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⚡️ What is FastEmbed?

FastEmbed is a lightweight, fast, Python library built for embedding generation. We support popular text models. Please open a Github issue if you want us to add a new model.

  1. Light & Fast

    • Quantized model weights
    • ONNX Runtime for inference

  2. Accuracy/Recall

    • Better than OpenAI Ada-002
    • Default is Flag Embedding, which has shown good results on the MTEB leaderboard
    • List of supported models - including multilingual models

Here is an example for Retrieval Embedding Generation and how to use FastEmbed with Qdrant.

🚀 Installation

To install the FastEmbed library, pip works:

pip install fastembed

📖 Usage

from fastembed import TextEmbedding

documents: list[str] = [
    "passage: Hello, World!",
    "query: Hello, World!",
    "passage: This is an example passage.",
    "fastembed is supported by and maintained by Qdrant."
]
embedding_model = TextEmbedding()
embeddings: list[np.ndarray] = embedding_model.embed(documents)

Usage with Qdrant

Installation with Qdrant Client in Python:

pip install qdrant-client[fastembed]

Might have to use pip install 'qdrant-client[fastembed]' on zsh.

from qdrant_client import QdrantClient

# Initialize the client
client = QdrantClient(":memory:")  # Using an in-process Qdrant

# Prepare your documents, metadata, and IDs
docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"]
metadata = [
    {"source": "Langchain-docs"},
    {"source": "Llama-index-docs"},
]
ids = [42, 2]

client.add(
    collection_name="demo_collection",
    documents=docs,
    metadata=metadata,
    ids=ids
)

search_result = client.query(
    collection_name="demo_collection",
    query_text="This is a query document"
)
print(search_result)