> ## 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 Configuration

> Configure retrieval behaviour for AI agents with knowledge bases

Control how agents retrieve and inject knowledge through the `knowledge` parameter — one surface for sources, chunking, reranking, and retrieval policy.

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

agent = Agent(
    name="Research Agent",
    instructions="Answer questions based on the provided documents.",
    knowledge={
        "sources": ["docs/", "papers.pdf"],
        "retrieval_k": 5,
        "rerank": True,
    },
)
response = agent.start("What are the key findings?")
```

The user asks a question; the agent retrieves relevant chunks from configured knowledge sources before answering.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Retrieval"
        Query[📋 User Query] --> Agent[🤖 Agent]
        Agent --> Retrieve[🔍 Retrieve Context]
        Retrieve --> Response[✅ Answer + Citations]
    end

    classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
    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 Query input
    class Agent agent
    class Retrieve tool
    class Response output
```

## Quick Start

<Steps>
  <Step title="Simple Usage">
    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonaiagents import Agent

    agent = Agent(
        name="Research Agent",
        instructions="Answer questions based on the provided documents.",
        knowledge={
            "sources": ["docs/"],
            "retrieval_k": 5,
            "rerank": True,
        },
    )
    response = agent.start("What are the key findings?")
    ```
  </Step>

  <Step title="With Configuration">
    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonaiagents import Agent, KnowledgeConfig

    agent = Agent(
        name="Agent",
        instructions="You are a helpful assistant.",
        knowledge=KnowledgeConfig(
            sources=["documents/"],
            embedder="openai",
            chunking_strategy="semantic",
            chunk_size=1000,
            chunk_overlap=200,
            retrieval_k=5,
            retrieval_threshold=0.3,
            rerank=True,
            auto_retrieve=True,
            vector_store={"provider": "chroma", "collection_name": "default"},
        ),
    )
    ```
  </Step>
</Steps>

***

## How It Works

The user asks a question; the agent embeds the query, retrieves the top-k chunks, then generates an answer with citations.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant User
    participant Agent
    participant Knowledge

    User->>Agent: start("What are the findings?")
    Agent->>Knowledge: Retrieve top-k chunks
    Knowledge-->>Agent: Relevant context + scores
    Agent->>Agent: Generate answer with context
    Agent-->>User: Response + citations
```

***

## Configuration Options

| Option                | Type        | Default      | Description                          |
| --------------------- | ----------- | ------------ | ------------------------------------ |
| `sources`             | `List[str]` | `[]`         | Files, directories, or URLs to index |
| `embedder`            | `str`       | `"openai"`   | Embedding provider                   |
| `chunking_strategy`   | `str`       | `"semantic"` | Chunking method                      |
| `chunk_size`          | `int`       | `1000`       | Target chunk size in tokens          |
| `chunk_overlap`       | `int`       | `200`        | Overlap between chunks               |
| `retrieval_k`         | `int`       | `5`          | Number of chunks to retrieve         |
| `retrieval_threshold` | `float`     | `0.0`        | Minimum relevance score (0.0–1.0)    |
| `rerank`              | `bool`      | `False`      | Enable reranking                     |
| `rerank_model`        | `str`       | `None`       | Custom rerank model                  |
| `auto_retrieve`       | `bool`      | `True`       | Inject context automatically         |
| `vector_store`        | `dict`      | `None`       | Vector store provider config         |

***

## Agent Methods

### `agent.start()` / `agent.chat()`

Conversation with automatic retrieval when `auto_retrieve=True`:

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
response = agent.start("What is the main topic?")
response = agent.start("Hello", force_retrieval=True)
response = agent.start("What is 2+2?", skip_retrieval=True)
```

### `agent.query()`

Structured answer with citations:

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
result = agent.query("What are the main findings?")
print(result.answer)
for citation in result.citations:
    print(f"[{citation.id}] {citation.source} (score: {citation.score:.2f})")
```

### `agent.retrieve()`

Retrieval only — no LLM generation:

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
context_pack = agent.retrieve("What are the key points?")
print(f"Found {len(context_pack.citations)} sources")
```

***

## Multi-Agent Shared Knowledge

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

shared = Knowledge(config={"vector_store": {"provider": "chroma", "config": {"collection_name": "shared_docs"}}})
shared.add("company_docs/")

analyst = Agent(
    name="Analyst",
    instructions="Analyse data from documents.",
    knowledge={"sources": shared, "retrieval_k": 10},
)

summariser = Agent(
    name="Summariser",
    instructions="Summarise key points.",
    knowledge={"sources": shared, "retrieval_k": 5},
)
```

***

## Best Practices

<AccordionGroup>
  <Accordion title="Set retrieval_threshold for noisy corpora">
    Use `retrieval_threshold=0.3` or higher when low-scoring chunks pollute answers.
  </Accordion>

  <Accordion title="Retrieve more, rerank down">
    For large bases, set `retrieval_k=20` with `rerank=True` — cast a wide net, then keep the best chunks.
  </Accordion>

  <Accordion title="Use skip_retrieval for non-RAG prompts">
    Arithmetic, greetings, and meta questions do not need retrieval — pass `skip_retrieval=True`.
  </Accordion>

  <Accordion title="Index once, share across agents">
    Create one `Knowledge` instance and pass it as `sources` to multiple agents to avoid re-indexing.
  </Accordion>
</AccordionGroup>

***

## Related

<CardGroup cols={2}>
  <Card title="Retrieval Strategies" icon="route" href="/docs/features/retrieval-strategies">
    Auto-select strategy by corpus size
  </Card>

  <Card title="CLI Retrieval" icon="terminal" href="/docs/cli/retrieval">
    Run retrieval from the command line
  </Card>
</CardGroup>