Bitcoin-PAL

📚 Personal Learning Assistant for Bitcoin

Problem

Where do users go for trusted Bitcoin educational resources? In the ever-changing and evolving world of Bitcoin, false information, distrust, and confusion about complex topics is common. This makes navigating the Bitcoin landscape challenging for many, especially for new users.

Solution

We believe open-source LLMs will develop exponentially faster than closed-source LLMs. In light of this our team created Bitcoin-PAL, a bitcoin focused AI chatbot coupled with an incentivized crowd-sourcing platform to train LLMs with bitcoin.

Using Bitcoin-PAL, bitcoiners can rely on and contribute to trusted and vetted bitcoin documentation rather than query the general internet and GPT models.

Bitcoin-PAL is a proof of concept and was built during the #Ai4ALL Hackathon presented by Bolt.fun, which ran from July 1 - August 1, 2023. The project also were the winners of the Training Track!

Resources

1. Project Charter and User Stories
2. Backend Documentation
3. Project Board
4. Figma designs
5. GitHub Actions
6. Demo Video
7. Presentation Deck

Demo and Presentation Video

Setup

Back End - Console

The backend can be used as a standalone console or a python flask based webservice.

Install and Usage - General

1. git clone this repo

2. pip3 install -r requirements.txt - install python dependencies

3. Then, download the LLM model and place it in a directory of your choice:

   • LLM: default to ggml-gpt4all-j-v1.3-groovy.bin. If you prefer a different GPT4All-J compatible model, just download it and reference it in your .env file.

4. Copy the example.env template into .env

   cp example.env .env

   and edit the variables appropriately in the .env file.

   MODEL_TYPE: supports LlamaCpp or GPT4All
   PERSIST_DIRECTORY: is the folder you want your vectorstore in
   MODEL_PATH: Path to your GPT4All or LlamaCpp supported LLM
   MODEL_N_CTX: Maximum token limit for the LLM model
   MODEL_N_BATCH: Number of tokens in the prompt that are fed into the model at a time. Optimal value differs a lot depending on the model (8 works well for GPT4All, and 1024 is better for LlamaCpp)
   EMBEDDINGS_MODEL_NAME: SentenceTransformers embeddings model name (see https://www.sbert.net/docs/pretrained_models.html)
   TARGET_SOURCE_CHUNKS: The amount of chunks (sources) that will be used to answer a question
   

   Note: because of the way langchain loads the SentenceTransformers embeddings, the first time you run the script it will require internet connection to download the embeddings model itself.

Usage - Ingestion

To ingest custom documentation the source_documents folder must be populated first and ingestion must be ran.

1. python3 ingest.py

Usage - Console

1. python3 bitcoinPAL.py
2. Ask your question on the command line

Usage - Server

1. python3 server.py
2. Ask a question by submitting a curl command with a json payload to localhost:8000 curl -X POST -H "Content-Type: application/json" -d '{"query":"What is bitcoin?"}' http://localhost:8000

Front End - Client

The back end server is required to be running also in order for the front end to talk to API for querying.

Install and Usage - Console

1. git clone this repo
2. cd to this repo directory
3. cd frontend - change directory to the front end
4. npm install - installs all node modules
5. npm start- starts front end on localhost:3000
6. Navigate to http://localhost:3000 and ask your question

Screenshot

Standing on the shoulders of giants

In addition to the libraries listed in requirements.txt and npm packages, this project also uses:

• PrivateGPT
• Markdown Badges by Ileriayo
• GPT4All-J v1.3-groovy .bin

Note

This software has no guarantees and was created during a hackathon. All software should be considered beta unless otherwise explicitly specified. The user assumes all risk by executing any code contained in this repository.

License

Bitcoin PAL is released under the terms of the MIT license. See LICENSE for more information or see https://opensource.org/licenses/MIT