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.
The default text embedding (TextEmbedding) model is Flag Embedding, the top model in the MTEB leaderboard. It supports "query" and "passage" prefixes for the input text. Here is an example for Retrieval Embedding Generation and how to use FastEmbed with Qdrant.
-
Light & Fast
- Quantized model weights
- ONNX Runtime, no PyTorch dependency
- CPU-first design
- Data-parallelism for encoding of large datasets
-
Accuracy/Recall
- Better than OpenAI Ada-002
- Default is Flag Embedding, which is top of the MTEB leaderboard
- List of supported models - including multilingual models
To install the FastEmbed library, pip works:
pip install fastembedfrom fastembed import TextEmbedding
from typing import List
import numpy as np
documents: List[str] = [
"passage: Hello, World!",
"query: Hello, World!", # these are two different embedding
"passage: This is an example passage.",
"fastembed is supported by and maintained by Qdrant." # You can leave out the prefix but it's recommended
]
embedding_model = TextEmbedding(model_name="BAAI/bge-base-en")
embeddings: List[np.ndarray] = list(embedding_model.embed(documents)) # Note the list() call - this is a generatorInstallation 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("localhost", port=6333) # For production
# client = QdrantClient(":memory:") # For small experiments
# 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]
# If you want to change the model:
# client.set_model("sentence-transformers/all-MiniLM-L6-v2")
# List of supported models: https://qdrant.github.io/fastembed/examples/Supported_Models
# Use the new add() instead of upsert()
# This internally calls embed() of the configured embedding model
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)Ilyas M. wrote about using FlagEmbeddings with Optimum over CUDA.