Explore sample applications and tutorials demonstrating the prowess of Amazon Bedrock with Python. Learn to integrate Bedrock with databases, use RAG techniques, and showcase experiments with langchain and streamlit.

Completely local RAG. Chat with your PDF documents (with open LLM) and UI to that uses LangChain, Streamlit, Ollama (Llama 3.1), Qdrant and advanced methods like reranking and semantic chunking.

Chat with PDF using LangChain, Streamlit, Ollama (for LLM inference) and PDFPlumber. Overall which is an example of a Retrieval-Augmented Generation (RAG) system with Deepseek r1 model.

A RAG based Generative AI Attorney fed with Indian Penal Code data. Developed using Streamlit, LangChain and TogetherAI API.

Healthcare RAG agent with Neo4j knowledge graphs - Query medical data using LangChain, FastAPI & Streamlit

A basic application using langchain, streamlit, and large language models to build a system for Retrieval-Augmented Generation (RAG) based on documents, also includes how to use Groq and deploy your own applications.

Analyse environmental, social, and governance policies for potential gaps using AI - powered by LLMs, RAG techniques, and a regulatory knowledge base using OpenAI API, LangChain, Pinecone and Streamlit.

In this article, I will present an open-source AI tool for writing grant applications, using Microsoft AutoGen combined with Retrieval-Augmented Generation (RAG) via a vector database in PostgreSQL/ pgvector and LangChain - served in FastAPI/ Streamlit. Dockerised to run locally.

AI-powered chatbot that provides guidance using YouTube video content as a knowledge base. Built with RAG architecture using LangChain, Qdrant, and Google Gemini AI, ElevenLabs and Streamlit.

An Offline and Secure Retrieval-Augmented Generation (RAG) system designed for efficient processing of diverse content types with minimal computational overhead. This system use only open source tools such as LangChain, FAISS , Docling, Llamafile, Mistral Nemo, Streamlit, Hugging Face Transformers, and so on ...

RAG-ChatBot using Google's Gemini-Pro model, Langchain, ChromaDB and Streamlit. The ChatBot can answer questions asked in natural language about PDF documents that the user uploads

This is a RAG project to chat with your uploaded PDF , made using Langchain and Anthropic Claude 3 used as LLM , hosted using Streamlit

Playing with RAG using Ollama, Langchain, and Streamlit. This project aims to demonstrate how a recruiter or HR personnel can benefit from a chatbot that answers questions regarding candidates.

Resume RAG Chatbot using LangChain, FAISS, Streamlit, and Ollama

A simple Retrieval-Augmented Generation (RAG) project built with LangChain and Streamlit. Upload documents (PDF/TXT) and interact with them using natural language questions powered by embeddings and vector search.

Chat with your PDFs using AI! This Streamlit app uses RAG, LangChain, FAISS, and OpenAI to let you ask questions and get answers with page and file references.

Developing a RAG-powered chatbot for CreditTrust Financial that analyzes customer complaints across five financial products. The AI tool retrieves relevant complaint narratives using semantic search and generates actionable insights, helping product managers identify trends faster. Built with Python, LangChain, FAISS/ChromaDB, and Gradio/Streamlit.

a Retrieval-Augmented Generation (RAG) multi-document chatbot application using Llama 3, Langchain, Streamlit, and Groq API

bili-core is an open-source framework for LLM benchmarking using LangChain, LangGraph, Streamlit, and Flask. It enables effective LLM model comparisons, Retrieval-Augmented Generation (RAG), and customizable decision workflows. Part of MSU Denver’s Sustainability Hub, bili-core promotes data democracy and transparent, reproducible AI research. 🚀

This is a simple lab I have implemented to test Knowledge Augmented or Retrieval Augmented Generation (RAG) with Large Language Models. In particular, I am using LangChain, Streamlit, and OpenAI ChatGPT API.

first attempt at creating and deploying rag based systems using langchain + ollama + streamlit

A basic application using langchain, streamlit, and large language models to build a system for Retrieval-Augmented Generation (RAG) based on documents, also includes how to use Groq and deploy your own applications.

Langchain RAG model, with output streaming on Streamlit and using persistent VectorStore in disk

An end-to-end RAG chatbot using FastAPI, LangChain, ChromaDB & Streamlit with Multi-LLM support to answer questions from uploaded PDFs.

A production-style AI customer support assistant for eCommerce platforms. Built with LangChain + LangGraph, powered by a local Ollama LLM, enhanced with RAG for shipping, return, and FAQ policies, and deployed using Streamlit.

In this project, a Voice Assistant Application has been built using Streamlit in Python with Langchain, RAG and Llama3.3 model (Open Source LLM). pyttsx3 (text-to-speech library), speech_recognition (speech-to-text library), Hugging Face Instruct Embeddings and Chroma (open-source vector database), PyPDFLoader etc.

Chat with your PDFs using AI! This Streamlit app uses RAG, LangChain, FAISS, and OpenAI to let you ask questions and get answers with page and file references.

This project is a web-based AI chatbot an implementation of the Retrieval-Augmented Generation (RAG) model, built using Streamlit and Langchain. It answers questions relevant to the data provided by the user. The chatbot utilizes OpenAI's GPT-4 model and accepts data in CSV format. Users can upload multiple CSV files, clear uploaded files, ask ques

Developed an end-to-end LLM sommelier chatbot that uses Microsoft Azure Open AI, LangChain, and the RAG method for wine recommendations. Powered by FastAPI and Streamlit. CI/CD with GitHub Actions

Customizable RAG chatbot made with LangChain, ChromaDB, Streamlit using gpt-3.5-turbo, text-embedding-ada-002 also sporting database integration