pgvector

基于 Spring AI 2.0 的生产级 ERP 智能助手｜Production-ready ERP AI assistant with RAG, Tool Calling, chat memory, dynamic SQL tools, multi-tenancy and ECharts.

Samples showing architectural patterns for Modular RAG using Spring AI and Ollama.

This application uses LLMs like DeepSeek, GPT-5, Claude, Gemini 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

Example chat-with-pdf app showing how to provision a dedicated database instance for each user. In this app, every database uses pgvector for similarity search. Powered by Neon

Private RAG app sample using Llama3, Ollama and PostgreSQL

(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

Local-first hybrid semantic code search tool. Indexes codebases into PostgreSQL with pgvector embeddings via Ollama, combines vector similarity + keyword search with RRF fusion. Supports 30+ languages. Features CLI, MCP server, WEB dashboard and interactive REPL.

🧠 BrainX V6 — The First Brain for OpenClaw. Persistent AI agent memory with PostgreSQL, pgvector, OpenAI embeddings, semantic search, cross-agent learning, and an OpenClaw runtime plugin.

A project to show howto use SpringAI with Ollama to chat with the documents in a library. Documents are stored in a normal/vector database. The AI is used to create embeddings from documents that are stored in the vector database. The vector database is used to query for the nearest document. That document is used by the AI to generate the answer.

🧠 AI Second Brain — 100% Local Knowledge Management Private, self-hosted second brain. Store, search, and synthesize documents, images, and ideas with local LLMs (LLaVA, CLIP) and PostgreSQL + pgvector. Features multimodal search, knowledge graphs, gDrive streaming, and real-time analysis — all offline, no API keys, no cloud, no limits.

Self-hosted AI knowledge base with hybrid semantic search (pgvector + FTS + RRF), MCP server, multi-provider LLM inference (Ollama, OpenAI, OpenRouter, llama.cpp), multimodal ingestion (vision, audio transcription, speaker diarization), and knowledge graph. Rust + PostgreSQL.

Fast vector search in PostgreSQL

An application that enable the users to upload PDF files and ask questions regarding their content using Retrieval Augmented Generation (RAG)

Modular, level-based project template for Claude Code + Obsidian + Figma. Pick your maturity level (L1-L4). Plug in your specs. Ship.

Knowledge base Q&A program using LangChain for retrieval-augmented prompting and PGVector as vector store.

Reference Java & Spring implementation of the Open Ingestion Standard (OIS). It provides a secure, decoupled, and vendor-neutral enterprise data integration platform leveraging modern Java 25 features, Spring Boot, Spring AI and vector search infrastructure to orchestrate data flows

Question Answering application with Large Language Models (LLMs) and Amazon Postgresql using pgvector

🔒 Privacy-first MCP server for Claude using PostgreSQL + Ollama. Local alternative to cloud-based code context with full data sovereignty. No API keys, no external calls, 100% local.

PostgreSQL TurboQuant Index for PGVector