Lecture Notes: Pre-Processing Unstructured Data for RAG Applications

Introduction

• Speaker: Maria, a developer at Unstructured.io
• Topic: Pre-processing unstructured data for RAG (Retrieval-Augmented Generation) and Gen AI applications
• Importance: Unstructured data is prevalent in personal and business documents

Challenges of Unstructured Data

• Scattered Documents: Personal and business information is often scattered in various formats (PDFs, emails, Markdown, etc.)
• Lack of Organization: Data stored in various native formats with no clear organization
• Document Complexity: Different layouts, tables, images, and text orientations

Pre-Processing Unstructured Data

• ETL Pipelines: Necessary for extracting and transforming data
• Building Expertise: Requires knowledge of multiple APIs and parsing techniques
• Advanced Processing: Use of OCR and document understanding models for complex documents

Unstructured.io Tools

• Open Source Library: Supports 25 document types and 20+ data sources/destinations
  • Deployable in any environment
• Serverless API: Offers fine-tuned OCR models for better performance on complex documents
  • Includes additional chunking strategies
• Enterprise Platform: No-code solution (currently in beta)

Document Pre-Processing Workflow

• Source Connectors: For ingesting data from various sources
• Partitioning Strategies:
  • Fast Strategy: Uses rule-based parsers for text documents
  • High-Resolution Strategy: Uses OCR for image-based documents
  • Auto Strategy: Automatically selects the partitioning strategy
• JSON Output: Extracts text with metadata, preserving document structure

Chunking and Embedding

• Chunking:
  • Maintain semantic separation of topics
  • Strategies include by title, by page, and by similarity
• Embedding: Integration with providers like OpenAI, Hugging Face, AWS Bedrock

Implementation Example

• Pipeline Setup:
  • Configuration files for processing parameters, S3 bucket, partitioning, and more
  • Example of processing PDFs from S3, chunking, and embedding before loading into Elasticsearch

Practical Considerations

• Dependencies: Need for additional installations like Tesseract for local OCR
• API Usage: Options for local or API-based processing with trial access
• Error Handling: Strategies for troubleshooting local setup issues

Conclusion

• Unstructured.io provides tools to make unstructured data usable for AI applications
• Facilitates transformation and loading of diverse document types into a structured format suitable for RAG applications