GoPenAI·2yBuilding a Custom Chatbot with Next.js, Langchain, OpenAI, and Supabase.
Build a custom chatbot with Next.js for the frontend, MaterialUI for UI components, Langchain and OpenAI for language model interactions, and Supabase to store data and embeddings. The chatbot can be trained with custom data from PDF files and answer questions based on the content.
Sam Witteveen·2yGemma 2 - Local RAG with Ollama and LangChain
Gemma 2 has been released for multiple formats including Keras, PyTorch, and Hugging Face transformers. This post details the author's experience using the 9B and 27B models in Ollama, highlighting the better performance of the 9B model for real-time responses. A straightforward script is provided to create a fully local Retrieval-Augmented Generation (RAG) system using Gemma 2, Nomic embeddings, and ChromaDB, all executed within VSCode. The steps involve setting up an indexer, embedding transcripts from Alex Hormozi's YouTube channel, and handling text splitting methods. Debugging tips and additional add-ons for the RAG system are also discussed.
GoPenAI·2yBuilding a Multi PDF RAG Chatbot: Langchain, Streamlit with code
This post provides a step-by-step breakdown of how to build a Multi PDF RAG Chatbot using Langchain and Streamlit. It covers the necessary tools, reading and processing PDF files, creating a searchable text database, setting up conversational AI using OpenAI, and user interaction through a user-friendly interface.
Towards AI·2yA Complete Guide to RAG
Retrieval-Augmented Generation (RAG) is a powerful technique that combines a strong existing language model with a retrieval system to efficiently handle company-specific information. Unlike retraining models, which is often impractical, RAG leverages a vector-based search to fetch relevant company documents and uses a language model to generate answers. This approach involves a retriever for searching and a generator for response crafting, significantly improving efficiency. Advanced techniques like RAG Fusion, Cross and Bi-Encoders, and ensemble retrievers enhance the system's accuracy and relevance. Tuning methods such as RELP and FLARE further optimize model performance, making RAG an effective solution for handling unstructured data and varying queries.
GoPenAI·2y🚀 Revolutionizing Document Interaction: An AI-Powered PDF Text-2-Voice Chatbot Using LlamaIndex 🐑, Langchain 🔗 and Azure AI Speech 🔊
The post delves into creating an AI-powered PDF Text-2-Voice chatbot using LangChain, LlamaIndex, and Azure AI Speech. The chatbot is designed to read out answers from the content in PDF documents. The technologies used include LangChain for chaining language models, LlamaIndex for document indexing, Azure AI Speech for speech synthesis, and Streamlit for the user interface. Detailed steps for setting up the environment, uploading files, vectorization, fetching data, and voice synthesis are provided. The end product is a Streamlit app where users can upload PDFs, ask questions, and receive voice responses.