--- a
+++ b/src/clustering/utils.py
@@ -0,0 +1,112 @@
+import torch
+import torch.nn.functional as F
+import numpy as np
+
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.dirname(__file__)))
+from config import *
+from utils.parse_data import parse_concept
+
+
+#Mean Pooling - Take average of all tokens
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output.last_hidden_state #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+#Encode text
+def encode(texts, tokenizer = tokenizer, model= model):
+    # Tokenize sentences
+    encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
+
+    # Compute token embeddings
+    with torch.no_grad():
+        model_output = model(**encoded_input, return_dict=True)
+
+    # Perform pooling
+    embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+    # Normalize embeddings
+    embeddings = F.normalize(embeddings, p=2, dim=1)
+    
+    return embeddings
+
+def find_cluster(query_emb, clustered_data, similarity=similarity):
+    best_cluster = None
+    best_score = -1
+    for i in clustered_data.keys():
+        center = clustered_data[i]["center"]
+        score = similarity(query_emb, center)
+        if score >= best_score:
+            best_cluster = i
+            best_score = score
+    return best_cluster
+
+def text_splitter(text, file_path):
+    con_file_path = os.path.dirname(os.path.dirname(file_path)) + os.sep + "concept" + os.sep + os.path.basename(file_path).split(".")[0] + ".con"
+    concepts_lines = list(set(parse_concept(con_file_path)["start_line"]))
+    concepts_lines.sort()
+    texts = text.split("\n")
+    concepts = []
+    for line in concepts_lines:
+        concepts.append(texts[line-1])
+    return concepts
+
+def semantic_search_base(query_emb, doc_emb, docs):
+    #Compute dot score between query and all document embeddings
+    scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
+
+    #Combine docs & scores
+    doc_score_pairs = list(zip(docs, scores))
+
+    #Sort by decreasing score
+    doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
+    print(doc_score_pairs)
+    #Output passages & scores
+    for doc, score in doc_score_pairs:
+        print("==> ",score) 
+        print(doc)
+        
+def forward(texts, tokenizer= tokenizer, model= model):
+    # Tokenize sentences
+    encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
+
+    # Compute token embeddings
+    model_output = model(**encoded_input, return_dict=True)
+
+    # Perform pooling
+    embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+    # Normalize embeddings
+    embeddings = F.normalize(embeddings, p=2, dim=1)
+    
+    return embeddings
+
+
+def forward_doc(text, file_path, tokenizer= tokenizer, model= model, no_grad= False):
+    texts = text_splitter(text, file_path) 
+    if len(texts) == 0:
+        return []
+    # Tokenize sentences
+    encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
+    
+    # Compute token embeddings
+    if no_grad:
+        with torch.no_grad():
+            model_output = model(**encoded_input, return_dict=True)
+    else :
+        model_output = model(**encoded_input, return_dict=True)
+
+    # Perform pooling
+    embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+    # NOTE: This is an easy approach
+    # another mean pooling over the lines of the document
+    # embeddings = torch.mean(embeddings_lines, 0).unsqueeze(0)
+    
+    # Normalize embeddings
+    embeddings = F.normalize(embeddings,  p=2, dim=1)
+    
+    return embeddings