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@libraryofcelsus
libraryofcelsus authored Apr 17, 2023
1 parent 0f2c608 commit b4e5c05
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47 changes: 25 additions & 22 deletions scripts/Main_Bot/GPT_3_5_Chat_Auto.py
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Original file line number Diff line number Diff line change
Expand Up @@ -188,10 +188,10 @@ def load_conversation_cadence(results):


# if __name__ == '__main__':
def GPT_3_5_Chat_Auto():
def GPT_3_5_Chat_Training():
vdb = pinecone.Index("aetherius")
index_info = vdb.describe_index_stats()
# # Number of Messages before conversation is summarized, higher number, higher api cost. Change to 3 to use GPT 3.5
# # Number of Messages before conversation is summarized, higher number, higher api cost. Change to 3 when using GPT 3.5
conv_length = 3
print("Type [Save and Exit] to summarize the conversation and exit.")
print("Type [Exit] to exit without saving.")
Expand Down Expand Up @@ -227,25 +227,25 @@ def GPT_3_5_Chat_Auto():
if a == 'Exit':
return
if a == 'Save and Exit':
conversation2.append({'role': 'user', 'content': "Read the previous messages and extract the salient points in single bullet point format to serve as %s's memories. Every bullet point should contain the given timestat, [%s], and the memory in full context.\nSummary:" % (bot_name, timestring)})
conversation2.append({'role': 'user', 'content': "Review the previous messages and summarize the key points of the conversation in a single bullet point format to serve as %s's memories. Include the full context for each bullet point. Start from the end and work towards the beginning.\nMemories:" % bot_name})
conv_summary = chatgpt35summary_completion(conversation2)
print(conv_summary)
while True:
print('\n\nSYSTEM: Upload to long term memory? Heavily increases token usage, not recommended.\n Press Y for yes or N for no.')
print('\n\nSYSTEM: Upload to long term memory?\n Press Y for yes or N for no.')
user_input = input("'Y' or 'N': ")
if user_input == 'y':
lines = conv_summary.splitlines()
for line in lines:
print(line)
vector = gpt3_embedding(line)
# print(timestring + line)
vector = gpt3_embedding(timestring + line)
unique_id = str(uuid4())
metadata = {'speaker': bot_name, 'time': timestamp, 'message': line,
metadata = {'speaker': bot_name, 'time': timestamp, 'message': (timestring + line),
'timestring': timestring, 'uuid': unique_id}
save_json('nexus/episodic_memories_nexus/%s.json' % unique_id, metadata)
save_json('nexus/episodic_memory_nexus/%s.json' % unique_id, metadata)
payload.append((unique_id, vector))
vdb.upsert(payload, namespace='episodic_memories')
print('\n\nSYSTEM: Upload Successful!')
payload.clear()
print('\n\nSYSTEM: Upload Successful!')
return
elif user_input == 'n':
print('\n\nSYSTEM: Memories have been Deleted')
Expand All @@ -260,24 +260,25 @@ def GPT_3_5_Chat_Auto():
message_input = a
vector_input = gpt3_embedding(message_input)
# # Search Memory DB
results = vdb.query(vector=vector_input, top_k=12, namespace='memories')
results = vdb.query(vector=vector_input, top_k=13, namespace='memories')
db_search = load_conversation_memory(results)
results = vdb.query(vector=vector_input, top_k=3, namespace='episodic_memories')
results = vdb.query(vector=vector_input, top_k=5, namespace='episodic_memories')
db_search_7 = load_conversation_episodic_memory(results)
# print(db_search)
# # Search Heuristics DB
results = vdb.query(vector=vector_input, top_k=10, namespace='heuristics')
results = vdb.query(vector=vector_input, top_k=7, namespace='heuristics')
db_search_2= load_conversation_heuristics(results)
# print(db_search_2)
# # Inner Monologue Generation
conversation.append({'role': 'assistant', 'content': "MEMORIES: %s;\n%s;\nHEURISTICS: %s;\nUSER MESSAGE: %s;\nBased on %s's memories and the user, %s's message, compose a brief silent soliloquy that reflects on %s's deepest contemplations and emotions in relation to the user's message.\n\nINNER_MONOLOGUE: " % (db_search, db_search_7, db_search_2, a, bot_name, username, bot_name)})
conversation.append({'role': 'assistant', 'content': "MEMORIES: %s;\n%s;\nHEURISTICS: %s;\nUSER MESSAGE: %s;\nBased on %s's memories and the user, %s's message, compose a brief silent soliloquy as %s's inner monologue that reflects on %s's deepest contemplations and emotions in relation to the user's message.\n\nINNER_MONOLOGUE: " % (db_search, db_search_7, db_search_2, a, bot_name, username, bot_name, bot_name)})
output = chatgpt250_completion(conversation)
message = output
vector_monologue = gpt3_embedding(message)
print('\n\nINNER_MONOLOGUE: %s' % output)
output_log = f'\nUSER: {a} \n\n {bot_name}: {output}'
filename = '%s_inner_monologue.txt' % time()
save_file('logs/inner_monologue_logs/%s' % filename, output_log)
# # Clear Conversation List for Intuition Generation
conversation.clear()
conversation.append({'role': 'system', 'content': '%s' % main_prompt})
# # Give Greeting if first Message
Expand All @@ -304,7 +305,7 @@ def GPT_3_5_Chat_Auto():
output_two_log = f'\nUSER: {a} \n\n {bot_name}: {output_two}'
filename = '%s_intuition.txt' % time()
save_file('logs/intuition_logs/%s' % filename, output_two_log)
# # Update Second Conversation List for Gpt-4
# # Update Second Conversation List for Response
if 'response_two' in locals():
conversation2.append({'role': 'assistant', 'content': "%s" % response_two})
else:
Expand All @@ -318,15 +319,17 @@ def GPT_3_5_Chat_Auto():
conversation2.append({'role': 'user', 'content': a})
# # Search Inner_Loop/Memory DB
while True:
results = vdb.query(vector=vector, top_k=2, namespace='inner_loop')
results = vdb.query(vector=vector_input, top_k=4, namespace='inner_loop')
db_search_4 = load_conversation_inner_loop(results)
# print(db_search_4)
results = vdb.query(vector=vector, top_k=3, namespace='memories')
results = vdb.query(vector=vector_input, top_k=4, namespace='memories')
db_search_5 = load_conversation_memory(results)
# print(db_search_5)
results = vdb.query(vector=vector_monologue, top_k=3, namespace='episodic_memories')
db_search_8 = load_conversation_episodic_memory(results)
break
# # Generate Aetherius's Response
conversation2.append({'role': 'assistant', 'content': "SUBCONSIOUS: %s;\n\nMEMORIES: %s;\n\nINNER THOUGHTS: %s;\n%s\nI am in the middle of a conversation with my user, %s. USER MESSAGE: %s; I will do my best to speak naturally and show emotional intelligence. I will intuit their needs: %s;\nMy current message window is limited to 2300 characters.\nI will now give a response with the diction of a real person: " % (db_search_4, db_search_5, output, second_prompt, username, a, output_two)})
conversation2.append({'role': 'assistant', 'content': "SUBCONSIOUS: %s;\n\nMEMORIES: %s\n%s\n\nINNER THOUGHTS: %s;\n%s\nI am in the middle of a conversation with my user, %s. USER MESSAGE: %s; I will do my best to speak naturally and show emotional intelligence. I will intuit their needs: %s;\nMy current message window is limited to 2300 characters.\nI will now give a response with the diction of a real person: " % (db_search_4, db_search_5, db_search_8, output, second_prompt, username, a, output_two)})
response_two = chatgpt500_completion(conversation2)
print('\n\n%s: %s' % (bot_name, response_two))
# # Save Chat Logs
Expand All @@ -338,7 +341,7 @@ def GPT_3_5_Chat_Auto():
save_file('logs/final_response_logs/%s' % filename, final_message)
# # Generate Summary for Memory DB
db_msg = f'\nUSER: {a} \n\n INNER_MONOLOGUE: {output} \n\n {bot_name}: {response_two}'
summary.append({'role': 'user', 'content': "LOG: %s;\n\nRead the previous messages and extract the salient points in single bullet point format to serve as %s's memories. Each bullet point should contain the memory in full context. Exclude irrelevant information.\nSummary:\n" % (db_msg, bot_name)})
summary.append({'role': 'user', 'content': "LOG:\n%s\n\Read the log and extract the salient points in single bullet point format to serve as %s's memories. Include the full context for each bullet point. Start from the end and work towards the beginning. Exclude the starting prompt and cadence.\nMemories: " % (db_msg, bot_name)})
db_upload = chatgpt250_completion(summary)
db_upsert = db_upload
# # Auto Upload to Memory DB
Expand Down Expand Up @@ -375,7 +378,7 @@ def GPT_3_5_Chat_Auto():
counter += 1
# # Summary loop to avoid Max Token Limit.
if counter % conv_length == 0:
conversation2.append({'role': 'user', 'content': "Read the previous messages and extract the salient points in single bullet point format to serve as %s's memories. Every bullet point should contain the given timestat, [%s], and the memory in full context.\nSummary:" % (bot_name, timestring)})
conversation2.append({'role': 'user', 'content': "Review the previous messages and summarize the key points of the conversation in a single bullet point format to serve as %s's memories. Include the full context for each bullet point. Start from the end and work towards the beginning.\nMemories:" % bot_name})
conv_summary = chatgpt35summary_completion(conversation2)
print(conv_summary)
conversation2.clear()
Expand All @@ -389,11 +392,11 @@ def GPT_3_5_Chat_Auto():
if user_input == 'y':
lines = conv_summary.splitlines()
for line in lines:
vector = gpt3_embedding(line)
vector = gpt3_embedding(timestring + line)
unique_id = str(uuid4())
metadata = {'speaker': bot_name, 'time': timestamp, 'message': line,
metadata = {'speaker': bot_name, 'time': timestamp, 'message': (timestring + line),
'timestring': timestring, 'uuid': unique_id}
save_json('nexus/episodic_memories_nexus/%s.json' % unique_id, metadata)
save_json('nexus/episodic_memory_nexus/%s.json' % unique_id, metadata)
payload.append((unique_id, vector))
vdb.upsert(payload, namespace='episodic_memories')
payload.clear()
Expand Down
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