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name: providers description: "Choose, configure, inspect, and troubleshoot LangExtract model providers and batch backends." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

LangExtract Providers

Use this sub-skill when a task depends on selecting or configuring the LangExtract language-model backend: Gemini, Vertex AI, OpenAI/GPT, OpenAI-compatible endpoints, Ollama/local models, provider routing, provider kwargs, or batch behavior.

Fast route

1. Decide whether the user is doing provider setup or prompt/extraction design.
   • Provider/backend choice, API keys, ModelConfig, language_model_params, batch mode, Ollama daemon/model checks: stay here.
   • Prompt, examples, output_schema shape, tokenization, resolver tuning, or extraction result grounding: route to extraction.
   • Custom provider package scaffolding, entry points, plugin conflicts, or schema implementation for a new provider: route to provider-plugins.
   • JSONL persistence or HTML output: route to visualization.
2. Inspect routing without secrets or network by running the bundled route checker:

   python sub-skills/providers/scripts/check_provider_routes.py
   

3. Use references/providers.md for exact provider patterns, factory APIs, environment-variable precedence, schema/fence interactions, and batch configuration.
4. Use references/troubleshooting.md when provider creation or inference fails.
5. For local Ollama, start with a preflight-only check before inference:

   python sub-skills/providers/scripts/ollama_demo.py --model gemma2:2b --preflight-only
   

Provider selection checklist

• Prefer model_id auto-routing for known IDs: gemini... for Gemini, gpt-4... or gpt-5... for OpenAI, and common local IDs such as gemma2:2b, llama3.2:1b, qwen..., or gpt-oss... for Ollama.
• Use langextract.factory.ModelConfig(provider=..., provider_kwargs=...) when a model ID does not match a built-in pattern, when an OpenAI-compatible endpoint uses a non-GPT ID, or when multiple providers could match.
• Do not put secrets in generated code. Read API keys from environment variables or caller-owned secret stores.
• Treat live Gemini/OpenAI/Vertex/Ollama checks as optional. This sub-skill only establishes configuration and local/no-network inspection unless the caller explicitly supplies credentials or a local daemon.
• Batch APIs are opt-in and only trigger when the prompt/chunk count meets the configured threshold.

Runtime helpers

• scripts/check_provider_routes.py: safe, no-network provider registry and sample model-ID resolver.
• scripts/ollama_demo.py: local Ollama preflight plus optional tiny extraction demo. It does not require the source repository checkout.

Evidence basis

This guidance is distilled from README.md, langextract/providers/README.md, docs/examples/batch_api_example.md, examples/ollama/, langextract/factory.py, langextract/providers/router.py, builtin_registry.py, patterns.py, gemini.py, openai.py, ollama.py, gemini_batch.py, openai_batch.py, and provider schema modules. Related native verification candidates include factory_test.py, factory_schema_test.py, provider_schema_test.py, inference_test.py, test_kwargs_passthrough.py, test_gemini_batch_api.py, gemini_retry_test.py, openai_batch_test.py, optional test_live_api.py, and optional test_ollama_integration.py; do not run those from this sub-skill unless the root verification plan asks for them.