AD-Finance-Agent

In this repo, we are building a bot that can mimic the style of Prof. Aswath Damodaran based on his YouTube lectures and textbooks. Here, we are working on three techniques, Retrieval-Augmented Generation (RAG), Hypothetical document embeddings (HyDE) and Modified HyDE, which is a novel concept of finetuning a decoder-only model on our raw data followed by HyDE from the generated answer. It is a research question that we are looking to explore.

The vector database are stored here. You can download from here and place the two folders inside the VectorDB, named AD-DB-LARGE AND AD-DB-SMALL, and store it inside the rag folder

We have tried out three implementations here:

FROZEN RAG

It is the basic RAG architecture with the vector database from embedding model from OpenAI.

HyDE

Here we send our question to an LLM first to hallucinate an answer to the question, and then we do RAG with the hypothetical generated answer

MODIFIED HyDE (Our novel architecture)

Here we finetune a CausalLanguage Model like GPT-2 on our raw text, and then we do RAG with the autocompleted answer.

Environment Setup

Create

python -m venv <NAME_OF_THE_ENVIRONMENT>

Activate

source <NAME_OF_THE_ENVIRONMENT>/bin/activate

Install

pip install -r requirements.txt

Setup environment variables

Please add .env files with your OPENAI_API_KEY at the below shown positions.

ad-finance-agent
    │
    ├── rag
    │   ├── .env
    ├── text_to_sql
    │   ├── .env
    └── app.py
    └── .env

Content

OPENAI_API_KEY=<INSERT_YOUR_OPENAI_GENERATEDKEY>

Add AD-DB Files for model

Finally download the context files from here. and move the two folders in VectorDB archive to the /rag directory as shown below

ad-finance-agent
    │
    ├── rag
    │   ├── AD-DB-LARGE
    │   ├── AD-DB-SMALL

Run the App

flask run

Front-end

To Start the Front-end service please refer here.

Results

Relevance score computed of Modified HyDE approach on the Validation data using TruLens

Demo Video