Provider Reference

LangExtract ships with three built-in providers (Gemini, OpenAI, Ollama) and supports custom providers via a plugin system.

Gemini (default, recommended)

python

result = lx.extract(
    text_or_documents=text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.5-flash",  # or "gemini-2.5-pro" for complex reasoning
)

Env: GEMINI_API_KEY (falls back to LANGEXTRACT_API_KEY).

OpenAI

python

result = lx.extract(
    text_or_documents=text,
    examples=examples,
    prompt_description=prompt,
    model_id="gpt-4o",
)

Env: OPENAI_API_KEY (falls back to LANGEXTRACT_API_KEY).

The OpenAI provider uses JSON mode (response_format={"type":"json_object"}) and reports requires_fence_output=False. Leave fence_output unset — extract() and the factory/provider layer auto-determine fence behavior from the provider's schema. The OpenAI provider parallelizes batched prompts with a ThreadPoolExecutor when max_workers > 1.

The OpenAI provider does not expose a schema class, so use_schema_constraints is a no-op here. You can omit it (as shown above) or leave it at its default.

Auto-routing scope: the built-in OpenAI provider only auto-matches GPT-style model_ids (^gpt-4, ^gpt4\., ^gpt-5, ^gpt5\.), and the environment-default API-key lookup keys off "gpt" rather than "openai". For OpenAI-compatible endpoints (LiteLLM, local servers, custom base URLs) or non-GPT model IDs, use ModelConfig with an explicit provider and kwargs:

python

from langextract.factory import ModelConfig

result = lx.extract(
    text_or_documents=text,
    examples=examples,
    prompt_description=prompt,
    config=ModelConfig(
        model_id="my-openai-compatible-model",
        provider="openai",
        provider_kwargs={"api_key": "sk-...", "base_url": "https://..."},
    ),
)

Ollama (local)

No API key needed. Requires a running Ollama server.

python

result = lx.extract(
    text_or_documents=text,
    examples=examples,
    prompt_description=prompt,
    model_id="gemma2:2b",
    model_url="http://localhost:11434",
)

The Ollama provider exposes FormatModeSchema for JSON mode. Leave fence_output and use_schema_constraints unset so extract() and the factory/provider layer auto-configure from the provider's schema. The Ollama provider processes batched prompts sequentially — max_workers > 1 will not parallelize it.

ModelConfig (advanced)

For provider-specific kwargs (custom base_url, explicit api_key, etc.):

python

from langextract.factory import ModelConfig

result = lx.extract(
    text_or_documents=text,
    examples=examples,
    config=ModelConfig(
        model_id="gpt-4o",
        provider_kwargs={"api_key": "your_key", "base_url": "https://..."},
    ),
)

ModelConfig fields: model_id, provider (for disambiguation when multiple providers match a model_id pattern), provider_kwargs.

Custom provider plugins

Subclass BaseLanguageModel and register a regex pattern for the model_ids your provider should handle:

python

from langextract.core.base_model import BaseLanguageModel
from langextract.providers import router

@router.register(r"^my-model")
class MyProvider(BaseLanguageModel):
    ...

For external packages, declare an entry point in your pyproject.toml so LangExtract discovers the provider at model-creation time:

toml

[project.entry-points."langextract.providers"]
my-model = "my_package.provider:MyProvider"

Plugin loading is lazy: load_plugins_once() runs the first time a model is created via the factory, not at package import. Once loaded, any registered provider whose pattern matches the requested model_id is selected automatically.

langextract.inference and langextract.providers.registry still exist as backward-compatibility aliases, but new code should import from langextract.core.base_model and langextract.providers.router as shown above. A complete working example lives at examples/custom_provider_plugin/ in the repo.