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update agents to use tool call messages (langchain-ai#20074)
```python
from langchain.agents import AgentExecutor, create_tool_calling_agent, tool
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant"),
MessagesPlaceholder("chat_history", optional=True),
("human", "{input}"),
MessagesPlaceholder("agent_scratchpad"),
]
)
model = ChatAnthropic(model="claude-3-opus-20240229")
@tool
def magic_function(input: int) -> int:
"""Applies a magic function to an input."""
return input + 2
tools = [magic_function]
agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "what is the value of magic_function(3)?"})
```
```
> Entering new AgentExecutor chain...
Invoking: `magic_function` with `{'input': 3}`
responded: [{'text': '<thinking>\nThe user has asked for the value of magic_function applied to the input 3. Looking at the available tools, magic_function is the relevant one to use here, as it takes an integer input and returns an integer output.\n\nThe magic_function has one required parameter:\n- input (integer)\n\nThe user has directly provided the value 3 for the input parameter. Since the required parameter is present, we can proceed with calling the function.\n</thinking>', 'type': 'text'}, {'id': 'toolu_01HsTheJPA5mcipuFDBbJ1CW', 'input': {'input': 3}, 'name': 'magic_function', 'type': 'tool_use'}]
5
Therefore, the value of magic_function(3) is 5.
> Finished chain.
{'input': 'what is the value of magic_function(3)?',
'output': 'Therefore, the value of magic_function(3) is 5.'}
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>- Loading branch information
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60 changes: 3 additions & 57 deletions
60
libs/langchain/langchain/agents/format_scratchpad/openai_tools.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,59 +1,5 @@ | ||
| import json | ||
| from typing import List, Sequence, Tuple | ||
|
|
||
| from langchain_core.agents import AgentAction | ||
| from langchain_core.messages import ( | ||
| AIMessage, | ||
| BaseMessage, | ||
| ToolMessage, | ||
| from langchain.agents.format_scratchpad.tools import ( | ||
| format_to_tool_messages as format_to_openai_tool_messages, | ||
| ) | ||
|
|
||
| from langchain.agents.output_parsers.openai_tools import OpenAIToolAgentAction | ||
|
|
||
|
|
||
| def _create_tool_message( | ||
| agent_action: OpenAIToolAgentAction, observation: str | ||
| ) -> ToolMessage: | ||
| """Convert agent action and observation into a function message. | ||
| Args: | ||
| agent_action: the tool invocation request from the agent | ||
| observation: the result of the tool invocation | ||
| Returns: | ||
| FunctionMessage that corresponds to the original tool invocation | ||
| """ | ||
| if not isinstance(observation, str): | ||
| try: | ||
| content = json.dumps(observation, ensure_ascii=False) | ||
| except Exception: | ||
| content = str(observation) | ||
| else: | ||
| content = observation | ||
| return ToolMessage( | ||
| tool_call_id=agent_action.tool_call_id, | ||
| content=content, | ||
| additional_kwargs={"name": agent_action.tool}, | ||
| ) | ||
|
|
||
|
|
||
| def format_to_openai_tool_messages( | ||
| intermediate_steps: Sequence[Tuple[AgentAction, str]], | ||
| ) -> List[BaseMessage]: | ||
| """Convert (AgentAction, tool output) tuples into FunctionMessages. | ||
| Args: | ||
| intermediate_steps: Steps the LLM has taken to date, along with observations | ||
| Returns: | ||
| list of messages to send to the LLM for the next prediction | ||
| """ | ||
| messages = [] | ||
| for agent_action, observation in intermediate_steps: | ||
| if isinstance(agent_action, OpenAIToolAgentAction): | ||
| new_messages = list(agent_action.message_log) + [ | ||
| _create_tool_message(agent_action, observation) | ||
| ] | ||
| messages.extend([new for new in new_messages if new not in messages]) | ||
| else: | ||
| messages.append(AIMessage(content=agent_action.log)) | ||
| return messages | ||
| __all__ = ["format_to_openai_tool_messages"] |
59 changes: 59 additions & 0 deletions
59
libs/langchain/langchain/agents/format_scratchpad/tools.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,59 @@ | ||
| import json | ||
| from typing import List, Sequence, Tuple | ||
|
|
||
| from langchain_core.agents import AgentAction | ||
| from langchain_core.messages import ( | ||
| AIMessage, | ||
| BaseMessage, | ||
| ToolMessage, | ||
| ) | ||
|
|
||
| from langchain.agents.output_parsers.tools import ToolAgentAction | ||
|
|
||
|
|
||
| def _create_tool_message( | ||
| agent_action: ToolAgentAction, observation: str | ||
| ) -> ToolMessage: | ||
| """Convert agent action and observation into a function message. | ||
| Args: | ||
| agent_action: the tool invocation request from the agent | ||
| observation: the result of the tool invocation | ||
| Returns: | ||
| FunctionMessage that corresponds to the original tool invocation | ||
| """ | ||
| if not isinstance(observation, str): | ||
| try: | ||
| content = json.dumps(observation, ensure_ascii=False) | ||
| except Exception: | ||
| content = str(observation) | ||
| else: | ||
| content = observation | ||
| return ToolMessage( | ||
| tool_call_id=agent_action.tool_call_id, | ||
| content=content, | ||
| additional_kwargs={"name": agent_action.tool}, | ||
| ) | ||
|
|
||
|
|
||
| def format_to_tool_messages( | ||
| intermediate_steps: Sequence[Tuple[AgentAction, str]], | ||
| ) -> List[BaseMessage]: | ||
| """Convert (AgentAction, tool output) tuples into FunctionMessages. | ||
| Args: | ||
| intermediate_steps: Steps the LLM has taken to date, along with observations | ||
| Returns: | ||
| list of messages to send to the LLM for the next prediction | ||
| """ | ||
| messages = [] | ||
| for agent_action, observation in intermediate_steps: | ||
| if isinstance(agent_action, ToolAgentAction): | ||
| new_messages = list(agent_action.message_log) + [ | ||
| _create_tool_message(agent_action, observation) | ||
| ] | ||
| messages.extend([new for new in new_messages if new not in messages]) | ||
| else: | ||
| messages.append(AIMessage(content=agent_action.log)) | ||
| return messages |
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76 changes: 23 additions & 53 deletions
76
libs/langchain/langchain/agents/output_parsers/openai_tools.py
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102 changes: 102 additions & 0 deletions
102
libs/langchain/langchain/agents/output_parsers/tools.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,102 @@ | ||
| import json | ||
| from json import JSONDecodeError | ||
| from typing import List, Union | ||
|
|
||
| from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish | ||
| from langchain_core.exceptions import OutputParserException | ||
| from langchain_core.messages import ( | ||
| AIMessage, | ||
| BaseMessage, | ||
| ToolCall, | ||
| ) | ||
| from langchain_core.outputs import ChatGeneration, Generation | ||
|
|
||
| from langchain.agents.agent import MultiActionAgentOutputParser | ||
|
|
||
|
|
||
| class ToolAgentAction(AgentActionMessageLog): | ||
| tool_call_id: str | ||
| """Tool call that this message is responding to.""" | ||
|
|
||
|
|
||
| def parse_ai_message_to_tool_action( | ||
| message: BaseMessage, | ||
| ) -> Union[List[AgentAction], AgentFinish]: | ||
| """Parse an AI message potentially containing tool_calls.""" | ||
| if not isinstance(message, AIMessage): | ||
| raise TypeError(f"Expected an AI message got {type(message)}") | ||
|
|
||
| actions: List = [] | ||
| if message.tool_calls: | ||
| tool_calls = message.tool_calls | ||
| else: | ||
| if not message.additional_kwargs.get("tool_calls"): | ||
| return AgentFinish( | ||
| return_values={"output": message.content}, log=str(message.content) | ||
| ) | ||
| # Best-effort parsing | ||
| tool_calls = [] | ||
| for tool_call in message.additional_kwargs["tool_calls"]: | ||
| function = tool_call["function"] | ||
| function_name = function["name"] | ||
| try: | ||
| args = json.loads(function["arguments"] or "{}") | ||
| tool_calls.append( | ||
| ToolCall(name=function_name, args=args, id=tool_call["id"]) | ||
| ) | ||
| except JSONDecodeError: | ||
| raise OutputParserException( | ||
| f"Could not parse tool input: {function} because " | ||
| f"the `arguments` is not valid JSON." | ||
| ) | ||
| for tool_call in tool_calls: | ||
| # HACK HACK HACK: | ||
| # The code that encodes tool input into Open AI uses a special variable | ||
| # name called `__arg1` to handle old style tools that do not expose a | ||
| # schema and expect a single string argument as an input. | ||
| # We unpack the argument here if it exists. | ||
| # Open AI does not support passing in a JSON array as an argument. | ||
| function_name = tool_call["name"] | ||
| _tool_input = tool_call["args"] | ||
| if "__arg1" in _tool_input: | ||
| tool_input = _tool_input["__arg1"] | ||
| else: | ||
| tool_input = _tool_input | ||
|
|
||
| content_msg = f"responded: {message.content}\n" if message.content else "\n" | ||
| log = f"\nInvoking: `{function_name}` with `{tool_input}`\n{content_msg}\n" | ||
| actions.append( | ||
| ToolAgentAction( | ||
| tool=function_name, | ||
| tool_input=tool_input, | ||
| log=log, | ||
| message_log=[message], | ||
| tool_call_id=tool_call["id"], | ||
| ) | ||
| ) | ||
| return actions | ||
|
|
||
|
|
||
| class ToolsAgentOutputParser(MultiActionAgentOutputParser): | ||
| """Parses a message into agent actions/finish. | ||
| If a tool_calls parameter is passed, then that is used to get | ||
| the tool names and tool inputs. | ||
| If one is not passed, then the AIMessage is assumed to be the final output. | ||
| """ | ||
|
|
||
| @property | ||
| def _type(self) -> str: | ||
| return "tools-agent-output-parser" | ||
|
|
||
| def parse_result( | ||
| self, result: List[Generation], *, partial: bool = False | ||
| ) -> Union[List[AgentAction], AgentFinish]: | ||
| if not isinstance(result[0], ChatGeneration): | ||
| raise ValueError("This output parser only works on ChatGeneration output") | ||
| message = result[0].message | ||
| return parse_ai_message_to_tool_action(message) | ||
|
|
||
| def parse(self, text: str) -> Union[List[AgentAction], AgentFinish]: | ||
| raise ValueError("Can only parse messages") |
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