Creating a Chatbot with Python, Langchain, and OpenAI: Handling Guidance Queries Using NLU

Question

I am in the process of creating a chatbot for my website using Python, Langchain, and OpenAI. My chatbot needs to handle a variety of user queries, including those that request assistance or guidance, such as:

• "I have a query."

• "Can you help me?"

• "How does this work?"

• "Tell me more."

I've attempted to implement a solution using Natural Language Understanding (NLU) systems to detect these guidance queries. Here's a simplified version of my code:

I have attempted to use a list of keywords to detect guidance queries, but I'm concerned that this approach may lead to false positives or miss certain variations. I expected the NLU system to automatically recognize these queries and trigger the appropriate responses.

# Function to detect guidance queries
def detect_guidance_query(text):
    guidance_keywords = ["query", "question", "help", "assistance", "work", "more", "capabilities", "assist"]
    # Check if any of the guidance keywords are present in the text
    for keyword in guidance_keywords:
        if re.search(r'\b' + re.escape(keyword) + r'\b', text, re.IGNORECASE):
            return True
    return False

# Function to generate AI-driven guidance response
def generate_guidance_response(query):
    # Add your logic to generate guidance responses here
    # For simplicity, we'll provide a fixed guidance response
    return "Of course! How can I assist you?"

# Function to generate answers using your chain
def generate_chain_answer(question):
    # Add your logic to use the chain to get answers here
    # For simplicity, we'll provide a fixed response
    return "I can provide information on a wide range of topics."

# Define a route to handle user input and return answers
@app.route('/ask', methods=['POST'])
def ask():
    if request.method == 'POST':
        question = request.form['question']

        # Check if the user query is a guidance query
        if detect_guidance_query(question):
            response = generate_guidance_response(question)
        else:
            # Use your chain to get answers for non-guidance queries
            response = generate_chain_answer(question)

        return jsonify({'answer': response})

if __name__ == '__main__':
    app.run(debug=True)

Answer 1

the problem is how you calculate the similarity between the query and your guidance_keywords. A feasible way is both encoding the query and guidance_keywords into vectors using embedding models (OpenAI api can also do this), then calculate the cos similarity, if the silarity large than a threshold, your detect_guidance_query return True.