically inject relevant context into prompts.
  </Accordion>
</AccordionGroup>

***

## Related

<CardGroup cols={2}>
  <Card title="Knowledge Overview" icon="book" href="/docs/knowledge/overview">
    Learn about the knowledge system
  </Card>

  <Card title="RAG" icon="magnifying-glass" href="/docs/rag/overview">
    RAG configuration and features
  </Card>
</CardGroup>