---
title: "NeumAI"
type: "tool"
slug: "neumtry-neumai"
canonical_url: "https://www.graphcanon.com/tools/neumtry-neumai"
github_url: "https://github.com/NeumTry/NeumAI"
homepage_url: "https://neum.ai"
stars: 865
forks: 50
primary_language: "Python"
license: "Apache-2.0"
categories: ["vector-databases", "data-retrieval"]
tags: ["llmops", "ml", "pipelines", "data-engineering", "ai", "neumarag", "chatgpt", "embedding"]
updated_at: "2026-07-07T18:49:03.024286+00:00"
---

# NeumAI

> Neum AI is a comprehensive solution for RAG with scalable vector embedding management.

Neum AI facilitates efficient data processing and retrieval augmenting for large-scale application needs utilizing vector embeddings. It provides tools like high throughput distributed architecture, real-time synchronization, and customizable pre-processing.

## Facts

- Repository: https://github.com/NeumTry/NeumAI
- Homepage: https://neum.ai
- Stars: 865 · Forks: 50 · Open issues: 9 · Watchers: 6
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2024-01-15T23:00:58+00:00

## Categories

- [Vector Databases](/categories/vector-databases.md)
- [Data & Retrieval](/categories/data-retrieval.md)

## Tags

llmops, ml, pipelines, data-engineering, ai, neumarag, chatgpt, embedding

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## README (excerpt)

```text
<h1 align="center">Neum AI</h1>

<div align="center">
  
  [Homepage](https://www.neum.ai) | [Documentation](https://docs.neum.ai) | [Blog](https://neum.ai/blog) | [Discord](https://discord.gg/mJeNZYRz4m) | [Twitter](https://twitter.com/neum_ai)
  
  <a href="https://www.ycombinator.com/companies/neum-ai"><img src="https://badgen.net/badge/Y%20Combinator/S23/orange"/></a> 
  <a href="https://pypi.org/project/neumai/">
    <img src="https://img.shields.io/pypi/v/neumai" alt="PyPI">
  </a>
</div>



**[Neum AI](https://neum.ai) is a data platform that helps developers leverage their data to contextualize Large Language Models through Retrieval Augmented Generation (RAG)** This includes
extracting data from existing data sources like document storage and NoSQL, processing the contents into vector embeddings and ingesting the vector embeddings into vector databases for similarity search. 

It provides you a comprehensive solution for RAG that can scale with your application and reduce the time spent integrating services like data connectors, embedding models and vector databases.

## Features

- 🏭 **High throughput distributed architecture** to handle billions of data points. Allows high degrees of parallelization to optimize embedding generation and ingestion.
- 🧱 **Built-in data connectors** to common data sources, embedding services and vector stores.
- 🔄 **Real-time synchronization** of data sources to ensure your data is always up-to-date. 
- ♻ **Customizable data pre-processing** in the form of loading, chunking and selecting.
- 🤝 **Cohesive data management** to support hybrid retrieval with metadata. Neum AI automatically augments and tracks metadata to provide rich retrieval experience.

## Talk to us

You can reach our team either through email ([founders@tryneum.com](mailto:founders@tryneum.com)), on [discord](https://discord.gg/mJeNZYRz4m) or by [scheduling a call wit us](https://calendly.com/neum-ai/neum-ai-demo?month=2023-12).

## Getting Started

### Neum AI Cloud

Sign up today at [dashboard.neum.ai](https://dashboard.neum.ai). See our [quickstart](https://docs.neum.ai/get-started/quickstart) to get started.

The Neum AI Cloud supports a large-scale, distributed architecture to run millions of documents through vector embedding. For the full set of features see: [Cloud vs Local](https://neumai.mintlify.app/get-started/cloud-vs-local)

### Local Development

Install the [`neumai`](https://pypi.org/project/neumai/) package:

```bash
pip install neumai
```

To create your first data pipelines visit our [quickstart](https://docs.neum.ai/get-started/quickstart).

At a high level, a pipeline consists of one or multiple sources to pull data from, one embed connector to vectorize the content, and one sink connector to store said vectors.
With this snippet of code we will craft all of these and run a pipeline:
<details open><summary>
  
  ### Creating and running a pipeline
  </summary>
  
  ```python
  
  from neumai.DataConnectors.WebsiteConnector import WebsiteConnector
  from neumai.Shared.Selector import Selector
  from neumai.Loaders.HTMLLoader import HTMLLoader
  from neumai.Chunkers.RecursiveChunker import RecursiveChunker
  from neumai.Sources.SourceConnector import SourceConnector
  from neumai.EmbedConnectors import OpenAIEmbed
  from neumai.SinkConnectors import WeaviateSink
  from neumai.Pipelines import Pipeline

  website_connector =  WebsiteConnector(
      url = "https://www.neum.ai/post/retrieval-augmented-generation-at-scale",
      selector = Selector(
          to_metadata=['url']
      )
  )
  source = SourceConnector(
      data_connector = website_connector, 
      loader = HTMLLoader(), 
      chunker = RecursiveChunker()
  )

  openai_embed = OpenAIEmbed(
      api_key = "<OPEN AI KEY>",
  )

  weaviate_sink = WeaviateSink(
      url = "your-weaviate-url",
      api_key = "your-api-key",
      class_name = "your-class-name",
  )

  pipeline = Pipeline(
      sources=[source], 
      embed=openai
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/neumtry-neumai`](/api/graphcanon/tools/neumtry-neumai)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_