diff --git a/README.md b/README.md
index 01342a8a4..8d05f10fa 100644
--- a/README.md
+++ b/README.md
@@ -153,9 +153,35 @@ Allow unauthenticated request : Yes
 | GCS_FILE_CACHE          | Optional           | False         | If set to True, will save the files to process into GCS. If set to False, will save the files locally   |
 | ENTITY_EMBEDDING        | Optional           | False         | If set to True, It will add embeddings for each entity in database |
 | LLM_MODEL_CONFIG_ollama_<model_name>         | Optional      |               | Set ollama config as - model_name,model_local_url for local deployments |
+| RAGAS_EMBEDDING_MODEL         | Optional      | openai              | embedding model used by ragas evaluation framework                               |
 
 
-
+## For local llms (Ollama)
+1. Pull the docker imgage of ollama
+```bash
+docker pull ollama/ollama
+```
+2. Run the ollama docker image
+```bash
+docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
+```
+3. Execute any llm model ex🦙3
+```bash
+docker exec -it ollama ollama run llama3
+```
+4. Configure  env variable in docker compose or backend enviournment.
+```env
+LLM_MODEL_CONFIG_ollama_<model_name>
+#example
+LLM_MODEL_CONFIG_ollama_llama3=${LLM_MODEL_CONFIG_ollama_llama3-llama3,
+http://host.docker.internal:11434}
+```
+5. Configure the backend API url
+```env
+VITE_BACKEND_API_URL=${VITE_BACKEND_API_URL-backendurl}
+```
+6. Open the application in browser and select the ollama model for the extraction.
+7. Enjoy Graph Building.
 
 
 ## Usage
diff --git a/backend/example.env b/backend/example.env
index 75b817220..7fa3cb480 100644
--- a/backend/example.env
+++ b/backend/example.env
@@ -2,6 +2,7 @@ OPENAI_API_KEY = ""
 DIFFBOT_API_KEY = ""
 GROQ_API_KEY = ""
 EMBEDDING_MODEL = "all-MiniLM-L6-v2"
+RAGAS_EMBEDDING_MODEL = "openai"
 IS_EMBEDDING = "true"
 KNN_MIN_SCORE = "0.94"
 # Enable Gemini (default is False) | Can be False or True
@@ -40,4 +41,5 @@ LLM_MODEL_CONFIG_anthropic_claude_3_5_sonnet="model_name,anthropic_api_key"
 LLM_MODEL_CONFIG_fireworks_llama_v3_70b="model_name,fireworks_api_key"
 LLM_MODEL_CONFIG_bedrock_claude_3_5_sonnet="model_name,aws_access_key_id,aws_secret__access_key,region_name"
 LLM_MODEL_CONFIG_ollama_llama3="model_name,model_local_url"
+YOUTUBE_TRANSCRIPT_PROXY="https://user:pass@domain:port"
 
diff --git a/backend/score.py b/backend/score.py
index 46c4ba137..f7b8c4082 100644
--- a/backend/score.py
+++ b/backend/score.py
@@ -190,22 +190,22 @@ async def extract_knowledge_graph_from_file(
         if source_type == 'local file':
             merged_file_path = os.path.join(MERGED_DIR,file_name)
             logging.info(f'File path:{merged_file_path}')
-            result = await extract_graph_from_file_local_file(uri, userName, password, database, model, merged_file_path, file_name, allowedNodes, allowedRelationship, retry_condition)
+            uri_latency, result = await extract_graph_from_file_local_file(uri, userName, password, database, model, merged_file_path, file_name, allowedNodes, allowedRelationship, retry_condition)
 
         elif source_type == 's3 bucket' and source_url:
-            result = await extract_graph_from_file_s3(uri, userName, password, database, model, source_url, aws_access_key_id, aws_secret_access_key, file_name, allowedNodes, allowedRelationship, retry_condition)
+            uri_latency, result = await extract_graph_from_file_s3(uri, userName, password, database, model, source_url, aws_access_key_id, aws_secret_access_key, file_name, allowedNodes, allowedRelationship, retry_condition)
         
         elif source_type == 'web-url':
-            result = await extract_graph_from_web_page(uri, userName, password, database, model, source_url, file_name, allowedNodes, allowedRelationship, retry_condition)
+            uri_latency, result = await extract_graph_from_web_page(uri, userName, password, database, model, source_url, file_name, allowedNodes, allowedRelationship, retry_condition)
 
         elif source_type == 'youtube' and source_url:
-            result = await extract_graph_from_file_youtube(uri, userName, password, database, model, source_url, file_name, allowedNodes, allowedRelationship, retry_condition)
+            uri_latency, result = await extract_graph_from_file_youtube(uri, userName, password, database, model, source_url, file_name, allowedNodes, allowedRelationship, retry_condition)
 
         elif source_type == 'Wikipedia' and wiki_query:
-            result = await extract_graph_from_file_Wikipedia(uri, userName, password, database, model, wiki_query, language, file_name, allowedNodes, allowedRelationship, retry_condition)
+            uri_latency, result = await extract_graph_from_file_Wikipedia(uri, userName, password, database, model, wiki_query, language, file_name, allowedNodes, allowedRelationship, retry_condition)
 
         elif source_type == 'gcs bucket' and gcs_bucket_name:
-            result = await extract_graph_from_file_gcs(uri, userName, password, database, model, gcs_project_id, gcs_bucket_name, gcs_bucket_folder, gcs_blob_filename, access_token, file_name, allowedNodes, allowedRelationship, retry_condition)
+            uri_latency, result = await extract_graph_from_file_gcs(uri, userName, password, database, model, gcs_project_id, gcs_bucket_name, gcs_bucket_folder, gcs_blob_filename, access_token, file_name, allowedNodes, allowedRelationship, retry_condition)
         else:
             return create_api_response('Failed',message='source_type is other than accepted source')
         extract_api_time = time.time() - start_time
@@ -218,12 +218,13 @@ async def extract_knowledge_graph_from_file(
             result['logging_time'] = formatted_time(datetime.now(timezone.utc))
             result['elapsed_api_time'] = f'{extract_api_time:.2f}'
         logger.log_struct(result, "INFO")
+        result.update(uri_latency)
         logging.info(f"extraction completed in {extract_api_time:.2f} seconds for file name {file_name}")
         return create_api_response('Success', data=result, file_source= source_type)
     except Exception as e:
         message=f"Failed To Process File:{file_name} or LLM Unable To Parse Content "
         error_message = str(e)
-        graphDb_data_Access.update_exception_db(file_name,error_message)
+        graphDb_data_Access.update_exception_db(file_name,error_message, retry_condition)
         gcs_file_cache = os.environ.get('GCS_FILE_CACHE')
         if source_type == 'local file':
             if gcs_file_cache == 'True':
@@ -748,7 +749,7 @@ async def merge_duplicate_nodes(uri=Form(), userName=Form(), password=Form(), da
         gc.collect()
         
 @app.post("/drop_create_vector_index")
-async def merge_duplicate_nodes(uri=Form(), userName=Form(), password=Form(), database=Form(), isVectorIndexExist=Form()):
+async def drop_create_vector_index(uri=Form(), userName=Form(), password=Form(), database=Form(), isVectorIndexExist=Form()):
     try:
         payload_json_obj = {'api_name':'drop_create_vector_index', 'db_url':uri, 'userName':userName, 'database':database,
                             'isVectorIndexExist':isVectorIndexExist, 'logging_time': formatted_time(datetime.now(timezone.utc))}
diff --git a/backend/src/QA_integration.py b/backend/src/QA_integration.py
index 468069531..b7fcbd665 100644
--- a/backend/src/QA_integration.py
+++ b/backend/src/QA_integration.py
@@ -390,7 +390,7 @@ def get_neo4j_retriever(graph, document_names,chat_mode_settings, score_threshol
     try:
 
         neo_db = initialize_neo4j_vector(graph, chat_mode_settings)
-        document_names= list(map(str.strip, json.loads(document_names)))
+        # document_names= list(map(str.strip, json.loads(document_names)))
         search_k = chat_mode_settings["top_k"]
         retriever = create_retriever(neo_db, document_names,chat_mode_settings, search_k, score_threshold)
         return retriever
@@ -434,10 +434,6 @@ def process_chat_response(messages, history, question, model, graph, document_na
             result = {"sources": list(), "nodedetails": list(), "entities": list()}
             total_tokens = 0
             formatted_docs = ""
-
-        # question = transformed_question if transformed_question else question
-        # metrics = get_ragas_metrics(question,formatted_docs,content)
-        # print(metrics)
         
         ai_response = AIMessage(content=content)
         messages.append(ai_response)
@@ -580,7 +576,7 @@ def process_graph_response(model, graph, question, messages, history):
         summarization_thread = threading.Thread(target=summarize_and_log, args=(history, messages, qa_llm))
         summarization_thread.start()
         logging.info("Summarization thread started.")
-        
+        metric_details = {"question":question,"contexts":graph_response.get("context", ""),"answer":ai_response_content}
         result = {
             "session_id": "", 
             "message": ai_response_content,
@@ -589,7 +585,8 @@ def process_graph_response(model, graph, question, messages, history):
                 "cypher_query": graph_response.get("cypher_query", ""),
                 "context": graph_response.get("context", ""),
                 "mode": "graph",
-                "response_time": 0
+                "response_time": 0,
+                "metric_details": metric_details,
             },
             "user": "chatbot"
         }
@@ -659,7 +656,25 @@ def QA_RAG(graph,model, question, document_names, session_id, mode, write_access
         result = process_graph_response(model, graph, question, messages, history)
     else:
         chat_mode_settings = get_chat_mode_settings(mode=mode)
-        result = process_chat_response(messages,history, question, model, graph, document_names,chat_mode_settings)
+        document_names= list(map(str.strip, json.loads(document_names)))
+        if document_names and not chat_mode_settings["document_filter"]:
+            result =  {
+                "session_id": "",  
+                "message": "This chat mode does support document selection",
+                "info": {
+                    "sources": [],
+                    "model": "",
+                    "nodedetails": [],
+                    "total_tokens": 0,
+                    "response_time": 0,
+                    "mode": chat_mode_settings["mode"],
+                    "entities": [],
+                    "metric_details": [],
+                },
+                "user": "chatbot"
+            }
+        else:
+            result = process_chat_response(messages,history, question, model, graph, document_names,chat_mode_settings)
 
     result["session_id"] = session_id
     
diff --git a/backend/src/document_sources/youtube.py b/backend/src/document_sources/youtube.py
index fce62774d..e30de301e 100644
--- a/backend/src/document_sources/youtube.py
+++ b/backend/src/document_sources/youtube.py
@@ -17,22 +17,25 @@
 def get_youtube_transcript(youtube_id):
   try:
     #transcript = YouTubeTranscriptApi.get_transcript(youtube_id)
-    transcript_list = YouTubeTranscriptApi.list_transcripts(youtube_id)
-    transcript = transcript_list.find_transcript(["en"])
-    transcript_pieces: List[Dict[str, Any]] = transcript.fetch()
+    # transcript_list = YouTubeTranscriptApi.list_transcripts(youtube_id)
+    # transcript = transcript_list.find_transcript(["en"])
+    # transcript_pieces: List[Dict[str, Any]] = transcript.fetch()
+    proxy = os.environ.get("YOUTUBE_TRANSCRIPT_PROXY") 
+    proxies = { 'https': proxy }
+    transcript_pieces = YouTubeTranscriptApi.get_transcript(youtube_id, proxies = proxies)
     return transcript_pieces
   except Exception as e:
     message = f"Youtube transcript is not available for youtube Id: {youtube_id}"
     raise Exception(message)
   
-def get_youtube_combined_transcript(youtube_id):
-  try:
-    transcript_dict = get_youtube_transcript(youtube_id)
-    transcript = YouTubeTranscriptApi.get_transcript(youtube_id)
-    return transcript
-  except Exception as e:
-    message = f"Youtube transcript is not available for youtube Id: {youtube_id}"
-    raise Exception(message)
+# def get_youtube_combined_transcript(youtube_id):
+#   try:
+#     transcript_dict = get_youtube_transcript(youtube_id)
+#     transcript = YouTubeTranscriptApi.get_transcript(youtube_id)
+#     return transcript
+#   except Exception as e:
+#     message = f"Youtube transcript is not available for youtube Id: {youtube_id}"
+#     raise Exception(message)
   
 def get_youtube_combined_transcript(youtube_id):
   try:
diff --git a/backend/src/graphDB_dataAccess.py b/backend/src/graphDB_dataAccess.py
index e3b923f37..189d581a6 100644
--- a/backend/src/graphDB_dataAccess.py
+++ b/backend/src/graphDB_dataAccess.py
@@ -17,14 +17,19 @@ class graphDBdataAccess:
     def __init__(self, graph: Neo4jGraph):
         self.graph = graph
 
-    def update_exception_db(self, file_name, exp_msg):
+    def update_exception_db(self, file_name, exp_msg, retry_condition):
         try:
             job_status = "Failed"
             result = self.get_current_status_document_node(file_name)
             is_cancelled_status = result[0]['is_cancelled']
             if bool(is_cancelled_status) == True:
                 job_status = 'Cancelled'
-            self.graph.query("""MERGE(d:Document {fileName :$fName}) SET d.status = $status, d.errorMessage = $error_msg""",
+            if retry_condition is not None: 
+                retry_condition = None
+                self.graph.query("""MERGE(d:Document {fileName :$fName}) SET d.status = $status, d.errorMessage = $error_msg, d.retry_condition = $retry_condition""",
+                            {"fName":file_name, "status":job_status, "error_msg":exp_msg, "retry_condition":retry_condition})
+            else :    
+                self.graph.query("""MERGE(d:Document {fileName :$fName}) SET d.status = $status, d.errorMessage = $error_msg""",
                             {"fName":file_name, "status":job_status, "error_msg":exp_msg})
         except Exception as e:
             error_message = str(e)
@@ -385,13 +390,14 @@ def get_duplicate_nodes_list(self):
                 [s in similar | s {.id, .description, labels:labels(s), elementId: elementId(s)}] as similar,
                 collect(distinct doc.fileName) as documents, count(distinct c) as chunkConnections
                 ORDER BY e.id ASC
+                LIMIT 100
                 """
-        return_query_duplicate_nodes_total = "RETURN COUNT(DISTINCT(n)) as total"
+        total_duplicate_nodes = "RETURN COUNT(DISTINCT(n)) as total"
         
         param = {"duplicate_score_value": score_value, "duplicate_text_distance" : text_distance}
         
         nodes_list = self.execute_query(query_duplicate_nodes.format(return_statement=return_query_duplicate_nodes),param=param)
-        total_nodes = self.execute_query(query_duplicate_nodes.format(return_statement=return_query_duplicate_nodes_total),param=param)
+        total_nodes = self.execute_query(query_duplicate_nodes.format(return_statement=total_duplicate_nodes),param=param)
         return nodes_list, total_nodes[0]
     
     def merge_duplicate_nodes(self,duplicate_nodes_list):
diff --git a/backend/src/main.py b/backend/src/main.py
index 425a24451..05f83f073 100644
--- a/backend/src/main.py
+++ b/backend/src/main.py
@@ -155,7 +155,7 @@ def create_source_node_graph_url_youtube(graph, model, source_url, source_type):
     #   logging.warning("credntial file path not exist")
 
     video_id = parse_qs(urlparse(youtube_url).query).get('v')
-    print(f'Video Id Youtube: {video_id}')
+   
     # google_api_client = GoogleApiClient(service_account_path=Path(file_path))
     # youtube_loader_channel = GoogleApiYoutubeLoader(
     # google_api_client=google_api_client,
@@ -165,6 +165,7 @@ def create_source_node_graph_url_youtube(graph, model, source_url, source_type):
     # page_content = youtube_transcript[0].page_content
 
     obj_source_node.file_name = match.group(1)#youtube_transcript[0].metadata["snippet"]["title"]
+    #obj_source_node.file_name = YouTube(youtube_url).title
     transcript= get_youtube_combined_transcript(match.group(1))
     print(transcript)
     if transcript==None or len(transcript)==0:
@@ -423,19 +424,20 @@ async def processing_source(uri, userName, password, database, model, file_name,
         uri_latency["Per_entity_latency"] = 'N/A'
       else:  
         uri_latency["Per_entity_latency"] = f'{int(processing_source_func)/node_count}/s'
-      uri_latency["fileName"] = file_name
-      uri_latency["nodeCount"] = node_count
-      uri_latency["relationshipCount"] = rel_count
-      uri_latency["total_processing_time"] = round(processed_time.total_seconds(),2)
-      uri_latency["status"] = job_status
-      uri_latency["model"] = model
-      uri_latency["success_count"] = 1
+      response = {}  
+      response["fileName"] = file_name
+      response["nodeCount"] = node_count
+      response["relationshipCount"] = rel_count
+      response["total_processing_time"] = round(processed_time.total_seconds(),2)
+      response["status"] = job_status
+      response["model"] = model
+      response["success_count"] = 1
       
-      return uri_latency
+      return uri_latency, response
     else:
       logging.info('File does not process because it\'s already in Processing status')
   else:
-    error_message = "Unable to get the status of docuemnt node."
+    error_message = "Unable to get the status of document node."
     logging.error(error_message)
     raise Exception(error_message)
 
diff --git a/backend/src/shared/constants.py b/backend/src/shared/constants.py
index cde354f16..b58fd3a67 100644
--- a/backend/src/shared/constants.py
+++ b/backend/src/shared/constants.py
@@ -560,12 +560,12 @@
 
 CHAT_VECTOR_MODE = "vector"
 CHAT_FULLTEXT_MODE = "fulltext"
-CHAT_ENTITY_VECTOR_MODE = "entity search+vector"
-CHAT_VECTOR_GRAPH_MODE = "graph+vector"
-CHAT_VECTOR_GRAPH_FULLTEXT_MODE = "graph+vector+fulltext"
-CHAT_GLOBAL_VECTOR_FULLTEXT_MODE = "global search+vector+fulltext"
+CHAT_ENTITY_VECTOR_MODE = "entity_vector"
+CHAT_VECTOR_GRAPH_MODE = "graph_vector"
+CHAT_VECTOR_GRAPH_FULLTEXT_MODE = "graph_vector_fulltext"
+CHAT_GLOBAL_VECTOR_FULLTEXT_MODE = "global_vector"
 CHAT_GRAPH_MODE = "graph"
-CHAT_DEFAULT_MODE = "graph+vector+fulltext"
+CHAT_DEFAULT_MODE = "graph_vector_fulltext"
 
 CHAT_MODE_CONFIG_MAP= {
         CHAT_VECTOR_MODE : {
diff --git a/backend/test_integrationqa.py b/backend/test_integrationqa.py
index 0281c91f8..548e9706f 100644
--- a/backend/test_integrationqa.py
+++ b/backend/test_integrationqa.py
@@ -1,15 +1,19 @@
 import json
+import asyncio
 import os
 import shutil
 import logging
 import pandas as pd
 from datetime import datetime as dt
 from dotenv import load_dotenv
-from score import *
+# from score import *
 from src.main import *
 from src.QA_integration import QA_RAG
 from langserve import add_routes
-
+from src.ragas_eval import get_ragas_metrics
+from datasets import Dataset
+from ragas import evaluate
+from ragas.metrics import answer_relevancy, context_utilization, faithfulness
 # Load environment variables if needed
 load_dotenv()
 import os 
@@ -57,11 +61,11 @@ def test_graph_from_file_local(model_name):
    create_source_node_local(graph, model_name, file_name)
    merged_file_path = os.path.join(MERGED_DIR, file_name)
 
-   local_file_result = extract_graph_from_file_local_file(
-       URI, USERNAME, PASSWORD, DATABASE, model_name, merged_file_path, file_name, '', '',None
-   )
+   local_file_result = asyncio.run(extract_graph_from_file_local_file(
+       URI, USERNAME, PASSWORD, DATABASE, model_name, merged_file_path, file_name, '', '',None))
    logging.info("Local file processing complete")
    print(local_file_result)
+   return local_file_result
 
 #    try:
 #        assert local_file_result['status'] == 'Completed'
@@ -72,19 +76,19 @@ def test_graph_from_file_local(model_name):
 #        print("Fail: ", e)
 
     # Delete the file after processing
-   delete_extracted_files(merged_file_path)
+#    delete_extracted_fiKles(merged_file_path)
 
-   return local_file_result
+   #return local_file_result
 
 def test_graph_from_wikipedia(model_name):
    # try:
        """Test graph creation from a Wikipedia page."""
-       wiki_query = 'https://en.wikipedia.org/wiki/Ram_Mandir'
+       wiki_query = 'https://en.wikipedia.org/wiki/Berkshire_Hathaway'
        source_type = 'Wikipedia'
-       file_name = "Ram_Mandir"
+       file_name = "Berkshire_Hathaway"
        create_source_node_graph_url_wikipedia(graph, model_name, wiki_query, source_type)
 
-       wiki_result = extract_graph_from_file_Wikipedia(URI, USERNAME, PASSWORD, DATABASE, model_name, file_name, 'en',file_name, '', '',None)
+       wiki_result = asyncio.run(extract_graph_from_file_Wikipedia(URI, USERNAME, PASSWORD, DATABASE, model_name, file_name, 'en',file_name, '', '',None))
        logging.info("Wikipedia test done")
        print(wiki_result)
        try:
@@ -107,7 +111,7 @@ def test_graph_website(model_name):
    file_name = []
    create_source_node_graph_web_url(graph, model_name, source_url, source_type)
 
-   weburl_result = extract_graph_from_web_page(URI, USERNAME, PASSWORD, DATABASE, model_name, source_url,file_name, '', '',None)
+   weburl_result = asyncio.run(extract_graph_from_web_page(URI, USERNAME, PASSWORD, DATABASE, model_name, source_url,file_name, '', '',None))
    logging.info("WebUrl test done")
    print(weburl_result)
 
@@ -164,9 +168,9 @@ def disconected_nodes():
    print(nodes_list[0]["e"]["elementId"])
    status = "False"
    if total_nodes['total']>0:
-       status = "True"
+       status = "get_unconnected_nodes_list.. records loaded successfully"
    else:
-       status = "False"
+       status = "get_unconnected_nodes_list ..records not loaded"
    return nodes_list[0]["e"]["elementId"], status
   
 #Test Delete delete_disconnected_nodes list
@@ -177,9 +181,9 @@ def delete_disconected_nodes(lst_element_id):
    result = graphDb_data_Access.delete_unconnected_nodes(json.dumps(lst_element_id))
    print(f'delete disconnect api result {result}')
    if not result:
-       return "True"
+       return "delete_unconnected_nodes..Succesfully deleted first index of disconnected nodes"
    else:
-       return "False"
+       return "delete_unconnected_nodes..Unable to delete Nodes"
 
 #Test Get Duplicate_nodes
 def get_duplicate_nodes():
@@ -187,28 +191,34 @@ def get_duplicate_nodes():
        graphDb_data_Access = graphDBdataAccess(graph)
        nodes_list, total_nodes = graphDb_data_Access.get_duplicate_nodes_list()
        if total_nodes['total']>0:
-           return "True"
+           return "Data successfully loaded"
        else:
-           return "False"
+           return "Unable to load data"
       
 #Test populate_graph_schema
-def test_populate_graph_schema_from_text(model):
-   result_schema = populate_graph_schema_from_text('When Amazon was founded in 1993 by creator Jeff Benzos, it was mostly an online bookstore. Initially Amazon’s growth was very slow, not turning a profit until over 7 years after its founding. This was thanks to the great momentum provided by the dot-com bubble.', model, True)
-   print(result_schema)
-   return result_schema
-
+def test_populate_graph_schema_from_text(model_name):
+    schema_text =('Amazon was founded on July 5, 1994, by Jeff Bezos in Bellevue, Washington.The company originally started as an online marketplace for books but gradually expanded its offerings to include a wide range of product categories. This diversification led to it being referred.')
+    #result_schema=''
+    try:
+        result_schema = populate_graph_schema_from_text(schema_text, model_name, True)
+        print(result_schema)
+        return result_schema
+    except Exception as e:
+       print("Failed to get schema from text", e)
+       return e
 
 def run_tests():
    final_list = []
    error_list = []
-   models = ['openai-gpt-3.5','openai-gpt-4o','openai-gpt-4o-mini','gemini-1.5-pro','gemini 1.5 Flash','azure_ai_gpt_35','azure_ai_gpt_4o','ollama_llama3','ollama','groq_llama3_70b','anthropic_claude_3_5_sonnet','bedrock_claude_3_5_sonnet','fireworks_llama_v3p2_90b']
+   
+   models = ['openai_gpt_3_5','openai_gpt_4o','openai_gpt_4o_mini','azure-ai-gpt-35','azure-ai-gpt-4o','gemini_1_5_pro','gemini_1_5_flash','anthropic-claude-3-5-sonnet','bedrock-claude-3-5-sonnet','groq-llama3-70b','fireworks-llama-v3-70b']
 
    for model_name in models:
        try:
                 final_list.append(test_graph_from_file_local(model_name))
                 final_list.append(test_graph_from_wikipedia(model_name))
-                final_list.append(test_populate_graph_schema_from_text(model_name))
                 final_list.append(test_graph_website(model_name))
+                final_list.append(test_populate_graph_schema_from_text(model_name))
                 final_list.append(test_graph_from_youtube_video(model_name))
                 final_list.append(test_chatbot_qna(model_name))
                 final_list.append(test_chatbot_qna(model_name, mode='vector'))
@@ -219,23 +229,25 @@ def run_tests():
                 
        except Exception as e:
            error_list.append((model_name, str(e)))
-   # #Compare and log diffrences in graph results
-   # # compare_graph_results(final_list)  # Pass the final_list to comapre_graph_results
-   # test_populate_graph_schema_from_text('openai-gpt-4o')
-#    dis_elementid, dis_status = disconected_nodes()
-#    lst_element_id = [dis_elementid]
-#    delt = delete_disconected_nodes(lst_element_id)
+
+#    test_populate_graph_schema_from_text('openai-gpt-4o')
+#delete diconnected nodes
+   dis_elementid, dis_status = disconected_nodes()
+   lst_element_id = [dis_elementid]
+   delt = delete_disconected_nodes(lst_element_id)
 #    dup = get_duplicate_nodes()
    print(final_list)
-   # schma = test_populate_graph_schema_from_text(model)
+   schma = test_populate_graph_schema_from_text(model_name)
    # Save final results to CSV
    df = pd.DataFrame(final_list)
    print(df)
    df['execution_date'] = dt.today().strftime('%Y-%m-%d')
-#    df['disconnected_nodes']=dis_status
+#diconnected nodes   
+   df['disconnected_nodes']=dis_status
 #    df['get_duplicate_nodes']=dup
-#    df['delete_disconected_nodes']=delt
-   # df['test_populate_graph_schema_from_text'] = schma
+
+   df['delete_disconected_nodes']=delt
+   df['test_populate_graph_schema_from_text'] = schma
    df.to_csv(f"Integration_TestResult_{dt.now().strftime('%Y%m%d_%H%M%S')}.csv", index=False)
 
    # Save error details to CSV
diff --git a/docker-compose.yml b/docker-compose.yml
index 7761704c7..ea6d2c050 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -52,16 +52,17 @@ services:
       dockerfile: Dockerfile
       args:
         - VITE_BACKEND_API_URL=${VITE_BACKEND_API_URL-http://localhost:8000}
-        - VITE_REACT_APP_SOURCES=${VITE_REACT_APP_SOURCES-local,youtube,wiki,s3}
-        - VITE_LLM_MODELS=${VITE_LLM_MODELS-diffbot,openai-gpt-3.5,openai-gpt-4o}
-        - VITE_GOOGLE_CLIENT_ID=${VITE_GOOGLE_CLIENT_ID-""}
+        - VITE_REACT_APP_SOURCES=${VITE_REACT_APP_SOURCES-local,wiki,s3}
+        - VITE_LLM_MODELS=${VITE_LLM_MODELS-}
+        - VITE_GOOGLE_CLIENT_ID=${VITE_GOOGLE_CLIENT_ID-}
         - VITE_BLOOM_URL=${VITE_BLOOM_URL-https://workspace-preview.neo4j.io/workspace/explore?connectURL={CONNECT_URL}&search=Show+me+a+graph&featureGenAISuggestions=true&featureGenAISuggestionsInternal=true}
         - VITE_TIME_PER_PAGE=${VITE_TIME_PER_PAGE-50}
         - VITE_CHUNK_SIZE=${VITE_CHUNK_SIZE-5242880}
         - VITE_LARGE_FILE_SIZE=${VITE_LARGE_FILE_SIZE-5242880}
         - VITE_ENV=${VITE_ENV-DEV}
-        - VITE_CHAT_MODES=${VITE_CHAT_MODES-""}
+        - VITE_CHAT_MODES=${VITE_CHAT_MODES-}
         - VITE_BATCH_SIZE=${VITE_BATCH_SIZE-2}
+        - VITE_LLM_MODELS_PROD=${VITE_LLM_MODELS_PROD-openai_gpt_4o,openai_gpt_4o_mini,diffbot,gemini_1.5_flash}
     volumes:
       - ./frontend:/app
       - /app/node_modules
diff --git a/docs/backend/backend_docs.adoc b/docs/backend/backend_docs.adoc
index 91b39260c..2591ac47e 100644
--- a/docs/backend/backend_docs.adoc
+++ b/docs/backend/backend_docs.adoc
@@ -22,9 +22,15 @@ Neo4j database connection on frontend is done with this API.
 [source,json,indent=0]
 ----
 {
-    "status":"Success",
-    "message":"Connection Successful"
-}
+    "status": "Success",
+    "data": {
+        "db_vector_dimension": 384,
+        "application_dimension": 384,
+        "message": "Connection Successful",
+        "gds_status": true,
+        "write_access": true,
+        "elapsed_api_time": "5.48"
+    }
 ----
 
 
@@ -361,10 +367,12 @@ The API responsible for a chatbot system designed to leverage multiple AI models
 
 **Components :** 
  
-** Embedding Models - Includes OpenAI Embeddings, VertexAI Embeddings, and SentenceTransformer Embeddings to support vector-based query operations.
-** AI Models - OpenAI GPT 3.5, GPT 4o, Gemini Pro, Gemini 1.5 Pro and Groq llama3 can be configured for the chatbot backend to generate responses and process natural language.
+** Embedding Models - Includes OpenAI Embeddings, VertexAI Embeddings, and SentenceTransformer Embeddings(Default) to support vector-based query operations.
+** AI Models - OpenAI GPT 3.5, GPT 4o, GPT 40 mini, gemini_1.5_flash can be configured for the chatbot backend to generate responses and process natural language.
 ** Graph Database (Neo4jGraph) - Manages interactions with the Neo4j database, retrieving, and storing conversation histories.
 ** Response Generation - Utilizes Vector Embeddings from the Neo4j database, chat history, and the knowledge base of the LLM used.
+** Chat Modes - Vector , Graph, Vector + Graph, Fulltext, Vector + Graph+Fulltext, Entity Search + Vector, Global search Vector 
+
 
 **API Parameters :**
 
@@ -373,7 +381,9 @@ The API responsible for a chatbot system designed to leverage multiple AI models
 * `password`= Neo4j database password
 * `model`= LLM model
 * `question`= User query for the chatbot
-* `session_id`= Session ID used to maintain the history of chats during the user's connection
+* `session_id`= Session ID used to maintain the history of chats during the user's connection 
+* `mode` = chat mode to use 
+* `document_names` = the names of documents to be filtered works for vector mode and vector+Graph mode 
 
 **Response :**
 [source,json,indent=0]
@@ -381,26 +391,46 @@ The API responsible for a chatbot system designed to leverage multiple AI models
 {
     "status": "Success",
     "data": {
-        "session_id": "0901",
-        "message": "Fibrosis, also known as fibrotic scarring, is a pathological wound healing process where connective tissue replaces normal parenchymal tissue."
+
+        "session_id": "0cbd04a8-abc3-4776-b393-6a9a2cea36b3",
+        "message": "response generated by the chat",
         "info": {
             "sources": [
-                {
-                    "source_name": "https://en.wikipedia.org/wiki/Fibrosis",
-                    "page_numbers": [],
-                    "start_time": []
-                }
+                "About Amazon.pdf"
             ],
-            "model": "gpt-4o",
-            "chunkids": [
-                "54d8c0dbefb67f1ed3f6939d59267e1ff557a94c",
-                "4cc02ee8419706c8decdf71ab0d3896aad5c7dca",
-                "266ce95311bb1921791b4f1cd29a48d433027139",
-                "11e19513247e1e396475728fa6a197695045b248",
-                "8bafa01b6d851f70822bcb86863e485e1785a64c"
-            ],
-            "total_tokens": 2213,
-            "response_time": 10.17
+            "model": "gpt-4o-2024-08-06",
+            "nodedetails": {
+                "chunkdetails": [
+                    {
+                        "id": "73bc9c9170bcd807d2fa87d87a0eeb3d82f95160",
+                        "score": 1.0
+                    },
+                    {
+                        "id": "de5486776978353c9f8ac530bcff33eeecbdbbad",
+                        "score": 0.9425
+                    }
+                ],
+                "entitydetails": [],
+                "communitydetails": []
+            },
+            "total_tokens": 4575,
+            "response_time": 17.19,
+            "mode": "graph_vector_fulltext",
+            "entities": {
+                "entityids": [
+                    "4:98e5e9bb-8095-440d-9462-03985fed2fa2:307",
+                    "4:98e5e9bb-8095-440d-9462-03985fed2fa2:1877",
+                ],
+                "relationshipids": [
+                    "5:98e5e9bb-8095-440d-9462-03985fed2fa2:8072566611095062357",
+                    "5:98e5e9bb-8095-440d-9462-03985fed2fa2:8072566508015847224"
+                ]
+            },
+            "metric_details": {
+                "question": "tell me about amazon ",
+                "contexts": "context sent to LLM"
+                "answer": "response generated by the LLM"
+            }
         },
         "user": "chatbot"
     }
@@ -420,7 +450,8 @@ This API is used to  get the entities and relations associated with a particular
 * `userName`= Neo4j db username, 
 * `password`= Neo4j db password, 
 * `database`= Neo4j database name
-* `chunk_ids` = Chunk ids of document
+* `nodedetails` = Node element id's to get information(chunks,entities,communities)
+* `entities` = entities received from the retriver for graph based modes
 
 
 **Response :**
@@ -431,42 +462,51 @@ This API is used to  get the entities and relations associated with a particular
     "data": {
         "nodes": [
             {
-                "element_id": "4:a69712a5-1102-40da-a96d-70c1143ea8e5:73267",
+                "element_id": "4:98e5e9bb-8095-440d-9462-03985fed2fa2:307",
                 "labels": [
-                    "Condition"
+                    "Company"
                 ],
                 "properties": {
-                    "id": "Fibrosis"
+                    "id": "Amazon",
+                    "description": "Initially an online bookstore, Amazon has transformed into a $48 billion retail giant, offering products in over forty categories, from books and electronics to groceries. Today, it operates as a logistics platform, a search engine, an Internet advertising platform, an e-commerce platform, and an IT platform."
                 }
-            },
- 
+            }
         ],
         "relationships": [
             {
-                "element_id": "5:a69712a5-1102-40da-a96d-70c1143ea8e5:1153057844048764467",
-                "type": "AFFECTS",
-                "start_node_element_id": "4:a69712a5-1102-40da-a96d-70c1143ea8e5:73267",
-                "end_node_element_id": "4:a69712a5-1102-40da-a96d-70c1143ea8e5:73282"
-            },
-            {
-                "element_id": "5:a69712a5-1102-40da-a96d-70c1143ea8e5:1155309643862449715",
-                "type": "AFFECTS",
-                "start_node_element_id": "4:a69712a5-1102-40da-a96d-70c1143ea8e5:73267",
-                "end_node_element_id": "4:a69712a5-1102-40da-a96d-70c1143ea8e5:73294"
-            },
+                "element_id": "5:98e5e9bb-8095-440d-9462-03985fed2fa2:6917952339617775946",
+                "type": "OFFERS",
+                "start_node_element_id": "4:98e5e9bb-8095-440d-9462-03985fed2fa2:307",
+                "end_node_element_id": "4:98e5e9bb-8095-440d-9462-03985fed2fa2:330"
+            }
         ],
         "chunk_data": [
             {
-                "id": "54d8c0dbefb67f1ed3f6939d59267e1ff557a94c",
-                "position": 1,
-                "text": "Fibrosis, also known as fibrotic scarring, is a pathological wound healing ...",
-                "content_offset": 0,
-                "fileName": "fibrosis",
-                "length": 1002,
+                "element_id": "4:98e5e9bb-8095-440d-9462-03985fed2fa2:14",
+                "id": "d1e92be81a0872d621242cee9fed69d14b0cd68d",
+                "position": 13,
+                "text": " 6 eBay, operating as the biggest online auction house and focusing as a service provider, employs cost leadership strategy by solely operating e-commerce as an intermediary without holding any inventories or physical infrastructures. It also applies a differentiation strategy by providing a ....",
+                "content_offset": 9886,
+                "fileName": "About Amazon.pdf",
+                "page_number": 7,
+                "length": 1024,
+                "fileSource": "local file",
                 "embedding": null
             }
+        ],
+        "community_data": [
+            {
+                "element_id": "4:98e5e9bb-8095-440d-9462-03985fed2fa2:1026",
+                "summary": "Google, led by CEO Sundar Pichai, is actively involved in various business and product initiatives.",
+                "id": "0-311",
+                "level": 0,
+                "weight": 7,
+                "embedding": null,
+                "community_rank": 1
+            }
         ]
-    }
+    },
+    "message": "Total elapsed API time 3.75"
 }
 ....
 
@@ -524,6 +564,63 @@ This API is used to view graph for a particular file.
 }    
 ....
 
+=== Get neighbour nodes 
+----
+POST /get_neighbours
+----
+
+This API is used to retrive the neighbor nodes of the given element id of the node.
+
+**API Parameters :**
+
+* `uri`=Neo4j uri, 
+* `userName`= Neo4j db username, 
+* `password`= Neo4j db password, 
+* `database`= Neo4j database name,
+* `elementId` = Element id of the node to retrive its neighbours
+
+
+**Response :**
+[source,json,indent=0]
+....
+{
+    "status": "Success",
+    "data": {
+        "nodes": [
+            {
+                "summary": null,
+                "element_id": "4:98e5e9bb-8095-440d-9462-03985fed2fa2:3",
+                "id": "73bc9c9170bcd807d2fa87d87a0eeb3d82f95160",
+                "position": 2,
+                "text": null,
+                "content_offset": 186,
+                "labels": [
+                    "Chunk"
+                ],
+                "page_number": 2,
+                "fileName": "About Amazon.pdf",
+                "length": 904,
+                "properties": {
+                    "id": "73bc9c9170bcd807d2fa87d87a0eeb3d82f95160"
+                },
+                "embedding": null
+            }
+        ],
+        "relationships": [
+            {
+                "element_id": "5:98e5e9bb-8095-440d-9462-03985fed2fa2:1175445000301838339",
+                "end_node_element_id": "4:98e5e9bb-8095-440d-9462-03985fed2fa2:18",
+                "start_node_element_id": "4:98e5e9bb-8095-440d-9462-03985fed2fa2:3",
+                "type": "HAS_ENTITY"
+            },
+        ]
+    },
+    "message": "Total elapsed API time 0.24"
+}
+....
+
+
+
 === Clear chat history
 ----
 POST /clear_chat_bot
@@ -699,7 +796,7 @@ The API is used to delete unconnected entities from database.
 ....
 
 
-== Decisions
+==== Decisions
 
 * Process only 1st page of Wikipedia
 * Split document content into chunks of size 200 and overlap of 20
@@ -709,3 +806,176 @@ The API is used to delete unconnected entities from database.
 ** Embedding model
 ** minimum score for KNN graph
 ** Uploaded file storage location (GCS bucket or container)
+
+
+=== Get duplicate nodes
+----
+POST /get_duplicate_nodes
+----
+
+The API is used to fetch duplicate entities from database.
+
+**API Parameters :**
+
+* `uri`=Neo4j uri, 
+* `userName`= Neo4j db username, 
+* `password`= Neo4j db password, 
+* `database`= Neo4j database name,
+
+
+**Response :**
+[source,json,indent=0]
+....
+{
+    "status": "Success",
+    "data": [
+        {
+            "e": {
+                "id": "13 September 2024",
+                "elementId": "4:b104b2e7-e2ed-4902-b78b-7ad1518ca04f:14007",
+                "communities": [
+                    2969,
+                    383,
+                    81
+                ],
+                "labels": [
+                    "__Entity__",
+                    "Date"
+                ],
+                "embedding": null
+            },
+            "similar": [
+                {
+                    "id": "20 September 2024",
+                    "elementId": "4:b104b2e7-e2ed-4902-b78b-7ad1518ca04f:14153",
+                    "description": null,
+                    "labels": [
+                        "__Entity__",
+                        "Date"
+                    ]
+                }
+            ],
+            "documents": [],
+            "chunkConnections": 0
+        }
+    ],
+    "message": {
+        "total": 1
+    }
+}
+....
+
+
+=== Merge duplicate nodes
+----
+POST /merge_duplicate_nodes
+----
+
+The API is used to merge duplicate entities from database selected by user.
+
+**API Parameters :**
+
+* `uri`=Neo4j uri, 
+* `userName`= Neo4j db username, 
+* `password`= Neo4j db password, 
+* `database`= Neo4j database name,
+* `duplicate_nodes_list`= selected entities list to merge of with similar entities.
+
+**Response :**
+[source,json,indent=0]
+....
+{
+    "status": "Success",
+    "data": [
+        {
+            "totalMerged": 2
+        }
+    ],
+    "message": "Duplicate entities merged successfully"
+}
+....
+=== Drop and create vector index
+----
+POST /drop_create_vector_index
+----
+
+The API is used to drop and create the vector index when vector index dimesion are different.
+
+**API Parameters :**
+
+* `uri`=Neo4j uri, 
+* `userName`= Neo4j db username, 
+* `password`= Neo4j db password, 
+* `database`= Neo4j database name,
+* `isVectorIndexExist`= True or False based on whether vector index exist in database,
+
+**Response :**
+[source,json,indent=0]
+....
+{
+    "status": "Success",
+    "message": "Drop and Re-Create vector index succesfully"
+}
+....
+
+=== Reprocessing of sources
+----
+POST /retry_processing
+----
+ 
+This API is used to reprocess cancelled, completed or failed file sources.
+Users have 3 options to reprocess files:
+ 
+* Start from begnning - In this condition file will be processed from the begnning i.e. 1st chunk again.
+* Delete entities and start from begnning - If the file source is already processed and have any existing nodes and relations then those will be deleted and file will be reprocessed from the 1st chunk.
+* Start from last processed postion - Cancelled or failed files will be processed from the last successfully processed chunk position. This option is not available for completed files.
+ 
+Ones the status is set to 'Reprocess', user can again click on Generate graph to process the file for knowledge graph creation.
+ 
+**API Parameters :**
+ 
+* `uri`=Neo4j uri,
+* `userName`= Neo4j db username,
+* `password`= Neo4j db password,
+* `database`= Neo4j database name,
+* `file_name`= Name of the file which user want to reprocess.
+* `retry_condition` = One of the above 3 conditions which is selected for reprocessing.
+ 
+ 
+**Response :**
+[source,json,indent=0]
+....
+{
+    "status": "Success",
+    "message": "Status set to Reprocess for filename : $filename"
+}
+....
+
+=== Evaluate response
+----
+POST /metric
+----
+
+The API responsible for a evaluating chatbot responses on the basis of different metrics such as faithfulness and answer relevancy. This utilises RAGAS library to calculate these metrics.
+
+**API Parameters :**
+
+* `question`= User query for the chatbot
+* `context`= context retrieved by retrieval mode used for answer generation
+* `answer`= answer generated by chatbot
+* `model`= LLM model
+* `mode`= Retrieval mode used for answer generationRetrieval mode used for answer generation
+
+**Response :**
+[source,json,indent=0]
+....
+{
+    "status": "Success",
+    "data": {
+        "graph+vector+fulltext": {
+            "faithfulness": 1.0,
+            "answer_relevancy": 0.9699
+        }
+    }
+}
+....
diff --git a/example.env b/example.env
index 227c51904..6b542daf1 100644
--- a/example.env
+++ b/example.env
@@ -31,3 +31,4 @@ VITE_CHUNK_SIZE=5242880
 VITE_GOOGLE_CLIENT_ID=""
 VITE_CHAT_MODES=""
 VITE_BATCH_SIZE=2
+VITE_LLM_MODELS_PROD="openai_gpt_4o,openai_gpt_4o_mini,diffbot,gemini_1.5_flash"
diff --git a/frontend/Dockerfile b/frontend/Dockerfile
index c3a7c1c82..3053e1ba9 100644
--- a/frontend/Dockerfile
+++ b/frontend/Dockerfile
@@ -12,6 +12,7 @@ ARG VITE_CHUNK_SIZE=5242880
 ARG VITE_CHAT_MODES=""
 ARG VITE_ENV="DEV"
 ARG VITE_BATCH_SIZE=2
+ARG VITE_LLM_MODELS_PROD="openai_gpt_4o,openai_gpt_4o_mini,diffbot,gemini_1.5_flash"
 
 WORKDIR /app
 COPY package.json yarn.lock ./
@@ -28,6 +29,7 @@ RUN VITE_BACKEND_API_URL=$VITE_BACKEND_API_URL \
     VITE_LARGE_FILE_SIZE=${VITE_LARGE_FILE_SIZE} \
     VITE_CHAT_MODES=$VITE_CHAT_MODES \
     VITE_BATCH_SIZE=$VITE_BATCH_SIZE \
+    VITE_LLM_MODELS_PROD=$VITE_LLM_MODELS_PROD \
     yarn run build
 
 # Step 2: Serve the application using Nginx
diff --git a/frontend/example.env b/frontend/example.env
index 9bc5bc0d5..4063fbc37 100644
--- a/frontend/example.env
+++ b/frontend/example.env
@@ -9,3 +9,4 @@ VITE_LARGE_FILE_SIZE=5242880
 VITE_GOOGLE_CLIENT_ID=""
 VITE_CHAT_MODES=""
 VITE_BATCH_SIZE=2
+VITE_LLM_MODELS_PROD="openai_gpt_4o,openai_gpt_4o_mini,diffbot,gemini_1.5_flash"
diff --git a/frontend/src/components/ChatBot/ChatInfoModal.tsx b/frontend/src/components/ChatBot/ChatInfoModal.tsx
index 757503fb1..4ffdd9acd 100644
--- a/frontend/src/components/ChatBot/ChatInfoModal.tsx
+++ b/frontend/src/components/ChatBot/ChatInfoModal.tsx
@@ -24,7 +24,7 @@ import ChunkInfo from './ChunkInfo';
 import EntitiesInfo from './EntitiesInfo';
 import SourcesInfo from './SourcesInfo';
 import CommunitiesInfo from './CommunitiesInfo';
-import { chatModeLables, supportedLLmsForRagas } from '../../utils/Constants';
+import { chatModeLables, chatModeReadableLables, supportedLLmsForRagas } from '../../utils/Constants';
 import { Relationship } from '@neo4j-nvl/base';
 import { getChatMetrics } from '../../services/GetRagasMetric';
 import MetricsTab from './MetricsTab';
@@ -71,14 +71,20 @@ const ChatInfoModal: React.FC<chatInfoMessage> = ({
   const { breakpoints } = tokens;
   const isTablet = useMediaQuery(`(min-width:${breakpoints.xs}) and (max-width: ${breakpoints.lg})`);
   const [activeTab, setActiveTab] = useState<number>(
-    error?.length ? 10 : mode === chatModeLables.global_vector ? 7 : mode === chatModeLables.graph ? 4 : 3
+    error?.length
+      ? 10
+      : mode === chatModeLables['global search+vector+fulltext']
+      ? 7
+      : mode === chatModeLables.graph
+      ? 4
+      : 3
   );
   const { userCredentials } = useCredentials();
   const themeUtils = useContext(ThemeWrapperContext);
   const [, copy] = useCopyToClipboard();
   const [copiedText, setcopiedText] = useState<boolean>(false);
   const [showMetricsTable, setShowMetricsTable] = useState<boolean>(Boolean(metricDetails));
-  const [showMultiModeMetrics, setShowMultiModeMetrics] = useState<boolean>(Boolean(multiModelMetrics.length))
+  const [showMultiModeMetrics, setShowMultiModeMetrics] = useState<boolean>(Boolean(multiModelMetrics.length));
   const [multiModeError, setMultiModeError] = useState<string>('');
 
   const actions: CypherCodeBlockProps['actions'] = useMemo(
@@ -201,7 +207,7 @@ const ChatInfoModal: React.FC<chatInfoMessage> = ({
           }
         }
       } else {
-        setShowMultiModeMetrics(true)
+        setShowMultiModeMetrics(true);
         toggleMetricsLoading();
         const contextarray = Object.values(activeChatmodes).map((r) => {
           return r.metric_contexts;
@@ -255,7 +261,7 @@ const ChatInfoModal: React.FC<chatInfoMessage> = ({
             To generate this response, the process took <span className='font-bold'>{response_time} seconds,</span>
             utilizing <span className='font-bold'>{total_tokens}</span> tokens with the model{' '}
             <span className='font-bold'>{model}</span> in{' '}
-            <span className='font-bold'>{mode !== 'vector' ? mode.replace(/\+/g, ' & ') : mode}</span> mode.
+            <span className='font-bold'>{chatModeReadableLables[mode] !== 'vector' ? chatModeReadableLables[mode].replace(/\+/g, ' & ') : chatModeReadableLables[mode]}</span> mode.
           </Typography>
         </Box>
       </Box>
@@ -263,16 +269,16 @@ const ChatInfoModal: React.FC<chatInfoMessage> = ({
         <Banner type='danger'>{error}</Banner>
       ) : (
         <Tabs size='large' fill='underline' onChange={onChangeTabs} value={activeTab}>
-          {mode === chatModeLables.global_vector ? (
+          {mode === chatModeLables['global search+vector+fulltext'] ? (
             <Tabs.Tab tabId={7}>Communities</Tabs.Tab>
           ) : (
             <>
               {mode != chatModeLables.graph ? <Tabs.Tab tabId={3}>Sources used</Tabs.Tab> : <></>}
               {mode != chatModeLables.graph ? <Tabs.Tab tabId={5}>Chunks</Tabs.Tab> : <></>}
-              {mode === chatModeLables.graph_vector ||
+              {mode === chatModeLables['graph+vector'] ||
               mode === chatModeLables.graph ||
-              mode === chatModeLables.graph_vector_fulltext ||
-              mode === chatModeLables.entity_vector ? (
+              mode === chatModeLables['graph+vector+fulltext'] ||
+              mode === chatModeLables['entity search+vector'] ? (
                 <Tabs.Tab tabId={4}>Top Entities used</Tabs.Tab>
               ) : (
                 <></>
@@ -282,7 +288,7 @@ const ChatInfoModal: React.FC<chatInfoMessage> = ({
               ) : (
                 <></>
               )}
-              {mode === chatModeLables.entity_vector && communities.length ? (
+              {mode === chatModeLables['entity search+vector'] && communities.length ? (
                 <Tabs.Tab tabId={7}>Communities</Tabs.Tab>
               ) : (
                 <></>
@@ -387,7 +393,7 @@ const ChatInfoModal: React.FC<chatInfoMessage> = ({
             className='min-h-40'
           />
         </Tabs.TabPanel>
-        {mode === chatModeLables.entity_vector || mode === chatModeLables.global_vector ? (
+        {mode === chatModeLables['entity search+vector'] || mode === chatModeLables['global search+vector+fulltext'] ? (
           <Tabs.TabPanel className='n-flex n-flex-col n-gap-token-4 n-p-token-6' value={activeTab} tabId={7}>
             <CommunitiesInfo loading={infoLoading} communities={communities} mode={mode} />
           </Tabs.TabPanel>
diff --git a/frontend/src/components/ChatBot/ChatModeToggle.tsx b/frontend/src/components/ChatBot/ChatModeToggle.tsx
index a892ca01d..52df01a4a 100644
--- a/frontend/src/components/ChatBot/ChatModeToggle.tsx
+++ b/frontend/src/components/ChatBot/ChatModeToggle.tsx
@@ -2,7 +2,7 @@ import { StatusIndicator, Typography } from '@neo4j-ndl/react';
 import { useMemo, useEffect } from 'react';
 import { useFileContext } from '../../context/UsersFiles';
 import CustomMenu from '../UI/Menu';
-import { chatModeLables, chatModes as AvailableModes } from '../../utils/Constants';
+import { chatModeLables, chatModes as AvailableModes, chatModeReadableLables } from '../../utils/Constants';
 import { capitalize } from '@mui/material';
 import { capitalizeWithPlus } from '../../utils/Utils';
 import { useCredentials } from '../../context/UserCredentials';
@@ -30,16 +30,16 @@ export default function ChatModeToggle({
       if (
         chatModes.includes(chatModeLables.graph) ||
         chatModes.includes(chatModeLables.fulltext) ||
-        chatModes.includes(chatModeLables.graph_vector_fulltext)
+        chatModes.includes(chatModeLables['global search+vector+fulltext'])
       ) {
         setchatModes((prev) =>
           prev.filter(
-            (m) => ![chatModeLables.graph, chatModeLables.fulltext, chatModeLables.graph_vector_fulltext].includes(m)
+            (m) => ![chatModeLables.graph, chatModeLables.fulltext, chatModeLables['graph+vector+fulltext']].includes(m)
           )
         );
       }
-      if (!chatModes.includes(chatModeLables.vector)) {
-        setchatModes([chatModeLables.vector]);
+      if (!(chatModes.includes(chatModeLables.vector) || chatModes.includes(chatModeLables['graph+vector']))) {
+        setchatModes([chatModeLables['graph+vector']]);
       }
     }
   }, [selectedRows.length, chatModes.length]);
@@ -47,16 +47,16 @@ export default function ChatModeToggle({
   const memoizedChatModes = useMemo(() => {
     return isGdsActive && isCommunityAllowed
       ? AvailableModes
-      : AvailableModes?.filter((m) => !m.mode.includes(chatModeLables.global_vector));
+      : AvailableModes?.filter((m) => !m.mode.includes(chatModeLables['global search+vector+fulltext']));
   }, [isGdsActive, isCommunityAllowed]);
   const menuItems = useMemo(() => {
     return memoizedChatModes?.map((m) => {
       const isDisabled = Boolean(
-        selectedRows.length && !(m.mode === chatModeLables.vector || m.mode === chatModeLables.graph_vector)
+        selectedRows.length && !(m.mode === chatModeLables.vector || m.mode === chatModeLables['graph+vector'])
       );
       const handleModeChange = () => {
         if (isDisabled) {
-          setchatModes([chatModeLables.graph_vector]);
+          setchatModes([chatModeLables['graph+vector']]);
         } else if (chatModes.includes(m.mode)) {
           setchatModes((prev) => prev.filter((i) => i != m.mode));
         } else {
@@ -68,7 +68,9 @@ export default function ChatModeToggle({
         title: (
           <div>
             <Typography variant='subheading-small'>
-              {m.mode.includes('+') ? capitalizeWithPlus(m.mode) : capitalize(m.mode)}
+              {chatModeReadableLables[m.mode].includes('+')
+                ? capitalizeWithPlus(chatModeReadableLables[m.mode])
+                : capitalize(chatModeReadableLables[m.mode])}
             </Typography>
             <div>
               <Typography variant='body-small'>{m.description}</Typography>
@@ -97,7 +99,7 @@ export default function ChatModeToggle({
 
   useEffect(() => {
     if (!selectedRows.length && !chatModes.length) {
-      setchatModes([chatModeLables.graph_vector_fulltext]);
+      setchatModes([]);
     }
   }, [selectedRows.length, chatModes.length]);
   return (
diff --git a/frontend/src/components/ChatBot/ChatModesSwitch.tsx b/frontend/src/components/ChatBot/ChatModesSwitch.tsx
index a958b1a06..db91e3372 100644
--- a/frontend/src/components/ChatBot/ChatModesSwitch.tsx
+++ b/frontend/src/components/ChatBot/ChatModesSwitch.tsx
@@ -2,6 +2,7 @@ import { Flex, IconButton } from '@neo4j-ndl/react';
 import { ChevronLeftIconSolid, ChevronRightIconSolid } from '@neo4j-ndl/react/icons';
 import TipWrapper from '../UI/TipWrapper';
 import { capitalize, capitalizeWithPlus } from '../../utils/Utils';
+import { chatModeReadableLables } from '../../utils/Constants';
 
 export default function ChatModesSwitch({
   switchToOtherMode,
@@ -16,7 +17,7 @@ export default function ChatModesSwitch({
   currentMode: string;
   isFullScreen: boolean;
 }) {
-  const chatmodetoshow = currentMode.includes('+') ? capitalizeWithPlus(currentMode) : capitalize(currentMode);
+  const chatmodetoshow =chatModeReadableLables[currentMode].includes('+') ? capitalizeWithPlus(chatModeReadableLables[currentMode]) : capitalize(chatModeReadableLables[currentMode]);
   return (
     <Flex flexDirection='row' gap='1' alignItems='center'>
       <IconButton
diff --git a/frontend/src/components/ChatBot/Chatbot.tsx b/frontend/src/components/ChatBot/Chatbot.tsx
index 3c1992645..3fa666e1e 100644
--- a/frontend/src/components/ChatBot/Chatbot.tsx
+++ b/frontend/src/components/ChatBot/Chatbot.tsx
@@ -64,7 +64,7 @@ const Chatbot: FC<ChatbotProps> = (props) => {
   const [responseTime, setResponseTime] = useState<number>(0);
   const [tokensUsed, setTokensUsed] = useState<number>(0);
   const [cypherQuery, setcypherQuery] = useState<string>('');
-  const [chatsMode, setChatsMode] = useState<string>(chatModeLables.graph_vector_fulltext);
+  const [chatsMode, setChatsMode] = useState<string>(chatModeLables['graph+vector+fulltext']);
   const [graphEntitites, setgraphEntitites] = useState<[]>([]);
   const [messageError, setmessageError] = useState<string>('');
   const [entitiesModal, setEntitiesModal] = useState<string[]>([]);
@@ -583,7 +583,7 @@ const Chatbot: FC<ChatbotProps> = (props) => {
           }}
           onClose={() => setShowInfoModal(false)}
           open={showInfoModal}
-          size={activeChat?.currentMode === chatModeLables.entity_vector ? 'large' : 'medium'}
+          size={activeChat?.currentMode === chatModeLables['entity search+vector'] ? 'large' : 'medium'}
         >
           <div style={{ display: 'flex', justifyContent: 'flex-end', alignItems: 'center' }}>
             <IconButton
diff --git a/frontend/src/components/ChatBot/ChunkInfo.tsx b/frontend/src/components/ChatBot/ChunkInfo.tsx
index 4664adbe5..7f64b3c27 100644
--- a/frontend/src/components/ChatBot/ChunkInfo.tsx
+++ b/frontend/src/components/ChatBot/ChunkInfo.tsx
@@ -60,8 +60,8 @@ const ChunkInfo: FC<ChunkProps> = ({ loading, chunks, mode }) => {
                         </Typography>
                       </>
                     </div>
-                    {mode !== chatModeLables.global_vector &&
-                      mode !== chatModeLables.entity_vector &&
+                    {mode !== chatModeLables['global search+vector+fulltext'] &&
+                      mode !== chatModeLables['entity search+vector'] &&
                       mode !== chatModeLables.graph &&
                       chunk.score && (
                         <Typography variant='subheading-small'>Similarity Score: {chunk?.score}</Typography>
@@ -71,11 +71,10 @@ const ChunkInfo: FC<ChunkProps> = ({ loading, chunks, mode }) => {
                     </div>
                     <div>
                       <TextLink
-                       as='medium'
                         label='Graph view'
                         className={`${loadingGraphView ? 'cursor-wait' : 'cursor-pointer'}`}
                         onClick={() => handleChunkClick(chunk.element_id, 'Chunk')}
-                      >{'Graph'}
+                      >{'View Graph'}
                       </TextLink>
                     </div>
                   </>
@@ -92,8 +91,8 @@ const ChunkInfo: FC<ChunkProps> = ({ loading, chunks, mode }) => {
                         </Typography>
                       </TextLink>
                     </div>
-                    {mode !== chatModeLables.global_vector &&
-                      mode !== chatModeLables.entity_vector &&
+                    {mode !== chatModeLables['global search+vector+fulltext'] &&
+                      mode !== chatModeLables['entity search+vector'] &&
                       mode !== chatModeLables.graph && (
                         <>
                           <Typography variant='subheading-small'>Similarity Score: {chunk?.score}</Typography>
@@ -115,18 +114,17 @@ const ChunkInfo: FC<ChunkProps> = ({ loading, chunks, mode }) => {
                       <img src={wikipedialogo} width={20} height={20} className='mr-2' />
                       <Typography variant='subheading-medium'>{chunk?.fileName}</Typography>
                     </div>
-                    {mode !== chatModeLables.global_vector &&
-                      mode !== chatModeLables.entity_vector &&
+                    {mode !== chatModeLables['global search+vector+fulltext'] &&
+                      mode !== chatModeLables['entity search+vector'] &&
                       mode !== chatModeLables.graph && (
                         <>
                           <Typography variant='subheading-small'>Similarity Score: {chunk?.score}</Typography>
                           <div>
                             <TextLink
-                             as='medium'
                              className={`${loadingGraphView ? 'cursor-wait' : 'cursor-pointer'}`}
                               label='Graph view'
                               onClick={() => handleChunkClick(chunk.element_id, 'Chunk')}
-                            >{'Graph'}
+                            >{'View Graph'}
                             </TextLink>
                           </div>
                         </>
@@ -138,18 +136,17 @@ const ChunkInfo: FC<ChunkProps> = ({ loading, chunks, mode }) => {
                       <img src={gcslogo} width={20} height={20} className='mr-2' />
                       <Typography variant='subheading-medium'>{chunk?.fileName}</Typography>
                     </div>
-                    {mode !== chatModeLables.global_vector &&
-                      mode !== chatModeLables.entity_vector &&
+                    {mode !== chatModeLables['global search+vector+fulltext'] &&
+                      mode !== chatModeLables['entity search+vector'] &&
                       mode !== chatModeLables.graph && (
                         <>
                           <Typography variant='subheading-small'>Similarity Score: {chunk?.score}</Typography>
                           <div>
                             <TextLink
-                             as='medium'
                              className={`${loadingGraphView ? 'cursor-wait' : 'cursor-pointer'}`}
                               label='Graph view'
                               onClick={() => handleChunkClick(chunk.element_id, 'Chunk')}
-                            >{'Graph'}
+                            >{'View Graph'}
                             </TextLink>
                           </div>
                         </>
@@ -161,18 +158,17 @@ const ChunkInfo: FC<ChunkProps> = ({ loading, chunks, mode }) => {
                       <img src={s3logo} width={20} height={20} className='mr-2' />
                       <Typography variant='subheading-medium'>{chunk?.fileName}</Typography>
                     </div>
-                    {mode !== chatModeLables.global_vector &&
-                      mode !== chatModeLables.entity_vector &&
+                    {mode !== chatModeLables['global search+vector+fulltext'] &&
+                      mode !== chatModeLables['entity search+vector'] &&
                       mode !== chatModeLables.graph && (
                         <>
                           <Typography variant='subheading-small'>Similarity Score: {chunk?.score}</Typography>
                           <div>
                             <TextLink
-                             as='medium'
                              className={`${loadingGraphView ? 'cursor-wait' : 'cursor-pointer'}`}
                               label='Graph view'
                               onClick={() => handleChunkClick(chunk.element_id, 'Chunk')}
-                            >{'Graph'}
+                            >{'View Graph'}
                             </TextLink>
                           </div>
                         </>
@@ -188,18 +184,17 @@ const ChunkInfo: FC<ChunkProps> = ({ loading, chunks, mode }) => {
                         <Typography variant='body-medium'>{chunk?.url}</Typography>
                       </TextLink>
                     </div>
-                    {mode !== chatModeLables.global_vector &&
-                      mode !== chatModeLables.entity_vector &&
+                    {mode !== chatModeLables['global search+vector+fulltext'] &&
+                      mode !== chatModeLables['entity search+vector'] &&
                       mode !== chatModeLables.graph && (
                         <>
                           <Typography variant='subheading-small'>Similarity Score: {chunk?.score}</Typography>
                           <div>
                             <TextLink
-                             as='medium'
                              className={`${loadingGraphView ? 'cursor-wait' : 'cursor-pointer'}`}
                               label='Graph view'
                               onClick={() => handleChunkClick(chunk.element_id, 'Chunk')}
-                            >{'Graph'}
+                            >{'View Graph'}
                             </TextLink>
                           </div>
                         </>
@@ -227,11 +222,10 @@ const ChunkInfo: FC<ChunkProps> = ({ loading, chunks, mode }) => {
                         </Typography>
                         <div>
                           <TextLink
-                           as='medium'
                            className={`${loadingGraphView ? 'cursor-wait' : 'cursor-pointer'}`}
                             label='Graph view'
                             onClick={() => handleChunkClick(chunk.element_id, 'Chunk')}
-                          >{'Graph'}
+                          >{'View Graph'}
                           </TextLink>
                         </div>
                       </>
diff --git a/frontend/src/components/ChatBot/CommunitiesInfo.tsx b/frontend/src/components/ChatBot/CommunitiesInfo.tsx
index 1a769a1f3..bc8e5e8d3 100644
--- a/frontend/src/components/ChatBot/CommunitiesInfo.tsx
+++ b/frontend/src/components/ChatBot/CommunitiesInfo.tsx
@@ -47,7 +47,7 @@ const CommunitiesInfo: FC<CommunitiesProps> = ({ loading, communities, mode }) =
                       onClick={() => handleCommunityClick(community.element_id, 'chatInfoView')}
                     >{`ID : ${community.id}`}</TextLink>
                   </Flex>
-                  {mode === chatModeLables.global_vector && community.score && (
+                  {mode === chatModeLables['global search+vector+fulltext'] && community.score && (
                     <Flex flexDirection='row' gap='2'>
                       <Typography variant='subheading-medium'>Score : </Typography>
                       <Typography variant='subheading-medium'>{community.score}</Typography>
diff --git a/frontend/src/components/ChatBot/SourcesInfo.tsx b/frontend/src/components/ChatBot/SourcesInfo.tsx
index 35d76aefb..a934913d9 100644
--- a/frontend/src/components/ChatBot/SourcesInfo.tsx
+++ b/frontend/src/components/ChatBot/SourcesInfo.tsx
@@ -16,10 +16,9 @@ const filterUniqueChunks = (chunks: Chunk[]) => {
     const sourceCheck = `${chunk.fileName}-${chunk.fileSource}`;
     if (chunkSource.has(sourceCheck)) {
       return false;
-    } 
-      chunkSource.add(sourceCheck);
-      return true;
-    
+    }
+    chunkSource.add(sourceCheck);
+    return true;
   });
 };
 
diff --git a/frontend/src/components/Content.tsx b/frontend/src/components/Content.tsx
index 70ac18cab..83c2c20b4 100644
--- a/frontend/src/components/Content.tsx
+++ b/frontend/src/components/Content.tsx
@@ -331,6 +331,7 @@ const Content: React.FC<ContentProps> = ({
               const apiRes = apiResponse?.data;
               return {
                 ...curfile,
+                processingProgress: apiRes?.processingTime?.toFixed(2),
                 processingTotalTime: apiRes?.processingTime?.toFixed(2),
                 status: apiRes?.status,
                 nodesCount: apiRes?.nodeCount,
@@ -521,9 +522,8 @@ const Content: React.FC<ContentProps> = ({
   const handleOpenGraphClick = () => {
     const bloomUrl = process.env.VITE_BLOOM_URL;
     const uriCoded = userCredentials?.uri.replace(/:\d+$/, '');
-    const connectURL = `${uriCoded?.split('//')[0]}//${userCredentials?.userName}@${uriCoded?.split('//')[1]}:${
-      userCredentials?.port ?? '7687'
-    }`;
+    const connectURL = `${uriCoded?.split('//')[0]}//${userCredentials?.userName}@${uriCoded?.split('//')[1]}:${userCredentials?.port ?? '7687'
+      }`;
     const encodedURL = encodeURIComponent(connectURL);
     const replacedUrl = bloomUrl?.replace('{CONNECT_URL}', encodedURL);
     window.open(replacedUrl, '_blank');
@@ -533,10 +533,10 @@ const Content: React.FC<ContentProps> = ({
     isLeftExpanded && isRightExpanded
       ? 'contentWithExpansion'
       : isRightExpanded
-      ? 'contentWithChatBot'
-      : !isLeftExpanded && !isRightExpanded
-      ? 'w-[calc(100%-128px)]'
-      : 'contentWithDropzoneExpansion';
+        ? 'contentWithChatBot'
+        : !isLeftExpanded && !isRightExpanded
+          ? 'w-[calc(100%-128px)]'
+          : 'contentWithDropzoneExpansion';
 
   const handleGraphView = () => {
     setOpenGraphView(true);
@@ -552,7 +552,7 @@ const Content: React.FC<ContentProps> = ({
     setSelectedNodes([]);
     setSelectedRels([]);
     setClearHistoryData(true);
-    setchatModes([chatModeLables.graph_vector_fulltext]);
+    setchatModes([chatModeLables['graph+vector+fulltext']]);
   };
 
   const retryHandler = async (filename: string, retryoption: string) => {
@@ -568,12 +568,12 @@ const Content: React.FC<ContentProps> = ({
         return prev.map((f) => {
           return f.name === filename
             ? {
-                ...f,
-                status: 'Reprocess',
-                processingProgress: isStartFromBegining ? 0 : f.processingProgress,
-                nodesCount: isStartFromBegining ? 0 : f.nodesCount,
-                relationshipCount: isStartFromBegining ? 0 : f.relationshipsCount,
-              }
+              ...f,
+              status: 'Reprocess',
+              processingProgress: isStartFromBegining ? 0 : f.processingProgress,
+              nodesCount: isStartFromBegining ? 0 : f.nodesCount,
+              relationshipCount: isStartFromBegining ? 0 : f.relationshipsCount,
+            }
             : f;
         });
       });
@@ -862,9 +862,8 @@ const Content: React.FC<ContentProps> = ({
           handleGenerateGraph={processWaitingFilesOnRefresh}
         ></FileTable>
         <Flex
-          className={`${
-            !isLeftExpanded && !isRightExpanded ? 'w-[calc(100%-128px)]' : 'w-full'
-          } p-2.5 absolute bottom-4 mt-1.5 self-start`}
+          className={`${!isLeftExpanded && !isRightExpanded ? 'w-[calc(100%-128px)]' : 'w-full'
+            } p-2.5 absolute bottom-4 mt-1.5 self-start`}
           justifyContent='space-between'
           flexDirection={isTablet ? 'column' : 'row'}
         >
diff --git a/frontend/src/components/Dropdown.tsx b/frontend/src/components/Dropdown.tsx
index 087e0d71c..8c434309f 100644
--- a/frontend/src/components/Dropdown.tsx
+++ b/frontend/src/components/Dropdown.tsx
@@ -2,7 +2,7 @@ import { Dropdown, Tip } from '@neo4j-ndl/react';
 import { OptionType, ReusableDropdownProps } from '../types';
 import { memo, useMemo, useReducer } from 'react';
 import { capitalize, capitalizeWithUnderscore } from '../utils/Utils';
-
+// import { LLMDropdownLabel } from '../utils/Constants';
 const DropdownComponent: React.FC<ReusableDropdownProps> = ({
   options,
   placeholder,
@@ -14,6 +14,8 @@ const DropdownComponent: React.FC<ReusableDropdownProps> = ({
   value,
 }) => {
   const [disableTooltip, toggleDisableState] = useReducer((state) => !state, false);
+  const isProdEnv = process.env.VITE_ENV === 'PROD';
+  const supportedModels = process.env.VITE_LLM_MODELS_PROD;
   const handleChange = (selectedOption: OptionType | null | void) => {
     onSelect(selectedOption);
   };
@@ -33,9 +35,11 @@ const DropdownComponent: React.FC<ReusableDropdownProps> = ({
                   const label =
                     typeof option === 'string' ? capitalizeWithUnderscore(option) : capitalize(option.label);
                   const value = typeof option === 'string' ? option : option.value;
+                  const isModelSupported = !isProdEnv || supportedModels?.includes(value);
                   return {
                     label,
                     value,
+                    isDisabled: !isModelSupported,
                   };
                 }),
                 placeholder: placeholder || 'Select an option',
@@ -60,6 +64,13 @@ const DropdownComponent: React.FC<ReusableDropdownProps> = ({
         </Tip>
         {children}
       </div>
+      {/* {isProdEnv && (<Typography className={'pt-4 absolute top-14 self-start'} variant='body-small'>
+        {LLMDropdownLabel.disabledModels}
+        <a href="https://dev-frontend-dcavk67s4a-uc.a.run.app/" target="_blank" style={{ textDecoration: 'underline' }}>
+          {LLMDropdownLabel.devEnv}
+        </a>
+        {'.'}
+      </Typography>)} */}
     </>
   );
 };
diff --git a/frontend/src/components/FileTable.tsx b/frontend/src/components/FileTable.tsx
index 7b0b271f0..b3fbafd55 100644
--- a/frontend/src/components/FileTable.tsx
+++ b/frontend/src/components/FileTable.tsx
@@ -824,7 +824,7 @@ const FileTable = forwardRef<ChildRef, FileTableProps>((props, ref) => {
               ...curfile,
               status: status,
               nodesCount: nodeCount,
-              relationshipsCount: relationshipCount,
+              relationshipCount: relationshipCount,
               model: model,
               processingTotalTime: processingTime?.toFixed(2),
               processingProgress: Math.floor((processed_chunk / total_chunks) * 100),
@@ -854,7 +854,7 @@ const FileTable = forwardRef<ChildRef, FileTableProps>((props, ref) => {
               ...curfile,
               status: status,
               nodesCount: nodeCount,
-              relationshipsCount: relationshipCount,
+              relationshipCount: relationshipCount,
               processingProgress: Math.floor((processed_chunk / total_chunks) * 100),
             };
           }
diff --git a/frontend/src/components/Popups/GraphEnhancementDialog/Deduplication/index.tsx b/frontend/src/components/Popups/GraphEnhancementDialog/Deduplication/index.tsx
index 420fbe590..1e6b0c213 100644
--- a/frontend/src/components/Popups/GraphEnhancementDialog/Deduplication/index.tsx
+++ b/frontend/src/components/Popups/GraphEnhancementDialog/Deduplication/index.tsx
@@ -45,7 +45,7 @@ export default function DeduplicationTab() {
   const [neoRels, setNeoRels] = useState<any[]>([]);
   const [openGraphView, setOpenGraphView] = useState(false);
   const [viewPoint, setViewPoint] = useState('');
-
+  const [nodesCount, setNodesCount] = useState<number>(0);
   const fetchDuplicateNodes = useCallback(async () => {
     try {
       setLoading(true);
@@ -56,6 +56,8 @@ export default function DeduplicationTab() {
       }
       if (duplicateNodesData.data.data.length) {
         setDuplicateNodes(duplicateNodesData.data.data);
+        //@ts-ignore
+        setNodesCount(duplicateNodesData.data.message.total)
       } else {
         setDuplicateNodes([]);
       }
@@ -272,9 +274,9 @@ export default function DeduplicationTab() {
               accuracy and clarity of your knowledge graph.
             </Typography>
           </Flex>
-          {duplicateNodes.length > 0 && (
+          {nodesCount > 0 && (
             <Typography variant={isTablet ? 'subheading-medium' : 'subheading-large'}>
-              Total Duplicate Nodes: {duplicateNodes.length}
+              Total Duplicate Nodes: {nodesCount}
             </Typography>
           )}
         </Flex>
diff --git a/frontend/src/components/Popups/GraphEnhancementDialog/PostProcessingCheckList/SelectedJobList.tsx b/frontend/src/components/Popups/GraphEnhancementDialog/PostProcessingCheckList/SelectedJobList.tsx
index 1cb3c7310..348c97af2 100644
--- a/frontend/src/components/Popups/GraphEnhancementDialog/PostProcessingCheckList/SelectedJobList.tsx
+++ b/frontend/src/components/Popups/GraphEnhancementDialog/PostProcessingCheckList/SelectedJobList.tsx
@@ -11,18 +11,19 @@ export default function SelectedJobList({
 }) {
   const ongoingPostProcessingTasks = useMemo(
     () =>
-      (isGdsActive
-        ? postProcessingTasks.includes('enable_communities')
-          ? postProcessingTasks
-          : postProcessingTasks.filter((s) => s != 'enable_communities')
-        : postProcessingTasks.filter((s) => s != 'enable_communities')),
+    (isGdsActive
+      ? postProcessingTasks.includes('enable_communities')
+        ? postProcessingTasks
+        : postProcessingTasks.filter((s) => s != 'enable_communities')
+      : postProcessingTasks.filter((s) => s != 'enable_communities')),
     [isGdsActive, postProcessingTasks]
   );
   return (
     <Flex justifyContent='space-between' flexDirection='column' gap='4'>
-      {ongoingPostProcessingTasks.map((task) => {
+      {ongoingPostProcessingTasks.map((task, idx) => {
         return (
           <Checkbox
+            key={idx}
             label={
               <Typography variant='label'>
                 {task
diff --git a/frontend/src/context/UsersFiles.tsx b/frontend/src/context/UsersFiles.tsx
index 14f5a0245..935f75f10 100644
--- a/frontend/src/context/UsersFiles.tsx
+++ b/frontend/src/context/UsersFiles.tsx
@@ -29,7 +29,7 @@ const FileContextProvider: FC<FileContextProviderProps> = ({ children }) => {
   const [selectedSchemas, setSelectedSchemas] = useState<readonly OptionType[]>([]);
   const [rowSelection, setRowSelection] = useState<Record<string, boolean>>({});
   const [selectedRows, setSelectedRows] = useState<string[]>([]);
-  const [chatModes, setchatModes] = useState<string[]>([chatModeLables.graph_vector_fulltext]);
+  const [chatModes, setchatModes] = useState<string[]>([chatModeLables['graph+vector+fulltext']]);
   const [isSchema, setIsSchema] = useState<boolean>(false);
   const [showTextFromSchemaDialog, setShowTextFromSchemaDialog] = useState<showTextFromSchemaDialogType>({
     triggeredFrom: '',
diff --git a/frontend/src/utils/Constants.ts b/frontend/src/utils/Constants.ts
index 8ab428999..112f8bbec 100644
--- a/frontend/src/utils/Constants.ts
+++ b/frontend/src/utils/Constants.ts
@@ -47,6 +47,17 @@ export const supportedLLmsForRagas = [
   'bedrock_claude_3_5_sonnet',
 ];
 export const chatModeLables = {
+  vector: 'vector',
+  graph: 'graph',
+  'graph+vector': 'graph_vector',
+  fulltext: 'fulltext',
+  'graph+vector+fulltext': 'graph_vector_fulltext',
+  'entity search+vector': 'entity_vector',
+  unavailableChatMode: 'Chat mode is unavailable when files are selected',
+  selected: 'Selected',
+  'global search+vector+fulltext': 'global_vector',
+};
+export const chatModeReadableLables: Record<string, string> = {
   vector: 'vector',
   graph: 'graph',
   graph_vector: 'graph+vector',
@@ -73,7 +84,7 @@ export const chatModes =
           description: 'Translates text to Cypher queries for precise data retrieval from a graph database.',
         },
         {
-          mode: chatModeLables.graph_vector,
+          mode: chatModeLables['graph+vector'],
           description: 'Combines vector indexing and graph connections for contextually enhanced semantic search.',
         },
         {
@@ -81,15 +92,15 @@ export const chatModes =
           description: 'Conducts fast, keyword-based search using full-text indexing on text chunks.',
         },
         {
-          mode: chatModeLables.graph_vector_fulltext,
+          mode: chatModeLables['graph+vector+fulltext'],
           description: 'Integrates vector, graph, and full-text indexing for comprehensive search results.',
         },
         {
-          mode: chatModeLables.entity_vector,
+          mode: chatModeLables['entity search+vector'],
           description: 'Uses vector indexing on entity nodes for highly relevant entity-based search.',
         },
         {
-          mode: chatModeLables.global_vector,
+          mode: chatModeLables['global search+vector+fulltext'],
           description:
             'Use vector and full-text indexing on community nodes to provide accurate, context-aware answers globally.',
         },
@@ -295,3 +306,8 @@ export const appLabels = {
   ownSchema: 'Or Define your own Schema',
   predefinedSchema: 'Select a Pre-defined Schema',
 };
+
+export const LLMDropdownLabel ={
+  disabledModels: 'Disabled models are available in the development version. Access more models in our ',
+  devEnv: 'development environment'
+}
diff --git a/frontend/src/utils/Utils.ts b/frontend/src/utils/Utils.ts
index 226fd03f9..6945c17ca 100644
--- a/frontend/src/utils/Utils.ts
+++ b/frontend/src/utils/Utils.ts
@@ -417,15 +417,15 @@ export const getDescriptionForChatMode = (mode: string): string => {
       return 'Utilizes vector indexing on text chunks to enable semantic similarity search.';
     case chatModeLables.graph:
       return 'Leverages text-to-cypher translation to query a database and retrieve relevant data, ensuring a highly targeted and contextually accurate response.';
-    case chatModeLables.graph_vector:
+    case chatModeLables['graph+vector']:
       return 'Combines vector indexing on text chunks with graph connections, enhancing search results with contextual relevance by considering relationships between concepts.';
     case chatModeLables.fulltext:
       return 'Employs a fulltext index on text chunks for rapid keyword-based search, efficiently identifying documents containing specific words or phrases.';
-    case chatModeLables.graph_vector_fulltext:
+    case chatModeLables['graph+vector+fulltext']:
       return 'Merges vector indexing, graph connections, and fulltext indexing for a comprehensive search approach, combining semantic similarity, contextual relevance, and keyword-based search for optimal results.';
-    case chatModeLables.entity_vector:
+    case chatModeLables['entity search+vector']:
       return 'Combines entity node vector indexing with graph connections for accurate entity-based search, providing the most relevant response.';
-    case chatModeLables.global_vector:
+    case chatModeLables['global search+vector+fulltext']:
       return 'Use vector and full-text indexing on community nodes to provide accurate, context-aware answers globally.';
     default:
       return 'Chat mode description not available'; // Fallback description
@@ -510,7 +510,7 @@ export function downloadClickHandler<Type>(
 }
 export function getNodes<Type extends Entity | ExtendedNode>(nodesData: Array<Type>, mode: string) {
   return nodesData.map((n) => {
-    if (!n.labels.length && mode === chatModeLables.entity_vector) {
+    if (!n.labels.length && mode === chatModeLables['entity search+vector']) {
       return {
         ...n,
         labels: ['Entity'],