FastEmbed Embeddings

Overview

FastEmbed provides fast, local embedding models powered by ONNX runtime. Supports GPU acceleration, sparse embeddings, and multiple model architectures including BGE, E5, and multilingual models. No API costs - runs entirely locally. Provider Class: FastEmbedProvider Config Class: FastEmbedConfig

Dependencies

uv pip install fastembed

Examples

from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import FastEmbedProvider, FastEmbedConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Create embedding provider
embedding = FastEmbedProvider(FastEmbedConfig(
    model_name="BAAI/bge-small-en-v1.5",
    enable_gpu=True
))

# Setup KnowledgeBase
vectordb = ChromaProvider(ChromaConfig(
    collection_name="fastembed_docs",
    vector_size=384,
    connection=ConnectionConfig(mode=Mode.IN_MEMORY)
))

kb = KnowledgeBase(
    sources=["document.txt"],
    embedding_provider=embedding,
    vectordb=vectordb
)

# Query with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task("What is this document about?", context=[kb])
result = agent.do(task)
print(result)

Parameters

Parameter	Type	Description	Default	Source
`model_name`	`str`	FastEmbed model name	`"BAAI/bge-small-en-v1.5"`	Specific
`cache_dir`	`str \| None`	Model cache directory	`None`	Specific
`threads`	`int \| None`	Number of threads (auto-detected if None)	`None`	Specific
`providers`	`list[str]`	ONNX execution providers	`["CPUExecutionProvider"]`	Specific
`enable_gpu`	`bool`	Enable GPU acceleration if available	`False`	Specific
`enable_parallel_processing`	`bool`	Enable parallel text processing	`True`	Specific
`doc_embed_type`	`str`	Document embedding type (default, passage)	`"default"`	Specific
`max_memory_mb`	`int \| None`	Maximum memory usage in MB	`None`	Specific
`model_warmup`	`bool`	Warm up model on initialization	`True`	Specific
`enable_sparse_embeddings`	`bool`	Use sparse embeddings for better performance	`False`	Specific
`sparse_model_name`	`str \| None`	Sparse model name if different from dense	`None`	Specific
`batch_size`	`int`	Batch size for document embedding	`100`	Base
`max_retries`	`int`	Maximum number of retries on failure	`3`	Base
`normalize_embeddings`	`bool`	Whether to normalize embeddings to unit length	`True`	Base
`show_progress`	`bool`	Whether to show progress during batch operations	`True`	Base

​Overview

​Dependencies

​Examples

​Parameters

Overview

Dependencies

Examples

Parameters