A suite of applications demonstrating document processing, chatbots, and AI-powered question answering using LangChain and various LLMs.
Automated book summarization system that processes PDFs/epubs and generates concise summaries using OpenAI's models.
Features:
- PDF text extraction
- Chunking and embeddings
- Summary generation with temperature control
Multi-document QA system that allows conversational interaction with your document collection.
Tech Stack:
- Document loading (PDF, DOCX, TXT)
- Text splitting with RecursiveCharacterTextSplitter
- FAISS vector store for efficient similarity search
Custom chatbot that can be trained on any website content through web scraping.
Implementation:
- URL-based content ingestion
- Context-aware responses
- Conversation memory
Educational materials covering LangChain fundamentals through practical examples.
Topics Include:
- Chains, Agents, and Memory
- Document Loaders and Text Splitters
- Vector Stores and Retrievers
Collection of implementations using Meta's Llama2 models:
- PDF question answering system
- Custom prompt templates
- Retrieval-Augmented Generation (RAG)
- Free-tier Colab implementation
- Quantized model support
- Gradio interface
- Optimized for CPU-only environments
- Quantized model loading
- Basic chat interface
git clone https://github.com/yourusername/llm-projects.git
cd llm-projects
pip install -r requirements.txt