# RAG Support Agent

The Lilypad RAG Support Agent is a Retrieval-Augmented Generation (RAG) AI assistant that retrieves relevant information and generates AI-powered responses using the Lilypad Network. It enables automated support and troubleshooting by leveraging vector search and AI-based text generation.

## **Agent Details**

* **Context-Aware Responses** – Uses `all-MiniLM-L6-v2` embeddings to retrieve relevant information.
* **AI-Powered Answer Generation** – Sends retrieved context and user query to the Lilypad API, which processes it using Llama3 8B.
* **Customizable Knowledge Base** – Modify the agent’s context source (`issues.md`) to adapt it for different use cases.

### **How It Works**

1. **Embedding Queries with `all-MiniLM`**
   * Converts user queries and stored knowledge into dense vector embeddings for similarity-based retrieval
2. **Retrieving Relevant Context**
   * Searches a pre-indexed database to find the most relevant information.
3. **Generating Responses with Llama3 using the** Lilypad API
   * Sends retrieved context and user prompt to the Lilypad API, where Llama3 8B generates a structured response

### **Expanding to Your Own Support Agent**

The Lilypad RAG Support Agent can be adapted to different projects by modifying its retrieval source.

#### **Updating the Knowledge Base (`issues.md`)**

By default, the agent retrieves information from `issues.md`, a markdown file containing troubleshooting steps.

To customize:

* Open `issues.md` in the repository.
* Replace or expand the content with relevant support information for your project.
* Format the content clearly to improve retrieval accuracy.
* Restart the agent to index the updated knowledge base.

For more advanced use cases, the agent can be extended to support multiple files or external knowledge sources.

## **Getting Started**

A guide to launch the RAG support agent locally and run inference on the Lilypad Network with the [Lilypad Inference API](https://github.com/Lilypad-Tech/HubSpot-LinkedIn-AgentSync).

#### **Get a Lilypad API Key**

Sign up at [Anura API](https://anura.lilypad.tech) and generate an API key.

#### **Clone the Repository**

```sh
git clone https://github.com/PBillingsby/lilypad-rag-agent.git
cd lilypad-rag-agent
```

#### **Configure Your API Key**

Export your Lilypad API Token as an environment variable:

```sh
export LILYPAD_API_TOKEN="your-api-key-here"
```

To make it persistent, add it to `~/.bashrc` or `~/.zshrc`.

### **Setup**

Ensure Python 3 is installed, then run:

```sh
pip install -r requirements.txt
```

### Usage

After setting up the API key and dependencies, the agent is ready to process queries using Lilypad’s AI-powered retrieval system.

#### **Run the Agent**

Execute the agent from the project's root directory:

```sh
python3 cli.py
```

### **Resources**

* [Lilypad Docs](https://docs.lilypad.tech)
* [Lilypad Anura API](https://anura.lilypad.tech/)
* [Source code](https://github.com/PBillingsby/lilypad-rag-agent)


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# 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.lilypad.tech/lilypad/use-cases-agents-and-projects/agents/rag-support-agent.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.