> ## 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.

# Ollama Embeddings

> Using Ollama local embedding models with Upsonic

## Overview

Ollama provides local embedding models that run entirely on your machine. Supports models like nomic-embed-text, mxbai-embed-large, and snowflake-arctic-embed. No API costs and works offline. Requires an Ollama server running locally.

**Provider Class:** `OllamaEmbedding`

**Config Class:** `OllamaEmbeddingConfig`

## Dependencies

```bash theme={null}
uv pip install aiohttp requests
```

Requires Ollama server running locally. Install from [ollama.ai](https://ollama.ai).

## Examples

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OllamaEmbedding, OllamaEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Create embedding provider
embedding = OllamaEmbedding(OllamaEmbeddingConfig(
    model_name="nomic-embed-text",
    base_url="http://localhost:11434",
    auto_pull_model=True
))

# Setup KnowledgeBase
vectordb = ChromaProvider(ChromaConfig(
    collection_name="ollama_docs",
    vector_size=768,
    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`           | Ollama embedding model name                      | `"nomic-embed-text"`       | Specific |
| `base_url`             | `str`           | Ollama server URL                                | `"http://localhost:11434"` | Specific |
| `auto_pull_model`      | `bool`          | Automatically pull model if not available        | `True`                     | Specific |
| `keep_alive`           | `str \| None`   | Keep model loaded for duration                   | `"5m"`                     | Specific |
| `temperature`          | `float \| None` | Model temperature                                | `None`                     | Specific |
| `top_p`                | `float \| None` | Top-p sampling                                   | `None`                     | Specific |
| `num_ctx`              | `int \| None`   | Context window size                              | `None`                     | Specific |
| `request_timeout`      | `float`         | Request timeout in seconds                       | `120.0`                    | Specific |
| `connection_timeout`   | `float`         | Connection timeout in seconds                    | `10.0`                     | Specific |
| `enable_keep_alive`    | `bool`          | Keep model loaded between requests               | `True`                     | Specific |
| `enable_model_preload` | `bool`          | Preload model on startup                         | `True`                     | 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     |