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What Are Retrievers?

Concept

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.

Want a deep dive? Check out the RAG Cookbook for advanced agent + retrieval use cases.

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Types of Retrievers

Vector Retriever

Converts documents into vectors using an embedding model, stores them, and retrieves by semantic similarity. Best for “meaning-based” search, RAG, and LLM workflows.

  • Chunks data, embeds with OpenAI or custom model
  • Stores in vector DB (like Qdrant)
  • Finds the most relevant info even with different wording

Keyword Retriever

Classic keyword search! Breaks documents and queries into tokens/keywords, and matches on those.

  • Tokenizes documents
  • Indexes by keyword
  • Fast, transparent, great for exact matches

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How To Use

Semantic Retrieval with VectorRetriever

This example uses OpenAI embeddings and Qdrant vector storage for semantic search.

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)

# Embed and store your data (URL or file)
content_input_path = "https://www.camel-ai.org/"
vr.process(content=content_input_path)

# Run a query for semantic search
query = "What is CAMEL"
results = vr.query(query=query, similarity_threshold=0)
print(results)

AutoRetriever: Quick RAG with One Call

AutoRetriever simplifies everything: just specify storage and content, and it handles embedding, storage, and querying.

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)

Use AutoRetriever for fast experiments and RAG workflows; for advanced control, use VectorRetriever directly.

KeywordRetriever (Classic Search)

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.)

See RAG Agents in Action

Build retrieval-augmented agents using these retrievers.

Reference API

Full configuration and options for all retriever classes.