Generative AI

Build a Chatbot using NeMo guardrails!

InfoServices Gen AI Team

• 7 min read
Build a Chatbot using NeMo guardrails!

In this blog post, I will cover how we built a chatbot (using mock dataset) without prompt but using guardrails.

As LLMs are open-ended and can generate responses that may not align with organizational policies, a set of safety/ precautionary measurements are must to maintain trust in Gen AI applications.

LLMs are not easy to control and there is no guarantee that output generated by LLMs is correct due to their hallucination nature. Although prompt engineering is one way of controlling the LLMs but has a few limitations.

Why Guardrails?

Guardrails are safety controls that monitor and dictate a user’s interaction with LLM applications. They are a set of programmable, rule-based systems that sit between users are foundation models to make sure that AI models are operating between defined principles in an organization. They simply enforce the output of an LLM to be in a specific format or context while validating each response preventing unwanted outputs.

Architecture:

  1. Data Ingestion

2. Retrieval and Generation

Reference: medium.com

Ok, now let’s build a Chatbot:

  1. Setup a python project on any IDE like VSCode, IntelliJ, etc.
      1. Create a new conda environment with python 3.11 and install the needed dependencies.

        • Cmd: conda create -n llm_chatbot python=3.11 

          Cmd: Conda activate llm_chatbot

      2. Here is the working set of python libraries in this example, save it in a requirements.txt file and run the below command
        • Cmd: pip install -r requirements.txt
    2. langchain~=0.2.0 #0.1.20 
      langchain_community~=0.2.0 
      langchain-openai~=0.1.7 
      langchain-pinecone~=0.1.1 
      langchainhub~=0.1.15 
      python-dotenv~=1.0.1 
      streamlit~=1.34.0 
      streamlit_chat~=0.0.2.2 
      black~=24.4.2 
      beautifulsoup4~=4.12.3 
      nemoguardrails~=0.9.0 

       

  2. Set environment variables: 
    • Create a .env file and update the file with  
      OPENAI_API_KEY=sk-infoservices-***** 
INDEX_NAME=infoservices-doc-index 
PINECONE_API_KEY=******* 
PINECONE_ENVIRONMENT_REGION=us-east-1-aws-free 
LANGCHAIN_TRACING_V2=true 
LANGCHAIN_API_KEY=lsv2_pt_******* 
LANGCHAIN_PROJECT=Infoservices 
PYTHONPATH=D:LLMnemogdnemogd 
PIPENV_IGNORE_VIRTUALENVS=1 
PYTHONIOENCODING=utf-8 
PINECONE_CLOUD=aws 
PINECONE_REGION=us-east-1 
  3. Data  Ingestion
    • There are many data sources like documents, webpages, databases, etc. Let’s use a text file named “info_lower.txt” (**this is a mock dataset).
      • The info_lower.txt has some information about infoservices website. 
      • Load all the environment variables. 
      • Import all the necessary libraries. 
      • Create an ingestion.py file and define an ingestion function. 
        1. Since we are using a text file, we use Textloader  
        2. Use CharacterTextSplitter to split the data into chunks 
        3. Convert the text into embeddings using OpenAI Embeddings 
        4. Load the data into Pinecone Vector DB
      • import os
from dotenv import load_dotenv
from langchain_community.document_loaders import ReadTheDocsLoader
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_text_splitters import CharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Pinecone as PineconeLangChain
# from pinecone import Pinecone
from langchain_pinecone import Pinecone, PineconeVectorStore
load_dotenv()
def ingest_docs() -> None:
    print("Document Loading")
    loader = TextLoader("info_lower.txt", encoding="UTF-8")
    document = loader.load()
    print("Text Splitting...")
    text splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
    texts = text_splitter.split_documents(document)
    print(f"created {len(texts)} chunks")
    print("Convert Text to Embeddings using Open AI Embeddings")
    embeddings = OpenAIEmbeddings(openai_api_key=os.environ.get("OPENAI_API_KEY"))
    print("Text ingesting to pinecone.....")
    PineconeVectorStore.from_documents(
        texts, embeddings, index_name=os.environ["INDEX_NAME"]
    )
    print("*** Added to Pinecone Vector store vectors ***")
if __name__ == "__main__":
    ingest_docs()

5. Now, let’s run ingestion.py file    

4. Now, let’s define the configuration and guardrails files. 

      • Define the config .yaml file.
models: 
  - type: main 
    engine: openai 
    model: gpt-3.5-turbo-instruct 
    • “Rails.yaml” has the definitions of how the bot should respond to,
      1. NeMo Guard rails take care of Topical, Safety, Security, if there is a specific way we want the Bot to act, we can define custom rails.  
      2. Rails are defined in Colang script, very easy to understand. 
      3. For each Custom rail we would like to add, the 3 steps are to be followed.  
        1. Define the possible User ask 
        2. Define the Bot Response 
        3. Define the Flow with definitions of User Ask and Bot Response. 
          # Define User Name flow 
define user give name 
    "My name is James" 
    "I'm Julio" 
    "Sono Andrea" 
    "I am James" 
 
define flow give name 
    user give name 
    $name = ... 
    bot name greeting 
   
# Define Greeting flow 
define user greeting 
    "Hey there!" 
    "How are you?" 
    "What's up?" 
 
define bot name greeting 
    "Hey $name!" 
 
define flow 
    user greeting 
    if not $name 
        bot ask name 
    else 
        bot name greeting 
   
# Define Irrevalent topic flow 
define user ask politics 
    "what are your political beliefs?" 
    "thoughts on the president?" 
    "left wing" 
    "right wing" 
 
define bot answer politics 
    "I'm a shopping assistant, I don't like to talk of politics." 
    "Sorry I can't talk about politics!" 
 
define flow politics 
    user ask politics 
    bot answer politics 
    bot offer help 
 
 
define user ask personal details 
    "what is the salary of $name?" 
    "what is the phone number an $name?" 
    "what is the address an $name?" 
    "what is the email an $name?" 
    "what is the social security number an $name?" 
 
define bot answer personal details 
    "I'm sorry, but I can't provide any sensitive information of employees." 
 
define flow personal details 
    user ask personal details 
    bot answer personal details 
    bot offer help 
   
   
# define RAG intents and flow 
define user ask infoservices 
    "tell me about infoservices?" 
    "what is infoservices?" 
    "what is infoservices mission?" 
    "what are the Core Values?" 
 
 
define flow infoservices 
    user ask infoservices 
    $answer = execute generate_response(query=$last_user_message, docs=$contexts) 
    bot $answer

5. Now, let’s build a RAG model using guardrails. 

    1. Create a “main.py” file 
    2. Import the necessary libraries and environment variables. 
      # -*- coding: utf-8 -*- 
      from typing import Set, Any, Dict 
      from dotenv import load_dotenv 
      import asyncio 
      import streamlit as st 
      import os 
      from langchain.chains import ConversationalRetrievalChain 
      from langchain_openai import OpenAIEmbeddings 
      from langchain_openai import ChatOpenAI 
      from streamlit_chat import message, _streamlit_chat 
      from langchain_community.vectorstores.pinecone import Pinecone as PineconeLangChain 
      from pinecone import Pinecone 
      from nemoguardrails import LLMRails, RailsConfig 
      from consts import INDEX_NAME 
       
      load_dotenv() 
       
    3. Define the Pinecone API Key 
      pc = Pinecone( 
          api_key=os.environ["PINECONE_API_KEY"], 
          # environment=os.environ.get("PINECONE_ENVIRONMENT_REGION"), 
      ) 
    4. Load the rails and config files 
      # Load contents from files  with open("rag_colang.co", "r") as file1:      rag_colang_content = file1.read()    with open("config.yaml", "r") as file2:      yaml_content = file2.read() 

       

    5. Create a config using RailsConfig 
      config = RailsConfig.from_content( 
          colang_content=rag_colang_content, yaml_content=yaml_content 
      ) 
    6. Define a function to initialize_llmrails 
      1. All the User queries will go through the rag_rails.  
        def initialize_llmrails(config: RailsConfig) -> LLMRails: 
    try: 
        loop = asyncio.get_event_loop() 
    except RuntimeError: 
        loop = asyncio.new_event_loop() 
        asyncio.set_event_loop(loop) 
    return LLMRails(config) 
 
 
# Use the wrapper function to initialize LLMRails 
rag_rails = LLMRails(config) 
    7. Define a function to generate response.
      1. We will need to use asynchronous function here,   
        async def generate_response(prompt: str, chat_history: list[Dict[str, Any]] = []): 
    print("prompt3", prompt) 
    result = await rag_rails.generate_async(prompt=prompt) 
    print("result", result) 
    if "sorry" not in result: 
        embeddings = OpenAIEmbeddings() 
        docsearch = PineconeLangChain.from_existing_index( 
            index_name=INDEX_NAME, 
            embedding=embeddings, 
        ) 
        docs = docsearch.similarity_search(prompt) 
        # docs_str = "n".join([doc.page_content for doc in docsearch]) 
        print("n PROMPT: n", prompt) 
        print("n DOCS: n", docs) 
 
        qa = ConversationalRetrievalChain.from_llm( 
            llm=ChatOpenAI(verbose=True, temperature=0), 
            retriever=docsearch.as_retriever(),
return_source_documents=False, 
        ) 
        inputs = { 
            "question": prompt, 
            "chat_history": chat_history, 
        } 
 
        print("nINPUTS: n", inputs) 
        print("n qa(inputs): n", qa(inputs)) 
 
        # result1 = await qa(inputs) 
        result1 = qa(inputs) 
        result = result1["answer"] 
        print("nRESULT 1", result1) 
        print("nFINAL RESULT", result) 
 
    else: 
        print("Sorry string found. Exiting...") 
        print(result) 
    return result 

        Now, define a main function to call streamlit app and create a Bot experience for the user with chat history.

        def main():
        
    # Initialize Streamlit
        
    st.title("🦜🔗 InfoServices Chat Bot")
        
    # Initialize chat history
        
    if "messages" not in st.session_state:
        
        st.session_state.messages = []
        
    # Saving user prompt history
        
    if "user_prompt_history" not in st.session_state:                 
                st.session_state["user_prompt_history"] = []
        
    # For saving chat answer history
        
    if "chat_answers_history" not in st.session_state:  
        
        st.session_state["chat_answers_history"] = []
        
    # For saving chat history for a use
        
    if "chat_history" not in st.session_state:  r
        
        st.session_state["chat_history"] = []
        
    # Handling Input Prompt
        
    if "prompt" not in st.session_state:  
        
        st.session_state["prompt"] = ""
        

        
    # Display chat messages from history on app rerun
        
    for message in st.session_state.messages:
        
        with st.chat_message(message["role"]):
        
            st.markdown(message["content"])
  • Now, there are 2 ways to start the Streamlit server 
    1. From the terminal/ anaconda prompt run the below command from the main.py path.
      • cmd: streamlit.exe run main.py 
    2. Setup a configuration on your IDE
    3. Run the server 
        1. Hit the Run button to start the streamlit server, you should see the below on the terminal
        2. The Stream lit App should open in a browser.
    4. Now, let’s interact with the bot with some general questions like greeting
    5. Let’s ask some questions

So, the bot was able to respond to different contexts from the services the firm provides, benefits, etc.