增加了claude api的调用方法

bot/claudeapi/claude_api_bot.py

@@ -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

bot/claudeapi/claude_api_session.py

@@ -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
+
+
+
+