戒酒的李白
95 lines
3.1 KiB
Python
95 lines
3.1 KiB
Python
import time
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import numpy as np
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from tqdm import tqdm
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from typing import Any, List, Mapping
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from bertopic.backend import BaseEmbedder
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class CohereBackend(BaseEmbedder):
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"""Cohere Embedding Model.
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Arguments:
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client: A `cohere` client.
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embedding_model: A Cohere model. Default is "large".
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For an overview of models see:
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https://docs.cohere.ai/docs/generation-card
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delay_in_seconds: If a `batch_size` is given, use this set
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the delay in seconds between batches.
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batch_size: The size of each batch.
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embed_kwargs: Kwargs passed to `cohere.Client.embed`.
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Can be used to define additional parameters
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such as `input_type`
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Examples:
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```python
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import cohere
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from bertopic.backend import CohereBackend
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client = cohere.Client("APIKEY")
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cohere_model = CohereBackend(client)
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```
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If you want to specify `input_type`:
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```python
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cohere_model = CohereBackend(
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client,
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embedding_model="embed-english-v3.0",
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embed_kwargs={"input_type": "clustering"}
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)
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```
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"""
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def __init__(
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self,
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client,
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embedding_model: str = "large",
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delay_in_seconds: float = None,
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batch_size: int = None,
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embed_kwargs: Mapping[str, Any] = {},
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):
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super().__init__()
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self.client = client
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self.embedding_model = embedding_model
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self.delay_in_seconds = delay_in_seconds
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self.batch_size = batch_size
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self.embed_kwargs = embed_kwargs
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if self.embed_kwargs.get("model"):
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self.embedding_model = embed_kwargs.get("model")
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else:
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self.embed_kwargs["model"] = self.embedding_model
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def embed(self, documents: List[str], verbose: bool = False) -> np.ndarray:
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"""Embed a list of n documents/words into an n-dimensional
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matrix of embeddings.
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Arguments:
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documents: A list of documents or words to be embedded
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verbose: Controls the verbosity of the process
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Returns:
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Document/words embeddings with shape (n, m) with `n` documents/words
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that each have an embeddings size of `m`
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"""
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# Batch-wise embedding extraction
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if self.batch_size is not None:
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embeddings = []
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for batch in tqdm(self._chunks(documents), disable=not verbose):
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response = self.client.embed(texts=batch, **self.embed_kwargs)
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embeddings.extend(response.embeddings)
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# Delay subsequent calls
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if self.delay_in_seconds:
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time.sleep(self.delay_in_seconds)
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# Extract embeddings all at once
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else:
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response = self.client.embed(texts=documents, **self.embed_kwargs)
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embeddings = response.embeddings
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return np.array(embeddings)
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def _chunks(self, documents):
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for i in range(0, len(documents), self.batch_size):
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yield documents[i : i + self.batch_size]
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