langchain/docs/modules/agents/examples/custom_agent.ipynb

Custom Agent

This notebook goes through how to create your own custom agent.

An agent consists of three parts:

- Tools: The tools the agent has available to use.
- LLMChain: The LLMChain that produces the text that is parsed in a certain way to determine which action to take.
- The agent class itself: this parses the output of the LLMChain to determin which action to take.

In this notebook we walk through two types of custom agents. The first type shows how to create a custom LLMChain, but still use an existing agent class to parse the output. The second shows how to create a custom agent class.

Custom LLMChain

The first way to create a custom agent is to use an existing Agent class, but use a custom LLMChain. This is the simplest way to create a custom Agent. It is highly reccomended that you work with the ZeroShotAgent, as at the moment that is by far the most generalizable one.

Most of the work in creating the custom LLMChain comes down to the prompt. Because we are using an existing agent class to parse the output, it is very important that the prompt say to produce text in that format. Additionally, we currently require an agent_scratchpad input variable to put notes on previous actions and observations. This should almost always be the final part of the prompt. However, besides those instructions, you can customize the prompt as you wish.

To ensure that the prompt contains the appropriate instructions, we will utilize a helper method on that class. The helper method for the ZeroShotAgent takes the following arguments:

• tools: List of tools the agent will have access to, used to format the prompt.
• prefix: String to put before the list of tools.
• suffix: String to put after the list of tools.
• input_variables: List of input variables the final prompt will expect.

For this exercise, we will give our agent access to Google Search, and we will customize it in that we will have it answer as a pirate.

In [1]:

from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain import OpenAI, SerpAPIWrapper, LLMChain

In [2]:

search = SerpAPIWrapper()
tools = [
    Tool(
        name = "Search",
        func=search.run,
        description="useful for when you need to answer questions about current events"
    )
]

In [3]:

prefix = """Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:"""
suffix = """Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Args"

Question: {input}
{agent_scratchpad}"""

prompt = ZeroShotAgent.create_prompt(
    tools, 
    prefix=prefix, 
    suffix=suffix, 
    input_variables=["input", "agent_scratchpad"]
)

In case we are curious, we can now take a look at the final prompt template to see what it looks like when its all put together.

In [4]:

print(prompt.template)

Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:

Search: useful for when you need to answer questions about current events

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [Search]
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

Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Args"

Question: {input}
{agent_scratchpad}

Note that we are able to feed agents a self-defined prompt template, i.e. not restricted to the prompt generated by the create_prompt function, assuming it meets the agent's requirements.

For example, for ZeroShotAgent, we will need to ensure that it meets the following requirements. There should a string starting with "Action:" and a following string starting with "Action Input:", and both should be separated by a newline.

In [5]:

llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)

In [7]:

tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)

In [8]:

agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)

In [9]:

agent_executor.run("How many people live in canada?")

Out [9]:

> Entering new AgentExecutor chain...
Thought: I need to find out how many people live in Canada
Action: Search
Action Input: Population of Canada
Observation: Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.
Thought: I need to find out the population of Canada
Action: Search
Action Input: Population of Canada
Observation: Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.
Thought: I now know the population of Canada
Final Answer: Arrr, Canada be home to over 37 million people!

> Finished chain.

'Arrr, Canada be home to over 37 million people!'

Multiple inputs

Agents can also work with prompts that require multiple inputs.

In [20]:

prefix = """Answer the following questions as best you can. You have access to the following tools:"""
suffix = """When answering, you MUST speak in the following language: {language}.

Question: {input}
{agent_scratchpad}"""

prompt = ZeroShotAgent.create_prompt(
    tools, 
    prefix=prefix, 
    suffix=suffix, 
    input_variables=["input", "language", "agent_scratchpad"]
)

In [21]:

llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)

In [22]:

agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools)

In [23]:

agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)

In [24]:

agent_executor.run(input="How many people live in canada?", language="italian")

Out [24]:

> Entering new AgentExecutor chain...
Thought: I should look up the population of Canada.
Action: Search
Action Input: Population of Canada
Observation: Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.
Thought: I should look for the population of Canada.
Action: Search
Action Input: Population of Canada
Observation: Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.
Thought: I should look for the population of Canada.
Action: Search
Action Input: Population of Canada
Observation: Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.
Thought: I should look for the population of Canada.
Action: Search
Action Input: Population of Canada
Observation: Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.
Thought: I should look for the population of Canada.
Action: Search
Action Input: Population of Canada
Observation: Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.
Thought: I should look for the population of Canada.
Action: Search
Action Input: Population of Canada
Observation: Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.
Thought: I should look for the population of Canada.
Action: Search
Action Input: Population of Canada
Observation: Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.
Thought: I should look for the population of Canada.
Action: Search
Action Input: Population of Canada
Observation: Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.
Thought: I now know the population of Canada.
Final Answer: La popolazione del Canada è di circa 37 milioni di persone.
> Finished AgentExecutor chain.

'La popolazione del Canada è di circa 37 milioni di persone.'

Custom Agent Class

Coming soon.

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