#vector-database

Operational database for AI agents , causal lineage (why()), semantic memory, live dashboards, MCP + Python/TypeScript SDK. Self-host or use db.zizka.ai.

Hands-on Generative AI: 15 RAG modules from document loaders to guardrails, plus Papeer — a research paper assistant with citations and claim verification. Also LangGraph workflows and LangSmith tracing.

Collaborative AI memory for engineering teams - share context across Claude Code, Cursor, and teammates

Multi-Agent AI Orchestrator 2026 🚀 | YAML, 6+ LLM Providers, ReAct & Swarm

HermesClaw Zero‑Config Sidecar is a lightweight local vector‑memory engine for Hermes and OpenClaw. It runs as a zero‑config sidecar, providing fast embeddings, persistent memory, and instant retrieval for agents and MCP tools. Built for speed, simplicity, and full local control.

Self-healing RAG on n8n with LLM-as-judge eval, drift detection, investigator agent, and canary A/B deploys.

🧠 A persistent memory RAG chatbot that never forgets. Uses Phi-3-mini LLM, ChromaDB vector database, and Ebbinghaus memory decay curves.

A reference n8n workflow that turns a folder of business documents into a chat agent that only answers from those documents. Retrieval-augmented, grounded, with refusal when the answer is not in the indexed sources.

Agente RAG conversacional sobre IA Generativa con Google Gemini, ChromaDB y LangGraph. Memoria por thread, interfaz Streamlit y base de conocimiento propia. Práctica final del Máster en Data Science e IA.

Chatbot developed during Tata Steel Summer Internship, uses live IoT Telemetry data for various machines and allows users to query and fetch details for the same.

AI agent that answers questions about YouTube tutorials and your own codebase — available as a Chrome extension, desktop app, and web app.

AI-powered research assistant that enables semantic search, question answering, and citation-grounded insights from academic papers using RAG.

AI-powered PDF Question Answering Chatbot using RAG and Mistral AI

AI-powered career intelligence platform built with FastAPI, PostgreSQL, pgvector, and Ollama. Analyze resumes, identify skill gaps, generate learning roadmaps, prepare for interviews, and chat with an intelligent career assistant using Retrieval-Augmented Generation (RAG).

Personalized AI assistant capable of handling automated conversations across WhatsApp and Gmail using LLM APIs, Retrieval-Augmented Generation (RAG), semantic search, conversation memory, and real-time information retrieval.

Pragmatic AI Engineering Skills Library for LLMs, RAG, Agents, MCP & Spring AI

🤖 An advanced, policy-compliant RAG-powered Internal Knowledge Assistant for TechNova. Built with LangChain, Chroma DB, and Llama 3.3 (Groq). Features strict source grounding, 100% retrieval precision, comprehensive factual citations, automated evaluation pipelines, and a dynamic multi-category Gradio chat interface. 🚀

Federal Acquisition Regulation Retrieval-Augmented Generation (RAG) Chatbot

Chat with your PDF documents using RAG. Built with LangChain, FAISS vector store, Groq LLM, and Streamlit. Every answer cites its source.

An AI-powered YouTube Q&A system that lets users chat with video content using RAG pipelines, LangChain, FastAPI, and vector search.

AI-powered Retrieval-Augmented Generation (RAG) chatbot built with n8n and Google Gemini. Supports PDF upload, semantic search, document question answering, and summarization using vector embeddings.

Production-grade RAG PDF Chatbot built with FastAPI, LangChain, OpenAI GPT-4o, and ChromaDB.

Production-grade multi-agent AI system with LangGraph supervisor pattern, RAG knowledge base (ChromaDB), and Anthropic Claude — routes queries between specialized triage, scheduling, and eligibility agents

Flowise-based multi-agent AI orchestration system using RAG, OpenAI embeddings, FAISS vector search, and sequential agent workflows for retrieval-grounded content generation and enterprise AI pipeline design.

RAG multi-agente con validación QA anti-alucinaciones (OpenAI + Pinecone + LangChain). Modo offline reproducible.

Hands-on implementation of Retrieval-Augmented Generation (RAG) using vector databases, embeddings, semantic search, and Anthropic Claude.

Hands-on Generative AI Engineering repository covering LLMs, Prompt Engineering, RAG, Vector Databases, Fine-Tuning, Multimodal AI, AI Agents, LLMOps, Evaluation, Deployment, and production-ready GenAI applications using OpenAI, Hugging Face, LangChain, LangGraph, and modern AI engineering practices.

Digital Garage Enterprise

AI-powered legacy code migration platform that transforms .NET (C#) applications into Java using LangChain, Milvus Vector Database, RAG, validation pipelines, security analysis, and automated project generation.

Telegram assistant with long-term semantic memory powered by Pinecone and OpenAI

🧠 RepoMind — A RAG-powered chatbot that lets you ask natural-language questions about any GitHub repository, with answers grounded in the actual source code and cited by file & line number.

Generic, provider-agnostic RAG backend with verified citations. FastAPI + SSE; switchable LLMs (Ollama/Anthropic/OpenAI) and vector stores (Chroma/pgvector); evals, tracing, tests, Docker.