修改内容：

调整llm引用模式
添加对星火大模型的支持
在模型报错时可以人工输入
可以通过config直接全人工输入

metagpt/actions/action.py

@@ -11,15 +11,16 @@ from typing import Optional
 from tenacity import retry, stop_after_attempt, wait_fixed
 
 from metagpt.actions.action_output import ActionOutput
-from metagpt.llm import LLM
+import metagpt.llm as LLM
 from metagpt.utils.common import OutputParser
 from metagpt.logs import logger
+from metagpt.config import CONFIG
 
 class Action(ABC):
     def __init__(self, name: str = '', context=None, llm: LLM = None):
         self.name: str = name
         if llm is None:
-            llm = LLM()
+            llm=LLM.DEFAULT_LLM
         self.llm = llm
         self.context = context
         self.prefix = ""
@@ -54,13 +55,42 @@ class Action(ABC):
         if not system_msgs:
             system_msgs = []
         system_msgs.append(self.prefix)
-        content = await self.llm.aask(prompt, system_msgs)
-        logger.debug(content)
-        output_class = ActionOutput.create_model_class(output_class_name, output_data_mapping)
-        parsed_data = OutputParser.parse_data_with_mapping(content, output_data_mapping)
-        logger.debug(parsed_data)
-        instruct_content = output_class(**parsed_data)
-        return ActionOutput(content, instruct_content)
+        if not CONFIG.no_api_mode:
+            content = await self.llm.aask(prompt, system_msgs)
+            logger.debug(content)
+            output_class = ActionOutput.create_model_class(output_class_name, output_data_mapping)
+            parsed_data = OutputParser.parse_data_with_mapping(content, output_data_mapping)
+            logger.debug(parsed_data)
+        try:
+            instruct_content = output_class(**parsed_data)
+            return ActionOutput(content, instruct_content)
+        except Exception as e:
+            print('Error:',e)
+        print('自动运行出错，切换为手动运行')
+        print('prompt为')
+        print('\n'.join( system_msgs)+prompt)
+        print('输入格式:')
+        print(output_data_mapping)
+        print('请准备输入,输入完成按ctrl+Z')
+        while True:
+            try:
+                lines=[]
+                while True:
+                    try:
+                        lines.append(input())
+                    except:
+                        break
+
+                content ='\n'.join(lines)
+                output_class = ActionOutput.create_model_class(output_class_name, output_data_mapping)
+                parsed_data = OutputParser.parse_data_with_mapping(content, output_data_mapping)
+                logger.debug(parsed_data)
+                instruct_content = output_class(**parsed_data)
+                return ActionOutput(content, instruct_content)
+            except Exception as e:
+                print('Error:',e)
+                print('输入错误，请重试')
+
 
     async def run(self, *args, **kwargs):
         """Run action"""

metagpt/actions/write_prd.py

@@ -143,4 +143,5 @@ class WritePRD(Action):
                                         format_example=FORMAT_EXAMPLE)
         logger.debug(prompt)
         prd = await self._aask_v1(prompt, "prd", OUTPUT_MAPPING)
+
         return prd

metagpt/config.py

@@ -45,8 +45,18 @@ class Config(metaclass=Singleton):
         self.global_proxy = self._get("GLOBAL_PROXY")
         self.openai_api_key = self._get("OPENAI_API_KEY")
         self.anthropic_api_key = self._get("Anthropic_API_KEY")
+
+        #星火大模型相关
+        self.xinghuo_appid = self._get("xinghuo_appid")
+        self.xinghuo_api_secret = self._get("xinghuo_api_secret")
+        self.xinghuo_api_key = self._get("xinghuo_api_key")
+        self.domain=self._get("domain")
+        self.Spark_url=self._get("Spark_url")
+        self.no_api_mode=self._get("no_api_mode")
         if (not self.openai_api_key or "YOUR_API_KEY" == self.openai_api_key) and (
             not self.anthropic_api_key or "YOUR_API_KEY" == self.anthropic_api_key
+        )and (
+            not self.xinghuo_api_key or "APIKey" == self.xinghuo_api_key
         ):
             raise NotConfiguredException("Set OPENAI_API_KEY or Anthropic_API_KEY first")
         self.openai_api_base = self._get("OPENAI_API_BASE")

metagpt/llm.py

@@ -8,10 +8,11 @@
 
 from metagpt.provider.anthropic_api import Claude2 as Claude
 from metagpt.provider.openai_api import OpenAIGPTAPI as LLM
+from metagpt.provider.spark_api import Spark
 
-DEFAULT_LLM = LLM()
+DEFAULT_LLM = Spark()
 CLAUDE_LLM = Claude()
-
+SPARK_LLM = Spark()
 
 async def ai_func(prompt):
     """使用LLM进行QA

metagpt/management/skill_manager.py

@@ -8,7 +8,7 @@
 from metagpt.actions import Action
 from metagpt.const import PROMPT_PATH
 from metagpt.document_store.chromadb_store import ChromaStore
-from metagpt.llm import LLM
+import metagpt.llm as LLM
 from metagpt.logs import logger
 
 Skill = Action
@@ -18,7 +18,7 @@ class SkillManager:
     """用来管理所有技能"""
 
     def __init__(self):
-        self._llm = LLM()
+        self._llm=LLM.DEFAULT_LLM
         self._store = ChromaStore('skill_manager')
         self._skills: dict[str: Skill] = {}
 

metagpt/manager.py

@@ -5,13 +5,13 @@
 @Author  : alexanderwu
 @File    : manager.py
 """
-from metagpt.llm import LLM
+import metagpt.llm as LLM
 from metagpt.logs import logger
 from metagpt.schema import Message
 
 
 class Manager:
-    def __init__(self, llm: LLM = LLM()):
+    def __init__(self, llm: llm=LLM.DEFAULT_LLM):
         self.llm = llm  # Large Language Model
         self.role_directions = {
             "BOSS": "Product Manager",

metagpt/prompts/generate_skill.md

@@ -10,10 +10,10 @@
 ```python
 from typing import Optional
 from abc import ABC
-from metagpt.llm import LLM # 大语言模型，类似GPT
+import metagpt.llm as LLM # 大语言模型，类似GPT
 
 class Action(ABC):
-    def __init__(self, name='', context=None, llm: LLM = LLM()):
+    def __init__(self, name='', context=None, llm: llm=LLM.DEFAULT_LLM):
         self.name = name
         self.llm = llm
         self.context = context

metagpt/provider/SparkApi.py

@@ -0,0 +1,137 @@
+import _thread as thread
+import base64
+import datetime
+import hashlib
+import hmac
+import json
+from urllib.parse import urlparse
+import ssl
+from datetime import datetime
+from time import mktime
+from urllib.parse import urlencode
+from wsgiref.handlers import format_date_time
+
+import websocket  # 使用websocket_client
+answer = ""
+
+class Ws_Param(object):
+    # 初始化
+    def __init__(self, APPID, APIKey, APISecret, Spark_url):
+        self.APPID = APPID
+        self.APIKey = APIKey
+        self.APISecret = APISecret
+        self.host = urlparse(Spark_url).netloc
+        self.path = urlparse(Spark_url).path
+        self.Spark_url = Spark_url
+
+    # 生成url
+    def create_url(self):
+        # 生成RFC1123格式的时间戳
+        now = datetime.now()
+        date = format_date_time(mktime(now.timetuple()))
+
+        # 拼接字符串
+        signature_origin = "host: " + self.host + "\n"
+        signature_origin += "date: " + date + "\n"
+        signature_origin += "GET " + self.path + " HTTP/1.1"
+
+        # 进行hmac-sha256进行加密
+        signature_sha = hmac.new(self.APISecret.encode('utf-8'), signature_origin.encode('utf-8'),
+                                 digestmod=hashlib.sha256).digest()
+
+        signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding='utf-8')
+
+        authorization_origin = f'api_key="{self.APIKey}", algorithm="hmac-sha256", headers="host date request-line", signature="{signature_sha_base64}"'
+
+        authorization = base64.b64encode(authorization_origin.encode('utf-8')).decode(encoding='utf-8')
+
+        # 将请求的鉴权参数组合为字典
+        v = {
+            "authorization": authorization,
+            "date": date,
+            "host": self.host
+        }
+        # 拼接鉴权参数，生成url
+        url = self.Spark_url + '?' + urlencode(v)
+        # 此处打印出建立连接时候的url,参考本demo的时候可取消上方打印的注释，比对相同参数时生成的url与自己代码生成的url是否一致
+        return url
+
+
+# 收到websocket错误的处理
+def on_error(ws, error):
+    print("### error:", error)
+
+
+# 收到websocket关闭的处理
+def on_close(ws,one,two):
+    print(" ")
+
+
+# 收到websocket连接建立的处理
+def on_open(ws):
+    thread.start_new_thread(run, (ws,))
+
+
+def run(ws, *args):
+    data = json.dumps(gen_params(appid=ws.appid, domain= ws.domain,question=ws.question))
+    ws.send(data)
+
+
+# 收到websocket消息的处理
+def on_message(ws, message):
+    # print(message)
+    data = json.loads(message)
+    code = data['header']['code']
+    if code != 0:
+        print(f'请求错误: {code}, {data}')
+        ws.close()
+    else:
+        choices = data["payload"]["choices"]
+        status = choices["status"]
+        content = choices["text"][0]["content"]
+        print(content,end ="")
+        global answer
+        answer += content
+        # print(1)
+        if status == 2:
+            ws.close()
+
+
+def gen_params(appid, domain,question):
+    """
+    通过appid和用户的提问来生成请参数
+    """
+    data = {
+        "header": {
+            "app_id": appid,
+            "uid": "1234"
+        },
+        "parameter": {
+            "chat": {
+                "domain": domain,
+                "random_threshold": 0.5,
+                "max_tokens": 2048,
+                "auditing": "default"
+            }
+        },
+        "payload": {
+            "message": {
+                "text": question
+            }
+        }
+    }
+    return data
+
+
+def main(appid, api_key, api_secret, Spark_url,domain, question):
+    # print("星火:")
+    wsParam = Ws_Param(appid, api_key, api_secret, Spark_url)
+    websocket.enableTrace(False)
+    wsUrl = wsParam.create_url()
+    ws = websocket.WebSocketApp(wsUrl, on_message=on_message, on_error=on_error, on_close=on_close, on_open=on_open)
+    ws.appid = appid
+    ws.question = question
+    ws.domain = domain
+    ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE})
+
+

metagpt/provider/spark_api.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2023/7/21 11:15
+@Author  : Leo Xiao
+@File    : anthropic_api.py
+"""
+
+from typing import Optional
+from metagpt.provider import SparkApi
+
+from metagpt.config import CONFIG
+
+def getlength(text):
+    length = 0
+    for content in text:
+        temp = content["content"]
+        leng = len(temp)
+        length += leng
+    return length
+
+def checklen(text):
+    while (getlength(text) > 8000):
+        del text[0]
+    return text
+
+class Spark:
+    system_prompt = 'You are a helpful assistant.'
+
+    def _user_msg(self, msg: str) -> dict[str, str]:
+        return {"role": "user", "content": msg}
+
+    def _assistant_msg(self, msg: str) -> dict[str, str]:
+        return {"role": "assistant", "content": msg}
+
+    def _system_msg(self, msg: str) -> dict[str, str]:
+        return {"role": "system", "content": msg}
+
+    def _system_msgs(self, msgs: list[str]) -> list[dict[str, str]]:
+        return [self._system_msg(msg) for msg in msgs]
+
+    def _default_system_msg(self):
+        return self._system_msg(self.system_prompt)
+    def ask(self, msg: str):
+        message = [self._user_msg(msg)]
+        SparkApi.main(CONFIG.xinghuo_appid,CONFIG.xinghuo_api_key,CONFIG.xinghuo_api_secret,"ws://spark-api.xf-yun.com/v2.1/chat","generalv2",message)
+        rsp = SparkApi.answer
+        return rsp
+
+    async def aask(self, msg: str, system_msgs: Optional[list[str]] = None) -> str:
+        if system_msgs:
+            message = self._system_msgs(system_msgs) + [self._user_msg(msg)]
+        else:
+            message = [self._user_msg(msg)]
+        SparkApi.main(CONFIG.xinghuo_appid,CONFIG.xinghuo_api_key,CONFIG.xinghuo_api_secret,"ws://spark-api.xf-yun.com/v2.1/chat","generalv2",message)
+        rsp = SparkApi.answer
+        return rsp

metagpt/roles/role.py

@@ -14,7 +14,7 @@ from pydantic import BaseModel, Field
 # from metagpt.environment import Environment
 from metagpt.config import CONFIG
 from metagpt.actions import Action, ActionOutput
-from metagpt.llm import LLM
+from metagpt import llm as LLM
 from metagpt.logs import logger
 from metagpt.memory import Memory, LongTermMemory
 from metagpt.schema import Message
@@ -94,7 +94,7 @@ class Role:
     """角色/代理"""
 
     def __init__(self, name="", profile="", goal="", constraints="", desc=""):
-        self._llm = LLM()
+        self._llm=LLM.DEFAULT_LLM
         self._setting = RoleSetting(name=name, profile=profile, goal=goal, constraints=constraints, desc=desc)
         self._states = []
         self._actions = []