Debugged reference to random_seed in vectorisation and reference to torch in representation_model.py

funcs/embeddings.py

@@ -25,7 +25,7 @@ else:
 
 
 @spaces.GPU
-def make_or_load_embeddings(docs: list, file_list: list, embeddings_out: np.ndarray, embeddings_super_compress: str, high_quality_mode_opt: str, embeddings_name:str="mixedbread-ai/mxbai-embed-xsmall-v1") -> np.ndarray:
+def make_or_load_embeddings(docs: list, file_list: list, embeddings_out: np.ndarray, embeddings_super_compress: str, high_quality_mode_opt: str, embeddings_name:str="mixedbread-ai/mxbai-embed-xsmall-v1", random_seed:int=42) -> np.ndarray:
     """
     Create or load embeddings for the given documents.
 
@@ -35,6 +35,7 @@ def make_or_load_embeddings(docs: list, file_list: list, embeddings_out: np.ndar
         embeddings_out (np.ndarray): Array to store the embeddings.
         embeddings_super_compress (str): Option to super compress embeddings ("Yes" or "No").
         high_quality_mode_opt (str): Option for high quality mode ("Yes" or "No").
+        random_seed (int): Random seed for vectorisation
 
     Returns:
         np.ndarray: The generated or loaded embeddings.

funcs/topic_core_funcs.py

@@ -326,7 +326,7 @@ def extract_topics(
     # UMAP model uses Bertopic defaults
     umap_model = UMAP(n_neighbors=umap_n_neighbours, n_components=5, min_dist=umap_min_dist, metric=umap_metric, low_memory=True, random_state=random_seed)
 
-    embeddings_out, embedding_model = make_or_load_embeddings(docs, file_list, embeddings_out, embeddings_super_compress, high_quality_mode, embeddings_name)
+    embeddings_out, embedding_model = make_or_load_embeddings(docs, file_list, embeddings_out, embeddings_super_compress, high_quality_mode, embeddings_name, random_seed)
 
      # If you want to save your embedding files
     if return_intermediate_files == "Yes":