Guardrails Langchain integration with streamable AgentExecutor about guardrails HOT 7 CLOSED

from langchain import hub
from langchain.agents import AgentExecutor
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain.tools import tool
from langchain_openai import ChatOpenAI
from langchain_core.documents.base import Document
from guardrails.hub import RegexMatch
from guardrails import Guard

from langchain_core.runnables import RunnablePassthrough
from langchain.agents.format_scratchpad.openai_tools import (
    format_to_openai_tool_messages,
)
from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser

prompt = hub.pull("hwchase17/openai-tools-agent")


@tool
def get_retriever_docs(query: str) -> list[Document]:
    """Returns a list of documents from the retriever."""
    return [
        Document(
            page_content="# test file\n\nThis is a test file with a secret code of 'blue-green-apricot-brownie-cake-mousepad'.",
            metadata={"source": "./test.md"},
        )
    ]


# Set up a Guard
topic = "apricot"
guard = Guard().use(RegexMatch(topic, match_type="search", on_fail="filter"))
model = ChatOpenAI(temperature=0, streaming=False)
llm = model
tools = [get_retriever_docs]


############################################ this is a copy-paste from langchain.agents.create_openai_tools_agent
llm_with_tools = llm.bind(tools=[convert_to_openai_tool(tool) for tool in tools])



agent = (
    RunnablePassthrough.assign(
        agent_scratchpad=lambda x: format_to_openai_tool_messages(
            x["intermediate_steps"]
        )
    )
    | prompt
    | llm_with_tools
    | OpenAIToolsAgentOutputParser()
)
############################################

agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_config(
    {"run_name": "Agent"}
)

chain = agent_executor | guard

query = "call get_retriever_docs and tell me a secret from the docs"
print(chain.invoke({"input": query}))

  RunnablePassthrough.assign(
      agent_scratchpad=lambda x: format_to_openai_tool_messages(
          x["intermediate_steps"]
      )
  )
  | prompt
  | llm_with_tools
  | OpenAIToolsAgentOutputParser()
  | AgentExecutor().with_config("run_name": "Agent")
  | guard
  | [other output parsers]