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financial_bot

README.md

Financial Assistant Bot

Inference pipeline that uses LangChain to create a chain that:

  • downloads the fine-tuned model from Comet's model registry
  • takes user questions as input
  • queries the Qdrant Vector DB and enhances the prompt with related financial news
  • calls the fine-tuned LLM for financial advice using the initial query, the context from the vector DB, and the chat history
  • persists the chat history into memory
  • logs the prompt & answer into Comet ML's LLMOps monitoring feature

The inference pipeline is deployed using Beam as a serverless GPU infrastructure, as a RESTful API. Also, it is wrapped under a UI for demo purposes, implemented in Gradio.

Table of Contents

1. Motivation

The inference pipeline defines how the user interacts with all the components we've built so far. We will combine all the components and make the actual financial assistant chatbot.

Thus, using LangChain, we will create a series of chains that:

  • download & load the fine-tuned model from Comet's model registry
  • take the user's input, embed it, and query the Qdrant Vector DB to extract related financial news
  • build the prompt based on the user input, financial news context, and chat history
  • call the LLM
  • persist the history in memory

Also, the final step is to put the financial assistant to good use and deploy it as a serverless RESTful API using Beam.

architecture

2. Install

2.1. Dependencies

Main dependencies you have to install yourself:

  • Python 3.10
  • Poetry 1.5.1
  • GNU Make 4.3

Installing all the other dependencies is as easy as running:

make install

When developing run:

make install_dev

Prepare credentials:

cp .env.example .env

--> and complete the .env file with your external services credentials.

2.2. Qdrant & Beam

Check out the Setup External Services section to see how to create API keys for them.

3. Usage

3.1. Local

Run the bot locally with a predefined question:

make run

For debugging & testing, run the bot locally with a predefined question, while mocking the LLM:

make run_dev

3.2. Beam | RESTful API

deploy the financial bot as a RESTful API to Beam [optional]

First, you must set up Beam, as explained in the Setup External Services section.

Deploy the bot under a RESTful API using Beam:

make deploy_beam

For debugging & testing, deploy the bot under a RESTful API using Beam while mocking the LLM:

make deploy_beam_dev

To test the deployment, make a request to the bot calling the RESTful API as follows (the first request will take a while as the LLM needs to load):

export BEAM_DEPLOYMENT_ID=<BEAM_DEPLOYMENT_ID> # e.g., <xxxxx> from https://<xxxxx>.apps.beam.cloud
export BEAM_AUTH_TOKEN=<BEAM_AUTH_TOKEN> # e.g., <xxxxx> from Authorization: Basic <xxxxx>

make call_restful_api DEPLOYMENT_ID=${BEAM_DEPLOYMENT_ID} TOKEN=${BEAM_AUTH_TOKEN}

Note: To find out BEAM_DEPLOYMENT_ID and BEAM_AUTH_TOKEN navigate to your financial_bot or financial_bot_dev Beam app.

IMPORTANT NOTE 1: After you finish testing your project, don't forget to stop your Beam deployment. IMPORTANT NOTE 2: The financial bot will work only on CUDA-enabled Nvidia GPUs with ~8 GB VRAM. If you don't have one and wish to run the code, you must deploy it to Beam.

3.3. Gradio UI

To test out & play with the financial bot, you can run it locally under a Gradio UI.

Start the Gradio UI:

make run_ui

Start the Gradio UI in debug mode while mocking the LLM:

make run_ui_dev

After starting the Gradio UI, you can access it at: http://localhost:7860/

Financial Bot Gradio UI

NOTE: Running the commands from above will host the UI on your computer. To run them, you need a CUDA-enabled Nvidia GPU with enough resources (e.g., to run the inference using Falcon 7B, you need ~8 GB VRAM). If you don't have that available, you can deploy it to Gradio Spaces on HuggingFace. It is straightforward to do so. Here are some docs to get you started.

3.4. Linting & Formatting

Check the code for linting issues:

make lint_check

Fix the code for linting issues (note that some issues can't automatically be fixed, so you might need to solve them manually):

make lint_fix

Check the code for formatting issues:

make format_check

Fix the code for formatting issues:

make format_fix