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EXAMPLES.md

Usage Examples

This document provides practical examples of using the Big RAG Plugin with different types of document collections.

Example 1: Technical Documentation Library

Scenario

You have a large collection of technical documentation, API references, and tutorials that you want to query using natural language.

Setup

# Directory structure
~/Documents/tech-library/
├── python/
│   ├── official-docs/
│   ├── tutorials/
│   └── api-reference/
├── javascript/
│   ├── mdn-docs/
│   └── frameworks/
└── databases/
    ├── postgresql/
    └── mongodb/

Configuration

  • Documents Directory: ~/Documents/tech-library
  • Vector Store Directory: ~/.lmstudio/tech-library-db
  • Chunk Size: 1024 (larger chunks for technical content)
  • Chunk Overlap: 200
  • Retrieval Limit: 7
  • Affinity Threshold: 0.6
  • Max Concurrent Files: 5
  • OCR: Disabled

Example Queries

Query 1: "How do I connect to PostgreSQL using Python?"

Expected Behavior:

  • Retrieves relevant passages from both Python and PostgreSQL documentation
  • Combines information from multiple sources
  • Provides code examples if available in the docs

Query 2: "What are the differences between var, let, and const in JavaScript?"

Expected Behavior:

  • Finds relevant sections from JavaScript documentation
  • Returns explanations with examples
  • May include best practices if documented

Query 3: "Show me MongoDB aggregation pipeline examples"

Expected Behavior:

  • Retrieves MongoDB-specific documentation
  • Includes practical examples
  • May reference multiple documents showing different use cases

Example 2: Research Paper Collection

Scenario

You're a researcher with hundreds of PDF papers that you want to search and reference.

Setup

# Directory structure
~/Research/papers/
├── machine-learning/
│   ├── deep-learning/
│   ├── reinforcement-learning/
│   └── nlp/
├── computer-vision/
└── robotics/

Configuration

  • Documents Directory: ~/Research/papers
  • Vector Store Directory: ~/.lmstudio/research-db
  • Chunk Size: 768 (balanced for academic writing)
  • Chunk Overlap: 150
  • Retrieval Limit: 10 (more results for research)
  • Affinity Threshold: 0.55
  • Max Concurrent Files: 3 (PDFs are slower to parse)
  • OCR: Disabled (assuming text-based PDFs)

Example Queries

Query 1: "What are the latest approaches to attention mechanisms in transformers?"

Expected Behavior:

  • Searches across all NLP papers
  • Retrieves relevant sections discussing attention
  • May include citations from multiple papers

Query 2: "Compare different reinforcement learning algorithms for robotics"

Expected Behavior:

  • Finds content from both RL and robotics papers
  • Retrieves comparative information
  • Provides context from multiple sources

Query 3: "What datasets are commonly used for object detection?"

Expected Behavior:

  • Searches computer vision papers
  • Lists datasets mentioned in papers
  • May include performance benchmarks

Scenario

A law firm with thousands of legal documents, contracts, and case files.

Setup

# Directory structure
~/Legal/documents/
├── contracts/
│   ├── 2020/
│   ├── 2021/
│   ├── 2022/
│   ├── 2023/
│   └── 2024/
├── case-files/
└── regulations/

Configuration

  • Documents Directory: ~/Legal/documents
  • Vector Store Directory: ~/.lmstudio/legal-db
  • Chunk Size: 512 (standard for legal text)
  • Chunk Overlap: 100
  • Retrieval Limit: 5
  • Affinity Threshold: 0.7 (higher precision for legal)
  • Max Concurrent Files: 2 (careful processing)
  • OCR: Enabled (for scanned documents)

Example Queries

Query 1: "Find all contracts with non-compete clauses"

Expected Behavior:

  • Searches across all contract documents
  • Retrieves sections containing non-compete language
  • Provides file references for review

Query 2: "What are the standard terms for intellectual property rights?"

Expected Behavior:

  • Finds IP-related clauses across documents
  • Shows variations in language
  • Helps identify standard vs. custom terms

Query 3: "Show precedents for breach of contract cases"

Expected Behavior:

  • Searches case files
  • Retrieves relevant case information
  • Provides context for similar situations

Example 4: Personal Knowledge Base

Scenario

Personal collection of notes, articles, ebooks, and saved web pages.

Setup

# Directory structure
~/Knowledge/
├── books/
│   ├── fiction/
│   └── non-fiction/
├── articles/
│   ├── saved-webpages/
│   └── pdfs/
├── notes/
│   ├── work/
│   └── personal/
└── recipes/

Configuration

  • Documents Directory: ~/Knowledge
  • Vector Store Directory: ~/.lmstudio/knowledge-db
  • Chunk Size: 512
  • Chunk Overlap: 100
  • Retrieval Limit: 5
  • Affinity Threshold: 0.5
  • Max Concurrent Files: 4
  • OCR: Enabled (for recipe images, etc.)

Example Queries

Query 1: "What did I save about productivity techniques?"

Expected Behavior:

  • Searches across articles and notes
  • Finds productivity-related content
  • Combines information from multiple sources

Query 2: "Find that recipe for chocolate cake"

Expected Behavior:

  • Searches recipe directory
  • May use OCR if recipe is an image
  • Returns recipe details

Query 3: "What books have I read about history?"

Expected Behavior:

  • Searches book collection
  • Identifies history-related books
  • May provide summaries or key points

Example 5: Software Development Project

Scenario

Large codebase with documentation, README files, and code comments.

Setup

# Directory structure
~/Projects/myapp/
├── docs/
│   ├── api/
│   ├── guides/
│   └── tutorials/
├── README.md
├── CONTRIBUTING.md
└── src/
    └── (various .md files for documentation)

Configuration

  • Documents Directory: ~/Projects/myapp
  • Vector Store Directory: ~/.lmstudio/myapp-docs-db
  • Chunk Size: 768
  • Chunk Overlap: 150
  • Retrieval Limit: 6
  • Affinity Threshold: 0.55
  • Max Concurrent Files: 5
  • OCR: Disabled

Example Queries

Query 1: "How do I set up the development environment?"

Expected Behavior:

  • Finds setup instructions from README or guides
  • Provides step-by-step information
  • May reference multiple documentation files

Query 2: "What's the API for user authentication?"

Expected Behavior:

  • Searches API documentation
  • Retrieves authentication-related endpoints
  • Shows usage examples

Query 3: "How do I contribute to this project?"

Expected Behavior:

  • Finds CONTRIBUTING.md content
  • Provides guidelines and workflow
  • May include code style requirements

Example 6: Medical/Healthcare Records (Anonymized)

Scenario

Healthcare provider with anonymized patient records, research notes, and medical literature.

Setup

# Directory structure
~/Medical/data/
├── research/
├── literature/
└── case-studies/

Configuration

  • Documents Directory: ~/Medical/data
  • Vector Store Directory: ~/.lmstudio/medical-db
  • Chunk Size: 512
  • Chunk Overlap: 100
  • Retrieval Limit: 8
  • Affinity Threshold: 0.65 (higher precision for medical)
  • Max Concurrent Files: 3
  • OCR: Enabled (for scanned records)

Example Queries

Query 1: "What are common treatments for condition X?"

Expected Behavior:

  • Searches medical literature and case studies
  • Retrieves treatment protocols
  • Provides evidence-based information

Query 2: "Find cases with similar symptoms"

Expected Behavior:

  • Searches case studies
  • Identifies similar presentations
  • Helps with differential diagnosis

Performance Tips by Use Case

Large Text Collections (>10GB)

  • Use higher concurrency (5-8)
  • Disable OCR unless needed
  • Consider processing in batches
  • Use SSD for vector store

PDF-Heavy Collections

  • Lower concurrency (2-3)
  • Increase chunk size (1024+)
  • Allow more time for initial indexing
  • Monitor memory usage

Mixed Media Collections

  • Enable OCR selectively
  • Use moderate concurrency (3-4)
  • Adjust threshold based on quality
  • Test with small subset first

Frequently Updated Collections

  • Enable auto-reindex
  • Use file watching (future feature)
  • Keep vector store on fast storage
  • Regular maintenance

Troubleshooting Examples

Example: No Results for Known Content

Problem: Querying for content you know exists returns no results.

Solutions:

  • Lower affinity threshold (try 0.3-0.4)
  • Rephrase query to match document language
  • Check that file was actually indexed
  • Verify file format is supported

Example: Too Many Irrelevant Results

Problem: Getting too many low-quality matches.

Solutions:

  • Increase affinity threshold (try 0.7-0.8)
  • Reduce retrieval limit
  • Use more specific queries
  • Adjust chunk size for content type

Example: Slow Indexing

Problem: Initial indexing taking too long.

Solutions:

  • Reduce max concurrent files
  • Disable OCR if not needed
  • Process subdirectories separately
  • Check disk I/O performance

Best Practices

  • Start Small: Test with a subset before indexing everything
  • Tune Settings: Adjust based on your specific content
  • Monitor Performance: Watch memory and disk usage
  • Regular Maintenance: Periodically rebuild index for optimization
  • Backup: Keep backups of your vector store
  • Document: Note what settings work best for your use case
  • Iterate: Refine queries and settings based on results