L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Développement · SKILL.md

model-providers

model-providers

Configure and troubleshoot Kotaemon LLM, embedding, reranking, web-search, local-model, and GraphRAG providers.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: model-providers description: "Configure and troubleshoot Kotaemon LLM, embedding, reranking, web-search, local-model, and GraphRAG providers." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Kotaemon Model Providers

Use this sub-skill when a task involves Kotaemon model/provider setup: LLMs, embeddings, rerankers, local model servers, OpenAI-compatible endpoints, web search credentials, or GraphRAG model configuration.

Use This For

  • Mapping .env, flowsettings.py, Resources UI, and manager database entries to provider specs for LLM, embedding, and reranking models.
  • Configuring OpenAI, Azure OpenAI, Ollama, OpenAI-compatible local servers, llama-cpp-python/GGUF, Cohere, VoyageAI, Google, Mistral, Groq, Anthropic, TEI, HuggingFace, and FastEmbed options.
  • Checking required credential pairs, endpoint suffixes, placeholder keys, optional provider packages, and Docker host-vs-localhost URL mistakes without making network calls.
  • Enabling or diagnosing GraphRAG, NanoGraphRAG, LightRAG, custom GraphRAG settings, and local GraphRAG model URLs.
  • Configuring Tavily or Jina web-search retrievers and reranker provider entries.

Route Elsewhere

  • Use ../app-deployment/SKILL.md for Docker/local launch, Gradio login, app data, PDF.js, update, migration, or general .env deployment checks.
  • Use ../rag-core/SKILL.md for programmatic RAG composition, Document/RetrievedDocument, vector index APIs, retrieval pipelines, reranker execution inside retrieval, QA, citations, and reasoning.
  • Use ../document-ingestion/SKILL.md for file readers, OCR/table parsers, splitters, metadata validation, and parser optional dependencies.
  • Use ../extensions/SKILL.md for adding custom providers, extra vendors, pages, components, templates, or plugin UI integrations.

Fast Path

  1. Decide where the provider is configured: .env seeds flowsettings.py; the Resources UI persists LLM, embedding, and reranking entries after the app database exists.
  2. Use references/provider-configuration.md to map provider choices to exact keys, manager names, class types, UI fields, and required packages.
  3. For local/private RAG, use references/local-models.md before changing URLs: OpenAI-compatible entries use /v1/; native Ollama chat uses the non-/v1/ Ollama base.
  4. For graph indexes, use references/graphrag.md to choose MS GraphRAG vs NanoGraphRAG vs LightRAG and verify toggles, API key strategy, and custom settings.
  5. Run the safe validator before launch or UI testing:
python scripts/check_provider_env.py --env-file .env --select openai --select embeddings

Safety Rules

  • Do not print secrets, call provider APIs, start model servers, pull Ollama models, download GGUF weights, install optional packages, or index data unless the user explicitly approves side effects.
  • Treat placeholders such as <YOUR_OPENAI_KEY>, your-key, dummy, and empty strings as not configured unless they are intentionally used for a local OpenAI-compatible endpoint.
  • Validate endpoint shapes separately from connectivity; this sub-skill's bundled script is offline-only.
  • Remember that .env registration is not the only source of truth after first run: existing app database rows in the Resources UI can override or outlive changed environment values.

Difficult Cases

  • Ollama chat plus embeddings: configure two OpenAI-compatible entries with /v1/ base URLs, api_key: ollama, and distinct chat/embedding model names; use references/local-models.md and the offline validator before trying network calls.
  • GraphRAG/local-model diagnosis: validate GRAPHRAG_API_KEY, USE_CUSTOMIZED_GRAPHRAG_SETTING, local model base URLs, and settings.yaml shape without calling external services; route app launch fixes to ../app-deployment/SKILL.md.

Bundled References

  • references/provider-configuration.md - provider map for env keys, class specs, managers, UI fields, web search, reranking, and optional packages.
  • references/local-models.md - Ollama, Docker host URLs, OpenAI-compatible local servers, GGUF/llama-cpp-python, and local RAG tuning.
  • references/graphrag.md - MS GraphRAG, NanoGraphRAG, LightRAG, toggles, custom settings, and model dependencies.
  • references/troubleshooting.md - provider failure-mode decision tree and safe remediation steps.

Bundled Script

python skills/kotaemon/sub-skills/model-providers/scripts/check_provider_env.py --env-file .env --select auto

The validator reads .env-style files plus inherited environment values, redacts all key-like values, performs no network calls, and exits nonzero when selected providers have missing required pairs, placeholder keys, incompatible URL suffixes, or missing local GGUF paths.

Skills enregistrées