Unstructured” Open-Source ETL for LLMs

Summary

Matt Robinson, Head of Product at unstructured.io, discusses strategies for improving Retrieval-Augmented Generation (RAG) systems through data pre-processing. He introduces unstructured.io's focus on ETL solutions for NLP, their open-source library for data pre-processing, and how it aids in RAG systems by enhancing query relevance, ensuring timely document retrieval, identifying response sources, and reducing text generation costs.

Takeaways

• 👨‍💼 Matt Robinson, Head of Product at unstructured.io, discusses strategies to enhance retrieval augmented generation (RAG) systems.
• 🔧 unstructured.io provides ETL solutions for natural language processing, focusing on making unstructured data accessible for downstream tasks.
• 💡 The company noticed a need for better data pre-processing tools when they found customers' valuable data trapped in various formats like PDFs and Word Documents.
• 📚 unstructured.io's solution includes three stages: connecting to data sources, transforming data, and staging it for applications like RAG systems.
• 🛠️ The unstructured open source library is a Python package that helps with data pre-processing, making it easier to prepare data for RAG systems.
• 📈 The library supports over 25 file types and offers functionalities like data connectors, partitioning, and staging.
• 🔗 unstructured.io offers different service tiers: open source, hosted API, and an upcoming production platform with enterprise features.
• 📊 The library's chunking feature helps improve query relevance by returning more timely and contextually relevant document chunks.
• ⏰ Metadata extraction from documents helps in identifying the source of a response and ensures the timeliness of the information retrieved.
• 💰 Using unstructured.io's pre-processed data can reduce the cost of text generation by requiring fewer tokens and allowing the use of less powerful, cheaper models.
• 🔄 The unstructured library integrates with tools like Lang chain, streamlining the process of setting up RAG systems.

Q & A

Who is Matt Robinson and what is his role at Unstructured?

Matt Robinson is the Head of Product at Unstructured. Before this role, he was an original member of the engineering team and is a frequent contributor to their open source project.

What is the primary focus of Unstructured?

Unstructured primarily focuses on providing ETL (Extract, Transform, Load) Solutions for natural language processing.

What is the main challenge that Unstructured aims to address?

Unstructured aims to address the challenge of making unstructured data, such as data trapped in PDFs, Word Documents, and PowerPoint files, easily accessible and ready for downstream natural language processing tasks.

What are the three stages of Unstructured's ETL process?

The three stages of Unstructured's ETL process are: 1) Connecting to the upstream data source, 2) Transforming the data by extracting relevant information and normalizing it into a standard format, and 3) Staging the data for downstream applications like retrieval augmented generation.

What types of data sources does Unstructured support?

Unstructured supports various data sources including Azure blob storage, S3, SharePoint, local file systems, and more.

How does Unstructured handle different document types during the transformation step?

Unstructured handles different document types by routing them to different pre-processing logic based on the file type. For example, PDFs and images might use computer vision models, while HTML or Word documents use separate parsing logic.

What is the purpose of the Unstructured open source Library?

The Unstructured open source Library is a Python Library that allows users to easily pre-process data and get it ready for small-scale LLM (Large Language Model) prototype applications.

What is the significance of the metadata extracted by Unstructured?

The metadata extracted by Unstructured is significant as it helps in identifying the source of a response, validating the information, and improving the user experience with RAG systems.

How does Unstructured help improve the timeliness of results in RAG systems?

Unstructured helps improve the timeliness of results by extracting metadata such as the document's date, allowing users to bias towards more recent information.

What is 'chunking' in the context of retrieval augmented generation systems?

Chunking in retrieval augmented generation systems refers to breaking down long documents into manageable chunks, which can fit into the attention window of an LM (Language Model) and ensure only relevant content is sent for processing.

How can using Unstructured potentially reduce costs associated with text generation?

Using Unstructured can reduce costs by pre-processing documents to remove unnecessary content, resulting in more compact prompts for LLMs. This can lead to using less powerful, less expensive models while achieving equivalent results.