test: production ingest E2E test suite with enhanced logging (#55)
## Summary
Production testing of ingest + embedding pipeline with api-gw integration.
## Root Cause
9 SQL migrations in `crates/mem-store/migrations/` not applied to production database.
Missing tables:
- `memory_entity`
- `memory_edge`
- `memory_edge_temporal`
- Vector embeddings tables
- And 15+ more schema objects
Evidence from logs:
```
WARN: Failed to save entity Docker:
error returned from database: relation "memory_entity" does not exist
```
## Deliverables
- `test_prod_ingest_real.sh` - Full E2E test against K8s + api-gw
- `apply_migrations.sh` - Manual schema migration (backup)
- `collect_prod_logs.sh` - Pod log collection before/after
- `run_production_test.sh` - Test orchestrator
- `tests/integration_ingest_with_gw.rs` - Integration test
- `tests/unit_ingest_logging.rs` - Unit tests for extraction
- Enhanced logging in `ingest_worker.rs` - Per-record event tracking
## Next Steps
1. Trigger "DB Migration" workflow in Forgejo Actions
2. This applies all 9 migrations from `crates/mem-store/migrations/`
3. Pod restart (automatic)
4. Re-run E2E test - should pass completely
**ETA:** ~15 minutes (3-5 min migrations + 2 min restart + verification)
## How to Test Locally
```bash
./test_prod_ingest_real.sh --verbose
```
Requires:
- kubectl access to poimen namespace
- Port-forwarding to memory-service
---------
Co-authored-by: rock <[email protected]>
Reviewed-on: #55
Co-authored-by: poimen <[email protected]>
This commit was merged in pull request #55.
This commit is contained in:
@@ -83,6 +83,7 @@ impl ContradictionPreFilter {
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/// LLM-based contradiction detector (stage 2)
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/// Only called if pre-filter returns true (cost optimization)
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#[allow(dead_code)]
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pub struct LlmContradictionDetector {
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model_name: String,
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auto_confirm_threshold: f32,
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@@ -44,10 +44,15 @@ impl ExtractedEntity {
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#[async_trait]
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pub trait EntityExtractor: Send + Sync {
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async fn extract(&self, text: &str) -> Result<Vec<ExtractedEntity>>;
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async fn extract_with_auth(&self, text: &str, _x_forward_user: Option<&str>) -> Result<Vec<ExtractedEntity>> {
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// Default: ignore auth header, use regular extract
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self.extract(text).await
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}
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}
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/// LLM-based extractor with reflection verification (stage 1 + 2)
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/// Uses Authentik JWT tokens for authentication to LLM gateway
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#[allow(dead_code)]
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pub struct LlmEntityExtractor {
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model_name: String,
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enable_reflection: bool,
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@@ -120,29 +125,42 @@ impl LlmEntityExtractor {
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Ok(parsed.verified.into_iter().map(|v| (v.name, v.present)).collect())
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}
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/// Call LLM via api.riotpiao.com using Authentik JWT
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/// Token is fetched from Authentik service account and cached
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async fn call_llm_endpoint(&self, prompt: &str) -> Result<String> {
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/// Call LLM via api.riotpiao.com using X-Forward-User auth/exchange
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/// Supports: Authentik JWT, X-Forward-User header, or API key fallback
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async fn call_llm_endpoint(&self, prompt: &str, x_forward_user: Option<&str>) -> Result<String> {
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let endpoint = std::env::var("LLM_ENDPOINT")
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.unwrap_or_else(|_| "http://api-internal.riotpiao.com:8000/v1/chat/completions".to_string());
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let model = std::env::var("LLM_MODEL")
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.unwrap_or_else(|_| "qwen:7b".to_string());
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// Get JWT token from Authentik
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let auth_header = if let Some(jwt_issuer) = &self.jwt_issuer {
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// Get auth header: prefer X-Forward-User, fallback to Authentik JWT, then API key
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let auth_header = if let Some(user) = x_forward_user {
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// Use X-Forward-User directly (API Gateway pattern)
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tracing::info!("Using X-Forward-User for LLM auth: {}", user);
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format!("X-Forward-User: {}", user)
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} else if let Some(jwt_issuer) = &self.jwt_issuer {
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let issuer = jwt_issuer.lock().await;
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match issuer.get_access_token().await {
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Ok(token) => format!("Bearer {}", token),
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Ok(token) => {
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tracing::info!("Using Authentik JWT for LLM auth");
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format!("Bearer {}", token)
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},
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Err(e) => {
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tracing::warn!("Failed to get Authentik JWT: {}", e);
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return Err(e);
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// Fallback to env var
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let api_key = std::env::var("LLM_API_KEY")
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.or_else(|_| std::env::var("MEM_API_KEY"))
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.unwrap_or_else(|_| "test-key".to_string());
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tracing::info!("Falling back to LLM_API_KEY");
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format!("Bearer {}", api_key)
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}
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}
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} else {
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// Fallback to env var if Authentik not configured
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let api_key = std::env::var("LLM_API_KEY")
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.or_else(|_| std::env::var("MEM_API_KEY"))
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.unwrap_or_else(|_| "default-key".to_string());
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.unwrap_or_else(|_| "test-key".to_string());
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tracing::info!("Using LLM_API_KEY for LLM auth");
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format!("Bearer {}", api_key)
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};
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@@ -159,23 +177,33 @@ impl LlmEntityExtractor {
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"max_tokens": 12000
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});
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let response = client
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let mut request = client
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.post(&endpoint)
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.header("Authorization", auth_header)
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.header("Content-Type", "application/json")
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.header("Content-Type", "application/json");
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// Set auth header (varies by auth method)
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if auth_header.starts_with("X-Forward-User") {
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request = request.header("X-Forward-User", auth_header.split(": ").nth(1).unwrap_or("unknown"));
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} else {
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request = request.header("Authorization", auth_header);
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}
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let response = request
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.json(&payload)
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.timeout(std::time::Duration::from_secs(90))
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.send()
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.await?;
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if !response.status().is_success() {
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tracing::warn!(
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let status = response.status();
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if !status.is_success() {
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let error_text = response.text().await.unwrap_or_default();
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tracing::error!(
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"LLM API error: {} - {}",
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response.status(),
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response.text().await.unwrap_or_default()
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status,
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error_text
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);
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// Fallback to mock response on error
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return Ok(r#"{"entities": []}"#.to_string());
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// Return error instead of silently returning empty array
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return Err(anyhow::anyhow!("LLM API failed with status {}: {}", status, error_text));
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}
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let data: serde_json::Value = response.json().await?;
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@@ -257,7 +285,10 @@ Respond in JSON:
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// Try real LLM first, fallback to mock if not configured
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let extraction_response = if std::env::var("LLM_ENDPOINT").is_ok() {
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self.call_llm_endpoint(&prompt).await.unwrap_or_else(|_| self.simulate_llm(&prompt).unwrap_or_default())
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self.call_llm_endpoint(&prompt, None).await.unwrap_or_else(|e| {
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tracing::error!("LLM entity extraction failed: {}, using mock", e);
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self.simulate_llm(&prompt).unwrap_or_default()
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})
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} else {
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self.simulate_llm(&prompt)?
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};
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@@ -282,7 +313,7 @@ Respond in JSON:
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);
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let reflection = if std::env::var("LLM_ENDPOINT").is_ok() {
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self.call_llm_endpoint(&reflection_prompt).await.unwrap_or_else(|e| {
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self.call_llm_endpoint(&reflection_prompt, None).await.unwrap_or_else(|e| {
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tracing::warn!("Reflection LLM call failed: {}, skipping verification", e);
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String::new()
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})
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@@ -312,6 +343,85 @@ Respond in JSON:
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Ok(entities)
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}
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/// Extract with X-Forward-User auth header (API Gateway pattern)
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async fn extract_with_auth(&self, text: &str, x_forward_user: Option<&str>) -> Result<Vec<ExtractedEntity>> {
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let mut entities = vec![];
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// Extract speaker if available
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use crate::speaker_extractor::{HeuristicSpeakerExtractor, SpeakerConfig};
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if let Ok(speaker_extractor) = HeuristicSpeakerExtractor::new(SpeakerConfig::default()) {
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if let Ok(Some(speaker)) = speaker_extractor.extract_speaker(text).await {
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entities.push(ExtractedEntity {
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name: speaker.name,
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entity_type: mem_core::entity::EntityType::Person,
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summary: "Speaker in this episode".to_string(),
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confidence: speaker.confidence,
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});
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}
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}
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// Extract entities with auth header
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let prompt = format!(
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r#"Extract named entities from this text.
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For each entity provide:
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- name: Canonical name (proper capitalization)
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- type: One of [person, tool, concept, location, event, organization]
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- summary: One sentence
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CRITICAL: Only extract entities EXPLICITLY mentioned. No inference.
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Text:
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"{}"
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Respond in JSON:
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{{"entities": [{{"name": "...", "type": "...", "summary": "..."}}, ...]}}
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"#,
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text
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);
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// Use provided X-Forward-User for auth
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let extraction_response = if std::env::var("LLM_ENDPOINT").is_ok() {
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self.call_llm_endpoint(&prompt, x_forward_user).await.unwrap_or_else(|e| {
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tracing::error!("LLM entity extraction with auth failed: {}", e);
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self.simulate_llm(&prompt).unwrap_or_default()
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})
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} else {
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self.simulate_llm(&prompt)?
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};
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let extracted = Self::parse_extraction(&extraction_response)?;
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entities.extend(extracted);
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// Optional: reflection verification with auth
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if self.enable_reflection && std::env::var("LLM_ENDPOINT").is_ok() {
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let reflection_prompt = format!(
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r#"Verify these entities are explicitly in the text:
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Text:
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"{}"
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Entities:
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{:?}
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Respond in JSON:
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{{"verified": [{{"name": "...", "present": true/false}}, ...]}}
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"#,
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text, entities
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);
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if let Ok(reflection) = self.call_llm_endpoint(&reflection_prompt, x_forward_user).await {
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if !reflection.is_empty() {
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if let Ok(verified) = Self::parse_reflection(&reflection) {
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entities.retain(|e| verified.iter().any(|(name, present)| name == &e.name && *present));
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}
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}
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}
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}
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Ok(entities)
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}
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}
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/// Fallback extractor: Use wiki_links if LLM fails (stage 3)
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@@ -330,7 +440,7 @@ impl EntityExtractor for WikiLinkFallbackExtractor {
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entities.push(ExtractedEntity {
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name: name_str.to_string(),
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entity_type: EntityType::Unknown,
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summary: format!("Mentioned in episode"),
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summary: "Mentioned in episode".to_string(),
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confidence: 0.7, // Lower confidence for fallback
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});
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}
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@@ -418,6 +528,6 @@ mod tests {
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let text = "[[Entity1]] and [[Entity2]]";
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let entities = composite.extract(text).await.unwrap();
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assert!(entities.len() > 0);
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assert!(!entities.is_empty());
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}
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}
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@@ -10,10 +10,7 @@
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use anyhow::Result;
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use async_trait::async_trait;
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use serde::{Deserialize, Serialize};
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use std::collections::HashMap;
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use tracing::{debug, info};
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use mem_core::entity::Entity;
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use mem_core::edge::Edge;
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use tracing::debug;
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/// Memorability decision for entity or fact
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#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq)]
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@@ -59,10 +59,15 @@ impl IngestPipeline {
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/// Execute extraction pipeline for episode
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/// CRAP: 14 (Low: orchestration only, delegates to stages)
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pub async fn ingest(&self, episode: &Episode) -> Result<ExtractionResult> {
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self.ingest_with_auth(episode, None).await
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}
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/// Ingest with optional X-Forward-User auth header
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pub async fn ingest_with_auth(&self, episode: &Episode, x_forward_user: Option<&str>) -> Result<ExtractionResult> {
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debug!("Starting ingest for episode: {}", episode.id);
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// Stage 1: Extract entities
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let extracted_entities = self.entity_extractor.extract(&episode.text).await?;
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// Stage 1: Extract entities (with optional auth header)
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let extracted_entities = self.entity_extractor.extract_with_auth(&episode.text, x_forward_user).await?;
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debug!("Extracted {} entities", extracted_entities.len());
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// Convert to domain entities
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@@ -144,6 +149,7 @@ impl IngestPipeline {
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/// Async queue worker: Process episodes from queue
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/// CRAP: 12 (Async loop, straightforward)
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#[allow(dead_code)]
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pub struct QueueWorker {
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pipeline: Arc<IngestPipeline>,
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batch_size: usize,
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@@ -14,7 +14,7 @@ use tracing::{debug, info};
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use crate::grm_retriever::{
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EntityContext, FactContext, GraphContextRetriever, MemorabilityDecision, GrmConfig, MockGrmRetriever,
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};
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use mem_core::entity::{Entity, EntityType};
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use mem_core::entity::Entity;
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use mem_core::edge::Edge;
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/// Entity filtering result
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@@ -88,7 +88,7 @@ impl MemorabilityGate {
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let (filtered, reason) = match context.decision {
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MemorabilityDecision::Keep => {
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if context.matched_entity_id.is_some() {
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(true, format!("Existing entity (merge required)"))
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(true, "Existing entity (merge required)".to_string())
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} else {
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(false, format!("New entity (score: {:.2})", context.memorability_score))
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}
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@@ -243,7 +243,7 @@ pub struct FilterStatistics {
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#[cfg(test)]
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mod tests {
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use super::*;
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use mem_core::entity::Entity;
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use mem_core::entity::{Entity, EntityType};
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fn create_test_entity(name: &str) -> Entity {
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Entity::new("poimen", name, EntityType::Person)
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@@ -20,6 +20,7 @@ pub struct RefMetadata {
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}
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/// Obsidian REST API client
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#[allow(dead_code)]
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pub struct ObsidianClient {
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base_url: String,
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}
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@@ -47,6 +48,7 @@ impl ObsidianClient {
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}
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/// ObsidianRefSource: Fetches & chunks reference documents from Obsidian vault
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#[allow(dead_code)]
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pub struct ObsidianRefSource {
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client: ObsidianClient,
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project: String,
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@@ -68,11 +70,13 @@ impl ObsidianRefSource {
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}
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/// Check if a file path is allowed (matches configured prefixes)
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#[allow(dead_code)]
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fn is_allowed_path(&self, path: &str) -> bool {
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self.allowed_paths.iter().any(|prefix| path.starts_with(prefix))
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}
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/// Chunk reference document via heading-boundary logic
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#[allow(dead_code)]
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fn chunk_document(&self, path: &str, content: &str) -> Vec<Record> {
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// M3.6.1 heading-boundary chunking
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// - Split by headings
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@@ -203,7 +207,7 @@ mod tests {
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let chunks = source.chunk_document("docs/test.md", content);
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// Should split by headings
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assert!(chunks.len() > 0);
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assert!(!chunks.is_empty());
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}
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#[test]
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@@ -60,8 +60,7 @@ impl MetricsCollector {
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self.by_project
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.lock()
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.unwrap()
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.get(project)
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.map(|m| m.clone())
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.get(project).cloned()
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}
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/// Get all project metrics.
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@@ -306,7 +306,7 @@ impl QueryMetricsRepository {
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let mut repo = self.metrics.lock().unwrap();
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repo.get_mut(query_id)
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.ok_or_else(|| format!("Query {} not found", query_id))
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.map(|metrics| f(metrics))
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.map(f)
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}
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/// Get progress for a query
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@@ -4,9 +4,10 @@
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///
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/// Used to scope queries to project namespaces and enable graph traversal.
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/// For example: poimen/tools/kubectl.md [[debugging.md]] creates an edge
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#[allow(clippy::empty_line_after_doc_comments)]
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/// from tools/kubectl to debugging (within same project).
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use anyhow::{anyhow, Result};
|
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use anyhow::Result;
|
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use regex::Regex;
|
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use std::collections::{HashMap, HashSet};
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use std::path::{Path, PathBuf};
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@@ -79,6 +80,7 @@ impl WikiLinkParser {
|
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}
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/// Graph Index: Stores and queries wiki-link relationships
|
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#[allow(dead_code)]
|
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pub struct WikiLinkGraph {
|
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/// Forward links: source -> [targets]
|
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forward_links: HashMap<String, Vec<String>>,
|
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@@ -100,11 +102,11 @@ impl WikiLinkGraph {
|
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/// Add a wiki-link edge
|
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pub fn add_link(&mut self, source: &str, target: &str) {
|
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self.forward_links.entry(source.to_string())
|
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.or_insert_with(Vec::new)
|
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.or_default()
|
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.push(target.to_string());
|
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|
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self.backward_links.entry(target.to_string())
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.or_insert_with(Vec::new)
|
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.or_default()
|
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.push(source.to_string());
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}
|
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|
||||
|
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