langextract-llamacpp 0.1.0

LangExtract provider plugin for llama-cpp-python

LangExtract llama-cpp-python Provider

A provider plugin for LangExtract that supports llama-cpp-python models.

Installation

pip install -e .

Supported Model IDs

Model ID using the format as such:

1. HuggingFace repo with file name: hf:<hf_repo_id>:<filename>
2. HuggingFace repo without file name: hf:<hf_repo_id>, in this case the filename will be None
3. Local file: file:<path_to_model>

hf_repo_id is existing huggingface model repository.

Usage

Using HuggingFace repository; this will call Llama.from_pretrained(...).

import langextract as lx

config = lx.factory.ModelConfig(
    model_id="hf:MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF:*Q4_K_M.gguf",
    provider="LlamaCppLanguageModel", # optional as hf: will resolve to the model
    provider_kwargs=dict(
        n_gpu_layers=-1,
        n_ctx=4096,
        verbose=False,
        completion_kwargs=dict(
            temperature=1.1,
            seed=42,
        ),
    ),
)

result = lx.extract(
    config=config,
    text_or_documents="Your input text",
    prompt_description="Extract entities",
    examples=[...],
)

Using local file path; this will call Llama(...).

import langextract as lx

config = lx.factory.ModelConfig(
    model_id="file:Mistral-7B-Instruct-v0.3.Q4_K_M.gguf",
    provider="LlamaCppLanguageModel", # optional as file: will resolve to the model
    provider_kwargs=dict(
        ...
    ),
)

...

For provider_kwargs refer to documentation for Llama class.

For completion_kwargs refer to documentation for crate_chat_completion method.

OpenAI compatible Web Server

When using llama-cpp-python server (or llama.cpp), you can use OpenAILanguageModel in the provider field as they implement OpenAI compatible web server.

To set this up, choose OpenAILanguageModel as the provider and supply the server’s base URL and an API key (any value) in provider_kwargs. The model_id field is optional.

config = lx.factory.ModelConfig(
    model_id="local", # optional
    provider="OpenAILanguageModel", # explicitly set the provider to `OpenAILanguageModel`
    provider_kwargs=dict(
        base_url="http://localhost:8000/v1/",
        api_key="llama-cpp", # any value; mandatory
    ),
)

result = lx.extract(
    config=config,
    ...
)

Development

1. Install in development mode: uv pip install -e .
2. Run tests: uv run test_plugin.py
3. Build package: uv build
4. Publish to PyPI: uv publish