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Merge pull request #1810 from FB208/master

增加了claude api的调用方法
master
zhayujie GitHub pirms 8 mēnešiem
vecāks
revīzija
674fbc3f69
Šim parakstam datu bāzē netika atrasta zināma atslēga GPG atslēgas ID: B5690EEEBB952194
9 mainītis faili ar 217 papildinājumiem un 5 dzēšanām
  1. +2
    -1
      README.md
  2. +4
    -1
      bot/bot_factory.py
  3. +125
    -0
      bot/claudeapi/claude_api_bot.py
  4. +74
    -0
      bot/claudeapi/claude_api_session.py
  5. +5
    -2
      bridge/bridge.py
  6. +3
    -1
      common/const.py
  7. +1
    -0
      config-template.json
  8. +2
    -0
      config.py
  9. +1
    -0
      requirements.txt

+ 2
- 1
README.md Parādīt failu

@@ -118,7 +118,8 @@ pip3 install -r requirements-optional.txt
# config.json文件内容示例
{
"open_ai_api_key": "YOUR API KEY", # 填入上面创建的 OpenAI API KEY
"model": "gpt-3.5-turbo", # 模型名称, 支持 gpt-3.5-turbo, gpt-3.5-turbo-16k, gpt-4, wenxin, xunfei
"model": "gpt-3.5-turbo", # 模型名称, 支持 gpt-3.5-turbo, gpt-3.5-turbo-16k, gpt-4, wenxin, xunfei, claude-3-opus-20240229
"claude_api_key":"YOUR API KEY" # 如果选用claude3模型的话,配置这个key,同时如想使用生图,语音等功能,仍需配置open_ai_api_key
"proxy": "", # 代理客户端的ip和端口,国内环境开启代理的需要填写该项,如 "127.0.0.1:7890"
"single_chat_prefix": ["bot", "@bot"], # 私聊时文本需要包含该前缀才能触发机器人回复
"single_chat_reply_prefix": "[bot] ", # 私聊时自动回复的前缀,用于区分真人


+ 4
- 1
bot/bot_factory.py Parādīt failu

@@ -2,6 +2,7 @@
channel factory
"""
from common import const
from common.log import logger


def create_bot(bot_type):
@@ -43,7 +44,9 @@ def create_bot(bot_type):
elif bot_type == const.CLAUDEAI:
from bot.claude.claude_ai_bot import ClaudeAIBot
return ClaudeAIBot()

elif bot_type == const.CLAUDEAPI:
from bot.claudeapi.claude_api_bot import ClaudeAPIBot
return ClaudeAPIBot()
elif bot_type == const.QWEN:
from bot.ali.ali_qwen_bot import AliQwenBot
return AliQwenBot()


+ 125
- 0
bot/claudeapi/claude_api_bot.py Parādīt failu

@@ -0,0 +1,125 @@
# encoding:utf-8

import time

import openai
import openai.error
import anthropic

from bot.bot import Bot
from bot.openai.open_ai_image import OpenAIImage
from bot.claudeapi.claude_api_session import ClaudeAPISession
from bot.session_manager import SessionManager
from bridge.context import ContextType
from bridge.reply import Reply, ReplyType
from common.log import logger
from config import conf

user_session = dict()


# OpenAI对话模型API (可用)
class ClaudeAPIBot(Bot, OpenAIImage):
def __init__(self):
super().__init__()
self.claudeClient = anthropic.Anthropic(
api_key=conf().get("claude_api_key")
)
openai.api_key = conf().get("open_ai_api_key")
if conf().get("open_ai_api_base"):
openai.api_base = conf().get("open_ai_api_base")
proxy = conf().get("proxy")
if proxy:
openai.proxy = proxy

self.sessions = SessionManager(ClaudeAPISession, model=conf().get("model") or "text-davinci-003")

def reply(self, query, context=None):
# acquire reply content
if context and context.type:
if context.type == ContextType.TEXT:
logger.info("[CLAUDE_API] query={}".format(query))
session_id = context["session_id"]
reply = None
if query == "#清除记忆":
self.sessions.clear_session(session_id)
reply = Reply(ReplyType.INFO, "记忆已清除")
elif query == "#清除所有":
self.sessions.clear_all_session()
reply = Reply(ReplyType.INFO, "所有人记忆已清除")
else:
session = self.sessions.session_query(query, session_id)
result = self.reply_text(session)
logger.info(result)
total_tokens, completion_tokens, reply_content = (
result["total_tokens"],
result["completion_tokens"],
result["content"],
)
logger.debug(
"[CLAUDE_API] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(str(session), session_id, reply_content, completion_tokens)
)

if total_tokens == 0:
reply = Reply(ReplyType.ERROR, reply_content)
else:
self.sessions.session_reply(reply_content, session_id, total_tokens)
reply = Reply(ReplyType.TEXT, reply_content)
return reply
elif context.type == ContextType.IMAGE_CREATE:
ok, retstring = self.create_img(query, 0)
reply = None
if ok:
reply = Reply(ReplyType.IMAGE_URL, retstring)
else:
reply = Reply(ReplyType.ERROR, retstring)
return reply

def reply_text(self, session: ClaudeAPISession, retry_count=0):
try:
logger.info("[CLAUDE_API] sendMessage={}".format(str(session)))
response = self.claudeClient.messages.create(
model=conf().get("model"),
max_tokens=1024,
# system=conf().get("system"),
messages=[
{"role": "user", "content": "{}".format(str(session))}
]
)
# response = openai.Completion.create(prompt=str(session), **self.args)
res_content = response.content[0].text.strip().replace("<|endoftext|>", "")
total_tokens = response.usage.input_tokens+response.usage.output_tokens
completion_tokens = response.usage.output_tokens
logger.info("[CLAUDE_API] reply={}".format(res_content))
return {
"total_tokens": total_tokens,
"completion_tokens": completion_tokens,
"content": res_content,
}
except Exception as e:
need_retry = retry_count < 2
result = {"completion_tokens": 0, "content": "我现在有点累了,等会再来吧"}
if isinstance(e, openai.error.RateLimitError):
logger.warn("[CLAUDE_API] RateLimitError: {}".format(e))
result["content"] = "提问太快啦,请休息一下再问我吧"
if need_retry:
time.sleep(20)
elif isinstance(e, openai.error.Timeout):
logger.warn("[CLAUDE_API] Timeout: {}".format(e))
result["content"] = "我没有收到你的消息"
if need_retry:
time.sleep(5)
elif isinstance(e, openai.error.APIConnectionError):
logger.warn("[CLAUDE_API] APIConnectionError: {}".format(e))
need_retry = False
result["content"] = "我连接不到你的网络"
else:
logger.warn("[CLAUDE_API] Exception: {}".format(e))
need_retry = False
self.sessions.clear_session(session.session_id)

if need_retry:
logger.warn("[CLAUDE_API] 第{}次重试".format(retry_count + 1))
return self.reply_text(session, retry_count + 1)
else:
return result

+ 74
- 0
bot/claudeapi/claude_api_session.py Parādīt failu

@@ -0,0 +1,74 @@
from bot.session_manager import Session
from common.log import logger


class ClaudeAPISession(Session):
def __init__(self, session_id, system_prompt=None, model="text-davinci-003"):
super().__init__(session_id, system_prompt)
self.model = model
self.reset()

def __str__(self):
# 构造对话模型的输入
"""
e.g. Q: xxx
A: xxx
Q: xxx
"""
prompt = ""
for item in self.messages:
if item["role"] == "system":
prompt += item["content"] + "<|endoftext|>\n\n\n"
elif item["role"] == "user":
prompt += "Q: " + item["content"] + "\n"
elif item["role"] == "assistant":
prompt += "\n\nA: " + item["content"] + "<|endoftext|>\n"

if len(self.messages) > 0 and self.messages[-1]["role"] == "user":
prompt += "A: "
return prompt

def discard_exceeding(self, max_tokens, cur_tokens=None):
precise = True

try:
cur_tokens = self.calc_tokens()
except Exception as e:
precise = False
if cur_tokens is None:
raise e
logger.debug("Exception when counting tokens precisely for query: {}".format(e))
while cur_tokens > max_tokens:
if len(self.messages) > 1:
self.messages.pop(0)
elif len(self.messages) == 1 and self.messages[0]["role"] == "assistant":
self.messages.pop(0)
if precise:
cur_tokens = self.calc_tokens()
else:
cur_tokens = len(str(self))
break
elif len(self.messages) == 1 and self.messages[0]["role"] == "user":
logger.warn("user question exceed max_tokens. total_tokens={}".format(cur_tokens))
break
else:
logger.debug("max_tokens={}, total_tokens={}, len(conversation)={}".format(max_tokens, cur_tokens, len(self.messages)))
break
if precise:
cur_tokens = self.calc_tokens()
else:
cur_tokens = len(str(self))
return cur_tokens
def calc_tokens(self):
return num_tokens_from_string(str(self), self.model)


# refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
def num_tokens_from_string(string: str, model: str) -> int:
"""Returns the number of tokens in a text string."""
num_tokens = len(string)
return num_tokens





+ 5
- 2
bridge/bridge.py Parādīt failu

@@ -18,6 +18,7 @@ class Bridge(object):
"text_to_voice": conf().get("text_to_voice", "google"),
"translate": conf().get("translate", "baidu"),
}
# 这边取配置的模型
model_type = conf().get("model") or const.GPT35
if model_type in ["text-davinci-003"]:
self.btype["chat"] = const.OPEN_AI
@@ -33,6 +34,8 @@ class Bridge(object):
self.btype["chat"] = const.GEMINI
if model_type in [const.ZHIPU_AI]:
self.btype["chat"] = const.ZHIPU_AI
if model_type in [const.CLAUDE3]:
self.btype["chat"] = const.CLAUDEAPI

if conf().get("use_linkai") and conf().get("linkai_api_key"):
self.btype["chat"] = const.LINKAI
@@ -40,12 +43,12 @@ class Bridge(object):
self.btype["voice_to_text"] = const.LINKAI
if not conf().get("text_to_voice") or conf().get("text_to_voice") in ["openai", const.TTS_1, const.TTS_1_HD]:
self.btype["text_to_voice"] = const.LINKAI

if model_type in ["claude"]:
self.btype["chat"] = const.CLAUDEAI

self.bots = {}
self.chat_bots = {}
# 模型对应的接口
def get_bot(self, typename):
if self.bots.get(typename) is None:
logger.info("create bot {} for {}".format(self.btype[typename], typename))


+ 3
- 1
common/const.py Parādīt failu

@@ -6,12 +6,14 @@ XUNFEI = "xunfei"
CHATGPTONAZURE = "chatGPTOnAzure"
LINKAI = "linkai"
CLAUDEAI = "claude"
CLAUDEAPI= "claudeAPI"
QWEN = "qwen"
GEMINI = "gemini"
ZHIPU_AI = "glm-4"


# model
CLAUDE3="claude-3-opus-20240229"
GPT35 = "gpt-3.5-turbo"
GPT4 = "gpt-4"
GPT4_TURBO_PREVIEW = "gpt-4-0125-preview"
@@ -20,7 +22,7 @@ WHISPER_1 = "whisper-1"
TTS_1 = "tts-1"
TTS_1_HD = "tts-1-hd"

MODEL_LIST = ["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-4", "wenxin", "wenxin-4", "xunfei", "claude", "gpt-4-turbo",
MODEL_LIST = ["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-4", "wenxin", "wenxin-4", "xunfei", "claude","claude-3-opus-20240229", "gpt-4-turbo",
"gpt-4-turbo-preview", "gpt-4-1106-preview", GPT4_TURBO_PREVIEW, QWEN, GEMINI, ZHIPU_AI]

# channel


+ 1
- 0
config-template.json Parādīt failu

@@ -2,6 +2,7 @@
"channel_type": "wx",
"model": "",
"open_ai_api_key": "YOUR API KEY",
"claude_api_key": "YOUR API KEY",
"text_to_image": "dall-e-2",
"voice_to_text": "openai",
"text_to_voice": "openai",


+ 2
- 0
config.py Parādīt failu

@@ -67,6 +67,8 @@ available_setting = {
# claude 配置
"claude_api_cookie": "",
"claude_uuid": "",
# claude api key
"claude_api_key":"",
# 通义千问API, 获取方式查看文档 https://help.aliyun.com/document_detail/2587494.html
"qwen_access_key_id": "",
"qwen_access_key_secret": "",


+ 1
- 0
requirements.txt Parādīt failu

@@ -7,3 +7,4 @@ chardet>=5.1.0
Pillow
pre-commit
web.py
anthropic

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