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Llama Index Milvus Example

Llama Index Milvus Example is an open-source Python project demonstrating a website-to-RAG workflow: Apify crawls a chosen site, OpenAI APIs generate embeddings, Milvus stores them, and a Gradio chat answers questions using retrieved context.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Llama Index Milvus Example — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a documented Python example of the stages connecting website crawling, embeddings, vector storage and contextual chat.

Who it is for

  • Developers learning how to build a retrieval-augmented question-answering workflow from website content.
  • Teams prototyping a searchable knowledge base for a selected website.

Documented uses

  • Crawl a chosen website and prepare its content for retrieval.
  • Generate embeddings and store them in a Milvus vector database.
  • Ask questions about the crawled content through a context-grounded chat interface.
  • Inspect Milvus collections and stored vectors using Attu.
Before you choose

Requirements & limits

The project is a public GitHub repository licensed under Apache-2.0. The README requires an Apify account and an OpenAI account for the documented workflow.

  • The workflow uses Apify for crawling, OpenAI APIs for embeddings, Milvus for vector storage and Gradio for chat.
  • Docker Compose is included to start Milvus; the README uses Pipenv but allows another Python environment manager.
  • The repository separates crawling and querying into spider.py and query.py.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and run the website RAG example

Start Milvus, configure the required tokens and website URL, then run the documented crawl and query scripts.

Before you start

  • The project repository and a Python environment; the README uses Pipenv but permits another environment manager.
  • Docker and Docker Compose for starting Milvus.
  • An Apify account and token for web crawling.
  • An OpenAI account and token for embedding generation.

Steps

  1. From the repository, enter the milvus directory and run sudo docker compose up -d, then return to the repository root.

  2. Install the Python dependencies with pipenv install or the environment manager you use.

  3. Copy .env.sample to .env and fill in the OpenAI and Apify tokens.

  4. Set the website URL in spider.py, then run the command below followed by the command below.

    Documented command
    python spider.py
    Documented command
    python query.py

Expected outcome

The example runs its website crawl and provides a Gradio chat for questions answered with retrieved context. The README also describes viewing the Milvus setup, collections and vectors through Attu at localhost:8000.

Guide limitations

  • The documented workflow requires credentials for both Apify and OpenAI.
  • The README describes an example workflow and does not document production deployment or operational guarantees.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Llama Index Milvus ExampleCrawl a chosen website and prepare its content for retrieval.Docker, Linux, Server, WebThe project is a public GitHub repository licensed under Apache-2.0. The README requires an Apify account and an OpenAI account for the documented workflow.
Chatbot with PDF for Semantic SearchPrepare document content as embeddings and save it to a supported vector database.WebThe repository is identified as open source and publicly visible on GitHub.
AI LLM PlaygroundAsk questions about PDF documents through an assistant interface.Web
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
sion42x/llama-index-milvus-example · Official homepage title · retained in its source language
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