戒酒的李白
89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
import time
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import openai
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import numpy as np
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from tqdm import tqdm
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from typing import List, Mapping, Any
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from bertopic.backend import BaseEmbedder
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class OpenAIBackend(BaseEmbedder):
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"""OpenAI Embedding Model.
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Arguments:
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client: A `openai.OpenAI` client.
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embedding_model: An OpenAI model. Default is
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For an overview of models see:
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https://platform.openai.com/docs/models/embeddings
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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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generator_kwargs: Kwargs passed to `openai.Embedding.create`.
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Can be used to define custom engines or
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deployment_ids.
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Examples:
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```python
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import openai
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from bertopic.backend import OpenAIBackend
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client = openai.OpenAI(api_key="sk-...")
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openai_embedder = OpenAIBackend(client, "text-embedding-ada-002")
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```
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"""
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def __init__(
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self,
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client: openai.OpenAI,
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embedding_model: str = "text-embedding-ada-002",
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delay_in_seconds: float = None,
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batch_size: int = None,
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generator_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.generator_kwargs = generator_kwargs
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if self.generator_kwargs.get("model"):
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self.embedding_model = generator_kwargs.get("model")
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elif not self.generator_kwargs.get("engine"):
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self.generator_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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# Prepare documents, replacing empty strings with a single space
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prepared_documents = [" " if doc == "" else doc for doc in documents]
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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(prepared_documents), disable=not verbose):
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response = self.client.embeddings.create(input=batch, **self.generator_kwargs)
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embeddings.extend([r.embedding for r in response.data])
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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.embeddings.create(input=prepared_documents, **self.generator_kwargs)
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embeddings = [r.embedding for r in response.data]
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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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