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💬 Retrieval Augmented Generation (RAG) system based on Deloitte's Weekly Global Economic Update

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Deloitte-Insightbot

Overview

The deloitte-insightbot is a question and answer system designed to provide insights based on Deloitte's weekly economic updates. These updates offer a brief overview of the global political and economic situation, summarizing key impacts and trends.

Features

  • Data Ingestion: Fetches content from Deloitte's weekly economic update URL.
  • Embeddings Storage: Stores embeddings of the content in a VectorDB.
  • Retrieval-Augmented Generation: Retrieves relevant passages to generate answers for user queries using an LLM.

Components

  • Data Ingestion: A module to scrape and parse content from the specified URL.
    • UnstructuredURLLoader class to fetch and parse the content from the URL.
  • Embeddings Model: Utilizes an embedding model to convert content into vector representations.
    • OpenAIEmbeddings model with the model name text-embedding-3-large.
  • VectorDB: Stores the embeddings for efficient retrieval.
    • Chroma class from langchain_chroma is used to interact with ChromaDB.
  • LLM: Generates answers based on the retrieved passages.
    • ChatOpenAI class with the model name gpt-3.5-turbo.

Usage

  1. Ingest Data: Run the data ingestion script to fetch and parse the content.
  2. Store Embeddings: Use the embeddings model to convert the content into vectors and store them in the VectorDB.
  3. Query System: Input a user query to retrieve relevant passages and generate an answer using the LLM.

Commands

Install the required packages

pip install -r requirements.txt

Start the ChromaDB container

docker compose up -d

Ping the ChromaDB container to check if it is running

curl localhost:8000/api/v1/heartbeat

Run the application

python src/main.py

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💬 Retrieval Augmented Generation (RAG) system based on Deloitte's Weekly Global Economic Update

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