Refactor nomic log fix

.beamignore

@@ -0,0 +1,7 @@
+.venv
+venv
+.idea
+.vscode
+.git
+*.pyc
+__pycache__

ai_ta_backend/beam/ingest.py

@@ -41,7 +41,8 @@
 from langchain.schema import Document
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import Qdrant
-from nomic_logging import delete_from_document_map, log_to_document_map
+
+from nomic_logging import delete_from_document_map, log_to_document_map, rebuild_map
 from OpenaiEmbeddings import OpenAIAPIProcessor
 from PIL import Image
 from posthog import Posthog
@@ -156,6 +157,7 @@ def loader():
     loader=loader,
     autoscaler=autoscaler)
 def ingest(**inputs: Dict[str, Any]):
+
   qdrant_client, vectorstore, s3_client, supabase_client, posthog = inputs["context"]
 
   course_name: List[str] | str = inputs.get('course_name', '')
@@ -171,6 +173,8 @@ def ingest(**inputs: Dict[str, Any]):
 
   ingester = Ingest(qdrant_client, vectorstore, s3_client, supabase_client, posthog)
 
+
+
   def run_ingest(course_name, s3_paths, base_url, url, readable_filename, content):
     if content:
       return ingester.ingest_single_web_text(course_name, base_url, url, content, readable_filename)
@@ -194,7 +198,10 @@ def run_ingest(course_name, s3_paths, base_url, url, readable_filename, content)
       # s3_paths = success_fail_dict['failure_ingest'] # retry only failed paths.... what if this is a URL instead?
       success_fail_dict = run_ingest(course_name, s3_paths, base_url, url, readable_filename, content)
       time.sleep(13 * retry_num)  # max is 65
-
+
+  # rebuild nomic document map after all ingests are done
+  rebuild_status = rebuild_map(course_name, map_type='document')
+
   print(f"Final success_fail_dict: {success_fail_dict}")
   return json.dumps(success_fail_dict)
 

ai_ta_backend/beam/nomic_logging.py

@@ -215,6 +215,7 @@ def log_convo_to_nomic(course_name: str, conversation) -> str:
       raise Exception({"exception": str(e)})
 
 
+
 def get_nomic_map(course_name: str, type: str):
   """
   Returns the variables necessary to construct an iframe of the Nomic map given a course name.
@@ -241,8 +242,7 @@ def get_nomic_map(course_name: str, type: str):
   except Exception as e:
     # Error: ValueError: You must specify a unique_id_field when creating a new project.
     if str(e) == 'You must specify a unique_id_field when creating a new project.':  # type: ignore
-      print("Nomic map does not exist yet, probably because you have less than 20 queries/documents on your project: ",
-            e)
+      print("Nomic map does not exist yet, probably because you have less than 20 queries/documents on your project: ", e)
     else:
       print("ERROR in get_nomic_map():", e)
       sentry_sdk.capture_exception(e)
@@ -405,8 +405,8 @@ def create_nomic_map(course_name: str, log_data: list):
     return "failed"
 
 
-## -------------------------------- DOCUMENT MAP FUNCTIONS --------------------------------- ##
 
+## -------------------------------- DOCUMENT MAP FUNCTIONS --------------------------------- ##
 
 def create_document_map(course_name: str):
   """
@@ -444,13 +444,13 @@ def create_document_map(course_name: str):
                                                                                               desc=False).execute()
     if not response.count:
       return "No documents found for this course."
-
+    
     total_doc_count = response.count
     print("Total number of documents in Supabase: ", total_doc_count)
 
     # minimum 20 docs needed to create map
-    if total_doc_count > 19:
 
+    if total_doc_count > 19:
       first_id = response.data[0]['id']
       combined_dfs = []
       curr_total_doc_count = 0
@@ -469,6 +469,7 @@ def create_document_map(course_name: str):
         curr_total_doc_count += len(response.data)
         doc_count += len(response.data)
 
+
         if doc_count >= 1000:  # upload to Nomic every 1000 docs
 
           # concat all dfs from the combined_dfs list
@@ -495,13 +496,15 @@ def create_document_map(course_name: str):
             # add project lock logic here
             result = append_to_map(embeddings, metadata, project_name)
 
+
           # reset variables
           combined_dfs = []
           doc_count = 0
 
         # set first_id for next iteration
         first_id = response.data[-1]['id'] + 1
 
+
       # upload last set of docs
       final_df = pd.concat(combined_dfs, ignore_index=True)
       embeddings, metadata = data_prep_for_doc_map(final_df)
@@ -528,6 +531,7 @@ def create_document_map(course_name: str):
   except Exception as e:
     print(e)
     sentry_sdk.capture_exception(e)
+
     return "failed"
 
 
@@ -615,13 +619,6 @@ def log_to_document_map(data: dict):
     project_name = "Document Map for " + course_name
     result = append_to_map(embeddings, metadata, project_name)
 
-    # check if project is accepting new datums
-    if project.is_accepting_data:
-      with project.wait_for_project_lock():
-        project.rebuild_maps()
-
-    # with project.wait_for_project_lock():
-    #   project.rebuild_maps()
     return result
 
   except Exception as e:
@@ -642,7 +639,6 @@ def create_map(embeddings, metadata, map_name, index_name, topic_label_field, co
     colorable_fields: list of str
   """
   nomic.login(os.getenv('NOMIC_API_KEY'))
-
   try:
     project = atlas.map_embeddings(embeddings=embeddings,
                                    data=metadata,
@@ -658,7 +654,6 @@ def create_map(embeddings, metadata, map_name, index_name, topic_label_field, co
     print(e)
     return "Error in creating map: {e}"
 
-
 def append_to_map(embeddings, metadata, map_name):
   """
   Generic function to append new data to an existing Nomic map.
@@ -667,6 +662,7 @@ def append_to_map(embeddings, metadata, map_name):
     metadata: pd.DataFrame of Nomic upload metadata
     map_name: str
   """
+
   nomic.login(os.getenv('NOMIC_API_KEY'))
   try:
     project = atlas.AtlasProject(name=map_name, add_datums_if_exists=True)
@@ -691,10 +687,10 @@ def data_prep_for_doc_map(df: pd.DataFrame):
 
   metadata = []
   embeddings = []
+
   texts = []
 
   for index, row in df.iterrows():
-
     current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
     if row['url'] == None:
       row['url'] = ""
@@ -725,9 +721,7 @@ def data_prep_for_doc_map(df: pd.DataFrame):
   # check dimension if embeddings_np is (n, 1536)
   if len(embeddings_np.shape) < 2:
     print("Creating new embeddings...")
-    # embeddings_model = OpenAIEmbeddings(openai_api_type=OPENAI_API_TYPE,
-    #                                     openai_api_base=os.getenv('AZURE_OPENAI_BASE'),
-    #                                     openai_api_key=os.getenv('AZURE_OPENAI_KEY')) # type: ignore
+
     embeddings_model = OpenAIEmbeddings(openai_api_type="openai",
                                         openai_api_base="https://api.openai.com/v1/",
                                         openai_api_key=os.getenv('VLADS_OPENAI_KEY'))  # type: ignore
@@ -738,6 +732,32 @@ def data_prep_for_doc_map(df: pd.DataFrame):
 
   return embeddings, metadata
 
+def rebuild_map(course_name:str, map_type:str):
+  """
+  This function rebuilds a given map in Nomic.
+  """
+  print("in rebuild_map()")
+  nomic.login(os.getenv('NOMIC_API_KEY'))
+  if map_type.lower() == 'document':
+    NOMIC_MAP_NAME_PREFIX = 'Document Map for '
+  else:
+    NOMIC_MAP_NAME_PREFIX = 'Conversation Map for '
+
+  try:
+    # fetch project from Nomic
+    project_name = NOMIC_MAP_NAME_PREFIX + course_name
+    project = AtlasProject(name=project_name, add_datums_if_exists=True)
+
+    with project.wait_for_project_lock():
+      project.rebuild_maps()
+    return "Successfully rebuilt map"
+  except Exception as e:
+    print(e)
+    sentry_sdk.capture_exception(e)
+    return "Error in rebuilding map: {e}"
+
+
 
 if __name__ == '__main__':
   pass
+