Source code for langchain_community.embeddings.aleph_alpha

from typing import Any, Dict, List, Optional

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, root_validator
from langchain_core.utils import get_from_dict_or_env


[docs]class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings): """Aleph Alpha's asymmetric semantic embedding. AA provides you with an endpoint to embed a document and a query. The models were optimized to make the embeddings of documents and the query for a document as similar as possible. To learn more, check out: https://docs.aleph-alpha.com/docs/tasks/semantic_embed/ Example: .. code-block:: python from aleph_alpha import AlephAlphaAsymmetricSemanticEmbedding embeddings = AlephAlphaAsymmetricSemanticEmbedding( normalize=True, compress_to_size=128 ) document = "This is a content of the document" query = "What is the content of the document?" doc_result = embeddings.embed_documents([document]) query_result = embeddings.embed_query(query) """ client: Any #: :meta private: # Embedding params model: str = "luminous-base" """Model name to use.""" compress_to_size: Optional[int] = None """Should the returned embeddings come back as an original 5120-dim vector, or should it be compressed to 128-dim.""" normalize: bool = False """Should returned embeddings be normalized""" contextual_control_threshold: Optional[int] = None """Attention control parameters only apply to those tokens that have explicitly been set in the request.""" control_log_additive: bool = True """Apply controls on prompt items by adding the log(control_factor) to attention scores.""" # Client params aleph_alpha_api_key: Optional[str] = None """API key for Aleph Alpha API.""" host: str = "https://api.aleph-alpha.com" """The hostname of the API host. The default one is "https://api.aleph-alpha.com")""" hosting: Optional[str] = None """Determines in which datacenters the request may be processed. You can either set the parameter to "aleph-alpha" or omit it (defaulting to None). Not setting this value, or setting it to None, gives us maximal flexibility in processing your request in our own datacenters and on servers hosted with other providers. Choose this option for maximal availability. Setting it to "aleph-alpha" allows us to only process the request in our own datacenters. Choose this option for maximal data privacy.""" request_timeout_seconds: int = 305 """Client timeout that will be set for HTTP requests in the `requests` library's API calls. Server will close all requests after 300 seconds with an internal server error.""" total_retries: int = 8 """The number of retries made in case requests fail with certain retryable status codes. If the last retry fails a corresponding exception is raised. Note, that between retries an exponential backoff is applied, starting with 0.5 s after the first retry and doubling for each retry made. So with the default setting of 8 retries a total wait time of 63.5 s is added between the retries.""" nice: bool = False """Setting this to True, will signal to the API that you intend to be nice to other users by de-prioritizing your request below concurrent ones.""" @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" aleph_alpha_api_key = get_from_dict_or_env( values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY" ) try: from aleph_alpha_client import Client values["client"] = Client( token=aleph_alpha_api_key, host=values["host"], hosting=values["hosting"], request_timeout_seconds=values["request_timeout_seconds"], total_retries=values["total_retries"], nice=values["nice"], ) except ImportError: raise ImportError( "Could not import aleph_alpha_client python package. " "Please install it with `pip install aleph_alpha_client`." ) return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call out to Aleph Alpha's asymmetric Document endpoint. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ try: from aleph_alpha_client import ( Prompt, SemanticEmbeddingRequest, SemanticRepresentation, ) except ImportError: raise ImportError( "Could not import aleph_alpha_client python package. " "Please install it with `pip install aleph_alpha_client`." ) document_embeddings = [] for text in texts: document_params = { "prompt": Prompt.from_text(text), "representation": SemanticRepresentation.Document, "compress_to_size": self.compress_to_size, "normalize": self.normalize, "contextual_control_threshold": self.contextual_control_threshold, "control_log_additive": self.control_log_additive, } document_request = SemanticEmbeddingRequest(**document_params) document_response = self.client.semantic_embed( request=document_request, model=self.model ) document_embeddings.append(document_response.embedding) return document_embeddings
[docs] def embed_query(self, text: str) -> List[float]: """Call out to Aleph Alpha's asymmetric, query embedding endpoint Args: text: The text to embed. Returns: Embeddings for the text. """ try: from aleph_alpha_client import ( Prompt, SemanticEmbeddingRequest, SemanticRepresentation, ) except ImportError: raise ImportError( "Could not import aleph_alpha_client python package. " "Please install it with `pip install aleph_alpha_client`." ) symmetric_params = { "prompt": Prompt.from_text(text), "representation": SemanticRepresentation.Query, "compress_to_size": self.compress_to_size, "normalize": self.normalize, "contextual_control_threshold": self.contextual_control_threshold, "control_log_additive": self.control_log_additive, } symmetric_request = SemanticEmbeddingRequest(**symmetric_params) symmetric_response = self.client.semantic_embed( request=symmetric_request, model=self.model ) return symmetric_response.embedding
[docs]class AlephAlphaSymmetricSemanticEmbedding(AlephAlphaAsymmetricSemanticEmbedding): """Symmetric version of the Aleph Alpha's semantic embeddings. The main difference is that here, both the documents and queries are embedded with a SemanticRepresentation.Symmetric Example: .. code-block:: python from aleph_alpha import AlephAlphaSymmetricSemanticEmbedding embeddings = AlephAlphaAsymmetricSemanticEmbedding( normalize=True, compress_to_size=128 ) text = "This is a test text" doc_result = embeddings.embed_documents([text]) query_result = embeddings.embed_query(text) """ def _embed(self, text: str) -> List[float]: try: from aleph_alpha_client import ( Prompt, SemanticEmbeddingRequest, SemanticRepresentation, ) except ImportError: raise ImportError( "Could not import aleph_alpha_client python package. " "Please install it with `pip install aleph_alpha_client`." ) query_params = { "prompt": Prompt.from_text(text), "representation": SemanticRepresentation.Symmetric, "compress_to_size": self.compress_to_size, "normalize": self.normalize, "contextual_control_threshold": self.contextual_control_threshold, "control_log_additive": self.control_log_additive, } query_request = SemanticEmbeddingRequest(**query_params) query_response = self.client.semantic_embed( request=query_request, model=self.model ) return query_response.embedding
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call out to Aleph Alpha's Document endpoint. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ document_embeddings = [] for text in texts: document_embeddings.append(self._embed(text)) return document_embeddings
[docs] def embed_query(self, text: str) -> List[float]: """Call out to Aleph Alpha's asymmetric, query embedding endpoint Args: text: The text to embed. Returns: Embeddings for the text. """ return self._embed(text)