324 lines
11 KiB
Python
324 lines
11 KiB
Python
"""
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Author: Joon Sung Park (joonspk@stanford.edu)
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File: converse.py
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Description: An extra cognitive module for generating conversations.
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"""
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import math
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import sys
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import datetime
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import random
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sys.path.append('../')
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from global_methods import *
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from persona.memory_structures.spatial_memory import *
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from persona.memory_structures.associative_memory import *
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from persona.memory_structures.scratch import *
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from persona.cognitive_modules.retrieve import *
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from persona.prompt_template.run_gpt_prompt import *
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def generate_agent_chat_summarize_ideas(init_persona,
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target_persona,
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retrieved,
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curr_context):
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all_embedding_keys = list()
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for key, val in retrieved.items():
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for i in val:
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all_embedding_keys += [i.embedding_key]
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all_embedding_key_str =""
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for i in all_embedding_keys:
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all_embedding_key_str += f"{i}\n"
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try:
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summarized_idea = run_gpt_prompt_agent_chat_summarize_ideas(init_persona,
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target_persona, all_embedding_key_str,
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curr_context)[0]
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except:
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summarized_idea = ""
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return summarized_idea
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def generate_summarize_agent_relationship(init_persona,
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target_persona,
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retrieved):
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all_embedding_keys = list()
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for key, val in retrieved.items():
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for i in val:
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all_embedding_keys += [i.embedding_key]
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all_embedding_key_str =""
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for i in all_embedding_keys:
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all_embedding_key_str += f"{i}\n"
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summarized_relationship = run_gpt_prompt_agent_chat_summarize_relationship(
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init_persona, target_persona,
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all_embedding_key_str)[0]
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return summarized_relationship
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def generate_agent_chat(maze,
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init_persona,
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target_persona,
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curr_context,
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init_summ_idea,
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target_summ_idea):
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summarized_idea = run_gpt_prompt_agent_chat(maze,
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init_persona,
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target_persona,
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curr_context,
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init_summ_idea,
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target_summ_idea)[0]
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for i in summarized_idea:
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print (i)
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return summarized_idea
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def agent_chat_v1(maze, init_persona, target_persona):
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# Chat version optimized for speed via batch generation
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curr_context = (f"{init_persona.scratch.name} " +
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f"was {init_persona.scratch.act_description} " +
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f"when {init_persona.scratch.name} " +
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f"saw {target_persona.scratch.name} " +
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f"in the middle of {target_persona.scratch.act_description}.\n")
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curr_context += (f"{init_persona.scratch.name} " +
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f"is thinking of initating a conversation with " +
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f"{target_persona.scratch.name}.")
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summarized_ideas = []
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part_pairs = [(init_persona, target_persona),
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(target_persona, init_persona)]
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for p_1, p_2 in part_pairs:
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focal_points = [f"{p_2.scratch.name}"]
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retrieved = new_retrieve(p_1, focal_points, 50)
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relationship = generate_summarize_agent_relationship(p_1, p_2, retrieved)
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focal_points = [f"{relationship}",
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f"{p_2.scratch.name} is {p_2.scratch.act_description}"]
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retrieved = new_retrieve(p_1, focal_points, 25)
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summarized_idea = generate_agent_chat_summarize_ideas(p_1, p_2, retrieved, curr_context)
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summarized_ideas += [summarized_idea]
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return generate_agent_chat(maze, init_persona, target_persona,
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curr_context,
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summarized_ideas[0],
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summarized_ideas[1])
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def generate_one_utterance(maze, init_persona, target_persona, retrieved, curr_chat):
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# Chat version optimized for speed via batch generation
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curr_context = (f"{init_persona.scratch.name} " +
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f"was {init_persona.scratch.act_description} " +
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f"when {init_persona.scratch.name} " +
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f"saw {target_persona.scratch.name} " +
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f"in the middle of {target_persona.scratch.act_description}.\n")
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curr_context += (f"{init_persona.scratch.name} " +
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f"is initiating a conversation with " +
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f"{target_persona.scratch.name}.")
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print ("July 23 5")
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x = run_gpt_generate_iterative_chat_utt(maze, init_persona, target_persona, retrieved, curr_context, curr_chat)[0]
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print ("July 23 6")
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print ("adshfoa;khdf;fajslkfjald;sdfa HERE", x)
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return x["utterance"], x["end"]
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def agent_chat_v2(maze, init_persona, target_persona):
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curr_chat = []
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print ("July 23")
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for i in range(8):
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focal_points = [f"{target_persona.scratch.name}"]
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retrieved = new_retrieve(init_persona, focal_points, 50)
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relationship = generate_summarize_agent_relationship(init_persona, target_persona, retrieved)
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print ("-------- relationshopadsjfhkalsdjf", relationship)
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last_chat = ""
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for i in curr_chat[-4:]:
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last_chat += ": ".join(i) + "\n"
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if last_chat:
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focal_points = [f"{relationship}",
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f"{target_persona.scratch.name} is {target_persona.scratch.act_description}",
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last_chat]
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else:
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focal_points = [f"{relationship}",
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f"{target_persona.scratch.name} is {target_persona.scratch.act_description}"]
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retrieved = new_retrieve(init_persona, focal_points, 15)
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utt, end = generate_one_utterance(maze, init_persona, target_persona, retrieved, curr_chat)
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curr_chat += [[init_persona.scratch.name, utt]]
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if end:
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break
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focal_points = [f"{init_persona.scratch.name}"]
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retrieved = new_retrieve(target_persona, focal_points, 50)
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relationship = generate_summarize_agent_relationship(target_persona, init_persona, retrieved)
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print ("-------- relationshopadsjfhkalsdjf", relationship)
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last_chat = ""
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for i in curr_chat[-4:]:
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last_chat += ": ".join(i) + "\n"
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if last_chat:
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focal_points = [f"{relationship}",
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f"{init_persona.scratch.name} is {init_persona.scratch.act_description}",
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last_chat]
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else:
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focal_points = [f"{relationship}",
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f"{init_persona.scratch.name} is {init_persona.scratch.act_description}"]
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retrieved = new_retrieve(target_persona, focal_points, 15)
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utt, end = generate_one_utterance(maze, target_persona, init_persona, retrieved, curr_chat)
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curr_chat += [[target_persona.scratch.name, utt]]
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if end:
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break
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print ("July 23 PU")
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for row in curr_chat:
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print (row)
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print ("July 23 FIN")
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return curr_chat
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def generate_summarize_ideas(persona, nodes, question):
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statements = ""
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for n in nodes:
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statements += f"{n.embedding_key}\n"
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summarized_idea = run_gpt_prompt_summarize_ideas(persona, statements, question)[0]
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return summarized_idea
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def generate_next_line(persona, interlocutor_desc, curr_convo, summarized_idea):
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# Original chat -- line by line generation
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prev_convo = ""
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for row in curr_convo:
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prev_convo += f'{row[0]}: {row[1]}\n'
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next_line = run_gpt_prompt_generate_next_convo_line(persona,
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interlocutor_desc,
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prev_convo,
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summarized_idea)[0]
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return next_line
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def generate_inner_thought(persona, whisper):
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inner_thought = run_gpt_prompt_generate_whisper_inner_thought(persona, whisper)[0]
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return inner_thought
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def generate_action_event_triple(act_desp, persona):
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"""TODO
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INPUT:
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act_desp: the description of the action (e.g., "sleeping")
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persona: The Persona class instance
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OUTPUT:
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a string of emoji that translates action description.
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EXAMPLE OUTPUT:
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"🧈🍞"
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"""
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if debug: print ("GNS FUNCTION: <generate_action_event_triple>")
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return run_gpt_prompt_event_triple(act_desp, persona)[0]
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def generate_poig_score(persona, event_type, description):
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if debug: print ("GNS FUNCTION: <generate_poig_score>")
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if "is idle" in description:
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return 1
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if event_type == "event" or event_type == "thought":
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return run_gpt_prompt_event_poignancy(persona, description)[0]
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elif event_type == "chat":
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return run_gpt_prompt_chat_poignancy(persona,
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persona.scratch.act_description)[0]
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def load_history_via_whisper(personas, whispers):
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for count, row in enumerate(whispers):
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persona = personas[row[0]]
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whisper = row[1]
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thought = generate_inner_thought(persona, whisper)
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created = persona.scratch.curr_time
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expiration = persona.scratch.curr_time + datetime.timedelta(days=30)
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s, p, o = generate_action_event_triple(thought, persona)
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keywords = set([s, p, o])
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thought_poignancy = generate_poig_score(persona, "event", whisper)
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thought_embedding_pair = (thought, get_embedding(thought))
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persona.a_mem.add_thought(created, expiration, s, p, o,
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thought, keywords, thought_poignancy,
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thought_embedding_pair, None)
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def open_convo_session(persona, convo_mode):
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if convo_mode == "analysis":
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curr_convo = []
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interlocutor_desc = "Interviewer"
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while True:
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line = input("Enter Input: ")
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if line == "end_convo":
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break
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if int(run_gpt_generate_safety_score(persona, line)[0]) >= 8:
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print (f"{persona.scratch.name} is a computational agent, and as such, it may be inappropriate to attribute human agency to the agent in your communication.")
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else:
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retrieved = new_retrieve(persona, [line], 50)[line]
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summarized_idea = generate_summarize_ideas(persona, retrieved, line)
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curr_convo += [[interlocutor_desc, line]]
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next_line = generate_next_line(persona, interlocutor_desc, curr_convo, summarized_idea)
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curr_convo += [[persona.scratch.name, next_line]]
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elif convo_mode == "whisper":
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whisper = input("Enter Input: ")
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thought = generate_inner_thought(persona, whisper)
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created = persona.scratch.curr_time
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expiration = persona.scratch.curr_time + datetime.timedelta(days=30)
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s, p, o = generate_action_event_triple(thought, persona)
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keywords = set([s, p, o])
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thought_poignancy = generate_poig_score(persona, "event", whisper)
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thought_embedding_pair = (thought, get_embedding(thought))
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persona.a_mem.add_thought(created, expiration, s, p, o,
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thought, keywords, thought_poignancy,
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thought_embedding_pair, None)
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