pymilvus.model
Model components for PyMilvus, the Python SDK for Milvus
Decision gist · record as of 2026-08-14
Yes, if you need a standard way to integrate embedding or reranking models with Milvus. The package has low install friction, permissive licensing, and no known vulnerabilities. However, the aging maintenance status (501 days since last release) warrants checking that its dependencies remain compatible with your environment.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or above.
- Depending on the model provider chosen, you may need API keys or additional model files downloaded by transformers.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache Software License (permissive), allowing commercial use and modification with minimal restrictions.
last release 2025-03-31 (501 days) · last repo commit 2025-03-31 · 60 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 399,404 downloads/mo, #6,947 on PyPI
Alternatives
Verify before relying
pip install pymilvus.model
from pymilvus_model.dense import SentenceTransformerEmbeddingFunction
embedding_fn = SentenceTransformerEmbeddingFunction()
embeddings = embedding_fn(["hello world"])- Whether the package receives active maintenance independent of parent project releases or only through bundled updates.
- Current state of compatibility with the latest versions of transformers, onnxruntime, scipy, protobuf, and numpy.
- Whether pymilvus-model is included as a dependency in pymilvus or installed separately.
What it is and what it does
pymilvus-model is a library that bridges embedding and reranking models with Milvus, an open-source vector database for AI applications. It abstracts away the complexity of integrating different model providers—including commercial services like OpenAI and Voyage AI, as well as open-source options via SentenceTransformers and Hugging Face—so you can generate embeddings or rerank results without writing provider-specific integration code.
The package depends on transformers, onnxruntime, scipy, protobuf, and numpy to handle model loading, inference, and numerical operations. It is designed to work with Python 3.8 and above. The library is intended for developers building AI search or retrieval systems who need a standard way to generate or rerank embeddings.
Use it for
- Generate embeddings from text using OpenAI, Cohere, or open-source models and store them in a vector database for semantic search.
- Rerank search results using different reranker models to improve relevance without reindexing.
- Build a retrieval-augmented generation pipeline where embeddings are created and managed through a unified interface.
- Integrate SentenceTransformers models into an application without writing custom model loading code.
- Switch between embedding providers with minimal code changes.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need a standard way to integrate embedding or reranking models with Milvus.
The package has low install friction, permissive licensing, and no known vulnerabilities. However, the aging maintenance status (501 days since last release) warrants checking that its dependencies remain compatible with your environment.
Install
pymilvus-model on PyPI
Before you install
Low install friction with a pure-wheel distribution. Maintenance status is aging—last release was 501 days ago—but the repository remains active, suggesting ongoing support.
Requires Python 3.8 or above. Depending on the model provider chosen, you may need API keys or additional model files downloaded by transformers.
License in practice
Licensed under Apache Software License (permissive), allowing commercial use and modification with minimal restrictions.
Quickstart
pip install pymilvus.model
from pymilvus_model.dense import SentenceTransformerEmbeddingFunction
embedding_fn = SentenceTransformerEmbeddingFunction()
embeddings = embedding_fn(["hello world"])Verify before relying
- Whether the package receives active maintenance independent of parent project releases or only through bundled updates.
- Current state of compatibility with the latest versions of transformers, onnxruntime, scipy, protobuf, and numpy.
- Whether pymilvus-model is included as a dependency in pymilvus or installed separately.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagestransformersonnxruntimescipyprotobufnumpy |
| Maintenance | Aging 501 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 399,404 / month, #6,947 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3 |
Evidence: pymilvus_model-0.3.2-py3-none-any.whl
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