> ## Documentation Index
> Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# FastEmbed Embeddings

> Using FastEmbed local embedding models with Upsonic

## 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

```bash theme={null}
uv pip install fastembed
```

## Examples

```python theme={null}
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     |