Introduction
LLM’s have revolutionized the way we analyze text data. These models, however, have their limitations, particularly in the context size - the maximum amount of text they can process in a single analysis. This limitation was a significant challenge for one of our clients who needed to analyze extensive text datasets and extract both general and detailed insights. While language models continually evolve, with context sizes expanding, the challenge of analyzing vast text data stays. To address this, we developed a solution that not only facilitates asking general questions about a text but also enables cross-document analysis.
General Questions for Single Documents
Let’s dive into how we handle general questions for a single document. This process involves loading the entire text into the model's context. For this purpose, we employed Anthropic's Claude2 language model, capable of handling a context size of about 100,000 tokens, equivalent to roughly 75,000 words. We chose Python and the Langchain library for this task due to its popularity, robust community support, and compatibility with various language models. A important feature of Langchain is its ability to create 'chains' that enable a series of questions to be posed to the language model sequentially. The answers from previous queries are fed into subsequent ones allowing the model to extract essential information from the text and tailor responses to the user’s specific questions. Not only do we utilize Claude2, but we also harness the capabilities of GPT-3.5 and GPT-4, each chosen based on the specific task. GPT-4, for instance, excels in transforming responses into formats suitable for diverse applications. This approach is particularly effective for general inquiries, as the model can access the entire document.
Specific Inquiries Across Multiple Documents
When dealing with even larger datasets, we encountered the inevitable context size limitations. To overcome this and allow our clients to query across multiple documents, we integrated vector databases with the power of language models. Vector databases operate on a multi-dimensional plane, grouping similar pieces of information close together. By dividing each document into smaller segments and storing them in a vector database, we effectively bypass the context size limitation of language models. This method enables us to analyze only the relevant sections of a document to derive answers.
Moreover, this approach can be scaled up to include a vector database of all documents, allowing queries to span across the entire dataset. This holistic method of data analysis using language models and vector databases represents a significant leap in our ability to process and understand large volumes of text data, offering our clients unparalleled insights into their business challenges.
Conclusion: Bridging Technology and Business Needs
The culmination of this project is a web application representing a major leap in data analysis capabilities:
- Enhanced Efficiency: The application drastically cuts down the time and resources needed for processing large text datasets.
- Depth and Precision in Analysis: Leveraging multiple LLMs and a vector database, the application achieves deep and accurate analysis, uncovering nuanced insights.
- Scalability and Flexibility: The application's design is scalable and versatile, catering to various industries and data types, and easily adaptable to evolving business needs.
- Strategic Business Advantage: With advanced data processing capabilities, the application provides businesses a significant competitive advantage, enabling them to make strategic, data-driven decisions.
This project is a testament to the power of merging technical innovation with practical business solutions. It highlights how engineering creativity and expertise can be directed to construct tools that address complex technical challenges and concurrently deliver substantial business value, particularly in the sphere of data-driven decision-making.
