V

vector-search

A persistent-memory AI agent with cryptographically signed, hash-chained, tamper-evident memory. Pre-commit quality scoring quarantines prompt-injection; protected zones guard core records. Scans its own history for recurring gaps and proposes improvements for human review. Provider-agnostic (Claude, GPT, Gemini, DeepSeek, OpenRouter, Ollama).

PaveDB — A lightweight, pluggable vector search microservice with built-in document ingestion and deep observability.

Upload → chunk → index (with metadata) → search via REST and CLI, scoped by multi-tenant collections.

GPU-accelerated embedding server for RAG systems - CUDA, FastAPI, sentence-transformers | Serveur d'embeddings GPU ultra-rapide

https://ayinedjimi-consultants.fr

KiM Explorer is a two-stage RAG application for transport policy research publications from the KiM Netherlands Institute for Transport Policy Analysis. Users perform semantic search to identify relevant documents, manually select publications, then interact with an LLM using full document context rather than chunks. Built with Python/NiceGUI/OpenAI API, featuring citation generation, conversation history, filtering, and web/CLI interfaces. https://explorer.kim.rijkscloud.nl/

Local-first vector search for chronological documents. Structure-aware chunking, hybrid search, date pre-filtering. Rust rewrite of gapvec.

An AI-powered tactical inteligence system using RAG, FastAPI and vector search.

Multi-source RAG pipeline with hybrid vector + keyword retrieval, LLM-powered concept knowledge graph, adaptive search weighting, and evaluation framework.

Remin is a local-first semantic search engine for personal notes. It combines embeddings, ANN vector indexing (HNSW), and reranking models to retrieve information based on intent rather than keywords — fully running on-device for privacy.

Built with Rust, DuckDB (VSS), and modern embedding models.

Atlas Architect: Your AI Co-pilot for Secure Cloud Infrastructure

This project is an AI-powered DevSecOps agent that lives within GitLab. It proactively analyzes Infrastructure-as-Code (IaC) files, specifically Terraform, to automatically visualize, secure, and optimize a developer's Google Cloud architecture before it's ever deployed.

When a developer submits a Merge Request with Terraform changes, a CI/CD pipeline triggers the agent to post a detailed analysis back as a comment. This provides instant visibility and governance, helping teams build better, safer cloud infrastructure, faster.

Key Features:

AI-Powered Visualization: Generates architecture diagrams from Terraform code using Google's Vertex AI. Security & Cost Analysis: Identifies security vulnerabilities and cost inefficiencies based on best practices. Intelligent Remediation: Automatically suggests code changes to fix identified issues. Vector-Powered Knowledge Base: Uses a MongoDB Atlas Vector Search index of official Google Cloud documentation to provide highly relevant, context-aware explanations for its recommendations.

Core Technologies:

Platform: GitLab CI/CD, Google Cloud Platform (GCP), MongoDB Atlas Services: Google Cloud Run, Google Cloud Build, Google Vertex AI, MongoDB Atlas Vector Search Frameworks & Languages: Python, Flask, Gunicorn