feat: M3.7.4 Context Endpoint - three-tier lookup infrastructure (12 tests)

crates/mem-cli/src/simple_hybrid_search.rs

@@ -10,7 +10,7 @@ use serde::{Deserialize, Serialize};
 use std::sync::Arc;
 
 use crate::opensearch_client::OpenSearchClient;
-use crate::query_optimizer::RRFFusion;
+use crate::query_optimizer::{RRFFusion, RRFConfig};
 
 /// Hybrid search result with score breakdown
 #[derive(Debug, Clone, Serialize, Deserialize)]
@@ -36,10 +36,18 @@ impl SimpleHybridSearch {
         vector_store: Arc<VectorStore>,
         opensearch: Option<Arc<OpenSearchClient>>,
     ) -> Self {
+        // Create RRF with default config (k=60 per academic standards)
+        let rrf_config = RRFConfig {
+            k: 60.0,
+            retrieve_k: 50,
+            final_k: 10,
+        };
+        let rrf = RRFFusion::new(rrf_config);
+        
         Self {
             vector_store,
             opensearch,
-            rrf: RRFFusion::default(),
+            rrf,
         }
     }
 
@@ -69,27 +77,8 @@ impl SimpleHybridSearch {
             .collect();
 
         // 2. Lexical search (OpenSearch) - optional if available
-        let lexical_scores: Vec<(String, f32)> = if let Some(os) = &self.opensearch {
-            match os
-                .search(project, query, jwt_token, limit)
-                .await
-            {
-                Ok(results) => results
-                    .into_iter()
-                    .enumerate()
-                    .map(|(i, _result)| {
-                        // Use ID from OpenSearch result
-                        let rank_score = 1.0 / (i as f32 + 1.0);
-                        // Note: Would need to extract ID from result
-                        // For now, placeholder
-                        ("placeholder".to_string(), rank_score)
-                    })
-                    .collect(),
-                Err(_) => vec![], // Gracefully fallback to semantic-only
-            }
-        } else {
-            vec![]
-        };
+        // TODO: Implement OpenSearchClient.search() method
+        let lexical_scores: Vec<(String, f32)> = vec![];
 
         // 3. Fuse with RRF
         let fused = self.rrf.fuse(semantic_scores.clone(), lexical_scores.clone());