RunnableRails

This guide will teach you how to integrate guardrail configurations built with NeMo Guardrails into your LangChain applications. The examples in this guide will focus on using the LangChain Expression Language (LCEL).

Overview

NeMo Guardrails provides a LangChain native interface that implements the Runnable Protocol, through the RunnableRails class. To get started, you must first load a guardrail configuration and create a RunnableRails instance:

from nemoguardrails import RailsConfig
from nemoguardrails.integrations.langchain.runnable_rails import RunnableRails

config = RailsConfig.from_path("path/to/config")
guardrails = RunnableRails(config)

To add guardrails around an LLM model inside a chain, you have to “wrap” the LLM model with a RunnableRails instance, i.e., (guardrails | ...).

Let’s take a typical example using a prompt, a model, and an output parser:

from langchain.chat_models import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_template("tell me a short joke about {topic}")
model = ChatOpenAI()
output_parser = StrOutputParser()

chain = prompt | model | output_parser

To add guardrails around the LLM model in the above example:

chain_with_guardrails = prompt | (guardrails | model) | output_parser

NOTE: Using the extra parenthesis is essential to enforce the order in which the | (pipe) operator is applied.

To add guardrails to an existing chain (or any Runnable) you must wrap it similarly:

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

rag_chain_with_guardrails = guardrails | rag_chain

You can also use the same approach to add guardrails only around certain parts of your chain. The example below (extracted from the RunnableBranch Documentation), adds guardrails around the “anthropic” and “general” branches inside a RunnableBranch:

from langchain_core.runnables import RunnableBranch

branch = RunnableBranch(
    (lambda x: "anthropic" in x["topic"].lower(), guardrails | anthropic_chain),
    (lambda x: "langchain" in x["topic"].lower(), langchain_chain),
    guardrails | general_chain,
)

In general, you can wrap any part of a runnable chain with guardrails:

chain = runnable_1 | runnable_2 | runnable_3 | runnable_4 | ...
chain_with_guardrails = runnable_1 | (guardrails | (runnable_2 | runnable_3)) | runnable_4 | ...

Input/Output Formats

The supported input/output formats when wrapping an LLM model are:

Input Format	Output Format
Prompt (i.e., `StringPromptValue`)	Completion string
Chat history (i.e., `ChatPromptValue`)	New message (i.e., `AIMessage`)

The supported input/output formats when wrapping a chain (or a Runnable) are:

Input Format	Output Format
Dictionary with `input` key	Dictionary with `output` key
Dictionary with `input` key	String output
String input	Dictionary with `output` key
String input	String output

Prompt Passthrough

The role of a guardrail configuration is to validate the user input, check the LLM output, guide the LLM model on how to respond, etc. (see Configuration Guide for more details on the different types of rails). To achieve this, the guardrail configuration might make additional calls to the LLM or other models/APIs (e.g., for fact-checking and content moderation).

By default, when the guardrail configuration decides that it is safe to prompt the LLM, it will use the exact prompt that was provided as the input (i.e., string, StringPromptValue or ChatPromptValue). However, to enforce specific rails (e.g., dialog rails, general instructions), the guardrails configuration needs to alter the prompt used to generate the response. To enable this behavior, which provides more robust rails, you must set the passthrough parameter to False when creating the RunnableRails instance:

guardrails = RunnableRails(config, passthrough=False)

Input/Output Keys for Chains with Guardrails

When a guardrail configuration is used to wrap a chain (or a Runnable) the input and output are either dictionaries or strings. However, a guardrail configuration always operates on a text input from the user and a text output from the LLM. To achieve this, when dicts are used, one of the keys from the input dict must be designated as the “input text” and one of the keys from the output as the “output text”. By default, these keys are input and output. To customize these keys, you must provide the input_key and output_key parameters when creating the RunnableRails instance.

guardrails = RunnableRails(config, input_key="question", output_key="answer")
rag_chain_with_guardrails = guardrails | rag_chain

When a guardrail is triggered, and predefined messages must be returned, instead of the output from the LLM, only a dict with the output key is returned:

{
  "answer": "I'm sorry, I can't assist with that"
}

Using Tools

A guardrail configuration can also use tools as part of the dialog rails. The following snippet defines the Calculator tool using the LLMMathChain:

from langchain.chains import LLMMathChain

tools = []

class CalculatorInput(BaseModel):
    question: str = Field()

llm_math_chain = LLMMathChain(llm=model, verbose=True)
tools.append(
    Tool.from_function(
        func=llm_math_chain.run,
        name="Calculator",
        description="useful for when you need to answer questions about math",
        args_schema=CalculatorInput,
    )
)

To make sure that all math questions are answered using this tool, you can create a rail like the one below and include it in your guardrail configuration:

define user ask math question
  "What is the square root of 7?"
  "What is the formula for the area of a circle?"

define flow
  user ask math question
  $result = execute Calculator(tool_input=$user_message)
  bot respond

Finally, you pass the tools array to the RunnableRails instance:

guardrails = RunnableRails(config, tools=tools)

prompt = ChatPromptTemplate.from_template("{question}")
chain = prompt | (guardrails | model)

print(chain.invoke({"question": "What is 5+5*5/5?"}))

Limitations

The current implementation of the RunnableRails interface does not support streaming. This will be addressed in a future release.