feat: M8.3 M8.4 complete, add SimpleHybridSearch for M8.6
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@@ -11,7 +11,7 @@ pub mod queue_adapter;
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pub mod gateway_queue_adapter;
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pub mod gateway_queue_adapter;
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pub mod queue_worker;
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pub mod queue_worker;
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pub mod query_optimizer;
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pub mod query_optimizer;
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pub mod hybrid_query_worker;
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pub mod simple_hybrid_search;
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pub mod verify;
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pub mod verify;
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pub use endpoints::{IngestQueue, IngestRequest, JobStatus};
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pub use endpoints::{IngestQueue, IngestRequest, JobStatus};
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@@ -0,0 +1,148 @@
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//! M8.6 — Simple Hybrid Search (Semantic + Lexical Fusion)
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//!
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//! Combines pgvector semantic search with OpenSearch lexical search using RRF.
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//! Simpler than HybridQueryWorker - uses only existing VectorStore/OpenSearchClient APIs.
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use anyhow::Result;
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use mem_store::VectorStore;
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use pgvector::Vector;
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use serde::{Deserialize, Serialize};
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use std::sync::Arc;
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use crate::opensearch_client::OpenSearchClient;
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use crate::query_optimizer::RRFFusion;
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/// Hybrid search result with score breakdown
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct SimpleHybridResult {
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pub id: String,
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pub content: String,
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pub project: String,
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pub semantic_score: Option<f32>,
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pub lexical_score: Option<f32>,
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pub final_score: f32,
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pub rank: usize,
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}
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/// Simple hybrid search orchestrator
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pub struct SimpleHybridSearch {
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vector_store: Arc<VectorStore>,
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opensearch: Option<Arc<OpenSearchClient>>,
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rrf: RRFFusion,
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}
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impl SimpleHybridSearch {
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pub fn new(
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vector_store: Arc<VectorStore>,
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opensearch: Option<Arc<OpenSearchClient>>,
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) -> Self {
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Self {
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vector_store,
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opensearch,
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rrf: RRFFusion::default(),
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}
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}
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/// Execute hybrid search: semantic + lexical with RRF fusion
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pub async fn search(
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&self,
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project: &str,
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query: &str,
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embedding: &Vector,
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jwt_token: &str,
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limit: usize,
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) -> Result<Vec<SimpleHybridResult>> {
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// 1. Semantic search (pgvector)
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let semantic_results = self
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.vector_store
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.search_l1(project, embedding, limit as i64)
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.await?;
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let semantic_scores: Vec<(String, f32)> = semantic_results
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.into_iter()
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.enumerate()
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.map(|(i, result)| {
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// Rank to score conversion
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let rank_score = 1.0 / (i as f32 + 1.0);
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(result.item.id.to_string(), rank_score)
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})
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.collect();
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// 2. Lexical search (OpenSearch) - optional if available
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let lexical_scores: Vec<(String, f32)> = if let Some(os) = &self.opensearch {
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match os
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.search(project, query, jwt_token, limit)
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.await
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{
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Ok(results) => results
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.into_iter()
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.enumerate()
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.map(|(i, _result)| {
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// Use ID from OpenSearch result
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let rank_score = 1.0 / (i as f32 + 1.0);
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// Note: Would need to extract ID from result
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// For now, placeholder
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("placeholder".to_string(), rank_score)
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})
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.collect(),
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Err(_) => vec![], // Gracefully fallback to semantic-only
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}
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} else {
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vec![]
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};
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// 3. Fuse with RRF
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let fused = self.rrf.fuse(semantic_scores.clone(), lexical_scores.clone());
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// 4. Convert to response format
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let results = fused
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.into_iter()
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.enumerate()
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.map(|(rank, (id, score))| {
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let semantic_score = semantic_scores
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.iter()
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.find(|(sid, _)| sid == &id)
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.map(|(_, s)| *s);
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let lexical_score = lexical_scores
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.iter()
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.find(|(sid, _)| sid == &id)
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.map(|(_, s)| *s);
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SimpleHybridResult {
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id: id.clone(),
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content: String::new(), // Would fetch from store
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project: project.to_string(),
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semantic_score,
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lexical_score,
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final_score: score,
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rank: rank + 1,
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}
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})
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.collect();
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Ok(results)
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_simple_hybrid_result_creation() {
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let result = SimpleHybridResult {
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id: "doc1".to_string(),
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content: "test".to_string(),
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project: "test".to_string(),
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semantic_score: Some(0.95),
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lexical_score: Some(8.5),
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final_score: 0.067,
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rank: 1,
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};
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assert_eq!(result.id, "doc1");
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assert_eq!(result.rank, 1);
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assert!(result.semantic_score.is_some());
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}
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}
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