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2. Add .env sample file 3. Optimized the keyword filtering logic, removed empty strings 4. Remove GPT response in search to improve search speed 5. Removed the search logic inside the chatgpt2api.py file, and deleted the gptsearch function inside the search_web_and_summary in agent.py. 6. Optimized the extraction prompt for search keywords. 7. Refactored the chatgpt2api.py code, using precise token calculation, and fixed the error in the gpt-3.5-turbo max token parameter. 8. Optimized log display
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,8 @@ | ||
| BOT_TOKEN= | ||
| API_URL= | ||
| API= | ||
| GOOGLE_API_KEY= | ||
| GOOGLE_CSE_ID= | ||
| claude_api_key= | ||
| ADMIN_LIST= | ||
| GROUP_LIST= |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,94 @@ | ||
| import os | ||
| import sys | ||
| sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | ||
| import tiktoken | ||
| from utils.function_call import function_call_list | ||
| import config | ||
| import requests | ||
| import json | ||
| import re | ||
|
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| from dotenv import load_dotenv | ||
| load_dotenv() | ||
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| def get_token_count(messages) -> int: | ||
| tiktoken.get_encoding("cl100k_base") | ||
| encoding = tiktoken.encoding_for_model("gpt-3.5-turbo") | ||
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| num_tokens = 0 | ||
| for message in messages: | ||
| # every message follows <im_start>{role/name}\n{content}<im_end>\n | ||
| num_tokens += 5 | ||
| for key, value in message.items(): | ||
| if value: | ||
| num_tokens += len(encoding.encode(value)) | ||
| if key == "name": # if there's a name, the role is omitted | ||
| num_tokens += 5 # role is always required and always 1 token | ||
| num_tokens += 5 # every reply is primed with <im_start>assistant | ||
| return num_tokens | ||
| # print(get_token_count(message_list)) | ||
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| def get_message_token(url, json_post): | ||
| headers = {"Authorization": f"Bearer {os.environ.get('API', None)}"} | ||
| response = requests.Session().post( | ||
| url, | ||
| headers=headers, | ||
| json=json_post, | ||
| timeout=None, | ||
| ) | ||
| if response.status_code != 200: | ||
| json_response = json.loads(response.text) | ||
| string = json_response["error"]["message"] | ||
| print(string) | ||
| string = re.findall(r"\((.*?)\)", string)[0] | ||
| numbers = re.findall(r"\d+\.?\d*", string) | ||
| numbers = [int(i) for i in numbers] | ||
| if len(numbers) == 2: | ||
| return { | ||
| "messages": numbers[0], | ||
| "total": numbers[0], | ||
| } | ||
| elif len(numbers) == 3: | ||
| return { | ||
| "messages": numbers[0], | ||
| "functions": numbers[1], | ||
| "total": numbers[0] + numbers[1], | ||
| } | ||
| else: | ||
| raise Exception("Unknown error") | ||
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| if __name__ == "__main__": | ||
| # message_list = [{'role': 'system', 'content': 'You are ChatGPT, a large language model trained by OpenAI. Respond conversationally in Simplified Chinese. Knowledge cutoff: 2021-09. Current date: [ 2023-12-12 ]'}, {'role': 'user', 'content': 'hi'}] | ||
| messages = [{'role': 'system', 'content': 'You are ChatGPT, a large language model trained by OpenAI. Respond conversationally in Simplified Chinese. Knowledge cutoff: 2021-09. Current date: [ 2023-12-12 ]'}, {'role': 'user', 'content': 'hi'}, {'role': 'assistant', 'content': '你好！有什么我可以帮助你的吗？'}] | ||
|
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| model = "gpt-3.5-turbo" | ||
| temperature = 0.5 | ||
| top_p = 0.7 | ||
| presence_penalty = 0.0 | ||
| frequency_penalty = 0.0 | ||
| reply_count = 1 | ||
| role = "user" | ||
| model_max_tokens = 5000 | ||
| url = config.bot_api_url.chat_url | ||
|
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| json_post = { | ||
| "model": model, | ||
| "messages": messages, | ||
| "stream": True, | ||
| "temperature": temperature, | ||
| "top_p": top_p, | ||
| "presence_penalty": presence_penalty, | ||
| "frequency_penalty": frequency_penalty, | ||
| "n": reply_count, | ||
| "user": role, | ||
| "max_tokens": model_max_tokens, | ||
| } | ||
| # json_post.update(function_call_list["base"]) | ||
| # if config.SEARCH_USE_GPT: | ||
| # json_post["functions"].append(function_call_list["web_search"]) | ||
| # json_post["functions"].append(function_call_list["url_fetch"]) | ||
| # print(get_token_count(message_list)) | ||
| print(get_message_token(url, json_post)) |
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