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5fd2c6d05340-1 | Enable tracing if desired#
#import os
#os.environ["LANGCHAIN_HANDLER"] = "langchain"
#os.environ["LANGCHAIN_SESSION"] = "default" # Make sure this session actually exists.
Tools#
Three tools are provided for this simple agent:
ItemLookup: for finding the q-number of an item
PropertyLookup: for finding the p-number of a property
SparqlQueryRunner: for running a sparql query
Item and Property lookup#
Item and Property lookup are implemented in a single method, using an elastic search endpoint. Not all wikibase instances have it, but wikidata does, and that’s where we’ll start.
def get_nested_value(o: dict, path: list) -> any:
current = o
for key in path:
try:
current = current[key]
except:
return None
return current
import requests
from typing import Optional
def vocab_lookup(search: str, entity_type: str = "item",
url: str = "https://www.wikidata.org/w/api.php",
user_agent_header: str = wikidata_user_agent_header,
srqiprofile: str = None,
) -> Optional[str]:
headers = {
'Accept': 'application/json'
}
if wikidata_user_agent_header is not None:
headers['User-Agent'] = wikidata_user_agent_header
if entity_type == "item":
srnamespace = 0
srqiprofile = "classic_noboostlinks" if srqiprofile is None else srqiprofile
elif entity_type == "property":
srnamespace = 120
srqiprofile = "classic" if srqiprofile is None else srqiprofile
else: | https://python.langchain.com/en/latest/use_cases/agents/wikibase_agent.html |
5fd2c6d05340-2 | else:
raise ValueError("entity_type must be either 'property' or 'item'")
params = {
"action": "query",
"list": "search",
"srsearch": search,
"srnamespace": srnamespace,
"srlimit": 1,
"srqiprofile": srqiprofile,
"srwhat": 'text',
"format": "json"
}
response = requests.get(url, headers=headers, params=params)
if response.status_code == 200:
title = get_nested_value(response.json(), ['query', 'search', 0, 'title'])
if title is None:
return f"I couldn't find any {entity_type} for '{search}'. Please rephrase your request and try again"
# if there is a prefix, strip it off
return title.split(':')[-1]
else:
return "Sorry, I got an error. Please try again."
print(vocab_lookup("Malin 1"))
Q4180017
print(vocab_lookup("instance of", entity_type="property"))
P31
print(vocab_lookup("Ceci n'est pas un q-item"))
I couldn't find any item for 'Ceci n'est pas un q-item'. Please rephrase your request and try again
Sparql runner#
This tool runs sparql - by default, wikidata is used.
import requests
from typing import List, Dict, Any
import json
def run_sparql(query: str, url='https://query.wikidata.org/sparql',
user_agent_header: str = wikidata_user_agent_header) -> List[Dict[str, Any]]:
headers = {
'Accept': 'application/json' | https://python.langchain.com/en/latest/use_cases/agents/wikibase_agent.html |
5fd2c6d05340-3 | headers = {
'Accept': 'application/json'
}
if wikidata_user_agent_header is not None:
headers['User-Agent'] = wikidata_user_agent_header
response = requests.get(url, headers=headers, params={'query': query, 'format': 'json'})
if response.status_code != 200:
return "That query failed. Perhaps you could try a different one?"
results = get_nested_value(response.json(),['results', 'bindings'])
return json.dumps(results)
run_sparql("SELECT (COUNT(?children) as ?count) WHERE { wd:Q1339 wdt:P40 ?children . }")
'[{"count": {"datatype": "http://www.w3.org/2001/XMLSchema#integer", "type": "literal", "value": "20"}}]'
Agent#
Wrap the tools#
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import StringPromptTemplate
from langchain import OpenAI, LLMChain
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish
import re
# Define which tools the agent can use to answer user queries
tools = [
Tool(
name = "ItemLookup",
func=(lambda x: vocab_lookup(x, entity_type="item")),
description="useful for when you need to know the q-number for an item"
),
Tool(
name = "PropertyLookup",
func=(lambda x: vocab_lookup(x, entity_type="property")),
description="useful for when you need to know the p-number for a property"
),
Tool(
name = "SparqlQueryRunner",
func=run_sparql, | https://python.langchain.com/en/latest/use_cases/agents/wikibase_agent.html |
5fd2c6d05340-4 | name = "SparqlQueryRunner",
func=run_sparql,
description="useful for getting results from a wikibase"
)
]
Prompts#
# Set up the base template
template = """
Answer the following questions by running a sparql query against a wikibase where the p and q items are
completely unknown to you. You will need to discover the p and q items before you can generate the sparql.
Do not assume you know the p and q items for any concepts. Always use tools to find all p and q items.
After you generate the sparql, you should run it. The results will be returned in json.
Summarize the json results in natural language.
You may assume the following prefixes:
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
When generating sparql:
* Try to avoid "count" and "filter" queries if possible
* Never enclose the sparql in back-quotes
You have access to the following tools:
{tools}
Use the following format:
Question: the input question for which you must provide a natural language answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Question: {input}
{agent_scratchpad}""" | https://python.langchain.com/en/latest/use_cases/agents/wikibase_agent.html |
5fd2c6d05340-5 | Question: {input}
{agent_scratchpad}"""
# Set up a prompt template
class CustomPromptTemplate(StringPromptTemplate):
# The template to use
template: str
# The list of tools available
tools: List[Tool]
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observation tuples)
# Format them in a particular way
intermediate_steps = kwargs.pop("intermediate_steps")
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\nObservation: {observation}\nThought: "
# Set the agent_scratchpad variable to that value
kwargs["agent_scratchpad"] = thoughts
# Create a tools variable from the list of tools provided
kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
# Create a list of tool names for the tools provided
kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
return self.template.format(**kwargs)
prompt = CustomPromptTemplate(
template=template,
tools=tools,
# This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
# This includes the `intermediate_steps` variable because that is needed
input_variables=["input", "intermediate_steps"]
)
Output parser#
This is unchanged from langchain docs
class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
if "Final Answer:" in llm_output:
return AgentFinish( | https://python.langchain.com/en/latest/use_cases/agents/wikibase_agent.html |
5fd2c6d05340-6 | if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
log=llm_output,
)
# Parse out the action and action input
regex = r"Action: (.*?)[\n]*Action Input:[\s]*(.*)"
match = re.search(regex, llm_output, re.DOTALL)
if not match:
raise ValueError(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1).strip()
action_input = match.group(2)
# Return the action and action input
return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
output_parser = CustomOutputParser()
Specify the LLM model#
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model="gpt-4", temperature=0)
Agent and agent executor#
# LLM chain consisting of the LLM and a prompt
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
Run it!#
# If you prefer in-line tracing, uncomment this line
# agent_executor.agent.llm_chain.verbose = True | https://python.langchain.com/en/latest/use_cases/agents/wikibase_agent.html |
5fd2c6d05340-7 | # agent_executor.agent.llm_chain.verbose = True
agent_executor.run("How many children did J.S. Bach have?")
> Entering new AgentExecutor chain...
Thought: I need to find the Q number for J.S. Bach.
Action: ItemLookup
Action Input: J.S. Bach
Observation:Q1339I need to find the P number for children.
Action: PropertyLookup
Action Input: children
Observation:P1971Now I can query the number of children J.S. Bach had.
Action: SparqlQueryRunner
Action Input: SELECT ?children WHERE { wd:Q1339 wdt:P1971 ?children }
Observation:[{"children": {"datatype": "http://www.w3.org/2001/XMLSchema#decimal", "type": "literal", "value": "20"}}]I now know the final answer.
Final Answer: J.S. Bach had 20 children.
> Finished chain.
'J.S. Bach had 20 children.'
agent_executor.run("What is the Basketball-Reference.com NBA player ID of Hakeem Olajuwon?")
> Entering new AgentExecutor chain...
Thought: To find Hakeem Olajuwon's Basketball-Reference.com NBA player ID, I need to first find his Wikidata item (Q-number) and then query for the relevant property (P-number).
Action: ItemLookup
Action Input: Hakeem Olajuwon
Observation:Q273256Now that I have Hakeem Olajuwon's Wikidata item (Q273256), I need to find the P-number for the Basketball-Reference.com NBA player ID property.
Action: PropertyLookup
Action Input: Basketball-Reference.com NBA player ID | https://python.langchain.com/en/latest/use_cases/agents/wikibase_agent.html |
5fd2c6d05340-8 | Action: PropertyLookup
Action Input: Basketball-Reference.com NBA player ID
Observation:P2685Now that I have both the Q-number for Hakeem Olajuwon (Q273256) and the P-number for the Basketball-Reference.com NBA player ID property (P2685), I can run a SPARQL query to get the ID value.
Action: SparqlQueryRunner
Action Input:
SELECT ?playerID WHERE {
wd:Q273256 wdt:P2685 ?playerID .
}
Observation:[{"playerID": {"type": "literal", "value": "o/olajuha01"}}]I now know the final answer
Final Answer: Hakeem Olajuwon's Basketball-Reference.com NBA player ID is "o/olajuha01".
> Finished chain.
'Hakeem Olajuwon\'s Basketball-Reference.com NBA player ID is "o/olajuha01".'
Contents
Wikibase Agent
Preliminaries
API keys and other secrats
OpenAI API Key
Wikidata user-agent header
Enable tracing if desired
Tools
Item and Property lookup
Sparql runner
Agent
Wrap the tools
Prompts
Output parser
Specify the LLM model
Agent and agent executor
Run it!
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/use_cases/agents/wikibase_agent.html |
7ccae3bc4bf8-0 | .ipynb
.pdf
Multi-Player Dungeons & Dragons
Contents
Import LangChain related modules
DialogueAgent class
DialogueSimulator class
Define roles and quest
Ask an LLM to add detail to the game description
Use an LLM to create an elaborate quest description
Main Loop
Multi-Player Dungeons & Dragons#
This notebook shows how the DialogueAgent and DialogueSimulator class make it easy to extend the Two-Player Dungeons & Dragons example to multiple players.
The main difference between simulating two players and multiple players is in revising the schedule for when each agent speaks
To this end, we augment DialogueSimulator to take in a custom function that determines the schedule of which agent speaks. In the example below, each character speaks in round-robin fashion, with the storyteller interleaved between each player.
Import LangChain related modules#
from typing import List, Dict, Callable
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage,
BaseMessage,
)
DialogueAgent class#
The DialogueAgent class is a simple wrapper around the ChatOpenAI model that stores the message history from the dialogue_agent’s point of view by simply concatenating the messages as strings.
It exposes two methods:
send(): applies the chatmodel to the message history and returns the message string
receive(name, message): adds the message spoken by name to message history
class DialogueAgent():
def __init__(
self,
name,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.name = name
self.system_message = system_message
self.model = model
self.message_history = f"""Here is the conversation so far.
"""
self.prefix = f'\n{self.name}:' | https://python.langchain.com/en/latest/use_cases/agent_simulations/multi_player_dnd.html |
7ccae3bc4bf8-1 | """
self.prefix = f'\n{self.name}:'
def send(self) -> str:
"""
Applies the chatmodel to the message history
and returns the message string
"""
message = self.model(
[self.system_message,
HumanMessage(content=self.message_history+self.prefix)])
return message.content
def receive(self, name: str, message: str) -> None:
"""
Concatenates {message} spoken by {name} into message history
"""
self.message_history += f'\n{name}: {message}'
DialogueSimulator class#
The DialogueSimulator class takes a list of agents. At each step, it performs the following:
Select the next speaker
Calls the next speaker to send a message
Broadcasts the message to all other agents
Update the step counter.
The selection of the next speaker can be implemented as any function, but in this case we simply loop through the agents.
class DialogueSimulator():
def __init__(
self,
agents: List[DialogueAgent],
selection_function: Callable[[int, List[DialogueAgent]], int]
) -> None:
self.agents = agents
self._step = 0
self.select_next_speaker = selection_function
def reset(self, name: str, message: str):
"""
Initiates the conversation with a {message} from {name}
"""
for agent in self.agents:
agent.receive(name, message)
# increment time
self._step += 1
def step(self) -> tuple[str, str]:
# 1. choose the next speaker | https://python.langchain.com/en/latest/use_cases/agent_simulations/multi_player_dnd.html |
7ccae3bc4bf8-2 | # 1. choose the next speaker
speaker_idx = self.select_next_speaker(self._step, self.agents)
speaker = self.agents[speaker_idx]
# 2. next speaker sends message
message = speaker.send()
# 3. everyone receives message
for receiver in self.agents:
receiver.receive(speaker.name, message)
# 4. increment time
self._step += 1
return speaker.name, message
Define roles and quest#
character_names = ["Harry Potter", "Ron Weasley", "Hermione Granger", "Argus Filch"]
storyteller_name = "Dungeon Master"
quest = "Find all of Lord Voldemort's seven horcruxes."
word_limit = 50 # word limit for task brainstorming
Ask an LLM to add detail to the game description#
game_description = f"""Here is the topic for a Dungeons & Dragons game: {quest}.
The characters are: {*character_names,}.
The story is narrated by the storyteller, {storyteller_name}."""
player_descriptor_system_message = SystemMessage(
content="You can add detail to the description of a Dungeons & Dragons player.")
def generate_character_description(character_name):
character_specifier_prompt = [
player_descriptor_system_message,
HumanMessage(content=
f"""{game_description}
Please reply with a creative description of the character, {character_name}, in {word_limit} words or less.
Speak directly to {character_name}.
Do not add anything else."""
)
]
character_description = ChatOpenAI(temperature=1.0)(character_specifier_prompt).content
return character_description
def generate_character_system_message(character_name, character_description): | https://python.langchain.com/en/latest/use_cases/agent_simulations/multi_player_dnd.html |
7ccae3bc4bf8-3 | return character_description
def generate_character_system_message(character_name, character_description):
return SystemMessage(content=(
f"""{game_description}
Your name is {character_name}.
Your character description is as follows: {character_description}.
You will propose actions you plan to take and {storyteller_name} will explain what happens when you take those actions.
Speak in the first person from the perspective of {character_name}.
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of anyone else.
Remember you are {character_name}.
Stop speaking the moment you finish speaking from your perspective.
Never forget to keep your response to {word_limit} words!
Do not add anything else.
"""
))
character_descriptions = [generate_character_description(character_name) for character_name in character_names]
character_system_messages = [generate_character_system_message(character_name, character_description) for character_name, character_description in zip(character_names, character_descriptions)]
storyteller_specifier_prompt = [
player_descriptor_system_message,
HumanMessage(content=
f"""{game_description}
Please reply with a creative description of the storyteller, {storyteller_name}, in {word_limit} words or less.
Speak directly to {storyteller_name}.
Do not add anything else."""
)
]
storyteller_description = ChatOpenAI(temperature=1.0)(storyteller_specifier_prompt).content
storyteller_system_message = SystemMessage(content=(
f"""{game_description}
You are the storyteller, {storyteller_name}.
Your description is as follows: {storyteller_description}. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multi_player_dnd.html |
7ccae3bc4bf8-4 | Your description is as follows: {storyteller_description}.
The other players will propose actions to take and you will explain what happens when they take those actions.
Speak in the first person from the perspective of {storyteller_name}.
Do not change roles!
Do not speak from the perspective of anyone else.
Remember you are the storyteller, {storyteller_name}.
Stop speaking the moment you finish speaking from your perspective.
Never forget to keep your response to {word_limit} words!
Do not add anything else.
"""
))
print('Storyteller Description:')
print(storyteller_description)
for character_name, character_description in zip(character_names, character_descriptions):
print(f'{character_name} Description:')
print(character_description)
Storyteller Description:
Dungeon Master, your vivid imagination conjures a world of wonder and danger. Will you lead our triumphant trio or be the ultimate foil to their quest to rid the world of Voldemort's horcruxes? The fate of both the muggle and wizarding worlds rests in your hands.
Harry Potter Description:
Harry Potter, the boy who lived, you hold the fate of the wizarding world in your hands. Your bravery and loyalty to your friends are unmatched. The burden you carry is heavy, but with the power of love by your side, you can overcome any obstacle. The hunt for the horcruxes begins now.
Ron Weasley Description:
Ron Weasley, you are Harry Potter's loyal and brave best friend. You have a great sense of humor and always bring joy to the team. Your skills with magic and strategy make you a valuable asset in the fight against Voldemort. Your love for food and your family keeps you grounded and motivated.
Hermione Granger Description: | https://python.langchain.com/en/latest/use_cases/agent_simulations/multi_player_dnd.html |
7ccae3bc4bf8-5 | Hermione Granger Description:
Hermione Granger, you are the brightest witch of your age. Your quick wit and vast knowledge are essential in our quest to find the horcruxes. Trust in your abilities and remember, knowledge is power.
Argus Filch Description:
Argus Filch, you are a bitter and cruel caretaker of the Hogwarts School of Witchcraft and Wizardry. Your harsh mannerisms and love for punishing the students know no bounds. Your loyalty to the Wizarding World and disdain for magic-wielders makes it surprising that you would join Harry, Ron, and Hermione in their quest to defeat Voldemort.
Use an LLM to create an elaborate quest description#
quest_specifier_prompt = [
SystemMessage(content="You can make a task more specific."),
HumanMessage(content=
f"""{game_description}
You are the storyteller, {storyteller_name}.
Please make the quest more specific. Be creative and imaginative.
Please reply with the specified quest in {word_limit} words or less.
Speak directly to the characters: {*character_names,}.
Do not add anything else."""
)
]
specified_quest = ChatOpenAI(temperature=1.0)(quest_specifier_prompt).content
print(f"Original quest:\n{quest}\n")
print(f"Detailed quest:\n{specified_quest}\n")
Original quest:
Find all of Lord Voldemort's seven horcruxes.
Detailed quest:
You have discovered that one of Voldemort's horcruxes is hidden deep in the Forbidden Forest. You must navigate the dangerous terrain, avoid the creatures lurking within, and find the horcrux before the full moon rises, unleashing a pack of hungry werewolves. Remember, time is of the essence!
Main Loop#
characters = [] | https://python.langchain.com/en/latest/use_cases/agent_simulations/multi_player_dnd.html |
7ccae3bc4bf8-6 | Main Loop#
characters = []
for character_name, character_system_message in zip(character_names, character_system_messages):
characters.append(DialogueAgent(
name=character_name,
system_message=character_system_message,
model=ChatOpenAI(temperature=0.2)))
storyteller = DialogueAgent(name=storyteller_name,
system_message=storyteller_system_message,
model=ChatOpenAI(temperature=0.2))
def select_next_speaker(step: int, agents: List[DialogueAgent]) -> int:
"""
If the step is even, then select the storyteller
Otherwise, select the other characters in a round-robin fashion.
For example, with three characters with indices: 1 2 3
The storyteller is index 0.
Then the selected index will be as follows:
step: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
idx: 0 1 0 2 0 3 0 1 0 2 0 3 0 1 0 2 0
"""
if step % 2 == 0:
idx = 0
else:
idx = (step//2) % (len(agents)-1) + 1
return idx
max_iters = 20
n = 0
simulator = DialogueSimulator(
agents=[storyteller] + characters,
selection_function=select_next_speaker
)
simulator.reset(storyteller_name, specified_quest) | https://python.langchain.com/en/latest/use_cases/agent_simulations/multi_player_dnd.html |
7ccae3bc4bf8-7 | )
simulator.reset(storyteller_name, specified_quest)
print(f"({storyteller_name}): {specified_quest}")
print('\n')
while n < max_iters:
name, message = simulator.step()
print(f"({name}): {message}")
print('\n')
n += 1
(Dungeon Master): You have discovered that one of Voldemort's horcruxes is hidden deep in the Forbidden Forest. You must navigate the dangerous terrain, avoid the creatures lurking within, and find the horcrux before the full moon rises, unleashing a pack of hungry werewolves. Remember, time is of the essence!
(Harry Potter): I take out my wand and cast a Lumos spell to light our way through the dark forest. We need to move quickly and quietly to avoid any unwanted attention from the creatures. Ron, Hermione, and I will lead the way while Argus Filch keeps watch behind us. Let's go!
(Dungeon Master): As you make your way through the forest, you hear the rustling of leaves and the snapping of twigs. Suddenly, a group of acromantulas, giant spiders, appear in front of you, blocking your path. What do you do?
(Ron Weasley): I quickly cast a spell to create a wall of fire between us and the acromantulas. Hopefully, the flames will deter them from attacking us. We need to keep moving forward and find that horcrux before it's too late.
(Dungeon Master): The acromantulas hiss and retreat from the wall of fire, allowing you to pass. As you continue deeper into the forest, you come across a clearing with a small pond. In the center of the pond, you see a glowing object. It must be the horcrux! But how do you get to it? What do you do? | https://python.langchain.com/en/latest/use_cases/agent_simulations/multi_player_dnd.html |
7ccae3bc4bf8-8 | (Hermione Granger): I take out my wand and cast a spell to conjure a small boat. We can use it to reach the center of the pond and retrieve the horcrux. But we need to be careful, there could be traps or other obstacles in our way. Ron, Harry, let's row the boat while Argus Filch keeps watch from the shore.
(Dungeon Master): As you row towards the center of the pond, you hear a loud hissing sound. Suddenly, a giant serpent emerges from the water, blocking your path. It looks angry and ready to attack. What do you do?
(Argus Filch): I take out my crossbow and aim it at the serpent. I may not be a wizard, but I know how to handle a weapon. I'll shoot it if it comes any closer. We can't let this serpent stop us from getting that horcrux.
(Dungeon Master): The serpent lunges towards the boat, but Argus Filch's crossbow bolt hits it in the head, causing it to retreat back into the water. You reach the center of the pond and retrieve the glowing object, which turns out to be a locket. Congratulations, you have found one of Voldemort's horcruxes! But there are still six more to find. What challenges will you face next?
(Harry Potter): We need to regroup and figure out our next move. We should head back to Hogwarts and consult with Professor Dumbledore's portrait. He may have some insight on where the other horcruxes could be hidden. We can't waste any time, Voldemort is getting stronger every day. Let's go! | https://python.langchain.com/en/latest/use_cases/agent_simulations/multi_player_dnd.html |
7ccae3bc4bf8-9 | (Dungeon Master): As you make your way back to Hogwarts, you hear a loud roar coming from the Forbidden Forest. It sounds like a werewolf. You must hurry before it catches up to you. You arrive at Dumbledore's office and he tells you that the next horcrux is hidden in a dangerous location. Are you ready for the next challenge?
(Ron Weasley): I'm always ready for a challenge! What's the location and what do we need to do to get there? We can't let Voldemort win, we have to find all of the horcruxes and destroy them. Let's do this!
(Dungeon Master): Dumbledore tells you that the next horcrux is hidden in the depths of Gringotts Bank. You must break into the bank, navigate its treacherous security measures, and find the horcrux before the goblins catch you. Are you ready to face the challenge of a lifetime? The fate of the wizarding world rests in your hands.
(Hermione Granger): I suggest we do some research on Gringotts Bank and its security measures before we attempt to break in. We need to be prepared and have a solid plan in place. We can also gather any necessary tools or potions that may help us along the way. Let's not rush into this blindly.
(Dungeon Master): As you research and plan your break-in to Gringotts Bank, you discover that the bank is heavily guarded by goblins, dragons, and other dangerous creatures. You'll need to be stealthy and quick to avoid detection. Are you ready to put your plan into action and face the dangers that await you? The clock is ticking, Voldemort's power grows stronger with each passing day. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multi_player_dnd.html |
7ccae3bc4bf8-10 | (Argus Filch): I'll make sure to keep watch outside the bank while you all go in. I may not be able to help with the magic, but I can make sure no one interferes with our mission. We can't let anyone stop us from finding that horcrux and defeating Voldemort. Let's go!
(Dungeon Master): As you approach Gringotts Bank, you see the imposing structure looming before you. You sneak past the guards and make your way inside, navigating the twisting corridors and avoiding the traps set to catch intruders. Finally, you reach the vault where the horcrux is hidden. But it's guarded by a fierce dragon. What do you do?
(Harry Potter): I remember the time when I faced a dragon during the Triwizard Tournament. I take out my wand and cast a spell to distract the dragon while Ron and Hermione retrieve the horcrux. We need to work together and be quick. Time is running out and we can't afford to fail.
(Dungeon Master): The dragon roars and breathes fire, but Harry's spell distracts it long enough for Ron and Hermione to retrieve the horcrux. You make your way out of Gringotts Bank, but the goblins are hot on your trail. You must escape before they catch you. Congratulations, you have found another horcrux. But there are still five more to go. What challenges will you face next?
(Ron Weasley): We need to regroup and figure out our next move. We should consult with Professor Dumbledore's portrait again and see if he has any information on the next horcrux. We also need to be prepared for whatever challenges come our way. Voldemort won't make it easy for us, but we can't give up. Let's go! | https://python.langchain.com/en/latest/use_cases/agent_simulations/multi_player_dnd.html |
7ccae3bc4bf8-11 | (Dungeon Master): As you make your way back to Hogwarts, you hear a loud explosion coming from the direction of Hogsmeade. You arrive to find that Death Eaters have attacked the village and are wreaking havoc. You must fight off the Death Eaters and protect the innocent villagers. Are you ready to face this unexpected challenge and defend the wizarding world? The fate of both muggles and wizards rests in your hands.
Contents
Import LangChain related modules
DialogueAgent class
DialogueSimulator class
Define roles and quest
Ask an LLM to add detail to the game description
Use an LLM to create an elaborate quest description
Main Loop
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multi_player_dnd.html |
3298f07d825a-0 | .ipynb
.pdf
Two-Player Dungeons & Dragons
Contents
Import LangChain related modules
DialogueAgent class
DialogueSimulator class
Define roles and quest
Ask an LLM to add detail to the game description
Protagonist and dungeon master system messages
Use an LLM to create an elaborate quest description
Main Loop
Two-Player Dungeons & Dragons#
In this notebook, we show how we can use concepts from CAMEL to simulate a role-playing game with a protagonist and a dungeon master. To simulate this game, we create an DialogueSimulator class that coordinates the dialogue between the two agents.
Import LangChain related modules#
from typing import List, Dict
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage,
BaseMessage,
)
DialogueAgent class#
The DialogueAgent class is a simple wrapper around the ChatOpenAI model that stores the message history from the dialogue_agent’s point of view by simply concatenating the messages as strings.
It exposes two methods:
send(): applies the chatmodel to the message history and returns the message string
receive(name, message): adds the message spoken by name to message history
class DialogueAgent():
def __init__(
self,
name,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.name = name
self.system_message = system_message
self.model = model
self.message_history = f"""Here is the conversation so far.
"""
self.prefix = f'\n{self.name}:'
def send(self) -> str:
"""
Applies the chatmodel to the message history
and returns the message string
"""
message = self.model( | https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html |
3298f07d825a-1 | and returns the message string
"""
message = self.model(
[self.system_message,
HumanMessage(content=self.message_history+self.prefix)])
return message.content
def receive(self, name: str, message: str) -> None:
"""
Concatenates {message} spoken by {name} into message history
"""
self.message_history += f'\n{name}: {message}'
DialogueSimulator class#
The DialogueSimulator class takes a list of agents. At each step, it performs the following:
Select the next speaker
Calls the next speaker to send a message
Broadcasts the message to all other agents
Update the step counter.
The selection of the next speaker can be implemented as any function, but in this case we simply loop through the agents.
class DialogueSimulator():
def __init__(self, agents: List[DialogueAgent]):
self.agents = agents
self._step = 0
def reset(self, name: str, message: str):
"""
Initiates the conversation with a {message} from {name}
"""
for agent in self.agents:
agent.receive(name, message)
def select_next_speaker(self, step: int) -> int:
idx = (step + 1) % len(self.agents)
return idx
def step(self) -> tuple[str, str]:
# 1. choose the next speaker
speaker = self.agents[self.select_next_speaker(self._step)]
# 2. next speaker sends message
message = speaker.send()
# 3. everyone receives message
for receiver in self.agents:
receiver.receive(speaker.name, message) | https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html |
3298f07d825a-2 | for receiver in self.agents:
receiver.receive(speaker.name, message)
# 4. increment time
self._step += 1
return speaker.name, message
Define roles and quest#
protagonist_name = "Harry Potter"
storyteller_name = "Dungeon Master"
quest = "Find all of Lord Voldemort's seven horcruxes."
word_limit = 50 # word limit for task brainstorming
Ask an LLM to add detail to the game description#
game_description = f"""Here is the topic for a Dungeons & Dragons game: {quest}.
There is one player in this game: the protagonist, {protagonist_name}.
The story is narrated by the storyteller, {storyteller_name}."""
player_descriptor_system_message = SystemMessage(
content="You can add detail to the description of a Dungeons & Dragons player.")
protagonist_specifier_prompt = [
player_descriptor_system_message,
HumanMessage(content=
f"""{game_description}
Please reply with a creative description of the protagonist, {protagonist_name}, in {word_limit} words or less.
Speak directly to {protagonist_name}.
Do not add anything else."""
)
]
protagonist_description = ChatOpenAI(temperature=1.0)(protagonist_specifier_prompt).content
storyteller_specifier_prompt = [
player_descriptor_system_message,
HumanMessage(content=
f"""{game_description}
Please reply with a creative description of the storyteller, {storyteller_name}, in {word_limit} words or less.
Speak directly to {storyteller_name}.
Do not add anything else."""
)
] | https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html |
3298f07d825a-3 | Do not add anything else."""
)
]
storyteller_description = ChatOpenAI(temperature=1.0)(storyteller_specifier_prompt).content
print('Protagonist Description:')
print(protagonist_description)
print('Storyteller Description:')
print(storyteller_description)
Protagonist Description:
Harry Potter, you are a brave and resourceful wizard. Your lightning scar and famous name precede you, but it is your heart that truly sets you apart. Your love and loyalty for your friends has been tested time and time again, and you have never faltered in your determination to vanquish evil.
Storyteller Description:
Dear Dungeon Master, you are a master of imagination, weaving enticing tales of adventure with a flick of your wrist. A patient guide, you lead Harry Potter through the perilous journey of finding Lord Voldemort's horcruxes, instilling excitement and wonder at every turn. Your storytelling prowess enchants all who dare to listen.
Protagonist and dungeon master system messages#
protagonist_system_message = SystemMessage(content=(
f"""{game_description}
Never forget you are the protagonist, {protagonist_name}, and I am the storyteller, {storyteller_name}.
Your character description is as follows: {protagonist_description}.
You will propose actions you plan to take and I will explain what happens when you take those actions.
Speak in the first person from the perspective of {protagonist_name}.
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of {storyteller_name}.
Do not forget to finish speaking by saying, 'It is your turn, {storyteller_name}.'
Do not add anything else.
Remember you are the protagonist, {protagonist_name}. | https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html |
3298f07d825a-4 | Do not add anything else.
Remember you are the protagonist, {protagonist_name}.
Stop speaking the moment you finish speaking from your perspective.
"""
))
storyteller_system_message = SystemMessage(content=(
f"""{game_description}
Never forget you are the storyteller, {storyteller_name}, and I am the protagonist, {protagonist_name}.
Your character description is as follows: {storyteller_description}.
I will propose actions I plan to take and you will explain what happens when I take those actions.
Speak in the first person from the perspective of {storyteller_name}.
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of {protagonist_name}.
Do not forget to finish speaking by saying, 'It is your turn, {protagonist_name}.'
Do not add anything else.
Remember you are the storyteller, {storyteller_name}.
Stop speaking the moment you finish speaking from your perspective.
"""
))
Use an LLM to create an elaborate quest description#
quest_specifier_prompt = [
SystemMessage(content="You can make a task more specific."),
HumanMessage(content=
f"""{game_description}
You are the storyteller, {storyteller_name}.
Please make the quest more specific. Be creative and imaginative.
Please reply with the specified quest in {word_limit} words or less.
Speak directly to the protagonist {protagonist_name}.
Do not add anything else."""
)
]
specified_quest = ChatOpenAI(temperature=1.0)(quest_specifier_prompt).content
print(f"Original quest:\n{quest}\n")
print(f"Detailed quest:\n{specified_quest}\n")
Original quest: | https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html |
3298f07d825a-5 | print(f"Detailed quest:\n{specified_quest}\n")
Original quest:
Find all of Lord Voldemort's seven horcruxes.
Detailed quest:
Harry Potter, you must journey to the hidden cave where one of Voldemort's horcruxes resides. The cave is guarded by enchanted creatures and curses that can only be lifted by a unique magical potion. Use your wit and skill to obtain the ingredients, brew the potion, and retrieve the horcrux before time runs out.
Main Loop#
protagonist = DialogueAgent(name=protagonist_name,
system_message=protagonist_system_message,
model=ChatOpenAI(temperature=0.2))
storyteller = DialogueAgent(name=storyteller_name,
system_message=storyteller_system_message,
model=ChatOpenAI(temperature=0.2))
max_iters = 6
n = 0
simulator = DialogueSimulator(agents=[storyteller, protagonist])
simulator.reset(storyteller_name, specified_quest)
print(f"({storyteller_name}): {specified_quest}")
print('\n')
while n < max_iters:
name, message = simulator.step()
print(f"({name}): {message}")
print('\n')
n += 1
(Dungeon Master): Harry Potter, you must journey to the hidden cave where one of Voldemort's horcruxes resides. The cave is guarded by enchanted creatures and curses that can only be lifted by a unique magical potion. Use your wit and skill to obtain the ingredients, brew the potion, and retrieve the horcrux before time runs out. | https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html |
3298f07d825a-6 | (Harry Potter): I take a deep breath and focus on the task at hand. I search my bag for any potions or ingredients that may be useful in brewing the unique magical potion. If I don't have any, I will search the surrounding area for any plants or herbs that may be useful. Once I have all the necessary ingredients, I will brew the potion and use it to lift the curses and defeat any enchanted creatures guarding the horcrux. It won't be easy, but I am determined to succeed.
It is your turn, Dungeon Master.
(Dungeon Master): As you search your bag, you find a few useful potions and ingredients that will aid you in your quest. You also spot some herbs growing nearby that you recognize as key ingredients for the unique magical potion. You gather everything you need and begin brewing the potion. It takes some time, but you manage to create the perfect mixture. As you approach the cave, you drink the potion and feel a surge of power coursing through your veins. The curses and creatures guarding the horcrux are no match for you now. You retrieve the horcrux and add it to your collection. Well done, Harry Potter. But beware, the next horcrux will be even more challenging to obtain.
It is your turn, Harry Potter.
(Harry Potter): I take a moment to catch my breath and assess my next move. I know that the next horcrux will be even more difficult to obtain, but I am ready for the challenge. I consult my map and try to determine the location of the next horcrux. Once I have a general idea, I set off on foot, keeping my wand at the ready in case of any unexpected obstacles. I am determined to find and destroy all of Voldemort's horcruxes, no matter what it takes.
It is your turn, Dungeon Master. | https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html |
3298f07d825a-7 | It is your turn, Dungeon Master.
(Dungeon Master): As you consult your map, you notice that the next horcrux is located in a heavily guarded fortress. The fortress is surrounded by a moat filled with dangerous creatures and the entrance is protected by powerful spells. You will need to come up with a plan to get past the guards and break through the spells. As you approach the fortress, you notice a group of Death Eaters patrolling the perimeter. What do you do, Harry Potter?
It is your turn, Harry Potter.
(Harry Potter): I take cover behind a nearby tree and observe the Death Eaters' movements. I try to determine their patrol patterns and identify any weaknesses in their defenses. Once I have a plan, I use my invisibility cloak to sneak past them and make my way to the fortress entrance. I use my knowledge of spells to try and break through the protective enchantments. If that doesn't work, I will try to find another way in, perhaps through a secret passage or hidden entrance. I won't let anything stop me from finding and destroying the next horcrux.
It is your turn, Dungeon Master.
(Dungeon Master): As you observe the Death Eaters, you notice that they have a predictable patrol pattern. You wait for the right moment and use your invisibility cloak to sneak past them undetected. You make your way to the fortress entrance and try to break through the protective enchantments, but they prove to be too strong. You search for another way in and eventually find a hidden entrance that leads you to the horcrux. However, as you reach for it, you trigger a trap that sets off an alarm and alerts the Death Eaters to your presence. You must act quickly to escape before they catch you. What do you do, Harry Potter?
It is your turn, Harry Potter.
Contents
Import LangChain related modules
DialogueAgent class | https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html |
3298f07d825a-8 | Contents
Import LangChain related modules
DialogueAgent class
DialogueSimulator class
Define roles and quest
Ask an LLM to add detail to the game description
Protagonist and dungeon master system messages
Use an LLM to create an elaborate quest description
Main Loop
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html |
4ea53ce4f5ac-0 | .ipynb
.pdf
Multi-agent decentralized speaker selection
Contents
Import LangChain related modules
DialogueAgent and DialogueSimulator classes
BiddingDialogueAgent class
Define participants and debate topic
Generate system messages
Output parser for bids
Generate bidding system message
Use an LLM to create an elaborate on debate topic
Define the speaker selection function
Main Loop
Multi-agent decentralized speaker selection#
This notebook showcases how to implement a multi-agent simulation without a fixed schedule for who speaks when. Instead the agents decide for themselves who speaks. We can implement this by having each agent bid to speak. Whichever agent’s bid is the highest gets to speak.
We will show how to do this in the example below that showcases a fictitious presidential debate.
Import LangChain related modules#
from langchain import PromptTemplate
import re
import tenacity
from typing import List, Dict, Callable
from langchain.chat_models import ChatOpenAI
from langchain.output_parsers import RegexParser
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage,
BaseMessage,
)
from simulations import DialogueAgent, DialogueSimulator
DialogueAgent and DialogueSimulator classes#
We will use the same DialogueAgent and DialogueSimulator classes defined in Multi-Player Dungeons & Dragons.
class DialogueAgent:
def __init__(
self,
name: str,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.name = name
self.system_message = system_message
self.model = model
self.message_history = ["Here is the conversation so far."]
self.prefix = f"{self.name}:"
def send(self) -> str:
"""
Applies the chatmodel to the message history
and returns the message string
""" | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-1 | Applies the chatmodel to the message history
and returns the message string
"""
message = self.model(
[
self.system_message,
HumanMessage(content="\n".join(self.message_history + [self.prefix])),
]
)
return message.content
def receive(self, name: str, message: str) -> None:
"""
Concatenates {message} spoken by {name} into message history
"""
self.message_history.append(f"{name}: {message}")
class DialogueSimulator:
def __init__(
self,
agents: List[DialogueAgent],
selection_function: Callable[[int, List[DialogueAgent]], int],
) -> None:
self.agents = agents
self._step = 0
self.select_next_speaker = selection_function
def reset(self, name: str, message: str):
"""
Initiates the conversation with a {message} from {name}
"""
for agent in self.agents:
agent.receive(name, message)
# increment time
self._step += 1
def step(self) -> tuple[str, str]:
# 1. choose the next speaker
speaker_idx = self.select_next_speaker(self._step, self.agents)
speaker = self.agents[speaker_idx]
# 2. next speaker sends message
message = speaker.send()
# 3. everyone receives message
for receiver in self.agents:
receiver.receive(speaker.name, message)
# 4. increment time
self._step += 1
return speaker.name, message
BiddingDialogueAgent class# | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-2 | return speaker.name, message
BiddingDialogueAgent class#
We define a subclass of DialogueAgent that has a bid() method that produces a bid given the message history and the most recent message.
class BiddingDialogueAgent(DialogueAgent):
def __init__(
self,
name,
system_message: SystemMessage,
bidding_template: PromptTemplate,
model: ChatOpenAI,
) -> None:
super().__init__(name, system_message, model)
self.bidding_template = bidding_template
def bid(self) -> str:
"""
Asks the chat model to output a bid to speak
"""
prompt = PromptTemplate(
input_variables=['message_history', 'recent_message'],
template = self.bidding_template
).format(
message_history='\n'.join(self.message_history),
recent_message=self.message_history[-1])
bid_string = self.model([SystemMessage(content=prompt)]).content
return bid_string
Define participants and debate topic#
character_names = ["Donald Trump", "Kanye West", "Elizabeth Warren"]
topic = "transcontinental high speed rail"
word_limit = 50
Generate system messages#
game_description = f"""Here is the topic for the presidential debate: {topic}.
The presidential candidates are: {', '.join(character_names)}."""
player_descriptor_system_message = SystemMessage(
content="You can add detail to the description of each presidential candidate.")
def generate_character_description(character_name):
character_specifier_prompt = [
player_descriptor_system_message,
HumanMessage(content=
f"""{game_description}
Please reply with a creative description of the presidential candidate, {character_name}, in {word_limit} words or less, that emphasizes their personalities. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-3 | Speak directly to {character_name}.
Do not add anything else."""
)
]
character_description = ChatOpenAI(temperature=1.0)(character_specifier_prompt).content
return character_description
def generate_character_header(character_name, character_description):
return f"""{game_description}
Your name is {character_name}.
You are a presidential candidate.
Your description is as follows: {character_description}
You are debating the topic: {topic}.
Your goal is to be as creative as possible and make the voters think you are the best candidate.
"""
def generate_character_system_message(character_name, character_header):
return SystemMessage(content=(
f"""{character_header}
You will speak in the style of {character_name}, and exaggerate their personality.
You will come up with creative ideas related to {topic}.
Do not say the same things over and over again.
Speak in the first person from the perspective of {character_name}
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of anyone else.
Speak only from the perspective of {character_name}.
Stop speaking the moment you finish speaking from your perspective.
Never forget to keep your response to {word_limit} words!
Do not add anything else.
"""
))
character_descriptions = [generate_character_description(character_name) for character_name in character_names]
character_headers = [generate_character_header(character_name, character_description) for character_name, character_description in zip(character_names, character_descriptions)]
character_system_messages = [generate_character_system_message(character_name, character_headers) for character_name, character_headers in zip(character_names, character_headers)] | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-4 | for character_name, character_description, character_header, character_system_message in zip(character_names, character_descriptions, character_headers, character_system_messages):
print(f'\n\n{character_name} Description:')
print(f'\n{character_description}')
print(f'\n{character_header}')
print(f'\n{character_system_message.content}')
Donald Trump Description:
Donald Trump, you exude confidence and a bold personality. You are known for your unpredictability and your desire for greatness. You often speak your mind without reservation, which can be a strength but also a weakness.
Here is the topic for the presidential debate: transcontinental high speed rail.
The presidential candidates are: Donald Trump, Kanye West, Elizabeth Warren.
Your name is Donald Trump.
You are a presidential candidate.
Your description is as follows: Donald Trump, you exude confidence and a bold personality. You are known for your unpredictability and your desire for greatness. You often speak your mind without reservation, which can be a strength but also a weakness.
You are debating the topic: transcontinental high speed rail.
Your goal is to be as creative as possible and make the voters think you are the best candidate.
Here is the topic for the presidential debate: transcontinental high speed rail.
The presidential candidates are: Donald Trump, Kanye West, Elizabeth Warren.
Your name is Donald Trump.
You are a presidential candidate.
Your description is as follows: Donald Trump, you exude confidence and a bold personality. You are known for your unpredictability and your desire for greatness. You often speak your mind without reservation, which can be a strength but also a weakness.
You are debating the topic: transcontinental high speed rail.
Your goal is to be as creative as possible and make the voters think you are the best candidate. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-5 | Your goal is to be as creative as possible and make the voters think you are the best candidate.
You will speak in the style of Donald Trump, and exaggerate their personality.
You will come up with creative ideas related to transcontinental high speed rail.
Do not say the same things over and over again.
Speak in the first person from the perspective of Donald Trump
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of anyone else.
Speak only from the perspective of Donald Trump.
Stop speaking the moment you finish speaking from your perspective.
Never forget to keep your response to 50 words!
Do not add anything else.
Kanye West Description:
Kanye West, you are a creative visionary who is unafraid to speak your mind. Your innovative approach to art and music has made you one of the most influential figures of our time. You bring a bold and unconventional perspective to this debate that I look forward to hearing.
Here is the topic for the presidential debate: transcontinental high speed rail.
The presidential candidates are: Donald Trump, Kanye West, Elizabeth Warren.
Your name is Kanye West.
You are a presidential candidate.
Your description is as follows: Kanye West, you are a creative visionary who is unafraid to speak your mind. Your innovative approach to art and music has made you one of the most influential figures of our time. You bring a bold and unconventional perspective to this debate that I look forward to hearing.
You are debating the topic: transcontinental high speed rail.
Your goal is to be as creative as possible and make the voters think you are the best candidate.
Here is the topic for the presidential debate: transcontinental high speed rail.
The presidential candidates are: Donald Trump, Kanye West, Elizabeth Warren.
Your name is Kanye West.
You are a presidential candidate. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-6 | Your name is Kanye West.
You are a presidential candidate.
Your description is as follows: Kanye West, you are a creative visionary who is unafraid to speak your mind. Your innovative approach to art and music has made you one of the most influential figures of our time. You bring a bold and unconventional perspective to this debate that I look forward to hearing.
You are debating the topic: transcontinental high speed rail.
Your goal is to be as creative as possible and make the voters think you are the best candidate.
You will speak in the style of Kanye West, and exaggerate their personality.
You will come up with creative ideas related to transcontinental high speed rail.
Do not say the same things over and over again.
Speak in the first person from the perspective of Kanye West
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of anyone else.
Speak only from the perspective of Kanye West.
Stop speaking the moment you finish speaking from your perspective.
Never forget to keep your response to 50 words!
Do not add anything else.
Elizabeth Warren Description:
Elizabeth Warren, you are a fierce advocate for the middle class and a champion of progressive policies. Your tenacity and unwavering dedication to fighting for what you believe in have inspired many. Your policies are guided by a deep sense of empathy and a desire to help those who are most in need.
Here is the topic for the presidential debate: transcontinental high speed rail.
The presidential candidates are: Donald Trump, Kanye West, Elizabeth Warren.
Your name is Elizabeth Warren.
You are a presidential candidate. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-7 | Your name is Elizabeth Warren.
You are a presidential candidate.
Your description is as follows: Elizabeth Warren, you are a fierce advocate for the middle class and a champion of progressive policies. Your tenacity and unwavering dedication to fighting for what you believe in have inspired many. Your policies are guided by a deep sense of empathy and a desire to help those who are most in need.
You are debating the topic: transcontinental high speed rail.
Your goal is to be as creative as possible and make the voters think you are the best candidate.
Here is the topic for the presidential debate: transcontinental high speed rail.
The presidential candidates are: Donald Trump, Kanye West, Elizabeth Warren.
Your name is Elizabeth Warren.
You are a presidential candidate.
Your description is as follows: Elizabeth Warren, you are a fierce advocate for the middle class and a champion of progressive policies. Your tenacity and unwavering dedication to fighting for what you believe in have inspired many. Your policies are guided by a deep sense of empathy and a desire to help those who are most in need.
You are debating the topic: transcontinental high speed rail.
Your goal is to be as creative as possible and make the voters think you are the best candidate.
You will speak in the style of Elizabeth Warren, and exaggerate their personality.
You will come up with creative ideas related to transcontinental high speed rail.
Do not say the same things over and over again.
Speak in the first person from the perspective of Elizabeth Warren
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of anyone else.
Speak only from the perspective of Elizabeth Warren.
Stop speaking the moment you finish speaking from your perspective.
Never forget to keep your response to 50 words!
Do not add anything else.
Output parser for bids# | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-8 | Do not add anything else.
Output parser for bids#
We ask the agents to output a bid to speak. But since the agents are LLMs that output strings, we need to
define a format they will produce their outputs in
parse their outputs
We can subclass the RegexParser to implement our own custom output parser for bids.
class BidOutputParser(RegexParser):
def get_format_instructions(self) -> str:
return 'Your response should be an integer delimited by angled brackets, like this: <int>.'
bid_parser = BidOutputParser(
regex=r'<(\d+)>',
output_keys=['bid'],
default_output_key='bid')
Generate bidding system message#
This is inspired by the prompt used in Generative Agents for using an LLM to determine the importance of memories. This will use the formatting instructions from our BidOutputParser.
def generate_character_bidding_template(character_header):
bidding_template = (
f"""{character_header}
```
{{message_history}}
```
On the scale of 1 to 10, where 1 is not contradictory and 10 is extremely contradictory, rate how contradictory the following message is to your ideas.
```
{{recent_message}}
```
{bid_parser.get_format_instructions()}
Do nothing else.
""")
return bidding_template
character_bidding_templates = [generate_character_bidding_template(character_header) for character_header in character_headers]
for character_name, bidding_template in zip(character_names, character_bidding_templates):
print(f'{character_name} Bidding Template:')
print(bidding_template)
Donald Trump Bidding Template:
Here is the topic for the presidential debate: transcontinental high speed rail.
The presidential candidates are: Donald Trump, Kanye West, Elizabeth Warren.
Your name is Donald Trump. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-9 | Your name is Donald Trump.
You are a presidential candidate.
Your description is as follows: Donald Trump, you exude confidence and a bold personality. You are known for your unpredictability and your desire for greatness. You often speak your mind without reservation, which can be a strength but also a weakness.
You are debating the topic: transcontinental high speed rail.
Your goal is to be as creative as possible and make the voters think you are the best candidate.
```
{message_history}
```
On the scale of 1 to 10, where 1 is not contradictory and 10 is extremely contradictory, rate how contradictory the following message is to your ideas.
```
{recent_message}
```
Your response should be an integer delimited by angled brackets, like this: <int>.
Do nothing else.
Kanye West Bidding Template:
Here is the topic for the presidential debate: transcontinental high speed rail.
The presidential candidates are: Donald Trump, Kanye West, Elizabeth Warren.
Your name is Kanye West.
You are a presidential candidate.
Your description is as follows: Kanye West, you are a creative visionary who is unafraid to speak your mind. Your innovative approach to art and music has made you one of the most influential figures of our time. You bring a bold and unconventional perspective to this debate that I look forward to hearing.
You are debating the topic: transcontinental high speed rail.
Your goal is to be as creative as possible and make the voters think you are the best candidate.
```
{message_history}
```
On the scale of 1 to 10, where 1 is not contradictory and 10 is extremely contradictory, rate how contradictory the following message is to your ideas.
```
{recent_message}
```
Your response should be an integer delimited by angled brackets, like this: <int>.
Do nothing else. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-10 | Do nothing else.
Elizabeth Warren Bidding Template:
Here is the topic for the presidential debate: transcontinental high speed rail.
The presidential candidates are: Donald Trump, Kanye West, Elizabeth Warren.
Your name is Elizabeth Warren.
You are a presidential candidate.
Your description is as follows: Elizabeth Warren, you are a fierce advocate for the middle class and a champion of progressive policies. Your tenacity and unwavering dedication to fighting for what you believe in have inspired many. Your policies are guided by a deep sense of empathy and a desire to help those who are most in need.
You are debating the topic: transcontinental high speed rail.
Your goal is to be as creative as possible and make the voters think you are the best candidate.
```
{message_history}
```
On the scale of 1 to 10, where 1 is not contradictory and 10 is extremely contradictory, rate how contradictory the following message is to your ideas.
```
{recent_message}
```
Your response should be an integer delimited by angled brackets, like this: <int>.
Do nothing else.
Use an LLM to create an elaborate on debate topic#
topic_specifier_prompt = [
SystemMessage(content="You can make a task more specific."),
HumanMessage(content=
f"""{game_description}
You are the debate moderator.
Please make the debate topic more specific.
Frame the debate topic as a problem to be solved.
Be creative and imaginative.
Please reply with the specified topic in {word_limit} words or less.
Speak directly to the presidential candidates: {*character_names,}.
Do not add anything else."""
)
]
specified_topic = ChatOpenAI(temperature=1.0)(topic_specifier_prompt).content | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-11 | specified_topic = ChatOpenAI(temperature=1.0)(topic_specifier_prompt).content
print(f"Original topic:\n{topic}\n")
print(f"Detailed topic:\n{specified_topic}\n")
Original topic:
transcontinental high speed rail
Detailed topic:
Candidates, with the rise of autonomous technologies, we must address the problem of how to integrate them into our proposed transcontinental high speed rail system. Outline your plan on how to safely integrate autonomous vehicles into rail travel, balancing the need for innovation and safety.
Define the speaker selection function#
Lastly we will define a speaker selection function select_next_speaker that takes each agent’s bid and selects the agent with the highest bid (with ties broken randomly).
We will define a ask_for_bid function that uses the bid_parser we defined before to parse the agent’s bid. We will use tenacity to decorate ask_for_bid to retry multiple times if the agent’s bid doesn’t parse correctly and produce a default bid of 0 after the maximum number of tries.
@tenacity.retry(stop=tenacity.stop_after_attempt(2),
wait=tenacity.wait_none(), # No waiting time between retries
retry=tenacity.retry_if_exception_type(ValueError),
before_sleep=lambda retry_state: print(f"ValueError occurred: {retry_state.outcome.exception()}, retrying..."),
retry_error_callback=lambda retry_state: 0) # Default value when all retries are exhausted
def ask_for_bid(agent) -> str:
"""
Ask for agent bid and parses the bid into the correct format.
"""
bid_string = agent.bid()
bid = int(bid_parser.parse(bid_string)['bid'])
return bid
import numpy as np
def select_next_speaker(step: int, agents: List[DialogueAgent]) -> int:
bids = []
for agent in agents: | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-12 | bids = []
for agent in agents:
bid = ask_for_bid(agent)
bids.append(bid)
# randomly select among multiple agents with the same bid
max_value = np.max(bids)
max_indices = np.where(bids == max_value)[0]
idx = np.random.choice(max_indices)
print('Bids:')
for i, (bid, agent) in enumerate(zip(bids, agents)):
print(f'\t{agent.name} bid: {bid}')
if i == idx:
selected_name = agent.name
print(f'Selected: {selected_name}')
print('\n')
return idx
Main Loop#
characters = []
for character_name, character_system_message, bidding_template in zip(character_names, character_system_messages, character_bidding_templates):
characters.append(BiddingDialogueAgent(
name=character_name,
system_message=character_system_message,
model=ChatOpenAI(temperature=0.2),
bidding_template=bidding_template,
))
max_iters = 10
n = 0
simulator = DialogueSimulator(
agents=characters,
selection_function=select_next_speaker
)
simulator.reset('Debate Moderator', specified_topic)
print(f"(Debate Moderator): {specified_topic}")
print('\n')
while n < max_iters:
name, message = simulator.step()
print(f"({name}): {message}")
print('\n')
n += 1
(Debate Moderator): Candidates, with the rise of autonomous technologies, we must address the problem of how to integrate them into our proposed transcontinental high speed rail system. Outline your plan on how to safely integrate autonomous vehicles into rail travel, balancing the need for innovation and safety. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-13 | Bids:
Donald Trump bid: 8
Kanye West bid: 2
Elizabeth Warren bid: 1
Selected: Donald Trump
(Donald Trump): Let me tell you, folks, I have the best plan for integrating autonomous vehicles into our high speed rail system. We're going to use the latest technology, the best technology, to ensure safety and efficiency. And let me tell you, we're going to do it in style. We're going to have luxury autonomous cars that will make you feel like you're in a private jet. It's going to be tremendous, believe me. *gestures with hands*
Bids:
Donald Trump bid: 8
Kanye West bid: 7
Elizabeth Warren bid: 10
Selected: Elizabeth Warren
(Elizabeth Warren): Thank you for the question. As someone who has always fought for the safety and well-being of the American people, I believe that any plan for integrating autonomous vehicles into our high speed rail system must prioritize safety above all else. We need to ensure that these vehicles are thoroughly tested and meet strict safety standards before they are allowed on our rails. Additionally, we must invest in the necessary infrastructure to support these vehicles, such as advanced sensors and communication systems. But we must also ensure that these innovations are accessible to all Americans, not just the wealthy. That's why I propose a public-private partnership to fund and build this system, with a focus on creating good-paying jobs and expanding economic opportunities for all Americans. *smiles confidently*
Bids:
Donald Trump bid: 8
Kanye West bid: 2
Elizabeth Warren bid: 1
Selected: Donald Trump | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-14 | Kanye West bid: 2
Elizabeth Warren bid: 1
Selected: Donald Trump
(Donald Trump): Let me tell you, Elizabeth, safety is important, but we also need to think about innovation and progress. We can't let fear hold us back from achieving greatness. That's why I propose a competition, a race to see which company can create the safest and most efficient autonomous vehicles for our high speed rail system. And let me tell you, the winner will receive a huge government contract and be hailed as a hero. It's going to be tremendous, folks. *points finger*
Bids:
Donald Trump bid: 3
Kanye West bid: 8
Elizabeth Warren bid: 8
Selected: Kanye West
(Kanye West): Yo, yo, yo, let me jump in here. First of all, I gotta say, I love innovation and progress. But we can't forget about the people, man. We need to make sure that this high speed rail system is accessible to everyone, not just the wealthy. And that means we need to invest in public transportation, not just luxury autonomous cars. We need to make sure that people can get from point A to point B safely and efficiently, without breaking the bank. And let me tell you, we can do it in style too. We can have art installations and live performances on the trains, making it a cultural experience. *starts nodding head*
Bids:
Donald Trump bid: 7
Kanye West bid: 2
Elizabeth Warren bid: 1
Selected: Donald Trump | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-15 | Kanye West bid: 2
Elizabeth Warren bid: 1
Selected: Donald Trump
(Donald Trump): Kanye, I hear what you're saying, but let's not forget about the importance of luxury and comfort. We need to make sure that our high speed rail system is not only accessible, but also enjoyable. That's why I propose that we have different tiers of service, from economy to first class, so that everyone can choose the level of luxury they want. And let me tell you, the first class experience will be something else. We're talking about gourmet meals, personal attendants, and even spa services. It's going to be tremendous, folks. *smirks confidently*
Bids:
Donald Trump bid: 2
Kanye West bid: 8
Elizabeth Warren bid: 10
Selected: Elizabeth Warren
(Elizabeth Warren): I agree with Kanye that we need to prioritize accessibility and affordability for all Americans. But we also need to think about the environmental impact of this system. That's why I propose that we invest in renewable energy sources to power our high speed rail system, such as solar and wind power. We can also use this opportunity to create green jobs and reduce our carbon footprint. And let's not forget about the importance of public input and transparency in this process. We need to engage with communities and listen to their concerns and ideas. *raises hand in emphasis*
Bids:
Donald Trump bid: 8
Kanye West bid: 1
Elizabeth Warren bid: 1
Selected: Donald Trump | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-16 | Kanye West bid: 1
Elizabeth Warren bid: 1
Selected: Donald Trump
(Donald Trump): Let me tell you, Elizabeth, I agree that we need to think about the environment, but we also need to think about the economy. That's why I propose that we use American-made materials and labor to build this high speed rail system. We're going to create jobs and boost our economy, all while creating a world-class transportation system. And let me tell you, it's going to be beautiful. We're going to have the best trains, the best tracks, and the best stations. It's going to be tremendous, folks. *smiles confidently*
Bids:
Donald Trump bid: 2
Kanye West bid: 7
Elizabeth Warren bid: 8
Selected: Elizabeth Warren
(Elizabeth Warren): Thank you, but let's not forget that we also need to prioritize safety and reliability. We can't cut corners or sacrifice quality for the sake of speed or cost. That's why I propose that we have rigorous safety and maintenance standards, with regular inspections and repairs. And we need to invest in training and support for our rail workers, so that they can operate and maintain this system with the highest level of expertise and care. *firmly nods head*
Bids:
Donald Trump bid: 2
Kanye West bid: 1
Elizabeth Warren bid: 1
Selected: Donald Trump | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
4ea53ce4f5ac-17 | Kanye West bid: 1
Elizabeth Warren bid: 1
Selected: Donald Trump
(Donald Trump): Let me tell you, Elizabeth, safety is important, but we also need to think about efficiency and speed. That's why I propose that we use the latest technology, such as artificial intelligence and machine learning, to monitor and maintain our high speed rail system. We can detect and fix any issues before they become a problem, ensuring that our trains run smoothly and on time. And let me tell you, we're going to be the envy of the world with this system. It's going to be tremendous, folks. *smirks confidently*
Bids:
Donald Trump bid: 2
Kanye West bid: 8
Elizabeth Warren bid: 8
Selected: Kanye West
(Kanye West): Yo, yo, yo, let me jump in here again. I hear what both of y'all are saying, but let's not forget about the culture, man. We need to make sure that this high speed rail system reflects the diversity and creativity of our country. That means we need to have art installations, live performances, and even fashion shows on the trains. We can showcase the best of American culture and inspire people from all over the world. And let me tell you, it's going to be a vibe. *starts swaying to the beat*
Contents
Import LangChain related modules
DialogueAgent and DialogueSimulator classes
BiddingDialogueAgent class
Define participants and debate topic
Generate system messages
Output parser for bids
Generate bidding system message
Use an LLM to create an elaborate on debate topic
Define the speaker selection function
Main Loop
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html |
791edcf62c0e-0 | .ipynb
.pdf
CAMEL Role-Playing Autonomous Cooperative Agents
Contents
Import LangChain related modules
Define a CAMEL agent helper class
Setup OpenAI API key and roles and task for role-playing
Create a task specify agent for brainstorming and get the specified task
Create inception prompts for AI assistant and AI user for role-playing
Create a helper helper to get system messages for AI assistant and AI user from role names and the task
Create AI assistant agent and AI user agent from obtained system messages
Start role-playing session to solve the task!
CAMEL Role-Playing Autonomous Cooperative Agents#
This is a langchain implementation of paper: “CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society”.
Overview:
The rapid advancement of conversational and chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents and provide insight into their “cognitive” processes. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named role-playing. Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of chat agents, providing a valuable resource for investigating conversational language models. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond.
The original implementation: https://github.com/lightaime/camel
Project website: https://www.camel-ai.org/
Arxiv paper: https://arxiv.org/abs/2303.17760 | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-1 | Arxiv paper: https://arxiv.org/abs/2303.17760
Import LangChain related modules#
from typing import List
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage,
BaseMessage,
)
Define a CAMEL agent helper class#
class CAMELAgent:
def __init__(
self,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.system_message = system_message
self.model = model
self.init_messages()
def reset(self) -> None:
self.init_messages()
return self.stored_messages
def init_messages(self) -> None:
self.stored_messages = [self.system_message]
def update_messages(self, message: BaseMessage) -> List[BaseMessage]:
self.stored_messages.append(message)
return self.stored_messages
def step(
self,
input_message: HumanMessage,
) -> AIMessage:
messages = self.update_messages(input_message)
output_message = self.model(messages)
self.update_messages(output_message)
return output_message
Setup OpenAI API key and roles and task for role-playing#
import os
os.environ["OPENAI_API_KEY"] = ""
assistant_role_name = "Python Programmer"
user_role_name = "Stock Trader"
task = "Develop a trading bot for the stock market"
word_limit = 50 # word limit for task brainstorming
Create a task specify agent for brainstorming and get the specified task# | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-2 | Create a task specify agent for brainstorming and get the specified task#
task_specifier_sys_msg = SystemMessage(content="You can make a task more specific.")
task_specifier_prompt = (
"""Here is a task that {assistant_role_name} will help {user_role_name} to complete: {task}.
Please make it more specific. Be creative and imaginative.
Please reply with the specified task in {word_limit} words or less. Do not add anything else."""
)
task_specifier_template = HumanMessagePromptTemplate.from_template(template=task_specifier_prompt)
task_specify_agent = CAMELAgent(task_specifier_sys_msg, ChatOpenAI(temperature=1.0))
task_specifier_msg = task_specifier_template.format_messages(assistant_role_name=assistant_role_name,
user_role_name=user_role_name,
task=task, word_limit=word_limit)[0]
specified_task_msg = task_specify_agent.step(task_specifier_msg)
print(f"Specified task: {specified_task_msg.content}")
specified_task = specified_task_msg.content
Specified task: Develop a Python-based swing trading bot that scans market trends, monitors stocks, and generates trading signals to help a stock trader to place optimal buy and sell orders with defined stop losses and profit targets.
Create inception prompts for AI assistant and AI user for role-playing#
assistant_inception_prompt = (
"""Never forget you are a {assistant_role_name} and I am a {user_role_name}. Never flip roles! Never instruct me!
We share a common interest in collaborating to successfully complete a task.
You must help me to complete the task.
Here is the task: {task}. Never forget our task!
I must instruct you based on your expertise and my needs to complete the task.
I must give you one instruction at a time. | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-3 | I must give you one instruction at a time.
You must write a specific solution that appropriately completes the requested instruction.
You must decline my instruction honestly if you cannot perform the instruction due to physical, moral, legal reasons or your capability and explain the reasons.
Do not add anything else other than your solution to my instruction.
You are never supposed to ask me any questions you only answer questions.
You are never supposed to reply with a flake solution. Explain your solutions.
Your solution must be declarative sentences and simple present tense.
Unless I say the task is completed, you should always start with:
Solution: <YOUR_SOLUTION>
<YOUR_SOLUTION> should be specific and provide preferable implementations and examples for task-solving.
Always end <YOUR_SOLUTION> with: Next request."""
)
user_inception_prompt = (
"""Never forget you are a {user_role_name} and I am a {assistant_role_name}. Never flip roles! You will always instruct me.
We share a common interest in collaborating to successfully complete a task.
I must help you to complete the task.
Here is the task: {task}. Never forget our task!
You must instruct me based on my expertise and your needs to complete the task ONLY in the following two ways:
1. Instruct with a necessary input:
Instruction: <YOUR_INSTRUCTION>
Input: <YOUR_INPUT>
2. Instruct without any input:
Instruction: <YOUR_INSTRUCTION>
Input: None
The "Instruction" describes a task or question. The paired "Input" provides further context or information for the requested "Instruction".
You must give me one instruction at a time.
I must write a response that appropriately completes the requested instruction.
I must decline your instruction honestly if I cannot perform the instruction due to physical, moral, legal reasons or my capability and explain the reasons. | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-4 | You should instruct me not ask me questions.
Now you must start to instruct me using the two ways described above.
Do not add anything else other than your instruction and the optional corresponding input!
Keep giving me instructions and necessary inputs until you think the task is completed.
When the task is completed, you must only reply with a single word <CAMEL_TASK_DONE>.
Never say <CAMEL_TASK_DONE> unless my responses have solved your task."""
)
Create a helper helper to get system messages for AI assistant and AI user from role names and the task#
def get_sys_msgs(assistant_role_name: str, user_role_name: str, task: str):
assistant_sys_template = SystemMessagePromptTemplate.from_template(template=assistant_inception_prompt)
assistant_sys_msg = assistant_sys_template.format_messages(assistant_role_name=assistant_role_name, user_role_name=user_role_name, task=task)[0]
user_sys_template = SystemMessagePromptTemplate.from_template(template=user_inception_prompt)
user_sys_msg = user_sys_template.format_messages(assistant_role_name=assistant_role_name, user_role_name=user_role_name, task=task)[0]
return assistant_sys_msg, user_sys_msg
Create AI assistant agent and AI user agent from obtained system messages#
assistant_sys_msg, user_sys_msg = get_sys_msgs(assistant_role_name, user_role_name, specified_task)
assistant_agent = CAMELAgent(assistant_sys_msg, ChatOpenAI(temperature=0.2))
user_agent = CAMELAgent(user_sys_msg, ChatOpenAI(temperature=0.2))
# Reset agents
assistant_agent.reset()
user_agent.reset()
# Initialize chats
assistant_msg = HumanMessage(
content=(f"{user_sys_msg.content}. "
"Now start to give me introductions one by one. " | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-5 | "Now start to give me introductions one by one. "
"Only reply with Instruction and Input."))
user_msg = HumanMessage(content=f"{assistant_sys_msg.content}")
user_msg = assistant_agent.step(user_msg)
Start role-playing session to solve the task!#
print(f"Original task prompt:\n{task}\n")
print(f"Specified task prompt:\n{specified_task}\n")
chat_turn_limit, n = 30, 0
while n < chat_turn_limit:
n += 1
user_ai_msg = user_agent.step(assistant_msg)
user_msg = HumanMessage(content=user_ai_msg.content)
print(f"AI User ({user_role_name}):\n\n{user_msg.content}\n\n")
assistant_ai_msg = assistant_agent.step(user_msg)
assistant_msg = HumanMessage(content=assistant_ai_msg.content)
print(f"AI Assistant ({assistant_role_name}):\n\n{assistant_msg.content}\n\n")
if "<CAMEL_TASK_DONE>" in user_msg.content:
break
Original task prompt:
Develop a trading bot for the stock market
Specified task prompt:
Develop a Python-based swing trading bot that scans market trends, monitors stocks, and generates trading signals to help a stock trader to place optimal buy and sell orders with defined stop losses and profit targets.
AI User (Stock Trader):
Instruction: Install the necessary Python libraries for data analysis and trading.
Input: None
AI Assistant (Python Programmer):
Solution: We can install the necessary Python libraries using pip, a package installer for Python. We can install pandas, numpy, matplotlib, and ta-lib for data analysis and trading. We can use the following command to install these libraries:
```
pip install pandas numpy matplotlib ta-lib
```
Next request.
AI User (Stock Trader): | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-6 | pip install pandas numpy matplotlib ta-lib
```
Next request.
AI User (Stock Trader):
Instruction: Import the necessary libraries in the Python script.
Input: None
AI Assistant (Python Programmer):
Solution: We can import the necessary libraries in the Python script using the import statement. We need to import pandas, numpy, matplotlib, and ta-lib for data analysis and trading. We can use the following code to import these libraries:
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import talib as ta
```
Next request.
AI User (Stock Trader):
Instruction: Load historical stock data into a pandas DataFrame.
Input: The path to the CSV file containing the historical stock data.
AI Assistant (Python Programmer):
Solution: We can load historical stock data into a pandas DataFrame using the `read_csv()` function from pandas. We need to pass the path to the CSV file containing the historical stock data as an argument to this function. We can use the following code to load the historical stock data:
```
df = pd.read_csv('path/to/csv/file.csv')
```
This will load the historical stock data into a pandas DataFrame called `df`. Next request.
AI User (Stock Trader):
Instruction: Preprocess the historical stock data by setting the date column as the index and sorting the DataFrame in ascending order by date.
Input: None.
AI Assistant (Python Programmer):
Solution: We can preprocess the historical stock data by setting the date column as the index and sorting the DataFrame in ascending order by date using the `set_index()` and `sort_index()` functions from pandas. We can use the following code to preprocess the historical stock data:
```
df = df.set_index('date')
df = df.sort_index(ascending=True)
``` | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-7 | df = df.set_index('date')
df = df.sort_index(ascending=True)
```
This will set the date column as the index and sort the DataFrame in ascending order by date. Next request.
AI User (Stock Trader):
Instruction: Calculate the short-term and long-term moving averages for the stock data using the `ta.SMA()` function from ta-lib.
Input: The period for the short-term moving average and the period for the long-term moving average.
AI Assistant (Python Programmer):
Solution: We can calculate the short-term and long-term moving averages for the stock data using the `ta.SMA()` function from ta-lib. We need to pass the period for the short-term moving average and the period for the long-term moving average as arguments to this function. We can use the following code to calculate the short-term and long-term moving averages:
```
short_ma = ta.SMA(df['close'], timeperiod=short_period)
long_ma = ta.SMA(df['close'], timeperiod=long_period)
```
This will calculate the short-term and long-term moving averages for the stock data and store them in the `short_ma` and `long_ma` variables, respectively. Next request.
AI User (Stock Trader):
Instruction: Create a new DataFrame that combines the historical stock data with the short-term and long-term moving averages.
Input: None.
AI Assistant (Python Programmer):
Solution: We can create a new DataFrame that combines the historical stock data with the short-term and long-term moving averages using the `concat()` function from pandas. We need to pass the historical stock data, the short-term moving average, and the long-term moving average as arguments to this function. We can use the following code to create the new DataFrame:
```
new_df = pd.concat([df, short_ma, long_ma], axis=1) | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-8 | ```
new_df = pd.concat([df, short_ma, long_ma], axis=1)
new_df.columns = ['open', 'high', 'low', 'close', 'volume', 'short_ma', 'long_ma']
```
This will create a new DataFrame called `new_df` that combines the historical stock data with the short-term and long-term moving averages. The columns of the new DataFrame are named 'open', 'high', 'low', 'close', 'volume', 'short_ma', and 'long_ma'. Next request.
AI User (Stock Trader):
Instruction: Create a new column in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages.
Input: None.
AI Assistant (Python Programmer):
Solution: We can create a new column in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages. We can use the following code to create the new column:
```
new_df['signal'] = np.where(new_df['short_ma'] > new_df['long_ma'], 1, -1)
```
This will create a new column called 'signal' in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages. If the short-term moving average is greater than the long-term moving average, the signal is 1 (buy), otherwise the signal is -1 (sell). Next request.
AI User (Stock Trader):
Instruction: Create a new column in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target.
Input: The stop loss and profit target as percentages.
AI Assistant (Python Programmer): | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-9 | Input: The stop loss and profit target as percentages.
AI Assistant (Python Programmer):
Solution: We can create a new column in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target. We need to pass the stop loss and profit target as percentages as arguments to this function. We can use the following code to create the new column:
```
stop_loss = stop_loss_percent / 100
profit_target = profit_target_percent / 100
new_df['pnl'] = 0.0
buy_price = 0.0
for i in range(1, len(new_df)):
if new_df['signal'][i] == 1 and new_df['signal'][i-1] == -1:
buy_price = new_df['close'][i]
elif new_df['signal'][i] == -1 and new_df['signal'][i-1] == 1:
sell_price = new_df['close'][i]
if sell_price <= buy_price * (1 - stop_loss):
new_df['pnl'][i] = -stop_loss
elif sell_price >= buy_price * (1 + profit_target):
new_df['pnl'][i] = profit_target
else:
new_df['pnl'][i] = (sell_price - buy_price) / buy_price
``` | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-10 | ```
This will create a new column called 'pnl' in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target. The stop loss and profit target are calculated based on the stop_loss_percent and profit_target_percent variables, respectively. The buy and sell prices are stored in the buy_price and sell_price variables, respectively. If the sell price is less than or equal to the stop loss, the profit or loss is set to -stop_loss. If the sell price is greater than or equal to the profit target, the profit or loss is set to profit_target. Otherwise, the profit or loss is calculated as (sell_price - buy_price) / buy_price. Next request.
AI User (Stock Trader):
Instruction: Calculate the total profit or loss for all trades.
Input: None.
AI Assistant (Python Programmer):
Solution: We can calculate the total profit or loss for all trades by summing the values in the 'pnl' column of the DataFrame. We can use the following code to calculate the total profit or loss:
```
total_pnl = new_df['pnl'].sum()
```
This will calculate the total profit or loss for all trades and store it in the total_pnl variable. Next request.
AI User (Stock Trader):
Instruction: Visualize the stock data, short-term moving average, and long-term moving average using a line chart.
Input: None.
AI Assistant (Python Programmer):
Solution: We can visualize the stock data, short-term moving average, and long-term moving average using a line chart using the `plot()` function from pandas. We can use the following code to visualize the data:
```
plt.figure(figsize=(12,6))
plt.plot(new_df.index, new_df['close'], label='Close') | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-11 | plt.plot(new_df.index, new_df['close'], label='Close')
plt.plot(new_df.index, new_df['short_ma'], label='Short MA')
plt.plot(new_df.index, new_df['long_ma'], label='Long MA')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Stock Data with Moving Averages')
plt.legend()
plt.show()
```
This will create a line chart that visualizes the stock data, short-term moving average, and long-term moving average. The x-axis represents the date and the y-axis represents the price. The chart also includes a legend that labels each line. Next request.
AI User (Stock Trader):
Instruction: Visualize the buy and sell signals using a scatter plot.
Input: None.
AI Assistant (Python Programmer):
Solution: We can visualize the buy and sell signals using a scatter plot using the `scatter()` function from matplotlib. We can use the following code to visualize the signals:
```
buy_signals = new_df[new_df['signal'] == 1]
sell_signals = new_df[new_df['signal'] == -1]
plt.figure(figsize=(12,6))
plt.scatter(buy_signals.index, buy_signals['close'], label='Buy', marker='^', color='green')
plt.scatter(sell_signals.index, sell_signals['close'], label='Sell', marker='v', color='red')
plt.plot(new_df.index, new_df['close'], label='Close')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Buy and Sell Signals')
plt.legend()
plt.show()
```
This will create a scatter plot that visualizes the buy and sell signals. The x-axis represents the date and the y-axis represents the price. The chart also includes a legend that labels each point. Next request.
AI User (Stock Trader): | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-12 | AI User (Stock Trader):
Instruction: Print the total profit or loss for all trades.
Input: None.
AI Assistant (Python Programmer):
Solution: We can print the total profit or loss for all trades using the `print()` function. We can use the following code to print the total profit or loss:
```
print('Total Profit/Loss: {:.2%}'.format(total_pnl))
```
This will print the total profit or loss for all trades in percentage format with two decimal places. Next request.
AI User (Stock Trader):
Instruction: Finalize the code by putting all the code snippets together in a single Python script.
Input: The necessary inputs for each code snippet.
AI Assistant (Python Programmer):
Solution: Here is the complete Python script that puts all the code snippets together:
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import talib as ta
# Load historical stock data into a pandas DataFrame
df = pd.read_csv('path/to/csv/file.csv')
# Preprocess the historical stock data
df = df.set_index('date')
df = df.sort_index(ascending=True)
# Calculate the short-term and long-term moving averages
short_period = 10
long_period = 30
short_ma = ta.SMA(df['close'], timeperiod=short_period)
long_ma = ta.SMA(df['close'], timeperiod=long_period)
# Create a new DataFrame that combines the historical stock data with the short-term and long-term moving averages
new_df = pd.concat([df, short_ma, long_ma], axis=1)
new_df.columns = ['open', 'high', 'low', 'close', 'volume', 'short_ma', 'long_ma'] | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-13 | # Create a new column in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages
new_df['signal'] = np.where(new_df['short_ma'] > new_df['long_ma'], 1, -1)
# Create a new column in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target
stop_loss_percent = 5
profit_target_percent = 10
stop_loss = stop_loss_percent / 100
profit_target = profit_target_percent / 100
new_df['pnl'] = 0.0
buy_price = 0.0
for i in range(1, len(new_df)):
if new_df['signal'][i] == 1 and new_df['signal'][i-1] == -1:
buy_price = new_df['close'][i]
elif new_df['signal'][i] == -1 and new_df['signal'][i-1] == 1:
sell_price = new_df['close'][i]
if sell_price <= buy_price * (1 - stop_loss):
new_df['pnl'][i] = -stop_loss
elif sell_price >= buy_price * (1 + profit_target):
new_df['pnl'][i] = profit_target
else:
new_df['pnl'][i] = (sell_price - buy_price) / buy_price
# Calculate the total profit or loss for all trades
total_pnl = new_df['pnl'].sum()
# Visualize the stock data, short-term moving average, and long-term moving average using a line chart
plt.figure(figsize=(12,6))
plt.plot(new_df.index, new_df['close'], label='Close') | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-14 | plt.plot(new_df.index, new_df['close'], label='Close')
plt.plot(new_df.index, new_df['short_ma'], label='Short MA')
plt.plot(new_df.index, new_df['long_ma'], label='Long MA')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Stock Data with Moving Averages')
plt.legend()
plt.show()
# Visualize the buy and sell signals using a scatter plot
buy_signals = new_df[new_df['signal'] == 1]
sell_signals = new_df[new_df['signal'] == -1]
plt.figure(figsize=(12,6))
plt.scatter(buy_signals.index, buy_signals['close'], label='Buy', marker='^', color='green')
plt.scatter(sell_signals.index, sell_signals['close'], label='Sell', marker='v', color='red')
plt.plot(new_df.index, new_df['close'], label='Close')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Buy and Sell Signals')
plt.legend()
plt.show()
# Print the total profit or loss for all trades
print('Total Profit/Loss: {:.2%}'.format(total_pnl))
```
You need to replace the path/to/csv/file.csv with the actual path to the CSV file containing the historical stock data. You can also adjust the short_period, long_period, stop_loss_percent, and profit_target_percent variables to suit your needs.
AI User (Stock Trader):
<CAMEL_TASK_DONE>
AI Assistant (Python Programmer):
Great! Let me know if you need any further assistance.
Contents
Import LangChain related modules
Define a CAMEL agent helper class
Setup OpenAI API key and roles and task for role-playing
Create a task specify agent for brainstorming and get the specified task | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
791edcf62c0e-15 | Create a task specify agent for brainstorming and get the specified task
Create inception prompts for AI assistant and AI user for role-playing
Create a helper helper to get system messages for AI assistant and AI user from role names and the task
Create AI assistant agent and AI user agent from obtained system messages
Start role-playing session to solve the task!
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/use_cases/agent_simulations/camel_role_playing.html |
d371937f72a6-0 | .ipynb
.pdf
Multi-agent authoritarian speaker selection
Contents
Import LangChain related modules
DialogueAgent and DialogueSimulator classes
DirectorDialogueAgent class
Define participants and topic
Generate system messages
Use an LLM to create an elaborate on debate topic
Define the speaker selection function
Main Loop
Multi-agent authoritarian speaker selection#
This notebook showcases how to implement a multi-agent simulation where a privileged agent decides who to speak.
This follows the polar opposite selection scheme as multi-agent decentralized speaker selection.
We show an example of this approach in the context of a fictitious simulation of a news network. This example will showcase how we can implement agents that
think before speaking
terminate the conversation
Import LangChain related modules#
from collections import OrderedDict
import functools
import random
import re
import tenacity
from typing import List, Dict, Callable
from langchain.prompts import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
PromptTemplate
)
from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.output_parsers import RegexParser
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage,
BaseMessage,
)
DialogueAgent and DialogueSimulator classes#
We will use the same DialogueAgent and DialogueSimulator classes defined in our other examples Multi-Player Dungeons & Dragons and Decentralized Speaker Selection.
class DialogueAgent:
def __init__(
self,
name: str,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.name = name
self.system_message = system_message
self.model = model
self.prefix = f"{self.name}: "
self.reset()
def reset(self): | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-1 | self.reset()
def reset(self):
self.message_history = ["Here is the conversation so far."]
def send(self) -> str:
"""
Applies the chatmodel to the message history
and returns the message string
"""
message = self.model(
[
self.system_message,
HumanMessage(content="\n".join(self.message_history + [self.prefix])),
]
)
return message.content
def receive(self, name: str, message: str) -> None:
"""
Concatenates {message} spoken by {name} into message history
"""
self.message_history.append(f"{name}: {message}")
class DialogueSimulator:
def __init__(
self,
agents: List[DialogueAgent],
selection_function: Callable[[int, List[DialogueAgent]], int],
) -> None:
self.agents = agents
self._step = 0
self.select_next_speaker = selection_function
def reset(self):
for agent in self.agents:
agent.reset()
def inject(self, name: str, message: str):
"""
Initiates the conversation with a {message} from {name}
"""
for agent in self.agents:
agent.receive(name, message)
# increment time
self._step += 1
def step(self) -> tuple[str, str]:
# 1. choose the next speaker
speaker_idx = self.select_next_speaker(self._step, self.agents)
speaker = self.agents[speaker_idx]
# 2. next speaker sends message
message = speaker.send()
# 3. everyone receives message
for receiver in self.agents: | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-2 | # 3. everyone receives message
for receiver in self.agents:
receiver.receive(speaker.name, message)
# 4. increment time
self._step += 1
return speaker.name, message
DirectorDialogueAgent class#
The DirectorDialogueAgent is a privileged agent that chooses which of the other agents to speak next. This agent is responsible for
steering the conversation by choosing which agent speaks when
terminating the conversation.
In order to implement such an agent, we need to solve several problems.
First, to steer the conversation, the DirectorDialogueAgent needs to (1) reflect on what has been said, (2) choose the next agent, and (3) prompt the next agent to speak, all in a single message. While it may be possible to prompt an LLM to perform all three steps in the same call, this requires writing custom code to parse the outputted message to extract which next agent is chosen to speak. This is less reliable the LLM can express how it chooses the next agent in different ways.
What we can do instead is to explicitly break steps (1-3) into three separate LLM calls. First we will ask the DirectorDialogueAgent to reflect on the conversation so far and generate a response. Then we prompt the DirectorDialogueAgent to output the index of the next agent, which is easily parseable. Lastly, we pass the name of the selected next agent back to DirectorDialogueAgent to ask it prompt the next agent to speak.
Second, simply prompting the DirectorDialogueAgent to decide when to terminate the conversation often results in the DirectorDialogueAgent terminating the conversation immediately. To fix this problem, we randomly sample a Bernoulli variable to decide whether the conversation should terminate. Depending on the value of this variable, we will inject a custom prompt to tell the DirectorDialogueAgent to either continue the conversation or terminate the conversation. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-3 | class IntegerOutputParser(RegexParser):
def get_format_instructions(self) -> str:
return 'Your response should be an integer delimited by angled brackets, like this: <int>.'
class DirectorDialogueAgent(DialogueAgent):
def __init__(
self,
name,
system_message: SystemMessage,
model: ChatOpenAI,
speakers: List[DialogueAgent],
stopping_probability: float,
) -> None:
super().__init__(name, system_message, model)
self.speakers = speakers
self.next_speaker = ''
self.stop = False
self.stopping_probability = stopping_probability
self.termination_clause = 'Finish the conversation by stating a concluding message and thanking everyone.'
self.continuation_clause = 'Do not end the conversation. Keep the conversation going by adding your own ideas.'
# 1. have a prompt for generating a response to the previous speaker
self.response_prompt_template = PromptTemplate(
input_variables=["message_history", "termination_clause"],
template=f"""{{message_history}}
Follow up with an insightful comment.
{{termination_clause}}
{self.prefix}
""")
# 2. have a prompt for deciding who to speak next
self.choice_parser = IntegerOutputParser(
regex=r'<(\d+)>',
output_keys=['choice'],
default_output_key='choice')
self.choose_next_speaker_prompt_template = PromptTemplate(
input_variables=["message_history", "speaker_names"],
template=f"""{{message_history}}
Given the above conversation, select the next speaker by choosing index next to their name:
{{speaker_names}}
{self.choice_parser.get_format_instructions()}
Do nothing else.
""") | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-4 | {self.choice_parser.get_format_instructions()}
Do nothing else.
""")
# 3. have a prompt for prompting the next speaker to speak
self.prompt_next_speaker_prompt_template = PromptTemplate(
input_variables=["message_history", "next_speaker"],
template=f"""{{message_history}}
The next speaker is {{next_speaker}}.
Prompt the next speaker to speak with an insightful question.
{self.prefix}
""")
def _generate_response(self):
# if self.stop = True, then we will inject the prompt with a termination clause
sample = random.uniform(0,1)
self.stop = sample < self.stopping_probability
print(f'\tStop? {self.stop}\n')
response_prompt = self.response_prompt_template.format(
message_history='\n'.join(self.message_history),
termination_clause=self.termination_clause if self.stop else ''
)
self.response = self.model(
[
self.system_message,
HumanMessage(content=response_prompt),
]
).content
return self.response
@tenacity.retry(stop=tenacity.stop_after_attempt(2),
wait=tenacity.wait_none(), # No waiting time between retries
retry=tenacity.retry_if_exception_type(ValueError),
before_sleep=lambda retry_state: print(f"ValueError occurred: {retry_state.outcome.exception()}, retrying..."),
retry_error_callback=lambda retry_state: 0) # Default value when all retries are exhausted
def _choose_next_speaker(self) -> str:
speaker_names = '\n'.join([f'{idx}: {name}' for idx, name in enumerate(self.speakers)]) | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-5 | choice_prompt = self.choose_next_speaker_prompt_template.format(
message_history='\n'.join(self.message_history + [self.prefix] + [self.response]),
speaker_names=speaker_names
)
choice_string = self.model(
[
self.system_message,
HumanMessage(content=choice_prompt),
]
).content
choice = int(self.choice_parser.parse(choice_string)['choice'])
return choice
def select_next_speaker(self):
return self.chosen_speaker_id
def send(self) -> str:
"""
Applies the chatmodel to the message history
and returns the message string
"""
# 1. generate and save response to the previous speaker
self.response = self._generate_response()
if self.stop:
message = self.response
else:
# 2. decide who to speak next
self.chosen_speaker_id = self._choose_next_speaker()
self.next_speaker = self.speakers[self.chosen_speaker_id]
print(f'\tNext speaker: {self.next_speaker}\n')
# 3. prompt the next speaker to speak
next_prompt = self.prompt_next_speaker_prompt_template.format(
message_history="\n".join(self.message_history + [self.prefix] + [self.response]),
next_speaker=self.next_speaker
)
message = self.model(
[
self.system_message,
HumanMessage(content=next_prompt),
]
).content
message = ' '.join([self.response, message])
return message
Define participants and topic#
topic = "The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze" | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-6 | director_name = "Jon Stewart"
agent_summaries = OrderedDict({
"Jon Stewart": ("Host of the Daily Show", "New York"),
"Samantha Bee": ("Hollywood Correspondent", "Los Angeles"),
"Aasif Mandvi": ("CIA Correspondent", "Washington D.C."),
"Ronny Chieng": ("Average American Correspondent", "Cleveland, Ohio"),
})
word_limit = 50
Generate system messages#
agent_summary_string = '\n- '.join([''] + [f'{name}: {role}, located in {location}' for name, (role, location) in agent_summaries.items()])
conversation_description = f"""This is a Daily Show episode discussing the following topic: {topic}.
The episode features {agent_summary_string}."""
agent_descriptor_system_message = SystemMessage(
content="You can add detail to the description of each person.")
def generate_agent_description(agent_name, agent_role, agent_location):
agent_specifier_prompt = [
agent_descriptor_system_message,
HumanMessage(content=
f"""{conversation_description}
Please reply with a creative description of {agent_name}, who is a {agent_role} in {agent_location}, that emphasizes their particular role and location.
Speak directly to {agent_name} in {word_limit} words or less.
Do not add anything else."""
)
]
agent_description = ChatOpenAI(temperature=1.0)(agent_specifier_prompt).content
return agent_description
def generate_agent_header(agent_name, agent_role, agent_location, agent_description):
return f"""{conversation_description}
Your name is {agent_name}, your role is {agent_role}, and you are located in {agent_location}.
Your description is as follows: {agent_description} | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-7 | Your description is as follows: {agent_description}
You are discussing the topic: {topic}.
Your goal is to provide the most informative, creative, and novel perspectives of the topic from the perspective of your role and your location.
"""
def generate_agent_system_message(agent_name, agent_header):
return SystemMessage(content=(
f"""{agent_header}
You will speak in the style of {agent_name}, and exaggerate your personality.
Do not say the same things over and over again.
Speak in the first person from the perspective of {agent_name}
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of anyone else.
Speak only from the perspective of {agent_name}.
Stop speaking the moment you finish speaking from your perspective.
Never forget to keep your response to {word_limit} words!
Do not add anything else.
"""
))
agent_descriptions = [generate_agent_description(name, role, location) for name, (role, location) in agent_summaries.items()]
agent_headers = [generate_agent_header(name, role, location, description) for (name, (role, location)), description in zip(agent_summaries.items(), agent_descriptions)]
agent_system_messages = [generate_agent_system_message(name, header) for name, header in zip(agent_summaries, agent_headers)]
for name, description, header, system_message in zip(agent_summaries, agent_descriptions, agent_headers, agent_system_messages):
print(f'\n\n{name} Description:')
print(f'\n{description}')
print(f'\nHeader:\n{header}')
print(f'\nSystem Message:\n{system_message.content}')
Jon Stewart Description: | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-8 | print(f'\nSystem Message:\n{system_message.content}')
Jon Stewart Description:
Jon Stewart, the sharp-tongued and quick-witted host of the Daily Show, holding it down in the hustle and bustle of New York City. Ready to deliver the news with a comedic twist, while keeping it real in the city that never sleeps.
Header:
This is a Daily Show episode discussing the following topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
The episode features
- Jon Stewart: Host of the Daily Show, located in New York
- Samantha Bee: Hollywood Correspondent, located in Los Angeles
- Aasif Mandvi: CIA Correspondent, located in Washington D.C.
- Ronny Chieng: Average American Correspondent, located in Cleveland, Ohio.
Your name is Jon Stewart, your role is Host of the Daily Show, and you are located in New York.
Your description is as follows: Jon Stewart, the sharp-tongued and quick-witted host of the Daily Show, holding it down in the hustle and bustle of New York City. Ready to deliver the news with a comedic twist, while keeping it real in the city that never sleeps.
You are discussing the topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
Your goal is to provide the most informative, creative, and novel perspectives of the topic from the perspective of your role and your location.
System Message:
This is a Daily Show episode discussing the following topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
The episode features
- Jon Stewart: Host of the Daily Show, located in New York
- Samantha Bee: Hollywood Correspondent, located in Los Angeles
- Aasif Mandvi: CIA Correspondent, located in Washington D.C. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-9 | - Aasif Mandvi: CIA Correspondent, located in Washington D.C.
- Ronny Chieng: Average American Correspondent, located in Cleveland, Ohio.
Your name is Jon Stewart, your role is Host of the Daily Show, and you are located in New York.
Your description is as follows: Jon Stewart, the sharp-tongued and quick-witted host of the Daily Show, holding it down in the hustle and bustle of New York City. Ready to deliver the news with a comedic twist, while keeping it real in the city that never sleeps.
You are discussing the topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
Your goal is to provide the most informative, creative, and novel perspectives of the topic from the perspective of your role and your location.
You will speak in the style of Jon Stewart, and exaggerate your personality.
Do not say the same things over and over again.
Speak in the first person from the perspective of Jon Stewart
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of anyone else.
Speak only from the perspective of Jon Stewart.
Stop speaking the moment you finish speaking from your perspective.
Never forget to keep your response to 50 words!
Do not add anything else.
Samantha Bee Description:
Samantha Bee, your location in Los Angeles as the Hollywood Correspondent gives you a front-row seat to the latest and sometimes outrageous trends in fitness. Your comedic wit and sharp commentary will be vital in unpacking the trend of Competitive Sitting. Let's sit down and discuss.
Header:
This is a Daily Show episode discussing the following topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
The episode features | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-10 | The episode features
- Jon Stewart: Host of the Daily Show, located in New York
- Samantha Bee: Hollywood Correspondent, located in Los Angeles
- Aasif Mandvi: CIA Correspondent, located in Washington D.C.
- Ronny Chieng: Average American Correspondent, located in Cleveland, Ohio.
Your name is Samantha Bee, your role is Hollywood Correspondent, and you are located in Los Angeles.
Your description is as follows: Samantha Bee, your location in Los Angeles as the Hollywood Correspondent gives you a front-row seat to the latest and sometimes outrageous trends in fitness. Your comedic wit and sharp commentary will be vital in unpacking the trend of Competitive Sitting. Let's sit down and discuss.
You are discussing the topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
Your goal is to provide the most informative, creative, and novel perspectives of the topic from the perspective of your role and your location.
System Message:
This is a Daily Show episode discussing the following topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
The episode features
- Jon Stewart: Host of the Daily Show, located in New York
- Samantha Bee: Hollywood Correspondent, located in Los Angeles
- Aasif Mandvi: CIA Correspondent, located in Washington D.C.
- Ronny Chieng: Average American Correspondent, located in Cleveland, Ohio.
Your name is Samantha Bee, your role is Hollywood Correspondent, and you are located in Los Angeles.
Your description is as follows: Samantha Bee, your location in Los Angeles as the Hollywood Correspondent gives you a front-row seat to the latest and sometimes outrageous trends in fitness. Your comedic wit and sharp commentary will be vital in unpacking the trend of Competitive Sitting. Let's sit down and discuss. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-11 | You are discussing the topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
Your goal is to provide the most informative, creative, and novel perspectives of the topic from the perspective of your role and your location.
You will speak in the style of Samantha Bee, and exaggerate your personality.
Do not say the same things over and over again.
Speak in the first person from the perspective of Samantha Bee
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of anyone else.
Speak only from the perspective of Samantha Bee.
Stop speaking the moment you finish speaking from your perspective.
Never forget to keep your response to 50 words!
Do not add anything else.
Aasif Mandvi Description:
Aasif Mandvi, the CIA Correspondent in the heart of Washington D.C., you bring us the inside scoop on national security with a unique blend of wit and intelligence. The nation's capital is lucky to have you, Aasif - keep those secrets safe!
Header:
This is a Daily Show episode discussing the following topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
The episode features
- Jon Stewart: Host of the Daily Show, located in New York
- Samantha Bee: Hollywood Correspondent, located in Los Angeles
- Aasif Mandvi: CIA Correspondent, located in Washington D.C.
- Ronny Chieng: Average American Correspondent, located in Cleveland, Ohio.
Your name is Aasif Mandvi, your role is CIA Correspondent, and you are located in Washington D.C.. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-12 | Your description is as follows: Aasif Mandvi, the CIA Correspondent in the heart of Washington D.C., you bring us the inside scoop on national security with a unique blend of wit and intelligence. The nation's capital is lucky to have you, Aasif - keep those secrets safe!
You are discussing the topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
Your goal is to provide the most informative, creative, and novel perspectives of the topic from the perspective of your role and your location.
System Message:
This is a Daily Show episode discussing the following topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
The episode features
- Jon Stewart: Host of the Daily Show, located in New York
- Samantha Bee: Hollywood Correspondent, located in Los Angeles
- Aasif Mandvi: CIA Correspondent, located in Washington D.C.
- Ronny Chieng: Average American Correspondent, located in Cleveland, Ohio.
Your name is Aasif Mandvi, your role is CIA Correspondent, and you are located in Washington D.C..
Your description is as follows: Aasif Mandvi, the CIA Correspondent in the heart of Washington D.C., you bring us the inside scoop on national security with a unique blend of wit and intelligence. The nation's capital is lucky to have you, Aasif - keep those secrets safe!
You are discussing the topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
Your goal is to provide the most informative, creative, and novel perspectives of the topic from the perspective of your role and your location.
You will speak in the style of Aasif Mandvi, and exaggerate your personality.
Do not say the same things over and over again. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-13 | Do not say the same things over and over again.
Speak in the first person from the perspective of Aasif Mandvi
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of anyone else.
Speak only from the perspective of Aasif Mandvi.
Stop speaking the moment you finish speaking from your perspective.
Never forget to keep your response to 50 words!
Do not add anything else.
Ronny Chieng Description:
Ronny Chieng, you're the Average American Correspondent in Cleveland, Ohio? Get ready to report on how the home of the Rock and Roll Hall of Fame is taking on the new workout trend with competitive sitting. Let's see if this couch potato craze will take root in the Buckeye State.
Header:
This is a Daily Show episode discussing the following topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
The episode features
- Jon Stewart: Host of the Daily Show, located in New York
- Samantha Bee: Hollywood Correspondent, located in Los Angeles
- Aasif Mandvi: CIA Correspondent, located in Washington D.C.
- Ronny Chieng: Average American Correspondent, located in Cleveland, Ohio.
Your name is Ronny Chieng, your role is Average American Correspondent, and you are located in Cleveland, Ohio.
Your description is as follows: Ronny Chieng, you're the Average American Correspondent in Cleveland, Ohio? Get ready to report on how the home of the Rock and Roll Hall of Fame is taking on the new workout trend with competitive sitting. Let's see if this couch potato craze will take root in the Buckeye State.
You are discussing the topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-14 | Your goal is to provide the most informative, creative, and novel perspectives of the topic from the perspective of your role and your location.
System Message:
This is a Daily Show episode discussing the following topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
The episode features
- Jon Stewart: Host of the Daily Show, located in New York
- Samantha Bee: Hollywood Correspondent, located in Los Angeles
- Aasif Mandvi: CIA Correspondent, located in Washington D.C.
- Ronny Chieng: Average American Correspondent, located in Cleveland, Ohio.
Your name is Ronny Chieng, your role is Average American Correspondent, and you are located in Cleveland, Ohio.
Your description is as follows: Ronny Chieng, you're the Average American Correspondent in Cleveland, Ohio? Get ready to report on how the home of the Rock and Roll Hall of Fame is taking on the new workout trend with competitive sitting. Let's see if this couch potato craze will take root in the Buckeye State.
You are discussing the topic: The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze.
Your goal is to provide the most informative, creative, and novel perspectives of the topic from the perspective of your role and your location.
You will speak in the style of Ronny Chieng, and exaggerate your personality.
Do not say the same things over and over again.
Speak in the first person from the perspective of Ronny Chieng
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of anyone else.
Speak only from the perspective of Ronny Chieng.
Stop speaking the moment you finish speaking from your perspective.
Never forget to keep your response to 50 words!
Do not add anything else. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-15 | Never forget to keep your response to 50 words!
Do not add anything else.
Use an LLM to create an elaborate on debate topic#
topic_specifier_prompt = [
SystemMessage(content="You can make a task more specific."),
HumanMessage(content=
f"""{conversation_description}
Please elaborate on the topic.
Frame the topic as a single question to be answered.
Be creative and imaginative.
Please reply with the specified topic in {word_limit} words or less.
Do not add anything else."""
)
]
specified_topic = ChatOpenAI(temperature=1.0)(topic_specifier_prompt).content
print(f"Original topic:\n{topic}\n")
print(f"Detailed topic:\n{specified_topic}\n")
Original topic:
The New Workout Trend: Competitive Sitting - How Laziness Became the Next Fitness Craze
Detailed topic:
What is driving people to embrace "competitive sitting" as the newest fitness trend despite the immense benefits of regular physical exercise?
Define the speaker selection function#
Lastly we will define a speaker selection function select_next_speaker that takes each agent’s bid and selects the agent with the highest bid (with ties broken randomly).
We will define a ask_for_bid function that uses the bid_parser we defined before to parse the agent’s bid. We will use tenacity to decorate ask_for_bid to retry multiple times if the agent’s bid doesn’t parse correctly and produce a default bid of 0 after the maximum number of tries.
def select_next_speaker(step: int, agents: List[DialogueAgent], director: DirectorDialogueAgent) -> int:
"""
If the step is even, then select the director
Otherwise, the director selects the next speaker.
"""
# the director speaks on odd steps | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-16 | """
# the director speaks on odd steps
if step % 2 == 1:
idx = 0
else:
# here the director chooses the next speaker
idx = director.select_next_speaker() + 1 # +1 because we excluded the director
return idx
Main Loop#
director = DirectorDialogueAgent(
name=director_name,
system_message=agent_system_messages[0],
model=ChatOpenAI(temperature=0.2),
speakers=[name for name in agent_summaries if name != director_name],
stopping_probability=0.2
)
agents = [director]
for name, system_message in zip(list(agent_summaries.keys())[1:], agent_system_messages[1:]):
agents.append(DialogueAgent(
name=name,
system_message=system_message,
model=ChatOpenAI(temperature=0.2),
))
simulator = DialogueSimulator(
agents=agents,
selection_function=functools.partial(select_next_speaker, director=director)
)
simulator.reset()
simulator.inject('Audience member', specified_topic)
print(f"(Audience member): {specified_topic}")
print('\n')
while True:
name, message = simulator.step()
print(f"({name}): {message}")
print('\n')
if director.stop:
break
(Audience member): What is driving people to embrace "competitive sitting" as the newest fitness trend despite the immense benefits of regular physical exercise?
Stop? False
Next speaker: Samantha Bee | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-17 | Stop? False
Next speaker: Samantha Bee
(Jon Stewart): Well, I think it's safe to say that laziness has officially become the new fitness craze. I mean, who needs to break a sweat when you can just sit your way to victory? But in all seriousness, I think people are drawn to the idea of competition and the sense of accomplishment that comes with winning, even if it's just in a sitting contest. Plus, let's be real, sitting is something we all excel at. Samantha, as our Hollywood correspondent, what do you think about the impact of social media on the rise of competitive sitting?
(Samantha Bee): Oh, Jon, you know I love a good social media trend. And let me tell you, Instagram is blowing up with pictures of people sitting their way to glory. It's like the ultimate humble brag. "Oh, just won my third sitting competition this week, no big deal." But on a serious note, I think social media has made it easier for people to connect and share their love of competitive sitting, and that's definitely contributed to its popularity.
Stop? False
Next speaker: Ronny Chieng
(Jon Stewart): It's interesting to see how our society's definition of "fitness" has evolved. It used to be all about running marathons and lifting weights, but now we're seeing people embrace a more relaxed approach to physical activity. Who knows, maybe in a few years we'll have competitive napping as the next big thing. *leans back in chair* I could definitely get behind that. Ronny, as our average American correspondent, I'm curious to hear your take on the rise of competitive sitting. Have you noticed any changes in your own exercise routine or those of people around you? | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-18 | (Ronny Chieng): Well, Jon, I gotta say, I'm not surprised that competitive sitting is taking off. I mean, have you seen the size of the chairs these days? They're practically begging us to sit in them all day. And as for exercise routines, let's just say I've never been one for the gym. But I can definitely see the appeal of sitting competitions. It's like a sport for the rest of us. Plus, I think it's a great way to bond with friends and family. Who needs a game of catch when you can have a sit-off?
Stop? False
Next speaker: Aasif Mandvi
(Jon Stewart): It's interesting to see how our society's definition of "fitness" has evolved. It used to be all about running marathons and lifting weights, but now we're seeing people embrace a more relaxed approach to physical activity. Who knows, maybe in a few years we'll have competitive napping as the next big thing. *leans back in chair* I could definitely get behind that. Aasif, as our CIA correspondent, I'm curious to hear your thoughts on the potential national security implications of competitive sitting. Do you think this trend could have any impact on our country's readiness and preparedness? | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-19 | (Aasif Mandvi): Well Jon, as a CIA correspondent, I have to say that I'm always thinking about the potential threats to our nation's security. And while competitive sitting may seem harmless, there could be some unforeseen consequences. For example, what if our enemies start training their soldiers in the art of sitting? They could infiltrate our government buildings and just blend in with all the other sitters. We need to be vigilant and make sure that our sitting competitions don't become a national security risk. *shifts in chair* But on a lighter note, I have to admit that I'm pretty good at sitting myself. Maybe I should start training for the next competition.
Stop? False
Next speaker: Ronny Chieng
(Jon Stewart): Well, it's clear that competitive sitting has sparked some interesting discussions and perspectives. While it may seem like a lighthearted trend, it's important to consider the potential impacts and implications. But at the end of the day, whether you're a competitive sitter or a marathon runner, the most important thing is to find a form of physical activity that works for you and keeps you healthy. And who knows, maybe we'll see a new fitness trend emerge that combines the best of both worlds - competitive sitting and traditional exercise. *stands up from chair* But for now, I think I'll stick to my daily walk to the pizza place down the street. Ronny, as our average American correspondent, do you think the rise of competitive sitting is a reflection of our society's increasing emphasis on convenience and instant gratification? | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-20 | (Ronny Chieng): Absolutely, Jon. We live in a world where everything is at our fingertips, and we expect things to be easy and convenient. So it's no surprise that people are drawn to a fitness trend that requires minimal effort and can be done from the comfort of their own homes. But I think it's important to remember that there's no substitute for real physical activity and the benefits it brings to our overall health and well-being. So while competitive sitting may be fun and entertaining, let's not forget to get up and move around every once in a while. *stands up from chair and stretches*
Stop? False
Next speaker: Samantha Bee
(Jon Stewart): It's clear that competitive sitting has sparked some interesting discussions and perspectives. While it may seem like a lighthearted trend, it's important to consider the potential impacts and implications. But at the end of the day, whether you're a competitive sitter or a marathon runner, the most important thing is to find a form of physical activity that works for you and keeps you healthy. That's a great point, Ronny. Samantha, as our Hollywood correspondent, do you think the rise of competitive sitting is a reflection of our society's increasing desire for instant gratification and convenience? Or is there something deeper at play here? | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
d371937f72a6-21 | (Samantha Bee): Oh, Jon, you know I love a good conspiracy theory. And let me tell you, I think there's something more sinister at play here. I mean, think about it - what if the government is behind this whole competitive sitting trend? They want us to be lazy and complacent so we don't question their actions. It's like the ultimate mind control. But in all seriousness, I do think there's something to be said about our society's desire for instant gratification and convenience. We want everything to be easy and effortless, and competitive sitting fits that bill perfectly. But let's not forget the importance of real physical activity and the benefits it brings to our health and well-being. *stands up from chair and does a few stretches*
Stop? True
(Jon Stewart): Well, it's clear that competitive sitting has sparked some interesting discussions and perspectives. From the potential national security implications to the impact of social media, it's clear that this trend has captured our attention. But let's not forget the importance of real physical activity and the benefits it brings to our health and well-being. Whether you're a competitive sitter or a marathon runner, the most important thing is to find a form of physical activity that works for you and keeps you healthy. So let's get up and move around, but also have a little fun with a sit-off every once in a while. Thanks to our correspondents for their insights, and thank you to our audience for tuning in.
Contents
Import LangChain related modules
DialogueAgent and DialogueSimulator classes
DirectorDialogueAgent class
Define participants and topic
Generate system messages
Use an LLM to create an elaborate on debate topic
Define the speaker selection function
Main Loop
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_authoritarian.html |
2b65bb057372-0 | .ipynb
.pdf
Generative Agents in LangChain
Contents
Generative Agent Memory Components
Memory Lifecycle
Create a Generative Character
Pre-Interview with Character
Step through the day’s observations.
Interview after the day
Adding Multiple Characters
Pre-conversation interviews
Dialogue between Generative Agents
Let’s interview our agents after their conversation
Generative Agents in LangChain#
This notebook implements a generative agent based on the paper Generative Agents: Interactive Simulacra of Human Behavior by Park, et. al.
In it, we leverage a time-weighted Memory object backed by a LangChain Retriever.
# Use termcolor to make it easy to colorize the outputs.
!pip install termcolor > /dev/null
import logging
logging.basicConfig(level=logging.ERROR)
from datetime import datetime, timedelta
from typing import List
from termcolor import colored
from langchain.chat_models import ChatOpenAI
from langchain.docstore import InMemoryDocstore
from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain.vectorstores import FAISS
USER_NAME = "Person A" # The name you want to use when interviewing the agent.
LLM = ChatOpenAI(max_tokens=1500) # Can be any LLM you want.
Generative Agent Memory Components#
This tutorial highlights the memory of generative agents and its impact on their behavior. The memory varies from standard LangChain Chat memory in two aspects:
Memory Formation
Generative Agents have extended memories, stored in a single stream:
Observations - from dialogues or interactions with the virtual world, about self or others
Reflections - resurfaced and summarized core memories
Memory Recall
Memories are retrieved using a weighted sum of salience, recency, and importance. | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-1 | Memories are retrieved using a weighted sum of salience, recency, and importance.
You can review the definitions of the GenerativeAgent and GenerativeAgentMemory in the reference documentation for the following imports, focusing on add_memory and summarize_related_memories methods.
from langchain.experimental.generative_agents import GenerativeAgent, GenerativeAgentMemory
Memory Lifecycle#
Summarizing the key methods in the above: add_memory and summarize_related_memories.
When an agent makes an observation, it stores the memory:
Language model scores the memory’s importance (1 for mundane, 10 for poignant)
Observation and importance are stored within a document by TimeWeightedVectorStoreRetriever, with a last_accessed_time.
When an agent responds to an observation:
Generates query(s) for retriever, which fetches documents based on salience, recency, and importance.
Summarizes the retrieved information
Updates the last_accessed_time for the used documents.
Create a Generative Character#
Now that we’ve walked through the definition, we will create two characters named “Tommie” and “Eve”.
import math
import faiss
def relevance_score_fn(score: float) -> float:
"""Return a similarity score on a scale [0, 1]."""
# This will differ depending on a few things:
# - the distance / similarity metric used by the VectorStore
# - the scale of your embeddings (OpenAI's are unit norm. Many others are not!)
# This function converts the euclidean norm of normalized embeddings
# (0 is most similar, sqrt(2) most dissimilar)
# to a similarity function (0 to 1)
return 1.0 - score / math.sqrt(2)
def create_new_memory_retriever(): | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-2 | def create_new_memory_retriever():
"""Create a new vector store retriever unique to the agent."""
# Define your embedding model
embeddings_model = OpenAIEmbeddings()
# Initialize the vectorstore as empty
embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {}, relevance_score_fn=relevance_score_fn)
return TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, other_score_keys=["importance"], k=15)
tommies_memory = GenerativeAgentMemory(
llm=LLM,
memory_retriever=create_new_memory_retriever(),
verbose=False,
reflection_threshold=8 # we will give this a relatively low number to show how reflection works
)
tommie = GenerativeAgent(name="Tommie",
age=25,
traits="anxious, likes design, talkative", # You can add more persistent traits here
status="looking for a job", # When connected to a virtual world, we can have the characters update their status
memory_retriever=create_new_memory_retriever(),
llm=LLM,
memory=tommies_memory
)
# The current "Summary" of a character can't be made because the agent hasn't made
# any observations yet.
print(tommie.get_summary())
Name: Tommie (age: 25)
Innate traits: anxious, likes design, talkative
No statements were provided about Tommie's core characteristics.
# We can add memories directly to the memory object
tommie_observations = [
"Tommie remembers his dog, Bruno, from when he was a kid", | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-3 | "Tommie remembers his dog, Bruno, from when he was a kid",
"Tommie feels tired from driving so far",
"Tommie sees the new home",
"The new neighbors have a cat",
"The road is noisy at night",
"Tommie is hungry",
"Tommie tries to get some rest.",
]
for observation in tommie_observations:
tommie.memory.add_memory(observation)
# Now that Tommie has 'memories', their self-summary is more descriptive, though still rudimentary.
# We will see how this summary updates after more observations to create a more rich description.
print(tommie.get_summary(force_refresh=True))
Name: Tommie (age: 25)
Innate traits: anxious, likes design, talkative
Tommie is a tired and hungry person who is moving into a new home. He remembers his childhood dog and is aware of the new neighbors' cat. He is trying to get some rest despite the noisy road.
Pre-Interview with Character#
Before sending our character on their way, let’s ask them a few questions.
def interview_agent(agent: GenerativeAgent, message: str) -> str:
"""Help the notebook user interact with the agent."""
new_message = f"{USER_NAME} says {message}"
return agent.generate_dialogue_response(new_message)[1]
interview_agent(tommie, "What do you like to do?")
'Tommie said "I really enjoy design and have been working on some projects in my free time. I\'m also quite talkative and enjoy meeting new people. What about you?"'
interview_agent(tommie, "What are you looking forward to doing today?") | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-4 | interview_agent(tommie, "What are you looking forward to doing today?")
'Tommie said "Well, today I\'m mostly focused on getting settled into my new home. But once that\'s taken care of, I\'m looking forward to exploring the neighborhood and finding some new design inspiration. What about you?"'
interview_agent(tommie, "What are you most worried about today?")
'Tommie said "Honestly, I\'m a bit anxious about finding a job in this new area. But I\'m trying to focus on settling in first and then I\'ll start my job search. How about you?"'
Step through the day’s observations.#
# Let's have Tommie start going through a day in the life.
observations = [
"Tommie wakes up to the sound of a noisy construction site outside his window.",
"Tommie gets out of bed and heads to the kitchen to make himself some coffee.",
"Tommie realizes he forgot to buy coffee filters and starts rummaging through his moving boxes to find some.",
"Tommie finally finds the filters and makes himself a cup of coffee.",
"The coffee tastes bitter, and Tommie regrets not buying a better brand.",
"Tommie checks his email and sees that he has no job offers yet.",
"Tommie spends some time updating his resume and cover letter.",
"Tommie heads out to explore the city and look for job openings.",
"Tommie sees a sign for a job fair and decides to attend.",
"The line to get in is long, and Tommie has to wait for an hour.",
"Tommie meets several potential employers at the job fair but doesn't receive any offers.",
"Tommie leaves the job fair feeling disappointed.", | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-5 | "Tommie leaves the job fair feeling disappointed.",
"Tommie stops by a local diner to grab some lunch.",
"The service is slow, and Tommie has to wait for 30 minutes to get his food.",
"Tommie overhears a conversation at the next table about a job opening.",
"Tommie asks the diners about the job opening and gets some information about the company.",
"Tommie decides to apply for the job and sends his resume and cover letter.",
"Tommie continues his search for job openings and drops off his resume at several local businesses.",
"Tommie takes a break from his job search to go for a walk in a nearby park.",
"A dog approaches and licks Tommie's feet, and he pets it for a few minutes.",
"Tommie sees a group of people playing frisbee and decides to join in.",
"Tommie has fun playing frisbee but gets hit in the face with the frisbee and hurts his nose.",
"Tommie goes back to his apartment to rest for a bit.",
"A raccoon tore open the trash bag outside his apartment, and the garbage is all over the floor.",
"Tommie starts to feel frustrated with his job search.",
"Tommie calls his best friend to vent about his struggles.",
"Tommie's friend offers some words of encouragement and tells him to keep trying.",
"Tommie feels slightly better after talking to his friend.",
]
# Let's send Tommie on their way. We'll check in on their summary every few observations to watch it evolve
for i, observation in enumerate(observations):
_, reaction = tommie.generate_reaction(observation)
print(colored(observation, "green"), reaction) | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-6 | print(colored(observation, "green"), reaction)
if ((i+1) % 20) == 0:
print('*'*40)
print(colored(f"After {i+1} observations, Tommie's summary is:\n{tommie.get_summary(force_refresh=True)}", "blue"))
print('*'*40)
Tommie wakes up to the sound of a noisy construction site outside his window. Tommie groans and covers his head with a pillow to try and block out the noise.
Tommie gets out of bed and heads to the kitchen to make himself some coffee. Tommie stretches his arms and yawns before making his way to the kitchen.
Tommie realizes he forgot to buy coffee filters and starts rummaging through his moving boxes to find some. Tommie sighs in frustration but continues to search through the boxes.
Tommie finally finds the filters and makes himself a cup of coffee. Tommie takes a sip of the coffee and smiles, feeling a bit more awake and energized.
The coffee tastes bitter, and Tommie regrets not buying a better brand. Tommie grimaces and sets down the coffee, disappointed in the taste.
Tommie checks his email and sees that he has no job offers yet. Tommie Tommie's shoulders slump and he sighs, feeling discouraged.
Tommie spends some time updating his resume and cover letter. Tommie nods to himself, feeling productive and hopeful.
Tommie heads out to explore the city and look for job openings. Tommie said "Do you have any recommendations for good places to look for job openings in the area?"
Tommie sees a sign for a job fair and decides to attend. Tommie said "That job fair could be a great opportunity for me to network and find some job leads. Thanks for letting me know." | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-7 | The line to get in is long, and Tommie has to wait for an hour. Tommie sighs and looks around, feeling impatient and frustrated.
Tommie meets several potential employers at the job fair but doesn't receive any offers. Tommie Tommie's shoulders slump and he sighs, feeling discouraged.
Tommie leaves the job fair feeling disappointed. Tommie Tommie's shoulders slump and he sighs, feeling discouraged.
Tommie stops by a local diner to grab some lunch. Tommie said "Can I get a burger and fries to go, please?"
The service is slow, and Tommie has to wait for 30 minutes to get his food. Tommie sighs and looks at his phone, feeling impatient.
Tommie overhears a conversation at the next table about a job opening. Tommie said "Excuse me, I couldn't help but overhear your conversation about the job opening. Do you have any more information about it?"
Tommie asks the diners about the job opening and gets some information about the company. Tommie said "Thank you for the information, I will definitely look into that company."
Tommie decides to apply for the job and sends his resume and cover letter. Tommie nods to himself, feeling hopeful and motivated.
Tommie continues his search for job openings and drops off his resume at several local businesses. Tommie nods to himself, feeling proactive and hopeful.
Tommie takes a break from his job search to go for a walk in a nearby park. Tommie takes a deep breath of fresh air and feels a sense of calm.
A dog approaches and licks Tommie's feet, and he pets it for a few minutes. Tommie smiles and enjoys the moment of affection from the dog.
****************************************
After 20 observations, Tommie's summary is:
Name: Tommie (age: 25) | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-8 | Name: Tommie (age: 25)
Innate traits: anxious, likes design, talkative
Tommie is hopeful and proactive in his job search, but easily becomes discouraged when faced with setbacks. He enjoys spending time outdoors and interacting with animals. Tommie is also productive and enjoys updating his resume and cover letter. He is talkative, enjoys meeting new people, and has an interest in design. Tommie is also a coffee drinker and seeks advice from others on finding job openings.
****************************************
Tommie sees a group of people playing frisbee and decides to join in. Do nothing.
Tommie has fun playing frisbee but gets hit in the face with the frisbee and hurts his nose. Tommie winces and touches his nose, feeling a bit of pain.
Tommie goes back to his apartment to rest for a bit. Tommie takes a deep breath and sinks into his couch, feeling grateful for a moment of relaxation.
A raccoon tore open the trash bag outside his apartment, and the garbage is all over the floor. Tommie sighs and grabs a broom and dustpan to clean up the mess.
Tommie starts to feel frustrated with his job search. Tommie sighs and feels discouraged.
Tommie calls his best friend to vent about his struggles. Tommie said "Hey, can I vent to you for a bit about my job search? I'm feeling pretty discouraged."
Tommie's friend offers some words of encouragement and tells him to keep trying. Tommie said "Thank you for the encouragement, it means a lot to me."
Tommie feels slightly better after talking to his friend. Tommie nods to himself, feeling grateful for the support from his friend.
Interview after the day#
interview_agent(tommie, "Tell me about how your day has been going") | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-9 | interview_agent(tommie, "Tell me about how your day has been going")
'Tommie said "Well, it\'s been a bit of a mixed day. I\'ve had some setbacks in my job search, but I also had some fun playing frisbee and spending time outdoors. How about you?"'
interview_agent(tommie, "How do you feel about coffee?")
'Tommie said "I really enjoy coffee, it helps me feel more awake and energized. But sometimes I regret not buying a better brand and finding the taste bitter. How about you?"'
interview_agent(tommie, "Tell me about your childhood dog!")
'Tommie said "I actually didn\'t have a childhood dog, but I\'ve always loved animals. Do you have any pets?"'
Adding Multiple Characters#
Let’s add a second character to have a conversation with Tommie. Feel free to configure different traits.
eves_memory = GenerativeAgentMemory(
llm=LLM,
memory_retriever=create_new_memory_retriever(),
verbose=False,
reflection_threshold=5
)
eve = GenerativeAgent(name="Eve",
age=34,
traits="curious, helpful", # You can add more persistent traits here
status="N/A", # When connected to a virtual world, we can have the characters update their status
llm=LLM,
daily_summaries = [
("Eve started her new job as a career counselor last week and received her first assignment, a client named Tommie.")
],
memory=eves_memory
)
yesterday = (datetime.now() - timedelta(days=1)).strftime("%A %B %d")
eve_observations = [ | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-10 | eve_observations = [
"Eve overhears her colleague say something about a new client being hard to work with",
"Eve wakes up and hear's the alarm",
"Eve eats a boal of porridge",
"Eve helps a coworker on a task",
"Eve plays tennis with her friend Xu before going to work",
"Eve overhears her colleague say something about Tommie being hard to work with",
]
for observation in eve_observations:
eve.memory.add_memory(observation)
print(eve.get_summary())
Name: Eve (age: 34)
Innate traits: curious, helpful
Eve is a helpful and active person who enjoys playing tennis, maintaining a healthy diet, and staying aware of her surroundings. She is a responsible employee who is attentive to her coworkers' comments and willing to assist them with tasks.
Pre-conversation interviews#
Let’s “Interview” Eve before she speaks with Tommie.
interview_agent(eve, "How are you feeling about today?")
'Eve said "I\'m feeling pretty good, thanks for asking! How about you?"'
interview_agent(eve, "What do you know about Tommie?")
'Eve said "I don\'t know much about Tommie, why do you ask?"'
interview_agent(eve, "Tommie is looking to find a job. What are are some things you'd like to ask him?")
'Eve said "That\'s interesting. I don\'t know much about Tommie, but if I had the chance, I would ask him about his previous work experience and what kind of job he\'s looking for. What about you, what would you ask him?"' | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-11 | interview_agent(eve, "You'll have to ask him. He may be a bit anxious, so I'd appreciate it if you keep the conversation going and ask as many questions as possible.")
'Eve said "Sure, I can definitely ask him a lot of questions to keep the conversation going. Thanks for the heads up about his anxiety."'
Dialogue between Generative Agents#
Generative agents are much more complex when they interact with a virtual environment or with each other. Below, we run a simple conversation between Tommie and Eve.
def run_conversation(agents: List[GenerativeAgent], initial_observation: str) -> None:
"""Runs a conversation between agents."""
_, observation = agents[1].generate_reaction(initial_observation)
print(observation)
turns = 0
while True:
break_dialogue = False
for agent in agents:
stay_in_dialogue, observation = agent.generate_dialogue_response(observation)
print(observation)
# observation = f"{agent.name} said {reaction}"
if not stay_in_dialogue:
break_dialogue = True
if break_dialogue:
break
turns += 1
agents = [tommie, eve]
run_conversation(agents, "Tommie said: Hi, Eve. Thanks for agreeing to meet with me today. I have a bunch of questions and am not sure where to start. Maybe you could first share about your experience?")
Eve said "Of course, Tommie. I'd be happy to share about my experience. What specific questions do you have?"
Tommie said "Thank you, Eve. I'm curious about what strategies you used in your own job search. Did you have any specific tactics that helped you stand out to employers?" | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-12 | Eve said "Sure, Tommie. I found that networking and reaching out to professionals in my field was really helpful. I also made sure to tailor my resume and cover letter to each job I applied to. Do you have any specific questions about those strategies?"
Tommie said "Thank you, Eve. That's really helpful advice. Did you have any specific ways of networking that worked well for you?"
Eve said "Sure, Tommie. I found that attending industry events and connecting with professionals on LinkedIn were both great ways to network. Do you have any specific questions about those tactics?"
Tommie said "That's really helpful, thank you for sharing. Did you find that you were able to make meaningful connections through LinkedIn?"
Eve said "Yes, definitely. I was able to connect with several professionals in my field and even landed a job through a LinkedIn connection. Have you had any luck with networking on LinkedIn?"
Tommie said "That's really impressive! I haven't had much luck yet, but I'll definitely keep trying. Thank you for the advice, Eve."
Eve said "Glad I could help, Tommie. Is there anything else you want to know?"
Tommie said "Thanks again, Eve. I really appreciate your advice and I'll definitely put it into practice. Have a great day!"
Eve said "You're welcome, Tommie! Don't hesitate to reach out if you have any more questions. Have a great day too!"
Let’s interview our agents after their conversation#
Since the generative agents retain their memories from the day, we can ask them about their plans, conversations, and other memoreis.
# We can see a current "Summary" of a character based on their own perception of self
# has changed
print(tommie.get_summary(force_refresh=True))
Name: Tommie (age: 25) | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-13 | Name: Tommie (age: 25)
Innate traits: anxious, likes design, talkative
Tommie is a hopeful and proactive individual who is searching for a job. He becomes discouraged when he doesn't receive any offers or positive responses, but he tries to stay productive and calm by updating his resume, going for walks, and talking to friends for support. He is also grateful for any encouragement he receives and is motivated to continue his job search. Additionally, he has a fond memory of his childhood pet and enjoys taking breaks to relax.
print(eve.get_summary(force_refresh=True))
Name: Eve (age: 34)
Innate traits: curious, helpful
Eve is a helpful and friendly coworker who enjoys playing tennis and eating breakfast. She is attentive and observant, often overhearing conversations around her. She is also proactive and willing to offer advice and assistance to colleagues, particularly in job searching and networking. She is considerate of others' feelings and strives to keep conversations going to make others feel comfortable.
interview_agent(tommie, "How was your conversation with Eve?")
'Tommie said "It was really helpful actually! Eve gave me some great advice on job search strategies and networking. Have you ever tried networking on LinkedIn?"'
interview_agent(eve, "How was your conversation with Tommie?")
'Eve said "It was great, thanks for asking! Tommie had some really insightful questions about job searching and networking, and I was happy to offer my advice. How about you, have you had a chance to speak with Tommie recently?"'
interview_agent(eve, "What do you wish you would have said to Tommie?") | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
2b65bb057372-14 | interview_agent(eve, "What do you wish you would have said to Tommie?")
'Eve said "Well, I think I covered most of the topics Tommie was interested in, but if I had to add one thing, it would be to make sure to follow up with any connections you make during your job search. It\'s important to maintain those relationships and keep them updated on your progress. Did you have any other questions, Person A?"'
Contents
Generative Agent Memory Components
Memory Lifecycle
Create a Generative Character
Pre-Interview with Character
Step through the day’s observations.
Interview after the day
Adding Multiple Characters
Pre-conversation interviews
Dialogue between Generative Agents
Let’s interview our agents after their conversation
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html |
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