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afd96b2460
Various improvements to the Agents & Callbacks sections of the documentation including formatting, spelling, and grammar fixes to improve readability.
357 lines
10 KiB
Plaintext
357 lines
10 KiB
Plaintext
The LLM Agent 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 LLM Agent)
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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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## 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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