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

# Retrievers

## What Are Retrievers?

<Card title="Concept" icon="laptop-file">
  Retrievers are your AI search engine for large text collections or knowledge bases. They let you find the most relevant information based on a query—using either advanced embeddings (semantic search) or classic keyword matching.
</Card>

<Tip>
  Want a deep dive? Check out the <a href="/cookbooks/advanced_features/agents_with_rag">RAG Cookbook</a> for advanced agent + retrieval use cases.
</Tip>

## Types of Retrievers

<CardGroup cols={2}>
  <Card title="Vector Retriever" icon="box">
    Converts documents into vectors using an embedding model, stores them, and retrieves by semantic similarity. Best for “meaning-based” search, RAG, and LLM workflows.

    <ul>
      <li>Chunks data, embeds with OpenAI or custom model</li>
      <li>Stores in vector DB (like Qdrant)</li>
      <li>Finds the most relevant info even with different wording</li>
    </ul>
  </Card>

  <Card title="Keyword Retriever" icon="hashtag">
    Classic keyword search! Breaks documents and queries into tokens/keywords, and matches on those.

    <ul>
      <li>Tokenizes documents</li>
      <li>Indexes by keyword</li>
      <li>Fast, transparent, great for exact matches</li>
    </ul>
  </Card>
</CardGroup>

## How To Use

<Card title="Semantic Retrieval with VectorRetriever" icon="box">
  This example uses OpenAI embeddings and Qdrant vector storage for semantic search.

  <CodeGroup>
    ```python vector_retriever_setup.py theme={"system"}
    from camel.embeddings import OpenAIEmbedding
    from camel.retrievers import VectorRetriever
    from camel.storages.vectordb_storages import QdrantStorage

    # Set up vector DB for embeddings
    vector_storage = QdrantStorage(
        vector_dim=OpenAIEmbedding().get_output_dim(),
        collection_name="my first collection",
        path="storage_customized_run",
    )
    vr = VectorRetriever(embedding_model=OpenAIEmbedding(), storage=vector_storage)
    ```
  </CodeGroup>

  <CodeGroup>
    ```python vector_retriever_ingest.py theme={"system"}
    # Embed and store your data (URL or file)
    content_input_path = "https://www.camel-ai.org/"
    vr.process(content=content_input_path)
    ```
  </CodeGroup>

  <CodeGroup>
    ```python vector_retriever_query.py theme={"system"}
    # Run a query for semantic search
    query = "What is CAMEL"
    results = vr.query(query=query, similarity_threshold=0)
    print(results)
    ```

    ```markdown vector_retriever_output.md theme={"system"}
    >>>  [{'similarity score': '0.81...', 'content path': 'https://www.camel-ai.org/', 'metadata': {...}, 'text': '...CAMEL-AI.org is an open-source community dedicated to the study of autonomous and communicative agents...'}]
    ```
  </CodeGroup>
</Card>

<Card title="AutoRetriever: Quick RAG with One Call" icon="zap">
  AutoRetriever simplifies everything: just specify storage and content, and it handles embedding, storage, and querying.

  <CodeGroup>
    ```python auto_retriever.py theme={"system"}
    from camel.retrievers import AutoRetriever
    from camel.types import StorageType

    ar = AutoRetriever(
        vector_storage_local_path="camel/retrievers",
        storage_type=StorageType.QDRANT,
    )

    retrieved_info = ar.run_vector_retriever(
        contents=["https://www.camel-ai.org/"],   # One or many URLs/files
        query="What is CAMEL-AI",
        return_detailed_info=True,
    )
    print(retrieved_info)
    ```

    ```markdown auto_retriever_output.md theme={"system"}
    >>> Original Query: {What is CAMEL-AI}
    >>> Retrieved Context:
    >>> {'similarity score': '0.83...', 'content path': 'https://www.camel-ai.org/', 'metadata': {...}, 'text': 'Mission\n\nCAMEL-AI.org is an open-source community dedicated to the study of autonomous and communicative agents...'}
    ```
  </CodeGroup>

  <Tip>
    Use AutoRetriever for fast experiments and RAG workflows; for advanced control, use VectorRetriever directly.
  </Tip>
</Card>

<Card title="KeywordRetriever (Classic Search)" icon="hashtag">
  For simple, blazing-fast search by keyword—use KeywordRetriever.
  Great for small data, transparency, or keyword-driven tasks.
  *(API and code example coming soon—see RAG Cookbook for details.)*
</Card>

<CardGroup cols={2}>
  <Card title="See RAG Agents in Action" icon="rocket" href="../cookbooks/advanced_features/agents_with_rag.ipynb">
    Build retrieval-augmented agents using these retrievers.
  </Card>

  <Card title="Reference API" icon="book" href="/reference/#retrievers">
    Full configuration and options for all retriever classes.
  </Card>
</CardGroup>