Source code for langchain_together.embeddings

"""Wrapper around Together AI's Embeddings API."""

import logging
import os
import warnings
from typing import (
    Any,
    Dict,
    List,
    Literal,
    Mapping,
    Optional,
    Sequence,
    Set,
    Tuple,
    Union,
)

import openai
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import (
    BaseModel,
    Extra,
    Field,
    SecretStr,
    root_validator,
)
from langchain_core.utils import (
    convert_to_secret_str,
    get_from_dict_or_env,
    get_pydantic_field_names,
)

logger = logging.getLogger(__name__)


[docs]class TogetherEmbeddings(BaseModel, Embeddings): """TogetherEmbeddings embedding model. To use, set the environment variable `TOGETHER_API_KEY` with your API key or pass it as a named parameter to the constructor. Example: .. code-block:: python from langchain_together import TogetherEmbeddings model = TogetherEmbeddings() """ client: Any = Field(default=None, exclude=True) #: :meta private: async_client: Any = Field(default=None, exclude=True) #: :meta private: model: str = "togethercomputer/m2-bert-80M-8k-retrieval" """Embeddings model name to use. Instead, use 'togethercomputer/m2-bert-80M-8k-retrieval' for example. """ dimensions: Optional[int] = None """The number of dimensions the resulting output embeddings should have. Not yet supported. """ together_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") """API Key for Solar API.""" together_api_base: str = Field( default="https://api.together.ai/v1/", alias="base_url" ) """Endpoint URL to use.""" embedding_ctx_length: int = 4096 """The maximum number of tokens to embed at once. Not yet supported. """ allowed_special: Union[Literal["all"], Set[str]] = set() """Not yet supported.""" disallowed_special: Union[Literal["all"], Set[str], Sequence[str]] = "all" """Not yet supported.""" chunk_size: int = 1000 """Maximum number of texts to embed in each batch. Not yet supported. """ max_retries: int = 2 """Maximum number of retries to make when generating.""" request_timeout: Optional[Union[float, Tuple[float, float], Any]] = Field( default=None, alias="timeout" ) """Timeout for requests to Together embedding API. Can be float, httpx.Timeout or None.""" show_progress_bar: bool = False """Whether to show a progress bar when embedding. Not yet supported. """ model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" skip_empty: bool = False """Whether to skip empty strings when embedding or raise an error. Defaults to not skipping. Not yet supported.""" default_headers: Union[Mapping[str, str], None] = None default_query: Union[Mapping[str, object], None] = None # Configure a custom httpx client. See the # [httpx documentation](https://www.python-httpx.org/api/#client) for more details. http_client: Union[Any, None] = None """Optional httpx.Client. Only used for sync invocations. Must specify http_async_client as well if you'd like a custom client for async invocations. """ http_async_client: Union[Any, None] = None """Optional httpx.AsyncClient. Only used for async invocations. Must specify http_client as well if you'd like a custom client for sync invocations.""" class Config: extra = Extra.forbid allow_population_by_field_name = True @root_validator(pre=True) def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) for field_name in list(values): if field_name in extra: raise ValueError(f"Found {field_name} supplied twice.") if field_name not in all_required_field_names: warnings.warn( f"""WARNING! {field_name} is not default parameter. {field_name} was transferred to model_kwargs. Please confirm that {field_name} is what you intended.""" ) extra[field_name] = values.pop(field_name) invalid_model_kwargs = all_required_field_names.intersection(extra.keys()) if invalid_model_kwargs: raise ValueError( f"Parameters {invalid_model_kwargs} should be specified explicitly. " f"Instead they were passed in as part of `model_kwargs` parameter." ) values["model_kwargs"] = extra return values @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" together_api_key = get_from_dict_or_env( values, "together_api_key", "TOGETHER_API_KEY" ) values["together_api_key"] = ( convert_to_secret_str(together_api_key) if together_api_key else None ) values["together_api_base"] = values["together_api_base"] or os.getenv( "TOGETHER_API_BASE" ) client_params = { "api_key": ( values["together_api_key"].get_secret_value() if values["together_api_key"] else None ), "base_url": values["together_api_base"], "timeout": values["request_timeout"], "max_retries": values["max_retries"], "default_headers": values["default_headers"], "default_query": values["default_query"], } if not values.get("client"): sync_specific = ( {"http_client": values["http_client"]} if values["http_client"] else {} ) values["client"] = openai.OpenAI( **client_params, **sync_specific ).embeddings if not values.get("async_client"): async_specific = ( {"http_client": values["http_async_client"]} if values["http_async_client"] else {} ) values["async_client"] = openai.AsyncOpenAI( **client_params, **async_specific ).embeddings return values @property def _invocation_params(self) -> Dict[str, Any]: params: Dict = {"model": self.model, **self.model_kwargs} if self.dimensions is not None: params["dimensions"] = self.dimensions return params
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed a list of document texts using passage model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ embeddings = [] params = self._invocation_params params["model"] = params["model"] for text in texts: response = self.client.create(input=text, **params) if not isinstance(response, dict): response = response.model_dump() embeddings.extend([i["embedding"] for i in response["data"]]) return embeddings
[docs] def embed_query(self, text: str) -> List[float]: """Embed query text using query model. Args: text: The text to embed. Returns: Embedding for the text. """ params = self._invocation_params params["model"] = params["model"] response = self.client.create(input=text, **params) if not isinstance(response, dict): response = response.model_dump() return response["data"][0]["embedding"]
[docs] async def aembed_documents(self, texts: List[str]) -> List[List[float]]: """Embed a list of document texts using passage model asynchronously. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ embeddings = [] params = self._invocation_params params["model"] = params["model"] for text in texts: response = await self.async_client.create(input=text, **params) if not isinstance(response, dict): response = response.model_dump() embeddings.extend([i["embedding"] for i in response["data"]]) return embeddings
[docs] async def aembed_query(self, text: str) -> List[float]: """Asynchronous Embed query text using query model. Args: text: The text to embed. Returns: Embedding for the text. """ params = self._invocation_params params["model"] = params["model"] response = await self.async_client.create(input=text, **params) if not isinstance(response, dict): response = response.model_dump() return response["data"][0]["embedding"]