Langchain Based

LCOpenAIEmbeddings

Bases: LCEmbeddingMixin, BaseEmbeddings

Wrapper around Langchain's OpenAI embedding, focusing on key parameters

Source code in libs/kotaemon/kotaemon/embeddings/langchain_based.py

class LCOpenAIEmbeddings(LCEmbeddingMixin, BaseEmbeddings):
    """Wrapper around Langchain's OpenAI embedding, focusing on key parameters"""

    def __init__(
        self,
        model: str = "text-embedding-ada-002",
        openai_api_version: Optional[str] = None,
        openai_api_base: Optional[str] = None,
        openai_api_type: Optional[str] = None,
        openai_api_key: Optional[str] = None,
        request_timeout: Optional[float] = None,
        **params,
    ):
        super().__init__(
            model=model,
            openai_api_version=openai_api_version,
            openai_api_base=openai_api_base,
            openai_api_type=openai_api_type,
            openai_api_key=openai_api_key,
            request_timeout=request_timeout,
            **params,
        )

    def _get_lc_class(self):
        try:
            from langchain_openai import OpenAIEmbeddings
        except ImportError:
            from langchain.embeddings import OpenAIEmbeddings

        return OpenAIEmbeddings

LCAzureOpenAIEmbeddings

Bases: LCEmbeddingMixin, BaseEmbeddings

Wrapper around Langchain's AzureOpenAI embedding, focusing on key parameters

Source code in libs/kotaemon/kotaemon/embeddings/langchain_based.py

class LCAzureOpenAIEmbeddings(LCEmbeddingMixin, BaseEmbeddings):
    """Wrapper around Langchain's AzureOpenAI embedding, focusing on key parameters"""

    def __init__(
        self,
        azure_endpoint: Optional[str] = None,
        deployment: Optional[str] = None,
        openai_api_key: Optional[str] = None,
        api_version: Optional[str] = None,
        request_timeout: Optional[float] = None,
        **params,
    ):
        super().__init__(
            azure_endpoint=azure_endpoint,
            deployment=deployment,
            api_version=api_version,
            openai_api_key=openai_api_key,
            request_timeout=request_timeout,
            **params,
        )

    def _get_lc_class(self):
        try:
            from langchain_openai import AzureOpenAIEmbeddings
        except ImportError:
            from langchain.embeddings import AzureOpenAIEmbeddings

        return AzureOpenAIEmbeddings

LCCohereEmbeddings

Bases: LCEmbeddingMixin, BaseEmbeddings

Wrapper around Langchain's Cohere embedding, focusing on key parameters

Source code in libs/kotaemon/kotaemon/embeddings/langchain_based.py

class LCCohereEmbeddings(LCEmbeddingMixin, BaseEmbeddings):
    """Wrapper around Langchain's Cohere embedding, focusing on key parameters"""

    cohere_api_key: str = Param(
        help="API key (https://dashboard.cohere.com/api-keys)",
        default=None,
        required=True,
    )
    model: str = Param(
        help="Model name to use (https://docs.cohere.com/docs/models)",
        default=None,
        required=True,
    )
    user_agent: str = Param(
        help="User agent (leave default)", default="default", required=True
    )

    def __init__(
        self,
        model: str = "embed-english-v2.0",
        cohere_api_key: Optional[str] = None,
        truncate: Optional[str] = None,
        request_timeout: Optional[float] = None,
        **params,
    ):
        super().__init__(
            model=model,
            cohere_api_key=cohere_api_key,
            truncate=truncate,
            request_timeout=request_timeout,
            **params,
        )

    def _get_lc_class(self):
        try:
            from langchain_cohere import CohereEmbeddings
        except ImportError:
            from langchain.embeddings import CohereEmbeddings

        return CohereEmbeddings

LCHuggingFaceEmbeddings

Bases: LCEmbeddingMixin, BaseEmbeddings

Wrapper around Langchain's HuggingFace embedding, focusing on key parameters

Source code in libs/kotaemon/kotaemon/embeddings/langchain_based.py

class LCHuggingFaceEmbeddings(LCEmbeddingMixin, BaseEmbeddings):
    """Wrapper around Langchain's HuggingFace embedding, focusing on key parameters"""

    model_name: str = Param(
        help=(
            "Model name to use (https://huggingface.co/models?"
            "pipeline_tag=sentence-similarity&sort=trending)"
        ),
        default=None,
        required=True,
    )

    def __init__(
        self,
        model_name: str = "sentence-transformers/all-mpnet-base-v2",
        **params,
    ):
        super().__init__(
            model_name=model_name,
            **params,
        )

    def _get_lc_class(self):
        try:
            from langchain_community.embeddings import HuggingFaceBgeEmbeddings
        except ImportError:
            from langchain.embeddings import HuggingFaceBgeEmbeddings

        return HuggingFaceBgeEmbeddings