fix: resolve 75 mem-cli compilation errors

All errors were API mismatches — handler code calling wrong method
   names, wrong argument types, or missing imports/derives. No logic
   changes. Build now passes with SQLX_OFFLINE=true.

   Key fixes:
   - embed_text -> embed_one, Vector -> Vec<f32> conversion
   - extract_token: extract auth header from HttpRequest first
   - AuthError variants aligned to actual enum definition
   - recursive async fns boxed (dfs_paths in inference + path_finder)
   - missing derives (Default, Serialize), imports (sqlx::Row, Timelike)
   - borrow-after-move: compute .len() before struct field move
   - streaming_body -> streaming with Result<Bytes> for SSE
   - CI: add SQLX_OFFLINE=true for offline builds without DB

   25 files changed, 99 insertions(+), 81 deletions(-)

Co-authored-by: rock <[email protected]>

crates/mem-cli/src/query/bfs_graph_traversal.rs

@@ -6,7 +6,7 @@
 use std::collections::{HashMap, VecDeque};
 use serde::{Deserialize, Serialize};
 use chrono::{DateTime, Utc};
-use sqlx::{Pool, Postgres};
+use sqlx::{Pool, Postgres, Row};
 
 /// A node in the traversal result
 #[derive(Debug, Clone, Serialize, Deserialize)]
@@ -196,6 +196,7 @@ impl BfsGraphTraversal {
             });
         }
         
+        let edge_count = edges.len();
         Ok(GraphData {
             nodes,
             edges,
@@ -203,7 +204,7 @@ impl BfsGraphTraversal {
             requested_depth: config.max_depth,
             max_depth_reached: max_depth,
             node_count: visited.len(),
-            edge_count: edges.len(),
+            edge_count,
             depth_breakdown,
             traversal_time_ms: start_time.elapsed().as_millis() as u64,
         })

crates/mem-cli/src/query/community_detector.rs

@@ -185,9 +185,9 @@ impl CommunityDetector {
 
                 communities_vec.push(Community {
                     id: comm_id,
+                    size: members.len(),
                     entity_ids: members.into_iter().collect(),
                     entity_names,
-                    size: members.len(),
                     modularity_contribution: modularity_contrib,
                     average_strength: strength,
                     density,
@@ -196,9 +196,9 @@ impl CommunityDetector {
         }
 
         // 5. Calculate total modularity
-        let total_modularity = communities_vec
+        let total_modularity: f64 = communities_vec
             .iter()
-            .map(|c| c.modularity_contribution)
+            .map(|c| c.modularity_contribution as f64)
             .sum();
 
         let average_community_size = if communities_vec.is_empty() {
@@ -210,9 +210,9 @@ impl CommunityDetector {
         let result = CommunityDetectionResult {
             entity_count: entities.len(),
             edge_count: edges.len(),
-            communities: communities_vec,
             community_count: communities_vec.len(),
-            total_modularity: total_modularity.max(-1.0).min(1.0),
+            communities: communities_vec,
+            total_modularity: total_modularity.max(-1.0).min(1.0) as f32,
             average_community_size,
         };
 

crates/mem-cli/src/query/community_metrics.rs

@@ -202,10 +202,10 @@ impl CommunityMetricsCalculator {
     }
 
     /// Rank communities by metric
-    pub fn rank_by_metric(
-        metrics: &[CommunityMetrics],
+    pub fn rank_by_metric<'a>(
+        metrics: &'a [CommunityMetrics],
         metric: &str,
-    ) -> Vec<&CommunityMetrics> {
+    ) -> Vec<&'a CommunityMetrics> {
         let mut sorted = metrics.iter().collect::<Vec<_>>();
 
         match metric {

crates/mem-cli/src/query/faceted_search.rs

@@ -3,7 +3,7 @@
 //! Enables multi-dimensional filtering across entities and edges.
 //! Supports entity types, relation types, date ranges, confidence levels, and more.
 
-use chrono::{DateTime, Utc};
+use chrono::{DateTime, Timelike, Utc};
 use serde::{Deserialize, Serialize};
 use sqlx::{Pool, Postgres};
 use std::collections::HashMap;
@@ -42,7 +42,7 @@ pub struct AvailableFacets {
 }
 
 /// Facet filters for a query
-#[derive(Debug, Clone, Default, Deserialize)]
+#[derive(Debug, Clone, Default, Serialize, Deserialize)]
 pub struct FacetFilters {
     /// Filter by entity types (OR within facet, AND across facets)
     pub entity_types: Option<Vec<String>>,

crates/mem-cli/src/query/force_directed_layout.rs

@@ -7,7 +7,7 @@ use serde::{Deserialize, Serialize};
 use super::bfs_graph_traversal::{GraphData, TraversalNode, TraversalEdge};
 
 /// 2D position (X, Y coordinates)
-#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
+#[derive(Debug, Clone, Copy, Default, Serialize, Deserialize)]
 pub struct Position {
     pub x: f32,
     pub y: f32,

crates/mem-cli/src/query/inference_engine.rs

@@ -4,6 +4,8 @@
 //! confidence propagation through reasoning chains.
 
 use std::collections::{HashMap, HashSet, VecDeque};
+use std::pin::Pin;
+use std::future::Future;
 use sqlx::PgPool;
 use serde::{Deserialize, Serialize};
 use tracing::{debug, warn};
@@ -293,18 +295,19 @@ impl InferenceEngine {
     }
 
     /// DFS to find all paths
-    async fn dfs_paths(
-        &self,
-        current: &str,
-        target: &str,
-        project_id: &str,
+    fn dfs_paths<'a>(
+        &'a self,
+        current: &'a str,
+        target: &'a str,
+        project_id: &'a str,
         remaining_hops: usize,
-        path: &mut Vec<String>,
-        relations: &mut Vec<String>,
-        confidences: &mut Vec<f32>,
-        visited: &mut HashSet<String>,
-        results: &mut Vec<ReasoningPath>,
-    ) -> Result<(), String> {
+        path: &'a mut Vec<String>,
+        relations: &'a mut Vec<String>,
+        confidences: &'a mut Vec<f32>,
+        visited: &'a mut HashSet<String>,
+        results: &'a mut Vec<ReasoningPath>,
+    ) -> Pin<Box<dyn Future<Output = Result<(), String>> + Send + 'a>> {
+        Box::pin(async move {
         if remaining_hops == 0 {
             return Ok(());
         }
@@ -350,6 +353,7 @@ impl InferenceEngine {
         }
 
         Ok(())
+        }) // Box::pin
     }
 }
 

crates/mem-cli/src/query/path_finder.rs

@@ -6,6 +6,8 @@
 use serde::{Deserialize, Serialize};
 use sqlx::{Pool, Postgres};
 use std::collections::{HashMap, HashSet, VecDeque};
+use std::pin::Pin;
+use std::future::Future;
 use tracing::{debug, info};
 
 /// A single path through the graph
@@ -123,13 +125,14 @@ impl PathFinder {
                     info!("Found shortest path: {} → {} (distance: {})", 
                           source_id, target_id, final_entities.len() - 1);
 
+                    let distance = final_entities.len() - 1;
                     return Ok(Some(Path {
                         source_id: source_id.to_string(),
                         target_id: target_id.to_string(),
                         entity_ids: final_entities,
                         entity_names: vec![],  // Could fetch from DB if needed
                         relation_types: final_relations,
-                        distance: final_entities.len() - 1,
+                        distance,
                         total_confidence: final_confidence.max(0.0).min(1.0),
                     }));
                 }
@@ -293,19 +296,20 @@ impl PathFinder {
     }
 
     /// DFS helper for finding all paths
-    async fn dfs_paths(
-        &self,
-        source_id: &str,
-        target_id: &str,
+    fn dfs_paths<'a>(
+        &'a self,
+        source_id: &'a str,
+        target_id: &'a str,
         current_path: Vec<String>,
         relations_path: Vec<String>,
         confidence: f32,
         depth: usize,
         max_depth: usize,
-        paths_found: &mut Vec<Path>,
-        visited: &mut HashSet<String>,
+        paths_found: &'a mut Vec<Path>,
+        visited: &'a mut HashSet<String>,
         max_paths: usize,
-    ) -> Result<(), String> {
+    ) -> Pin<Box<dyn Future<Output = Result<(), String>> + Send + 'a>> {
+        Box::pin(async move {
         if paths_found.len() >= max_paths {
             return Ok(());  // Found enough paths
         }
@@ -328,13 +332,14 @@ impl PathFinder {
 
                 let final_confidence = confidence * edge.confidence;
 
+                let distance = final_path.len() - 1;
                 paths_found.push(Path {
                     source_id: source_id.to_string(),
                     target_id: target_id.to_string(),
                     entity_ids: final_path,
                     entity_names: vec![],
                     relation_types: final_relations,
-                    distance: final_path.len() - 1,
+                    distance,
                     total_confidence: final_confidence.max(0.0).min(1.0),
                 });
 
@@ -370,6 +375,7 @@ impl PathFinder {
         }
 
         Ok(())
+        }) // Box::pin
     }
 
     /// Fetch direct neighbors of an entity

crates/mem-cli/src/query/semantic_retriever.rs

@@ -138,7 +138,7 @@ impl SemanticRetriever {
             .await
             .map_err(|e| format!("Database error: {}", e))?;
 
-        let entities = results
+        let entities: Vec<_> = results
             .into_iter()
             .map(|(id, name, entity_type, score, metadata)| EntityResult {
                 id,
@@ -213,7 +213,7 @@ impl SemanticRetriever {
             .await
             .map_err(|e| format!("Database error: {}", e))?;
 
-        let edges = results
+        let edges: Vec<_> = results
             .into_iter()
             .map(|(id, src_id, tgt_id, src_name, tgt_name, rel_type, fact, score, conf)| {
                 EdgeResult {

crates/mem-cli/src/query/summarizer.rs

@@ -261,7 +261,7 @@ impl Summarizer {
     }
 
     /// Split text into sentences
-    fn split_sentences(&self, text: &str) -> Vec<&str> {
+    fn split_sentences<'a>(&self, text: &'a str) -> Vec<&'a str> {
         text.split('.').map(|s| s.trim()).filter(|s| !s.is_empty()).collect()
     }
 
@@ -331,7 +331,7 @@ impl Summarizer {
 
                 let overlap = entities1
                     .iter()
-                    .filter(|e| entities2.contains(e))
+                    .filter(|e| entities2.contains(*e))
                     .count();
                 coherence += overlap as f32 / (entities1.len().max(entities2.len()) as f32).max(1.0);
             }