Human-in-the-loop

There are certain tools that we don’t trust a model to execute on its own. One thing we can do in such situations is require human approval before the tool is invoked.

Setup

We’ll need to install the following packages:

%pip install --upgrade --quiet langchain

And set these environment variables:

import getpass
import os

# If you'd like to use LangSmith, uncomment the below:
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()

Chain

Suppose we have the following (dummy) tools and tool-calling chain:

OpenAI

Install dependencies

pip install -qU langchain-openai

Set environment variables

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

Anthropic

Install dependencies

pip install -qU langchain-anthropic

Set environment variables

import getpass
import os

os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-sonnet-20240229")

Google

Install dependencies

pip install -qU langchain-google-vertexai

Set environment variables

import getpass
import os

os.environ["GOOGLE_API_KEY"] = getpass.getpass()

from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-pro")

Cohere

Install dependencies

pip install -qU langchain-cohere

Set environment variables

import getpass
import os

os.environ["COHERE_API_KEY"] = getpass.getpass()

from langchain_cohere import ChatCohere

llm = ChatCohere(model="command-r")

FireworksAI

Install dependencies

pip install -qU langchain-fireworks

Set environment variables

import getpass
import os

os.environ["FIREWORKS_API_KEY"] = getpass.getpass()

from langchain_fireworks import ChatFireworks

llm = ChatFireworks(model="accounts/fireworks/models/mixtral-8x7b-instruct")

MistralAI

Install dependencies

pip install -qU langchain-mistralai

Set environment variables

import getpass
import os

os.environ["MISTRAL_API_KEY"] = getpass.getpass()

from langchain_mistralai import ChatMistralAI

llm = ChatMistralAI(model="mistral-large-latest")

TogetherAI

Install dependencies

pip install -qU langchain-openai

Set environment variables

import getpass
import os

os.environ["TOGETHER_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.together.xyz/v1",
    api_key=os.environ["TOGETHER_API_KEY"],
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",)

from operator import itemgetter
from typing import Dict, List

from langchain_core.messages import AIMessage
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import tool


@tool
def count_emails(last_n_days: int) -> int:
    """Multiply two integers together."""
    return last_n_days * 2


@tool
def send_email(message: str, recipient: str) -> str:
    "Add two integers."
    return f"Successfully sent email to {recipient}."


tools = [count_emails, send_email]
llm_with_tools = llm.bind_tools(tools)


def call_tools(msg: AIMessage) -> List[Dict]:
    """Simple sequential tool calling helper."""
    tool_map = {tool.name: tool for tool in tools}
    tool_calls = msg.tool_calls.copy()
    for tool_call in tool_calls:
        tool_call["output"] = tool_map[tool_call["name"]].invoke(tool_call["args"])
    return tool_calls


chain = llm_with_tools | call_tools
chain.invoke("how many emails did i get in the last 5 days?")

[{'name': 'count_emails',
  'args': {'last_n_days': 5},
  'id': 'toolu_012VHuh7vk5dVNct5SgZj3gh',
  'output': 10}]

Adding human approval

We can add a simple human approval step to our tool_chain function:

import json


def human_approval(msg: AIMessage) -> Runnable:
    tool_strs = "\n\n".join(
        json.dumps(tool_call, indent=2) for tool_call in msg.tool_calls
    )
    input_msg = (
        f"Do you approve of the following tool invocations\n\n{tool_strs}\n\n"
        "Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no."
    )
    resp = input(input_msg)
    if resp.lower() not in ("yes", "y"):
        raise ValueError(f"Tool invocations not approved:\n\n{tool_strs}")
    return msg

chain = llm_with_tools | human_approval | call_tools
chain.invoke("how many emails did i get in the last 5 days?")

Do you approve of the following tool invocations

{
  "name": "count_emails",
  "args": {
    "last_n_days": 5
  },
  "id": "toolu_01LCpjpFxrRspygDscnHYyPm"
}

Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no. yes

[{'name': 'count_emails',
  'args': {'last_n_days': 5},
  'id': 'toolu_01LCpjpFxrRspygDscnHYyPm',
  'output': 10}]

chain.invoke("Send sally@gmail.com an email saying 'What's up homie'")

Do you approve of the following tool invocations

{
  "name": "send_email",
  "args": {
    "message": "What's up homie",
    "recipient": "sally@gmail.com"
  },
  "id": "toolu_0158qJVd1AL32Y1xxYUAtNEy"
}

Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no. no

ValueError: Tool invocations not approved:

{
  "name": "send_email",
  "args": {
    "message": "What's up homie",
    "recipient": "sally@gmail.com"
  },
  "id": "toolu_0158qJVd1AL32Y1xxYUAtNEy"
}