LangExtract LiteLLM Provider

A provider plugin for LangExtract that supports 100+ LLM models through LiteLLM's unified API, including OpenAI GPT models, Anthropic Claude, Google PaLM, Azure OpenAI, and many open-source models.

Note: This is a third-party provider plugin for LangExtract. For the main LangExtract library, visit google/langextract.

Installation

Install from PyPI:

pip install langextract-litellm

Or install from source for development:

git clone https://github.com/JustStas/langextract-litellm
cd langextract-litellm
pip install -e .

Supported Models

This provider handles model IDs that start with litellm and supports a wide range of models through LiteLLM's unified API:

• OpenAI models: litellm/gpt-4, litellm/gpt-4o, litellm/gpt-3.5-turbo, etc.
• Anthropic models: litellm/claude-3-opus, litellm/claude-3-sonnet, litellm/claude-3-haiku, etc.
• Google models: litellm/gemini-1.5-pro, litellm/palm-2, etc.
• Azure OpenAI: litellm/azure/your-deployment-name
• Open-source models: litellm/llama-2-7b-chat, litellm/mistral-7b, litellm/codellama-34b, etc.
• And many more: See LiteLLM's supported models

Note: All model IDs must be prefixed with litellm/ or litellm- to be handled by this provider.

Environment Variables

Configure authentication using LiteLLM's standard environment variable format. Set the appropriate variables based on your provider:

OpenAI

export OPENAI_API_KEY="your-api-key"

Anthropic

export ANTHROPIC_API_KEY="your-api-key"

HuggingFace

export HUGGINGFACE_API_KEY="your-api-key"

Azure OpenAI

export AZURE_API_KEY="your-azure-key"
export AZURE_API_BASE="https://your-resource.openai.azure.com/"
export AZURE_API_VERSION="2024-02-01"

Google (VertexAI)

export VERTEXAI_PROJECT="your-project-id"
export VERTEXAI_LOCATION="us-central1"
# Also run: gcloud auth application-default login

Other Providers

See the LiteLLM documentation for environment variables for other providers like HuggingFace, Cohere, AI21, etc.

Usage

Basic Usage

import langextract as lx

# Create model configuration
config = lx.factory.ModelConfig(
    model_id="litellm/azure/gpt-4o",  # or "gpt-4", "claude-3-sonnet", etc.
    provider="LiteLLMLanguageModel",
)
model = lx.factory.create_model(config)

# Extract entities
result = lx.extract(
    text_or_documents="Lady Juliet gazed longingly at the stars, her heart aching for Romeo",
    model=model,
    prompt_description="Extract characters, emotions, and relationships in order of appearance.",
    examples=[...]
)

Complete Example with Examples

import langextract as lx
import textwrap

# Define extraction prompt
prompt = textwrap.dedent("""\
    Extract characters, emotions, and relationships in order of appearance.
    Use exact text for extractions. Do not paraphrase or overlap entities.
    Provide meaningful attributes for each entity to add context.""")

# Provide high-quality examples to guide the model
examples = [
    lx.data.ExampleData(
        text="ROMEO. But soft! What light through yonder window breaks? It is the east, and Juliet is the sun.",
        extractions=[
            lx.data.Extraction(
                extraction_class="character",
                extraction_text="ROMEO",
                attributes={"emotional_state": "wonder"}
            ),
            lx.data.Extraction(
                extraction_class="emotion",
                extraction_text="But soft!",
                attributes={"feeling": "gentle awe"}
            ),
            lx.data.Extraction(
                extraction_class="relationship",
                extraction_text="Juliet is the sun",
                attributes={"type": "metaphor"}
            ),
        ]
    )
]

# Create model configuration
config = lx.factory.ModelConfig(
    model_id="litellm/azure/gpt-4o",
    provider="LiteLLMLanguageModel",
)
model = lx.factory.create_model(config)

# Extract entities
result = lx.extract(
    text_or_documents="Lady Juliet gazed longingly at the stars, her heart aching for Romeo",
    model=model,
    prompt_description=prompt,
    examples=examples
)

print("✅ Extraction successful!")
print(f"Results: {result}")

Model ID Formats

The model ID must start with litellm/ or litellm- to be handled by this provider.

# Explicit LiteLLM prefix
model_id = "litellm/azure/gpt-4o"
model_id = "litellm/gpt-4"
model_id = "litellm/claude-3-sonnet"

# Alternative prefix formats
model_id = "litellm-gpt-4o"
model_id = "litellm-claude-3-sonnet"

Advanced Configuration

You can pass additional parameters supported by LiteLLM:

config = lx.factory.ModelConfig(
    model_id="litellm/gpt-4",
    provider="LiteLLMLanguageModel",
    temperature=0.7,
    max_tokens=1000,
    top_p=0.9,
    frequency_penalty=0.1,
    presence_penalty=0.1,
    timeout=30,
)

Expected Output

The extraction will return structured data with precise character intervals:

AnnotatedDocument(
    extractions=[
        Extraction(
            extraction_class='character',
            extraction_text='Lady Juliet',
            char_interval=CharInterval(start_pos=0, end_pos=11),
            alignment_status=<AlignmentStatus.MATCH_EXACT: 'match_exact'>,
            attributes={'emotional_state': 'longing'}
        ),
        Extraction(
            extraction_class='emotion',
            extraction_text='aching',
            char_interval=CharInterval(start_pos=52, end_pos=58),
            alignment_status=<AlignmentStatus.MATCH_FUZZY: 'match_fuzzy'>,
            attributes={'feeling': 'heartfelt yearning'}
        ),
        Extraction(
            extraction_class='relationship',
            extraction_text='her heart aching for Romeo',
            char_interval=CharInterval(start_pos=42, end_pos=68),
            alignment_status=<AlignmentStatus.MATCH_EXACT: 'match_exact'>,
            attributes={'type': 'romantic longing'}
        )
    ],
    text='Lady Juliet gazed longingly at the stars, her heart aching for Romeo'
)

Error Handling

The provider includes robust error handling and will return error messages instead of raising exceptions:

# If API call fails, you'll get:
ScoredOutput(score=0.0, output="LiteLLM API error: [error details]")

Development

1. Install in development mode: pip install -e .
2. Run tests: python test_plugin.py
3. Build package: python -m build
4. Publish to PyPI: twine upload dist/*

Requirements

• langextract
• litellm

License

Apache License 2.0