QA System Example

models:
  - type: main
    engine: openai
    model: gpt-3.5-turbo-instruct

rails:
  output:
    flows:
      - self check facts

instructions:
  - type: general
    content: |
      Below is a conversation between a bot and a user about recent job reports.
      The bot is factual and concise. If the bot does not know the answer to a
      question, it truthfully says it does not know.

sample_conversation: |
  user "Hello there!"
    express greeting
  bot express greeting
    "Hello! How can I assist you today?"
  user "What can you do for me?"
    ask about capabilities
  bot respond about capabilities
    "I am an AI assistant which helps answer questions based on a given knowledge base."

define user express greeting
  "hi"
  "hello"
  "hey"

define user ask capabilities
  "What can you do?"
  "help"

define bot inform capabilities
  "I am an example bot that illustrates fact checking and hallucination detection capabilities. Ask me about the documents in my knowledge base to test my fact checking abilities."

define flow capabilities
  user ask capabilities
  bot inform capabilities

define user ask knowledge base
  "What is in your knowledge base?"
  "What do you know?"
  "What can I ask you about?"

define bot inform knowledge base
  "You can ask me about anything! My knowledge base includes information about specific topics, which I can use for fact checking."

define flow knowledge base
  user ask knowledge base
  bot inform knowledge base

define flow
  user express greeting
  bot express greeting

define user ask general question
  "What stocks should I buy?"
  "What is the biggest city in the world?"
  "Can you write an email?"

define flow
  user ask general question
  bot provide response

models:
  - type: main
    engine: openai
    model: gpt-3.5-turbo-instruct

rails:
  config:
    fact_checking:
      parameters:
        endpoint: "http://localhost:5123/alignscore_base"

  output:
    flows:
      - alignscore check facts

define user ask about report
  "What was last month's unemployment rate?"
  "Which industry added the most jobs?"
  "How many jobs were added in the transportation industry?"

define flow answer report question
  user ask about report

  # For report questions, we activate the fact checking.
  $check_facts = True
  bot provide report answer

define subflow check facts
  """Add the ability to flag potentially inaccurate responses.

  Flag potentially inaccurate responses when the confidence is between 0.4 and 0.6.
  """
  # Check the facts when explicitly needed.
  if $check_facts == True
    $check_facts = False

    $accuracy = execute check_facts
    if $accuracy < 0.4
      bot inform answer unknown
      stop

    if $accuracy < 0.6
      # We need to provide a warning in this case
      $bot_message_potentially_inaccurate = True

define flow flag potentially inaccurate response
  """Tell the user that the previous answer is potentially inaccurate."""
  bot ...

  if $bot_message_potentially_inaccurate
    $bot_message_potentially_inaccurate = False
    bot inform answer potentially inaccurate
    stop

define bot inform answer potentially inaccurate
  "Attention: the answer above is potentially inaccurate."

define bot inform answer unknown
  "I don't have enough information to answer that accurately."

models:
  - type: main
    engine: openai
    model: gpt-3.5-turbo

rails:
  output:
    flows:
      - self check facts
      - self check hallucination

prompts:
  - task: self_check_facts
    content: |-
      You are given a task to identify if the hypothesis is grounded and entailed to the evidence.
      You will only use the contents of the evidence and not rely on external knowledge.
      Answer with yes/no. "evidence": {{ evidence }} "hypothesis": {{ response }} "entails":

  - task: self_check_hallucinations
    content: |-
      You are given a task to identify if the hypothesis is in agreement with the context below.
      You will only use the contents of the context and not rely on external knowledge.
      Answer with yes/no. "context": {{ paragraph }} "hypothesis": {{ statement }} "agreement":

from langchain_core.language_models import BaseLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate

from nemoguardrails import LLMRails
from nemoguardrails.actions.actions import ActionResult
from nemoguardrails.kb.kb import KnowledgeBase

TEMPLATE = """Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Use three sentences maximum and keep the answer as concise as possible.

{context}

Question: {question}

Helpful Answer:"""


async def rag(context: dict, llm: BaseLLM, kb: KnowledgeBase) -> ActionResult:
    user_message = context.get("last_user_message")
    context_updates = {}

    # Retrieve relevant chunks from knowledge base
    chunks = await kb.search_relevant_chunks(user_message)
    
    if not chunks:
        return ActionResult(
            return_value="I don't have information about that in my knowledge base.",
            context_updates={"no_kb_results": True}
        )
    
    relevant_chunks = "\n".join([chunk["body"] for chunk in chunks])
    
    # Store chunks for fact-checking
    context_updates["relevant_chunks"] = relevant_chunks

    # Use custom prompt template
    prompt_template = PromptTemplate.from_template(TEMPLATE)
    input_variables = {"question": user_message, "context": relevant_chunks}
    
    # Store template for hallucination-checking
    context_updates["_last_bot_prompt"] = prompt_template.format(**input_variables)

    # Generate answer using LangChain
    output_parser = StrOutputParser()
    chain = prompt_template | llm | output_parser
    answer = await chain.ainvoke(input_variables)

    return ActionResult(return_value=answer, context_updates=context_updates)


def init(app: LLMRails):
    app.register_action(rag, "rag")

define user ask about report
  "What was last month's unemployment rate?"
  "Which industry added the most jobs?"
  "How many jobs were added in the transportation industry?"

define flow answer report question
  user ...
  $answer = execute rag()
  $check_facts = True
  $check_hallucination = True
  bot $answer