# End to end RAG pipeline with Documentation

## Overview

This example shows how to build your own RAG Doctor :woman\_health\_worker::

* Build an embeddings database from documentation CSVs
* Query the database with LLMs
* Serve the RAG model as an API endpoint

### Steps

{% stepper %}
{% step %}
**Data Preparation**

Compile CSV documentation and create an embeddings database, ensuring data is organized for efficient retrieval.
{% endstep %}

{% step %}
**Embedding Database**

Utilize Large Language Models (LLMs) to build and query the embeddings database effectively.
{% endstep %}

{% step %}
**API Deployment**

Configure and deploy the RAG model as an API endpoint to facilitate seamless interaction and integration.
{% endstep %}
{% endstepper %}

### GitHub Repository

The repository walks you through how to go through the above steps:

{% embed url="<https://github.com/valohai/rag-doc-example>" %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.valohai.com/project-gallery/nlp-and-llm/rag-doc-example.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.