Milvus vs Pinecone MCP Server

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 acceleration 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

$ pip install -U pymilvus

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

from pymilvus import MilvusClient

• You can also try Milvus Lite for quickstart by installing pymilvus[milvus-lite]. To create a local vector database, simply instantiate a client with a local file name for persisting data:

  client = MilvusClient("milvus_demo.db")
  

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

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

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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:

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 embeddings such as SPLADE and BGE-M3. Users can store sparse vectors and dense vectors 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 businesses 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.

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 services, in text, image and video modalities. Milvus also provides a convenient utility 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 contributions 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 && CMake < 4
  GCC: >= 11
  Python: > 3.8 and  <= 3.11
  

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

  Go: >= 1.21
  CMake: >= 3.26.4 && CMake < 4
  llvm: >= 15
  Python: > 3.8 and  <= 3.11
  

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

  Go: >= 1.21 (Arch=ARM64)
  CMake: >= 3.26.4 && CMake < 4
  llvm: >= 15
  Python: > 3.8 and  <= 3.11
  

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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 the latest news about Milvus, follow us on social media:

• X
• LinkedIn
• YouTube
• Medium

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

• Technical Steering Committee
• Technical Discussions
• Announcement

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}
}

Pinecone Developer MCP Server

The Model Context Protocol (MCP) is a standard that allows coding assistants and other AI tools to interact with platforms like Pinecone. The Pinecone Developer MCP Server allows you to connect these tools with Pinecone projects and documentation.

Once connected, AI tools can:

• Search Pinecone documentation to answer questions accurately.
• Help you configure indexes based on your application's needs.
• Generate code informed by your index configuration and data, as well as Pinecone documentation and examples.
• Upsert and search for data in indexes, allowing you to test queries and evaluate results within your dev environment.

See the docs for more detailed information.

This MCP server is focused on improving the experience of developers working with Pinecone as part of their technology stack. It is intended for use with coding assistants. Pinecone also offers the Assistant MCP, which is designed to provide AI assistants with relevant context sourced from your knowledge base.

Setup

To configure the MCP server to access your Pinecone project, you will need to generate an API key using the console. Without an API key, your AI tool will still be able to search documentation. However, it will not be able to manage or query your indexes.

The MCP server requires Node.js v22 or later. Ensure that node and npx are available in your PATH.

Next, you will need to configure your AI assistant to use the MCP server.

Configure Cursor

To add the Pinecone MCP server to a project, create a .cursor/mcp.json file in the project root (if it doesn't already exist) and add the following configuration:

{
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y", "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "<your pinecone api key>"
      }
    }
  }
}

You can check the status of the server in Cursor Settings > MCP.

To enable the server globally, add the configuration to the .cursor/mcp.json in your home directory instead.

It is recommended to use rules to instruct Cursor on proper usage of the MCP server. Check out the docs for some suggestions.

Configure Claude desktop

Use Claude desktop to locate the claude_desktop_config.json file by navigating to Settings > Developer > Edit Config. Add the following configuration:

{
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y", "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "<your pinecone api key>"
      }
    }
  }
}

Restart Claude desktop. On the new chat screen, you should see a hammer (MCP) icon appear with the new MCP tools available.

Use as a Gemini CLI extension

To install this as a Gemini CLI extension, run the following command:

gemini extensions install https://github.com/pinecone-io/pinecone-mcp

You will need to provide your Pinecone API key in the PINECONE_API_KEY environment variable.

export PINECONE_API_KEY=<your pinecone api key>

When you run gemini and press ctrl+t, pinecone should now be shown in the list of installed MCP servers.

Usage

Once configured, your AI tool will automatically make use of the MCP to interact with Pinecone. You may be prompted for permission before a tool can be used.

Example prompts

Here are some prompts you can try with your AI assistant:

• "Search the Pinecone docs for information about metadata filtering"
• "List all my Pinecone indexes and describe their configurations"
• "Create a new index called 'my-docs' using the multilingual-e5-large model"
• "Upsert these documents into my index: [paste your documents]"
• "Search my index for records related to 'authentication best practices'"
• "What namespaces exist in my index, and how many records are in each?"

Tools

Pinecone Developer MCP Server provides the following tools for AI assistants to use:

• search-docs: Search the official Pinecone documentation.
• list-indexes: Lists all Pinecone indexes.
• describe-index: Describes the configuration of an index.
• describe-index-stats: Provides statistics about the data in the index, including the number of records and available namespaces.
• create-document-index: Creates a document index with searchable string fields, each indexed for full-text search, embedded by a hosted model (integrated embedding), or both.
• upsert-documents: Inserts or updates documents in a document index.
• search-document-index: Searches a document index by meaning (embed, sparse_embed) or by keywords (text, query_string), with optional filters.
• create-index-for-model: Creates a legacy integrated index that uses an integrated inference model to embed text as vectors.
• upsert-records: Inserts or updates records in an index with integrated inference.
• search-records: Searches for records in an index based on a text query, using integrated inference for embedding. Has options for metadata filtering and reranking.
• cascading-search: Searches for records across multiple indexes, deduplicating and reranking the results.
• rerank-documents: Reranks a collection of records or text documents using a specialized reranking model.

Limitations

Document indexes with string fields (full-text search and integrated embedding) and legacy integrated indexes are supported. Assistants, standalone embeddings, and search with your own vectors are not supported.

Troubleshooting

MCP server not appearing in your AI tool

• Ensure Node.js v22 or later is installed: node --version
• Verify npx is available in your PATH: which npx
• Check that your configuration file is in the correct location and has valid JSON syntax
• Restart your AI tool after making configuration changes

"Invalid API key" or authentication errors

• Verify your API key is correct in the Pinecone console
• Check that the PINECONE_API_KEY environment variable is set correctly in your MCP configuration
• Ensure there are no extra spaces or quotes around the API key value

Tools not working as expected

• The MCP server only supports indexes with integrated inference. If you're trying to use a serverless index without integrated inference, you'll need to create a new index with an embedding model
• Check the MCP server logs for error messages. In Cursor, view logs in Cursor Settings > MCP

Connection issues

• If using a corporate network, ensure your firewall allows connections to api.pinecone.io
• Try running the server manually to see detailed error output: PINECONE_API_KEY=<your-key> npx @pinecone-database/mcp

Contributing

We welcome your collaboration in improving the developer MCP experience. Please submit issues in the GitHub issue tracker. Information about contributing can be found in CONTRIBUTING.md.