FastEmbed by Qdrant

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.

Dependencies

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

%pip install --upgrade --quiet  fastembed

Imports

from langchain_community.embeddings.fastembed import FastEmbedEmbeddings

API Reference:FastEmbedEmbeddings

Instantiating FastEmbed

Parameters

• 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]

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

• threads: Optional[int]

  The number of threads a single onnxruntime session can use. Defaults to None.

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

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

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

embeddings = FastEmbedEmbeddings()

Usage

Generating document embeddings

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

Generating query embeddings

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