Resolve mrege conflicts with updated processor conversation data model

This commit is contained in:
Saba 2021-11-28 16:22:52 -05:00
commit 7ca4fc3453
3 changed files with 55 additions and 21 deletions

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@ -15,7 +15,7 @@ from src.utils.helpers import get_absolute_path
from src.utils.cli import cli from src.utils.cli import cli
from src.utils.config import SearchType, SearchModels, TextSearchConfig, ImageSearchConfig, SearchConfig, ProcessorConfig, ConversationProcessorConfig from src.utils.config import SearchType, SearchModels, TextSearchConfig, ImageSearchConfig, SearchConfig, ProcessorConfig, ConversationProcessorConfig
from src.utils.rawconfig import FullConfig from src.utils.rawconfig import FullConfig
from src.processor.conversation.gpt import converse, message_to_prompt from src.processor.conversation.gpt import converse, message_to_log, message_to_prompt, understand
# Application Global State # Application Global State
model = SearchModels() model = SearchModels()
@ -114,13 +114,16 @@ def regenerate(t: Optional[SearchType] = None):
@app.get('/chat') @app.get('/chat')
def chat(q: str): def chat(q: str):
# Load Conversation History # Load Conversation History
conversation_history = processor_config.conversation.conversation_history chat_log = processor_config.conversation.chat_log
meta_log = processor_config.conversation.meta_log
# Converse with OpenAI GPT # Converse with OpenAI GPT
gpt_response = converse(q, conversation_history, api_key=processor_config.conversation.openai_api_key) user_message_metadata = understand(q, api_key=processor_config.conversation.openai_api_key)
gpt_response = converse(q, chat_log, api_key=processor_config.conversation.openai_api_key)
# Update Conversation History # Update Conversation History
processor_config.conversation.conversation_history = message_to_prompt(q, conversation_history, gpt_response) processor_config.conversation.chat_log = message_to_prompt(q, chat_log, gpt_message=gpt_response)
processor_config.conversation.meta_log= message_to_log(q, user_message_metadata, gpt_response, meta_log)
return {'status': 'ok', 'response': gpt_response} return {'status': 'ok', 'response': gpt_response}
@ -158,31 +161,44 @@ def initialize_processor(verbose):
# Initialize Conversation Processor # Initialize Conversation Processor
processor_config.conversation = ConversationProcessorConfig(config.processor.conversation, verbose) processor_config.conversation = ConversationProcessorConfig(config.processor.conversation, verbose)
# Load or Initialize Conversation History from Disk
conversation_logfile = processor_config.conversation.conversation_logfile conversation_logfile = processor_config.conversation.conversation_logfile
if processor_config.conversation.verbose: if processor_config.conversation.verbose:
print('Saving conversation logs to disk...') print('INFO:\tLoading conversation logs from disk...')
if conversation_logfile.expanduser().absolute().is_file(): if conversation_logfile.expanduser().absolute().is_file():
# Load Metadata Logs from Conversation Logfile
with open(get_absolute_path(conversation_logfile), 'r') as f: with open(get_absolute_path(conversation_logfile), 'r') as f:
processor_config.conversation.conversation_history = json.load(f).get('chat', '') processor_config.conversation.meta_log = json.load(f)
# Extract Chat Logs from Metadata
processor_config.conversation.chat_log = ''.join(
[f'\n{item["by"]}: {item["message"]}'
for item
in processor_config.conversation.meta_log])
print('INFO:\tConversation logs loaded from disk.')
else: else:
processor_config.conversation.conversation_history = '' # Initialize Conversation Logs
processor_config.conversation.meta_log = []
processor_config.conversation.chat_log = ""
return processor_config return processor_config
@app.on_event('shutdown') @app.on_event('shutdown')
def shutdown_event(): def shutdown_event():
if processor_config.conversation.verbose: # No need to create empty log file
print('Saving conversation logs to disk...') if not processor_config.conversation.meta_log:
return
elif processor_config.conversation.verbose:
print('INFO:\tSaving conversation logs to disk...')
# Save Conversation History to Disk # Save Conversation Metadata Logs to Disk
conversation_logfile = get_absolute_path(processor_config.conversation.conversation_logfile) conversation_logfile = get_absolute_path(processor_config.conversation.conversation_logfile)
with open(conversation_logfile, "w+", encoding='utf-8') as logfile: with open(conversation_logfile, "w+", encoding='utf-8') as logfile:
json.dump({"chat": processor_config.conversation.conversation_history}, logfile) json.dump(processor_config.conversation.meta_log, logfile)
print('Conversation logs saved to disk.') print('INFO:\tConversation logs saved to disk.')
if __name__ == '__main__': if __name__ == '__main__':

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@ -1,5 +1,7 @@
# Standard Packages # Standard Packages
import os import os
import json
from datetime import datetime
# External Packages # External Packages
import openai import openai
@ -11,12 +13,12 @@ def understand(text, api_key=None, temperature=0.5, max_tokens=100):
""" """
# Initialize Variables # Initialize Variables
openai.api_key = api_key or os.getenv("OPENAI_API_KEY") openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
understand_primer="Extract information from each chat message\n\nremember(memory-type, data);\nmemory-type=[\"companion\", \"notes\", \"ledger\", \"image\", \"music\"]\nsearch(search-type, data);\nsearch-type=[\"google\", \"youtube\"]\ngenerate(activity);\nactivity=[\"paint\",\"write\", \"chat\"]\ntrigger-emotion(emotion);\nemotion=[\"happy\",\"confidence\",\"fear\",\"surprise\",\"sadness\",\"disgust\",\"anger\", \"curiosity\", \"calm\"]\n\nQ: How are you doing?\nA: activity(\"chat\"); trigger-emotion(\"surprise\")\nQ: Do you remember what I told you about my brother Antoine when we were at the beach?\nA: remember(\"notes\", \"Brother Antoine when we were at the beach\"); trigger-emotion(\"curiosity\");\nQ: what did we talk about last time?\nA: remember(\"notes\", \"talk last time\"); trigger-emotion(\"curiosity\");\nQ: Let's make some drawings!\nA: generate(\"paint\"); trigger-emotion(\"happy\");\nQ: Do you know anything about Lebanon?\nA: search(\"google\", \"lebanon\"); trigger-emotion(\"confidence\");\nQ: Find a video about a panda rolling in the grass\nA: search(\"youtube\",\"panda rolling in the grass\"); trigger-emotion(\"happy\"); \nQ: Tell me a scary story\nA: generate(\"write\" \"A story about some adventure\"); trigger-emotion(\"fear\");\nQ: What fiction book was I reading last week about AI starship?\nA: remember(\"notes\", \"read fiction book about AI starship last week\"); trigger-emotion(\"curiosity\");\nQ: How much did I spend at Subway for dinner last time?\nA: remember(\"ledger\", \"last Subway dinner\"); trigger-emotion(\"curiosity\");\nQ: I'm feeling sleepy\nA: activity(\"chat\"); trigger-emotion(\"calm\")\nQ: What was that popular Sri lankan song that Alex showed me recently?\nA: remember(\"music\", \"popular Sri lankan song that Alex showed recently\"); trigger-emotion(\"curiosity\"); \nQ: You're pretty funny!\nA: activity(\"chat\"); trigger-emotion(\"pride\")" understand_primer = "Extract intent, trigger emotion information as JSON from each chat message\n\n- intent\n - remember(memory-type, data);\n - memory-type=[\"companion\", \"notes\", \"ledger\", \"image\", \"music\"]\n - search(search-type, data);\n - search-type=[\"google\"]\n - generate(activity);\n - activity=[\"paint\",\"write\", \"chat\"]\n- trigger-emotion(emotion)\n - emotion=[\"happy\",\"confidence\",\"fear\",\"surprise\",\"sadness\",\"disgust\",\"anger\", \"curiosity\", \"calm\"]\n\nQ: How are you doing?\nA: { \"intent\": [\"activity\", \"chat\"], \"trigger-emotion\": \"surprise\" }\nQ: Do you remember what I told you about my brother Antoine when we were at the beach?\nA: { \"intent\": [\"remember\", \"notes\", \"Brother Antoine when we were at the beach\"], \"trigger-emotion\": \"curiosity\" }\nQ: what did we talk about last time?\nA: { \"intent\": [\"remember\", \"notes\", \"talk last time\"], \"trigger-emotion\": \"curiosity\" }\nQ: Let's make some drawings!\nA: { \"intent\": [\"generate\", \"paint\"], \"trigger-emotion: \"happy\" }\nQ: Do you know anything about Lebanon cuisine in the 18th century?\nA: { \"intent\": [\"search\", \"google\", \"lebanon cusine in the 18th century\"], \"trigger-emotion; \"confidence\" }\nQ: Tell me a scary story\nA: { \"intent\": [\"generate\", \"write\", \"A story about some adventure\"], \"trigger-emotion\": \"fear\" }\nQ: What fiction book was I reading last week about AI starship?\nA: { \"intent\": [\"remember\", \"notes\", \"fiction book about AI starship last week\"], \"trigger-emotion\": \"curiosity\" }\nQ: How much did I spend at Subway for dinner last time?\nA: { \"intent\": [\"remember\", \"ledger\", \"last Subway dinner\"], \"trigger-emotion\": \"curiosity\" }\nQ: I'm feeling sleepy\nA: { \"intent\": [\"activity\", \"chat\"], \"trigger-emotion\": \"calm\" }\nQ: What was that popular Sri lankan song that Alex had mentioned?\nA: { \"intent\": [\"remember\", \"music\", \"popular Sri lankan song mentioned by Alex\"], \"trigger-emotion\": \"curiosity\" } \nQ: You're pretty funny!\nA: { \"intent\": [\"activity\", \"chat\"], \"trigger-emotion\": \"pride\" }\nQ: Can you recommend a movie to watch from my notes?\nA: { \"intent\": [\"remember\", \"notes\", \"movie to watch\"], \"trigger-emotion\": \"curiosity\" }",
# Setup Prompt with Understand Primer # Setup Prompt with Understand Primer
prompt = message_to_prompt(text, understand_primer, start_sequence="\nA:", restart_sequence="\nQ:") prompt = message_to_prompt(text, understand_primer, start_sequence="\nA:", restart_sequence="\nQ:")
# Get Reponse from GPT # Get Response from GPT
response = openai.Completion.create( response = openai.Completion.create(
engine="davinci", engine="davinci",
prompt=prompt, prompt=prompt,
@ -63,8 +65,22 @@ def converse(text, conversation_history=None, api_key=None, temperature=0.9, max
def message_to_prompt(user_message, conversation_history="", gpt_message=None, start_sequence="\nAI:", restart_sequence="\nHuman:"): def message_to_prompt(user_message, conversation_history="", gpt_message=None, start_sequence="\nAI:", restart_sequence="\nHuman:"):
"""Create prompt for GPT from message""" """Create prompt for GPT from messages and conversation history"""
if gpt_message: gpt_message = f" {gpt_message}" if gpt_message else ""
return f"{conversation_history}{restart_sequence} {user_message}{start_sequence}{gpt_message}" return f"{conversation_history}{restart_sequence} {user_message}{start_sequence}{gpt_message}"
else:
return f"{conversation_history}{restart_sequence} {user_message}{start_sequence}"
def message_to_log(user_message, user_message_metadata, gpt_message, conversation_log=[]):
"""Create json logs from messages, metadata for conversation log"""
# Create json log from Human's message
human_log = json.loads(user_message_metadata)
human_log["message"] = user_message
human_log["by"] = "Human"
human_log["created"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
# Create json log from GPT's response
ai_log = {"message": gpt_message, "by": "AI", "created": datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
conversation_log.extend([human_log, ai_log])
return conversation_log

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@ -74,6 +74,8 @@ class ConversationProcessorConfig():
def __init__(self, processor_config: ProcessorConversationConfig, verbose: bool): def __init__(self, processor_config: ProcessorConversationConfig, verbose: bool):
self.openai_api_key = processor_config.open_api_key self.openai_api_key = processor_config.open_api_key
self.conversation_logfile = Path(processor_config.conversation_logfile) self.conversation_logfile = Path(processor_config.conversation_logfile)
self.chat_log = ''
self.meta_log = []
self.conversation_history = Path(processor_config.conversation_history) self.conversation_history = Path(processor_config.conversation_history)
self.verbose = verbose self.verbose = verbose