---
id: llama-index-embeddings-ollama
version: "0.9.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# llama-index-embeddings-ollama — llama-index embeddings ollama integration

License: permissive · Maintenance: active · Downloads: 171.4K/mo

## What it is and what it does

This package bridges Ollama, a tool for running large language models locally, with LlamaIndex's embedding and retrieval infrastructure. It provides a drop-in embedding provider that generates vector representations of text using Ollama models running on your machine or a remote server, enabling private, offline-capable semantic search and document indexing without reliance on external APIs.

The integration supports both synchronous and asynchronous embedding generation, batch processing, and configuration options like query/text instructions and model keep-alive settings. It works seamlessly with LlamaIndex's VectorStoreIndex and other retrieval components, allowing you to build RAG systems entirely on local infrastructure.

Use it for:

- Build a private document search system using local embedding models without sending data to external services.
- Create offline-capable semantic search for applications that must work without internet connectivity.
- Integrate Ollama embeddings into LlamaIndex retrieval pipelines for cost-effective large-scale indexing.
- Combine local embeddings with Ollama LLMs to run end-to-end generative AI workflows on-premises.
- Batch-embed large document collections using configurable batch sizes and model memory management.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Integrates Ollama's local embedding models with LlamaIndex for generating text embeddings without external API calls.

Yes. Low install friction, active maintenance, MIT license, and no known vulnerabilities make this a straightforward choice for anyone building LlamaIndex applications with local Ollama infrastructure. Install it if you're already running Ollama and want seamless vector embedding support; skip it if you rely on cloud embedding APIs.

## Install

pip install llama-index-embeddings-ollama
uv add llama-index-embeddings-ollama
poetry add llama-index-embeddings-ollama

## Installing llama-index-embeddings-ollama

Before you install: Low friction: pure Python wheel with only three runtime dependencies (llama-index-core, ollama, pytest-asyncio). Active maintenance status with recent release.

License in practice: MIT license permits commercial and private use with minimal restrictions—suitable for most projects.

Quickstart:

pip install llama-index-embeddings-ollama

from llama_index.embeddings.ollama import OllamaEmbedding

embed_model = OllamaEmbedding(
    model_name="nomic-embed-text",
    base_url="http://localhost:11434"
)
embedding = embed_model.get_text_embedding("Hello, world!")

Ollama must be installed and running locally (or on a remote server) with an embedding model already pulled before use.

Verify before relying:

- Performance characteristics and latency compared to cloud-based embedding APIs.
- Memory and CPU requirements for different embedding models on typical hardware.
- Compatibility with specific versions of llama-index-core beyond the stated dependency.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 171.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

local embedding models, ollama embeddings, llamaindex integration, offline text embeddings, vector embeddings local, embedding generation, private embeddings, embeddings, local-inference, rag

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