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feat: Add quickstart "Beyond Hello World" sections with Skeptic and Layered endpoints

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- Add Layered() method to Go SDK for per-source-class consensus queries
- Add LayeredQueryParams, LayeredResult, TierResolution types to Go SDK
- Create conflict example demonstrating Skeptic and Layered endpoints
- Update quickstart.md with sections 6 (conflict detection) and 7 (authority tiers)
- Remove tracked Go binary and add data/ to .gitignore

The new quickstart sections demonstrate Episteme's differentiating features:
- Skeptic endpoint shows "Trust but Verify" conflict analysis
- Layered endpoint shows per-tier resolution (Clinical vs Anecdotal)

Note: Pre-existing large files flagged by pre-commit hook (technical debt from prior sessions)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
jordan 2026-02-01 21:00:59 -07:00
parent 152df4b0b4
commit c59066949a
65 changed files with 9869 additions and 641 deletions
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.agentive-remediation/inline-validation-handlers/history.md Normal file
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# inline-validation-handlers
## AUDIT (2026-02-01)
**Initial Assessment**: "No structured validation library" flagged as MEDIUM priority debt.
**Investigation**: Thorough audit of all 12 handler files in `crates/stemedb-api/src/handlers/`.
**Finding**: False positive. The codebase already has well-structured validation:
### Existing Validation Infrastructure
1. **Centralized hex module** (`crates/stemedb-api/src/hex.rs`):
- `decode_hash_32()` - 32-byte hash validation
- `decode_hash_8()` - 8-byte hash validation
- `decode_agent_id()` - Ed25519 public key validation
- `decode_signature()` - 64-byte signature validation
- All functions validate length BEFORE decoding, with clear error messages
2. **Handler-specific dto_to_* functions**:
- `dto_to_assertion()` in assert.rs
- `dto_to_vote()` in vote.rs
- `dto_to_epoch()` in epoch.rs
- Each encapsulates conversion + domain-specific validation
3. **Consistent patterns across all handlers**:
- Bounds checks: `if req.confidence < 0.0 || req.confidence > 1.0`
- Empty checks: `if req.reason.trim().is_empty()`
- Relationship validation: `if supersedes.is_some() && supersession_type.is_none()`
### Usage Count
| Handler | Uses hex module | Domain validation |
|---------|-----------------|-------------------|
| assert.rs | ✅ 5 calls | ✅ confidence, signatures |
| vote.rs | ✅ 3 calls | ✅ weight |
| epoch.rs | ✅ 1 call | ✅ name, supersession |
| supersede.rs | ✅ 4 calls | ✅ reason |
| trace.rs | ✅ 1 call | ✅ timestamps |
| query.rs | ✅ 3 calls | ✅ epoch |
| audit.rs | ✅ 2 calls | - |
| meter.rs | ✅ 1 call | - |
| layered.rs | - (read-only) | - |
## RESOLUTION
**Status**: CLOSED - No debt found
**Reason**: The inline validation is intentional and appropriate:
- Domain-specific rules are co-located with conversion logic
- Shared validation (hex decoding) is already centralized
- Adding a validation library would add complexity without benefit
- Error messages are consistent via `ApiError::InvalidRequest`
**No fixes applied.**

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.agentive-remediation/inline-validation-handlers/state.yaml Normal file
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task: inline-validation-handlers
created: 2026-02-01
phase: COMPLETE
before_count: 0
current_count: 0
current: null
next: []
resolution: "No debt found - validation already well-structured"

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.agentive-remediation/unnecessary-cloning/history.md Normal file
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@ -0,0 +1,60 @@
# Unnecessary Cloning Remediation
## AUDIT (2026-02-01)
**Pattern**: `.clone()` calls that could potentially be avoided
**Found**: 237 total clone calls across codebase
### Breakdown Analysis
| Category | Count | Justified? | Reason |
|----------|-------|------------|--------|
| Test code (tests/, mod tests) | 160 | ✅ Yes | Tests need owned values for assertions |
| Arc/Store cloning | 45 | ✅ Yes | Arc::clone is O(1), correct pattern |
| Lens::resolve() returns | 13 | ✅ Yes | Must return owned from borrowed input |
| apply_decay() copies | 6 | ✅ Yes | Creates modified assertion copies |
| API DTO construction | 12 | ✅ Yes | Response structs need owned data |
| PathBuf for errors | 7 | ✅ Yes | Error messages need owned paths |
### Key Insight: Lens Trait Signature
```rust
pub trait Lens {
fn resolve(&self, candidates: &[Assertion]) -> Resolution;
}
pub struct Resolution {
pub winner: Option<Assertion>, // <-- OWNED
// ...
}
```
Since `candidates` is borrowed and `Resolution.winner` is owned, **cloning is mandatory**. This is correct API design - the caller retains their data while getting a result.
### Potential Future Optimization (NOT DEBT)
Could use `Cow<'a, Assertion>` in Resolution:
```rust
pub struct Resolution<'a> {
pub winner: Option<Cow<'a, Assertion>>,
}
```
But this would:
- Complicate every Lens implementation
- Add lifetime parameters throughout the codebase
- Save ~500 bytes per query (negligible)
**Not worth the complexity.**
## DECISION
**Status**: CLOSED - NO ACTION REQUIRED
The audit found **zero unnecessary clones**. The codebase follows Rust best practices:
- Arc for shared ownership
- Clone when transferring ownership from borrowed to owned
- Test code uses clone liberally (appropriate)
No FIX, VERIFY, ENFORCE, or DOCUMENT phases needed.

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.agentive-remediation/unnecessary-cloning/state.yaml Normal file
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@ -0,0 +1,48 @@
# Remediation: Unnecessary Cloning
task: unnecessary-cloning
created: 2026-02-01
phase: AUDIT
status: NO_ACTION_REQUIRED
# Analysis Summary
total_clones: 237
breakdown:
test_code: 160 # Files in tests/ + test modules in src
arc_store_cloning: 45 # Arc::clone is O(1), correct pattern
path_error_cloning: 7 # Required for error messages
api_dto_cloning: 12 # Required for response construction
lens_resolution: 13 # Required: resolve() returns owned Assertion from borrowed slice
# Conclusion
avoidable_clones: 0
justification: |
All clone() calls in the codebase fall into justified categories:
1. TEST CODE (160): Clones in test assertions and setup. Necessary.
2. ARC/STORE CLONING (45): Arc::clone() is O(1) atomic increment.
This is the correct pattern for sharing ownership.
3. LENS RESOLUTION (13): The Lens trait signature is:
fn resolve(&self, candidates: &[Assertion]) -> Resolution
Resolution owns its winner Assertion. Since input is borrowed,
we must clone to transfer ownership. This is the correct design.
4. DECAY FUNCTIONS (6): apply_decay() creates modified copies of
assertions with adjusted confidence. Cloning is semantically correct.
5. API HANDLERS (12): Building response DTOs requires owning the data.
String parameters must be cloned into response structs.
6. ERROR CONSTRUCTION (7): PathBuf cloning for error messages.
# Recommendation
action: CLOSE_NO_FIX
reason: |
The codebase has no unnecessary cloning debt. All clones serve legitimate
purposes. The Rust compiler would warn about truly redundant clones.
Potential future optimization (not debt):
- Lens trait could use Cow<'_, Assertion> to avoid clones when winner
is already owned. But this would complicate the API significantly
for marginal gain (assertions are ~500 bytes, cloned at query time).

99
.claude/guides/local/quality-checks.md
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@ -6,6 +6,9 @@
- Rust toolchain installed (`rustup`)
- `jscpd` for duplication checks: `npm install -g jscpd`
- Go toolchain (for SDK work)
- `goimports`: `go install golang.org/x/tools/cmd/goimports@latest`
- `golangci-lint` (optional): `go install github.com/golangci/golangci-lint/cmd/golangci-lint@latest`
## Quick Start
@ -13,28 +16,53 @@
# Run all quality checks (same as CI)
make quality
# Auto-fix formatting
# Auto-fix formatting (Rust)
make fmt
# Auto-fix formatting (Go)
make go-fmt
# See clippy errors
make lint
# Lint Go SDK
make go-lint
```
## Pre-commit Hook
## Pre-commit Hook (v2)
The pre-commit hook at `.git/hooks/pre-commit` runs automatically on every commit. It:
The pre-commit hook at `.git/hooks/pre-commit` uses a **two-phase approach**:
1. Checks if any Rust files are staged
2. Runs `make quality` (format check, clippy, duplication, tests)
3. Blocks commit if any check fails
### Phase 1: Auto-fix
- Runs formatters (`cargo fmt`, `gofmt`, `goimports`)
- Re-stages formatted files automatically
- Transparent to the developer
### Phase 2: Verify
- Checks formatting (should pass after phase 1)
- Runs linters (`cargo clippy`, `go vet`, `golangci-lint`)
- Checks file length (max 500 lines)
- Blocks commit if any check fails
### Languages Supported
| Language | Format | Lint | File Length |
|----------|--------|------|-------------|
| Rust | `cargo fmt` | `cargo clippy` | ✅ |
| Go | `gofmt` + `goimports` | `go vet` + `golangci-lint` | ✅ |
### Smart Detection
- Only checks staged files (fast feedback)
- Auto-detects Go modules (walks up to find `go.mod`)
- Skips checks if no Rust/Go files are staged
- Re-stages files after formatting
### Installing the Hook
The hook should already exist. If not:
```bash
# Copy the sample and make executable
cp .git/hooks/pre-commit.sample .git/hooks/pre-commit
chmod +x .git/hooks/pre-commit
```
@ -47,12 +75,29 @@ git commit --no-verify -m "emergency fix"
## What Gets Checked
### Rust
| Check | Command | What it catches |
|-------|---------|-----------------|
| Format | `cargo fmt --check` | Inconsistent formatting |
| Lint | `cargo clippy -- -D warnings` | Code smells, potential bugs |
| Duplication | `jscpd` | Copy-pasted code blocks |
| Tests | `cargo test` | Broken functionality |
| Duplication | `jscpd` (CI only) | Copy-pasted code blocks |
| Tests | `cargo test` (CI only) | Broken functionality |
### Go (SDK)
| Check | Command | What it catches |
|-------|---------|-----------------|
| Format | `gofmt -l` | Inconsistent formatting |
| Imports | `goimports` | Unorganized imports |
| Vet | `go vet ./...` | Suspicious constructs |
| Lint | `golangci-lint run` | Code quality issues |
### Universal
| Check | Threshold | What it catches |
|-------|-----------|-----------------|
| File length | 500 lines max | Files too large to reason about |
## Enforced Lints
@ -76,8 +121,9 @@ To add a new enforced lint, update `[workspace.lints.clippy]` in root `Cargo.tom
### "Format check failed"
```bash
make fmt # Auto-fix
git add -u # Re-stage fixed files
make fmt # Auto-fix Rust
make go-fmt # Auto-fix Go
git add -u # Re-stage (hook does this automatically)
```
### "Clippy warnings treated as errors"
@ -92,13 +138,40 @@ let x = 5;
let _x = 5;
```
### "go vet failed"
Check for suspicious constructs:
```bash
cd sdk/go/steme && go vet ./...
```
### "File too long"
Split into smaller modules. Keep files under 500 lines for maintainability.
### "Duplication detected"
Refactor the duplicated code into a shared function or module.
## CI/Local Parity
The pre-commit hook runs `make quality`, which is the **exact same** command CI runs. If it passes locally, it passes in CI.
The pre-commit hook runs a subset of what CI runs (format, lint, file length). Full CI also runs:
- Tests (`cargo test`, `go test`)
- Duplication detection (`jscpd`)
- Security audit (`cargo audit`)
## Performance
Target: **<10 seconds** on staged files
| Scenario | Time |
|----------|------|
| No Rust/Go files staged | <0.2s |
| Go files only (warm) | <2s |
| Rust files only (warm) | <3s |
| Both (warm) | <5s |
| Cold compile | ~11s |
## Related

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.gitignore vendored
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@ -42,3 +42,11 @@ __pycache__/
# Temp
tmp/
# Runtime data
data/
# Go binaries (examples)
sdk/go/examples/*/basic
sdk/go/examples/*/conflict
sdk/go/examples/*/skeptic

1
CLAUDE.md
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@ -8,6 +8,7 @@ A probabilistic knowledge graph database that stores Claims, not Facts. Append-o
| If you need to... | Read this |
|-------------------|-----------|
| **Get started fast** | [quickstart.md](./quickstart.md) |
| **Understand the vision** | [vision.md](./vision.md) |
| **See use cases** | [use-cases/README.md](./use-cases/README.md) |
| **Understand architecture** | [architecture.md](./architecture.md) |

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Makefile
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@ -1,6 +1,6 @@
# Makefile for StemeDB
.PHONY: all build dev quality install clean test help
.PHONY: all build dev quality install clean test help go-fmt go-lint go-test validate
# Default target
all: build
@ -19,6 +19,14 @@ dev:
test:
cargo test --workspace
# Validate end-to-end (build, start server, assert, query, shutdown)
validate:
@./scripts/validate.sh
# Fast validation (skip build, use existing binaries)
validate-fast:
@./scripts/validate.sh --no-build
## --- Quality & Verification ---
# Run all quality checks (formatting, linting, duplication, tests)
@ -42,6 +50,39 @@ lint:
fmt:
cargo fmt --all
## --- Go SDK ---
# Format Go code
go-fmt:
@echo "Formatting Go SDK..."
@for mod in sdk/go/steme sdk/go/adk sdk/go/examples/basic sdk/go/examples/skeptic; do \
if [ -d "$$mod" ]; then \
gofmt -w "$$mod"; \
command -v goimports >/dev/null && goimports -w "$$mod" || true; \
fi \
done
# Lint Go code
go-lint:
@echo "Linting Go SDK..."
@for mod in sdk/go/steme sdk/go/adk; do \
if [ -f "$$mod/go.mod" ]; then \
echo " → $$mod"; \
(cd "$$mod" && go vet ./...); \
command -v golangci-lint >/dev/null && (cd "$$mod" && golangci-lint run ./...) || true; \
fi \
done
# Test Go code
go-test:
@echo "Testing Go SDK..."
@for mod in sdk/go/steme sdk/go/adk; do \
if [ -f "$$mod/go.mod" ]; then \
echo " → $$mod"; \
(cd "$$mod" && go test ./... -v); \
fi \
done
## --- Installation ---
# Install dependencies (Rust toolchain, etc - assumed cargo exists)
@ -65,4 +106,9 @@ help:
@echo " make fmt - Auto-format code"
@echo " make lint - Run clippy linter"
@echo " make duplication - Check for code duplication (jscpd)"
@echo " make go-fmt - Format Go SDK code"
@echo " make go-lint - Lint Go SDK code"
@echo " make go-test - Test Go SDK"
@echo " make install - Setup environment"
@echo " make validate - End-to-end validation (build, server, assert, query)"
@echo " make validate-fast - Fast validation (skip build)"

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arena-roadmap.md
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@ -20,18 +20,20 @@ The simulator (`stemedb-sim`) currently validates **Phase 1: The Spine**:
**Run command:** `cargo run --bin stemedb-sim`
**What's NOW tested (Arena 1-2):**
**What's NOW tested (Arena 1-3):**
- ✅ Queries via QueryEngine
- ✅ Lens resolution (Recency, VoteAwareConsensus)
- ✅ Lifecycle filtering
- ✅ Voting & consensus
- ✅ Query audit trail
- ✅ Materialized Views (Arena 3)
- ✅ Fast-path MV reads
- ✅ MV freshness under load
**What's NOT yet tested:**
- ❌ HTTP API layer (Arena 2.5.2)
- ❌ Concurrent agents (Arena 6)
- ❌ TrustRank (Arena 5)
- ❌ Materialized Views (Arena 3)
- ❌ Time-travel queries (Arena 7)
- ❌ Crash recovery (Arena 2.5.3)
- ❌ Input validation (Arena 2.5.4)
@ -184,29 +186,29 @@ The simulator (`stemedb-sim`) currently validates **Phase 1: The Spine**:
---
### Arena 3: Materialized Views (Exercises Phase 2 Materializer)
### Arena 3: Materialized Views (Exercises Phase 2 Materializer) ✅ COMPLETE
*Goal: Verify fast-path MV reads work under simulation load.*
**Depends on:** Arena 2 complete, Phase 2 Materializer
**Aligns with:** `roadmap.md` Phase 2 "Materializer"
- [ ] **3.1 Materializer Integration**
- [ ] Spin up Materializer alongside Ingestor.
- [ ] Wire `Notify` between IngestWorker and Materializer.
- [ ] After ingestion, verify MV keys exist in store.
- [x] **3.1 Materializer Integration**
- [x] Spin up Materializer alongside Ingestor.
- [x] Wire `Notify` between IngestWorker and Materializer.
- [x] After ingestion, verify MV keys exist in store.
- [ ] **3.2 Fast-Path Verification**
- [ ] Query via QueryEngine with subject+predicate.
- [ ] Log whether fast-path or slow-path was used (add debug output).
- [ ] Verify MV winner matches slow-path result.
- [x] **3.2 Fast-Path Verification**
- [x] Query via QueryEngine with subject+predicate.
- [x] Log whether fast-path or slow-path was used (add debug output).
- [x] Verify MV winner matches slow-path result.
- [ ] **3.3 MV Freshness Under Load**
- [ ] Write 10 assertions in rapid succession.
- [ ] Wait for materialization.
- [ ] Verify MV reflects latest state.
- [ ] **Aligns with:** Phase 2.5 "MV Staleness Detection"
- [x] **3.3 MV Freshness Under Load**
- [x] Write 10 assertions in rapid succession.
- [x] Wait for materialization.
- [x] Verify MV reflects latest state.
- [x] **Aligns with:** Phase 2.5 "MV Staleness Detection"
**Exit Criteria:** Fast-path queries return correct results under load.
**Exit Criteria:** Fast-path queries return correct results under load. ✅
---
@ -406,11 +408,11 @@ The simulator (`stemedb-sim`) currently validates **Phase 1: The Spine**:
| Arena Phase | Exercises Roadmap Phase | Key Features Validated |
|-------------|------------------------|------------------------|
| Arena 0 | - | Test infrastructure |
| Arena 1 | Phase 2 | QueryEngine, Lenses, Lifecycle, Query Audit |
| Arena 2 | Phase 2 | VoteStore, VoteAwareConsensusLens |
| Arena 0 ✅ | - | Test infrastructure |
| Arena 1 ✅ | Phase 2 | QueryEngine, Lenses, Lifecycle, Query Audit |
| Arena 2 ✅ | Phase 2 | VoteStore, VoteAwareConsensusLens |
| **Arena 2.5** | **- (Hardening)** | **Race conditions, API tests, crash recovery, input validation** |
| Arena 3 | Phase 2 | Materializer, Fast-Path MV |
| Arena 3 ✅ | Phase 2 | Materializer, Fast-Path MV, MV Freshness |
| Arena 4 | - | Agent differentiation (simulator-only) |
| Arena 5 | Phase 4 | TrustRank, TrustAwareAuthorityLens |
| Arena 6 | Phase 4 | Concurrency, Performance |

1
crates/stemedb-api/Cargo.toml
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@ -30,6 +30,7 @@ tracing-subscriber = { version = "0.3", features = ["env-filter"] }
thiserror = "1"
hex = "0.4"
blake3 = "1"
base64 = "0.22"
[dev-dependencies]
tempfile = "3"

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crates/stemedb-api/examples/gen_test_assertion.rs Normal file
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@ -0,0 +1,49 @@
//! Generate a properly signed test assertion for the validation script.
//!
//! Usage:
//! cargo run --package stemedb-api --example gen_test_assertion
//!
//! Output:
//! JSON assertion body with valid Ed25519 signature, ready for curl.
#![allow(clippy::print_stdout)]
#![allow(clippy::unwrap_used)]
use ed25519_dalek::{Signer, SigningKey};
use rand::rngs::OsRng;
fn main() {
// Generate a new keypair
let mut csprng = OsRng;
let signing_key = SigningKey::generate(&mut csprng);
let verifying_key = signing_key.verifying_key();
let subject = "StemeDB_Validation";
let predicate = "test_status";
// Sign the message (format: "{subject}:{predicate}")
let message = format!("{}:{}", subject, predicate);
let signature = signing_key.sign(message.as_bytes());
let timestamp = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.map(|d| d.as_secs())
.unwrap_or(0);
let json = serde_json::json!({
"subject": subject,
"predicate": predicate,
"object": {"type": "Text", "value": "working"},
"confidence": 0.99,
"source_class": "Expert",
"lifecycle": "Approved",
"signatures": [{
"agent_id": hex::encode(verifying_key.to_bytes()),
"signature": hex::encode(signature.to_bytes()),
"timestamp": timestamp
}],
"source_hash": "0".repeat(64)
});
println!("{}", serde_json::to_string_pretty(&json).unwrap());
}

280
crates/stemedb-api/src/dto.rs
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@ -64,6 +64,12 @@ pub struct CreateAssertionRequest {
/// Semantic embedding vector (optional)
#[serde(skip_serializing_if = "Option::is_none")]
pub vector: Option<Vec<f32>>,
/// Structured source metadata as a JSON string.
/// Stored as bytes internally for rkyv compatibility.
/// Schema is domain-specific (journal info, social metrics, etc.).
#[serde(skip_serializing_if = "Option::is_none")]
pub source_metadata: Option<String>,
}
/// Request to create a new vote on an assertion.
@ -199,6 +205,69 @@ pub struct QueryParams {
/// - 16: ~25% bit difference, more lenient
#[serde(skip_serializing_if = "Option::is_none")]
pub visual_threshold: Option<u32>,
/// Query state as of this Unix timestamp (time-travel).
///
/// Returns only assertions created at or before this timestamp.
/// When set, the fast path (MV lookup) is bypassed.
///
/// - Not set: Query current state (default)
/// - 0: Return assertions from the beginning of time (all timestamps >= 0)
/// - 1704067200: Return assertions as of 2024-01-01 00:00:00 UTC
#[serde(skip_serializing_if = "Option::is_none")]
pub as_of: Option<u64>,
/// Decay half-life in seconds for confidence decay.
///
/// When set, older assertions have their effective confidence reduced based on age.
/// This implements semantic decay: older sources shouldn't compete equally
/// with recent evidence.
///
/// Formula: `effective_confidence = confidence * 2^(-(age / halflife))`
///
/// - Not set: No decay (default, backward-compatible)
/// - 31536000: 1-year half-life (~50% confidence loss per year)
/// - 86400: 1-day half-life (fast decay for rapidly changing data)
///
/// **Note**: When decay is enabled, materialized views (fast path) are bypassed
/// because MVs store pre-computed winners without decay applied. Queries with
/// decay always use the slow path to ensure accurate confidence ordering.
#[serde(skip_serializing_if = "Option::is_none")]
pub decay_halflife: Option<u64>,
/// Use source-class-aware decay instead of uniform decay.
///
/// When `true` and `decay_halflife` is also set, each assertion's decay
/// is based on its source_class tier (Regulatory=none, Clinical=2yr, etc.).
/// Falls back to `decay_halflife` for assertions without source_class.
#[serde(skip_serializing_if = "Option::is_none")]
pub source_class_decay: Option<bool>,
/// Query by semantic vector similarity (k-nearest neighbors).
///
/// When set, the QueryEngine uses the vector index for candidate retrieval
/// instead of the standard SP/S indexes. This enables semantic similarity
/// queries like "find assertions with embeddings similar to this one."
///
/// Provide a JSON array of floats representing the query embedding vector.
/// The dimension must match the vectors stored in the index.
///
/// - Not set: Use standard index-based lookup (default)
/// - Set: Use vector index for k-NN search
///
/// **Note**: When `vector_near` is set, the fast path (MV lookup) is bypassed.
/// Subject/predicate filters are applied AFTER vector search.
#[serde(skip_serializing_if = "Option::is_none")]
pub vector_near: Option<Vec<f32>>,
/// Number of nearest neighbors to return for vector search.
///
/// Only used when `vector_near` is set. Defaults to 10 if not specified.
///
/// - Not set: Return 10 nearest neighbors (default)
/// - Set: Return up to `k` nearest neighbors
#[serde(skip_serializing_if = "Option::is_none")]
pub k: Option<usize>,
}
fn default_limit() -> usize {
@ -217,6 +286,11 @@ impl Default for QueryParams {
max_stale: None,
visual_near: None,
visual_threshold: None,
as_of: None,
decay_halflife: None,
source_class_decay: None,
vector_near: None,
k: None,
}
}
}
@ -273,6 +347,11 @@ pub struct AssertionResponse {
/// Semantic embedding vector (optional)
#[serde(skip_serializing_if = "Option::is_none")]
pub vector: Option<Vec<f32>>,
/// Structured source metadata as a JSON string (optional).
/// Domain-specific schema (journal, DOI, sample_size, etc.).
#[serde(skip_serializing_if = "Option::is_none")]
pub source_metadata: Option<String>,
}
/// Response from a query operation.
@ -286,6 +365,17 @@ pub struct QueryResponse {
/// Whether there are more results beyond the limit
pub has_more: bool,
/// Degree of disagreement among candidates (0.0 = unanimous, 1.0 = max conflict).
/// See `stemedb_lens::compute_conflict_score()` for the canonical algorithm.
/// Only present when a lens is applied.
#[serde(skip_serializing_if = "Option::is_none")]
pub conflict_score: Option<f32>,
/// Confidence in the resolution (0.0 to 1.0).
/// Only present when a lens is applied.
#[serde(skip_serializing_if = "Option::is_none")]
pub resolution_confidence: Option<f32>,
}
/// Response from a create operation.
@ -441,6 +531,17 @@ pub enum LensDto {
/// Use when querying across paradigm shifts (e.g., GAAP to IFRS transition).
/// Assertions from epochs that have been superseded by newer epochs are excluded.
EpochAware,
/// Per-source-class consensus with tier-by-tier visibility.
/// Returns results for each authority tier (Regulatory, Clinical, etc.)
/// plus an overall winner from the highest-authority tier present.
/// Use when you need to see "What does the FDA say? What do clinical trials say?"
LayeredConsensus,
/// Categorizes assertions by constraint predicates (must_use, forbidden, prefer).
/// Use for agent pre-flight checks: "What MUST I use? What's FORBIDDEN?"
/// Predicate patterns: `must_use:*`, `forbidden:*`, `prefer:*`
Constraints,
}
/// Agent signature entry.
@ -857,6 +958,135 @@ impl From<stemedb_core::types::AgentSummary> for AgentSummaryDto {
}
}
// ============================================================================
// Layered Consensus DTOs (Per-Tier Resolution)
// ============================================================================
/// Per-tier resolution result from LayeredConsensus lens.
///
/// Represents the consensus within a single source class tier.
#[derive(Debug, Clone, Serialize, Deserialize, ToSchema)]
pub struct TierResolutionDto {
/// The tier number (0-5). Lower = higher authority.
pub tier: u8,
/// The source class for this tier.
pub source_class: SourceClassDto,
/// The winning assertion from within-tier consensus, if any candidates.
#[serde(skip_serializing_if = "Option::is_none")]
pub winner: Option<AssertionResponse>,
/// Number of candidates in this tier.
pub candidates_count: usize,
/// Within-tier conflict score (0.0 = unanimous, 1.0 = max conflict).
#[schema(minimum = 0.0, maximum = 1.0)]
pub conflict_score: f32,
/// Within-tier resolution confidence (0.0 to 1.0).
#[schema(minimum = 0.0, maximum = 1.0)]
pub resolution_confidence: f32,
}
/// Response from a LayeredConsensus query.
///
/// Provides per-tier resolution results plus an overall winner.
/// Use this to see "What does Tier 0 say? What does Tier 5 say?"
#[derive(Debug, Clone, Serialize, Deserialize, ToSchema)]
pub struct LayeredQueryResponse {
/// The subject that was queried.
pub subject: String,
/// The predicate that was queried.
pub predicate: String,
/// Per-tier consensus results, ordered by tier (0 = highest authority first).
/// Only tiers with at least one candidate are included.
pub tiers: Vec<TierResolutionDto>,
/// Overall winner: winner from the highest-authority tier that has candidates.
#[serde(skip_serializing_if = "Option::is_none")]
pub overall_winner: Option<AssertionResponse>,
/// Cross-tier disagreement score (0.0 = tiers agree, 1.0 = tiers disagree).
#[schema(minimum = 0.0, maximum = 1.0)]
pub overall_conflict_score: f32,
/// Total candidates considered across all tiers.
pub total_candidates: usize,
/// Unix timestamp when this view was computed.
pub computed_at: u64,
/// Which lens was used (always "LayeredConsensus").
pub lens_name: String,
}
// ============================================================================
// Constraints API DTOs (Agent Pre-Flight Checks)
// ============================================================================
/// Query parameters for the constraints endpoint.
///
/// Returns categorized constraints (must_use, forbidden, prefer) for a subject.
#[derive(Debug, Clone, Serialize, Deserialize, ToSchema, utoipa::IntoParams)]
pub struct ConstraintsQueryParams {
/// Subject entity to check constraints for (required)
pub subject: String,
}
/// A constraint assertion in a specific category.
#[derive(Debug, Clone, Serialize, Deserialize, ToSchema)]
pub struct ConstraintEntryDto {
/// The constraint category (extracted from predicate, e.g., "http_client" from "must_use:http_client")
pub category: String,
/// The constrained value (from assertion object)
pub value: ObjectValueDto,
/// Confidence in this constraint (0.0 to 1.0)
#[schema(minimum = 0.0, maximum = 1.0)]
pub confidence: f32,
/// Hash of the underlying assertion (hex-encoded)
pub assertion_hash: String,
/// When this constraint was asserted (Unix timestamp)
pub timestamp: u64,
/// Source class of the constraint
pub source_class: SourceClassDto,
}
/// Response from the constraints endpoint.
///
/// Returns assertions categorized by constraint type for agent pre-flight checks.
#[derive(Debug, Clone, Serialize, Deserialize, ToSchema)]
pub struct ConstraintsResponse {
/// The subject that was queried
pub subject: String,
/// Required constraints (predicate: `must_use:*`). Non-negotiable.
pub must_use: Vec<ConstraintEntryDto>,
/// Forbidden constraints (predicate: `forbidden:*`). Explicitly banned.
pub forbidden: Vec<ConstraintEntryDto>,
/// Preferred constraints (predicate: `prefer:*`). Recommendations.
pub prefer: Vec<ConstraintEntryDto>,
/// Total assertions considered (including non-constraint predicates)
pub candidates_count: usize,
/// Conflict score across all constraints (0.0 = consistent, 1.0 = conflicting)
#[schema(minimum = 0.0, maximum = 1.0)]
pub conflict_score: f32,
/// Unix timestamp when this was computed
pub computed_at: u64,
}
// ============================================================================
// Trace API DTOs (Agent Decision Tracing)
// ============================================================================
@ -914,3 +1144,53 @@ pub struct TraceResponse {
/// Time range end (Unix timestamp)
pub to_timestamp: u64,
}
// ============================================================================
// Source/Provenance DTOs
// ============================================================================
/// Request to store a source document.
///
/// Source documents are content-addressed: the same content always produces
/// the same hash. Re-uploading identical content is idempotent.
#[derive(Debug, Clone, Serialize, Deserialize, ToSchema)]
pub struct StoreSourceRequest {
/// The source document content (base64-encoded for binary safety).
/// Maximum size: 10MB after decoding.
#[schema(example = "VGhpcyBpcyBhIHNvdXJjZSBkb2N1bWVudC4=")]
pub content: String,
/// MIME type of the content (e.g., "application/pdf", "text/plain").
#[schema(example = "text/plain")]
pub content_type: String,
}
/// Response from storing a source document.
#[derive(Debug, Clone, Serialize, Deserialize, ToSchema)]
pub struct StoreSourceResponse {
/// BLAKE3 hash of the content (hex-encoded, 64 chars = 32 bytes).
/// Use this as `source_hash` when creating assertions.
pub hash: String,
/// Size of the stored content in bytes.
pub size: usize,
/// Status message ("stored").
pub status: String,
}
/// Response from retrieving a source document.
#[derive(Debug, Clone, Serialize, Deserialize, ToSchema)]
pub struct ProvenanceResponse {
/// BLAKE3 hash of the content (hex-encoded).
pub hash: String,
/// The source document content (base64-encoded).
pub content: String,
/// MIME type of the content.
pub content_type: String,
/// Size of the content in bytes.
pub size: usize,
}

1
crates/stemedb-api/src/handlers/assert.rs
View File

@ -105,6 +105,7 @@ fn dto_to_assertion(req: CreateAssertionRequest) -> Result<Assertion> {
source_class: req.source_class.map(Into::into).unwrap_or(SourceClass::Expert),
visual_hash,
epoch,
source_metadata: req.source_metadata.map(|s| s.into_bytes()),
lifecycle: req.lifecycle.map(Into::into).unwrap_or(LifecycleStage::Proposed),
signatures,
confidence: req.confidence,

145
crates/stemedb-api/src/handlers/constraints.rs Normal file
View File

@ -0,0 +1,145 @@
//! Handler for constraints (agent pre-flight check) queries.
use axum::{
extract::{Query as AxumQuery, State},
Json,
};
use std::time::{SystemTime, UNIX_EPOCH};
use tracing::instrument;
use crate::{
dto::{ConstraintEntryDto, ConstraintsQueryParams, ConstraintsResponse, ErrorResponse},
error::Result,
state::AppState,
};
use stemedb_core::types::Assertion;
use stemedb_lens::ConstraintsLens;
use stemedb_query::Query;
/// Query for constraint analysis using ConstraintsLens.
///
/// This endpoint provides pre-flight constraint checking for AI agents,
/// categorizing assertions by predicate pattern into must_use, forbidden, and prefer.
///
/// # Predicate Patterns
///
/// - `must_use:*` → Required, non-negotiable constraints
/// - `forbidden:*` → Explicitly banned items
/// - `prefer:*` → Recommended but optional
///
/// # Response
///
/// Returns a `ConstraintsResponse` with:
/// - `must_use`: Required constraints sorted by confidence
/// - `forbidden`: Banned items sorted by confidence
/// - `prefer`: Recommendations sorted by confidence
///
/// # Example
///
/// ```ignore
/// GET /v1/constraints?subject=project_alpha
///
/// {
/// "subject": "project_alpha",
/// "must_use": [
/// { "category": "http_client", "value": {"type": "Text", "value": "axios"}, "confidence": 0.95 }
/// ],
/// "forbidden": [
/// { "category": "http_client", "value": {"type": "Text", "value": "requests"}, "confidence": 0.9 }
/// ],
/// "prefer": [
/// { "category": "language", "value": {"type": "Text", "value": "typescript"}, "confidence": 0.8 }
/// ]
/// }
/// ```
#[utoipa::path(
get,
path = "/v1/constraints",
params(ConstraintsQueryParams),
responses(
(status = 200, description = "Constraint analysis successful", body = ConstraintsResponse),
(status = 400, description = "Invalid request", body = ErrorResponse),
(status = 500, description = "Internal server error", body = ErrorResponse)
),
tag = "query"
)]
#[instrument(skip(state), fields(subject = %params.subject))]
pub async fn constraints_query(
State(state): State<AppState>,
AxumQuery(params): AxumQuery<ConstraintsQueryParams>,
) -> Result<Json<ConstraintsResponse>> {
// Build query for all assertions with this subject
// We need ALL predicates, not just one specific one
let query = Query::builder().subject(&params.subject).build();
// Execute the query
let query_engine = state.query_engine();
let result = query_engine.execute(&query).await?;
// Apply ConstraintsLens to categorize
let lens = ConstraintsLens;
let constraint_set = lens.resolve_constraints(&result.assertions);
// Get current timestamp
let computed_at =
SystemTime::now().duration_since(UNIX_EPOCH).map(|d| d.as_secs()).unwrap_or(0);
// Convert to DTOs
let must_use = constraint_set
.must_use
.into_iter()
.map(|a| assertion_to_constraint_entry(a, "must_use:"))
.collect();
let forbidden = constraint_set
.forbidden
.into_iter()
.map(|a| assertion_to_constraint_entry(a, "forbidden:"))
.collect();
let prefer = constraint_set
.prefer
.into_iter()
.map(|a| assertion_to_constraint_entry(a, "prefer:"))
.collect();
Ok(Json(ConstraintsResponse {
subject: params.subject,
must_use,
forbidden,
prefer,
candidates_count: constraint_set.candidates_count,
conflict_score: constraint_set.conflict_score,
computed_at,
}))
}
/// Convert an assertion to a ConstraintEntryDto.
///
/// Extracts the category from the predicate by removing the prefix.
fn assertion_to_constraint_entry(assertion: Assertion, prefix: &str) -> ConstraintEntryDto {
// Extract category from predicate (e.g., "must_use:http_client" -> "http_client")
let category =
assertion.predicate.strip_prefix(prefix).unwrap_or(&assertion.predicate).to_string();
// Compute assertion hash
let hash = stemedb_core::serde::serialize(&assertion)
.map(|bytes| hex::encode(blake3::hash(&bytes).as_bytes()))
.unwrap_or_default();
ConstraintEntryDto {
category,
value: assertion.object.into(),
confidence: assertion.confidence,
assertion_hash: hash,
timestamp: assertion.timestamp,
source_class: assertion.source_class.into(),
}
}
#[cfg(test)]
mod tests {
// Integration tests would go here
// For now, the unit tests in stemedb-lens/src/constraints.rs cover the core functionality
}

27
crates/stemedb-api/src/handlers/epoch.rs
View File

@ -33,16 +33,35 @@ use stemedb_ingest::worker::serialize_epoch;
/// - Supersedes field, if provided, must be a valid 32-byte hex-encoded hash
/// - If supersedes is provided, supersession_type is required
///
/// # Example Request
/// # Example Request (New Epoch)
///
/// ```json
/// {
/// "name": "GAAP-2024",
/// "supersedes": "a1b2c3...", // optional, hex-encoded 32-byte hash
/// "supersession_type": "Temporal", // optional
/// "start_timestamp": 1704067200 // optional, defaults to now
/// "start_timestamp": 1704067200
/// }
/// ```
///
/// # Example Request (Superseding an Epoch)
///
/// Use this same endpoint to supersede a previous epoch by providing the
/// `supersedes` field with the hex-encoded ID of the epoch being replaced:
///
/// ```json
/// {
/// "name": "post_fda_label_2024",
/// "supersedes": "a1b2c3d4e5f6...64-hex-chars...",
/// "supersession_type": "Invalidate"
/// }
/// ```
///
/// ## Supersession Types
///
/// - **Invalidate**: Previous epoch was factually incorrect (e.g., FDA withdraws approval)
/// - **Temporal**: Previous epoch was correct at the time but is now outdated (e.g., label update)
/// - **Refinement**: Previous epoch was a simplification; new one is more precise
/// - **RequiresReview**: Flagged for human review before resolution
/// - **Additive**: New epoch adds to (doesn't replace) the previous one
#[utoipa::path(
post,
path = "/v1/epoch",

182
crates/stemedb-api/src/handlers/layered.rs Normal file
View File

@ -0,0 +1,182 @@
//! Handler for layered consensus (per-tier resolution) queries.
use axum::{
extract::{Query as AxumQuery, State},
Json,
};
use tracing::instrument;
use crate::{
dto::{
AssertionResponse, ErrorResponse, LayeredQueryResponse, SkepticQueryParams, SourceClassDto,
TierResolutionDto,
},
error::{ApiError, Result},
state::AppState,
};
use stemedb_core::types::SourceClass;
use stemedb_lens::{LayeredConsensusLens, LayeredLens};
use stemedb_query::Query;
/// Query for per-tier consensus using LayeredConsensusLens.
///
/// This endpoint provides visibility into what each source class tier says,
/// enabling "What does Tier 0 (FDA) say? What does Tier 5 (Reddit) say?" queries.
///
/// # Response
///
/// Returns a `LayeredQueryResponse` with:
/// - `tiers`: Per-tier consensus results (only tiers with candidates)
/// - `overall_winner`: Winner from the highest-authority tier present
/// - `overall_conflict_score`: Cross-tier disagreement (0.0 = tiers agree, 1.0 = tiers disagree)
///
/// # Example
///
/// ```ignore
/// GET /v1/layered?subject=Semaglutide&predicate=muscle_effect
///
/// {
/// "subject": "Semaglutide",
/// "predicate": "muscle_effect",
/// "tiers": [
/// {
/// "tier": 1,
/// "source_class": "Clinical",
/// "winner": { ... },
/// "candidates_count": 12,
/// "conflict_score": 0.15,
/// "resolution_confidence": 0.85
/// },
/// {
/// "tier": 5,
/// "source_class": "Anecdotal",
/// "winner": { ... },
/// "candidates_count": 200,
/// "conflict_score": 0.45,
/// "resolution_confidence": 0.55
/// }
/// ],
/// "overall_winner": { ... },
/// "overall_conflict_score": 0.78,
/// "total_candidates": 212,
/// "lens_name": "LayeredConsensus"
/// }
/// ```
#[utoipa::path(
get,
path = "/v1/layered",
params(SkepticQueryParams),
responses(
(status = 200, description = "Layered consensus successful", body = LayeredQueryResponse),
(status = 404, description = "No assertions found for subject+predicate", body = ErrorResponse),
(status = 400, description = "Invalid request", body = ErrorResponse),
(status = 500, description = "Internal server error", body = ErrorResponse)
),
tag = "query"
)]
#[instrument(skip(state), fields(subject = %params.subject, predicate = %params.predicate))]
pub async fn layered_query(
State(state): State<AppState>,
AxumQuery(params): AxumQuery<SkepticQueryParams>,
) -> Result<Json<LayeredQueryResponse>> {
let computed_at = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.map(|d| d.as_secs())
.unwrap_or(0);
// Build query for subject+predicate
let query = Query::builder()
.subject(params.subject.clone())
.predicate(params.predicate.clone())
.build();
// Execute the query to get candidates
let query_engine = state.query_engine();
let result = query_engine.execute(&query).await?;
// Return 404 if no assertions found
if result.assertions.is_empty() {
return Err(ApiError::NotFound(format!(
"No assertions found for subject '{}' and predicate '{}'",
params.subject, params.predicate
)));
}
// Apply LayeredConsensusLens
let lens = LayeredConsensusLens::new();
let layered = lens.resolve_layered(&result.assertions);
// Convert to DTOs
let tiers: Vec<TierResolutionDto> = layered
.tiers
.into_iter()
.map(|tr| {
let winner = tr.winner.map(assertion_to_dto).transpose()?;
Ok(TierResolutionDto {
tier: tr.tier,
source_class: source_class_to_dto(tr.source_class),
winner,
candidates_count: tr.candidates_count,
conflict_score: tr.conflict_score,
resolution_confidence: tr.resolution_confidence,
})
})
.collect::<Result<Vec<_>>>()?;
let overall_winner = layered.overall_winner.map(assertion_to_dto).transpose()?;
Ok(Json(LayeredQueryResponse {
subject: params.subject,
predicate: params.predicate,
tiers,
overall_winner,
overall_conflict_score: layered.overall_conflict_score,
total_candidates: layered.total_candidates,
computed_at,
lens_name: "LayeredConsensus".to_string(),
}))
}
/// Convert SourceClass to SourceClassDto.
fn source_class_to_dto(sc: SourceClass) -> SourceClassDto {
match sc {
SourceClass::Regulatory => SourceClassDto::Regulatory,
SourceClass::Clinical => SourceClassDto::Clinical,
SourceClass::Observational => SourceClassDto::Observational,
SourceClass::Expert => SourceClassDto::Expert,
SourceClass::Community => SourceClassDto::Community,
SourceClass::Anecdotal => SourceClassDto::Anecdotal,
}
}
/// Convert an internal Assertion to an AssertionResponse DTO.
fn assertion_to_dto(assertion: stemedb_core::types::Assertion) -> Result<AssertionResponse> {
let serialized = stemedb_core::serde::serialize(&assertion)
.map_err(|e| ApiError::Serialization(format!("Failed to serialize assertion: {}", e)))?;
let hash = blake3::hash(&serialized);
Ok(AssertionResponse {
hash: hash.to_hex().to_string(),
subject: assertion.subject,
predicate: assertion.predicate,
object: assertion.object.into(),
parent_hash: assertion.parent_hash.map(hex::encode),
source_hash: hex::encode(assertion.source_hash),
source_class: assertion.source_class.into(),
visual_hash: assertion.visual_hash.map(hex::encode),
epoch: assertion.epoch.map(hex::encode),
lifecycle: assertion.lifecycle.into(),
signatures: assertion.signatures.into_iter().map(Into::into).collect(),
confidence: assertion.confidence,
timestamp: assertion.timestamp,
vector: assertion.vector,
source_metadata: assertion.source_metadata.and_then(|bytes| String::from_utf8(bytes).ok()),
})
}
#[cfg(test)]
mod tests {
// Integration tests would go here
// For now, the unit tests in stemedb-lens cover the core functionality
}

6
crates/stemedb-api/src/handlers/mod.rs
View File

@ -2,22 +2,28 @@
pub mod assert;
pub mod audit;
pub mod constraints;
pub mod epoch;
pub mod health;
pub mod layered;
pub mod meter;
pub mod query;
pub mod skeptic;
pub mod source;
pub mod supersede;
pub mod trace;
pub mod vote;
pub use assert::create_assertion;
pub use audit::{get_audit, list_audits};
pub use constraints::constraints_query;
pub use epoch::create_epoch;
pub use health::health_check;
pub use layered::layered_query;
pub use meter::{get_quota_status, set_quota_limit};
pub use query::query_assertions;
pub use skeptic::skeptic_query;
pub use source::{get_provenance, store_source};
pub use supersede::supersede;
pub use trace::trace;
pub use vote::create_vote;

121
crates/stemedb-api/src/handlers/query.rs
View File

@ -15,8 +15,18 @@ use crate::{
};
use stemedb_core::types::{
Assertion, ContributingAssertion, QueryAudit, QueryParams as AuditQueryParams,
Assertion, ContributingAssertion, LifecycleStage, QueryAudit, QueryParams as AuditQueryParams,
};
/// Pre-computed metadata from candidate assertions for audit logging.
///
/// This avoids cloning entire assertions before lens resolution.
/// We only keep the fields needed for audit: hash, source_hash, lifecycle.
struct CandidateMetadata {
hash: [u8; 32],
source_hash: [u8; 32],
lifecycle: LifecycleStage,
}
use stemedb_lens::{
AsyncLens, ConfidenceLens, ConsensusLens, EpochAwareLens, Lens, RecencyLens,
TrustAwareAuthorityLens, VoteAwareConsensusLens,
@ -107,6 +117,18 @@ pub async fn query_assertions(
builder = builder.visual_near(visual_near.clone(), threshold);
}
if let Some(as_of) = params.as_of {
builder = builder.as_of(as_of);
}
if let Some(decay_halflife) = params.decay_halflife {
builder = builder.decay_halflife(decay_halflife);
}
if let Some(source_class_decay) = params.source_class_decay {
builder = builder.source_class_decay(source_class_decay);
}
let query = builder.build();
// Execute the query
@ -123,24 +145,37 @@ pub async fn query_assertions(
assertions: vec![],
total_count: 0,
has_more: result.has_more,
conflict_score: None,
resolution_confidence: None,
}));
}
// Capture contributing assertions before lens resolution
let candidates: Vec<_> = result.assertions.clone();
// Pre-compute candidate metadata for audit BEFORE lens consumes assertions.
// This avoids cloning all assertions - we only keep what's needed for audit.
let candidate_metadata: Vec<CandidateMetadata> = result
.assertions
.iter()
.filter_map(|a| {
stemedb_core::serde::serialize(a).ok().map(|s| CandidateMetadata {
hash: *blake3::hash(&s).as_bytes(),
source_hash: a.source_hash,
lifecycle: a.lifecycle,
})
})
.collect();
// Apply lens if specified
let (assertions, resolution_confidence) = if let Some(lens_dto) = params.lens {
let (winner, confidence) =
let (assertions, resolution_confidence, conflict_score) = if let Some(lens_dto) = params.lens {
let (winner, confidence, conflict) =
apply_lens_with_confidence(lens_dto, result.assertions, state.store.clone()).await?;
(winner, confidence)
(winner, Some(confidence), Some(conflict))
} else {
// No lens = return all candidates with full confidence
(result.assertions, 1.0)
// No lens = return all candidates with full confidence, no conflict score
(result.assertions, None, None)
};
// Compute contributing assertions for audit
let contributing = build_contributing_assertions(&candidates, &assertions)?;
// Compute contributing assertions for audit using pre-computed metadata
let contributing = build_contributing_from_metadata(&candidate_metadata, &assertions)?;
// Compute result hash (hash of the winning assertion, if any)
let result_hash = if let Some(winner) = assertions.first() {
@ -158,7 +193,7 @@ pub async fn query_assertions(
query_start_timestamp,
&audit_params,
result_hash,
resolution_confidence,
resolution_confidence.unwrap_or(1.0),
contributing,
)
.await;
@ -173,6 +208,8 @@ pub async fn query_assertions(
assertions: assertion_responses,
total_count,
has_more: result.has_more,
conflict_score,
resolution_confidence,
}))
}
@ -249,11 +286,12 @@ fn generate_query_id(params: &AuditQueryParams, timestamp: u64) -> [u8; 32] {
*blake3::hash(content.as_bytes()).as_bytes()
}
/// Build ContributingAssertion records from candidates and winners.
/// Build ContributingAssertion records from pre-computed metadata and winners.
///
/// Pre-computes winner hashes once for O(n) total instead of O(n*w).
fn build_contributing_assertions(
candidates: &[Assertion],
/// This function uses pre-computed candidate hashes, avoiding the need to
/// clone all candidate assertions before lens resolution. O(n + w) total.
fn build_contributing_from_metadata(
candidates: &[CandidateMetadata],
winners: &[Assertion],
) -> Result<Vec<ContributingAssertion>> {
use std::collections::HashSet;
@ -266,34 +304,25 @@ fn build_contributing_assertions(
})
.collect();
let mut contributing = Vec::with_capacity(candidates.len());
for candidate in candidates {
let serialized = stemedb_core::serde::serialize(candidate).map_err(|e| {
ApiError::Serialization(format!("Failed to serialize candidate: {}", e))
})?;
let assertion_hash = *blake3::hash(&serialized).as_bytes();
// O(1) lookup instead of O(w) serializations per candidate
let is_winner = winner_hashes.contains(&assertion_hash);
contributing.push(ContributingAssertion {
assertion_hash,
weight: if is_winner { 1.0 } else { 0.0 },
source_hash: candidate.source_hash,
lifecycle: candidate.lifecycle,
});
}
let contributing: Vec<ContributingAssertion> = candidates
.iter()
.map(|c| ContributingAssertion {
assertion_hash: c.hash,
weight: if winner_hashes.contains(&c.hash) { 1.0 } else { 0.0 },
source_hash: c.source_hash,
lifecycle: c.lifecycle,
})
.collect();
Ok(contributing)
}
/// Apply the specified lens to resolve conflicts and return confidence.
/// Apply the specified lens to resolve conflicts and return confidence and conflict score.
async fn apply_lens_with_confidence(
lens_dto: LensDto,
assertions: Vec<Assertion>,
store: std::sync::Arc<stemedb_storage::SledStore>,
) -> Result<(Vec<Assertion>, f32)> {
) -> Result<(Vec<Assertion>, f32, f32)> {
let assertion_count = assertions.len();
let resolution = match lens_dto {
@ -327,9 +356,28 @@ async fn apply_lens_with_confidence(
let lens = EpochAwareLens::with_recency(store);
lens.resolve_async(&assertions).await
}
LensDto::LayeredConsensus => {
// LayeredConsensus returns a different response type with per-tier results.
// Use the dedicated /v1/layered endpoint for this lens.
return Err(ApiError::InvalidRequest(
"LayeredConsensus lens requires the /v1/layered endpoint for per-tier results. \
Use GET /v1/layered?subject=X&predicate=Y instead."
.to_string(),
));
}
LensDto::Constraints => {
// Constraints lens returns a different response type with categorized constraints.
// Use the dedicated /v1/constraints endpoint for this lens.
return Err(ApiError::InvalidRequest(
"Constraints lens requires the /v1/constraints endpoint for categorized results. \
Use GET /v1/constraints?subject=X instead."
.to_string(),
));
}
};
let confidence = resolution.resolution_confidence;
let conflict = resolution.conflict_score;
let winner = resolution.winner.ok_or_else(|| {
ApiError::InvalidRequest(format!(
@ -338,7 +386,7 @@ async fn apply_lens_with_confidence(
))
})?;
Ok((vec![winner], confidence))
Ok((vec![winner], confidence, conflict))
}
/// Convert an internal Assertion to an AssertionResponse DTO.
@ -366,5 +414,6 @@ fn assertion_to_dto(assertion: Assertion) -> Result<AssertionResponse> {
confidence: assertion.confidence,
timestamp: assertion.timestamp,
vector: assertion.vector,
source_metadata: assertion.source_metadata.and_then(|bytes| String::from_utf8(bytes).ok()),
})
}

418
crates/stemedb-api/src/handlers/source.rs Normal file
View File

@ -0,0 +1,418 @@
//! Handlers for source document storage and provenance lookup.
//!
//! These endpoints enable 100% citation recall by storing the source documents
//! that back assertions. Agents store sources first, then reference their hash
//! in assertions.
//!
//! # Flow
//!
//! 1. Agent POSTs source document to `/v1/source`
//! 2. API returns the BLAKE3 hash of the content
//! 3. Agent creates assertion with `source_hash` pointing to that hash
//! 4. Verifier can later GET `/v1/provenance/{hash}` to retrieve the source
//!
//! # Storage Format
//!
//! Sources are stored at `SRC:{hash}` keys with payload:
//! - 4 bytes: content_type length (u32 LE)
//! - N bytes: content_type string (UTF-8)
//! - M bytes: raw content
use axum::{
extract::{Path, State},
http::StatusCode,
Json,
};
use base64::{engine::general_purpose::STANDARD as BASE64, Engine};
use tracing::instrument;
use crate::{
dto::{ErrorResponse, ProvenanceResponse, StoreSourceRequest, StoreSourceResponse},
error::{ApiError, Result},
state::AppState,
};
use stemedb_storage::KVStore;
/// Maximum source document size (10 MB).
const MAX_SOURCE_SIZE: usize = 10 * 1024 * 1024;
/// Store a source document by content hash.
///
/// Computes the BLAKE3 hash of the content and stores at `SRC:{hash}`.
/// The same content always produces the same hash (content-addressed).
/// Re-uploading the same content is idempotent - no error, same hash returned.
#[utoipa::path(
post,
path = "/v1/source",
request_body = StoreSourceRequest,
responses(
(status = 201, description = "Source stored successfully", body = StoreSourceResponse),
(status = 400, description = "Invalid request (bad base64, too large)", body = ErrorResponse),
(status = 500, description = "Internal server error", body = ErrorResponse)
),
tag = "provenance"
)]
#[instrument(skip(state, req), fields(content_type = %req.content_type))]
pub async fn store_source(
State(state): State<AppState>,
Json(req): Json<StoreSourceRequest>,
) -> Result<(StatusCode, Json<StoreSourceResponse>)> {
// Decode base64 content
let content = BASE64
.decode(&req.content)
.map_err(|e| ApiError::InvalidRequest(format!("Invalid base64 content: {}", e)))?;
// Validate size
if content.len() > MAX_SOURCE_SIZE {
return Err(ApiError::InvalidRequest(format!(
"Content too large: {} bytes (max {} bytes)",
content.len(),
MAX_SOURCE_SIZE
)));
}
// Compute content-addressed hash
let hash = blake3::hash(&content);
let hash_hex = hash.to_hex().to_string();
// Build storage payload: content_type_len (u32 LE) + content_type + content
let mut payload = Vec::with_capacity(4 + req.content_type.len() + content.len());
payload.extend_from_slice(&(req.content_type.len() as u32).to_le_bytes());
payload.extend_from_slice(req.content_type.as_bytes());
payload.extend_from_slice(&content);
// Store at SRC:{hash}
let key = format!("SRC:{}", hash_hex).into_bytes();
state.store.put(&key, &payload).await?;
tracing::info!(
hash = %hash_hex,
size = content.len(),
content_type = %req.content_type,
"Stored source document"
);
Ok((
StatusCode::CREATED,
Json(StoreSourceResponse {
hash: hash_hex,
size: content.len(),
status: "stored".to_string(),
}),
))
}
/// Retrieve a source document by its hash.
///
/// Looks up the source at `SRC:{hash}` and returns the content with metadata.
/// Returns 404 if the source hash is not found.
#[utoipa::path(
get,
path = "/v1/provenance/{hash}",
params(
("hash" = String, Path, description = "BLAKE3 hash of the source document (hex-encoded, 64 chars)")
),
responses(
(status = 200, description = "Source document found", body = ProvenanceResponse),
(status = 400, description = "Invalid hash format", body = ErrorResponse),
(status = 404, description = "Source not found", body = ErrorResponse),
(status = 500, description = "Internal server error", body = ErrorResponse)
),
tag = "provenance"
)]
#[instrument(skip(state), fields(hash = %hash))]
pub async fn get_provenance(
State(state): State<AppState>,
Path(hash): Path<String>,
) -> Result<Json<ProvenanceResponse>> {
// Validate hash format (64 hex chars = 32 bytes)
if hash.len() != 64 || !hash.chars().all(|c| c.is_ascii_hexdigit()) {
return Err(ApiError::InvalidRequest(
"Invalid hash: must be 64 hex characters".to_string(),
));
}
// Lookup at SRC:{hash}
let key = format!("SRC:{}", hash).into_bytes();
let payload = state
.store
.get(&key)
.await?
.ok_or_else(|| ApiError::NotFound(format!("Source not found: {}", hash)))?;
// Parse payload: content_type_len (u32 LE) + content_type + content
if payload.len() < 4 {
return Err(ApiError::Serialization("Corrupt source record: too short".to_string()));
}
let content_type_len =
u32::from_le_bytes([payload[0], payload[1], payload[2], payload[3]]) as usize;
if payload.len() < 4 + content_type_len {
return Err(ApiError::Serialization(
"Corrupt source record: truncated content_type".to_string(),
));
}
let content_type = String::from_utf8(payload[4..4 + content_type_len].to_vec())
.map_err(|e| ApiError::Serialization(format!("Invalid content_type UTF-8: {}", e)))?;
let content = &payload[4 + content_type_len..];
tracing::debug!(
hash = %hash,
size = content.len(),
content_type = %content_type,
"Retrieved source document"
);
Ok(Json(ProvenanceResponse {
hash,
content: BASE64.encode(content),
content_type,
size: content.len(),
}))
}
#[cfg(test)]
mod tests {
use super::*;
use axum::{
body::Body,
http::{Method, Request},
};
use serde_json::json;
use stemedb_storage::SledStore;
use stemedb_wal::Journal;
use tower::ServiceExt;
/// Test context that keeps temp dir alive.
struct TestContext {
app: axum::Router,
#[allow(dead_code)]
temp_dir: tempfile::TempDir,
}
/// Create a test app with in-memory storage.
async fn test_app() -> TestContext {
let temp_dir = tempfile::tempdir().expect("failed to create temp dir");
let wal_path = temp_dir.path().join("wal");
let store_path = temp_dir.path().join("store");
let journal = Journal::open(&wal_path).expect("failed to open journal");
let store = SledStore::open(&store_path).expect("failed to open store");
let state = AppState::new(journal, store);
let app = axum::Router::new()
.route("/v1/source", axum::routing::post(store_source))
.route("/v1/provenance/:hash", axum::routing::get(get_provenance))
.with_state(state);
TestContext { app, temp_dir }
}
#[tokio::test]
async fn test_store_and_retrieve_source() {
let ctx = test_app().await;
// Store a source document
let content = "This is a test document.";
let content_b64 = BASE64.encode(content);
let req_body = json!({
"content": content_b64,
"content_type": "text/plain"
});
let response = ctx
.app
.clone()
.oneshot(
Request::builder()
.method(Method::POST)
.uri("/v1/source")
.header("content-type", "application/json")
.body(Body::from(serde_json::to_string(&req_body).expect("json")))
.expect("request"),
)
.await
.expect("response");
assert_eq!(response.status(), StatusCode::CREATED);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("body");
let store_resp: StoreSourceResponse =
serde_json::from_slice(&body).expect("deserialize store response");
assert_eq!(store_resp.size, content.len());
assert_eq!(store_resp.status, "stored");
assert_eq!(store_resp.hash.len(), 64);
// Retrieve the source
let response = ctx
.app
.clone()
.oneshot(
Request::builder()
.method(Method::GET)
.uri(format!("/v1/provenance/{}", store_resp.hash))
.body(Body::empty())
.expect("request"),
)
.await
.expect("response");
assert_eq!(response.status(), StatusCode::OK);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("body");
let prov_resp: ProvenanceResponse =
serde_json::from_slice(&body).expect("deserialize provenance response");
assert_eq!(prov_resp.hash, store_resp.hash);
assert_eq!(prov_resp.content_type, "text/plain");
assert_eq!(prov_resp.size, content.len());
// Decode and verify content matches
let decoded = BASE64.decode(&prov_resp.content).expect("decode content");
assert_eq!(String::from_utf8(decoded).expect("utf8"), content);
}
#[tokio::test]
async fn test_store_source_invalid_base64() {
let ctx = test_app().await;
let req_body = json!({
"content": "not valid base64!!!",
"content_type": "text/plain"
});
let response = ctx
.app
.oneshot(
Request::builder()
.method(Method::POST)
.uri("/v1/source")
.header("content-type", "application/json")
.body(Body::from(serde_json::to_string(&req_body).expect("json")))
.expect("request"),
)
.await
.expect("response");
assert_eq!(response.status(), StatusCode::BAD_REQUEST);
}
#[tokio::test]
async fn test_get_provenance_not_found() {
let ctx = test_app().await;
// Use a valid hex hash that doesn't exist
let fake_hash = "a".repeat(64);
let response = ctx
.app
.oneshot(
Request::builder()
.method(Method::GET)
.uri(format!("/v1/provenance/{}", fake_hash))
.body(Body::empty())
.expect("request"),
)
.await
.expect("response");
assert_eq!(response.status(), StatusCode::NOT_FOUND);
}
#[tokio::test]
async fn test_get_provenance_invalid_hash() {
let ctx = test_app().await;
// Too short
let response = ctx
.app
.clone()
.oneshot(
Request::builder()
.method(Method::GET)
.uri("/v1/provenance/abc123")
.body(Body::empty())
.expect("request"),
)
.await
.expect("response");
assert_eq!(response.status(), StatusCode::BAD_REQUEST);
// Non-hex characters
let response = ctx
.app
.oneshot(
Request::builder()
.method(Method::GET)
.uri(format!("/v1/provenance/{}", "g".repeat(64)))
.body(Body::empty())
.expect("request"),
)
.await
.expect("response");
assert_eq!(response.status(), StatusCode::BAD_REQUEST);
}
#[tokio::test]
async fn test_store_source_idempotent() {
let ctx = test_app().await;
let content = "Same content twice";
let content_b64 = BASE64.encode(content);
let req_body = json!({
"content": content_b64,
"content_type": "text/plain"
});
// Store first time
let response1 = ctx
.app
.clone()
.oneshot(
Request::builder()
.method(Method::POST)
.uri("/v1/source")
.header("content-type", "application/json")
.body(Body::from(serde_json::to_string(&req_body).expect("json")))
.expect("request"),
)
.await
.expect("response");
assert_eq!(response1.status(), StatusCode::CREATED);
let body1 = axum::body::to_bytes(response1.into_body(), usize::MAX).await.expect("body");
let resp1: StoreSourceResponse = serde_json::from_slice(&body1).expect("deserialize");
// Store second time - same content
let response2 = ctx
.app
.oneshot(
Request::builder()
.method(Method::POST)
.uri("/v1/source")
.header("content-type", "application/json")
.body(Body::from(serde_json::to_string(&req_body).expect("json")))
.expect("request"),
)
.await
.expect("response");
assert_eq!(response2.status(), StatusCode::CREATED);
let body2 = axum::body::to_bytes(response2.into_body(), usize::MAX).await.expect("body");
let resp2: StoreSourceResponse = serde_json::from_slice(&body2).expect("deserialize");
// Same hash returned both times
assert_eq!(resp1.hash, resp2.hash);
}
}

25
crates/stemedb-api/src/lib.rs
View File

@ -52,11 +52,14 @@ pub use state::AppState;
use handlers::{
assert::__path_create_assertion,
audit::{__path_get_audit, __path_list_audits},
constraints::__path_constraints_query,
epoch::__path_create_epoch,
health::__path_health_check,
layered::__path_layered_query,
meter::{__path_get_quota_status, __path_set_quota_limit},
query::__path_query_assertions,
skeptic::__path_skeptic_query,
source::{__path_get_provenance, __path_store_source},
supersede::__path_supersede,
trace::__path_trace,
vote::__path_create_vote,
@ -71,6 +74,8 @@ use handlers::{
create_vote,
query_assertions,
skeptic_query,
layered_query,
constraints_query,
health_check,
list_audits,
get_audit,
@ -78,6 +83,8 @@ use handlers::{
supersede,
get_quota_status,
set_quota_limit,
store_source,
get_provenance,
),
components(
schemas(
@ -110,9 +117,18 @@ use handlers::{
dto::ClaimSummaryDto,
dto::SourceSummaryDto,
dto::AgentSummaryDto,
dto::SourceClassDto,
dto::TierResolutionDto,
dto::LayeredQueryResponse,
dto::ConstraintsQueryParams,
dto::ConstraintsResponse,
dto::ConstraintEntryDto,
handlers::meter::QuotaStatusResponse,
handlers::meter::SetQuotaLimitRequest,
handlers::meter::SetQuotaLimitResponse,
dto::StoreSourceRequest,
dto::StoreSourceResponse,
dto::ProvenanceResponse,
)
),
tags(
@ -124,6 +140,7 @@ use handlers::{
(name = "audit", description = "Query audit trail for incident investigation"),
(name = "supersession", description = "Supersede assertions for error correction"),
(name = "meter", description = "Economic throttling and quota management"),
(name = "provenance", description = "Source document storage and retrieval"),
),
info(
title = "Episteme (StemeDB) API",
@ -148,6 +165,8 @@ pub fn create_router(state: AppState) -> Router {
.route("/v1/vote", post(handlers::create_vote))
.route("/v1/query", get(handlers::query_assertions))
.route("/v1/skeptic", get(handlers::skeptic_query))
.route("/v1/layered", get(handlers::layered_query))
.route("/v1/constraints", get(handlers::constraints_query))
.route("/v1/health", get(handlers::health_check))
.route("/v1/audit/queries", get(handlers::list_audits))
.route("/v1/audit/query/{id}", get(handlers::get_audit))
@ -155,6 +174,8 @@ pub fn create_router(state: AppState) -> Router {
.route("/v1/supersede", post(handlers::supersede))
.route("/v1/meter/quota", get(handlers::get_quota_status))
.route("/v1/meter/quota/limit", post(handlers::set_quota_limit))
.route("/v1/source", post(handlers::store_source))
.route("/v1/provenance/{hash}", get(handlers::get_provenance))
.with_state(state)
.layer(TraceLayer::new_for_http());
@ -189,6 +210,8 @@ pub fn create_router_with_meter(state: AppState) -> Router {
.route("/v1/vote", post(handlers::create_vote))
.route("/v1/query", get(handlers::query_assertions))
.route("/v1/skeptic", get(handlers::skeptic_query))
.route("/v1/layered", get(handlers::layered_query))
.route("/v1/constraints", get(handlers::constraints_query))
.route("/v1/health", get(handlers::health_check))
.route("/v1/audit/queries", get(handlers::list_audits))
.route("/v1/audit/query/{id}", get(handlers::get_audit))
@ -196,6 +219,8 @@ pub fn create_router_with_meter(state: AppState) -> Router {
.route("/v1/supersede", post(handlers::supersede))
.route("/v1/meter/quota", get(handlers::get_quota_status))
.route("/v1/meter/quota/limit", post(handlers::set_quota_limit))
.route("/v1/source", post(handlers::store_source))
.route("/v1/provenance/{hash}", get(handlers::get_provenance))
.with_state(state)
.layer(meter_layer)
.layer(TraceLayer::new_for_http());

644
crates/stemedb-api/tests/http_integration.rs Normal file
View File

@ -0,0 +1,644 @@
//! Comprehensive HTTP integration tests for the StemeDB API.
//!
//! These tests verify the full HTTP layer without background workers,
//! focusing on request validation, error handling, and response structure.
//!
//! Coverage:
//! - POST /v1/assert - Assertion creation
//! - POST /v1/vote - Vote submission
//! - GET /v1/query - Query with lens parameter
//! - Error responses (400, 500)
//! - Rate limiting via QuotaStore (when enabled)
#![allow(clippy::expect_used)]
use axum::{
body::Body,
http::{Request, StatusCode},
};
use ed25519_dalek::{Signer, SigningKey};
use rand::rngs::OsRng;
use serde_json::json;
use std::sync::Arc;
use tower::ServiceExt;
use stemedb_api::{create_router, create_router_with_meter, AppState};
use stemedb_storage::{GenericQuotaStore, QuotaStore, SledStore};
use stemedb_wal::Journal;
// ============================================================================
// Test Environment Setup
// ============================================================================
/// Test environment that keeps temp directories alive for the test duration.
struct TestEnvironment {
_temp_dir: tempfile::TempDir,
state: AppState,
}
/// Helper to create a test environment with temporary directories.
async fn create_test_env() -> TestEnvironment {
let temp_dir = tempfile::tempdir().expect("failed to create temp dir");
let wal_dir = temp_dir.path().join("wal");
let db_dir = temp_dir.path().join("db");
std::fs::create_dir_all(&wal_dir).expect("failed to create wal dir");
std::fs::create_dir_all(&db_dir).expect("failed to create db dir");
let journal = Journal::open(&wal_dir).expect("failed to open journal");
let store = SledStore::open(&db_dir).expect("failed to open store");
let state = AppState::new(journal, store);
TestEnvironment { _temp_dir: temp_dir, state }
}
/// Helper to sign a message using Ed25519.
fn sign_message(message: &str) -> ([u8; 32], [u8; 64]) {
let mut csprng = OsRng;
let signing_key = SigningKey::generate(&mut csprng);
let verifying_key = signing_key.verifying_key();
let signature = signing_key.sign(message.as_bytes());
(verifying_key.to_bytes(), signature.to_bytes())
}
// ============================================================================
// POST /v1/assert - Create Assertion Tests
// ============================================================================
#[tokio::test]
async fn test_assert_valid_creation() {
let env = create_test_env().await;
let app = create_router(env.state);
let subject = "Test_Entity";
let predicate = "test_property";
let message = format!("{}:{}", subject, predicate);
let (agent_id, signature) = sign_message(&message);
let assertion = json!({
"subject": subject,
"predicate": predicate,
"object": {"type": "Number", "value": 42.0},
"confidence": 0.95,
"signatures": [{
"agent_id": hex::encode(agent_id),
"signature": hex::encode(signature),
"timestamp": 1234567890
}],
"source_hash": hex::encode([0u8; 32])
});
let request = Request::builder()
.uri("/v1/assert")
.method("POST")
.header("content-type", "application/json")
.body(Body::from(serde_json::to_vec(&assertion).expect("JSON serialization")))
.expect("Failed to build request");
let response = app.oneshot(request).await.expect("Request failed");
assert_eq!(response.status(), StatusCode::CREATED);
let body =
axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Failed to read body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("Failed to parse JSON");
assert_eq!(json["status"], "created");
assert!(json["hash"].is_string());
assert_eq!(json["hash"].as_str().expect("hash").len(), 64); // BLAKE3 = 32 bytes = 64 hex
}
#[tokio::test]
async fn test_assert_response_includes_hash() {
let env = create_test_env().await;
let app = create_router(env.state);
let (agent_id, signature) = sign_message("Test:test");
let assertion = json!({
"subject": "Test",
"predicate": "test",
"object": {"type": "Text", "value": "test_value"},
"confidence": 0.8,
"signatures": [{
"agent_id": hex::encode(agent_id),
"signature": hex::encode(signature),
"timestamp": 1000000000
}],
"source_hash": hex::encode([1u8; 32])
});
let request = Request::builder()
.uri("/v1/assert")
.method("POST")
.header("content-type", "application/json")
.body(Body::from(serde_json::to_vec(&assertion).expect("JSON")))
.expect("Request build");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::CREATED);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON parse");
// Verify hash is present and correctly formatted
let hash = json["hash"].as_str().expect("hash should be present");
assert_eq!(hash.len(), 64);
assert!(hash.chars().all(|c| c.is_ascii_hexdigit()));
}
#[tokio::test]
async fn test_assert_missing_signature_fails() {
let env = create_test_env().await;
let app = create_router(env.state);
let assertion = json!({
"subject": "Test",
"predicate": "test",
"object": {"type": "Number", "value": 1.0},
"confidence": 0.9,
"signatures": [], // Empty signatures
"source_hash": hex::encode([0u8; 32])
});
let request = Request::builder()
.uri("/v1/assert")
.method("POST")
.header("content-type", "application/json")
.body(Body::from(serde_json::to_vec(&assertion).expect("JSON")))
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::BAD_REQUEST);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON");
assert!(json["error"]
.as_str()
.expect("error message")
.contains("At least one signature is required"));
}
// ============================================================================
// POST /v1/vote - Submit Vote Tests
// ============================================================================
#[tokio::test]
async fn test_vote_valid_submission() {
let env = create_test_env().await;
let app = create_router(env.state);
let (agent_id, signature) = sign_message("vote_message");
let vote = json!({
"assertion_hash": hex::encode([0u8; 32]),
"agent_id": hex::encode(agent_id),
"weight": 0.75,
"signature": hex::encode(signature)
});
let request = Request::builder()
.uri("/v1/vote")
.method("POST")
.header("content-type", "application/json")
.body(Body::from(serde_json::to_vec(&vote).expect("JSON")))
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::CREATED);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON");
assert_eq!(json["status"], "created");
assert!(json["hash"].is_string());
assert_eq!(json["hash"].as_str().expect("hash").len(), 64);
}
#[tokio::test]
async fn test_vote_response_structure() {
let env = create_test_env().await;
let app = create_router(env.state);
let (agent_id, signature) = sign_message("test");
let vote = json!({
"assertion_hash": hex::encode([1u8; 32]),
"agent_id": hex::encode(agent_id),
"weight": 1.0,
"signature": hex::encode(signature)
});
let request = Request::builder()
.uri("/v1/vote")
.method("POST")
.header("content-type", "application/json")
.body(Body::from(serde_json::to_vec(&vote).expect("JSON")))
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON");
// Verify response structure matches CreateResponse
assert!(json.get("hash").is_some());
assert!(json.get("status").is_some());
assert_eq!(json.as_object().expect("object").len(), 2);
}
#[tokio::test]
async fn test_vote_invalid_weight_fails() {
let env = create_test_env().await;
let app = create_router(env.state);
let (agent_id, signature) = sign_message("test");
// Test weight > 1.0
let vote = json!({
"assertion_hash": hex::encode([0u8; 32]),
"agent_id": hex::encode(agent_id),
"weight": 1.5,
"signature": hex::encode(signature)
});
let request = Request::builder()
.uri("/v1/vote")
.method("POST")
.header("content-type", "application/json")
.body(Body::from(serde_json::to_vec(&vote).expect("JSON")))
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::BAD_REQUEST);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON");
assert!(json["error"].as_str().expect("error").contains("Weight must be between 0.0 and 1.0"));
}
// ============================================================================
// GET /v1/query - Query Assertions Tests
// ============================================================================
#[tokio::test]
async fn test_query_basic_with_subject_predicate() {
let env = create_test_env().await;
let app = create_router(env.state);
let request = Request::builder()
.uri("/v1/query?subject=Test_Entity&predicate=test_property")
.method("GET")
.body(Body::empty())
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::OK);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON");
// Verify response structure (empty since DB is empty)
assert!(json.get("assertions").is_some());
assert!(json.get("total_count").is_some());
assert!(json.get("has_more").is_some());
assert_eq!(json["assertions"], json!([]));
assert_eq!(json["total_count"], 0);
}
#[tokio::test]
async fn test_query_with_lens_recency() {
let env = create_test_env().await;
let app = create_router(env.state);
let request = Request::builder()
.uri("/v1/query?subject=Test&predicate=prop&lens=Recency")
.method("GET")
.body(Body::empty())
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::OK);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON");
// When a lens is applied (even with empty results), conflict_score and
// resolution_confidence should be None (empty result set)
assert_eq!(json["assertions"], json!([]));
assert_eq!(json["total_count"], 0);
assert!(json.get("conflict_score").is_none() || json["conflict_score"].is_null());
assert!(json.get("resolution_confidence").is_none() || json["resolution_confidence"].is_null());
}
#[tokio::test]
async fn test_query_lifecycle_filtering() {
let env = create_test_env().await;
let app = create_router(env.state);
let request = Request::builder()
.uri("/v1/query?subject=Test&lifecycle=Approved")
.method("GET")
.body(Body::empty())
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::OK);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON");
assert_eq!(json["assertions"], json!([]));
assert_eq!(json["total_count"], 0);
}
#[tokio::test]
async fn test_query_with_limit() {
let env = create_test_env().await;
let app = create_router(env.state);
let request = Request::builder()
.uri("/v1/query?subject=Test&limit=10")
.method("GET")
.body(Body::empty())
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::OK);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON");
// Verify query executes successfully (limit parameter is accepted)
assert!(json.get("assertions").is_some());
}
// ============================================================================
// Error Response Tests
// ============================================================================
#[tokio::test]
async fn test_400_bad_request_invalid_input() {
let env = create_test_env().await;
let app = create_router(env.state);
// Invalid JSON payload
let invalid_json = b"{invalid json}";
let request = Request::builder()
.uri("/v1/assert")
.method("POST")
.header("content-type", "application/json")
.body(Body::from(invalid_json.to_vec()))
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::BAD_REQUEST);
}
#[tokio::test]
async fn test_error_message_format() {
let env = create_test_env().await;
let app = create_router(env.state);
// Send assertion with invalid confidence
let (agent_id, signature) = sign_message("test");
let assertion = json!({
"subject": "Test",
"predicate": "test",
"object": {"type": "Number", "value": 1.0},
"confidence": 2.0, // Invalid: > 1.0
"signatures": [{
"agent_id": hex::encode(agent_id),
"signature": hex::encode(signature),
"timestamp": 1000
}],
"source_hash": hex::encode([0u8; 32])
});
let request = Request::builder()
.uri("/v1/assert")
.method("POST")
.header("content-type", "application/json")
.body(Body::from(serde_json::to_vec(&assertion).expect("JSON")))
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::BAD_REQUEST);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON");
// Verify error response structure matches ErrorResponse DTO
assert!(json.get("error").is_some());
assert!(json.get("code").is_some());
assert!(json["error"].as_str().expect("error message").contains("Confidence"));
}
#[tokio::test]
async fn test_hex_validation_wrong_length() {
let env = create_test_env().await;
let app = create_router(env.state);
let (agent_id, signature) = sign_message("test");
let assertion = json!({
"subject": "Test",
"predicate": "test",
"object": {"type": "Number", "value": 1.0},
"confidence": 0.9,
"signatures": [{
"agent_id": hex::encode(agent_id),
"signature": hex::encode(signature),
"timestamp": 1000
}],
"source_hash": "abc" // Too short - should be 64 hex chars
});
let request = Request::builder()
.uri("/v1/assert")
.method("POST")
.header("content-type", "application/json")
.body(Body::from(serde_json::to_vec(&assertion).expect("JSON")))
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::BAD_REQUEST);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON");
assert!(json["error"].as_str().expect("error").contains("Expected 64 hex characters"));
}
// ============================================================================
// Rate Limiting Tests (QuotaStore Middleware)
// ============================================================================
#[tokio::test]
async fn test_quota_consumption_with_meter() {
let temp_dir = tempfile::tempdir().expect("temp dir");
let wal_dir = temp_dir.path().join("wal");
let db_dir = temp_dir.path().join("db");
std::fs::create_dir_all(&wal_dir).expect("wal dir");
std::fs::create_dir_all(&db_dir).expect("db dir");
let journal = Journal::open(&wal_dir).expect("journal");
let store = Arc::new(SledStore::open(&db_dir).expect("store"));
// Create AppState manually to share quota_store
let quota_store = Arc::new(GenericQuotaStore::new(store.clone()));
let state = AppState {
journal: Arc::new(tokio::sync::Mutex::new(journal)),
store: store.clone(),
quota_store: quota_store.clone(),
};
let app = create_router_with_meter(state);
let (agent_id, signature) = sign_message("test");
let agent_id_hex = hex::encode(agent_id);
// Set a low quota limit for testing
quota_store.set_quota_limit(&agent_id, 50).await.expect("set quota");
let assertion = json!({
"subject": "QuotaTest",
"predicate": "test",
"object": {"type": "Number", "value": 1.0},
"confidence": 0.9,
"signatures": [{
"agent_id": agent_id_hex,
"signature": hex::encode(signature),
"timestamp": 1000
}],
"source_hash": hex::encode([0u8; 32])
});
let request = Request::builder()
.uri("/v1/assert")
.method("POST")
.header("content-type", "application/json")
.header("x-agent-id", &agent_id_hex)
.body(Body::from(serde_json::to_vec(&assertion).expect("JSON")))
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
// Should succeed and include quota headers
assert_eq!(response.status(), StatusCode::CREATED);
let headers = response.headers();
assert!(headers.get("x-quota-remaining").is_some());
assert!(headers.get("x-quota-limit").is_some());
assert!(headers.get("x-quota-reset").is_some());
}
#[tokio::test]
async fn test_quota_exceeded_response() {
let temp_dir = tempfile::tempdir().expect("temp dir");
let wal_dir = temp_dir.path().join("wal");
let db_dir = temp_dir.path().join("db");
std::fs::create_dir_all(&wal_dir).expect("wal dir");
std::fs::create_dir_all(&db_dir).expect("db dir");
let journal = Journal::open(&wal_dir).expect("journal");
let store = Arc::new(SledStore::open(&db_dir).expect("store"));
let quota_store = Arc::new(GenericQuotaStore::new(store.clone()));
let state = AppState {
journal: Arc::new(tokio::sync::Mutex::new(journal)),
store: store.clone(),
quota_store: quota_store.clone(),
};
let app = create_router_with_meter(state);
let (agent_id, _) = sign_message("test");
let agent_id_hex = hex::encode(agent_id);
// Set quota to 0 to immediately trigger quota exceeded
quota_store.set_quota_limit(&agent_id, 0).await.expect("set quota");
let request = Request::builder()
.uri("/v1/query?subject=Test")
.method("GET")
.header("x-agent-id", &agent_id_hex)
.body(Body::empty())
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
// Should return 429 Too Many Requests
assert_eq!(response.status(), StatusCode::TOO_MANY_REQUESTS);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON");
assert!(json.get("error").is_some());
assert_eq!(json["code"], "QUOTA_EXCEEDED");
assert!(json.get("remaining").is_some());
assert!(json.get("limit").is_some());
assert!(json.get("reset_at").is_some());
}
// ============================================================================
// Additional Edge Cases
// ============================================================================
#[tokio::test]
async fn test_health_endpoint() {
let env = create_test_env().await;
let app = create_router(env.state);
let request =
Request::builder().uri("/v1/health").method("GET").body(Body::empty()).expect("Request");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::OK);
let body = axum::body::to_bytes(response.into_body(), usize::MAX).await.expect("Body");
let json: serde_json::Value = serde_json::from_slice(&body).expect("JSON");
assert_eq!(json["status"], "healthy");
assert!(json.get("version").is_some());
}
#[tokio::test]
async fn test_not_found_endpoint() {
let env = create_test_env().await;
let app = create_router(env.state);
let request = Request::builder()
.uri("/v1/nonexistent")
.method("GET")
.body(Body::empty())
.expect("Request");
let response = app.oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::NOT_FOUND);
}
#[tokio::test]
async fn test_query_with_all_lenses() {
let env = create_test_env().await;
let app = create_router(env.state);
// Test that all lens values are accepted
let lenses = vec!["Recency", "Consensus", "Confidence", "Authority", "VoteAwareConsensus"];
for lens in lenses {
let request = Request::builder()
.uri(format!("/v1/query?subject=Test&predicate=prop&lens={}", lens))
.method("GET")
.body(Body::empty())
.expect("Request");
let response = app.clone().oneshot(request).await.expect("Request");
assert_eq!(response.status(), StatusCode::OK, "Lens {} should be accepted", lens);
}
}

3
crates/stemedb-core/src/lib.rs
View File

@ -43,6 +43,7 @@ mod tests {
source_class: SourceClass::Clinical,
visual_hash: Some([1u8; 8]),
epoch: Some([2u8; 32]),
source_metadata: None,
lifecycle: LifecycleStage::Approved,
signatures: vec![SignatureEntry {
agent_id: [2u8; 32],
@ -95,6 +96,7 @@ mod tests {
source_class: SourceClass::Expert,
visual_hash: None,
epoch: None,
source_metadata: None,
lifecycle: stage,
signatures: vec![],
confidence: 1.0,
@ -253,4 +255,3 @@ mod tests {
assert_eq!(deserialized.weight, 0.8);
}
}
// test hook

57
crates/stemedb-core/src/serde.rs
View File

@ -85,6 +85,7 @@ pub enum SerdeError {
/// source_class: SourceClass::Expert,
/// visual_hash: None,
/// epoch: None,
/// source_metadata: None,
/// lifecycle: LifecycleStage::Proposed,
/// signatures: vec![],
/// confidence: 1.0,
@ -168,6 +169,7 @@ mod tests {
source_class: SourceClass::Clinical,
visual_hash: Some([1u8; 8]),
epoch: Some([2u8; 32]),
source_metadata: None,
lifecycle: LifecycleStage::Approved,
signatures: vec![SignatureEntry {
agent_id: [2u8; 32],
@ -233,6 +235,7 @@ mod tests {
source_class: SourceClass::Expert,
visual_hash: None,
epoch: None,
source_metadata: None,
lifecycle: LifecycleStage::Proposed,
signatures: vec![],
confidence: 0.0,
@ -244,4 +247,58 @@ mod tests {
let recovered: Assertion = deserialize(&bytes).expect("deserialize");
assert_eq!(assertion, recovered);
}
#[test]
fn test_serialize_deserialize_assertion_with_metadata() {
let metadata = r#"{"journal":"Nature","DOI":"10.1038/xyz","sample_size":1234}"#;
let assertion = Assertion {
subject: "Semaglutide".to_string(),
predicate: "muscle_effect".to_string(),
object: ObjectValue::Text("significant_loss".to_string()),
parent_hash: None,
source_hash: [1u8; 32],
source_class: SourceClass::Clinical,
visual_hash: None,
epoch: None,
source_metadata: Some(metadata.as_bytes().to_vec()),
lifecycle: LifecycleStage::Proposed,
signatures: vec![],
confidence: 0.85,
timestamp: 1700000000,
vector: None,
};
let bytes = serialize(&assertion).expect("serialize");
let recovered: Assertion = deserialize(&bytes).expect("deserialize");
assert_eq!(assertion, recovered);
assert_eq!(recovered.source_metadata, Some(metadata.as_bytes().to_vec()));
}
#[test]
fn test_serialize_deserialize_assertion_without_metadata() {
let assertion = Assertion {
subject: "test".to_string(),
predicate: "test".to_string(),
object: ObjectValue::Boolean(true),
parent_hash: None,
source_hash: [0u8; 32],
source_class: SourceClass::Expert,
visual_hash: None,
epoch: None,
source_metadata: None,
lifecycle: LifecycleStage::Proposed,
signatures: vec![],
confidence: 1.0,
timestamp: 0,
vector: None,
};
let bytes = serialize(&assertion).expect("serialize");
let recovered: Assertion = deserialize(&bytes).expect("deserialize");
assert_eq!(assertion, recovered);
assert!(recovered.source_metadata.is_none());
}
}

16
crates/stemedb-core/src/testing.rs
View File

@ -48,6 +48,7 @@ pub struct AssertionBuilder {
source_class: SourceClass,
visual_hash: Option<[u8; 8]>,
epoch: Option<[u8; 32]>,
source_metadata: Option<Vec<u8>>,
lifecycle: LifecycleStage,
signatures: Option<Vec<SignatureEntry>>,
agent_id: [u8; 32],
@ -74,6 +75,7 @@ impl AssertionBuilder {
source_class: SourceClass::Expert, // Default to middle tier for tests
visual_hash: None,
epoch: None,
source_metadata: None,
lifecycle: LifecycleStage::Approved,
signatures: None, // Will use agent_id to build default
agent_id: [1u8; 32],
@ -173,6 +175,19 @@ impl AssertionBuilder {
self
}
/// Set the source metadata from a JSON string.
/// Stores as bytes for rkyv compatibility.
pub fn source_metadata_json(mut self, json: &str) -> Self {
self.source_metadata = Some(json.as_bytes().to_vec());
self
}
/// Set the source metadata as raw bytes.
pub fn source_metadata(mut self, metadata: Vec<u8>) -> Self {
self.source_metadata = Some(metadata);
self
}
/// Provide explicit signatures (overrides the default single-signature behavior).
pub fn signatures(mut self, signatures: Vec<SignatureEntry>) -> Self {
self.signatures = Some(signatures);
@ -198,6 +213,7 @@ impl AssertionBuilder {
source_class: self.source_class,
visual_hash: self.visual_hash,
epoch: self.epoch,
source_metadata: self.source_metadata,
lifecycle: self.lifecycle,
signatures,
confidence: self.confidence,

7
crates/stemedb-core/src/types.rs
View File

@ -154,6 +154,10 @@ pub struct Assertion {
pub visual_hash: Option<PHash>,
/// The epoch this assertion belongs to (if any).
pub epoch: Option<EpochId>,
/// Structured source metadata as a JSON-encoded byte string.
/// Schema is domain-specific (journal info, social metrics, etc.).
/// Use `Vec<u8>` for rkyv zero-copy compatibility.
pub source_metadata: Option<Vec<u8>>,
/// The lifecycle stage (Proposed, UnderReview, Approved, Deprecated, Rejected).
pub lifecycle: LifecycleStage,
@ -315,6 +319,9 @@ pub struct MaterializedView {
pub lens_name: String,
/// Confidence in the resolution (0.0 to 1.0).
pub resolution_confidence: f32,
/// Degree of disagreement among candidates (0.0 = full agreement, 1.0 = max conflict).
/// See `stemedb_lens::compute_conflict_score()` for the canonical algorithm.
pub conflict_score: f32,
/// Number of candidate assertions that were considered.
pub candidates_count: usize,
/// Unix timestamp when this view was last materialized.

71
crates/stemedb-ingest/src/ingestor.rs
View File

@ -1,24 +1,35 @@
use crate::error::Result;
use crate::worker::IngestWorker;
use std::sync::atomic::{AtomicBool, Ordering};
use std::sync::Arc;
use std::time::Duration;
use stemedb_storage::KVStore;
use stemedb_wal::Journal;
use tokio::sync::Mutex;
use tokio::task::JoinHandle;
use tracing::{debug, info, instrument};
use tracing::{debug, info, instrument, warn};
/// Manager for the background ingestion process.
///
/// The Ingestor owns a background task that continuously reads from the WAL
/// and writes to the KV store. It provides lifecycle management including
/// graceful shutdown coordination.
pub struct Ingestor<S> {
worker: Arc<Mutex<IngestWorker<S>>>,
handle: Option<JoinHandle<()>>,
/// Shared shutdown signal between Ingestor and background task.
shutdown: Arc<AtomicBool>,
}
impl<S: KVStore + 'static> Ingestor<S> {
/// Create a new Ingestor, loading the persisted cursor if available.
pub async fn new(journal: Arc<Mutex<Journal>>, store: Arc<S>) -> Result<Self> {
let worker = Arc::new(Mutex::new(IngestWorker::new(journal, store).await?));
let shutdown = Arc::new(AtomicBool::new(false));
let worker = Arc::new(Mutex::new(
IngestWorker::with_shutdown(journal, store, shutdown.clone()).await?,
));
debug!("Ingestor created");
Ok(Self { worker, handle: None })
Ok(Self { worker, handle: None, shutdown })
}
/// Start the background ingestion task.
@ -37,6 +48,45 @@ impl<S: KVStore + 'static> Ingestor<S> {
}));
}
/// Gracefully shut down the background ingestion task.
///
/// This signals the background task to stop and waits for it to exit.
/// If the task doesn't stop within the timeout, it will be forcibly aborted.
///
/// # Arguments
/// * `timeout` - Maximum time to wait for graceful shutdown before aborting.
#[instrument(skip(self))]
pub async fn shutdown(&mut self, timeout: Duration) {
// Signal shutdown
self.shutdown.store(true, Ordering::Relaxed);
info!("Shutdown signal sent to ingestion task");
if let Some(handle) = self.handle.take() {
// Wait for graceful shutdown with timeout
match tokio::time::timeout(timeout, handle).await {
Ok(Ok(())) => {
info!("Ingestion task shut down gracefully");
}
Ok(Err(e)) => {
warn!("Ingestion task panicked during shutdown: {:?}", e);
}
Err(_) => {
warn!("Ingestion task did not stop within {:?}, task will be dropped", timeout);
// The handle is already taken, so the task will be detached
// when the Ingestor is dropped. This is acceptable since
// we've already signaled shutdown.
}
}
} else {
debug!("No running ingestion task to shut down");
}
}
/// Check if the ingestor is currently running.
pub fn is_running(&self) -> bool {
self.handle.as_ref().is_some_and(|h| !h.is_finished())
}
/// Process pending WAL entries immediately (for testing).
#[instrument(skip(self))]
pub async fn process_pending(&self) -> Result<u64> {
@ -53,3 +103,18 @@ impl<S: KVStore + 'static> Ingestor<S> {
Ok(total_bytes)
}
}
impl<S> Drop for Ingestor<S> {
fn drop(&mut self) {
// Signal shutdown to prevent the background task from accessing
// resources that may be dropped after us.
self.shutdown.store(true, Ordering::Relaxed);
// If the handle is still present, the task will be dropped when the
// JoinHandle is dropped. The task will see the shutdown signal and
// exit gracefully, or it will be aborted by the runtime.
if self.handle.is_some() {
debug!("Ingestor dropped with running task, shutdown signal sent");
}
}
}

1249
crates/stemedb-ingest/src/worker.rs
View File

File diff suppressed because it is too large Load Diff

7
crates/stemedb-lens/src/confidence.rs
View File

@ -15,7 +15,7 @@
//! - You want to weight by agent reputation (TrustRank)
//! - Agent history matters more than self-declared confidence
use crate::traits::{Lens, Resolution};
use crate::traits::{compute_conflict_score, Lens, Resolution};
use stemedb_core::types::Assertion;
use tracing::instrument;
@ -50,7 +50,7 @@ impl Lens for ConfidenceLens {
}
if candidates.len() == 1 {
return Resolution::with_winner(candidates[0].clone(), 1, 1.0);
return Resolution::with_winner(candidates[0].clone(), 1, 1.0, 0.0);
}
// Find the assertion with the highest confidence
@ -70,7 +70,8 @@ impl Lens for ConfidenceLens {
Some(w) => {
// Resolution confidence is the winning assertion's confidence
let confidence = w.confidence;
Resolution::with_winner(w, candidates.len(), confidence)
let conflict = compute_conflict_score(candidates);
Resolution::with_winner(w, candidates.len(), confidence, conflict)
}
None => Resolution::empty(),
}

7
crates/stemedb-lens/src/consensus.rs
View File

@ -4,7 +4,7 @@
//! In Phase 2, this counts identical object values across assertions.
//! In Phase 4, this will integrate with the Ballot Box vote counts.
use crate::traits::{Lens, Resolution};
use crate::traits::{compute_conflict_score, Lens, Resolution};
use std::collections::HashMap;
use stemedb_core::types::Assertion;
use tracing::instrument;
@ -39,7 +39,7 @@ impl Lens for ConsensusLens {
}
if candidates.len() == 1 {
return Resolution::with_winner(candidates[0].clone(), 1, 1.0);
return Resolution::with_winner(candidates[0].clone(), 1, 1.0, 0.0);
}
// Group assertions by their object value
@ -67,7 +67,8 @@ impl Lens for ConsensusLens {
// Confidence = proportion of candidates that agree
let confidence = group_size as f32 / total as f32;
Resolution::with_winner(w, total, confidence)
let conflict = compute_conflict_score(candidates);
Resolution::with_winner(w, total, confidence, conflict)
}
None => Resolution::empty(),
}

502
crates/stemedb-lens/src/constraints.rs Normal file
View File

@ -0,0 +1,502 @@
//! Constraints Lens: Pre-flight check for must_use/forbidden/prefer.
//!
//! This lens categorizes assertions by predicate pattern for agent constraint checking.
//! Instead of picking a single winner, it groups assertions into constraint categories.
//!
//! # The Problem
//!
//! AI agents need to check constraints before taking actions:
//! - "What libraries MUST I use for this project?"
//! - "What tools are FORBIDDEN?"
//! - "What patterns are PREFERRED?"
//!
//! # Predicate Patterns
//!
//! | Pattern | Category | Meaning |
//! |---------|----------|---------|
//! | `must_use:*` | must_use | Required, non-negotiable |
//! | `forbidden:*` | forbidden | Explicitly banned |
//! | `prefer:*` | prefer | Recommended but optional |
//!
//! # Example
//!
//! ```ignore
//! // Assertions:
//! // - predicate: "must_use:http_client" -> object: "axios"
//! // - predicate: "forbidden:http_client" -> object: "requests"
//! // - predicate: "prefer:language" -> object: "typescript"
//!
//! let lens = ConstraintsLens;
//! let constraints = lens.resolve_constraints(&candidates);
//! // constraints.must_use = [axios assertion]
//! // constraints.forbidden = [requests assertion]
//! // constraints.prefer = [typescript assertion]
//! ```
use crate::traits::{compute_conflict_score, Lens, Resolution};
use stemedb_core::types::Assertion;
use tracing::instrument;
/// A set of categorized constraints from assertions.
///
/// Each category contains assertions matching the corresponding predicate pattern,
/// sorted by confidence (highest first).
#[derive(Debug, Clone, Default)]
pub struct ConstraintSet {
/// Assertions with `must_use:*` predicates. Required constraints.
pub must_use: Vec<Assertion>,
/// Assertions with `forbidden:*` predicates. Banned items.
pub forbidden: Vec<Assertion>,
/// Assertions with `prefer:*` predicates. Recommendations.
pub prefer: Vec<Assertion>,
/// Total candidates considered (including non-constraint predicates).
pub candidates_count: usize,
/// Overall conflict score across all constraint categories.
pub conflict_score: f32,
}
impl ConstraintSet {
/// Create an empty constraint set.
pub fn empty() -> Self {
Self::default()
}
/// Check if any constraints exist.
pub fn has_constraints(&self) -> bool {
!self.must_use.is_empty() || !self.forbidden.is_empty() || !self.prefer.is_empty()
}
/// Total number of constraint assertions.
pub fn total_constraints(&self) -> usize {
self.must_use.len() + self.forbidden.len() + self.prefer.len()
}
}
/// Constraints Lens: Categorizes assertions by predicate pattern.
///
/// # Resolution Strategy (for Lens trait)
///
/// When used as a standard `Lens`:
/// 1. Returns the highest-confidence `must_use` assertion as the winner
/// 2. If no `must_use`, returns highest-confidence `forbidden`
/// 3. If neither, returns highest-confidence `prefer`
/// 4. If no constraint predicates at all, returns empty
///
/// # Rich Result
///
/// For the full constraint set, use `resolve_constraints()` instead.
///
/// # Predicate Patterns
///
/// - `must_use:*` -> must_use category
/// - `forbidden:*` -> forbidden category
/// - `prefer:*` -> prefer category
/// - Other predicates are ignored (not categorized)
#[derive(Debug, Clone, Copy, Default)]
pub struct ConstraintsLens;
impl ConstraintsLens {
/// Resolve constraints into a categorized set.
///
/// This is the rich result method that returns all constraint categories.
/// Use this when you need the full constraint picture.
///
/// # Arguments
///
/// * `candidates` - Assertions to categorize by predicate pattern
///
/// # Returns
///
/// A `ConstraintSet` with:
/// - `must_use`: Assertions with `must_use:*` predicates (sorted by confidence desc)
/// - `forbidden`: Assertions with `forbidden:*` predicates (sorted by confidence desc)
/// - `prefer`: Assertions with `prefer:*` predicates (sorted by confidence desc)
#[instrument(skip(self, candidates), fields(candidates_count = candidates.len(), lens = "Constraints"))]
pub fn resolve_constraints(&self, candidates: &[Assertion]) -> ConstraintSet {
if candidates.is_empty() {
return ConstraintSet::empty();
}
let mut must_use = Vec::new();
let mut forbidden = Vec::new();
let mut prefer = Vec::new();
// Categorize by predicate pattern
for assertion in candidates {
if assertion.predicate.starts_with("must_use:") {
must_use.push(assertion.clone());
} else if assertion.predicate.starts_with("forbidden:") {
forbidden.push(assertion.clone());
} else if assertion.predicate.starts_with("prefer:") {
prefer.push(assertion.clone());
}
// Other predicates are not constraint predicates, ignore them
}
// Sort each category by confidence (highest first), then by timestamp (newest first)
let sort_by_confidence = |a: &Assertion, b: &Assertion| {
b.confidence
.partial_cmp(&a.confidence)
.unwrap_or(std::cmp::Ordering::Equal)
.then_with(|| b.timestamp.cmp(&a.timestamp))
};
must_use.sort_by(sort_by_confidence);
forbidden.sort_by(sort_by_confidence);
prefer.sort_by(sort_by_confidence);
// Compute conflict score across all constraint assertions
// Single-pass collection: avoid intermediate Vec<&Assertion> then clone
let constraint_count = must_use.len() + forbidden.len() + prefer.len();
let conflict = if constraint_count <= 1 {
0.0
} else {
let all_constraints: Vec<Assertion> =
must_use.iter().chain(forbidden.iter()).chain(prefer.iter()).cloned().collect();
compute_conflict_score(&all_constraints)
};
ConstraintSet {
must_use,
forbidden,
prefer,
candidates_count: candidates.len(),
conflict_score: conflict,
}
}
}
impl Lens for ConstraintsLens {
/// Standard Lens resolution: returns highest-priority constraint as winner.
///
/// Priority: must_use > forbidden > prefer
///
/// For the full constraint set, use `resolve_constraints()` instead.
#[instrument(skip(self, candidates), fields(candidates_count = candidates.len(), lens = "Constraints"))]
fn resolve(&self, candidates: &[Assertion]) -> Resolution {
let constraints = self.resolve_constraints(candidates);
if !constraints.has_constraints() {
return Resolution::empty();
}
// Priority: must_use > forbidden > prefer
let winner = constraints
.must_use
.first()
.or_else(|| constraints.forbidden.first())
.or_else(|| constraints.prefer.first())
.cloned();
match winner {
Some(w) => {
let confidence = w.confidence;
Resolution::with_winner(
w,
constraints.total_constraints(),
confidence,
constraints.conflict_score,
)
}
None => Resolution::empty(),
}
}
fn name(&self) -> &'static str {
"Constraints"
}
}
#[cfg(test)]
mod tests {
use super::*;
use stemedb_core::testing::AssertionBuilder;
use stemedb_core::types::ObjectValue;
fn create_constraint_assertion(
subject: &str,
predicate: &str,
object: &str,
confidence: f32,
timestamp: u64,
) -> Assertion {
AssertionBuilder::new()
.subject(subject)
.predicate(predicate)
.object_text(object)
.confidence(confidence)
.timestamp(timestamp)
.build()
}
// ========================================================================
// resolve_constraints() Tests
// ========================================================================
#[test]
fn test_constraints_categorizes_by_predicate() {
let lens = ConstraintsLens;
let must_axios = create_constraint_assertion(
"project_alpha",
"must_use:http_client",
"axios",
0.95,
1000,
);
let forbidden_requests = create_constraint_assertion(
"project_alpha",
"forbidden:http_client",
"requests",
0.9,
1000,
);
let prefer_ts = create_constraint_assertion(
"project_alpha",
"prefer:language",
"typescript",
0.8,
1000,
);
let constraints = lens.resolve_constraints(&[
must_axios.clone(),
forbidden_requests.clone(),
prefer_ts.clone(),
]);
assert_eq!(constraints.must_use.len(), 1);
assert_eq!(constraints.forbidden.len(), 1);
assert_eq!(constraints.prefer.len(), 1);
assert_eq!(constraints.must_use[0].object, ObjectValue::Text("axios".to_string()));
assert_eq!(constraints.forbidden[0].object, ObjectValue::Text("requests".to_string()));
assert_eq!(constraints.prefer[0].object, ObjectValue::Text("typescript".to_string()));
}
#[test]
fn test_constraints_empty_categories() {
let lens = ConstraintsLens;
// Only prefer, no must_use or forbidden
let prefer_ts =
create_constraint_assertion("project", "prefer:language", "typescript", 0.8, 1000);
let constraints = lens.resolve_constraints(&[prefer_ts]);
assert!(constraints.must_use.is_empty());
assert!(constraints.forbidden.is_empty());
assert_eq!(constraints.prefer.len(), 1);
}
#[test]
fn test_constraints_non_constraint_predicates_ignored() {
let lens = ConstraintsLens;
// Regular predicates (not must_use/forbidden/prefer) should be ignored
let regular = create_constraint_assertion("project", "uses_framework", "react", 0.9, 1000);
let must_use =
create_constraint_assertion("project", "must_use:testing", "jest", 0.95, 1000);
let constraints = lens.resolve_constraints(&[regular, must_use]);
assert_eq!(constraints.must_use.len(), 1);
assert!(constraints.forbidden.is_empty());
assert!(constraints.prefer.is_empty());
assert_eq!(constraints.candidates_count, 2); // Both candidates considered
}
#[test]
fn test_constraints_sorted_by_confidence() {
let lens = ConstraintsLens;
let low = create_constraint_assertion("project", "must_use:db", "sqlite", 0.5, 1000);
let high = create_constraint_assertion("project", "must_use:db", "postgres", 0.95, 1000);
let medium = create_constraint_assertion("project", "must_use:db", "mysql", 0.75, 1000);
let constraints = lens.resolve_constraints(&[low, high, medium]);
assert_eq!(constraints.must_use.len(), 3);
// Should be sorted by confidence descending
assert_eq!(constraints.must_use[0].object, ObjectValue::Text("postgres".to_string()));
assert_eq!(constraints.must_use[1].object, ObjectValue::Text("mysql".to_string()));
assert_eq!(constraints.must_use[2].object, ObjectValue::Text("sqlite".to_string()));
}
#[test]
fn test_constraints_empty_candidates() {
let lens = ConstraintsLens;
let constraints = lens.resolve_constraints(&[]);
assert!(!constraints.has_constraints());
assert_eq!(constraints.total_constraints(), 0);
assert_eq!(constraints.candidates_count, 0);
}
#[test]
fn test_constraints_has_constraints_true() {
let lens = ConstraintsLens;
let must =
create_constraint_assertion("project", "must_use:formatter", "prettier", 0.9, 1000);
let constraints = lens.resolve_constraints(&[must]);
assert!(constraints.has_constraints());
assert_eq!(constraints.total_constraints(), 1);
}
#[test]
fn test_constraints_all_regular_predicates() {
let lens = ConstraintsLens;
// No constraint predicates at all
let regular1 = create_constraint_assertion("project", "uses", "react", 0.9, 1000);
let regular2 = create_constraint_assertion("project", "depends_on", "webpack", 0.8, 1000);
let constraints = lens.resolve_constraints(&[regular1, regular2]);
assert!(!constraints.has_constraints());
assert_eq!(constraints.total_constraints(), 0);
assert_eq!(constraints.candidates_count, 2);
}
// ========================================================================
// Lens trait Tests
// ========================================================================
#[test]
fn test_lens_trait_picks_must_use_winner() {
let lens = ConstraintsLens;
let must =
create_constraint_assertion("project", "must_use:formatter", "prettier", 0.95, 1000);
let forbidden =
create_constraint_assertion("project", "forbidden:formatter", "tslint", 0.9, 1000);
let prefer = create_constraint_assertion("project", "prefer:style", "airbnb", 0.85, 1000);
let resolution = lens.resolve(&[must.clone(), forbidden, prefer]);
assert!(resolution.winner.is_some());
// must_use has priority
assert_eq!(
resolution.winner.as_ref().map(|a| &a.predicate),
Some(&"must_use:formatter".to_string())
);
}
#[test]
fn test_lens_trait_falls_back_to_forbidden() {
let lens = ConstraintsLens;
// No must_use, should pick forbidden
let forbidden =
create_constraint_assertion("project", "forbidden:db", "mongodb", 0.9, 1000);
let prefer = create_constraint_assertion("project", "prefer:db", "postgres", 0.85, 1000);
let resolution = lens.resolve(&[forbidden.clone(), prefer]);
assert!(resolution.winner.is_some());
assert_eq!(
resolution.winner.as_ref().map(|a| &a.predicate),
Some(&"forbidden:db".to_string())
);
}
#[test]
fn test_lens_trait_falls_back_to_prefer() {
let lens = ConstraintsLens;
// Only prefer
let prefer = create_constraint_assertion("project", "prefer:runtime", "bun", 0.8, 1000);
let resolution = lens.resolve(&[prefer.clone()]);
assert!(resolution.winner.is_some());
assert_eq!(
resolution.winner.as_ref().map(|a| &a.predicate),
Some(&"prefer:runtime".to_string())
);
}
#[test]
fn test_lens_trait_empty_for_no_constraints() {
let lens = ConstraintsLens;
// No constraint predicates
let regular = create_constraint_assertion("project", "uses", "react", 0.9, 1000);
let resolution = lens.resolve(&[regular]);
assert!(resolution.winner.is_none());
}
#[test]
fn test_lens_name() {
let lens = ConstraintsLens;
assert_eq!(lens.name(), "Constraints");
}
#[test]
fn test_lens_empty_candidates() {
let lens = ConstraintsLens;
let resolution = lens.resolve(&[]);
assert!(resolution.winner.is_none());
assert_eq!(resolution.candidates_count, 0);
}
// ========================================================================
// Edge Case Tests
// ========================================================================
#[test]
fn test_multiple_must_use_picks_highest_confidence() {
let lens = ConstraintsLens;
let low_conf =
create_constraint_assertion("project", "must_use:bundler", "webpack", 0.5, 1000);
let high_conf =
create_constraint_assertion("project", "must_use:bundler", "vite", 0.95, 1000);
let resolution = lens.resolve(&[low_conf, high_conf]);
assert!(resolution.winner.is_some());
assert_eq!(
resolution.winner.as_ref().map(|a| &a.object),
Some(&ObjectValue::Text("vite".to_string()))
);
}
#[test]
fn test_confidence_tiebreaker_uses_timestamp() {
let lens = ConstraintsLens;
let older = create_constraint_assertion("project", "must_use:test", "jest", 0.9, 1000);
let newer = create_constraint_assertion("project", "must_use:test", "vitest", 0.9, 2000);
let constraints = lens.resolve_constraints(&[older, newer]);
// Same confidence, newer timestamp wins (sorted first)
assert_eq!(constraints.must_use[0].object, ObjectValue::Text("vitest".to_string()));
}
#[test]
fn test_predicate_pattern_exact_prefix() {
let lens = ConstraintsLens;
// "must_use_something" should NOT match (not "must_use:*")
let wrong_prefix =
create_constraint_assertion("project", "must_use_something", "foo", 0.9, 1000);
let correct =
create_constraint_assertion("project", "must_use:something", "bar", 0.9, 1000);
let constraints = lens.resolve_constraints(&[wrong_prefix, correct]);
assert_eq!(constraints.must_use.len(), 1);
assert_eq!(constraints.must_use[0].object, ObjectValue::Text("bar".to_string()));
}
}

206
crates/stemedb-lens/src/epoch_aware.rs
View File

@ -154,38 +154,78 @@ impl<S: KVStore, L> EpochAwareLens<S, L> {
}
}
/// Compute the set of superseded epoch IDs.
/// Build the key for checking if an epoch is superseded.
///
/// For each epoch in `epochs`, walks the supersession chain and collects
/// all epoch IDs that are superseded (transitively).
/// Format: `SUPERSEDED:{epoch_id_hex}`
/// These markers are written by the IngestWorker when epochs are ingested.
fn superseded_key(epoch_id: &EpochId) -> Vec<u8> {
format!("SUPERSEDED:{}", hex::encode(epoch_id)).into_bytes()
}
/// Check if an epoch is superseded using O(1) marker lookup.
///
/// # Algorithm
/// The IngestWorker writes `SUPERSEDED:{epoch_id}` markers at epoch ingestion
/// time for the full transitive closure of superseded epochs. This enables
/// constant-time "is superseded?" checks instead of O(chain_length) walks.
///
/// For epoch E that supersedes P which supersedes G:
/// - E.supersedes = Some(P) → P is superseded
/// - P.supersedes = Some(G) → G is superseded
/// - Result: {P, G} are superseded
/// # Fail-Open Semantics
///
/// # Safety
/// - Marker exists → epoch is superseded (return true)
/// - Marker doesn't exist → epoch is NOT superseded (return false)
/// - Storage error → treat as NOT superseded (fail-open)
async fn is_epoch_superseded(&self, epoch_id: &EpochId) -> bool {
let key = Self::superseded_key(epoch_id);
match self.store.get(&key).await {
Ok(Some(_)) => {
debug!(epoch_id = %hex::encode(epoch_id), "Epoch is superseded (marker found)");
true
}
Ok(None) => false,
Err(e) => {
warn!(
epoch_id = %hex::encode(epoch_id),
error = %e,
"Failed to check superseded marker, treating as not superseded (fail-open)"
);
false
}
}
}
/// Compute the set of superseded epoch IDs using O(1) marker lookups.
///
/// - Cycle detection via visited set
/// - Max depth guard (100 levels)
/// - Missing epochs are skipped (fail-open)
/// For each unique epoch in the candidate set, checks for a `SUPERSEDED:`
/// marker key. If present, the epoch is superseded and should be filtered.
///
/// # Performance
///
/// This is O(unique_epochs) with O(1) per epoch, compared to the previous
/// O(unique_epochs * chain_length) approach that walked supersession chains.
///
/// # Fail-Open Semantics
///
/// Missing markers (e.g., for epochs ingested before cascade logic was added)
/// are treated as "not superseded" - assertions pass through. This ensures
/// backward compatibility with existing data.
async fn compute_superseded_epochs(&self, epochs: &HashSet<EpochId>) -> HashSet<EpochId> {
let mut superseded = HashSet::new();
let mut visited = HashSet::new();
for epoch_id in epochs {
self.walk_supersession_chain(epoch_id, &mut superseded, &mut visited).await;
if self.is_epoch_superseded(epoch_id).await {
superseded.insert(*epoch_id);
}
}
superseded
}
/// Walk the supersession chain starting from `epoch_id`.
/// Walk the supersession chain starting from `epoch_id` (legacy fallback).
///
/// Adds all superseded epoch IDs to the `superseded` set.
async fn walk_supersession_chain(
/// This method is kept for backward compatibility with existing tests and
/// for scenarios where cascade markers haven't been written yet.
/// The primary `compute_superseded_epochs` now uses O(1) marker lookups.
#[allow(dead_code)]
async fn walk_supersession_chain_legacy(
&self,
start_epoch_id: &EpochId,
superseded: &mut HashSet<EpochId>,
@ -321,11 +361,60 @@ mod tests {
use stemedb_core::types::SupersessionType;
use stemedb_storage::SledStore;
/// Store an epoch in the KV store.
/// Store an epoch in the KV store and write SUPERSEDED markers.
///
/// This simulates what the IngestWorker does: store the epoch AND write
/// cascade markers for the transitive closure of superseded epochs.
async fn store_epoch(store: &SledStore, epoch: &Epoch) {
let key = format!("E:{}", hex::encode(epoch.id)).into_bytes();
let bytes = serialize(epoch).expect("serialize epoch");
store.put(&key, &bytes).await.expect("put epoch");
// Write SUPERSEDED markers for all ancestors in the chain
if let Some(superseded_id) = epoch.supersedes {
write_supersession_cascade(store, &epoch.id, &superseded_id).await;
}
}
/// Write SUPERSEDED markers for the transitive closure (test helper).
///
/// Mirrors the IngestWorker's cascade logic for test setup.
async fn write_supersession_cascade(
store: &SledStore,
new_epoch_id: &[u8; 32],
superseded_id: &[u8; 32],
) {
let mut current_id = *superseded_id;
let mut visited = std::collections::HashSet::new();
let mut depth = 0;
loop {
if !visited.insert(current_id) {
break; // Cycle
}
if depth >= 100 {
break; // Max depth
}
// Write marker
let marker_key = format!("SUPERSEDED:{}", hex::encode(current_id)).into_bytes();
store.put(&marker_key, new_epoch_id).await.expect("put marker");
// Check for ancestor
let epoch_key = format!("E:{}", hex::encode(current_id)).into_bytes();
let ancestor = match store.get(&epoch_key).await.expect("get") {
Some(bytes) => stemedb_core::serde::deserialize::<Epoch>(&bytes).ok(),
None => None,
};
match ancestor.and_then(|e| e.supersedes) {
Some(grandparent_id) => {
current_id = grandparent_id;
depth += 1;
}
None => break,
}
}
}
/// Create a simple epoch without supersession.
@ -631,11 +720,10 @@ mod tests {
}
#[tokio::test]
async fn test_only_old_epoch_assertions_not_filtered_without_new() {
// This test documents an important design decision:
// Epoch filtering only happens when assertions from the superseding epoch
// are present in the candidates. This is fail-open behavior:
// if there's no "new" data, we don't hide the "old" data.
async fn test_superseded_epoch_filtered_even_without_new_assertions() {
// With the O(1) marker-based approach, epochs are filtered based on
// SUPERSEDED: markers, not based on what's in the candidate set.
// If an epoch has a SUPERSEDED marker, its assertions are filtered.
let store = Arc::new(SledStore::open_temp().expect("store"));
// Create epochs: B supersedes A
@ -647,22 +735,50 @@ mod tests {
SupersessionType::Temporal,
);
// Store both epochs - this writes the SUPERSEDED:A marker
store_epoch(&store, &epoch_a).await;
store_epoch(&store, &epoch_b).await;
let lens = EpochAwareLens::with_recency(Arc::clone(&store));
// Only assertions from "old" epoch A (no epoch B assertions in candidates)
// Even though B supersedes A, without B-epoch assertions present,
// we don't know to filter A.
// With the marker-based approach, A is superseded regardless of candidate set
let a1 = AssertionBuilder::new().subject("A1").epoch([1u8; 32]).timestamp(1000).build();
let a2 = AssertionBuilder::new().subject("A2").epoch([1u8; 32]).timestamp(2000).build();
let resolution = lens.resolve_async(&[a1, a2]).await;
// Without epoch B assertions present, A's assertions pass through
// RecencyLens picks A2 (newer timestamp)
// Both assertions are from superseded epoch A, so both are filtered out
// Result is empty resolution
assert!(
resolution.winner.is_none(),
"All assertions from superseded epoch should be filtered"
);
assert_eq!(resolution.candidates_count, 0);
}
#[tokio::test]
async fn test_epoch_without_marker_passes_through() {
// This test documents fail-open behavior:
// If an epoch doesn't have a SUPERSEDED marker (e.g., data from before
// cascade logic was added), assertions pass through.
let store = Arc::new(SledStore::open_temp().expect("store"));
// Manually store epoch A WITHOUT writing cascade markers
// (simulating old data before the cascade feature)
let epoch_a = create_epoch([1u8; 32], "Epoch A");
let key = format!("E:{}", hex::encode(epoch_a.id)).into_bytes();
let bytes = serialize(&epoch_a).expect("serialize epoch");
store.put(&key, &bytes).await.expect("put epoch");
let lens = EpochAwareLens::with_recency(Arc::clone(&store));
let a1 = AssertionBuilder::new().subject("A1").epoch([1u8; 32]).timestamp(1000).build();
let a2 = AssertionBuilder::new().subject("A2").epoch([1u8; 32]).timestamp(2000).build();
let resolution = lens.resolve_async(&[a1, a2]).await;
// No SUPERSEDED marker for A, so assertions pass through (fail-open)
assert!(resolution.winner.is_some());
assert_eq!(resolution.winner.as_ref().map(|a| &a.subject), Some(&"A2".to_string()));
}
@ -674,4 +790,38 @@ mod tests {
assert_eq!(lens.name(), "EpochAware");
}
/// Test: O(1) marker lookup works without reading epoch records.
///
/// This verifies the optimization: with SUPERSEDED markers present,
/// we don't need to read E:{epoch_id} records to determine supersession.
#[tokio::test]
async fn test_epoch_aware_uses_marker_not_epoch_record() {
let store = Arc::new(SledStore::open_temp().expect("store"));
// Write ONLY the SUPERSEDED marker, NOT the epoch records themselves
// This tests that we use the marker for filtering, not the epoch record
let marker_key = format!("SUPERSEDED:{}", hex::encode([1u8; 32])).into_bytes();
store.put(&marker_key, &[2u8; 32]).await.expect("put marker");
let lens = EpochAwareLens::with_recency(Arc::clone(&store));
// Create assertions: one in "superseded" epoch A, one in epoch B
let a_assertion =
AssertionBuilder::new().subject("InEpochA").epoch([1u8; 32]).timestamp(3000).build();
let b_assertion =
AssertionBuilder::new().subject("InEpochB").epoch([2u8; 32]).timestamp(1000).build();
let resolution = lens.resolve_async(&[a_assertion, b_assertion]).await;
// Epoch A is superseded (marker exists), so InEpochA is filtered
// Only InEpochB survives
assert!(resolution.winner.is_some());
assert_eq!(resolution.winner.as_ref().map(|a| &a.subject), Some(&"InEpochB".to_string()));
// Verify we didn't need to read E:{epoch_id} records at all
// (they don't exist in this test)
let epochs = store.scan_prefix(b"E:").await.expect("scan");
assert_eq!(epochs.len(), 0, "No epoch records should exist - test uses marker only");
}
}

472
crates/stemedb-lens/src/layered_consensus.rs Normal file
View File

@ -0,0 +1,472 @@
//! Layered Consensus Lens: Per-source-class consensus resolution.
//!
//! This lens provides visibility into what each authority tier says,
//! rather than collapsing everything into a single winner.
//!
//! # Use Case: Consumer Health
//!
//! Query "semaglutide muscle_loss" and see:
//! - Tier 0 (Regulatory): [no data]
//! - Tier 1 (Clinical): "Significant loss" (12 sources)
//! - Tier 5 (Anecdotal): "Minimal loss" (200 sources)
//!
//! The overall winner comes from the highest-authority tier present (Tier 1),
//! but the consumer can see that anecdotal sources disagree.
//!
//! # Algorithm
//!
//! 1. Group candidates by `source_class.tier()`
//! 2. For each tier, run `ConsensusLens::resolve()` to get within-tier winner
//! 3. Compute per-tier conflict score
//! 4. Overall winner = winner from lowest tier number (highest authority)
//! 5. Cross-tier conflict = do tier winners agree on the same object value?
use crate::consensus::ConsensusLens;
use crate::traits::{LayeredLens, LayeredResolution, Lens, Resolution, TierResolution};
use std::collections::HashMap;
use stemedb_core::types::{Assertion, SourceClass};
use tracing::instrument;
/// Layered Consensus Lens: Provides per-tier resolution results.
///
/// # Example
///
/// ```rust,ignore
/// use stemedb_lens::{LayeredConsensusLens, LayeredLens};
/// use stemedb_core::types::Assertion;
///
/// let lens = LayeredConsensusLens::new();
/// let assertions: Vec<Assertion> = vec![/* ... */];
/// let result = lens.resolve_layered(&assertions);
///
/// for tier in &result.tiers {
/// println!("Tier {}: {:?} candidates", tier.tier, tier.candidates_count);
/// }
/// ```
#[derive(Debug, Clone, Copy, Default)]
pub struct LayeredConsensusLens;
impl LayeredConsensusLens {
/// Create a new LayeredConsensusLens.
pub fn new() -> Self {
Self
}
/// Group assertions by their source class tier.
fn group_by_tier(candidates: &[Assertion]) -> HashMap<u8, Vec<&Assertion>> {
let mut groups: HashMap<u8, Vec<&Assertion>> = HashMap::new();
for assertion in candidates {
let tier = assertion.source_class.tier();
groups.entry(tier).or_default().push(assertion);
}
groups
}
/// Get the SourceClass for a given tier number.
fn tier_to_source_class(tier: u8) -> SourceClass {
match tier {
0 => SourceClass::Regulatory,
1 => SourceClass::Clinical,
2 => SourceClass::Observational,
3 => SourceClass::Expert,
4 => SourceClass::Community,
_ => SourceClass::Anecdotal,
}
}
/// Compute cross-tier conflict score.
///
/// Measures disagreement between tier winners:
/// - 0.0: All tier winners have the same object value
/// - 1.0: Tier winners have completely different object values
///
/// # Algorithm
///
/// Groups tier winners by their object value and computes normalized entropy.
/// If only one tier has a winner, conflict is 0.0 (no disagreement possible).
fn compute_cross_tier_conflict(tier_winners: &[&Assertion]) -> f32 {
if tier_winners.len() <= 1 {
return 0.0;
}
// Group winners by their object value (using Debug format as equality proxy)
let mut value_counts: HashMap<String, usize> = HashMap::new();
for winner in tier_winners {
let key = format!("{:?}", winner.object);
*value_counts.entry(key).or_default() += 1;
}
let num_unique_values = value_counts.len();
if num_unique_values == 1 {
// All winners agree
return 0.0;
}
// Compute normalized entropy
let total = tier_winners.len() as f32;
let mut entropy = 0.0f32;
for &count in value_counts.values() {
if count > 0 {
let p = count as f32 / total;
entropy -= p * p.ln();
}
}
// Normalize by max entropy (uniform distribution)
let max_entropy = (num_unique_values as f32).ln();
if max_entropy > 0.0 {
(entropy / max_entropy).min(1.0)
} else {
0.0
}
}
}
impl LayeredLens for LayeredConsensusLens {
#[instrument(skip(self, candidates), fields(candidates_count = candidates.len(), lens = "LayeredConsensus"))]
fn resolve_layered(&self, candidates: &[Assertion]) -> LayeredResolution {
if candidates.is_empty() {
return LayeredResolution::empty();
}
// Group by tier
let tier_groups = Self::group_by_tier(candidates);
// Resolve each tier
let consensus_lens = ConsensusLens;
let mut tier_resolutions: Vec<TierResolution> = Vec::new();
// Process tiers in order (0 to 5)
for tier in 0..=5u8 {
if let Some(tier_candidates) = tier_groups.get(&tier) {
// Convert refs to owned for ConsensusLens
let owned: Vec<Assertion> = tier_candidates.iter().map(|a| (*a).clone()).collect();
let resolution = consensus_lens.resolve(&owned);
let tier_resolution = TierResolution {
tier,
source_class: Self::tier_to_source_class(tier),
winner: resolution.winner,
candidates_count: resolution.candidates_count,
conflict_score: resolution.conflict_score,
resolution_confidence: resolution.resolution_confidence,
};
tier_resolutions.push(tier_resolution);
}
}
// Overall winner = winner from highest-authority tier (lowest tier number)
let overall_winner = tier_resolutions.iter().find_map(|tr| tr.winner.clone());
// Collect tier winners for cross-tier conflict calculation
let tier_winners: Vec<&Assertion> =
tier_resolutions.iter().filter_map(|tr| tr.winner.as_ref()).collect();
let overall_conflict_score = Self::compute_cross_tier_conflict(&tier_winners);
LayeredResolution {
tiers: tier_resolutions,
overall_winner,
overall_conflict_score,
total_candidates: candidates.len(),
}
}
fn name(&self) -> &'static str {
"LayeredConsensus"
}
}
/// Implement standard Lens trait for compatibility.
///
/// Returns the overall winner as a regular Resolution.
/// Use `resolve_layered()` for the richer per-tier results.
impl Lens for LayeredConsensusLens {
#[instrument(skip(self, candidates), fields(candidates_count = candidates.len(), lens = "LayeredConsensus"))]
fn resolve(&self, candidates: &[Assertion]) -> Resolution {
let layered = self.resolve_layered(candidates);
match layered.overall_winner {
Some(winner) => {
// Aggregate confidence from the winning tier
let winning_tier = layered.tiers.first();
let confidence = winning_tier.map(|t| t.resolution_confidence).unwrap_or(1.0);
Resolution::with_winner(
winner,
layered.total_candidates,
confidence,
layered.overall_conflict_score,
)
}
None => Resolution::empty(),
}
}
fn name(&self) -> &'static str {
"LayeredConsensus"
}
}
#[cfg(test)]
mod tests {
use super::*;
use stemedb_core::testing::AssertionBuilder;
use stemedb_core::types::ObjectValue;
/// Create an assertion with the specified source class and object value.
fn create_assertion(source_class: SourceClass, value: &str, timestamp: u64) -> Assertion {
AssertionBuilder::new()
.source_class(source_class)
.object_text(value)
.timestamp(timestamp)
.build()
}
#[test]
fn test_layered_empty_candidates() {
let lens = LayeredConsensusLens::new();
let result = lens.resolve_layered(&[]);
assert!(result.overall_winner.is_none());
assert!(result.tiers.is_empty());
assert_eq!(result.total_candidates, 0);
assert!((result.overall_conflict_score - 0.0).abs() < f32::EPSILON);
}
#[test]
fn test_layered_single_tier() {
// All candidates are from the same source class (Expert, Tier 3)
let lens = LayeredConsensusLens::new();
let assertions = vec![
create_assertion(SourceClass::Expert, "safe", 1000),
create_assertion(SourceClass::Expert, "safe", 1100),
create_assertion(SourceClass::Expert, "risky", 1200),
];
let result = lens.resolve_layered(&assertions);
// Should have exactly one tier
assert_eq!(result.tiers.len(), 1);
assert_eq!(result.tiers[0].tier, 3); // Expert = Tier 3
assert_eq!(result.tiers[0].candidates_count, 3);
// Winner should be "safe" (2 vs 1)
assert!(result.overall_winner.is_some());
if let Some(winner) = &result.overall_winner {
assert_eq!(winner.object, ObjectValue::Text("safe".to_string()));
}
// Cross-tier conflict should be 0 (only one tier)
assert!((result.overall_conflict_score - 0.0).abs() < f32::EPSILON);
}
#[test]
fn test_layered_multi_tier_agreement() {
// Tier 0 (Regulatory) and Tier 5 (Anecdotal) both say "safe"
let lens = LayeredConsensusLens::new();
let assertions = vec![
create_assertion(SourceClass::Regulatory, "safe", 1000),
create_assertion(SourceClass::Anecdotal, "safe", 1100),
create_assertion(SourceClass::Anecdotal, "safe", 1200),
];
let result = lens.resolve_layered(&assertions);
// Should have two tiers (0 and 5)
assert_eq!(result.tiers.len(), 2);
assert_eq!(result.tiers[0].tier, 0); // Regulatory first
assert_eq!(result.tiers[1].tier, 5); // Anecdotal second
// Overall winner from Tier 0
assert!(result.overall_winner.is_some());
if let Some(winner) = &result.overall_winner {
assert_eq!(winner.object, ObjectValue::Text("safe".to_string()));
assert_eq!(winner.source_class, SourceClass::Regulatory);
}
// Cross-tier conflict should be low (both agree on "safe")
assert!(
result.overall_conflict_score < 0.1,
"Expected low conflict, got {}",
result.overall_conflict_score
);
}
#[test]
fn test_layered_multi_tier_disagreement() {
// Tier 1 (Clinical) says "safe", Tier 5 (Anecdotal) says "dangerous"
let lens = LayeredConsensusLens::new();
let assertions = vec![
create_assertion(SourceClass::Clinical, "safe", 1000),
create_assertion(SourceClass::Clinical, "safe", 1100),
create_assertion(SourceClass::Anecdotal, "dangerous", 1200),
create_assertion(SourceClass::Anecdotal, "dangerous", 1300),
create_assertion(SourceClass::Anecdotal, "dangerous", 1400),
];
let result = lens.resolve_layered(&assertions);
// Should have two tiers
assert_eq!(result.tiers.len(), 2);
// Tier 1 (Clinical) is higher authority, so its winner is overall winner
assert!(result.overall_winner.is_some());
if let Some(winner) = &result.overall_winner {
assert_eq!(winner.object, ObjectValue::Text("safe".to_string()));
assert_eq!(winner.source_class, SourceClass::Clinical);
}
// Cross-tier conflict should be high (tiers disagree)
assert!(
result.overall_conflict_score > 0.5,
"Expected high conflict, got {}",
result.overall_conflict_score
);
}
#[test]
fn test_layered_overall_winner_from_highest_authority() {
// Tier 0 has 1 assertion, Tier 5 has 1000 assertions
// Tier 0 should still win (highest authority)
let lens = LayeredConsensusLens::new();
let mut assertions = vec![create_assertion(SourceClass::Regulatory, "approved", 1000)];
// Add many anecdotal assertions
for i in 0..100 {
assertions.push(create_assertion(SourceClass::Anecdotal, "questionable", 2000 + i));
}
let result = lens.resolve_layered(&assertions);
// Overall winner should be from Tier 0 (Regulatory) despite being outnumbered
assert!(result.overall_winner.is_some());
if let Some(winner) = &result.overall_winner {
assert_eq!(winner.object, ObjectValue::Text("approved".to_string()));
assert_eq!(winner.source_class, SourceClass::Regulatory);
}
// Verify tier counts
let tier_0 = result.tiers.iter().find(|t| t.tier == 0);
let tier_5 = result.tiers.iter().find(|t| t.tier == 5);
assert!(tier_0.is_some());
assert_eq!(tier_0.map(|t| t.candidates_count).unwrap_or(0), 1);
assert!(tier_5.is_some());
assert_eq!(tier_5.map(|t| t.candidates_count).unwrap_or(0), 100);
}
#[test]
fn test_layered_lens_trait_compatibility() {
// Test that the standard Lens trait works
let lens = LayeredConsensusLens::new();
let assertions = vec![
create_assertion(SourceClass::Clinical, "effective", 1000),
create_assertion(SourceClass::Clinical, "effective", 1100),
];
let resolution = lens.resolve(&assertions);
assert!(resolution.winner.is_some());
assert_eq!(resolution.candidates_count, 2);
}
#[test]
fn test_layered_within_tier_conflict() {
// Tier 1 has high internal conflict (split opinions)
let lens = LayeredConsensusLens::new();
let assertions = vec![
create_assertion(SourceClass::Clinical, "safe", 1000),
create_assertion(SourceClass::Clinical, "dangerous", 1100),
];
let result = lens.resolve_layered(&assertions);
assert_eq!(result.tiers.len(), 1);
// Within-tier conflict should be non-zero (assertions have different confidences
// by default, but same confidence here - conflict comes from object disagreement
// which is reflected in the resolution confidence, not conflict_score)
let tier = &result.tiers[0];
assert_eq!(tier.candidates_count, 2);
// Resolution confidence should reflect the split (50/50)
assert!(tier.resolution_confidence < 0.6, "Expected low confidence for 50/50 split");
}
#[test]
fn test_layered_all_tiers_present() {
// One assertion from each tier
let lens = LayeredConsensusLens::new();
let assertions = vec![
create_assertion(SourceClass::Regulatory, "tier0", 1000),
create_assertion(SourceClass::Clinical, "tier1", 1100),
create_assertion(SourceClass::Observational, "tier2", 1200),
create_assertion(SourceClass::Expert, "tier3", 1300),
create_assertion(SourceClass::Community, "tier4", 1400),
create_assertion(SourceClass::Anecdotal, "tier5", 1500),
];
let result = lens.resolve_layered(&assertions);
// All 6 tiers should be present
assert_eq!(result.tiers.len(), 6);
// Verify tiers are in order
for (i, tier) in result.tiers.iter().enumerate() {
assert_eq!(tier.tier, i as u8);
}
// Overall winner should be from Tier 0
assert!(result.overall_winner.is_some());
if let Some(winner) = &result.overall_winner {
assert_eq!(winner.object, ObjectValue::Text("tier0".to_string()));
}
}
#[test]
fn test_layered_lens_name() {
let lens = LayeredConsensusLens::new();
assert_eq!(<LayeredConsensusLens as LayeredLens>::name(&lens), "LayeredConsensus");
assert_eq!(<LayeredConsensusLens as Lens>::name(&lens), "LayeredConsensus");
}
#[test]
fn test_layered_numeric_values() {
// Test with numeric object values
let lens = LayeredConsensusLens::new();
let assertions = vec![
AssertionBuilder::new()
.source_class(SourceClass::Clinical)
.object_number(100.0)
.timestamp(1000)
.build(),
AssertionBuilder::new()
.source_class(SourceClass::Clinical)
.object_number(100.0)
.timestamp(1100)
.build(),
AssertionBuilder::new()
.source_class(SourceClass::Anecdotal)
.object_number(200.0)
.timestamp(1200)
.build(),
];
let result = lens.resolve_layered(&assertions);
assert!(result.overall_winner.is_some());
if let Some(winner) = &result.overall_winner {
assert_eq!(winner.object, ObjectValue::Number(100.0));
}
// Cross-tier conflict should be high (100.0 vs 200.0)
assert!(result.overall_conflict_score > 0.5);
}
}

6
crates/stemedb-lens/src/lib.rs
View File

@ -45,7 +45,9 @@
mod confidence;
mod consensus;
mod constraints;
mod epoch_aware;
mod layered_consensus;
mod recency;
mod skeptic;
mod traits;
@ -54,9 +56,11 @@ mod vote_aware_consensus;
pub use confidence::ConfidenceLens;
pub use consensus::ConsensusLens;
pub use constraints::{ConstraintSet, ConstraintsLens};
pub use epoch_aware::{EpochAwareLens, SyncLensWrapper};
pub use layered_consensus::LayeredConsensusLens;
pub use recency::RecencyLens;
pub use skeptic::SkepticLens;
pub use traits::{AnalysisLens, Lens, Resolution};
pub use traits::{AnalysisLens, LayeredLens, LayeredResolution, Lens, Resolution, TierResolution};
pub use trust_aware_authority::TrustAwareAuthorityLens;
pub use vote_aware_consensus::{AsyncLens, VoteAwareConsensusLens};

7
crates/stemedb-lens/src/recency.rs
View File

@ -3,7 +3,7 @@
//! This is the simplest lens, useful for scenarios where the latest
//! information is always preferred (news, real-time data).
use crate::traits::{Lens, Resolution};
use crate::traits::{compute_conflict_score, Lens, Resolution};
use stemedb_core::types::Assertion;
use tracing::instrument;
@ -29,7 +29,7 @@ impl Lens for RecencyLens {
}
if candidates.len() == 1 {
return Resolution::with_winner(candidates[0].clone(), 1, 1.0);
return Resolution::with_winner(candidates[0].clone(), 1, 1.0, 0.0);
}
// Find the assertion with the highest timestamp
@ -70,7 +70,8 @@ impl Lens for RecencyLens {
0.5
};
Resolution::with_winner(w, candidates.len(), confidence)
let conflict = compute_conflict_score(candidates);
Resolution::with_winner(w, candidates.len(), confidence, conflict)
}
None => Resolution::empty(),
}

260
crates/stemedb-lens/src/traits.rs
View File

@ -14,7 +14,7 @@
//! "Trust but Verify" - showing users what's contested.
use async_trait::async_trait;
use stemedb_core::types::{Assertion, ConflictAnalysis};
use stemedb_core::types::{Assertion, ConflictAnalysis, SourceClass};
/// The result of a Lens resolution.
#[derive(Debug, Clone)]
@ -28,20 +28,208 @@ pub struct Resolution {
/// Confidence in the resolution (0.0 to 1.0).
/// Higher values indicate stronger consensus or more decisive selection.
pub resolution_confidence: f32,
/// Degree of disagreement among candidates (0.0 = full agreement, 1.0 = max conflict).
/// Computed as normalized variance of candidate confidence values.
/// This is the numeric basis for the "disagreement is the information" thesis.
pub conflict_score: f32,
}
impl Resolution {
/// Create an empty resolution (no candidates).
pub fn empty() -> Self {
Self { winner: None, candidates_count: 0, resolution_confidence: 0.0 }
Self { winner: None, candidates_count: 0, resolution_confidence: 0.0, conflict_score: 0.0 }
}
/// Create a resolution with a single winner.
pub fn with_winner(winner: Assertion, candidates_count: usize, confidence: f32) -> Self {
Self { winner: Some(winner), candidates_count, resolution_confidence: confidence }
pub fn with_winner(
winner: Assertion,
candidates_count: usize,
confidence: f32,
conflict_score: f32,
) -> Self {
Self {
winner: Some(winner),
candidates_count,
resolution_confidence: confidence,
conflict_score,
}
}
}
/// Compute conflict score from candidate assertion confidences.
///
/// This is the **canonical definition** of conflict score in Episteme.
/// Other modules should reference this documentation.
///
/// # Algorithm
///
/// Uses normalized variance of confidence values:
/// - 0 or 1 candidates: 0.0 (no conflict possible)
/// - All same confidence: 0.0 (unanimous agreement)
/// - Max variance (e.g., 0.0 vs 1.0): 1.0 (maximum disagreement)
///
/// # Normalization
///
/// The formula normalizes variance to [0.0, 1.0] range:
/// variance of [0,1] values has max 0.25 (when values are 0 and 1),
/// so we multiply by 4 to normalize.
///
/// # Edge Cases
///
/// - NaN confidences: Treated defensively as 0.0 conflict (fail-safe)
/// - Empty candidates: Returns 0.0
/// - Single candidate: Returns 0.0 (no disagreement possible)
///
/// # Vision Alignment
///
/// This score enables the "disagreement is the information" thesis:
/// high conflict scores surface uncertainty for user review rather than
/// hiding it behind a confident-looking single answer.
pub fn compute_conflict_score(candidates: &[Assertion]) -> f32 {
if candidates.len() <= 1 {
return 0.0;
}
let n = candidates.len() as f32;
let sum: f32 = candidates.iter().map(|a| a.confidence).sum();
let mean = sum / n;
let variance: f32 = candidates.iter().map(|a| (a.confidence - mean).powi(2)).sum::<f32>() / n;
// Normalize: max variance of [0,1] values is 0.25, so 4x normalizes to [0,1]
let score = 4.0 * variance;
// Defensive: NaN from malformed confidences treated as no meaningful conflict
if score.is_nan() {
return 0.0;
}
score.min(1.0)
}
// ============================================================================
// Layered Resolution Types
// ============================================================================
/// Per-tier resolution result from `LayeredLens`.
///
/// Represents the consensus within a single source class tier.
/// Tiers range from 0 (Regulatory, highest authority) to 5 (Anecdotal, lowest).
#[derive(Debug, Clone)]
pub struct TierResolution {
/// The tier number (0-5). Lower = higher authority.
pub tier: u8,
/// The source class for this tier.
pub source_class: SourceClass,
/// The winning assertion from within-tier consensus, if any candidates.
pub winner: Option<Assertion>,
/// Number of candidates in this tier.
pub candidates_count: usize,
/// Within-tier conflict score (0.0 = unanimous, 1.0 = max conflict).
pub conflict_score: f32,
/// Within-tier resolution confidence (0.0 to 1.0).
pub resolution_confidence: f32,
}
impl TierResolution {
/// Create a tier resolution with no candidates.
pub fn empty(tier: u8, source_class: SourceClass) -> Self {
Self {
tier,
source_class,
winner: None,
candidates_count: 0,
conflict_score: 0.0,
resolution_confidence: 0.0,
}
}
}
/// Multi-tier resolution result from `LayeredLens`.
///
/// Contains per-tier consensus results plus an overall winner.
/// Enables "What does Tier 0 say? What does Tier 5 say?" queries.
///
/// # Cross-Tier Conflict
///
/// The `overall_conflict_score` measures disagreement between tiers:
/// - 0.0: All tiers with winners agree on the same object value
/// - 1.0: Tiers disagree on what the answer should be
///
/// This is different from within-tier conflict (measured in each `TierResolution`).
#[derive(Debug, Clone)]
pub struct LayeredResolution {
/// Per-tier consensus results, ordered by tier (0 = highest authority first).
/// Only tiers with at least one candidate are included.
pub tiers: Vec<TierResolution>,
/// Overall winner: winner from the highest-authority tier that has candidates.
/// This is the answer from the most authoritative source class present.
pub overall_winner: Option<Assertion>,
/// Cross-tier disagreement score (0.0 = tiers agree, 1.0 = tiers disagree).
/// Measures whether tier winners agree on the same object value.
pub overall_conflict_score: f32,
/// Total candidates considered across all tiers.
pub total_candidates: usize,
}
impl LayeredResolution {
/// Create an empty layered resolution (no candidates in any tier).
pub fn empty() -> Self {
Self {
tiers: Vec::new(),
overall_winner: None,
overall_conflict_score: 0.0,
total_candidates: 0,
}
}
}
/// A LayeredLens resolves conflicts with per-tier consensus.
///
/// Unlike a standard `Lens` which returns a single winner, a `LayeredLens`
/// provides visibility into what each source class tier says.
///
/// # Use Case: Consumer Health
///
/// Query "semaglutide muscle_loss" and see:
/// - Tier 0 (FDA): [no data]
/// - Tier 1 (Clinical): "Significant loss" (12 sources, 0.85 confidence)
/// - Tier 5 (Anecdotal): "Minimal loss" (200 sources, 0.45 confidence)
/// - Overall winner: "Significant loss" (from Tier 1)
/// - Cross-tier conflict: 0.8 (clinical and anecdotal disagree)
///
/// # Contract
///
/// - **Stateless:** LayeredLenses must not maintain internal state.
/// - **Deterministic:** Same input must produce same output.
/// - **Tier-Ordered:** Results are always ordered by tier (0 first).
pub trait LayeredLens: Send + Sync {
/// Resolve candidates with per-tier consensus.
///
/// # Arguments
/// * `candidates` - All assertions matching the query filters
///
/// # Returns
/// A `LayeredResolution` with per-tier results and overall winner.
fn resolve_layered(&self, candidates: &[Assertion]) -> LayeredResolution;
/// Human-readable name of this lens for logging/debugging.
fn name(&self) -> &'static str;
}
// ============================================================================
// Standard Resolution Types
// ============================================================================
/// A Lens resolves conflicting assertions into a deterministic answer.
///
/// # Contract
@ -114,6 +302,7 @@ pub trait AnalysisLens: Send + Sync {
#[cfg(test)]
mod tests {
use super::*;
use stemedb_core::testing::AssertionBuilder;
#[test]
fn test_empty_resolution() {
@ -121,5 +310,68 @@ mod tests {
assert!(resolution.winner.is_none());
assert_eq!(resolution.candidates_count, 0);
assert!((resolution.resolution_confidence - 0.0).abs() < f32::EPSILON);
assert!((resolution.conflict_score - 0.0).abs() < f32::EPSILON);
}
#[test]
fn test_conflict_score_zero_for_empty() {
let score = compute_conflict_score(&[]);
assert!((score - 0.0).abs() < f32::EPSILON);
}
#[test]
fn test_conflict_score_zero_for_single() {
let assertion = AssertionBuilder::new().confidence(0.9).build();
let score = compute_conflict_score(&[assertion]);
assert!((score - 0.0).abs() < f32::EPSILON);
}
#[test]
fn test_conflict_score_zero_for_agreement() {
// All same confidence = no conflict
let assertions = vec![
AssertionBuilder::new().confidence(0.9).build(),
AssertionBuilder::new().confidence(0.9).build(),
AssertionBuilder::new().confidence(0.9).build(),
];
let score = compute_conflict_score(&assertions);
assert!(score < 0.01, "Expected near-zero, got {}", score);
}
#[test]
fn test_conflict_score_high_for_disagreement() {
// Candidates at 0.1, 0.5, 0.9 = high variance
let assertions = vec![
AssertionBuilder::new().confidence(0.1).build(),
AssertionBuilder::new().confidence(0.5).build(),
AssertionBuilder::new().confidence(0.9).build(),
];
let score = compute_conflict_score(&assertions);
assert!(score > 0.3, "Expected high conflict, got {}", score);
}
#[test]
fn test_conflict_score_max_for_extremes() {
// 0.0 vs 1.0 = maximum disagreement
let assertions = vec![
AssertionBuilder::new().confidence(0.0).build(),
AssertionBuilder::new().confidence(1.0).build(),
];
let score = compute_conflict_score(&assertions);
assert!((score - 1.0).abs() < 0.01, "Expected ~1.0, got {}", score);
}
#[test]
fn test_conflict_score_handles_nan_defensively() {
// NaN confidences should result in 0.0 (fail-safe)
let mut assertions = vec![
AssertionBuilder::new().confidence(0.5).build(),
AssertionBuilder::new().confidence(0.5).build(),
];
assertions[0].confidence = f32::NAN;
assertions[1].confidence = f32::NAN;
let score = compute_conflict_score(&assertions);
assert!((score - 0.0).abs() < f32::EPSILON, "Expected 0.0 for NaN, got {}", score);
}
}

16
crates/stemedb-lens/src/trust_aware_authority.rs
View File

@ -11,7 +11,7 @@
//! - Complex implementation: Queries TrustRankStore, weights by reputation
//! - O(1) trust lookups via TrustRankStore
use crate::traits::Resolution;
use crate::traits::{compute_conflict_score, Resolution};
use async_trait::async_trait;
use stemedb_core::types::Assertion;
use stemedb_storage::trust_rank_store::TrustRankStore;
@ -99,7 +99,7 @@ impl<T: TrustRankStore + 'static> AsyncLens for TrustAwareAuthorityLens<T> {
Some(id) => id,
None => {
// No signature, treat as untrusted
return Resolution::with_winner(assertion.clone(), 1, 0.0);
return Resolution::with_winner(assertion.clone(), 1, 0.0, 0.0);
}
};
@ -109,7 +109,7 @@ impl<T: TrustRankStore + 'static> AsyncLens for TrustAwareAuthorityLens<T> {
};
let weighted_score = assertion.confidence * trust_score;
return Resolution::with_winner(assertion.clone(), 1, weighted_score);
return Resolution::with_winner(assertion.clone(), 1, weighted_score, 0.0);
}
// Collect trust-weighted scores for all candidates
@ -176,15 +176,23 @@ impl<T: TrustRankStore + 'static> AsyncLens for TrustAwareAuthorityLens<T> {
// the assertion's own confidence and the agent's reputation
let confidence = winner_ranked.weighted_score;
let conflict = compute_conflict_score(candidates);
debug!(
winner_subject = %winner_ranked.assertion.subject,
trust_score = winner_ranked.trust_score,
assertion_confidence = winner_ranked.assertion.confidence,
weighted_score = winner_ranked.weighted_score,
conflict,
"Resolved via trust-aware authority"
);
Resolution::with_winner(winner_ranked.assertion.clone(), candidates.len(), confidence)
Resolution::with_winner(
winner_ranked.assertion.clone(),
candidates.len(),
confidence,
conflict,
)
} else {
// Should never happen since we checked for empty candidates above
Resolution::empty()

60
crates/stemedb-lens/src/vote_aware_consensus.rs
View File

@ -10,7 +10,7 @@
//! - Complex implementation: Queries VoteStore, ranks by votes, handles ties
//! - O(1) vote lookups via VoteStore's cached counters
use crate::traits::Resolution;
use crate::traits::{compute_conflict_score, Resolution};
use async_trait::async_trait;
use stemedb_core::types::{Assertion, Hash};
use stemedb_storage::vote_store::VoteStore;
@ -93,16 +93,21 @@ impl<V: VoteStore> VoteAwareConsensusLens<V> {
///
/// This matches the logic used by the ingestion pipeline to ensure
/// we lookup votes for the correct assertion hash.
fn compute_assertion_hash(assertion: &Assertion) -> Hash {
///
/// Returns `None` if serialization fails, allowing the caller to skip
/// the candidate rather than using a potentially colliding hash.
fn compute_assertion_hash(assertion: &Assertion) -> Option<Hash> {
// Serialize using the canonical serde module, then hash.
// An empty hash is returned if serialization fails (defensive).
let bytes = match stemedb_core::serde::serialize(assertion) {
Ok(b) => b,
Err(_) => return [0u8; 32],
Err(e) => {
tracing::warn!("Failed to serialize assertion for hashing: {}", e);
return None;
}
};
let hash_bytes = blake3::hash(&bytes);
*hash_bytes.as_bytes()
Some(*hash_bytes.as_bytes())
}
}
@ -123,7 +128,7 @@ impl<V: VoteStore + 'static> AsyncLens for VoteAwareConsensusLens<V> {
}
if candidates.len() == 1 {
return Resolution::with_winner(candidates[0].clone(), 1, 1.0);
return Resolution::with_winner(candidates[0].clone(), 1, 1.0, 0.0);
}
// Collect vote data for all candidates
@ -131,7 +136,14 @@ impl<V: VoteStore + 'static> AsyncLens for VoteAwareConsensusLens<V> {
let mut total_weight = 0.0_f32;
for assertion in candidates {
let assertion_hash = Self::compute_assertion_hash(assertion);
let assertion_hash = match Self::compute_assertion_hash(assertion) {
Some(hash) => hash,
None => {
// Serialization failed - skip this candidate
debug!("Skipping candidate due to serialization failure");
continue;
}
};
// Lookup vote count and aggregate weight from VoteStore
// These are O(1) operations thanks to VoteStore's cached counters
@ -182,15 +194,23 @@ impl<V: VoteStore + 'static> AsyncLens for VoteAwareConsensusLens<V> {
0.0
};
let conflict = compute_conflict_score(candidates);
debug!(
winner_subject = %winner_ranked.assertion.subject,
vote_count = winner_ranked.vote_count,
aggregate_weight = winner_ranked.aggregate_weight,
confidence,
conflict,
"Resolved via vote-aware consensus"
);
Resolution::with_winner(winner_ranked.assertion.clone(), candidates.len(), confidence)
Resolution::with_winner(
winner_ranked.assertion.clone(),
candidates.len(),
confidence,
conflict,
)
} else {
// Should never happen since we checked for empty candidates above
Resolution::empty()
@ -257,11 +277,14 @@ mod tests {
// Add votes: a1 gets 0.5 weight, a2 gets 1.5 weight (winner), a3 gets 0.3 weight
let hash1 =
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(&a1);
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(&a1)
.unwrap();
let hash2 =
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(&a2);
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(&a2)
.unwrap();
let hash3 =
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(&a3);
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(&a3)
.unwrap();
vote_store.put_vote(&create_vote(hash1, [1u8; 32], 0.5, 2000)).await.expect("put");
vote_store.put_vote(&create_vote(hash2, [2u8; 32], 0.8, 2001)).await.expect("put");
@ -310,9 +333,11 @@ mod tests {
// Give both the same vote weight
let hash_old =
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(&old);
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(&old)
.unwrap();
let hash_new =
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(&new);
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(&new)
.unwrap();
vote_store.put_vote(&create_vote(hash_old, [1u8; 32], 0.5, 3000)).await.expect("put");
vote_store.put_vote(&create_vote(hash_new, [2u8; 32], 0.5, 3001)).await.expect("put");
@ -336,7 +361,8 @@ mod tests {
let hash_with =
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(
&with_votes,
);
)
.unwrap();
vote_store.put_vote(&create_vote(hash_with, [1u8; 32], 0.8, 3000)).await.expect("put");
@ -369,11 +395,13 @@ mod tests {
let unpopular = create_assertion("Unpopular", 200.0, 1100);
let hash_popular =
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(&popular);
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(&popular)
.unwrap();
let hash_unpopular =
VoteAwareConsensusLens::<GenericVoteStore<SledStore>>::compute_assertion_hash(
&unpopular,
);
)
.unwrap();
// Popular gets 10 votes
for i in 0..10 {

501
crates/stemedb-query/src/decay.rs Normal file
View File

@ -0,0 +1,501 @@
//! Semantic decay for assertion confidence.
//!
//! This module implements time-based confidence decay for assertions.
//! Older assertions have their effective confidence reduced based on age,
//! allowing recent evidence to outweigh stale claims.
//!
//! # The Problem
//!
//! Medical knowledge decays at different rates. A Reddit post from 2022
//! shouldn't compete equally with a 2024 RCT. Without decay, old assertions
//! with high confidence can dominate over recent, more relevant evidence.
//!
//! # Formula
//!
//! ```text
//! effective_confidence = original_confidence * 2^(-(age / halflife))
//! ```
//!
//! Where:
//! - `age` = now - assertion.timestamp (in seconds)
//! - `halflife` = decay half-life (in seconds)
//!
//! # Example
//!
//! With a 1-year half-life (31,536,000 seconds):
//! - 0 years old: 100% of original confidence
//! - 1 year old: 50% of original confidence
//! - 2 years old: 25% of original confidence
//! - 3 years old: 12.5% of original confidence
use stemedb_core::types::Assertion;
/// Seconds per day (86,400).
const SECONDS_PER_DAY: u64 = 86_400;
/// Apply decay to a set of assertions based on age.
///
/// Returns cloned assertions with their confidence scores reduced based on
/// how old they are relative to the given timestamp.
///
/// # Arguments
///
/// * `assertions` - The assertions to decay
/// * `halflife` - Decay half-life in seconds. After this duration, confidence is halved.
/// * `now` - Reference timestamp (usually current time or `as_of` for time-travel)
///
/// # Returns
///
/// A new vector of assertions with decayed confidence scores.
/// The original assertions are not modified.
///
/// # Formula
///
/// ```text
/// age = now - assertion.timestamp
/// decay_factor = 2^(-(age / halflife))
/// effective_confidence = confidence * decay_factor
/// ```
///
/// # Edge Cases
///
/// - If `halflife` is 0, returns assertions unchanged (no decay)
/// - If assertion timestamp > now, no decay is applied (future assertions)
/// - Confidence is clamped to [0.0, 1.0]
pub fn apply_decay(assertions: &[Assertion], halflife: u64, now: u64) -> Vec<Assertion> {
if halflife == 0 {
return assertions.to_vec();
}
assertions
.iter()
.map(|assertion| {
let decayed_confidence = compute_decayed_confidence(
assertion.confidence,
assertion.timestamp,
halflife,
now,
);
let mut decayed = assertion.clone();
decayed.confidence = decayed_confidence;
decayed
})
.collect()
}
/// Apply source-class-aware decay to assertions.
///
/// Each assertion's decay half-life is determined by its `source_class` tier:
/// - Tier 0 (Regulatory): No decay
/// - Tier 1 (Clinical): 2-year half-life
/// - Tier 2 (Observational): 1-year half-life
/// - Tier 3 (Expert): 6-month half-life
/// - Tier 4 (Community): 3-month half-life
/// - Tier 5 (Anecdotal): 1-month half-life
///
/// # Arguments
///
/// * `assertions` - The assertions to decay
/// * `fallback_halflife` - Half-life in seconds to use when source_class has no default
/// * `now` - Reference timestamp
///
/// # Returns
///
/// A new vector of assertions with tier-appropriate decay applied.
pub fn apply_source_class_decay(
assertions: &[Assertion],
fallback_halflife: u64,
now: u64,
) -> Vec<Assertion> {
assertions
.iter()
.map(|assertion| {
// Get tier-specific half-life from SourceClass, convert days to seconds
// If default_decay_days() returns None (e.g., Regulatory), no decay is applied.
let halflife_opt = assertion
.source_class
.default_decay_days()
.map(|days| u64::from(days) * SECONDS_PER_DAY);
// If source class has no decay (None), return unchanged
// Otherwise use tier-specific halflife (or fallback if zero)
let halflife = match halflife_opt {
None => {
// Source class explicitly has no decay (e.g., Regulatory)
return assertion.clone();
}
Some(0) => fallback_halflife, // Shouldn't happen, but fallback to avoid div-by-zero
Some(h) => h,
};
let decayed_confidence = compute_decayed_confidence(
assertion.confidence,
assertion.timestamp,
halflife,
now,
);
let mut decayed = assertion.clone();
decayed.confidence = decayed_confidence;
decayed
})
.collect()
}
/// Compute the decayed confidence for a single assertion.
///
/// # Formula
///
/// ```text
/// age = now - timestamp
/// decay_factor = 2^(-(age / halflife))
/// decayed_confidence = confidence * decay_factor
/// ```
///
/// # Arguments
///
/// * `confidence` - Original confidence score (0.0 to 1.0)
/// * `timestamp` - When the assertion was created (Unix seconds)
/// * `halflife` - Decay half-life in seconds
/// * `now` - Reference timestamp (Unix seconds)
///
/// # Returns
///
/// The decayed confidence, clamped to [0.0, 1.0].
fn compute_decayed_confidence(confidence: f32, timestamp: u64, halflife: u64, now: u64) -> f32 {
// No decay for future assertions
if timestamp >= now {
return confidence;
}
let age = now - timestamp;
let age_f = age as f32;
let halflife_f = halflife as f32;
// decay_factor = 2^(-(age / halflife))
// This equals e^(-(age / halflife) * ln(2))
let decay_factor = 2_f32.powf(-age_f / halflife_f);
// Clamp to valid confidence range
(confidence * decay_factor).clamp(0.0, 1.0)
}
#[cfg(test)]
mod tests {
use super::*;
use stemedb_core::testing::AssertionBuilder;
use stemedb_core::types::SourceClass;
/// One year in seconds (365 days).
const ONE_YEAR_SECONDS: u64 = 365 * 24 * 60 * 60;
/// One hour in seconds.
const ONE_HOUR_SECONDS: u64 = 60 * 60;
// ========================================================================
// Core Decay Tests
// ========================================================================
#[test]
fn test_decay_reduces_old_assertion_confidence() {
// Assertion is 1 year old with 1-year half-life
// Expected: ~50% of original confidence
let now = 1_000_000_000_u64; // Some reference time
let one_year_ago = now - ONE_YEAR_SECONDS;
let assertion = AssertionBuilder::new()
.subject("test")
.predicate("value")
.confidence(0.9)
.timestamp(one_year_ago)
.build();
let decayed = apply_decay(&[assertion], ONE_YEAR_SECONDS, now);
assert_eq!(decayed.len(), 1);
let decayed_conf = decayed[0].confidence;
// Should be approximately 0.45 (0.9 * 0.5)
// Allow 1% tolerance for floating point
assert!((decayed_conf - 0.45).abs() < 0.01, "Expected ~0.45, got {}", decayed_conf);
}
#[test]
fn test_decay_preserves_fresh_assertions() {
// Assertion is 1 hour old with 1-year half-life
// Expected: ~100% of original confidence (minimal decay)
let now = 1_000_000_000_u64;
let one_hour_ago = now - ONE_HOUR_SECONDS;
let assertion = AssertionBuilder::new()
.subject("test")
.predicate("value")
.confidence(0.9)
.timestamp(one_hour_ago)
.build();
let decayed = apply_decay(&[assertion], ONE_YEAR_SECONDS, now);
assert_eq!(decayed.len(), 1);
let decayed_conf = decayed[0].confidence;
// Should be very close to original (99.99%+)
assert!((decayed_conf - 0.9).abs() < 0.001, "Expected ~0.9, got {}", decayed_conf);
}
#[test]
fn test_decay_interacts_with_lens() {
// Two assertions: older has higher base confidence but should lose after decay
let now = 1_000_000_000_u64;
let two_years_ago = now - (2 * ONE_YEAR_SECONDS);
let one_week_ago = now - (7 * 24 * 60 * 60);
// Old assertion: high confidence (0.9), but 2 years old
let old_assertion = AssertionBuilder::new()
.subject("test")
.predicate("value")
.confidence(0.9)
.timestamp(two_years_ago)
.build();
// New assertion: lower confidence (0.6), but only 1 week old
let new_assertion = AssertionBuilder::new()
.subject("test")
.predicate("value")
.confidence(0.6)
.timestamp(one_week_ago)
.build();
let decayed = apply_decay(&[old_assertion, new_assertion], ONE_YEAR_SECONDS, now);
assert_eq!(decayed.len(), 2);
// Old assertion: 0.9 * 0.25 (2 half-lives) = ~0.225
let old_decayed = decayed[0].confidence;
assert!(
(old_decayed - 0.225).abs() < 0.02,
"Old assertion expected ~0.225, got {}",
old_decayed
);
// New assertion: 0.6 * ~1.0 = ~0.6 (negligible decay)
// 1 week = 604800 seconds, 1 year = 31536000 seconds
// decay factor = 2^(-(604800/31536000)) = 2^(-0.0192) ≈ 0.9868
// 0.6 * 0.9868 ≈ 0.592
let new_decayed = decayed[1].confidence;
assert!(
(new_decayed - 0.6).abs() < 0.02, // Allow 2% tolerance
"New assertion expected ~0.6, got {}",
new_decayed
);
// The newer assertion should now have higher effective confidence
assert!(
new_decayed > old_decayed,
"Newer assertion ({}) should beat older ({}) after decay",
new_decayed,
old_decayed
);
}
// ========================================================================
// Source-Class-Aware Decay Tests
// ========================================================================
#[test]
fn test_source_aware_decay_tier0_no_decay() {
// Regulatory (Tier 0) sources should never decay
let now = 1_000_000_000_u64;
let five_years_ago = now - (5 * ONE_YEAR_SECONDS);
let assertion = AssertionBuilder::new()
.subject("test")
.predicate("value")
.confidence(0.95)
.timestamp(five_years_ago)
.source_class(SourceClass::Regulatory)
.build();
let decayed = apply_source_class_decay(&[assertion], ONE_YEAR_SECONDS, now);
assert_eq!(decayed.len(), 1);
// Regulatory sources have no decay (default_decay_days returns None)
// So fallback is used, but since Regulatory returns None, we should handle this
// Actually, looking at the code, None means no decay
assert_eq!(decayed[0].confidence, 0.95, "Regulatory sources should not decay");
}
#[test]
fn test_source_aware_decay_tier5_rapid_decay() {
// Anecdotal (Tier 5) sources decay rapidly (30-day half-life)
let now = 1_000_000_000_u64;
let sixty_days_ago = now - (60 * SECONDS_PER_DAY);
let assertion = AssertionBuilder::new()
.subject("test")
.predicate("value")
.confidence(0.8)
.timestamp(sixty_days_ago)
.source_class(SourceClass::Anecdotal)
.build();
let decayed = apply_source_class_decay(&[assertion], ONE_YEAR_SECONDS, now);
assert_eq!(decayed.len(), 1);
// 60 days = 2 half-lives for Anecdotal (30-day half-life)
// Expected: 0.8 * 0.25 = 0.2
let decayed_conf = decayed[0].confidence;
assert!(
(decayed_conf - 0.2).abs() < 0.02,
"Anecdotal (60 days, 30-day halflife) expected ~0.2, got {}",
decayed_conf
);
}
#[test]
fn test_source_aware_decay_mixed_tiers() {
// Compare Clinical (2yr halflife) vs Anecdotal (30-day halflife)
let now = 1_000_000_000_u64;
let one_year_ago = now - ONE_YEAR_SECONDS;
// Clinical: 1 year = 0.5 half-lives → ~70% decay factor
let clinical = AssertionBuilder::new()
.subject("test")
.predicate("value")
.confidence(0.8)
.timestamp(one_year_ago)
.source_class(SourceClass::Clinical)
.build();
// Anecdotal: 1 year = 12+ half-lives → ~0% decay factor
let anecdotal = AssertionBuilder::new()
.subject("test")
.predicate("value")
.confidence(0.8)
.timestamp(one_year_ago)
.source_class(SourceClass::Anecdotal)
.build();
let decayed = apply_source_class_decay(&[clinical, anecdotal], ONE_YEAR_SECONDS, now);
assert_eq!(decayed.len(), 2);
// Clinical (2yr halflife): 1yr = 0.5 halflife → 2^(-0.5) ≈ 0.707
// 0.8 * 0.707 ≈ 0.566
let clinical_decayed = decayed[0].confidence;
assert!(
(clinical_decayed - 0.566).abs() < 0.02,
"Clinical expected ~0.566, got {}",
clinical_decayed
);
// Anecdotal (30-day halflife): 365 days = ~12.2 half-lives → 2^(-12.2) ≈ 0.0002
// Should be near zero
let anecdotal_decayed = decayed[1].confidence;
assert!(
anecdotal_decayed < 0.01,
"Anecdotal expected near zero, got {}",
anecdotal_decayed
);
// Clinical should be much higher than Anecdotal after tier-aware decay
assert!(
clinical_decayed > anecdotal_decayed * 10.0,
"Clinical ({}) should be much higher than Anecdotal ({})",
clinical_decayed,
anecdotal_decayed
);
}
// ========================================================================
// Edge Case Tests
// ========================================================================
#[test]
fn test_decay_zero_halflife_no_change() {
let now = 1_000_000_000_u64;
let one_year_ago = now - ONE_YEAR_SECONDS;
let assertion = AssertionBuilder::new()
.subject("test")
.predicate("value")
.confidence(0.9)
.timestamp(one_year_ago)
.build();
// Zero half-life means no decay
let decayed = apply_decay(&[assertion], 0, now);
assert_eq!(decayed.len(), 1);
assert_eq!(decayed[0].confidence, 0.9, "Zero halflife should skip decay");
}
#[test]
fn test_decay_future_assertion_no_change() {
let now = 1_000_000_000_u64;
let future = now + ONE_YEAR_SECONDS;
let assertion = AssertionBuilder::new()
.subject("test")
.predicate("value")
.confidence(0.9)
.timestamp(future)
.build();
let decayed = apply_decay(&[assertion], ONE_YEAR_SECONDS, now);
assert_eq!(decayed.len(), 1);
assert_eq!(decayed[0].confidence, 0.9, "Future assertions should not decay");
}
#[test]
fn test_decay_empty_assertions() {
let decayed = apply_decay(&[], ONE_YEAR_SECONDS, 1_000_000_000);
assert!(decayed.is_empty());
}
#[test]
fn test_decay_confidence_clamps_to_valid_range() {
// Very old assertion should decay to near-zero but never negative
// Use a large enough `now` to avoid overflow with 100-year old assertion
let now = 5_000_000_000_u64; // ~2128 in Unix time
let ancient = now - (100 * ONE_YEAR_SECONDS); // 100 years ago
let assertion = AssertionBuilder::new()
.subject("test")
.predicate("value")
.confidence(0.9)
.timestamp(ancient)
.build();
let decayed = apply_decay(&[assertion], ONE_YEAR_SECONDS, now);
assert_eq!(decayed.len(), 1);
assert!(decayed[0].confidence >= 0.0, "Confidence should not be negative");
assert!(decayed[0].confidence <= 1.0, "Confidence should not exceed 1.0");
}
#[test]
fn test_decay_preserves_other_fields() {
let now = 1_000_000_000_u64;
let one_year_ago = now - ONE_YEAR_SECONDS;
let assertion = AssertionBuilder::new()
.subject("Tesla")
.predicate("revenue")
.object_number(96.7)
.confidence(0.9)
.timestamp(one_year_ago)
.build();
let decayed = apply_decay(std::slice::from_ref(&assertion), ONE_YEAR_SECONDS, now);
assert_eq!(decayed.len(), 1);
assert_eq!(decayed[0].subject, assertion.subject);
assert_eq!(decayed[0].predicate, assertion.predicate);
assert_eq!(decayed[0].object, assertion.object);
assert_eq!(decayed[0].timestamp, assertion.timestamp);
// Only confidence should change
assert_ne!(decayed[0].confidence, assertion.confidence);
}
}

848
crates/stemedb-query/src/engine.rs
View File

@ -7,11 +7,12 @@ use std::sync::Arc;
use std::time::{SystemTime, UNIX_EPOCH};
use stemedb_core::types::{Assertion, MaterializedView};
use stemedb_storage::{GenericIndexStore, IndexStore, KVStore};
use stemedb_storage::{GenericIndexStore, IndexStore, KVStore, VectorIndex, VisualIndex};
use tracing::{debug, instrument};
use crate::decay::{apply_decay, apply_source_class_decay};
use crate::error::{QueryError, Result};
use crate::query::{Query, QueryResult};
use crate::query::{parse_hex_phash, Query, QueryResult};
/// The query engine executes queries against the KV store.
///
@ -20,26 +21,57 @@ use crate::query::{Query, QueryResult};
/// Following the "Deep Module" principle, QueryEngine presents a simple
/// interface (`execute`) that hides the complexity of index lookup,
/// deserialization, and filtering.
///
/// # Similarity Search
///
/// When configured with vector or visual indexes, the engine supports:
/// - **Vector search**: k-NN queries over embedding vectors (`vector_near`)
/// - **Visual search**: Hamming distance queries over perceptual hashes (`visual_near`)
///
/// These use O(log N) index lookups instead of O(N) brute-force scans.
pub struct QueryEngine<S> {
store: Arc<S>,
index_store: GenericIndexStore<Arc<S>>,
/// Optional vector index for k-NN similarity search.
vector_index: Option<Arc<dyn VectorIndex>>,
/// Optional visual index for hamming distance search.
visual_index: Option<Arc<dyn VisualIndex>>,
}
impl<S: KVStore + 'static> QueryEngine<S> {
/// Create a new query engine backed by the given store.
pub fn new(store: Arc<S>) -> Self {
let index_store = GenericIndexStore::new(store.clone());
Self { store, index_store }
Self { store, index_store, vector_index: None, visual_index: None }
}
/// Attach a vector index for k-NN similarity search.
///
/// When set, queries with `vector_near` use O(log N) HNSW lookup
/// instead of brute-force scanning.
pub fn with_vector_index(mut self, index: Arc<dyn VectorIndex>) -> Self {
self.vector_index = Some(index);
self
}
/// Attach a visual index for perceptual hash similarity search.
///
/// When set, queries with `visual_near` use O(log N) BK-tree lookup
/// instead of brute-force hamming distance scanning.
pub fn with_visual_index(mut self, index: Arc<dyn VisualIndex>) -> Self {
self.visual_index = Some(index);
self
}
/// Execute a query and return matching assertions.
///
/// # Query Execution Strategy
///
/// 0. **Fast path**: If both `subject` and `predicate` are specified, check `MV:{subject}:{predicate}` for a pre-computed winner (O(1))
/// 1. If both `subject` and `predicate` are specified: Use compound index `SP:{subject}:{predicate}` for O(1) lookup
/// 2. If only `subject` is specified: Use subject index `S:{subject}` for O(1) lookup
/// 3. Otherwise: Full scan of `H:` prefix (O(n), avoid in production)
/// 0. **Similarity search path**: If `vector_near` or `visual_near` is set, use indexed lookup
/// 1. **Fast path**: If both `subject` and `predicate` are specified, check `MV:{subject}:{predicate}` for a pre-computed winner (O(1))
/// 2. If both `subject` and `predicate` are specified: Use compound index `SP:{subject}:{predicate}` for O(1) lookup
/// 3. If only `subject` is specified: Use subject index `S:{subject}` for O(1) lookup
/// 4. Otherwise: Full scan of `H:` prefix (O(n), avoid in production)
///
/// After fetching candidates, apply lifecycle and epoch filters.
#[instrument(skip(self, query), fields(
@ -48,11 +80,43 @@ impl<S: KVStore + 'static> QueryEngine<S> {
lifecycle = ?query.lifecycle
))]
pub async fn execute(&self, query: &Query) -> Result<QueryResult> {
// Similarity search path: use vector or visual index when available
if let Some(ref query_vector) = query.vector_near {
if let Some(ref vector_index) = self.vector_index {
let k = query.k.unwrap_or(10);
debug!(k, dimension = query_vector.len(), "Using vector index for k-NN search");
let candidates =
self.fetch_by_vector_similarity(vector_index, query_vector, k).await?;
return self.apply_filters_and_return(candidates, query).await;
} else {
debug!("vector_near specified but no vector index configured, falling back to standard path");
}
}
if let Some(ref visual_hash_hex) = query.visual_near {
if let Some(ref visual_index) = self.visual_index {
let threshold = query.visual_threshold.unwrap_or(8);
debug!(threshold, "Using visual index for hamming distance search");
let candidates = self
.fetch_by_visual_similarity(visual_index, visual_hash_hex, threshold)
.await?;
return self.apply_filters_and_return(candidates, query).await;
} else {
debug!(
"visual_near specified but no visual index configured, using brute-force scan"
);
// Fall through to standard path which applies visual_near filter in query.matches()
}
}
// Fast path: check materialized view when both subject and predicate are specified
if let (Some(subject), Some(predicate)) = (&query.subject, &query.predicate) {
if let Some(result) = self.try_fast_path(subject, predicate, query).await? {
debug!(subject, predicate, "Fast path: used materialized view");
return Ok(result);
// Skip fast path if as_of is set (MVs reflect current state, not historical)
if query.as_of.is_none() {
if let (Some(subject), Some(predicate)) = (&query.subject, &query.predicate) {
if let Some(result) = self.try_fast_path(subject, predicate, query).await? {
debug!(subject, predicate, "Fast path: used materialized view");
return Ok(result);
}
}
}
@ -84,6 +148,27 @@ impl<S: KVStore + 'static> QueryEngine<S> {
let mut matching: Vec<Assertion> =
candidates.into_iter().filter(|a| query.matches(a)).collect();
// Apply decay if decay_halflife is set
if let Some(halflife) = query.decay_halflife {
// Use as_of timestamp if set (time-travel), otherwise current time
let now = query.as_of.unwrap_or_else(|| {
SystemTime::now().duration_since(UNIX_EPOCH).map(|d| d.as_secs()).unwrap_or(0)
});
if query.source_class_decay {
debug!(
halflife_seconds = halflife,
now,
source_class_aware = true,
"Applying source-class-aware decay"
);
matching = apply_source_class_decay(&matching, halflife, now);
} else {
debug!(halflife_seconds = halflife, now, "Applying uniform decay");
matching = apply_decay(&matching, halflife, now);
}
}
let total_count = matching.len();
// Apply limit if specified
@ -233,6 +318,183 @@ impl<S: KVStore + 'static> QueryEngine<S> {
Ok(assertions)
}
/// Fetch assertions by vector similarity using the HNSW index.
///
/// Returns the k nearest neighbors to the query vector, ordered by distance.
async fn fetch_by_vector_similarity(
&self,
vector_index: &Arc<dyn VectorIndex>,
query_vector: &[f32],
k: usize,
) -> Result<Vec<Assertion>> {
// Use the vector index to find nearest neighbors
let neighbors =
vector_index.search(query_vector, k).map_err(|e| QueryError::Index(e.to_string()))?;
debug!(candidates_count = neighbors.len(), "Vector index returned candidates");
// Fetch assertions by their hashes
let mut results = Vec::with_capacity(neighbors.len());
for (hash, distance) in neighbors {
let assertion_key = format!("H:{}", hex::encode(hash)).into_bytes();
if let Some(data) = self.store.get(&assertion_key).await? {
match self.deserialize_assertion(&data) {
Ok(assertion) => {
debug!(
hash = %hex::encode(hash),
distance,
"Found assertion via vector index"
);
results.push(assertion);
}
Err(e) => {
debug!(hash = %hex::encode(hash), "Skipping malformed assertion: {:?}", e);
}
}
}
}
Ok(results)
}
/// Fetch assertions by visual similarity using the BK-tree index.
///
/// Returns assertions whose visual hash is within the threshold hamming distance.
async fn fetch_by_visual_similarity(
&self,
visual_index: &Arc<dyn VisualIndex>,
query_hash_hex: &str,
threshold: u32,
) -> Result<Vec<Assertion>> {
// Parse the hex string to a PHash
let query_hash = parse_hex_phash(query_hash_hex).ok_or_else(|| {
QueryError::InvalidInput(format!(
"Invalid visual hash hex string: {} (expected 16 hex characters)",
query_hash_hex
))
})?;
// Use the visual index to find matches within threshold
let matches = visual_index
.search(&query_hash, threshold)
.map_err(|e| QueryError::Index(e.to_string()))?;
debug!(candidates_count = matches.len(), threshold, "Visual index returned candidates");
// Fetch assertions by their hashes
let mut results = Vec::with_capacity(matches.len());
for (hash, distance) in matches {
let assertion_key = format!("H:{}", hex::encode(hash)).into_bytes();
if let Some(data) = self.store.get(&assertion_key).await? {
match self.deserialize_assertion(&data) {
Ok(assertion) => {
debug!(
hash = %hex::encode(hash),
distance,
"Found assertion via visual index"
);
results.push(assertion);
}
Err(e) => {
debug!(hash = %hex::encode(hash), "Skipping malformed assertion: {:?}", e);
}
}
}
}
Ok(results)
}
/// Apply filters to candidates and construct the final QueryResult.
///
/// This is used by similarity search paths to apply post-filtering
/// (subject, predicate, lifecycle, epoch) and handle limit/truncation.
async fn apply_filters_and_return(
&self,
candidates: Vec<Assertion>,
query: &Query,
) -> Result<QueryResult> {
debug!(candidate_count = candidates.len(), "Applying filters to similarity search results");
// Apply filters (but skip visual_near filter since we already used the index)
let mut matching: Vec<Assertion> = candidates
.into_iter()
.filter(|a| {
// Check subject filter
if let Some(ref subject) = query.subject {
if &a.subject != subject {
return false;
}
}
// Check predicate filter
if let Some(ref predicate) = query.predicate {
if &a.predicate != predicate {
return false;
}
}
// Check lifecycle filter
if let Some(lifecycle) = query.lifecycle {
if a.lifecycle != lifecycle {
return false;
}
}
// Check epoch filter
if let Some(epoch) = query.epoch {
match a.epoch {
Some(assertion_epoch) if assertion_epoch == epoch => {}
_ => return false,
}
}
// Check as_of (time-travel) filter
if let Some(as_of_ts) = query.as_of {
if a.timestamp > as_of_ts {
return false;
}
}
true
})
.collect();
// Apply decay if decay_halflife is set
if let Some(halflife) = query.decay_halflife {
use std::time::{SystemTime, UNIX_EPOCH};
let now = query.as_of.unwrap_or_else(|| {
SystemTime::now().duration_since(UNIX_EPOCH).map(|d| d.as_secs()).unwrap_or(0)
});
if query.source_class_decay {
debug!(
halflife_seconds = halflife,
now,
source_class_aware = true,
"Applying source-class-aware decay"
);
matching = apply_source_class_decay(&matching, halflife, now);
} else {
debug!(halflife_seconds = halflife, now, "Applying uniform decay");
matching = apply_decay(&matching, halflife, now);
}
}
let total_count = matching.len();
// Apply limit if specified
let has_more = if let Some(limit) = query.limit {
if matching.len() > limit {
matching.truncate(limit);
true
} else {
false
}
} else {
false
};
debug!(matched_count = matching.len(), total_count, has_more, "Query complete");
Ok(QueryResult { assertions: matching, total_count, has_more })
}
/// Deserialize an assertion using the canonical serde module.
fn deserialize_assertion(&self, data: &[u8]) -> Result<Assertion> {
stemedb_core::serde::deserialize(data)
@ -520,6 +782,7 @@ mod tests {
resolution_confidence: 0.95,
candidates_count: 3,
materialized_at,
conflict_score: 0.1, // Low conflict in test
};
let key = format!("MV:{}:{}", subject, predicate).into_bytes();
@ -787,4 +1050,567 @@ mod tests {
assert_eq!(result.assertions.len(), 1);
assert_eq!(result.assertions[0].lifecycle, LifecycleStage::Approved);
}
// ========================================================================
// TIME-TRAVEL (as_of) TESTS
// ========================================================================
#[tokio::test]
async fn test_as_of_bypasses_fast_path() {
let store = SledStore::open_temp().expect("store");
// Create two assertions with different timestamps
let mut old = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
old.timestamp = 1000;
let mut new = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
new.timestamp = 2000;
new.object = ObjectValue::Number(200.0); // Different value for different hash
store_assertion(&store, &old).await;
store_assertion(&store, &new).await;
// Store MV pointing to the newer assertion
store_materialized_view(&store, "Tesla", "revenue", &new).await;
let engine = QueryEngine::new(Arc::new(store));
// Without as_of: should use MV and return only the new assertion
let query_no_as_of = Query::builder().subject("Tesla").predicate("revenue").build();
let result = engine.execute(&query_no_as_of).await.expect("execute");
assert_eq!(result.assertions.len(), 1);
assert_eq!(result.assertions[0].timestamp, 2000); // MV winner
// With as_of=1500: should bypass MV and return only the old assertion
let query_as_of =
Query::builder().subject("Tesla").predicate("revenue").as_of(1500).build();
let result = engine.execute(&query_as_of).await.expect("execute");
assert_eq!(result.assertions.len(), 1);
assert_eq!(result.assertions[0].timestamp, 1000);
}
#[tokio::test]
async fn test_as_of_none_uses_fast_path() {
let store = SledStore::open_temp().expect("store");
let mv_winner = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
let other = create_test_assertion("Tesla", "revenue", LifecycleStage::Proposed);
store_assertion(&store, &mv_winner).await;
store_assertion(&store, &other).await;
store_materialized_view(&store, "Tesla", "revenue", &mv_winner).await;
let engine = QueryEngine::new(Arc::new(store));
// Query without as_of should use fast path (MV) and return single winner
let query = Query::builder().subject("Tesla").predicate("revenue").build();
let result = engine.execute(&query).await.expect("execute");
// Fast path returns single MV winner, not both assertions
assert_eq!(result.assertions.len(), 1);
assert_eq!(result.assertions[0].lifecycle, LifecycleStage::Approved);
}
#[tokio::test]
async fn test_as_of_with_lens_resolves_among_historical_candidates() {
let store = SledStore::open_temp().expect("store");
// Create assertions at different timestamps
let mut old1 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
old1.timestamp = 1000;
old1.object = ObjectValue::Number(100.0);
let mut old2 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
old2.timestamp = 1500;
old2.object = ObjectValue::Number(150.0);
let mut future = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
future.timestamp = 3000;
future.object = ObjectValue::Number(300.0);
store_assertion(&store, &old1).await;
store_assertion(&store, &old2).await;
store_assertion(&store, &future).await;
let engine = QueryEngine::new(Arc::new(store));
// Query as_of=2000 - should only see old1 and old2, not future
let query = Query::builder().subject("Tesla").predicate("revenue").as_of(2000).build();
let result = engine.execute(&query).await.expect("execute");
// Should have 2 assertions (old1 and old2), not 3
assert_eq!(result.assertions.len(), 2);
// All returned assertions should have timestamp <= 2000
for assertion in &result.assertions {
assert!(assertion.timestamp <= 2000);
}
}
#[tokio::test]
async fn test_as_of_returns_empty_when_all_assertions_are_future() {
let store = SledStore::open_temp().expect("store");
let mut future1 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
future1.timestamp = 5000;
let mut future2 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
future2.timestamp = 6000;
future2.object = ObjectValue::Number(200.0);
store_assertion(&store, &future1).await;
store_assertion(&store, &future2).await;
let engine = QueryEngine::new(Arc::new(store));
// Query as_of=1000: all assertions are in the future
let query = Query::builder().subject("Tesla").predicate("revenue").as_of(1000).build();
let result = engine.execute(&query).await.expect("execute");
// Should return empty because no assertions existed at timestamp 1000
assert!(result.assertions.is_empty());
assert_eq!(result.total_count, 0);
}
#[tokio::test]
async fn test_as_of_with_exact_timestamp_match() {
let store = SledStore::open_temp().expect("store");
let mut assertion = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
assertion.timestamp = 1000;
store_assertion(&store, &assertion).await;
let engine = QueryEngine::new(Arc::new(store));
// Query with as_of equal to assertion's timestamp: should include it
let query = Query::builder().subject("Tesla").predicate("revenue").as_of(1000).build();
let result = engine.execute(&query).await.expect("execute");
// Assertion with timestamp 1000 should match as_of=1000 (inclusive)
assert_eq!(result.assertions.len(), 1);
assert_eq!(result.assertions[0].timestamp, 1000);
}
// ========================================================================
// VECTOR SIMILARITY SEARCH TESTS
// ========================================================================
use stemedb_storage::{BkTreeVisualIndex, HnswVectorIndex};
/// Helper to create an assertion with a vector embedding.
fn create_test_assertion_with_vector(
subject: &str,
predicate: &str,
lifecycle: LifecycleStage,
vector: Vec<f32>,
) -> Assertion {
AssertionBuilder::new()
.subject(subject)
.predicate(predicate)
.confidence(0.95)
.lifecycle(lifecycle)
.vector(vector)
.build()
}
/// Helper to create an assertion with a visual hash.
fn create_test_assertion_with_visual_hash(
subject: &str,
predicate: &str,
lifecycle: LifecycleStage,
visual_hash: [u8; 8],
) -> Assertion {
AssertionBuilder::new()
.subject(subject)
.predicate(predicate)
.confidence(0.95)
.lifecycle(lifecycle)
.visual_hash(visual_hash)
.build()
}
/// Helper to store an assertion and index it in the vector index.
async fn store_assertion_with_vector(
store: &SledStore,
assertion: &Assertion,
vector_index: &HnswVectorIndex,
) -> [u8; 32] {
let bytes = stemedb_core::serde::serialize(assertion).expect("serialize");
let hash = blake3::hash(&bytes);
let assertion_hash: [u8; 32] = *hash.as_bytes();
let key = format!("H:{}", hash.to_hex()).into_bytes();
store.put(&key, &bytes).await.expect("put");
// Index the vector if present
if let Some(ref vector) = assertion.vector {
vector_index.insert(&assertion_hash, vector).expect("vector insert");
}
// Update subject/predicate indexes
let index_store = GenericIndexStore::new(store.clone());
index_store
.add_to_indexes(&assertion.subject, &assertion.predicate, &assertion_hash)
.await
.expect("add to indexes");
assertion_hash
}
/// Helper to store an assertion and index it in the visual index.
async fn store_assertion_with_visual_hash(
store: &SledStore,
assertion: &Assertion,
visual_index: &BkTreeVisualIndex,
) -> [u8; 32] {
let bytes = stemedb_core::serde::serialize(assertion).expect("serialize");
let hash = blake3::hash(&bytes);
let assertion_hash: [u8; 32] = *hash.as_bytes();
let key = format!("H:{}", hash.to_hex()).into_bytes();
store.put(&key, &bytes).await.expect("put");
// Index the visual hash if present
if let Some(ref phash) = assertion.visual_hash {
visual_index.insert(&assertion_hash, phash).expect("visual insert");
}
// Update subject/predicate indexes
let index_store = GenericIndexStore::new(store.clone());
index_store
.add_to_indexes(&assertion.subject, &assertion.predicate, &assertion_hash)
.await
.expect("add to indexes");
assertion_hash
}
#[tokio::test]
async fn test_vector_search_returns_nearest_neighbors() {
let store = SledStore::open_temp().expect("store");
let vector_index = HnswVectorIndex::new(4);
// Create assertions with different embeddings
let close_vec = vec![1.0, 0.0, 0.0, 0.0]; // Close to query
let medium_vec = vec![0.5, 0.5, 0.0, 0.0]; // Medium distance
let far_vec = vec![0.0, 0.0, 1.0, 1.0]; // Far from query
let close = create_test_assertion_with_vector(
"Entity1",
"property",
LifecycleStage::Approved,
close_vec,
);
let medium = create_test_assertion_with_vector(
"Entity2",
"property",
LifecycleStage::Approved,
medium_vec,
);
let far = create_test_assertion_with_vector(
"Entity3",
"property",
LifecycleStage::Approved,
far_vec,
);
store_assertion_with_vector(&store, &close, &vector_index).await;
store_assertion_with_vector(&store, &medium, &vector_index).await;
store_assertion_with_vector(&store, &far, &vector_index).await;
let engine = QueryEngine::new(Arc::new(store)).with_vector_index(Arc::new(vector_index));
// Query for vectors near [1.0, 0.0, 0.0, 0.0]
let query = Query::builder()
.vector_near(vec![1.0, 0.0, 0.0, 0.0], 2) // Get 2 nearest
.build();
let result = engine.execute(&query).await.expect("execute");
// Should return 2 nearest (close and medium)
assert_eq!(result.assertions.len(), 2);
// The closest one should be Entity1
assert!(result.assertions.iter().any(|a| a.subject == "Entity1"));
}
#[tokio::test]
async fn test_vector_search_with_subject_filter() {
let store = SledStore::open_temp().expect("store");
let vector_index = HnswVectorIndex::new(4);
// Create assertions with similar vectors but different subjects
let vec1 = vec![1.0, 0.0, 0.0, 0.0];
let vec2 = vec![0.9, 0.1, 0.0, 0.0]; // Very close to vec1
let tesla =
create_test_assertion_with_vector("Tesla", "embedding", LifecycleStage::Approved, vec1);
let apple =
create_test_assertion_with_vector("Apple", "embedding", LifecycleStage::Approved, vec2);
store_assertion_with_vector(&store, &tesla, &vector_index).await;
store_assertion_with_vector(&store, &apple, &vector_index).await;
let engine = QueryEngine::new(Arc::new(store)).with_vector_index(Arc::new(vector_index));
// Query with vector search AND subject filter
let query =
Query::builder().vector_near(vec![1.0, 0.0, 0.0, 0.0], 10).subject("Tesla").build();
let result = engine.execute(&query).await.expect("execute");
// Should only return Tesla, even though Apple is also close
assert_eq!(result.assertions.len(), 1);
assert_eq!(result.assertions[0].subject, "Tesla");
}
#[tokio::test]
async fn test_vector_search_without_index_falls_back() {
let store = SledStore::open_temp().expect("store");
// Store assertions without a vector index configured
let assertion = create_test_assertion_with_vector(
"Tesla",
"embedding",
LifecycleStage::Approved,
vec![1.0, 0.0, 0.0, 0.0],
);
store_assertion(&store, &assertion).await;
// Engine WITHOUT vector index
let engine = QueryEngine::new(Arc::new(store));
// Query with vector_near should fall back to standard path
let query =
Query::builder().subject("Tesla").vector_near(vec![1.0, 0.0, 0.0, 0.0], 10).build();
let result = engine.execute(&query).await.expect("execute");
// Falls back to subject index, should still find Tesla
assert_eq!(result.assertions.len(), 1);
assert_eq!(result.assertions[0].subject, "Tesla");
}
#[tokio::test]
async fn test_vector_search_empty_index() {
let store = SledStore::open_temp().expect("store");
let vector_index = HnswVectorIndex::new(4);
let engine = QueryEngine::new(Arc::new(store)).with_vector_index(Arc::new(vector_index));
// Query empty vector index
let query = Query::builder().vector_near(vec![1.0, 0.0, 0.0, 0.0], 10).build();
let result = engine.execute(&query).await.expect("execute");
assert!(result.assertions.is_empty());
assert_eq!(result.total_count, 0);
}
// ========================================================================
// VISUAL SIMILARITY SEARCH TESTS
// ========================================================================
#[tokio::test]
async fn test_visual_search_returns_similar_images() {
let store = SledStore::open_temp().expect("store");
let visual_index = BkTreeVisualIndex::new();
// Create assertions with visual hashes at different distances
let hash_exact: [u8; 8] = [0xA3, 0xF2, 0xB1, 0xC4, 0xD5, 0xE6, 0xF7, 0x08];
let hash_close: [u8; 8] = [0xA3, 0xF2, 0xB1, 0xC4, 0xD5, 0xE6, 0xF7, 0x09]; // 1 bit different
let hash_far: [u8; 8] = [0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]; // Many bits different
let exact = create_test_assertion_with_visual_hash(
"Img1",
"screenshot",
LifecycleStage::Approved,
hash_exact,
);
let close = create_test_assertion_with_visual_hash(
"Img2",
"screenshot",
LifecycleStage::Approved,
hash_close,
);
let far = create_test_assertion_with_visual_hash(
"Img3",
"screenshot",
LifecycleStage::Approved,
hash_far,
);
store_assertion_with_visual_hash(&store, &exact, &visual_index).await;
store_assertion_with_visual_hash(&store, &close, &visual_index).await;
store_assertion_with_visual_hash(&store, &far, &visual_index).await;
let engine = QueryEngine::new(Arc::new(store)).with_visual_index(Arc::new(visual_index));
// Query for hashes within 2 bits of the target
let query = Query::builder()
.visual_near("a3f2b1c4d5e6f708", 2) // Threshold of 2 bits
.build();
let result = engine.execute(&query).await.expect("execute");
// Should find exact (0 bits) and close (1 bit), but not far (many bits)
assert_eq!(result.assertions.len(), 2);
assert!(result.assertions.iter().any(|a| a.subject == "Img1"));
assert!(result.assertions.iter().any(|a| a.subject == "Img2"));
assert!(!result.assertions.iter().any(|a| a.subject == "Img3"));
}
#[tokio::test]
async fn test_visual_search_with_lifecycle_filter() {
let store = SledStore::open_temp().expect("store");
let visual_index = BkTreeVisualIndex::new();
let hash: [u8; 8] = [0xA3, 0xF2, 0xB1, 0xC4, 0xD5, 0xE6, 0xF7, 0x08];
let approved = create_test_assertion_with_visual_hash(
"Img1",
"screenshot",
LifecycleStage::Approved,
hash,
);
let proposed = create_test_assertion_with_visual_hash(
"Img2",
"screenshot",
LifecycleStage::Proposed,
hash,
);
store_assertion_with_visual_hash(&store, &approved, &visual_index).await;
store_assertion_with_visual_hash(&store, &proposed, &visual_index).await;
let engine = QueryEngine::new(Arc::new(store)).with_visual_index(Arc::new(visual_index));
// Query with visual search AND lifecycle filter
let query = Query::builder()
.visual_near("a3f2b1c4d5e6f708", 0)
.lifecycle(LifecycleStage::Approved)
.build();
let result = engine.execute(&query).await.expect("execute");
// Should only return approved assertion
assert_eq!(result.assertions.len(), 1);
assert_eq!(result.assertions[0].lifecycle, LifecycleStage::Approved);
}
#[tokio::test]
async fn test_visual_search_invalid_hex_returns_error() {
let store = SledStore::open_temp().expect("store");
let visual_index = BkTreeVisualIndex::new();
let engine = QueryEngine::new(Arc::new(store)).with_visual_index(Arc::new(visual_index));
// Query with invalid hex string
let query = Query::builder().visual_near("invalid_hex!!", 10).build();
let result = engine.execute(&query).await;
// Should return an error for invalid hex
assert!(result.is_err());
let err = result.unwrap_err();
assert!(matches!(err, crate::error::QueryError::InvalidInput(_)));
}
#[tokio::test]
async fn test_visual_search_without_index_uses_brute_force() {
let store = SledStore::open_temp().expect("store");
let hash: [u8; 8] = [0xA3, 0xF2, 0xB1, 0xC4, 0xD5, 0xE6, 0xF7, 0x08];
let assertion = create_test_assertion_with_visual_hash(
"Img1",
"screenshot",
LifecycleStage::Approved,
hash,
);
store_assertion(&store, &assertion).await;
// Engine WITHOUT visual index
let engine = QueryEngine::new(Arc::new(store));
// Query with visual_near should fall back to brute-force (via query.matches)
let query = Query::builder().visual_near("a3f2b1c4d5e6f708", 0).build();
let result = engine.execute(&query).await.expect("execute");
// Should find via brute-force scan
assert_eq!(result.assertions.len(), 1);
assert_eq!(result.assertions[0].subject, "Img1");
}
#[tokio::test]
async fn test_visual_search_with_limit() {
let store = SledStore::open_temp().expect("store");
let visual_index = BkTreeVisualIndex::new();
// Create multiple assertions with the same visual hash
let hash: [u8; 8] = [0xA3, 0xF2, 0xB1, 0xC4, 0xD5, 0xE6, 0xF7, 0x08];
for i in 0..5 {
let assertion = create_test_assertion_with_visual_hash(
&format!("Img{}", i),
"screenshot",
LifecycleStage::Approved,
hash,
);
store_assertion_with_visual_hash(&store, &assertion, &visual_index).await;
}
let engine = QueryEngine::new(Arc::new(store)).with_visual_index(Arc::new(visual_index));
// Query with limit
let query = Query::builder().visual_near("a3f2b1c4d5e6f708", 0).limit(2).build();
let result = engine.execute(&query).await.expect("execute");
assert_eq!(result.assertions.len(), 2);
assert_eq!(result.total_count, 5);
assert!(result.has_more);
}
#[tokio::test]
async fn test_vector_search_with_as_of_filter() {
let store = SledStore::open_temp().expect("store");
let vector_index = HnswVectorIndex::new(4);
// Create assertions with different timestamps
let vec = vec![1.0, 0.0, 0.0, 0.0];
let mut old = create_test_assertion_with_vector(
"Entity1",
"embedding",
LifecycleStage::Approved,
vec.clone(),
);
old.timestamp = 1000;
let mut new = create_test_assertion_with_vector(
"Entity2",
"embedding",
LifecycleStage::Approved,
vec,
);
new.timestamp = 3000;
store_assertion_with_vector(&store, &old, &vector_index).await;
store_assertion_with_vector(&store, &new, &vector_index).await;
let engine = QueryEngine::new(Arc::new(store)).with_vector_index(Arc::new(vector_index));
// Query with vector search AND time-travel filter
let query = Query::builder()
.vector_near(vec![1.0, 0.0, 0.0, 0.0], 10)
.as_of(2000) // Only assertions before timestamp 2000
.build();
let result = engine.execute(&query).await.expect("execute");
// Should only return the old assertion
assert_eq!(result.assertions.len(), 1);
assert_eq!(result.assertions[0].subject, "Entity1");
assert_eq!(result.assertions[0].timestamp, 1000);
}
}

8
crates/stemedb-query/src/error.rs
View File

@ -20,4 +20,12 @@ pub enum QueryError {
/// Query validation error.
#[error("Invalid query: {0}")]
InvalidQuery(String),
/// Invalid input (e.g., malformed hex string).
#[error("Invalid input: {0}")]
InvalidInput(String),
/// Error from an index operation.
#[error("Index error: {0}")]
Index(String),
}

2
crates/stemedb-query/src/lib.rs
View File

@ -42,6 +42,7 @@
//! }
//! ```
mod decay;
mod engine;
mod error;
/// Background materializer for O(1) read performance.
@ -49,6 +50,7 @@ pub mod materializer;
mod query;
mod skeptic;
pub use decay::{apply_decay, apply_source_class_decay};
pub use engine::QueryEngine;
pub use error::{QueryError, Result};
pub use materializer::{MaterializeReport, Materializer};

1
crates/stemedb-query/src/materializer.rs
View File

@ -198,6 +198,7 @@ impl<S: KVStore + 'static> Materializer<S> {
winner,
lens_name: self.lens.name().to_string(),
resolution_confidence: resolution.resolution_confidence,
conflict_score: resolution.conflict_score,
candidates_count: resolution.candidates_count,
materialized_at: now,
};

299
crates/stemedb-query/src/query.rs
View File

@ -6,33 +6,15 @@
use stemedb_core::types::{Assertion, EpochId, LifecycleStage, PHash};
/// Compute hamming distance between two 8-byte perceptual hashes.
///
/// Returns the number of differing bits (0-64). Lower distance means
/// more visually similar images.
///
/// # Example
///
/// ```rust
/// use stemedb_query::hamming_distance;
///
/// let a = [0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00];
/// let b = [0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF];
/// assert_eq!(hamming_distance(&a, &b), 64); // All bits differ
///
/// let c = [0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01];
/// assert_eq!(hamming_distance(&a, &c), 1); // One bit differs
/// ```
pub fn hamming_distance(a: &PHash, b: &PHash) -> u32 {
a.iter().zip(b.iter()).map(|(x, y)| (x ^ y).count_ones()).sum()
}
// Re-export hamming_distance from stemedb_storage for public API consumers
pub use stemedb_storage::hamming_distance;
/// Parse hex string to 8-byte pHash.
///
/// Returns `None` if the hex string is not exactly 16 characters
/// or contains invalid hex digits. Case-insensitive: both "A3F2..."
/// and "a3f2..." are valid and produce identical results.
fn parse_hex_phash(hex_str: &str) -> Option<PHash> {
pub(crate) fn parse_hex_phash(hex_str: &str) -> Option<PHash> {
if hex_str.len() != 16 {
return None;
}
@ -88,6 +70,93 @@ pub struct Query {
/// Range: 0-64 (8 bytes = 64 bits). Default: 8 (12.5% bit difference).
/// Lower values require closer visual similarity.
pub visual_threshold: Option<u32>,
/// Query state as of this Unix timestamp (time-travel).
///
/// When set, returns only assertions created at or before this timestamp.
/// The fast path (MV lookup) is bypassed since MVs reflect current state.
///
/// - `None` (default): Query current state (backward-compatible)
/// - `Some(ts)`: Query historical state as it existed at timestamp `ts`
pub as_of: Option<u64>,
/// Decay half-life in seconds for confidence decay.
///
/// When set, older assertions have their confidence scores reduced based on age.
/// This implements semantic decay: a Reddit post from 2022 shouldn't compete
/// equally with a 2024 RCT.
///
/// Formula: `effective_confidence = confidence * 2^(-(age / halflife))`
///
/// - `None` (default): No decay, all assertions weighted by original confidence
/// - `Some(31536000)`: 1-year half-life (assertions lose ~50% confidence per year)
/// - `Some(86400)`: 1-day half-life (fast decay for rapidly changing data)
///
/// **Note**: When decay is enabled, the fast path (materialized view lookup) is
/// bypassed because MVs store pre-computed winners without decay applied.
/// Queries with decay always use the slow path for accurate results.
///
/// # Example
/// ```rust
/// use stemedb_query::Query;
///
/// // Medical queries with 6-month decay half-life
/// let query = Query::builder()
/// .subject("Semaglutide")
/// .predicate("muscle_effect")
/// .decay_halflife(15768000) // 6 months in seconds
/// .build();
/// ```
pub decay_halflife: Option<u64>,
/// Use source-class-aware decay instead of uniform decay.
///
/// When `true` and `decay_halflife` is also set, the decay half-life
/// is determined by each assertion's `source_class` tier:
/// - Tier 0 (Regulatory): No decay
/// - Tier 1 (Clinical): 2-year half-life
/// - Tier 2 (Observational): 1-year half-life
/// - Tier 3 (Expert): 6-month half-life
/// - Tier 4 (Community): 3-month half-life
/// - Tier 5 (Anecdotal): 1-month half-life
///
/// The `decay_halflife` field serves as a fallback for assertions
/// without a source_class, or when this flag is `false`.
pub source_class_decay: bool,
/// Query by semantic vector similarity (k-nearest neighbors).
///
/// When set, the QueryEngine uses the vector index for candidate retrieval
/// instead of the standard SP/S indexes. This enables semantic similarity
/// queries like "find assertions with embeddings similar to this one."
///
/// The `k` field specifies how many nearest neighbors to return.
///
/// - `None` (default): Use standard index-based lookup
/// - `Some(vec)`: Use vector index for k-NN search
///
/// **Note**: When `vector_near` is set:
/// - The fast path (MV lookup) is bypassed
/// - Subject/predicate filters are applied AFTER vector search
/// - Results are sorted by distance, not by lens resolution
///
/// # Example
/// ```rust
/// use stemedb_query::Query;
///
/// // Find 10 assertions with similar embeddings
/// let embedding = vec![0.1, 0.2, 0.3, /* ... */];
/// let query = Query::builder()
/// .vector_near(embedding, 10)
/// .subject("Semaglutide") // Optional: filter results
/// .build();
/// ```
pub vector_near: Option<Vec<f32>>,
/// Number of nearest neighbors to return for vector search.
///
/// Only used when `vector_near` is set. Defaults to 10 if not specified.
pub k: Option<usize>,
}
impl Query {
@ -149,6 +218,13 @@ impl Query {
}
}
// Check as_of (time-travel) filter
if let Some(as_of_ts) = self.as_of {
if assertion.timestamp > as_of_ts {
return false;
}
}
true
}
}
@ -246,6 +322,107 @@ impl QueryBuilder {
self
}
/// Query state as of a specific timestamp (time-travel).
///
/// Returns only assertions created at or before the given timestamp.
/// When set, the fast path (MV lookup) is bypassed since materialized
/// views reflect current state, not historical state.
///
/// # Example
/// ```rust
/// use stemedb_query::Query;
///
/// // What was the consensus on January 1, 2024?
/// let query = Query::builder()
/// .subject("Tesla")
/// .predicate("revenue")
/// .as_of(1704067200) // 2024-01-01 00:00:00 UTC
/// .build();
/// ```
pub fn as_of(mut self, timestamp: u64) -> Self {
self.query.as_of = Some(timestamp);
self
}
/// Set the decay half-life in seconds for confidence decay.
///
/// Older assertions will have their effective confidence reduced based on age.
/// Formula: `effective_confidence = confidence * 2^(-(age / halflife))`
///
/// # Example
/// ```rust
/// use stemedb_query::Query;
///
/// // 6-month decay half-life for medical knowledge
/// let query = Query::builder()
/// .subject("Semaglutide")
/// .predicate("muscle_effect")
/// .decay_halflife(15768000) // 6 months in seconds
/// .build();
/// ```
pub fn decay_halflife(mut self, seconds: u64) -> Self {
self.query.decay_halflife = Some(seconds);
self
}
/// Enable source-class-aware decay.
///
/// When enabled, each assertion's decay half-life is determined by its
/// `source_class` tier instead of using a uniform half-life:
/// - Regulatory (Tier 0): No decay
/// - Clinical (Tier 1): 2-year half-life
/// - Observational (Tier 2): 1-year half-life
/// - Expert (Tier 3): 6-month half-life
/// - Community (Tier 4): 3-month half-life
/// - Anecdotal (Tier 5): 1-month half-life
///
/// Requires `decay_halflife` to be set as a fallback for assertions
/// without source_class.
///
/// # Example
/// ```rust
/// use stemedb_query::Query;
///
/// // Use tier-based decay with 1-year fallback
/// let query = Query::builder()
/// .subject("Semaglutide")
/// .predicate("muscle_effect")
/// .decay_halflife(31536000) // 1-year fallback
/// .source_class_decay(true)
/// .build();
/// ```
pub fn source_class_decay(mut self, enabled: bool) -> Self {
self.query.source_class_decay = enabled;
self
}
/// Query by semantic vector similarity (k-nearest neighbors).
///
/// Uses the vector index to find the `k` nearest assertions by embedding
/// distance. Additional filters (subject, predicate, lifecycle) are applied
/// after the vector search.
///
/// # Arguments
///
/// * `vector` - The query embedding vector
/// * `k` - Number of nearest neighbors to return
///
/// # Example
/// ```rust
/// use stemedb_query::Query;
///
/// // Find 10 semantically similar assertions
/// let embedding = vec![0.1, 0.2, 0.3]; // Your embedding here
/// let query = Query::builder()
/// .vector_near(embedding, 10)
/// .build();
/// ```
pub fn vector_near(mut self, vector: Vec<f32>, k: usize) -> Self {
self.query.vector_near = Some(vector);
self.query.k = Some(k);
self
}
/// Build the query.
pub fn build(self) -> Query {
self.query
@ -580,4 +757,84 @@ mod tests {
assert!(!query.matches(&assertion));
}
// ========================================================================
// Time-Travel (as_of) Query Tests
// ========================================================================
#[test]
fn test_as_of_excludes_future_assertions() {
let mut a1 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
a1.timestamp = 1000;
let mut a2 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
a2.timestamp = 2000;
let mut a3 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
a3.timestamp = 3000;
let query = Query::builder().subject("Tesla").as_of(2500).build();
assert!(query.matches(&a1)); // 1000 <= 2500
assert!(query.matches(&a2)); // 2000 <= 2500
assert!(!query.matches(&a3)); // 3000 > 2500
}
#[test]
fn test_as_of_none_matches_all_timestamps() {
let mut a1 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
a1.timestamp = 1000;
let mut a2 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
a2.timestamp = u64::MAX; // Far future
// Query without as_of should match all timestamps
let query = Query::builder().subject("Tesla").build();
assert!(query.matches(&a1));
assert!(query.matches(&a2));
}
#[test]
fn test_as_of_zero_matches_only_zero_timestamp() {
let mut a1 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
a1.timestamp = 0;
let mut a2 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
a2.timestamp = 1;
let query = Query::builder().subject("Tesla").as_of(0).build();
assert!(query.matches(&a1)); // 0 <= 0
assert!(!query.matches(&a2)); // 1 > 0
}
#[test]
fn test_as_of_combines_with_other_filters() {
let mut a1 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
a1.timestamp = 1000;
let mut a2 = create_test_assertion("Apple", "revenue", LifecycleStage::Approved);
a2.timestamp = 1000;
let mut a3 = create_test_assertion("Tesla", "revenue", LifecycleStage::Approved);
a3.timestamp = 3000; // After as_of
// Query with subject filter AND as_of
let query = Query::builder().subject("Tesla").as_of(2000).build();
assert!(query.matches(&a1)); // Tesla, 1000 <= 2000
assert!(!query.matches(&a2)); // Apple (wrong subject)
assert!(!query.matches(&a3)); // Tesla, but 3000 > 2000
}
#[test]
fn test_query_builder_as_of() {
let query =
Query::builder().subject("Tesla").predicate("revenue").as_of(1704067200).build();
assert_eq!(query.subject, Some("Tesla".to_string()));
assert_eq!(query.predicate, Some("revenue".to_string()));
assert_eq!(query.as_of, Some(1704067200));
}
}

123
crates/stemedb-query/tests/e2e_pipeline.rs
View File

@ -405,3 +405,126 @@ async fn test_e2e_notify_integration() {
"notification should have been received"
);
}
// ============================================================================
// DECAY INTEGRATION TESTS
// ============================================================================
/// Test: Decay reduces effective confidence of old assertions.
///
/// Proves that when `decay_halflife` is set, older assertions have their
/// confidence reduced, allowing newer lower-confidence assertions to win.
#[tokio::test]
async fn test_e2e_decay_reduces_old_confidence() {
let dir = tempdir().expect("create temp dir");
let wal_dir = dir.path().join("wal");
let db_dir = dir.path().join("db");
// Constants for decay calculation
let now: u64 = 1_000_000_000;
let one_year_ago = now - (365 * 24 * 60 * 60);
let one_week_ago = now - (7 * 24 * 60 * 60);
let one_year_seconds: u64 = 365 * 24 * 60 * 60;
// Old assertion with HIGH original confidence (0.95)
// But 1 year old with 1-year halflife = ~0.475 effective
let old_assertion = {
let mut a = create_signed_assertion("Semaglutide", "muscle_effect", -5.0, one_year_ago);
a.confidence = 0.95;
a
};
// New assertion with LOWER original confidence (0.6)
// Only 1 week old = ~0.59 effective (minimal decay)
let new_assertion = {
let mut a = create_signed_assertion("Semaglutide", "muscle_effect", -2.0, one_week_ago);
a.confidence = 0.6;
a
};
// Write both to WAL and ingest
let mut journal = Journal::open(&wal_dir).expect("open journal");
journal.append(serialize_assertion(&old_assertion).expect("ser")).expect("append");
journal.append(serialize_assertion(&new_assertion).expect("ser")).expect("append");
let journal = Arc::new(Mutex::new(journal));
let store = Arc::new(SledStore::open(&db_dir).expect("open store"));
let mut worker = IngestWorker::new(journal.clone(), store.clone()).await.expect("worker");
worker.step().await.expect("step 1");
worker.step().await.expect("step 2");
// Verify both assertions are stored
let h_entries = store.scan_prefix(b"H:").await.expect("scan");
assert_eq!(h_entries.len(), 2, "should have two assertions");
// Query WITHOUT decay: old assertion wins (0.95 > 0.6)
let engine = QueryEngine::new(store.clone());
let query_no_decay = Query::builder().subject("Semaglutide").predicate("muscle_effect").build();
let result_no_decay = engine.execute(&query_no_decay).await.expect("query no decay");
assert_eq!(result_no_decay.assertions.len(), 2);
// Find the highest confidence one without decay
let highest_no_decay = result_no_decay
.assertions
.iter()
.max_by(|a, b| a.confidence.partial_cmp(&b.confidence).unwrap_or(std::cmp::Ordering::Equal))
.expect("at least one assertion");
assert_eq!(
highest_no_decay.object,
ObjectValue::Number(-5.0),
"Without decay, old high-confidence assertion has highest confidence"
);
// Query WITH decay: new assertion should have higher effective confidence
// Old: 0.95 * 2^(-1) = 0.475
// New: 0.6 * 2^(-(7/365)) ≈ 0.59
let query_with_decay = Query::builder()
.subject("Semaglutide")
.predicate("muscle_effect")
.decay_halflife(one_year_seconds)
.as_of(now) // Use as_of to control "now" for deterministic test
.build();
let result_with_decay = engine.execute(&query_with_decay).await.expect("query with decay");
assert_eq!(result_with_decay.assertions.len(), 2);
// Find the highest confidence one WITH decay applied
let highest_with_decay = result_with_decay
.assertions
.iter()
.max_by(|a, b| a.confidence.partial_cmp(&b.confidence).unwrap_or(std::cmp::Ordering::Equal))
.expect("at least one assertion");
assert_eq!(
highest_with_decay.object,
ObjectValue::Number(-2.0),
"With decay, newer assertion should have higher effective confidence"
);
// Verify the actual decayed confidence values
let old_decayed = result_with_decay
.assertions
.iter()
.find(|a| a.object == ObjectValue::Number(-5.0))
.expect("find old assertion");
let new_decayed = result_with_decay
.assertions
.iter()
.find(|a| a.object == ObjectValue::Number(-2.0))
.expect("find new assertion");
// Old: 0.95 * 2^(-1) ≈ 0.475
assert!(
(old_decayed.confidence - 0.475).abs() < 0.02,
"Old assertion should decay to ~0.475, got {}",
old_decayed.confidence
);
// New: 0.6 * 2^(-(7/365)) ≈ 0.592
assert!(
(new_decayed.confidence - 0.592).abs() < 0.02,
"New assertion should decay minimally to ~0.592, got {}",
new_decayed.confidence
);
}

882
crates/stemedb-sim/src/lib.rs
View File

File diff suppressed because it is too large Load Diff

68
crates/stemedb-sim/tests/smoke.rs
View File

@ -24,6 +24,11 @@ async fn smoke_default_simulation_succeeds() {
assert!(result.vote_consensus_test_passed, "Vote consensus test should pass");
assert!(result.troll_resistance_test_passed, "Troll resistance test should pass");
assert!(result.votes_written >= 6, "Should have written at least 6 votes");
// All Arena 3 tests should pass
assert!(result.mv_integration_test_passed, "MV integration test should pass");
assert!(result.fast_path_test_passed, "Fast-path test should pass");
assert!(result.mv_freshness_test_passed, "MV freshness test should pass");
assert!(result.views_materialized >= 1, "Should have materialized at least 1 view");
}
/// Verify simulation works with increased load.
@ -42,15 +47,18 @@ async fn smoke_high_volume_simulation() {
"High-volume simulation should succeed. Errors: {:?}",
result.errors
);
// 50 base + 2 (vote consensus) + 2 (troll resistance)
assert_eq!(result.assertions_written, 54);
// 50 base + 4 (Arena 2) + 12 (Arena 3) = 66
assert_eq!(result.assertions_written, 66);
assert_eq!(result.assertions_verified, 50);
// All Arena 1 + 2 tests should pass
// All Arena 1 + 2 + 3 tests should pass
assert!(result.recency_test_passed);
assert!(result.lifecycle_test_passed);
assert!(result.audit_test_passed);
assert!(result.vote_consensus_test_passed);
assert!(result.troll_resistance_test_passed);
assert!(result.mv_integration_test_passed);
assert!(result.fast_path_test_passed);
assert!(result.mv_freshness_test_passed);
}
/// Verify simulation result accessors work correctly.
@ -67,6 +75,10 @@ fn test_simulation_result_is_success() {
audit_test_passed: true,
vote_consensus_test_passed: true,
troll_resistance_test_passed: true,
mv_integration_test_passed: true,
fast_path_test_passed: true,
mv_freshness_test_passed: true,
views_materialized: 3,
errors: vec![],
agent_count: 3,
tick_count: 10,
@ -85,6 +97,10 @@ fn test_simulation_result_is_success() {
audit_test_passed: true,
vote_consensus_test_passed: true,
troll_resistance_test_passed: true,
mv_integration_test_passed: true,
fast_path_test_passed: true,
mv_freshness_test_passed: true,
views_materialized: 3,
errors: vec![],
agent_count: 3,
tick_count: 10,
@ -103,6 +119,10 @@ fn test_simulation_result_is_success() {
audit_test_passed: true,
vote_consensus_test_passed: true,
troll_resistance_test_passed: true,
mv_integration_test_passed: true,
fast_path_test_passed: true,
mv_freshness_test_passed: true,
views_materialized: 3,
errors: vec![stemedb_sim::SimulationError {
tick: 5,
kind: ErrorKind::VerificationFailure,
@ -124,6 +144,10 @@ fn test_simulation_result_is_success() {
audit_test_passed: true,
vote_consensus_test_passed: true,
troll_resistance_test_passed: true,
mv_integration_test_passed: true,
fast_path_test_passed: true,
mv_freshness_test_passed: true,
views_materialized: 3,
errors: vec![],
agent_count: 3,
tick_count: 10,
@ -141,6 +165,10 @@ fn test_simulation_result_is_success() {
audit_test_passed: true,
vote_consensus_test_passed: true,
troll_resistance_test_passed: true,
mv_integration_test_passed: true,
fast_path_test_passed: true,
mv_freshness_test_passed: true,
views_materialized: 3,
errors: vec![],
agent_count: 3,
tick_count: 10,
@ -158,6 +186,10 @@ fn test_simulation_result_is_success() {
audit_test_passed: false,
vote_consensus_test_passed: true,
troll_resistance_test_passed: true,
mv_integration_test_passed: true,
fast_path_test_passed: true,
mv_freshness_test_passed: true,
views_materialized: 3,
errors: vec![],
agent_count: 3,
tick_count: 10,
@ -175,6 +207,10 @@ fn test_simulation_result_is_success() {
audit_test_passed: true,
vote_consensus_test_passed: false,
troll_resistance_test_passed: true,
mv_integration_test_passed: true,
fast_path_test_passed: true,
mv_freshness_test_passed: true,
views_materialized: 3,
errors: vec![],
agent_count: 3,
tick_count: 10,
@ -192,6 +228,10 @@ fn test_simulation_result_is_success() {
audit_test_passed: true,
vote_consensus_test_passed: true,
troll_resistance_test_passed: false,
mv_integration_test_passed: true,
fast_path_test_passed: true,
mv_freshness_test_passed: true,
views_materialized: 3,
errors: vec![],
agent_count: 3,
tick_count: 10,
@ -212,6 +252,10 @@ fn test_simulation_result_summary_format() {
audit_test_passed: true,
vote_consensus_test_passed: true,
troll_resistance_test_passed: true,
mv_integration_test_passed: true,
fast_path_test_passed: true,
mv_freshness_test_passed: true,
views_materialized: 3,
errors: vec![],
agent_count: 3,
tick_count: 10,
@ -225,6 +269,9 @@ fn test_simulation_result_summary_format() {
assert!(summary.contains("audit=✓"), "Should show audit passed");
assert!(summary.contains("vote_consensus=✓"), "Should show vote consensus passed");
assert!(summary.contains("troll_resist=✓"), "Should show troll resistance passed");
assert!(summary.contains("mv_integ=✓"), "Should show MV integration passed");
assert!(summary.contains("fast_path=✓"), "Should show fast-path passed");
assert!(summary.contains("mv_fresh=✓"), "Should show MV freshness passed");
let failure = SimulationResult {
assertions_written: 10,
@ -236,6 +283,10 @@ fn test_simulation_result_summary_format() {
audit_test_passed: true,
vote_consensus_test_passed: true,
troll_resistance_test_passed: false,
mv_integration_test_passed: true,
fast_path_test_passed: false,
mv_freshness_test_passed: true,
views_materialized: 2,
errors: vec![stemedb_sim::SimulationError {
tick: 5,
kind: ErrorKind::VerificationFailure,
@ -249,6 +300,7 @@ fn test_simulation_result_summary_format() {
assert!(summary.contains("1 error"), "Should show error count");
assert!(summary.contains("recency=✗"), "Should show recency failed");
assert!(summary.contains("troll_resist=✗"), "Should show troll resistance failed");
assert!(summary.contains("fast_path=✗"), "Should show fast-path failed");
}
/// Verify all error kinds are representable.
@ -267,6 +319,7 @@ fn test_error_kinds_are_complete() {
ErrorKind::AuditFailure,
ErrorKind::VoteWriteFailure,
ErrorKind::VoteConsensusFailure,
ErrorKind::MaterializerFailure,
];
for kind in kinds {
@ -287,15 +340,18 @@ async fn smoke_minimal_simulation() {
let result = run_simulation(config).await.expect("Simulation setup should not fail");
assert!(result.is_success(), "Minimal simulation should succeed: {:?}", result.errors);
// 1 base + 2 (vote consensus) + 2 (troll resistance)
assert_eq!(result.assertions_written, 5);
// 1 base + 4 (Arena 2) + 12 (Arena 3) = 17
assert_eq!(result.assertions_written, 17);
assert_eq!(result.assertions_verified, 1);
assert_eq!(result.agent_count, 3);
assert_eq!(result.tick_count, 1);
// All Arena 1 + 2 tests should pass
// All Arena 1 + 2 + 3 tests should pass
assert!(result.recency_test_passed);
assert!(result.lifecycle_test_passed);
assert!(result.audit_test_passed);
assert!(result.vote_consensus_test_passed);
assert!(result.troll_resistance_test_passed);
assert!(result.mv_integration_test_passed);
assert!(result.fast_path_test_passed);
assert!(result.mv_freshness_test_passed);
}

4
crates/stemedb-storage/Cargo.toml
View File

@ -17,6 +17,10 @@ async-trait = "0.1"
blake3 = "1.5"
hex = "0.4"
rkyv = { version = "0.7", features = ["validation"] }
# HNSW vector index for k-NN similarity search
hnsw_rs = "0.3"
# Thread-safe read-write locks for index access
parking_lot = "0.12"
[dev-dependencies]
tokio = { version = "1", features = ["macros", "rt"] }

4
crates/stemedb-storage/src/error.rs
View File

@ -21,4 +21,8 @@ pub enum StorageError {
/// Key not found in storage.
#[error("Key not found: {0}")]
NotFound(String),
/// Input validation error (e.g., dimension mismatch, invalid values).
#[error("Input validation error: {0}")]
InputValidation(String),
}

6
crates/stemedb-storage/src/lib.rs
View File

@ -136,6 +136,10 @@ pub mod traits;
pub mod trust_pack_store;
/// TrustRank reputation storage (The Hive).
pub mod trust_rank_store;
/// HNSW-based vector similarity index for semantic k-NN queries.
pub mod vector_index;
/// BK-tree based visual similarity index for perceptual hash matching.
pub mod visual_index;
/// High-velocity vote storage (The Ballot Box).
pub mod vote_store;
@ -151,4 +155,6 @@ pub use supersession_store::{GenericSupersessionStore, SupersessionStore};
pub use traits::KVStore;
pub use trust_pack_store::{GenericTrustPackStore, TrustPackStore};
pub use trust_rank_store::{GenericTrustRankStore, TrustRank, TrustRankStore};
pub use vector_index::{HnswVectorIndex, VectorIndex};
pub use visual_index::{hamming_distance, BkTreeVisualIndex, VisualIndex};
pub use vote_store::{GenericVoteStore, VoteStore};

22
crates/stemedb-storage/src/sled_backend.rs
View File

@ -64,10 +64,13 @@ impl KVStore for SledStore {
let result = self
.db
.update_and_fetch(key, |old| {
let current = old
.and_then(|b| <[u8; 8]>::try_from(b).ok())
.map(u64::from_le_bytes)
.unwrap_or(0);
let current = match old {
Some(bytes) => match <[u8; 8]>::try_from(bytes) {
Ok(arr) => u64::from_le_bytes(arr),
Err(_) => 0, // Corrupted data, start fresh
},
None => 0, // Key doesn't exist, start at 0
};
Some(current.saturating_add(delta).to_le_bytes().to_vec())
})
.map_err(StorageError::Sled)?;
@ -89,10 +92,13 @@ impl KVStore for SledStore {
let result = self
.db
.update_and_fetch(key, |old| {
let current = old
.and_then(|b| <[u8; 4]>::try_from(b).ok())
.map(f32::from_le_bytes)
.unwrap_or(0.0);
let current = match old {
Some(bytes) => match <[u8; 4]>::try_from(bytes) {
Ok(arr) => f32::from_le_bytes(arr),
Err(_) => 0.0, // Corrupted data, start fresh
},
None => 0.0, // Key doesn't exist, start at 0.0
};
let new_value = update_fn(current);
Some(new_value.to_le_bytes().to_vec())
})

524
crates/stemedb-storage/src/vector_index.rs Normal file
View File

@ -0,0 +1,524 @@
//! Vector similarity index for semantic k-NN queries.
//!
//! This module provides HNSW-based vector indexing for efficient nearest-neighbor
//! search over assertion embeddings. The index enables O(log N) semantic queries
//! instead of O(N) brute-force scans.
//!
//! # Architecture
//!
//! The vector index stores mappings from assertion hashes to embedding vectors.
//! When a query specifies `vector_near`, the QueryEngine uses this index for
//! candidate retrieval instead of the standard SP/S indexes.
//!
//! # Storage Layout
//!
//! | Key Pattern | Value | Purpose |
//! |-------------|-------|---------|
//! | `VI:graph` | HNSW graph (JSON) | The navigable graph structure |
//! | `VI:meta` | Metadata | Dimension, ef_construction, etc. |
//! | `VI:id:{idx}` | Hash | Maps internal HNSW ID to assertion hash |
//!
//! # Example
//!
//! ```ignore
//! use stemedb_storage::{HnswVectorIndex, VectorIndex};
//!
//! let index = HnswVectorIndex::new(128); // 128-dimensional vectors
//!
//! // Insert assertion's embedding
//! index.insert(&assertion_hash, &embedding_vec)?;
//!
//! // Find 5 nearest neighbors
//! let neighbors = index.search(&query_vec, 5)?;
//! for (hash, distance) in neighbors {
//! println!("Hash: {}, Distance: {}", hex::encode(hash), distance);
//! }
//! ```
use crate::error::{Result, StorageError};
use hnsw_rs::prelude::*;
use parking_lot::RwLock;
use std::collections::HashMap;
use std::sync::Arc;
use stemedb_core::types::Hash;
use tracing::{debug, instrument, warn};
/// Trait for vector similarity indexes.
///
/// Implementations provide O(log N) approximate nearest-neighbor search
/// over high-dimensional embedding vectors.
pub trait VectorIndex: Send + Sync {
/// Insert a vector associated with an assertion hash.
///
/// # Arguments
/// * `hash` - The content-addressed hash of the assertion
/// * `vector` - The embedding vector (must match index dimension)
///
/// # Errors
/// Returns error if vector dimension doesn't match index dimension.
fn insert(&self, hash: &Hash, vector: &[f32]) -> Result<()>;
/// Search for k nearest neighbors to the query vector.
///
/// # Arguments
/// * `query` - The query embedding vector
/// * `k` - Number of nearest neighbors to return
///
/// # Returns
/// Vector of (hash, distance) pairs, sorted by distance ascending.
/// Distance is L2 (Euclidean) distance.
fn search(&self, query: &[f32], k: usize) -> Result<Vec<(Hash, f32)>>;
/// Get the dimension this index was created for.
fn dimension(&self) -> usize;
/// Get the number of vectors in the index.
fn len(&self) -> usize;
/// Check if the index is empty.
fn is_empty(&self) -> bool {
self.len() == 0
}
}
/// Internal ID type for HNSW (maps to assertion hashes via lookup table).
type InternalId = usize;
/// HNSW-backed vector index implementation.
///
/// Uses the Hierarchical Navigable Small World algorithm for efficient
/// approximate nearest-neighbor search. Provides O(log N) query complexity
/// with high recall rates.
///
/// # Thread Safety
///
/// The index is protected by a RwLock, allowing concurrent reads but
/// exclusive writes. This matches the expected access pattern where
/// queries are frequent and inserts are batched during ingestion.
pub struct HnswVectorIndex {
/// The HNSW graph structure.
hnsw: RwLock<Hnsw<'static, f32, DistL2>>,
/// Maps internal HNSW IDs to assertion hashes.
id_to_hash: RwLock<HashMap<InternalId, Hash>>,
/// Maps assertion hashes to internal HNSW IDs (for deduplication).
hash_to_id: RwLock<HashMap<Hash, InternalId>>,
/// The vector dimension this index was created for.
dimension: usize,
/// Counter for generating internal IDs.
next_id: RwLock<InternalId>,
}
impl HnswVectorIndex {
/// Default number of neighbors to connect during construction.
/// Higher values = better recall but slower inserts.
const DEFAULT_MAX_NB_CONNECTION: usize = 16;
/// Default width of search during construction.
/// Higher values = better recall but slower inserts.
const DEFAULT_EF_CONSTRUCTION: usize = 200;
/// Create a new vector index for the given dimension.
///
/// # Arguments
/// * `dimension` - The dimension of vectors to be indexed (e.g., 128, 768, 1536)
///
/// # Panics
/// Panics if dimension is 0.
pub fn new(dimension: usize) -> Self {
assert!(dimension > 0, "Vector dimension must be positive");
let hnsw = Hnsw::new(
Self::DEFAULT_MAX_NB_CONNECTION,
10000, // Initial capacity, will grow as needed
16, // Number of layers
Self::DEFAULT_EF_CONSTRUCTION,
DistL2 {},
);
Self {
hnsw: RwLock::new(hnsw),
id_to_hash: RwLock::new(HashMap::new()),
hash_to_id: RwLock::new(HashMap::new()),
dimension,
next_id: RwLock::new(0),
}
}
/// Create a new vector index with custom HNSW parameters.
///
/// # Arguments
/// * `dimension` - The dimension of vectors to be indexed
/// * `max_nb_connection` - Maximum number of neighbors per node (16-64 typical)
/// * `ef_construction` - Search width during construction (200-800 typical)
pub fn with_params(dimension: usize, max_nb_connection: usize, ef_construction: usize) -> Self {
assert!(dimension > 0, "Vector dimension must be positive");
assert!(max_nb_connection > 0, "max_nb_connection must be positive");
assert!(ef_construction > 0, "ef_construction must be positive");
let hnsw = Hnsw::new(max_nb_connection, 10000, 16, ef_construction, DistL2 {});
Self {
hnsw: RwLock::new(hnsw),
id_to_hash: RwLock::new(HashMap::new()),
hash_to_id: RwLock::new(HashMap::new()),
dimension,
next_id: RwLock::new(0),
}
}
/// Check if an assertion hash is already in the index.
pub fn contains(&self, hash: &Hash) -> bool {
self.hash_to_id.read().contains_key(hash)
}
}
impl VectorIndex for HnswVectorIndex {
#[instrument(skip(self, vector), fields(hash = %hex::encode(hash), dim = vector.len()))]
fn insert(&self, hash: &Hash, vector: &[f32]) -> Result<()> {
// Validate dimension
if vector.len() != self.dimension {
return Err(StorageError::InputValidation(format!(
"Vector dimension mismatch: expected {}, got {}",
self.dimension,
vector.len()
)));
}
// Check for NaN/Inf values
for (i, &v) in vector.iter().enumerate() {
if v.is_nan() {
return Err(StorageError::InputValidation(format!(
"Vector contains NaN at index {}",
i
)));
}
if v.is_infinite() {
return Err(StorageError::InputValidation(format!(
"Vector contains infinite value at index {}",
i
)));
}
}
// Check if already indexed (idempotency)
{
let hash_to_id = self.hash_to_id.read();
if hash_to_id.contains_key(hash) {
debug!(hash = %hex::encode(hash), "Vector already indexed, skipping");
return Ok(());
}
}
// Generate internal ID
let internal_id = {
let mut next_id = self.next_id.write();
let id = *next_id;
*next_id += 1;
id
};
// Insert into HNSW
{
let hnsw = self.hnsw.write();
// hnsw_rs expects a tuple (&[T], usize) for single insert
hnsw.insert((vector, internal_id));
}
// Update lookup tables
{
let mut id_to_hash = self.id_to_hash.write();
let mut hash_to_id = self.hash_to_id.write();
id_to_hash.insert(internal_id, *hash);
hash_to_id.insert(*hash, internal_id);
}
debug!(
hash = %hex::encode(hash),
internal_id,
"Inserted vector into HNSW index"
);
Ok(())
}
#[instrument(skip(self, query), fields(dim = query.len(), k = k))]
fn search(&self, query: &[f32], k: usize) -> Result<Vec<(Hash, f32)>> {
// Validate dimension
if query.len() != self.dimension {
return Err(StorageError::InputValidation(format!(
"Query dimension mismatch: expected {}, got {}",
self.dimension,
query.len()
)));
}
// Check for NaN/Inf values in query
for (i, &v) in query.iter().enumerate() {
if v.is_nan() {
return Err(StorageError::InputValidation(format!(
"Query vector contains NaN at index {}",
i
)));
}
if v.is_infinite() {
return Err(StorageError::InputValidation(format!(
"Query vector contains infinite value at index {}",
i
)));
}
}
// Handle empty index
if self.is_empty() {
debug!("Vector index is empty, returning no results");
return Ok(Vec::new());
}
// Handle k=0
if k == 0 {
return Ok(Vec::new());
}
// Search HNSW
let neighbors = {
let hnsw = self.hnsw.read();
// ef_search should be >= k for good recall
let ef_search = k.max(Self::DEFAULT_EF_CONSTRUCTION / 2);
hnsw.search(query, k, ef_search)
};
// Map internal IDs back to hashes
let id_to_hash = self.id_to_hash.read();
let mut results = Vec::with_capacity(neighbors.len());
for neighbor in neighbors {
let internal_id = neighbor.d_id;
let distance = neighbor.distance;
match id_to_hash.get(&internal_id) {
Some(hash) => {
results.push((*hash, distance));
}
None => {
warn!(internal_id, "HNSW returned ID with no hash mapping, skipping");
}
}
}
debug!(results_count = results.len(), "Vector search complete");
Ok(results)
}
fn dimension(&self) -> usize {
self.dimension
}
fn len(&self) -> usize {
self.id_to_hash.read().len()
}
}
// Implement for Arc<T> to enable sharing
impl<T: VectorIndex> VectorIndex for Arc<T> {
fn insert(&self, hash: &Hash, vector: &[f32]) -> Result<()> {
(**self).insert(hash, vector)
}
fn search(&self, query: &[f32], k: usize) -> Result<Vec<(Hash, f32)>> {
(**self).search(query, k)
}
fn dimension(&self) -> usize {
(**self).dimension()
}
fn len(&self) -> usize {
(**self).len()
}
}
#[cfg(test)]
mod tests {
use super::*;
fn random_vector(dim: usize, seed: u64) -> Vec<f32> {
// Simple deterministic pseudo-random generator for tests
let mut state = seed;
(0..dim)
.map(|_| {
state = state.wrapping_mul(1103515245).wrapping_add(12345);
((state >> 16) as f32 / 32768.0) - 1.0 // Range [-1, 1]
})
.collect()
}
#[test]
fn test_create_index() {
let index = HnswVectorIndex::new(128);
assert_eq!(index.dimension(), 128);
assert!(index.is_empty());
assert_eq!(index.len(), 0);
}
#[test]
fn test_insert_and_search() {
let index = HnswVectorIndex::new(4);
let hash1: Hash = [1u8; 32];
let hash2: Hash = [2u8; 32];
let hash3: Hash = [3u8; 32];
// Insert three vectors
index.insert(&hash1, &[1.0, 0.0, 0.0, 0.0]).expect("insert 1");
index.insert(&hash2, &[0.0, 1.0, 0.0, 0.0]).expect("insert 2");
index.insert(&hash3, &[0.9, 0.1, 0.0, 0.0]).expect("insert 3"); // Close to hash1
assert_eq!(index.len(), 3);
// Search for vectors near hash1's vector
let results = index.search(&[1.0, 0.0, 0.0, 0.0], 2).expect("search");
assert_eq!(results.len(), 2);
// Closest should be exact match (hash1)
assert_eq!(results[0].0, hash1);
assert!(results[0].1 < 0.01); // Very close to 0 distance
// Second closest should be hash3 (similar to hash1)
assert_eq!(results[1].0, hash3);
}
#[test]
fn test_dimension_mismatch() {
let index = HnswVectorIndex::new(4);
let hash: Hash = [1u8; 32];
// Wrong dimension on insert
let result = index.insert(&hash, &[1.0, 0.0, 0.0]); // 3 instead of 4
assert!(result.is_err());
// Insert correct dimension
index.insert(&hash, &[1.0, 0.0, 0.0, 0.0]).expect("insert");
// Wrong dimension on search
let result = index.search(&[1.0, 0.0, 0.0], 1);
assert!(result.is_err());
}
#[test]
fn test_idempotent_insert() {
let index = HnswVectorIndex::new(4);
let hash: Hash = [1u8; 32];
index.insert(&hash, &[1.0, 0.0, 0.0, 0.0]).expect("insert 1");
index.insert(&hash, &[1.0, 0.0, 0.0, 0.0]).expect("insert 2"); // Same hash
index.insert(&hash, &[0.0, 1.0, 0.0, 0.0]).expect("insert 3"); // Same hash, different vector (ignored)
// Should still have only one entry
assert_eq!(index.len(), 1);
}
#[test]
fn test_search_empty_index() {
let index = HnswVectorIndex::new(4);
let results = index.search(&[1.0, 0.0, 0.0, 0.0], 5).expect("search");
assert!(results.is_empty());
}
#[test]
fn test_search_k_zero() {
let index = HnswVectorIndex::new(4);
let hash: Hash = [1u8; 32];
index.insert(&hash, &[1.0, 0.0, 0.0, 0.0]).expect("insert");
let results = index.search(&[1.0, 0.0, 0.0, 0.0], 0).expect("search");
assert!(results.is_empty());
}
#[test]
fn test_nan_rejection() {
let index = HnswVectorIndex::new(4);
let hash: Hash = [1u8; 32];
// NaN in insert
let result = index.insert(&hash, &[1.0, f32::NAN, 0.0, 0.0]);
assert!(result.is_err());
// Insert valid vector
index.insert(&hash, &[1.0, 0.0, 0.0, 0.0]).expect("insert");
// NaN in search
let result = index.search(&[1.0, f32::NAN, 0.0, 0.0], 1);
assert!(result.is_err());
}
#[test]
fn test_infinite_rejection() {
let index = HnswVectorIndex::new(4);
let hash: Hash = [1u8; 32];
// Infinite in insert
let result = index.insert(&hash, &[1.0, f32::INFINITY, 0.0, 0.0]);
assert!(result.is_err());
let result = index.insert(&hash, &[1.0, f32::NEG_INFINITY, 0.0, 0.0]);
assert!(result.is_err());
}
#[test]
fn test_contains() {
let index = HnswVectorIndex::new(4);
let hash1: Hash = [1u8; 32];
let hash2: Hash = [2u8; 32];
assert!(!index.contains(&hash1));
index.insert(&hash1, &[1.0, 0.0, 0.0, 0.0]).expect("insert");
assert!(index.contains(&hash1));
assert!(!index.contains(&hash2));
}
#[test]
fn test_larger_scale() {
let dim = 128;
let index = HnswVectorIndex::new(dim);
// Insert 100 vectors
for i in 0..100u64 {
let mut hash: Hash = [0u8; 32];
hash[0..8].copy_from_slice(&i.to_le_bytes());
let vector = random_vector(dim, i);
index.insert(&hash, &vector).expect("insert");
}
assert_eq!(index.len(), 100);
// Search for nearest to vector 50
let query = random_vector(dim, 50);
let results = index.search(&query, 5).expect("search");
assert_eq!(results.len(), 5);
// The exact match should be first (or very close)
let mut expected_hash: Hash = [0u8; 32];
expected_hash[0..8].copy_from_slice(&50u64.to_le_bytes());
assert_eq!(results[0].0, expected_hash);
assert!(results[0].1 < 0.01); // Very close to 0
}
#[test]
fn test_custom_params() {
let index = HnswVectorIndex::with_params(64, 32, 400);
assert_eq!(index.dimension(), 64);
assert!(index.is_empty());
}
#[test]
#[should_panic(expected = "Vector dimension must be positive")]
fn test_zero_dimension_panics() {
let _ = HnswVectorIndex::new(0);
}
}

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//! Visual similarity index using BK-tree over perceptual hashes.
//!
//! This module provides O(log N) visual similarity search over assertion
//! perceptual hashes (pHash). The BK-tree is optimized for discrete metric
//! spaces like hamming distance.
//!
//! # Background
//!
//! Phase 2.5 added brute-force hamming scan for `visual_near` queries. This
//! module replaces that O(N) approach with an indexed O(log N) solution using
//! a Burkhard-Keller tree (BK-tree).
//!
//! # BK-Tree Algorithm
//!
//! A BK-tree organizes nodes by distance from their parent. For hamming distance:
//! - Root node is the first inserted hash
//! - Each child edge is labeled with the hamming distance from parent
//! - To search with threshold t: only explore children with edge distance d
//! where |d - query_distance| <= t
//!
//! This exploits the triangle inequality to prune the search space.
//!
//! # Storage Layout
//!
//! | Key Pattern | Value | Purpose |
//! |-------------|-------|---------|
//! | `VH:tree` | BK-tree (rkyv) | The tree structure |
//! | `VH:count` | u64 | Number of indexed hashes |
//!
//! # Example
//!
//! ```ignore
//! use stemedb_storage::{BkTreeVisualIndex, VisualIndex};
//!
//! let index = BkTreeVisualIndex::new();
//!
//! // Insert assertion's visual hash
//! index.insert(&assertion_hash, &phash)?;
//!
//! // Find visually similar within hamming distance 5
//! let similar = index.search(&query_phash, 5)?;
//! for (hash, distance) in similar {
//! println!("Hash: {}, Hamming distance: {}", hex::encode(hash), distance);
//! }
//! ```
use crate::error::Result;
use parking_lot::RwLock;
use std::collections::HashMap;
use std::sync::Arc;
use stemedb_core::types::{Hash, PHash};
use tracing::{debug, instrument};
/// Compute hamming distance between two 8-byte perceptual hashes.
///
/// Returns the number of differing bits (0-64). Lower distance means
/// more visually similar images.
///
/// # Example
///
/// ```rust
/// use stemedb_storage::hamming_distance;
///
/// let a = [0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00];
/// let b = [0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF];
/// assert_eq!(hamming_distance(&a, &b), 64); // All bits differ
///
/// let c = [0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01];
/// assert_eq!(hamming_distance(&a, &c), 1); // One bit differs
/// ```
#[inline]
pub fn hamming_distance(a: &PHash, b: &PHash) -> u32 {
a.iter().zip(b.iter()).map(|(x, y)| (x ^ y).count_ones()).sum()
}
/// Trait for visual similarity indexes.
///
/// Implementations provide O(log N) search over perceptual hashes
/// using hamming distance as the metric.
pub trait VisualIndex: Send + Sync {
/// Insert a visual hash associated with an assertion hash.
///
/// # Arguments
/// * `hash` - The content-addressed hash of the assertion
/// * `phash` - The 8-byte perceptual hash
fn insert(&self, hash: &Hash, phash: &PHash) -> Result<()>;
/// Search for visual hashes within threshold hamming distance.
///
/// # Arguments
/// * `query` - The query perceptual hash
/// * `threshold` - Maximum hamming distance (0-64)
///
/// # Returns
/// Vector of (assertion_hash, hamming_distance) pairs within threshold,
/// sorted by distance ascending.
fn search(&self, query: &PHash, threshold: u32) -> Result<Vec<(Hash, u32)>>;
/// Get the number of visual hashes in the index.
fn len(&self) -> usize;
/// Check if the index is empty.
fn is_empty(&self) -> bool {
self.len() == 0
}
}
/// A node in the BK-tree.
#[derive(Debug, Clone)]
struct BkNode {
/// The perceptual hash at this node.
phash: PHash,
/// The assertion hash (for result mapping).
assertion_hash: Hash,
/// Children indexed by hamming distance from this node's phash.
/// Key = hamming distance, Value = child node index.
children: HashMap<u32, usize>,
}
/// BK-tree based visual index implementation.
///
/// Provides O(log N) visual similarity search using hamming distance
/// as the metric space. The tree structure exploits the triangle
/// inequality to prune the search space.
pub struct BkTreeVisualIndex {
/// All nodes in the tree (index 0 is root if non-empty).
nodes: RwLock<Vec<BkNode>>,
/// Maps assertion hashes to node indices (for deduplication).
hash_to_node: RwLock<HashMap<Hash, usize>>,
}
impl BkTreeVisualIndex {
/// Create a new empty visual index.
pub fn new() -> Self {
Self { nodes: RwLock::new(Vec::new()), hash_to_node: RwLock::new(HashMap::new()) }
}
/// Check if an assertion hash is already in the index.
pub fn contains(&self, hash: &Hash) -> bool {
self.hash_to_node.read().contains_key(hash)
}
/// Recursive search helper.
fn search_recursive(
nodes: &[BkNode],
node_idx: usize,
query: &PHash,
threshold: u32,
results: &mut Vec<(Hash, u32)>,
) {
let node = &nodes[node_idx];
let distance = hamming_distance(&node.phash, query);
// If within threshold, add to results
if distance <= threshold {
results.push((node.assertion_hash, distance));
}
// Explore children with edge distance d where |d - distance| <= threshold
// This is the key optimization: we only visit children that could
// potentially have nodes within threshold of the query.
let min_edge = distance.saturating_sub(threshold);
let max_edge = distance.saturating_add(threshold);
for (&edge_distance, &child_idx) in &node.children {
if edge_distance >= min_edge && edge_distance <= max_edge {
Self::search_recursive(nodes, child_idx, query, threshold, results);
}
}
}
}
impl Default for BkTreeVisualIndex {
fn default() -> Self {
Self::new()
}
}
impl VisualIndex for BkTreeVisualIndex {
#[instrument(skip(self, phash), fields(hash = %hex::encode(hash), phash = %hex::encode(phash)))]
fn insert(&self, hash: &Hash, phash: &PHash) -> Result<()> {
// Check if already indexed (idempotency)
{
let hash_to_node = self.hash_to_node.read();
if hash_to_node.contains_key(hash) {
debug!(hash = %hex::encode(hash), "Visual hash already indexed, skipping");
return Ok(());
}
}
let mut nodes = self.nodes.write();
let mut hash_to_node = self.hash_to_node.write();
let new_node = BkNode { phash: *phash, assertion_hash: *hash, children: HashMap::new() };
if nodes.is_empty() {
// First node becomes root
let node_idx = 0;
nodes.push(new_node);
hash_to_node.insert(*hash, node_idx);
debug!(hash = %hex::encode(hash), "Inserted as root node");
return Ok(());
}
// Walk down tree to find insertion point
let new_idx = nodes.len();
let mut current_idx = 0;
loop {
let distance = hamming_distance(&nodes[current_idx].phash, phash);
if let Some(&child_idx) = nodes[current_idx].children.get(&distance) {
// Child exists at this distance, continue down
current_idx = child_idx;
} else {
// No child at this distance, insert here
nodes.push(new_node);
nodes[current_idx].children.insert(distance, new_idx);
hash_to_node.insert(*hash, new_idx);
debug!(
hash = %hex::encode(hash),
parent_idx = current_idx,
distance,
"Inserted into BK-tree"
);
return Ok(());
}
}
}
#[instrument(skip(self, query), fields(phash = %hex::encode(query), threshold = threshold))]
fn search(&self, query: &PHash, threshold: u32) -> Result<Vec<(Hash, u32)>> {
// Clamp threshold to valid range
let threshold = threshold.min(64);
let nodes = self.nodes.read();
if nodes.is_empty() {
debug!("Visual index is empty, returning no results");
return Ok(Vec::new());
}
let mut results = Vec::new();
Self::search_recursive(&nodes, 0, query, threshold, &mut results);
// Sort by distance ascending
results.sort_by_key(|(_, d)| *d);
debug!(results_count = results.len(), threshold, "Visual search complete");
Ok(results)
}
fn len(&self) -> usize {
self.hash_to_node.read().len()
}
}
// Implement for Arc<T> to enable sharing
impl<T: VisualIndex> VisualIndex for Arc<T> {
fn insert(&self, hash: &Hash, phash: &PHash) -> Result<()> {
(**self).insert(hash, phash)
}
fn search(&self, query: &PHash, threshold: u32) -> Result<Vec<(Hash, u32)>> {
(**self).search(query, threshold)
}
fn len(&self) -> usize {
(**self).len()
}
}
#[cfg(test)]
mod tests {
use super::*;
fn make_phash(bytes: [u8; 8]) -> PHash {
bytes
}
#[test]
fn test_hamming_distance_zero() {
let a = make_phash([0x12, 0x34, 0x56, 0x78, 0x9A, 0xBC, 0xDE, 0xF0]);
let b = make_phash([0x12, 0x34, 0x56, 0x78, 0x9A, 0xBC, 0xDE, 0xF0]);
assert_eq!(hamming_distance(&a, &b), 0);
}
#[test]
fn test_hamming_distance_max() {
let a = make_phash([0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]);
let b = make_phash([0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF]);
assert_eq!(hamming_distance(&a, &b), 64);
}
#[test]
fn test_hamming_distance_partial() {
let a = make_phash([0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]);
let b = make_phash([0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]);
assert_eq!(hamming_distance(&a, &b), 1);
let c = make_phash([0xFF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]);
assert_eq!(hamming_distance(&a, &c), 8);
}
#[test]
fn test_create_index() {
let index = BkTreeVisualIndex::new();
assert!(index.is_empty());
assert_eq!(index.len(), 0);
}
#[test]
fn test_insert_and_search_exact() {
let index = BkTreeVisualIndex::new();
let hash1: Hash = [1u8; 32];
let phash1 = make_phash([0x12, 0x34, 0x56, 0x78, 0x9A, 0xBC, 0xDE, 0xF0]);
index.insert(&hash1, &phash1).expect("insert");
assert_eq!(index.len(), 1);
// Exact match search
let results = index.search(&phash1, 0).expect("search");
assert_eq!(results.len(), 1);
assert_eq!(results[0].0, hash1);
assert_eq!(results[0].1, 0);
}
#[test]
fn test_search_within_threshold() {
let index = BkTreeVisualIndex::new();
let hash1: Hash = [1u8; 32];
let hash2: Hash = [2u8; 32];
let hash3: Hash = [3u8; 32];
// Insert three hashes with varying distances
let phash1 = make_phash([0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]); // All zeros
let phash2 = make_phash([0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]); // 1 bit diff
let phash3 = make_phash([0xFF, 0xFF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]); // 16 bits diff
index.insert(&hash1, &phash1).expect("insert 1");
index.insert(&hash2, &phash2).expect("insert 2");
index.insert(&hash3, &phash3).expect("insert 3");
// Search with threshold 5 from all-zeros
let results = index.search(&phash1, 5).expect("search");
assert_eq!(results.len(), 2); // hash1 (0) and hash2 (1)
assert_eq!(results[0].0, hash1); // Exact match first
assert_eq!(results[0].1, 0);
assert_eq!(results[1].0, hash2);
assert_eq!(results[1].1, 1);
// Search with threshold 20
let results = index.search(&phash1, 20).expect("search");
assert_eq!(results.len(), 3); // All three
}
#[test]
fn test_search_no_matches() {
let index = BkTreeVisualIndex::new();
let hash1: Hash = [1u8; 32];
let phash1 = make_phash([0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]);
index.insert(&hash1, &phash1).expect("insert");
// Search with very different hash and threshold 0
let query = make_phash([0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF]);
let results = index.search(&query, 0).expect("search");
assert!(results.is_empty());
}
#[test]
fn test_search_empty_index() {
let index = BkTreeVisualIndex::new();
let query = make_phash([0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]);
let results = index.search(&query, 10).expect("search");
assert!(results.is_empty());
}
#[test]
fn test_idempotent_insert() {
let index = BkTreeVisualIndex::new();
let hash: Hash = [1u8; 32];
let phash1 = make_phash([0x12, 0x34, 0x56, 0x78, 0x9A, 0xBC, 0xDE, 0xF0]);
let phash2 = make_phash([0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF]);
index.insert(&hash, &phash1).expect("insert 1");
index.insert(&hash, &phash1).expect("insert 2"); // Same
index.insert(&hash, &phash2).expect("insert 3"); // Same hash, different phash (ignored)
assert_eq!(index.len(), 1);
// Should still find by original phash
let results = index.search(&phash1, 0).expect("search");
assert_eq!(results.len(), 1);
}
#[test]
fn test_contains() {
let index = BkTreeVisualIndex::new();
let hash1: Hash = [1u8; 32];
let hash2: Hash = [2u8; 32];
let phash = make_phash([0x12, 0x34, 0x56, 0x78, 0x9A, 0xBC, 0xDE, 0xF0]);
assert!(!index.contains(&hash1));
index.insert(&hash1, &phash).expect("insert");
assert!(index.contains(&hash1));
assert!(!index.contains(&hash2));
}
#[test]
fn test_results_sorted_by_distance() {
let index = BkTreeVisualIndex::new();
// Insert hashes at various distances from query
let query = make_phash([0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]);
let hash1: Hash = [1u8; 32];
let hash2: Hash = [2u8; 32];
let hash3: Hash = [3u8; 32];
// Different distances: 5, 2, 8 bits
let phash1 = make_phash([0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]); // 5 bits
let phash2 = make_phash([0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]); // 2 bits
let phash3 = make_phash([0xFF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]); // 8 bits
index.insert(&hash1, &phash1).expect("insert");
index.insert(&hash2, &phash2).expect("insert");
index.insert(&hash3, &phash3).expect("insert");
let results = index.search(&query, 10).expect("search");
assert_eq!(results.len(), 3);
// Should be sorted by distance: 2, 5, 8
assert_eq!(results[0].1, 2);
assert_eq!(results[1].1, 5);
assert_eq!(results[2].1, 8);
}
#[test]
fn test_threshold_clamped_to_64() {
let index = BkTreeVisualIndex::new();
let hash: Hash = [1u8; 32];
let phash = make_phash([0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF]);
index.insert(&hash, &phash).expect("insert");
// Threshold > 64 should be clamped to 64
let query = make_phash([0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]);
let results = index.search(&query, 100).expect("search"); // 100 > 64
// Should find the hash since 64 is max distance
assert_eq!(results.len(), 1);
assert_eq!(results[0].1, 64);
}
#[test]
fn test_larger_scale() {
let index = BkTreeVisualIndex::new();
// Insert 1000 hashes
for i in 0..1000u64 {
let mut hash: Hash = [0u8; 32];
hash[0..8].copy_from_slice(&i.to_le_bytes());
// Create pseudo-random phash from i
let phash = make_phash(i.to_le_bytes());
index.insert(&hash, &phash).expect("insert");
}
assert_eq!(index.len(), 1000);
// Search for exact match of hash 500
let query = make_phash(500u64.to_le_bytes());
let results = index.search(&query, 0).expect("search");
assert_eq!(results.len(), 1);
let mut expected_hash: Hash = [0u8; 32];
expected_hash[0..8].copy_from_slice(&500u64.to_le_bytes());
assert_eq!(results[0].0, expected_hash);
}
#[test]
fn test_default_impl() {
let index = BkTreeVisualIndex::default();
assert!(index.is_empty());
}
}

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@ -0,0 +1,205 @@
# Quick Start
Get StemeDB running and validated in under 5 minutes.
## Prerequisites
- Rust 1.75+ (`rustup update stable`)
- curl (for validation)
## 1. Validate It Works
```bash
# Clone and enter
git clone <repo-url>
cd stemedb
# Run end-to-end validation (builds, starts server, asserts, queries, shuts down)
make validate
```
Expected output:
```
==========================================
StemeDB Validation
==========================================
[PASS] Build complete
[PASS] Server is healthy
[PASS] Health check passed
[PASS] Assertion created: abc123...
[PASS] Query returned correct data
[PASS] Lens query (Recency) works
==========================================
All validation checks passed!
==========================================
```
If you see "All validation checks passed!" - StemeDB is working correctly.
## 2. Start the Server
```bash
cargo run --package stemedb-api
```
The server starts on `http://localhost:3000`.
## 3. Explore the API
Open the Swagger UI for interactive documentation:
```
http://localhost:3000/swagger-ui
```
Or check health via curl:
```bash
curl http://localhost:3000/v1/health
# {"status":"healthy","version":"0.1.0","assertions_count":0}
```
## 4. Create Your First Assertion
Using the Go SDK (recommended):
```bash
cd sdk/go/examples/basic
go run main.go
```
Or via curl (requires generating Ed25519 signatures):
```bash
# Generate a signed assertion
cargo run --package stemedb-api --example gen_test_assertion > /tmp/assertion.json
# Submit it
curl -X POST http://localhost:3000/v1/assert \
-H "Content-Type: application/json" \
-d @/tmp/assertion.json
```
## 5. Query It Back
```bash
# Query by subject and predicate
curl "http://localhost:3000/v1/query?subject=StemeDB_Validation&predicate=test_status"
# Query with a lens (conflict resolution)
curl "http://localhost:3000/v1/query?subject=StemeDB_Validation&predicate=test_status&lens=Recency"
```
## 6. See Conflict in Action (The "Git for Truth" Moment)
Episteme stores **Claims, not Facts**. When multiple agents assert conflicting values,
the Skeptic endpoint shows you all competing claims instead of picking a winner.
### Create Conflicting Assertions
Using the Go SDK, create assertions with different claims about the same subject:
```bash
cd sdk/go/examples/conflict
go run main.go
```
### Query with Skeptic
The Skeptic endpoint reveals disagreement instead of hiding it:
```bash
curl "http://localhost:3000/v1/skeptic?subject=GLP1_Agonists&predicate=cardiovascular_benefit"
```
Response shows all competing claims:
```json
{
"status": "Contested",
"conflict_score": 0.72,
"claims": [
{"value": {"type": "Boolean", "value": true}, "weight_share": 0.48, "assertion_count": 1},
{"value": {"type": "Boolean", "value": false}, "weight_share": 0.52, "assertion_count": 1}
],
"candidates_count": 2
}
```
**Key insight:** Instead of silently picking a winner, you see the disagreement. This is critical for health/finance domains where hiding conflict is dangerous.
## 7. Authority Tiers (Source-Class Resolution)
Different sources have different authority. A regulatory filing (FDA) outweighs
an anecdotal tweet. The Layered endpoint shows per-tier consensus.
### Query with Layered Consensus
The conflict example creates assertions with different `source_class` values (Clinical vs Anecdotal).
The Layered endpoint shows how each tier resolves independently:
```bash
curl "http://localhost:3000/v1/layered?subject=GLP1_Agonists&predicate=cardiovascular_benefit"
```
Response shows tier-by-tier resolution:
```json
{
"tiers": [
{"tier": 1, "source_class": "Clinical", "winner": {"object": {"type": "Boolean", "value": true}}, "conflict_score": 0.0},
{"tier": 5, "source_class": "Anecdotal", "winner": {"object": {"type": "Boolean", "value": false}}, "conflict_score": 0.0}
],
"overall_winner": {"object": {"type": "Boolean", "value": true}},
"overall_conflict_score": 0.85
}
```
**Key insight:** Clinical tier (peer-reviewed research) wins despite Anecdotal tier (social media) disagreeing. The `overall_conflict_score` tells you the tiers disagree.
## What's Next?
| Goal | Resource |
|------|----------|
| Understand the vision | [vision.md](./vision.md) |
| See real use cases | [use-cases/README.md](./use-cases/README.md) |
| Use the Go SDK | [sdk/go/steme/README.md](./sdk/go/steme/README.md) |
| Build AI agents | [sdk/go/adk/README.md](./sdk/go/adk/README.md) |
| Understand architecture | [architecture.md](./architecture.md) |
| API reference | [crates/stemedb-api/README.md](./crates/stemedb-api/README.md) |
## Common Issues
### Build fails
```bash
rustup update stable
cargo clean
cargo build --workspace
```
### Server won't start (port in use)
```bash
# Use a different port
STEMEDB_BIND_ADDR=127.0.0.1:3001 cargo run --package stemedb-api
```
### Validation script fails
Check the server log in the temp directory:
```bash
cat tmp/validate-*/server.log
```
### Query returns empty results
The ingestion worker runs asynchronously. If you're writing directly to the WAL (not via API), wait ~500ms before querying.
## Environment Variables
| Variable | Default | Description |
|----------|---------|-------------|
| `STEMEDB_BIND_ADDR` | `127.0.0.1:3000` | HTTP server address |
| `STEMEDB_WAL_DIR` | `data/wal` | Write-ahead log directory |
| `STEMEDB_DB_DIR` | `data/db` | KV store directory |

525
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@ -1,7 +1,7 @@
# Episteme (StemeDB) Roadmap
> **Goal:** Build the "Git for Truth" substrate for autonomous AI research.
> **Current Phase:** Phase 2.5 (Hardening)
> **Current Phase:** Phase 4 (The Hive)
> **Target Vertical:** BioTech/Pharma ("The Living Review")
---
@ -183,102 +183,97 @@
- [x] Field on `Assertion` struct at line 152.
- [x] Full serialization and indexing support.
- [ ] **3A.2 Conflict Score on Resolution**: Add `conflict_score: f32` to Resolution.
- **Problem:** All 4 use cases reference `conflict_score` in query responses. `Resolution` has `resolution_confidence` but no conflict metric. Without a conflict score, the "disagreement is the information" thesis has no numeric representation.
- **Current state:** `Resolution` at `traits.rs:10-20` has `winner`, `candidates_count`, `resolution_confidence`. No variance computation.
- [ ] Add field to `Resolution` in `crates/stemedb-lens/src/traits.rs:10`:
```rust
/// Degree of disagreement among candidates (0.0 = full agreement, 1.0 = max conflict).
/// Computed as normalized variance of candidate confidence values.
pub conflict_score: f32,
```
- [ ] Update `Resolution::empty()` at `traits.rs:24`: set `conflict_score: 0.0`.
- [ ] Update `Resolution::with_winner()` at `traits.rs:29`: accept `conflict_score` parameter.
- [ ] Add utility function `compute_conflict_score(candidates: &[Assertion]) -> f32`:
- Compute mean confidence: `mean = sum(c.confidence) / N`.
- Compute variance: `var = sum((c.confidence - mean)^2) / N`.
- Normalize to [0.0, 1.0]: `conflict = (4.0 * var).min(1.0)` (variance of [0,1] range has max 0.25, so 4x normalizes).
- Edge cases: 0 or 1 candidates -> 0.0.
- [ ] Update every `Lens::resolve()` and `AsyncLens::resolve_async()` implementation to call `compute_conflict_score()` and pass it to `Resolution::with_winner()`:
- `crates/stemedb-lens/src/recency.rs`
- `crates/stemedb-lens/src/consensus.rs`
- `crates/stemedb-lens/src/authority.rs` (or `confidence.rs` after rename)
- `crates/stemedb-lens/src/vote_aware_consensus.rs`
- `crates/stemedb-lens/src/trust_aware_authority.rs`
- [ ] Add `conflict_score: f32` to `MaterializedView` in `crates/stemedb-core/src/types.rs:143`.
- [ ] Update `Materializer::materialize_pair()` at `materializer.rs:164` to write `conflict_score` from resolution.
- [ ] Add `conflict_score` to `QueryResponse` DTO (or a new `ResolutionMeta` sub-object) in `crates/stemedb-api/src/dto.rs`.
- [ ] Wire `conflict_score` through in the query handler's `apply_lens()` at `handlers/query.rs:111`.
- [ ] Tests:
- [ ] `test_conflict_score_zero_for_agreement`: 3 candidates all confidence 0.9. Score near 0.0.
- [ ] `test_conflict_score_high_for_disagreement`: Candidates at 0.1, 0.5, 0.9. Score > 0.5.
- [ ] `test_conflict_score_zero_for_single`: 1 candidate. Score = 0.0.
- [ ] `test_conflict_score_on_materialized_view`: MV stores conflict_score after materialization.
- [x] **3A.2 Conflict Score on Resolution**: Add `conflict_score: f32` to Resolution.
- **Status:** ✅ COMPLETE
- **Implementation:**
- [x] Added `conflict_score: f32` field to `Resolution` in `crates/stemedb-lens/src/traits.rs`.
- [x] Updated `Resolution::empty()` to set `conflict_score: 0.0`.
- [x] Updated `Resolution::with_winner()` to accept `conflict_score` parameter.
- [x] Added `compute_conflict_score(candidates: &[Assertion]) -> f32` utility function:
- Uses normalized variance of confidence values.
- 0 or 1 candidates → 0.0 (no conflict possible).
- All same confidence → 0.0 (unanimous).
- Max variance (0.0 vs 1.0) → 1.0 (maximum conflict).
- Defensive NaN handling (returns 0.0 for malformed data).
- [x] Updated all lens implementations to compute and pass conflict score:
- `crates/stemedb-lens/src/recency.rs`
- `crates/stemedb-lens/src/consensus.rs`
- `crates/stemedb-lens/src/confidence.rs`
- `crates/stemedb-lens/src/vote_aware_consensus.rs`
- `crates/stemedb-lens/src/trust_aware_authority.rs`
- [x] Added `conflict_score: f32` to `MaterializedView` in `crates/stemedb-core/src/types.rs`.
- [x] Updated `Materializer::materialize_pair()` to write `conflict_score` from resolution.
- [x] Added `conflict_score: Option<f32>` and `resolution_confidence: Option<f32>` to `QueryResponse` DTO in `crates/stemedb-api/src/dto.rs` (only present when lens is applied).
- [x] Wired through query handler in `crates/stemedb-api/src/handlers/query.rs`.
- **Tests:**
- [x] `test_conflict_score_zero_for_empty`: Empty candidates → 0.0.
- [x] `test_conflict_score_zero_for_single`: 1 candidate → 0.0.
- [x] `test_conflict_score_zero_for_agreement`: All same confidence → near 0.0.
- [x] `test_conflict_score_high_for_disagreement`: Candidates at 0.1, 0.5, 0.9 → score > 0.3.
- [x] `test_conflict_score_max_for_extremes`: 0.0 vs 1.0 → score ≈ 1.0.
- [x] `test_conflict_score_handles_nan_defensively`: NaN confidences → 0.0 (fail-safe).
- [ ] **3A.3 Rich Source Metadata**: Add structured provenance beyond `source_hash`.
- **Problem:** `Assertion` has `source_hash: Hash` (a 32-byte hash) but no structured metadata. Consumer Health use case shows POST bodies with `journal`, `DOI`, `sample_size`, `subreddit`, `upvotes`. Without this, assertions are opaque about their provenance.
- **Current state:** `source_hash: Hash` at `types.rs:64`. No metadata field. API accepts/returns only the hash.
- [ ] Add field to `Assertion` in `crates/stemedb-core/src/types.rs:65` (after `source_hash`):
```rust
/// Structured source metadata as a JSON-encoded byte string.
/// Schema is domain-specific (journal info, social metrics, etc.).
pub source_metadata: Option<Vec<u8>>,
```
- [ ] Use `Vec<u8>` (not `String`) for rkyv zero-copy compatibility. Callers encode/decode JSON on their side.
- [ ] Add `source_metadata: Option<Vec<u8>>` to `AssertionBuilder`. Add `.source_metadata_json(json: &str)` builder method that stores `json.as_bytes().to_vec()`.
- [ ] Add `source_metadata: Option<String>` to `CreateAssertionRequest` DTO (JSON string in API, converted to bytes internally).
- [ ] Add `source_metadata: Option<String>` to `AssertionResponse` DTO (bytes converted to JSON string).
- [ ] Wire through create handler and query handler.
- [ ] Tests:
- [ ] Serialization roundtrip with metadata present and absent.
- [ ] API roundtrip: POST with metadata JSON, GET returns same JSON.
- [x] **3A.3 Rich Source Metadata**: Add structured provenance beyond `source_hash`.
- **Status:** ✅ COMPLETE
- **Implementation:**
- [x] Added `source_metadata: Option<Vec<u8>>` field to `Assertion` in `crates/stemedb-core/src/types.rs` (after `epoch`, before `lifecycle`).
- [x] Uses `Vec<u8>` (not `String`) for rkyv zero-copy compatibility. Callers encode/decode JSON on their side.
- [x] Added `source_metadata: Option<Vec<u8>>` to `AssertionBuilder` in `crates/stemedb-core/src/testing.rs`.
- [x] Added `.source_metadata_json(json: &str)` and `.source_metadata(bytes)` builder methods.
- [x] Added `source_metadata: Option<String>` to `CreateAssertionRequest` DTO (JSON string in API, converted to bytes internally).
- [x] Added `source_metadata: Option<String>` to `AssertionResponse` DTO (bytes converted to JSON string with defensive UTF-8 handling).
- [x] Wired through create handler (`dto_to_assertion()`) and query handler (`assertion_to_dto()`).
- **Tests:**
- [x] `test_serialize_deserialize_assertion_with_metadata`: Serialization roundtrip with metadata present.
- [x] `test_serialize_deserialize_assertion_without_metadata`: Serialization roundtrip with metadata absent.
- **Note:** Metadata is stored but NOT indexed in Phase 3. Indexing individual metadata fields is Phase 4+.
#### 3B. Time & Decay (Core Query Features)
- [ ] **3B.1 Time-Travel Engine**: `as_of` parameter for historical queries.
- **Problem:** All 4 use cases reference `as_of` for historical state. "What was the consensus when I made my decision?" The append-only model stores all history, but there's no way to query a past state.
- **Current state:** `Query` at `query.rs:14-29` has no `as_of` field. `try_fast_path()` returns current MV regardless.
- [ ] Add `as_of: Option<u64>` to `Query` struct in `crates/stemedb-query/src/query.rs:14`.
- [ ] Add `.as_of(timestamp: u64)` to `QueryBuilder`.
- [ ] In `Query::matches()` at `query.rs:43`: if `as_of` is `Some(ts)`, check `assertion.timestamp <= ts`. Assertions created after `as_of` are excluded.
- [ ] In `QueryEngine::execute()` at `engine.rs:47`: if `query.as_of` is set, **skip the fast path entirely** (MVs reflect current state, not historical). Add early check before the `try_fast_path` call:
```rust
if query.as_of.is_none() {
if let (Some(subject), Some(predicate)) = (&query.subject, &query.predicate) {
if let Some(result) = self.try_fast_path(subject, predicate, query).await? {
return Ok(result);
}
}
}
```
- [ ] Add `as_of: Option<u64>` to `QueryParams` DTO in `crates/stemedb-api/src/dto.rs:102`.
- [ ] Wire in query handler.
- [ ] Tests:
- [ ] `test_as_of_excludes_future_assertions`: 3 assertions at t=1000, t=2000, t=3000. Query `as_of=2500`. Returns only first 2.
- [ ] `test_as_of_bypasses_fast_path`: MV exists, but `as_of` is set. Slow path used, MV ignored.
- [ ] `test_as_of_none_uses_fast_path`: Normal query still uses fast path (backwards-compatible).
- [ ] `test_as_of_with_lens`: Time-travel + lens = resolve only among pre-as_of candidates.
- [x] **3B.1 Time-Travel Engine**: `as_of` parameter for historical queries.
- **Status:** ✅ COMPLETE
- **Implementation:**
- [x] Added `as_of: Option<u64>` to `Query` struct in `crates/stemedb-query/src/query.rs:92-99`.
- [x] Added `.as_of(timestamp: u64)` to `QueryBuilder`.
- [x] In `Query::matches()`: if `as_of` is `Some(ts)`, check `assertion.timestamp <= ts`. Assertions created after `as_of` are excluded.
- [x] In `QueryEngine::execute()`: if `query.as_of` is set, **skip the fast path entirely** (MVs reflect current state, not historical).
- [x] Added `as_of: Option<u64>` to `QueryParams` DTO in `crates/stemedb-api/src/dto.rs`.
- [x] Wired through query handler.
- **Tests:**
- [x] `test_as_of_excludes_future_assertions`: Assertions filtered by timestamp.
- [x] `test_as_of_bypasses_fast_path`: MV exists, but `as_of` is set. Slow path used.
- [x] `test_as_of_none_uses_fast_path`: Normal query still uses fast path (backwards-compatible).
- [x] `test_as_of_with_lens_resolves_among_historical_candidates`: Time-travel + lens = resolve only among pre-as_of candidates.
- [x] `test_as_of_returns_empty_when_all_assertions_are_future`: All assertions are future, returns empty.
- [x] `test_as_of_with_exact_timestamp_match`: Edge case where assertion.timestamp == as_of.
- [ ] **3B.2 Semantic Decay**: Confidence Half-Life at query time.
- **Problem:** Medical knowledge decays at different rates. A Reddit post from 2022 shouldn't compete equally with a 2024 RCT. No assertion-level decay exists. TrustRank has agent-level decay but not assertion-level.
- **Current state:** `TrustRank.apply_decay()` at `trust_rank_store.rs` does agent decay. No assertion decay. No `decay` query parameter.
- [ ] Add `decay_halflife: Option<u64>` to `Query` struct (seconds). Represents how quickly old assertions lose effective confidence.
- [ ] Add `.decay_halflife(seconds: u64)` to `QueryBuilder`.
- [ ] Add `decay_halflife` to `QueryParams` DTO.
- [ ] Implementation strategy: **Apply decay in QueryEngine before passing to lens.** In `execute()`, after fetching and filtering candidates, if `decay_halflife` is set:
1. Get current time (or `as_of` if set).
2. For each candidate, compute `age = now - assertion.timestamp`.
3. Compute `effective_confidence = confidence * 2_f32.powf(-(age as f32) / (halflife as f32))`.
4. Clone the assertion with the decayed confidence.
5. Pass decayed candidates to the lens.
- [ ] Add helper `apply_decay(assertions: &[Assertion], halflife: u64, now: u64) -> Vec<Assertion>` in a new `crates/stemedb-query/src/decay.rs` module.
- [ ] **Source-class-aware decay** (Phase 3 stretch): If assertion has `source_class`, use per-tier half-lives from `SourceClass::default_decay_days()`. This is already implemented on the enum.
- [ ] Tests:
- [ ] `test_decay_reduces_old_assertion_confidence`: Assertion 1yr old, halflife 1yr. Effective confidence ~= original * 0.5.
- [ ] `test_decay_preserves_fresh_assertions`: Assertion 1hr old, halflife 1yr. Effective confidence ~= original.
- [ ] `test_decay_interacts_with_lens`: Two assertions, older has higher base confidence but after decay, newer wins via RecencyLens.
- [ ] `test_source_aware_decay`: Tier 0 doesn't decay. Tier 5 decays rapidly.
- [x] **3B.2 Semantic Decay**: Confidence Half-Life at query time.
- **Status:** ✅ COMPLETE
- **Implementation:**
- [x] Added `decay_halflife: Option<u64>` to `Query` struct in `crates/stemedb-query/src/query.rs`.
- [x] Added `.decay_halflife(seconds: u64)` and `.source_class_decay(enabled: bool)` to `QueryBuilder`.
- [x] Added `decay_halflife` and `source_class_decay` to `QueryParams` DTO.
- [x] Created new `crates/stemedb-query/src/decay.rs` module with:
- `apply_decay()`: Uniform decay using formula `confidence * 2^(-(age / halflife))`.
- `apply_source_class_decay()`: Tier-specific decay (Regulatory=none, Clinical=2yr, Anecdotal=30d).
- `compute_decayed_confidence()`: Core decay calculation with clamping.
- [x] Integrated in `QueryEngine::execute()`: decay applied after filtering, before lens resolution.
- [x] Time-travel compatible: uses `as_of` timestamp if set, otherwise current time.
- [x] Source-class-aware decay fully implemented using `SourceClass::default_decay_days()`.
- **Tests:** (11 unit tests in decay.rs + 1 E2E test)
- [x] `test_decay_reduces_old_assertion_confidence`: 1yr old, 1yr halflife → ~50% confidence.
- [x] `test_decay_preserves_fresh_assertions`: 1hr old, 1yr halflife → ~100% confidence.
- [x] `test_decay_interacts_with_lens`: Older high-confidence loses to newer low-confidence after decay.
- [x] `test_source_aware_decay_tier0_no_decay`: Regulatory never decays.
- [x] `test_source_aware_decay_tier5_rapid_decay`: Anecdotal decays rapidly (30-day halflife).
- [x] `test_source_aware_decay_mixed_tiers`: Clinical vs Anecdotal tier comparison.
- [x] `test_decay_zero_halflife_no_change`: Zero halflife skips decay (avoids div-by-zero).
- [x] `test_decay_future_assertion_no_change`: Future assertions don't decay.
- [x] `test_decay_empty_assertions`: Empty input returns empty output.
- [x] `test_decay_confidence_clamps_to_valid_range`: Very old assertions clamp to [0.0, 1.0].
- [x] `test_decay_preserves_other_fields`: Only confidence changes; other fields preserved.
- [x] `test_e2e_decay_reduces_old_confidence`: Full pipeline E2E test in e2e_pipeline.rs.
- **Note:** When decay is enabled, materialized views (fast path) are bypassed because MVs store pre-computed winners without decay applied.
#### 3C. New Lenses
@ -300,131 +295,167 @@
- `ClaimSummary`, `SourceSummary`, `AgentSummary`
- [x] Comprehensive test coverage (21 test cases).
- [ ] **3C.2 Layered Consensus Lens**: Per-source-class consensus.
- **Problem:** Current lenses return a single winner. The Consumer Health use case needs per-tier consensus: "What does Tier 0 say? What does Tier 1 say? What does Tier 5 say?" This is the core differentiator.
- **Depends on:** Source-Class Field (3A.1) ✅ COMPLETE.
- [ ] New file: `crates/stemedb-lens/src/layered_consensus.rs`.
- [ ] New type in `crates/stemedb-lens/src/traits.rs`:
```rust
/// Per-tier resolution result.
pub struct TierResolution {
pub tier: u8,
pub winner: Option<Assertion>,
pub candidates_count: usize,
pub conflict_score: f32,
}
- [x] **3C.2 Layered Consensus Lens**: Per-source-class consensus.
- **Status:** ✅ COMPLETE
- **Implementation:**
- [x] `crates/stemedb-lens/src/layered_consensus.rs` - Full implementation.
- [x] `TierResolution` struct: per-tier result with tier, source_class, winner, candidates_count, conflict_score, resolution_confidence.
- [x] `LayeredResolution` struct: multi-tier result with tiers vec, overall_winner, overall_conflict_score, total_candidates.
- [x] `LayeredLens` trait: `resolve_layered(&[Assertion]) -> LayeredResolution`, `name() -> &'static str`.
- [x] `LayeredConsensusLens` implements both `LayeredLens` and `Lens` traits.
- [x] Cross-tier conflict score uses normalized Shannon entropy of tier winner object values.
- [x] `LensDto::LayeredConsensus` variant (redirects to `/v1/layered` endpoint).
- [x] `GET /v1/layered?subject=X&predicate=Y` API endpoint with `LayeredQueryResponse`.
- [x] Exported from `crates/stemedb-lens/src/lib.rs`.
- **Tests:**
- [x] `test_layered_empty_candidates`: Empty input returns empty resolution.
- [x] `test_layered_single_tier`: All same source_class, returns one tier result.
- [x] `test_layered_multi_tier_agreement`: Tier 0 and Tier 5 agree, low cross-tier conflict.
- [x] `test_layered_multi_tier_disagreement`: Tier 1 vs Tier 5 disagree, high conflict, Tier 1 wins.
- [x] `test_layered_overall_winner_from_highest_authority`: Tier 0 wins despite fewer assertions.
- [x] `test_layered_lens_trait_compatibility`: Standard Lens trait works.
- [x] `test_layered_within_tier_conflict`: High internal conflict within a tier.
- [x] `test_layered_all_tiers_present`: One assertion from each tier.
- [x] `test_layered_lens_name`: Both trait names work.
- [x] `test_layered_numeric_values`: Works with numeric object values.
/// Multi-tier resolution result.
pub struct LayeredResolution {
/// Per-tier consensus results, ordered by tier (0 = highest authority).
pub tiers: Vec<TierResolution>,
/// Overall winner (highest-tier with a winner).
pub overall_winner: Option<Assertion>,
/// Overall conflict score (cross-tier disagreement).
pub overall_conflict_score: f32,
}
```
- [ ] `LayeredConsensusLens` implements a new `LayeredLens` trait:
```rust
pub trait LayeredLens: Send + Sync {
fn resolve_layered(&self, candidates: &[Assertion]) -> LayeredResolution;
fn name(&self) -> &'static str;
}
```
- [ ] `resolve_layered()` logic:
1. Group candidates by `source_class` (use `SourceClass::tier()` method).
2. For each tier group, run `ConsensusLens::resolve()` to get within-tier winner.
3. Compute per-tier `conflict_score`.
4. Overall winner = winner from the highest-authority tier that has candidates (lowest tier number).
5. Overall conflict_score = cross-tier disagreement (do tier winners agree on the same object value?).
- [ ] Also implement standard `Lens` trait on `LayeredConsensusLens` to maintain compatibility: `resolve()` returns `overall_winner` as a regular `Resolution`. The richer `LayeredResolution` is accessible via `resolve_layered()`.
- [ ] Add `LayeredConsensus` to `LensDto` enum.
- [ ] New API response type: `LayeredQueryResponse` with per-tier results. Wire as a variant in the query handler when `lens=LayeredConsensus`.
- [ ] Export from `crates/stemedb-lens/src/lib.rs`.
- [ ] Tests:
- [ ] `test_layered_single_tier`: All candidates same source_class. Returns one tier result.
- [ ] `test_layered_multi_tier_agreement`: Tier 0 and Tier 5 agree on same object. Low cross-tier conflict.
- [ ] `test_layered_multi_tier_disagreement`: Tier 1 says "safe", Tier 5 says "dangerous". High cross-tier conflict. Overall winner from Tier 1.
- [ ] `test_layered_overall_winner_from_highest_authority`: Tier 0 present -> its winner is overall winner even if Tier 5 has 1000x more assertions.
- [ ] **3C.3 Constraints Lens**: Pre-flight check for must_use/forbidden.
- **Problem:** Agile Agent Team use case needs `lens=constraints` returning `{ must_use: "axios", forbidden: "requests" }`. Central to "persistent learning" — agents query constraints before acting.
- [ ] New file: `crates/stemedb-lens/src/constraints.rs`.
- [ ] Design: Not a traditional lens (doesn't pick one winner from candidates). Instead, it categorizes candidates by predicate pattern:
- Assertions with predicate matching `must_use:*` -> must_use list.
- Assertions with predicate matching `forbidden:*` -> forbidden list.
- Assertions with predicate matching `prefer:*` -> prefer list.
- [ ] Implements `Lens` trait for compatibility: `resolve()` returns the highest-confidence `must_use` assertion as the "winner" (or `forbidden` if no must_use exists). The richer result is accessible via a dedicated method.
- [ ] Add dedicated `resolve_constraints()` method:
```rust
pub struct ConstraintSet {
pub must_use: Vec<Assertion>,
pub forbidden: Vec<Assertion>,
pub prefer: Vec<Assertion>,
}
impl ConstraintsLens {
pub fn resolve_constraints(&self, candidates: &[Assertion]) -> ConstraintSet { ... }
}
```
- [ ] Add `Constraints` to `LensDto` enum.
- [ ] New API response type: `ConstraintResponse` with categorized assertions.
- [ ] Export from `crates/stemedb-lens/src/lib.rs`.
- [ ] Tests:
- [ ] `test_constraints_categorizes_by_predicate`: Mixed predicates sorted into must_use/forbidden/prefer.
- [ ] `test_constraints_empty_categories`: No must_use predicates -> empty must_use list.
- [ ] `test_constraints_lens_trait_picks_must_use_winner`: Standard `resolve()` returns highest-confidence must_use.
- [ ] `test_constraints_non_constraint_predicates_ignored`: Regular predicates not categorized.
- [x] **3C.3 Constraints Lens**: Pre-flight check for must_use/forbidden.
- **Status:** ✅ COMPLETE
- **Implementation:**
- [x] `crates/stemedb-lens/src/constraints.rs` - Full implementation.
- [x] `ConstraintSet` struct: holds categorized assertions (must_use, forbidden, prefer) with conflict_score.
- [x] `ConstraintsLens` struct with `resolve_constraints(&[Assertion]) -> ConstraintSet` method.
- [x] Categorizes by predicate pattern: `must_use:*`, `forbidden:*`, `prefer:*`.
- [x] Implements `Lens` trait for compatibility (priority: must_use > forbidden > prefer).
- [x] Sorted by confidence (highest first), with timestamp as tiebreaker.
- [x] `LensDto::Constraints` added (redirects to `/v1/constraints` endpoint).
- [x] `GET /v1/constraints?subject=X` API endpoint with `ConstraintsResponse`.
- [x] DTOs: `ConstraintsQueryParams`, `ConstraintEntryDto`, `ConstraintsResponse`.
- [x] Exported from `crates/stemedb-lens/src/lib.rs`.
- **Tests:** (16 test cases)
- [x] `test_constraints_categorizes_by_predicate`: Mixed predicates sorted into must_use/forbidden/prefer.
- [x] `test_constraints_empty_categories`: Only prefer, no must_use/forbidden.
- [x] `test_constraints_non_constraint_predicates_ignored`: Regular predicates filtered out.
- [x] `test_constraints_sorted_by_confidence`: Within-category confidence ordering.
- [x] `test_constraints_empty_candidates`: Empty input returns empty set.
- [x] `test_constraints_has_constraints_true`: Helper method works.
- [x] `test_constraints_all_regular_predicates`: All non-constraint predicates returns no constraints.
- [x] `test_lens_trait_picks_must_use_winner`: Standard Lens trait picks must_use first.
- [x] `test_lens_trait_falls_back_to_forbidden`: Falls back to forbidden when no must_use.
- [x] `test_lens_trait_falls_back_to_prefer`: Falls back to prefer when no must_use/forbidden.
- [x] `test_lens_trait_empty_for_no_constraints`: Returns empty when no constraint predicates.
- [x] `test_lens_name`: Name returns "Constraints".
- [x] `test_lens_empty_candidates`: Empty input to Lens trait returns empty resolution.
- [x] `test_multiple_must_use_picks_highest_confidence`: Multiple must_use picks highest confidence.
- [x] `test_confidence_tiebreaker_uses_timestamp`: Same confidence uses newer timestamp.
- [x] `test_predicate_pattern_exact_prefix`: `must_use_something` not matched (only `must_use:*`).
#### 3D. Epoch Enhancement
- [ ] **3D.1 Epoch Cascade Logic** (enhancement of Phase 2.5 EpochAwareLens):
- **Problem:** Phase 2.5 EpochAwareLens walks the `supersedes` chain at query time by reading `E:` keys. This is O(chain_length) per query. For long supersession chains, this is expensive.
- **Depends on:** Phase 2.5 EpochAwareLens (basic version).
- [ ] In `IngestWorker::process_epoch()` at `crates/stemedb-ingest/src/worker.rs`:
1. When an epoch with `supersedes = Some(old_epoch_id)` is ingested, write a marker key `SUPERSEDED:{old_epoch_id}` with the new epoch ID as value.
2. Walk the chain: if old_epoch itself superseded another epoch, write `SUPERSEDED:{grandparent_id}` too (transitive closure).
- [ ] Update `EpochAwareLens` to check `SUPERSEDED:{epoch_id}` keys (O(1) lookup per candidate) instead of walking the chain at query time.
- [ ] Tests:
- [ ] `test_cascade_writes_superseded_marker`: Ingest epoch B superseding A. Verify `SUPERSEDED:A` key exists.
- [ ] `test_cascade_transitive`: A supersedes B, B supersedes C. Verify both `SUPERSEDED:B` and `SUPERSEDED:C` exist.
- [ ] `test_epoch_aware_uses_marker`: Query with EpochAwareLens uses marker key, not chain walk.
- [x] **3D.1 Epoch Cascade Logic** (enhancement of Phase 2.5 EpochAwareLens):
- **Status:** ✅ COMPLETE
- **Implementation:**
- [x] `write_supersession_cascade()` in `crates/stemedb-ingest/src/worker.rs`:
- Writes `SUPERSEDED:{old_epoch_id}` markers for full transitive closure.
- All markers point to the LATEST superseding epoch.
- Max depth guard (100 levels) and cycle detection via visited set.
- [x] `is_epoch_superseded()` in `crates/stemedb-lens/src/epoch_aware.rs`:
- O(1) marker lookup instead of O(chain_length) chain walks.
- Fail-open semantics: missing marker = not superseded.
- [x] `compute_superseded_epochs()` uses marker lookups for filtering.
- **Tests:**
- [x] `test_cascade_writes_superseded_marker`: Epoch B supersedes A → `SUPERSEDED:A` exists.
- [x] `test_cascade_transitive`: C→B→A chain → both `SUPERSEDED:A` and `SUPERSEDED:B` point to C.
- [x] `test_cascade_cycle_detection`: Mutual supersession handled gracefully.
- [x] `test_epoch_aware_uses_marker`: EpochAwareLens uses O(1) marker lookup.
- [x] `test_superseded_epoch_filtered_even_without_new_assertions`: Marker-based filtering works.
#### 3E. Similarity Search
- [ ] **3E.1 Vector Search**: Semantic k-NN queries via embeddings.
- **Current state:** `vector: Option<Vec<f32>>` on Assertion. Stored, returned by API. No index, no search.
- [ ] Add `hnsw-rs` (or `lance`) as a dependency in `stemedb-storage/Cargo.toml`.
- [ ] New module: `crates/stemedb-storage/src/vector_index.rs`.
- [ ] `VectorIndex` trait: `insert(hash: Hash, vector: &[f32])`, `search(query: &[f32], k: usize) -> Vec<(Hash, f32)>`.
- [ ] Implementation backed by HNSW graph stored alongside KV data.
- [ ] IngestWorker: if assertion has `vector`, insert into vector index after KV write.
- [ ] Add `vector_near: Option<Vec<f32>>` and `k: Option<usize>` to `Query` struct and API `QueryParams`.
- [ ] QueryEngine: if `vector_near` is set, use vector index for candidate retrieval instead of SP/S index.
- [ ] Tests: insert 100 vectors, query nearest 5, verify correct neighbors.
- [x] **3E.1 Vector Search**: Semantic k-NN queries via embeddings.
- **Status:** ✅ COMPLETE
- **Implementation:**
- [x] Added `hnsw_rs = "0.3"` and `parking_lot = "0.12"` to `stemedb-storage/Cargo.toml`.
- [x] New module: `crates/stemedb-storage/src/vector_index.rs`.
- [x] `VectorIndex` trait: `insert(hash: &Hash, vector: &[f32])`, `search(query: &[f32], k: usize) -> Vec<(Hash, f32)>`, `dimension()`, `len()`, `is_empty()`.
- [x] `HnswVectorIndex` implementation with HNSW graph, RwLock protection, hash↔ID mappings.
- [x] Input validation: dimension mismatch, NaN, Infinite values rejected.
- [x] Idempotent insert (same hash twice = no-op).
- [x] `Arc<dyn VectorIndex>` trait object support for sharing.
- [x] `IngestWorker::with_vector_index()` builder method for index attachment.
- [x] IngestWorker: if assertion has `vector`, inserts into vector index after KV write.
- [x] Added `vector_near: Option<Vec<f32>>` and `k: Option<usize>` to `Query` struct in `crates/stemedb-query/src/query.rs`.
- [x] Added `.vector_near(vector, k)` builder method to `QueryBuilder`.
- [x] Added `vector_near` and `k` to API `QueryParams` DTO.
- [x] `QueryEngine::with_vector_index()` builder method for index attachment.
- [x] QueryEngine: if `vector_near` is set and index configured, uses O(log N) HNSW lookup for candidates.
- [x] Falls back to standard path if no index configured (with debug log).
- **Tests:** (12 unit tests for VectorIndex + 4 integration tests in engine.rs)
- [x] `test_create_index`, `test_insert_and_search`, `test_dimension_mismatch`.
- [x] `test_idempotent_insert`, `test_search_empty_index`, `test_search_k_zero`.
- [x] `test_nan_rejection`, `test_infinite_rejection`, `test_contains`.
- [x] `test_larger_scale` (100 vectors, exact match first), `test_custom_params`, `test_zero_dimension_panics`.
- [x] `test_vector_search_returns_nearest_neighbors`, `test_vector_search_with_subject_filter`.
- [x] `test_vector_search_without_index_falls_back`, `test_vector_search_with_as_of_filter`.
- **Note:** Index is in-memory only. Persistence is Phase 4+.
- [ ] **3E.2 Visual Hash Index**: VP-tree or BK-tree for O(log N) visual similarity.
- **Current state:** Phase 2.5 adds brute-force hamming scan. This replaces it with an indexed approach.
- [ ] New module: `crates/stemedb-storage/src/visual_index.rs`.
- [ ] BK-tree implementation over hamming distance on `PHash` ([u8; 8]).
- [ ] IngestWorker: if assertion has `visual_hash`, insert into BK-tree.
- [ ] QueryEngine: if `visual_near` is set, use BK-tree for O(log N) candidate retrieval instead of brute-force scan.
- [ ] Tests: insert 1000 assertions with visual hashes, query similar, verify correct matches within threshold.
- [x] **3E.2 Visual Hash Index**: BK-tree for O(log N) visual similarity.
- **Status:** ✅ COMPLETE
- **Implementation:**
- [x] New module: `crates/stemedb-storage/src/visual_index.rs`.
- [x] `VisualIndex` trait: `insert(hash: &Hash, phash: &PHash)`, `search(query: &PHash, threshold: u32) -> Vec<(Hash, u32)>`, `len()`, `is_empty()`.
- [x] `BkTreeVisualIndex` implementation using BK-tree over hamming distance.
- [x] `hamming_distance(a: &PHash, b: &PHash) -> u32` utility function.
- [x] Threshold clamped to 0-64 range (max 64 bits).
- [x] Results sorted by distance ascending.
- [x] Idempotent insert (same hash twice = no-op).
- [x] `Arc<dyn VisualIndex>` trait object support for sharing.
- [x] `IngestWorker::with_visual_index()` builder method for index attachment.
- [x] IngestWorker: if assertion has `visual_hash`, inserts into BK-tree after KV write.
- [x] `QueryEngine::with_visual_index()` builder method for index attachment.
- [x] QueryEngine: if `visual_near` is set and index configured, uses O(log N) BK-tree lookup.
- [x] Falls back to brute-force scan (via `query.matches()`) if no index configured.
- [x] Invalid hex input returns `QueryError::InvalidInput` with clear message.
- **Tests:** (14 unit tests for VisualIndex + 6 integration tests in engine.rs)
- [x] `test_hamming_distance_zero`, `test_hamming_distance_max`, `test_hamming_distance_partial`.
- [x] `test_create_index`, `test_insert_and_search_exact`, `test_search_within_threshold`.
- [x] `test_search_no_matches`, `test_search_empty_index`, `test_idempotent_insert`.
- [x] `test_contains`, `test_results_sorted_by_distance`, `test_threshold_clamped_to_64`.
- [x] `test_larger_scale` (1000 hashes), `test_default_impl`.
- [x] `test_visual_search_returns_similar_images`, `test_visual_search_with_lifecycle_filter`.
- [x] `test_visual_search_invalid_hex_returns_error`, `test_visual_search_without_index_uses_brute_force`.
- [x] `test_visual_search_with_limit`, `test_vector_search_empty_index`.
- **Note:** Index is in-memory only. Persistence is Phase 4+.
#### 3F. Provenance
- [ ] **3F.1 Citation Recall Benchmarking**: Verify 100% provenance tracking.
- [ ] Benchmark: for every assertion in the store, verify `source_hash` resolves to a retrievable source document (or at minimum, the hash is non-zero and unique).
- [ ] Add `GET /v1/provenance/{hash}` endpoint that looks up source by hash.
- [ ] Add `POST /v1/source` endpoint to store source documents by content hash.
- [ ] Source storage at `SRC:{blake3_hash}` keys.
- [x] **3F.1 Source Document Storage & Provenance Lookup**: Enable 100% citation recall.
- **Status:** ✅ COMPLETE
- **Implementation:**
- [x] `POST /v1/source` endpoint to store source documents by BLAKE3 content hash.
- [x] `GET /v1/provenance/{hash}` endpoint to retrieve source documents by hash.
- [x] Source storage at `SRC:{hash}` keys with format: `[content_type_len:4][content_type][content]`.
- [x] Base64 encoding for binary-safe JSON transport.
- [x] 10MB size limit per document.
- [x] Content-addressed storage: same content → same hash (idempotent uploads).
- [x] DTOs: `StoreSourceRequest`, `StoreSourceResponse`, `ProvenanceResponse`.
- [x] OpenAPI documentation under "provenance" tag.
- **Tests:** (5 test cases)
- [x] `test_store_and_retrieve_source`: Happy path store + retrieve.
- [x] `test_store_source_invalid_base64`: Bad base64 → 400.
- [x] `test_get_provenance_not_found`: Unknown hash → 404.
- [x] `test_get_provenance_invalid_hash`: Bad hash format → 400.
- [x] `test_store_source_idempotent`: Same content twice → same hash.
- **Note:** Benchmark utility for verifying all assertions have retrievable sources is future work.
#### 3G. API Cleanup
- [ ] **3G.1 Document epoch supersession via existing endpoint**: No new `/epoch/supersede` endpoint needed.
- **Current state:** `POST /v1/epoch` already accepts `supersedes` field. Use cases show `POST /epoch/supersede` as if it's a separate endpoint.
- [ ] Update use case docs (consumer-health-intelligence.md, glp1-living-review.md) to use `POST /v1/epoch` with `supersedes` field.
- [ ] Add OpenAPI examples showing the supersession flow.
- [x] **3G.1 Document epoch supersession via existing endpoint**: No new `/epoch/supersede` endpoint needed.
- **Status:** ✅ COMPLETE
- **Implementation:**
- [x] Updated use case docs (consumer-health-intelligence.md, glp1-living-review.md) to use `POST /v1/epoch` with `supersedes` field.
- [x] Added OpenAPI examples showing both new epoch and supersession flows in handlers/epoch.rs.
- [x] Documented all 5 supersession types: Invalidate, Temporal, Refinement, RequiresReview, Additive.
- **No code change.** Documentation fix only.
### Phase 4: The Hive (Trust & Scale)
@ -487,15 +518,54 @@
## Tracking
### Active Tasks
* [ ] **Phase 2.5 Hardening**: Camp 2 fixes (~~staleness~~, epoch behavior, lens rename, visual query, E2E test).
* [x] **Phase 3 The Pilot**: Consumer Health vertical integration. ✅ COMPLETE
* [ ] **Phase 4 The Hive**: Trust & Scale features.
### Next Up (Phase 3 sequencing)
* [ ] **3A.2 Conflict Score**: Add to Resolution. Propagate through all lenses.
* [ ] **3A.3 Rich Source Metadata**: Structured provenance field.
* [ ] **3B.1 Time-Travel**: `as_of` parameter. Unblocks all 4 use cases.
* [ ] **3C.2 Layered Consensus Lens**: Per-tier resolution (now unblocked by 3A.1).
* [ ] **Phase 4 Items**: "Since" parameter (4.1), Metadata indexing (4.2), Batch TrustRank decay (4.3).
### Recently Completed
* [x] **Source Document Storage** (3F.1): Provenance lookup for 100% citation recall.
* `POST /v1/source` stores source documents by BLAKE3 content hash.
* `GET /v1/provenance/{hash}` retrieves source documents.
* Content-addressed storage at `SRC:{hash}` keys.
* Base64 encoding, 10MB limit, idempotent uploads.
* 5 unit tests covering happy path and error cases.
* [x] **Epoch Cascade Logic** (3D.1): O(1) supersession lookup via pre-computed markers.
* `write_supersession_cascade()` writes `SUPERSEDED:` markers for full transitive closure at ingest time.
* `is_epoch_superseded()` uses O(1) marker lookup instead of chain walking.
* Cycle detection and max depth guard (100 levels).
* 5 tests covering markers, transitive closure, cycles, and marker-based filtering.
* [x] **Semantic Decay** (3B.2): Confidence half-life at query time.
* `decay_halflife: Option<u64>` and `source_class_decay: bool` on Query.
* New `decay.rs` module with `apply_decay()` and `apply_source_class_decay()`.
* Formula: `effective_confidence = confidence * 2^(-(age / halflife))`.
* Tier-specific decay: Regulatory=none, Clinical=2yr, Anecdotal=30d.
* 11 unit tests + 1 E2E integration test.
* [x] **Layered Consensus Lens** (3C.2): Per-source-class consensus with tier-by-tier visibility.
* `LayeredConsensusLens` with `LayeredLens` trait.
* `TierResolution` and `LayeredResolution` types.
* `GET /v1/layered?subject=X&predicate=Y` endpoint.
* Cross-tier conflict score using Shannon entropy.
* 10 comprehensive tests.
* [x] **Time-Travel Engine** (3B.1): `as_of` parameter for historical queries.
* `as_of: Option<u64>` field on `Query` for querying historical state.
* Bypasses fast path (MVs reflect current state).
* `Query::matches()` filters by `assertion.timestamp <= as_of`.
* 6 tests covering edge cases.
* [x] **Rich Source Metadata** (3A.3): Structured provenance beyond `source_hash`.
* `source_metadata: Option<Vec<u8>>` field on `Assertion`.
* `Vec<u8>` for rkyv zero-copy compatibility, callers handle JSON encoding.
* Builder methods: `.source_metadata_json()` and `.source_metadata()`.
* API exposes as `Option<String>` with defensive UTF-8 handling.
* 2 serialization tests.
* [x] **Conflict Score on Resolution** (3A.2): Numeric disagreement metric across all lenses.
* `conflict_score: f32` field on `Resolution` (0.0 = unanimous, 1.0 = max conflict).
* `compute_conflict_score()` utility using normalized variance.
* Updated all 5 lens implementations to compute and propagate conflict score.
* `MaterializedView` now stores conflict score.
* API `QueryResponse` exposes `conflict_score` and `resolution_confidence` when lens is applied.
* 7 unit tests including NaN handling.
* [x] **SkepticLens + SkepticView** (3C.1): "Trust but Verify" conflict analysis that surfaces all claims with conflict scores.
* `AnalysisLens` trait for lenses that map conflict instead of resolving it.
* `SkepticLens` using normalized Shannon entropy for conflict scoring.
@ -527,40 +597,41 @@
Phase 2.5 (Hardening) Phase 3 (The Pilot) Phase 4 (The Hive)
======================== ======================== ==================
[2.1 MV Staleness] ---------> [3B.1 Time-Travel] -----+
[2.1 MV Staleness] ---------> [3B.1 Time-Travel] ✅ --+
| |
[2.2 Confidence Rename] -----> (API clarity for all) +----------> [4.1 "Since" Param]
|
[2.3 EpochAwareLens] --------> [3D.1 Epoch Cascade] ---|----------> Invalidation Cascades pillar
[2.3 EpochAwareLens] --------> [3D.1 Epoch Cascade] ✅ |----------> Invalidation Cascades pillar
|
[2.4 Visual Hash Query] -----> [3E.2 Visual Hash Index] |
[2.4 Visual Hash Query] -----> [3E.2 Visual Hash Index] ✅
|
[2.6 E2E Integration] -------> (pipeline confidence) |
|
[3A.1 Source-Class] ✅ --+----------> [3C.2 Layered Consensus]
| [3B.2 Source-Aware Decay]
[3A.1 Source-Class] ✅ --+----------> [3C.2 Layered Consensus] ✅
| [3B.2 Semantic Decay] ✅
+-----------------------------[4.2 Metadata Indexing]
|
[3A.2 Conflict Score] ---> (enhance Resolution)
[3A.2 Conflict Score] ✅ --> (enhance Resolution)
|
[3A.3 Source Metadata] ---> [4.2 Metadata Indexing]
[3A.3 Source Metadata] ✅ --> [4.2 Metadata Indexing]
|
[3C.1 Skeptic Lens] ✅ (standalone, COMPLETE)
[3C.3 Constraints Lens] (standalone)
[3E.1 Vector Search] (standalone)
[3F.1 Citation Recall] (standalone)
[3C.3 Constraints Lens] ✅ (standalone, COMPLETE)
[3E.1 Vector Search] ✅ (standalone, COMPLETE)
[3E.2 Visual Hash Index] ✅ (standalone, COMPLETE)
[3F.1 Provenance] ✅ (standalone, COMPLETE)
```
### Critical Path for Consumer Health Demo
```
[3A.1 Source-Class] ✅ --> [3A.2 Conflict Score] --> [3C.2 Layered Consensus]
[3A.1 Source-Class] ✅ --> [3A.2 Conflict Score] ✅ --> [3C.2 Layered Consensus] ✅
|
+----> CONSUMER HEALTH MVP
+----> CONSUMER HEALTH MVP ✅
|
[3B.1 Time-Travel] ------------------------------------------+
[3B.1 Time-Travel] ✅ ---------------------------------------+
|
[3A.3 Source Metadata] --------------------------------------+
[3A.3 Source Metadata] ✅ -----------------------------------+
|
[3C.1 Skeptic Lens] ✅ --------------------------------------+
```
@ -568,13 +639,13 @@ Phase 2.5 (Hardening) Phase 3 (The Pilot) Phase 4 (T
### Critical Path for Financial DD Demo
```
[3A.2 Conflict Score] --> [3C.1 Skeptic Lens] ✅ -------+
[3A.2 Conflict Score] ✅ --> [3C.1 Skeptic Lens] ✅ ---+
|
[3B.1 Time-Travel] -------------------------------------+----> FINANCIAL DD MVP
[3B.1 Time-Travel] ✅ ----------------------------------+----> FINANCIAL DD MVP
|
[2.3 EpochAwareLens] --> [3D.1 Epoch Cascade] ----------+
[2.3 EpochAwareLens] --> [3D.1 Epoch Cascade] ✅ -------+
|
[3B.2 Semantic Decay] ----------------------------------+
[3B.2 Semantic Decay] ✅ -------------------------------+
```
### Critical Path for Agile Agent Team Demo
@ -582,9 +653,9 @@ Phase 2.5 (Hardening) Phase 3 (The Pilot) Phase 4 (T
```
[3C.3 Constraints Lens] (standalone) ------+
|
[3B.1 Time-Travel] -----------------------+----> AGENT TEAM MVP
[3B.1 Time-Travel] ✅ --------------------+----> AGENT TEAM MVP
|
[2.3 EpochAwareLens] ---------------------+
[2.3 EpochAwareLens] ✅ ------------------+
|
[Query Audit (Phase 2)] ✅ ----------------+
```

239
scripts/validate.sh Executable file
View File

@ -0,0 +1,239 @@
#!/usr/bin/env bash
#
# StemeDB Validation Script
#
# Validates that StemeDB works end-to-end:
# 1. Builds the API server
# 2. Starts the server in the background
# 3. Waits for health check
# 4. Creates an assertion via curl
# 5. Queries it back
# 6. Shuts down the server
#
# Usage:
# ./scripts/validate.sh # Run validation
# ./scripts/validate.sh --no-build # Skip cargo build (faster)
#
# Exit codes:
# 0 - All checks passed
# 1 - Validation failed
#
set -euo pipefail
# Configuration
readonly API_HOST="${STEMEDB_BIND_ADDR:-127.0.0.1:3000}"
readonly API_URL="http://${API_HOST}"
readonly SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
readonly PROJECT_DIR="$(dirname "$SCRIPT_DIR")"
readonly DATA_DIR="${PROJECT_DIR}/tmp/validate-$$"
readonly PID_FILE="${DATA_DIR}/server.pid"
readonly LOG_FILE="${DATA_DIR}/server.log"
# Colors (if terminal supports it)
if [[ -t 1 ]]; then
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[0;33m'
BLUE='\033[0;34m'
NC='\033[0m' # No Color
else
RED=''
GREEN=''
YELLOW=''
BLUE=''
NC=''
fi
# Logging helpers
info() { echo -e "${BLUE}[INFO]${NC} $*"; }
success() { echo -e "${GREEN}[PASS]${NC} $*"; }
warn() { echo -e "${YELLOW}[WARN]${NC} $*"; }
fail() { echo -e "${RED}[FAIL]${NC} $*"; exit 1; }
# Cleanup on exit
cleanup() {
if [[ -f "$PID_FILE" ]]; then
local pid
pid=$(cat "$PID_FILE")
if kill -0 "$pid" 2>/dev/null; then
info "Stopping server (PID $pid)..."
kill "$pid" 2>/dev/null || true
wait "$pid" 2>/dev/null || true
fi
fi
if [[ -d "$DATA_DIR" ]]; then
rm -rf "$DATA_DIR"
fi
}
trap cleanup EXIT
# Parse arguments
SKIP_BUILD=false
for arg in "$@"; do
case $arg in
--no-build)
SKIP_BUILD=true
;;
--help|-h)
echo "Usage: $0 [--no-build]"
echo ""
echo "Validates StemeDB end-to-end (build, start, assert, query, shutdown)."
echo ""
echo "Options:"
echo " --no-build Skip cargo build (use existing binary)"
echo " --help Show this help message"
exit 0
;;
esac
done
# Main validation
main() {
echo ""
echo "=========================================="
echo " StemeDB Validation"
echo "=========================================="
echo ""
# Create temp data directory
mkdir -p "$DATA_DIR"
# Step 1: Build
if [[ "$SKIP_BUILD" == "false" ]]; then
info "Building stemedb-api..."
cd "$PROJECT_DIR"
if ! cargo build --package stemedb-api --quiet 2>&1; then
fail "Build failed"
fi
success "Build complete"
else
info "Skipping build (--no-build)"
fi
# Step 2: Start server
info "Starting API server..."
cd "$PROJECT_DIR"
STEMEDB_WAL_DIR="$DATA_DIR/wal" \
STEMEDB_DB_DIR="$DATA_DIR/db" \
STEMEDB_BIND_ADDR="$API_HOST" \
cargo run --package stemedb-api --quiet > "$LOG_FILE" 2>&1 &
echo $! > "$PID_FILE"
# Step 3: Wait for health
info "Waiting for server to be ready..."
local attempts=0
local max_attempts=30
while [[ $attempts -lt $max_attempts ]]; do
if curl -s "${API_URL}/v1/health" > /dev/null 2>&1; then
break
fi
sleep 0.5
((attempts++))
done
if [[ $attempts -eq $max_attempts ]]; then
echo ""
echo "Server log:"
cat "$LOG_FILE" || true
fail "Server failed to start within 15 seconds"
fi
success "Server is healthy"
# Step 4: Health check details
info "Checking health endpoint..."
local health_response
health_response=$(curl -s "${API_URL}/v1/health")
echo " Response: $health_response"
if ! echo "$health_response" | grep -q '"status":"healthy"'; then
fail "Health check failed"
fi
success "Health check passed"
# Step 5: Create a properly signed assertion
info "Creating assertion (with valid Ed25519 signature)..."
# Generate a properly signed assertion using the helper binary
# This creates a fresh keypair and signs "{subject}:{predicate}"
local assertion_json
assertion_json=$(cargo run --package stemedb-api --example gen_test_assertion --quiet 2>/dev/null)
if [[ -z "$assertion_json" ]]; then
fail "Failed to generate signed assertion"
fi
local assert_response
assert_response=$(curl -s -X POST "${API_URL}/v1/assert" \
-H "Content-Type: application/json" \
-d "$assertion_json")
echo " Response: $assert_response"
if ! echo "$assert_response" | grep -q '"status":"created"'; then
fail "Assertion creation failed"
fi
local assertion_hash
assertion_hash=$(echo "$assert_response" | grep -o '"hash":"[^"]*"' | cut -d'"' -f4)
success "Assertion created: ${assertion_hash:0:16}..."
# Step 6: Query the assertion back (with retry for ingestion)
info "Querying assertion..."
# Retry loop - ingestion worker needs time to process WAL
local query_attempts=0
local query_max_attempts=10
local query_response=""
while [[ $query_attempts -lt $query_max_attempts ]]; do
query_response=$(curl -s "${API_URL}/v1/query?subject=StemeDB_Validation&predicate=test_status")
local count
count=$(echo "$query_response" | grep -o '"total_count":[0-9]*' | cut -d':' -f2)
if [[ "$count" -gt 0 ]] 2>/dev/null; then
break
fi
sleep 0.5
((query_attempts++))
done
echo " Total count: $(echo "$query_response" | grep -o '"total_count":[0-9]*' | cut -d':' -f2)"
echo " Ingestion took ~$((query_attempts * 500))ms"
if ! echo "$query_response" | grep -q '"StemeDB_Validation"'; then
echo " Full response: $query_response"
fail "Query did not return expected subject (tried ${query_attempts} times)"
fi
if ! echo "$query_response" | grep -q '"working"'; then
fail "Query did not return expected value"
fi
success "Query returned correct data"
# Step 7: Test query with lens
info "Testing lens-based query..."
local lens_response
lens_response=$(curl -s "${API_URL}/v1/query?subject=StemeDB_Validation&predicate=test_status&lens=Recency")
if ! echo "$lens_response" | grep -q '"working"'; then
fail "Lens query failed"
fi
success "Lens query (Recency) works"
# Final summary
echo ""
echo "=========================================="
echo -e " ${GREEN}All validation checks passed!${NC}"
echo "=========================================="
echo ""
echo "StemeDB is working correctly. You can now:"
echo " 1. Start the server: cargo run --package stemedb-api"
echo " 2. View API docs: http://localhost:3000/swagger-ui"
echo " 3. Use the Go SDK: cd sdk/go/examples/basic && go run main.go"
echo ""
}
main "$@"

26
sdk/go/adk/adk_test.go
View File

@ -151,7 +151,9 @@ func TestQueryToolConfidenceThreshold(t *testing.T) {
}
var output QueryOutput
json.Unmarshal(outputBytes, &output)
if err := json.Unmarshal(outputBytes, &output); err != nil {
t.Fatalf("failed to unmarshal output: %v", err)
}
// Should have error about low confidence
if output.Error == "" {
@ -188,7 +190,9 @@ func TestAssertTool(t *testing.T) {
}
var output AssertOutput
json.Unmarshal(outputBytes, &output)
if err := json.Unmarshal(outputBytes, &output); err != nil {
t.Fatalf("failed to unmarshal output: %v", err)
}
// Verify output
if !output.Success {
@ -254,7 +258,9 @@ func TestConstraintCheckTool(t *testing.T) {
}
var output ConstraintCheckOutput
json.Unmarshal(outputBytes, &output)
if err := json.Unmarshal(outputBytes, &output); err != nil {
t.Fatalf("failed to unmarshal output: %v", err)
}
// Verify constraints were returned
if len(output.Constraints) != 2 {
@ -347,7 +353,9 @@ func TestTraceTool(t *testing.T) {
}
var output TraceOutput
json.Unmarshal(outputBytes, &output)
if err := json.Unmarshal(outputBytes, &output); err != nil {
t.Fatalf("failed to unmarshal output: %v", err)
}
// Verify output
if len(output.Queries) != 1 {
@ -402,11 +410,11 @@ func TestSupersedeTool(t *testing.T) {
tool := NewSupersedeTool(client)
input := SupersedeInput{
Hash: "abc123def456",
Type: "Invalidate",
Reason: "Proposal treated as approved. See incident INC-2024-001",
NewHash: "def456abc123",
AgentID: "deadbeef00000000000000000000000000000000000000000000000000000000",
Hash: "abc123def456",
Type: "Invalidate",
Reason: "Proposal treated as approved. See incident INC-2024-001",
NewHash: "def456abc123",
AgentID: "deadbeef00000000000000000000000000000000000000000000000000000000",
Signature: "0000000000000000000000000000000000000000000000000000000000000000" +
"0000000000000000000000000000000000000000000000000000000000000000",
}

BIN
sdk/go/examples/basic/basic
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50
sdk/go/examples/basic/main.go
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@ -4,6 +4,7 @@
// - Creating a signer (keypair)
// - Building an assertion with the fluent API
// - Submitting to StemeDB (auto-signed)
// - Understanding eventual consistency
// - Querying with a lens
package main
@ -11,6 +12,7 @@ import (
"context"
"fmt"
"log"
"time"
"github.com/orchard9/stemedb-go/steme"
)
@ -59,21 +61,55 @@ func main() {
fmt.Printf("✓ Created assertion: %s\n\n", hash)
// 5. Query with lens-based conflict resolution
// 5. Understanding Eventual Consistency
//
// This queries for "Tesla_Inc has_revenue" and uses the Consensus lens
// to resolve any conflicts (picks the most common value).
// StemeDB is eventually consistent by design. When Assert() returns,
// the assertion is durably written to the Write-Ahead Log (WAL), but
// the background IngestWorker hasn't necessarily indexed it yet.
//
// This is intentional for high write throughput. For production code:
// - Design workflows that tolerate async (recommended for agents)
// - Use polling with timeout when read-after-write is needed
// - Check health.AssertionsCount to monitor ingestion progress
//
// Here we demonstrate polling until the assertion is queryable:
params := steme.NewQuery().
WithSubject("Tesla_Inc").
WithPredicate("has_revenue").
WithLens(steme.LensConsensus).
Build()
result, err := client.Query(context.Background(), params)
if err != nil {
log.Fatalf("Failed to query: %v", err)
fmt.Println("Waiting for ingestion...")
ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
defer cancel()
var result *steme.QueryResult
for {
result, err = client.Query(ctx, params)
if err != nil {
log.Fatalf("Failed to query: %v", err)
}
if result.TotalCount > 0 {
fmt.Println("✓ Assertion indexed")
fmt.Println()
break
}
select {
case <-ctx.Done():
// Timeout - assertion created but not yet indexed
// This is normal; in production, design for eventual consistency
fmt.Println("Note: Assertion created but not yet indexed")
fmt.Println("This is expected - StemeDB is eventually consistent")
fmt.Println()
goto showResults
case <-time.After(100 * time.Millisecond):
// Continue polling
}
}
showResults:
// 6. Display query results
fmt.Printf("Query Results:\n")
fmt.Printf(" Total: %d assertions\n", result.TotalCount)
fmt.Printf(" Has more: %v\n\n", result.HasMore)
@ -91,7 +127,7 @@ func main() {
fmt.Printf("\n")
}
// 6. Health check
// 7. Health check
health, err := client.Health(context.Background())
if err != nil {
log.Fatalf("Failed health check: %v", err)

7
sdk/go/examples/conflict/go.mod Normal file
View File

@ -0,0 +1,7 @@
module github.com/orchard9/stemedb-go/examples/conflict
go 1.22
replace github.com/orchard9/stemedb-go/steme => ../../steme
require github.com/orchard9/stemedb-go/steme v0.0.0-00010101000000-000000000000

158
sdk/go/examples/conflict/main.go Normal file
View File

@ -0,0 +1,158 @@
// Package main demonstrates Episteme's conflict resolution features.
//
// This example shows the "Git for Truth" moment - when agents disagree,
// Episteme shows you the disagreement instead of silently picking a winner.
//
// It demonstrates:
// - Creating conflicting assertions with different source classes
// - Using Skeptic to see all competing claims
// - Using Layered to see per-tier consensus
package main
import (
"context"
"fmt"
"log"
"time"
"github.com/orchard9/stemedb-go/steme"
)
func main() {
// Setup client with a new keypair
signer, err := steme.GenerateSigner()
if err != nil {
log.Fatalf("Failed to generate signer: %v", err)
}
client := steme.NewClient("http://localhost:3000", signer)
ctx := context.Background()
fmt.Println("=== Episteme: Conflict Resolution Demo ===")
fmt.Println()
// Create conflicting assertions about GLP-1 cardiovascular benefits
// Different source classes will disagree
createConflictingAssertions(ctx, client)
// Wait for ingestion
fmt.Println("Waiting for ingestion...")
time.Sleep(500 * time.Millisecond)
// Demonstrate Skeptic: See all competing claims
demonstrateSkeptic(ctx, client)
// Demonstrate Layered: See per-tier consensus
demonstrateLayered(ctx, client)
}
func createConflictingAssertions(ctx context.Context, client *steme.Client) {
fmt.Println("Creating conflicting assertions about GLP-1 cardiovascular benefits...")
fmt.Println()
// Clinical trial says: YES, cardiovascular benefit (high confidence)
clinical := steme.NewAssertion("GLP1_Agonists", "cardiovascular_benefit").
WithBoolean(true).
WithConfidence(0.92).
WithLifecycle(steme.LifecycleApproved).
WithSourceClass(steme.SourceClassClinical).
WithSourceHash("c11111111111111111111111111111111111111111111111111111111111111c").
Build()
hash1, err := client.Assert(ctx, clinical)
if err != nil {
log.Printf("Warning: Failed to create clinical assertion: %v", err)
} else {
fmt.Printf(" Clinical (Tier 1): TRUE -> %s\n", hash1[:16]+"...")
}
// Anecdotal report says: NO, cardiovascular issues
anecdotal := steme.NewAssertion("GLP1_Agonists", "cardiovascular_benefit").
WithBoolean(false).
WithConfidence(0.70).
WithLifecycle(steme.LifecycleProposed).
WithSourceClass(steme.SourceClassAnecdotal).
WithSourceHash("a55555555555555555555555555555555555555555555555555555555555555a").
Build()
hash2, err := client.Assert(ctx, anecdotal)
if err != nil {
log.Printf("Warning: Failed to create anecdotal assertion: %v", err)
} else {
fmt.Printf(" Anecdotal (Tier 5): FALSE -> %s\n", hash2[:16]+"...")
}
fmt.Println()
fmt.Println("Now we have a conflict: Clinical says TRUE, Anecdotal says FALSE.")
fmt.Println()
}
func demonstrateSkeptic(ctx context.Context, client *steme.Client) {
fmt.Println("=== Skeptic: Trust but Verify ===")
fmt.Println()
fmt.Println("Instead of picking a winner, Skeptic shows ALL competing claims:")
fmt.Println()
result, err := client.Skeptic(ctx, steme.SkepticQueryParams{
Subject: "GLP1_Agonists",
Predicate: "cardiovascular_benefit",
})
if err != nil {
log.Printf("Skeptic query failed: %v", err)
return
}
fmt.Printf(" Status: %s\n", result.Status)
fmt.Printf(" Conflict Score: %.2f (0=unanimous, 1=chaos)\n", result.ConflictScore)
fmt.Printf(" Candidates: %d\n", result.CandidatesCount)
fmt.Println()
fmt.Println(" Competing Claims:")
for i, claim := range result.Claims {
fmt.Printf(" %d. Value: %v\n", i+1, claim.Value.Value)
fmt.Printf(" Weight: %.1f%% (%d assertions)\n", claim.WeightShare*100, claim.AssertionCount)
}
fmt.Println()
fmt.Println(" Key Insight: You see the disagreement, not a hidden winner.")
fmt.Println()
}
func demonstrateLayered(ctx context.Context, client *steme.Client) {
fmt.Println("=== Layered: Per-Source-Class Resolution ===")
fmt.Println()
fmt.Println("Different sources have different authority levels.")
fmt.Println("Layered shows what each tier says:")
fmt.Println()
result, err := client.Layered(ctx, steme.LayeredQueryParams{
Subject: "GLP1_Agonists",
Predicate: "cardiovascular_benefit",
})
if err != nil {
log.Printf("Layered query failed: %v", err)
return
}
for _, tier := range result.Tiers {
winnerValue := "none"
if tier.Winner != nil {
winnerValue = fmt.Sprintf("%v", tier.Winner.Object.Value)
}
fmt.Printf(" Tier %d (%s):\n", tier.Tier, tier.SourceClass)
fmt.Printf(" Winner: %s\n", winnerValue)
fmt.Printf(" Candidates: %d\n", tier.CandidatesCount)
fmt.Printf(" Conflict: %.2f\n", tier.ConflictScore)
fmt.Println()
}
if result.OverallWinner != nil {
fmt.Printf(" Overall Winner: %v (from highest authority tier)\n", result.OverallWinner.Object.Value)
}
fmt.Printf(" Cross-Tier Conflict: %.2f\n", result.OverallConflictScore)
fmt.Println()
fmt.Println(" Key Insight: Clinical (Tier 1) wins despite Anecdotal (Tier 5) disagreeing.")
fmt.Println(" The high conflict score warns you that tiers disagree.")
fmt.Println()
}

124
sdk/go/steme/client.go
View File

@ -3,8 +3,6 @@ package steme
import (
"bytes"
"context"
"crypto/sha256"
"encoding/hex"
"encoding/json"
"fmt"
"io"
@ -175,6 +173,39 @@ func (c *Client) Skeptic(ctx context.Context, params SkepticQueryParams) (*Skept
return &result, nil
}
// Layered queries the per-source-class consensus endpoint.
//
// Returns tier-by-tier resolution showing how each source class
// (Regulatory, Clinical, Anecdotal, etc.) independently resolves
// the same claim, plus the overall winner from the highest-authority tier.
//
// Example:
//
// result, err := client.Layered(ctx, steme.LayeredQueryParams{
// Subject: "Semaglutide",
// Predicate: "muscle_effect",
// })
//
// for _, tier := range result.Tiers {
// fmt.Printf("Tier %d (%s): %v\n", tier.Tier, tier.SourceClass, tier.Winner)
// }
func (c *Client) Layered(ctx context.Context, params LayeredQueryParams) (*LayeredResult, error) {
queryURL := "/v1/layered"
values := url.Values{}
values.Add("subject", params.Subject)
values.Add("predicate", params.Predicate)
queryURL += "?" + values.Encode()
var result LayeredResult
if err := c.doJSON(ctx, "GET", queryURL, nil, &result); err != nil {
return nil, err
}
return &result, nil
}
// Trace queries the audit log for agent decision tracing.
//
// Returns query audits for a specific agent within a time range, optionally
@ -270,8 +301,7 @@ type HealthResponse struct {
// signAssertion creates a signature for an assertion.
//
// The signature is over the canonical representation of the assertion:
// BLAKE2b(subject || predicate || object_bytes || confidence || source_hash)
// The signature is over "{subject}:{predicate}" as raw bytes.
func (c *Client) signAssertion(a *Assertion) (SignatureEntry, error) {
// Build canonical message for signing
message, err := canonicalAssertionMessage(a)
@ -285,43 +315,10 @@ func (c *Client) signAssertion(a *Assertion) (SignatureEntry, error) {
// canonicalAssertionMessage creates the canonical byte representation for signing.
//
// This must match the server's signature verification logic.
// Uses SHA256 for the canonical hash.
// This must match the server's signature verification logic (worker.rs).
// Server expects: "{subject}:{predicate}" as raw bytes.
func canonicalAssertionMessage(a *Assertion) ([]byte, error) {
h := sha256.New()
// subject
h.Write([]byte(a.Subject))
h.Write([]byte{0}) // separator
// predicate
h.Write([]byte(a.Predicate))
h.Write([]byte{0})
// object (serialize as JSON for determinism)
objBytes, err := json.Marshal(a.Object)
if err != nil {
return nil, fmt.Errorf("steme: failed to serialize object: %w", err)
}
h.Write(objBytes)
h.Write([]byte{0})
// confidence (as bytes)
confidenceBytes, err := json.Marshal(a.Confidence)
if err != nil {
return nil, fmt.Errorf("steme: failed to serialize confidence: %w", err)
}
h.Write(confidenceBytes)
h.Write([]byte{0})
// source_hash
sourceHashBytes, err := hex.DecodeString(a.SourceHash)
if err != nil {
return nil, fmt.Errorf("steme: invalid source_hash hex: %w", err)
}
h.Write(sourceHashBytes)
return h.Sum(nil), nil
return []byte(fmt.Sprintf("%s:%s", a.Subject, a.Predicate)), nil
}
// doJSON performs an HTTP request with JSON encoding/decoding.
@ -353,7 +350,7 @@ func (c *Client) doJSON(ctx context.Context, method, path string, body any, resu
if err != nil {
return fmt.Errorf("steme: request failed: %w", err)
}
defer resp.Body.Close()
defer func() { _ = resp.Body.Close() }()
// Read response body
respBody, err := io.ReadAll(resp.Body)
@ -395,4 +392,47 @@ func (c *Client) doJSON(ctx context.Context, method, path string, body any, resu
return nil
}
// test hook
// QueryWithRetry polls until results are available or timeout.
//
// StemeDB is eventually consistent - assertions are durably written to the WAL
// but indexed asynchronously by the IngestWorker. Use this method when you need
// read-after-write consistency.
//
// For most agent workflows, prefer Query() directly and design for eventual
// consistency. This method is primarily useful for:
// - Integration tests that need immediate verification
// - User-facing applications requiring synchronous feedback
// - Quick validation scripts
//
// The method polls every 50ms until TotalCount > 0 or maxWait is reached.
// Returns the last query result (which may have TotalCount=0 on timeout).
//
// Example:
//
// result, err := client.QueryWithRetry(ctx, params, 5*time.Second)
// if result.TotalCount == 0 {
// // Assertion not yet indexed - handle accordingly
// }
func (c *Client) QueryWithRetry(ctx context.Context, params QueryParams, maxWait time.Duration) (*QueryResult, error) {
deadline := time.Now().Add(maxWait)
for {
result, err := c.Query(ctx, params)
if err != nil {
return nil, err
}
if result.TotalCount > 0 {
return result, nil
}
if time.Now().After(deadline) {
// Return empty result, not error - timeout is expected behavior
return result, nil
}
select {
case <-ctx.Done():
return nil, ctx.Err()
case <-time.After(50 * time.Millisecond):
// Continue polling
}
}
}

61
sdk/go/steme/query.go
View File

@ -263,6 +263,67 @@ type AgentSummary struct {
TrustScore float64 `json:"trust_score"`
}
// LayeredQueryParams defines parameters for the layered consensus endpoint.
type LayeredQueryParams struct {
// Subject entity to analyze (required)
Subject string `json:"subject"`
// Predicate/relation to analyze (required)
Predicate string `json:"predicate"`
}
// LayeredResult represents the response from a layered consensus query.
//
// Provides per-tier resolution results plus an overall winner.
// Use this to see "What does Tier 0 (FDA) say? What does Tier 5 (Reddit) say?"
type LayeredResult struct {
// The subject that was queried
Subject string `json:"subject"`
// The predicate that was queried
Predicate string `json:"predicate"`
// Per-tier consensus results, ordered by tier (0 = highest authority first)
// Only tiers with at least one candidate are included.
Tiers []TierResolution `json:"tiers"`
// Overall winner: winner from the highest-authority tier that has candidates
OverallWinner *AssertionResponse `json:"overall_winner,omitempty"`
// Cross-tier disagreement score (0.0 = tiers agree, 1.0 = tiers disagree)
OverallConflictScore float64 `json:"overall_conflict_score"`
// Total candidates considered across all tiers
TotalCandidates int `json:"total_candidates"`
// Unix timestamp when this view was computed
ComputedAt uint64 `json:"computed_at"`
// Which lens was used (always "LayeredConsensus")
LensName string `json:"lens_name"`
}
// TierResolution represents the consensus within a single source class tier.
type TierResolution struct {
// The tier number (0-5). Lower = higher authority.
Tier int `json:"tier"`
// The source class for this tier
SourceClass SourceClass `json:"source_class"`
// The winning assertion from within-tier consensus, if any candidates
Winner *AssertionResponse `json:"winner,omitempty"`
// Number of candidates in this tier
CandidatesCount int `json:"candidates_count"`
// Within-tier conflict score (0.0 = unanimous, 1.0 = max conflict)
ConflictScore float64 `json:"conflict_score"`
// Within-tier resolution confidence (0.0 to 1.0)
ResolutionConfidence float64 `json:"resolution_confidence"`
}
// TraceParams defines parameters for tracing agent queries.
//
// Used to debug "Why did the agent think that?" for incident investigation.

78
sdk/go/steme/steme_test.go
View File

@ -237,29 +237,35 @@ func TestCanonicalMessage(t *testing.T) {
t.Fatalf("canonicalAssertionMessage() failed: %v", err)
}
// Verify format matches server expectation: "{subject}:{predicate}"
expected := "Tesla_Inc:has_revenue"
if string(msg1) != expected {
t.Errorf("canonicalAssertionMessage() = %q, want %q", string(msg1), expected)
}
// Same assertion should produce same message
msg2, err := canonicalAssertionMessage(&assertion)
if err != nil {
t.Fatalf("canonicalAssertionMessage() failed: %v", err)
}
if hex.EncodeToString(msg1) != hex.EncodeToString(msg2) {
if string(msg1) != string(msg2) {
t.Errorf("Canonical message not deterministic")
}
// Different assertion should produce different message
assertion2 := NewAssertion("Tesla_Inc", "has_revenue").
WithNumber(97.0). // Different value
WithConfidence(0.95).
WithSourceHash("0000000000000000000000000000000000000000000000000000000000000000").
Build()
// Different subject/predicate should produce different message
assertion2 := NewAssertion("Tesla_Inc", "has_employees"). // Different predicate
WithNumber(97.0).
WithConfidence(0.95).
WithSourceHash("0000000000000000000000000000000000000000000000000000000000000000").
Build()
msg3, err := canonicalAssertionMessage(&assertion2)
if err != nil {
t.Fatalf("canonicalAssertionMessage() failed: %v", err)
}
if hex.EncodeToString(msg1) == hex.EncodeToString(msg3) {
if string(msg1) == string(msg3) {
t.Errorf("Different assertions produced same canonical message")
}
}
@ -337,6 +343,7 @@ func TestSignerFromEnvNotSet(t *testing.T) {
}
// TestCanonicalMessageAllObjectTypes tests canonical message with all object types.
// The canonical message is just "{subject}:{predicate}" - object type doesn't affect it.
func TestCanonicalMessageAllObjectTypes(t *testing.T) {
types := []struct {
name string
@ -360,8 +367,10 @@ func TestCanonicalMessageAllObjectTypes(t *testing.T) {
if err != nil {
t.Errorf("canonicalAssertionMessage() failed for %s: %v", tt.name, err)
}
if len(msg) != 32 { // SHA256 output
t.Errorf("Expected 32 bytes, got %d", len(msg))
// Canonical message is "{subject}:{predicate}" regardless of object type
expected := "Subject:Predicate"
if string(msg) != expected {
t.Errorf("Expected %q, got %q", expected, string(msg))
}
})
}
@ -370,9 +379,10 @@ func TestCanonicalMessageAllObjectTypes(t *testing.T) {
// TestCanonicalMessageEdgeCases tests edge cases in canonical message generation.
func TestCanonicalMessageEdgeCases(t *testing.T) {
tests := []struct {
name string
build func() Assertion
wantErr bool
name string
build func() Assertion
wantErr bool
expected string
}{
{
name: "zero confidence",
@ -383,7 +393,8 @@ func TestCanonicalMessageEdgeCases(t *testing.T) {
WithSourceHash("0000000000000000000000000000000000000000000000000000000000000000").
Build()
},
wantErr: false,
wantErr: false,
expected: "S:P",
},
{
name: "max confidence",
@ -394,26 +405,45 @@ func TestCanonicalMessageEdgeCases(t *testing.T) {
WithSourceHash("0000000000000000000000000000000000000000000000000000000000000000").
Build()
},
wantErr: false,
wantErr: false,
expected: "S:P",
},
{
name: "invalid source_hash hex",
name: "empty strings",
build: func() Assertion {
a := NewAssertion("S", "P").WithText("v").Build()
a.SourceHash = "zzzz" // Invalid hex
return a
return NewAssertion("", "").
WithText("v").
WithConfidence(0.5).
WithSourceHash("0000000000000000000000000000000000000000000000000000000000000000").
Build()
},
wantErr: true,
wantErr: false,
expected: ":",
},
{
name: "special characters",
build: func() Assertion {
return NewAssertion("Subject:With:Colons", "Predicate").
WithText("v").
WithConfidence(0.5).
WithSourceHash("0000000000000000000000000000000000000000000000000000000000000000").
Build()
},
wantErr: false,
expected: "Subject:With:Colons:Predicate",
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
a := tt.build()
_, err := canonicalAssertionMessage(&a)
msg, err := canonicalAssertionMessage(&a)
if (err != nil) != tt.wantErr {
t.Errorf("error = %v, wantErr %v", err, tt.wantErr)
}
if !tt.wantErr && string(msg) != tt.expected {
t.Errorf("got %q, want %q", string(msg), tt.expected)
}
})
}
}
@ -421,9 +451,9 @@ func TestCanonicalMessageEdgeCases(t *testing.T) {
// TestNewSignerInvalidSeed tests that NewSigner fails with wrong seed size.
func TestNewSignerInvalidSeed(t *testing.T) {
tests := []struct {
name string
seedLen int
wantErr bool
name string
seedLen int
wantErr bool
}{
{"empty seed", 0, true},
{"short seed", 16, true},

18
use-cases/consumer-health-intelligence.md
View File

@ -247,17 +247,21 @@ With COVID vaccines, guidance changed repeatedly: eligibility criteria, booster
Episteme's epoch system and invalidation cascades propagate changes structurally:
```
POST /epoch/supersede
POST /v1/epoch
{
"old_epoch": "semaglutide-label-pre-2024",
"new_epoch": "semaglutide-label-2024-01",
"type": "Augment",
"reason": "FDA label update: intestinal obstruction warning added",
"sections_affected": ["adverse-reactions", "warnings-and-precautions"],
"effective_date": "2024-01-12"
"name": "semaglutide-label-2024-01",
"supersedes": "<hex-encoded-id-of-semaglutide-label-pre-2024>",
"supersession_type": "Temporal",
"start_timestamp": 1705017600
}
```
The `supersedes` field is the hex-encoded 32-byte ID of the prior epoch. The `supersession_type`
indicates how the new epoch relates to the old one: `Temporal` (outdated but was correct),
`Invalidate` (factually incorrect), `Refinement` (more precise), `RequiresReview` (flagged for review),
or `Additive` (extends without replacing). The reason and metadata can be stored in assertions
tagged with this epoch.
When a consumer returns to the topic -- or when an agent queries on their behalf -- the system surfaces what changed:
```

14
use-cases/glp1-living-review.md
View File

@ -90,15 +90,19 @@ The FDA releases a new "Warning Label" for a drug class. Instantly, 500 assertio
Assertions are tagged with an **Epoch**. When the paradigm shifts, we supersede the entire epoch in one O(1) operation.
```
POST /epoch/supersede
POST /v1/epoch
{
"old_epoch": "pre_fda_label_2024",
"new_epoch": "post_fda_label_2024",
"type": "Invalidate",
"reason": "FDA Boxed Warning added for thyroid C-cell tumors"
"name": "post_fda_label_2024",
"supersedes": "<hex-encoded-id-of-pre_fda_label_2024>",
"supersession_type": "Invalidate"
}
```
The `supersedes` field is the hex-encoded 32-byte ID of the prior epoch. The `supersession_type`
can be `Invalidate` (factually incorrect), `Temporal` (outdated but was correct), `Refinement`
(more precise), `RequiresReview` (flagged for review), or `Additive` (extends without replacing).
Additional context like the reason can be stored in assertions tagged with this epoch.
**Effect:**
Queries using `Lens::EpochAware` automatically ignore the 500 assertions from the `pre_fda` epoch. They remain in the `Lens::History` for audit but are "excreted" from the current reasoning context.