Add BERTopic.

LLMTopicDetection_BERTopic/bertopic/backend/_cohere.py

@@ -0,0 +1,94 @@
+import time
+import numpy as np
+from tqdm import tqdm
+from typing import Any, List, Mapping
+from bertopic.backend import BaseEmbedder
+
+
+class CohereBackend(BaseEmbedder):
+    """Cohere Embedding Model.
+
+    Arguments:
+        client: A `cohere` client.
+        embedding_model: A Cohere model. Default is "large".
+                         For an overview of models see:
+                         https://docs.cohere.ai/docs/generation-card
+        delay_in_seconds: If a `batch_size` is given, use this set
+                          the delay in seconds between batches.
+        batch_size: The size of each batch.
+        embed_kwargs: Kwargs passed to `cohere.Client.embed`.
+                            Can be used to define additional parameters
+                            such as `input_type`
+
+    Examples:
+    ```python
+    import cohere
+    from bertopic.backend import CohereBackend
+
+    client = cohere.Client("APIKEY")
+    cohere_model = CohereBackend(client)
+    ```
+
+    If you want to specify `input_type`:
+
+    ```python
+    cohere_model = CohereBackend(
+        client,
+        embedding_model="embed-english-v3.0",
+        embed_kwargs={"input_type": "clustering"}
+    )
+    ```
+    """
+
+    def __init__(
+        self,
+        client,
+        embedding_model: str = "large",
+        delay_in_seconds: float = None,
+        batch_size: int = None,
+        embed_kwargs: Mapping[str, Any] = {},
+    ):
+        super().__init__()
+        self.client = client
+        self.embedding_model = embedding_model
+        self.delay_in_seconds = delay_in_seconds
+        self.batch_size = batch_size
+        self.embed_kwargs = embed_kwargs
+
+        if self.embed_kwargs.get("model"):
+            self.embedding_model = embed_kwargs.get("model")
+        else:
+            self.embed_kwargs["model"] = self.embedding_model
+
+    def embed(self, documents: List[str], verbose: bool = False) -> np.ndarray:
+        """Embed a list of n documents/words into an n-dimensional
+        matrix of embeddings.
+
+        Arguments:
+            documents: A list of documents or words to be embedded
+            verbose: Controls the verbosity of the process
+
+        Returns:
+            Document/words embeddings with shape (n, m) with `n` documents/words
+            that each have an embeddings size of `m`
+        """
+        # Batch-wise embedding extraction
+        if self.batch_size is not None:
+            embeddings = []
+            for batch in tqdm(self._chunks(documents), disable=not verbose):
+                response = self.client.embed(texts=batch, **self.embed_kwargs)
+                embeddings.extend(response.embeddings)
+
+                # Delay subsequent calls
+                if self.delay_in_seconds:
+                    time.sleep(self.delay_in_seconds)
+
+        # Extract embeddings all at once
+        else:
+            response = self.client.embed(texts=documents, **self.embed_kwargs)
+            embeddings = response.embeddings
+        return np.array(embeddings)
+
+    def _chunks(self, documents):
+        for i in range(0, len(documents), self.batch_size):
+            yield documents[i : i + self.batch_size]