How to Build a Multi-Agent AI App with AutoGen
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Python Notebook Introduction
This Jupyter notebook is designed to demonstrate the use of various Python libraries for text processing, document loading, and vector embeddings. It also showcases the use of the OpenAI API for generating embeddings and the SingleStoreDB for storing and retrieving documents.
The notebook is divided into several sections:
Installation of Required Libraries: This section covers the installation of necessary libraries such as
langchain_community
,pyautogen
,langchain_openai
,langchain_text_splitters
, andunstructured
.Data Loading and Preparation: This section involves loading a markdown document from a URL and preparing it for further processing.
Document Splitting and Embedding Generation: This section demonstrates how to split the loaded document into smaller parts and generate embeddings for each part using the OpenAI API.
SingleStoreDB Setup: This section covers the setup of SingleStoreDB for storing and retrieving documents.
Agent Setup and Group Chat Simulation: This section demonstrates the setup of various agents (like a boss, coder, product manager, and code reviewer) and simulates a group chat among them to solve a given problem.
Chat Simulation: This section runs the chat simulation without and with the Retrieve and Generate (RAG) model.
Please ensure that you have the necessary API keys and environment variables set up before running this notebook.
In [1]:
# Check if the database is running on a shared tiershared_tier_check = %sql show variables like 'is_shared_tier'# If not on a shared tier, or if the shared tier is turned off, drop the existing database and create a new oneif not shared_tier_check or shared_tier_check[0][1] == 'OFF':%sql DROP DATABASE IF EXISTS autogen%sql CREATE DATABASE autogen
In [2]:
!pip install --quiet langchain_community pyautogen langchain_openai langchain_text_splitters unstructured
In [3]:
!pip install --quiet markdown
In [4]:
import requestsr = requests.get("https://raw.githubusercontent.com/microsoft/FLAML/main/website/docs/Examples/Integrate%20-%20Spark.md")open('example.md', 'wb').write(r.content)
In [5]:
from langchain_community.vectorstores import SingleStoreDBfrom langchain_openai import OpenAIEmbeddingsfrom langchain_community.document_loaders import UnstructuredMarkdownLoaderfrom langchain_text_splitters import CharacterTextSplitterfrom typing import List, Dict, Unionimport osloader = UnstructuredMarkdownLoader("./example.md")os.environ["OPENAI_API_KEY"] = "api-key"data = loader.load()text_splitter = CharacterTextSplitter()docs = text_splitter.split_documents(data)embeddings = OpenAIEmbeddings()os.environ["SINGLESTOREDB_URL"] = "admin:pass@host:3306/db"
In [6]:
singlestore_db = SingleStoreDB.from_documents(docs,embeddings,table_name="notebook2", # use table with a custom name)
In [7]:
!pip install --quiet pyautogen[retrievechat]
In [8]:
import autogenfrom autogen.agentchat.contrib.retrieve_assistant_agent import RetrieveAssistantAgentfrom autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgentfrom autogen import config_list_from_jsonfrom autogen import AssistantAgent
In [9]:
class SingleStoreRetrieveUserProxyAgent(RetrieveUserProxyAgent):def __init__(self, singlestore_db: SingleStoreDB, **kwargs):super().__init__(**kwargs)self.singlestore_db = singlestore_dbdef query_vector_db(self,query_texts: List[str],n_results: int = 10,search_string: str = "",**kwargs,) -> Dict[str, List[List[str]]]:documents = []ids = []for query_index, query_text in enumerate(query_texts):searched_docs = self.singlestore_db.similarity_search(query=query_text,k=n_results,)# Assuming searched_docs is a list of documents with only 'page_content' propertybatch_documents = [doc.page_content for doc in searched_docs]documents.append(batch_documents)# Generate a unique ID for each document based on enumerationbatch_ids = [f"{query_index}-{i}" for i in range(len(batch_documents))]ids.append(batch_ids)return {"ids": ids,"documents": documents,}def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str = "", **kwargs):results = self.query_vector_db(query_texts=[problem],n_results=n_results,search_string=search_string,**kwargs,)self._results = results
In [10]:
import osos.environ["AUTOGEN_USE_DOCKER"] = "False"
In [11]:
llm_config = {"config_list": [{"model": "gpt-3.5-turbo", "api_key": os.environ["OPENAI_API_KEY"]}],}def termination_msg(x):return isinstance(x, dict) and "TERMINATE" == str(x.get("content", ""))[-9:].upper()boss = autogen.UserProxyAgent(name="Boss",is_termination_msg=termination_msg,human_input_mode="NEVER",code_execution_config=False, # we don't want to execute code in this case.default_auto_reply="Reply `TERMINATE` if the task is done.",description="The boss who ask questions and give tasks.",)boss_aid = SingleStoreRetrieveUserProxyAgent(name="Boss_Assistant",is_termination_msg=termination_msg,human_input_mode="NEVER",max_consecutive_auto_reply=3,retrieve_config={"task": "code",},code_execution_config=False, # we don't want to execute code in this case.description="Assistant who has extra content retrieval power for solving difficult problems.",singlestore_db=singlestore_db)coder = autogen.AssistantAgent(name="Senior_Python_Engineer",is_termination_msg=termination_msg,system_message="You are a senior python engineer, you provide python code to answer questions. Reply `TERMINATE` in the end when everything is done.",llm_config=llm_config,description="Senior Python Engineer who can write code to solve problems and answer questions.",)pm = autogen.AssistantAgent(name="Product_Manager",is_termination_msg=termination_msg,system_message="You are a product manager. Reply `TERMINATE` in the end when everything is done.",llm_config=llm_config,description="Product Manager who can design and plan the project.",)reviewer = autogen.AssistantAgent(name="Code_Reviewer",is_termination_msg=termination_msg,system_message="You are a code reviewer. Reply `TERMINATE` in the end when everything is done.",llm_config=llm_config,description="Code Reviewer who can review the code.",)PROBLEM = "How to use spark for parallel training in FLAML? Give me sample code."def _reset_agents():boss.reset()boss_aid.reset()coder.reset()pm.reset()reviewer.reset()def rag_chat():_reset_agents()groupchat = autogen.GroupChat(agents=[boss_aid, pm, coder, reviewer], messages=[], max_round=12, speaker_selection_method="round_robin")manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)# Start chatting with boss_aid as this is the user proxy agent.boss_aid.initiate_chat(manager,problem=PROBLEM,n_results=3,)def norag_chat():_reset_agents()groupchat = autogen.GroupChat(agents=[boss, pm, coder, reviewer],messages=[],max_round=12,speaker_selection_method="auto",allow_repeat_speaker=False,)manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)# Start chatting with the boss as this is the user proxy agent.boss.initiate_chat(manager,message=PROBLEM,)def call_rag_chat():_reset_agents()# In this case, we will have multiple user proxy agents and we don't initiate the chat# with RAG user proxy agent.# In order to use RAG user proxy agent, we need to wrap RAG agents in a function and call# it from other agents.def retrieve_content(message: Annotated[str,"Refined message which keeps the original meaning and can be used to retrieve content for code generation and question answering.",],n_results: Annotated[int, "number of results"] = 3,) -> str:boss_aid.n_results = n_results # Set the number of results to be retrieved.# Check if we need to update the context.update_context_case1, update_context_case2 = boss_aid._check_update_context(message)if (update_context_case1 or update_context_case2) and boss_aid.update_context:boss_aid.problem = message if not hasattr(boss_aid, "problem") else boss_aid.problem_, ret_msg = boss_aid._generate_retrieve_user_reply(message)else:ret_msg = boss_aid.generate_init_message(message, n_results=n_results)return ret_msg if ret_msg else messageboss_aid.human_input_mode = "NEVER" # Disable human input for boss_aid since it only retrieves content.for caller in [pm, coder, reviewer]:d_retrieve_content = caller.register_for_llm(description="retrieve content for code generation and question answering.", api_style="function")(retrieve_content)for executor in [boss, pm]:executor.register_for_execution()(d_retrieve_content)groupchat = autogen.GroupChat(agents=[boss, pm, coder, reviewer],messages=[],max_round=12,speaker_selection_method="round_robin",allow_repeat_speaker=False,)manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)# Start chatting with the boss as this is the user proxy agent.boss.initiate_chat(manager,message=PROBLEM,)
In [12]:
norag_chat()
In [13]:
rag_chat()
In [14]:
shared_tier_check = %sql show variables like 'is_shared_tier'if not shared_tier_check or shared_tier_check[0][1] == 'OFF':%sql DROP DATABASE IF EXISTS autogen
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About this Template
Learn how to build a multi-agent group chat with RAG using Autogen and SingleStore
This Notebook can be run in Shared Tier, Standard and Enterprise deployments.
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License
This Notebook has been released under the Apache 2.0 open source license.