ConsultBot_LLM

RAG based LLM for practicing case studies

About :

Final entry for LLM apps bootcamp held by CAC (Consulting Analytics Club) IITG (IIT Guwahati) and Pathway

My Details - Ayush Agarwal, 4th year ECE IIT BHU, Infoedge DS
My linkedin - https://www.linkedin.com/in/ayush-agarwal-261041215/ (Would be happy to connect, even better if we can convert this into a business)
Concepts - RAG ( Retrival Augmented Generation) in LLMs (Large Language Models)
My idea - explained very well below
Tech Stack - Docker, Python, OpenAI, Pathway

Bootcamp details can be accessed here - https://cac-iit-g-and-pathway.gitbook.io/3-week-bootcamp-building-realtime-llm-application/

Have written some notes below for bootcamp team who are checking this repo

Demo :

Pathway "llm_app" library based chatbot demo :

DiFy based chatbot demo :

Here is the link to my application hosted on DiFy cloud : https://udify.app/chat/I91qkxLZaxCOGHKY

(Please don't overuse it I don't have OpenAI credits so one person's greed will make it stop working for everyone)(Thank You) (Infact I have given enough information in this repo for you to be able to implement it yourself in few hours) (update - switched the dify app off since Ive shared this repo in linkedin now)

My Idea and Vision :

I was studying for placements and I realized that there is so much support availaible for SDE, yet so less of practice material curated specially for Business and related Domains (Consulting, Analytics, Product Management). I validated this observed population pain point by talking to my friends . As I was studying about RAG in LLM and its applications it struck to me that I could solve my friends problems : All I had to do was download casebooks (which are free and open source) from the IIMs, and deploy a RAG based LLM application to allow the users to practice case studies by talking to the chatbot. As the vision became clearer to me, I started working upon my idea :)

For those who don't know what case is, let me explain -

A case is a small problem statement which usually resembles a real life business problem, that are often asked in the fields of business and management (eg - Consulting, Analytics, Product Management) in interviews in India. IIMs are Indian Institute of Management, which are the elite MBA institutions of India, and they often release casebooks containing different types of cases (eg - guesstimates, Root Cause Analysis RCA, Profitability, Market Entry, Go to Market GTM, Product Design etc) which all the students use across the country. Interview preparation and Ed-Tech are very big and popular markets in India.

Notes to the bootcamp checking team on how to run (and anyone else implementing the code on their own) :

In the .env file, put the openai_api (which you can get from - https://platform.openai.com/api-keys),
and make a folder, put all IIM casebooks whose flavour you would like in your practice,
and then put the path to that folder in the DROPBOX_LOCAL_FOLDER_PATH field .
Then run the docker container (using docker-compose build and docker-compose up commands respectively) .
When you want to shut down use ctrl + C and then docker-compose down command.
While interacting with the app, try not to press enter button (sometimes code breaks) and remember that it will look like the demo , i.e. text will be below and your input will be above (Couldnt make it as clean as chatgpt, after all I'm more of ML person than a dev person, also I did this project in less than a week).
- Also I was facing some issues in using github and this web version of github dosent allow to upload folders so I put them in a zip file and uploaded it , please unzip them if they are needed , I dont think it would be neccessary tho .
My idea is original and I am really looking forward to the leaderboard :)
Do read my vision with the app also , would take only 10 20 seconds to read :)

There are 2 versions - pathway one and dify one

Thanks

Dataset -

IIMA Case Book - https://www.linkedin.com/posts/consult-club-iim-ahmedabad_iima-casebook-2022-23-activity-6972579962100834304-kfq-?utm_source=share&utm_medium=member_desktop

IIMB Case Book - https://www.linkedin.com/posts/icon-iimbconsultingclub_icon-casebook-2022-23volume-12-a-activity-6974450270873280512-y8wl?utm_source=share&utm_medium=member_desktop

IIMC Case Book - https://www.linkedin.com/posts/consult-club-iim-calcutta_iim-calcutta-casebook-2023-24-activity-7106291181445115904-HYOP?utm_source=share&utm_medium=member_desktop

If I had a minute to explain you RAG LLM concepts :

You ask something to chatgpt (prompt it) and it returns the answer.

But it might not have the latest information, or private information (eg - company internal documents)

So we can add this required information to the prompt .

If we didnt give this information , its called 0 shot learning . If we give 1 example and then ask a question then its 1 shot learning. Otherwise its multishot learning .

But now the problem is that the prompt length to an LLM can be of limited size only (measured in tokens), whereas company internal documents can be of huge size.

So we need a system to search the part of the documents (knowledge base) related to the query (question) asked by the user, and then add that part as context to the prompt in LLM.

But this search would be slow and unstructured.

So our pdfs (knowledge base) is broken into chunks, then we converted into vectors , then this is stored into vector database. (vector db) . Then there is a similarity search algorithm (LSH based maybe) which gets the relevent context for the query from the vector db which is added to the prompt .

Mentos ?

Oh and by the way , RAG LLM means Retrival Augmented Generation for Large Language Models . You can see the research paper here - https://arxiv.org/abs/2005.11401

Dev side of things -

ML models are made in tensorflow or pytorch

Large companies like OpenAI , HuggingFace have their own LLM trained on BigData using RLHF (Reinforcement Learning Using Human Feedback) which is availaible as API (Application Programming Interface) OpenAI API is not free, but Huggingface API is free as of now (dec 2023)

There are libraries to convert knowledge base to vector database like Chroma DB, Pinecone etc

There is algorithm and library called FAISS (Facebook AI Simlarity Search) which help in searching parts of vector db of knowledge base relevent to the query asked by the user . You can see the details here - https://ai.meta.com/tools/faiss/

There are libraries for making frontend part of chatbot such as StreamLit and Flask

There are PDF reading libraries to smoothen the data reading part too

There are libraries to manage the whole RAG setup - LangChain

Appreciation Note for Dify and Explanation for the abstraction layers -

First Data Scientists made models using programming

Then Libraries like Tensorflow and Pytorch came so that coding a neural network was no longer a rocket science. A layer of abstraction.

Then Large companies made their models and started selling the API access, and any developer could call these APIs to use ML model without bothering about the "ML Part" of it . Another layer of abstraction.

Then RAG came, and all the libraries came which I have described here.

But the Last layer of Abstraction is Dify . More details availaible on their website - https://dify.ai/ and https://cloud.dify.ai/apps. It allows to setup a RAG based pipeline very conveniently, giving the freedom to set the required hyperparameters , make a knowledge base, choose the models we need, choose vector db, editing the frontend etc. It was created sometime in 2023, and the company is much much more than a chatgpt wrapper , and I feel it will be the next popular layer of abstraction.

Appreciation note to pathway -

It was fun working with your app :) Pathway is a RAG based LLM making tool which runs with Rust in backend which makes it much faster than its competitors, and I appreciate their "llm_app" library much more after using it .

theadityasurana/ConsultBot_LLM