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milvus

Sat, 13 Sep 2025 15:24:43 +0800

milvus-io/milvus

What is Milvus?

🐦 Milvus is a high-performance vector database built for scale. It powers AI applications by efficiently organizing and searching vast amounts of unstructured data, such as text, images, and multi-modal information.

🧑‍💻 Written in Go and C++, Milvus implements hardware accelaration for CPU/GPU to achieve best-in-class vector search performance. Thanks to its fully-distributed and K8s-native architecture, Milvus can scale horizontally, handle tens of thousands of search queries on billions of vectors, and keep data fresh with real-time streaming updates. Milvus also supports Standalone mode for single machine deployment. Milvus Lite is a lightweight version good for quickstart in python with pip install.

Want to use Milvus with zero setup? Try out Zilliz Cloud ☁️ for free. Milvus is available as a fully managed service on Zilliz Cloud, with Serverless, Dedicated and BYOC options available.

For questions about how to use Milvus, join the community on Discord to get help. For reporting problems, file bugs and feature requests in GitHub Issues or ask in Discussions.

The Milvus open-source project is under LF AI & Data Foundation, distributed with Apache 2.0 License, with Zilliz as its major contributor.

Quickstart

`1`	`$ pip install -U pymilvus`

This installs pymilvus, the Python SDK for Milvus. Use MilvusClient to create a client:

`1`	`from pymilvus import MilvusClient`

pymilvus also includes Milvus Lite for quickstart. To create a local vector database, simply instantiate a client with a local file name for persisting data:
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client = MilvusClient("milvus_demo.db")

You can also specify the credentials to connect to your deployed Milvus server or Zilliz Cloud:

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client = MilvusClient(
  uri="<endpoint_of_self_hosted_milvus_or_zilliz_cloud>",
  token="<username_and_password_or_zilliz_cloud_api_key>")

With the client, you can create collection:

client.create_collection(
    collection_name="demo_collection",
    dimension=768,  # The vectors we will use in this demo have 768 dimensions
)

Ingest data:

`1`	`res = client.insert(collection_name="demo_collection", data=data)`

Perform vector search:

query_vectors = embedding_fn.encode_queries(["Who is Alan Turing?", "What is AI?"])
res = client.search(
    collection_name="demo_collection",  # target collection
    data=query_vectors,  # a list of one or more query vectors, supports batch
    limit=2,  # how many results to return (topK)
    output_fields=["vector", "text", "subject"],  # what fields to return
)

Why Milvus

Milvus is designed to handle vector search at scale. It stores vectors, which are learned representations of unstructured data, together with other scalar data types such as integers, strings, and JSON objects. Users can conduct efficient vector search with metadata filtering or hybrid search. Here are why developers choose Milvus as the vector database for AI applications:

High Performance at Scale and High Availability

Milvus features a distributed architecture that separates compute and storage. Milvus can horizontally scale and adapt to diverse traffic patterns, achieving optimal performance by independently increasing query nodes for read-heavy workload and data node for write-heavy workload. The stateless microservices on K8s allow quick recovery from failure, ensuring high availability. The support for replicas further enhances fault tolerance and throughput by loading data segments on multiple query nodes. See benchmark for performance comparison.

Support for Various Vector Index Types and Hardware Acceleration

Milvus separates the system and core vector search engine, allowing it to support all major vector index types that are optimized for different scenarios, including HNSW, IVF, FLAT (brute-force), SCANN, and DiskANN, with quantization-based variations and mmap. Milvus optimizes vector search for advanced features such as metadata filtering and range search. Additionally, Milvus implements hardware acceleration to enhance vector search performance and supports GPU indexing, such as NVIDIA’s CAGRA.

Flexible Multi-tenancy and Hot/Cold Storage

Milvus supports multi-tenancy through isolation at database, collection, partition, or partition key level. The flexible strategies allow a single cluster to handle hundreds to millions of tenants, also ensures optimized search performance and flexible access control. Milvus enhances cost-effectiveness with hot/cold storage. Frequently accessed hot data can be stored in memory or on SSDs for better performance, while less-accessed cold data is kept on slower, cost-effective storage. This mechanism can significantly reduce costs while maintaining high performance for critical tasks.

Sparse Vector for Full Text Search and Hybrid Search

In addition to semantic search through dense vector, Milvus also natively supports full text search with BM25 as well as learned sparse embedding such as SPLADE and BGE-M3. Users can store sparse vector and dense vector in the same collection, and define functions to rerank results from multiple search requests. See examples of Hybrid Search with semantic search + full text search.

Data Security and Fine-grain Access Control

Milvus ensures data security by implementing mandatory user authentication, TLS encryption, and Role-Based Access Control (RBAC). User authentication ensures that only authorized users with valid credentials can access the database, while TLS encryption secures all communications within the network. Additionally, RBAC allows for fine-grained access control by assigning specific permissions to users based on their roles. These features make Milvus a robust and secure choice for enterprise applications, protecting sensitive data from unauthorized access and potential breaches.

Milvus is trusted by AI developers to build applications such as text and image search, Retrieval-Augmented Generation (RAG), and recommendation systems. Milvus powers many mission-critical business for startups and enterprises.

Demos and Tutorials

Here is a selection of demos and tutorials to show how to build various types of AI applications made with Milvus:

You can explore a comprehensive Tutorials Overview covering topics such as Retrieval-Augmented Generation (RAG), Semantic Search, Hybrid Search, Question Answering, Recommendation Systems, and various quick-start guides. These resources are designed to help you get started quickly and efficiently.

Tutorial	Use Case	Related Milvus Features
Build RAG with Milvus	RAG	vector search
Advanced RAG Optimizations	RAG	vector search, full text search
Full Text Search with Milvus	Text Search	full text search
Hybrid Search with Milvus	Hybrid Search	hybrid search, multi vector, dense embedding, sparse embedding
Image Search with Milvus	Semantic Search	vector search, dynamic field
Multimodal Search using Multi Vectors	Semantic Search	multi vector, hybrid search
Movie Recommendation with Milvus	Recommendation System	vector search
Graph RAG with Milvus	RAG	graph search
Contextual Retrieval with Milvus	Quickstart	vector search
Vector Visualization	Quickstart	vector search
HDBSCAN Clustering with Milvus	Quickstart	vector search
Use ColPali for Multi-Modal Retrieval with Milvus	Quickstart	vector search

Image Search	RAG	Drug Discovery

Ecosystem and Integration

Milvus integrates with a comprehensive suite of AI development tools, such as LangChain, LlamaIndex, OpenAI and HuggingFace, making it an ideal vector store for GenAI applications such as Retrieval-Augmented Generation (RAG). Milvus works with both open-source embedding models and embedding service, in text, image and video modalities. Milvus also provides a convenient util pymilvus[model], users can use the simple wrapper code to transform unstructured data into vector embeddings and leverage reranking models for optimized search results. The Milvus ecosystem also includes Attu for GUI-based administration, Birdwatcher for system debugging, Prometheus/Grafana for monitoring, Milvus CDC for data synchronization, VTS for data migration and data connectors for Spark, Kafka, Fivetran, and Airbyte to build search pipelines.

Check out https://milvus.io/docs/integrations_overview.md for more details.

Documentation

For guidance on installation, usage, deployment, and administration, check out Milvus Docs. For technical milestones and enhancement proposals, check out issues on GitHub.

Contributing

The Milvus open-source project accepts contribution from everyone. See Guidelines for Contributing for details on submitting patches and the development workflow. See our community repository to learn about project governance and access more community resources.

Build Milvus from Source Code

Requirements:

Linux systems (Ubuntu 20.04 or later recommended):

go: >= 1.21
cmake: >= 3.26.4
gcc: 9.5
python: > 3.8 and  <= 3.11

MacOS systems with x86_64 (Big Sur 11.5 or later recommended):

go: >= 1.21
cmake: >= 3.26.4
llvm: >= 15
python: > 3.8 and  <= 3.11

MacOS systems with Apple Silicon (Monterey 12.0.1 or later recommended):

go: >= 1.21 (Arch=ARM64)
cmake: >= 3.26.4
llvm: >= 15
python: > 3.8 and  <= 3.11

Clone Milvus repo and build.

# Clone github repository.
$ git clone https://github.com/milvus-io/milvus.git

# Install third-party dependencies.
$ cd milvus/
$ ./scripts/install_deps.sh

# Compile Milvus.
$ make

For full instructions, see developer’s documentation.

Community

Join the Milvus community on Discord to share your suggestions, advice, and questions with our engineering team.

To learn latest news about Milvus, follow us on social media:

You can also check out our FAQ page to discover solutions or answers to your issues or questions, and subscribe to Milvus mailing lists:

Reference

Reference to cite when you use Milvus in a research paper:

@inproceedings{2021milvus,
  title={Milvus: A Purpose-Built Vector Data Management System},
  author={Wang, Jianguo and Yi, Xiaomeng and Guo, Rentong and Jin, Hai and Xu, Peng and Li, Shengjun and Wang, Xiangyu and Guo, Xiangzhou and Li, Chengming and Xu, Xiaohai and others},
  booktitle={Proceedings of the 2021 International Conference on Management of Data},
  pages={2614--2627},
  year={2021}
}

@article{2022manu,
  title={Manu: a cloud native vector database management system},
  author={Guo, Rentong and Luan, Xiaofan and Xiang, Long and Yan, Xiao and Yi, Xiaomeng and Luo, Jigao and Cheng, Qianya and Xu, Weizhi and Luo, Jiarui and Liu, Frank and others},
  journal={Proceedings of the VLDB Endowment},
  volume={15},
  number={12},
  pages={3548--3561},
  year={2022},
  publisher={VLDB Endowment}
}

ragflow

Thu, 19 Jun 2025 15:29:58 +0800

infiniflow/ragflow

Document | Roadmap | Twitter | Discord | Demo

📕 Table of Contents

💡 What is RAGFlow?
🎮 Demo
📌 Latest Updates
🌟 Key Features
🔎 System Architecture
🎬 Get Started
🔧 Configurations
🔧 Build a docker image without embedding models
🔧 Build a docker image including embedding models
🔨 Launch service from source for development
📚 Documentation
📜 Roadmap
🏄 Community
🙌 Contributing

💡 What is RAGFlow?

RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.

🎮 Demo

Try our demo at https://demo.ragflow.io.

🔥 Latest Updates

2025-05-23 Adds a Python/JavaScript code executor component to Agent.
2025-05-05 Supports cross-language query.
2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.
2025-02-28 Combined with Internet search (Tavily), supports reasoning like Deep Research for any LLMs.
2024-12-18 Upgrades Document Layout Analysis model in DeepDoc.
2024-08-22 Support text to SQL statements through RAG.

🎉 Stay Tuned

⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new releases! 🌟

🌟 Key Features

🍭 “Quality in, quality out”

Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
Finds “needle in a data haystack” of literally unlimited tokens.

🍱 Template-based chunking

Intelligent and explainable.
Plenty of template options to choose from.

🌱 Grounded citations with reduced hallucinations

Visualization of text chunking to allow human intervention.
Quick view of the key references and traceable citations to support grounded answers.

🍔 Compatibility with heterogeneous data sources

Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.

🛀 Automated and effortless RAG workflow

Streamlined RAG orchestration catered to both personal and large businesses.
Configurable LLMs as well as embedding models.
Multiple recall paired with fused re-ranking.
Intuitive APIs for seamless integration with business.

🔎 System Architecture

🎬 Get Started

📝 Prerequisites

CPU >= 4 cores
RAM >= 16 GB
Disk >= 50 GB
Docker >= 24.0.0 & Docker Compose >= v2.26.1
gVisor: Required only if you intend to use the code executor (sandbox) feature of RAGFlow.

[!TIP] If you have not installed Docker on your local machine (Windows, Mac, or Linux), see Install Docker Engine.

🚀 Start up the server

Ensure vm.max_map_count >= 262144:
To check the value of vm.max_map_count:
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$ sysctl vm.max_map_count
Reset vm.max_map_count to a value at least 262144 if it is not.
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# In this case, we set it to 262144: $ sudo sysctl -w vm.max_map_count=262144
This change will be reset after a system reboot. To ensure your change remains permanent, add or update the vm.max_map_count value in /etc/sysctl.conf accordingly:
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vm.max_map_count=262144

Clone the repo:

`1`	`$ git clone https://github.com/infiniflow/ragflow.git`

Start up the server using the pre-built Docker images:

[!CAUTION] All Docker images are built for x86 platforms. We don’t currently offer Docker images for ARM64. If you are on an ARM64 platform, follow this guide to build a Docker image compatible with your system.

The command below downloads the v0.19.1-slim edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from v0.19.1-slim, update the RAGFLOW_IMAGE variable accordingly in docker/.env before using docker compose to start the server. For example: set RAGFLOW_IMAGE=infiniflow/ragflow:v0.19.1 for the full edition v0.19.1.

$ cd ragflow/docker
# Use CPU for embedding and DeepDoc tasks:
$ docker compose -f docker-compose.yml up -d

# To use GPU to accelerate embedding and DeepDoc tasks:
# docker compose -f docker-compose-gpu.yml up -d

RAGFlow image tag	Image size (GB)	Has embedding models?	Stable?
v0.19.1	≈9	:heavy_check_mark:	Stable release
v0.19.1-slim	≈2	❌	Stable release
nightly	≈9	:heavy_check_mark:	Unstable nightly build
nightly-slim	≈2	❌	Unstable nightly build

Check the server status after having the server up and running:

`1`	`$ docker logs -f ragflow-server`

The following output confirms a successful launch of the system:


      ____   ___    ______ ______ __
     / __ \ /   |  / ____// ____// /____  _      __
    / /_/ // /| | / / __ / /_   / // __ \| | /| / /
   / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
  /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/

 * Running on all addresses (0.0.0.0)

If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a network anormal error because, at that moment, your RAGFlow may not be fully initialized.

In your web browser, enter the IP address of your server and log in to RAGFlow.

With the default settings, you only need to enter http://IP_OF_YOUR_MACHINE (sans port number) as the default HTTP serving port 80 can be omitted when using the default configurations.
In service_conf.yaml.template, select the desired LLM factory in user_default_llm and update the API_KEY field with the corresponding API key.

See llm_api_key_setup for more information.

The show is on!

🔧 Configurations

When it comes to system configurations, you will need to manage the following files:

.env: Keeps the fundamental setups for the system, such as SVR_HTTP_PORT, MYSQL_PASSWORD, and MINIO_PASSWORD.
service_conf.yaml.template: Configures the back-end services. The environment variables in this file will be automatically populated when the Docker container starts. Any environment variables set within the Docker container will be available for use, allowing you to customize service behavior based on the deployment environment.
docker-compose.yml: The system relies on docker-compose.yml to start up.

The ./docker/README file provides a detailed description of the environment settings and service configurations which can be used as ${ENV_VARS} in the service_conf.yaml.template file.

To update the default HTTP serving port (80), go to docker-compose.yml and change 80:80 to <YOUR_SERVING_PORT>:80.

Updates to the above configurations require a reboot of all containers to take effect:

`1`	`$ docker compose -f docker-compose.yml up -d`

Switch doc engine from Elasticsearch to Infinity

RAGFlow uses Elasticsearch by default for storing full text and vectors. To switch to Infinity, follow these steps:

Stop all running containers:

`1`	`$ docker compose -f docker/docker-compose.yml down -v`

[!WARNING] -v will delete the docker container volumes, and the existing data will be cleared.

Set DOC_ENGINE in docker/.env to infinity.

Start the containers:

`1`	`$ docker compose -f docker-compose.yml up -d`

[!WARNING] Switching to Infinity on a Linux/arm64 machine is not yet officially supported.

🔧 Build a Docker image without embedding models

This image is approximately 2 GB in size and relies on external LLM and embedding services.

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git clone https://github.com/infiniflow/ragflow.git
cd ragflow/
docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .

🔧 Build a Docker image including embedding models

This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.

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git clone https://github.com/infiniflow/ragflow.git
cd ragflow/
docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .

🔨 Launch service from source for development

Install uv, or skip this step if it is already installed:
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pipx install uv pre-commit

Clone the source code and install Python dependencies:

git clone https://github.com/infiniflow/ragflow.git
cd ragflow/
uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
uv run download_deps.py
pre-commit install

Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
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docker compose -f docker/docker-compose-base.yml up -d
Add the following line to /etc/hosts to resolve all hosts specified in docker/.env to 127.0.0.1:
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127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager
If you cannot access HuggingFace, set the HF_ENDPOINT environment variable to use a mirror site:
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export HF_ENDPOINT=https://hf-mirror.com

If your operating system does not have jemalloc, please install it as follows:

# ubuntu
sudo apt-get install libjemalloc-dev
# centos
sudo yum install jemalloc

Launch backend service:

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source .venv/bin/activate
export PYTHONPATH=$(pwd)
bash docker/launch_backend_service.sh

Install frontend dependencies:
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cd web npm install
Launch frontend service:
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npm run dev
The following output confirms a successful launch of the system:
Stop RAGFlow front-end and back-end service after development is complete:
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pkill -f "ragflow_server.py|task_executor.py"

📚 Documentation

📜 Roadmap

See the RAGFlow Roadmap 2025

🏄 Community

🙌 Contributing

RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. If you would like to be a part, review our Contribution Guidelines first.