init() — langchain Function Reference

Architecture documentation for the __init__() function in huggingface.py from the langchain codebase.

Function python LangChainCore ApiManagement

Entity Profile

LangChainCore→ ApiManagement→ __init__() — langchain Function Reference

Dependency Diagram

graph TD
  6f8d5d0a_4a99_c3be_d269_1921338dc479["__init__()"]
  bc29a19b_53a3_8e06_ab58_d933298d898b["HuggingFaceEmbeddings"]
  6f8d5d0a_4a99_c3be_d269_1921338dc479 -->|defined in| bc29a19b_53a3_8e06_ab58_d933298d898b
  style 6f8d5d0a_4a99_c3be_d269_1921338dc479 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py lines 64–99

    def __init__(self, **kwargs: Any):
        """Initialize the sentence_transformer."""
        super().__init__(**kwargs)
        try:
            import sentence_transformers  # type: ignore[import]
        except ImportError as exc:
            msg = (
                "Could not import sentence_transformers python package. "
                "Please install it with `pip install sentence-transformers`."
            )
            raise ImportError(msg) from exc

        if self.model_kwargs.get("backend", "torch") == "ipex":
            if not is_optimum_intel_available() or not is_ipex_available():
                msg = f"Backend: ipex {IMPORT_ERROR.format('optimum[ipex]')}"
                raise ImportError(msg)

            if is_optimum_intel_version("<", _MIN_OPTIMUM_VERSION):
                msg = (
                    f"Backend: ipex requires optimum-intel>="
                    f"{_MIN_OPTIMUM_VERSION}. You can install it with pip: "
                    "`pip install --upgrade --upgrade-strategy eager "
                    "`optimum[ipex]`."
                )
                raise ImportError(msg)

            from optimum.intel import IPEXSentenceTransformer  # type: ignore[import]

            model_cls = IPEXSentenceTransformer

        else:
            model_cls = sentence_transformers.SentenceTransformer

        self._client = model_cls(
            self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
        )