llama-index-embeddings-ollama
llama-index embeddings ollama integration
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Ollama must be installed and running locally (or on a remote server) with an embedding model already pulled before use.
- Low friction: pure Python wheel with only three runtime dependencies (llama-index-core, ollama, pytest-asyncio).
- Active maintenance status with recent release.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions—suitable for most projects.
last release 2026-03-12 (155 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 171,402 downloads/mo, #10,369 on PyPI
Alternatives
Verify before relying
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!")- 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.
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 on this page. Verify before relying on it.
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
llama-index-embeddings-ollama on PyPI
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.
Ollama must be installed and running locally (or on a remote server) with an embedding model already pulled before use.
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!")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 the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesllama-index-coreollamapytest-asyncio |
| Maintenance | Actively maintained 155 days since the last release |
| First released | |
| Downloads | 171,402 / month, #10,369 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_embeddings_ollama-0.9.0-py3-none-any.whl
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See also llama-index-embeddings-google-genai · llama-index-embeddings-langchain · llama-index-embeddings-bedrock · llama-index-embeddings-vertex · llama-index-embeddings-openai · llama-index-embeddings-huggingface · fastembed · llama-index-vector-stores-qdrant · llama-index-embeddings-azure-openai · ollama