Add BERTopic.

LLMTopicDetection_BERTopic/bertopic/backend/_langchain.py

@@ -0,0 +1,43 @@
+from typing import List
+
+import numpy as np
+from bertopic.backend import BaseEmbedder
+from langchain_core.embeddings import Embeddings
+
+
+class LangChainBackend(BaseEmbedder):
+    """LangChain Embedding Model.
+
+    This class uses the LangChain Embedding class to embed the documents.
+    Argument:
+        embedding_model: A LangChain Embedding Instance.
+
+    Examples:
+    ```python
+    from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+    from bertopic.backend import LangChainBackend
+
+    hf_embedding = HuggingFaceInstructEmbeddings()
+    langchain_embedder = LangChainBackend(hf_embedding)
+    ```
+    """
+
+    def __init__(self, embedding_model: Embeddings):
+        self.embedding_model = 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`
+        """
+        # Prepare documents, replacing empty strings with a single space
+        prepared_documents = [" " if doc == "" else doc for doc in documents]
+        response = self.embedding_model.embed_documents(prepared_documents)
+        return np.array(response)