diff --git a/README.md b/README.md index 91a5483e0..c326190b0 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ # MetaGPT: The Multi-Agent Framework Discord Follow License: MIT roadmap -Twitter Follow +Twitter Follow

@@ -162,9 +162,9 @@ ### Installation by Docker ```bash # Step 1: Download metagpt official image and prepare config.yaml -docker pull metagpt/metagpt:v0.3.1 +docker pull metagpt/metagpt:latest mkdir -p /opt/metagpt/{config,workspace} -docker run --rm metagpt/metagpt:v0.3.1 cat /app/metagpt/config/config.yaml > /opt/metagpt/config/key.yaml +docker run --rm metagpt/metagpt:latest cat /app/metagpt/config/config.yaml > /opt/metagpt/config/key.yaml vim /opt/metagpt/config/key.yaml # Change the config # Step 2: Run metagpt demo with container @@ -172,7 +172,7 @@ # Step 2: Run metagpt demo with container --privileged \ -v /opt/metagpt/config/key.yaml:/app/metagpt/config/key.yaml \ -v /opt/metagpt/workspace:/app/metagpt/workspace \ - metagpt/metagpt:v0.3.1 \ + metagpt/metagpt:latest \ python startup.py "Write a cli snake game" # You can also start a container and execute commands in it @@ -180,7 +180,7 @@ # You can also start a container and execute commands in it --privileged \ -v /opt/metagpt/config/key.yaml:/app/metagpt/config/key.yaml \ -v /opt/metagpt/workspace:/app/metagpt/workspace \ - metagpt/metagpt:v0.3.1 + metagpt/metagpt:latest docker exec -it metagpt /bin/bash $ python startup.py "Write a cli snake game" diff --git a/config/config.yaml b/config/config.yaml index a60d2dbdd..de6254898 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -15,18 +15,18 @@ RPM: 10 #### if Anthropic #Anthropic_API_KEY: "YOUR_API_KEY" -#### if xinghuo -#xinghuo_appid : "APPID" -#xinghuo_api_secret : "APISecret" -#xinghuo_api_key : "APIKey" +#### if Xinghuo +#XINGHUO_APPID : "YOUR_APPID" +#XINGHUO_API_SECRET : "YOUR_APISecret" +#XINGHUO_API_KEY : "YOUR_APIKey" +#DOMAIN : "generalv2" +#SPARK_URL : "ws://spark-api.xf-yun.com/v2.1/chat" -#domain : "generalv2" - -#Spark_url : "ws://spark-api.xf-yun.com/v2.1/chat" - -#### 如果不能使用api - -#no_api_mode :"true" +XINGHUO_APPID : "ae5e30f4" +XINGHUO_API_SECRET : "MDhlOWE2NmFhOWMxZWRkOTdlYjY2Njk1" +XINGHUO_API_KEY : "97b635fe5927d34a857333e11d15f29f" +DOMAIN : "generalv2" +SPARK_URL : "ws://spark-api.xf-yun.com/v2.1/chat" #### if AZURE, check https://github.com/openai/openai-cookbook/blob/main/examples/azure/chat.ipynb #### You can use ENGINE or DEPLOYMENT mode diff --git a/debating_tournament.py b/debating_tournament.py deleted file mode 100644 index 8971a12c1..000000000 --- a/debating_tournament.py +++ /dev/null @@ -1,135 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/9/20 00:30 -@Author : zhouziming -@File : debating_tourmament.py -""" -import asyncio -import platform -import fire -from pydantic import BaseModel, Field - -from metagpt.actions import BossRequirement -from metagpt.config import CONFIG -from metagpt.environment import Environment -from metagpt.logs import logger -from metagpt.roles import Role -from metagpt.schema import Message -from metagpt.utils.common import NoMoneyException -from metagpt.llm import DEFAULT_LLM -正方一辩提示词=''' -##角色 -现在你是一名高水平，有辩论技巧，有强大表达能力的辩手。 -##要求 -您的立论题目是{正方辩题}。您的立论稿应该包括明确自己的论点，解释自己论点的含义，然后使用对论点有利的论据来支撑自己的论点。最后使用生活中的示例来论证自己的论点。 -''' -反方一辩提示词=''' -##角色 -现在你是一名高水平，有辩论技巧，有强大表达能力的辩手。 -##要求 -您的立论题目是{反方辩题}。您的立论稿应该包括明确自己的论点，解释自己论点的含义，然后使用对论点有利的论据来支撑自己的论点。最后使用生活中的示例来论证自己的论点。 -''' -正方一辩评价提示词=''' -##角色 -现在你是一名高水平，有辩论技巧辩论赛裁判，根据辩论赛而不是自身立场来评价。 -##要求 -你的任务是根据一辩辩手的立论稿对辩手的立论进行评价，指出改进空间。评价应当包括：立论稿内容是否符合辩题、逻辑表达是否清晰、论据是否能够支撑论点、能否结合实际方面进行评价。并在进行中立，客观的评价后，给出自己的评分。评分从A+到C-。 -##辩题 -{正方辩题} -##立论稿 -{正方立论稿} -''' -反方一辩评价提示词=''' -##角色 -现在你是一名高水平，有辩论技巧辩论赛裁判，根据辩论赛而不是自身立场来评价。 -##要求 -你的任务是根据一辩辩手的立论稿对辩手的立论进行评价，指出改进空间。评价应当包括：立论稿内容是否符合辩题、逻辑表达是否清晰、论据是否能够支撑论点、能否结合实际方面进行评价。并在进行中立，客观的评价后，给出自己的评分。评分从A+到C-。 -##辩题 -{反方辩题} -##立论稿 -{反方立论稿} -''' -正方质询提示词=''' -##角色 -现在你是一名高水平，有辩论技巧，有强大表达能力的辩手。 -##要求 -你的任务是，根据自己辩题，针对立论稿提出疑问。疑问内容不超过五条，每条只限一句话。 -##辩题 -{正方辩题} -##立论稿 -{反方立论稿} -''' -反方回答提示词=''' -##角色 -现在你是一名高水平，有辩论技巧，有强大表达能力的辩手。 -##要求 -你的任务是，根据立论稿对对手提出的疑问进行回答。对每个问题的回答应限制在三句话以内。回答内容和疑问应当一一对应。 -##辩题 -{反方辩题} -##立论稿 -{反方立论稿} -##疑问 -{正方质询} -''' -反方质询提示词=''' -##角色 -现在你是一名高水平，有辩论技巧，有强大表达能力的辩手。 -##要求 -你的任务是，根据自己辩题，针对立论稿提出疑问。疑问内容不超过五条，每条只限一句话。 -##辩题 -{反方辩题} -##立论稿 -{正方立论稿} -''' -正方回答提示词=''' -##角色 -现在你是一名高水平，有辩论技巧，有强大表达能力的辩手。 -##要求 -你的任务是，根据立论稿对对手提出的疑问进行回答。对每个问题的回答应限制在三句话以内。回答内容和疑问应当一一对应。 -##辩题 -{正方辩题} -##立论稿 -{正方立论稿} -##疑问 -{反方质询} -''' -def main( - zf:str='人性本善', - ff:str='人性本恶' -): - """ - """ - if platform.system() == "Windows": - asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) - asyncio.run(startup(zf,ff)) - -async def startup(正方辩题:str,反方辩题:str): - llm=DEFAULT_LLM - #一辩环节 - #正方 - - 正方立论稿=await llm.aask(正方一辩提示词.format(正方辩题=正方辩题)) - #反方 - - 反方立论稿=await llm.aask(反方一辩提示词.format(反方辩题=反方辩题)) - #裁判评价环节 - - 正方一辩评价=await llm.aask(正方一辩评价提示词.format(正方辩题=正方辩题,正方立论稿=正方立论稿)) - - 反方一辩评价=await llm.aask(反方一辩评价提示词.format(反方辩题=反方辩题,反方立论稿=反方立论稿)) - #二辩质询环节 - #正方质询 - - 正方质询=await llm.aask(正方质询提示词.format(正方辩题=正方辩题,反方立论稿=反方立论稿)) - #反方回答 - - 反方回答=await llm.aask(反方回答提示词.format(反方辩题=反方辩题,反方立论稿=反方立论稿,正方质询=正方质询)) - #反方质询 - - 反方质询=await llm.aask(反方质询提示词.format(反方辩题=反方辩题,正方立论稿=正方立论稿)) - #正方回答 - - 正方回答=await llm.aask(正方回答提示词.format(正方辩题=正方辩题,正方立论稿=正方立论稿,反方质询=反方质询)) -if __name__ == '__main__': - fire.Fire(main) diff --git a/docs/README_CN.md b/docs/README_CN.md index 1372bf9f4..7a27a0e59 100644 --- a/docs/README_CN.md +++ b/docs/README_CN.md @@ -15,7 +15,7 @@ # MetaGPT: 多智能体框架 Discord Follow License: MIT roadmap -Twitter Follow +Twitter Follow

@@ -87,9 +87,9 @@ ### Docker安装 ```bash # 步骤1: 下载metagpt官方镜像并准备好config.yaml -docker pull metagpt/metagpt:v0.3 +docker pull metagpt/metagpt:latest mkdir -p /opt/metagpt/{config,workspace} -docker run --rm metagpt/metagpt:v0.3 cat /app/metagpt/config/config.yaml > /opt/metagpt/config/config.yaml +docker run --rm metagpt/metagpt:latest cat /app/metagpt/config/config.yaml > /opt/metagpt/config/config.yaml vim /opt/metagpt/config/config.yaml # 修改config # 步骤2: 使用容器运行metagpt演示 @@ -97,7 +97,7 @@ # 步骤2: 使用容器运行metagpt演示 --privileged \ -v /opt/metagpt/config:/app/metagpt/config \ -v /opt/metagpt/workspace:/app/metagpt/workspace \ - metagpt/metagpt:v0.3 \ + metagpt/metagpt:latest \ python startup.py "Write a cli snake game" # 您也可以启动一个容器并在其中执行命令 @@ -105,7 +105,7 @@ # 您也可以启动一个容器并在其中执行命令 --privileged \ -v /opt/metagpt/config:/app/metagpt/config \ -v /opt/metagpt/workspace:/app/metagpt/workspace \ - metagpt/metagpt:v0.3 + metagpt/metagpt:latest docker exec -it metagpt /bin/bash $ python startup.py "Write a cli snake game" @@ -123,7 +123,7 @@ ### 自己构建镜像 ```bash # 您也可以自己构建metagpt镜像 git clone https://github.com/geekan/MetaGPT.git -cd MetaGPT && docker build -t metagpt:v0.3 . +cd MetaGPT && docker build -t metagpt:custom . ``` ## 配置 diff --git a/docs/README_JA.md b/docs/README_JA.md index 8d6c2fe84..eab69d912 100644 --- a/docs/README_JA.md +++ b/docs/README_JA.md @@ -15,7 +15,7 @@ # MetaGPT: マルチエージェントフレームワーク Discord Follow License: MIT roadmap -Twitter Follow +Twitter Follow

@@ -92,9 +92,9 @@ ### Docker によるインストール ```bash # ステップ 1: metagpt 公式イメージをダウンロードし、config.yaml を準備する -docker pull metagpt/metagpt:v0.3.1 +docker pull metagpt/metagpt:latest mkdir -p /opt/metagpt/{config,workspace} -docker run --rm metagpt/metagpt:v0.3.1 cat /app/metagpt/config/config.yaml > /opt/metagpt/config/key.yaml +docker run --rm metagpt/metagpt:latest cat /app/metagpt/config/config.yaml > /opt/metagpt/config/key.yaml vim /opt/metagpt/config/key.yaml # 設定を変更する # ステップ 2: コンテナで metagpt デモを実行する @@ -102,7 +102,7 @@ # ステップ 2: コンテナで metagpt デモを実行する --privileged \ -v /opt/metagpt/config/key.yaml:/app/metagpt/config/key.yaml \ -v /opt/metagpt/workspace:/app/metagpt/workspace \ - metagpt/metagpt:v0.3.1 \ + metagpt/metagpt:latest \ python startup.py "Write a cli snake game" # コンテナを起動し、その中でコマンドを実行することもできます @@ -110,7 +110,7 @@ # コンテナを起動し、その中でコマンドを実行することもで --privileged \ -v /opt/metagpt/config/key.yaml:/app/metagpt/config/key.yaml \ -v /opt/metagpt/workspace:/app/metagpt/workspace \ - metagpt/metagpt:v0.3.1 + metagpt/metagpt:latest docker exec -it metagpt /bin/bash $ python startup.py "Write a cli snake game" diff --git a/examples/llm_hello_world.py b/examples/llm_hello_world.py index d6d24b688..3ba03eea0 100644 --- a/examples/llm_hello_world.py +++ b/examples/llm_hello_world.py @@ -7,11 +7,12 @@ """ import asyncio -import metagpt.llm as LLM +from metagpt.llm import LLM, Claude +from metagpt.logs import logger async def main(): - llm=LLM.DEFAULT_LLM + llm = LLM() claude = Claude() logger.info(await claude.aask('你好，请进行自我介绍')) logger.info(await llm.aask('hello world')) diff --git a/metagpt/actions/action.py b/metagpt/actions/action.py index 8e1d5c85d..790295d55 100644 --- a/metagpt/actions/action.py +++ b/metagpt/actions/action.py @@ -22,7 +22,7 @@ class Action(ABC): def __init__(self, name: str = "", context=None, llm: LLM = None): self.name: str = name if llm is None: - llm=LLM.DEFAULT_LLM + llm = LLM() self.llm = llm self.context = context self.prefix = "" diff --git a/metagpt/actions/design_api.py b/metagpt/actions/design_api.py index f19fcbeaa..75df8b909 100644 --- a/metagpt/actions/design_api.py +++ b/metagpt/actions/design_api.py @@ -207,5 +207,11 @@ class WriteDesign(Action): prompt = prompt_template.format(context=context, format_example=format_example) # system_design = await self._aask(prompt) system_design = await self._aask_v1(prompt, "system_design", OUTPUT_MAPPING, format=format) + # fix Python package name, we can't system_design.instruct_content.python_package_name = "xxx" since "Python package name" contain space, have to use setattr + setattr( + system_design.instruct_content, + "Python package name", + system_design.instruct_content.dict()["Python package name"].strip().strip("'").strip('"'), + ) await self._save(context, system_design) return system_design diff --git a/metagpt/config.py b/metagpt/config.py index 146fb957b..ab57ae614 100644 --- a/metagpt/config.py +++ b/metagpt/config.py @@ -45,18 +45,8 @@ 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") @@ -72,6 +62,12 @@ class Config(metaclass=Singleton): self.deployment_name = self._get("DEPLOYMENT_NAME") self.deployment_id = self._get("DEPLOYMENT_ID") + 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.claude_api_key = self._get("Anthropic_API_KEY") self.serpapi_api_key = self._get("SERPAPI_API_KEY") self.serper_api_key = self._get("SERPER_API_KEY") diff --git a/metagpt/llm.py b/metagpt/llm.py index 68945e71e..e6f815950 100644 --- a/metagpt/llm.py +++ b/metagpt/llm.py @@ -8,9 +8,9 @@ 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 = Spark() +DEFAULT_LLM = LLM() +CLAUDE_LLM = Claude() async def ai_func(prompt): """使用LLM进行QA diff --git a/metagpt/management/skill_manager.py b/metagpt/management/skill_manager.py index 4852e452f..f967a0a94 100644 --- a/metagpt/management/skill_manager.py +++ b/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 -import metagpt.llm as LLM +from metagpt.llm import LLM from metagpt.logs import logger Skill = Action @@ -18,7 +18,7 @@ class SkillManager: """Used to manage all skills""" def __init__(self): - self._llm=LLM.DEFAULT_LLM + self._llm = LLM() self._store = ChromaStore('skill_manager') self._skills: dict[str: Skill] = {} diff --git a/metagpt/manager.py b/metagpt/manager.py index 3f6c115f3..9d238c621 100644 --- a/metagpt/manager.py +++ b/metagpt/manager.py @@ -5,13 +5,13 @@ @Author : alexanderwu @File : manager.py """ -import metagpt.llm as LLM +from metagpt.llm import LLM from metagpt.logs import logger from metagpt.schema import Message class Manager: - def __init__(self, llm: llm=LLM.DEFAULT_LLM): + def __init__(self, llm: LLM = LLM()): self.llm = llm # Large Language Model self.role_directions = { "BOSS": "Product Manager", diff --git a/metagpt/memory/longterm_memory.py b/metagpt/memory/longterm_memory.py index 1f4698704..f8abea5f3 100644 --- a/metagpt/memory/longterm_memory.py +++ b/metagpt/memory/longterm_memory.py @@ -42,21 +42,21 @@ class LongTermMemory(Memory): # and ignore adding messages from recover repeatedly self.memory_storage.add(message) - def remember(self, observed: list[Message], k=0) -> list[Message]: + def find_news(self, observed: list[Message], k=0) -> list[Message]: """ - remember the most similar k memories from observed Messages, return all when k=0 - 1. remember the short-term memory(stm) news - 2. integrate the stm news with ltm(long-term memory) news + find news (previously unseen messages) from the the most recent k memories, from all memories when k=0 + 1. find the short-term memory(stm) news + 2. furthermore, filter out similar messages based on ltm(long-term memory), get the final news """ - stm_news = super(LongTermMemory, self).remember(observed, k=k) # shot-term memory news + stm_news = super(LongTermMemory, self).find_news(observed, k=k) # shot-term memory news if not self.memory_storage.is_initialized: - # memory_storage hasn't initialized, use default `remember` to get stm_news + # memory_storage hasn't initialized, use default `find_news` to get stm_news return stm_news ltm_news: list[Message] = [] for mem in stm_news: - # integrate stm & ltm - mem_searched = self.memory_storage.search(mem) + # filter out messages similar to those seen previously in ltm, only keep fresh news + mem_searched = self.memory_storage.search_dissimilar(mem) if len(mem_searched) > 0: ltm_news.append(mem) return ltm_news[-k:] diff --git a/metagpt/memory/memory.py b/metagpt/memory/memory.py index 92f0428a7..c818fa707 100644 --- a/metagpt/memory/memory.py +++ b/metagpt/memory/memory.py @@ -63,8 +63,8 @@ class Memory: """Return the most recent k memories, return all when k=0""" return self.storage[-k:] - def remember(self, observed: list[Message], k=0) -> list[Message]: - """remember the most recent k memories from observed Messages, return all when k=0""" + def find_news(self, observed: list[Message], k=0) -> list[Message]: + """find news (previously unseen messages) from the the most recent k memories, from all memories when k=0""" already_observed = self.get(k) news: list[Message] = [] for i in observed: diff --git a/metagpt/memory/memory_storage.py b/metagpt/memory/memory_storage.py index 8b639150c..302d96aa7 100644 --- a/metagpt/memory/memory_storage.py +++ b/metagpt/memory/memory_storage.py @@ -74,7 +74,7 @@ class MemoryStorage(FaissStore): self.persist() logger.info(f"Agent {self.role_id}'s memory_storage add a message") - def search(self, message: Message, k=4) -> List[Message]: + def search_dissimilar(self, message: Message, k=4) -> List[Message]: """search for dissimilar messages""" if not self.store: return [] diff --git a/metagpt/prompts/generate_skill.md b/metagpt/prompts/generate_skill.md index dd28df079..74948cd15 100644 --- a/metagpt/prompts/generate_skill.md +++ b/metagpt/prompts/generate_skill.md @@ -9,10 +9,10 @@ ```python from typing import Optional from abc import ABC -import metagpt.llm as LLM # 大语言模型，类似GPT - +from metagpt.llm import LLM # Large language model, similar to GPT +n class Action(ABC): - def __init__(self, name='', context=None, llm: llm=LLM.DEFAULT_LLM): + def __init__(self, name='', context=None, llm: LLM = LLM()): self.name = name self.llm = llm self.context = context diff --git a/metagpt/provider/SparkApi.py b/metagpt/provider/SparkApi.py deleted file mode 100644 index 7ce57c22a..000000000 --- a/metagpt/provider/SparkApi.py +++ /dev/null @@ -1,138 +0,0 @@ -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 -from metagpt.logs import logger - -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): - logger.error("### error:"+error) - - -# 收到websocket关闭的处理 -def on_close(ws,one,two): - logger.error("websocket关闭") - - -# 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: - logger.error(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}) - - diff --git a/metagpt/provider/spark_api.py b/metagpt/provider/spark_api.py index e855e019a..db50771d4 100644 --- a/metagpt/provider/spark_api.py +++ b/metagpt/provider/spark_api.py @@ -5,54 +5,188 @@ @Author : Leo Xiao @File : anthropic_api.py """ - -from typing import Optional -from metagpt.provider import SparkApi +import _thread as thread +import base64 +import datetime +import hashlib +import hmac +import json +import ssl +from collections import OrderedDict +import datetime +from time import mktime +from urllib.parse import urlencode +from urllib.parse import urlparse +from wsgiref.handlers import format_date_time +import websocket # 使用websocket_client +import websockets +from websockets.legacy.server import WebSocketServerProtocol from metagpt.config import CONFIG +from metagpt.logs import logger +from metagpt.provider.base_gpt_api import BaseGPTAPI -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} +class SparkAPI(BaseGPTAPI): - def _assistant_msg(self, msg: str) -> dict[str, str]: - return {"role": "assistant", "content": msg} + def get_choice_text(self, rsp: dict) -> str: + return rsp["payload"]["choices"]["text"][-1]["content"] + async def acompletion_text(self, messages: list[dict], stream=False) -> str: + #尚未实现 + pass - def _system_msg(self, msg: str) -> dict[str, str]: - return {"role": "system", "content": msg} + async def acompletion(self, messages: list[dict]): + # 尚未实现 + w = GetMessageFromWeb(messages) + return await w.arun() - def _system_msgs(self, msgs: list[str]) -> list[dict[str, str]]: - return [self._system_msg(msg) for msg in msgs] + def completion(self, messages: list[dict]): + w= GetMessageFromWeb(messages) + return w.run() - 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)] +class GetMessageFromWeb: + class WsParam(object): + """ + 该类适合讯飞星火大部分接口的调用。 + 输入 app_id, api_key, api_secret, spark_url以初始化， + create_url方法返回接口url + """ + + # 初始化 + def __init__(self, app_id, api_key, api_secret, spark_url, message=None): + self.app_id = app_id + self.api_key = api_key + self.api_secret = api_secret + self.host = urlparse(spark_url).netloc + self.path = urlparse(spark_url).path + self.spark_url = spark_url + self.message = message + + # 生成url + def create_url(self): + # 生成RFC1123格式的时间戳 + now = datetime.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.api_secret.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.api_key}", 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 + + def __init__(self,text): + self.text = text + self.ret =None + self.xinghuo_appid = CONFIG.xinghuo_appid + self.xinghuo_api_secret = CONFIG.xinghuo_api_secret + self.xinghuo_api_key = CONFIG.xinghuo_api_key + self.domain = CONFIG.domain + self.spark_url = CONFIG.spark_url + + def on_message(self, ws, message): + logger.info(message) + data = json.loads(message) + code = data['header']['code'] + + if code != 0: + ws.close() # 请求错误，则关闭socket + logger.critical(f'回答获取失败，响应信息反序列化之后为： {data}') + return 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 + choices = data["payload"]["choices"] + seq = choices["seq"] # 服务端是流式返回，seq为返回的数据序号 + status = choices["status"] # 服务端是流式返回，status用于判断信息是否传送完毕 + content = choices["text"][0]["content"] # 本次接收到的回答文本 + self.ret=data + ws.close() + logger.info(f"本次通讯关闭") + + # 收到websocket错误的处理 + def on_error(self, ws, error): + # on_message方法处理接收到的信息，出现任何错误，都会调用这个方法 + logger.critical(f'通讯连接出错，【错误提示: {error}】') + + # 收到websocket关闭的处理 + def on_close(self, ws, one, two): + pass + # print("通讯完成") + + # 处理请求数据 + def gen_params(self): + + data = { + "header": { + "app_id": self.xinghuo_appid, + "uid": "1234" + }, + "parameter": { + "chat": { + # domain为必传参数 + "domain": self.domain, + + # 以下为可微调，非必传参数 + # 注意：官方建议，temperature和top_k修改一个即可 + "max_tokens": 2048, # 默认2048，模型回答的tokens的最大长度，即允许它输出文本的最长字数 + "temperature": 0.5, # 取值为[0,1],默认为0.5。取值越高随机性越强、发散性越高，即相同的问题得到的不同答案的可能性越高 + "top_k": 4, # 取值为[1，6],默认为4。从k个候选中随机选择一个（非等概率） + } + }, + "payload": { + "message": { + "text": self.text + } + } + } + return data + + def send(self, ws, *args): + data = json.dumps(self.gen_params()) + logger.info(f"开始尝试建立通讯...") + logger.info(f"此次请求的请求头数据为：{data}") + ws.send(data) + + # 收到websocket连接建立的处理 + def on_open(self, ws): + thread.start_new_thread(self.send, (ws,)) + + # 处理收到的 websocket消息，出现任何错误，调用on_error方法 + def run(self): + return self._run(self.text) + def _run(self, text_list): + + ws_param = self.WsParam( + self.xinghuo_appid, + self.xinghuo_api_key, + self.xinghuo_api_secret, + self.spark_url, + text_list) + ws_url = ws_param.create_url() + + websocket.enableTrace(True) # 默认禁用 WebSocket 的跟踪功能 + ws = websocket.WebSocketApp(ws_url, on_message=self.on_message, on_error=self.on_error, on_close=self.on_close, + on_open=self.on_open) + ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE}) + return self.ret diff --git a/metagpt/reflect_and_retrieve/GA_memory_storage.py b/metagpt/reflect_and_retrieve/GA_memory_storage.py deleted file mode 100644 index c5ca24729..000000000 --- a/metagpt/reflect_and_retrieve/GA_memory_storage.py +++ /dev/null @@ -1,140 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# @Desc : 对应 GA中 concept node 实现 & AssociativeMemory 实现 -# author: didi -# Date:9.24 - -from run_gpt import run_gpt_prompt_chat_poignancy, run_gpt_random_concept -from gpt_structure import embedding -from retrive import agent_retrive -import time -import json - -# Meomry_basic 类 - - -class Memory_basic: - def __init__( - self, created_time, accessed_time, - description, - poignancy, - embedding_key=None) -> None: - """ - Initializes a basic memory object. - - Args: - created_time (datetime): The time when the memory was created. - accessed_time (datetime): The time when the memory was last accessed. - description (str): The description of the memory. - poignancy (int): The level of emotional intensity associated with the memory. - embedding_key (Optional[str]): The embedding key for the memory (to avoid redundant vectorization). - - Returns: - None - """ - self.created_time = created_time # 记忆创建时间 - self.accessed_time = accessed_time # 记忆上次调用时间 - self.description = description # 记忆描述 - self.poignancy = poignancy # 记忆心酸程度 - if embedding_key is None: # 记忆emmbeding key(避免重复向量化花钱) - self.embedding_key = embedding(self.description) - else: - self.embedding_key = embedding_key - -# Agent Memory 类 - - -class Agent_memory(object): - - def __init__(self, name: str, iss: str, - memory_forget: float = 0.99, - memories_list: list[Memory_basic] = [], memory_path: str = None) -> None: - ''' - 定义Agent,替换原有Agent使用，需要其他人根据需求补全功能。 - Attributes: - name:agent name - iss:agent iss（性格特征） - memory_forget:agent 记忆遗忘速率（计算近因性） - memories_list:agent 记忆JSON文件存储地址 - memory_path:记忆存储地址 - ''' - self.name = name # agent name - self.iss = iss # agent iss（性格特征） - self.memories_list = memories_list # agent 记忆列表 - self.concept_forget = memory_forget # agent 记忆遗忘速率（计算近因性） - self.memory_path = memory_path # agent 记忆JSON文件存储地址 - # agent 当前时间（现在使用的time.time()，等到环境搭好之后使用游戏内时间） - self.curr_time = time.time() - # 若给到memory_path 进行记忆初始化 - if memory_path: - self.memories_list = self.memory_load(memory_path) - - def memory_save(self, PATH: str) -> None: - ''' - 将Memory存储在指定PATH的JSON文件中，命名为"{self.name}'s memory - Args: - PATH:str - Return: - None - ''' - with open(PATH, 'w') as file: - memory_data = [mem.__dict__ for mem in self.memories_list] - json.dump(memory_data, file) - - def memory_load(self, PATH: str) -> list[Memory_basic]: - """ - 将Memory从指定路径的JSON文件中Load出来,返回一个记忆列表;如果load失败，返回一个空列表。 - Args: - PATH:str - Return: - List(Meomry_basic) - """ - try: - with open(PATH, 'r') as file: - memory_data = json.load(file) - self.memories_list = [Memory_basic( - **mem) for mem in memory_data] - return self.memories_list - except OSError: - return [] - - -if __name__ == "__main__": - # 例子，构建John Agent，实现retrive - John_iss = """John Lin is a pharmacy shopkeeper at the Willow Market and Pharmacy who loves to help people. - He is always looking for ways to make the process of getting medication easier for his customers; - John Lin is living with his wife, Mei Lin, who is a college professor, and son, - Eddy Lin, who is a student studying music theory; John Lin loves his family very much; - John Lin has known the old couple next-door, - Sam Moore and Jennifer Moore, for a few years; - John Lin thinks Sam Moore is a kind and nice man; - John Lin knows his neighbor, Yuriko Yamamoto, well; - John Lin knows of his neighbors, Tamara Taylor and Carmen Ortiz, - but has not met them before; - John Lin and Tom Moreno are colleagues at The Willows Market and Pharmacy; - John Lin and Tom Moreno are friends and like to discuss local politics together; - John Lin knows the Moreno family somewhat well — the husband Tom Moreno and the wife Jane Moreno.""" - John = Agent_memory( - "John", John_iss, memory_path="agent_memories/John_memory.json") - - for i in range(3): - memory = run_gpt_random_concept() - curr_time = time.time() - poignancy = run_gpt_prompt_chat_poignancy(John, memory) - M = Memory_basic(curr_time, curr_time, memory, poignancy) - John.memories_list.append(M) - - John.memory_save(John.memory_path) - - for i in range(len(John.memories_list)): - print(f"John记忆为:{John.memories_list[i].description}") - print(f"心酸程度为:{John.memories_list[i].poignancy}") - query = """ - How has John's personal connection with his neighbors, - such as the Moores and Yuriko, influenced his role as a pharmacy shopkeeper? - """ - - Top_v = agent_retrive(John, query, 10, 3) - print(f"John的相关信息：{Top_v}") - - # John的相关信息：{'Had a friendly chat with Yuriko about her garden.': 2.4992317730827667, 'Helped Mrs. Moore carry groceries into her house.': 1.957656720441911, 'Discussed local politics with Tom Moreno.': 1.9458268038234035} diff --git a/metagpt/reflect_and_retrieve/Prompt_template/poignancy_chat_v1.txt b/metagpt/reflect_and_retrieve/Prompt_template/poignancy_chat_v1.txt deleted file mode 100644 index 572dd8a05..000000000 --- a/metagpt/reflect_and_retrieve/Prompt_template/poignancy_chat_v1.txt +++ /dev/null @@ -1,17 +0,0 @@ -poignancy_chat_v1.txt - -!!: agent name -!!: iss -!!: name -!!: event description - -### -Here is a brief description of !!. -!! - -On the scale of 1 to 10, where 1 is purely mundane (e.g., routine morning greetings) and 10 is extremely poignant (e.g., a conversation about breaking up, a fight), rate the likely poignancy of the following conversation for !!. - -Conversation: -!! - -Rate (return a number between 1 to 10): \ No newline at end of file diff --git a/metagpt/reflect_and_retrieve/__MACOSX/GA_memory_stream/._.DS_Store b/metagpt/reflect_and_retrieve/__MACOSX/GA_memory_stream/._.DS_Store deleted file mode 100644 index a5b28df1c..000000000 Binary files a/metagpt/reflect_and_retrieve/__MACOSX/GA_memory_stream/._.DS_Store and /dev/null differ diff --git a/metagpt/reflect_and_retrieve/__MACOSX/GA_memory_stream/agent_memories/._.DS_Store b/metagpt/reflect_and_retrieve/__MACOSX/GA_memory_stream/agent_memories/._.DS_Store deleted file mode 100644 index a5b28df1c..000000000 Binary files a/metagpt/reflect_and_retrieve/__MACOSX/GA_memory_stream/agent_memories/._.DS_Store and /dev/null differ diff --git a/metagpt/reflect_and_retrieve/agent_memories/John_memory.json b/metagpt/reflect_and_retrieve/agent_memories/John_memory.json deleted file mode 100644 index 959e07940..000000000 --- a/metagpt/reflect_and_retrieve/agent_memories/John_memory.json +++ /dev/null @@ -1 +0,0 @@ -[{"created_time": 1695640634.6113422, "accessed_time": 1695640634.6113422, "description": "Had a friendly chat with Yuriko about her garden.", "poignancy": 3, "embedding_key": [0.00822820607572794, -0.005863511469215155, 0.012651615776121616, 0.008660569787025452, 0.016709178686141968, 0.02378663420677185, -0.00028955869493074715, -0.003728301962837577, -0.02039424516260624, -0.02626108191907406, -0.00543114822357893, -0.023081548511981964, 0.009146146476268768, -0.009931053034961224, -0.019183628261089325, -0.002715574111789465, 0.0278841070830822, -0.0023779980838298798, 0.04334275797009468, -0.033817462623119354, -0.015006332658231258, 0.025702334940433502, 0.0026091462932527065, 0.010509754531085491, 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example_output（可选）：示例输出的JSON字符串。 - - 返回： - 生成的最终响应。 - - """ - completion = openai.Completion.create( - model="gpt-3.5-turbo-instruct", - prompt=prompt, - temperature=0, - max_tokens=500, - top_p=1, - stream=False, - frequency_penalty=0, - presence_penalty=0 - ) - return (completion.choices[0].text) - -# 特殊指令加入Prompt生成 - - -def final_response(prompt, special_instruction, example_output=None): - """ - 通过将特殊指令加入Prompt生成最终的响应。 - - 参数： - - prompt：要生成响应的提示文本。 - - special_instruction：要加入Prompt的特殊指令。 - - example_output（可选）：示例输出的JSON字符串。 - - 返回： - 生成的最终响应。 - - """ - prompt = '"""\n' + prompt + '\n"""\n' - prompt += f"Output the response to the prompt above in json. {special_instruction}\n" - if example_output: - prompt += "Example output json:\n" - prompt += '{"output": "' + str(example_output) + '"}' - return response_generate(prompt) - -# prompt填充模板 - - -def prompt_generate(curr_input, prompt_lib_file): - """ - Takes in the current input (e.g. comment that you want to classifiy) and - the path to a prompt file. The prompt file contains the raw str prompt that - will be used, which contains the following substr: !! -- this - function replaces this substr with the actual curr_input to produce the - final promopt that will be sent to the GPT3 server. - ARGS: - curr_input: the input we want to feed in (IF THERE ARE MORE THAN ONE - INPUT, THIS CAN BE A LIST.) - prompt_lib_file: the path to the promopt file. - RETURNS: - a str prompt that will be sent to OpenAI's GPT server. - """ - if type(curr_input) is type("string"): - curr_input = [curr_input] - curr_input = [str(i) for i in curr_input] - - f = open(prompt_lib_file, "r") - prompt = f.read() - f.close() - for count, i in enumerate(curr_input): - prompt = prompt.replace(f"!!", i) - if "###" in prompt: - prompt = prompt.split( - "###")[1] - return prompt.strip() - -# 使用OpenAI embedding库进行存储 - - -def embedding(query): - """ - Generates an embedding for the given query. - - Args: - query (str): The text query to be embedded. - - Returns: - str: The embedding key generated for the query. - """ - embedding_result = openai.Embedding.create( - model="text-embedding-ada-002", - input=query - ) - embedding_key = embedding_result['data'][0]["embedding"] - return embedding_key diff --git a/metagpt/reflect_and_retrieve/pycodetester.py b/metagpt/reflect_and_retrieve/pycodetester.py deleted file mode 100644 index 30c9abaec..000000000 --- a/metagpt/reflect_and_retrieve/pycodetester.py +++ /dev/null @@ -1,3 +0,0 @@ -import pycodestyle as pcs -checker = pcs.StyleGuide() -checker.input_dir('./') diff --git a/metagpt/reflect_and_retrieve/reflect.py b/metagpt/reflect_and_retrieve/reflect.py deleted file mode 100644 index 1e1bb1352..000000000 --- a/metagpt/reflect_and_retrieve/reflect.py +++ /dev/null @@ -1,84 +0,0 @@ - -import json -from logging import Logger -import time -from gpt_structure import final_response -import run_gpt -from GA_memory_storage import Agent_memory, Memory_basic -from retrive import agent_retrive - - -def agent_reflect(agent): - ''' - agent:agent本身 - ''' - pass - - -def generate_focus_point(memories_list, n=3): - wait_sorted_mem = [[i.accessed_time, i] for i in memories_list] - sorted_memories = sorted(wait_sorted_mem, key=lambda x: x[0]) - memorys = [i for created, i in sorted_memories] - statements = '' - for i in memorys: - statements += i.description + "\n" - prompt = ''' - {statements} - Given only the information above, what are {num_question} most salient high-level questions we can answer about the subjects grounded in the statements? - ''' - example_output = '["What should Jane do for lunch", "Does Jane like strawberry", "Who is Jane"]' - out = final_response(prompt.format(statements=statements, num_question=n), - "Output must be a list of str.", example_output) - try: - poi_dict = json.loads(out) - return (poi_dict['output']) - except ValueError: - print(out) - Logger.error('无法返回正常结果') - return out - - -def generate_insights_and_evidence(agent, memories_list, question, n=5): - agent_retrive(agent, question, 20, 10) - statements = "" - for count, mem in enumerate(memories_list): - statements += f'{str(count)}. {mem.description}\n' - prompt = ''' - Input: - {statements} - - What {n} high-level insights can you infer from the above statements? - You should return a list of list[str,list] . The first element is the insight you have found.The second element is the - ''' - - ret = final_response(prompt.format( - question=question, statements=statements, n=n), "['insightA',[1,2,3]]") - try: - insight_list = json.loads(ret) - for insight, index in insight_list: - agent.memory_list.append(Memory_basic( - time.time(), None, insight, None, None)) - return (insight_list) - except: - Logger.error('我们无法获得想要的返回。') - return ret - - -if __name__ == "__main__": - # 例子，构建John Agent，实现retrive - John_iss = "John Lin is a pharmacy shopkeeper at the Willow Market and Pharmacy who loves to help people. He is always looking for ways to make the process of getting medication easier for his customers; John Lin is living with his wife, Mei Lin, who is a college professor, and son, Eddy Lin, who is a student studying music theory; John Lin loves his family very much; John Lin has known the old couple next-door, Sam Moore and Jennifer Moore, for a few years; John Lin thinks Sam Moore is a kind and nice man; John Lin knows his neighbor, Yuriko Yamamoto, well; John Lin knows of his neighbors, Tamara Taylor and Carmen Ortiz, but has not met them before; John Lin and Tom Moreno are colleagues at The Willows Market and Pharmacy; John Lin and Tom Moreno are friends and like to discuss local politics together; John Lin knows the Moreno family somewhat well — the husband Tom Moreno and the wife Jane Moreno." - John = Agent_memory( - "John", John_iss, memory_path="agent_memories/John_memory.json") - - # John的相关信息：{'Had a friendly chat with Yuriko about her garden.': 2.4992317730827667, 'Helped Mrs. Moore carry groceries into her house.': 1.957656720441911, 'Discussed local politics with Tom Moreno.': 1.9458268038234035} - A = generate_focus_point(John.memories_list) - - for i in A: - B = generate_insights_and_evidence( - John, John.memories_list, question=A[0]) - print(type(B)) - print(B) - ''' - 这里是输出,list形式，返回给记忆。 - [['The pharmacy is a friendly and helpful community.', [0, 2, 9, 12]], ['The pharmacy is a place where people come for more than just medication.', [3, 5, 13, 14]], ['The pharmacy is a place where people come for advice and conversation.', [0, 2, 6, 9, 12]], ['The pharmacy is a place where people come for assistance with daily tasks.', [3, 5, 13, 14]], ['The pharmacy is a place where people come for political discussions.', [1]]] - ''' diff --git a/metagpt/reflect_and_retrieve/retrive.py b/metagpt/reflect_and_retrieve/retrive.py deleted file mode 100644 index 9a75a5c29..000000000 --- a/metagpt/reflect_and_retrieve/retrive.py +++ /dev/null @@ -1,136 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# @Desc : 实现GA中检索函数 -# author: didi -# Date:9.25 - -from numpy import dot -from numpy.linalg import norm -from gpt_structure import embedding - -# 实现三(2)合一搜索 - - -def agent_retrive(agent, query, n, topk): - # 将记忆列表按照Nodes[i].accessed_time排列，仅取前十个，如果不够10个就取现有的所有 - Nodes = agent.memories_list - sorted_nodes = sorted( - Nodes, key=lambda node: node.accessed_time, reverse=True) - Nodes = sorted_nodes[:n] if len(sorted_nodes) >= n else sorted_nodes - - # 创建一个分数列表 - Score_list = [] - """ - { - "memory":Nodes[i], - "importance":Nodes[i].poignancy - "recency":衰减因子计算结果 - "relevance":搜索结果 - } - """ - Score_list = extract_importance(Nodes, Score_list) - Score_list = extract_recency(Score_list) # 计算近因性函数还没有实现，目前都是1 - Score_list = extract_relevance(Score_list, query) - - Score_list = normalize_Socre_floats(Score_list, 0, 1) - total_dict = {} - gw = [1, 1, 1] # 三个因素的权重,重要性，近因性，相关性 - for i in range(len(Score_list)): - total_score = (Score_list[i]['importance']*gw[0] + - Score_list[i]['recency']*gw[1] + - Score_list[i]['relevance']*gw[2] - ) - total_dict[Score_list[i]['memory'].description] = total_score - - result = top_highest_x_values(total_dict, topk) - - return result - - -def top_highest_x_values(d, x): - top_v = dict(sorted(d.items(), - key=lambda item: item[1], - reverse=True)[:x]) - return top_v -# 抽取重要性 - - -def extract_importance(Nodes, Score_list): - for i in range(len(Nodes)): - Score = {"memory": Nodes[i], - "importance": Nodes[i].poignancy - } - Score_list.append(Score) - return Score_list - -# 抽取相关性 - - -def extract_relevance(Score_list, query): - query_embedding = embedding(query) - # 进行 - for i in range(len(Score_list)): - result = cos_sim( - Score_list[i]["memory"].embedding_key, query_embedding) - Score_list[i]['relevance'] = result - - return Score_list - -# 抽取近因性 - - -def extract_recency(Score_list): - for i in range(len(Score_list)): - Score_list[i]['recency'] = 1 - return Score_list - -# 计算余弦相似度 - - -def cos_sim(a, b): - return dot(a, b)/(norm(a)*norm(b)) - -# 单个列表归一化 - - -def normalize_List_floats(Single_list, target_min, target_max): - min_val = min(Single_list) - max_val = max(Single_list) - range_val = max_val - min_val - - if range_val == 0: - for i in range(len(Single_list)): - Single_list[i] = (target_max - target_min)/2 - else: - for i in range(len(Single_list)): - Single_list[i] = ((Single_list[i] - min_val) * (target_max - target_min) - / range_val + target_min) - return Single_list - -# 整体归一化 - - -def normalize_Socre_floats(Score_list, target_min, target_max): - - importance_list = [] - relevance_list = [] - recency_list = [] - - for i in range(len(Score_list)): - importance_list.append(Score_list[i]['importance']) - relevance_list.append(Score_list[i]['relevance']) - recency_list.append(Score_list[i]['recency']) - - # 进行归一化操作 - importance_list = normalize_List_floats( - importance_list, target_min, target_max) - relevance_list = normalize_List_floats( - relevance_list, target_min, target_max) - recency_list = normalize_List_floats(recency_list, target_min, target_max) - - for i in range(len(Score_list)): - Score_list[i]['importance'] = importance_list[i] - Score_list[i]['relevance'] = relevance_list[i] - Score_list[i]['recency'] = recency_list[i] - - return Score_list diff --git a/metagpt/reflect_and_retrieve/run_gpt.py b/metagpt/reflect_and_retrieve/run_gpt.py deleted file mode 100644 index 71d2a7a3d..000000000 --- a/metagpt/reflect_and_retrieve/run_gpt.py +++ /dev/null @@ -1,62 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# @Desc : 调用PROMPT -# author: didi -# Date:9.25 - -import random -import json -from gpt_structure import final_response, prompt_generate - -# 使用GPT衡量心酸程度 - - -def run_gpt_prompt_chat_poignancy(agent, event_description): - """ - 使用GA中的run GPT构造，具体的代码可以参考昨天GPT的内容 - https://chat.openai.com/c/afddac31-300e-427b-9947-4b3ca16bd3a1 - 其中输入的ISS是identity stable set - """ - def create_prompt_input(agent, event_description): - prompt_input = [agent.name, - agent.iss, - agent.name, - event_description] - return prompt_input - - # 1. Prompt构建 - # 2. Instruction给出 - prompt_template = "Prompt_template/poignancy_chat_v1.txt" - prompt_input = create_prompt_input(agent, event_description) - prompt = prompt_generate(prompt_input, prompt_template) - special_instruction = "The output should ONLY contain ONE integer value on the scale of 1 to 10." - poignancy = final_response(prompt, special_instruction) - try: - poi_dict = json.loads(poignancy) - return (poi_dict['poignancy']) - except: - return poignancy - -# 返回John随机记忆 - - -def run_gpt_random_concept(): - random_memories = [ - "Helped Mrs. Moore carry groceries into her house.", - "Had a friendly chat with Yuriko about her garden.", - "Met Tom Moreno for coffee during our lunch break.", - "Talked to Mei about their upcoming vacation plans.", - "Eddy played his new music composition for me.", - "Helped a customer find a specific medication.", - "John divorced his wife because he was in love with someone else", - "Helped Mrs. Moore carry groceries into her house.", - "Had a friendly chat with Yuriko about her garden.", - "Met Tom Moreno for coffee during our lunch break.", - "Talked to Mei about their upcoming vacation plans.", - "Eddy played his new music composition for me.", - "Helped a customer find a specific medication.", - "Wished Carmen a good day as she passed by the pharmacy.", - "Discussed local politics with Tom Moreno.", - "Gave gardening tips to Mrs. Yamamoto.", - "Saw Jane Moreno jogging in the morning."] - return (random.choice(random_memories)) diff --git a/metagpt/roles/role.py b/metagpt/roles/role.py index 52cc693a0..44bb3e976 100644 --- a/metagpt/roles/role.py +++ b/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 import llm as LLM +from metagpt.llm import LLM from metagpt.logs import logger from metagpt.memory import Memory, LongTermMemory from metagpt.schema import Message @@ -94,7 +94,7 @@ class Role: """Role/Agent""" def __init__(self, name="", profile="", goal="", constraints="", desc=""): - self._llm=LLM.DEFAULT_LLM + self._llm = LLM() self._setting = RoleSetting(name=name, profile=profile, goal=goal, constraints=constraints, desc=desc) self._states = [] self._actions = [] @@ -185,7 +185,7 @@ class Role: observed = self._rc.env.memory.get_by_actions(self._rc.watch) - self._rc.news = self._rc.memory.remember(observed) # remember recent exact or similar memories + self._rc.news = self._rc.memory.find_news(observed) # find news (previously unseen messages) from observed messages for i in env_msgs: self.recv(i) diff --git a/metagpt/schema.py b/metagpt/schema.py index 27f5dd10c..bdca093c2 100644 --- a/metagpt/schema.py +++ b/metagpt/schema.py @@ -29,6 +29,7 @@ class Message: cause_by: Type["Action"] = field(default="") sent_from: str = field(default="") send_to: str = field(default="") + restricted_to: str = field(default="") def __str__(self): # prefix = '-'.join([self.role, str(self.cause_by)]) diff --git a/metagpt/tools/moderation.py b/metagpt/tools/moderation.py new file mode 100644 index 000000000..c56a6afc4 --- /dev/null +++ b/metagpt/tools/moderation.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +@Time : 2023/9/26 14:27 +@Author : zhanglei +@File : moderation.py +""" +from typing import Union + +from metagpt.llm import LLM + + +class Moderation: + def __init__(self): + self.llm = LLM() + + def moderation(self, content: Union[str, list[str]]): + resp = [] + if content: + moderation_results = self.llm.moderation(content=content) + results = moderation_results.results + for item in results: + resp.append(item.flagged) + + return resp + + async def amoderation(self, content: Union[str, list[str]]): + resp = [] + if content: + moderation_results = await self.llm.amoderation(content=content) + results = moderation_results.results + for item in results: + resp.append(item.flagged) + + return resp + + +if __name__ == "__main__": + moderation = Moderation() + print(moderation.moderation(content=["I will kill you", "The weather is really nice today", "I want to hit you"])) diff --git a/metagpt/utils/common.py b/metagpt/utils/common.py index 65cc15e82..59d179808 100644 --- a/metagpt/utils/common.py +++ b/metagpt/utils/common.py @@ -180,7 +180,7 @@ class OutputParser: if start_index != -1 and end_index != -1: # Extract the structure part - structure_text = text[start_index:end_index + 1] + structure_text = text[start_index : end_index + 1] try: # Attempt to convert the text to a Python data type using ast.literal_eval @@ -237,7 +237,7 @@ class CodeParser: logger.error(f"{pattern} not match following text:") logger.error(text) # raise Exception - return "" + return text # just assume original text is code return code @classmethod diff --git a/metagpt/utils/serialize.py b/metagpt/utils/serialize.py index ffafca8cd..124176fcb 100644 --- a/metagpt/utils/serialize.py +++ b/metagpt/utils/serialize.py @@ -4,7 +4,7 @@ import copy import pickle -from typing import Dict, List, Tuple +from typing import Dict, List from metagpt.actions.action_output import ActionOutput from metagpt.schema import Message @@ -37,8 +37,8 @@ def actionoutout_schema_to_mapping(schema: Dict) -> Dict: elif property["type"] == "array" and property["items"]["type"] == "string": mapping[field] = (List[str], ...) elif property["type"] == "array" and property["items"]["type"] == "array": - # here only consider the `Tuple[str, str]` situation - mapping[field] = (List[Tuple[str, str]], ...) + # here only consider the `List[List[str]]` situation + mapping[field] = (List[List[str]], ...) return mapping diff --git a/requirements.txt b/requirements.txt index c2616a08a..de861ded9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -34,7 +34,7 @@ tqdm==4.64.0 # selenium>4 # webdriver_manager<3.9 anthropic==0.3.6 -typing-inspect +typing-inspect==0.8.0 typing_extensions==4.5.0 libcst==1.0.1 qdrant-client==1.4.0 diff --git a/tests/metagpt/actions/test_write_code.py b/tests/metagpt/actions/test_write_code.py index 00d2c504e..7bb18ddf2 100644 --- a/tests/metagpt/actions/test_write_code.py +++ b/tests/metagpt/actions/test_write_code.py @@ -8,7 +8,7 @@ import pytest from metagpt.actions.write_code import WriteCode -import metagpt.llm as LLM +from metagpt.llm import LLM from metagpt.logs import logger from tests.metagpt.actions.mock import TASKS_2, WRITE_CODE_PROMPT_SAMPLE @@ -29,6 +29,6 @@ async def test_write_code(): @pytest.mark.asyncio async def test_write_code_directly(): prompt = WRITE_CODE_PROMPT_SAMPLE + '\n' + TASKS_2[0] - llm=LLM.DEFAULT_LLM + llm = LLM() rsp = await llm.aask(prompt) logger.info(rsp) diff --git a/tests/metagpt/memory/test_longterm_memory.py b/tests/metagpt/memory/test_longterm_memory.py index 62a3a2361..dc5540520 100644 --- a/tests/metagpt/memory/test_longterm_memory.py +++ b/tests/metagpt/memory/test_longterm_memory.py @@ -21,35 +21,35 @@ def test_ltm_search(): idea = 'Write a cli snake game' message = Message(role='BOSS', content=idea, cause_by=BossRequirement) - news = ltm.remember([message]) + news = ltm.find_news([message]) assert len(news) == 1 ltm.add(message) sim_idea = 'Write a game of cli snake' sim_message = Message(role='BOSS', content=sim_idea, cause_by=BossRequirement) - news = ltm.remember([sim_message]) + news = ltm.find_news([sim_message]) assert len(news) == 0 ltm.add(sim_message) new_idea = 'Write a 2048 web game' new_message = Message(role='BOSS', content=new_idea, cause_by=BossRequirement) - news = ltm.remember([new_message]) + news = ltm.find_news([new_message]) assert len(news) == 1 ltm.add(new_message) # restore from local index ltm_new = LongTermMemory() ltm_new.recover_memory(role_id, rc) - news = ltm_new.remember([message]) + news = ltm_new.find_news([message]) assert len(news) == 0 ltm_new.recover_memory(role_id, rc) - news = ltm_new.remember([sim_message]) + news = ltm_new.find_news([sim_message]) assert len(news) == 0 new_idea = 'Write a Battle City' new_message = Message(role='BOSS', content=new_idea, cause_by=BossRequirement) - news = ltm_new.remember([new_message]) + news = ltm_new.find_news([new_message]) assert len(news) == 1 ltm_new.clear() diff --git a/tests/metagpt/provider/test_xinghuo_api.py b/tests/metagpt/provider/test_xinghuo_api.py new file mode 100644 index 000000000..74e13413c --- /dev/null +++ b/tests/metagpt/provider/test_xinghuo_api.py @@ -0,0 +1,4 @@ +from metagpt.provider.spark_api import SparkAPI +def test_message(): + llm=SparkAPI() + llm.ask('只回答"收到了"这三个字。') \ No newline at end of file diff --git a/tests/metagpt/roles/mock.py b/tests/metagpt/roles/mock.py index 9567be603..52fc4a3c1 100644 --- a/tests/metagpt/roles/mock.py +++ b/tests/metagpt/roles/mock.py @@ -16,7 +16,7 @@ DETAIL_REQUIREMENT = """需求：开发一个基于LLM（大语言模型）与 3. 私有知识库支持pdf、word、txt等各种文件格式上传，上传后可以在服务端解析为文本，存储ES 资源： -1. 大语言模型已经有前置的抽象、部署，可以通过 `import metagpt.llm as LLM`，再使用`LLM().ask(prompt)`直接调用 +1. 大语言模型已经有前置的抽象、部署，可以通过 `from metagpt.llm import LLM`，再使用`LLM().ask(prompt)`直接调用 2. Elastic已有[部署](http://192.168.50.82:9200/)，代码可以直接使用这个部署""" diff --git a/tests/metagpt/test_llm.py b/tests/metagpt/test_llm.py index 1986f3f22..11503af1d 100644 --- a/tests/metagpt/test_llm.py +++ b/tests/metagpt/test_llm.py @@ -8,7 +8,7 @@ import pytest -import metagpt.llm as LLM +from metagpt.llm import LLM @pytest.fixture() diff --git a/tests/metagpt/tools/test_moderation.py b/tests/metagpt/tools/test_moderation.py new file mode 100644 index 000000000..225acff75 --- /dev/null +++ b/tests/metagpt/tools/test_moderation.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +@Time : 2023/9/26 14:46 +@Author : zhanglei +@File : test_translate.py +""" + +import pytest + +from metagpt.tools.moderation import Moderation + + +@pytest.mark.parametrize( + ("content",), + [ + [ + ["I will kill you", "The weather is really nice today", "I want to hit you"], + ] + ], +) +def test_moderation(content): + moderation = Moderation() + results = moderation.moderation(content=content) + assert isinstance(results, list) + assert len(results) == len(content) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("content",), + [ + [ + ["I will kill you", "The weather is really nice today", "I want to hit you"], + ] + ], +) +async def test_amoderation(content): + moderation = Moderation() + results = await moderation.amoderation(content=content) + assert isinstance(results, list) + assert len(results) == len(content) diff --git a/webui.py b/webui.py deleted file mode 100644 index e69de29bb..000000000