feat: Implemented graph-based RAG

.sisyphus/plans/graph-rag-spec.md

@@ -0,0 +1,371 @@
+# Graph RAG Design Spec
+
+## Status: COMPLETE
+
+### Verified From Code (all claims backed by actual file reads)
+
+---
+
+## Goal
+
+Extend the existing two-signal hybrid search (vector HNSW + BM25 → RRF) to a three-signal hybrid
+(vector + BM25 + knowledge graph → RRF). The graph captures entity/relationship knowledge extracted
+from documents at ingestion time via an LLM call per chunk. At query time, graph traversal expands
+context beyond semantic similarity.
+
+---
+
+## Verified Current Architecture
+
+### `Rag` struct (`src/rag/mod.rs:48`)
+```rust
+pub struct Rag {
+    app_config: Arc<AppConfig>,
+    name: String,
+    path: String,
+    embedding_model: Model,
+    hnsw: Hnsw<'static, f32, DistCosine>,  // ephemeral, rebuilt on load
+    bm25: SearchEngine<DocumentId>,          // ephemeral, rebuilt on load
+    data: RagData,                           // serialized to YAML
+    last_sources: RwLock<Option<String>>,
+}
+```
+
+### `RagData` struct (`src/rag/mod.rs:892`)
+```rust
+pub struct RagData {
+    pub embedding_model: String,
+    pub chunk_size: usize,
+    pub chunk_overlap: usize,
+    pub reranker_model: Option<String>,
+    pub top_k: usize,
+    pub batch_size: Option<usize>,
+    pub next_file_id: FileId,
+    pub document_paths: Vec<String>,
+    pub files: IndexMap<FileId, RagFile>,
+    #[serde(with = "serde_vectors")]
+    pub vectors: IndexMap<DocumentId, Vec<f32>>,
+}
+```
+
+### `RagData::new` callers (both need updating):
+1. `Rag::init` (`src/rag/mod.rs:219`) — interactive init path
+2. `Rag::resolve_init_data` (`src/rag/mod.rs:195`) — config-driven init path
+
+### `Rag::create` (`src/rag/mod.rs:253`) — all init paths converge here:
+```rust
+pub fn create(app: &AppConfig, name: &str, path: &Path, data: RagData) -> Result<Self> {
+    let hnsw = data.build_hnsw();
+    let bm25 = data.build_bm25();
+    let embedding_model = Model::retrieve_model(app, &data.embedding_model, ModelType::Embedding)?;
+    let rag = Rag { app_config: Arc::new(app.clone()), name: name.to_string(),
+                    path: path.display().to_string(), data, embedding_model, hnsw, bm25,
+                    last_sources: RwLock::new(None) };
+    Ok(rag)
+}
+```
+
+### `hybrid_search` (`src/rag/mod.rs:710`)
+```rust
+async fn hybrid_search(&self, query: &str, top_k: usize, rerank_model: Option<&str>)
+    -> Result<Vec<(DocumentId, String)>>
+```
+Runs `vector_search` + `keyword_search` in parallel via `tokio::join!`, then either reranks or
+applies `reciprocal_rank_fusion(vec![vector_ids, keyword_ids], vec![1.125, 1.0], top_k)`.
+
+### `reciprocal_rank_fusion` (`src/rag/mod.rs:1186`) — standalone fn, already weight-parameterized:
+```rust
+fn reciprocal_rank_fusion(
+    list_of_document_ids: Vec<Vec<DocumentId>>,
+    list_of_weights: Vec<f32>,
+    top_k: usize,
+) -> Vec<DocumentId>
+```
+
+### `RagData::del` (`src/rag/mod.rs:953`):
+```rust
+pub fn del(&mut self, file_ids: Vec<FileId>) {
+    for file_id in file_ids {
+        if let Some(file) = self.files.swap_remove(&file_id) {
+            for (document_index, _) in file.documents.iter().enumerate() {
+                let document_id = DocumentId::new(file_id, document_index);
+                self.vectors.swap_remove(&document_id);
+            }
+        }
+    }
+}
+```
+
+### `RagNode` (`src/graph/types.rs:331`):
+```rust
+pub struct RagNode {
+    pub documents: Vec<String>,
+    pub query: Option<String>,
+    pub top_k: Option<usize>,
+    pub embedding_model: Option<String>,
+    pub chunk_size: Option<usize>,
+    pub chunk_overlap: Option<usize>,
+    pub reranker_model: Option<String>,
+    pub batch_size: Option<usize>,
+    pub state_updates: Option<HashMap<String, String>>,
+    pub timeout: Option<u64>,
+}
+```
+
+### `Client` trait (`src/client/common.rs:40`):
+- `async fn chat_completions(&self, input: Input) -> Result<ChatCompletionsOutput>` — needs `Input`
+- `async fn chat_completions_inner(&self, client: &ReqwestClient, data: ChatCompletionsData) -> Result<ChatCompletionsOutput>` — accessible on `Box<dyn Client>` via vtable
+- `async fn embeddings(&self, data: &EmbeddingsData) -> Result<Vec<Vec<f32>>>`
+- `async fn rerank(&self, data: &RerankData) -> Result<RerankOutput>`
+- `fn build_client(&self) -> Result<ReqwestClient>`
+- `fn model(&self) -> &Model`
+
+**Key finding**: `Input` cannot be constructed without `RequestContext` (which `Rag` doesn't have).
+Instead, `extract_entities` uses `chat_completions_inner` directly with manually built
+`ChatCompletionsData`. This is accessible via `Box<dyn Client>`.
+
+### `Message` (`src/client/message.rs:22`):
+```rust
+pub fn new(role: MessageRole, content: MessageContent) -> Self
+```
+`MessageRole::User`, `MessageContent::Text(String)` — both confirmed.
+
+### `AppConfig` RAG fields (`src/config/app_config.rs:71`):
+```rust
+pub rag_embedding_model: Option<String>,
+pub rag_reranker_model: Option<String>,
+pub rag_top_k: usize,        // default: 5
+pub rag_chunk_size: Option<usize>,
+pub rag_chunk_overlap: Option<usize>,
+pub rag_template: Option<String>,
+```
+
+### `patch_messages` — confirmed exported from `crate::client::*` (used in `input.rs:5`)
+
+### `init_client(app_config, model)` — works for any `ModelType`, including `Chat`
+
+### `ModelType` variants: `Chat`, `Embedding`, `Reranker` (confirmed in `model.rs`)
+
+### petgraph serde: `NodeIndex` serializes as inner `u32`; `StableGraph` preserves index positions
+through roundtrip. `IndexMap<DocumentId, Vec<NodeIndex>>` safe for YAML (DocumentId is newtype over
+usize, serializes as integer key).
+
+---
+
+## New Dependency
+
+```toml
+petgraph = { version = "0.7", features = ["serde-1"] }
+```
+
+---
+
+## New File: `src/rag/graph.rs`
+
+All graph types and extraction logic. Module declared in `mod.rs` as `mod graph; use self::graph::*;`.
+
+### Types:
+- `Entity { name: String, entity_type: String, description: Option<String> }`
+- `Relationship { relation_type: String, weight: f32 }`
+- `ExtractionResult { entities: Vec<ExtractedEntity>, relationships: Vec<ExtractedRelationship> }`
+- `ExtractedEntity { name: String, r#type: String, description: Option<String> }`
+- `ExtractedRelationship { from: String, to: String, r#type: String, weight: Option<f32> }`
+- `KnowledgeGraph { graph: StableGraph<Entity, Relationship>, entity_index: IndexMap<String, NodeIndex>, document_entities: IndexMap<DocumentId, Vec<NodeIndex>> }`
+
+### Key methods on `KnowledgeGraph`:
+- `merge(doc_id: DocumentId, result: ExtractionResult)` — merges extraction into graph
+- `remove_documents(ids: &[DocumentId])` — removes entities exclusive to deleted documents
+- `build_node_to_docs(&self) -> IndexMap<NodeIndex, Vec<DocumentId>>` — ephemeral reverse map
+
+### `extract_entities(client: &dyn Client, chunk: &str) -> Result<ExtractionResult>`:
+- Builds `ChatCompletionsData` manually (no `Input` needed)
+- Calls `patch_messages` then `client.chat_completions_inner(&reqwest_client, data).await`
+- Strips markdown code fences from response before JSON parse
+- Temperature: `Some(0.0)` for deterministic extraction
+
+### Extraction prompt: structured JSON output requesting entities + relationships
+
+---
+
+## Changes to `src/rag/mod.rs`
+
+### `Rag` struct — add one ephemeral field:
+```rust
+node_to_docs: IndexMap<NodeIndex, Vec<DocumentId>>,  // ephemeral, rebuilt on load
+```
+
+### `Rag::create` — build node_to_docs before moving data:
+```rust
+let node_to_docs = data.knowledge_graph.build_node_to_docs();
+// then add to struct literal
+```
+
+### `Rag` Clone impl — add:
+```rust
+node_to_docs: self.data.knowledge_graph.build_node_to_docs(),
+```
+
+### `RagData` struct — three new fields (all `#[serde(default)]` for backward compat):
+```rust
+#[serde(default)]
+pub graph_enabled: bool,
+#[serde(default, skip_serializing_if = "Option::is_none")]
+pub extractor_model: Option<String>,
+#[serde(default)]
+pub knowledge_graph: KnowledgeGraph,
+```
+
+### `RagData::new` — two new params: `graph_enabled: bool, extractor_model: Option<String>`
+
+### `RagData::del` — collect doc_ids during existing loop, call `remove_documents` at end:
+```rust
+let mut doc_ids_to_remove = vec![];
+for file_id in file_ids {
+    if let Some(file) = self.files.swap_remove(&file_id) {
+        for (document_index, _) in file.documents.iter().enumerate() {
+            let document_id = DocumentId::new(file_id, document_index);
+            self.vectors.swap_remove(&document_id);
+            doc_ids_to_remove.push(document_id);
+        }
+    }
+}
+self.knowledge_graph.remove_documents(&doc_ids_to_remove);
+```
+
+### `Rag::init` (line 219) — add two params to `RagData::new`:
+```rust
+app.rag_graph_enabled,
+app.rag_extractor_model.clone(),
+```
+
+### `resolve_init_data` — resolve from config+app, pass to `RagData::new`:
+```rust
+let graph_enabled = config.graph_enabled.unwrap_or(app.rag_graph_enabled);
+let extractor_model = config.extractor_model.clone().or_else(|| app.rag_extractor_model.clone());
+```
+
+### `sync_documents` — entity extraction block after `rag_files` built, before embedding:
+```rust
+if self.data.graph_enabled {
+    if let Some(extractor_model_id) = self.data.extractor_model.clone() {
+        let model = Model::retrieve_model(&self.app_config, &extractor_model_id, ModelType::Chat)?;
+        let client = self.create_embeddings_client(model)?;
+        let total_chunks: usize = rag_files.iter().map(|f| f.documents.len()).sum();
+        let mut chunk_num = 0;
+        let file_offset = next_file_id;
+        for (batch_file_idx, rag_file) in rag_files.iter().enumerate() {
+            let file_id = file_offset + batch_file_idx;
+            for (doc_idx, doc) in rag_file.documents.iter().enumerate() {
+                chunk_num += 1;
+                progress(&spinner, format!("Extracting entities [{chunk_num}/{total_chunks}]"));
+                let doc_id = DocumentId::new(file_id, doc_idx);
+                match extract_entities(client.as_ref(), &doc.page_content).await {
+                    Ok(result) => self.data.knowledge_graph.merge(doc_id, result),
+                    Err(e) => debug!("Entity extraction failed for {doc_id:?}: {e}"),
+                }
+            }
+        }
+    }
+}
+```
+
+### After line 705 (after hnsw/bm25 rebuild in sync_documents):
+```rust
+self.node_to_docs = self.data.knowledge_graph.build_node_to_docs();
+```
+
+### `hybrid_search` — add third signal:
+```rust
+let graph_search_ids: Vec<DocumentId> = if self.data.graph_enabled
+    && !self.data.knowledge_graph.entity_index.is_empty()
+{
+    self.graph_search(query, &keyword_search_ids, top_k)
+} else {
+    vec![]
+};
+// RRF: extend to 3-way when graph has results, fall back to 2-way otherwise
+```
+
+### New `graph_search` method (sync):
+```rust
+fn graph_search(&self, query: &str, bm25_anchor_ids: &[DocumentId], top_k: usize) -> Vec<DocumentId>
+```
+Phase 1: entity names from query via substring match in `entity_index`.
+Phase 2: fallback — entities from top BM25 document chunks.
+Phase 3: expand 1-hop neighbors in `StableGraph`.
+Phase 4: score docs by entity overlap ratio, return top_k.
+
+### `RagInitConfig` — two new fields:
+```rust
+pub graph_enabled: Option<bool>,
+pub extractor_model: Option<String>,
+```
+
+---
+
+## Changes to `src/config/app_config.rs`
+
+New fields alongside existing `rag_*` block:
+```rust
+pub rag_graph_enabled: bool,            // default: false
+pub rag_extractor_model: Option<String>, // default: None
+```
+Defaults, env var overrides, and propagation all follow the same pattern as existing `rag_*` fields.
+
+---
+
+## Changes to `src/graph/types.rs` — `RagNode`
+
+```rust
+#[serde(default, skip_serializing_if = "Option::is_none")]
+pub graph_enabled: Option<bool>,
+#[serde(default, skip_serializing_if = "Option::is_none")]
+pub extractor_model: Option<String>,
+```
+
+---
+
+## Changes to `src/config/agent.rs`
+
+Pass new fields through to `RagInitConfig`:
+```rust
+graph_enabled: rag_node.graph_enabled,
+extractor_model: rag_node.extractor_model.clone(),
+```
+
+---
+
+## Backward Compatibility
+
+- All new `RagData` fields have `#[serde(default)]` — old YAML files load without migration
+- `graph_enabled` defaults `false` — existing RAG instances unchanged
+- `graph_search_ids` empty → 2-way RRF runs (identical to current behavior)
+- `node_to_docs` rebuild on `create()` is O(n) over empty map for old instances
+
+---
+
+## V1 Scope Exclusions
+
+- LLM entity extraction from query at search time (V1 uses substring match + BM25 anchoring)
+- Multi-hop traversal (field reserved, 1-hop only in V1)
+- Entity embeddings / fuzzy entity lookup
+- Bincode for large-corpus graph storage
+- Gleaning / multi-pass extraction
+
+---
+
+## Implementation Progress
+
+- [x] Cargo.toml — petgraph dependency
+- [x] src/rag/graph.rs — new file
+- [x] src/rag/mod.rs — mod/use, Rag struct, create, clone
+- [x] src/rag/mod.rs — RagData fields, new, del
+- [x] src/rag/mod.rs — Rag::init, resolve_init_data
+- [x] src/rag/mod.rs — sync_documents extraction block
+- [x] src/rag/mod.rs — hybrid_search + graph_search
+- [x] src/rag/mod.rs — RagInitConfig fields
+- [x] src/config/app_config.rs — new fields
+- [x] src/config/mod.rs — propagation
+- [x] src/graph/types.rs — RagNode fields
+- [x] src/config/agent.rs — propagation
+- [x] cargo check — clean (0 warnings, 1065 tests passing)

Cargo.lock

@@ -1455,6 +1455,7 @@ dependencies = [
  "os_info",
  "parking_lot",
  "path-absolutize",
+ "petgraph 0.7.1",
  "pretty_assertions",
  "rand 0.10.1",
  "reedline",
@@ -4128,6 +4129,18 @@ version = "2.3.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "9b4f627cb1b25917193a259e49bdad08f671f8d9708acfd5fe0a8c1455d87220"
 
+[[package]]
+name = "petgraph"
+version = "0.7.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3672b37090dbd86368a4145bc067582552b29c27377cad4e0a306c97f9bd7772"
+dependencies = [
+ "fixedbitset",
+ "indexmap 2.14.0",
+ "serde",
+ "serde_derive",
+]
+
 [[package]]
 name = "petgraph"
 version = "0.8.3"
@@ -6305,7 +6318,7 @@ checksum = "b8765b90061cba6c22b5831f675da109ae5561588290f9fa2317adab2714d5a6"
 dependencies = [
  "memchr",
  "nom 8.0.0",
- "petgraph",
+ "petgraph 0.8.3",
 ]
 
 [[package]]

Cargo.toml

@@ -74,6 +74,7 @@ html_to_markdown = "0.1.0"
 rust-embed = "8.5.0"
 os_info = { version = "3.8.2", default-features = false }
 bm25 = { version = "2.0.1", features = ["parallelism"] }
+petgraph = { version = "0.7", features = ["serde-1"] }
 which = "8.0.0"
 fuzzy-matcher = "0.3.7"
 terminal-colorsaurus = "0.4.8"

config.agent.example.yaml

@@ -92,6 +92,9 @@ conversation_starters:          # Optional conversation starters for the agent
   - What is the best way to exercise?
   - How do I manage my time effectively?
 documents:                     # Optional documents to load for the agent
+                               # To enable graph-based RAG (entity/relationship extraction + knowledge graph retrieval),
+                               # set `rag_extractor_model` in your global config.yaml.
+                               # See https://github.com/Dark-Alex-17/coyote/wiki/RAG#graph-based-rag
   - git:/some/repo             # Explicitly tell Coyote to use the 'git' document loader using an absolute path
   - pdf:some-pdf-file.pdf      # Explicitly tell Coyote to use the 'pdf' document loader using a relative path
   - https://some-website.com/some-page

config.example.yaml

@@ -197,6 +197,9 @@ rag_reranker_model: null         # Specifies the reranker model used for sorting
 rag_top_k: 5                     # Specifies the number of documents to retrieve for answering queries
 rag_chunk_size: null             # Defines the size of chunks for document processing in characters
 rag_chunk_overlap: null          # Defines the overlap between chunks
+rag_extractor_model: null        # LLM model for graph-based entity/relationship extraction; when set, enables a graph RAG signal alongside vector and BM25
+rag_extractor_prompt: null       # Custom extraction prompt template; must contain __CHUNK__ placeholder; defaults to built-in prompt when null
+rag_graph_hops: 1                # Number of hops to expand from matched entities at query time (1 = direct neighbors; increase for denser graphs)
 # Defines the query structure using variables like __CONTEXT__, __SOURCES__, and __INPUT__ to tailor searches to specific needs
 rag_template: |
   Answer the query based on the context while respecting the rules. (user query, some textual context and rules, all inside xml tags)

graph.example.yaml

@@ -225,6 +225,9 @@ nodes:
     chunk_size: 1000
     chunk_overlap: 100
     reranker_model: null       # Optional reranker for hybrid-search results
+    extractor_model: null      # Optional chat model for graph-based entity/relationship extraction; enables graph RAG signal when set
+    extractor_prompt: null     # Optional custom extraction prompt; must contain __CHUNK__ placeholder; uses built-in prompt when null
+    graph_hops: 1              # Graph expansion depth at query time (1 = direct neighbors; increase for denser knowledge graphs)
     batch_size: 100            # Optional embedding-request batch size
     state_updates:             # {{output}} = { context: <str>, sources: [<path>, ...] }
       context: "{{output.context}}"   # writes `context` -> `reducers.context = concat`

src/config/agent.rs

@@ -921,6 +921,9 @@ async fn init_graph_rags(
                 reranker_model: rag_node.reranker_model.clone(),
                 top_k: rag_node.top_k,
                 batch_size: rag_node.batch_size,
+                extractor_model: rag_node.extractor_model.clone(),
+                extractor_prompt: rag_node.extractor_prompt.clone(),
+                graph_hops: rag_node.graph_hops,
             };
             let fully_specified = config.embedding_model.is_some()
                 && config.chunk_size.is_some()

src/config/app_config.rs

@@ -74,6 +74,9 @@ pub struct AppConfig {
     pub rag_chunk_size: Option<usize>,
     pub rag_chunk_overlap: Option<usize>,
     pub rag_template: Option<String>,
+    pub rag_extractor_model: Option<String>,
+    pub rag_extractor_prompt: Option<String>,
+    pub rag_graph_hops: usize,
 
     #[serde(default)]
     pub document_loaders: HashMap<String, String>,
@@ -146,6 +149,9 @@ impl Default for AppConfig {
             rag_chunk_size: None,
             rag_chunk_overlap: None,
             rag_template: None,
+            rag_extractor_model: None,
+            rag_extractor_prompt: None,
+            rag_graph_hops: 1,
 
             document_loaders: Default::default(),
 
@@ -219,6 +225,9 @@ impl AppConfig {
             rag_chunk_size: config.rag_chunk_size,
             rag_chunk_overlap: config.rag_chunk_overlap,
             rag_template: config.rag_template,
+            rag_extractor_model: config.rag_extractor_model,
+            rag_extractor_prompt: config.rag_extractor_prompt,
+            rag_graph_hops: config.rag_graph_hops,
 
             document_loaders: config.document_loaders,
 
@@ -512,6 +521,15 @@ impl AppConfig {
         if let Some(v) = super::read_env_value::<String>(&get_env_name("rag_template")) {
             self.rag_template = v;
         }
+        if let Some(v) = super::read_env_value::<String>(&get_env_name("rag_extractor_model")) {
+            self.rag_extractor_model = v;
+        }
+        if let Some(v) = super::read_env_value::<String>(&get_env_name("rag_extractor_prompt")) {
+            self.rag_extractor_prompt = v;
+        }
+        if let Some(v) = super::read_env_value::<usize>(&get_env_name("rag_graph_hops")) {
+            self.rag_graph_hops = v.unwrap_or(1);
+        }
 
         if let Ok(v) = env::var(get_env_name("document_loaders"))
             && let Ok(v) = serde_json::from_str(&v)

src/config/mod.rs

@@ -250,6 +250,9 @@ pub struct Config {
     pub rag_chunk_size: Option<usize>,
     pub rag_chunk_overlap: Option<usize>,
     pub rag_template: Option<String>,
+    pub rag_extractor_model: Option<String>,
+    pub rag_extractor_prompt: Option<String>,
+    pub rag_graph_hops: usize,
 
     #[serde(default)]
     pub document_loaders: HashMap<String, String>,
@@ -322,6 +325,9 @@ impl Default for Config {
             rag_chunk_size: None,
             rag_chunk_overlap: None,
             rag_template: None,
+            rag_extractor_model: None,
+            rag_extractor_prompt: None,
+            rag_graph_hops: 1,
 
             document_loaders: Default::default(),
 

src/graph/types.rs

@@ -352,6 +352,15 @@ pub struct RagNode {
     #[serde(default, skip_serializing_if = "Option::is_none")]
     pub batch_size: Option<usize>,
 
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub extractor_model: Option<String>,
+
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub extractor_prompt: Option<String>,
+
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub graph_hops: Option<usize>,
+
     #[serde(default, skip_serializing_if = "Option::is_none")]
     pub state_updates: Option<HashMap<String, String>>,
 

src/graph/validator.rs

@@ -1027,6 +1027,9 @@ mod tests {
                 chunk_overlap: None,
                 reranker_model: None,
                 batch_size: None,
+                extractor_model: None,
+                extractor_prompt: None,
+                graph_hops: None,
                 state_updates,
                 timeout: None,
             }),

src/rag/graph.rs

@@ -0,0 +1,252 @@
+use super::DocumentId;
+use crate::client::*;
+
+use anyhow::{Context, Result};
+use indexmap::IndexMap;
+use petgraph::Direction;
+use petgraph::graph::NodeIndex;
+use petgraph::stable_graph::StableGraph;
+use serde::{Deserialize, Serialize};
+use std::collections::HashSet;
+
+const EXTRACTION_PROMPT: &str = r#"Extract entities and relationships from the following text chunk.
+
+Return a JSON object with this exact structure:
+{
+  "entities": [
+    {"name": "EntityName", "type": "EntityType", "description": "brief description"}
+  ],
+  "relationships": [
+    {"from": "EntityA", "to": "EntityB", "type": "relation_verb", "weight": 0.9}
+  ]
+}
+
+Rules:
+- Entity types: PERSON, ORGANIZATION, CONCEPT, TECHNOLOGY, LOCATION, EVENT, or OTHER
+- Relationship types should be short verb phrases (e.g., "uses", "depends_on", "implements", "part_of")
+- Weight is a float from 0.0 to 1.0 indicating relationship strength (default 1.0)
+- Only extract entities and relationships clearly stated or strongly implied in the text
+- Use exact entity names as they appear so relationships can be matched
+- Return ONLY the JSON object, no markdown fences, no explanation
+
+Text chunk:
+__CHUNK__"#;
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct Entity {
+    pub name: String,
+    pub entity_type: String,
+    pub description: Option<String>,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct Relationship {
+    pub relation_type: String,
+    pub weight: f32,
+}
+
+#[derive(Debug, Deserialize)]
+pub struct ExtractionResult {
+    pub entities: Vec<ExtractedEntity>,
+    pub relationships: Vec<ExtractedRelationship>,
+}
+
+#[derive(Debug, Deserialize)]
+pub struct ExtractedEntity {
+    pub name: String,
+    #[serde(rename = "type")]
+    pub entity_type: String,
+    pub description: Option<String>,
+}
+
+#[derive(Debug, Deserialize)]
+pub struct ExtractedRelationship {
+    pub from: String,
+    pub to: String,
+    #[serde(rename = "type")]
+    pub relation_type: String,
+    pub weight: Option<f32>,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct KnowledgeGraph {
+    pub graph: StableGraph<Entity, Relationship>,
+    /// Lowercased entity name → raw node index
+    pub entity_index: IndexMap<String, u32>,
+    /// DocumentId inner value → raw node indices for entities in that chunk
+    pub document_entities: IndexMap<usize, Vec<u32>>,
+}
+
+impl Default for KnowledgeGraph {
+    fn default() -> Self {
+        Self {
+            graph: StableGraph::new(),
+            entity_index: IndexMap::new(),
+            document_entities: IndexMap::new(),
+        }
+    }
+}
+
+impl KnowledgeGraph {
+    pub fn merge(&mut self, doc_id: DocumentId, result: ExtractionResult) {
+        let mut chunk_nodes: Vec<u32> = vec![];
+
+        for extracted in &result.entities {
+            let key = extracted.name.to_lowercase();
+            let node_raw = if let Some(&existing) = self.entity_index.get(&key) {
+                existing
+            } else {
+                let entity = Entity {
+                    name: extracted.name.clone(),
+                    entity_type: extracted.entity_type.clone(),
+                    description: extracted.description.clone(),
+                };
+                let idx = self.graph.add_node(entity);
+                let raw = idx.index() as u32;
+                self.entity_index.insert(key, raw);
+                raw
+            };
+            chunk_nodes.push(node_raw);
+        }
+
+        for extracted in &result.relationships {
+            let from_key = extracted.from.to_lowercase();
+            let to_key = extracted.to.to_lowercase();
+            if let (Some(&from_raw), Some(&to_raw)) = (
+                self.entity_index.get(&from_key),
+                self.entity_index.get(&to_key),
+            ) {
+                let from_idx = NodeIndex::new(from_raw as usize);
+                let to_idx = NodeIndex::new(to_raw as usize);
+                // Avoid duplicate edges
+                if !self.graph.contains_edge(from_idx, to_idx) {
+                    let rel = Relationship {
+                        relation_type: extracted.relation_type.clone(),
+                        weight: extracted.weight.unwrap_or(1.0),
+                    };
+                    self.graph.add_edge(from_idx, to_idx, rel);
+                }
+            }
+        }
+
+        self.document_entities
+            .entry(doc_id.0)
+            .or_default()
+            .extend(chunk_nodes);
+    }
+
+    pub fn remove_documents(&mut self, doc_ids: &[DocumentId]) {
+        if doc_ids.is_empty() {
+            return;
+        }
+
+        let removing: HashSet<usize> = doc_ids.iter().map(|d| d.0).collect();
+        for raw_id in &removing {
+            self.document_entities.swap_remove(raw_id);
+        }
+
+        let still_used: HashSet<u32> = self
+            .document_entities
+            .values()
+            .flat_map(|v| v.iter().copied())
+            .collect();
+
+        let to_remove: Vec<u32> = self
+            .entity_index
+            .values()
+            .copied()
+            .filter(|raw| !still_used.contains(raw))
+            .collect();
+
+        for raw in to_remove {
+            let idx = NodeIndex::new(raw as usize);
+            if self.graph.contains_node(idx) {
+                let name = self.graph[idx].name.to_lowercase();
+                self.graph.remove_node(idx);
+                self.entity_index.swap_remove(&name);
+            }
+        }
+    }
+
+    pub fn build_node_to_docs(&self) -> IndexMap<u32, Vec<DocumentId>> {
+        let mut map: IndexMap<u32, Vec<DocumentId>> = IndexMap::new();
+        for (&doc_raw, node_raws) in &self.document_entities {
+            let doc_id = DocumentId(doc_raw);
+            for &node_raw in node_raws {
+                map.entry(node_raw).or_default().push(doc_id);
+            }
+        }
+        map
+    }
+
+    pub fn expand_neighbors(&self, seed_nodes: &[u32], hops: usize) -> Vec<u32> {
+        let mut expanded: indexmap::IndexSet<u32> = seed_nodes.iter().copied().collect();
+        let mut frontier: Vec<u32> = seed_nodes.to_vec();
+        for _ in 0..hops {
+            let mut next_frontier: Vec<u32> = vec![];
+            for &raw in &frontier {
+                let idx = NodeIndex::new(raw as usize);
+                if self.graph.contains_node(idx) {
+                    for dir in [Direction::Outgoing, Direction::Incoming] {
+                        for neighbor in self.graph.neighbors_directed(idx, dir) {
+                            let n = neighbor.index() as u32;
+                            if expanded.insert(n) {
+                                next_frontier.push(n);
+                            }
+                        }
+                    }
+                }
+            }
+            frontier = next_frontier;
+            if frontier.is_empty() {
+                break;
+            }
+        }
+        expanded.into_iter().collect()
+    }
+}
+
+/// Uses chat_completions_inner directly (bypassing Input) because Rag has no
+/// RequestContext, which Input::from_str requires.
+pub async fn extract_entities(
+    client: &dyn Client,
+    chunk: &str,
+    prompt_template: Option<&str>,
+) -> Result<ExtractionResult> {
+    let template = prompt_template.unwrap_or(EXTRACTION_PROMPT);
+    let prompt = template.replace("__CHUNK__", chunk);
+    let mut messages = vec![Message::new(
+        MessageRole::User,
+        MessageContent::Text(prompt),
+    )];
+    patch_messages(&mut messages, client.model());
+    let reqwest_client = client
+        .build_client()
+        .context("Failed to build HTTP client for entity extraction")?;
+    let data = ChatCompletionsData {
+        messages,
+        temperature: Some(0.0),
+        top_p: None,
+        functions: None,
+        stream: false,
+    };
+    let output = client
+        .chat_completions_inner(&reqwest_client, data)
+        .await
+        .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)
+            .take_while(|l| !l.trim_start().starts_with("```"))
+            .collect::<Vec<_>>()
+            .join("\n")
+    } else {
+        text.to_string()
+    };
+
+    serde_json::from_str::<ExtractionResult>(&json)
+        .context("Failed to parse entity extraction JSON")
+}

src/rag/mod.rs

@@ -4,15 +4,19 @@ use crate::client::*;
 use crate::config::*;
 use crate::utils::*;
 
+mod graph;
 mod serde_vectors;
 mod splitter;
 
+use self::graph::{KnowledgeGraph, extract_entities};
+
 use anyhow::{Context, Result, anyhow, bail};
 use bm25::{Language, SearchEngine, SearchEngineBuilder};
 use hnsw_rs::prelude::*;
 use indexmap::{IndexMap, IndexSet};
 use inquire::{Confirm, Select, Text, required, validator::Validation};
 use parking_lot::RwLock;
+use petgraph::graph::NodeIndex;
 use serde::{Deserialize, Serialize};
 use serde_json::json;
 use std::{
@@ -54,6 +58,7 @@ pub struct Rag {
     bm25: SearchEngine<DocumentId>,
     data: RagData,
     last_sources: RwLock<Option<String>>,
+    node_to_docs: IndexMap<u32, Vec<DocumentId>>,
 }
 
 impl Debug for Rag {
@@ -76,6 +81,7 @@ impl Clone for Rag {
             embedding_model: self.embedding_model.clone(),
             hnsw: self.data.build_hnsw(),
             bm25: self.data.build_bm25(),
+            node_to_docs: self.data.knowledge_graph.build_node_to_docs(),
             data: self.data.clone(),
             last_sources: RwLock::new(None),
         }
@@ -90,6 +96,16 @@ pub struct RagInitConfig {
     pub reranker_model: Option<String>,
     pub top_k: Option<usize>,
     pub batch_size: Option<usize>,
+    pub extractor_model: Option<String>,
+    pub extractor_prompt: Option<String>,
+    pub graph_hops: Option<usize>,
+}
+
+#[derive(Debug, Clone, Default)]
+pub struct GraphRagConfig {
+    pub extractor_model: Option<String>,
+    pub extractor_prompt: Option<String>,
+    pub graph_hops: Option<usize>,
 }
 
 impl Rag {
@@ -199,6 +215,17 @@ impl Rag {
             reranker_model,
             top_k,
             batch_size,
+            GraphRagConfig {
+                extractor_model: config
+                    .extractor_model
+                    .clone()
+                    .or_else(|| app.rag_extractor_model.clone()),
+                extractor_prompt: config
+                    .extractor_prompt
+                    .clone()
+                    .or_else(|| app.rag_extractor_prompt.clone()),
+                graph_hops: Some(config.graph_hops.unwrap_or(app.rag_graph_hops)),
+            },
         ))
     }
 
@@ -216,6 +243,16 @@ impl Rag {
         let (embedding_model, chunk_size, chunk_overlap) = Self::create_config(app)?;
         let reranker_model = app.rag_reranker_model.clone();
         let top_k = app.rag_top_k;
+        let extractor_model = match app.rag_extractor_model.clone() {
+            Some(model) => Some(model),
+            None => select_extractor_model(app)?,
+        };
+        let graph_hops = if extractor_model.is_some() {
+            set_graph_hops(app.rag_graph_hops)?
+        } else {
+            app.rag_graph_hops
+        };
+        let extractor_prompt = app.rag_extractor_prompt.clone();
         let data = RagData::new(
             embedding_model.id(),
             chunk_size,
@@ -223,6 +260,11 @@ impl Rag {
             reranker_model,
             top_k,
             embedding_model.max_batch_size(),
+            GraphRagConfig {
+                extractor_model,
+                extractor_prompt,
+                graph_hops: Some(graph_hops),
+            },
         );
         let mut rag = Self::create(app, name, save_path, data)?;
         let mut paths = doc_paths.to_vec();
@@ -253,6 +295,7 @@ impl Rag {
     pub fn create(app: &AppConfig, name: &str, path: &Path, data: RagData) -> Result<Self> {
         let hnsw = data.build_hnsw();
         let bm25 = data.build_bm25();
+        let node_to_docs = data.knowledge_graph.build_node_to_docs();
         let embedding_model =
             Model::retrieve_model(app, &data.embedding_model, ModelType::Embedding)?;
         let rag = Rag {
@@ -263,6 +306,7 @@ impl Rag {
             embedding_model,
             hnsw,
             bm25,
+            node_to_docs,
             last_sources: RwLock::new(None),
         };
         Ok(rag)
@@ -413,6 +457,9 @@ impl Rag {
             "chunk_size": self.data.chunk_size,
             "chunk_overlap": self.data.chunk_overlap,
             "reranker_model": self.data.reranker_model,
+            "extractor_model": self.data.extractor_model,
+            "extractor_prompt": self.data.extractor_prompt,
+            "graph_hops": self.data.graph_hops.unwrap_or(1),
             "top_k": self.data.top_k,
             "batch_size": self.data.batch_size,
             "document_paths": self.data.document_paths,
@@ -673,13 +720,18 @@ impl Rag {
         let mut files = vec![];
         let mut document_ids = vec![];
         let mut embeddings = vec![];
+        let mut new_doc_contents: Vec<(DocumentId, String)> = vec![];
 
         if !rag_files.is_empty() {
             let mut texts = vec![];
             for file in rag_files.into_iter() {
                 for (document_index, document) in file.documents.iter().enumerate() {
-                    document_ids.push(DocumentId::new(next_file_id, document_index));
+                    let doc_id = DocumentId::new(next_file_id, document_index);
-                    texts.push(document.page_content.clone())
+                    document_ids.push(doc_id);
+                    texts.push(document.page_content.clone());
+                    if self.data.extractor_model.is_some() {
+                        new_doc_contents.push((doc_id, document.page_content.clone()));
+                    }
                 }
                 files.push((next_file_id, file));
                 next_file_id += 1;
@@ -700,9 +752,43 @@ impl Rag {
             bail!("No RAG files");
         }
 
+        if self.data.extractor_model.is_some()
+            && !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();
+                        for (i, (doc_id, content)) in new_doc_contents.into_iter().enumerate() {
+                            progress(
+                                &spinner,
+                                format!("Extracting entities [{}/{}]", i + 1, total),
+                            );
+                            match extract_entities(
+                                client.as_ref(),
+                                &content,
+                                self.data.extractor_prompt.as_deref(),
+                            )
+                            .await
+                            {
+                                Ok(result) => self.data.knowledge_graph.merge(doc_id, result),
+                                Err(e) => {
+                                    debug!("Entity extraction failed for doc {doc_id:?}: {e}")
+                                }
+                            }
+                        }
+                    }
+                    Err(e) => debug!("Failed to create extractor client: {e}"),
+                },
+                Err(e) => debug!("Extractor model not found: {e}"),
+            }
+        }
+
         progress(&spinner, "Building store".into());
         self.hnsw = self.data.build_hnsw();
         self.bm25 = self.data.build_bm25();
+        self.node_to_docs = self.data.knowledge_graph.build_node_to_docs();
 
         Ok(())
     }
@@ -755,11 +841,21 @@ impl Rag {
                 ids
             }
             None => {
-                let ids = reciprocal_rank_fusion(
+                let ids = if self.data.extractor_model.is_some() {
-                    vec![vector_search_ids, keyword_search_ids],
+                    let graph_ids = self.graph_search(query, top_k);
-                    vec![1.125, 1.0],
+                    debug!("graph_search_ids: {graph_ids:?}");
-                    top_k,
+                    reciprocal_rank_fusion(
-                );
+                        vec![vector_search_ids, keyword_search_ids, graph_ids],
+                        vec![1.125, 1.0, 0.9],
+                        top_k,
+                    )
+                } else {
+                    reciprocal_rank_fusion(
+                        vec![vector_search_ids, keyword_search_ids],
+                        vec![1.125, 1.0],
+                        top_k,
+                    )
+                };
                 debug!("rrf_ids: {ids:?}");
                 ids
             }
@@ -829,6 +925,93 @@ impl Rag {
         Ok(output)
     }
 
+    fn graph_search(&self, query: &str, top_k: usize) -> Vec<DocumentId> {
+        let kg = &self.data.knowledge_graph;
+        if kg.entity_index.is_empty() {
+            return vec![];
+        }
+        let query_lower = query.to_lowercase();
+
+        let mut seed_nodes: Vec<u32> = kg
+            .entity_index
+            .iter()
+            .filter(|(name, _)| {
+                let name_str = name.as_str();
+                if name_str.contains(' ') {
+                    query_lower.contains(name_str)
+                } else {
+                    // whole-word match: prevents "go" from seeding on every query containing "Django"
+                    query_lower
+                        .split_whitespace()
+                        .any(|token| token.trim_matches(|c: char| !c.is_alphanumeric()) == name_str)
+                }
+            })
+            .map(|(_, &raw)| raw)
+            .collect();
+
+        if seed_nodes.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());
+                    if seed_nodes.len() >= top_k {
+                        break 'outer;
+                    }
+                }
+            }
+        }
+
+        if seed_nodes.is_empty() {
+            return vec![];
+        }
+
+        let hops = self.data.graph_hops.unwrap_or(1);
+        let expanded = kg.expand_neighbors(&seed_nodes, 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));
+
+        let mut result_ids: IndexSet<DocumentId> = IndexSet::new();
+        for (raw, _) in scored {
+            if let Some(doc_ids) = self.node_to_docs.get(&raw) {
+                for &doc_id in doc_ids {
+                    result_ids.insert(doc_id);
+                    if result_ids.len() >= top_k {
+                        break;
+                    }
+                }
+            }
+            if result_ids.len() >= top_k {
+                break;
+            }
+        }
+        result_ids.into_iter().collect()
+    }
+
     async fn create_embeddings(
         &self,
         data: EmbeddingsData,
@@ -902,6 +1085,14 @@ pub struct RagData {
     pub files: IndexMap<FileId, RagFile>,
     #[serde(with = "serde_vectors")]
     pub vectors: IndexMap<DocumentId, Vec<f32>>,
+    #[serde(default)]
+    pub extractor_model: Option<String>,
+    #[serde(default)]
+    pub extractor_prompt: Option<String>,
+    #[serde(default)]
+    pub graph_hops: Option<usize>,
+    #[serde(default)]
+    pub knowledge_graph: KnowledgeGraph,
 }
 
 impl Debug for RagData {
@@ -916,6 +1107,9 @@ impl Debug for RagData {
             .field("next_file_id", &self.next_file_id)
             .field("document_paths", &self.document_paths)
             .field("files", &self.files)
+            .field("extractor_model", &self.extractor_model)
+            .field("extractor_prompt", &self.extractor_prompt)
+            .field("graph_hops", &self.graph_hops)
             .finish()
     }
 }
@@ -928,6 +1122,7 @@ impl RagData {
         reranker_model: Option<String>,
         top_k: usize,
         batch_size: Option<usize>,
+        graph: GraphRagConfig,
     ) -> Self {
         Self {
             embedding_model,
@@ -940,6 +1135,10 @@ impl RagData {
             document_paths: Default::default(),
             files: Default::default(),
             vectors: Default::default(),
+            extractor_model: graph.extractor_model,
+            extractor_prompt: graph.extractor_prompt,
+            graph_hops: graph.graph_hops,
+            knowledge_graph: KnowledgeGraph::default(),
         }
     }
 
@@ -951,14 +1150,17 @@ impl RagData {
     }
 
     pub fn del(&mut self, file_ids: Vec<FileId>) {
+        let mut graph_doc_ids = vec![];
         for file_id in file_ids {
             if let Some(file) = self.files.swap_remove(&file_id) {
                 for (document_index, _) in file.documents.iter().enumerate() {
                     let document_id = DocumentId::new(file_id, document_index);
                     self.vectors.swap_remove(&document_id);
+                    graph_doc_ids.push(document_id);
                 }
             }
         }
+        self.knowledge_graph.remove_documents(&graph_doc_ids);
     }
 
     pub fn add(
@@ -1055,29 +1257,70 @@ impl DocumentId {
 }
 
 fn select_embedding_model(models: &[&Model]) -> Result<String> {
+    let max_width = models.iter().map(|v| v.id().len()).max().unwrap_or(0);
     let models: Vec<_> = models
         .iter()
-        .map(|v| SelectOption::new(v.id(), v.description()))
+        .map(|v| SelectOption::new(v.id(), v.description(), max_width))
         .collect();
-    let result = Select::new("Select embedding model:", models).prompt()?;
+    let result = Select::new("Select embedding model:", models)
+        .with_formatter(&|opt| opt.value.value.clone())
+        .prompt()?;
     Ok(result.value)
 }
 
+const EXTRACTOR_SKIP: &str = "Skip";
+
+fn select_extractor_model(app: &AppConfig) -> Result<Option<String>> {
+    let models = list_models(app, ModelType::Chat);
+    if models.is_empty() {
+        return Ok(None);
+    }
+    let pad = models
+        .iter()
+        .map(|v| v.id().len())
+        .max()
+        .unwrap_or(0)
+        .max(EXTRACTOR_SKIP.len());
+    let mut options = vec![SelectOption::new(
+        EXTRACTOR_SKIP.to_string(),
+        "vector + full text search only (no graph)".to_string(),
+        pad,
+    )];
+    options.extend(
+        models
+            .iter()
+            .map(|v| SelectOption::new(v.id(), v.description(), pad)),
+    );
+    let result = Select::new("Extractor model for graph-based RAG (optional):", options)
+        .with_formatter(&|opt| opt.value.value.clone())
+        .prompt()?;
+    Ok(if result.value == EXTRACTOR_SKIP {
+        None
+    } else {
+        Some(result.value)
+    })
+}
+
 #[derive(Debug)]
 struct SelectOption {
     pub value: String,
-    pub description: String,
+    pub display: String,
 }
 
 impl SelectOption {
-    pub fn new(value: String, description: String) -> Self {
+    pub fn new(value: String, description: String, pad: usize) -> Self {
-        Self { value, description }
+        let display = if description.is_empty() {
+            format!("{value:<pad$}")
+        } else {
+            format!("{value:<pad$} ({description})")
+        };
+        Self { value, display }
     }
 }
 
 impl fmt::Display for SelectOption {
     fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
-        write!(f, "{} ({})", self.value, self.description)
+        write!(f, "{}", self.display)
     }
 }
 
@@ -1103,6 +1346,21 @@ fn set_chunk_size(model: &Model) -> Result<usize> {
     value.parse().map_err(|_| anyhow!("Invalid chunk_size"))
 }
 
+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_validator(move |text: &str| {
+            let out = match text.parse::<usize>() {
+                Ok(v) if v >= 1 => Validation::Valid,
+                _ => Validation::Invalid("Must be an integer >= 1".into()),
+            };
+            Ok(out)
+        })
+        .prompt()?;
+    value.parse().map_err(|_| anyhow!("Invalid graph_hops"))
+}
+
 fn set_chunk_overlay(default_value: usize) -> Result<usize> {
     let value = Text::new("Set chunk overlay:")
         .with_default(&default_value.to_string())
@@ -1277,7 +1535,15 @@ mod tests {
 
     #[test]
     fn rag_data_new_defaults() {
-        let data = RagData::new("model".into(), 1000, 20, None, 5, None);
+        let data = RagData::new(
+            "model".into(),
+            1000,
+            20,
+            None,
+            5,
+            None,
+            GraphRagConfig::default(),
+        );
         assert_eq!(data.embedding_model, "model");
         assert_eq!(data.chunk_size, 1000);
         assert_eq!(data.chunk_overlap, 20);
@@ -1291,7 +1557,15 @@ mod tests {
 
     #[test]
     fn rag_data_get_returns_document() {
-        let mut data = RagData::new("m".into(), 100, 10, None, 5, None);
+        let mut data = RagData::new(
+            "m".into(),
+            100,
+            10,
+            None,
+            5,
+            None,
+            GraphRagConfig::default(),
+        );
         let file = RagFile {
             hash: "abc".into(),
             path: "test.txt".into(),
@@ -1308,13 +1582,29 @@ mod tests {
 
     #[test]
     fn rag_data_get_returns_none_for_missing_file() {
-        let data = RagData::new("m".into(), 100, 10, None, 5, None);
+        let data = RagData::new(
+            "m".into(),
+            100,
+            10,
+            None,
+            5,
+            None,
+            GraphRagConfig::default(),
+        );
         assert!(data.get(DocumentId::new(99, 0)).is_none());
     }
 
     #[test]
     fn rag_data_get_returns_none_for_missing_document() {
-        let mut data = RagData::new("m".into(), 100, 10, None, 5, None);
+        let mut data = RagData::new(
+            "m".into(),
+            100,
+            10,
+            None,
+            5,
+            None,
+            GraphRagConfig::default(),
+        );
         let file = RagFile {
             hash: "abc".into(),
             path: "test.txt".into(),
@@ -1326,7 +1616,15 @@ mod tests {
 
     #[test]
     fn rag_data_del_removes_files_and_vectors() {
-        let mut data = RagData::new("m".into(), 100, 10, None, 5, None);
+        let mut data = RagData::new(
+            "m".into(),
+            100,
+            10,
+            None,
+            5,
+            None,
+            GraphRagConfig::default(),
+        );
         let file = RagFile {
             hash: "abc".into(),
             path: "test.txt".into(),
@@ -1347,14 +1645,30 @@ mod tests {
 
     #[test]
     fn rag_data_del_nonexistent_is_noop() {
-        let mut data = RagData::new("m".into(), 100, 10, None, 5, None);
+        let mut data = RagData::new(
+            "m".into(),
+            100,
+            10,
+            None,
+            5,
+            None,
+            GraphRagConfig::default(),
+        );
         data.del(vec![99]);
         assert!(data.files.is_empty());
     }
 
     #[test]
     fn rag_data_add_inserts_files_and_vectors() {
-        let mut data = RagData::new("m".into(), 100, 10, None, 5, None);
+        let mut data = RagData::new(
+            "m".into(),
+            100,
+            10,
+            None,
+            5,
+            None,
+            GraphRagConfig::default(),
+        );
         let file = RagFile {
             hash: "xyz".into(),
             path: "new.txt".into(),
@@ -1414,7 +1728,15 @@ mod tests {
 
     #[test]
     fn rag_data_build_bm25_empty() {
-        let data = RagData::new("m".into(), 100, 10, None, 5, None);
+        let data = RagData::new(
+            "m".into(),
+            100,
+            10,
+            None,
+            5,
+            None,
+            GraphRagConfig::default(),
+        );
         let engine = data.build_bm25();
         let results = engine.search("anything", 5);
         assert!(results.is_empty());
@@ -1422,7 +1744,15 @@ mod tests {
 
     #[test]
     fn rag_data_build_bm25_finds_documents() {
-        let mut data = RagData::new("m".into(), 100, 10, None, 5, None);
+        let mut data = RagData::new(
+            "m".into(),
+            100,
+            10,
+            None,
+            5,
+            None,
+            GraphRagConfig::default(),
+        );
         let file = RagFile {
             hash: "h".into(),
             path: "test.txt".into(),