Add BERTopic.

LLMTopicDetection_BERTopic/bertopic/backend/_base.py

@@ -0,0 +1,62 @@
+import numpy as np
+from typing import List
+
+
+class BaseEmbedder:
+    """The Base Embedder used for creating embedding models.
+
+    Arguments:
+        embedding_model: The main embedding model to be used for extracting
+                         document and word embedding
+        word_embedding_model: The embedding model used for extracting word
+                              embeddings only. If this model is selected,
+                              then the `embedding_model` is purely used for
+                              creating document embeddings.
+    """
+
+    def __init__(self, embedding_model=None, word_embedding_model=None):
+        self.embedding_model = embedding_model
+        self.word_embedding_model = word_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`
+        """
+        pass
+
+    def embed_words(self, words: List[str], verbose: bool = False) -> np.ndarray:
+        """Embed a list of n words into an n-dimensional
+        matrix of embeddings.
+
+        Arguments:
+            words: A list of words to be embedded
+            verbose: Controls the verbosity of the process
+
+        Returns:
+            Word embeddings with shape (n, m) with `n` words
+            that each have an embeddings size of `m`
+
+        """
+        return self.embed(words, verbose)
+
+    def embed_documents(self, document: List[str], verbose: bool = False) -> np.ndarray:
+        """Embed a list of n words into an n-dimensional
+        matrix of embeddings.
+
+        Arguments:
+            document: A list of documents to be embedded
+            verbose: Controls the verbosity of the process
+
+        Returns:
+            Document embeddings with shape (n, m) with `n` documents
+            that each have an embeddings size of `m`
+        """
+        return self.embed(document, verbose)