Merge pull request #19 from GoldWaterFall/chapter4

Chapter4

docs/chapter4/Multi Agent 概念模块.md

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+---
+comments: true
+---
+## 4.1 Multi Agent 概念概述

docs/chapter4/index.md

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+---
+comments: true
+---
+
+# 4.第四章：智能体综述及多智能体框架介绍
+
+# 前期准备
+本章我们会介绍MetaGPT多智能体的开发流程,通过案例实现多智能体的开发
+
+

docs/chapter4/多智能体开发作业.md

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+---
+comments: true
+---
+
+## 4.4 多智能体开发作业
+
+### 基础作业
+
+基于 env 或 team 设计一个你的多智能体团队，尝试让他们完成 你画我猜文字版 ，要求其中含有两个agent，其中一个agent负责接收来自用户提供的物体描述并转告另一个agent，另一个agent将猜测用户给出的物体名称，两个agent将不断交互直到另一个给出正确的答案
+
+（也可以在系统之上继续扩展，比如引入一个agent来生成词语，而人类参与你画我猜的过程中）
+
+### 进阶作业
+
+进阶作业只面向愿意深入探索MG潜力并检验自己学习成果的同学，只要完成基础作业视为学习完成。
+
+如何检验mg的Agent开发学习程度呢？我认为对于本次学习，如果对于一个问题（过往交由人解决的）SOP可以将SOP迅速抽象成MG的Role和Action形式并且用多智能体协同完成，即达到了本次学习的目的。 重写babyagi是一个非常合适的任务。 babyagi是[yoheinakajima](https://twitter.com/yoheinakajima)的一个demo，他的主业是一位投资人，在2023年上半年AutoGPT爆火之际，完成了这个demo，实际上是这位投资人的日常任务规划优先级解决的一套SOP 以下是babyagi的实现流程
+
+![homework](assets/images/homework.png)
+
+```python
+import openai
+import pinecone
+import time
+from collections import deque
+from typing import Dict, List
+
+#Set API Keys
+OPENAI_API_KEY = ""
+PINECONE_API_KEY = ""
+PINECONE_ENVIRONMENT = "us-east1-gcp" #Pinecone Environment (eg. "us-east1-gcp")
+
+#Set Variables
+YOUR_TABLE_NAME = "test-table"
+OBJECTIVE = "Solve world hunger."
+YOUR_FIRST_TASK = "Develop a task list."
+
+#Print OBJECTIVE
+print("\033[96m\033[1m"+"\n*****OBJECTIVE*****\n"+"\033[0m\033[0m")
+print(OBJECTIVE)
+
+# Configure OpenAI and Pinecone
+openai.api_key = OPENAI_API_KEY
+pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENVIRONMENT)
+
+# Create Pinecone index
+table_name = YOUR_TABLE_NAME
+dimension = 1536
+metric = "cosine"
+pod_type = "p1"
+if table_name not in pinecone.list_indexes():
+    pinecone.create_index(table_name, dimension=dimension, metric=metric, pod_type=pod_type)
+
+# Connect to the index
+index = pinecone.Index(table_name)
+
+# Task list
+task_list = deque([])
+
+def add_task(task: Dict):
+    task_list.append(task)
+
+def get_ada_embedding(text):
+    text = text.replace("\n", " ")
+    return openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]
+
+def task_creation_agent(objective: str, result: Dict, task_description: str, task_list: List[str]):
+    prompt = f"You are an task creation AI that uses the result of an execution agent to create new tasks with the following objective: {objective}, The last completed task has the result: {result}. This result was based on this task description: {task_description}. These are incomplete tasks: {', '.join(task_list)}. Based on the result, create new tasks to be completed by the AI system that do not overlap with incomplete tasks. Return the tasks as an array."
+    response = openai.Completion.create(engine="text-davinci-003",prompt=prompt,temperature=0.5,max_tokens=100,top_p=1,frequency_penalty=0,presence_penalty=0)
+    new_tasks = response.choices[0].text.strip().split('\n')
+    return [{"task_name": task_name} for task_name in new_tasks]
+
+def prioritization_agent(this_task_id:int):
+    global task_list
+    task_names = [t["task_name"] for t in task_list]
+    next_task_id = int(this_task_id)+1
+    prompt = f"""You are an task prioritization AI tasked with cleaning the formatting of and reprioritizing the following tasks: {task_names}. Consider the ultimate objective of your team:{OBJECTIVE}. Do not remove any tasks. Return the result as a numbered list, like:
+    #. First task
+    #. Second task
+    Start the task list with number {next_task_id}."""
+    response = openai.Completion.create(engine="text-davinci-003",prompt=prompt,temperature=0.5,max_tokens=1000,top_p=1,frequency_penalty=0,presence_penalty=0)
+    new_tasks = response.choices[0].text.strip().split('\n')
+    task_list = deque()
+    for task_string in new_tasks:
+        task_parts = task_string.strip().split(".", 1)
+        if len(task_parts) == 2:
+            task_id = task_parts[0].strip()
+            task_name = task_parts[1].strip()
+            task_list.append({"task_id": task_id, "task_name": task_name})
+
+def execution_agent(objective:str,task: str) -> str:
+    #context = context_agent(index="quickstart", query="my_search_query", n=5)
+    context=context_agent(index=YOUR_TABLE_NAME, query=objective, n=5)
+    #print("\n*******RELEVANT CONTEXT******\n")
+    #print(context)
+    response = openai.Completion.create(
+        engine="text-davinci-003",
+        prompt=f"You are an AI who performs one task based on the following objective: {objective}. Your task: {task}\nResponse:",
+        temperature=0.7,
+        max_tokens=2000,
+        top_p=1,
+        frequency_penalty=0,
+        presence_penalty=0
+    )
+    return response.choices[0].text.strip()
+
+def context_agent(query: str, index: str, n: int):
+    query_embedding = get_ada_embedding(query)
+    index = pinecone.Index(index_name=index)
+    results = index.query(query_embedding, top_k=n,
+    include_metadata=True)
+    #print("***** RESULTS *****")
+    #print(results)
+    sorted_results = sorted(results.matches, key=lambda x: x.score, reverse=True)    
+    return [(str(item.metadata['task'])) for item in sorted_results]
+
+# Add the first task
+first_task = {
+    "task_id": 1,
+    "task_name": YOUR_FIRST_TASK
+}
+
+add_task(first_task)
+# Main loop
+task_id_counter = 1
+while True:
+    if task_list:
+        # Print the task list
+        print("\033[95m\033[1m"+"\n*****TASK LIST*****\n"+"\033[0m\033[0m")
+        for t in task_list:
+            print(str(t['task_id'])+": "+t['task_name'])
+
+        # Step 1: Pull the first task
+        task = task_list.popleft()
+        print("\033[92m\033[1m"+"\n*****NEXT TASK*****\n"+"\033[0m\033[0m")
+        print(str(task['task_id'])+": "+task['task_name'])
+
+        # Send to execution function to complete the task based on the context
+        result = execution_agent(OBJECTIVE,task["task_name"])
+        this_task_id = int(task["task_id"])
+        print("\033[93m\033[1m"+"\n*****TASK RESULT*****\n"+"\033[0m\033[0m")
+        print(result)
+
+        # Step 2: Enrich result and store in Pinecone
+        enriched_result = {'data': result}  # This is where you should enrich the result if needed
+        result_id = f"result_{task['task_id']}"
+        vector = enriched_result['data']  # extract the actual result from the dictionary
+        index.upsert([(result_id, get_ada_embedding(vector),{"task":task['task_name'],"result":result})])
+
+    # Step 3: Create new tasks and reprioritize task list
+    new_tasks = task_creation_agent(OBJECTIVE,enriched_result, task["task_name"], [t["task_name"] for t in task_list])
+
+    for new_task in new_tasks:
+        task_id_counter += 1
+        new_task.update({"task_id": task_id_counter})
+        add_task(new_task)
+    prioritization_agent(this_task_id)
+
+time.sleep(1)  # Sleep before checking the task list again
+```
+
+以上是来自于babyagi仓库的源码，总行数140行涵盖prompt，同时任务为三个agent进行协同组织。
+
+这个是babyagi的webui演示https://babyagi-ui.vercel.app/zh，同学们可以先体验一下了解一下babyagi的输入输出的workflow结合上图用MG进行重写。 其中MG已经抽象好了许多上层类，以及react的规划模式和actions列表。 思考部分：
+
+1. 什么是enrich
+2. 何时应该creat new task，何时应该排序任务优先级
+3. 新的new task应该观察什么作为创建的依据（当前任务列表/目标/已完成的任务结果）
+4. 人类是否可以介入这个流程比如新任务的合入审核，任务执行时的拆解？
+
+你不一定要完全依据源码的逻辑进行重写，尝试找到更优秀的SOP.
+
+> Chroma/Weaviate 来存储和检索上下文的任务结果。该脚本根据 TABLE_NAME 变量中指定的表名创建 Chroma/Weaviate 集合。然后使用 Chroma/Weaviate 将任务结果以及任务名称和任何其他元数据存储在集合中。而向量数据库并不是本次学习的重点即便MG的example里有对向量检索进行支持，理论上loop的次数较少时是可以将上下文的任务结果完全的作为prompt的输入，所以在本次学习中可以将上下文的任务结果写入一个外部的txt或者直接在memory中调用的，学习者可以不使用向量数据库完成本次任务