plano/crates/brightstaff/src/handlers/integration_tests.rs

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use std::sync::Arc;
use hermesllm::apis::openai::{ChatCompletionsRequest, Message, MessageContent, Role};
use hyper::header::HeaderMap;
use crate::handlers::agent_selector::{AgentSelectionError, AgentSelector};
use crate::handlers::pipeline_processor::PipelineProcessor;
use crate::router::plano_orchestrator::OrchestratorService;
use common::errors::BrightStaffError;
use http_body_util::BodyExt;
use hyper::StatusCode;
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/// Integration test that demonstrates the modular agent chat flow
/// This test shows how the three main components work together:
/// 1. AgentSelector - selects the appropriate agents based on orchestration
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/// 2. PipelineProcessor - executes the agent pipeline
/// 3. ResponseHandler - handles response streaming
#[cfg(test)]
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mod tests {
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use super::*;
use common::configuration::{Agent, AgentFilterChain, Listener};
fn create_test_orchestrator_service() -> Arc<OrchestratorService> {
Arc::new(OrchestratorService::new(
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"http://localhost:8080".to_string(),
"test-model".to_string(),
"plano-orchestrator".to_string(),
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))
}
fn create_test_message(role: Role, content: &str) -> Message {
Message {
role,
content: Some(MessageContent::Text(content.to_string())),
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name: None,
tool_calls: None,
tool_call_id: None,
}
}
#[tokio::test]
async fn test_modular_agent_chat_flow() {
// Setup services
let orchestrator_service = create_test_orchestrator_service();
let agent_selector = AgentSelector::new(orchestrator_service);
let mut pipeline_processor = PipelineProcessor::default();
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// Create test data
let agents = vec![
Agent {
id: "filter-agent".to_string(),
agent_type: Some("filter".to_string()),
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url: "http://localhost:8081".to_string(),
tool: None,
transport: None,
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},
Agent {
id: "terminal-agent".to_string(),
agent_type: Some("terminal".to_string()),
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url: "http://localhost:8082".to_string(),
tool: None,
transport: None,
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},
];
let agent_pipeline = AgentFilterChain {
id: "terminal-agent".to_string(),
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filter_chain: Some(vec![
"filter-agent".to_string(),
"terminal-agent".to_string(),
]),
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description: Some("Test pipeline".to_string()),
default: Some(true),
};
let listener = Listener {
name: "test-listener".to_string(),
agents: Some(vec![agent_pipeline.clone()]),
port: 8080,
router: None,
};
let listeners = vec![listener];
let messages = vec![create_test_message(Role::User, "Hello world!")];
// Test 1: Agent Selection
let selected_listener = agent_selector
.find_listener(Some("test-listener"), &listeners)
.await;
assert!(selected_listener.is_ok());
let listener = selected_listener.unwrap();
assert_eq!(listener.name, "test-listener");
// Test 2: Agent Map Creation
let agent_map = agent_selector.create_agent_map(&agents);
assert_eq!(agent_map.len(), 2);
assert!(agent_map.contains_key("filter-agent"));
assert!(agent_map.contains_key("terminal-agent"));
// Test 3: Pipeline Processing (empty filter chain for testing)
let request = ChatCompletionsRequest {
messages: messages.clone(),
model: "test-model".to_string(),
..Default::default()
};
// Create a pipeline with empty filter chain to avoid network calls
let test_pipeline = AgentFilterChain {
id: "terminal-agent".to_string(),
filter_chain: Some(vec![]), // Empty filter chain - no network calls needed
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description: None,
default: None,
};
let headers = HeaderMap::new();
let result = pipeline_processor
.process_filter_chain(&request.messages, &test_pipeline, &agent_map, &headers)
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.await;
println!("Pipeline processing result: {:?}", result);
assert!(result.is_ok());
let processed_messages = result.unwrap();
// With empty filter chain, should return the original messages unchanged
assert_eq!(processed_messages.len(), 1);
if let Some(MessageContent::Text(content)) = &processed_messages[0].content {
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assert_eq!(content, "Hello world!");
} else {
panic!("Expected text content");
}
// Test 4: Error Response Creation
let err = BrightStaffError::ModelNotFound("gpt-5-secret".to_string());
let response = err.into_response();
assert_eq!(response.status(), StatusCode::NOT_FOUND);
// Helper to extract body as JSON
let body_bytes = response.into_body().collect().await.unwrap().to_bytes();
let body: serde_json::Value = serde_json::from_slice(&body_bytes).unwrap();
assert_eq!(body["error"]["code"], "ModelNotFound");
assert_eq!(
body["error"]["details"]["rejected_model_id"],
"gpt-5-secret"
);
assert!(body["error"]["message"]
.as_str()
.unwrap()
.contains("gpt-5-secret"));
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println!("✅ All modular components working correctly!");
}
#[tokio::test]
async fn test_error_handling_flow() {
let router_service = create_test_orchestrator_service();
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let agent_selector = AgentSelector::new(router_service);
// Test listener not found
let result = agent_selector.find_listener(Some("nonexistent"), &[]).await;
assert!(result.is_err());
assert!(matches!(
result.unwrap_err(),
AgentSelectionError::ListenerNotFound(_)
));
let technical_reason = "Database connection timed out";
let err = BrightStaffError::InternalServerError(technical_reason.to_string());
let response = err.into_response();
// --- 1. EXTRACT BYTES ---
let body_bytes = response.into_body().collect().await.unwrap().to_bytes();
// --- 2. DECLARE body_json HERE ---
let body_json: serde_json::Value =
serde_json::from_slice(&body_bytes).expect("Failed to parse JSON body");
// --- 3. USE body_json ---
assert_eq!(body_json["error"]["code"], "InternalServerError");
assert_eq!(body_json["error"]["details"]["reason"], technical_reason);
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println!("✅ Error handling working correctly!");
}
}