Java version of LangChain

Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM 等语言模型的本地知识库问答 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM) QA app with langchain

PostgreSQL Primary-Replica Servers (with Pgvector Extension) on Docker

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.

Magick is a cutting-edge toolkit for a new kind of AI builder. Make Magick with us!

Simple syslog collection server with Embedding and LLM Capabilities

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

A starter application that shows data collection, embedding creation and querying, and text completion with OpenAI.

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

Extensible API and framework to build your Retrieval Augmented Generation (RAG) and Information Extraction (IE) applications with LLMs

AI implementation using langchain4j openAPI and springAI with Java

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

a rag implementation example for document-based QA, using spring-ai, ollama, and postgres-pgvector vetor db.

Example of a Retrieval-Augmented Generation with Postgres, pgvector, ollama, Llama3 and Go.

Simple spring-boot application which ingest a document to apply RAG pattern in order to enrich model knowledge about doc content.

An intellligent AI assistant that can do anything!

A lightweight, fast, flexible, and easy-to-use benchmarking tool dedicated to pgvector.

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

IntelliSearch is an advanced retrieval-based question-answering and recommendation system that leverages embeddings and a large language model (LLM) to provide accurate and relevant information to users.

Pip-installable, embedded-like postgres server for your python app