> ## Documentation Index
> Fetch the complete documentation index at: https://docs-preprod.sambanova.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Milvus

[Milvus](https://milvus.io/) is an open-source vector database designed for building Retrieval-Augmented Generation (RAG) applications. By combining Milvus with SambaNova LLMs, you can create scalable, high-performance RAG pipelines.

## Prerequisites

* A [SambaNova Cloud](https://cloud.sambanova.ai/) account and API key
* Python 3.8 or higher

## Installation and setup

1. Create a virtual environment

```bash
python -m venv .venv
source .venv/bin/activate
```

2. Install dependencies

```bash
pip install openai
pip install pymilvus
pip install langchain-sambanova
```

3. Set your SambaNova API key

```bash
export api_key="your-sambanova-api-key"
```

## Example use cases

* Build Retrieval-Augmented Generation (RAG) systems
* Store and query embeddings efficiently
* Combine SambaNova LLMs with Milvus for scalable knowledge retrieval

## Example notebooks

You can explore example notebooks to learn how to build RAG systems with SambaCloud and Milvus:

* OpenAI Client guide: [Build RAG with Milvus and SambaCloud](https://github.com/sambanova/integrations/blob/main/milvus/build_RAG_with_milvus_and_SNCloud.ipynb)
* LangChain Connector guide: [RAG with Milvus and SambaCloud using LangChain](https://github.com/sambanova/integrations/blob/main/milvus/langchain_RAG_with_milvus_and_SNCloud.ipynb)

These guides walk you through setting up and integrating the systems to create a functional RAG application.