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433 lines
16 KiB
433 lines
16 KiB
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from collections import deque
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from typing import Dict, List, Optional, Any
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from langchain import LLMChain, OpenAI, PromptTemplate
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.llms import BaseLLM
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from langchain.vectorstores.base import VectorStore
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from pydantic import BaseModel, Field
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from langchain.chains.base import Chain
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from langchain.experimental import BabyAGI
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from langchain.vectorstores import FAISS
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from langchain.docstore import InMemoryDocstore
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from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
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from langchain import OpenAI, SerpAPIWrapper, LLMChain
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import faiss
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#-------------------------------------------------------------------------- WORKER NODE
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import pandas as pd
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from langchain.experimental.autonomous_agents.autogpt.agent import AutoGPT
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from langchain.chat_models import ChatOpenAI
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from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
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from langchain.docstore.document import Document
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import asyncio
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import nest_asyncio
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# Tools
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import os
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from contextlib import contextmanager
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from typing import Optional
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from langchain.tools.file_management.read import ReadFileTool
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from langchain.tools.file_management.write import WriteFileTool
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ROOT_DIR = "./data/"
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from langchain.tools import BaseTool, DuckDuckGoSearchRun
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain, BaseCombineDocumentsChain
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from langchain.tools.human.tool import HumanInputRun
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# from swarms.agents.workers.auto_agent import MultiModalVisualAgent
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# from swarms.agents.workers import multimodal_agent_tool
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from swarms.tools.main import Terminal, CodeWriter, CodeEditor, process_csv, WebpageQATool
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from swarms.tools.main import math_tool
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openai_api_key = os.environ["OPENAI_API_KEY"]
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llm = ChatOpenAI(model_name="gpt-4", temperature=1.0, openai_api_key=openai_api_key)
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query_website_tool = WebpageQATool(qa_chain=load_qa_with_sources_chain(llm))
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# !pip install duckduckgo_search
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web_search = DuckDuckGoSearchRun()
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tools = [
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web_search,
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WriteFileTool(root_dir="./data"),
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ReadFileTool(root_dir="./data"),
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process_csv,
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# multimodal_agent_tool,
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query_website_tool,
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Terminal,
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CodeWriter,
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CodeEditor,
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math_tool
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# HumanInputRun(), # Activate if you want the permit asking for help from the human
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]
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############## Vectorstore
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embeddings_model = OpenAIEmbeddings()
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embedding_size = 1536
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index = faiss.IndexFlatL2(embedding_size)
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vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
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####################################################################### => Worker Node
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worker_agent = AutoGPT.from_llm_and_tools(
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ai_name="WorkerX",
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ai_role="Assistant",
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tools=tools,
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llm=llm,
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memory=vectorstore.as_retriever(search_kwargs={"k": 8}),
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human_in_the_loop=True, # Set to True if you want to add feedback at each step.
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)
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worker_agent.chain.verbose = True
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class WorkerNode:
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def __init__(self, llm, tools, vectorstore):
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self.llm = llm
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self.tools = tools
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self.vectorstore = vectorstore
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def create_agent(self, ai_name, ai_role, human_in_the_loop, search_kwargs):
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# Instantiate the agent
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self.agent = AutoGPT.from_llm_and_tools(
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ai_name=ai_name,
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ai_role=ai_role,
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tools=tools,
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llm=self.llm,
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memory=self.vectorstore.as_retriever(search_kwargs=search_kwargs),
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human_in_the_loop=human_in_the_loop,
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)
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self.agent.chain.verbose = True
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def run_agent(self, prompt):
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# Run the agent with the given prompt
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tree_of_thoughts_prompt = """
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Imagine three different experts are answering this question. All experts will write down each chain of thought of each step of their thinking, then share it with the group. Then all experts will go on to the next step, etc. If any expert realises they're wrong at any point then they leave. The question is...
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"""
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self.agent.run([f"{tree_of_thoughts_prompt} {prompt}"])
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#inti worker node with llm
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worker_node = WorkerNode(llm=llm, tools=tools, vectorstore=vectorstore)
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# #create an agent within the worker node
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# worker_node.create_agent(ai_name="AI Assistant", ai_role="Assistant", human_in_the_loop=True, search_kwargs={})
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# #use the agent to perform a task
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# worker_node.run_agent("Find 20 potential customers for a Swarms based AI Agent automation infrastructure")
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class BossNode:
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def __init__(self, openai_api_key, llm, vectorstore, task_execution_chain, verbose, max_iterations):
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self.llm = llm
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self.openai_api_key = openai_api_key
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self.vectorstore = vectorstore
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self.task_execution_chain = task_execution_chain
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self.verbose = verbose
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self.max_iterations = max_iterations
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self.baby_agi = BabyAGI.from_llm(
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llm=self.llm,
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vectorstore=self.vectorstore,
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task_execution_chain=self.task_execution_chain
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)
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def create_task(self, objective):
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return {"objective": objective}
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def execute_task(self, task):
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self.baby_agi(task)
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########### ===============> inputs to boss None
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todo_prompt = PromptTemplate.from_template(
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"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}"""
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)
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todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt)
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search = SerpAPIWrapper()
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tools = [
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Tool(
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name="Search",
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func=search.run,
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description="useful for when you need to answer questions about current events",
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),
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Tool(
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name="TODO",
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func=todo_chain.run,
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description="useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!",
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),
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Tool(
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name="AUTONOMOUS Worker AGENT",
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func=worker_agent.run,
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description="Useful for when you need to spawn an autonomous agent instance as a worker to accomplish complex tasks, it can search the internet or spawn child multi-modality models to process and generate images and text or audio and so on"
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)
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]
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suffix = """Question: {task}
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{agent_scratchpad}"""
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prefix = """You are an Boss in a swarm who performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}.
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"""
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prompt = ZeroShotAgent.create_prompt(
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tools,
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prefix=prefix,
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suffix=suffix,
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input_variables=["objective", "task", "context", "agent_scratchpad"],
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)
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llm = OpenAI(temperature=0)
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llm_chain = LLMChain(llm=llm, prompt=prompt)
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tool_names = [tool.name for tool in tools]
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agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
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agent_executor = AgentExecutor.from_agent_and_tools(
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agent=agent, tools=tools, verbose=True
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)
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boss_node = BossNode(llm=llm, vectorstore=vectorstore, task_execution_chain=agent_executor, verbose=True, max_iterations=5)
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# #create a task
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# task = boss_node.create_task(objective="Write a research paper on the impact of climate change on global agriculture")
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# #execute the task
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# boss_node.execute_task(task)
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class Swarms:
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def __init__(self, openai_api_key):
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self.openai_api_key = openai_api_key
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def initialize_llm(self):
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return ChatOpenAI(model_name="gpt-4", temperature=1.0, openai_api_key=self.openai_api_key)
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def initialize_tools(self, llm):
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web_search = DuckDuckGoSearchRun()
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tools = [web_search, WriteFileTool(root_dir="./data"), ReadFileTool(root_dir="./data"), process_csv,
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# multimodal_agent_tool,
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WebpageQATool(qa_chain=load_qa_with_sources_chain(llm)),
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Terminal, CodeWriter, CodeEditor, math_tool]
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return tools
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def initialize_vectorstore(self):
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embeddings_model = OpenAIEmbeddings()
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embedding_size = 1536
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index = faiss.IndexFlatL2(embedding_size)
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return FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
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def initialize_worker_node(self, llm, tools, vectorstore):
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worker_node = WorkerNode(llm=llm, tools=tools, vectorstore=vectorstore)
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worker_node.create_agent(ai_name="AI Assistant", ai_role="Assistant", human_in_the_loop=True, search_kwargs={})
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return worker_node
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def initialize_boss_node(self, llm, vectorstore):
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todo_prompt = PromptTemplate.from_template("You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}""")
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todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt)
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search = SerpAPIWrapper()
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tools = [
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Tool(name="Search", func=search.run, description="useful for when you need to answer questions about current events"),
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Tool(name="TODO", func=todo_chain.run, description="useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!"),
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Tool(name="AUTONOMOUS Worker AGENT", func=self.worker_node.agent.run, description="Useful for when you need to spawn an autonomous agent instance as a worker to accomplish complex tasks, it can search the internet or spawn child multi-modality models to process and generate images and text or audio and so on")
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]
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suffix = """Question: {task}\n{agent_scratchpad}"""
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prefix = """You are an Boss in a swarm who performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}.\n"""
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prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix, suffix=suffix, input_variables=["objective", "task", "context", "agent_scratchpad"],)
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llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
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agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=[tool.name for tool in tools])
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agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
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return BossNode(self.openai_api_key, llm, vectorstore, agent_executor, verbose=True, max_iterations=5)
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def run_swarms(self, objective):
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llm = self.initialize_llm()
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tools = self.initialize_tools(llm)
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vectorstore = self.initialize_vectorstore()
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worker_node = self.initialize_worker_node(llm, tools, vectorstore)
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boss_node = self.initialize_boss_node(llm, vectorstore)
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task = boss_node.create_task(objective)
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boss_node.execute_task(task)
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worker_node.run_agent(objective)
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# class Swarms:
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# def __init__(self, num_nodes: int, llm: BaseLLM, self_scaling: bool):
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# self.nodes = [WorkerNode(llm) for _ in range(num_nodes)]
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# self.self_scaling = self_scaling
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# def add_worker(self, llm: BaseLLM):
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# self.nodes.append(WorkerNode(llm))
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# def remove_workers(self, index: int):
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# self.nodes.pop(index)
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# def execute(self, task):
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# #placeholer for main execution logic
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# pass
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# def scale(self):
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# #placeholder for self scaling logic
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# pass
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#special classes
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# class HierarchicalSwarms(Swarms):
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# def execute(self, task):
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# pass
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# class CollaborativeSwarms(Swarms):
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# def execute(self, task):
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# pass
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# class CompetitiveSwarms(Swarms):
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# def execute(self, task):
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# pass
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# class MultiAgentDebate(Swarms):
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# def execute(self, task):
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# pass
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#======================================> WorkerNode
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# class MetaWorkerNode:
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# def __init__(self, llm, tools, vectorstore):
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# self.llm = llm
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# self.tools = tools
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# self.vectorstore = vectorstore
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# self.agent = None
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# self.meta_chain = None
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# def init_chain(self, instructions):
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# self.agent = WorkerNode(self.llm, self.tools, self.vectorstore)
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# self.agent.create_agent("Assistant", "Assistant Role", False, {})
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# def initialize_meta_chain():
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# meta_template = """
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# Assistant has just had the below interactions with a User. Assistant followed their "Instructions" closely. Your job is to critique the Assistant's performance and then revise the Instructions so that Assistant would quickly and correctly respond in the future.
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# ####
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# {chat_history}
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# ####
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# Please reflect on these interactions.
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# You should first critique Assistant's performance. What could Assistant have done better? What should the Assistant remember about this user? Are there things this user always wants? Indicate this with "Critique: ...".
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# You should next revise the Instructions so that Assistant would quickly and correctly respond in the future. Assistant's goal is to satisfy the user in as few interactions as possible. Assistant will only see the new Instructions, not the interaction history, so anything important must be summarized in the Instructions. Don't forget any important details in the current Instructions! Indicate the new Instructions by "Instructions: ...".
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# """
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# meta_prompt = PromptTemplate(
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# input_variables=["chat_history"], template=meta_template
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# )
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# meta_chain = LLMChain(
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# llm=OpenAI(temperature=0),
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# prompt=meta_prompt,
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# verbose=True,
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# )
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# return meta_chain
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# def meta_chain(self):
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# #define meta template and meta prompting as per your needs
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# self.meta_chain = initialize_meta_chain()
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# def get_chat_history(chain_memory):
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# memory_key = chain_memory.memory_key
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# chat_history = chain_memory.load_memory_variables(memory_key)[memory_key]
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# return chat_history
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# def get_new_instructions(meta_output):
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# delimiter = "Instructions: "
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# new_instructions = meta_output[meta_output.find(delimiter) + len(delimiter) :]
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# return new_instructions
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# def main(self, task, max_iters=3, max_meta_iters=5):
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# failed_phrase = "task failed"
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# success_phrase = "task succeeded"
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# key_phrases = [success_phrase, failed_phrase]
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# instructions = "None"
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# for i in range(max_meta_iters):
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# print(f"[Episode {i+1}/{max_meta_iters}]")
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# self.initialize_chain(instructions)
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# output = self.agent.perform('Assistant', {'request': task})
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# for j in range(max_iters):
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# print(f"(Step {j+1}/{max_iters})")
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# print(f"Assistant: {output}")
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# print(f"Human: ")
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# human_input = input()
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# if any(phrase in human_input.lower() for phrase in key_phrases):
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# break
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# output = self.agent.perform('Assistant', {'request': human_input})
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# if success_phrase in human_input.lower():
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# print(f"You succeeded! Thanks for playing!")
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# return
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# self.initialize_meta_chain()
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# meta_output = self.meta_chain.predict(chat_history=self.get_chat_history())
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# print(f"Feedback: {meta_output}")
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# instructions = self.get_new_instructions(meta_output)
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# print(f"New Instructions: {instructions}")
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# print("\n" + "#" * 80 + "\n")
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# print(f"You failed! Thanks for playing!")
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# #init instance of MetaWorkerNode
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# meta_worker_node = MetaWorkerNode(llm=OpenAI, tools=tools, vectorstore=vectorstore)
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# #specify a task and interact with the agent
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# task = "Provide a sysmatic argument for why we should always eat past with olives"
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# meta_worker_node.main(task)
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####################################################################### => Boss Node
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####################################################################### => Boss Node
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####################################################################### => Boss Node
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