Unleash the Power of Design Documents: Building a Feature-Rich Gen-AI Chatbot with Python, OpenSearch, and LLMs

Imagine a world where design documents evolve from static files to interactive companions, readily answering your questions as you work. This vision becomes a reality with a Generative AI (Gen-AI) powered chatbot. This article delves into the intricate details of constructing such a chatbot using Python, harnessing the strengths of Large Language Models (LLMs) and a robust search engine like Provisioned OpenSearch.

1. Data Preparation: Extracting Knowledge from Design Documents

Document Collection: Gather all your design documents encompassing:

• Functional specifications (detailed descriptions of system behavior)
• Wireframes (low-fidelity visual representations of UI layouts)
• User interface (UI) mockups (high-fidelity visual representations of UI elements)
• Design rationale documents (explanations for design decisions)

Data Cleaning and Preprocessing:

• Regular Expressions in Action: Utilize Python’s re module to remove unnecessary headers, footers, and comments.

import re

def clean_document(text):
    cleaned_text = re.sub(r'^[^\n]*\n|\n[^\n]*$', '', text)  # Remove headers/footers
    cleaned_text = re.sub(r'//.*|\/\*.*?\*\/', '', cleaned_text)  # Remove comments
    return cleaned_text        

• Consistent Formatting: Ensure consistent formatting for text and code snippets. Python’s Pandas library offers effective data cleaning functionalities.
• Chunking for Efficiency: Break down large documents into smaller, manageable chunks. This enhances processing efficiency by the LLM.

2. Building the Search Engine: Unleashing the Power of OpenSearch

• Provisioning OpenSearch: Set up a Provisioned OpenSearch cluster on a cloud platform like GCP. Follow the official documentation for configuration: https://opensearch.org/docs/latest/
• Indexing Documents: Establish a connection to your OpenSearch cluster using Python libraries like opensearchpy. Leverage this connection to meticulously index the preprocessed design documents.

Code Snippet (Indexing with opensearchpy):

from opensearchpy import OpenSearch

# Connect to OpenSearch cluster (replace with your credentials)
client = OpenSearch(
    hosts=[{"host": "your_opensearch_endpoint", "port": 9200}],
    http_auth=("username", "password")
)

# Define the index name and document structure
index_name = "design_documents"
doc = {
    "title": "Functional Specifications - Project X",
    "content": clean_document(open("functional_specs.docx", "rb").read().decode("utf-8")),  # Handle binary data
    "type": "functional_spec"  # Add a document type field for categorization
}

# Index the document with custom ID (can be auto-generated)
client.index(index=index_name, id=1, body=doc)        

3. Integrating the LLM for Conversational Brilliance:

• LLM Selection: Choose an LLM service like Google Bard or OpenAI ChatGPT based on your specific needs and budget.
• Dialogue Management with Rasa: Integrate a dialogue management system like Rasa to manage user interactions. Rasa facilitates context preservation during conversations and efficiently routes queries to the LLM.

Rasa Action Server for Handling LLM Responses (Code Snippet):

from rasa import data, nlu, conversation

# Define custom actions based on LLM responses
def answer_design_question(text):
    # Leverage LLM API to query OpenSearch for relevant information
    # Process retrieved documents and generate a comprehensive response
    return f"Based on the design documents, here's what I found: ..."

# Create a Rasa action server with custom actions
action_server = conversation.ActionServer(actions=[answer_design_question])

# Build an Rasa NLU model to interpret user intent
nlu_model = data.load_agent("your_rasa_nlu_model.yml")

# Start the chatbot conversation loop
while True:
    user_input = input("Ask a design question: ")
    intent = nlu_model.parse(user_input)
    action_server.handle_text(user_input, intent)        

4. Querying OpenSearch with the LLM:

Craft a Python function that leverages the LLM API to formulate search queries. This function should:

• Understand the user’s intent from the Rasa dialogue management system.
• Utilize the LLM to rephrase the user question into a search query suitable for OpenSearch.
• Integrate the formulate_search_query function into your Rasa action server. When the answer_design_question action is triggered, use the formulated search query to retrieve relevant documents from OpenSearch.

Code Snippet (LLM-powered Query Formulation):

import requests  # Assuming a REST-based LLM API

def formulate_search_query(user_question, llm_endpoint, llm_api_key):
  # Preprocess user question for LLM (e.g., remove irrelevant phrases)
  preprocessed_question = preprocess_question(user_question)
  # Send the preprocessed question to the LLM API for reformulation
  payload = {"prompt": f"Can you rephrase this question for design document search: {preprocessed_question}?"}
  headers = {"Authorization": f"Bearer {llm_api_key}"}
  response = requests.post(llm_endpoint, json=payload, headers=headers)
  llm_response = response.json()["response"]
  # Extract the reformulated query from the LLM response
  search_query = llm_response.strip()
  return search_query        

5. Refining the Response with the LLM:

Utilize the LLM to process the retrieved documents from OpenSearch and generate a user-friendly response.

Code Snippet (LLM-based Response Generation):

def generate_response(search_results, llm_endpoint, llm_api_key):
  # Prepare relevant snippets or summaries of retrieved documents
  document_summaries = prepare_document_summaries(search_results)
  # Send the document summaries to the LLM for response generation
  payload = {"prompt": f"Can you summarize the following design document information for the user: {document_summaries}"}
  headers = {"Authorization": f"Bearer {llm_api_key}"}
  response = requests.post(llm_endpoint, json=payload, headers=headers)
  llm_response = response.json()["response"]
  # Craft the final chatbot response incorporating the LLM's generated summary
  return f"Here's what I found in the design documents: {llm_response}"        

6. Deployment and Refinement

• Deploy your chatbot on a platform accessible to your design team, such as your company website or a messaging app.
• Continuously monitor user interactions and gather feedback to improve the chatbot’s accuracy and performance.
• Fine-tune the LLM with additional design document data to enhance its ability to understand design-specific language and generate informative responses.

Additional Considerations

• Security: Implement robust security measures to protect sensitive design document information. Consider access control mechanisms and data encryption.
• Explainability: Explore techniques for the LLM to explain its reasoning behind responses. This fosters user trust and facilitates knowledge acquisition.
• Error Handling: Gracefully handle situations where the LLM or OpenSearch return unexpected results. Provide informative messages to the user and consider logging errors for further investigation.

By following these steps and incorporating the considerations, you can construct a powerful Gen-AI chatbot that empowers your design team by unlocking the knowledge within your design documents.