#Pgvector
Showing 60 of 70 repositories tagged #pgvector, ranked by stars
The Postgres development platform. Supabase gives you a dedicated Postgres database to build your web, mobile, and AI applications.
๐ฅ MaxKB is an open-source platform for building enterprise-grade agents. ๅผบๅคงๆ็จ็ๅผๆบไผไธ็บงๆบ่ฝไฝๅนณๅฐใ
LangChain4j is an idiomatic, open-source Java library for building LLM-powered applications on the JVM. It offers a unified API over popular LLM providers and vector stores, and makes implementing tool calling (including MCP support), agents and RAG easy. It integrates seamlessly with enterprise Java frameworks like Quarkus and Spring Boot.
The all-in-one, open-source backend platform for agentic coding. InsForge gives your coding agent database, auth, storage, compute, hosting, and AI gateway to ship full-stack apps end-to-end.
ๅบไบ Spring Boot 4.1 + Java 21 + Spring AI 2.0 + PostgreSQL + pgvector + RustFS + Redis๏ผๅฎ็ฐ็ฎๅๆบ่ฝๅๆใAIๆจกๆ้ข่ฏใ็ฅ่ฏๅบRAGๆฃ็ดข็ญๆ ธๅฟๅ่ฝใ้ๅธธ้ๅไฝไธบๅญฆไน ๅ็ฎๅ้กน็ฎ๏ผๅญฆไน ้จๆงไฝใ
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
๐ฅค RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL
AI equity research agent with resilient workflows, Redis Lua single-flight, pgvector RAG, versioned reports, evidence tracing, and RAG evaluation.
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.
AI-powered StartUp Accelerator Engine built with Next.js, LangChain, PostgreSQL + pgvector. Upload, organize, and chat with documents. Includes predictive missing-document detection, role-based workflows, and page-level insight extraction.
ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector
Magick is a cutting-edge toolkit for a new kind of AI builder. Make Magick with us!
A dead-simple API to build LLM-powered apps
This is the public release of MIRA OS. Discrete memories decay through momentum loss, tools auto-configure when dropped into tools/ folder, and the system prompt composes from modular trinkets. I would like to think I've made an elegant brain-in-box. You load it and send cURL requests - it talks back, learns, and uses tools. Contributions welcome.
๐ฅ๐ฅ๐ฅ Turn AI-written code into real apps. Nubase is an open-source, AI-native backend platform for AI Coding, agentic applications, and modern product teams: Memory, Database, Storage, and Auth in one self-hostable service.
Portable semantic memory for AI agents: core engine, TypeScript SDK, framework adapters, MCP server, CLI, and host plugins.
Governed shared memory for AI agent fleets โ multi-agent, multi-tenant, MCP-native. Trust tiers, keystone policies, audit trails, knowledge graph, self-improving retrieval. Apache 2.0.
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.
Agentic AI memory with Ebbinghaus forgetting curve decay. +16pp better recall than Mem0 on LoCoMo.
A modern desktop application for exploring, managing, and analyzing vector databases
Open-source memory runtime for AI agents โ reproducible, provenance-tagged context bundles instead of query-time retrieval. Apache-2.0, self-hosted on Postgres + pgvector, Python + TypeScript SDKs.
An agent framework for Go with graph-aware memory, UTCP-native tools, and multi-agent orchestration. Built for production.
Enterprise-ready Spring AI platform for RAG, tool calling, async ingestion, JWT/RBAC security, and observability.
Human-like memory for AI agents โ semantic, episodic & procedural. Experience-driven procedures that learn from failures. Free API, Python & JS SDKs, LangChain, CrewAI & OpenClaw integrations.
pgvector - a PostgreSQL extension (native compiled in Microsoft Windows environment)
ไธญๆไบ่็ฝๅ ๅฎน็ไธชไบบ AI ็จๅ่ฏป + ็ฅ่ฏๅบ ยท Read-later + AI knowledge base for the Chinese internet
Open-source AI customer support platform โ RAG knowledge base, multi-provider LLM agents, embeddable chat widget. FastAPI + Next.js + pgvector.
Open Source, Self-Hosted, AI Search and LLM.txt for your website
Memory that remembers the story not just the facts. File System Memory and Three layer sentence graph for AI agents -> Facts, Episodes, raw Sentences. One DB. Zero config.
SNDR Core Engine (Genesis) โ vLLM runtime patch-overlay for Qwen3.6 + Gemma4 on consumer NVIDIA (Ampere sm_86, 2ร A5000/3090). Qwen3.6-35B-A3B FP8 ~240 tok/s, 27B-int4 hybrid GDN+Mamba, Gemma4 26B/31B AWQ, 256K ctx. 321 patches: TurboQuant k8v4 KV, MTP/DFlash spec-decode, FULL cudagraph, hybrid GDN. vLLM pin dev424 + Control Center GUI.
Turn a production incident into a structured 9-section LLM response (severity, root cause, mitigation, postmortem). Ships with a 5-scenario regression suite + LLM-as-judge eval pipeline.
Opinionated sample on how to build/deploy a RAG web app on AWS powered by Amazon Bedrock and PGVector (on Amazon RDS)
Template for building your own custom ChatGPT style doc search powered by Fresh, Deno, OpenAI, and Supabase.
Lumen โ learner-owned AI education platform. Tell the AI what you want to learn: it builds you a private course in ~a minute, tutors you with course-scoped RAG + citations, and lets you share, clone & remix via a moderated catalog. BYOK, custom no-LangChain multi-agent orchestrator, golden evals in CI, MCP server. Live demo + public /eval.
Samples showing architectural patterns for Modular RAG using Spring AI and Ollama.
AKB โ Agent Knowledgebase. Organizational memory for AI agents: vault-scoped docs / tables / files unified by URI graph, served over MCP.
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
Private RAG app sample using Llama3, Ollama and PostgreSQL
Learning journey to develop a scalable and modular backend to manage your data and solve your problems
A local-first AI memory system with hybrid search, MCP integration, and a knowledge graph.
Search for similar images using NeonDB and Vertex AI.
Implement RAG (using LangChain and PostgreSQL) for Go applications to improve the accuracy and relevance of LLM outputs
(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.
MCP-enabled AI conversation engine with MCTS analysis, FastAPI backend, and async operations for building advanced LLM applications
One-click knowledge system for documents, internal bots, and AI agents
Enter a US stock ticker, get an analyst grade memo with DCF valuation, peer comparables, news sentiment, and earnings call tone analysis. Multi agent LLM pipeline (Claude + GPT-4o devil's advocate) with strict citation validation. FastAPI + Next.js + pgvector RAG. No hallucinated numbers
Hierarchical RAG architecture scaling to 693K chunks on consumer hardware (4GB VRAM). Features 3-address routing, hybrid vector+graph fusion, and SetFit classification.
This project contains source code to demonstrate the usage of Retrival Augmented Generation (RAG) feature using Spring AI using PG Vector as the Database and Ollama as the Local LLM
Next.js RAG with PGVector
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.
Extensible API and framework to build your Retrieval Augmented Generation (RAG) and Information Extraction (IE) applications with LLMs
๐ง BrainX V6 โ The First Brain for OpenClaw. Persistent AI agent memory with PostgreSQL, pgvector, OpenAI embeddings, semantic search, cross-agent learning, and an OpenClaw runtime plugin.
Learn how to talk to Django as any human should -- e.g. Semantic Search and Text-to-SQL.
This demo application showcases the implementation of Spring Boot microservices, highlighting their powerful features and capabilities, with a particular emphasis on integrating OpenAI for advanced AI functionalities.
First open-source AI knowledge layer for Togo โ 62K+ documents, RAG API, fine-tuned LLM. Built for developers, startups and institutions in francophone West Africa
Typical RAG implementation using Semantic Kernel, Semantic Memory and Aspire
A pragmatic approach to continuously vectorize your PostgreSQL tables with the flexibility of your own embedding pipelines.
Terraform code to instantiate a Cloud Run V2 connected to a Postgres Cloud SQL, on which the pgvector extension will be enabled, with a connection to Redis and continuous deployment through the automatic trigger of Cloud Build.
Zero-config MCP server for searchable documentation (SQLite default, PostgreSQL optional)
AI: Search engine using RAG course examples