langchain-guardrails 0.0.2

Langchain implementation for Guardrails: Nemo

Nemo Integration with LangChain

This package provides NeMo integration with LangChain, addressing a key limitation in existing NeMo runnables. Specifically, it enables configurable options for NeMo, offering greater control over what is generated. Additionally, it allows flexibility in using different LLMs for NeMo and text generation, enhancing adaptability for various use cases.

Features

• Customizable NeMo options: Control what NeMo generates.
• Flexible LLM integration: Use different LLMs for NeMo and response generation.
• Guardrails integration: Ensure AI-generated responses adhere to predefined constraints.

Installation

Ensure you have the necessary dependencies installed:

pip install langchain_openai nemoguardrails langchain_guardrails

Usage

Below is an example demonstrating how to integrate NeMo with LangChain and apply guardrails to control responses.

from langchain_openai import ChatOpenAI
from nemoguardrails import RailsConfig
from langchain_guardrails import NemoRails
from langchain.schema import HumanMessage
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import chain

# Initialize OpenAI model
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

# Load RailsConfig (configure with the actual path to your NeMo guardrails config)
rails_config = RailsConfig.from_path("./tests/config")

# Instantiate NemoRails with configuration and LLM
nemorails = NemoRails(config=rails_config, llm=llm, options={"rails": ["input"]})

# Sample user input
user_input = [HumanMessage(content="Tell me about the Avengers movie")]

# Define a simple passthrough function with an exit condition
@chain
def passthrough_or_exit(message_dict):
    if message_dict["stop"]:
        return "I'm sorry, I can't respond to that."
    return llm.invoke(message_dict["original"])

# Create the guardrail processing chain
guardrail_chain = nemorails.create_guardrail_chain()
response_chain = ChatPromptTemplate.from_messages(user_input) | guardrail_chain | passthrough_or_exit

# Invoke the chain and generate response
response = response_chain.invoke({})

# Print the output
print(response)

Notes

• The RailsConfig must be configured with the correct NeMo guardrails path.
• Modify options to customize how NeMo processes inputs.
• The passthrough_or_exit function ensures controlled responses.

This package enhances NeMo's capabilities within LangChain, making it more configurable and adaptable for diverse AI applications.