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

Intermediate Steps

In order to get more visibility into what an agent is doing, we can also return intermediate steps. This comes in the form of an extra key in the return value, which is a list of (action, observation) tuples.

In [1]:

from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.llms import OpenAI

Initialize the components needed for the agent.

In [2]:

llm = OpenAI(temperature=0, model_name='text-davinci-002')
tools = load_tools(["serpapi", "llm-math"], llm=llm)

Initialize the agent with return_intermediate_steps=True

In [3]:

agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True, return_intermediate_steps=True)

In [5]:

response = agent({"input":"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?"})

> Entering new AgentExecutor chain...
 I should look up who Leo DiCaprio is dating
Action: Search
Action Input: "Leo DiCaprio girlfriend"
Observation: Camila Morrone
Thought: I should look up how old Camila Morrone is
Action: Search
Action Input: "Camila Morrone age"
Observation: 25 years
Thought: I should calculate what 25 years raised to the 0.43 power is
Action: Calculator
Action Input: 25^0.43
Observation: Answer: 3.991298452658078

Thought: I now know the final answer
Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and she is 3.991298452658078 years old.

> Finished chain.

In [6]:

# The actual return type is a NamedTuple for the agent action, and then an observation
print(response["intermediate_steps"])

[(AgentAction(tool='Search', tool_input='Leo DiCaprio girlfriend', log=' I should look up who Leo DiCaprio is dating\nAction: Search\nAction Input: "Leo DiCaprio girlfriend"'), 'Camila Morrone'), (AgentAction(tool='Search', tool_input='Camila Morrone age', log=' I should look up how old Camila Morrone is\nAction: Search\nAction Input: "Camila Morrone age"'), '25 years'), (AgentAction(tool='Calculator', tool_input='25^0.43', log=' I should calculate what 25 years raised to the 0.43 power is\nAction: Calculator\nAction Input: 25^0.43'), 'Answer: 3.991298452658078\n')]

In [7]:

import json
print(json.dumps(response["intermediate_steps"], indent=2))

[
  [
    [
      "Search",
      "Leo DiCaprio girlfriend",
      " I should look up who Leo DiCaprio is dating\nAction: Search\nAction Input: \"Leo DiCaprio girlfriend\""
    ],
    "Camila Morrone"
  ],
  [
    [
      "Search",
      "Camila Morrone age",
      " I should look up how old Camila Morrone is\nAction: Search\nAction Input: \"Camila Morrone age\""
    ],
    "25 years"
  ],
  [
    [
      "Calculator",
      "25^0.43",
      " I should calculate what 25 years raised to the 0.43 power is\nAction: Calculator\nAction Input: 25^0.43"
    ],
    "Answer: 3.991298452658078\n"
  ]
]

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