Creating an AI Chatbot with Generative AI: 5 Easy Steps
Introduction
AI chatbots use Generative AI to deliver intelligent, contextualized responses 🧠. A hybrid approach is used, prioritizing predefined intents and answers based on frequently asked questions, and using the AI model when no intent matches. This ensures efficiency and allows the chatbot to dynamically handle complex queries ⚡.
Implementation Process
- Data Collection
Relevant data sources are collected to form the chatbot's knowledge base, including:
- PDFs, web pages and structured documents such as CSV, JSON.
- Customer-specific information relevant to the chatbot's domain.
- Original documents such as company policies, medical guidelines, or financial information.
- Data Preprocessing
De-identification & Image Removal: To comply with privacy regulations, sensitive information is removed from certain data sets.
Automated Preprocessing: Python scripts are used to process the files, ensuring that they only contain de-identified text.
Standard Formatting: Text is extracted, cleaned, and structured for indexing, ensuring consistency.
- Data Storage
All processed documents are stored in cloud solutions such as Azure Blob Storage, AWS S3, or Google Cloud Storage ☁️.
Supported formats for indexing include CSV, HTML, JSON, PDF, TXT, and Microsoft Office formats (Word, PPT, Excel).
Each chatbot implementation has a dedicated storage container to keep knowledge base documents organized.
- Creating Indexes
To optimize retrieval efficiency, an index is created using Azure OpenAI Studio, ElasticSearch, VectorDB, or other AI search tools.
The index is built by extracting text, dividing it into manageable sections, and saving these fragments for quick searching.
This allows the AI model to efficiently search and retrieve relevant information, rather than processing entire documents at runtime.
- Deployment of the Generative AI Model
AI models such as GPT-3.5, GPT-4, or other LLMs are deployed through cloud services.
Integration is required to access models, and quota limits can be adjusted based on usage needs 🔧.
Embedding models can be used when implementing a vector search index for semantic search capabilities, especially when handling large data sets.
- API Configuration
Once the search index is configured and the AI model is deployed, the Chat Completions API is configured:
- The chatbot integrates with the search index and AI model to obtain relevant data.
- API calls are structured to retrieve data, maintain chat history, and generate summary responses.
AI Chatbot Workflow
User Inquiry Processing: A user sends a question to the chatbot.
Search Index Recovery: The query is submitted to the search index, retrieving the K most relevant text fragments based on similarity.
Generating AI Model Responses: The retrieved text snippets, along with the user's query and chat history, are fed into the AI model.
Summary Answer & Quotes: The AI model generates a contextual response, often including references to the original sources.
Response Delivery: The chatbot provides the generated response, along with links to cited documents where applicable 🔗.
Additional clarifications on how to build a chatbot efficiently using different services
Document Storage & Access
Documents are stored in cloud storage solutions and indexed by AI search services.
The AI model retrieves indexed references and provides document URLs for users to access.
If de-identification is applied, users will still be redirected to the original files in storage.
Handling Different Types of Files
For HTML files: Instead of providing a link to a document, the chatbot can redirect users to a live webpage version via a middleware setting.
For PDFs and other static files: Direct access to the indexed document is provided via cloud storage URLs.
Search Index Update
New files are uploaded to cloud storage and must be indexed manually.
The index must be recreated every time updates, additions, or deletions occur.
This process is not fully automated and must be performed manually to ensure up-to-date data is available.
Customizing Search Performance
Parameters in Azure AI Search or other indexing services can be modified to improve response accuracy.
Settings include fragment size, sorting methods, and indexing frequency to optimize performance.
Quick Answers & Hierarchical Navigation
Chatbots often use quick response buttons (pickers) to guide users through hierarchical categories.
These pickers must be manually configured within the chatbot framework to align with the conversation flow.
Document Storage & Index Mapping
Below is a structured example of chatbot storage, search services, and indexed knowledge bases:
| Category | Resource Group | Storage Account | Container | Search Service | Index Name |
|---|---|---|---|---|---|
| Health | health chatbot | health documents | health data | health search service | health index |
| Finance | finance chatbot | financial documents | financial data | search for finances | finance index |
| Pharmaceutical | pharmacy chatbot | pharmacy documents | pharmacy data | pharmacy search | pharmacy index |
Conclusion
Building an AI chatbot leveraging Generative AI involves data collection, preprocessing, indexing, and deploying AI models. By integrating a robust search retrieval mechanism and generating API-based answers, chatbots can provide contextualized, accurate, and efficient responses to user queries.
Regular maintenance of the knowledge base and search index ensures that chatbot responses remain up-to-date. Future enhancements may include embedding-based semantic search, multimodal AI capabilities, and dynamic knowledge updates, enabling even smarter and more scalable chatbot solutions.
