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Using NLWeb with Milvus

Microsoft’s NLWeb is a proposed framework that enables natural language interfaces for websites, using Schema.org, formats like RSS and the emerging MCP protocol.

Milvus is supported as a vector database backend within NLWeb for embedding storage and efficient vector similarity search, enabling powerful context retrieval for natural language processing applications.

This documentation is primarily based on the official quick start documentation. If you find any outdated or inconsistent content, please prioritize the official documentation and feel free to raise an issue for us.

Usage

NLWeb can be configured to use Milvus as the retrieval engine. Below is a guide on how to set up and use NLWeb with Milvus.

Installation

Clone the repo and set up your environment:

git clone https://github.com/microsoft/NLWeb
cd NLWeb
python -m venv .venv
source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows
cd code
pip install -r requirements.txt
pip install pymilvus  # Add Milvus Python client

Configuring Milvus

To use Milvus, update your configuration.

Update config files in code/config

Open the config_retrieval.yaml file and add the Milvus configuration:

preferred_endpoint: milvus_local

endpoints:
  milvus_local:
    database_path: "../data/milvus.db"
    # Set the collection name to use
    index_name: nlweb_collection
    # Specify the database type
    db_type: milvus

Loading Data

Once configured, load your content using RSS feeds.

From the code directory:

python -m tools.db_load https://feeds.libsyn.com/121695/rss Behind-the-Tech

This will ingest the content into your Milvus collection, storing both the text data and vector embeddings.

Running the Server

To start NLWeb, from the code directory, run:

python app-file.py

You can now query your content via natural language using either the web UI at http://localhost:8000/ or directly through the MCP-compatible REST API.

Further Reading

Table of contents

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