LLM App Collection

This project is a collection of applications leveraging Large Language Models (LLMs) for various tasks such as chatbots, document summarization, and retrieval-augmented generation (RAG). The applications are built using LangChain and LangGraph for the backend processing and Streamlit for the UI.

Features

Chatbots

• Support for multiple LLM providers (OpenAI and Groq)
• Model selection for each provider:
  • OpenAI: gpt-4o-mini, gpt-4-turbo, gpt-4o
  • Groq: llama3-70b-8192, llama3-8b-8192
• Message history with auto-trimming
• Audio capabilities:
  • Voice input using OpenAI's Whisper for speech-to-text
  • Text-to-speech responses using OpenAI's TTS
  • Automatic audio playback of responses

Document Summarization

• Two summarization techniques:
  • Stuff: For shorter documents that fit within context window
  • Map-Reduce: For longer documents that exceed context limits
• PDF document support

Retrieval Augmented Generation (RAG)

• Agentic RAG: QA with Memory
  • Tool-calling: Tool calling enables the model to decide if a retrieval step is needed. If required, user queries are rewritten based on the chat history (contextualization). If not, the model responds directly without a retrieval step (e.g., in response to a generic greeting).
  • FAISS vector store for efficient similarity search
  • OpenAI embeddings integration

Technical Features

• Built with Streamlit for interactive UI
• LangChain and LangGraph integration for LLM operations
• Modular architecture with separate pages
• Authentication via API key or password

Demo App

Setup

1. Clone the repository:

git clone https://github.com/yourusername/llm-app-collection.git
cd llm-app-suite

2. Configure API keys in .streamlit/secrets.toml file:

OPENAI_API_KEY_DEV = "your-openai-key"
GROQ_API_KEY_DEV = "your-groq-key"
PASSWORDS = ["your-password"]

3. Create and activate virtual environment:

Ubuntu/Linux

pip install virtualenv
virtualenv .venv
source .venv/bin/activate

Windows:

pip install virtualenv
virtualenv venv
venv\Scripts\activate.bat

4. Install dependencies:

pip install -r requirements.txt

5. Run the application:

streamlit run app.py

Resources

• Two ways of text summarization, Stuff and Map-reduce (link)
• Retrieval Augmented Generation (RAG) using LangChain (link)