From 2f9121407b383576ccc405b13ead03ae0dd3d1b3 Mon Sep 17 00:00:00 2001 From: Adil Hafeez Date: Wed, 17 Dec 2025 17:30:14 -0800 Subject: [PATCH] Use mcp tools for filter chain (#621) * agents framework demo * more changes * add more changes * pending changes * fix tests * fix more * rebase with main and better handle error from mcp * add trace for filters * add test for client error, server error and for mcp error * update schema validate code and rename kind => type in agent_filter * fix agent description and pre-commit * fix tests * add provider specific request parsing in agents chat * fix precommit and tests * cleanup demo * update readme * fix pre-commit * refactor tracing * fix fmt * fix: handle MessageContent enum in responses API conversion - Update request.rs to handle new MessageContent enum structure from main - MessageContent can now be Text(String) or Items(Vec) - Handle new InputItem variants (ItemReference, FunctionCallOutput) - Fixes compilation error after merging latest main (#632) * address pr feedback * fix span * fix build * update openai version --- arch/arch_config_schema.yaml | 32 + arch/envoy.template.yaml | 32 +- arch/tools/cli/config_generator.py | 11 +- arch/tools/cli/utils.py | 4 + arch/tools/test/test_config_generator.py | 27 +- .../src/handlers/agent_chat_completions.rs | 155 +- .../src/handlers/agent_selector.rs | 43 +- .../src/handlers/integration_tests.rs | 20 +- crates/brightstaff/src/handlers/jsonrpc.rs | 49 + crates/brightstaff/src/handlers/mod.rs | 1 + .../src/handlers/pipeline_processor.rs | 798 ++++++- crates/brightstaff/src/main.rs | 62 +- crates/brightstaff/src/tracing/constants.rs | 22 +- crates/build.sh | 1 + crates/common/src/configuration.rs | 6 +- crates/common/src/consts.rs | 1 + crates/common/src/traces/mod.rs | 2 +- crates/common/src/traces/span_builder.rs | 11 +- crates/hermesllm/src/apis/amazon_bedrock.rs | 98 + crates/hermesllm/src/apis/anthropic.rs | 59 + crates/hermesllm/src/apis/openai.rs | 8 + crates/hermesllm/src/apis/openai_responses.rs | 134 ++ crates/hermesllm/src/providers/request.rs | 169 ++ demos/use_cases/mcp_filter/README.md | 106 + demos/use_cases/mcp_filter/arch_config.yaml | 41 + .../use_cases/mcp_filter/docker-compose.yaml | 17 + demos/use_cases/mcp_filter/mcp_query.rest | 86 + demos/use_cases/mcp_filter/pyproject.toml | 22 + demos/use_cases/mcp_filter/sample_queries.md | 64 + .../mcp_filter/src/rag_agent/__init__.py | 98 + .../mcp_filter/src/rag_agent/__main__.py | 4 + .../use_cases/mcp_filter/src/rag_agent/api.py | 36 + .../src/rag_agent/context_builder.py | 205 ++ .../src/rag_agent/query_rewriter.py | 119 ++ .../mcp_filter/src/rag_agent/rag_agent.py | 303 +++ .../src/rag_agent/sample_knowledge_base.csv | 257 +++ demos/use_cases/mcp_filter/start_agents.sh | 47 + demos/use_cases/mcp_filter/test.rest | 95 + demos/use_cases/mcp_filter/uv.lock | 1830 +++++++++++++++++ tests/e2e/docker-compose.yaml | 1 - 40 files changed, 4886 insertions(+), 190 deletions(-) create mode 100644 crates/brightstaff/src/handlers/jsonrpc.rs create mode 100644 crates/build.sh create mode 100644 demos/use_cases/mcp_filter/README.md create mode 100644 demos/use_cases/mcp_filter/arch_config.yaml create mode 100644 demos/use_cases/mcp_filter/docker-compose.yaml create mode 100644 demos/use_cases/mcp_filter/mcp_query.rest create mode 100644 demos/use_cases/mcp_filter/pyproject.toml create mode 100644 demos/use_cases/mcp_filter/sample_queries.md create mode 100644 demos/use_cases/mcp_filter/src/rag_agent/__init__.py create mode 100644 demos/use_cases/mcp_filter/src/rag_agent/__main__.py create mode 100644 demos/use_cases/mcp_filter/src/rag_agent/api.py create mode 100644 demos/use_cases/mcp_filter/src/rag_agent/context_builder.py create mode 100644 demos/use_cases/mcp_filter/src/rag_agent/query_rewriter.py create mode 100644 demos/use_cases/mcp_filter/src/rag_agent/rag_agent.py create mode 100644 demos/use_cases/mcp_filter/src/rag_agent/sample_knowledge_base.csv create mode 100644 demos/use_cases/mcp_filter/start_agents.sh create mode 100644 demos/use_cases/mcp_filter/test.rest create mode 100644 demos/use_cases/mcp_filter/uv.lock diff --git a/arch/arch_config_schema.yaml b/arch/arch_config_schema.yaml index 1474302c..61c24edb 100644 --- a/arch/arch_config_schema.yaml +++ b/arch/arch_config_schema.yaml @@ -14,6 +14,38 @@ properties: type: array items: type: object + properties: + id: + type: string + url: + type: string + additionalProperties: false + required: + - id + - url + filters: + type: array + items: + type: object + properties: + id: + type: string + url: + type: string + type: + type: string + enum: + - mcp + transport: + type: string + enum: + - streamable-http + tool: + type: string + additionalProperties: false + required: + - id + - url listeners: oneOf: - type: array diff --git a/arch/envoy.template.yaml b/arch/envoy.template.yaml index 3d618faf..4ead29c2 100644 --- a/arch/envoy.template.yaml +++ b/arch/envoy.template.yaml @@ -214,21 +214,21 @@ static_resources: - name: envoy.filters.network.http_connection_manager typed_config: "@type": type.googleapis.com/envoy.extensions.filters.network.http_connection_manager.v3.HttpConnectionManager - {% if "random_sampling" in arch_tracing and arch_tracing["random_sampling"] > 0 %} - generate_request_id: true - tracing: - provider: - name: envoy.tracers.opentelemetry - typed_config: - "@type": type.googleapis.com/envoy.config.trace.v3.OpenTelemetryConfig - grpc_service: - envoy_grpc: - cluster_name: opentelemetry_collector - timeout: 0.250s - service_name: tools - random_sampling: - value: {{ arch_tracing.random_sampling }} - {% endif %} + # {% if "random_sampling" in arch_tracing and arch_tracing["random_sampling"] > 0 %} + # generate_request_id: true + # tracing: + # provider: + # name: envoy.tracers.opentelemetry + # typed_config: + # "@type": type.googleapis.com/envoy.config.trace.v3.OpenTelemetryConfig + # grpc_service: + # envoy_grpc: + # cluster_name: opentelemetry_collector + # timeout: 0.250s + # service_name: tools + # random_sampling: + # value: {{ arch_tracing.random_sampling }} + # {% endif %} stat_prefix: outbound_api_traffic codec_type: AUTO scheme_header_transformation: @@ -299,7 +299,7 @@ static_resources: envoy_grpc: cluster_name: opentelemetry_collector timeout: 0.250s - service_name: arch_gateway + service_name: plano(inbound) random_sampling: value: {{ arch_tracing.random_sampling }} {% endif %} diff --git a/arch/tools/cli/config_generator.py b/arch/tools/cli/config_generator.py index fd306c60..3acdb932 100644 --- a/arch/tools/cli/config_generator.py +++ b/arch/tools/cli/config_generator.py @@ -101,8 +101,17 @@ def validate_and_render_schema(): # Process agents section and convert to endpoints agents = config_yaml.get("agents", []) - for agent in agents: + filters = config_yaml.get("filters", []) + agents_combined = agents + filters + agent_id_keys = set() + + for agent in agents_combined: agent_id = agent.get("id") + if agent_id in agent_id_keys: + raise Exception( + f"Duplicate agent id {agent_id}, please provide unique id for each agent" + ) + agent_id_keys.add(agent_id) agent_endpoint = agent.get("url") if agent_id and agent_endpoint: diff --git a/arch/tools/cli/utils.py b/arch/tools/cli/utils.py index 6db34585..ef0a37ea 100644 --- a/arch/tools/cli/utils.py +++ b/arch/tools/cli/utils.py @@ -57,6 +57,10 @@ def convert_legacy_listeners( "timeout": "30s", } + # Handle None case + if listeners is None: + return [llm_gateway_listener], llm_gateway_listener, prompt_gateway_listener + if isinstance(listeners, dict): # legacy listeners # check if type is array or object diff --git a/arch/tools/test/test_config_generator.py b/arch/tools/test/test_config_generator.py index 7016a34f..5a19c424 100644 --- a/arch/tools/test/test_config_generator.py +++ b/arch/tools/test/test_config_generator.py @@ -94,21 +94,16 @@ def test_validate_and_render_happy_path_agent_config(monkeypatch): version: v0.3.0 agents: - - name: query_rewriter - kind: openai - endpoint: http://localhost:10500 - - name: context_builder - kind: openai - endpoint: http://localhost:10501 - - name: response_generator - kind: openai - endpoint: http://localhost:10502 - - name: research_agent - kind: openai - endpoint: http://localhost:10500 - - name: input_guard_rails - kind: openai - endpoint: http://localhost:10503 + - id: query_rewriter + url: http://localhost:10500 + - id: context_builder + url: http://localhost:10501 + - id: response_generator + url: http://localhost:10502 + - id: research_agent + url: http://localhost:10500 + - id: input_guard_rails + url: http://localhost:10503 listeners: - name: tmobile @@ -156,7 +151,7 @@ listeners: mock.mock_open().return_value, # ARCH_CONFIG_FILE_RENDERED (write) ] with mock.patch("builtins.open", m_open): - with mock.patch("config_generator.Environment"): + with mock.patch("cli.config_generator.Environment"): validate_and_render_schema() diff --git a/crates/brightstaff/src/handlers/agent_chat_completions.rs b/crates/brightstaff/src/handlers/agent_chat_completions.rs index 3a90fc48..c6675b06 100644 --- a/crates/brightstaff/src/handlers/agent_chat_completions.rs +++ b/crates/brightstaff/src/handlers/agent_chat_completions.rs @@ -1,16 +1,24 @@ use std::sync::Arc; +use std::time::{Instant, SystemTime}; use bytes::Bytes; -use hermesllm::apis::openai::ChatCompletionsRequest; +use common::consts::TRACE_PARENT_HEADER; +use common::traces::{SpanBuilder, SpanKind, parse_traceparent, generate_random_span_id}; +use hermesllm::apis::OpenAIMessage; +use hermesllm::clients::SupportedAPIsFromClient; +use hermesllm::providers::request::ProviderRequest; +use hermesllm::ProviderRequestType; use http_body_util::combinators::BoxBody; use http_body_util::BodyExt; use hyper::{Request, Response}; +use serde::ser::Error as SerError; use tracing::{debug, info, warn}; use super::agent_selector::{AgentSelectionError, AgentSelector}; use super::pipeline_processor::{PipelineError, PipelineProcessor}; use super::response_handler::ResponseHandler; use crate::router::llm_router::RouterService; +use crate::tracing::{OperationNameBuilder, operation_component, http}; /// Main errors for agent chat completions #[derive(Debug, thiserror::Error)] @@ -33,8 +41,17 @@ pub async fn agent_chat( _: String, agents_list: Arc>>>, listeners: Arc>>, + trace_collector: Arc, ) -> Result>, hyper::Error> { - match handle_agent_chat(request, router_service, agents_list, listeners).await { + match handle_agent_chat( + request, + router_service, + agents_list, + listeners, + trace_collector, + ) + .await + { Ok(response) => Ok(response), Err(err) => { // Check if this is a client error from the pipeline that should be cascaded @@ -109,10 +126,11 @@ async fn handle_agent_chat( router_service: Arc, agents_list: Arc>>>, listeners: Arc>>, + trace_collector: Arc, ) -> Result>, AgentFilterChainError> { // Initialize services let agent_selector = AgentSelector::new(router_service); - let pipeline_processor = PipelineProcessor::default(); + let mut pipeline_processor = PipelineProcessor::default(); let response_handler = ResponseHandler::new(); // Extract listener name from headers @@ -132,6 +150,13 @@ async fn handle_agent_chat( info!("Handling request for listener: {}", listener.name); // Parse request body + let request_path = request + .uri() + .path() + .to_string() + .strip_prefix("/agents") + .unwrap() + .to_string(); let request_headers = request.headers().clone(); let chat_request_bytes = request.collect().await?.to_bytes(); @@ -140,61 +165,141 @@ async fn handle_agent_chat( String::from_utf8_lossy(&chat_request_bytes) ); - let chat_completions_request: ChatCompletionsRequest = - serde_json::from_slice(&chat_request_bytes).map_err(|err| { - warn!( - "Failed to parse request body as ChatCompletionsRequest: {}", - err - ); - AgentFilterChainError::RequestParsing(err) + // Determine the API type from the endpoint + let api_type = + SupportedAPIsFromClient::from_endpoint(request_path.as_str()).ok_or_else(|| { + let err_msg = format!("Unsupported endpoint: {}", request_path); + warn!("{}", err_msg); + AgentFilterChainError::RequestParsing(serde_json::Error::custom(err_msg)) })?; + let client_request = match ProviderRequestType::try_from((&chat_request_bytes[..], &api_type)) { + Ok(request) => request, + Err(err) => { + warn!("Failed to parse request as ProviderRequestType: {}", err); + let err_msg = format!("Failed to parse request: {}", err); + return Err(AgentFilterChainError::RequestParsing( + serde_json::Error::custom(err_msg), + )); + } + }; + + let message: Vec = client_request.get_messages(); + + // let chat_completions_request: ChatCompletionsRequest = + // serde_json::from_slice(&chat_request_bytes).map_err(|err| { + // warn!( + // "Failed to parse request body as ChatCompletionsRequest: {}", + // err + // ); + // AgentFilterChainError::RequestParsing(err) + // })?; + // Extract trace parent for routing let trace_parent = request_headers .iter() - .find(|(key, _)| key.as_str() == "traceparent") + .find(|(key, _)| key.as_str() == TRACE_PARENT_HEADER) .map(|(_, value)| value.to_str().unwrap_or_default().to_string()); - // Select appropriate agent using arch router llm model - let selected_agent = agent_selector - .select_agent(&chat_completions_request.messages, &listener, trace_parent) - .await?; - - debug!("Processing agent pipeline: {}", selected_agent.id); - - // Create agent map for pipeline processing + // Create agent map for pipeline processing and agent selection let agent_map = { let agents = agents_list.read().await; let agents = agents.as_ref().unwrap(); agent_selector.create_agent_map(agents) }; + // Parse trace parent to get trace_id and parent_span_id + let (trace_id, parent_span_id) = if let Some(ref tp) = trace_parent { + parse_traceparent(tp) + } else { + (String::new(), None) + }; + + // Select appropriate agent using arch router llm model + let selected_agent = agent_selector + .select_agent(&message, &listener, trace_parent.clone()) + .await?; + + debug!("Processing agent pipeline: {}", selected_agent.id); + + // Record the start time for agent span + let agent_start_time = SystemTime::now(); + let agent_start_instant = Instant::now(); + // let (span_id, trace_id) = trace_collector.start_span( + // trace_parent.clone(), + // operation_component::AGENT, + // &format!("/agents{}", request_path), + // &selected_agent.id, + // ); + + let span_id = generate_random_span_id(); + // Process the filter chain - let processed_messages = pipeline_processor + let chat_history = pipeline_processor .process_filter_chain( - &chat_completions_request, + &message, &selected_agent, &agent_map, &request_headers, + Some(&trace_collector), + trace_id.clone(), + span_id.clone(), ) .await?; // Get terminal agent and send final response - let terminal_agent_name = selected_agent.id; + let terminal_agent_name = selected_agent.id.clone(); let terminal_agent = agent_map.get(&terminal_agent_name).unwrap(); debug!("Processing terminal agent: {}", terminal_agent_name); debug!("Terminal agent details: {:?}", terminal_agent); let llm_response = pipeline_processor - .invoke_upstream_agent( - &processed_messages, - &chat_completions_request, + .invoke_agent( + &chat_history, + client_request, terminal_agent, &request_headers, + trace_id.clone(), + span_id.clone(), ) .await?; + // Record agent span after processing is complete + let agent_end_time = SystemTime::now(); + let agent_elapsed = agent_start_instant.elapsed(); + + // Build full path with /agents prefix + let full_path = format!("/agents{}", request_path); + + // Build operation name: POST {full_path} {agent_name} + let operation_name = OperationNameBuilder::new() + .with_method("POST") + .with_path(&full_path) + .with_target(&terminal_agent_name) + .build(); + + let mut span_builder = SpanBuilder::new(&operation_name) + .with_span_id(span_id) + .with_kind(SpanKind::Internal) + .with_start_time(agent_start_time) + .with_end_time(agent_end_time) + .with_attribute(http::METHOD, "POST") + .with_attribute(http::TARGET, full_path) + .with_attribute("agent.name", terminal_agent_name.clone()) + .with_attribute("duration_ms", format!("{:.2}", agent_elapsed.as_secs_f64() * 1000.0)); + + if !trace_id.is_empty() { + span_builder = span_builder.with_trace_id(trace_id); + } + if let Some(parent_id) = parent_span_id { + span_builder = span_builder.with_parent_span_id(parent_id); + } + + let span = span_builder.build(); + // Use plano(agent) as service name for the agent processing span + trace_collector.record_span(operation_component::AGENT, span); + // Create streaming response response_handler .create_streaming_response(llm_response) diff --git a/crates/brightstaff/src/handlers/agent_selector.rs b/crates/brightstaff/src/handlers/agent_selector.rs index 0fff1198..02e52df2 100644 --- a/crates/brightstaff/src/handlers/agent_selector.rs +++ b/crates/brightstaff/src/handlers/agent_selector.rs @@ -20,6 +20,8 @@ pub enum AgentSelectionError { RoutingError(String), #[error("Default agent not found for listener: {0}")] DefaultAgentNotFound(String), + #[error("MCP client error: {0}")] + McpError(String), } /// Service for selecting agents based on routing preferences and listener configuration @@ -29,7 +31,9 @@ pub struct AgentSelector { impl AgentSelector { pub fn new(router_service: Arc) -> Self { - Self { router_service } + Self { + router_service, + } } /// Find listener by name from the request headers @@ -77,7 +81,9 @@ impl AgentSelector { return Ok(agents[0].clone()); } - let usage_preferences = self.convert_agent_description_to_routing_preferences(agents); + let usage_preferences = self + .convert_agent_description_to_routing_preferences(agents) + .await; debug!( "Agents usage preferences for agent routing str: {}", serde_json::to_string(&usage_preferences).unwrap_or_default() @@ -131,20 +137,23 @@ impl AgentSelector { } /// Convert agent descriptions to routing preferences - fn convert_agent_description_to_routing_preferences( + async fn convert_agent_description_to_routing_preferences( &self, agents: &[AgentFilterChain], ) -> Vec { - agents - .iter() - .map(|agent| ModelUsagePreference { - model: agent.id.clone(), + let mut preferences = Vec::new(); + + for agent_chain in agents { + preferences.push(ModelUsagePreference { + model: agent_chain.id.clone(), routing_preferences: vec![RoutingPreference { - name: agent.id.clone(), - description: agent.description.as_ref().unwrap_or(&String::new()).clone(), + name: agent_chain.id.clone(), + description: agent_chain.description.clone().unwrap_or_default(), }], - }) - .collect() + }); + } + + preferences } } @@ -183,8 +192,10 @@ mod tests { fn create_test_agent_struct(name: &str) -> Agent { Agent { id: name.to_string(), - kind: Some("test".to_string()), + agent_type: Some("test".to_string()), url: "http://localhost:8080".to_string(), + tool: None, + transport: None, } } @@ -240,8 +251,8 @@ mod tests { assert!(agent_map.contains_key("agent2")); } - #[test] - fn test_convert_agent_description_to_routing_preferences() { + #[tokio::test] + async fn test_convert_agent_description_to_routing_preferences() { let router_service = create_test_router_service(); let selector = AgentSelector::new(router_service); @@ -250,7 +261,9 @@ mod tests { create_test_agent("agent2", "Second agent description", false), ]; - let preferences = selector.convert_agent_description_to_routing_preferences(&agents); + let preferences = selector + .convert_agent_description_to_routing_preferences(&agents) + .await; assert_eq!(preferences.len(), 2); assert_eq!(preferences[0].model, "agent1"); diff --git a/crates/brightstaff/src/handlers/integration_tests.rs b/crates/brightstaff/src/handlers/integration_tests.rs index e09ed4f2..42686796 100644 --- a/crates/brightstaff/src/handlers/integration_tests.rs +++ b/crates/brightstaff/src/handlers/integration_tests.rs @@ -42,19 +42,23 @@ mod integration_tests { // Setup services let router_service = create_test_router_service(); let agent_selector = AgentSelector::new(router_service); - let pipeline_processor = PipelineProcessor::default(); + let mut pipeline_processor = PipelineProcessor::default(); // Create test data let agents = vec![ Agent { id: "filter-agent".to_string(), - kind: Some("filter".to_string()), + agent_type: Some("filter".to_string()), url: "http://localhost:8081".to_string(), + tool: None, + transport: None, }, Agent { id: "terminal-agent".to_string(), - kind: Some("terminal".to_string()), + agent_type: Some("terminal".to_string()), url: "http://localhost:8082".to_string(), + tool: None, + transport: None, }, ]; @@ -107,7 +111,15 @@ mod integration_tests { let headers = HeaderMap::new(); let result = pipeline_processor - .process_filter_chain(&request, &test_pipeline, &agent_map, &headers) + .process_filter_chain( + &request.messages, + &test_pipeline, + &agent_map, + &headers, + None, + String::new(), + String::new(), + ) .await; println!("Pipeline processing result: {:?}", result); diff --git a/crates/brightstaff/src/handlers/jsonrpc.rs b/crates/brightstaff/src/handlers/jsonrpc.rs new file mode 100644 index 00000000..0f8b9373 --- /dev/null +++ b/crates/brightstaff/src/handlers/jsonrpc.rs @@ -0,0 +1,49 @@ +use serde::{Deserialize, Serialize}; +use std::collections::HashMap; + +pub const JSON_RPC_VERSION: &str = "2.0"; +pub const TOOL_CALL_METHOD : &str = "tools/call"; +pub const MCP_INITIALIZE: &str = "initialize"; +pub const MCP_INITIALIZE_NOTIFICATION: &str = "initialize/notification"; + +#[derive(Debug, Clone, Serialize, Deserialize)] +#[serde(untagged)] +pub enum JsonRpcId { + String(String), + Number(u64), +} + +#[derive(Debug, Clone, Serialize, Deserialize)] +pub struct JsonRpcRequest { + pub jsonrpc: String, + pub id: JsonRpcId, + pub method: String, + #[serde(skip_serializing_if = "Option::is_none")] + pub params: Option>, +} + +#[derive(Debug, Clone, Serialize, Deserialize)] +pub struct JsonRpcNotification { + pub jsonrpc: String, + pub method: String, + #[serde(skip_serializing_if = "Option::is_none")] + pub params: Option>, +} + +#[derive(Debug, Clone, Serialize, Deserialize)] +pub struct JsonRpcError { + pub code: i32, + pub message: String, + #[serde(skip_serializing_if = "Option::is_none")] + pub data: Option, +} + +#[derive(Debug, Clone, Serialize, Deserialize)] +pub struct JsonRpcResponse { + pub jsonrpc: String, + pub id: JsonRpcId, + #[serde(skip_serializing_if = "Option::is_none")] + pub result: Option>, + #[serde(skip_serializing_if = "Option::is_none")] + pub error: Option, +} diff --git a/crates/brightstaff/src/handlers/mod.rs b/crates/brightstaff/src/handlers/mod.rs index b7762916..e63958d5 100644 --- a/crates/brightstaff/src/handlers/mod.rs +++ b/crates/brightstaff/src/handlers/mod.rs @@ -7,6 +7,7 @@ pub mod function_calling; pub mod pipeline_processor; pub mod response_handler; pub mod utils; +pub mod jsonrpc; #[cfg(test)] mod integration_tests; diff --git a/crates/brightstaff/src/handlers/pipeline_processor.rs b/crates/brightstaff/src/handlers/pipeline_processor.rs index 4d83325a..88e8238e 100644 --- a/crates/brightstaff/src/handlers/pipeline_processor.rs +++ b/crates/brightstaff/src/handlers/pipeline_processor.rs @@ -1,10 +1,24 @@ use std::collections::HashMap; use common::configuration::{Agent, AgentFilterChain}; -use common::consts::{ARCH_UPSTREAM_HOST_HEADER, ENVOY_RETRY_HEADER}; -use hermesllm::apis::openai::{ChatCompletionsRequest, Message}; +use common::consts::{ + ARCH_UPSTREAM_HOST_HEADER, BRIGHT_STAFF_SERVICE_NAME, ENVOY_RETRY_HEADER, TRACE_PARENT_HEADER, +}; +use common::traces::{SpanBuilder, SpanKind, generate_random_span_id}; +use hermesllm::apis::openai::Message; +use hermesllm::{ProviderRequest, ProviderRequestType}; use hyper::header::HeaderMap; -use tracing::{debug, warn}; +use std::time::{Instant, SystemTime}; +use tracing::{debug, info, warn}; + +use crate::tracing::operation_component::{self}; +use crate::tracing::{http, OperationNameBuilder}; + +use crate::handlers::jsonrpc::{ + JsonRpcId, JsonRpcNotification, JsonRpcRequest, JsonRpcResponse, JSON_RPC_VERSION, + MCP_INITIALIZE, MCP_INITIALIZE_NOTIFICATION, TOOL_CALL_METHOD, +}; +use uuid::Uuid; /// Errors that can occur during pipeline processing #[derive(Debug, thiserror::Error)] @@ -19,6 +33,12 @@ pub enum PipelineError { NoChoicesInResponse(String), #[error("No content in response from agent '{0}'")] NoContentInResponse(String), + #[error("No result in response from agent '{0}'")] + NoResultInResponse(String), + #[error("No structured content in response from agent '{0}'")] + NoStructuredContentInResponse(String), + #[error("No messages in response from agent '{0}'")] + NoMessagesInResponse(String), #[error("Client error from agent '{agent}' (HTTP {status}): {body}")] ClientError { agent: String, @@ -37,13 +57,17 @@ pub enum PipelineError { pub struct PipelineProcessor { client: reqwest::Client, url: String, + agent_id_session_map: HashMap, } +const ENVOY_API_ROUTER_ADDRESS: &str = "http://localhost:11000"; + impl Default for PipelineProcessor { fn default() -> Self { Self { client: reqwest::Client::new(), - url: "http://localhost:11000/v1/chat/completions".to_string(), + url: ENVOY_API_ROUTER_ADDRESS.to_string(), + agent_id_session_map: HashMap::new(), } } } @@ -53,18 +77,128 @@ impl PipelineProcessor { Self { client: reqwest::Client::new(), url, + agent_id_session_map: HashMap::new(), } } + /// Record a span for filter execution + fn record_filter_span( + &self, + collector: &std::sync::Arc, + agent_name: &str, + tool_name: &str, + start_time: SystemTime, + end_time: SystemTime, + elapsed: std::time::Duration, + trace_id: String, + parent_span_id: String, + span_id: String, + ) -> String { + // let (trace_id, parent_span_id) = self.extract_trace_context(); + + // Build operation name: POST /agents/* {filter_name} + // Using generic path since we don't have access to specific endpoint here + let operation_name = OperationNameBuilder::new() + .with_method("POST") + .with_path("/agents/*") + .with_target(agent_name) + .build(); + + let mut span_builder = SpanBuilder::new(&operation_name) + .with_span_id(span_id.clone()) + .with_kind(SpanKind::Client) + .with_start_time(start_time) + .with_end_time(end_time) + .with_attribute(http::METHOD, "POST") + .with_attribute(http::TARGET, "/agents/*") + .with_attribute("filter.name", agent_name.to_string()) + .with_attribute("filter.tool_name", tool_name.to_string()) + .with_attribute( + "duration_ms", + format!("{:.2}", elapsed.as_secs_f64() * 1000.0), + ); + + if !trace_id.is_empty() { + span_builder = span_builder.with_trace_id(trace_id); + } + if !parent_span_id.is_empty() { + span_builder = span_builder.with_parent_span_id(parent_span_id); + } + + let span = span_builder.build(); + // Use plano(filter) as service name for filter execution spans + collector.record_span(operation_component::AGENT_FILTER, span); + span_id.clone() + } + + /// Record a span for MCP protocol interactions + fn record_mcp_span( + &self, + collector: &std::sync::Arc, + operation: &str, + agent_id: &str, + start_time: SystemTime, + end_time: SystemTime, + elapsed: std::time::Duration, + additional_attrs: Option>, + trace_id: String, + parent_span_id: String, + span_id: Option, + ) { + // let (trace_id, parent_span_id) = self.extract_trace_context(); + + // Build operation name: POST /mcp {agent_id} + let operation_name = OperationNameBuilder::new() + .with_method("POST") + .with_path("/mcp") + .with_operation(operation) + .with_target(agent_id) + .build(); + + let mut span_builder = SpanBuilder::new(&operation_name) + .with_span_id(span_id.unwrap_or_else(|| generate_random_span_id())) + .with_kind(SpanKind::Client) + .with_start_time(start_time) + .with_end_time(end_time) + .with_attribute(http::METHOD, "POST") + .with_attribute(http::TARGET, &format!("/mcp ({})", operation.to_string())) + .with_attribute("mcp.operation", operation.to_string()) + .with_attribute("mcp.agent_id", agent_id.to_string()) + .with_attribute( + "duration_ms", + format!("{:.2}", elapsed.as_secs_f64() * 1000.0), + ); + + if let Some(attrs) = additional_attrs { + for (key, value) in attrs { + span_builder = span_builder.with_attribute(key, value); + } + } + + if !trace_id.is_empty() { + span_builder = span_builder.with_trace_id(trace_id); + } + if !parent_span_id.is_empty() { + span_builder = span_builder.with_parent_span_id(parent_span_id); + } + + let span = span_builder.build(); + // MCP spans also use plano(filter) service name as they are part of filter operations + collector.record_span(operation_component::AGENT_FILTER, span); + } + /// Process the filter chain of agents (all except the terminal agent) pub async fn process_filter_chain( - &self, - initial_request: &ChatCompletionsRequest, + &mut self, + chat_history: &[Message], agent_filter_chain: &AgentFilterChain, agent_map: &HashMap, request_headers: &HeaderMap, + trace_collector: Option<&std::sync::Arc>, + trace_id: String, + parent_span_id: String, ) -> Result, PipelineError> { - let mut chat_completions_history = initial_request.messages.clone(); + let mut chat_history_updated = chat_history.to_vec(); for agent_name in &agent_filter_chain.filter_chain { debug!("Processing filter agent: {}", agent_name); @@ -73,123 +207,490 @@ impl PipelineProcessor { .get(agent_name) .ok_or_else(|| PipelineError::AgentNotFound(agent_name.clone()))?; - debug!("Agent details: {:?}", agent); + let tool_name = agent.tool.as_deref().unwrap_or(&agent.id); - let response_content = self - .send_agent_filter_chain_request( - &chat_completions_history, - initial_request, + info!( + "executing filter: {}/{}, url: {}, conversation length: {}", + agent_name, + tool_name, + agent.url, + chat_history.len() + ); + + let start_time = SystemTime::now(); + let start_instant = Instant::now(); + + // Generate filter span ID before execution so MCP spans can use it as parent + let filter_span_id = generate_random_span_id(); + + chat_history_updated = self + .execute_filter( + &chat_history_updated, agent, request_headers, + trace_collector, + trace_id.clone(), + filter_span_id.clone(), ) .await?; - debug!("Received response from filter agent {}", agent_name); + let end_time = SystemTime::now(); + let elapsed = start_instant.elapsed(); - // Parse the response content as new message history - chat_completions_history = - serde_json::from_str(&response_content).inspect_err(|err| { - warn!( - "Failed to parse response from agent {}, err: {}, response: {}", - agent_name, err, response_content - ) - })?; + info!( + "Filter '{}' completed in {:.2}ms, updated conversation length: {}", + agent_name, + elapsed.as_secs_f64() * 1000.0, + chat_history_updated.len() + ); + + // Record span for this filter execution + if let Some(collector) = trace_collector { + self.record_filter_span( + collector, + agent_name, + tool_name, + start_time, + end_time, + elapsed, + trace_id.clone(), + parent_span_id.clone(), + filter_span_id, + ); + } } - Ok(chat_completions_history) + Ok(chat_history_updated) } - /// Send request to a specific agent and return the response content - async fn send_agent_filter_chain_request( + /// Build common MCP headers for requests + fn build_mcp_headers( &self, - messages: &[Message], - original_request: &ChatCompletionsRequest, - agent: &Agent, request_headers: &HeaderMap, - ) -> Result { - let mut request = original_request.clone(); - request.messages = messages.to_vec(); + agent_id: &str, + session_id: Option<&str>, + trace_id: String, + parent_span_id: String, + ) -> Result { + let trace_parent = format!("00-{}-{}-01", trace_id, parent_span_id); + let mut headers = request_headers.clone(); + headers.remove(hyper::header::CONTENT_LENGTH); - let request_body = serde_json::to_string(&request)?; - debug!("Sending request to agent {}", agent.id); - - let mut agent_headers = request_headers.clone(); - agent_headers.remove(hyper::header::CONTENT_LENGTH); - agent_headers.insert( - ARCH_UPSTREAM_HOST_HEADER, - hyper::header::HeaderValue::from_str(&agent.id) - .map_err(|_| PipelineError::AgentNotFound(agent.id.clone()))?, + headers.remove(TRACE_PARENT_HEADER); + headers.insert( + TRACE_PARENT_HEADER, + hyper::header::HeaderValue::from_str(&trace_parent).unwrap(), ); - agent_headers.insert( + headers.insert( + ARCH_UPSTREAM_HOST_HEADER, + hyper::header::HeaderValue::from_str(agent_id) + .map_err(|_| PipelineError::AgentNotFound(agent_id.to_string()))?, + ); + + headers.insert( ENVOY_RETRY_HEADER, hyper::header::HeaderValue::from_str("3").unwrap(), ); + headers.insert( + "Accept", + hyper::header::HeaderValue::from_static("application/json, text/event-stream"), + ); + + headers.insert( + "Content-Type", + hyper::header::HeaderValue::from_static("application/json"), + ); + + if let Some(sid) = session_id { + headers.insert( + "mcp-session-id", + hyper::header::HeaderValue::from_str(sid).unwrap(), + ); + } + + Ok(headers) + } + + /// Parse SSE formatted response and extract JSON-RPC data + fn parse_sse_response( + &self, + response_bytes: &[u8], + agent_id: &str, + ) -> Result { + let response_str = String::from_utf8_lossy(response_bytes); + let lines: Vec<&str> = response_str.lines().collect(); + + // Validate SSE format: first line should be "event: message" + if lines.is_empty() || lines[0] != "event: message" { + warn!( + "Invalid SSE response format from agent {}: expected 'event: message' as first line, got: {:?}", + agent_id, + lines.first() + ); + return Err(PipelineError::NoContentInResponse(format!( + "Invalid SSE response format from agent {}: expected 'event: message' as first line", + agent_id + ))); + } + + // Find the data line + let data_lines: Vec<&str> = lines + .iter() + .filter(|line| line.starts_with("data: ")) + .copied() + .collect(); + + if data_lines.len() != 1 { + warn!( + "Expected exactly one 'data:' line from agent {}, found {}", + agent_id, + data_lines.len() + ); + return Err(PipelineError::NoContentInResponse(format!( + "Expected exactly one 'data:' line from agent {}, found {}", + agent_id, + data_lines.len() + ))); + } + + // Skip "data: " prefix + Ok(data_lines[0][6..].to_string()) + } + + /// Send an MCP request and return the response + async fn send_mcp_request( + &self, + json_rpc_request: &JsonRpcRequest, + headers: HeaderMap, + agent_id: &str, + ) -> Result { + let request_body = serde_json::to_string(json_rpc_request)?; + + debug!( + "Sending MCP request to agent {}: {}", + agent_id, request_body + ); + let response = self .client - .post(&self.url) - .headers(agent_headers) + .post(format!("{}/mcp", self.url)) + .headers(headers) .body(request_body) .send() .await?; - let status = response.status(); + Ok(response) + } + + /// Build a tools/call JSON-RPC request + fn build_tool_call_request( + &self, + tool_name: &str, + messages: &[Message], + ) -> Result { + let mut arguments = HashMap::new(); + arguments.insert("messages".to_string(), serde_json::to_value(messages)?); + + let mut params = HashMap::new(); + params.insert("name".to_string(), serde_json::to_value(tool_name)?); + params.insert("arguments".to_string(), serde_json::to_value(arguments)?); + + Ok(JsonRpcRequest { + jsonrpc: JSON_RPC_VERSION.to_string(), + id: JsonRpcId::String(Uuid::new_v4().to_string()), + method: TOOL_CALL_METHOD.to_string(), + params: Some(params), + }) + } + + /// Send request to a specific agent and return the response content + async fn execute_filter( + &mut self, + messages: &[Message], + agent: &Agent, + request_headers: &HeaderMap, + trace_collector: Option<&std::sync::Arc>, + trace_id: String, + filter_span_id: String, + ) -> Result, PipelineError> { + // Get or create MCP session + let mcp_session_id = if let Some(session_id) = self.agent_id_session_map.get(&agent.id) { + session_id.clone() + } else { + let session_id = self + .get_new_session_id( + &agent.id, + trace_id.clone(), + filter_span_id.clone(), + ) + .await; + self.agent_id_session_map + .insert(agent.id.clone(), session_id.clone()); + session_id + }; + + info!( + "Using MCP session ID {} for agent {}", + mcp_session_id, agent.id + ); + + // Build JSON-RPC request + let tool_name = agent.tool.as_deref().unwrap_or(&agent.id); + let json_rpc_request = self.build_tool_call_request(tool_name, messages)?; + + // Generate span ID for this MCP tool call (child of filter span) + let mcp_span_id = generate_random_span_id(); + + // Build headers + let agent_headers = + self.build_mcp_headers(request_headers, &agent.id, Some(&mcp_session_id), trace_id.clone(), mcp_span_id.clone())?; + + // Send request with tracing + let start_time = SystemTime::now(); + let start_instant = Instant::now(); + + let response = self + .send_mcp_request( + &json_rpc_request, + agent_headers, + &agent.id, + ) + .await?; + let http_status = response.status(); let response_bytes = response.bytes().await?; - // Check for HTTP errors and handle them appropriately - if !status.is_success() { - let error_body = String::from_utf8_lossy(&response_bytes).to_string(); + let end_time = SystemTime::now(); + let elapsed = start_instant.elapsed(); - if status.is_client_error() { - // 4xx errors - cascade back to developer - return Err(PipelineError::ClientError { - agent: agent.id.clone(), - status: status.as_u16(), - body: error_body, - }); - } else if status.is_server_error() { - // 5xx errors - server/agent error - return Err(PipelineError::ServerError { - agent: agent.id.clone(), - status: status.as_u16(), - body: error_body, - }); - } + // Record MCP tool call span + if let Some(collector) = trace_collector { + let mut attrs = HashMap::new(); + attrs.insert("mcp.method", "tools/call".to_string()); + attrs.insert("mcp.tool_name", tool_name.to_string()); + attrs.insert("mcp.session_id", mcp_session_id.clone()); + attrs.insert("http.status_code", http_status.as_u16().to_string()); + + self.record_mcp_span( + collector, + "tool_call", + &agent.id, + start_time, + end_time, + elapsed, + Some(attrs), + trace_id.clone(), + filter_span_id.clone(), + Some(mcp_span_id), + ); } - // Parse the response as JSON to extract the content - let response_json: serde_json::Value = serde_json::from_slice(&response_bytes)?; + // Handle HTTP errors + if !http_status.is_success() { + let error_body = String::from_utf8_lossy(&response_bytes).to_string(); + return Err(if http_status.is_client_error() { + PipelineError::ClientError { + agent: agent.id.clone(), + status: http_status.as_u16(), + body: error_body, + } + } else { + PipelineError::ServerError { + agent: agent.id.clone(), + status: http_status.as_u16(), + body: error_body, + } + }); + } - let content = response_json - .get("choices") - .and_then(|choices| choices.as_array()) - .and_then(|choices| choices.first()) - .and_then(|choice| choice.get("message")) - .and_then(|message| message.get("content")) - .and_then(|content| content.as_str()) - .ok_or_else(|| PipelineError::NoContentInResponse(agent.id.clone()))? + info!( + "Response from agent {}: {}", + agent.id, + String::from_utf8_lossy(&response_bytes) + ); + + // Parse SSE response + let data_chunk = self.parse_sse_response(&response_bytes, &agent.id)?; + let response: JsonRpcResponse = serde_json::from_str(&data_chunk)?; + let response_result = response + .result + .ok_or_else(|| PipelineError::NoResultInResponse(agent.id.clone()))?; + + // Check if error field is set in response result + if response_result + .get("isError") + .and_then(|v| v.as_bool()) + .unwrap_or(false) + { + let error_message = response_result + .get("content") + .and_then(|v| v.as_array()) + .and_then(|arr| arr.first()) + .and_then(|v| v.get("text")) + .and_then(|v| v.as_str()) + .unwrap_or("unknown_error") + .to_string(); + + return Err(PipelineError::ClientError { + agent: agent.id.clone(), + status: http_status.as_u16(), + body: error_message, + }); + } + + // Extract structured content and parse messages + let response_json = response_result + .get("structuredContent") + .ok_or_else(|| PipelineError::NoStructuredContentInResponse(agent.id.clone()))?; + + let messages: Vec = response_json + .get("result") + .and_then(|v| v.as_array()) + .ok_or_else(|| PipelineError::NoMessagesInResponse(agent.id.clone()))? + .iter() + .map(|msg_value| serde_json::from_value(msg_value.clone())) + .collect::, _>>() + .map_err(PipelineError::ParseError)?; + + Ok(messages) + } + + /// Build an initialize JSON-RPC request + fn build_initialize_request(&self) -> JsonRpcRequest { + JsonRpcRequest { + jsonrpc: JSON_RPC_VERSION.to_string(), + id: JsonRpcId::String(Uuid::new_v4().to_string()), + method: MCP_INITIALIZE.to_string(), + params: Some({ + let mut params = HashMap::new(); + params.insert( + "protocolVersion".to_string(), + serde_json::Value::String("2024-11-05".to_string()), + ); + params.insert("capabilities".to_string(), serde_json::json!({})); + params.insert( + "clientInfo".to_string(), + serde_json::json!({ + "name": BRIGHT_STAFF_SERVICE_NAME, + "version": "1.0.0" + }), + ); + params + }), + } + } + + /// Send initialized notification after session creation + async fn send_initialized_notification( + &self, + agent_id: &str, + session_id: &str, + trace_id: String, + parent_span_id: String, + ) -> Result<(), PipelineError> { + let initialized_notification = JsonRpcNotification { + jsonrpc: JSON_RPC_VERSION.to_string(), + method: MCP_INITIALIZE_NOTIFICATION.to_string(), + params: None, + }; + + let notification_body = serde_json::to_string(&initialized_notification)?; + debug!("Sending initialized notification for agent {}", agent_id); + + let headers = self.build_mcp_headers(&HeaderMap::new(), agent_id, Some(session_id), trace_id.clone(), parent_span_id.clone())?; + + let response = self + .client + .post(format!("{}/mcp", self.url)) + .headers(headers) + .body(notification_body) + .send() + .await?; + + info!( + "Initialized notification response status: {}", + response.status() + ); + + Ok(()) + } + + async fn get_new_session_id( + &self, + agent_id: &str, + trace_id: String, + parent_span_id: String, + ) -> String { + info!("Initializing MCP session for agent {}", agent_id); + + let initialize_request = self.build_initialize_request(); + let headers = self + .build_mcp_headers(&HeaderMap::new(), agent_id, None, trace_id.clone(), parent_span_id.clone()) + .expect("Failed to build headers for initialization"); + + let response = self + .send_mcp_request(&initialize_request, headers, agent_id) + .await + .expect("Failed to initialize MCP session"); + + info!("Initialize response status: {}", response.status()); + + let session_id = response + .headers() + .get("mcp-session-id") + .and_then(|v| v.to_str().ok()) + .expect("No mcp-session-id in response") .to_string(); - Ok(content) + info!( + "Created new MCP session for agent {}: {}", + agent_id, session_id + ); + + // Send initialized notification + self.send_initialized_notification( + agent_id, + &session_id, + trace_id.clone(), + parent_span_id.clone(), + ) + .await + .expect("Failed to send initialized notification"); + + session_id } /// Send request to terminal agent and return the raw response for streaming - pub async fn invoke_upstream_agent( + pub async fn invoke_agent( &self, messages: &[Message], - original_request: &ChatCompletionsRequest, + mut original_request: ProviderRequestType, terminal_agent: &Agent, request_headers: &HeaderMap, + trace_id: String, + agent_span_id: String, ) -> Result { - let mut request = original_request.clone(); - request.messages = messages.to_vec(); + // let mut request = original_request.clone(); + original_request.set_messages(messages); - let request_body = serde_json::to_string(&request)?; + let request_body = ProviderRequestType::to_bytes(&original_request).unwrap(); + // let request_body = serde_json::to_string(&request)?; debug!("Sending request to terminal agent {}", terminal_agent.id); let mut agent_headers = request_headers.clone(); agent_headers.remove(hyper::header::CONTENT_LENGTH); + + // Set traceparent header to make the egress span a child of the agent span + if !trace_id.is_empty() && !agent_span_id.is_empty() { + let trace_parent = format!("00-{}-{}-01", trace_id, agent_span_id); + agent_headers.remove(TRACE_PARENT_HEADER); + agent_headers.insert( + TRACE_PARENT_HEADER, + hyper::header::HeaderValue::from_str(&trace_parent).unwrap(), + ); + } + agent_headers.insert( ARCH_UPSTREAM_HOST_HEADER, hyper::header::HeaderValue::from_str(&terminal_agent.id) @@ -203,7 +704,7 @@ impl PipelineProcessor { let response = self .client - .post(&self.url) + .post(format!("{}/v1/chat/completions", self.url)) .headers(agent_headers) .body(request_body) .send() @@ -217,6 +718,7 @@ impl PipelineProcessor { mod tests { use super::*; use hermesllm::apis::openai::{Message, MessageContent, Role}; + use mockito::Server; use std::collections::HashMap; fn create_test_message(role: Role, content: &str) -> Message { @@ -240,23 +742,149 @@ mod tests { #[tokio::test] async fn test_agent_not_found_error() { - let processor = PipelineProcessor::default(); + let mut processor = PipelineProcessor::default(); let agent_map = HashMap::new(); let request_headers = HeaderMap::new(); - let initial_request = ChatCompletionsRequest { - messages: vec![create_test_message(Role::User, "Hello")], - model: "test-model".to_string(), - ..Default::default() - }; + let messages = vec![create_test_message(Role::User, "Hello")]; let pipeline = create_test_pipeline(vec!["nonexistent-agent", "terminal-agent"]); let result = processor - .process_filter_chain(&initial_request, &pipeline, &agent_map, &request_headers) + .process_filter_chain(&messages, &pipeline, &agent_map, &request_headers, None, String::new(), String::new()) .await; assert!(result.is_err()); matches!(result.unwrap_err(), PipelineError::AgentNotFound(_)); } + + #[tokio::test] + async fn test_execute_filter_http_status_error() { + let mut server = Server::new_async().await; + let _m = server + .mock("POST", "/mcp") + .with_status(500) + .with_body("boom") + .create(); + + let server_url = server.url(); + let mut processor = PipelineProcessor::new(server_url.clone()); + processor + .agent_id_session_map + .insert("agent-1".to_string(), "session-1".to_string()); + + let agent = Agent { + id: "agent-1".to_string(), + transport: None, + tool: None, + url: server_url, + agent_type: None, + }; + + let messages = vec![create_test_message(Role::User, "Hello")]; + let request_headers = HeaderMap::new(); + + let result = processor + .execute_filter(&messages, &agent, &request_headers, None, "trace-123".to_string(), "span-123".to_string()) + .await; + + match result { + Err(PipelineError::ServerError { status, body, .. }) => { + assert_eq!(status, 500); + assert_eq!(body, "boom"); + } + _ => panic!("Expected server error for 500 status"), + } + } + + #[tokio::test] + async fn test_execute_filter_http_client_error() { + let mut server = Server::new_async().await; + let _m = server + .mock("POST", "/mcp") + .with_status(400) + .with_body("bad request") + .create(); + + let server_url = server.url(); + let mut processor = PipelineProcessor::new(server_url.clone()); + processor + .agent_id_session_map + .insert("agent-3".to_string(), "session-3".to_string()); + + let agent = Agent { + id: "agent-3".to_string(), + transport: None, + tool: None, + url: server_url, + agent_type: None, + }; + + let messages = vec![create_test_message(Role::User, "Ping")]; + let request_headers = HeaderMap::new(); + + let result = processor + .execute_filter(&messages, &agent, &request_headers, None, "trace-456".to_string(), "span-456".to_string()) + .await; + + match result { + Err(PipelineError::ClientError { status, body, .. }) => { + assert_eq!(status, 400); + assert_eq!(body, "bad request"); + } + _ => panic!("Expected client error for 400 status"), + } + } + + #[tokio::test] + async fn test_execute_filter_mcp_error_flag() { + let rpc_body = serde_json::json!({ + "jsonrpc": JSON_RPC_VERSION, + "id": "1", + "result": { + "isError": true, + "content": [ + { "text": "bad tool call" } + ] + } + }); + + let sse_body = format!("event: message\ndata: {}\n\n", rpc_body.to_string()); + + let mut server = Server::new_async().await; + let _m = server + .mock("POST", "/mcp") + .with_status(200) + .with_body(sse_body) + .create(); + + let server_url = server.url(); + let mut processor = PipelineProcessor::new(server_url.clone()); + processor + .agent_id_session_map + .insert("agent-2".to_string(), "session-2".to_string()); + + let agent = Agent { + id: "agent-2".to_string(), + transport: None, + tool: None, + url: server_url, + agent_type: None, + }; + + let messages = vec![create_test_message(Role::User, "Hi")]; + let request_headers = HeaderMap::new(); + + let result = processor + .execute_filter(&messages, &agent, &request_headers, None, "trace-789".to_string(), "span-789".to_string()) + .await; + + match result { + Err(PipelineError::ClientError { status, body, .. }) => { + assert_eq!(status, 200); + assert_eq!(body, "bad tool call"); + } + _ => panic!("Expected client error when isError flag is set"), + } + } } diff --git a/crates/brightstaff/src/main.rs b/crates/brightstaff/src/main.rs index 325280e8..9a2b6706 100644 --- a/crates/brightstaff/src/main.rs +++ b/crates/brightstaff/src/main.rs @@ -1,14 +1,14 @@ use brightstaff::handlers::agent_chat_completions::agent_chat; +use brightstaff::handlers::function_calling::function_calling_chat_handler; use brightstaff::handlers::llm::llm_chat; use brightstaff::handlers::models::list_models; -use brightstaff::handlers::function_calling::{function_calling_chat_handler}; use brightstaff::router::llm_router::RouterService; use brightstaff::state::StateStorage; use brightstaff::state::postgresql::PostgreSQLConversationStorage; use brightstaff::state::memory::MemoryConversationalStorage; use brightstaff::utils::tracing::init_tracer; use bytes::Bytes; -use common::configuration::Configuration; +use common::configuration::{Agent, Configuration}; use common::consts::{CHAT_COMPLETIONS_PATH, MESSAGES_PATH, OPENAI_RESPONSES_API_PATH}; use common::traces::TraceCollector; use http_body_util::{combinators::BoxBody, BodyExt, Empty}; @@ -63,8 +63,18 @@ async fn main() -> Result<(), Box> { let arch_config = Arc::new(config); + // combine agents and filters into a single list of agents + let all_agents: Vec = arch_config + .agents + .as_deref() + .unwrap_or_default() + .iter() + .chain(arch_config.filters.as_deref().unwrap_or_default()) + .cloned() + .collect(); + let llm_providers = Arc::new(RwLock::new(arch_config.model_providers.clone())); - let agents_list = Arc::new(RwLock::new(arch_config.agents.clone())); + let combined_agents_filters_list = Arc::new(RwLock::new(Some(all_agents))); let listeners = Arc::new(RwLock::new(arch_config.listeners.clone())); let llm_provider_url = env::var("LLM_PROVIDER_ENDPOINT").unwrap_or_else(|_| "http://localhost:12001".to_string()); @@ -98,7 +108,9 @@ async fn main() -> Result<(), Box> { info!("Tracing configuration found in arch_config.yaml"); Some(true) } else { - info!("No tracing configuration in arch_config.yaml, will check OTEL_TRACING_ENABLED env var"); + info!( + "No tracing configuration in arch_config.yaml, will check OTEL_TRACING_ENABLED env var" + ); None }; let trace_collector = Arc::new(TraceCollector::new(tracing_enabled)); @@ -142,11 +154,13 @@ async fn main() -> Result<(), Box> { let io = TokioIo::new(stream); let router_service: Arc = Arc::clone(&router_service); - let model_aliases: Arc>> = Arc::clone(&model_aliases); + let model_aliases: Arc< + Option>, + > = Arc::clone(&model_aliases); let llm_provider_url = llm_provider_url.clone(); let llm_providers = llm_providers.clone(); - let agents_list = agents_list.clone(); + let agents_list = combined_agents_filters_list.clone(); let listeners = listeners.clone(); let trace_collector = trace_collector.clone(); let state_storage = state_storage.clone(); @@ -162,28 +176,36 @@ async fn main() -> Result<(), Box> { let state_storage = state_storage.clone(); async move { - match (req.method(), req.uri().path()) { - (&Method::POST, CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH) => { - let fully_qualified_url = - format!("{}{}", llm_provider_url, req.uri().path()); - llm_chat(req, router_service, fully_qualified_url, model_aliases, llm_providers, trace_collector, state_storage) - .with_context(parent_cx) - .await - } - (&Method::POST, "/agents/v1/chat/completions") => { - let fully_qualified_url = - format!("{}{}", llm_provider_url, req.uri().path()); - agent_chat( + let path = req.uri().path(); + // Check if path starts with /agents + if path.starts_with("/agents") { + // Check if it matches one of the agent API paths + let stripped_path = path.strip_prefix("/agents").unwrap(); + if matches!( + stripped_path, + CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH + ) { + let fully_qualified_url = format!("{}{}", llm_provider_url, stripped_path); + return agent_chat( req, router_service, fully_qualified_url, agents_list, listeners, + trace_collector, ) .with_context(parent_cx) - .await + .await; + } + } + match (req.method(), path) { + (&Method::POST, CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH) => { + let fully_qualified_url = + format!("{}{}", llm_provider_url, path); + llm_chat(req, router_service, fully_qualified_url, model_aliases, llm_providers, trace_collector, state_storage) + .with_context(parent_cx) + .await } - (&Method::POST, "/function_calling") => { let fully_qualified_url = format!("{}{}", llm_provider_url, "/v1/chat/completions"); diff --git a/crates/brightstaff/src/tracing/constants.rs b/crates/brightstaff/src/tracing/constants.rs index bd946aac..d3eafb2e 100644 --- a/crates/brightstaff/src/tracing/constants.rs +++ b/crates/brightstaff/src/tracing/constants.rs @@ -157,7 +157,7 @@ pub mod operation_component { pub const HANDOFF: &str = "plano(handoff)"; /// Agent filter execution - pub const AGENT_FILTER: &str = "plano(agent filter)"; + pub const AGENT_FILTER: &str = "plano(filter)"; /// Agent execution pub const AGENT: &str = "plano(agent)"; @@ -203,6 +203,7 @@ pub mod operation_component { pub struct OperationNameBuilder { method: Option, path: Option, + operation: Option, target: Option, } @@ -212,6 +213,7 @@ impl OperationNameBuilder { Self { method: None, path: None, + operation: None, target: None, } } @@ -234,6 +236,15 @@ impl OperationNameBuilder { self } + /// Set the operation type (optional, for MCP operations) + /// + /// # Arguments + /// * `operation` - Operation type (e.g., "tool_call", "session_init", "notification") + pub fn with_operation(mut self, operation: impl Into) -> Self { + self.operation = Some(operation.into()); + self + } + /// Set the target (model name, agent name, or filter name) /// /// # Arguments @@ -246,7 +257,8 @@ impl OperationNameBuilder { /// Build the operation name string /// /// # Format - /// - With all components: `{method} {path} {target}` + /// - With all components: `{method} {path} ({operation}) {target}` + /// - Without operation: `{method} {path} {target}` /// - Without target: `{method} {path}` /// - Without path: `{method}` /// - Empty: returns empty string @@ -258,7 +270,11 @@ impl OperationNameBuilder { } if let Some(path) = self.path { - parts.push(path); + if let Some(operation) = self.operation { + parts.push(format!("{} ({})", path, operation)); + } else { + parts.push(path); + } } if let Some(target) = self.target { diff --git a/crates/build.sh b/crates/build.sh new file mode 100644 index 00000000..9c3e59b2 --- /dev/null +++ b/crates/build.sh @@ -0,0 +1 @@ +cargo build --release --target wasm32-wasip1 -p prompt_gateway -p llm_gateway && cargo build --release -p brightstaff diff --git a/crates/common/src/configuration.rs b/crates/common/src/configuration.rs index b693a3a8..0f8bc78f 100644 --- a/crates/common/src/configuration.rs +++ b/crates/common/src/configuration.rs @@ -21,8 +21,11 @@ pub struct ModelAlias { #[derive(Debug, Clone, Serialize, Deserialize)] pub struct Agent { pub id: String, - pub kind: Option, + pub transport: Option, + pub tool: Option, pub url: String, + #[serde(rename = "type")] + pub agent_type: Option, } #[derive(Debug, Clone, Serialize, Deserialize)] @@ -71,6 +74,7 @@ pub struct Configuration { pub mode: Option, pub routing: Option, pub agents: Option>, + pub filters: Option>, pub listeners: Vec, pub state_storage: Option, } diff --git a/crates/common/src/consts.rs b/crates/common/src/consts.rs index 12d35ab4..ce34fad5 100644 --- a/crates/common/src/consts.rs +++ b/crates/common/src/consts.rs @@ -32,3 +32,4 @@ pub const OTEL_COLLECTOR_HTTP: &str = "opentelemetry_collector_http"; pub const OTEL_POST_PATH: &str = "/v1/traces"; pub const LLM_ROUTE_HEADER: &str = "x-arch-llm-route"; pub const ENVOY_RETRY_HEADER: &str = "x-envoy-max-retries"; +pub const BRIGHT_STAFF_SERVICE_NAME : &str = "brightstaff"; diff --git a/crates/common/src/traces/mod.rs b/crates/common/src/traces/mod.rs index c0d042fa..c4197995 100644 --- a/crates/common/src/traces/mod.rs +++ b/crates/common/src/traces/mod.rs @@ -18,7 +18,7 @@ pub use shapes::{ }; // Re-export new utilities -pub use span_builder::{SpanBuilder, SpanKind}; +pub use span_builder::{SpanBuilder, SpanKind, generate_random_span_id}; pub use resource_span_builder::ResourceSpanBuilder; pub use constants::*; diff --git a/crates/common/src/traces/span_builder.rs b/crates/common/src/traces/span_builder.rs index 187c1678..e07cfab9 100644 --- a/crates/common/src/traces/span_builder.rs +++ b/crates/common/src/traces/span_builder.rs @@ -37,6 +37,7 @@ pub struct SpanBuilder { end_time: Option, kind: SpanKind, attributes: HashMap, + span_id: Option, } impl SpanBuilder { @@ -53,6 +54,7 @@ impl SpanBuilder { end_time: None, kind: SpanKind::Internal, attributes: HashMap::new(), + span_id: None, } } @@ -62,6 +64,11 @@ impl SpanBuilder { self } + pub fn with_span_id(mut self, span_id: impl Into) -> Self { + self.span_id = Some(span_id.into()); + self + } + /// Set the parent span ID to link this span to its parent pub fn with_parent_span_id(mut self, parent_span_id: impl Into) -> Self { self.parent_span_id = Some(parent_span_id.into()); @@ -125,7 +132,7 @@ impl SpanBuilder { // Build span directly without going through Span::new() Span { trace_id, - span_id: generate_random_span_id(), + span_id: self.span_id.unwrap_or_else(|| generate_random_span_id()), parent_span_id: self.parent_span_id, name: self.name, start_time_unix_nano: format!("{}", start_nanos), @@ -145,7 +152,7 @@ fn system_time_to_nanos(time: SystemTime) -> u128 { } /// Generate a random span ID (16 hex characters = 8 bytes) -fn generate_random_span_id() -> String { +pub fn generate_random_span_id() -> String { use rand::RngCore; let mut rng = rand::thread_rng(); let mut random_bytes = [0u8; 8]; diff --git a/crates/hermesllm/src/apis/amazon_bedrock.rs b/crates/hermesllm/src/apis/amazon_bedrock.rs index 7b4a511f..3c953850 100644 --- a/crates/hermesllm/src/apis/amazon_bedrock.rs +++ b/crates/hermesllm/src/apis/amazon_bedrock.rs @@ -233,6 +233,104 @@ impl ProviderRequest for ConverseRequest { fn get_temperature(&self) -> Option { self.inference_config.as_ref()?.temperature } + + fn get_messages(&self) -> Vec { + use crate::apis::openai::{Message, MessageContent, Role}; + + let mut openai_messages = Vec::new(); + + // Add system messages if present + if let Some(system) = &self.system { + for sys_block in system { + match sys_block { + SystemContentBlock::Text { text } => { + openai_messages.push(Message { + role: Role::System, + content: MessageContent::Text(text.clone()), + name: None, + tool_calls: None, + tool_call_id: None, + }); + } + _ => {} // Skip other system content types + } + } + } + + // Convert conversation messages + if let Some(messages) = &self.messages { + for msg in messages { + let role = match msg.role { + ConversationRole::User => Role::User, + ConversationRole::Assistant => Role::Assistant, + }; + + // Extract text from content blocks + let content = msg.content.iter() + .filter_map(|block| { + if let ContentBlock::Text { text } = block { + Some(text.clone()) + } else { + None + } + }) + .collect::>() + .join("\n"); + + openai_messages.push(Message { + role, + content: MessageContent::Text(content), + name: None, + tool_calls: None, + tool_call_id: None, + }); + } + } + + openai_messages + } + + fn set_messages(&mut self, messages: &[crate::apis::openai::Message]) { + // Convert OpenAI messages to Bedrock format + use crate::apis::amazon_bedrock::{ContentBlock, ConversationRole, SystemContentBlock}; + + let mut system_blocks = Vec::new(); + let mut bedrock_messages = Vec::new(); + + for msg in messages { + match msg.role { + crate::apis::openai::Role::System => { + if let crate::apis::openai::MessageContent::Text(text) = &msg.content { + system_blocks.push(SystemContentBlock::Text { text: text.clone() }); + } + } + crate::apis::openai::Role::User | crate::apis::openai::Role::Assistant => { + let role = match msg.role { + crate::apis::openai::Role::User => ConversationRole::User, + crate::apis::openai::Role::Assistant => ConversationRole::Assistant, + _ => continue, + }; + + let content = if let crate::apis::openai::MessageContent::Text(text) = &msg.content { + vec![ContentBlock::Text { text: text.clone() }] + } else { + vec![] + }; + + bedrock_messages.push(crate::apis::amazon_bedrock::Message { + role, + content, + }); + } + _ => {} + } + } + + if !system_blocks.is_empty() { + self.system = Some(system_blocks); + } + self.messages = Some(bedrock_messages); + } } // ============================================================================ diff --git a/crates/hermesllm/src/apis/anthropic.rs b/crates/hermesllm/src/apis/anthropic.rs index 06d632d9..20f422e2 100644 --- a/crates/hermesllm/src/apis/anthropic.rs +++ b/crates/hermesllm/src/apis/anthropic.rs @@ -541,6 +541,65 @@ impl ProviderRequest for MessagesRequest { fn get_temperature(&self) -> Option { self.temperature } + + fn get_messages(&self) -> Vec { + use crate::apis::openai::Message; + + let mut openai_messages = Vec::new(); + + // Add system prompt as system message if present + if let Some(system) = &self.system { + openai_messages.push(system.clone().into()); + } + + // Convert each Anthropic message to OpenAI format + for msg in &self.messages { + if let Ok(converted_msgs) = TryInto::>::try_into(msg.clone()) { + openai_messages.extend(converted_msgs); + } + } + + openai_messages + } + + fn set_messages(&mut self, messages: &[crate::apis::openai::Message]) { + // Convert OpenAI messages to Anthropic format + // Separate system messages from regular messages + let mut system_messages = Vec::new(); + let mut regular_messages = Vec::new(); + + for msg in messages { + if msg.role == crate::apis::openai::Role::System { + system_messages.push(msg.clone()); + } else { + regular_messages.push(msg.clone()); + } + } + + // Set system prompt if there are system messages + if !system_messages.is_empty() { + // Combine all system messages into one + let system_text = system_messages.iter() + .filter_map(|msg| { + if let crate::apis::openai::MessageContent::Text(text) = &msg.content { + Some(text.as_str()) + } else { + None + } + }) + .collect::>() + .join("\n"); + + self.system = Some(crate::apis::anthropic::MessagesSystemPrompt::Single(system_text)); + } + + // Convert regular messages + self.messages = regular_messages.iter() + .filter_map(|msg| { + msg.clone().try_into().ok() + }) + .collect(); + } } impl MessagesResponse { diff --git a/crates/hermesllm/src/apis/openai.rs b/crates/hermesllm/src/apis/openai.rs index 4e006c3a..79154d39 100644 --- a/crates/hermesllm/src/apis/openai.rs +++ b/crates/hermesllm/src/apis/openai.rs @@ -735,6 +735,14 @@ impl ProviderRequest for ChatCompletionsRequest { fn get_temperature(&self) -> Option { self.temperature } + + fn get_messages(&self) -> Vec { + self.messages.clone() + } + + fn set_messages(&mut self, messages: &[crate::apis::openai::Message]) { + self.messages = messages.to_vec(); + } } /// Implementation of ProviderResponse for ChatCompletionsResponse diff --git a/crates/hermesllm/src/apis/openai_responses.rs b/crates/hermesllm/src/apis/openai_responses.rs index 33dea44a..6afe9f09 100644 --- a/crates/hermesllm/src/apis/openai_responses.rs +++ b/crates/hermesllm/src/apis/openai_responses.rs @@ -1134,6 +1134,140 @@ impl ProviderRequest for ResponsesAPIRequest { fn get_temperature(&self) -> Option { self.temperature } + + fn get_messages(&self) -> Vec { + use crate::apis::openai::{Message, MessageContent, Role}; + + let mut openai_messages = Vec::new(); + + // Add instructions as system message if present + if let Some(instructions) = &self.instructions { + openai_messages.push(Message { + role: Role::System, + content: MessageContent::Text(instructions.clone()), + name: None, + tool_calls: None, + tool_call_id: None, + }); + } + + // Convert input to messages + match &self.input { + InputParam::Text(text) => { + openai_messages.push(Message { + role: Role::User, + content: MessageContent::Text(text.clone()), + name: None, + tool_calls: None, + tool_call_id: None, + }); + } + InputParam::Items(items) => { + for item in items { + match item { + InputItem::Message(msg) => { + // Convert message role + let role = match msg.role { + MessageRole::User => Role::User, + MessageRole::Assistant => Role::Assistant, + MessageRole::System => Role::System, + MessageRole::Developer => Role::System, // Map developer to system + }; + + // Extract text from message content + let content = match &msg.content { + crate::apis::openai_responses::MessageContent::Text(text) => text.clone(), + crate::apis::openai_responses::MessageContent::Items(items) => { + items.iter() + .filter_map(|c| { + if let InputContent::InputText { text } = c { + Some(text.clone()) + } else { + None + } + }) + .collect::>() + .join("\n") + } + }; + + openai_messages.push(Message { + role, + content: MessageContent::Text(content), + name: None, + tool_calls: None, + tool_call_id: None, + }); + } + // Skip other input item types for now + InputItem::ItemReference { .. } | InputItem::FunctionCallOutput { .. } => { + // These are not yet supported in agent framework + } + } + } + } + } + + openai_messages + } + + fn set_messages(&mut self, messages: &[crate::apis::openai::Message]) { + // For ResponsesAPI, we need to convert messages back to input format + // Extract system messages as instructions + let system_text = messages.iter() + .filter(|msg| msg.role == crate::apis::openai::Role::System) + .filter_map(|msg| { + if let crate::apis::openai::MessageContent::Text(text) = &msg.content { + Some(text.as_str()) + } else { + None + } + }) + .collect::>() + .join("\n"); + + if !system_text.is_empty() { + self.instructions = Some(system_text); + } + + // Convert user/assistant messages to InputParam + // For simplicity, we'll use the last user message as the input + // or combine all non-system messages + let input_messages: Vec<_> = messages.iter() + .filter(|msg| msg.role != crate::apis::openai::Role::System) + .collect(); + + if !input_messages.is_empty() { + // If there's only one message, use Text format + if input_messages.len() == 1 { + if let crate::apis::openai::MessageContent::Text(text) = &input_messages[0].content { + self.input = crate::apis::openai_responses::InputParam::Text(text.clone()); + } + } else { + // Multiple messages - combine them as text for now + // A more sophisticated approach would use InputParam::Items + let combined_text = input_messages.iter() + .filter_map(|msg| { + if let crate::apis::openai::MessageContent::Text(text) = &msg.content { + Some(format!("{}: {}", + match msg.role { + crate::apis::openai::Role::User => "User", + crate::apis::openai::Role::Assistant => "Assistant", + _ => "Unknown", + }, + text + )) + } else { + None + } + }) + .collect::>() + .join("\n"); + + self.input = crate::apis::openai_responses::InputParam::Text(combined_text); + } + } + } } // ============================================================================ diff --git a/crates/hermesllm/src/providers/request.rs b/crates/hermesllm/src/providers/request.rs index eb8f0788..7cd951c3 100644 --- a/crates/hermesllm/src/providers/request.rs +++ b/crates/hermesllm/src/providers/request.rs @@ -47,6 +47,28 @@ pub trait ProviderRequest: Send + Sync { fn remove_metadata_key(&mut self, key: &str) -> bool; fn get_temperature(&self) -> Option; + + /// Get message history as OpenAI Message format + /// This is useful for processing chat history across different provider formats + fn get_messages(&self) -> Vec; + + /// Set message history from OpenAI Message format + /// This converts OpenAI messages to the appropriate format for each provider type + fn set_messages(&mut self, messages: &[crate::apis::openai::Message]); +} + +impl ProviderRequestType { + /// Set message history from OpenAI Message format + /// This converts OpenAI messages to the appropriate format for each provider type + pub fn set_messages(&mut self, messages: &[crate::apis::openai::Message]) { + match self { + Self::ChatCompletionsRequest(r) => r.set_messages(messages), + Self::MessagesRequest(r) => r.set_messages(messages), + Self::BedrockConverse(r) => r.set_messages(messages), + Self::BedrockConverseStream(r) => r.set_messages(messages), + Self::ResponsesAPIRequest(r) => r.set_messages(messages), + } + } } impl ProviderRequest for ProviderRequestType { @@ -149,6 +171,26 @@ impl ProviderRequest for ProviderRequestType { Self::ResponsesAPIRequest(r) => r.get_temperature(), } } + + fn get_messages(&self) -> Vec { + match self { + Self::ChatCompletionsRequest(r) => r.get_messages(), + Self::MessagesRequest(r) => r.get_messages(), + Self::BedrockConverse(r) => r.get_messages(), + Self::BedrockConverseStream(r) => r.get_messages(), + Self::ResponsesAPIRequest(r) => r.get_messages(), + } + } + + fn set_messages(&mut self, messages: &[crate::apis::openai::Message]) { + match self { + Self::ChatCompletionsRequest(r) => r.set_messages(messages), + Self::MessagesRequest(r) => r.set_messages(messages), + Self::BedrockConverse(r) => r.set_messages(messages), + Self::BedrockConverseStream(r) => r.set_messages(messages), + Self::ResponsesAPIRequest(r) => r.set_messages(messages), + } + } } /// Parse the client API from a byte slice. @@ -934,4 +976,131 @@ mod tests { .message .contains("OpenAI ChatCompletions, Anthropic Messages, and OpenAI Responses")); } + + #[test] + fn test_get_message_history_chat_completions() { + use crate::apis::openai::{Message, MessageContent, Role}; + + let chat_req = ChatCompletionsRequest { + model: "gpt-4".to_string(), + messages: vec![ + Message { + role: Role::System, + content: MessageContent::Text("You are helpful".to_string()), + name: None, + tool_calls: None, + tool_call_id: None, + }, + Message { + role: Role::User, + content: MessageContent::Text("Hello!".to_string()), + name: None, + tool_calls: None, + tool_call_id: None, + }, + ], + ..Default::default() + }; + + let provider_req = ProviderRequestType::ChatCompletionsRequest(chat_req); + let messages = provider_req.get_messages(); + + assert_eq!(messages.len(), 2); + assert_eq!(messages[0].role, Role::System); + assert_eq!(messages[1].role, Role::User); + } + + #[test] + fn test_get_message_history_anthropic_messages() { + use crate::apis::anthropic::{ + MessagesMessage, MessagesMessageContent, MessagesRequest, MessagesRole, + MessagesSystemPrompt, + }; + + let anthropic_req = MessagesRequest { + model: "claude-3-sonnet".to_string(), + messages: vec![MessagesMessage { + role: MessagesRole::User, + content: MessagesMessageContent::Single("Hello!".to_string()), + }], + system: Some(MessagesSystemPrompt::Single( + "You are helpful".to_string(), + )), + max_tokens: 100, + container: None, + mcp_servers: None, + metadata: None, + service_tier: None, + thinking: None, + temperature: None, + top_p: None, + top_k: None, + stream: None, + stop_sequences: None, + tools: None, + tool_choice: None, + }; + + let provider_req = ProviderRequestType::MessagesRequest(anthropic_req); + let messages = provider_req.get_messages(); + + // Should have system message + user message + assert_eq!(messages.len(), 2); + assert_eq!( + messages[0].role, + crate::apis::openai::Role::System + ); + assert_eq!( + messages[1].role, + crate::apis::openai::Role::User + ); + } + + #[test] + fn test_get_message_history_responses_api() { + use crate::apis::openai_responses::{InputParam, ResponsesAPIRequest}; + + let responses_req = ResponsesAPIRequest { + model: "gpt-4o".to_string(), + input: InputParam::Text("Hello, world!".to_string()), + instructions: Some("Be helpful".to_string()), + temperature: None, + max_output_tokens: None, + stream: None, + metadata: None, + tools: None, + tool_choice: None, + parallel_tool_calls: None, + modalities: None, + user: None, + store: None, + reasoning_effort: None, + include: None, + audio: None, + text: None, + service_tier: None, + top_p: None, + top_logprobs: None, + stream_options: None, + truncation: None, + conversation: None, + previous_response_id: None, + max_tool_calls: None, + background: None, + }; + + let provider_req = ProviderRequestType::ResponsesAPIRequest(responses_req); + let messages = provider_req.get_messages(); + + // Should have system message (instructions) + user message (input) + assert_eq!(messages.len(), 2); + assert_eq!( + messages[0].role, + crate::apis::openai::Role::System + ); + assert_eq!( + messages[1].role, + crate::apis::openai::Role::User + ); + } } diff --git a/demos/use_cases/mcp_filter/README.md b/demos/use_cases/mcp_filter/README.md new file mode 100644 index 00000000..a524c1b4 --- /dev/null +++ b/demos/use_cases/mcp_filter/README.md @@ -0,0 +1,106 @@ +# RAG Agent Demo + +A multi-agent RAG system demonstrating archgw's agent filter chain with MCP protocol. + +## Architecture + +This demo consists of three components: +1. **Query Rewriter** (MCP filter) - Rewrites user queries for better retrieval +2. **Context Builder** (MCP filter) - Retrieves relevant context from knowledge base +3. **RAG Agent** (REST) - Generates final responses based on augmented context + +## Components + +### Query Rewriter Filter (MCP) +- **Port**: 10501 +- **Tool**: `query_rewriter` +- Improves queries using LLM before retrieval + +### Context Builder Filter (MCP) +- **Port**: 10502 +- **Tool**: `context_builder` +- Augments queries with relevant passages from knowledge base + +### RAG Agent (REST/OpenAI) +- **Port**: 10505 +- **Endpoint**: `/v1/chat/completions` +- Generates responses using OpenAI-compatible API + +## Quick Start + +### 1. Start all agents +```bash +./start_agents.sh +``` + +This starts: +- Query Rewriter MCP server on port 10501 +- Context Builder MCP server on port 10502 +- RAG Agent REST server on port 10505 + +### 2. Start archgw +```bash +archgw up --foreground +``` + +### 3. Test the system +```bash +curl -X POST http://localhost:8001/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o", + "messages": [{"role": "user", "content": "What is the guaranteed uptime for TechCorp?"}] + }' +``` + +## Configuration + +The `arch_config.yaml` defines how agents are connected: + +```yaml +filters: + - id: query_rewriter + url: mcp://host.docker.internal:10500 + tool: rewrite_query_with_archgw # MCP tool name + + - id: context_builder + url: mcp://host.docker.internal:10501 + tool: chat_completions +``` +How It Works + +1. User sends request to archgw listener on port 8001 +2. Request passes through MCP filter chain: + - **Query Rewriter** rewrites the query for better retrieval + - **Context Builder** augments query with relevant knowledge base passages +3. Augmented request is forwarded to **RAG Agent** REST endpoint +4. RAG Agent generates final response using LLM + +## Configuration + +See `arch_config.yaml` for the complete filter chain setup. The MCP filters use default settings: +- `type: mcp` (default) +- `transport: streamable-http` (default) +- Tool name defaults to filter ID `sample_queries.md` for example queries to test the RAG system. + +Example request: +```bash +curl -X POST http://localhost:8001/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o", + "messages": [ + { + "role": "user", + "content": "What is the guaranteed uptime for TechCorp?" + } + ] + }' +``` +- `LLM_GATEWAY_ENDPOINT` - archgw endpoint (default: `http://localhost:12000/v1`) +- `OPENAI_API_KEY` - OpenAI API key for model providers + +## Additional Resources + +- See `sample_queries.md` for more example queries +- See `arch_config.yaml` for complete configuration details diff --git a/demos/use_cases/mcp_filter/arch_config.yaml b/demos/use_cases/mcp_filter/arch_config.yaml new file mode 100644 index 00000000..e5aacc03 --- /dev/null +++ b/demos/use_cases/mcp_filter/arch_config.yaml @@ -0,0 +1,41 @@ +version: v0.3.0 + +agents: + - id: rag_agent + url: http://host.docker.internal:10505 + +filters: + - id: query_rewriter + url: http://host.docker.internal:10501 + # type: mcp # default is mcp + # transport: streamable-http # default is streamable-http + # tool: query_rewriter # default name is the filter id + - id: context_builder + url: http://host.docker.internal:10502 + +model_providers: + - model: openai/gpt-4o-mini + access_key: $OPENAI_API_KEY + default: true + - model: openai/gpt-4o + access_key: $OPENAI_API_KEY + +model_aliases: + fast-llm: + target: gpt-4o-mini + smart-llm: + target: gpt-4o + +listeners: + - type: agent + name: agent_1 + port: 8001 + router: arch_agent_router + agents: + - id: rag_agent + description: virtual assistant for retrieval augmented generation tasks + filter_chain: + - query_rewriter + - context_builder +tracing: + random_sampling: 100 diff --git a/demos/use_cases/mcp_filter/docker-compose.yaml b/demos/use_cases/mcp_filter/docker-compose.yaml new file mode 100644 index 00000000..a5d45ed9 --- /dev/null +++ b/demos/use_cases/mcp_filter/docker-compose.yaml @@ -0,0 +1,17 @@ +services: + jaeger: + build: + context: ../../shared/jaeger + ports: + - "16686:16686" + - "4317:4317" + - "4318:4318" + open-web-ui: + image: dyrnq/open-webui:main + restart: always + ports: + - "8080:8080" + environment: + - DEFAULT_MODEL=gpt-4o-mini + - ENABLE_OPENAI_API=true + - OPENAI_API_BASE_URL=http://host.docker.internal:8001/v1 diff --git a/demos/use_cases/mcp_filter/mcp_query.rest b/demos/use_cases/mcp_filter/mcp_query.rest new file mode 100644 index 00000000..c08dd884 --- /dev/null +++ b/demos/use_cases/mcp_filter/mcp_query.rest @@ -0,0 +1,86 @@ +### Initialize MCP Session (SSE) +POST http://localhost:10501/mcp +Content-Type: application/json +Accept: application/json, text/event-stream + +{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"capabilities":{},"protocolVersion":"2024-11-05","clientInfo":{"name":"test","version":"1.0.0"}}} + +### Send Initialized Notification +POST http://localhost:10501/mcp +Content-Type: application/json +Accept: application/json, text/event-stream +mcp-session-id: 35d455dc07b8400887f86668590f12bb + +{ + "jsonrpc": "2.0", + "method": "notifications/initialized" +} + +### List Tools +POST http://localhost:10501/mcp +Content-Type: application/json +Accept: application/json, text/event-stream +mcp-session-id: eb10a691b36e4547b6c93c5dc5b47e11 + +{ + "jsonrpc": "2.0", + "id": "list-tools-1", + "method": "tools/list" +} + +### Call Query Rewriter Tool +POST http://localhost:10501/mcp +Content-Type: application/json +Accept: application/json, text/event-stream +mcp-session-id: 6b95ff75825a402b90eb3ea07e23fbce + +{ + "jsonrpc": "2.0", + "id": "3d3b886a-6216-4a26-a422-7a972529c0e7", + "method": "tools/call", + "params": { + "arguments": { + "messages": [ + { + "content": "What is the guaranteed uptime percentage for TechCorp's cloud services?", + "role": "user" + } + ] + }, + "name": "query_rewriter" + } +} + +### another test + +# Content-Type: application/json +# Accept: application/json, text/event-stream +# mcp-session-id: ed7a81a1d39549ecaadb867a6b2daf1e + +POST http://localhost:10501/mcp +content-type: application/json +mcp-session-id: e4ec1ae904e14e06b7d194da10e5f74c +accept: application/json, text/event-stream + +{"jsonrpc":"2.0","id":"4bb1043a-2953-4bcd-b801-f270b0ae8c39","method":"tools/call","params":{"arguments":{"messages":[{"content":"What is the guaranteed uptime percentage for TechCorp's cloud services?","role":"user"}]},"name":"query_rewriter"}} + + + +### stream test + +POST http://localhost:10501/mcp +content-type: application/json +mcp-session-id: 35d455dc07b8400887f86668590f12bb +accept: application/json, text/event-stream + +{ + "jsonrpc": "2.0", + "id": 1, + "method": "tools/call", + "params": { + "name": "long_job", + "arguments": { + "n": 3 + } + } +} diff --git a/demos/use_cases/mcp_filter/pyproject.toml b/demos/use_cases/mcp_filter/pyproject.toml new file mode 100644 index 00000000..7cd9411b --- /dev/null +++ b/demos/use_cases/mcp_filter/pyproject.toml @@ -0,0 +1,22 @@ +[project] +name = "rag_agent" +version = "0.1.0" +description = "RAG Agent" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "click>=8.2.1", + "mcp>=1.13.1", + "fastmcp>=2.14", + "pydantic>=2.11.7", + "fastapi>=0.104.1", + "uvicorn>=0.24.0", + "openai==2.13.0", +] + +[project.scripts] +rag_agent = "rag_agent:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/demos/use_cases/mcp_filter/sample_queries.md b/demos/use_cases/mcp_filter/sample_queries.md new file mode 100644 index 00000000..ba98cdfd --- /dev/null +++ b/demos/use_cases/mcp_filter/sample_queries.md @@ -0,0 +1,64 @@ +# Sample Queries for Knowledge Base RAG Agent + +## Service Level Agreement Queries +- What is the guaranteed uptime percentage for TechCorp's cloud services? +- What remedies are available if the API response time exceeds the agreed threshold? +- How quickly must TechCorp respond to critical support issues? +- What monitoring and reporting requirements are specified in the SLA? +- When was the TechCorp service agreement signed and by whom? + +## Privacy Policy Queries +- What encryption methods does DataSecure use to protect data? +- How long does DataSecure retain personal data after account deletion? +- What rights do users have regarding their personal information? +- Can DataSecure sell user data to third parties for marketing? +- Who should be contacted for privacy-related concerns at DataSecure? + +## Supply Chain Agreement Queries +- What types of automotive components does PrecisionParts supply? +- What are the payment terms and volume discount structure? +- What quality standards must the supplied components meet? +- What are the penalties for late delivery? +- What insurance coverage requirements apply to the supplier? + +## Student Data Management Queries +- What federal laws must EduTech comply with regarding student data? +- What security measures are in place to protect student information? +- How long are student records retained after graduation? +- What consent is required for students under 13 years old? +- Who can access student educational records? + +## Investment Advisory Queries +- What is FinanceFirst's management fee structure? +- What types of investments are included in the advisory services? +- What regulatory body oversees FinanceFirst Advisors? +- How often are portfolio reviews conducted? +- What are the client's responsibilities under this agreement? + +## Healthcare Standards Queries +- What is the target response time for emergency code teams? +- What hand hygiene compliance rate is required? +- How quickly must medical records be completed after patient encounters? +- What continuing education requirements apply to nursing staff? +- What patient safety protocols are mandatory upon admission? + +## Cross-Document Queries +- Which agreements include confidentiality or data protection provisions? +- What are the common termination notice periods across different contract types? +- Which documents specify insurance or liability coverage requirements? +- What compliance and regulatory requirements are mentioned across agreements? +- Which contracts include performance metrics or service level commitments? + +## Complex Analysis Queries +- Compare the data retention policies across the privacy policy and student data management documents. +- What are the different approaches to risk management across the supply chain and investment advisory agreements? +- How do the security measures in the healthcare standards compare to those in the privacy policy? +- Which agreements provide the most detailed compliance and regulatory frameworks? +- What common themes exist in the quality assurance requirements across different industries? + +## Document-Specific Detail Queries +- List all the specific percentages, timeframes, and numerical requirements mentioned in the SLA. +- What are all the contact persons and their roles mentioned across the documents? +- Identify all the compliance standards and certifications referenced in the supply chain agreement. +- What are the specific consequences or penalties mentioned for non-compliance across agreements? +- List all the third-party systems, tools, or services mentioned in the documents. diff --git a/demos/use_cases/mcp_filter/src/rag_agent/__init__.py b/demos/use_cases/mcp_filter/src/rag_agent/__init__.py new file mode 100644 index 00000000..08f8e21f --- /dev/null +++ b/demos/use_cases/mcp_filter/src/rag_agent/__init__.py @@ -0,0 +1,98 @@ +import click +from fastmcp import FastMCP + +mcp = None + + +@click.command() +@click.option( + "--transport", + "transport", + default="streamable-http", + help="Transport type: stdio or sse", +) +@click.option("--host", "host", default="localhost", help="Host to bind MCP server to") +@click.option("--port", "port", type=int, default=10500, help="Port for MCP server") +@click.option( + "--agent", + "agent", + required=True, + help="Agent name: query_rewriter, context_builder, or response_generator", +) +@click.option( + "--name", + "agent_name", + default=None, + help="Custom MCP server name (defaults to agent type)", +) +@click.option( + "--rest-server", + "rest_server", + is_flag=True, + help="Start REST server instead of MCP server", +) +@click.option("--rest-port", "rest_port", default=8000, help="Port for REST server") +def main(host, port, agent, transport, agent_name, rest_server, rest_port): + """Start a RAG agent as an MCP server or REST server.""" + + # Map friendly names to agent modules + agent_map = { + "query_rewriter": ("rag_agent.query_rewriter", "Query Rewriter Agent"), + "context_builder": ("rag_agent.context_builder", "Context Builder Agent"), + "response_generator": ( + "rag_agent.rag_agent", + "Response Generator Agent", + ), + } + + if agent not in agent_map: + print(f"Error: Unknown agent '{agent}'") + print(f"Available agents: {', '.join(agent_map.keys())}") + return + + module_name, default_name = agent_map[agent] + mcp_name = agent_name or default_name + + if rest_server: + # Only response_generator supports REST server mode + if agent != "response_generator": + print(f"Error: Agent '{agent}' does not support REST server mode.") + print(f"REST server is only supported for: response_generator") + print(f"Remove --rest-server flag to start {agent} as an MCP server.") + return + + print(f"Starting REST server on {host}:{rest_port} for agent: {agent}") + from rag_agent.rag_agent import start_server + + start_server(host=host, port=rest_port) + return + else: + # Only query_rewriter and context_builder support MCP + if agent not in ["query_rewriter", "context_builder"]: + print(f"Error: Agent '{agent}' does not support MCP mode.") + print(f"MCP is only supported for: query_rewriter, context_builder") + print(f"Use --rest-server flag to start {agent} as a REST server.") + return + + global mcp + mcp = FastMCP(mcp_name, host=host, port=port) + + print(f"Starting MCP server: {mcp_name}") + print(f" Agent: {agent}") + print(f" Transport: {transport}") + print(f" Host: {host}") + print(f" Port: {port}") + + # Import the agent module to register its tools + import importlib + + importlib.import_module(module_name) + + print(f"Agent '{agent}' loaded successfully") + print(f"MCP server ready on {transport}://{host}:{port}") + + mcp.run(transport=transport) + + +if __name__ == "__main__": + main() diff --git a/demos/use_cases/mcp_filter/src/rag_agent/__main__.py b/demos/use_cases/mcp_filter/src/rag_agent/__main__.py new file mode 100644 index 00000000..868d99ef --- /dev/null +++ b/demos/use_cases/mcp_filter/src/rag_agent/__main__.py @@ -0,0 +1,4 @@ +from . import main + +if __name__ == "__main__": + main() diff --git a/demos/use_cases/mcp_filter/src/rag_agent/api.py b/demos/use_cases/mcp_filter/src/rag_agent/api.py new file mode 100644 index 00000000..eb63ea99 --- /dev/null +++ b/demos/use_cases/mcp_filter/src/rag_agent/api.py @@ -0,0 +1,36 @@ +from pydantic import BaseModel +from typing import List, Optional, Dict, Any + + +class ChatMessage(BaseModel): + role: str + content: str + + +class ChatCompletionRequest(BaseModel): + model: str + messages: List[ChatMessage] + temperature: Optional[float] = 1.0 + max_tokens: Optional[int] = None + top_p: Optional[float] = 1.0 + frequency_penalty: Optional[float] = 0.0 + presence_penalty: Optional[float] = 0.0 + stream: Optional[bool] = False + stop: Optional[List[str]] = None + + +class ChatCompletionResponse(BaseModel): + id: str + object: str = "chat.completion" + created: int + model: str + choices: List[Dict[str, Any]] + usage: Dict[str, int] + + +class ChatCompletionStreamResponse(BaseModel): + id: str + object: str = "chat.completion.chunk" + created: int + model: str + choices: List[Dict[str, Any]] diff --git a/demos/use_cases/mcp_filter/src/rag_agent/context_builder.py b/demos/use_cases/mcp_filter/src/rag_agent/context_builder.py new file mode 100644 index 00000000..2fa6e307 --- /dev/null +++ b/demos/use_cases/mcp_filter/src/rag_agent/context_builder.py @@ -0,0 +1,205 @@ +import json +from typing import List, Optional, Dict, Any +from openai import AsyncOpenAI +import os +import logging +import csv +from pathlib import Path + +from .api import ChatMessage +from . import mcp +from fastmcp.server.dependencies import get_http_headers + +# Set up logging +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - [CONTEXT_BUILDER] - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + + +# Configuration for archgw LLM gateway +LLM_GATEWAY_ENDPOINT = os.getenv("LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1") +RAG_MODEL = "gpt-4o-mini" + +# Initialize OpenAI client for archgw +archgw_client = AsyncOpenAI( + base_url=LLM_GATEWAY_ENDPOINT, + api_key="EMPTY", # archgw doesn't require a real API key +) + +# Global variable to store the knowledge base +knowledge_base = [] + + +def load_knowledge_base(): + """Load the sample_knowledge_base.csv file into memory on startup.""" + global knowledge_base + + # Get the path to the CSV file relative to this script + current_dir = Path(__file__).parent + csv_path = current_dir / "sample_knowledge_base.csv" + + print(f"Loading knowledge base from {csv_path}") + + try: + knowledge_base = [] + with open(csv_path, "r", encoding="utf-8-sig") as file: + csv_reader = csv.DictReader(file) + for row in csv_reader: + knowledge_base.append({"path": row["path"], "content": row["content"]}) + + logger.info(f"Loaded {len(knowledge_base)} documents from knowledge base") + + except Exception as e: + logger.error(f"Error loading knowledge base: {e}") + knowledge_base = [] + + +async def find_relevant_passages( + query: str, traceparent: Optional[str] = None, top_k: int = 3 +) -> List[Dict[str, str]]: + """Use the LLM to find the most relevant passages from the knowledge base.""" + + if not knowledge_base: + logger.warning("Knowledge base is empty") + return [] + + # Create a system prompt for passage selection + system_prompt = f"""You are a retrieval assistant that selects the most relevant document passages for a given query. + + Given a user query and a list of document passages, identify the {top_k} most relevant passages that would help answer the query. + + Query: {query} + + Available passages: + """ + + # Add all passages with indices + for i, doc in enumerate(knowledge_base): + system_prompt += ( + f"\n[{i}] Path: {doc['path']}\nContent: {doc['content'][:500]}...\n" + ) + + system_prompt += f""" + + Please respond with ONLY the indices of the {top_k} most relevant passages, separated by commas (e.g., "0,3,7"). + If fewer than {top_k} passages are relevant, return only the relevant ones. + If no passages are relevant, return "NONE".""" + + try: + # Call archgw to select relevant passages + logger.info(f"Calling archgw to find relevant passages for query: '{query}'") + + # Prepare extra headers if traceparent is provided + extra_headers = {"x-envoy-max-retries": "3"} + if traceparent: + extra_headers["traceparent"] = traceparent + + response = await archgw_client.chat.completions.create( + model=RAG_MODEL, + messages=[{"role": "system", "content": system_prompt}], + temperature=0.1, + max_tokens=50, + extra_headers=extra_headers, + ) + + result = response.choices[0].message.content.strip() + logger.info(f"LLM selected passages: {result}") + + # Parse the indices + if result.upper() == "NONE": + return [] + + selected_passages = [] + indices = [ + int(idx.strip()) for idx in result.split(",") if idx.strip().isdigit() + ] + + for idx in indices: + if 0 <= idx < len(knowledge_base): + selected_passages.append(knowledge_base[idx]) + + logger.info(f"Selected {len(selected_passages)} relevant passages") + return selected_passages + + except Exception as e: + logger.error(f"Error finding relevant passages: {e}") + return [] + + +async def augment_query_with_context( + messages: List[ChatMessage], traceparent: Optional[str] = None +) -> List[ChatMessage]: + """Extract user query, find relevant context, and augment the messages.""" + + # Find the last user message + last_user_message = None + last_user_index = -1 + + for i in range(len(messages) - 1, -1, -1): + if messages[i].role == "user": + last_user_message = messages[i].content + last_user_index = i + break + + if not last_user_message: + logger.warning("No user message found in conversation") + return messages + + logger.info(f"Processing user query: '{last_user_message}'") + + # Find relevant passages + relevant_passages = await find_relevant_passages(last_user_message, traceparent) + + if not relevant_passages: + logger.info("No relevant passages found, returning original messages") + return messages + + # Build context from relevant passages + context_parts = [] + for i, passage in enumerate(relevant_passages): + context_parts.append( + f"Document {i+1} ({passage['path']}):\n{passage['content']}" + ) + + context = "\n\n".join(context_parts) + + # Create augmented content with original query and context + augmented_content = f"""{last_user_message} RELEVANT CONTEXT: + {context}""" + + # Create updated messages with the augmented query + updated_messages = messages.copy() + updated_messages[last_user_index] = ChatMessage( + role="user", content=augmented_content + ) + + logger.info(f"Augmented user query with {len(relevant_passages)} relevant passages") + + return updated_messages + + +# Load knowledge base on module import +load_knowledge_base() + + +@mcp.tool() +async def context_builder(messages: List[ChatMessage]) -> List[ChatMessage]: + """MCP tool that augments user queries with relevant context from the knowledge base.""" + logger.info(f"Received chat completion request with {len(messages)} messages") + + # Get traceparent header from MCP request + headers = get_http_headers() + traceparent_header = headers.get("traceparent") + + if traceparent_header: + logger.info(f"Received traceparent header: {traceparent_header}") + else: + logger.info("No traceparent header found") + + # Augment the user query with relevant context + updated_messages = await augment_query_with_context(messages, traceparent_header) + + # Return as dict to minimize text serialization + return [{"role": msg.role, "content": msg.content} for msg in updated_messages] diff --git a/demos/use_cases/mcp_filter/src/rag_agent/query_rewriter.py b/demos/use_cases/mcp_filter/src/rag_agent/query_rewriter.py new file mode 100644 index 00000000..89e5b200 --- /dev/null +++ b/demos/use_cases/mcp_filter/src/rag_agent/query_rewriter.py @@ -0,0 +1,119 @@ +import asyncio +import json +from typing import List, Optional, Dict, Any +from openai import AsyncOpenAI +import os +import logging + +from .api import ChatMessage +from . import mcp +from fastmcp.server.dependencies import get_http_headers + +# Set up logging +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - [QUERY_REWRITER] - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + + +# Configuration for archgw LLM gateway +LLM_GATEWAY_ENDPOINT = os.getenv("LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1") +QUERY_REWRITE_MODEL = "gpt-4o-mini" + +# Initialize OpenAI client for archgw +archgw_client = AsyncOpenAI( + base_url=LLM_GATEWAY_ENDPOINT, + api_key="EMPTY", # archgw doesn't require a real API key +) + + +async def rewrite_query_with_archgw( + messages: List[ChatMessage], traceparent_header: str +) -> str: + """Rewrite the user query using LLM for better retrieval.""" + system_prompt = """You are a query rewriter that improves user queries for better retrieval. + + Given a conversation history, rewrite the last user message to be more specific and context-aware. + The rewritten query should: + 1. Include relevant context from previous messages + 2. Be clear and specific for information retrieval + 3. Maintain the user's intent + 4. Be concise but comprehensive + + Return only the rewritten query, nothing else.""" + + # Prepare messages for the query rewriter - just add system prompt to existing messages + rewrite_messages = [{"role": "system", "content": system_prompt}] + + # Add conversation history + for msg in messages: + rewrite_messages.append({"role": msg.role, "content": msg.content}) + + try: + # Call archgw using OpenAI client + extra_headers = {"x-envoy-max-retries": "3"} + if traceparent_header: + extra_headers["traceparent"] = traceparent_header + logger.info(f"Calling archgw at {LLM_GATEWAY_ENDPOINT} to rewrite query") + response = await archgw_client.chat.completions.create( + model=QUERY_REWRITE_MODEL, + messages=rewrite_messages, + temperature=0.3, + max_tokens=200, + extra_headers=extra_headers, + ) + + rewritten_query = response.choices[0].message.content.strip() + logger.info(f"Query rewritten successfully: '{rewritten_query}'") + return rewritten_query + + except Exception as e: + logger.error(f"Error rewriting query: {e}") + + # If rewriting fails, return the original last user message + logger.info("Falling back to original user message") + for message in reversed(messages): + if message.role == "user": + return message.content + return "" + + +@mcp.tool() +async def query_rewriter(messages: List[ChatMessage]) -> List[ChatMessage]: + """Chat completions endpoint that rewrites the last user query using archgw. + + Returns a dict with a 'messages' key containing the updated message list. + """ + import time + import uuid + + logger.info(f"Received chat completion request with {len(messages)} messages") + + # Get traceparent header from HTTP request using FastMCP's dependency function + headers = get_http_headers() + traceparent_header = headers.get("traceparent") + + if traceparent_header: + logger.info(f"Received traceparent header: {traceparent_header}") + else: + logger.info("No traceparent header found") + + # Call archgw to rewrite the last user query + rewritten_query = await rewrite_query_with_archgw(messages, traceparent_header) + + # Create updated messages with the rewritten query + updated_messages = messages.copy() + + # Find and update the last user message with the rewritten query + for i in range(len(updated_messages) - 1, -1, -1): + if updated_messages[i].role == "user": + original_query = updated_messages[i].content + updated_messages[i] = ChatMessage(role="user", content=rewritten_query) + logger.info( + f"Updated user query from '{original_query}' to '{rewritten_query}'" + ) + break + + # Return as dict to minimize text serialization + return [{"role": msg.role, "content": msg.content} for msg in updated_messages] diff --git a/demos/use_cases/mcp_filter/src/rag_agent/rag_agent.py b/demos/use_cases/mcp_filter/src/rag_agent/rag_agent.py new file mode 100644 index 00000000..b590248a --- /dev/null +++ b/demos/use_cases/mcp_filter/src/rag_agent/rag_agent.py @@ -0,0 +1,303 @@ +import json +from fastapi import FastAPI, Request +from fastapi.responses import StreamingResponse +from openai import AsyncOpenAI +import os +import logging +import time +import uuid +import uvicorn +import asyncio + +from .api import ( + ChatCompletionRequest, + ChatCompletionResponse, + ChatCompletionStreamResponse, +) + +# Set up logging +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - [RESPONSE_GENERATOR] - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + +# Configuration for archgw LLM gateway +LLM_GATEWAY_ENDPOINT = os.getenv("LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1") +RESPONSE_MODEL = "gpt-4o" + +# System prompt for response generation +SYSTEM_PROMPT = """You are a helpful assistant that generates coherent, contextual responses. + +Given a conversation history, generate a helpful and relevant response based on all the context available in the messages. +Your response should: +1. Be contextually aware of the entire conversation +2. Address the user's needs appropriately +3. Be helpful and informative +4. Maintain a natural conversational tone + +Generate a complete response to assist the user.""" + +# Initialize OpenAI client for archgw +archgw_client = AsyncOpenAI( + base_url=LLM_GATEWAY_ENDPOINT, + api_key="EMPTY", # archgw doesn't require a real API key +) + +# FastAPI app for REST server +app = FastAPI(title="RAG Agent Response Generator", version="1.0.0") + + +def prepare_response_messages(request_body: ChatCompletionRequest): + """Prepare messages for response generation by adding system prompt.""" + response_messages = [{"role": "system", "content": SYSTEM_PROMPT}] + + # Add conversation history + for msg in request_body.messages: + response_messages.append({"role": msg.role, "content": msg.content}) + + return response_messages + + +@app.post("/v1/chat/completions") +async def chat_completion_http(request: Request, request_body: ChatCompletionRequest): + """HTTP endpoint for chat completions with streaming support.""" + logger.info( + f"Received chat completion request with {len(request_body.messages)} messages" + ) + + # Get traceparent header from HTTP request + traceparent_header = request.headers.get("traceparent") + + if traceparent_header: + logger.info(f"Received traceparent header: {traceparent_header}") + else: + logger.info("No traceparent header found") + + # Check if streaming is requested + if request_body.stream: + return StreamingResponse( + stream_chat_completions(request_body, traceparent_header), + media_type="text/plain", + headers={ + "content-type": "text/event-stream", + }, + ) + else: + return await non_streaming_chat_completions(request_body, traceparent_header) + + +async def stream_chat_completions( + request_body: ChatCompletionRequest, traceparent_header: str = None +): + """Generate streaming chat completions.""" + # Prepare messages for response generation + response_messages = prepare_response_messages(request_body) + + try: + # Call archgw using OpenAI client for streaming + logger.info( + f"Calling archgw at {LLM_GATEWAY_ENDPOINT} to generate streaming response" + ) + + # Prepare extra headers if traceparent is provided + extra_headers = {"x-envoy-max-retries": "3"} + if traceparent_header: + extra_headers["traceparent"] = traceparent_header + + response_stream = await archgw_client.chat.completions.create( + model=RESPONSE_MODEL, + messages=response_messages, + temperature=request_body.temperature or 0.7, + max_tokens=request_body.max_tokens or 1000, + stream=True, + extra_headers=extra_headers, + ) + + completion_id = f"chatcmpl-{uuid.uuid4().hex[:8]}" + created_time = int(time.time()) + collected_content = [] + + async for chunk in response_stream: + if chunk.choices and chunk.choices[0].delta.content: + content = chunk.choices[0].delta.content + collected_content.append(content) + + # Create streaming response chunk + stream_chunk = ChatCompletionStreamResponse( + id=completion_id, + created=created_time, + model=request_body.model, + choices=[ + { + "index": 0, + "delta": {"content": content}, + "finish_reason": None, + } + ], + ) + + yield f"data: {stream_chunk.model_dump_json()}\n\n" + + # Send final chunk with complete response in expected format + full_response = "".join(collected_content) + updated_history = [{"role": "assistant", "content": full_response}] + + final_chunk = ChatCompletionStreamResponse( + id=completion_id, + created=created_time, + model=request_body.model, + choices=[ + { + "index": 0, + "delta": {}, + "finish_reason": "stop", + "message": { + "role": "assistant", + "content": json.dumps(updated_history), + }, + } + ], + ) + + yield f"data: {final_chunk.model_dump_json()}\n\n" + yield "data: [DONE]\n\n" + + except Exception as e: + logger.error(f"Error generating streaming response: {e}") + + # Send error as streaming response + error_chunk = ChatCompletionStreamResponse( + id=f"chatcmpl-{uuid.uuid4().hex[:8]}", + created=int(time.time()), + model=request_body.model, + choices=[ + { + "index": 0, + "delta": { + "content": "I apologize, but I'm having trouble generating a response right now. Please try again." + }, + "finish_reason": "stop", + } + ], + ) + + yield f"data: {error_chunk.model_dump_json()}\n\n" + yield "data: [DONE]\n\n" + + +async def non_streaming_chat_completions( + request_body: ChatCompletionRequest, traceparent_header: str = None +): + """Generate non-streaming chat completions.""" + # Prepare messages for response generation + response_messages = prepare_response_messages(request_body) + + try: + # Call archgw using OpenAI client + logger.info(f"Calling archgw at {LLM_GATEWAY_ENDPOINT} to generate response") + + # Prepare extra headers if traceparent is provided + extra_headers = {"x-envoy-max-retries": "3"} + if traceparent_header: + extra_headers["traceparent"] = traceparent_header + + response = await archgw_client.chat.completions.create( + model=RESPONSE_MODEL, + messages=response_messages, + temperature=request_body.temperature or 0.7, + max_tokens=request_body.max_tokens or 1000, + extra_headers=extra_headers, + ) + + generated_response = response.choices[0].message.content.strip() + logger.info(f"Response generated successfully") + + return ChatCompletionResponse( + id=f"chatcmpl-{uuid.uuid4().hex[:8]}", + created=int(time.time()), + model=request_body.model, + choices=[ + { + "index": 0, + "message": { + "role": "assistant", + "content": generated_response, + }, + "finish_reason": "stop", + } + ], + usage={ + "prompt_tokens": sum( + len(msg.content.split()) for msg in request_body.messages + ), + "completion_tokens": len(generated_response.split()), + "total_tokens": sum( + len(msg.content.split()) for msg in request_body.messages + ) + + len(generated_response.split()), + }, + ) + + except Exception as e: + logger.error(f"Error generating response: {e}") + + # Fallback response + fallback_message = "I apologize, but I'm having trouble generating a response right now. Please try again." + return ChatCompletionResponse( + id=f"chatcmpl-{uuid.uuid4().hex[:8]}", + created=int(time.time()), + model=request_body.model, + choices=[ + { + "index": 0, + "message": {"role": "assistant", "content": fallback_message}, + "finish_reason": "stop", + } + ], + usage={ + "prompt_tokens": sum( + len(msg.content.split()) for msg in request_body.messages + ), + "completion_tokens": len(fallback_message.split()), + "total_tokens": sum( + len(msg.content.split()) for msg in request_body.messages + ) + + len(fallback_message.split()), + }, + ) + + +@app.get("/health") +async def health_check(): + """Health check endpoint.""" + return {"status": "healthy"} + + +def start_server(host: str = "localhost", port: int = 8000): + """Start the REST server.""" + uvicorn.run( + app, + host=host, + port=port, + log_config={ + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "default": { + "format": "%(asctime)s - [RESPONSE_GENERATOR] - %(levelname)s - %(message)s", + }, + }, + "handlers": { + "default": { + "formatter": "default", + "class": "logging.StreamHandler", + "stream": "ext://sys.stdout", + }, + }, + "root": { + "level": "INFO", + "handlers": ["default"], + }, + }, + ) diff --git a/demos/use_cases/mcp_filter/src/rag_agent/sample_knowledge_base.csv b/demos/use_cases/mcp_filter/src/rag_agent/sample_knowledge_base.csv new file mode 100644 index 00000000..b9e6e8f0 --- /dev/null +++ b/demos/use_cases/mcp_filter/src/rag_agent/sample_knowledge_base.csv @@ -0,0 +1,257 @@ +path,content +TechCorp_CloudServices_SLA_Agreement_2024,"SERVICE LEVEL AGREEMENT +This Service Level Agreement (""SLA"") is entered into on March 15, 2024, between TechCorp Solutions Inc., a Delaware corporation (""Provider""), and CloudFirst Enterprises LLC (""Customer""). + +DEFINITIONS +Service Availability: The percentage of time during which the cloud services are operational and accessible. +Downtime: Any period when the services are unavailable or inaccessible to Customer. +Response Time: The time between service request submission and initial response from Provider. + +SERVICE COMMITMENTS +Provider guarantees 99.9% uptime for all cloud infrastructure services during any calendar month. +Average response time for API calls shall not exceed 200 milliseconds under normal operating conditions. +Customer support response times: Critical issues within 1 hour, Standard issues within 4 hours. + +REMEDIES +For each full percentage point below 99.9% availability, Customer receives 10% credit on monthly fees. +If response times exceed 500ms for more than 5 minutes in any hour, Customer receives 5% monthly credit. + +MONITORING AND REPORTING +Provider will maintain real-time monitoring systems and provide monthly performance reports. +All metrics will be measured from Provider's monitoring systems located in primary data centers. + +This SLA remains in effect for the duration of the underlying service agreement. + +Executed by: +TechCorp Solutions Inc. +Sarah Mitchell, VP Operations +Date: March 15, 2024 + +CloudFirst Enterprises LLC +Robert Chen, CTO +Date: March 16, 2024" + +DataSecure_Privacy_Policy_v3.2,"PRIVACY POLICY +DataSecure Analytics, Inc. (""Company"") Privacy Policy +Effective Date: January 1, 2024 +Last Updated: February 28, 2024 + +INFORMATION COLLECTION +We collect information you provide directly, such as account details, usage preferences, and communication records. +Automatically collected data includes IP addresses, browser types, device information, and service interaction logs. +Third-party integrations may provide additional user behavior and demographic information with consent. + +DATA USAGE +Personal information is used to provide services, improve user experience, and communicate service updates. +Aggregated, non-identifiable data may be used for analytics, research, and service enhancement. +We do not sell personal information to third parties for marketing purposes. + +DATA PROTECTION +All data is encrypted in transit using TLS 1.3 and at rest using AES-256 encryption. +Access controls limit data access to authorized personnel only on a need-to-know basis. +Regular security audits and penetration testing ensure ongoing protection measures. + +DATA RETENTION +Personal data is retained for the duration of active service plus 24 months. +Logs and analytics data are retained for 12 months unless legally required otherwise. +Upon account deletion, personal data is permanently removed within 30 days. + +USER RIGHTS +Users may request access to, correction of, or deletion of their personal information. +Data portability requests will be fulfilled in standard formats within 30 days. +Marketing communications can be opted out of at any time. + +CONTACT +For privacy concerns, contact: privacy@datasecure.com +Data Protection Officer: Jennifer Walsh, jwalsh@datasecure.com" + +GlobalManufacturing_SupplyChain_Contract_Q2_2024,"SUPPLY CHAIN AGREEMENT +This Supply Chain Agreement is entered into between GlobalManufacturing Corp (""Buyer"") and PrecisionParts Ltd (""Supplier"") effective April 1, 2024. + +SCOPE OF SERVICES +Supplier will provide automotive components including brake assemblies, suspension parts, and electrical harnesses. +All products must meet ISO 9001 quality standards and automotive industry specifications. +Delivery schedule: Weekly shipments every Tuesday, with 48-hour advance shipping notifications. + +PRICING AND PAYMENT +Component pricing is fixed for initial 6-month term with quarterly price review thereafter. +Payment terms: Net 45 days from invoice date via electronic transfer. +Volume discounts apply: 5% for orders exceeding 10,000 units per month, 8% for orders exceeding 25,000 units. + +QUALITY REQUIREMENTS +All components must pass incoming inspection with less than 0.1% defect rate. +Supplier maintains quality certifications including IATF 16949 and environmental compliance. +Batch tracking and traceability required for all delivered components. + +LOGISTICS AND DELIVERY +Supplier responsible for packaging, labeling, and delivery to Buyer's distribution centers. +Delivery windows: 8 AM - 4 PM, Monday through Friday, with advance appointment scheduling. +Late delivery penalties: 2% of shipment value for each day beyond scheduled delivery. + +RISK MANAGEMENT +Supplier maintains business continuity plans and alternative sourcing strategies. +Force majeure events must be reported within 24 hours with mitigation plans. +Insurance requirements: $5M general liability, $2M product liability coverage. + +INTELLECTUAL PROPERTY +All custom tooling and specifications remain property of Buyer. +Supplier grants license to use necessary patents for component manufacturing. + +This agreement shall remain in effect for 24 months with automatic renewal unless terminated. + +GlobalManufacturing Corp +Michael Rodriguez, Supply Chain Director +Date: April 1, 2024 + +PrecisionParts Ltd +Amanda Foster, VP Sales +Date: April 2, 2024" + +EduTech_StudentData_Management_Policy_2024,"STUDENT DATA MANAGEMENT POLICY +EduTech Learning Platform - Data Management and Protection Policy +Document Version: 2.1 +Effective Date: August 15, 2024 + +SCOPE AND PURPOSE +This policy governs the collection, use, storage, and protection of student educational records and personal information. +Applies to all employees, contractors, and third-party service providers accessing student data. +Compliance with FERPA, COPPA, and state student privacy laws is mandatory. + +DATA CLASSIFICATION +Educational Records: Grades, attendance, assignments, and academic progress information. +Personal Information: Names, addresses, contact details, and demographic information. +Behavioral Data: Learning patterns, platform usage, and engagement metrics. + +COLLECTION PRINCIPLES +Data collection is limited to educational purposes and service improvement only. +Parental consent required for students under 13 years of age. +Students and parents have right to review and request corrections to educational records. + +ACCESS CONTROLS +Role-based access ensures personnel see only data necessary for their functions. +Multi-factor authentication required for all system access. +Access logs maintained and reviewed monthly for unauthorized activity. + +DATA SHARING +Educational records shared only with authorized school personnel and parents/students. +No data sharing with third parties for commercial purposes without explicit consent. +Research data must be de-identified and aggregated before external sharing. + +SECURITY MEASURES +Data encrypted using industry-standard protocols during transmission and storage. +Regular security assessments and vulnerability testing conducted quarterly. +Incident response plan includes notification procedures for data breaches. + +RETENTION AND DISPOSAL +Student records retained according to school district policies, typically 5-7 years post-graduation. +Inactive accounts and associated data purged after 2 years of non-use. +Secure data destruction protocols ensure complete removal of sensitive information. + +COMPLIANCE MONITORING +Annual privacy training required for all staff handling student data. +Regular audits ensure ongoing compliance with applicable privacy regulations. +Privacy impact assessments conducted for new features or data uses. + +Contact: Dr. Lisa Thompson, Chief Privacy Officer +Email: privacy@edutech-learning.com +Phone: (555) 123-4567" + +FinanceFirst_Investment_Advisory_Agreement_2024,"INVESTMENT ADVISORY AGREEMENT +This Investment Advisory Agreement is entered into between FinanceFirst Advisors LLC (""Advisor"") and Madison Investment Group (""Client"") on May 20, 2024. + +ADVISORY SERVICES +Advisor will provide comprehensive investment management and financial planning services. +Services include portfolio construction, asset allocation, risk assessment, and performance monitoring. +Regular portfolio reviews conducted quarterly with detailed performance reporting. + +INVESTMENT AUTHORITY +Client grants Advisor discretionary authority to make investment decisions within agreed parameters. +Investment universe includes stocks, bonds, ETFs, mutual funds, and alternative investments as appropriate. +All trades executed through qualified broker-dealers with best execution practices. + +FEE STRUCTURE +Management fee: 1.25% annually on assets under management, calculated and billed quarterly. +Performance fee: 15% of returns exceeding S&P 500 benchmark, calculated annually. +Additional fees may apply for specialized services such as tax planning or estate planning. + +CLIENT RESPONSIBILITIES +Client must provide accurate financial information and promptly communicate changes in circumstances. +Investment objectives and risk tolerance should be reviewed and updated annually. +Client responsible for reviewing and approving investment policy statement. + +RISK DISCLOSURE +All investments carry risk of loss, and past performance does not guarantee future results. +Diversification does not ensure profit or protect against loss in declining markets. +Alternative investments may have limited liquidity and higher volatility. + +REGULATORY COMPLIANCE +Advisor is registered with the Securities and Exchange Commission as an investment advisor. +All activities conducted in accordance with Investment Advisers Act of 1940 and applicable regulations. +Form ADV Part 2 brochure provided annually with material updates. + +CONFIDENTIALITY +All client information treated as confidential and shared only as necessary for service provision. +Third-party service providers bound by confidentiality agreements. +Client data protected through secure systems and access controls. + +TERMINATION +Either party may terminate agreement with 30 days written notice. +Upon termination, Advisor will assist with orderly transfer of assets to new custodian or advisor. +Final fee calculation prorated to date of termination. + +FinanceFirst Advisors LLC +Thomas Anderson, Managing Partner +Date: May 20, 2024 + +Madison Investment Group +Rebecca Martinez, Chief Investment Officer +Date: May 21, 2024" + +HealthSystem_PatientCare_Standards_2024,"PATIENT CARE STANDARDS AND PROTOCOLS +Metropolitan Health System - Clinical Care Standards +Document ID: MHS-PCS-2024-001 +Effective Date: June 1, 2024 + +PATIENT SAFETY PROTOCOLS +All patients must have proper identification verification using two unique identifiers. +Medication administration requires independent double-check for high-risk medications. +Fall risk assessments completed within 4 hours of admission with appropriate interventions. + +CLINICAL DOCUMENTATION +Medical records must be completed within 24 hours of patient encounter. +All entries require electronic signature with timestamp and provider identification. +Critical values and abnormal results must be communicated and documented immediately. + +INFECTION CONTROL +Hand hygiene compliance monitored with target rate of 95% or higher. +Personal protective equipment used according to transmission-based precautions. +Isolation procedures implemented within 2 hours of identification of infectious conditions. + +EMERGENCY RESPONSE +Code team response time target: 3 minutes from activation to arrival. +Crash cart and emergency equipment checks performed daily and documented. +All staff required to maintain current CPR and emergency response certifications. + +PATIENT COMMUNICATION +Patient rights and responsibilities communicated upon admission. +Informed consent obtained and documented prior to procedures and treatments. +Family involvement encouraged with respect for patient privacy preferences. + +QUALITY MEASURES +Patient satisfaction scores monitored monthly with target of 4.5/5.0 or higher. +Medication error rates tracked with goal of less than 1 per 1000 patient days. +Hospital-acquired infection rates measured and benchmarked against national standards. + +STAFF COMPETENCY +Annual competency assessments required for all clinical staff. +Continuing education requirements: 24 hours annually for nurses, 40 hours for physicians. +Specialty certifications maintained according to department and role requirements. + +TECHNOLOGY STANDARDS +Electronic health record system used for all patient documentation. +Telemedicine capabilities available for remote consultations and monitoring. +Clinical decision support tools integrated to assist with diagnosis and treatment decisions. + +Contact: Dr. Patricia Williams, Chief Medical Officer +Email: pwilliams@metrohealthsystem.org +Phone: (555) 987-6543" diff --git a/demos/use_cases/mcp_filter/start_agents.sh b/demos/use_cases/mcp_filter/start_agents.sh new file mode 100644 index 00000000..e044fdda --- /dev/null +++ b/demos/use_cases/mcp_filter/start_agents.sh @@ -0,0 +1,47 @@ +#!/bin/bash +set -e + +WAIT_FOR_PIDS=() + +log() { + timestamp=$(python3 -c 'from datetime import datetime; print(datetime.now().strftime("%Y-%m-%d %H:%M:%S,%f")[:23])') + message="$*" + echo "$timestamp - $message" +} + +cleanup() { + log "Caught signal, terminating all user processes ..." + for PID in "${WAIT_FOR_PIDS[@]}"; do + if kill $PID 2> /dev/null; then + log "killed process: $PID" + fi + done + exit 1 +} + +trap cleanup EXIT + +# log "Starting input guards filter on port 10500..." +# uv run python -m rag_agent --host 0.0.0.0 --port 10500 --agent input_guards & +# WAIT_FOR_PIDS+=($!) + + +log "Starting query_parser agent on port 10501..." +uv run python -m rag_agent --host 0.0.0.0 --port 10501 --agent query_rewriter & +WAIT_FOR_PIDS+=($!) + +log "Starting context_builder agent on port 10502..." +uv run python -m rag_agent --host 0.0.0.0 --port 10502 --agent context_builder & +WAIT_FOR_PIDS+=($!) + +# log "Starting response_generator agent on port 10400..." +# uv run python -m rag_agent --host 0.0.0.0 --port 10400 --agent response_generator & +# WAIT_FOR_PIDS+=($!) + +log "Starting response_generator agent on port 10505..." +uv run python -m rag_agent --rest-server --host 0.0.0.0 --rest-port 10505 --agent response_generator & +WAIT_FOR_PIDS+=($!) + +for PID in "${WAIT_FOR_PIDS[@]}"; do + wait "$PID" +done diff --git a/demos/use_cases/mcp_filter/test.rest b/demos/use_cases/mcp_filter/test.rest new file mode 100644 index 00000000..13d773b1 --- /dev/null +++ b/demos/use_cases/mcp_filter/test.rest @@ -0,0 +1,95 @@ +@baseUrl = http://0.0.0.0:10502 +@model = gpt-4o + +# Health Check +GET {{baseUrl}}/health + +### + +# Test 1: Simple Non-Streaming Chat Completion +POST {{baseUrl}}/v1/chat/completions +Content-Type: application/json + +{ + "model": "{{model}}", + "messages": [ + { + "role": "user", + "content": "Hello! Can you help me understand what machine learning is?" + } + ] +} + +### + +# Test 2: Simple Streaming Chat Completion +POST {{baseUrl}}/v1/chat/completions +Content-Type: application/json + +{ + "model": "{{model}}", + "messages": [ + { + "role": "user", + "content": "Explain the concept of artificial intelligence in simple terms." + } + ], + "stream": true +} + +### Test 3 +POST http://localhost:8001/v1/chat/completions +Content-Type: application/json + +{ + "model": "{{model}}", + "messages": [ + { + "role": "user", + "content": "What is the guaranteed uptime percentage for TechCorp's cloud services?" + } + ], + "stream": true +} + +### send request to context builder agent +POST http://localhost:10501/v1/chat/completions +Content-Type: application/json + +{ + "model": "gpt-4o-mini", + "messages": [ + { + "role": "user", + "content": "What is the guaranteed uptime percentage for TechCorp's cloud services?" + } + ] +} + +### test fast-llm +POST 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