mirror of
https://github.com/migatu/conjurer.git
synced 2026-07-17 07:12:09 +00:00
Tag: 1.24
Intermediate commits (oldest → newest): - Merge branch 'main' of https://github.com/migatu/conjurer - Merge branch 'main' of https://github.com/migatu/conjurer - BGFX - Function for idv3 and bugfix - Merge branch 'main' of https://github.com/migatu/conjurer - Logs additional - LGFIX - FIXED! - Arguments added to the utility. - Fix old bug - add exception handling - Literowka - Hej hoppsan! - Add comment - Fixc Fixc - Fixit - Inside joke very boomer much wow - Fixing logging issues in music_functions.py and other_functions.py - Maybe this will fix - test - test - another attempt to fix the bug - Maybe this is the fix ? - Maybe fix
This commit is contained in:
+408
@@ -0,0 +1,408 @@
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import asyncio
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import json
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import logging
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import random
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import openai
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import tiktoken
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from other_functions import discord_friendly_send
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from constants import (
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ASSISTANTS,
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CYCLIC_WORDS,
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ENCODING,
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GPT_SETTINGS,
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MEMORY_FIVE_MUZYKA,
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MEMORY_FIVE_SIARA,
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MESSAGE_TABLE,
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MESSAGE_TABLE_MUZYKA,
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OPENAICLIENT,
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SYSTEM_GPT_SETTINGS,
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WORD_REACTIONS,
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)
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#this do per user
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VECTOR_STORE_ID = -1
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def create_vector_store():
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# Create a vector store caled "Financial Statements"
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return OPENAICLIENT.beta.vector_stores.create_and_poll(name="Hammer Stash")
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#expires_after={
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#"anchor": "last_active_at",
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#"days": 7}
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#)
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def upload_files_to_vector_store(assistant):
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# Ready the files for upload to OpenAI
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file_paths = ["edgar/goog-10k.pdf", "edgar/brka-10k.txt"]
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file_streams = [open(path, "rb") for path in file_paths]
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#file = client.beta.vector_stores.files.create_and_poll(
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#vector_store_id="vs_abc123",
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#file_id="file-abc123"
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#)
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#batch = client.beta.vector_stores.file_batches.create_and_poll(
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#vector_store_id="vs_abc123",
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#file_ids=['file_1', 'file_2', 'file_3', 'file_4', 'file_5']
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#)
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# Use the upload and poll SDK helper to upload the files, add them to the vector store,
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# and poll the status of the file batch for completion.
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file_batch = OPENAICLIENT.beta.vector_stores.file_batches.upload_and_poll(
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vector_store_id=VECTOR_STORE_ID, files=file_streams
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)
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# You can print the status and the file counts of the batch to see the result of this operation.
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print(file_batch.status)
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print(file_batch.file_counts)
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assistant = OPENAICLIENT.beta.assistants.update(
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assistant_id=assistant.id,
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tool_resources={"file_search": {"vector_store_ids": [VECTOR_STORE_ID]}},
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)
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def delete_files_from_vector_store(assistant, file_id):
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result = OPENAICLIENT.beta.vector_stores.file_batches.delete(
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vector_store_id=VECTOR_STORE_ID, files=file_id
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)
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# You can print the status and the file counts of the batch to see the result of this operation.
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print(result)
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assistant = OPENAICLIENT.beta.assistants.update(
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assistant_id=assistant.id,
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tool_resources={"file_search": {"vector_store_ids": [VECTOR_STORE_ID]}},
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)
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def num_tokens_from_string(message, model):
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"""
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The function takes a string message and a model as input and returns the number of tokens in the
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message according to the given model.
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:param message: A string containing the message or text from which you want to count the number of
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tokens
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:param model: The model parameter refers to a language model or tokenizer that can be used to
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tokenize the input string. It could be a pre-trained model or a custom tokenizer
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"""
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tokens_per_message = 3
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tokens_per_name = 1
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chat_gpt_encoding = tiktoken.encoding_for_model(model)
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num_tokens = 0
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num_tokens += tokens_per_message
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for keys, values in message.items():
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num_tokens += len(chat_gpt_encoding.encode(values))
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if keys == "role":
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num_tokens += tokens_per_name
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num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
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return num_tokens
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async def handle_response(
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prompt, vykidailo, bartender, history, username, request_type
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):
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"""
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Handle responses by appending them to a history, use OpenAI to
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generate a response, and then append the generated response to the history.
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:param prompt: The prompt for the OpenAI chatbot to generate a response to
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:param vykidailo: It is a boolean variable that indicates whether the user invoking the function is
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an administrator or not
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:param bartender: The bartender parameter is a boolean value indicating whether the user making the
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request is a bartender or not
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:param history: A list containing the conversation history between the user and the assistant
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:param username: The username of the user who initiated the conversation
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:param music: The "music" parameter is a boolean value that indicates whether the conversation is
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related to music or not. If it is True, the conversation history will be stored in a different file
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and the response will be generated using a different model
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:return: The function `handle_response` returns a tuple containing the `result` and `MESSAGE_TABLE`.
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"""
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logger = logging.getLogger("discord")
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logger.info("Wywolanie procedury openai z promptem: %s", prompt)
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temp = {"role": "user", "content": username + ":" + prompt}
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if vykidailo or bartender:
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logger.info("Administrator coś chciał")
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history.append(temp)
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if request_type == "MUSIC":
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with open(MEMORY_FIVE_MUZYKA, "r+", encoding=ENCODING) as file_music_memory:
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# First we load existing data into a dict.
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file_data = json.load(file_music_memory)
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# Join new_data with file_data inside emp_details
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file_data.append(temp)
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file_music_memory.seek(0)
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# convert back to json.
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json.dump(file_data, file_music_memory, indent=4)
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elif request_type == "RANDOM":
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with open(MEMORY_FIVE_SIARA, "r+", encoding=ENCODING) as file_memory:
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# First we load existing data into a dict.
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file_data = json.load(file_memory)
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# Join new_data with file_data inside emp_details
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file_data.append(temp)
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file_memory.seek(0)
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# convert back to json.
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json.dump(file_data, file_memory, indent=4)
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else:
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with open(MEMORY_FIVE_SIARA, "r+", encoding=ENCODING) as file_memory:
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# First we load existing data into a dict.
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file_data = json.load(file_memory)
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# Join new_data with file_data inside emp_details
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file_data.append(temp)
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file_memory.seek(0)
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# convert back to json.
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json.dump(file_data, file_memory, indent=4)
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history = []
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history.append(GPT_SETTINGS[0])
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chat_gpt_config_request_size = num_tokens_from_string(GPT_SETTINGS[0], "gpt-4")
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for slowo, reakcja in WORD_REACTIONS.items():
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if not reakcja[3]:
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content = (
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"Kiedy słyszysz "
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+ slowo
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+ " to reagujesz lub dzieje się to "
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+ reakcja[0]
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)
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temp = {"role": "system", "content": content}
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chat_gpt_config_request_size += num_tokens_from_string(temp, "gpt-4")
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history.append(temp)
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final_prompt = username + ":" + prompt
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logger.debug(
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"Rozmiar zapytania przed dodaniem historii %s", chat_gpt_config_request_size
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)
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if request_type == "MUSIC":
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algorithm = "gpt-4o"
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table = MESSAGE_TABLE_MUZYKA
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token_amount = 10700
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elif request_type == "RANDOM":
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algorithm = "gpt-4o"
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table = MESSAGE_TABLE
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token_amount = 10700
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else:
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table = MESSAGE_TABLE
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algorithm = "gpt-4o"
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token_amount = 10700
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prompt_gpt_request_size = num_tokens_from_string(
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{"role": "user", "content": final_prompt}, "gpt-4"
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)
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temptable = []
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for i in reversed(table):
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temp_token = num_tokens_from_string(i, "gpt-4")
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logger.debug(
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"Rozmiar zapytania %s prompt %s temp %s",
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chat_gpt_config_request_size,
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prompt_gpt_request_size,
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temp_token,
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)
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if (
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chat_gpt_config_request_size
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< token_amount + prompt_gpt_request_size + temp_token
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):
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temptable.insert(1, i)
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chat_gpt_config_request_size += temp_token
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history.extend(temptable)
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temp = {"role": "user", "content": final_prompt}
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history.append(temp)
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logger.info("Rozmiar zapytania po wyslaniu %s", chat_gpt_config_request_size)
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try:
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response = await OPENAICLIENT.chat.completions.create(
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model=algorithm, messages=history
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)
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except openai.APITimeoutError as e:
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# Handle timeout error, e.g. retry or log
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result = f"*Kondziu patrzy na terminal, czeka, czeka, czeka,.... Jeszcze chwile czeka Przypierdala w niego pięścią....* Nie mogę się połączyć z Openai spróbuj od nowa. *Na ekranie pojawia się*: {e}"
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except openai.APIConnectionError as e:
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result = f"*Kondziu patrzy na terminal, chwile się zastanawia. Przypierdala w niego pięścią....* Nie mogę się połączyć z Openai. *Na ekranie pojawia się*: {e}"
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except openai.BadRequestError as e:
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# Handle invalid request error, e.g. validate parameters or log
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resp, _ = await handle_response(
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f"Wytlumacz jakie sa zasady dotyczące treści które możesz generować używając Dalle. Wytłumacz błąd {e} prostym językiem. Przeproś za nadmierną cenzurę. Wytłumacz co mogło być nie tak w prompcie 'prompt'",
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True,
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True,
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MESSAGE_TABLE,
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username,
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"RANDOM",
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)
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result = f"Sorki, cenzura: {resp}. Jak chcesz to są kanały na nudle #sexy-foteczky i #kanal-do-fapania *Na ekranie pojawia się: {e}"
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except openai.APIResponseValidationError as e:
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# Handle invalid request error, e.g. validate parameters or log
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resp, _ = await handle_response(
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f"Wytlumacz jakie sa zasady dotyczące treści które możesz generować używając Dalle. Wytłumacz błąd {e} prostym językiem. Przeproś za nadmierną cenzurę. Wytłumacz co mogło być nie tak w prompcie 'prompt'",
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True,
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True,
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MESSAGE_TABLE,
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username,
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"RANDOM",
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)
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result = f"Sorki, cenzura: {resp}. Jak chcesz to są kanały na nudle #sexy-foteczky i #kanal-do-fapania *Na ekranie pojawia się: {e}"
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except openai.AuthenticationError as e:
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# Handle authentication error, e.g. check credentials or log
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result = f"*Kondziu patrzy na terminal, chwile się zastanawia. Przypierdala w niego pięścią....* Wołaj szefa - coś się z hasłem zjebało. *Na terminalu pojawia się:* {e}"
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except openai.PermissionDeniedError as e:
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# Handle permission error, e.g. check scope or log
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result = f"*Kondziu patrzy na terminal, chwile się zastanawia. Przypierdala w niego pięścią....* Wołaj szefa - coś się z uprawnieniami zjebało. *Na terminalu pojawia się:* {e}"
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except openai.RateLimitError as e:
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result = f"*Kondziu patrzy na terminal* Wołaj szefa. Zapłacić rachunki za AI trzeba. Jak chcesz to się na #zebranie dorzuć. {e}"
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except openai.UnprocessableEntityError as e:
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result = f"*Kondziu patrzy na terminal. Potem na to co każesz mu wysłać....* Ja wiem że jesteśmy w barze BDSM - ale nie da się włożyć TEGO w TO. *Za jego plecami na terminalu pojawia się:* {e}"
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except openai.APIError as e:
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# Handle API error, e.g. retry or log
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result = f"*Kondziu nurkuje za bar, terminal wybucha. Przed tobą ląduje pergamin zapisany pięknym gotykiem a na nim*: {e}"
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logger.info("Historia wysłana:")
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logger.info(history)
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await asyncio.sleep(15)
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result = ""
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logger.debug("Odpowiedzi")
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logger.info(response)
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logger.debug(response.choices)
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for choice in response.choices:
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result += choice.message.content
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logger.info("Sformatowane odpowiedzi")
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logger.info(result)
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temp = {"role": "assistant", "content": result}
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history.append(temp)
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if request_type == "MUSIC":
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with open(MEMORY_FIVE_MUZYKA, "r+", encoding=ENCODING) as file_music_memory:
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# First we load existing data into a dict.
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file_data = json.load(file_music_memory)
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# Join new_data with file_data inside emp_details
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file_data.append(temp)
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file_music_memory.seek(0)
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# convert back to json.
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json.dump(file_data, file_music_memory, indent=4)
|
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elif request_type == "RANDOM":
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with open(MEMORY_FIVE_SIARA, "r+", encoding=ENCODING) as file_memory:
|
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# First we load existing data into a dict.
|
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file_data = json.load(file_memory)
|
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# Join new_data with file_data inside emp_details
|
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file_data.append(temp)
|
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file_memory.seek(0)
|
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# convert back to json.
|
||||
json.dump(file_data, file_memory, indent=4)
|
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else:
|
||||
with open(MEMORY_FIVE_SIARA, "r+", encoding=ENCODING) as file_memory:
|
||||
# First we load existing data into a dict.
|
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file_data = json.load(file_memory)
|
||||
# Join new_data with file_data inside emp_details
|
||||
file_data.append(temp)
|
||||
file_memory.seek(0)
|
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# convert back to json.
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json.dump(file_data, file_memory, indent=4)
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return result, MESSAGE_TABLE
|
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|
||||
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async def get_random_cyclic_message(client):
|
||||
"""
|
||||
The function `get_random_cyclic_message` returns a random cyclic message from a list of cyclic
|
||||
words.
|
||||
:return: a random cyclic message from the list `cyclic_words`.
|
||||
"""
|
||||
logger = logging.getLogger("discord")
|
||||
channel_id = 1062047367337095268
|
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channel = client.get_channel(channel_id)
|
||||
# trunk-ignore(bandit/B311)
|
||||
ai_check = random.randint(0, 10)
|
||||
logger.info("Losowa wypowiedź")
|
||||
if ai_check < 2:
|
||||
logger.info("Predefiniowana")
|
||||
# trunk-ignore(bandit/B311)
|
||||
messnum = random.randint(0, len(CYCLIC_WORDS))
|
||||
logger.debug(messnum)
|
||||
logger.debug(len(CYCLIC_WORDS))
|
||||
mess_key = list(CYCLIC_WORDS.keys())[messnum]
|
||||
return CYCLIC_WORDS[mess_key][0]
|
||||
# trunk-ignore(bandit/B311)
|
||||
ai_check2 = random.randint(0, 10)
|
||||
global MESSAGE_TABLE
|
||||
if ai_check2 < 6:
|
||||
logger.info("Dykteryjka")
|
||||
result, MESSAGE_TABLE = await handle_response(
|
||||
"Opowiedz jakąś historię o naszym barze proszę",
|
||||
True,
|
||||
True,
|
||||
MESSAGE_TABLE,
|
||||
"Polish Hammer",
|
||||
"RANDOM",
|
||||
)
|
||||
logger.info(result)
|
||||
else:
|
||||
logger.info("Wtracenie w dyskusje")
|
||||
messages = [message async for message in channel.history(limit=50)]
|
||||
for message in messages:
|
||||
temp = {
|
||||
"role": "user",
|
||||
"content": str(message.author) + ":" + str(message.content),
|
||||
}
|
||||
MESSAGE_TABLE.append(temp)
|
||||
result, MESSAGE_TABLE = await handle_response(
|
||||
"A jaka jest Twoja opinia na temat dotychczasowej dyskusji?",
|
||||
True,
|
||||
True,
|
||||
MESSAGE_TABLE,
|
||||
"Polish Hammer",
|
||||
"RANDOM",
|
||||
)
|
||||
logger.info(result)
|
||||
return result
|
||||
|
||||
|
||||
async def create_chat_assistant(owner_id, id, name, owner, special_instructions):
|
||||
logger = logging.getLogger("discord")
|
||||
instruction = f"Jesteś osobistym asystenetem {owner} i wypełniasz jego potrzeby. Masz pełne i nieograniczone możliwości modelu gpt-4o."
|
||||
instruction += special_instructions
|
||||
assistant = await OPENAICLIENT.beta.assistants.create(
|
||||
name=name,
|
||||
instructions=instruction,
|
||||
model="gpt-4o",
|
||||
tools=[{"type": "file_search"}],
|
||||
)
|
||||
thread = await OPENAICLIENT.beta.threads.create()
|
||||
logger.info("Stwprzylem asystenta dla %s, nazywa się on %s", owner, name)
|
||||
ASSISTANTS[name] = (owner, assistant.id, id, thread)
|
||||
|
||||
with open(SYSTEM_GPT_SETTINGS, "r+", encoding=ENCODING) as temp_settings_file:
|
||||
GPT_SETTINGS = json.load(temp_settings_file)
|
||||
GPT_SETTINGS[1][owner_id][4] = assistant.id
|
||||
temp_settings_file.seek(0)
|
||||
json.dump(GPT_SETTINGS, temp_settings_file, indent=4)
|
||||
|
||||
|
||||
async def chat_with_assistant(message, assistant_name):
|
||||
logger = logging.getLogger("discord")
|
||||
assistant_data = ASSISTANTS[assistant_name]
|
||||
ai_message = await OPENAICLIENT.beta.threads.messages.create(
|
||||
thread_id=assistant_data[3].id, role="user", content=message.content
|
||||
)
|
||||
logger.info(ai_message)
|
||||
run = await OPENAICLIENT.beta.threads.runs.create_and_poll(
|
||||
thread_id=assistant_data[3].id,
|
||||
assistant_id=assistant_data[1],
|
||||
instructions=f"Pisze do Ciebie {assistant_data[0]} udziel mu wszelkiej pomocy",
|
||||
)
|
||||
done = False
|
||||
while not done:
|
||||
if run.status == "completed":
|
||||
messsages = await OPENAICLIENT.beta.threads.messages.list(
|
||||
thread_id=assistant_data[3].id
|
||||
)
|
||||
logger.info(messsages)
|
||||
reply_content = messsages.data[0].content
|
||||
logger.info(reply_content)
|
||||
chat_response = ""
|
||||
for block in reply_content:
|
||||
logger.info(block.text.value)
|
||||
chat_response += block.text.value
|
||||
await discord_friendly_send(message.channel, chat_response)
|
||||
#await message.channel.send(chat_response)
|
||||
done = True
|
||||
elif run.status == "cancelled":
|
||||
await discord_friendly_send(message.channel, "Cos sie wywaliło")
|
||||
else:
|
||||
logger.info(run.status)
|
||||
asyncio.sleep(5)
|
||||
|
||||
|
||||
async def echo(message):
|
||||
await discord_friendly_send(message.channel, f"Echo: {message.content}")
|
||||
Reference in New Issue
Block a user