Building HyDE powered RAG chatbots using Microsoft Azure AI Models & Dataloop

Customer service is undergoing an AI revolution, driven by the demand for smarter, more efficient solutions. HyDE-powered RAG chatbots offer a breakthrough technology that combines vast knowledge bases with real-time data retrieval and hypothetical document embeddings (HyDE) to deliver superior accuracy and context-specific responses. Yet, building and managing these complex systems remains a significant challenge due to the intricate integration of diverse AI components, real-time processing requirements, and the need for specialized expertise in AI and data engineering.

Simplifying GenAI solutions with Microsoft and Dataloop

The Microsoft-Dataloop partnership abstracts the deployment of powerful chatbot applications. By integrating Microsoft's PHI-3-MINI foundation model with Dataloop's data platform, we've made HyDE-powered RAG chatbots accessible to a wider developer community with minimal coding. Developers can leave the documentation behind and start utilizing these capabilities instantly, accelerating time to value.

This announcement follows our successful integration with Microsoft Azure AI Model as a Service and Azure AI Video Indexer, further enhancing our ability to deliver advanced AI solutions. These integrations enable developers to seamlessly incorporate state-of-the-art AI models into their workflows, significantly accelerating development cycles.

About Dataloop AI development platform

Dataloop is an enterprise-grade end-to-end AI development platform designed to streamline the creation and deployment of powerful GenAI applications. The platform offers a comprehensive suite of tools and services, enabling efficient AI model development and management.

Key features include:

• Orchestration: Dataloop provides seamless pipeline management, access to a marketplace for AI models, and a serverless architecture to simplify deployment and scalability.
• Data Management: The Dataloop platform supports extensive dataset exploration, allowing users to query, visualize, and curate data efficiently.
• Human Knowledge: Dataloop facilitates knowledge-based ground truth creation through tools for annotation, review, and monitoring, ensuring high-quality data labeling.
• MLOps: With reliable model management capabilities, Dataloop ensures efficient inference, training, and evaluation of AI models.

Dataloop is also available on Azure Marketplace.

About Azure AI Models as a Service

Azure AI Models as a Service (MaaS) offers developers and businesses access to a robust ecosystem of powerful AI models. This service includes a wide range of models, from pre-trained and custom models to foundation models, covering tasks such as natural language processing, computer vision, and more. The service is backed by Azure's stringent data privacy and security commitments, ensuring that all data, including prompts and responses, remains private and secure.

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Figure: HyDE-powered RAG Chatbot Workflow – This pipeline, created using the Dataloop platform, demonstrates the process of transforming user queries into hypothetical answers, generating embeddings, and retrieving relevant documents from a vector store. This internal Slack chatbot is optimizing information retrieval to ensure that users receive accurate and contextually relevant responses, enhancing the chatbot’s ability to search for answers in the documentation.

This is how we do it!

Powering Efficient AI Inference at Scale: Microsoft’s AI tools build upon a powerful foundation of inference engines like Azure Machine Learning and ONNX Runtime. This robust toolkit ensures smooth, high-performance AI inferencing at scale. These tools specifically fine-tune neural networks for exceptional speed and efficiency, making them ideal for demanding applications like large language models (LLMs). This translates to rapid inference and scalable AI deployment across various environments.

End-to-End AI Development with Drag-and-Drop Ease: Dataloop empowers users to build and manage advanced AI capabilities entirely within its intuitive no-code interface. Simply drag and drop models provided or developed by Microsoft through our marketplace to seamlessly integrate them into your workflows. Pre-built pipeline templates specifically designed for RAG chatbots further streamline development. This eliminates the need for additional tools, making Dataloop your one-stop shop for building next-generation RAG-based chatbots.

A Node-by-Node Look at a RAG-based Document Assistant Chatbot with Microsoft and Dataloop

This section takes you behind the scenes of our RAG-based document assistant chatbot creation, utilizing Microsoft’s AI tools and the Dataloop platform. This breakdown will help you understand each component's role and how they work together to deliver efficient and accurate responses. Below is a detailed node-by-node explanation of the system.

Node 1: Slack (or Messaging App) - Prompt Entry Point

Description: This node acts as the interface between users and the chatbot system. It integrates with a messaging platform like Slack and receives user interactions (messages, queries, commands) and starts the pipeline.

Functionality: It captures and processes the user input to be forwarded to the predictive model.

Configuration:

• Integration:
•   Specify the target messaging platform (e.g., Slack API token, login credentials for other messaging apps).
•   Define event types to handle (e.g., messages, direct mentions, specific commands).
• Message Handling:
•   Define how to pre-process messages (e.g., removing emojis, formatting, language detection).
•   Configure how to identify user intent and extract relevant information from the message.

Node 2 - PHI-3-MINI - Predict Model

Description: This node utilizes a generative prediction model, PHI-3-MINI, optimized with Microsoft’s AI tools.

Functionality: The node takes input from the Slack node and generates hypothetical responses. Research in Zero-Shot Learning suggests that this approach, leveraging contextual understanding and broad knowledge, can often outperform traditional methods.

Configuration:

• Model Selection: Choose any LLM optimized using Microsoft’s AI tools. In our chatbot, we leverage PHI-3-MINI, specifically optimized for efficient resource usage.
• System Prompt Configuration: A system prompt guides the AI's behavior by setting tone, style, and content rules, ensuring consistent, relevant, and appropriate responses. For our case, we configure the LLM to give a hypothetical and concise answer.
• Parameters: Set parameters for the model (e.g., beam search size, temperature for sampling).

Node 3 - Embed Item

Description: This node is responsible for embedding items, transforming text or data into a format that can be easily used for further processing or retrieval.

Functionality: It generates vector embeddings from the text. These embeddings represent the text in a high-dimensional space, allowing for efficient similarity searches in the next node.

Configuration:

• Embedding Model: Choose the model for generating vector embeddings from text (e.g., pre-trained Word2Vec, Sentence Transformers). You can also utilize Microsoft’s embedding tools. Each embedding model comes with its own dimensionality of the vectors.
• Normalization: Specify the normalization technique for the embeddings (e.g., L2 normalization).

Node 4 - Retriever Prompt (Search)

Description: This node acts as a retrieval mechanism, responsible for fetching relevant information or context based on the embedded item.

Functionality: It uses the embeddings to search a database or knowledge base, retrieving information that is relevant to the query or input provided by the user. It could use various retrieval techniques, including vector searches, to find the best matching results.

Configuration:

• Dataset: Specify your dataset, with all the existing chunks and embeddings.
• Similarity Metric: Define the metric for measuring similarity between the query embedding and candidate items (e.g., cosine similarity, dot product).
• Retrieval Strategy: Choose the retrieval strategy. In our case, we used our feature store based on SingleStore, a database optimized for fast searches. This allows for efficient vector-based search to quickly retrieve the most relevant information.

Node 5 - PHI-3-MINI - (Refine)

Description: Similar to the earlier PHI-3-MINI node, this node also involves a predictive model, another instance of the PHI-3-MINI model optimized by Microsoft.

Functionality: Processes the retrieved information using the predictive model to generate a response or further refine the data, ensuring a contextually accurate output for the user.

Configuration: Model Selection: Specify another instance of the PHI-3-MINI model optimized with Microsoft’s AI tools.

• Task Definition: Instruct the model to take all chunks of documentation and reply accurately to the user's question.
• System Prompt Configuration: Instruct the chatbot on how to respond. In our case, we configured it to respond kindly, act as a helpful documentation assistant, clearly state when it doesn't know an answer, and avoid inventing information.

Accelerate AI Development with Dataloop's Integration of Microsoft Foundation Models

Discover a vast ecosystem of pre-built solutions, models, and datasets tailored to your specific needs. Easily filter options by provider, media type, and compatibility to find the perfect fit. Build and customize AI workflows with easy-to-use pipeline tools and out-of-the-box end-to-end AI and GenAI workflows. We are incredibly excited to see what you can create with your new capabilities!