from dataclasses import asdict, dataclass
from enum import Enum
from typing import (
Any,
Dict,
List,
Mapping,
Optional,
Sequence,
Tuple,
)
from aleph_alpha_client.prompt import Prompt
[docs]
@dataclass(frozen=True)
class EmbeddingRequest:
"""
Embeds a text and returns vectors that can be used for downstream tasks (e.g. semantic similarity) and models (e.g. classifiers).
Parameters:
prompt
The text and/or image(s) to be embedded.
layers
A list of layer indices from which to return embeddings.
* Index 0 corresponds to the word embeddings used as input to the first transformer layer
* Index 1 corresponds to the hidden state as output by the first transformer layer, index 2 to the output of the second layer etc.
* Index -1 corresponds to the last transformer layer (not the language modelling head), index -2 to the second last layer etc.
pooling
Pooling operation to use.
Pooling operations include:
* mean: aggregate token embeddings across the sequence dimension using an average
* max: aggregate token embeddings across the sequence dimension using a maximum
* last_token: just use the last token
* abs_max: aggregate token embeddings across the sequence dimension using a maximum of absolute values
type
Type of the embedding (e.g. symmetric or asymmetric)
tokens
Flag indicating whether the tokenized prompt is to be returned (True) or not (False)
normalize
Return normalized embeddings. This can be used to save on additional compute when applying a cosine similarity metric.
Note that at the moment this parameter does not yet have any effect. This will change as soon as the
corresponding feature is available in the backend
contextual_control_threshold (float, default None)
If set to None, attention control parameters only apply to those tokens that have
explicitly been set in the request.
If set to a non-None value, we apply the control parameters to similar tokens as well.
Controls that have been applied to one token will then be applied to all other tokens
that have at least the similarity score defined by this parameter.
The similarity score is the cosine similarity of token embeddings.
control_log_additive (bool, default True)
True: apply control by adding the log(control_factor) to attention scores.
False: apply control by (attention_scores - - attention_scores.min(-1)) * control_factor
Examples:
>>> prompt = Prompt.from_text("This is an example.")
>>> EmbeddingRequest(prompt=prompt, layers=[-1], pooling=["mean"])
"""
prompt: Prompt
layers: List[int]
pooling: List[str]
type: Optional[str] = None
tokens: bool = False
normalize: bool = False
contextual_control_threshold: Optional[float] = None
control_log_additive: Optional[bool] = True
[docs]
def to_json(self) -> Mapping[str, Any]:
return {
**self._asdict(),
"prompt": self.prompt.to_json(),
}
def _asdict(self) -> Mapping[str, Any]:
return asdict(self)
[docs]
@dataclass(frozen=True)
class EmbeddingResponse:
model_version: str
num_tokens_prompt_total: int
embeddings: Optional[Dict[Tuple[str, str], List[float]]]
tokens: Optional[List[str]]
message: Optional[str] = None
[docs]
@staticmethod
def from_json(json: Dict[str, Any]) -> "EmbeddingResponse":
return EmbeddingResponse(
model_version=json["model_version"],
embeddings={
(layer, pooling): embedding
for layer, pooling_dict in json["embeddings"].items()
for pooling, embedding in pooling_dict.items()
},
tokens=json.get("tokens"),
message=json.get("message"),
num_tokens_prompt_total=json["num_tokens_prompt_total"],
)
[docs]
class SemanticRepresentation(Enum):
"""
Available types of semantic representations that prompts can be embedded with.
Symmetric:
`Symmetric` is useful for comparing prompts to each other, in use cases such as clustering, classification, similarity, etc. `Symmetric` embeddings should be compared with other `Symmetric` embeddings.
Document:
`Document` and `Query` are used together in use cases such as search where you want to compare shorter queries against larger documents.
`Document` embeddings are optimized for larger pieces of text to compare queries against.
Query:
`Document` and `Query` are used together in use cases such as search where you want to compare shorter queries against larger documents.
`Query` embeddings are optimized for shorter texts, such as questions or keywords.
"""
Symmetric = "symmetric"
Document = "document"
Query = "query"
[docs]
@dataclass(frozen=True)
class SemanticEmbeddingRequest:
"""
Embeds a text and returns vectors that can be used for downstream tasks (e.g. semantic similarity) and models (e.g. classifiers).
Parameters:
prompt
The text and/or image(s) to be embedded.
representation
Semantic representation to embed the prompt with.
compress_to_size
Options available: 128
The default behavior is to return the full embedding, but you can optionally request an embedding compressed to a smaller set of dimensions.
Full embedding sizes for supported models:
- luminous-base: 5120
The 128 size is expected to have a small drop in accuracy performance (4-6%), with the benefit of being much smaller, which makes comparing these embeddings much faster for use cases where speed is critical.
The 128 size can also perform better if you are embedding really short texts or documents.
normalize
Return normalized embeddings. This can be used to save on additional compute when applying a cosine similarity metric.
Note that at the moment this parameter does not yet have any effect. This will change as soon as the
corresponding feature is available in the backend
contextual_control_threshold (float, default None)
If set to None, attention control parameters only apply to those tokens that have
explicitly been set in the request.
If set to a non-None value, we apply the control parameters to similar tokens as well.
Controls that have been applied to one token will then be applied to all other tokens
that have at least the similarity score defined by this parameter.
The similarity score is the cosine similarity of token embeddings.
control_log_additive (bool, default True)
True: apply control by adding the log(control_factor) to attention scores.
False: apply control by (attention_scores - - attention_scores.min(-1)) * control_factor
Examples
>>> texts = [
"deep learning",
"artificial intelligence",
"deep diving",
"artificial snow",
]
>>> # Texts to compare
>>> embeddings = []
>>> for text in texts:
request = SemanticEmbeddingRequest(prompt=Prompt.from_text(text), representation=SemanticRepresentation.Symmetric)
result = model.semantic_embed(request)
embeddings.append(result.embedding)
"""
prompt: Prompt
representation: SemanticRepresentation
compress_to_size: Optional[int] = None
normalize: bool = False
contextual_control_threshold: Optional[float] = None
control_log_additive: Optional[bool] = True
[docs]
def to_json(self) -> Mapping[str, Any]:
return {
**self._asdict(),
"representation": self.representation.value,
"prompt": self.prompt.to_json(),
}
def _asdict(self) -> Mapping[str, Any]:
return asdict(self)
[docs]
@dataclass(frozen=True)
class BatchSemanticEmbeddingRequest:
"""
Embeds multiple multi-modal prompts and returns their embeddings in the same order as they were supplied.
Parameters:
prompts
A list of texts and/or images to be embedded.
representation
Semantic representation to embed the prompt with.
compress_to_size
Options available: 128
The default behavior is to return the full embedding, but you can optionally request an embedding compressed to a smaller set of dimensions.
Full embedding sizes for supported models:
- luminous-base: 5120
The 128 size is expected to have a small drop in accuracy performance (4-6%), with the benefit of being much smaller, which makes comparing these embeddings much faster for use cases where speed is critical.
The 128 size can also perform better if you are embedding really short texts or documents.
normalize
Return normalized embeddings. This can be used to save on additional compute when applying a cosine similarity metric.
Note that at the moment this parameter does not yet have any effect. This will change as soon as the
corresponding feature is available in the backend
contextual_control_threshold (float, default None)
If set to None, attention control parameters only apply to those tokens that have
explicitly been set in the request.
If set to a non-None value, we apply the control parameters to similar tokens as well.
Controls that have been applied to one token will then be applied to all other tokens
that have at least the similarity score defined by this parameter.
The similarity score is the cosine similarity of token embeddings.
control_log_additive (bool, default True)
True: apply control by adding the log(control_factor) to attention scores.
False: apply control by (attention_scores - - attention_scores.min(-1)) * control_factor
Examples
>>> texts = [
"deep learning",
"artificial intelligence",
"deep diving",
"artificial snow",
]
>>> # Texts to compare
>>> request = BatchSemanticEmbeddingRequest(prompts=[Prompt.from_text(text) for text in texts], representation=SemanticRepresentation.Symmetric)
result = model.batch_semantic_embed(request)
"""
prompts: Sequence[Prompt]
representation: SemanticRepresentation
compress_to_size: Optional[int] = None
normalize: bool = False
contextual_control_threshold: Optional[float] = None
control_log_additive: Optional[bool] = True
[docs]
def to_json(self) -> Mapping[str, Any]:
return {
**self._asdict(),
"representation": self.representation.value,
"prompts": [prompt.to_json() for prompt in self.prompts],
}
def _asdict(self) -> Mapping[str, Any]:
return asdict(self)
EmbeddingVector = List[float]
[docs]
@dataclass(frozen=True)
class SemanticEmbeddingResponse:
"""
Response of a semantic embedding request
Parameters:
model_version
Model name and version (if any) of the used model for inference
embedding
A list of floats that can be used to compare against other embeddings.
message
This field is no longer used.
"""
model_version: str
embedding: EmbeddingVector
num_tokens_prompt_total: int
message: Optional[str] = None
[docs]
@staticmethod
def from_json(json: Dict[str, Any]) -> "SemanticEmbeddingResponse":
return SemanticEmbeddingResponse(
model_version=json["model_version"],
embedding=json["embedding"],
message=json.get("message"),
num_tokens_prompt_total=json["num_tokens_prompt_total"],
)
[docs]
@dataclass(frozen=True)
class BatchSemanticEmbeddingResponse:
"""
Response of a batch semantic embedding request
Parameters:
model_version
Model name and version (if any) of the used model for inference
embeddings
A list of embeddings.
"""
model_version: str
embeddings: Sequence[EmbeddingVector]
num_tokens_prompt_total: int
[docs]
@staticmethod
def from_json(json: Dict[str, Any]) -> "BatchSemanticEmbeddingResponse":
return BatchSemanticEmbeddingResponse(
model_version=json["model_version"],
embeddings=json["embeddings"],
num_tokens_prompt_total=json["num_tokens_prompt_total"],
)
[docs]
def to_json(self) -> Mapping[str, Any]:
return {
**asdict(self),
"embeddings": [embedding for embedding in self.embeddings],
}
@staticmethod
def _from_model_version_and_embeddings(
model_version: str,
embeddings: Sequence[EmbeddingVector],
num_tokens_prompt_total: int,
) -> "BatchSemanticEmbeddingResponse":
return BatchSemanticEmbeddingResponse(
model_version=model_version,
embeddings=embeddings,
num_tokens_prompt_total=num_tokens_prompt_total,
)