🧠RAG Support Agent
A retrieval-augmented generation (RAG) agent that retrieves relevant context and generates AI-powered responses using the Lilypad Network.
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A retrieval-augmented generation (RAG) agent that retrieves relevant context and generates AI-powered responses using the Lilypad Network.
Last updated
Was this helpful?
Was this helpful?
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.
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.
Embedding Queries with all-MiniLM
Converts user queries and stored knowledge into dense vector embeddings for similarity-based retrieval
Retrieving Relevant Context
Searches a pre-indexed database to find the most relevant information.
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
The Lilypad RAG Support Agent can be adapted to different projects by modifying its retrieval source.
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.
A guide to launch the RAG support agent locally and run inference on the Lilypad Network with the Lilypad Inference API.
Sign up at Anura API and generate an API key.
git clone https://github.com/PBillingsby/lilypad-rag-agent.git
cd lilypad-rag-agentExport your Lilypad API Token as an environment variable:
export LILYPAD_API_TOKEN="your-api-key-here"To make it persistent, add it to ~/.bashrc or ~/.zshrc.
Ensure Python 3 is installed, then run:
pip install -r requirements.txtAfter setting up the API key and dependencies, the agent is ready to process queries using Lilypad’s AI-powered retrieval system.
Execute the agent from the project's root directory:
python3 cli.py