README.md

---
title: Chat with PCI DSS v4
emoji: 👀
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 4.16.0
app_file: app.py
pinned: false
license: apache-2.0
---

# Evaluation of Orca 2 against other LLMs for Retrieval Augmented Generation
 
This project contains the source code, datasets and results for the titled paper.

## How it works

We're using an AI methodology, namely Retrieval Augmentated Generation (RAG), which uses LLMs off the shelf (i.e., without any fine-tuning), then controls their behavior through clever prompting and conditioning on private “contextual” data, e.g., texts extracted from your PDF files.

At a very high level, the workflow can be divided into three stages:

1. Data preprocessing / embedding: This stage involves storing private data (your PDF files) to be retrieved later. Typically, the documents are broken into chunks, passed through an embedding model, then stored the created embeddings in a vectorstore.

2. Prompt construction / retrieval: When a user submits a query, the application constructs a series of prompts to submit to the language model. A compiled prompt typically combines a prompt template and a set of relevant documents retrieved from the vectorstore.

3. Prompt execution / inference: Once the prompts have been compiled, they are submitted to a pre-trained LLM for inference—including both proprietary model APIs and open-source or self-trained models.

![Retrieval Augmentated Generation (RAG) - Workflow Overview](./assets/rag-workflow.png)

Tech stack used includes LangChain, Gradio, Chroma and FAISS. 
- LangChain is an open-source framework that makes it easier to build scalable AI/LLM apps and chatbots. 
- Gradio is an open-source Python library that is used to build machine learning and data science demos and web applications.
- Chroma and FAISS are open-source vectorstores for storing embeddings for your files.

## Running Locally

1. Check pre-conditions:

- [Git Large File Storage (LFS)](https://git-lfs.com/) must have been installed.
- Run `python --version` to make sure you're running Python version 3.10 or above.
- The latest PyTorch with GPU support must have been installed. Here is a sample `conda` command:
```
conda install -y pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
```
- [CMake](https://cmake.org/) must have been installed. Here is a sample command to install `CMake` on `ubuntu`:
```
sudo apt install cmake
```

2. Clone the repo

```
git lfs install
git clone --recursive https://github.com/smu-ai/Evaluation-of-Orca-2-Models-for-Conversational-RAG.git
```


3. Install packages


On Linux/WSL2:
```
pip install -r requirements.txt
```

On Mac:
```
pip install -r requirements-mac.txt
```

4. Set up your environment variables

- By default, environment variables are loaded from `.env.example` file
- If you don't want to use the default settings, copy `.env.example` into `.env`. Your can then update it for your local runs.


5. Start the local server at `http://localhost:7860`:

```
python app.py
```

## Talk to Your Own PDF Files

- The sample PDF files are downloaded from [PCI DSS official website](https://www.pcisecuritystandards.org/document_library/?category=pcidss) and the corresponding embeddings are stored in folders `data/chromadb_1024_512` and `data/faiss_1024_512` with Chroma & FAISS formats respectively, which allows you to run locally without any additional effort.

- You can also put your own PDF files into any folder specified in `SOURCE_PDFS_PATH` and run the command below to generate embeddings which will be stored in folder `FAISS_INDEX_PATH` or `CHROMADB_INDEX_PATH`. If both `*_INDEX_PATH` env vars are set, `FAISS_INDEX_PATH` takes precedence. Make sure the folder specified by `*_INDEX_PATH` doesn't exist; other wise the command will simply try to load index from the folder and do a simple similarity search, as a way to verify if embeddings are generated and stored properly. Please note the HuggingFace Embedding model specified by `HF_EMBEDDINGS_MODEL_NAME` will be used to generate the embeddings.

```
python ingest.py
```

- Once embeddings are generated, you can test them out locally, or check them into your duplicated space. Please note HF Spaces git server does not allow PDF files to be checked in.

## Play with Different Large Language Models

The source code supports different LLM types - as shown at the top of `.env.example`

```
# LLM_MODEL_TYPE=openai
# LLM_MODEL_TYPE=gpt4all-j
# LLM_MODEL_TYPE=gpt4all
# LLM_MODEL_TYPE=llamacpp
LLM_MODEL_TYPE=huggingface
# LLM_MODEL_TYPE=mosaicml
# LLM_MODEL_TYPE=stablelm
# LLM_MODEL_TYPE=openllm
# LLM_MODEL_TYPE=hftgi
```

- By default, the app runs `microsoft/orca-2-13b` model with HF Text Generation Interface, which runs on a research server and might be down from time to time.

- Uncomment/comment the above to play with different LLM types. You may also want to update other related env vars. E.g., here's the list of HF models which have been tested with the code:

```
# HUGGINGFACE_MODEL_NAME_OR_PATH="microsoft/orca-2-7b"
HUGGINGFACE_MODEL_NAME_OR_PATH="microsoft/orca-2-13b"
# HUGGINGFACE_MODEL_NAME_OR_PATH="TheBloke/wizardLM-7B-HF"
# HUGGINGFACE_MODEL_NAME_OR_PATH="TheBloke/vicuna-7B-1.1-HF"
# HUGGINGFACE_MODEL_NAME_OR_PATH="nomic-ai/gpt4all-j"
# HUGGINGFACE_MODEL_NAME_OR_PATH="nomic-ai/gpt4all-falcon"
# HUGGINGFACE_MODEL_NAME_OR_PATH="lmsys/fastchat-t5-3b-v1.0"
# HUGGINGFACE_MODEL_NAME_OR_PATH="meta-llama/Llama-2-7b-chat-hf"
# HUGGINGFACE_MODEL_NAME_OR_PATH="meta-llama/Llama-2-13b-chat-hf"
# HUGGINGFACE_MODEL_NAME_OR_PATH="meta-llama/Llama-2-70b-chat-hf"
HUGGINGFACE_MODEL_NAME_OR_PATH="microsoft/Orca-2-7b"
# HUGGINGFACE_MODEL_NAME_OR_PATH="microsoft/Orca-2-13b"
```