import re
from typing import Any, List, Optional, Sequence, Tuple
from langchain_core._api import deprecated
from langchain_core.agents import AgentAction
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain_core.pydantic_v1 import Field
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import JSONAgentOutputParser
from langchain.agents.structured_chat.output_parser import (
StructuredChatOutputParserWithRetries,
)
from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
from langchain.chains.llm import LLMChain
from langchain.tools.render import ToolsRenderer, render_text_description_and_args
HUMAN_MESSAGE_TEMPLATE = "{input}\n\n{agent_scratchpad}"
[docs]@deprecated("0.1.0", alternative="create_structured_chat_agent", removal="0.2.0")
class StructuredChatAgent(Agent):
"""Structured Chat Agent."""
output_parser: AgentOutputParser = Field(
default_factory=StructuredChatOutputParserWithRetries
)
"""Output parser for the agent."""
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
def _construct_scratchpad(
self, intermediate_steps: List[Tuple[AgentAction, str]]
) -> str:
agent_scratchpad = super()._construct_scratchpad(intermediate_steps)
if not isinstance(agent_scratchpad, str):
raise ValueError("agent_scratchpad should be of type string.")
if agent_scratchpad:
return (
f"This was your previous work "
f"(but I haven't seen any of it! I only see what "
f"you return as final answer):\n{agent_scratchpad}"
)
else:
return agent_scratchpad
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
pass
@classmethod
def _get_default_output_parser(
cls, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
) -> AgentOutputParser:
return StructuredChatOutputParserWithRetries.from_llm(llm=llm)
@property
def _stop(self) -> List[str]:
return ["Observation:"]
[docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
memory_prompts: Optional[List[BasePromptTemplate]] = None,
) -> BasePromptTemplate:
tool_strings = []
for tool in tools:
args_schema = re.sub("}", "}}", re.sub("{", "{{", str(tool.args)))
tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
formatted_tools = "\n".join(tool_strings)
tool_names = ", ".join([tool.name for tool in tools])
format_instructions = format_instructions.format(tool_names=tool_names)
template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
_memory_prompts = memory_prompts or []
messages = [
SystemMessagePromptTemplate.from_template(template),
*_memory_prompts,
HumanMessagePromptTemplate.from_template(human_message_template),
]
return ChatPromptTemplate(input_variables=input_variables, messages=messages)
@property
def _agent_type(self) -> str:
raise ValueError
[docs]def create_structured_chat_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: ChatPromptTemplate,
tools_renderer: ToolsRenderer = render_text_description_and_args,
) -> Runnable:
"""Create an agent aimed at supporting tools with multiple inputs.
Args:
llm: LLM to use as the agent.
tools: Tools this agent has access to.
prompt: The prompt to use. See Prompt section below for more.
tools_renderer: This controls how the tools are converted into a string and
then passed into the LLM. Default is `render_text_description`.
Returns:
A Runnable sequence representing an agent. It takes as input all the same input
variables as the prompt passed in does. It returns as output either an
AgentAction or AgentFinish.
Examples:
.. code-block:: python
from langchain import hub
from langchain_community.chat_models import ChatOpenAI
from langchain.agents import AgentExecutor, create_structured_chat_agent
prompt = hub.pull("hwchase17/structured-chat-agent")
model = ChatOpenAI()
tools = ...
agent = create_structured_chat_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
agent_executor.invoke({"input": "hi"})
# Using with chat history
from langchain_core.messages import AIMessage, HumanMessage
agent_executor.invoke(
{
"input": "what's my name?",
"chat_history": [
HumanMessage(content="hi! my name is bob"),
AIMessage(content="Hello Bob! How can I assist you today?"),
],
}
)
Prompt:
The prompt must have input keys:
* `tools`: contains descriptions and arguments for each tool.
* `tool_names`: contains all tool names.
* `agent_scratchpad`: contains previous agent actions and tool outputs as a string.
Here's an example:
.. code-block:: python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
system = '''Respond to the human as helpfully and accurately as possible. You have access to the following tools:
{tools}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid "action" values: "Final Answer" or {tool_names}
Provide only ONE action per $JSON_BLOB, as shown:
```
{{
"action": $TOOL_NAME,
"action_input": $INPUT
}}
```
Follow this format:
Question: input question to answer
Thought: consider previous and subsequent steps
Action:
```
$JSON_BLOB
```
Observation: action result
... (repeat Thought/Action/Observation N times)
Thought: I know what to respond
Action:
```
{{
"action": "Final Answer",
"action_input": "Final response to human"
}}
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation'''
human = '''{input}
{agent_scratchpad}
(reminder to respond in a JSON blob no matter what)'''
prompt = ChatPromptTemplate.from_messages(
[
("system", system),
MessagesPlaceholder("chat_history", optional=True),
("human", human),
]
)
""" # noqa: E501
missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
prompt.input_variables
)
if missing_vars:
raise ValueError(f"Prompt missing required variables: {missing_vars}")
prompt = prompt.partial(
tools=tools_renderer(list(tools)),
tool_names=", ".join([t.name for t in tools]),
)
llm_with_stop = llm.bind(stop=["Observation"])
agent = (
RunnablePassthrough.assign(
agent_scratchpad=lambda x: format_log_to_str(x["intermediate_steps"]),
)
| prompt
| llm_with_stop
| JSONAgentOutputParser()
)
return agent