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.
Dependenciesโ
To use FastEmbed with LangChain, install the fastembed
Python package.
%pip install --upgrade --quiet fastembed
Importsโ
from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
API Reference:
Instantiating FastEmbedโ
Parametersโ
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()
Usageโ
Generating document embeddingsโ
document_embeddings = embeddings.embed_documents(
["This is a document", "This is some other document"]
)
Generating query embeddingsโ
query_embeddings = embeddings.embed_query("This is a query")