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return agent intermediate steps

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harrison/agent_intermediate_steps
Harrison Chase 1 year ago
parent e02d6b2288
commit 27601a13c4
4 changed files with 132 additions and 29 deletions
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118
docs/getting_started/agents.ipynb
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@ -118,40 +118,40 @@
"name": "stdout",
"output_type": "stream",
"text": [
"What is the age of Olivia Wilde's boyfriend raised to the 0.23 power?\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to find the age of Olivia Wilde's boyfriend\n",
"Action: Search\n",
"Action Input: \"Olivia Wilde's boyfriend\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mOlivia Wilde started dating Harry Styles after ending her years-long engagement to Jason Sudeikis — see their relationship timeline.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to find the age of Harry Styles\n",
"\n",
"\n",
"\u001b[1m> Entering new ZeroShotAgent chain...\u001b[0m\n",
"How old is Olivia Wilde's boyfriend? What is that number raised to the 0.23 power?\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to find out how old Olivia Wilde's boyfriend is, and then use a calculator to calculate the power.\n",
"Action: Search\n",
"Action Input: \"Harry Styles age\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to calculate 28 to the 0.23 power\n",
"Action Input: Olivia Wilde's boyfriend age\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mWhile Wilde, 37, and Styles, 27, have both kept a low profile when it comes to talking about their relationship, Wilde did address their ...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m Olivia Wilde's boyfriend is 27 years old.\n",
"Action: Calculator\n",
"Action Input: 28^0.23\u001b[0m\n",
"Action Input: 27^0.23\u001b[0m\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"28^0.23\u001b[32;1m\u001b[1;3m\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"27^0.23\u001b[32;1m\u001b[1;3m\n",
"\n",
"```python\n",
"print(28**0.23)\n",
"import math\n",
"print(math.pow(27, 0.23))\n",
"```\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m2.1520202182226886\n",
"Answer: \u001b[33;1m\u001b[1;3m2.1340945944237553\n",
"\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\u001b[1m> Finished LLMMathChain chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1520202182226886\n",
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1340945944237553\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: 2.1520202182226886\u001b[0m"
"Thought:\n",
"\u001b[1m> Finished ZeroShotAgent chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'2.1520202182226886'"
"'2.1340945944237553'"
]
},
"execution_count": 4,
@ -165,10 +165,86 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"id": "2f0852ff",
"metadata": {},
"outputs": [],
"source": [
"# We can also return the intermediate steps\n",
"llm = OpenAI(temperature=0)\n",
"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True, return_intermediate_steps=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "837211e8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new ZeroShotAgent chain...\u001b[0m\n",
"How old is Olivia Wilde's boyfriend? What is that number raised to the 0.23 power?\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to find out how old Olivia Wilde's boyfriend is, and then use a calculator to calculate the power.\n",
"Action: Search\n",
"Action Input: Olivia Wilde's boyfriend age\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mWhile Wilde, 37, and Styles, 27, have both kept a low profile when it comes to talking about their relationship, Wilde did address their ...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m Olivia Wilde's boyfriend is 27 years old.\n",
"Action: Calculator\n",
"Action Input: 27^0.23\u001b[0m\n",
"\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"27^0.23\u001b[32;1m\u001b[1;3m\n",
"\n",
"```python\n",
"import math\n",
"print(math.pow(27, 0.23))\n",
"```\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m2.1340945944237553\n",
"\u001b[0m\n",
"\u001b[1m> Finished LLMMathChain chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1340945944237553\n",
"\u001b[0m\n",
"Thought:\n",
"\u001b[1m> Finished ZeroShotAgent chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'input': \"How old is Olivia Wilde's boyfriend? What is that number raised to the 0.23 power?\",\n",
" 'output': '2.1340945944237553',\n",
" 'intermediate_steps': [{'log': \" I need to find out how old Olivia Wilde's boyfriend is, and then use a calculator to calculate the power.\\nAction: Search\\nAction Input: Olivia Wilde's boyfriend age\",\n",
" 'tool': 'Search',\n",
" 'tool_input': \"Olivia Wilde's boyfriend age\",\n",
" 'observation': 'While Wilde, 37, and Styles, 27, have both kept a low profile when it comes to talking about their relationship, Wilde did address their ...'},\n",
" {'log': \" Olivia Wilde's boyfriend is 27 years old.\\nAction: Calculator\\nAction Input: 27^0.23\",\n",
" 'tool': 'Calculator',\n",
" 'tool_input': '27^0.23',\n",
" 'observation': 'Answer: 2.1340945944237553\\n'}]}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent({\"input\":\"How old is Olivia Wilde's boyfriend? What is that number raised to the 0.23 power?\"})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9256ff6b",
"metadata": {},
"outputs": [],
"source": []
}
],
@ -188,7 +264,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
"version": "3.10.8"
}
},
"nbformat": 4,

21
langchain/agents/agent.py
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@ -22,6 +22,7 @@ class Agent(Chain, BaseModel, ABC):
prompt: ClassVar[BasePromptTemplate]
llm_chain: LLMChain
tools: List[Tool]
return_intermediate_steps: bool = False
input_key: str = "input" #: :meta private:
output_key: str = "output" #: :meta private:
@ -39,7 +40,10 @@ class Agent(Chain, BaseModel, ABC):
:meta private:
"""
return [self.output_key]
if self.return_intermediate_steps:
return [self.output_key, "intermediate_steps"]
else:
return [self.output_key]
@property
@abstractmethod
@ -115,7 +119,7 @@ class Agent(Chain, BaseModel, ABC):
tool, tool_input = parsed_output
return AgentAction(tool, tool_input, full_output)
def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
"""Run text through and get agent response."""
text = inputs[self.input_key]
# Do any preparation necessary when receiving a new input.
@ -138,12 +142,17 @@ class Agent(Chain, BaseModel, ABC):
while True:
# Call the LLM to see what to do.
output = self.get_action(chained_input.input)
# Add the log to the Chained Input.
chained_input.add_action(output, color="green")
# If the tool chosen is the finishing tool, then we end and return.
if output.tool == self.finish_tool_name:
return {self.output_key: output.tool_input}
# Otherwise we lookup the tool
final_output: dict = {self.output_key: output.tool_input}
if self.return_intermediate_steps:
final_output[
"intermediate_steps"
] = chained_input.intermediate_steps
return final_output
# Other we add the log to the Chained Input.
chained_input.add_action(output, color="green")
# And then we lookup the tool
if output.tool in name_to_tool_map:
chain = name_to_tool_map[output.tool]
# We then call the tool on the tool input to get an observation

20
langchain/agents/input.py
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@ -1,5 +1,5 @@
"""Input manager for agents."""
from typing import Optional
from typing import List, Optional
import langchain
from langchain.schema import AgentAction
@ -14,12 +14,29 @@ class ChainedInput:
if self._verbose:
langchain.logger.log_agent_start(text)
self._input = text
self._intermediate_actions: List[AgentAction] = []
self._intermediate_observations: List[str] = []
@property
def intermediate_steps(self) -> List:
"""Return intermediate steps the agent took."""
steps = []
for i, action in enumerate(self._intermediate_actions):
step = {
"log": action.log,
"tool": action.tool,
"tool_input": action.tool_input,
"observation": self._intermediate_observations[i],
}
steps.append(step)
return steps
def add_action(self, action: AgentAction, color: Optional[str] = None) -> None:
"""Add text to input, print if in verbose mode."""
if self._verbose:
langchain.logger.log_agent_action(action, color=color)
self._input += action.log
self._intermediate_actions.append(action)
def add_observation(
self,
@ -37,6 +54,7 @@ class ChainedInput:
llm_prefix=llm_prefix,
)
self._input += f"\n{observation_prefix}{observation}\n{llm_prefix}"
self._intermediate_observations.append(observation)
@property
def input(self) -> str:

2
langchain/chains/base.py
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@ -71,7 +71,7 @@ class Chain(BaseModel, ABC):
def __call__(
self, inputs: Dict[str, Any], return_only_outputs: bool = False
) -> Dict[str, str]:
) -> Dict[str, Any]:
"""Run the logic of this chain and add to output if desired.
Args:

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