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Ajouter la récupération vectorielle native Postgres aux workflo…

Stockez les embeddings à côté des données applicatives dans Postgres, créez des index vectoriels et interrogez les plus proches voisins pour la recherche sémantique, le RAG, les recommandations ou la récupération de mémoire d'agent.

Qu'est-ce que Ajouter la récupération vectorielle native Postgres aux workflo… ?

Cette compétence permet aux agents d'IA et aux pipelines RAG d'effectuer des recherches vectorielles sémantiques directement dans Postgres en utilisant l'extension pgvector. Vous pouvez stocker les embeddings générés par des modèles d'embedding à côté de vos données relationnelles existantes, créer des index vectoriels optimisés (comme IVFFlat ou HNSW) pour une interrogation efficace, et exécuter des requêtes de plus proches voisins pour la recherche sémantique, les systèmes de recommandation, ou la récupération de mémoire d'agent. Elle prend en charge les environnements utilisant Postgres 13+ et nécessite l'installation de l'extension pgvector via Git, Docker, Homebrew ou d'autres gestionnaires de paquets. Le workflow typique consiste à installer l'extension, à créer une colonne vectorielle, à insérer des embeddings, à créer un index vectoriel, puis à interroger les plus proches voisins avec une requête SQL utilisant l'opérateur de distance. Cette approche élimine le besoin d'une base de données vectorielle séparée, simplifiant l'architecture et réduisant la latence pour les applications d'IA qui nécessitent une persistance combinée des données et des embeddings.

Compatible avec~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/add-postgres-native-vector-retrieval-to-agent-and-rag-workflows-with-pgvector
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Documentation

Ajouter la récupération vectorielle native Postgres aux workflows d'agents et de RAG avec pgvector

Stockez les embeddings à côté des données applicatives dans Postgres, créez des index vectoriels et interrogez les plus proches voisins pour la recherche sémantique, le RAG, les recommandations ou la récupération de mémoire d'agent.

Prérequis

Postgres 13+, extension pgvector, accès SQL à la base de données cible, embeddings provenant de l'agent ou du pipeline RAG, et code applicatif capable d'interroger Postgres

Installation

Utilisez le chemin d'installation ou de configuration en amont correspondant à votre environnement :

  • git clone --branch v0.8.2 https://github.com/pgvector/pgvector.git
  • make
  • make install # peut nécessiter sudo
  • Puis réexécutez les instructions d'installation (lancez make clean avant make si nécessaire). Si sudo est nécessaire pour make install, utilisez :

Exigences et précautions issues de la source amont :

Notes de base ou de démarrage :

Documentation

Source

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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Add Claude design-system and accessibility workflows with UX/UI

Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.

Add executable smoke tests for shell scripts and CLIs before ref

Use Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.

Add graph-backed memory and context retrieval to agent workflows

Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

Add IDE-style semantic code navigation and symbol-safe refactors

Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.

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Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

Add multimodal lifelong memory to MCP and Python agents with Sim

Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.

Add persistent project memory and hook-driven synthesis with Cla

Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.

Address GitHub PR review comments from the current branch with g

Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.

Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

Adobe Firefly API Batch Image Generator

Calls Adobe Firefly

Adobe Photoshop API Batch Processor

Automates image editing workflows via the Adobe Photoshop API (Firefly Services). Supports smart object replacement, action playback, and PSD layer manipulation at scale.

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