---
title: Financial Bot
emoji: 🚀
colorFrom: red
colorTo: green
sdk: gradio
sdk_version: 4.16.0
app_file: app.py
pinned: false
license: mit
---

# Friendly Fincancial Bot

This is the **Inference component** of a 3-part **prod-ready** FTI feature-training-inference **RAG-framework LLMOps** course. \
In this iteration, I've **replaced Falcon 7B Instruct** with the **currently-SoTa (Jan'24) Mistral-7B-Instruct-v0.2**, \
fine-tuned using **Unsloth** on an expanded dataset of financial questions and answers generated with the help of GPT-4, \
quantized and augmented with a 4bit QLoRa. \
\
Prompt analysis and model registry is handled by **Comet LLM**, and finance news is streamed via **Bytewax** using an \
**Alpaca API**, then parsed, cleaned, and chunked with **unstructured**, and finally sent as a vector embedding to \
**Qdrant**'s serverless vector store. **LangChain** chains the prompt and most relevant news article with real-time \
finance information, **contextualizing the output**. \
\
**#TODO:** Add citations to output to show end-user which article has been used to generate the output.

I have contributed to the original MIT licensed (ka-ching!) course which can be found here:\
https://medium.com/decoding-ml/the-llms-kit-build-a-production-ready-real-time-financial-advisor-system-using-streaming-ffdcb2b50714