Supercharge Your Research Analysis with SLIM Models on CPU using LLMWare

Supercharge Your Research Analysis with SLIM Models on CPU using LLMWare

Table of Contents:

  1. Introduction
  2. What is a slim?
  3. The Need for Multi-Step Research and Analytics
  4. Building an AI-Ready Knowledge Base
  5. The Microsoft and IBM Partnership
  6. Conducting Multi-Step Research with LLmware
  7. Code Walkthrough: Creating an AI-Ready Knowledge Base
  8. Running the Multi-Step Research Demo
  9. Analyzing the Results
  10. Conclusion

⭐ Introduction

In today's video, we will be delving into the topic of multi-step research and analytics. We'll explore how we can go beyond simply answering questions or summarizing information, and instead, focus on the complexity of conducting multi-step analytics using LLmware and slim models. The best part? All of this can be achieved with the power of a CPU. So, let's dive right in!

⭐ What is a slim?

A slim, or a structured language instruction model, is a specialized function that provides structured outputs such as Python dictionaries, JSON, and SQL. These small LLmware models are designed to seamlessly integrate into a multi-step process, allowing for more complex types of deliverables. This means that Generative AI can be deployed for true knowledge-based automation in the enterprise, which typically involves multiple specialized skills and a linear pipeline of tasks.

⭐ The Need for Multi-Step Research and Analytics

When deploying generative AI for knowledge-based automation in the enterprise, it is often not a single step but a multi-step process. This process involves various specialized models and tools, each performing specific tasks based on the insights gained from previous steps. The goal is to extract key information, perform lookups in knowledge bases, classify data, and enable further processing based on the outcomes. Ultimately, all these steps need to come together and be seamlessly integrated into existing business processes.

⭐ Building an AI-Ready Knowledge Base

At the heart of effective multi-step research and analytics is an AI-ready knowledge base. This knowledge base consists of ingested, parsed, and extracted unstructured information from documents and files. These documents are then chunked at Scale and indexed, typically using text collections and embedding models optimized for the specific domain and industry. The vectorized data obtained is then integrated with SQL table data stores, which house valuable enterprise information.

⭐ The Microsoft and IBM Partnership

Before we delve into the demo, let's take a moment to explore one of the most iconic partnerships and rivalries that shaped the software industry: that between Microsoft and IBM. The collaboration between IBM and Microsoft in the early 80s led to Microsoft's dominant position in the operating system market. However, over time, this partnership turned into a rivalry that significantly influenced the direction of the software industry.

⭐ Conducting Multi-Step Research with LLmware

Now, let's walk through an end-to-end demo showcasing multi-step research using LLmware. First, we will create a knowledge base consisting of Microsoft materials to demonstrate the scalability of the process. Then, we will run a query to identify instances where both IBM and Microsoft are Mentioned, specifically focusing on passages with negative sentiment. This is where the real research begins, as we dive deep into analyzing and extracting insights from this negative sentiment data related to IBM.

⭐ Code Walkthrough: Creating an AI-Ready Knowledge Base

To better understand the process, let's break down the code that creates an AI-ready knowledge base. We start by parsing text, chunking it, and indexing it from documents. We then query this library for passages mentioning IBM. Next, we instantiate our agent as an instance of the LLm FX class. The agent loads various tools, which are slim models designed for efficient CPU usage. We pass the search results to the agent and iteratively run sentiment analysis, creating a structured journal of all the work performed.

⭐ Running the Multi-Step Research Demo

Now, let's witness the multi-step research demo in action. Using the code we reviewed earlier, we create the knowledge base, run the query, load the tools, and iterate through the search results. The sentiment analysis is conducted on each passage containing negative sentiment, providing us with valuable insights into the IBM-Microsoft rivalry. This blog post will not dive into the specific code details, but you'll find the complete code and examples on our LLmware platform.

⭐ Analyzing the Results

After completing the analysis, it's time to make sense of the results. Each report gives us an overview of the passages where IBM and negative sentiment intersect, along with sentiment scores, tags, emotions, topics, and named entities. These reports provide a clear understanding of the historical context and reveal the tension between IBM and Microsoft. By examining these detailed reports, we can identify specific areas of interest for further research.

⭐ Conclusion

In this video, we explored the concept of multi-step research and analytics using LLmware. By going beyond simple question answering, we can achieve complex analytics and deliver actionable insights. The integration of AI-ready knowledge bases, slim models, and the power of CPUs enable us to perform sophisticated research tasks. By understanding the historical significance of partnerships like Microsoft and IBM, we gain valuable insights into the software industry. We hope this demo has shed light on the potential of multi-step research and analytics using LLmware, opening doors for further explorations and applications in the enterprise.


Highlights:

  • Explore the power of multi-step research and analytics using LLmware
  • Create an AI-ready knowledge base for efficient research processes
  • Analyze the historic Microsoft and IBM partnership and rivalry
  • Run an end-to-end demo showcasing multi-step research
  • Leverage slim models and CPU capabilities for complex research tasks
  • Uncover valuable insights by conducting sentiment analysis
  • Generate detailed reports for deeper analysis
  • Take advantage of LLmware's tools and capabilities for actionable insights

FAQ:

Q: How can multi-step research and analytics benefit enterprises? A: Multi-step research and analytics allow enterprises to go beyond simple question answering and summarization, enabling complex analysis and the generation of actionable insights.

Q: Can the LLmware platform be used with CPUs? A: Yes, LLmware can be used on CPUs, making it accessible and efficient for research tasks.

Q: What is the significance of the Microsoft and IBM partnership? A: The partnership between Microsoft and IBM in the early 80s shaped the software industry and led to the domination of Microsoft in the operating system market.

Q: How can LLmware facilitate sentiment analysis? A: LLmware provides the tools and capabilities to perform sentiment analysis, allowing researchers to uncover sentiment-related insights and trends.

Q: What are the key components of an AI-ready knowledge base? A: An AI-ready knowledge base comprises ingested, parsed, and extracted unstructured information, which is then indexed and integrated with SQL table data stores.

Q: How can LLmware help in conducting detailed research analyses? A: LLmware's slim models and tools enable researchers to perform detailed analyses of tags, emotions, topics, and named entities, providing a comprehensive understanding of the data.

Q: Is LLmware suitable for large-scale research projects? A: Yes, LLmware can handle large-scale research projects, ensuring scalability and efficiency in processing vast amounts of data.

Q: Can LLmware generate actionable reports? A: Yes, LLmware can generate detailed reports that combine relevant information, including sentiment analysis results, tags, emotions, topics, and named entities, enabling researchers to derive actionable insights.

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