https://github.com/mmabrouk/qa_llama_index_playground

https://github.com/mmabrouk/qa_llama_index_playground

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• Host: GitHub
• URL: https://github.com/mmabrouk/qa_llama_index_playground
• Owner: mmabrouk
• Created: 2023-12-21T11:43:32.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2023-12-21T11:44:54.000Z (10 months ago)
• Last Synced: 2023-12-21T14:02:59.036Z (10 months ago)
• Language: Python
• Size: 1.95 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Question Answering RAG using LlamaIndex in agenta

This templates is a question answering application with a RAG architecture using LlamaIndex, openAI. It provides a playground to experiment with different prompts and parameters in LlamaIndex and evaluate the results.

It runs with agenta. [Agenta](https://github.com/agenta-ai/agenta) is an open-source LLMOps 

platform that allows you to 1) create a playground from the code of any LLM app to quickly experiment, version, and collaborate in your team 2) evaluate LLM applications, and 3) deploy applications easily. 

## How to use

### 0. Prerequisites

-  Install the agenta CLI

```bash

pip install agenta-cli

```

- Either create an account in [agenta cloud](https://cloud.agenta.ai/) or 

[self-host agenta](/self-host/host-locally)

### 1. Clone the repository

```bash

git clone https://github.com/Agenta-AI/job_extractor_template

```

### 2. Initialize the project

```bash

agenta init

```

### 3. Setup your openAI API key

Create a .env file by copying the .env.example file and add your openAI 

API key to it.

```bash

OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx

```

### 4. Deploy the application to agenta

```bash

agenta variant serve app.py

```

### 5. Experiment with the prompts in a playground and evaluate different variants in agenta

https://github.com/Agenta-AI/job_extractor_template/assets/4510758/30271188-8d46-4d02-8207-ddb60ad0e284