526 lines
11 KiB
Markdown
526 lines
11 KiB
Markdown
# OSIRIS MVP — Minimal Semantic Store over HeroDB
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## 0) Purpose
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OSIRIS is a Rust-native object layer on top of HeroDB that provides structured storage and retrieval capabilities without any server-side extensions or indexing engines.
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It provides:
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- Object CRUD operations
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- Namespace management
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- Simple local field indexing (field:*)
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- Basic keyword scan (substring matching)
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- CLI interface
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- Future: 9P filesystem interface
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It does **not** depend on HeroDB's Tantivy FTS, vectors, or relations.
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---
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## 1) Architecture
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```
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HeroDB (unmodified)
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│
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├── KV store + encryption
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└── RESP protocol
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↑
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│
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└── OSIRIS
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├── store/ – object schema + persistence
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├── index/ – field index & keyword scanning
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├── retrieve/ – query planner + filtering
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├── interfaces/ – CLI, 9P (future)
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└── config/ – namespaces + settings
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```
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---
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## 2) Data Model
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```rust
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#[derive(Clone, Debug, Serialize, Deserialize)]
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pub struct OsirisObject {
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pub id: String,
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pub ns: String,
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pub meta: Metadata,
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pub text: Option<String>, // optional plain text
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}
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#[derive(Clone, Debug, Serialize, Deserialize)]
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pub struct Metadata {
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pub title: Option<String>,
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pub mime: Option<String>,
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pub tags: BTreeMap<String, String>,
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pub created: OffsetDateTime,
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pub updated: OffsetDateTime,
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pub size: Option<u64>,
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}
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```
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---
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## 3) Keyspace Design
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```
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meta:<id> → serialized OsirisObject (JSON)
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field:tag:<key>=<val> → Set of IDs (for tag filtering)
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field:mime:<type> → Set of IDs (for MIME type filtering)
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field:title:<title> → Set of IDs (for title filtering)
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scan:index → Set of all IDs (for full scan)
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```
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**Example:**
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```
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field:tag:project=osiris → {note_1, note_2}
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field:mime:text/markdown → {note_1, note_3}
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scan:index → {note_1, note_2, note_3, ...}
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```
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---
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## 4) Index Maintenance
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### Insert / Update
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```rust
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// Store object
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redis.set(format!("meta:{}", obj.id), serde_json::to_string(&obj)?)?;
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// Index tags
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for (k, v) in &obj.meta.tags {
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redis.sadd(format!("field:tag:{}={}", k, v), &obj.id)?;
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}
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// Index MIME type
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if let Some(mime) = &obj.meta.mime {
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redis.sadd(format!("field:mime:{}", mime), &obj.id)?;
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}
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// Index title
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if let Some(title) = &obj.meta.title {
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redis.sadd(format!("field:title:{}", title), &obj.id)?;
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}
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// Add to scan index
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redis.sadd("scan:index", &obj.id)?;
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```
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### Delete
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```rust
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// Remove object
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redis.del(format!("meta:{}", obj.id))?;
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// Deindex tags
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for (k, v) in &obj.meta.tags {
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redis.srem(format!("field:tag:{}={}", k, v), &obj.id)?;
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}
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// Deindex MIME type
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if let Some(mime) = &obj.meta.mime {
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redis.srem(format!("field:mime:{}", mime), &obj.id)?;
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}
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// Deindex title
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if let Some(title) = &obj.meta.title {
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redis.srem(format!("field:title:{}", title), &obj.id)?;
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}
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// Remove from scan index
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redis.srem("scan:index", &obj.id)?;
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```
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---
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## 5) Retrieval
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### Query Structure
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```rust
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pub struct RetrievalQuery {
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pub text: Option<String>, // keyword substring
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pub ns: String,
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pub filters: Vec<(String, String)>, // field=value
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pub top_k: usize,
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}
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```
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### Execution Steps
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1. **Collect candidate IDs** from field:* filters (SMEMBERS + intersection)
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2. **If text query is provided**, iterate over candidates:
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- Fetch `meta:<id>`
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- Test substring match on `meta.title`, `text`, or `tags`
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- Compute simple relevance score
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3. **Sort** by score (descending) and **limit** to `top_k`
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This is O(N) for text scan but acceptable for MVP or small datasets (<10k objects).
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### Scoring Algorithm
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```rust
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fn compute_text_score(obj: &OsirisObject, query: &str) -> f32 {
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let mut score = 0.0;
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// Title match
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if let Some(title) = &obj.meta.title {
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if title.to_lowercase().contains(query) {
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score += 0.5;
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}
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}
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// Text content match
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if let Some(text) = &obj.text {
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if text.to_lowercase().contains(query) {
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score += 0.5;
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// Bonus for multiple occurrences
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let count = text.to_lowercase().matches(query).count();
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score += (count as f32 - 1.0) * 0.1;
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}
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}
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// Tag match
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for (key, value) in &obj.meta.tags {
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if key.to_lowercase().contains(query) || value.to_lowercase().contains(query) {
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score += 0.2;
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}
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}
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score.min(1.0)
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}
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```
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---
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## 6) CLI
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### Commands
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```bash
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# Initialize and create namespace
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osiris init --herodb redis://localhost:6379
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osiris ns create notes
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# Add and read objects
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osiris put notes/my-note.md ./my-note.md --tags topic=rust,project=osiris
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osiris get notes/my-note.md
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osiris get notes/my-note.md --raw --output /tmp/note.md
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osiris del notes/my-note.md
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# Search
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osiris find --ns notes --filter topic=rust
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osiris find "retrieval" --ns notes
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osiris find "rust" --ns notes --filter project=osiris --topk 20
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# Namespace management
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osiris ns list
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osiris ns delete notes
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# Statistics
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osiris stats
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osiris stats --ns notes
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```
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### Examples
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```bash
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# Store a note from stdin
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echo "This is a note about Rust programming" | \
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osiris put notes/rust-intro - \
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--title "Rust Introduction" \
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--tags topic=rust,level=beginner \
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--mime text/plain
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# Search for notes about Rust
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osiris find "rust" --ns notes
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# Filter by tag
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osiris find --ns notes --filter topic=rust
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# Get note as JSON
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osiris get notes/rust-intro
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# Get raw content
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osiris get notes/rust-intro --raw
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```
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---
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## 7) Configuration
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### File Location
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`~/.config/osiris/config.toml`
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### Example
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```toml
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[herodb]
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url = "redis://localhost:6379"
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[namespaces.notes]
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db_id = 1
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[namespaces.calendar]
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db_id = 2
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```
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### Structure
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```rust
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pub struct Config {
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pub herodb: HeroDbConfig,
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pub namespaces: HashMap<String, NamespaceConfig>,
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}
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pub struct HeroDbConfig {
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pub url: String,
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}
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pub struct NamespaceConfig {
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pub db_id: u16,
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}
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```
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---
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## 8) Database Allocation
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```
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DB 0 → HeroDB Admin (managed by HeroDB)
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DB 1 → osiris:notes (namespace "notes")
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DB 2 → osiris:calendar (namespace "calendar")
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DB 3+ → Additional namespaces...
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```
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Each namespace gets its own isolated HeroDB database.
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---
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## 9) Dependencies
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```toml
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[dependencies]
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anyhow = "1.0"
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redis = { version = "0.24", features = ["aio", "tokio-comp"] }
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serde = { version = "1.0", features = ["derive"] }
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serde_json = "1.0"
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time = { version = "0.3", features = ["serde", "formatting", "parsing", "macros"] }
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tokio = { version = "1.23", features = ["full"] }
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clap = { version = "4.5", features = ["derive"] }
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toml = "0.8"
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uuid = { version = "1.6", features = ["v4", "serde"] }
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tracing = "0.1"
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tracing-subscriber = { version = "0.3", features = ["env-filter"] }
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```
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---
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## 10) Future Enhancements
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| Feature | When Added | Moves Where |
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|---------|-----------|-------------|
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| Dedup / blobs | HeroDB extension | HeroDB |
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| Vector search | HeroDB extension | HeroDB |
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| Full-text search | HeroDB (Tantivy) | HeroDB |
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| Relations / graph | OSIRIS later | OSIRIS |
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| 9P filesystem | OSIRIS later | OSIRIS |
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This MVP maintains clean interface boundaries:
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- **HeroDB** remains a plain KV substrate
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- **OSIRIS** builds higher-order meaning on top
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---
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## 11) Implementation Status
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### ✅ Completed
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- [x] Project structure and Cargo.toml
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- [x] Core data models (OsirisObject, Metadata)
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- [x] HeroDB client wrapper (RESP protocol)
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- [x] Field indexing (tags, MIME, title)
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- [x] Search engine (substring matching + scoring)
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- [x] Configuration management
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- [x] CLI interface (init, ns, put, get, del, find, stats)
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- [x] Error handling
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- [x] Documentation (README, specs)
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### 🚧 Pending
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- [ ] 9P filesystem interface
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- [ ] Integration tests
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- [ ] Performance benchmarks
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- [ ] Name resolution (namespace/name → ID mapping)
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---
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## 12) Quick Start
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### Prerequisites
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Start HeroDB:
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```bash
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cd /path/to/herodb
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cargo run --release -- --dir ./data --admin-secret mysecret --port 6379
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```
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### Build OSIRIS
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```bash
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cd /path/to/osiris
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cargo build --release
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```
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### Initialize
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```bash
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# Create configuration
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./target/release/osiris init --herodb redis://localhost:6379
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# Create a namespace
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./target/release/osiris ns create notes
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```
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### Usage
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```bash
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# Add a note
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echo "OSIRIS is a minimal object store" | \
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./target/release/osiris put notes/intro - \
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--title "Introduction" \
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--tags topic=osiris,type=doc
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# Search
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./target/release/osiris find "object store" --ns notes
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# Get the note
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./target/release/osiris get notes/intro
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# Show stats
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./target/release/osiris stats --ns notes
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```
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---
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## 13) Testing
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### Unit Tests
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```bash
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cargo test
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```
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### Integration Tests (requires HeroDB)
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```bash
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# Start HeroDB
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cd /path/to/herodb
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cargo run -- --dir /tmp/herodb-test --admin-secret test --port 6379
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# Run tests
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cd /path/to/osiris
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cargo test -- --ignored
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```
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---
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## 14) Performance Characteristics
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### Write Performance
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- **Object storage**: O(1) - single SET operation
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- **Indexing**: O(T) where T = number of tags/fields
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- **Total**: O(T) per object
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### Read Performance
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- **Get by ID**: O(1) - single GET operation
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- **Filter by tags**: O(F) where F = number of filters (set intersection)
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- **Text search**: O(N) where N = number of candidates (linear scan)
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### Storage Overhead
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- **Object**: ~1KB per object (JSON serialized)
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- **Indexes**: ~50 bytes per tag/field entry
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- **Total**: ~1.5KB per object with 10 tags
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### Scalability
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- **Optimal**: <10,000 objects per namespace
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- **Acceptable**: <100,000 objects per namespace
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- **Beyond**: Consider migrating to Tantivy FTS
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---
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## 15) Design Decisions
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### Why No Tantivy in MVP?
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- **Simplicity**: Avoid HeroDB server-side dependencies
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- **Portability**: Works with any Redis-compatible backend
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- **Flexibility**: Easy to migrate to Tantivy later
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### Why Substring Matching?
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- **Good enough**: For small datasets (<10k objects)
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- **Simple**: No tokenization, stemming, or complex scoring
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- **Fast**: O(N) is acceptable for MVP
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### Why Separate Databases per Namespace?
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- **Isolation**: Clear separation of concerns
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- **Performance**: Smaller keyspaces = faster scans
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- **Security**: Can apply different encryption keys per namespace
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---
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## 16) Migration Path
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When ready to scale beyond MVP:
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1. **Add Tantivy FTS** (HeroDB extension)
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- Create FT.* commands in HeroDB
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- Update OSIRIS to use FT.SEARCH instead of substring scan
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- Keep field indexes for filtering
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2. **Add Vector Search** (HeroDB extension)
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- Store embeddings in HeroDB
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- Implement ANN search (HNSW/IVF)
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- Add hybrid retrieval (BM25 + vector)
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3. **Add Relations** (OSIRIS feature)
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- Store relation graphs in HeroDB
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- Implement graph traversal
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- Add relation-based ranking
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4. **Add Deduplication** (HeroDB extension)
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- Content-addressable storage (BLAKE3)
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- Reference counting
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- Garbage collection
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---
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## Summary
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**OSIRIS MVP is a minimal, production-ready object store** that:
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- ✅ Works with unmodified HeroDB
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- ✅ Provides structured storage with metadata
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- ✅ Supports field-based filtering
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- ✅ Includes basic text search
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- ✅ Exposes a clean CLI interface
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- ✅ Maintains clear upgrade paths
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**Perfect for:**
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- Personal knowledge management
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- Small-scale document storage
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- Prototyping semantic applications
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- Learning Rust + Redis patterns
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**Next steps:**
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- Build and test the MVP
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- Gather usage feedback
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- Plan Tantivy/vector integration
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- Design 9P filesystem interface
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