diff --git a/ai_ta_backend/beam/nomic_logging.py b/ai_ta_backend/beam/nomic_logging.py new file mode 100644 index 00000000..18591a05 --- /dev/null +++ b/ai_ta_backend/beam/nomic_logging.py @@ -0,0 +1,743 @@ +import datetime +import json +import os +import time + +import backoff +import nomic +import numpy as np +import pandas as pd +import sentry_sdk +import supabase +from langchain.embeddings import OpenAIEmbeddings +from nomic import AtlasProject, atlas + +OPENAI_API_TYPE = "azure" + +SUPABASE_CLIENT = supabase.create_client( # type: ignore + supabase_url=os.getenv('SUPABASE_URL'), # type: ignore + supabase_key=os.getenv('SUPABASE_API_KEY')) # type: ignore + +LOCK_EXCEPTIONS = [ + 'Project is locked for state access! Please wait until the project is unlocked to access embeddings.', + 'Project is locked for state access! Please wait until the project is unlocked to access data.', + 'Project is currently indexing and cannot ingest new datums. Try again later.' +] + + +def giveup_hdlr(e): + """ + Function to handle giveup conditions in backoff decorator + Args: + e: Exception raised by the decorated function + Returns: + True if we want to stop retrying, False otherwise + """ + (e_args,) = e.args + e_str = e_args['exception'] + + print("giveup_hdlr() called with exception:", e_str) + if e_str in LOCK_EXCEPTIONS: + return False + else: + sentry_sdk.capture_exception(e) + return True + + +def backoff_hdlr(details): + """ + Function to handle backup conditions in backoff decorator. + Currently just prints the details of the backoff. + """ + print( + "\nBacking off {wait:0.1f} seconds after {tries} tries, calling function {target} with args {args} and kwargs {kwargs}" + .format(**details)) + + +def backoff_strategy(): + """ + Function to define retry strategy. Is usualy defined in the decorator, + but passing parameters to it is giving errors. + """ + return backoff.expo(base=10, factor=1.5) + + +@backoff.on_exception(backoff_strategy, + Exception, + max_tries=5, + raise_on_giveup=False, + giveup=giveup_hdlr, + on_backoff=backoff_hdlr) +def log_convo_to_nomic(course_name: str, conversation) -> str: + nomic.login(os.getenv('NOMIC_API_KEY')) # login during start of flask app + NOMIC_MAP_NAME_PREFIX = 'Conversation Map for ' + """ + Logs conversation to Nomic. + 1. Check if map exists for given course + 2. Check if conversation ID exists + - if yes, delete and add new data point + - if no, add new data point + 3. Keep current logic for map doesn't exist - update metadata + """ + + print(f"in log_convo_to_nomic() for course: {course_name}") + print("type of conversation:", type(conversation)) + #conversation = json.loads(conversation) + messages = conversation['conversation']['messages'] + if 'user_email' not in conversation['conversation']: + user_email = "NULL" + else: + user_email = conversation['conversation']['user_email'] + conversation_id = conversation['conversation']['id'] + + # 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. + # 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) + + map_metadata_df = project.maps[1].data.df # type: ignore + map_embeddings_df = project.maps[1].embeddings.latent + # create a function which returns project, data and embeddings df here + map_metadata_df['id'] = map_metadata_df['id'].astype(int) + last_id = map_metadata_df['id'].max() + + if conversation_id in map_metadata_df.values: + # store that convo metadata locally + prev_data = map_metadata_df[map_metadata_df['conversation_id'] == conversation_id] + prev_index = prev_data.index.values[0] + embeddings = map_embeddings_df[prev_index - 1].reshape(1, 1536) + prev_convo = prev_data['conversation'].values[0] + prev_id = prev_data['id'].values[0] + created_at = pd.to_datetime(prev_data['created_at'].values[0]).strftime('%Y-%m-%d %H:%M:%S') + + # delete that convo data point from Nomic, and print result + print("Deleting point from nomic:", project.delete_data([str(prev_id)])) + + # prep for new point + first_message = prev_convo.split("\n")[1].split(": ")[1] + + # 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: + if message['role'] == 'user': + emoji = "🙋 " + else: + emoji = "🤖 " + + if isinstance(message['content'], list): + text = message['content'][0]['text'] + else: + text = message['content'] + + prev_convo += "\n>>> " + emoji + message['role'] + ": " + text + "\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 + }] + else: + print("conversation_id does not exist") + + # add new data point + user_queries = [] + conversation_string = "" + + first_message = messages[0]['content'] + if isinstance(first_message, list): + first_message = first_message[0]['text'] + user_queries.append(first_message) + + for message in messages: + if message['role'] == 'user': + emoji = "🙋 " + else: + emoji = "🤖 " + + if isinstance(message['content'], list): + text = message['content'][0]['text'] + else: + text = message['content'] + + conversation_string += "\n>>> " + emoji + message['role'] + ": " + text + "\n" + + # 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 + }] + + # create embeddings + embeddings_model = OpenAIEmbeddings(openai_api_type=OPENAI_API_TYPE) # type: ignore + embeddings = embeddings_model.embed_documents(user_queries) + + # add embeddings to the project - create a new function for this + project = atlas.AtlasProject(name=project_name, add_datums_if_exists=True) + with project.wait_for_project_lock(): + project.add_embeddings(embeddings=np.array(embeddings), data=pd.DataFrame(metadata)) + project.rebuild_maps() + + print(f"⏰ Nomic logging runtime: {(time.monotonic() - start_time):.2f} seconds") + return f"Successfully logged for {course_name}" + + except Exception as e: + if str(e) == 'You must specify a unique_id_field when creating a new project.': + print("Attempting to create Nomic map...") + result = create_nomic_map(course_name, conversation) + print("result of create_nomic_map():", result) + else: + # raising exception again to trigger backoff and passing parameters to use in create_nomic_map() + 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. + We just need the ID and URL. + Example values: + map link: https://atlas.nomic.ai/map/ed222613-97d9-46a9-8755-12bbc8a06e3a/f4967ad7-ff37-4098-ad06-7e1e1a93dd93 + map id: f4967ad7-ff37-4098-ad06-7e1e1a93dd93 + """ + nomic.login(os.getenv('NOMIC_API_KEY')) # login during start of flask app + if type.lower() == 'document': + NOMIC_MAP_NAME_PREFIX = 'Document Map for ' + else: + NOMIC_MAP_NAME_PREFIX = 'Conversation Map for ' + + project_name = NOMIC_MAP_NAME_PREFIX + course_name + start_time = time.monotonic() + + try: + project = atlas.AtlasProject(name=project_name, add_datums_if_exists=True) + 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} + 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) + else: + print("ERROR in get_nomic_map():", e) + sentry_sdk.capture_exception(e) + return {"map_id": None, "map_link": None} + + +def create_nomic_map(course_name: str, log_data: list): + """ + Creates a Nomic map for new courses and those which previously had < 20 queries. + 1. fetches supabase conversations for course + 2. appends current embeddings and metadata to it + 2. creates map if there are at least 20 queries + """ + nomic.login(os.getenv('NOMIC_API_KEY')) # login during start of flask app + NOMIC_MAP_NAME_PREFIX = 'Conversation Map for ' + + print(f"in create_nomic_map() for {course_name}") + # 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 + + try: + # 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: + # get all queries for course and create metadata + user_queries = [] + metadata = [] + i = 1 + conversation_exists = False + + # current log details + log_messages = log_data['conversation']['messages'] # type: ignore + log_user_email = log_data['conversation']['user_email'] # type: ignore + log_conversation_id = log_data['conversation']['id'] # type: ignore + + 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') + convo = row['convo'] + messages = convo['messages'] + + first_message = messages[0]['content'] + if isinstance(first_message, list): + first_message = first_message[0]['text'] + + user_queries.append(first_message) + + # create metadata for multi-turn conversation + conversation = "" + for message in messages: + # string of role: content, role: content, ... + if message['role'] == 'user': # type: ignore + emoji = "🙋 " + else: + emoji = "🤖 " + + if isinstance(message['content'], list): + text = message['content'][0]['text'] + else: + text = message['content'] + + conversation += "\n>>> " + emoji + message['role'] + ": " + text + "\n" + + # append current chat to previous chat if convo already exists + if convo['id'] == log_conversation_id: + conversation_exists = True + + for m in log_messages: + if m['role'] == 'user': # type: ignore + emoji = "🙋 " + else: + emoji = "🤖 " + + if isinstance(m['content'], list): + text = m['content'][0]['text'] + else: + text = m['content'] + conversation += "\n>>> " + emoji + m['role'] + ": " + text + "\n" + + # 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.append(metadata_row) + i += 1 + + # add current log as a new data point if convo doesn't exist + if not conversation_exists: + user_queries.append(log_messages[0]['content']) + conversation = "" + for message in log_messages: + if message['role'] == 'user': + emoji = "🙋 " + else: + emoji = "🤖 " + + if isinstance(message['content'], list): + text = message['content'][0]['text'] + else: + text = message['content'] + conversation += "\n>>> " + emoji + message['role'] + ": " + text + "\n" + + # 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.append(metadata_row) + + metadata = pd.DataFrame(metadata) + embeddings_model = OpenAIEmbeddings(openai_api_type=OPENAI_API_TYPE) # 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" + project = atlas.map_embeddings( + embeddings=np.array(embeddings), + data=metadata, # type: ignore - this is the correct type, the func 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) + return f"Successfully created Nomic map for {course_name}" + 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 on your project: ", e) + else: + print("ERROR in create_nomic_map():", e) + sentry_sdk.capture_exception(e) + + return "failed" + + +## -------------------------------- DOCUMENT MAP FUNCTIONS --------------------------------- ## + + +def create_document_map(course_name: str): + """ + This is a function which creates a document map for a given course from scratch + 1. Gets count of documents for the course + 2. If less than 20, returns a message that a map cannot be created + 3. If greater than 20, iteratively fetches documents in batches of 25 + 4. Prepares metadata and embeddings for nomic upload + 5. Creates a new map and uploads the data + + Args: + course_name: str + Returns: + str: success or failed + """ + print("in create_document_map()") + nomic.login(os.getenv('NOMIC_API_KEY')) + NOMIC_MAP_NAME_PREFIX = 'Document Map for ' + + # 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 + + try: + # check if map exists + response = supabase_client.table("projects").select("doc_map_id").eq("course_name", course_name).execute() + if response.data: + return "Map already exists for this course." + + # fetch relevant document data from Supabase + response = supabase_client.table("documents").select("id", + count="exact").eq("course_name", + course_name).order('id', + 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: + + first_id = response.data[0]['id'] + combined_dfs = [] + curr_total_doc_count = 0 + doc_count = 0 + first_batch = True + + # iteratively query in batches of 25 + while curr_total_doc_count < total_doc_count: + + response = supabase_client.table("documents").select( + "id, created_at, s3_path, url, readable_filename, contexts").eq("course_name", course_name).gte( + 'id', first_id).order('id', desc=False).limit(25).execute() + df = pd.DataFrame(response.data) + combined_dfs.append(df) # list of dfs + + 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 + final_df = pd.concat(combined_dfs, ignore_index=True) + + # prep data for nomic upload + embeddings, metadata = data_prep_for_doc_map(final_df) + + if first_batch: + # create a new map + print("Creating new map...") + project_name = NOMIC_MAP_NAME_PREFIX + course_name + index_name = course_name + "_doc_index" + topic_label_field = "text" + colorable_fields = ["readable_filename", "text"] + result = create_map(embeddings, metadata, project_name, index_name, topic_label_field, colorable_fields) + # update flag + first_batch = False + + else: + # append to existing map + print("Appending data to existing map...") + project_name = NOMIC_MAP_NAME_PREFIX + course_name + # 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) + project_name = NOMIC_MAP_NAME_PREFIX + course_name + if first_batch: + index_name = course_name + "_doc_index" + topic_label_field = "text" + colorable_fields = ["readable_filename", "text"] + result = create_map(embeddings, metadata, project_name, index_name, topic_label_field, colorable_fields) + else: + result = append_to_map(embeddings, metadata, project_name) + print("Atlas upload status: ", result) + + # log info to supabase + project = AtlasProject(name=project_name, add_datums_if_exists=True) + project_id = project.id + project.rebuild_maps() + project_info = {'course_name': course_name, 'doc_map_id': project_id} + response = supabase_client.table("projects").insert(project_info).execute() + print("Response from supabase: ", response) + return "success" + else: + return "Cannot create a map because there are less than 20 documents in the course." + except Exception as e: + print(e) + sentry_sdk.capture_exception(e) + return "failed" + + +def delete_from_document_map(course_name: str, ids: list): + """ + This function is used to delete datapoints from a document map. + Currently used within the delete_data() function in vector_database.py + Args: + course_name: str + ids: list of str + """ + print("in delete_from_document_map()") + + try: + # check if project exists + response = SUPABASE_CLIENT.table("projects").select("doc_map_id").eq("course_name", course_name).execute() + if response.data: + project_id = response.data[0]['doc_map_id'] + else: + return "No document map found for this course" + + # fetch project from Nomic + project = AtlasProject(project_id=project_id, add_datums_if_exists=True) + + # delete the ids from Nomic + print("Deleting point from document map:", project.delete_data(ids)) + with project.wait_for_project_lock(): + project.rebuild_maps() + return "Successfully deleted from Nomic map" + except Exception as e: + print(e) + sentry_sdk.capture_exception(e) + return "Error in deleting from document map: {e}" + + +def log_to_document_map(data: dict): + """ + This is a function which appends new documents to an existing document map. It's called + at the end of split_and_upload() after inserting data to Supabase. + Args: + data: dict - the response data from Supabase insertion + """ + print("in add_to_document_map()") + + try: + # check if map exists + course_name = data['course_name'] + response = SUPABASE_CLIENT.table("projects").select("doc_map_id").eq("course_name", course_name).execute() + if response.data: + project_id = response.data[0]['doc_map_id'] + else: + # create a map + map_creation_result = create_document_map(course_name) + if map_creation_result != "success": + return "The project has less than 20 documents and a map cannot be created." + else: + # fetch project id + response = SUPABASE_CLIENT.table("projects").select("doc_map_id").eq("course_name", course_name).execute() + project_id = response.data[0]['doc_map_id'] + + project = AtlasProject(project_id=project_id, add_datums_if_exists=True) + #print("Inserted data: ", data) + + embeddings = [] + metadata = [] + context_count = 0 + # prep data for nomic upload + for row in data['contexts']: + context_count += 1 + embeddings.append(row['embedding']) + metadata.append({ + "id": str(data['id']) + "_" + str(context_count), + "doc_ingested_at": data['created_at'], + "s3_path": data['s3_path'], + "url": data['url'], + "readable_filename": data['readable_filename'], + "created_at": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "text": row['text'] + }) + embeddings = np.array(embeddings) + metadata = pd.DataFrame(metadata) + print("Shape of embeddings: ", embeddings.shape) + + # append to existing map + 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: + print(e) + sentry_sdk.capture_exception(e) + return "Error in appending to map: {e}" + + +def create_map(embeddings, metadata, map_name, index_name, topic_label_field, colorable_fields): + """ + Generic function to create a Nomic map from given parameters. + Args: + embeddings: np.array of embeddings + metadata: pd.DataFrame of metadata + map_name: str + index_name: str + topic_label_field: str + colorable_fields: list of str + """ + nomic.login(os.getenv('NOMIC_API_KEY')) + + try: + project = atlas.map_embeddings(embeddings=embeddings, + data=metadata, + id_field="id", + build_topic_model=True, + name=map_name, + topic_label_field=topic_label_field, + colorable_fields=colorable_fields, + add_datums_if_exists=True) + project.create_index(index_name, build_topic_model=True) + return "success" + except Exception as e: + 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. + Args: + embeddings: np.array of embeddings + 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) + with project.wait_for_project_lock(): + project.add_embeddings(embeddings=embeddings, data=metadata) + return "Successfully appended to Nomic map" + except Exception as e: + print(e) + return "Error in appending to map: {e}" + + +def data_prep_for_doc_map(df: pd.DataFrame): + """ + This function prepares embeddings and metadata for nomic upload in document map creation. + Args: + df: pd.DataFrame - the dataframe of documents from Supabase + Returns: + embeddings: np.array of embeddings + metadata: pd.DataFrame of metadata + """ + print("in data_prep_for_doc_map()") + + 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'] = "" + # iterate through all contexts and create separate entries for each + context_count = 0 + for context in row['contexts']: + context_count += 1 + text_row = context['text'] + embeddings_row = context['embedding'] + + meta_row = { + "id": str(row['id']) + "_" + str(context_count), + "doc_ingested_at": row['created_at'], + "s3_path": row['s3_path'], + "url": row['url'], + "readable_filename": row['readable_filename'], + "created_at": current_time, + "text": text_row + } + + embeddings.append(embeddings_row) + metadata.append(meta_row) + texts.append(text_row) + + embeddings_np = np.array(embeddings, dtype=object) + print("Shape of embeddings: ", embeddings_np.shape) + + # 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 + embeddings = embeddings_model.embed_documents(texts) + + metadata = pd.DataFrame(metadata) + embeddings = np.array(embeddings) + + return embeddings, metadata + + +if __name__ == '__main__': + pass