├── .gitattributes ├── .gitignore ├── LICENSE ├── README.md ├── group.py ├── research.py └── simple.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 105 | __pypackages__/ 106 | 107 | # Celery stuff 108 | celerybeat-schedule 109 | celerybeat.pid 110 | 111 | # SageMath parsed files 112 | *.sage.py 113 | 114 | # Environments 115 | .env 116 | .venv 117 | env/ 118 | venv/ 119 | ENV/ 120 | env.bak/ 121 | venv.bak/ 122 | 123 | # Spyder project settings 124 | .spyderproject 125 | .spyproject 126 | 127 | # Rope project settings 128 | .ropeproject 129 | 130 | # mkdocs documentation 131 | /site 132 | 133 | # mypy 134 | .mypy_cache/ 135 | .dmypy.json 136 | dmypy.json 137 | 138 | # Pyre type checker 139 | .pyre/ 140 | 141 | # pytype static type analyzer 142 | .pytype/ 143 | 144 | # Cython debug symbols 145 | cython_debug/ 146 | 147 | # PyCharm 148 | # JetBrains specific template is maintainted in a separate JetBrains.gitignore that can 149 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 150 | # and can be added to the global gitignore or merged into this file. 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Do not involve in future conversations or error fixing", 24 | llm_config=llm_config, 25 | ) 26 | 27 | # 创建 组 groupchat 28 | groupchat = autogen.GroupChat( 29 | agents=[user_proxy, coder, pm], messages=[]) 30 | manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config) 31 | 32 | # 初始化 开始干活 33 | user_proxy.initiate_chat( 34 | manager, message="Build a classic & basic pong game with 2 players in python") 35 | -------------------------------------------------------------------------------- /research.py: -------------------------------------------------------------------------------- 1 | import os 2 | from autogen import config_list_from_json 3 | import autogen 4 | 5 | import requests 6 | from bs4 import BeautifulSoup 7 | import json 8 | 9 | from langchain.agents import initialize_agent 10 | from langchain.chat_models import ChatOpenAI 11 | from langchain.text_splitter import RecursiveCharacterTextSplitter 12 | from langchain.chains.summarize import load_summarize_chain 13 | from langchain import PromptTemplate 14 | import openai 15 | from dotenv import load_dotenv 16 | 17 | # 获取各个 API Key 18 | load_dotenv() 19 | config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST") 20 | #具体可参见第三期视频 21 | openai.api_key = os.getenv("OPENAI_API_KEY") 22 | serper_api_key=os.getenv("SERPER_API_KEY") 23 | browserless_api_key=os.getenv("BROWSERLESS_API_KEY") 24 | 25 | # research 工具模块 26 | 27 | #调用 Google search by Serper 28 | def search(query): 29 | url = "https://google.serper.dev/search" 30 | 31 | payload = json.dumps({ 32 | "q": query 33 | }) 34 | headers = { 35 | 'X-API-KEY': serper_api_key, 36 | 'Content-Type': 'application/json' 37 | } 38 | 39 | response = requests.request("POST", url, headers=headers, data=payload) 40 | 41 | return response.json() 42 | 43 | 44 | #抓取网站内容 45 | def scrape(url: str): 46 | # scrape website, and also will summarize the content based on objective if the content is too large 47 | # objective is the original objective & task that user give to the agent, url is the url of the website to be scraped 48 | 49 | print("Scraping website...") 50 | # Define the headers for the request 51 | headers = { 52 | 'Cache-Control': 'no-cache', 53 | 'Content-Type': 'application/json', 54 | } 55 | 56 | # Define the data to be sent in the request 57 | data = { 58 | "url": url 59 | } 60 | 61 | # Convert Python object to JSON string 62 | data_json = json.dumps(data) 63 | 64 | # Send the POST request 65 | post_url = f"https://chrome.browserless.io/content?token={browserless_api_key}" 66 | response = requests.post( 67 | post_url, headers=headers, data=data_json) 68 | 69 | # Check the response status code 70 | if response.status_code == 200: 71 | soup = BeautifulSoup(response.content, "html.parser") 72 | text = soup.get_text() 73 | print("CONTENT:", text) 74 | if len(text) > 8000: 75 | output = summary(text) 76 | return output 77 | else: 78 | return text 79 | else: 80 | print(f"HTTP request failed with status code {response.status_code}") 81 | 82 | #总结网站内容 83 | def summary(content): 84 | llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613") 85 | text_splitter = RecursiveCharacterTextSplitter( 86 | separators=["\n\n", "\n"], chunk_size=10000, chunk_overlap=500) 87 | docs = text_splitter.create_documents([content]) 88 | map_prompt = """ 89 | Write a detailed summary of the following text for a research purpose: 90 | "{text}" 91 | SUMMARY: 92 | """ 93 | map_prompt_template = PromptTemplate( 94 | template=map_prompt, input_variables=["text"]) 95 | 96 | summary_chain = load_summarize_chain( 97 | llm=llm, 98 | chain_type='map_reduce', #内容切片 防止超过 LLM 的 Token 限制 99 | map_prompt=map_prompt_template, 100 | combine_prompt=map_prompt_template, 101 | verbose=True 102 | ) 103 | 104 | output = summary_chain.run(input_documents=docs,) 105 | 106 | return output 107 | 108 | # 信息收集 109 | def research(query): 110 | llm_config_researcher = { 111 | "functions": [ 112 | { 113 | "name": "search", 114 | "description": "google search for relevant information", 115 | "parameters": { 116 | "type": "object", 117 | "properties": { 118 | "query": { 119 | "type": "string", 120 | "description": "Google search query", 121 | } 122 | }, 123 | "required": ["query"], 124 | }, 125 | }, 126 | { 127 | "name": "scrape", 128 | "description": "Scraping website content based on url", 129 | "parameters": { 130 | "type": "object", 131 | "properties": { 132 | "url": { 133 | "type": "string", 134 | "description": "Website url to scrape", 135 | } 136 | }, 137 | "required": ["url"], 138 | }, 139 | }, 140 | ], 141 | "config_list": config_list} 142 | 143 | researcher = autogen.AssistantAgent( 144 | name="researcher", 145 | system_message="Research about a given query, collect as many information as possible, and generate detailed research results with loads of technique details with all reference links attached; Add TERMINATE to the end of the research report;", 146 | llm_config=llm_config_researcher, 147 | ) 148 | 149 | user_proxy = autogen.UserProxyAgent( 150 | name="User_proxy", 151 | code_execution_config={"last_n_messages": 2, "work_dir": "research"}, 152 | is_termination_msg=lambda x: x.get("content", "") and x.get( 153 | "content", "").rstrip().endswith("TERMINATE"), 154 | human_input_mode="TERMINATE", 155 | function_map={ 156 | "search": search, 157 | "scrape": scrape, 158 | } 159 | ) 160 | 161 | user_proxy.initiate_chat(researcher, message=query) 162 | 163 | # set the receiver to be researcher, and get a summary of the research report 164 | user_proxy.stop_reply_at_receive(researcher) 165 | user_proxy.send( 166 | "Give me the research report that just generated again, return ONLY the report & reference links", researcher) 167 | 168 | # return the last message the expert received 169 | return user_proxy.last_message()["content"] 170 | 171 | 172 | # 编辑 配置不同的 agent 角色 173 | def write_content(research_material, topic): 174 | editor = autogen.AssistantAgent( 175 | name="editor", 176 | system_message="You are a senior editor of an AI blogger, you will define the structure of a short blog post based on material provided by the researcher, and give it to the writer to write the blog post", 177 | llm_config={"config_list": config_list}, 178 | ) 179 | 180 | writer = autogen.AssistantAgent( 181 | name="writer", 182 | system_message="You are a professional AI blogger who is writing a blog post about AI, you will write a short blog post based on the structured provided by the editor, and feedback from reviewer; After 2 rounds of content iteration, add TERMINATE to the end of the message", 183 | llm_config={"config_list": config_list}, 184 | ) 185 | 186 | reviewer = autogen.AssistantAgent( 187 | name="reviewer", 188 | system_message="You are a world class hash tech blog content critic, you will review & critic the written blog and provide feedback to writer.After 2 rounds of content iteration, add TERMINATE to the end of the message", 189 | llm_config={"config_list": config_list}, 190 | ) 191 | 192 | user_proxy = autogen.UserProxyAgent( 193 | name="admin", 194 | system_message="A human admin. Interact with editor to discuss the structure. Actual writing needs to be approved by this admin.", 195 | code_execution_config=False, 196 | is_termination_msg=lambda x: x.get("content", "") and x.get( 197 | "content", "").rstrip().endswith("TERMINATE"), 198 | human_input_mode="TERMINATE", #终止模式 199 | ) 200 | 201 | #创建 组 202 | groupchat = autogen.GroupChat( 203 | agents=[user_proxy, editor, writer, reviewer], 204 | messages=[], 205 | max_round=20) 206 | manager = autogen.GroupChatManager(groupchat=groupchat) 207 | 208 | #消息交换机制部分 重点 209 | user_proxy.initiate_chat( 210 | manager, message=f"Write a blog about {topic}, here are the material: {research_material}") 211 | 212 | user_proxy.stop_reply_at_receive(manager) 213 | user_proxy.send( 214 | "Give me the blog that just generated again, return ONLY the blog, and add TERMINATE in the end of the message", manager) 215 | 216 | # return the last message the expert received 217 | return user_proxy.last_message()["content"] 218 | 219 | 220 | # 出版 221 | llm_config_content_assistant = { 222 | "functions": [ 223 | { 224 | "name": "research", 225 | "description": "research about a given topic, return the research material including reference links", 226 | "parameters": { 227 | "type": "object", 228 | "properties": { 229 | "query": { 230 | "type": "string", 231 | "description": "The topic to be researched about", 232 | } 233 | }, 234 | "required": ["query"], 235 | }, 236 | }, 237 | { 238 | "name": "write_content", 239 | "description": "Write content based on the given research material & topic", 240 | "parameters": { 241 | "type": "object", 242 | "properties": { 243 | "research_material": { 244 | "type": "string", 245 | "description": "research material of a given topic, including reference links when available", 246 | }, 247 | "topic": { 248 | "type": "string", 249 | "description": "The topic of the content", 250 | } 251 | }, 252 | "required": ["research_material", "topic"], 253 | }, 254 | }, 255 | ], 256 | "config_list": config_list} 257 | 258 | writing_assistant = autogen.AssistantAgent( 259 | name="writing_assistant", 260 | system_message="You are a writing assistant, you can use research function to collect latest information about a given topic, and then use write_content function to write a very well written content; Reply TERMINATE when your task is done", 261 | llm_config=llm_config_content_assistant, 262 | ) 263 | 264 | user_proxy = autogen.UserProxyAgent( 265 | name="User_proxy", 266 | human_input_mode="TERMINATE", #注意此处的模式选择 267 | function_map={ 268 | "write_content": write_content, #调用编辑和信息 组 269 | "research": research, 270 | } 271 | ) 272 | 273 | #最初 需求 启动干活 274 | user_proxy.initiate_chat( 275 | writing_assistant, message="write a blog about autogen multi AI agent framework") 276 | -------------------------------------------------------------------------------- /simple.py: -------------------------------------------------------------------------------- 1 | from autogen import AssistantAgent, UserProxyAgent, config_list_from_json 2 | 3 | # 配置 openai api key 4 | config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST") 5 | 6 | # 创建 assistant agent 7 | assistant = AssistantAgent(name="assistant", llm_config={ 8 | "config_list": config_list}) 9 | 10 | # 创建 user proxy agent 11 | user_proxy = UserProxyAgent( 12 | name="user_proxy", 13 | code_execution_config={"work_dir": "sources"}) 14 | 15 | 16 | # 设置目标 开始干活 17 | user_proxy.initiate_chat( 18 | assistant, message="Plot a chart of NVDA and TESLA stock price change YTD.") 19 | --------------------------------------------------------------------------------