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FastEmbed by Qdrant | 🦜️🔗 LangChain

FastEmbed from Qdrant is a lightweight, fast, Python library built for embedding generation.

• Quantized model weights
• ONNX Runtime, no PyTorch dependency
• CPU-first design
• Data-parallelism for encoding of large datasets.

To use FastEmbed with LangChain, install the fastembed Python package.

%pip install --upgrade --quiet  fastembed

from langchain_community.embeddings.fastembed import FastEmbedEmbeddings

• model_name: str (default: "BAAI/bge-small-en-v1.5")

  Name of the FastEmbedding model to use. You can find the list of supported models here.

• max_length: int (default: 512)

  The maximum number of tokens. Unknown behavior for values > 512.

• cache_dir: Optional[str] (default: None)

  The path to the cache directory. Defaults to local_cache in the parent directory.

• threads: Optional[int] (default: None)

  The number of threads a single onnxruntime session can use.

• doc_embed_type: Literal["default", "passage"] (default: "default")

  "default": Uses FastEmbed's default embedding method.

  "passage": Prefixes the text with "passage" before embedding.

• batch_size: int (default: 256)

  Batch size for encoding. Higher values will use more memory, but be faster.

• parallel: Optional[int] (default: None)

  If >1, data-parallel encoding will be used, recommended for offline encoding of large datasets. If 0, use all available cores. If None, don't use data-parallel processing, use default onnxruntime threading instead.

embeddings = FastEmbedEmbeddings()

document_embeddings = embeddings.embed_documents(
    ["This is a document", "This is some other document"]
)

query_embeddings = embeddings.embed_query("This is a query")

• Embedding model conceptual guide
• Embedding model how-to guides

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