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f57b7f65ea
To prompt improve readability: - Remove newline escape sequence and use actual newline directly - This avoids one long line of text as prompt and - Remove escaping of double quotes
171 lines
7.5 KiB
Python
171 lines
7.5 KiB
Python
# Standard Packages
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import os
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import json
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from datetime import datetime
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# External Packages
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import openai
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# Internal Packages
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from src.utils.constants import empty_escape_sequences
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def summarize(text, summary_type, user_query=None, api_key=None, temperature=0.5, max_tokens=100):
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"""
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Summarize user input using OpenAI's GPT
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"""
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# Initialize Variables
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openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
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# Setup Prompt based on Summary Type
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if summary_type == "chat":
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prompt = f"You are an AI. Summarize the conversation below from your perspective:\n\n{text}\n\nSummarize the conversation from the AI's first-person perspective:"
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elif summary_type == "notes":
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prompt = f"Summarize the below notes about {user_query}:\n\n{text}\n\nSummarize the notes in second person perspective and use past tense:"
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# Get Response from GPT
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response = openai.Completion.create(
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engine="davinci-instruct-beta-v3",
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prompt=prompt,
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temperature=temperature,
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max_tokens=max_tokens,
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top_p=1,
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frequency_penalty=0.2,
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presence_penalty=0,
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stop="\"\"\"")
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# Extract, Clean Message from GPT's Response
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story = response['choices'][0]['text']
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return str(story).replace("\n\n", "")
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def understand(text, api_key=None, temperature=0.5, max_tokens=100, verbose=0):
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"""
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Understand user input using OpenAI's GPT
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"""
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# Initialize Variables
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openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
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understand_primer = '''
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Objective: Extract intent and trigger emotion information as JSON from each chat message
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Potential intent types and valid argument values are listed below:
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- intent
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- remember(memory-type, query);
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- memory-type=["companion","notes","ledger","image","music"]
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- search(search-type, query);
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- search-type=["google"]
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- generate(activity, query);
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- activity=["paint","write","chat"]
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- trigger-emotion(emotion)
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- emotion=["happy","confidence","fear","surprise","sadness","disgust","anger","shy","curiosity","calm"]
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Some examples are given below for reference:
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Q: How are you doing?
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A: { "intent": {"type": "generate", "activity": "chat", "query": "How are you doing?"}, "trigger-emotion": "happy" }
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Q: Do you remember what I told you about my brother Antoine when we were at the beach?
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A: { "intent": {"type": "remember", "memory-type": "companion", "query": "Brother Antoine when we were at the beach"}, "trigger-emotion": "curiosity" }
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Q: what was that fantasy story you told me last time?
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A: { "intent": {"type": "remember", "memory-type": "companion", "query": "fantasy story told last time"}, "trigger-emotion": "curiosity" }
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Q: Let's make some drawings about the stars on a clear full moon night!
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A: { "intent": {"type": "generate", "activity": "paint", "query": "stars on a clear full moon night"}, "trigger-emotion: "happy" }
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Q: Do you know anything about Lebanon cuisine in the 18th century?
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A: { "intent": {"type": "search", "search-type": "google", "query": "lebanon cusine in the 18th century"}, "trigger-emotion; "confidence" }
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Q: Tell me a scary story
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A: { "intent": {"type": "generate", "activity": "write", "query": "A scary story"}, "trigger-emotion": "fear" }
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Q: What fiction book was I reading last week about AI starship?
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A: { "intent": {"type": "remember", "memory-type": "notes", "query": "fiction book about AI starship last week"}, "trigger-emotion": "curiosity" }
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Q: How much did I spend at Subway for dinner last time?
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A: { "intent": {"type": "remember", "memory-type": "ledger", "query": "last Subway dinner"}, "trigger-emotion": "calm" }
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Q: I'm feeling sleepy
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A: { "intent": {"type": "generate", "activity": "chat", "query": "I'm feeling sleepy"}, "trigger-emotion": "calm" }
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Q: What was that popular Sri lankan song that Alex had mentioned?
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A: { "intent": {"type": "remember", "memory-type": "music", "query": "popular Sri lankan song mentioned by Alex"], "trigger-emotion": "curiosity" }
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Q: You're pretty funny!
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A: { "intent": {"type": "generate", "activity": "chat", "query": "You're pretty funny!"}, "trigger-emotion": "shy" }
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Q: Can you recommend a movie to watch from my notes?
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A: { "intent": {"type": "remember", "memory-type": "notes", "query": "recommend movie to watch"], "trigger-emotion": "curiosity" }
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Q: When did I go surfing last?
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A: { "intent": {"type": "remember", "memory-type": "notes", "query": "When did I go surfing last"], "trigger-emotion": "calm" }
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Q: Can you dance for me?
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A: { "intent": {"type": "generate", "activity": "chat", "query": "Can you dance for me?"}, "trigger-emotion": "sad" }'''
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# Setup Prompt with Understand Primer
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prompt = message_to_prompt(text, understand_primer, start_sequence="\nA:", restart_sequence="\nQ:")
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if verbose > 1:
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print(f"Message -> Prompt: {text} -> {prompt}")
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# Get Response from GPT
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response = openai.Completion.create(
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engine="davinci",
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prompt=prompt,
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temperature=temperature,
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max_tokens=max_tokens,
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top_p=1,
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frequency_penalty=0.2,
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presence_penalty=0,
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stop=["\n"])
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# Extract, Clean Message from GPT's Response
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story = str(response['choices'][0]['text'])
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return json.loads(story.strip(empty_escape_sequences))
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def converse(text, conversation_history=None, api_key=None, temperature=0.9, max_tokens=150):
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"""
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Converse with user using OpenAI's GPT
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"""
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# Initialize Variables
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openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
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ai_prompt = "AI:"
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human_prompt = "Human:"
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conversation_primer = f'''
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The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and a very friendly companion.
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{human_prompt} Hello, who are you?
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{ai_prompt} Hi, I am an AI conversational companion created by OpenAI. How can I help you today?'''
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# Setup Prompt with Primer or Conversation History
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prompt = message_to_prompt(text, conversation_history or conversation_primer, start_sequence=ai_prompt, restart_sequence=human_prompt)
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# Get Response from GPT
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response = openai.Completion.create(
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engine="davinci",
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prompt=prompt,
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temperature=temperature,
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max_tokens=max_tokens,
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top_p=1,
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frequency_penalty=0,
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presence_penalty=0.6,
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stop=["\n", "Human:", "AI:"])
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# Extract, Clean Message from GPT's Response
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story = str(response['choices'][0]['text'])
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return story.strip(empty_escape_sequences)
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def message_to_prompt(user_message, conversation_history="", gpt_message=None, start_sequence="\nAI:", restart_sequence="\nHuman:"):
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"""Create prompt for GPT from messages and conversation history"""
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gpt_message = f" {gpt_message}" if gpt_message else ""
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return f"{conversation_history}{restart_sequence} {user_message}{start_sequence}{gpt_message}"
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def message_to_log(user_message, user_message_metadata, gpt_message, conversation_log=[]):
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"""Create json logs from messages, metadata for conversation log"""
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# Create json log from Human's message
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human_log = user_message_metadata
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human_log["message"] = user_message
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human_log["by"] = "Human"
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human_log["created"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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# Create json log from GPT's response
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ai_log = {"message": gpt_message, "by": "AI", "created": datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
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conversation_log.extend([human_log, ai_log])
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return conversation_log
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def extract_summaries(metadata):
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"""Extract summaries from metadata"""
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return ''.join(
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[f'\n{session["summary"]}' for session in metadata]) |