Wire up GPT understand method to /chat API. Log conversation metadata too

src/main.py

@@ -12,7 +12,7 @@ from src.search_type import asymmetric, symmetric_ledger, image_search
 from src.utils.helpers import get_absolute_path
 from src.utils.cli import cli
 from src.utils.config import SearchType, SearchModels, TextSearchConfig, ImageSearchConfig, SearchConfig, ProcessorConfig, ConversationProcessorConfig
-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
@@ -91,13 +91,16 @@ def regenerate(t: Optional[SearchType] = None):
 @app.get('/chat')
 def chat(q: str):
     # 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
-    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
-    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}
 
@@ -130,36 +133,48 @@ def initialize_search(config, regenerate, verbose):
 
 
 def initialize_processor(config, verbose):
-    processor_config = ProcessorConfig()
-
     # Initialize Conversation Processor
+    processor_config = ProcessorConfig()
     processor_config.conversation = ConversationProcessorConfig.create_from_dictionary(config, ('processor', 'conversation'), verbose)
 
-    # Load or Initialize Conversation History from Disk
     conversation_logfile = processor_config.conversation.conversation_logfile
     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():
+        # Load Metadata Logs from Conversation Logfile
         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:
-        processor_config.conversation.conversation_history = ''
+        # Initialize Conversation Logs
+        processor_config.conversation.meta_log = []
+        processor_config.conversation.chat_log = ""
 
     return processor_config
 
 
 @app.on_event('shutdown')
 def shutdown_event():
-    if processor_config.conversation.verbose:
-        print('Saving conversation logs to disk...')
+    # No need to create empty log file
+    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)
     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__':

src/processor/conversation/gpt.py

@@ -1,5 +1,7 @@
 # Standard Packages
 import os
+import json
+from datetime import datetime
 
 # External Packages
 import openai
@@ -11,12 +13,12 @@ def understand(text, api_key=None, temperature=0.5, max_tokens=100):
     """
     # Initialize Variables
     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
     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(
         engine="davinci",
         prompt=prompt,
@@ -55,7 +57,7 @@ def converse(text, conversation_history=None, api_key=None, temperature=0.9, max
         top_p=1,
         frequency_penalty=0,
         presence_penalty=0.6,
-        stop=["\n", " Human:", " AI:"])
+        stop=["\n", "Human:", "AI:"])
 
     # Extract, Clean Message from GPT's Response
     story = response['choices'][0]['text']
@@ -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:"):
-    """Create prompt for GPT from message"""
-    if 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}"
+    """Create prompt for GPT from messages and conversation history"""
+    gpt_message = f" {gpt_message}" if gpt_message else ""
+
+    return f"{conversation_history}{restart_sequence} {user_message}{start_sequence}{gpt_message}"
+
+
+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

src/utils/config.py

@@ -96,10 +96,11 @@ class SearchConfig():
 
 
 class ConversationProcessorConfig():
-    def __init__(self, conversation_logfile, conversation_history, openai_api_key, verbose):
+    def __init__(self, conversation_logfile, chat_log, meta_log, openai_api_key, verbose):
         self.openai_api_key = openai_api_key
         self.conversation_logfile = conversation_logfile
-        self.conversation_history = conversation_history
+        self.chat_log = chat_log
+        self.meta_log = meta_log
         self.verbose = verbose
 
     def create_from_dictionary(config, key_tree, verbose):
@@ -109,7 +110,8 @@ class ConversationProcessorConfig():
 
         return ConversationProcessorConfig(
             openai_api_key = conversation_config['openai-api-key'],
-            conversation_history = '',
+            chat_log = '',
+            meta_log = [],
             conversation_logfile = Path(conversation_config['conversation-logfile']),
             verbose = verbose)