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Author SHA1 Message Date
Dark-Alex-17 d8eec1d427 docs: Documented the new no_workspace_mcp configuration property that disables workspace-local MCP configurations
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2026-07-13 17:14:06 -06:00
Dark-Alex-17 382916c3ee style: Removed redundant '&' from paths module function calls 2026-07-13 17:12:58 -06:00
Dark-Alex-17 bc3cc10a7b feat: Support workspace-local skill definitions and MCP configurations 2026-07-13 17:12:34 -06:00
Dark-Alex-17 b91f738209 docs: updated the configuratino examples for graph-based RAG
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2026-07-13 16:55:18 -06:00
Dark-Alex-17 4f0dae9b49 feat: fully functional graph-based RAG
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2026-07-13 16:50:07 -06:00
Dark-Alex-17 deb673ebc9 fmt: applied some formatting changes 2026-07-13 16:07:19 -06:00
15 changed files with 679 additions and 99 deletions
+5 -1
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@@ -134,6 +134,10 @@ enabled_mcp_servers: null # Which MCP servers to enable by default.
# - slack
# Example (comma-separated form):
# enabled_mcp_servers: github,slack,ddg-search
no_workspace_mcp: false # Disable loading workspace-local MCP servers from .coyote/mcp.json (default: false).
# When false (the default), Coyote merges .coyote/mcp.json from the current directory
# into the global MCP registry at startup. Workspace entries shadow global ones on
# name collision. Set to true (or pass --no-workspace-mcp) to skip this entirely.
# ---- Skills ----
# Skills are modular knowledge or capability packs the LLM can load and unload mid-conversation.
@@ -199,7 +203,7 @@ rag_chunk_size: null # Defines the size of chunks for document proce
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)
rag_graph_hops: 1 # Number of hops to expand from matched entities at query time (0 = seed nodes only; 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)
+1 -1
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@@ -227,7 +227,7 @@ nodes:
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)
graph_hops: 1 # Graph expansion depth at query time (0 = seed nodes only; 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`
+3
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@@ -195,6 +195,9 @@ pub struct Cli {
/// Skip discovery and application of all sbx mixins (user and built-in)
#[arg(long, requires = "sandbox")]
pub no_mixins: bool,
/// Disable loading workspace MCP servers from .coyote/mcp.json
#[arg(long)]
pub no_workspace_mcp: bool,
}
impl Cli {
+2 -2
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@@ -50,7 +50,7 @@ fn prepare_chat_completions(
let url = format!(
"{}/openai/deployments/{}/chat/completions?api-version=2024-12-01-preview",
&api_base,
api_base,
self_.model.real_name()
);
@@ -69,7 +69,7 @@ fn prepare_embeddings(self_: &AzureOpenAIClient, data: &EmbeddingsData) -> Resul
let url = format!(
"{}/openai/deployments/{}/embeddings?api-version=2024-10-21",
&api_base,
api_base,
self_.model.real_name()
);
+3
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@@ -88,6 +88,7 @@ pub struct AppConfig {
pub user_agent: Option<String>,
pub save_shell_history: bool,
pub no_workspace_mcp: bool,
pub sync_models_url: Option<String>,
pub clients: Vec<ClientConfig>,
@@ -162,6 +163,7 @@ impl Default for AppConfig {
user_agent: None,
save_shell_history: true,
no_workspace_mcp: false,
sync_models_url: None,
clients: vec![],
@@ -238,6 +240,7 @@ impl AppConfig {
user_agent: config.user_agent,
save_shell_history: config.save_shell_history,
no_workspace_mcp: false,
sync_models_url: config.sync_models_url,
clients: config.clients,
+10 -10
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@@ -7,7 +7,7 @@ use serde::{Deserialize, Serialize};
use crate::config::{
GIT_DIR_NAME, GITIGNORE_FILE_NAME, MEMORY_DIR_NAME, MEMORY_INDEX_FILE_NAME,
WORKSPACE_MEMORY_DIR_NAME, WORKSPACE_MEMORY_FILE_NAME, paths,
WORKSPACE_COYOTE_DIR_NAME, WORKSPACE_MEMORY_FILE_NAME, paths,
};
pub const DEFAULT_MEMORY_CAP_WITH_TOOLS: usize = 6_000;
@@ -27,7 +27,7 @@ pub enum WorkspaceMemory {
pub fn discover_workspace_memory(start: &Path) -> Option<WorkspaceMemory> {
for dir in start.ancestors() {
let structured = dir.join(WORKSPACE_MEMORY_DIR_NAME).join(MEMORY_DIR_NAME);
let structured = dir.join(WORKSPACE_COYOTE_DIR_NAME).join(MEMORY_DIR_NAME);
if structured.join(MEMORY_INDEX_FILE_NAME).exists() {
return Some(WorkspaceMemory::Structured {
workspace_root: dir.to_path_buf(),
@@ -84,8 +84,8 @@ pub fn bootstrap_workspace_memory(git_root: &Path) -> Result<PathBuf> {
fn append_gitignore_entry(git_root: &Path) -> Result<bool> {
let gitignore = git_root.join(GITIGNORE_FILE_NAME);
let entry = format!("{WORKSPACE_MEMORY_DIR_NAME}/{MEMORY_DIR_NAME}/");
let entry_no_slash = format!("{WORKSPACE_MEMORY_DIR_NAME}/{MEMORY_DIR_NAME}");
let entry = format!("{WORKSPACE_COYOTE_DIR_NAME}/{MEMORY_DIR_NAME}/");
let entry_no_slash = format!("{WORKSPACE_COYOTE_DIR_NAME}/{MEMORY_DIR_NAME}");
let existing = fs::read_to_string(&gitignore).unwrap_or_default();
let already_present = existing.lines().any(|line| {
@@ -347,7 +347,7 @@ mod tests {
let root = temp_root("phase1");
let workspace = root.join("workspace");
let workspace_memory_dir = workspace
.join(WORKSPACE_MEMORY_DIR_NAME)
.join(WORKSPACE_COYOTE_DIR_NAME)
.join(MEMORY_DIR_NAME);
fs::create_dir_all(&workspace_memory_dir).unwrap();
fs::write(
@@ -382,7 +382,7 @@ mod tests {
let root = temp_root("prefer");
let workspace = root.join("ws");
let structured = workspace
.join(WORKSPACE_MEMORY_DIR_NAME)
.join(WORKSPACE_COYOTE_DIR_NAME)
.join(MEMORY_DIR_NAME);
fs::create_dir_all(&structured).unwrap();
fs::write(structured.join(MEMORY_INDEX_FILE_NAME), "s").unwrap();
@@ -415,7 +415,7 @@ mod tests {
let root = temp_root("indexes_only");
let workspace = root.join("ws");
let structured = workspace
.join(WORKSPACE_MEMORY_DIR_NAME)
.join(WORKSPACE_COYOTE_DIR_NAME)
.join(MEMORY_DIR_NAME);
fs::create_dir_all(&structured).unwrap();
fs::write(
@@ -450,7 +450,7 @@ mod tests {
let root = temp_root("drill_bodies");
let workspace = root.join("ws");
let structured = workspace
.join(WORKSPACE_MEMORY_DIR_NAME)
.join(WORKSPACE_COYOTE_DIR_NAME)
.join(MEMORY_DIR_NAME);
fs::create_dir_all(&structured).unwrap();
fs::write(structured.join(MEMORY_INDEX_FILE_NAME), "idx").unwrap();
@@ -485,7 +485,7 @@ mod tests {
let root = temp_root("cap");
let workspace = root.join("ws");
let structured = workspace
.join(WORKSPACE_MEMORY_DIR_NAME)
.join(WORKSPACE_COYOTE_DIR_NAME)
.join(MEMORY_DIR_NAME);
fs::create_dir_all(&structured).unwrap();
fs::write(structured.join(MEMORY_INDEX_FILE_NAME), "idx").unwrap();
@@ -575,7 +575,7 @@ mod tests {
let root = temp_root("walk_up");
let workspace = root.join("ws");
let mem_dir = workspace
.join(WORKSPACE_MEMORY_DIR_NAME)
.join(WORKSPACE_COYOTE_DIR_NAME)
.join(MEMORY_DIR_NAME);
fs::create_dir_all(&mem_dir).unwrap();
fs::write(mem_dir.join(MEMORY_INDEX_FILE_NAME), "idx").unwrap();
+1 -1
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@@ -143,7 +143,7 @@ const MCP_FILE_NAME: &str = "mcp.json";
const MEMORY_DIR_NAME: &str = "memory";
const MEMORY_INDEX_FILE_NAME: &str = "MEMORY.md";
const WORKSPACE_MEMORY_FILE_NAME: &str = "COYOTE.md";
const WORKSPACE_MEMORY_DIR_NAME: &str = ".coyote";
const WORKSPACE_COYOTE_DIR_NAME: &str = ".coyote";
const SBX_KIT_DIR_NAME: &str = "sbx-kit";
const SBX_KIT_HASH_FILE: &str = "kit.sha256";
const SBX_MIXIN_FILE_NAME: &str = "sbx-mixin.yaml";
+29 -5
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@@ -5,7 +5,7 @@ use super::{
GLOBAL_TOOLS_UTILS_DIR_NAME, MACROS_DIR_NAME, MCP_FILE_NAME, MEMORY_DIR_NAME,
MEMORY_INDEX_FILE_NAME, ModelsOverride, RAGS_DIR_NAME, ROLES_DIR_NAME, SBX_KIT_DIR_NAME,
SBX_KIT_HASH_FILE, SBX_MIXIN_FILE_NAME, SBX_MIXIN_KITS_DIR_NAME, SBX_VAULT_MIXINS_DIR_NAME,
SKILLS_DIR_NAME, WORKSPACE_MEMORY_DIR_NAME,
SKILLS_DIR_NAME, WORKSPACE_COYOTE_DIR_NAME,
};
use crate::client::ProviderModels;
use crate::config::REPL_HISTORY_DIR_NAME;
@@ -118,7 +118,7 @@ pub fn global_tools_sbx_mixin_file() -> PathBuf {
pub fn find_workspace_sbx_mixin(start: &Path) -> Option<PathBuf> {
for dir in start.ancestors() {
let candidate = dir
.join(WORKSPACE_MEMORY_DIR_NAME)
.join(WORKSPACE_COYOTE_DIR_NAME)
.join(SBX_MIXIN_FILE_NAME);
if candidate.exists() {
return Some(candidate);
@@ -193,6 +193,24 @@ pub fn skill_file(name: &str) -> PathBuf {
skill_dir(name).join("SKILL.md")
}
pub fn workspace_skills_dir() -> PathBuf {
env::current_dir()
.unwrap_or_default()
.join(WORKSPACE_COYOTE_DIR_NAME)
.join(SKILLS_DIR_NAME)
}
pub fn workspace_skill_file(name: &str) -> PathBuf {
workspace_skills_dir().join(name).join("SKILL.md")
}
pub fn workspace_mcp_config_file() -> PathBuf {
env::current_dir()
.unwrap_or_default()
.join(WORKSPACE_COYOTE_DIR_NAME)
.join(MCP_FILE_NAME)
}
pub fn validate_skill_name(name: &str) -> Result<()> {
if name.is_empty() {
bail!("Skill name cannot be empty");
@@ -318,7 +336,7 @@ pub fn global_memory_index_path() -> PathBuf {
pub fn workspace_memory_dir_for(workspace_root: &Path) -> PathBuf {
workspace_root
.join(WORKSPACE_MEMORY_DIR_NAME)
.join(WORKSPACE_COYOTE_DIR_NAME)
.join(MEMORY_DIR_NAME)
}
@@ -405,25 +423,31 @@ pub fn has_macro(name: &str) -> bool {
pub fn list_skills() -> Vec<String> {
let mut names = Vec::new();
if let Ok(rd) = read_dir(skills_dir()) {
let mut seen = HashSet::new();
for dir in [workspace_skills_dir(), skills_dir()] {
if let Ok(rd) = read_dir(dir) {
for entry in rd.flatten() {
if let Ok(file_type) = entry.file_type()
&& file_type.is_dir()
&& let Some(name) = entry.file_name().to_str()
&& !seen.contains(name)
&& entry.path().join("SKILL.md").is_file()
&& validate_skill_name(name).is_ok()
{
seen.insert(name.to_string());
names.push(name.to_string());
}
}
}
}
names.sort_unstable();
names
}
pub fn has_skill(name: &str) -> bool {
skill_file(name).is_file()
workspace_skill_file(name).is_file() || skill_file(name).is_file()
}
pub fn local_models_override() -> Result<Vec<ProviderModels>> {
+1 -1
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@@ -261,7 +261,7 @@ impl Session {
data["messages"] = json!(self.messages);
let output = serde_yaml::to_string(&data)
.with_context(|| format!("Unable to show info about session '{}'", &self.name))?;
.with_context(|| format!("Unable to show info about session '{}'", self.name))?;
Ok(output)
}
+5 -1
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@@ -117,7 +117,11 @@ impl Skill {
pub fn load(name: &str) -> Result<Self> {
paths::validate_skill_name(name)?;
let path = paths::skill_file(name);
let path = if paths::workspace_skill_file(name).is_file() {
paths::workspace_skill_file(name)
} else {
paths::skill_file(name)
};
let content = read_to_string(&path)
.with_context(|| format!("Failed to read skill '{name}' at {}", path.display()))?;
Ok(Skill::new(name, &content))
+4 -4
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@@ -147,10 +147,10 @@ pub async fn eval_tool_calls(
let mut is_all_null = true;
for call in calls {
if let Some(msg) = ctx.tool_scope.tool_tracker.check_loop(&call.clone()) {
let dup_msg = format!("{{\"tool_call_loop_alert\":{}}}", &msg.trim());
let dup_msg = format!("{{\"tool_call_loop_alert\":{}}}", msg.trim());
println!(
"{}",
warning_text(format!("{}: ⚠️ Tool-call loop detected! ⚠️", &call.name).as_str())
warning_text(format!("{}: ⚠️ Tool-call loop detected! ⚠️", call.name).as_str())
);
let val = json!(dup_msg);
output.push(ToolResult::new(call, val));
@@ -870,7 +870,7 @@ impl Functions {
let root_dir = paths::functions_dir();
let tool_path = format!(
"{}/{binary_name}",
&paths::global_tools_dir().to_string_lossy()
paths::global_tools_dir().to_string_lossy()
);
content_template
.replace("{function_name}", binary_name)
@@ -881,7 +881,7 @@ impl Functions {
let root_dir = paths::agent_data_dir(agent_name);
let tool_path = format!(
"{}/{binary_name}",
&paths::global_tools_dir().to_string_lossy()
paths::global_tools_dir().to_string_lossy()
);
content_template
.replace("{function_name}", binary_name)
+6 -2
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@@ -187,7 +187,11 @@ async fn main() -> Result<()> {
let abort_signal = create_abort_signal();
let start_mcp_servers = cli.agent.is_none() && cli.role.is_none();
let cfg = Config::load_with_interpolation(info_flag).await?;
let app_config: Arc<AppConfig> = Arc::new(AppConfig::from_config(cfg)?);
let mut app_config = AppConfig::from_config(cfg)?;
if cli.no_workspace_mcp {
app_config.no_workspace_mcp = true;
}
let app_config: Arc<AppConfig> = Arc::new(app_config);
let app_state: Arc<AppState> = Arc::new(
AppState::init(
app_config,
@@ -559,7 +563,7 @@ async fn shell_execute(
match answer_char {
'e' => {
debug!("{} {:?}", shell.cmd, &[&shell.arg, &eval_str]);
debug!("{} {:?}", shell.cmd, [&shell.arg, &eval_str]);
let code = run_command(&shell.cmd, &[&shell.arg, &eval_str], None)?;
if code == 0 && app.save_shell_history {
let _ = append_to_shell_history(&shell.name, &eval_str, code);
+47 -1
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@@ -214,7 +214,53 @@ impl McpRegistry {
spec.validate(name)?;
}
registry.config = Some(mcp_servers_config);
let mut merged = mcp_servers_config;
if !app_config.no_workspace_mcp {
let ws_path = paths::workspace_mcp_config_file();
if ws_path.try_exists().unwrap_or(false) {
match tokio::fs::read_to_string(&ws_path).await {
Ok(ws_content) if !ws_content.trim().is_empty() => {
match interpolate_secrets(&ws_content, vault) {
Ok((parsed, missing)) if missing.is_empty() => {
match serde_json::from_str::<McpServersConfig>(&parsed) {
Ok(ws_config) => {
let mut loaded = Vec::new();
for (name, spec) in ws_config.mcp_servers {
match spec.validate(&name) {
Ok(_) => {
loaded.push(name.clone());
merged.mcp_servers.insert(name, spec);
}
Err(e) => warn!(
"Invalid workspace MCP server '{name}': {e}. Skipping."
),
}
}
if !loaded.is_empty() {
eprintln!(
"Loading workspace MCP servers: {}",
loaded.join(", ")
);
}
}
Err(e) => warn!(
"Failed to parse workspace MCP config: {e}. Skipping."
),
}
}
Ok((_, missing)) => warn!(
"Workspace MCP config references missing vault secrets: {missing:?}. Skipping."
),
Err(e) => {
warn!("Failed to process workspace MCP config: {e}. Skipping.")
}
}
}
_ => {}
}
}
}
registry.config = Some(merged);
if start_mcp_servers && app_config.mcp_server_support {
abortable_run_with_spinner(
+502 -20
View File
@@ -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.
@@ -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
if !self.graph.contains_edge(from_idx, to_idx) {
let already_exists = self
.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>> {
@@ -179,30 +257,79 @@ impl KnowledgeGraph {
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();
/// BFS from seed nodes with weight-decayed scoring.
///
/// 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![];
for &raw in &frontier {
let idx = NodeIndex::new(raw as usize);
if self.graph.contains_node(idx) {
if node_scores.len() >= MAX_GRAPH_NODES {
break;
}
frontier.sort_unstable_by(|a, b| {
b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal)
});
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 neighbor in self.graph.neighbors_directed(idx, dir) {
let n = neighbor.index() as u32;
if expanded.insert(n) {
next_frontier.push(n);
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
}
}
@@ -213,6 +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(
@@ -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)));
}
}
+50 -40
View File
@@ -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()
&& !new_doc_contents.is_empty()
if !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}"),
}
}
@@ -930,9 +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, _)| {
@@ -946,52 +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());
if seed_nodes.len() >= top_k {
for &raw in node_raws {
seed_scores.push((raw, BM25_SEED_SCORE));
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 query_tokens: Vec<&str> = query_lower.split_whitespace().collect();
let token_count = query_tokens.len().max(1);
let mut scored: Vec<(u32, f32)> = expanded
let mut scored: Vec<(u32, f32)> = kg
.expand_neighbors_scored(&seed_scores, hops)
.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));
@@ -1349,11 +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,
_ => Validation::Invalid("Must be an integer >= 1".into()),
Ok(_) => Validation::Valid,
_ => Validation::Invalid("Must be a non-negative integer".into()),
};
Ok(out)
})