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available for the input text after accounting for the question + available_length_for_input = max_length - len(question) - len( + "Based on the input text: \n Give me answers for this question: ") + + # Truncate the input text to fit the available length + truncated_input_text = input_text[:available_length_for_input] + + # Construct the temporary question with the truncated input text + tmp_question = f"Based on the input text: {truncated_input_text}\nGive me answers for this question: {question}" + + return tmp_question + + +def answer_with_gpt3_with_function_calls(input_text, question, model): + question = truncate_input_text(input_text, question) + + messages = [ + { + "role": "system", + "content": "".join([ + "You are a professional, knowledgeable, supportive, friendly but not overly casual assistant who will help the user to answer questions about a lab. ", + "In order to do so, you may use semantic_search to find relevant documents. ", + ]) + }, + { + "role": "user", + "content": question + } + ] + + response = openai.ChatCompletion.create( + model=model, + messages=messages, + functions=OPENAI_FUNCTIONS_DEFINITIONS, + max_tokens=200 + ) + response_message = response["choices"][0]["message"] + + messages.append( + { + "role": "assistant", + "content": response_message.get("content"), + "function_call": response_message.get("function_call"), + } + ) + + # Check if GPT wanted to call a function + if response_message.get("function_call"): + # Call the function + # Note: the JSON response may not always be valid; be sure to handle errors + available_functions = OPENAI_AVAILABLE_FUNCTIONS # only one function in this example, but you can have multiple + function_name = response_message["function_call"]["name"] + + # Step 4: send the info on the function call and function response to GPT + + function_to_call = available_functions[function_name] + function_args = json.loads(response_message["function_call"]["arguments"]) + function_response = function_to_call(**function_args) + messages.append(response_message) # extend conversation with assistant's reply + messages.append( + { + "role": "function", + "name": function_name, + "content": function_response, + } + ) # extend conversation with function response + second_response = openai.ChatCompletion.create( + model=model, + messages=messages, + ) # get a new response from GPT where it can see the function response + return second_response.choices[0].message.content + else: + return response.choices[0].message.content # add input parameter: need api_key for demo def get_response_from_model(user_input, top_k=3, annoy_metric='dot', model_name="gpt-3.5-turbo", user_query_preprocess=False): diff --git a/model_main.py b/model_main.py deleted file mode 100644 index 8a1e7c5b7772e80829471335802c4c8ee4a8e922..0000000000000000000000000000000000000000 --- a/model_main.py +++ /dev/null @@ -1,65 +0,0 @@ -import argparse - -from utils import get_embeddings, search_document_annoy, \ - answer_with_gpt3_with_function_calls, transform_user_question, debug_print - -def main(args): - """ - - Get & embed user's question - - Find top choices of documents based on embedding search - - Generate response - """ - top_k = args.top_k - annoy_metric = args.annoy_metric - - # Get & embed user's question - while True: - print("Hi! What question do you have for ANGR? press 0 to exit") - user_input = input() - if user_input == '0': - break - - assert top_k > 0, 'k must be a integer greater than 0' - - if args.user_query_preprocess: - chatgpt_question = transform_user_question(user_input, args.model) - else: - chatgpt_question = user_input - debug_print("chatgpt_question: ", chatgpt_question) - - try: - user_q_embedding = get_embeddings(chatgpt_question) - document = search_document_annoy(user_q_embedding, top_k=top_k, metric=annoy_metric) - reply = answer_with_gpt3_with_function_calls(document, user_input, args.model) - print(reply) - except Exception as e: - print(f"error: {e}") - return e - # print("Error when trying to get embedding for the user query. Please try with a shorter question.") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument('--top_k', default=3, type=int, help="Number of documents to retrieve") - parser.add_argument('--annoy_metric', default='angular', - choices=['angular', 'euclidean', 'manhattan', 'hamming', 'dot'], - help="metric for annoy algorithm") - parser.add_argument('--model', default="gpt-3.5-turbo") - parser.add_argument('--user_query_preprocess', default=False, help="Whether to ask ChatGPT to generate an additional query for itself based on user's question") - parser.add_argument('--debug', default=False, help="define whether to print the debug statement") - args = parser.parse_args() - - main(args) - -""" -python main.py --top_k 3 --annoy_metric dot --user_query_preprocess True - -Questions: -- list math-related publications -- list all publications -- Give me a publication written by J Coleman - - Could you please provide me with a publication authored by J Coleman? - - Can you find a publication authored by J Coleman? -- Give me the link to Jared Coleman's homepage - -""" diff --git a/openai_function_utils/openai_function_impl.py b/openai_function_utils/openai_function_impl.py index 490e7b525e6fde0f43adf9e4b203bd359bd96ccb..c46698e9610d23f8335afc8e423d755aea2f5748 100644 --- a/openai_function_utils/openai_function_impl.py +++ b/openai_function_utils/openai_function_impl.py @@ -4,6 +4,7 @@ Util functions for openai api import json import os from thefuzz import process +import openai def get_lab_member_info(name: str): @@ -11,10 +12,17 @@ def get_lab_member_info(name: str): with open(database_addr, 'r') as fin: all_members_info = json.load(fin) + choices = set() for field in all_members_info: toSearch = all_members_info[field] for i in toSearch: - if name.lower() in i['name'].lower(): + choices.add(i['name']) + best = process.extractOne(name, choices) + + for field in all_members_info: + toSearch = all_members_info[field] + for i in toSearch: + if best[0].lower() in i['name'].lower(): return json.dumps(i) return json.dumps({}) @@ -25,10 +33,17 @@ def get_lab_member_detailed_info(name: str, detailed_info: str): with open(database_addr, 'r') as fin: all_members_info = json.load(fin) + choices = set() + for field in all_members_info: + toSearch = all_members_info[field] + for i in toSearch: + choices.add(i['name']) + best = process.extractOne(name, choices) + for field in all_members_info: toSearch = all_members_info[field] for i in toSearch: - if name.lower() in i['name'].lower(): + if best[0].lower() in i['name'].lower(): if "link" in detailed_info.lower() or "homepage" in detailed_info.lower(): return json.dumps(i['links']) elif "photo" in detailed_info.lower() or "pic" in detailed_info.lower() or "picture" in detailed_info.lower(): @@ -38,12 +53,14 @@ def get_lab_member_detailed_info(name: str, detailed_info: str): return json.dumps({}) + def get_publication_by_year(year: str): database_addr = os.path.join(os.getcwd(), 'database/original_documents/publications.json') with open(database_addr, 'r') as fin: all_pub_info = json.load(fin) data = {} for field in all_pub_info: + print(f'field: {field}') to_search = all_pub_info[field] for i in to_search: if int(year) == i['year']: @@ -59,10 +76,19 @@ def get_pub_info(name: str): for i in all_members_info: if name.lower() in i['title'].lower(): return json.dumps(i) - return json.dumps({}) + def get_pub_by_name(name: str): + choices = set() + database_addr = os.path.join(os.getcwd(), 'database/original_documents/publications.json') + with open(database_addr, 'r') as fin: + all_members_info = json.load(fin) + for i in all_members_info: + for author in i['authors']: + choices.add(author) + best = process.extractOne(name, choices) + database_addr = os.path.join(os.getcwd(), 'database/original_documents/publications.json') with open(database_addr, 'r') as fin: all_members_info = json.load(fin) @@ -70,28 +96,42 @@ def get_pub_by_name(name: str): data = {} for i in all_members_info: for author in i['authors']: - if name.lower() in author.lower(): + if best[0].lower() in author.lower(): data.update(i) return json.dumps(data) -def get_fuzz_name(name: str): - choices = {} - database_addr = os.path.join(os.getcwd(), 'database/original_documents/members.json') - with open(database_addr, 'r') as fin: - all_members_info = json.load(fin) - for field in all_members_info: - toSearch = all_members_info[field] - for i in toSearch: - choices.add(i['name']) +def semantic_search(query: str): + response = openai.Embedding.create(model="text-embedding-ada-002", input=query) + embedding = response['data'][0]['embedding'] + function_response = search_document(embedding, 3) + return function_response + + +def search_downloads(input_title: str): + # Search in downloads.json file based on title of video demos, code and datasets, and course materials + download_fn = os.path.join(os.getcwd(), 'database/original_documents/parsed_downloads.json') + with open(download_fn, 'r') as fin: + all_download_info = json.load(fin) + choices = set() + for i in all_download_info.keys(): + choices.add(i) + best = process.extractOne(input_title, choices) + data = {} + for title, entry in all_download_info.items(): + if best[0].lower() in title.lower(): + data.update(entry) - best = process.extractOne(name, choices, score_cutoff=50) - if best: - return best[0] - else: - return None + return json.dumps(data) -def semantic_search(query: str): - return \ No newline at end of file +def get_member_list_by_edu_status(criterion: str): + # Get list of member info by education level and status (whether still in the lab). + # Choices for criterion are ['undergrad', 'current_phd', 'director', 'former_postdoc', 'former_visiting', 'graduated', 'master'] + + member_json = os.path.join(os.getcwd(), f'database/original_documents/members/{criterion}.json') + with open(member_json, 'r') as fin: + member_info_list = json.load(fin) + + return json.dumps(member_info_list) diff --git a/openai_function_utils/openai_function_interface.py b/openai_function_utils/openai_function_interface.py index 2b32842a3bf6d3ab901be7fc618edbf33bc59780..4a72779dc7253b1f64e7869b91870e55df695377 100644 --- a/openai_function_utils/openai_function_interface.py +++ b/openai_function_utils/openai_function_interface.py @@ -1,6 +1,5 @@ - from openai_function_utils.openai_function_impl import get_lab_member_info, get_pub_info, get_lab_member_detailed_info, \ - get_publication_by_year, get_pub_by_name, get_fuzz_name, semantic_search + get_publication_by_year, get_pub_by_name, semantic_search, search_downloads, get_member_list_by_edu_status OPENAI_FUNCTIONS_DEFINITIONS = [ { @@ -71,38 +70,53 @@ OPENAI_FUNCTIONS_DEFINITIONS = [ "properties": { "name": { "type": "string", - "description": "Name of a lab member", + "description": "Name of a lab member, for example, Jared Coleman, J Coleman", } }, "required": ["name"], }, }, { - "name": "get_fuzz_name", - "description": "When user mentions a name that is cannot be found, use this function to search for the most similar name.", + "name": "semantic_search", + "description": "The function performs a semantic search over the documents based on query. Specifically, it will retrieve related documents with the maximum cosine similarity between the embedding of the query with that of all database documents.", "parameters": { "type": "object", "properties": { - "name": { + "query": { "type": "string", - "description": "Name of a lab member", + "description": "The query to search for", } }, - "required": ["name"], - }, + "required": ["query"], + } }, { - "name": "semantic_search", - "description": "does a semantic search over the documents based on query", + "name": "search_downloads", + "description": "This function returns information (including info such as textual description, link to code and dataset, etc.) of video demos, code and datasets, and course materials based on the title of the video/code/dataset/course.", "parameters": { "type": "object", "properties": { - "query": { + "input_title": { "type": "string", - "description": "The query to search for", + "description": "Title of things such as video demos, code, datasets, and course materials to search for.", } }, - "required": ["query"], + "required": ["input_title"], + } + }, + { + "name": "get_member_list_by_edu_status", + "description": "Get list of member info by education level and status (whether still in the lab), which are indicated in the name of the choice that you will be choosing from.", + "parameters": { + "type": "object", + "properties": { + "criterion": { + "type": "string", + "description": "Choice of education and his/her status. e.g.: former_postdoc means people pursuing postdoc but have already graduated from and left the lab.", + "enum": ['undergrad', 'current_phd', 'director', 'former_postdoc', 'former_visiting', 'graduated', 'master'] + } + }, + "required": ["criterion"], } }, ] @@ -113,6 +127,7 @@ OPENAI_AVAILABLE_FUNCTIONS = { "get_lab_member_detailed_info": get_lab_member_detailed_info, "get_publication_by_year": get_publication_by_year, "get_pub_by_name": get_pub_by_name, - "get_fuzz_name": get_fuzz_name, "semantic_search": semantic_search, + "search_downloads": search_downloads, + "get_member_list_by_edu_status": get_member_list_by_edu_status } diff --git a/static/index.html b/static/index.html index c75ae6a1652fb318213620def0950c34da0a35a0..fdccacedaa8369c786fc57a58fde73e2f81e0b1b 100644 --- a/static/index.html +++ b/static/index.html @@ -31,7 +31,7 @@

Demo Questions

First enter your own api-key and then ask question through user input or click on one of the demo questions

- + diff --git a/utils.py b/utils.py index 3845071f2ab240216346b8554d930f2cb6cdf9c9..177c219fcc380ab20204554272f5b2005783332b 100644 --- a/utils.py +++ b/utils.py @@ -10,7 +10,7 @@ from tqdm import tqdm from annoy import AnnoyIndex -from openai_function_utils.openai_function_interface import OPENAI_AVAILABLE_FUNCTIONS, OPENAI_FUNCTIONS_DEFINITIONS +# from openai_function_utils.openai_function_interface import OPENAI_AVAILABLE_FUNCTIONS, OPENAI_FUNCTIONS_DEFINITIONS DEBUG_PRINT = False # openai.api_key = OPENAI_KEY # openai.organization = 'org-dsEkob5KeBBq3lbBLhnCXcJt' @@ -43,104 +43,6 @@ def transform_user_question(question, model): return chagpt_question -def answer_with_gpt3_with_function_calls(input_text, question, model): - question = f"Based on the input text: {input_text}\n Give me answers for this question: {question}" - messages = [ - { - "role": "system", - "content": "".join([ - "You are a helpful assistant for ChatGPT that will answer the user's questions. " - ]) - }, - { - "role": "user", - "content": question - } - ] - - response = openai.ChatCompletion.create( - model=model, - messages=messages, - functions=OPENAI_FUNCTIONS_DEFINITIONS, - max_tokens=200 - ) - response_message = response["choices"][0]["message"] - - messages.append( - { - "role": "assistant", - "content": response_message.get("content"), - "function_call": response_message.get("function_call"), - } - ) - - # Check if GPT wanted to call a function - if response_message.get("function_call"): - # Call the function - # Note: the JSON response may not always be valid; be sure to handle errors - available_functions = OPENAI_AVAILABLE_FUNCTIONS # only one function in this example, but you can have multiple - function_name = response_message["function_call"]["name"] - - # Step 4: send the info on the function call and function response to GPT - if function_name == "semantic_search": - # print("Running semantic search") - # print(response_message["function_call"]["arguments"]) - function_args = json.loads(response_message["function_call"]["arguments"]) - embedding = get_embeddings(function_args['query']) - function_response = search_document(embedding, 3) - messages.append({ - "role": "function", - "name": "semantic_search", - "content": function_response - }) - second_response = openai.ChatCompletion.create( - model=model, - messages=messages, - ) # get a new response from GPT where it can see the function response - return second_response.choices[0].message.content - else: - function_to_call = available_functions[function_name] - function_args = json.loads(response_message["function_call"]["arguments"]) - function_response = function_to_call(**function_args) - messages.append(response_message) # extend conversation with assistant's reply - messages.append( - { - "role": "function", - "name": function_name, - "content": function_response, - } - ) # extend conversation with function response - # messages.append( - # { - # "role": "user", - # "content": "give me publication of J Coleman" - # } - # ) - print("DEBUG: messages", messages) - second_response = openai.ChatCompletion.create( - model=model, - messages=messages, - ) # get a new response from GPT where it can see the function response - return second_response.choices[0].message.content - else: - return response.choices[0].message.content - - -def answer_with_gpt3(input_text, question): - messages = [{"role": "system", - "content": "You are an intelligent chatbot for answering user's questions related to a research lab."}] - message = f"Based on the input text: {input_text}\n Give me answers for this question: {question}" - messages.append({"role": "user", "content": message}) - chat = openai.ChatCompletion.create( - model="gpt-3.5-turbo", - messages=messages, - functions=OPENAI_FUNCTIONS_DEFINITIONS, - max_tokens=200 - ) - reply = chat.choices[0].message.content - return reply - - def search_document(user_question_embed: list, top_k: int = 1): csv_filename = 'database/document_name_to_embedding.csv' if not os.path.exists(csv_filename): @@ -180,7 +82,7 @@ def search_document_annoy(user_question_embed: list, top_k: int, metric): print("This won't happen!") return - df = pd.read_csv(csv_filename) + df = pd.read_csv(csv_filename, index_col=0) # Convert the embedding column from string to list/array df['embedding'] = df['embedding'].apply(ast.literal_eval).apply(np.array) @@ -320,5 +222,19 @@ def main(): final_df.to_csv('database/document_name_to_embedding.csv') +def parse_downloads_to_title_to_info(): + download_fn = os.path.join(os.getcwd(), 'database/original_documents/downloads.json') + with open(download_fn, 'r') as fin: + all_download_info = json.load(fin) + title_to_info = {} + for k, v in all_download_info.items(): + tmp_list = v[0] if len(v) == 1 else v + for entry in tmp_list: + title_to_info.setdefault(entry['title'], entry) + download_fn = os.path.join(os.getcwd(), 'database/original_documents/parsed_downloads.json') + with open(download_fn, 'w') as fout: + json.dump(title_to_info, fout) + + if __name__ == "__main__": - main() + main() \ No newline at end of file