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https://github.com/cdryzun/tg_bot_collections.git
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feat: answer_it screenless llm to ph page
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Alter-xyz
parent
d1d84aa0e3
commit
bde2fd061b
@ -7,6 +7,7 @@ from os import environ
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import time
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import datetime
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from concurrent.futures import ThreadPoolExecutor
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import re
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from . import *
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@ -18,18 +19,6 @@ markdown_symbol.link = "🔗" # If you want, Customizing the link symbol
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chat_message_dict = ExpiringDict(max_len=100, max_age_seconds=120)
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chat_user_dict = ExpiringDict(max_len=100, max_age_seconds=20)
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#### Customization ####
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Language = "zh-cn" # "en" or "zh-cn".
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SUMMARY = "gemini" # "cohere" or "gemini" or None
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Extra_clean = True # Will Delete command message
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GEMINI_USE = True
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CHATGPT_USE = True
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COHERE_USE = True
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QWEN_USE = True
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CLADUE_USE = True
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LLAMA_USE = True
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#### Telegra.ph init ####
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# Will auto generate a token if not provided, restart will lose all TODO
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TELEGRA_PH_TOKEN = environ.get("TELEGRA_PH_TOKEN")
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@ -37,6 +26,23 @@ TELEGRA_PH_TOKEN = environ.get("TELEGRA_PH_TOKEN")
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ph = TelegraphAPI(TELEGRA_PH_TOKEN)
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#### Customization ####
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Language = "zh-cn" # "en" or "zh-cn".
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SUMMARY = "gemini" # "cohere" or "gemini" or None
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General_clean = True # Will Delete LLM message
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Extra_clean = True # Will Delete command message too
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#### LLMs ####
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GEMINI_USE = True
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CHATGPT_USE = True
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COHERE_USE = False # Slow, but web search
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QWEN_USE = True
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CLADUE_USE = False # Untested
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LLAMA_USE = False # prompted for Language
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COHERE_USE_BACKGROUND = True # Only display in telegra.ph
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LLAMA_USE_BACKGROUND = True
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#### LLMs init ####
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#### OpenAI init ####
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CHATGPT_API_KEY = environ.get("OPENAI_API_KEY")
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@ -72,7 +78,7 @@ if GEMINI_USE and GOOGLE_GEMINI_KEY:
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]
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model = genai.GenerativeModel(
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model_name="gemini-1.5-flash-latest",
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model_name="gemini-1.5-pro-latest",
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generation_config=generation_config,
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safety_settings=safety_settings,
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)
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@ -84,9 +90,9 @@ if GEMINI_USE and GOOGLE_GEMINI_KEY:
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The user asked a question, and multiple AI have given answers to the same question.
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Your task is to summarize the responses from them in a concise and clear manner.
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The summary should:
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In one to two short sentences, as less as possible, and should not exceed 150 characters.
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In one to three short sentences, as less as possible.
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Your must use language of {Language} to respond.
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Start with "Summary:" or "总结:"
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Start with "Summary:" or"总结:"
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""",
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)
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convo = model.start_chat()
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@ -96,7 +102,7 @@ Start with "Summary:" or "总结:"
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#### Cohere init ####
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COHERE_API_KEY = environ.get("COHERE_API_KEY")
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if COHERE_USE and COHERE_API_KEY:
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if (COHERE_USE or COHERE_USE_BACKGROUND) and COHERE_API_KEY:
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import cohere
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COHERE_MODEL = "command-r-plus"
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@ -124,11 +130,11 @@ if CLADUE_USE and ANTHROPIC_API_KEY:
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#### llama init ####
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LLAMA_API_KEY = environ.get("GROQ_API_KEY")
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if LLAMA_USE and LLAMA_API_KEY:
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if (LLAMA_USE or LLAMA_USE_BACKGROUND) and LLAMA_API_KEY:
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from groq import Groq
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llama_client = Groq(api_key=LLAMA_API_KEY)
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LLAMA_MODEL = "llama3-8b-8192"
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LLAMA_MODEL = "llama3-70b-8192"
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#### init end ####
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@ -194,7 +200,9 @@ def answer_it_handler(message: Message, bot: TeleBot) -> None:
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latest_message = chat_message_dict.get(chat_id)
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m = latest_message.text.strip()
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m = enrich_text_with_urls(m)
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full_answer = f"Question:\n{m}\n---\n"
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full_answer = f"Question:\n{m}\n" if len(m) < 300 else ""
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if Extra_clean: # delete the command message
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bot.delete_message(chat_id, message.message_id)
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#### Answers Thread ####
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executor = ThreadPoolExecutor(max_workers=5)
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@ -240,53 +248,32 @@ def answer_it_handler(message: Message, bot: TeleBot) -> None:
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print(full_chat_id_list)
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if len(m) > 300:
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full_answer += llm_answer("Question", m)
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##### Telegraph #####
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final_answer(latest_message, bot, full_answer, full_chat_id_list)
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if Extra_clean:
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bot.delete_message(chat_id, message.message_id)
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# def thread_answers(latest_message: Message, bot: TeleBot, m: str):
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# #### answers function init ####
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# USE = {
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# "gemini_answer": GEMINI_USE and GOOGLE_GEMINI_KEY,
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# "chatgpt_answer": CHATGPT_USE and CHATGPT_API_KEY,
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# "cohere_answer": COHERE_USE and COHERE_API_KEY,
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# "qwen_answer": QWEN_USE and QWEN_API_KEY,
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# # More LLMs
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# }
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def update_time():
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"""Return the current time in UTC+8. Good for testing completion of content."""
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return f"\nLast Update{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} at UTC+8\n"
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# results = []
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# full_chat_id_list = []
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def llm_answer(who: str, s: str) -> str:
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"""Universal llm answer format for telegra.ph. Use title so 'link#title' can be used."""
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return f"\n\n---\n## {who}\n{s}"
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# with ThreadPoolExecutor(max_workers=5) as executor:
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# futures = {
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# executor.submit(func, latest_message, bot, m): func
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# for func, use in USE.items()
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# if use
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# }
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# for future in as_completed(futures):
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# try:
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# answer, message_id = future.result()
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# # Store the answer and message_id
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# results.append((message_id, answer))
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# full_chat_id_list.append(message_id)
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# except Exception as e:
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# print(f"\n------\nthread_answers Error:\n{e}\n------\n")
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# continue
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# # rank the results by message_id
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# sorted_results = sorted(results)
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# full_chat_id_list.sort()
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# # final answer
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# full_answer = f"Question:\n{m}\n---\n"
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# for _, answer in sorted_results:
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# full_answer += answer
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# return full_answer, full_chat_id_list
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def llm_background(path: str, full_answer: str, m: str) -> str:
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"""Update the telegra.ph page with background answer result. Return new full answer."""
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ph_path = re.search(r"https?://telegra\.ph/(.+)", path).group(1)
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full_answer += m + update_time()
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try:
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ph.edit_page_md(path=ph_path, title="Answer it", markdown_text=full_answer)
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except Exception as e:
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print(f"\n------\nllm_background Error:\n{e}\n------\n")
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return full_answer
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def gemini_answer(latest_message: Message, bot: TeleBot, m):
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@ -312,8 +299,7 @@ def gemini_answer(latest_message: Message, bot: TeleBot, m):
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bot_reply_markdown(reply_id, who, "Error", bot)
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return f"\n---\n{who}:\nAnswer wrong", reply_id.message_id
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answer = f"\n---\n{who}:\n{s}"
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return answer, reply_id.message_id
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return llm_answer(who, s), reply_id.message_id
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def chatgpt_answer(latest_message: Message, bot: TeleBot, m):
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@ -350,8 +336,7 @@ def chatgpt_answer(latest_message: Message, bot: TeleBot, m):
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bot_reply_markdown(reply_id, who, "answer wrong", bot)
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return f"\n---\n{who}:\nAnswer wrong", reply_id.message_id
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answer = f"\n---\n{who}:\n{s}"
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return answer, reply_id.message_id
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return llm_answer(who, s), reply_id.message_id
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def claude_answer(latest_message: Message, bot: TeleBot, m):
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@ -387,7 +372,7 @@ def claude_answer(latest_message: Message, bot: TeleBot, m):
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return f"\n---\n{who}:\nAnswer wrong", reply_id.message_id
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answer = f"\n---\n{who}:\n{s}"
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return answer, reply_id.message_id
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return llm_answer(who, s), reply_id.message_id
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def cohere_answer(latest_message: Message, bot: TeleBot, m):
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@ -456,8 +441,8 @@ def cohere_answer(latest_message: Message, bot: TeleBot, m):
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print(f"\n------\n{who} function inner Error:\n{e}\n------\n")
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bot_reply_markdown(reply_id, who, "Answer wrong", bot)
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return f"\n---\n{who}:\nAnswer wrong", reply_id.message_id
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answer = f"\n---\n{who}:\n{content}"
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return answer, reply_id.message_id
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return llm_answer(who, content), reply_id.message_id
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def qwen_answer(latest_message: Message, bot: TeleBot, m):
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@ -491,8 +476,7 @@ def qwen_answer(latest_message: Message, bot: TeleBot, m):
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bot_reply_markdown(reply_id, who, "answer wrong", bot)
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return f"\n---\n{who}:\nAnswer wrong", reply_id.message_id
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answer = f"\n---\n{who}:\n{s}"
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return answer, reply_id.message_id
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return llm_answer(who, s), reply_id.message_id
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def llama_answer(latest_message: Message, bot: TeleBot, m):
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@ -503,9 +487,10 @@ def llama_answer(latest_message: Message, bot: TeleBot, m):
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r = llama_client.chat.completions.create(
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messages=[
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{
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"role": "user",
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"content": f"{m}\nMotes: You must use language of {Language} to respond.",
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}
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"role": "system",
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"content": f"You must use language of {Language} to respond.",
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},
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{"role": "user", "content": m},
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],
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max_tokens=8192,
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model=LLAMA_MODEL,
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@ -531,8 +516,7 @@ def llama_answer(latest_message: Message, bot: TeleBot, m):
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bot_reply_markdown(reply_id, who, "answer wrong", bot)
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return f"\n---\n{who}:\nAnswer wrong", reply_id.message_id
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answer = f"\n---\n{who}:\n{s}"
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return answer, reply_id.message_id
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return llm_answer(who, s), reply_id.message_id
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# TODO: Perplexity looks good. `pplx_answer`
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@ -542,9 +526,16 @@ def final_answer(latest_message: Message, bot: TeleBot, full_answer: str, answer
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"""final answer"""
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who = "Answer it"
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reply_id = bot_reply_first(latest_message, who, bot)
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# If disappeared means the answer is not complete in telegra.ph
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full_answer += update_time()
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# greate new telegra.ph page
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ph_s = ph.create_page_md(title="Answer it", markdown_text=full_answer)
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bot_reply_markdown(reply_id, who, f"**[Full Answer]({ph_s})**", bot)
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# delete the chat message, only leave a telegra.ph link
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if General_clean:
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for i in answers_list:
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bot.delete_message(latest_message.chat.id, i)
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@ -558,6 +549,77 @@ def final_answer(latest_message: Message, bot: TeleBot, full_answer: str, answer
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else:
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pass
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#### Background LLM ####
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# Run background llm, no show to telegram, just update the page, Good for slow llm
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if LLAMA_USE_BACKGROUND and LLAMA_API_KEY:
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llama_b_m = background_llama(latest_message.text)
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print(llama_b_m)
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full_answer = llm_background(ph_s, full_answer, llama_b_m)
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if COHERE_USE_BACKGROUND and COHERE_API_KEY:
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cohere_b_m = background_cohere(latest_message.text)
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print(cohere_b_m)
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full_answer = llm_background(ph_s, full_answer, cohere_b_m)
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def background_cohere(m: str) -> str:
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"""we run cohere get the full answer in background"""
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who = "Command R Plus"
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try:
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stream = co.chat_stream(
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model=COHERE_MODEL,
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message=m,
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temperature=0.8,
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chat_history=[], # One time, so no need for chat history
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prompt_truncation="AUTO",
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connectors=[{"id": "web-search"}],
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citation_quality="accurate",
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preamble="",
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)
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s = ""
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source = ""
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for event in stream:
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if event.event_type == "search-results":
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for doc in event.documents:
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source += f"\n{doc['title']}\n{doc['url']}\n"
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elif event.event_type == "text-generation":
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s += event.text.encode("utf-8").decode("utf-8", "ignore")
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elif event.event_type == "stream-end":
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break
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content = llm_answer(who, f"{s}\n\n---\n{source}")
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except Exception as e:
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print(f"\n------\nbackground_cohere Error:\n{e}\n------\n")
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content = llm_answer(who, "Background Answer wrong")
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return content
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def background_llama(m: str) -> str:
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"""we run llama get the full answer in background"""
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who = "llama"
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try:
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r = llama_client.chat.completions.create(
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messages=[
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{
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"role": "system",
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"content": f"You must use language of {Language} to respond.",
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},
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{"role": "user", "content": m},
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],
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max_tokens=8192,
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model=LLAMA_MODEL,
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stream=True,
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)
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s = ""
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for chunk in r:
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if chunk.choices[0].delta.content is None:
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break
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s += chunk.choices[0].delta.content
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except Exception as e:
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print(f"\n------\nbackground_llama Error:\n{e}\n------\n")
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s = "Background Answer wrong"
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return llm_answer(who, s)
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def summary_cohere(bot: TeleBot, full_answer: str, ph_s: str, reply_id: int) -> None:
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"""Receive the full text, and the final_answer's chat_id, update with a summary."""
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