US-Elections-GPT:

Analyzing and Predicting US Election Trends with RAG-enabled GPT

This project leverages the power of Generative Pre-trained Transformers (GPT) in understanding, analyzing, and predicting trends in US elections. It does this by sourcing news articles from websites such as Fox News, CNN, NPR, and Politico ensuring that whether you are liberal or conservative, your political views can be represented in the answers generated by the LLM.

Architecture Design

A simplified architecture diagram of the product is shown below:

SERVICES

SCRAPER SERVICE & MONGODB

First, news articles are scraped from news sources and loaded into MongoDB. To ensure that all sides of the political spectrum are represented in the LLM, both right-wing and left-wing news sources, as well as those in the middle ground, are scraped. Users can select which side of the political spectrum they prefer when querying the LLM..

AIRFLOW, NEWS SUMMARY, & NAMED ENTITY EXTRACTION

Airflow is used with Papermill and Jupyter Notebook to summarize news articles using HuggingFace summarization pipeline, extract named entities using spaCy, and obtain vector embeddings using HuggingFace SentenceTransformer. The news articles, their summaries, and extracted entities including PERSON, ORG, and LOCATION are loaded into ChromaDB datastore.

REDIS CACHE

The news article URLs are cached in Redis to ensure that each webpage is visited and scraped only once

CHAT SERVICE & LANGCHAIN

The chat service is created using LangChain and HuggingFace pipeline. First, a Mistral 7B model (which may be substituted with a different model in the future) is fine-tuned into a 4-bit float model using bitsandbytes. This is done to reduce model footprint and speed up inference. LangChain is used to create a RAG-based model using the quantized model and ChromaDB, where the news article vector embeddings are stored. Redis is used to store chat sessions so that sessions can be revisited with ease.

FASTAPI & WEBSOCKET

A WebSocket server is created using FastAPI to serve the langchain model.

MODEL OPTIMIZATIONS

To ensure that accurate retrieval is done during model chat querying, some LLM optimizations were performed. These optimizations include small-to-big retrievals, re-ranking, and metadata search using extracted named entity recognition

TO RUN

• Rename .env.example to .env and fill in the variables.
• Run docker compose build to build the images and
• Run docker compose up to start the services.