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@ -16,10 +16,11 @@ class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
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Example:
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.. code-block:: python
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from aleph_alpha import AlephAlphaAsymmetricSemanticEmbedding
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embeddings = AlephAlphaSymmetricSemanticEmbedding()
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embeddings = AlephAlphaAsymmetricSemanticEmbedding(
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normalize=True, compress_to_size=128
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)
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document = "This is a content of the document"
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query = "What is the content of the document?"
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@ -30,24 +31,55 @@ class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
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"""
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client: Any #: :meta private:
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"""Aleph Alpha client."""
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model: Optional[str] = "luminous-base"
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# Embedding params
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model: str = "luminous-base"
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"""Model name to use."""
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hosting: Optional[str] = "https://api.aleph-alpha.com"
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"""Optional parameter that specifies which datacenters may process the request."""
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normalize: Optional[bool] = True
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"""Should returned embeddings be normalized"""
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compress_to_size: Optional[int] = 128
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compress_to_size: Optional[int] = None
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"""Should the returned embeddings come back as an original 5120-dim vector,
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or should it be compressed to 128-dim."""
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normalize: Optional[bool] = None
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"""Should returned embeddings be normalized"""
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contextual_control_threshold: Optional[int] = None
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"""Attention control parameters only apply to those tokens that have
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explicitly been set in the request."""
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control_log_additive: Optional[bool] = True
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control_log_additive: bool = True
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"""Apply controls on prompt items by adding the log(control_factor)
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to attention scores."""
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# Client params
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aleph_alpha_api_key: Optional[str] = None
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"""API key for Aleph Alpha API."""
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host: str = "https://api.aleph-alpha.com"
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"""The hostname of the API host.
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The default one is "https://api.aleph-alpha.com")"""
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hosting: Optional[str] = None
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"""Determines in which datacenters the request may be processed.
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You can either set the parameter to "aleph-alpha" or omit it (defaulting to None).
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Not setting this value, or setting it to None, gives us maximal flexibility
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in processing your request in our
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own datacenters and on servers hosted with other providers.
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Choose this option for maximal availability.
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Setting it to "aleph-alpha" allows us to only process the request
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in our own datacenters.
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Choose this option for maximal data privacy."""
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request_timeout_seconds: int = 305
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"""Client timeout that will be set for HTTP requests in the
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`requests` library's API calls.
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Server will close all requests after 300 seconds with an internal server error."""
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total_retries: int = 8
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"""The number of retries made in case requests fail with certain retryable
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status codes. If the last
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retry fails a corresponding exception is raised. Note, that between retries
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an exponential backoff
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is applied, starting with 0.5 s after the first retry and doubling for each
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retry made. So with the
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default setting of 8 retries a total wait time of 63.5 s is added between
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the retries."""
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nice: bool = False
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"""Setting this to True, will signal to the API that you intend to be
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nice to other users
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by de-prioritizing your request below concurrent ones."""
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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@ -57,12 +89,21 @@ class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
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)
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try:
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from aleph_alpha_client import Client
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values["client"] = Client(
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token=aleph_alpha_api_key,
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host=values["host"],
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hosting=values["hosting"],
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request_timeout_seconds=values["request_timeout_seconds"],
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total_retries=values["total_retries"],
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nice=values["nice"],
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)
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except ImportError:
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raise ValueError(
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"Could not import aleph_alpha_client python package. "
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"Please install it with `pip install aleph_alpha_client`."
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)
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values["client"] = Client(token=aleph_alpha_api_key)
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return values
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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@ -152,7 +193,9 @@ class AlephAlphaSymmetricSemanticEmbedding(AlephAlphaAsymmetricSemanticEmbedding
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from aleph_alpha import AlephAlphaSymmetricSemanticEmbedding
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embeddings = AlephAlphaAsymmetricSemanticEmbedding()
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embeddings = AlephAlphaAsymmetricSemanticEmbedding(
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normalize=True, compress_to_size=128
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)
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text = "This is a test text"
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doc_result = embeddings.embed_documents([text])
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