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

# Retrieval Strategies

> Automatic strategy selection based on corpus size and complexity

PraisonAI picks the best retrieval strategy from your indexed corpus size — from direct loading for tiny folders to hierarchical search for massive libraries.

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

agent = Agent(
    name="Researcher",
    instructions="Answer from the knowledge base.",
    knowledge={"sources": ["docs/"], "retrieval_k": 10, "rerank": True},
)
response = agent.start("What are the key features?")
```

The user asks a question; PraisonAI selects the best retrieval strategy for the indexed corpus size.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    Corpus[Indexed Corpus] --> Select{Auto Select}
    Select --> Direct[DIRECT]
    Select --> Basic[BASIC]
    Select --> Hybrid[HYBRID]
    Select --> Reranked[RERANKED]
    Select --> Compressed[COMPRESSED]
    Select --> Hierarchical[HIERARCHICAL]
    Hybrid --> Agent[Agent Response]

    classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef tool fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef output fill:#10B981,stroke:#7C90A0,color:#fff

    class Corpus,Agent agent
    class Select,Direct,Basic,Hybrid,Reranked,Compressed,Hierarchical tool
```

## How It Works

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant User
    participant Agent
    participant Feature as Retrieval Strategies

    User->>Agent: Request
    Agent->>Feature: Process request
    Feature-->>Agent: Result    Agent-->>User: Response
```

## Quick Start

<Steps>
  <Step title="Simple Usage">
    Strategy selection is automatic when you attach knowledge to an agent:

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

    agent = Agent(
        name="Researcher",
        instructions="Answer from the knowledge base.",
        knowledge={"sources": ["docs/"], "retrieval_k": 10, "rerank": True},
    )
    response = agent.start("What are the key features?")
    ```
  </Step>

  <Step title="With Configuration">
    Override the auto-selected strategy via `KnowledgeConfig.config`:

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

    agent = Agent(
        name="Researcher",
        instructions="Answer from documents.",
        knowledge=KnowledgeConfig(
            sources=["docs/"],
            rerank=True,
            config={"strategy": "hybrid", "top_k": 10, "hybrid": True},
        ),
    )
    ```
  </Step>
</Steps>

***

## Strategy Selection

| File count | Strategy     | Technique              |
| ---------- | ------------ | ---------------------- |
| \< 10      | DIRECT       | Load all content       |
| \< 100     | BASIC        | Semantic search        |
| \< 1,000   | HYBRID       | Keyword + semantic     |
| \< 10,000  | RERANKED     | Hybrid + reranking     |
| \< 100,000 | COMPRESSED   | Reranked + compression |
| ≥ 100,000  | HIERARCHICAL | Multi-level summaries  |

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents.rag import select_strategy
from praisonaiagents import CorpusStats

stats = CorpusStats(file_count=500)
strategy = select_strategy(stats)
print(strategy.value)  # hybrid
```

***

## Configuration Options

| Option     | Type   | Default  | Description                                                                   |
| ---------- | ------ | -------- | ----------------------------------------------------------------------------- |
| `strategy` | `str`  | `"auto"` | `auto`, `direct`, `basic`, `hybrid`, `reranked`, `compressed`, `hierarchical` |
| `top_k`    | `int`  | `5`      | Chunks to retrieve                                                            |
| `rerank`   | `bool` | `False`  | Enable cross-encoder reranking                                                |
| `hybrid`   | `bool` | `False`  | Combine keyword and semantic search                                           |
| `compress` | `bool` | `False`  | Compress retrieved context                                                    |

***

## CLI Usage

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai knowledge search "query" --strategy hybrid
praisonai knowledge search "query" --rerank --compress --max-context-tokens 4000
```

***

## Best Practices

<AccordionGroup>
  <Accordion title="Start with auto-selection">
    Let the SDK pick the strategy from corpus size unless you have measured latency or quality requirements.
  </Accordion>

  <Accordion title="Enable rerank for large corpora">
    Set `rerank=True` when `retrieval_k` is high — reranking improves relevance on noisy result sets.
  </Accordion>

  <Accordion title="Use hybrid for mixed terminology">
    Technical docs with exact identifiers benefit from `strategy="hybrid"` or `hybrid=True`.
  </Accordion>

  <Accordion title="Profile before overriding">
    Run a few queries with auto-selection first; only pin a strategy when benchmarks show a clear win.
  </Accordion>
</AccordionGroup>

***

## Related

<CardGroup cols={2}>
  <Card title="Retrieval Configuration" icon="magnifying-glass" href="/docs/features/retrieval">
    Configure retrieval behaviour on agents
  </Card>

  <Card title="Vector Store" icon="database" href="/docs/features/vector-store">
    Pluggable embedding store with namespace support
  </Card>
</CardGroup>