Add RAG embedding and AI chat feature

Implement end-to-end RAG pipeline: AST-aware code chunking, LiteLLM
embedding generation, MongoDB vector storage with brute-force cosine
similarity fallback for self-hosted instances, and a chat API with
RAG-augmented responses. Add dedicated /chat/:repo_id dashboard page
with embedding build controls, message history, and source reference
cards.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

compliance-agent/src/agent.rs

@@ -20,6 +20,7 @@ impl ComplianceAgent {
             config.litellm_url.clone(),
             config.litellm_api_key.clone(),
             config.litellm_model.clone(),
+            config.litellm_embed_model.clone(),
         ));
         Self {
             config,

compliance-agent/src/api/handlers/chat.rs

@@ -0,0 +1,238 @@
+use std::sync::Arc;
+
+use axum::extract::{Extension, Path};
+use axum::http::StatusCode;
+use axum::Json;
+use mongodb::bson::doc;
+
+use compliance_core::models::chat::{ChatRequest, ChatResponse, SourceReference};
+use compliance_core::models::embedding::EmbeddingBuildRun;
+use compliance_graph::graph::embedding_store::EmbeddingStore;
+
+use crate::agent::ComplianceAgent;
+use crate::rag::pipeline::RagPipeline;
+
+use super::ApiResponse;
+
+type AgentExt = Extension<Arc<ComplianceAgent>>;
+
+/// POST /api/v1/chat/:repo_id — Send a chat message with RAG context
+pub async fn chat(
+    Extension(agent): AgentExt,
+    Path(repo_id): Path<String>,
+    Json(req): Json<ChatRequest>,
+) -> Result<Json<ApiResponse<ChatResponse>>, StatusCode> {
+    let pipeline = RagPipeline::new(agent.llm.clone(), agent.db.inner());
+
+    // Step 1: Embed the user's message
+    let query_vectors = agent
+        .llm
+        .embed(vec![req.message.clone()])
+        .await
+        .map_err(|e| {
+            tracing::error!("Failed to embed query: {e}");
+            StatusCode::INTERNAL_SERVER_ERROR
+        })?;
+
+    let query_embedding = query_vectors.into_iter().next().ok_or_else(|| {
+        tracing::error!("Empty embedding response");
+        StatusCode::INTERNAL_SERVER_ERROR
+    })?;
+
+    // Step 2: Vector search — retrieve top 8 chunks
+    let search_results = pipeline
+        .store()
+        .vector_search(&repo_id, query_embedding, 8, 0.5)
+        .await
+        .map_err(|e| {
+            tracing::error!("Vector search failed: {e}");
+            StatusCode::INTERNAL_SERVER_ERROR
+        })?;
+
+    // Step 3: Build system prompt with code context
+    let mut context_parts = Vec::new();
+    let mut sources = Vec::new();
+
+    for (embedding, score) in &search_results {
+        context_parts.push(format!(
+            "--- {} ({}, {}:L{}-L{}) ---\n{}",
+            embedding.qualified_name,
+            embedding.kind,
+            embedding.file_path,
+            embedding.start_line,
+            embedding.end_line,
+            embedding.content,
+        ));
+
+        // Truncate snippet for the response
+        let snippet: String = embedding
+            .content
+            .lines()
+            .take(10)
+            .collect::<Vec<_>>()
+            .join("\n");
+        sources.push(SourceReference {
+            file_path: embedding.file_path.clone(),
+            qualified_name: embedding.qualified_name.clone(),
+            start_line: embedding.start_line,
+            end_line: embedding.end_line,
+            language: embedding.language.clone(),
+            snippet,
+            score: *score,
+        });
+    }
+
+    let code_context = if context_parts.is_empty() {
+        "No relevant code context found.".to_string()
+    } else {
+        context_parts.join("\n\n")
+    };
+
+    let system_prompt = format!(
+        "You are an expert code assistant for a software repository. \
+         Answer the user's question based on the code context below. \
+         Reference specific files and functions when relevant. \
+         If the context doesn't contain enough information, say so.\n\n\
+         ## Code Context\n\n{code_context}"
+    );
+
+    // Step 4: Build messages array with history
+    let mut messages: Vec<(String, String)> = Vec::new();
+    messages.push(("system".to_string(), system_prompt));
+
+    for msg in &req.history {
+        messages.push((msg.role.clone(), msg.content.clone()));
+    }
+    messages.push(("user".to_string(), req.message));
+
+    // Step 5: Call LLM
+    let response_text = agent
+        .llm
+        .chat_with_messages(messages, Some(0.3))
+        .await
+        .map_err(|e| {
+            tracing::error!("LLM chat failed: {e}");
+            StatusCode::INTERNAL_SERVER_ERROR
+        })?;
+
+    Ok(Json(ApiResponse {
+        data: ChatResponse {
+            message: response_text,
+            sources,
+        },
+        total: None,
+        page: None,
+    }))
+}
+
+/// POST /api/v1/chat/:repo_id/build-embeddings — Trigger embedding build
+pub async fn build_embeddings(
+    Extension(agent): AgentExt,
+    Path(repo_id): Path<String>,
+) -> Result<Json<serde_json::Value>, StatusCode> {
+    let agent_clone = (*agent).clone();
+    tokio::spawn(async move {
+        let repo = match agent_clone
+            .db
+            .repositories()
+            .find_one(doc! { "_id": mongodb::bson::oid::ObjectId::parse_str(&repo_id).ok() })
+            .await
+        {
+            Ok(Some(r)) => r,
+            _ => {
+                tracing::error!("Repository {repo_id} not found for embedding build");
+                return;
+            }
+        };
+
+        // Get latest graph build
+        let build = match agent_clone
+            .db
+            .graph_builds()
+            .find_one(doc! { "repo_id": &repo_id })
+            .sort(doc! { "started_at": -1 })
+            .await
+        {
+            Ok(Some(b)) => b,
+            _ => {
+                tracing::error!("[{repo_id}] No graph build found — build graph first");
+                return;
+            }
+        };
+
+        let graph_build_id = build
+            .id
+            .map(|id| id.to_hex())
+            .unwrap_or_else(|| "unknown".to_string());
+
+        // Get nodes
+        let nodes: Vec<compliance_core::models::graph::CodeNode> = match agent_clone
+            .db
+            .graph_nodes()
+            .find(doc! { "repo_id": &repo_id })
+            .await
+        {
+            Ok(cursor) => {
+                use futures_util::StreamExt;
+                let mut items = Vec::new();
+                let mut cursor = cursor;
+                while let Some(Ok(item)) = cursor.next().await {
+                    items.push(item);
+                }
+                items
+            }
+            Err(e) => {
+                tracing::error!("[{repo_id}] Failed to fetch nodes: {e}");
+                return;
+            }
+        };
+
+        let git_ops = crate::pipeline::git::GitOps::new(&agent_clone.config.git_clone_base_path);
+        let repo_path = match git_ops.clone_or_fetch(&repo.git_url, &repo.name) {
+            Ok(p) => p,
+            Err(e) => {
+                tracing::error!("Failed to clone repo for embedding build: {e}");
+                return;
+            }
+        };
+
+        let pipeline = RagPipeline::new(agent_clone.llm.clone(), agent_clone.db.inner());
+        match pipeline
+            .build_embeddings(&repo_id, &repo_path, &graph_build_id, &nodes)
+            .await
+        {
+            Ok(run) => {
+                tracing::info!(
+                    "[{repo_id}] Embedding build complete: {}/{} chunks",
+                    run.embedded_chunks,
+                    run.total_chunks
+                );
+            }
+            Err(e) => {
+                tracing::error!("[{repo_id}] Embedding build failed: {e}");
+            }
+        }
+    });
+
+    Ok(Json(
+        serde_json::json!({ "status": "embedding_build_triggered" }),
+    ))
+}
+
+/// GET /api/v1/chat/:repo_id/status — Get latest embedding build status
+pub async fn embedding_status(
+    Extension(agent): AgentExt,
+    Path(repo_id): Path<String>,
+) -> Result<Json<ApiResponse<Option<EmbeddingBuildRun>>>, StatusCode> {
+    let store = EmbeddingStore::new(agent.db.inner());
+    let build = store.get_latest_build(&repo_id).await.map_err(|e| {
+        tracing::error!("Failed to get embedding status: {e}");
+        StatusCode::INTERNAL_SERVER_ERROR
+    })?;
+
+    Ok(Json(ApiResponse {
+        data: build,
+        total: None,
+        page: None,
+    }))
+}

compliance-agent/src/api/handlers/mod.rs

@@ -1,3 +1,4 @@
+pub mod chat;
 pub mod dast;
 pub mod graph;
 

compliance-agent/src/api/routes.rs

@@ -23,10 +23,7 @@ pub fn build_router() -> Router {
         .route("/api/v1/issues", get(handlers::list_issues))
         .route("/api/v1/scan-runs", get(handlers::list_scan_runs))
         // Graph API endpoints
-        .route(
+        .route("/api/v1/graph/{repo_id}", get(handlers::graph::get_graph))
-            "/api/v1/graph/{repo_id}",
-            get(handlers::graph::get_graph),
-        )
         .route(
             "/api/v1/graph/{repo_id}/nodes",
             get(handlers::graph::get_nodes),
@@ -52,14 +49,8 @@ pub fn build_router() -> Router {
             post(handlers::graph::trigger_build),
         )
         // DAST API endpoints
-        .route(
+        .route("/api/v1/dast/targets", get(handlers::dast::list_targets))
-            "/api/v1/dast/targets",
+        .route("/api/v1/dast/targets", post(handlers::dast::add_target))
-            get(handlers::dast::list_targets),
-        )
-        .route(
-            "/api/v1/dast/targets",
-            post(handlers::dast::add_target),
-        )
         .route(
             "/api/v1/dast/targets/{id}/scan",
             post(handlers::dast::trigger_scan),
@@ -68,12 +59,19 @@ pub fn build_router() -> Router {
             "/api/v1/dast/scan-runs",
             get(handlers::dast::list_scan_runs),
         )
-        .route(
+        .route("/api/v1/dast/findings", get(handlers::dast::list_findings))
-            "/api/v1/dast/findings",
-            get(handlers::dast::list_findings),
-        )
         .route(
             "/api/v1/dast/findings/{id}",
             get(handlers::dast::get_finding),
         )
+        // Chat / RAG API endpoints
+        .route("/api/v1/chat/{repo_id}", post(handlers::chat::chat))
+        .route(
+            "/api/v1/chat/{repo_id}/build-embeddings",
+            post(handlers::chat::build_embeddings),
+        )
+        .route(
+            "/api/v1/chat/{repo_id}/status",
+            get(handlers::chat::embedding_status),
+        )
 }

compliance-agent/src/config.rs

@@ -24,6 +24,8 @@ pub fn load_config() -> Result<AgentConfig, AgentError> {
             .unwrap_or_else(|| "http://localhost:4000".to_string()),
         litellm_api_key: SecretString::from(env_var_opt("LITELLM_API_KEY").unwrap_or_default()),
         litellm_model: env_var_opt("LITELLM_MODEL").unwrap_or_else(|| "gpt-4o".to_string()),
+        litellm_embed_model: env_var_opt("LITELLM_EMBED_MODEL")
+            .unwrap_or_else(|| "text-embedding-3-small".to_string()),
         github_token: env_secret_opt("GITHUB_TOKEN"),
         github_webhook_secret: env_secret_opt("GITHUB_WEBHOOK_SECRET"),
         gitlab_url: env_var_opt("GITLAB_URL"),

compliance-agent/src/database.rs

@@ -127,11 +127,7 @@ impl Database {
 
         // dast_targets: index on repo_id
         self.dast_targets()
-            .create_index(
+            .create_index(IndexModel::builder().keys(doc! { "repo_id": 1 }).build())
-                IndexModel::builder()
-                    .keys(doc! { "repo_id": 1 })
-                    .build(),
-            )
             .await?;
 
         // dast_scan_runs: compound (target_id, started_at DESC)
@@ -152,6 +148,24 @@ impl Database {
             )
             .await?;
 
+        // code_embeddings: compound (repo_id, graph_build_id)
+        self.code_embeddings()
+            .create_index(
+                IndexModel::builder()
+                    .keys(doc! { "repo_id": 1, "graph_build_id": 1 })
+                    .build(),
+            )
+            .await?;
+
+        // embedding_builds: compound (repo_id, started_at DESC)
+        self.embedding_builds()
+            .create_index(
+                IndexModel::builder()
+                    .keys(doc! { "repo_id": 1, "started_at": -1 })
+                    .build(),
+            )
+            .await?;
+
         tracing::info!("Database indexes ensured");
         Ok(())
     }
@@ -210,6 +224,17 @@ impl Database {
         self.inner.collection("dast_findings")
     }
 
+    // Embedding collections
+    pub fn code_embeddings(&self) -> Collection<compliance_core::models::embedding::CodeEmbedding> {
+        self.inner.collection("code_embeddings")
+    }
+
+    pub fn embedding_builds(
+        &self,
+    ) -> Collection<compliance_core::models::embedding::EmbeddingBuildRun> {
+        self.inner.collection("embedding_builds")
+    }
+
     #[allow(dead_code)]
     pub fn raw_collection(&self, name: &str) -> Collection<mongodb::bson::Document> {
         self.inner.collection(name)

compliance-agent/src/llm/client.rs

@@ -8,6 +8,7 @@ pub struct LlmClient {
     base_url: String,
     api_key: SecretString,
     model: String,
+    embed_model: String,
     http: reqwest::Client,
 }
 
@@ -42,16 +43,46 @@ struct ChatResponseMessage {
     content: String,
 }
 
+/// Request body for the embeddings API
+#[derive(Serialize)]
+struct EmbeddingRequest {
+    model: String,
+    input: Vec<String>,
+}
+
+/// Response from the embeddings API
+#[derive(Deserialize)]
+struct EmbeddingResponse {
+    data: Vec<EmbeddingData>,
+}
+
+/// A single embedding result
+#[derive(Deserialize)]
+struct EmbeddingData {
+    embedding: Vec<f64>,
+    index: usize,
+}
+
 impl LlmClient {
-    pub fn new(base_url: String, api_key: SecretString, model: String) -> Self {
+    pub fn new(
+        base_url: String,
+        api_key: SecretString,
+        model: String,
+        embed_model: String,
+    ) -> Self {
         Self {
             base_url,
             api_key,
             model,
+            embed_model,
             http: reqwest::Client::new(),
         }
     }
 
+    pub fn embed_model(&self) -> &str {
+        &self.embed_model
+    }
+
     pub async fn chat(
         &self,
         system_prompt: &str,
@@ -169,4 +200,49 @@ impl LlmClient {
             .map(|c| c.message.content.clone())
             .ok_or_else(|| AgentError::Other("Empty response from LiteLLM".to_string()))
     }
+
+    /// Generate embeddings for a batch of texts
+    pub async fn embed(&self, texts: Vec<String>) -> Result<Vec<Vec<f64>>, AgentError> {
+        let url = format!("{}/v1/embeddings", self.base_url.trim_end_matches('/'));
+
+        let request_body = EmbeddingRequest {
+            model: self.embed_model.clone(),
+            input: texts,
+        };
+
+        let mut req = self
+            .http
+            .post(&url)
+            .header("content-type", "application/json")
+            .json(&request_body);
+
+        let key = self.api_key.expose_secret();
+        if !key.is_empty() {
+            req = req.header("Authorization", format!("Bearer {key}"));
+        }
+
+        let resp = req
+            .send()
+            .await
+            .map_err(|e| AgentError::Other(format!("Embedding request failed: {e}")))?;
+
+        if !resp.status().is_success() {
+            let status = resp.status();
+            let body = resp.text().await.unwrap_or_default();
+            return Err(AgentError::Other(format!(
+                "Embedding API returned {status}: {body}"
+            )));
+        }
+
+        let body: EmbeddingResponse = resp
+            .json()
+            .await
+            .map_err(|e| AgentError::Other(format!("Failed to parse embedding response: {e}")))?;
+
+        // Sort by index to maintain input order
+        let mut data = body.data;
+        data.sort_by_key(|d| d.index);
+
+        Ok(data.into_iter().map(|d| d.embedding).collect())
+    }
 }

compliance-agent/src/main.rs

@@ -7,6 +7,7 @@ mod database;
 mod error;
 mod llm;
 mod pipeline;
+mod rag;
 mod scheduler;
 #[allow(dead_code)]
 mod trackers;

compliance-agent/src/rag/mod.rs

@@ -0,0 +1 @@
+pub mod pipeline;

compliance-agent/src/rag/pipeline.rs

@@ -0,0 +1,164 @@
+use std::path::Path;
+use std::sync::Arc;
+
+use chrono::Utc;
+use compliance_core::models::embedding::{CodeEmbedding, EmbeddingBuildRun, EmbeddingBuildStatus};
+use compliance_core::models::graph::CodeNode;
+use compliance_graph::graph::chunking::extract_chunks;
+use compliance_graph::graph::embedding_store::EmbeddingStore;
+use tracing::{error, info};
+
+use crate::error::AgentError;
+use crate::llm::LlmClient;
+
+/// RAG pipeline for building embeddings and performing retrieval
+pub struct RagPipeline {
+    llm: Arc<LlmClient>,
+    embedding_store: EmbeddingStore,
+}
+
+impl RagPipeline {
+    pub fn new(llm: Arc<LlmClient>, db: &mongodb::Database) -> Self {
+        Self {
+            llm,
+            embedding_store: EmbeddingStore::new(db),
+        }
+    }
+
+    pub fn store(&self) -> &EmbeddingStore {
+        &self.embedding_store
+    }
+
+    /// Build embeddings for all code nodes in a repository
+    pub async fn build_embeddings(
+        &self,
+        repo_id: &str,
+        repo_path: &Path,
+        graph_build_id: &str,
+        nodes: &[CodeNode],
+    ) -> Result<EmbeddingBuildRun, AgentError> {
+        let embed_model = self.llm.embed_model().to_string();
+        let mut build =
+            EmbeddingBuildRun::new(repo_id.to_string(), graph_build_id.to_string(), embed_model);
+
+        // Step 1: Extract chunks
+        let chunks = extract_chunks(repo_path, nodes, 2048);
+        build.total_chunks = chunks.len() as u32;
+        info!(
+            "[{repo_id}] Extracted {} chunks for embedding",
+            chunks.len()
+        );
+
+        // Store the initial build record
+        self.embedding_store
+            .store_build(&build)
+            .await
+            .map_err(|e| AgentError::Other(format!("Failed to store build: {e}")))?;
+
+        if chunks.is_empty() {
+            build.status = EmbeddingBuildStatus::Completed;
+            build.completed_at = Some(Utc::now());
+            self.embedding_store
+                .update_build(
+                    repo_id,
+                    graph_build_id,
+                    EmbeddingBuildStatus::Completed,
+                    0,
+                    None,
+                )
+                .await
+                .map_err(|e| AgentError::Other(format!("Failed to update build: {e}")))?;
+            return Ok(build);
+        }
+
+        // Step 2: Delete old embeddings for this repo
+        self.embedding_store
+            .delete_repo_embeddings(repo_id)
+            .await
+            .map_err(|e| AgentError::Other(format!("Failed to delete old embeddings: {e}")))?;
+
+        // Step 3: Batch embed (small batches to stay within model limits)
+        let batch_size = 20;
+        let mut all_embeddings = Vec::new();
+        let mut embedded_count = 0u32;
+
+        for batch_start in (0..chunks.len()).step_by(batch_size) {
+            let batch_end = (batch_start + batch_size).min(chunks.len());
+            let batch_chunks = &chunks[batch_start..batch_end];
+
+            // Prepare texts: context_header + content
+            let texts: Vec<String> = batch_chunks
+                .iter()
+                .map(|c| format!("{}\n{}", c.context_header, c.content))
+                .collect();
+
+            match self.llm.embed(texts).await {
+                Ok(vectors) => {
+                    for (chunk, embedding) in batch_chunks.iter().zip(vectors) {
+                        all_embeddings.push(CodeEmbedding {
+                            id: None,
+                            repo_id: repo_id.to_string(),
+                            graph_build_id: graph_build_id.to_string(),
+                            qualified_name: chunk.qualified_name.clone(),
+                            kind: chunk.kind.clone(),
+                            file_path: chunk.file_path.clone(),
+                            start_line: chunk.start_line,
+                            end_line: chunk.end_line,
+                            language: chunk.language.clone(),
+                            content: chunk.content.clone(),
+                            context_header: chunk.context_header.clone(),
+                            embedding,
+                            token_estimate: chunk.token_estimate,
+                            created_at: Utc::now(),
+                        });
+                    }
+                    embedded_count += batch_chunks.len() as u32;
+                }
+                Err(e) => {
+                    error!("[{repo_id}] Embedding batch failed: {e}");
+                    build.status = EmbeddingBuildStatus::Failed;
+                    build.error_message = Some(e.to_string());
+                    build.completed_at = Some(Utc::now());
+                    let _ = self
+                        .embedding_store
+                        .update_build(
+                            repo_id,
+                            graph_build_id,
+                            EmbeddingBuildStatus::Failed,
+                            embedded_count,
+                            Some(e.to_string()),
+                        )
+                        .await;
+                    return Ok(build);
+                }
+            }
+        }
+
+        // Step 4: Store all embeddings
+        self.embedding_store
+            .store_embeddings(&all_embeddings)
+            .await
+            .map_err(|e| AgentError::Other(format!("Failed to store embeddings: {e}")))?;
+
+        // Step 5: Update build status
+        build.status = EmbeddingBuildStatus::Completed;
+        build.embedded_chunks = embedded_count;
+        build.completed_at = Some(Utc::now());
+        self.embedding_store
+            .update_build(
+                repo_id,
+                graph_build_id,
+                EmbeddingBuildStatus::Completed,
+                embedded_count,
+                None,
+            )
+            .await
+            .map_err(|e| AgentError::Other(format!("Failed to update build: {e}")))?;
+
+        info!(
+            "[{repo_id}] Embedding build complete: {embedded_count}/{} chunks",
+            build.total_chunks
+        );
+        Ok(build)
+    }
+}

compliance-core/src/config.rs

@@ -8,6 +8,7 @@ pub struct AgentConfig {
     pub litellm_url: String,
     pub litellm_api_key: SecretString,
     pub litellm_model: String,
+    pub litellm_embed_model: String,
     pub github_token: Option<SecretString>,
     pub github_webhook_secret: Option<SecretString>,
     pub gitlab_url: Option<String>,

compliance-core/src/models/chat.rs

@@ -0,0 +1,35 @@
+use serde::{Deserialize, Serialize};
+
+/// A message in the chat history
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ChatMessage {
+    pub role: String,
+    pub content: String,
+}
+
+/// Request body for the chat endpoint
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ChatRequest {
+    pub message: String,
+    #[serde(default)]
+    pub history: Vec<ChatMessage>,
+}
+
+/// A source reference from the RAG retrieval
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct SourceReference {
+    pub file_path: String,
+    pub qualified_name: String,
+    pub start_line: u32,
+    pub end_line: u32,
+    pub language: String,
+    pub snippet: String,
+    pub score: f64,
+}
+
+/// Response from the chat endpoint
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ChatResponse {
+    pub message: String,
+    pub sources: Vec<SourceReference>,
+}

compliance-core/src/models/embedding.rs

@@ -0,0 +1,100 @@
+use chrono::{DateTime, Utc};
+use serde::{Deserialize, Serialize};
+
+/// Status of an embedding build operation
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
+#[serde(rename_all = "snake_case")]
+pub enum EmbeddingBuildStatus {
+    Running,
+    Completed,
+    Failed,
+}
+
+/// A code embedding stored in MongoDB Atlas Vector Search
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct CodeEmbedding {
+    #[serde(rename = "_id", skip_serializing_if = "Option::is_none")]
+    pub id: Option<bson::oid::ObjectId>,
+    pub repo_id: String,
+    pub graph_build_id: String,
+    pub qualified_name: String,
+    pub kind: String,
+    pub file_path: String,
+    pub start_line: u32,
+    pub end_line: u32,
+    pub language: String,
+    pub content: String,
+    pub context_header: String,
+    pub embedding: Vec<f64>,
+    pub token_estimate: u32,
+    #[serde(with = "bson::serde_helpers::chrono_datetime_as_bson_datetime")]
+    pub created_at: DateTime<Utc>,
+}
+
+/// Tracks an embedding build operation for a repository
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct EmbeddingBuildRun {
+    #[serde(rename = "_id", skip_serializing_if = "Option::is_none")]
+    pub id: Option<bson::oid::ObjectId>,
+    pub repo_id: String,
+    pub graph_build_id: String,
+    pub status: EmbeddingBuildStatus,
+    pub total_chunks: u32,
+    pub embedded_chunks: u32,
+    pub embedding_model: String,
+    pub error_message: Option<String>,
+    #[serde(with = "bson::serde_helpers::chrono_datetime_as_bson_datetime")]
+    pub started_at: DateTime<Utc>,
+    #[serde(
+        default,
+        skip_serializing_if = "Option::is_none",
+        with = "opt_chrono_as_bson"
+    )]
+    pub completed_at: Option<DateTime<Utc>>,
+}
+
+impl EmbeddingBuildRun {
+    pub fn new(repo_id: String, graph_build_id: String, embedding_model: String) -> Self {
+        Self {
+            id: None,
+            repo_id,
+            graph_build_id,
+            status: EmbeddingBuildStatus::Running,
+            total_chunks: 0,
+            embedded_chunks: 0,
+            embedding_model,
+            error_message: None,
+            started_at: Utc::now(),
+            completed_at: None,
+        }
+    }
+}
+
+/// Serde helper for Option<DateTime<Utc>> as BSON DateTime
+mod opt_chrono_as_bson {
+    use chrono::{DateTime, Utc};
+    use serde::{Deserialize, Deserializer, Serialize, Serializer};
+
+    #[derive(Serialize, Deserialize)]
+    struct BsonDt(
+        #[serde(with = "bson::serde_helpers::chrono_datetime_as_bson_datetime")] DateTime<Utc>,
+    );
+
+    pub fn serialize<S>(value: &Option<DateTime<Utc>>, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        match value {
+            Some(dt) => BsonDt(*dt).serialize(serializer),
+            None => serializer.serialize_none(),
+        }
+    }
+
+    pub fn deserialize<'de, D>(deserializer: D) -> Result<Option<DateTime<Utc>>, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        let opt: Option<BsonDt> = Option::deserialize(deserializer)?;
+        Ok(opt.map(|d| d.0))
+    }
+}

compliance-core/src/models/mod.rs

@@ -1,5 +1,7 @@
+pub mod chat;
 pub mod cve;
 pub mod dast;
+pub mod embedding;
 pub mod finding;
 pub mod graph;
 pub mod issue;
@@ -7,15 +9,16 @@ pub mod repository;
 pub mod sbom;
 pub mod scan;
 
+pub use chat::{ChatMessage, ChatRequest, ChatResponse, SourceReference};
 pub use cve::{CveAlert, CveSource};
 pub use dast::{
     DastAuthConfig, DastEvidence, DastFinding, DastScanPhase, DastScanRun, DastScanStatus,
     DastTarget, DastTargetType, DastVulnType,
 };
+pub use embedding::{CodeEmbedding, EmbeddingBuildRun, EmbeddingBuildStatus};
 pub use finding::{Finding, FindingStatus, Severity};
 pub use graph::{
-    CodeEdge, CodeEdgeKind, CodeNode, CodeNodeKind, GraphBuildRun, GraphBuildStatus,
+    CodeEdge, CodeEdgeKind, CodeNode, CodeNodeKind, GraphBuildRun, GraphBuildStatus, ImpactAnalysis,
-    ImpactAnalysis,
 };
 pub use issue::{IssueStatus, TrackerIssue, TrackerType};
 pub use repository::{ScanTrigger, TrackedRepository};

compliance-dashboard/assets/main.css

@@ -1710,3 +1710,240 @@ tbody tr:last-child td {
     white-space: nowrap;
     margin-left: auto;
 }
+
+/* ── AI Chat ── */
+
+.chat-embedding-banner {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    padding: 12px 20px;
+    background: var(--bg-card);
+    border: 1px solid var(--border);
+    border-radius: var(--radius);
+    margin-bottom: 16px;
+    font-size: 13px;
+    color: var(--text-secondary);
+}
+
+.chat-embedding-banner .btn-sm {
+    padding: 6px 14px;
+    font-size: 12px;
+    background: var(--accent-muted);
+    color: var(--accent);
+    border: 1px solid var(--border-accent);
+    border-radius: var(--radius-sm);
+    cursor: pointer;
+    transition: all 0.2s var(--ease-out);
+}
+
+.chat-embedding-banner .btn-sm:hover:not(:disabled) {
+    background: var(--accent);
+    color: var(--bg-primary);
+}
+
+.chat-embedding-banner .btn-sm:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+.chat-container {
+    display: flex;
+    flex-direction: column;
+    height: calc(100vh - 240px);
+    background: var(--bg-card);
+    border: 1px solid var(--border);
+    border-radius: var(--radius);
+    overflow: hidden;
+}
+
+.chat-messages {
+    flex: 1;
+    overflow-y: auto;
+    padding: 20px;
+    display: flex;
+    flex-direction: column;
+    gap: 16px;
+}
+
+.chat-empty {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+    height: 100%;
+    color: var(--text-tertiary);
+    text-align: center;
+}
+
+.chat-empty h3 {
+    font-family: var(--font-display);
+    font-size: 18px;
+    font-weight: 600;
+    color: var(--text-secondary);
+    margin-bottom: 8px;
+}
+
+.chat-empty p {
+    font-size: 13px;
+    max-width: 400px;
+}
+
+.chat-message {
+    max-width: 80%;
+    padding: 12px 16px;
+    border-radius: var(--radius);
+    font-size: 14px;
+    line-height: 1.6;
+}
+
+.chat-message-user {
+    align-self: flex-end;
+    background: var(--accent-muted);
+    border: 1px solid var(--border-accent);
+    color: var(--text-primary);
+}
+
+.chat-message-assistant {
+    align-self: flex-start;
+    background: var(--bg-elevated);
+    border: 1px solid var(--border);
+    color: var(--text-primary);
+}
+
+.chat-message-role {
+    font-family: var(--font-display);
+    font-size: 11px;
+    font-weight: 600;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+    color: var(--text-tertiary);
+    margin-bottom: 6px;
+}
+
+.chat-message-content {
+    white-space: pre-wrap;
+    word-break: break-word;
+}
+
+.chat-typing {
+    color: var(--text-tertiary);
+    font-style: italic;
+}
+
+.chat-sources {
+    margin-top: 12px;
+    border-top: 1px solid var(--border);
+    padding-top: 10px;
+}
+
+.chat-sources-label {
+    font-size: 11px;
+    font-weight: 600;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+    color: var(--text-tertiary);
+    display: block;
+    margin-bottom: 8px;
+}
+
+.chat-source-card {
+    background: var(--bg-secondary);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-sm);
+    padding: 10px 12px;
+    margin-bottom: 6px;
+}
+
+.chat-source-header {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    margin-bottom: 6px;
+}
+
+.chat-source-name {
+    font-family: var(--font-mono);
+    font-size: 12px;
+    font-weight: 500;
+    color: var(--accent);
+}
+
+.chat-source-location {
+    font-family: var(--font-mono);
+    font-size: 11px;
+    color: var(--text-tertiary);
+}
+
+.chat-source-snippet {
+    margin: 0;
+    padding: 8px;
+    background: var(--bg-primary);
+    border-radius: 4px;
+    overflow-x: auto;
+    max-height: 120px;
+}
+
+.chat-source-snippet code {
+    font-family: var(--font-mono);
+    font-size: 11px;
+    color: var(--text-secondary);
+    white-space: pre;
+}
+
+.chat-input-area {
+    display: flex;
+    gap: 10px;
+    padding: 16px 20px;
+    border-top: 1px solid var(--border);
+    background: var(--bg-secondary);
+}
+
+.chat-input {
+    flex: 1;
+    background: var(--bg-primary);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-sm);
+    color: var(--text-primary);
+    font-family: var(--font-body);
+    font-size: 14px;
+    padding: 10px 14px;
+    resize: none;
+    min-height: 42px;
+    max-height: 120px;
+    outline: none;
+    transition: border-color 0.2s var(--ease-out);
+}
+
+.chat-input:focus {
+    border-color: var(--accent);
+}
+
+.chat-input:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+.chat-send-btn {
+    padding: 10px 20px;
+    background: var(--accent);
+    color: var(--bg-primary);
+    border: none;
+    border-radius: var(--radius-sm);
+    font-family: var(--font-display);
+    font-weight: 600;
+    font-size: 13px;
+    cursor: pointer;
+    transition: all 0.2s var(--ease-out);
+    align-self: flex-end;
+}
+
+.chat-send-btn:hover:not(:disabled) {
+    background: var(--accent-hover);
+    box-shadow: var(--accent-glow);
+}
+
+.chat-send-btn:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}

compliance-dashboard/src/app.rs

@@ -26,6 +26,10 @@ pub enum Route {
         GraphExplorerPage { repo_id: String },
         #[route("/graph/:repo_id/impact/:finding_id")]
         ImpactAnalysisPage { repo_id: String, finding_id: String },
+        #[route("/chat")]
+        ChatIndexPage {},
+        #[route("/chat/:repo_id")]
+        ChatPage { repo_id: String },
         #[route("/dast")]
         DastOverviewPage {},
         #[route("/dast/targets")]

compliance-dashboard/src/components/sidebar.rs

@@ -46,6 +46,11 @@ pub fn Sidebar() -> Element {
             route: Route::GraphIndexPage {},
             icon: rsx! { Icon { icon: BsDiagram3, width: 18, height: 18 } },
         },
+        NavItem {
+            label: "AI Chat",
+            route: Route::ChatIndexPage {},
+            icon: rsx! { Icon { icon: BsChatDots, width: 18, height: 18 } },
+        },
         NavItem {
             label: "DAST",
             route: Route::DastOverviewPage {},
@@ -58,7 +63,11 @@ pub fn Sidebar() -> Element {
         },
     ];
 
-    let sidebar_class = if collapsed() { "sidebar collapsed" } else { "sidebar" };
+    let sidebar_class = if collapsed() {
+        "sidebar collapsed"
+    } else {
+        "sidebar"
+    };
 
     rsx! {
         nav { class: "{sidebar_class}",
@@ -76,6 +85,7 @@ pub fn Sidebar() -> Element {
                             (Route::GraphIndexPage {}, Route::GraphIndexPage {}) => true,
                             (Route::GraphExplorerPage { .. }, Route::GraphIndexPage {}) => true,
                             (Route::ImpactAnalysisPage { .. }, Route::GraphIndexPage {}) => true,
+                            (Route::ChatPage { .. }, Route::ChatIndexPage {}) => true,
                             (Route::DastTargetsPage {}, Route::DastOverviewPage {}) => true,
                             (Route::DastFindingsPage {}, Route::DastOverviewPage {}) => true,
                             (Route::DastFindingDetailPage { .. }, Route::DastOverviewPage {}) => true,

compliance-dashboard/src/infrastructure/chat.rs

@@ -0,0 +1,126 @@
+use dioxus::prelude::*;
+use serde::{Deserialize, Serialize};
+
+// ── Response types ──
+
+#[derive(Debug, Clone, Serialize, Deserialize, Default)]
+pub struct ChatApiResponse {
+    pub data: ChatResponseData,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, Default)]
+pub struct ChatResponseData {
+    pub message: String,
+    #[serde(default)]
+    pub sources: Vec<SourceRef>,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, Default)]
+pub struct SourceRef {
+    pub file_path: String,
+    pub qualified_name: String,
+    pub start_line: u32,
+    pub end_line: u32,
+    pub language: String,
+    pub snippet: String,
+    pub score: f64,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, Default)]
+pub struct EmbeddingStatusResponse {
+    pub data: Option<EmbeddingBuildData>,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, Default)]
+pub struct EmbeddingBuildData {
+    pub repo_id: String,
+    pub status: String,
+    pub total_chunks: u32,
+    pub embedded_chunks: u32,
+    pub embedding_model: String,
+    pub error_message: Option<String>,
+    #[serde(default)]
+    pub started_at: Option<serde_json::Value>,
+    #[serde(default)]
+    pub completed_at: Option<serde_json::Value>,
+}
+
+// ── Chat message history type ──
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ChatHistoryMessage {
+    pub role: String,
+    pub content: String,
+}
+
+// ── Server functions ──
+
+#[server]
+pub async fn send_chat_message(
+    repo_id: String,
+    message: String,
+    history: Vec<ChatHistoryMessage>,
+) -> Result<ChatApiResponse, ServerFnError> {
+    let state: super::server_state::ServerState =
+        dioxus_fullstack::FullstackContext::extract().await?;
+
+    let url = format!("{}/api/v1/chat/{repo_id}", state.agent_api_url);
+    let client = reqwest::Client::builder()
+        .timeout(std::time::Duration::from_secs(120))
+        .build()
+        .map_err(|e| ServerFnError::new(e.to_string()))?;
+    let resp = client
+        .post(&url)
+        .json(&serde_json::json!({
+            "message": message,
+            "history": history,
+        }))
+        .send()
+        .await
+        .map_err(|e| ServerFnError::new(format!("Request failed: {e}")))?;
+
+    let text = resp
+        .text()
+        .await
+        .map_err(|e| ServerFnError::new(format!("Failed to read response: {e}")))?;
+
+    let body: ChatApiResponse = serde_json::from_str(&text)
+        .map_err(|e| ServerFnError::new(format!("Failed to parse response: {e} — body: {text}")))?;
+    Ok(body)
+}
+
+#[server]
+pub async fn trigger_embedding_build(repo_id: String) -> Result<(), ServerFnError> {
+    let state: super::server_state::ServerState =
+        dioxus_fullstack::FullstackContext::extract().await?;
+
+    let url = format!(
+        "{}/api/v1/chat/{repo_id}/build-embeddings",
+        state.agent_api_url
+    );
+    let client = reqwest::Client::new();
+    client
+        .post(&url)
+        .send()
+        .await
+        .map_err(|e| ServerFnError::new(e.to_string()))?;
+    Ok(())
+}
+
+#[server]
+pub async fn fetch_embedding_status(
+    repo_id: String,
+) -> Result<EmbeddingStatusResponse, ServerFnError> {
+    let state: super::server_state::ServerState =
+        dioxus_fullstack::FullstackContext::extract().await?;
+
+    let url = format!("{}/api/v1/chat/{repo_id}/status", state.agent_api_url);
+    let resp = reqwest::get(&url)
+        .await
+        .map_err(|e| ServerFnError::new(e.to_string()))?;
+    let body: EmbeddingStatusResponse = resp
+        .json()
+        .await
+        .map_err(|e| ServerFnError::new(e.to_string()))?;
+    Ok(body)
+}

compliance-dashboard/src/infrastructure/mod.rs

@@ -1,5 +1,6 @@
 // Server function modules (compiled for both web and server;
 // the #[server] macro generates client stubs for the web target)
+pub mod chat;
 pub mod dast;
 pub mod findings;
 pub mod graph;

compliance-dashboard/src/pages/chat.rs

@@ -0,0 +1,232 @@
+use dioxus::prelude::*;
+
+use crate::components::page_header::PageHeader;
+use crate::infrastructure::chat::{
+    fetch_embedding_status, send_chat_message, trigger_embedding_build, ChatHistoryMessage,
+    SourceRef,
+};
+
+/// A UI-level chat message
+#[derive(Clone, Debug)]
+struct UiChatMessage {
+    role: String,
+    content: String,
+    sources: Vec<SourceRef>,
+}
+
+#[component]
+pub fn ChatPage(repo_id: String) -> Element {
+    let mut messages: Signal<Vec<UiChatMessage>> = use_signal(Vec::new);
+    let mut input_text = use_signal(String::new);
+    let mut loading = use_signal(|| false);
+    let mut building = use_signal(|| false);
+
+    let repo_id_for_status = repo_id.clone();
+    let mut embedding_status = use_resource(move || {
+        let rid = repo_id_for_status.clone();
+        async move { fetch_embedding_status(rid).await.ok() }
+    });
+
+    let has_embeddings = {
+        let status = embedding_status.read();
+        match &*status {
+            Some(Some(resp)) => resp
+                .data
+                .as_ref()
+                .map(|d| d.status == "completed")
+                .unwrap_or(false),
+            _ => false,
+        }
+    };
+
+    let embedding_status_text = {
+        let status = embedding_status.read();
+        match &*status {
+            Some(Some(resp)) => match &resp.data {
+                Some(d) => match d.status.as_str() {
+                    "completed" => format!(
+                        "Embeddings ready: {}/{} chunks",
+                        d.embedded_chunks, d.total_chunks
+                    ),
+                    "running" => format!(
+                        "Building embeddings: {}/{}...",
+                        d.embedded_chunks, d.total_chunks
+                    ),
+                    "failed" => format!(
+                        "Embedding build failed: {}",
+                        d.error_message.as_deref().unwrap_or("unknown error")
+                    ),
+                    s => format!("Status: {s}"),
+                },
+                None => "No embeddings built yet".to_string(),
+            },
+            Some(None) => "Failed to check embedding status".to_string(),
+            None => "Checking embedding status...".to_string(),
+        }
+    };
+
+    let repo_id_for_build = repo_id.clone();
+    let on_build = move |_| {
+        let rid = repo_id_for_build.clone();
+        building.set(true);
+        spawn(async move {
+            let _ = trigger_embedding_build(rid).await;
+            building.set(false);
+            embedding_status.restart();
+        });
+    };
+
+    let repo_id_for_send = repo_id.clone();
+    let mut do_send = move || {
+        let text = input_text.read().trim().to_string();
+        if text.is_empty() || *loading.read() {
+            return;
+        }
+
+        let rid = repo_id_for_send.clone();
+        let user_msg = text.clone();
+
+        // Add user message to UI
+        messages.write().push(UiChatMessage {
+            role: "user".to_string(),
+            content: user_msg.clone(),
+            sources: Vec::new(),
+        });
+        input_text.set(String::new());
+        loading.set(true);
+
+        spawn(async move {
+            // Build history from existing messages
+            let history: Vec<ChatHistoryMessage> = messages
+                .read()
+                .iter()
+                .filter(|m| m.role == "user" || m.role == "assistant")
+                .rev()
+                .skip(1) // skip the message we just added
+                .take(10) // limit history
+                .collect::<Vec<_>>()
+                .into_iter()
+                .rev()
+                .map(|m| ChatHistoryMessage {
+                    role: m.role.clone(),
+                    content: m.content.clone(),
+                })
+                .collect();
+
+            match send_chat_message(rid, user_msg, history).await {
+                Ok(resp) => {
+                    messages.write().push(UiChatMessage {
+                        role: "assistant".to_string(),
+                        content: resp.data.message,
+                        sources: resp.data.sources,
+                    });
+                }
+                Err(e) => {
+                    messages.write().push(UiChatMessage {
+                        role: "assistant".to_string(),
+                        content: format!("Error: {e}"),
+                        sources: Vec::new(),
+                    });
+                }
+            }
+            loading.set(false);
+        });
+    };
+
+    let mut do_send_click = do_send.clone();
+
+    rsx! {
+        PageHeader { title: "AI Chat" }
+
+        // Embedding status banner
+        div { class: "chat-embedding-banner",
+            span { "{embedding_status_text}" }
+            button {
+                class: "btn btn-sm",
+                disabled: *building.read(),
+                onclick: on_build,
+                if *building.read() { "Building..." } else { "Build Embeddings" }
+            }
+        }
+
+        div { class: "chat-container",
+            // Message list
+            div { class: "chat-messages",
+                if messages.read().is_empty() && !*loading.read() {
+                    div { class: "chat-empty",
+                        h3 { "Ask anything about your codebase" }
+                        p { "Build embeddings first, then ask questions about functions, architecture, patterns, and more." }
+                    }
+                }
+                for (i, msg) in messages.read().iter().enumerate() {
+                    {
+                        let class = if msg.role == "user" {
+                            "chat-message chat-message-user"
+                        } else {
+                            "chat-message chat-message-assistant"
+                        };
+                        let content = msg.content.clone();
+                        let sources = msg.sources.clone();
+                        rsx! {
+                            div { class: class, key: "{i}",
+                                div { class: "chat-message-role",
+                                    if msg.role == "user" { "You" } else { "Assistant" }
+                                }
+                                div { class: "chat-message-content", "{content}" }
+                                if !sources.is_empty() {
+                                    div { class: "chat-sources",
+                                        span { class: "chat-sources-label", "Sources:" }
+                                        for src in sources {
+                                            div { class: "chat-source-card",
+                                                div { class: "chat-source-header",
+                                                    span { class: "chat-source-name",
+                                                        "{src.qualified_name}"
+                                                    }
+                                                    span { class: "chat-source-location",
+                                                        "{src.file_path}:{src.start_line}-{src.end_line}"
+                                                    }
+                                                }
+                                                pre { class: "chat-source-snippet",
+                                                    code { "{src.snippet}" }
+                                                }
+                                            }
+                                        }
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+                if *loading.read() {
+                    div { class: "chat-message chat-message-assistant",
+                        div { class: "chat-message-role", "Assistant" }
+                        div { class: "chat-message-content chat-typing", "Thinking..." }
+                    }
+                }
+            }
+
+            // Input area
+            div { class: "chat-input-area",
+                textarea {
+                    class: "chat-input",
+                    placeholder: "Ask about your codebase...",
+                    value: "{input_text}",
+                    disabled: !has_embeddings,
+                    oninput: move |e| input_text.set(e.value()),
+                    onkeydown: move |e: Event<KeyboardData>| {
+                        if e.key() == Key::Enter && !e.modifiers().shift() {
+                            e.prevent_default();
+                            do_send();
+                        }
+                    },
+                }
+                button {
+                    class: "btn chat-send-btn",
+                    disabled: *loading.read() || !has_embeddings,
+                    onclick: move |_| do_send_click(),
+                    "Send"
+                }
+            }
+        }
+    }
+}

compliance-dashboard/src/pages/chat_index.rs

@@ -0,0 +1,71 @@
+use dioxus::prelude::*;
+
+use crate::app::Route;
+use crate::components::page_header::PageHeader;
+use crate::infrastructure::chat::fetch_embedding_status;
+use crate::infrastructure::repositories::fetch_repositories;
+
+#[component]
+pub fn ChatIndexPage() -> Element {
+    let repos = use_resource(|| async { fetch_repositories(1).await.ok() });
+
+    rsx! {
+        PageHeader {
+            title: "AI Chat",
+            description: "Ask questions about your codebase using RAG-augmented AI",
+        }
+
+        match &*repos.read() {
+            Some(Some(data)) => {
+                let repo_list = &data.data;
+                if repo_list.is_empty() {
+                    rsx! {
+                        div { class: "card",
+                            p { "No repositories found. Add a repository first." }
+                        }
+                    }
+                } else {
+                    rsx! {
+                        div { class: "graph-index-grid",
+                            for repo in repo_list {
+                                {
+                                    let repo_id = repo.id.map(|id| id.to_hex()).unwrap_or_default();
+                                    let name = repo.name.clone();
+                                    let url = repo.git_url.clone();
+                                    let branch = repo.default_branch.clone();
+                                    rsx! {
+                                        Link {
+                                            to: Route::ChatPage { repo_id },
+                                            class: "graph-repo-card",
+                                            div { class: "graph-repo-card-header",
+                                                div { class: "graph-repo-card-icon", "\u{1F4AC}" }
+                                                h3 { class: "graph-repo-card-name", "{name}" }
+                                            }
+                                            if !url.is_empty() {
+                                                p { class: "graph-repo-card-url", "{url}" }
+                                            }
+                                            div { class: "graph-repo-card-meta",
+                                                span { class: "graph-repo-card-tag",
+                                                    "\u{E0A0} {branch}"
+                                                }
+                                                span { class: "graph-repo-card-tag",
+                                                    "AI Chat"
+                                                }
+                                            }
+                                        }
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            },
+            Some(None) => rsx! {
+                div { class: "card", p { "Failed to load repositories." } }
+            },
+            None => rsx! {
+                div { class: "loading", "Loading repositories..." }
+            },
+        }
+    }
+}

compliance-dashboard/src/pages/mod.rs

@@ -1,3 +1,5 @@
+pub mod chat;
+pub mod chat_index;
 pub mod dast_finding_detail;
 pub mod dast_findings;
 pub mod dast_overview;
@@ -13,6 +15,8 @@ pub mod repositories;
 pub mod sbom;
 pub mod settings;
 
+pub use chat::ChatPage;
+pub use chat_index::ChatIndexPage;
 pub use dast_finding_detail::DastFindingDetailPage;
 pub use dast_findings::DastFindingsPage;
 pub use dast_overview::DastOverviewPage;

compliance-graph/src/graph/chunking.rs

@@ -0,0 +1,96 @@
+use std::path::Path;
+
+use compliance_core::models::graph::CodeNode;
+
+/// A chunk of code extracted from a source file, ready for embedding
+#[derive(Debug, Clone)]
+pub struct CodeChunk {
+    pub qualified_name: String,
+    pub kind: String,
+    pub file_path: String,
+    pub start_line: u32,
+    pub end_line: u32,
+    pub language: String,
+    pub content: String,
+    pub context_header: String,
+    pub token_estimate: u32,
+}
+
+/// Extract embeddable code chunks from parsed CodeNodes.
+///
+/// For each node, reads the corresponding source lines from disk,
+/// builds a context header, and estimates tokens.
+pub fn extract_chunks(
+    repo_path: &Path,
+    nodes: &[CodeNode],
+    max_chunk_tokens: u32,
+) -> Vec<CodeChunk> {
+    let mut chunks = Vec::new();
+
+    for node in nodes {
+        let file = repo_path.join(&node.file_path);
+        let source = match std::fs::read_to_string(&file) {
+            Ok(s) => s,
+            Err(_) => continue,
+        };
+
+        let lines: Vec<&str> = source.lines().collect();
+        let start = node.start_line.saturating_sub(1) as usize;
+        let end = (node.end_line as usize).min(lines.len());
+        if start >= end {
+            continue;
+        }
+
+        let content: String = lines[start..end].join("\n");
+
+        // Skip tiny chunks
+        if content.len() < 50 {
+            continue;
+        }
+
+        // Estimate tokens (~4 chars per token)
+        let mut token_estimate = (content.len() / 4) as u32;
+
+        // Truncate if too large
+        let final_content = if token_estimate > max_chunk_tokens {
+            let max_chars = (max_chunk_tokens as usize) * 4;
+            token_estimate = max_chunk_tokens;
+            content.chars().take(max_chars).collect()
+        } else {
+            content
+        };
+
+        // Build context header: file path + containing scope hint
+        let context_header = build_context_header(
+            &node.file_path,
+            &node.qualified_name,
+            &node.kind.to_string(),
+        );
+
+        chunks.push(CodeChunk {
+            qualified_name: node.qualified_name.clone(),
+            kind: node.kind.to_string(),
+            file_path: node.file_path.clone(),
+            start_line: node.start_line,
+            end_line: node.end_line,
+            language: node.language.clone(),
+            content: final_content,
+            context_header,
+            token_estimate,
+        });
+    }
+
+    chunks
+}
+
+fn build_context_header(file_path: &str, qualified_name: &str, kind: &str) -> String {
+    // Extract containing module/class from qualified name
+    // e.g. "src/main.rs::MyStruct::my_method" → parent is "MyStruct"
+    let parts: Vec<&str> = qualified_name.split("::").collect();
+    if parts.len() >= 2 {
+        let parent = parts[..parts.len() - 1].join("::");
+        format!("// {file_path} | {kind} in {parent}")
+    } else {
+        format!("// {file_path} | {kind}")
+    }
+}

compliance-graph/src/graph/embedding_store.rs

@@ -0,0 +1,238 @@
+use compliance_core::error::CoreError;
+use compliance_core::models::embedding::{CodeEmbedding, EmbeddingBuildRun, EmbeddingBuildStatus};
+use futures_util::TryStreamExt;
+use mongodb::bson::doc;
+use mongodb::{Collection, Database, IndexModel};
+use tracing::info;
+
+/// MongoDB persistence layer for code embeddings and vector search
+pub struct EmbeddingStore {
+    embeddings: Collection<CodeEmbedding>,
+    builds: Collection<EmbeddingBuildRun>,
+}
+
+impl EmbeddingStore {
+    pub fn new(db: &Database) -> Self {
+        Self {
+            embeddings: db.collection("code_embeddings"),
+            builds: db.collection("embedding_builds"),
+        }
+    }
+
+    /// Create standard indexes. NOTE: The Atlas Vector Search index must be
+    /// created via the Atlas UI or CLI with the following definition:
+    /// ```json
+    /// {
+    ///   "fields": [
+    ///     { "type": "vector", "path": "embedding", "numDimensions": 1536, "similarity": "cosine" },
+    ///     { "type": "filter", "path": "repo_id" }
+    ///   ]
+    /// }
+    /// ```
+    pub async fn ensure_indexes(&self) -> Result<(), CoreError> {
+        self.embeddings
+            .create_index(
+                IndexModel::builder()
+                    .keys(doc! { "repo_id": 1, "graph_build_id": 1 })
+                    .build(),
+            )
+            .await?;
+
+        self.builds
+            .create_index(
+                IndexModel::builder()
+                    .keys(doc! { "repo_id": 1, "started_at": -1 })
+                    .build(),
+            )
+            .await?;
+
+        Ok(())
+    }
+
+    /// Delete all embeddings for a repository
+    pub async fn delete_repo_embeddings(&self, repo_id: &str) -> Result<u64, CoreError> {
+        let result = self
+            .embeddings
+            .delete_many(doc! { "repo_id": repo_id })
+            .await?;
+        info!(
+            "Deleted {} embeddings for repo {repo_id}",
+            result.deleted_count
+        );
+        Ok(result.deleted_count)
+    }
+
+    /// Store embeddings in batches of 500
+    pub async fn store_embeddings(&self, embeddings: &[CodeEmbedding]) -> Result<u64, CoreError> {
+        let mut total_inserted = 0u64;
+        for batch in embeddings.chunks(500) {
+            let result = self.embeddings.insert_many(batch).await?;
+            total_inserted += result.inserted_ids.len() as u64;
+        }
+        info!("Stored {total_inserted} embeddings");
+        Ok(total_inserted)
+    }
+
+    /// Store a new build run
+    pub async fn store_build(&self, build: &EmbeddingBuildRun) -> Result<(), CoreError> {
+        self.builds.insert_one(build).await?;
+        Ok(())
+    }
+
+    /// Update an existing build run
+    pub async fn update_build(
+        &self,
+        repo_id: &str,
+        graph_build_id: &str,
+        status: EmbeddingBuildStatus,
+        embedded_chunks: u32,
+        error_message: Option<String>,
+    ) -> Result<(), CoreError> {
+        let mut update = doc! {
+            "$set": {
+                "status": mongodb::bson::to_bson(&status).unwrap_or_default(),
+                "embedded_chunks": embedded_chunks as i64,
+            }
+        };
+
+        if status == EmbeddingBuildStatus::Completed || status == EmbeddingBuildStatus::Failed {
+            update
+                .get_document_mut("$set")
+                .unwrap()
+                .insert("completed_at", mongodb::bson::DateTime::now());
+        }
+
+        if let Some(msg) = error_message {
+            update
+                .get_document_mut("$set")
+                .unwrap()
+                .insert("error_message", msg);
+        }
+
+        self.builds
+            .update_one(
+                doc! { "repo_id": repo_id, "graph_build_id": graph_build_id },
+                update,
+            )
+            .await?;
+        Ok(())
+    }
+
+    /// Get the latest embedding build for a repository
+    pub async fn get_latest_build(
+        &self,
+        repo_id: &str,
+    ) -> Result<Option<EmbeddingBuildRun>, CoreError> {
+        Ok(self
+            .builds
+            .find_one(doc! { "repo_id": repo_id })
+            .sort(doc! { "started_at": -1 })
+            .await?)
+    }
+
+    /// Perform vector search. Tries Atlas $vectorSearch first, falls back to
+    /// brute-force cosine similarity for local MongoDB instances.
+    pub async fn vector_search(
+        &self,
+        repo_id: &str,
+        query_embedding: Vec<f64>,
+        limit: u32,
+        min_score: f64,
+    ) -> Result<Vec<(CodeEmbedding, f64)>, CoreError> {
+        match self
+            .atlas_vector_search(repo_id, &query_embedding, limit, min_score)
+            .await
+        {
+            Ok(results) => Ok(results),
+            Err(e) => {
+                info!(
+                    "Atlas $vectorSearch unavailable ({e}), falling back to brute-force cosine similarity"
+                );
+                self.bruteforce_vector_search(repo_id, &query_embedding, limit, min_score)
+                    .await
+            }
+        }
+    }
+
+    /// Atlas $vectorSearch aggregation stage (requires Atlas Vector Search index)
+    async fn atlas_vector_search(
+        &self,
+        repo_id: &str,
+        query_embedding: &[f64],
+        limit: u32,
+        min_score: f64,
+    ) -> Result<Vec<(CodeEmbedding, f64)>, CoreError> {
+        use mongodb::bson::{Bson, Document};
+
+        let pipeline = vec![
+            doc! {
+                "$vectorSearch": {
+                    "index": "embedding_vector_index",
+                    "path": "embedding",
+                    "queryVector": query_embedding.iter().map(|&v| Bson::Double(v)).collect::<Vec<_>>(),
+                    "numCandidates": (limit * 10) as i64,
+                    "limit": limit as i64,
+                    "filter": { "repo_id": repo_id },
+                }
+            },
+            doc! {
+                "$addFields": {
+                    "search_score": { "$meta": "vectorSearchScore" }
+                }
+            },
+            doc! {
+                "$match": {
+                    "search_score": { "$gte": min_score }
+                }
+            },
+        ];
+
+        let mut cursor = self.embeddings.aggregate(pipeline).await?;
+
+        let mut results = Vec::new();
+        while let Some(doc) = cursor.try_next().await? {
+            let score = doc.get_f64("search_score").unwrap_or(0.0);
+            let mut clean_doc: Document = doc;
+            clean_doc.remove("search_score");
+            if let Ok(embedding) = mongodb::bson::from_document::<CodeEmbedding>(clean_doc) {
+                results.push((embedding, score));
+            }
+        }
+
+        Ok(results)
+    }
+
+    /// Brute-force cosine similarity fallback for local MongoDB without Atlas
+    async fn bruteforce_vector_search(
+        &self,
+        repo_id: &str,
+        query_embedding: &[f64],
+        limit: u32,
+        min_score: f64,
+    ) -> Result<Vec<(CodeEmbedding, f64)>, CoreError> {
+        let mut cursor = self.embeddings.find(doc! { "repo_id": repo_id }).await?;
+
+        let query_norm = dot(query_embedding, query_embedding).sqrt();
+        let mut scored: Vec<(CodeEmbedding, f64)> = Vec::new();
+
+        while let Some(emb) = cursor.try_next().await? {
+            let doc_norm = dot(&emb.embedding, &emb.embedding).sqrt();
+            let score = if query_norm > 0.0 && doc_norm > 0.0 {
+                dot(query_embedding, &emb.embedding) / (query_norm * doc_norm)
+            } else {
+                0.0
+            };
+            if score >= min_score {
+                scored.push((emb, score));
+            }
+        }
+
+        scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
+        scored.truncate(limit as usize);
+        Ok(scored)
+    }
+}
+
+fn dot(a: &[f64], b: &[f64]) -> f64 {
+    a.iter().zip(b.iter()).map(|(x, y)| x * y).sum()
+}

compliance-graph/src/graph/mod.rs

@@ -1,4 +1,6 @@
+pub mod chunking;
 pub mod community;
+pub mod embedding_store;
 pub mod engine;
 pub mod impact;
 pub mod persistence;