From fe0de52b7d5387c0ca746fc2ff3bd107044e7e66 Mon Sep 17 00:00:00 2001 From: Kastan Day Date: Fri, 15 Sep 2023 15:06:53 -0700 Subject: [PATCH] Yapf format ONLY --- ai_ta_backend/nomic_logging.py | 119 +++++++++++++++++++++------------ 1 file changed, 77 insertions(+), 42 deletions(-) diff --git a/ai_ta_backend/nomic_logging.py b/ai_ta_backend/nomic_logging.py index c86142ca..9e625f05 100644 --- a/ai_ta_backend/nomic_logging.py +++ b/ai_ta_backend/nomic_logging.py @@ -9,9 +9,10 @@ import pandas as pd import supabase -nomic.login(os.getenv('NOMIC_API_KEY')) # login during start of flask app +nomic.login(os.getenv('NOMIC_API_KEY')) # login during start of flask app NOMIC_MAP_NAME_PREFIX = 'Conversation Map for ' + def log_convo_to_nomic(course_name: str, conversation) -> str: """ Logs conversation to Nomic. @@ -24,22 +25,22 @@ def log_convo_to_nomic(course_name: str, conversation) -> str: print("in log_convo_to_nomic()") print("conversation: ", conversation) - + messages = conversation['conversation']['messages'] user_email = conversation['conversation']['user_email'] conversation_id = conversation['conversation']['id'] #print("conversation: ", conversation) - + # we have to upload whole conversations # check what the fetched data looks like - pandas df or pyarrow table - # check if conversation ID exists in Nomic, if yes fetch all data from it and delete it. + # check if conversation ID exists in Nomic, if yes fetch all data from it and delete it. # will have current QA and historical QA from Nomic, append new data and add_embeddings() project_name = NOMIC_MAP_NAME_PREFIX + course_name start_time = time.monotonic() emoji = "" - + try: # fetch project metadata and embbeddings project = AtlasProject(name=project_name, add_datums_if_exists=True) @@ -48,29 +49,29 @@ def log_convo_to_nomic(course_name: str, conversation) -> str: map_metadata_df['id'] = map_metadata_df['id'].astype(int) last_id = map_metadata_df['id'].max() print("last_id: ", last_id) - + if conversation_id in map_metadata_df.values: print("conversation_id exists") - + # store that convo metadata locally prev_data = map_metadata_df[map_metadata_df['conversation_id'] == conversation_id] prev_index = prev_data.index.values[0] print("prev_index: ", prev_index) - embeddings = map_embeddings_df[prev_index-1].reshape(1, 1536) + embeddings = map_embeddings_df[prev_index - 1].reshape(1, 1536) prev_convo = prev_data['conversation'].values[0] prev_id = prev_data['id'].values[0] print("prev_id: ", prev_id) created_at = pd.to_datetime(prev_data['created_at'].values[0]).strftime('%Y-%m-%d %H:%M:%S') print("prev_created_at: ", created_at) print("before delete") - + # delete that convo data point from Nomic print(project.delete_data([str(prev_id)])) - + # prep for new point first_message = prev_convo.split("\n")[1].split(": ")[1] print("first_message: ", first_message) - + # select the last 2 messages and append new convo to prev convo messages_to_be_logged = messages[-2:] for message in messages_to_be_logged: @@ -78,16 +79,23 @@ def log_convo_to_nomic(course_name: str, conversation) -> str: emoji = "🙋 " else: emoji = "🤖 " - + prev_convo += "\n>>> " + emoji + message['role'] + ": " + message['content'] + "\n" # modified timestamp current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") # update metadata - metadata = [{"course": course_name, "conversation": prev_convo, "conversation_id": conversation_id, - "id": last_id+1, "user_email": user_email, "first_query": first_message, "created_at": created_at, - "modified_at": current_time}] + metadata = [{ + "course": course_name, + "conversation": prev_convo, + "conversation_id": conversation_id, + "id": last_id + 1, + "user_email": user_email, + "first_query": first_message, + "created_at": created_at, + "modified_at": current_time + }] else: print("conversation_id does not exist") @@ -107,19 +115,26 @@ def log_convo_to_nomic(course_name: str, conversation) -> str: # modified timestamp current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") - metadata = [{"course": course_name, "conversation": conversation_string, "conversation_id": conversation_id, - "id": last_id+1, "user_email": user_email, "first_query": first_message, "created_at": current_time, - "modified_at": current_time}] - + metadata = [{ + "course": course_name, + "conversation": conversation_string, + "conversation_id": conversation_id, + "id": last_id + 1, + "user_email": user_email, + "first_query": first_message, + "created_at": current_time, + "modified_at": current_time + }] + # create embeddings embeddings_model = OpenAIEmbeddings() embeddings = embeddings_model.embed_documents(user_queries) - + # add embeddings to the project project = atlas.AtlasProject(name=project_name, add_datums_if_exists=True) project.add_embeddings(embeddings=np.array(embeddings), data=pd.DataFrame(metadata)) project.rebuild_maps() - + except Exception as e: # if project doesn't exist, create it print(e) @@ -155,13 +170,13 @@ def get_nomic_map(course_name: str): # Moved this to the logging function to keep our UI fast. # with project.wait_for_project_lock() as project: # project.rebuild_maps() - + map = project.get_map(project_name) print(f"⏰ Nomic Full Map Retrieval: {(time.monotonic() - start_time):.2f} seconds") - return {"map_id": f"iframe{map.id}", - "map_link": map.map_link} + return {"map_id": f"iframe{map.id}", "map_link": map.map_link} + def create_nomic_map(course_name: str, log_data: list): """ @@ -173,14 +188,14 @@ def create_nomic_map(course_name: str, log_data: list): print("in create_nomic_map()") # initialize supabase supabase_client = supabase.create_client( # type: ignore - supabase_url=os.getenv('SUPABASE_URL'), # type: ignore - supabase_key=os.getenv('SUPABASE_API_KEY')) # type: ignore + supabase_url=os.getenv('SUPABASE_URL'), # type: ignore + supabase_key=os.getenv('SUPABASE_API_KEY')) # type: ignore # fetch all conversations with this new course (we expect <=20 conversations, because otherwise the map should be made already) response = supabase_client.table("llm-convo-monitor").select("*").eq("course_name", course_name).execute() data = response.data df = pd.DataFrame(data) - + if len(data) < 19: return None else: @@ -197,7 +212,7 @@ def create_nomic_map(course_name: str, log_data: list): for index, row in df.iterrows(): user_email = row['user_email'] - created_at = pd.to_datetime(row['created_at']).strftime('%Y-%m-%d %H:%M:%S') + created_at = pd.to_datetime(row['created_at']).strftime('%Y-%m-%d %H:%M:%S') convo = row['convo'] messages = convo['messages'] first_message = messages[0]['content'] @@ -206,13 +221,13 @@ def create_nomic_map(course_name: str, log_data: list): # create metadata for multi-turn conversation conversation = "" if message['role'] == 'user': - emoji = "🙋 " + emoji = "🙋 " else: - emoji = "🤖 " + emoji = "🤖 " for message in messages: # string of role: content, role: content, ... conversation += "\n>>> " + emoji + message['role'] + ": " + message['content'] + "\n" - + # append current chat to previous chat if convo already exists if convo['id'] == log_conversation_id: conversation_exists = True @@ -225,11 +240,18 @@ def create_nomic_map(course_name: str, log_data: list): # adding modified timestamp current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") - + # add to metadata - metadata_row = {"course": row['course_name'], "conversation": conversation, "conversation_id": convo['id'], - "id": i, "user_email": user_email, "first_query": first_message, "created_at": created_at, - "modified_at": current_time} + metadata_row = { + "course": row['course_name'], + "conversation": conversation, + "conversation_id": convo['id'], + "id": i, + "user_email": user_email, + "first_query": first_message, + "created_at": created_at, + "modified_at": current_time + } metadata.append(metadata_row) i += 1 @@ -247,27 +269,40 @@ def create_nomic_map(course_name: str, log_data: list): # adding timestamp current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") - metadata_row = {"course": course_name, "conversation": conversation, "conversation_id": log_conversation_id, - "id": i, "user_email": log_user_email, "first_query": log_messages[0]['content'], "created_at": current_time, - "modified_at": current_time} + metadata_row = { + "course": course_name, + "conversation": conversation, + "conversation_id": log_conversation_id, + "id": i, + "user_email": log_user_email, + "first_query": log_messages[0]['content'], + "created_at": current_time, + "modified_at": current_time + } metadata.append(metadata_row) print("length of metadata: ", len(metadata)) metadata = pd.DataFrame(metadata) - embeddings_model = OpenAIEmbeddings() # type: ignore + embeddings_model = OpenAIEmbeddings() # type: ignore embeddings = embeddings_model.embed_documents(user_queries) # create Atlas project project_name = NOMIC_MAP_NAME_PREFIX + course_name index_name = course_name + "_convo_index" print("project_name: ", project_name) - project = atlas.map_embeddings(embeddings=np.array(embeddings), data=metadata, # type: ignore -- this is actually the correc type, the function signature from Nomic is incomplete - id_field='id', build_topic_model=True, topic_label_field='first_query', - name=project_name, colorable_fields=['conversation_id', 'first_query']) + project = atlas.map_embeddings( + embeddings=np.array(embeddings), + data=metadata, # type: ignore -- this is actually the correc type, the function signature from Nomic is incomplete + id_field='id', + build_topic_model=True, + topic_label_field='first_query', + name=project_name, + colorable_fields=['conversation_id', 'first_query']) project.create_index(index_name, build_topic_model=True) print("project: ", project) return f"Successfully created Nomic map for {course_name}" + if __name__ == '__main__': pass