Ollama Embeddings - Upsonic AI

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

uv pip install aiohttp requests

Requires Ollama server running locally. Install from ollama.ai.

Examples

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

Documentation Index

​Overview

​Dependencies

​Examples

​Parameters

Overview

Dependencies

Examples

Parameters