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357 lines
10 KiB
Markdown
357 lines
10 KiB
Markdown
The LLMAgent is used in an AgentExecutor. This AgentExecutor can largely be thought of as a loop that:
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1. Passes user input and any previous steps to the Agent (in this case, the LLMAgent)
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2. If the Agent returns an `AgentFinish`, then return that directly to the user
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3. If the Agent returns an `AgentAction`, then use that to call a tool and get an `Observation`
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4. Repeat, passing the `AgentAction` and `Observation` back to the Agent until an `AgentFinish` is emitted.
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`AgentAction` is a response that consists of `action` and `action_input`. `action` refers to which tool to use, and `action_input` refers to the input to that tool. `log` can also be provided as more context (that can be used for logging, tracing, etc).
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`AgentFinish` is a response that contains the final message to be sent back to the user. This should be used to end an agent run.
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In this notebook we walk through how to create a custom LLM agent.
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<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! Instead, edit the notebook w/the location & name as this file. -->
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## Set up environment
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Do necessary imports, etc.
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```python
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from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
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from langchain.prompts import StringPromptTemplate
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from langchain import OpenAI, SerpAPIWrapper, LLMChain
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from typing import List, Union
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from langchain.schema import AgentAction, AgentFinish, OutputParserException
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import re
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```
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## Set up tool
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Set up any tools the agent may want to use. This may be necessary to put in the prompt (so that the agent knows to use these tools).
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```python
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# Define which tools the agent can use to answer user queries
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search = SerpAPIWrapper()
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tools = [
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Tool(
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name = "Search",
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func=search.run,
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description="useful for when you need to answer questions about current events"
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)
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]
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```
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## Prompt Template
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This instructs the agent on what to do. Generally, the template should incorporate:
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- `tools`: which tools the agent has access and how and when to call them.
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- `intermediate_steps`: These are tuples of previous (`AgentAction`, `Observation`) pairs. These are generally not passed directly to the model, but the prompt template formats them in a specific way.
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- `input`: generic user input
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```python
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# Set up the base template
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template = """Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:
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{tools}
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Use the following format:
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Question: the input question you must answer
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Thought: you should always think about what to do
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Action: the action to take, should be one of [{tool_names}]
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Action Input: the input to the action
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Observation: the result of the action
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... (this Thought/Action/Action Input/Observation can repeat N times)
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Thought: I now know the final answer
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Final Answer: the final answer to the original input question
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Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Arg"s
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Question: {input}
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{agent_scratchpad}"""
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```
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```python
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# Set up a prompt template
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class CustomPromptTemplate(StringPromptTemplate):
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# The template to use
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template: str
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# The list of tools available
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tools: List[Tool]
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def format(self, **kwargs) -> str:
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# Get the intermediate steps (AgentAction, Observation tuples)
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# Format them in a particular way
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intermediate_steps = kwargs.pop("intermediate_steps")
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thoughts = ""
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for action, observation in intermediate_steps:
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thoughts += action.log
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thoughts += f"\nObservation: {observation}\nThought: "
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# Set the agent_scratchpad variable to that value
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kwargs["agent_scratchpad"] = thoughts
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# Create a tools variable from the list of tools provided
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kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
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# Create a list of tool names for the tools provided
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kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
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return self.template.format(**kwargs)
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```
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```python
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prompt = CustomPromptTemplate(
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template=template,
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tools=tools,
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# This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
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# This includes the `intermediate_steps` variable because that is needed
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input_variables=["input", "intermediate_steps"]
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)
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```
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## Output Parser
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The output parser is responsible for parsing the LLM output into `AgentAction` and `AgentFinish`. This usually depends heavily on the prompt used.
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This is where you can change the parsing to do retries, handle whitespace, etc
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```python
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class CustomOutputParser(AgentOutputParser):
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def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
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# Check if agent should finish
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if "Final Answer:" in llm_output:
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return AgentFinish(
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# Return values is generally always a dictionary with a single `output` key
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# It is not recommended to try anything else at the moment :)
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return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
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log=llm_output,
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)
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# Parse out the action and action input
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regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
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match = re.search(regex, llm_output, re.DOTALL)
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if not match:
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raise OutputParserException(f"Could not parse LLM output: `{llm_output}`")
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action = match.group(1).strip()
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action_input = match.group(2)
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# Return the action and action input
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return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
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```
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```python
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output_parser = CustomOutputParser()
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```
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## Set up LLM
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Choose the LLM you want to use!
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```python
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llm = OpenAI(temperature=0)
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```
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## Define the stop sequence
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This is important because it tells the LLM when to stop generation.
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This depends heavily on the prompt and model you are using. Generally, you want this to be whatever token you use in the prompt to denote the start of an `Observation` (otherwise, the LLM may hallucinate an observation for you).
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## Set up the Agent
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We can now combine everything to set up our agent
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```python
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# LLM chain consisting of the LLM and a prompt
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llm_chain = LLMChain(llm=llm, prompt=prompt)
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```
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```python
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tool_names = [tool.name for tool in tools]
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agent = LLMSingleActionAgent(
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llm_chain=llm_chain,
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output_parser=output_parser,
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stop=["\nObservation:"],
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allowed_tools=tool_names
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)
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```
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## Use the Agent
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Now we can use it!
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```python
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agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
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```
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```python
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agent_executor.run("How many people live in canada as of 2023?")
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new AgentExecutor chain...
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Thought: I need to find out the population of Canada in 2023
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Action: Search
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Action Input: Population of Canada in 2023
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Observation:The current population of Canada is 38,658,314 as of Wednesday, April 12, 2023, based on Worldometer elaboration of the latest United Nations data. I now know the final answer
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Final Answer: Arrr, there be 38,658,314 people livin' in Canada as of 2023!
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> Finished chain.
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"Arrr, there be 38,658,314 people livin' in Canada as of 2023!"
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```
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</CodeOutputBlock>
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## Adding Memory
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If you want to add memory to the agent, you'll need to:
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1. Add a place in the custom prompt for the chat_history
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2. Add a memory object to the agent executor.
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```python
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# Set up the base template
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template_with_history = """Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:
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{tools}
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Use the following format:
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Question: the input question you must answer
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Thought: you should always think about what to do
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Action: the action to take, should be one of [{tool_names}]
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Action Input: the input to the action
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Observation: the result of the action
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... (this Thought/Action/Action Input/Observation can repeat N times)
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Thought: I now know the final answer
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Final Answer: the final answer to the original input question
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Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Arg"s
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Previous conversation history:
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{history}
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New question: {input}
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{agent_scratchpad}"""
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```
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```python
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prompt_with_history = CustomPromptTemplate(
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template=template_with_history,
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tools=tools,
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# This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
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# This includes the `intermediate_steps` variable because that is needed
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input_variables=["input", "intermediate_steps", "history"]
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)
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```
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```python
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llm_chain = LLMChain(llm=llm, prompt=prompt_with_history)
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```
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```python
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tool_names = [tool.name for tool in tools]
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agent = LLMSingleActionAgent(
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llm_chain=llm_chain,
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output_parser=output_parser,
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stop=["\nObservation:"],
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allowed_tools=tool_names
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)
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```
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```python
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from langchain.memory import ConversationBufferWindowMemory
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```
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```python
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memory=ConversationBufferWindowMemory(k=2)
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```
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```python
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agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)
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```
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```python
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agent_executor.run("How many people live in canada as of 2023?")
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new AgentExecutor chain...
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Thought: I need to find out the population of Canada in 2023
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Action: Search
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Action Input: Population of Canada in 2023
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Observation:The current population of Canada is 38,658,314 as of Wednesday, April 12, 2023, based on Worldometer elaboration of the latest United Nations data. I now know the final answer
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Final Answer: Arrr, there be 38,658,314 people livin' in Canada as of 2023!
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> Finished chain.
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"Arrr, there be 38,658,314 people livin' in Canada as of 2023!"
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```
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</CodeOutputBlock>
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```python
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agent_executor.run("how about in mexico?")
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new AgentExecutor chain...
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Thought: I need to find out how many people live in Mexico.
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Action: Search
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Action Input: How many people live in Mexico as of 2023?
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Observation:The current population of Mexico is 132,679,922 as of Tuesday, April 11, 2023, based on Worldometer elaboration of the latest United Nations data. Mexico 2020 ... I now know the final answer.
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Final Answer: Arrr, there be 132,679,922 people livin' in Mexico as of 2023!
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> Finished chain.
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"Arrr, there be 132,679,922 people livin' in Mexico as of 2023!"
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```
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</CodeOutputBlock>
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