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Litellm

LiteLLMEmbedding #

Bases: BaseEmbedding

Embedding class using the LiteLLM unified API.

Parameters:

Name Type Description Default
model_name str

Name of the embedding model to use. Examples include: - "text-embedding-3-small" - "text-embedding-3-large" - Any OpenAI-compatible embedding model exposed through LiteLLM.

 required
api_key Optional[str]

API key for direct OpenAI-compatible requests. Not required when using a LiteLLM proxy with configured credentials.

 required
api_base Optional[str]

Base URL of a LiteLLM proxy server

 required
dimensions Optional[int]

Output embedding dimensionality. Supported for text-embedding-3 models.

 required
timeout int

Timeout (in seconds) for embedding requests. Defaults to 60.

 required
Source code in llama_index/embeddings/litellm/base.py 
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class LiteLLMEmbedding(BaseEmbedding):
    """
    Embedding class using the LiteLLM unified API.

    Args:
        model_name (str): Name of the embedding model to use.
            Examples include:
            - "text-embedding-3-small"
            - "text-embedding-3-large"
            - Any OpenAI-compatible embedding model exposed through LiteLLM.

        api_key (Optional[str]): API key for direct OpenAI-compatible requests.
            Not required when using a LiteLLM proxy with configured credentials.

        api_base (Optional[str]): Base URL of a LiteLLM proxy server

        dimensions (Optional[int]): Output embedding dimensionality.
            Supported for text-embedding-3 models.

        timeout (int): Timeout (in seconds) for embedding requests.
            Defaults to 60.

    """

    model_name: str = Field(description="The name of the embedding model.")
    api_key: Optional[str] = Field(
        default=None,
        description="OpenAI key. If not provided, the proxy server must be configured with the key.",
    )
    api_base: Optional[str] = Field(
        default=None, description="The base URL of the LiteLLM proxy."
    )
    dimensions: Optional[int] = Field(
        default=None,
        description=(
            "The number of dimensions the resulting output embeddings should have. "
            "Only supported in text-embedding-3 and later models."
        ),
    )
    timeout: Optional[int] = Field(
        default=60, description="Timeout for each request.", ge=0
    )

    @classmethod
    def class_name(cls) -> str:
        return "lite-llm"

    async def _aget_query_embedding(self, query: str) -> List[float]:
        return self._get_query_embedding(query)

    async def _aget_text_embedding(self, text: str) -> List[float]:
        return self._get_text_embedding(text)

    def _get_query_embedding(self, query: str) -> List[float]:
        embeddings = get_embeddings(
            api_key=self.api_key,
            api_base=self.api_base,
            model_name=self.model_name,
            dimensions=self.dimensions,
            timeout=self.timeout,
            input=[query],
        )
        return embeddings[0]

    def _get_text_embedding(self, text: str) -> List[float]:
        embeddings = get_embeddings(
            api_key=self.api_key,
            api_base=self.api_base,
            model_name=self.model_name,
            dimensions=self.dimensions,
            timeout=self.timeout,
            input=[text],
        )
        return embeddings[0]

    def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
        return get_embeddings(
            api_key=self.api_key,
            api_base=self.api_base,
            model_name=self.model_name,
            dimensions=self.dimensions,
            timeout=self.timeout,
            input=texts,
        )

options: members: - LiteLLMEmbedding