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docs: consolidate and archive redundant documentation

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- Archived 13 API design iterations to docs/api-design-archive/
- Consolidated augmentation docs to docs/augmentations-archive/
- Maintained ONE definitive API doc at docs/api/README.md
- Cleaned up documentation structure for 2.0 release
- Preserved all historical documents for reference
This commit is contained in:
David Snelling 2025-08-25 10:15:38 -07:00
parent ef8f35ec2a
commit 994276f09f
35 changed files with 444 additions and 239 deletions
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4
docs/guides/getting-started.md
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@ -1,6 +1,6 @@
# Getting Started with Brainy
This guide will help you get up and running with Brainy, the multi-dimensional AI database that combines vector similarity, graph relationships, and field filtering.
This guide will help you get up and running with Brainy, the multi-dimensional AI database that combines vector similarity, graph relationships, and metadata filtering.
## Installation
@ -90,7 +90,7 @@ results.forEach(result => {
const results = await brain.find("show me technology articles about AI from 2023")
// Automatically interprets: topic, category, and time range
// Structured queries with vector similarity and field filtering
// Structured queries with vector similarity and metadata filtering
const structured = await brain.find({
like: "artificial intelligence",
where: {

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docs/guides/natural-language.md
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@ -162,7 +162,7 @@ The natural language is converted to a structured Triple Intelligence query:
### 5. Execution
The structured query is executed using Triple Intelligence, combining:
- Vector similarity search
- Field filtering
- Metadata filtering
- Graph traversal
## Advanced Features