Popular GitHub Repositories for Pgvector

? Ready-to-use & flexible RAG Chatbot, supporting mainstream large language models (LLMs) such as DeepSeek-R1, Llama 3.3, Qwen2, OpenAI and more.

Java version of LangChain

A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK on AWS

ChatGTP (gpt3.5-turbo) starter app

ChatWeb can crawl web pages, read PDF, DOCX, TXT, and extract the main content, then answer your questions based on the content, or summarize the key points.

A dead-simple API to build LLM-powered apps

ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector

A RAG app to ask questions about rows in a database table. Deployable on Azure Container Apps with PostgreSQL Flexible Server.

A web UI Project In order to learn the large language model. This project includes features such as chat, quantization, fine-tuning, prompt engineering templates, and multimodality.

? DiscovAI-Search: An AI-powered search engine for AI tools and custom data. Built with Next.js, OpenAI, Supabase, and more. Features vector-based search, Redis caching, and LLM-powered responses.

Timescale Vector Cookbook. A collection of recipes to build applications with LLMs using PostgreSQL and Timescale Vector.

Opinionated sample on how to build/deploy a RAG web app on AWS powered by Amazon Bedrock and PGVector (on Amazon RDS)

Batch convert video to text using openai's whisper or the local coreML via whisper.cpp on your MacBook

Open Source, Self-Hosted, AI Search and LLM.txt for your website

AI chat over the US Constitution ? ? ??

Private RAG app sample using Llama3, Ollama and PostgreSQL

Search for similar images using NeonDB and Vertex AI.

This application uses LLMs like DeepSeek, GPT-4o, Claude, Gemini2 or Llama, Mixtral (locally) in order to generate text based on the user input. The user input is used to retrieve relevant information from the database and then the retrieved information is used to generate the text. This approach combines power of LLMs and access to source documen

(Let's start with a) Scalable question-answering system utilizing FastAPI, LangChain (LCEL), and PGVector, featuring an ingestion pipeline. Deployed on GCP Cloud Run via Terraform.

Implement RAG (using LangChain and PostgreSQL) for Go applications to improve the accuracy and relevance of LLM outputs