onireimu / rag-examples

Retrieval Augmented Generation Examples - Original, GPT based, Semantic Search based.

RAG-implementation

Information

Example of Retrieval Augmented Generation with a private dataset.

We have demonstrated three different ways to utilise RAG Implementations over the document for Question/Answering and Parsing.

1 - Original MetaAI RAG Paper Implementation for user dataset.

2 - Llama-Index, LangChain and OpenAI RAG Implementation for user dataset.

3 - Minimilistic Implementation of LaBSE + OpenAI RAG Implementation for user dataset.

Note: 1 - Implementation 2 and Implementation 3 has metrics result for the test dataset on WER (Word Error Rate used for Transcription) and RoUGE Scores (used for Translation)
2 - These also have implementation of Multilingual Input/Output because of LID from NLLB (MetaAI) with ChatGPT and LID + LaBSE with ChatGPT.

Setting Up

Notebooks

All the notebook implementations are mostly self-explanatory.
It consists of usually 5 sections -

1. Package Installation and Setup
2. Data Cleaning and Preprocessing
3. Main CodeBlock with Indexing and Usage.
4. Inference Example and WrapUp for Productionizing.
5. Evaluation Metrics.

I mostly used the evaluation metrics which made sense to me and usually towards established and statistical metrics.
But for further work,

1. BertScore - https://huggingface.co/spaces/evaluate-metric/bertscore
2. ColBERT - https://github.com/stanford-futuredata/ColBERT
3. RAGAS - https://github.com/explodinggradients/ragas

Gradio Application

Gradio Application is an Inference Code for Implementation 2 - LlamaIndex/LangChain/OpenAI.

It uses gpt-3.5-turbo model with temperature 0 (zero) for answer generation.

You need put OpenAI Key in line 22 for Gradio Application and similarly in the notebook instance.

os.environ['OPENAI_API_KEY'] = <openai-api-key>

Then spin up the gradio application with given configuration, change question examples if using for different dataset.

python gradio_app.py

Alternatively, you can opt for connecting to an HTTP end-point via postman.

python http_app.py

Future Work

Audio Implementation can also be added to these implementations via OpenAI/Whisper and Microsoft/speecht5_tts but has not been implemented.

Can be added later based on request.