big-rag
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Forked from mindstudio/big-rag
Forked from mindstudio/big-rag
Forked from mindstudio/big-rag
EXAMPLES.md
EXAMPLES.md
This document provides practical examples of using the Big RAG Plugin with different types of document collections.
You have a large collection of technical documentation, API references, and tutorials that you want to query using natural language.
# Directory structure ~/Documents/tech-library/ āāā python/ ā āāā official-docs/ ā āāā tutorials/ ā āāā api-reference/ āāā javascript/ ā āāā mdn-docs/ ā āāā frameworks/ āāā databases/ āāā postgresql/ āāā mongodb/
~/Documents/tech-library~/.lmstudio/tech-library-dbQuery 1: "How do I connect to PostgreSQL using Python?"
Expected Behavior:
Query 2: "What are the differences between var, let, and const in JavaScript?"
Expected Behavior:
Query 3: "Show me MongoDB aggregation pipeline examples"
Expected Behavior:
You're a researcher with hundreds of PDF papers that you want to search and reference.
Query 1: "What are the latest approaches to attention mechanisms in transformers?"
Expected Behavior:
Query 2: "Compare different reinforcement learning algorithms for robotics"
Expected Behavior:
Query 3: "What datasets are commonly used for object detection?"
Expected Behavior:
A law firm with thousands of legal documents, contracts, and case files.
Query 1: "Find all contracts with non-compete clauses"
Expected Behavior:
Query 2: "What are the standard terms for intellectual property rights?"
Expected Behavior:
Query 3: "Show precedents for breach of contract cases"
Expected Behavior:
Personal collection of notes, articles, ebooks, and saved web pages.
Query 1: "What did I save about productivity techniques?"
Expected Behavior:
Query 2: "Find that recipe for chocolate cake"
Expected Behavior:
Query 3: "What books have I read about history?"
Expected Behavior:
Large codebase with documentation, README files, and code comments.
Query 1: "How do I set up the development environment?"
Expected Behavior:
Query 2: "What's the API for user authentication?"
Expected Behavior:
Query 3: "How do I contribute to this project?"
Expected Behavior:
Healthcare provider with anonymized patient records, research notes, and medical literature.
Query 1: "What are common treatments for condition X?"
Expected Behavior:
Query 2: "Find cases with similar symptoms"
Expected Behavior:
Problem: Querying for content you know exists returns no results.
Solutions:
Problem: Getting too many low-quality matches.
Solutions:
Problem: Initial indexing taking too long.
Solutions:
This document provides practical examples of using the Big RAG Plugin with different types of document collections.
You have a large collection of technical documentation, API references, and tutorials that you want to query using natural language.
# Directory structure ~/Documents/tech-library/ āāā python/ ā āāā official-docs/ ā āāā tutorials/ ā āāā api-reference/ āāā javascript/ ā āāā mdn-docs/ ā āāā frameworks/ āāā databases/ āāā postgresql/ āāā mongodb/
~/Documents/tech-library~/.lmstudio/tech-library-dbQuery 1: "How do I connect to PostgreSQL using Python?"
Expected Behavior:
Query 2: "What are the differences between var, let, and const in JavaScript?"
Expected Behavior:
Query 3: "Show me MongoDB aggregation pipeline examples"
Expected Behavior:
You're a researcher with hundreds of PDF papers that you want to search and reference.
Query 1: "What are the latest approaches to attention mechanisms in transformers?"
Expected Behavior:
Query 2: "Compare different reinforcement learning algorithms for robotics"
Expected Behavior:
Query 3: "What datasets are commonly used for object detection?"
Expected Behavior:
A law firm with thousands of legal documents, contracts, and case files.
Query 1: "Find all contracts with non-compete clauses"
Expected Behavior:
Query 2: "What are the standard terms for intellectual property rights?"
Expected Behavior:
Query 3: "Show precedents for breach of contract cases"
Expected Behavior:
Personal collection of notes, articles, ebooks, and saved web pages.
Query 1: "What did I save about productivity techniques?"
Expected Behavior:
Query 2: "Find that recipe for chocolate cake"
Expected Behavior:
Query 3: "What books have I read about history?"
Expected Behavior:
Large codebase with documentation, README files, and code comments.
Query 1: "How do I set up the development environment?"
Expected Behavior:
Query 2: "What's the API for user authentication?"
Expected Behavior:
Query 3: "How do I contribute to this project?"
Expected Behavior:
Healthcare provider with anonymized patient records, research notes, and medical literature.
Query 1: "What are common treatments for condition X?"
Expected Behavior:
Query 2: "Find cases with similar symptoms"
Expected Behavior:
Problem: Querying for content you know exists returns no results.
Solutions:
Problem: Getting too many low-quality matches.
Solutions:
Problem: Initial indexing taking too long.
Solutions:
~/Research/papers~/.lmstudio/research-db~/Legal/documents~/.lmstudio/legal-db~/Knowledge~/.lmstudio/knowledge-db~/Projects/myapp~/.lmstudio/myapp-docs-db~/Medical/data~/.lmstudio/medical-db# Directory structure
~/Research/papers/
āāā machine-learning/
ā āāā deep-learning/
ā āāā reinforcement-learning/
ā āāā nlp/
āāā computer-vision/
āāā robotics/
# Directory structure
~/Legal/documents/
āāā contracts/
ā āāā 2020/
ā āāā 2021/
ā āāā 2022/
ā āāā 2023/
ā āāā 2024/
āāā case-files/
āāā regulations/
# Directory structure
~/Knowledge/
āāā books/
ā āāā fiction/
ā āāā non-fiction/
āāā articles/
ā āāā saved-webpages/
ā āāā pdfs/
āāā notes/
ā āāā work/
ā āāā personal/
āāā recipes/
# Directory structure
~/Projects/myapp/
āāā docs/
ā āāā api/
ā āāā guides/
ā āāā tutorials/
āāā README.md
āāā CONTRIBUTING.md
āāā src/
āāā (various .md files for documentation)
# Directory structure
~/Medical/data/
āāā research/
āāā literature/
āāā case-studies/
~/Research/papers~/.lmstudio/research-db~/Legal/documents~/.lmstudio/legal-db~/Knowledge~/.lmstudio/knowledge-db~/Projects/myapp~/.lmstudio/myapp-docs-db~/Medical/data~/.lmstudio/medical-db# Directory structure
~/Research/papers/
āāā machine-learning/
ā āāā deep-learning/
ā āāā reinforcement-learning/
ā āāā nlp/
āāā computer-vision/
āāā robotics/
# Directory structure
~/Legal/documents/
āāā contracts/
ā āāā 2020/
ā āāā 2021/
ā āāā 2022/
ā āāā 2023/
ā āāā 2024/
āāā case-files/
āāā regulations/
# Directory structure
~/Knowledge/
āāā books/
ā āāā fiction/
ā āāā non-fiction/
āāā articles/
ā āāā saved-webpages/
ā āāā pdfs/
āāā notes/
ā āāā work/
ā āāā personal/
āāā recipes/
# Directory structure
~/Projects/myapp/
āāā docs/
ā āāā api/
ā āāā guides/
ā āāā tutorials/
āāā README.md
āāā CONTRIBUTING.md
āāā src/
āāā (various .md files for documentation)
# Directory structure
~/Medical/data/
āāā research/
āāā literature/
āāā case-studies/