feat: fully functional graph-based RAG

src/rag/graph.rs

@@ -2,12 +2,21 @@ use super::DocumentId;
 use crate::client::*;
 
 use anyhow::{Context, Result};
-use indexmap::IndexMap;
+use indexmap::{IndexMap, IndexSet};
 use petgraph::Direction;
 use petgraph::graph::NodeIndex;
 use petgraph::stable_graph::StableGraph;
+use petgraph::visit::EdgeRef;
 use serde::{Deserialize, Serialize};
-use std::collections::HashSet;
+use std::collections::{HashMap, HashSet};
+
+/// Heuristic upper bound on chunk size before warning the user that the
+/// extraction LLM call may be truncated. Not a hard limit.
+const MAX_CHUNK_CHARS: usize = 24_000;
+
+/// Maximum number of nodes the BFS may visit during a single graph_search.
+/// Keeps the synchronous traversal bounded on dense graphs.
+pub const MAX_GRAPH_NODES: usize = 500;
 
 const EXTRACTION_PROMPT: &str = r#"Extract entities and relationships from the following text chunk.
 
@@ -71,9 +80,9 @@ pub struct ExtractedRelationship {
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct KnowledgeGraph {
     pub graph: StableGraph<Entity, Relationship>,
-    /// Lowercased entity name -> raw node index
+    /// Lowercased entity name → raw node index
     pub entity_index: IndexMap<String, u32>,
-    /// DocumentId inner value -> raw node indices for entities in that chunk
+    /// DocumentId inner value → raw node indices for entities in that chunk
     pub document_entities: IndexMap<usize, Vec<u32>>,
 }
 
@@ -89,16 +98,27 @@ impl Default for KnowledgeGraph {
 
 impl KnowledgeGraph {
     pub fn merge(&mut self, doc_id: DocumentId, result: ExtractionResult) {
-        let mut chunk_nodes: Vec<u32> = vec![];
+        let mut chunk_nodes: IndexSet<u32> = IndexSet::new();
 
         for extracted in &result.entities {
             let key = extracted.name.to_lowercase();
+            let normalized_type = extracted.entity_type.to_uppercase();
             let node_raw = if let Some(&existing) = self.entity_index.get(&key) {
+                let idx = NodeIndex::new(existing as usize);
+                if self.graph.contains_node(idx) {
+                    let node = &mut self.graph[idx];
+                    if node.entity_type == "OTHER" && normalized_type != "OTHER" {
+                        node.entity_type = normalized_type;
+                    }
+                    if node.description.is_none() {
+                        node.description = extracted.description.clone();
+                    }
+                }
                 existing
             } else {
                 let entity = Entity {
                     name: extracted.name.clone(),
-                    entity_type: extracted.entity_type.clone(),
+                    entity_type: normalized_type,
                     description: extracted.description.clone(),
                 };
                 let idx = self.graph.add_node(entity);
@@ -106,7 +126,7 @@ impl KnowledgeGraph {
                 self.entity_index.insert(key, raw);
                 raw
             };
-            chunk_nodes.push(node_raw);
+            chunk_nodes.insert(node_raw);
         }
 
         for extracted in &result.relationships {
@@ -118,11 +138,14 @@ impl KnowledgeGraph {
             ) {
                 let from_idx = NodeIndex::new(from_raw as usize);
                 let to_idx = NodeIndex::new(to_raw as usize);
-                // Avoid duplicate edges
+                let already_exists = self
-                if !self.graph.contains_edge(from_idx, to_idx) {
+                    .graph
+                    .edges_connecting(from_idx, to_idx)
+                    .any(|e| e.weight().relation_type == extracted.relation_type);
+                if !already_exists {
                     let rel = Relationship {
                         relation_type: extracted.relation_type.clone(),
-                        weight: extracted.weight.unwrap_or(1.0),
+                        weight: extracted.weight.unwrap_or(1.0).clamp(0.0, 1.0),
                     };
                     self.graph.add_edge(from_idx, to_idx, rel);
                 }
@@ -158,6 +181,10 @@ impl KnowledgeGraph {
             .filter(|raw| !still_used.contains(raw))
             .collect();
 
+        if to_remove.is_empty() {
+            return;
+        }
+
         for raw in to_remove {
             let idx = NodeIndex::new(raw as usize);
             if self.graph.contains_node(idx) {
@@ -166,6 +193,57 @@ impl KnowledgeGraph {
                 self.entity_index.swap_remove(&name);
             }
         }
+
+        self.compact();
+    }
+
+    /// Rebuild the internal graph with consecutive node indices. Eliminates
+    /// the null tombstone slots that petgraph's StableGraph accumulates after
+    /// repeated `remove_node` calls, keeping serialized YAML size in check.
+    fn compact(&mut self) {
+        let mut new_graph: StableGraph<Entity, Relationship> = StableGraph::new();
+        let mut old_to_new: HashMap<u32, u32> = HashMap::new();
+
+        for &old_raw in self.entity_index.values() {
+            let old_idx = NodeIndex::new(old_raw as usize);
+            if self.graph.contains_node(old_idx) {
+                let entity = self.graph[old_idx].clone();
+                let new_idx = new_graph.add_node(entity);
+                old_to_new.insert(old_raw, new_idx.index() as u32);
+            }
+        }
+
+        for edge_idx in self.graph.edge_indices() {
+            if let Some((from, to)) = self.graph.edge_endpoints(edge_idx) {
+                let from_raw = from.index() as u32;
+                let to_raw = to.index() as u32;
+                if let (Some(&new_from), Some(&new_to)) =
+                    (old_to_new.get(&from_raw), old_to_new.get(&to_raw))
+                {
+                    let rel = self.graph[edge_idx].clone();
+                    new_graph.add_edge(
+                        NodeIndex::new(new_from as usize),
+                        NodeIndex::new(new_to as usize),
+                        rel,
+                    );
+                }
+            }
+        }
+
+        for raw in self.entity_index.values_mut() {
+            if let Some(&new_raw) = old_to_new.get(raw) {
+                *raw = new_raw;
+            }
+        }
+
+        for node_raws in self.document_entities.values_mut() {
+            *node_raws = node_raws
+                .iter()
+                .filter_map(|raw| old_to_new.get(raw).copied())
+                .collect();
+        }
+
+        self.graph = new_graph;
     }
 
     pub fn build_node_to_docs(&self) -> IndexMap<u32, Vec<DocumentId>> {
@@ -176,34 +254,82 @@ impl KnowledgeGraph {
                 map.entry(node_raw).or_default().push(doc_id);
             }
         }
-
         map
     }
 
-    pub fn expand_neighbors(&self, seed_nodes: &[u32], hops: usize) -> Vec<u32> {
+    /// BFS from seed nodes with weight-decayed scoring.
-        let mut expanded: indexmap::IndexSet<u32> = seed_nodes.iter().copied().collect();
+    ///
-        let mut frontier: Vec<u32> = seed_nodes.to_vec();
+    /// Seed node scores are provided by the caller (typically token-overlap
+    /// ratios). Each neighbor's score is `edge_weight * parent_score`, so
+    /// strongly-connected neighbors rank higher and weakly-connected ones
+    /// naturally contribute less. Traversal is capped at `MAX_GRAPH_NODES`
+    /// total nodes; the highest-scored frontier nodes are expanded first so
+    /// the budget is spent on the most relevant entities.
+    ///
+    /// Returns a map of raw node index → score (includes seed nodes).
+    pub fn expand_neighbors_scored(
+        &self,
+        seed_scores: &[(u32, f32)],
+        hops: usize,
+    ) -> IndexMap<u32, f32> {
+        let mut node_scores: IndexMap<u32, f32> = IndexMap::new();
+        for &(raw, score) in seed_scores {
+            node_scores.insert(raw, score);
+        }
+
+        let mut frontier: Vec<(u32, f32)> = seed_scores.to_vec();
+
         for _ in 0..hops {
-            let mut next_frontier: Vec<u32> = vec![];
+            if node_scores.len() >= MAX_GRAPH_NODES {
-            for &raw in &frontier {
+                break;
-                let idx = NodeIndex::new(raw as usize);
+            }
-                if self.graph.contains_node(idx) {
+
-                    for dir in [Direction::Outgoing, Direction::Incoming] {
+            frontier.sort_unstable_by(|a, b| {
-                        for neighbor in self.graph.neighbors_directed(idx, dir) {
+                b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal)
-                            let n = neighbor.index() as u32;
+            });
-                            if expanded.insert(n) {
+
-                                next_frontier.push(n);
+            let mut next_frontier: Vec<(u32, f32)> = vec![];
+
+            'nodes: for (raw, parent_score) in &frontier {
+                let idx = NodeIndex::new(*raw as usize);
+                if !self.graph.contains_node(idx) {
+                    continue;
+                }
+                for dir in [Direction::Outgoing, Direction::Incoming] {
+                    for edge_ref in self.graph.edges_directed(idx, dir) {
+                        let neighbor_idx = match dir {
+                            Direction::Outgoing => edge_ref.target(),
+                            Direction::Incoming => edge_ref.source(),
+                        };
+                        let neighbor_raw = neighbor_idx.index() as u32;
+                        let candidate = edge_ref.weight().weight * parent_score;
+
+                        match node_scores.entry(neighbor_raw) {
+                            indexmap::map::Entry::Vacant(e) => {
+                                e.insert(candidate);
+                                next_frontier.push((neighbor_raw, candidate));
                             }
+                            indexmap::map::Entry::Occupied(mut e) => {
+                                if candidate > *e.get() {
+                                    *e.get_mut() = candidate;
+                                }
+                            }
+                        }
+
+                        if node_scores.len() >= MAX_GRAPH_NODES {
+                            break 'nodes;
                         }
                     }
                 }
             }
+
             frontier = next_frontier;
             if frontier.is_empty() {
                 break;
             }
         }
-        expanded.into_iter().collect()
+
+        node_scores
     }
 }
 
@@ -214,6 +340,14 @@ pub async fn extract_entities(
     chunk: &str,
     prompt_template: Option<&str>,
 ) -> Result<ExtractionResult> {
+    if chunk.len() > MAX_CHUNK_CHARS {
+        warn!(
+            "Entity extraction chunk is {} chars (heuristic limit: {}); \
+             the LLM response may be truncated",
+            chunk.len(),
+            MAX_CHUNK_CHARS
+        );
+    }
     let template = prompt_template.unwrap_or(EXTRACTION_PROMPT);
     let prompt = template.replace("__CHUNK__", chunk);
     let mut messages = vec![Message::new(
@@ -237,6 +371,7 @@ pub async fn extract_entities(
         .context("Entity extraction LLM call failed")?;
 
     let text = output.text.trim();
+    // Strip markdown code fences if the model wraps in ```json ... ```
     let json: String = if text.starts_with("```") {
         text.lines()
             .skip(1)
@@ -250,3 +385,350 @@ pub async fn extract_entities(
     serde_json::from_str::<ExtractionResult>(&json)
         .context("Failed to parse entity extraction JSON")
 }
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    fn entity(name: &str, entity_type: &str) -> ExtractedEntity {
+        ExtractedEntity {
+            name: name.to_string(),
+            entity_type: entity_type.to_string(),
+            description: None,
+        }
+    }
+
+    fn rel(from: &str, to: &str, rel_type: &str, weight: f32) -> ExtractedRelationship {
+        ExtractedRelationship {
+            from: from.to_string(),
+            to: to.to_string(),
+            relation_type: rel_type.to_string(),
+            weight: Some(weight),
+        }
+    }
+
+    fn doc(id: usize) -> DocumentId {
+        DocumentId(id)
+    }
+
+    fn extraction(
+        entities: Vec<ExtractedEntity>,
+        rels: Vec<ExtractedRelationship>,
+    ) -> ExtractionResult {
+        ExtractionResult {
+            entities,
+            relationships: rels,
+        }
+    }
+
+    #[test]
+    fn merge_deduplicates_by_lowercase_name() {
+        let mut kg = KnowledgeGraph::default();
+        kg.merge(
+            doc(0),
+            extraction(
+                vec![
+                    entity("Python", "TECHNOLOGY"),
+                    entity("python", "TECHNOLOGY"),
+                ],
+                vec![],
+            ),
+        );
+        assert_eq!(kg.entity_index.len(), 1);
+        assert_eq!(kg.graph.node_count(), 1);
+    }
+
+    #[test]
+    fn merge_chunk_nodes_no_duplicate_doc_entries() {
+        let mut kg = KnowledgeGraph::default();
+        kg.merge(
+            doc(1),
+            extraction(
+                vec![
+                    entity("Python", "TECHNOLOGY"),
+                    entity("python", "TECHNOLOGY"),
+                ],
+                vec![],
+            ),
+        );
+        let count = kg.document_entities.get(&1).map(|v| v.len()).unwrap_or(0);
+        assert_eq!(
+            count, 1,
+            "duplicate entity in one chunk should produce one doc_entity entry"
+        );
+    }
+
+    #[test]
+    fn merge_normalizes_entity_type_to_uppercase() {
+        let mut kg = KnowledgeGraph::default();
+        kg.merge(
+            doc(0),
+            extraction(vec![entity("Django", "technology")], vec![]),
+        );
+        let raw = kg.entity_index["django"];
+        assert_eq!(
+            kg.graph[NodeIndex::new(raw as usize)].entity_type,
+            "TECHNOLOGY"
+        );
+    }
+
+    #[test]
+    fn merge_promotes_type_from_other_to_specific() {
+        let mut kg = KnowledgeGraph::default();
+        kg.merge(doc(0), extraction(vec![entity("Python", "OTHER")], vec![]));
+        kg.merge(
+            doc(1),
+            extraction(vec![entity("Python", "TECHNOLOGY")], vec![]),
+        );
+        let raw = kg.entity_index["python"];
+        assert_eq!(
+            kg.graph[NodeIndex::new(raw as usize)].entity_type,
+            "TECHNOLOGY"
+        );
+    }
+
+    #[test]
+    fn merge_does_not_demote_specific_type_to_other() {
+        let mut kg = KnowledgeGraph::default();
+        kg.merge(
+            doc(0),
+            extraction(vec![entity("Python", "TECHNOLOGY")], vec![]),
+        );
+        kg.merge(doc(1), extraction(vec![entity("Python", "OTHER")], vec![]));
+        let raw = kg.entity_index["python"];
+        assert_eq!(
+            kg.graph[NodeIndex::new(raw as usize)].entity_type,
+            "TECHNOLOGY"
+        );
+    }
+
+    #[test]
+    fn merge_allows_multiple_relation_types_between_same_pair() {
+        let mut kg = KnowledgeGraph::default();
+        kg.merge(
+            doc(0),
+            extraction(
+                vec![
+                    entity("Python", "TECHNOLOGY"),
+                    entity("Django", "TECHNOLOGY"),
+                ],
+                vec![rel("Python", "Django", "implements", 0.9)],
+            ),
+        );
+        kg.merge(
+            doc(1),
+            extraction(
+                vec![
+                    entity("Python", "TECHNOLOGY"),
+                    entity("Django", "TECHNOLOGY"),
+                ],
+                vec![rel("Python", "Django", "uses", 0.8)],
+            ),
+        );
+        let from_idx = NodeIndex::new(kg.entity_index["python"] as usize);
+        let to_idx = NodeIndex::new(kg.entity_index["django"] as usize);
+        let count = kg.graph.edges_connecting(from_idx, to_idx).count();
+        assert_eq!(
+            count, 2,
+            "two different relation types should produce two edges"
+        );
+    }
+
+    #[test]
+    fn merge_deduplicates_same_relation_type() {
+        let mut kg = KnowledgeGraph::default();
+        kg.merge(
+            doc(0),
+            extraction(
+                vec![entity("A", "CONCEPT"), entity("B", "CONCEPT")],
+                vec![rel("A", "B", "uses", 1.0)],
+            ),
+        );
+        kg.merge(
+            doc(1),
+            extraction(
+                vec![entity("A", "CONCEPT"), entity("B", "CONCEPT")],
+                vec![rel("A", "B", "uses", 0.5)],
+            ),
+        );
+        let from_idx = NodeIndex::new(kg.entity_index["a"] as usize);
+        let to_idx = NodeIndex::new(kg.entity_index["b"] as usize);
+        let count = kg.graph.edges_connecting(from_idx, to_idx).count();
+        assert_eq!(
+            count, 1,
+            "same relation type should not create a duplicate edge"
+        );
+    }
+
+    #[test]
+    fn remove_documents_preserves_entity_shared_across_docs() {
+        let mut kg = KnowledgeGraph::default();
+        kg.merge(
+            doc(0),
+            extraction(
+                vec![entity("Python", "TECHNOLOGY"), entity("A", "CONCEPT")],
+                vec![],
+            ),
+        );
+        kg.merge(
+            doc(1),
+            extraction(
+                vec![entity("Python", "TECHNOLOGY"), entity("B", "CONCEPT")],
+                vec![],
+            ),
+        );
+        kg.remove_documents(&[doc(0)]);
+        assert!(
+            kg.entity_index.contains_key("python"),
+            "shared entity should survive"
+        );
+        assert!(
+            !kg.entity_index.contains_key("a"),
+            "exclusive entity should be removed"
+        );
+        assert!(
+            kg.entity_index.contains_key("b"),
+            "other doc's entity should survive"
+        );
+    }
+
+    #[test]
+    fn remove_documents_noop_on_empty_slice() {
+        let mut kg = KnowledgeGraph::default();
+        kg.merge(doc(0), extraction(vec![entity("X", "CONCEPT")], vec![]));
+        kg.remove_documents(&[]);
+        assert_eq!(kg.entity_index.len(), 1);
+    }
+
+    #[test]
+    fn remove_documents_compacts_graph() {
+        let mut kg = KnowledgeGraph::default();
+        // doc 0: A, B with an edge
+        kg.merge(
+            doc(0),
+            extraction(
+                vec![entity("A", "CONCEPT"), entity("B", "CONCEPT")],
+                vec![rel("A", "B", "uses", 1.0)],
+            ),
+        );
+        // doc 1: C only
+        kg.merge(doc(1), extraction(vec![entity("C", "CONCEPT")], vec![]));
+
+        kg.remove_documents(&[doc(0)]);
+
+        assert_eq!(kg.graph.node_count(), 1);
+        let c_raw = kg.entity_index["c"];
+        assert_eq!(
+            c_raw, 0,
+            "compacted graph should give surviving node index 0"
+        );
+        let refs = kg.document_entities.get(&1).cloned().unwrap_or_default();
+        assert_eq!(refs, vec![0u32]);
+    }
+
+    #[test]
+    fn expand_zero_hops_returns_seeds_only() {
+        let mut kg = KnowledgeGraph::default();
+        kg.merge(
+            doc(0),
+            extraction(
+                vec![entity("A", "CONCEPT"), entity("B", "CONCEPT")],
+                vec![rel("A", "B", "uses", 0.9)],
+            ),
+        );
+        let a_raw = kg.entity_index["a"];
+        let result = kg.expand_neighbors_scored(&[(a_raw, 1.0)], 0);
+        assert_eq!(result.len(), 1);
+        assert_eq!(result[&a_raw], 1.0);
+    }
+
+    #[test]
+    fn expand_one_hop_decays_score_by_edge_weight() {
+        let mut kg = KnowledgeGraph::default();
+        kg.merge(
+            doc(0),
+            extraction(
+                vec![entity("A", "CONCEPT"), entity("B", "CONCEPT")],
+                vec![rel("A", "B", "uses", 0.8)],
+            ),
+        );
+        let a_raw = kg.entity_index["a"];
+        let b_raw = kg.entity_index["b"];
+        let result = kg.expand_neighbors_scored(&[(a_raw, 1.0)], 1);
+        assert_eq!(result.len(), 2);
+        assert_eq!(result[&a_raw], 1.0);
+        let b_score = result[&b_raw];
+        assert!(
+            (b_score - 0.8).abs() < 1e-6,
+            "neighbor score should be edge_weight * parent_score = 0.8, got {b_score}"
+        );
+    }
+
+    #[test]
+    fn expand_incoming_edges_also_traversed() {
+        let mut kg = KnowledgeGraph::default();
+        // Edge goes B → A; seeding A should still discover B via incoming edge
+        kg.merge(
+            doc(0),
+            extraction(
+                vec![entity("A", "CONCEPT"), entity("B", "CONCEPT")],
+                vec![rel("B", "A", "uses", 0.7)],
+            ),
+        );
+        let a_raw = kg.entity_index["a"];
+        let b_raw = kg.entity_index["b"];
+        let result = kg.expand_neighbors_scored(&[(a_raw, 1.0)], 1);
+        assert!(
+            result.contains_key(&b_raw),
+            "B should be reachable via incoming edge from A"
+        );
+        let b_score = result[&b_raw];
+        assert!((b_score - 0.7).abs() < 1e-6);
+    }
+
+    #[test]
+    fn expand_picks_best_path_score() {
+        let mut kg = KnowledgeGraph::default();
+        // A(0.5) → C(0.9): score 0.45; B(1.0) → C(0.4): score 0.40 — A→C path wins.
+        kg.merge(
+            doc(0),
+            extraction(
+                vec![
+                    entity("A", "CONCEPT"),
+                    entity("B", "CONCEPT"),
+                    entity("C", "CONCEPT"),
+                ],
+                vec![rel("A", "C", "uses", 0.9), rel("B", "C", "uses", 0.4)],
+            ),
+        );
+        let a_raw = kg.entity_index["a"];
+        let b_raw = kg.entity_index["b"];
+        let c_raw = kg.entity_index["c"];
+        let seeds = vec![(a_raw, 0.5f32), (b_raw, 1.0f32)];
+        let result = kg.expand_neighbors_scored(&seeds, 1);
+        let c_score = result[&c_raw];
+        // Best path: B(1.0) * 0.4 = 0.4, A(0.5) * 0.9 = 0.45 → should be 0.45
+        assert!(
+            (c_score - 0.45).abs() < 1e-6,
+            "C score should reflect best path (0.45), got {c_score}"
+        );
+    }
+
+    #[test]
+    fn build_node_to_docs_maps_shared_entity_to_multiple_docs() {
+        let mut kg = KnowledgeGraph::default();
+        kg.merge(
+            doc(0),
+            extraction(vec![entity("Python", "TECHNOLOGY")], vec![]),
+        );
+        kg.merge(
+            doc(1),
+            extraction(vec![entity("Python", "TECHNOLOGY")], vec![]),
+        );
+        let n2d = kg.build_node_to_docs();
+        let raw = kg.entity_index["python"];
+        let docs = &n2d[&raw];
+        assert!(docs.contains(&DocumentId(0)));
+        assert!(docs.contains(&DocumentId(1)));
+    }
+}

src/rag/mod.rs

@@ -25,6 +25,8 @@ use std::{
 };
 use tokio::time::sleep;
 
+const BM25_SEED_SCORE: f32 = 0.5;
+
 const RAG_TEMPLATE: &str = r#"Answer the query based on the context while respecting the rules. (user query, some textual context and rules, all inside xml tags)
 
 <context>
@@ -752,14 +754,14 @@ impl Rag {
             bail!("No RAG files");
         }
 
-        if self.data.extractor_model.is_some()
+        if !new_doc_contents.is_empty()
-            && !new_doc_contents.is_empty()
             && let Some(extractor_model_id) = self.data.extractor_model.clone()
         {
             match Model::retrieve_model(&self.app_config, &extractor_model_id, ModelType::Chat) {
                 Ok(model) => match self.create_embeddings_client(model) {
                     Ok(client) => {
                         let total = new_doc_contents.len();
+                        let mut failures = 0usize;
                         for (i, (doc_id, content)) in new_doc_contents.into_iter().enumerate() {
                             progress(
                                 &spinner,
@@ -774,14 +776,21 @@ impl Rag {
                             {
                                 Ok(result) => self.data.knowledge_graph.merge(doc_id, result),
                                 Err(e) => {
-                                    debug!("Entity extraction failed for doc {doc_id:?}: {e}")
+                                    warn!("Entity extraction failed for doc {doc_id:?}: {e}");
+                                    failures += 1;
                                 }
                             }
                         }
+                        if failures > 0 {
+                            progress(
+                                &spinner,
+                                format!("Entity extraction: {failures}/{total} chunks failed"),
+                            );
+                        }
                     }
-                    Err(e) => debug!("Failed to create extractor client: {e}"),
+                    Err(e) => warn!("Failed to create extractor client: {e}"),
                 },
-                Err(e) => debug!("Extractor model not found: {e}"),
+                Err(e) => warn!("Extractor model not found: {e}"),
             }
         }
 
@@ -930,10 +939,31 @@ impl Rag {
         if kg.entity_index.is_empty() {
             return vec![];
         }
-        
-        let query_lower = query.to_lowercase();
 
-        let mut seed_nodes: Vec<u32> = kg
+        let query_lower = query.to_lowercase();
+        let query_tokens: Vec<&str> = query_lower.split_whitespace().collect();
+        let token_count = query_tokens.len().max(1);
+
+        let score_node = |raw: u32| -> f32 {
+            let idx = NodeIndex::new(raw as usize);
+            if !kg.graph.contains_node(idx) {
+                return 0.0;
+            }
+            let entity = &kg.graph[idx];
+            let combined = format!(
+                "{} {}",
+                entity.name,
+                entity.description.as_deref().unwrap_or("")
+            )
+            .to_lowercase();
+            query_tokens
+                .iter()
+                .filter(|t| combined.contains(*t))
+                .count() as f32
+                / token_count as f32
+        };
+
+        let mut seed_scores: Vec<(u32, f32)> = kg
             .entity_index
             .iter()
             .filter(|(name, _)| {
@@ -947,52 +977,31 @@ impl Rag {
                         .any(|token| token.trim_matches(|c: char| !c.is_alphanumeric()) == name_str)
                 }
             })
-            .map(|(_, &raw)| raw)
+            .map(|(_, &raw)| (raw, score_node(raw).max(BM25_SEED_SCORE)))
             .collect();
 
-        if seed_nodes.is_empty() {
+        if seed_scores.is_empty() {
             let bm25_results = self.bm25.search(query, top_k * 2);
             'outer: for result in bm25_results {
                 if let Some(node_raws) = kg.document_entities.get(&result.document.id.0) {
-                    seed_nodes.extend(node_raws.iter().copied());
+                    for &raw in node_raws {
-                    if seed_nodes.len() >= top_k {
+                        seed_scores.push((raw, BM25_SEED_SCORE));
-                        break 'outer;
+                        if seed_scores.len() >= top_k {
+                            break 'outer;
+                        }
                     }
                 }
             }
         }
 
-        if seed_nodes.is_empty() {
+        if seed_scores.is_empty() {
             return vec![];
         }
 
         let hops = self.data.graph_hops.unwrap_or(1);
-        let expanded = kg.expand_neighbors(&seed_nodes, hops);
+        let mut scored: Vec<(u32, f32)> = kg
-
+            .expand_neighbors_scored(&seed_scores, hops)
-        let query_tokens: Vec<&str> = query_lower.split_whitespace().collect();
-        let token_count = query_tokens.len().max(1);
-        let mut scored: Vec<(u32, f32)> = expanded
             .into_iter()
-            .map(|raw| {
-                let idx = NodeIndex::new(raw as usize);
-                let score = if kg.graph.contains_node(idx) {
-                    let entity = &kg.graph[idx];
-                    let combined = format!(
-                        "{} {}",
-                        entity.name,
-                        entity.description.as_deref().unwrap_or("")
-                    )
-                    .to_lowercase();
-                    query_tokens
-                        .iter()
-                        .filter(|t| combined.contains(*t))
-                        .count() as f32
-                        / token_count as f32
-                } else {
-                    0.0
-                };
-                (raw, score)
-            })
             .collect();
         scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(Ordering::Equal));
 
@@ -1350,11 +1359,11 @@ fn set_chunk_size(model: &Model) -> Result<usize> {
 fn set_graph_hops(default_value: usize) -> Result<usize> {
     let value = Text::new("Set graph expansion hops:")
         .with_default(&default_value.to_string())
-        .with_help_message("Number of hops to expand from matched entities (1 = direct neighbors, 2 = neighbors of neighbors)")
+        .with_help_message("Number of hops to expand from matched entities (0 = seed nodes only, 1 = direct neighbors, 2 = neighbors of neighbors)")
         .with_validator(move |text: &str| {
             let out = match text.parse::<usize>() {
-                Ok(v) if v >= 1 => Validation::Valid,
+                Ok(_) => Validation::Valid,
-                _ => Validation::Invalid("Must be an integer >= 1".into()),
+                _ => Validation::Invalid("Must be a non-negative integer".into()),
             };
             Ok(out)
         })