Deploy a Digital Assistant today with RAG on IBM Power10

A Digital Assistant with Generative AI capabilities represents a significant advancement over traditional chatbots, offering more intelligent, personalized, and dynamic interactions. This makes them suitable for more complex and varied applications, providing greater value to users and organizations.

The heart of the technology is called Retrieval Augmented Generation or RAG that has become a standard industry practice in a very short time.

Let me try and explain RAG with analogy that many of us are familiar with: Imagine you're a student working on a research project. First, you go to the library to find books and articles on your topic (retrieval). Then, you read through the materials and take notes on the important points (augmentation). Finally, you use these notes, along with your own knowledge, to write your paper (generation). Similarly, RAG in AI involves searching for relevant information, using it to enhance understanding, and then generating a detailed response.

There are 3 stages in RAG:

1. Retrieval: When you ask a question, the system first searches a large database of documents to find the most relevant information. To perform retrieval, full-text search and analytics tools will be used.
2. Augmentation: The retrieved information is then used to help the AI generate a more accurate and detailed answer to your question.
3. Generation: Finally, the AI combines the retrieved information with its own knowledge to produce a response. An example of processing the retrieved information is Hugging Face Transformers which is A popular library for natural language processing tasks, which includes pre-trained models like Llama.

So, RAG combines searching for information and generating text to give better answers.

The good news is: If you’re running Power10 systems for your core workloads, you can test drive a digital assistant use case today using the same system side by side your core workload.

Here’s an architectural overview of a Digital Assistant solution that uses RAG in a Power10 system:

Let’s go through the workflow when a user asks a question:

1. User types a question via front end application
2. Present the question to an LLM (e.g. Llama2, DeepSeek to handle Chinese Language ..etc.) to perform inference
3. Pass through the “knowledge base” created using a vector DB (e.g. Milvus) to provide additional domain context to LLM
4. Contextual answer presented to user.

Going a little deeper, these are the essential steps that a prospective client organization ?will have to go through to design a RAG Application:

1. Define the Use Case and Requirements: Clearly define what you want the RAG application to achieve. Determine the type and amount of data needed. Establish how you will measure the success of your application (e.g., accuracy, latency).
2. Data Collection: Clean and pre-process the data to ensure it is in a usable format.
3. Choose a Base Language Model Choose a pre-trained large language model. ensure it meets your application's needs
4. Build the Retrieval Component Index your data using tools and vector database. Implement or configure the retrieval algorithm to fetch relevant documents based on user queries. Fine-tune the retrieval system to improve relevance and accuracy.
5. Integrate the Generation Component Integrate the chosen language model with the retrieval component
6. Develop the Application Interface or re-purpose the existing one

Clients will have significant benefits when they adopt a RAG based Digital Assistant:

1. RAG is Highly adaptable to multiple use cases by changing the knowledge base.
2. RAG is simple and cost-effective compared to other customization approaches, enabling organizations to deploy it without extensive model customization.
3. Leveraging RAG allows LLMs to provide contextually relevant responses tailored to an organization's proprietary or domain-specific data.

The industry is brimming with use cases for Digital Assistants. Here are some of the popular ones that clients are exploring:

1. Question Answering: Providing detailed and contextually accurate answers, Offering precise technical solutions
2. Content Creation: Helping content creators with relevant information to enhance their writing, automatically generating reports by retrieving relevant data and presenting it in a coherent and readable format.
3. Document Summarization: Summarizing lengthy legal documents or medical records by retrieving relevant sections and generating concise summaries.
4. Knowledge Management: Enhancing internal knowledge management systems by retrieving relevant documents and generating insights for employees.
5. Translation and Localization: Enhancing translation accuracy by retrieving contextually relevant examples and generating translations that better capture the intended meaning.
6. Financial Analysis: Analyzing market trends by retrieving relevant financial reports and generating insights.
7. Tutoring Assistant: Creating customized learning materials by retrieving relevant educational content and generating personalized study guides.

The next big question is: “Why IBM Power10 for RAG”?

These are the top five reasons I can think of:

1. IBM Power10 servers offer a compelling security advantage for enterprises.
2. Run the inference close to the data and application.
3. Each Power10 core has 4x MMAs or Matrix Math Accelerator units that can efficiently execute dense matrix multiplication operations. Instead of executing these operations on general-purpose CPU cores, the MMA units can perform them in a vectorized manner with much higher throughput. This is ideal for AI inferencing when discrete GPUs are not available or feasible.
4. MMAs enable AI inferencing in back-offices, remote sites, or network edges where GPUs aren't viable.
5. Power10 already supports many of the Open-Source software components needed to build a RAG solution.

Here are a few call to actions for your consideration:

1. Experience the future of AI with our cutting-edge Digital Assistant powered by Retrieval-Augmented Generation (RAG) on IBM Power10.
2. Enjoy intelligent, personalized interactions that go beyond traditional chatbots.
3. Leverage your existing Power10 resources without needing a GPU server.
4. Connect with us to schedule a use case alignment workshop and see how this innovative technology can transform your operations.

Discover the potential of RAG and IBM Power10 today!