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Construire des workflows de récupération basés sur des graphes…

Indexez du texte privé dans des structures de récupération adossées à des graphes, ajustez les prompts et interrogez le graphe de connaissances résultant pour des workflows RAG.

Qu'est-ce que Construire des workflows de récupération basés sur des graphes… ?

GraphRAG est une méthodologie qui utilise des structures mémoire de graphes de connaissances pour améliorer les résultats des LLM. Cette compétence permet d'indexer des corpus de texte privé dans des graphes de connaissances, d'ajuster les prompts et d'interroger ces graphes pour des workflows de retrieval-augmented generation (RAG). Elle est particulièrement utile pour les développeurs qui souhaitent améliorer la qualité et la pertinence des réponses générées par les LLM en exploitant des bases de connaissances structurées. L'outil s'utilise via le CLI ou le package Python GraphRAG, avec des credentials LLM et un corpus de texte délimité. Attention : l'indexation GraphRAG peut être coûteuse, il est recommandé de bien comprendre le processus et de commencer par des tests à petite échelle.

Compatible avec~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/build-graph-based-retrieval-workflows-with-graphrag
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Documentation

Construire des workflows de récupération basés sur des graphes avec GraphRAG

Indexez du texte privé dans des structures de récupération adossées à des graphes, ajustez les prompts et interrogez le graphe de connaissances résultant pour des workflows RAG.

Prérequis

Python, GraphRAG CLI/package, credentials LLM, corpus de texte délimité

Installation

Exigences et précautions issues de l'upstream :

  • Les marques commerciales ou logos doivent être utilisés conformément aux règles.
  • L'utilisation des marques commerciales ou logos Microsoft dans des versions modifiées de ce projet ne doit pas prêter à confusion ni suggérer un parrainage de Microsoft.

Notes d'utilisation de base ou de démarrage :

  • Pour commencer avec le système GraphRAG, nous recommandons d'essayer le guide de démarrage en ligne de commande.

  • Ce dépôt présente une méthodologie pour utiliser des structures mémoire de graphes de connaissances afin d'améliorer les sorties des LLM. Veuillez noter que le code fourni sert de démonstration et n'est pas une offre officiellement prise en charge par Microsoft.

  • ⚠️ Avertissement : L'indexation GraphRAG peut être une opération coûteuse. Veuillez lire toute la documentation pour comprendre le processus et les coûts impliqués, et commencez par de petits volumes.

  • Source : https://github.com/microsoft/graphrag

  • Extrait de la documentation upstream : https://raw.githubusercontent.com/microsoft/graphrag/HEAD/README.md

Documentation

Source

Individual skills in this repo

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

Academic Paper Citation Network Mapper

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ActivityWatch Cross-Platform Automated Time Tracker and Producti

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act Local GitHub Actions Runner

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Use AgentClick when an agent should pause before risky commands, plans, drafts, or code changes so a human can inspect, edit, approve, or reject them in a purpose-built browser UI.

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.

Add managed long-term memory to LangGraph agents with LangMem

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.

Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

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

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