diff --git a/.claude/skills/release/SKILL.md b/.claude/skills/release/SKILL.md index 80510004..ba101bd3 100644 --- a/.claude/skills/release/SKILL.md +++ b/.claude/skills/release/SKILL.md @@ -25,4 +25,6 @@ Update the version string in ALL of these files: Do NOT change version strings in `*.lock` files or `Cargo.lock`. +After updating all version strings, run `cd cli && uv lock` to update the lock file with the new version. + After making changes, show a summary of all files modified and the old → new version. diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 25e6f99d..2d844498 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -133,13 +133,13 @@ jobs: load: true tags: | ${{ env.PLANO_DOCKER_IMAGE }} - ${{ env.DOCKER_IMAGE }}:0.4.11 + ${{ env.DOCKER_IMAGE }}:0.4.14 ${{ env.DOCKER_IMAGE }}:latest cache-from: type=gha cache-to: type=gha,mode=max - name: Save image as artifact - run: docker save ${{ env.PLANO_DOCKER_IMAGE }} ${{ env.DOCKER_IMAGE }}:0.4.11 ${{ env.DOCKER_IMAGE }}:latest -o /tmp/plano-image.tar + run: docker save ${{ env.PLANO_DOCKER_IMAGE }} ${{ env.DOCKER_IMAGE }}:0.4.14 ${{ env.DOCKER_IMAGE }}:latest -o /tmp/plano-image.tar - name: Upload image artifact uses: actions/upload-artifact@v6 diff --git a/.gitignore b/.gitignore index af706ea4..391c17fa 100644 --- a/.gitignore +++ b/.gitignore @@ -152,3 +152,4 @@ apps/*/dist/ .cursor/ .agents +docs/do/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 84001c45..22a18416 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,6 +4,7 @@ repos: hooks: - id: check-yaml exclude: config/envoy.template* + args: [--allow-multiple-documents] - id: end-of-file-fixer - id: trailing-whitespace - repo: local diff --git a/Dockerfile b/Dockerfile index 1e27f8ed..ad0ca707 100644 --- a/Dockerfile +++ b/Dockerfile @@ -49,7 +49,8 @@ FROM python:3.14-slim AS arch RUN set -eux; \ apt-get update; \ - apt-get install -y --no-install-recommends gettext-base curl; \ + apt-get upgrade -y; \ + apt-get install -y --no-install-recommends gettext-base curl procps; \ apt-get clean; rm -rf /var/lib/apt/lists/* RUN pip install --no-cache-dir supervisor diff --git a/apps/www/src/components/Hero.tsx b/apps/www/src/components/Hero.tsx index 7952c68f..d61fad2c 100644 --- a/apps/www/src/components/Hero.tsx +++ b/apps/www/src/components/Hero.tsx @@ -24,7 +24,7 @@ export function Hero() { >
- v0.4.11 + v0.4.14 — diff --git a/build_filter_image.sh b/build_filter_image.sh index 8e041894..dcbf32f5 100644 --- a/build_filter_image.sh +++ b/build_filter_image.sh @@ -1 +1 @@ -docker build -f Dockerfile . -t katanemo/plano -t katanemo/plano:0.4.11 +docker build -f Dockerfile . -t katanemo/plano -t katanemo/plano:0.4.14 diff --git a/cli/planoai/__init__.py b/cli/planoai/__init__.py index b94eadc2..71def36d 100644 --- a/cli/planoai/__init__.py +++ b/cli/planoai/__init__.py @@ -1,3 +1,3 @@ """Plano CLI - Intelligent Prompt Gateway.""" -__version__ = "0.4.11" +__version__ = "0.4.14" diff --git a/cli/planoai/config_generator.py b/cli/planoai/config_generator.py index 522968c9..929b7657 100644 --- a/cli/planoai/config_generator.py +++ b/cli/planoai/config_generator.py @@ -3,18 +3,17 @@ import os from planoai.utils import convert_legacy_listeners from jinja2 import Environment, FileSystemLoader import yaml -from jsonschema import validate +from jsonschema import validate, ValidationError from urllib.parse import urlparse from copy import deepcopy from planoai.consts import DEFAULT_OTEL_TRACING_GRPC_ENDPOINT - SUPPORTED_PROVIDERS_WITH_BASE_URL = [ "azure_openai", "ollama", "qwen", "amazon_bedrock", - "arch", + "plano", ] SUPPORTED_PROVIDERS_WITHOUT_BASE_URL = [ @@ -368,47 +367,52 @@ def validate_and_render_schema(): llms_with_endpoint.append(model_provider) llms_with_endpoint_cluster_names.add(cluster_name) - if len(model_usage_name_keys) > 0: - routing_model_provider = config_yaml.get("routing", {}).get( - "model_provider", None + overrides_config = config_yaml.get("overrides", {}) + # Build lookup of model names (already prefix-stripped by config processing) + model_name_set = {mp.get("model") for mp in updated_model_providers} + + # Auto-add arch-router provider if routing preferences exist and no provider matches the router model + router_model = overrides_config.get("llm_routing_model", "Arch-Router") + # Strip provider prefix for comparison since config processing strips prefixes from model names + router_model_id = ( + router_model.split("/", 1)[1] if "/" in router_model else router_model + ) + if len(model_usage_name_keys) > 0 and router_model_id not in model_name_set: + updated_model_providers.append( + { + "name": "arch-router", + "provider_interface": "plano", + "model": router_model_id, + "internal": True, + } ) - if ( - routing_model_provider - and routing_model_provider not in model_provider_name_set - ): - raise Exception( - f"Routing model_provider {routing_model_provider} is not defined in model_providers" - ) - if ( - routing_model_provider is None - and "arch-router" not in model_provider_name_set - ): - updated_model_providers.append( - { - "name": "arch-router", - "provider_interface": "arch", - "model": config_yaml.get("routing", {}).get("model", "Arch-Router"), - "internal": True, - } - ) # Always add arch-function model provider if not already defined if "arch-function" not in model_provider_name_set: updated_model_providers.append( { "name": "arch-function", - "provider_interface": "arch", + "provider_interface": "plano", "model": "Arch-Function", "internal": True, } ) - if "plano-orchestrator" not in model_provider_name_set: + # Auto-add plano-orchestrator provider if no provider matches the orchestrator model + orchestrator_model = overrides_config.get( + "agent_orchestration_model", "Plano-Orchestrator" + ) + orchestrator_model_id = ( + orchestrator_model.split("/", 1)[1] + if "/" in orchestrator_model + else orchestrator_model + ) + if orchestrator_model_id not in model_name_set: updated_model_providers.append( { - "name": "plano-orchestrator", - "provider_interface": "arch", - "model": "Plano-Orchestrator", + "name": "plano/orchestrator", + "provider_interface": "plano", + "model": orchestrator_model_id, "internal": True, } ) @@ -426,6 +430,16 @@ def validate_and_render_schema(): "Please provide model_providers either under listeners or at root level, not both. Currently we don't support multiple listeners with model_providers" ) + # Validate input_filters IDs on listeners reference valid agent/filter IDs + for listener in listeners: + listener_input_filters = listener.get("input_filters", []) + for fc_id in listener_input_filters: + if fc_id not in agent_id_keys: + raise Exception( + f"Listener '{listener.get('name', 'unknown')}' references input_filters id '{fc_id}' " + f"which is not defined in agents or filters. Available ids: {', '.join(sorted(agent_id_keys))}" + ) + # Validate model aliases if present if "model_aliases" in config_yaml: model_aliases = config_yaml["model_aliases"] @@ -503,11 +517,15 @@ def validate_prompt_config(plano_config_file, plano_config_schema_file): try: validate(config_yaml, config_schema_yaml) - except Exception as e: - print( - f"Error validating plano_config file: {plano_config_file}, schema file: {plano_config_schema_file}, error: {e}" + except ValidationError as e: + path = ( + " → ".join(str(p) for p in e.absolute_path) if e.absolute_path else "root" ) - raise e + raise ValidationError( + f"{e.message}\n Location: {path}\n Value: {e.instance}" + ) from None + except Exception as e: + raise if __name__ == "__main__": diff --git a/cli/planoai/consts.py b/cli/planoai/consts.py index 145fb640..e80c8b72 100644 --- a/cli/planoai/consts.py +++ b/cli/planoai/consts.py @@ -5,7 +5,7 @@ PLANO_COLOR = "#969FF4" SERVICE_NAME_ARCHGW = "plano" PLANO_DOCKER_NAME = "plano" -PLANO_DOCKER_IMAGE = os.getenv("PLANO_DOCKER_IMAGE", "katanemo/plano:0.4.11") +PLANO_DOCKER_IMAGE = os.getenv("PLANO_DOCKER_IMAGE", "katanemo/plano:0.4.14") DEFAULT_OTEL_TRACING_GRPC_ENDPOINT = "http://localhost:4317" # Native mode constants diff --git a/cli/planoai/core.py b/cli/planoai/core.py index 174f37c0..54a181d6 100644 --- a/cli/planoai/core.py +++ b/cli/planoai/core.py @@ -165,7 +165,7 @@ def _resolve_cli_agent_endpoint(plano_config_yaml: dict) -> tuple[str, int]: if isinstance(listeners, list): for listener in listeners: - if listener.get("type") in ["model", "model_listener"]: + if listener.get("type") == "model": host = listener.get("host") or listener.get("address") or "0.0.0.0" port = listener.get("port", 12000) return host, port diff --git a/cli/planoai/native_runner.py b/cli/planoai/native_runner.py index 0e39a1fd..ed44e8ad 100644 --- a/cli/planoai/native_runner.py +++ b/cli/planoai/native_runner.py @@ -420,9 +420,16 @@ def native_validate_config(plano_config_file): with _temporary_env(overrides): from planoai.config_generator import validate_and_render_schema - # Suppress verbose print output from config_generator - with contextlib.redirect_stdout(io.StringIO()): - validate_and_render_schema() + # Suppress verbose print output from config_generator but capture errors + captured = io.StringIO() + try: + with contextlib.redirect_stdout(captured): + validate_and_render_schema() + except SystemExit: + # validate_and_render_schema calls exit(1) on failure after + # printing to stdout; re-raise so the caller gets a useful message. + output = captured.getvalue().strip() + raise Exception(output) if output else Exception("Config validation failed") def native_logs(debug=False, follow=False): diff --git a/cli/planoai/utils.py b/cli/planoai/utils.py index 171006f1..822d6e3c 100644 --- a/cli/planoai/utils.py +++ b/cli/planoai/utils.py @@ -89,7 +89,7 @@ def convert_legacy_listeners( ) -> tuple[list, dict | None, dict | None]: llm_gateway_listener = { "name": "egress_traffic", - "type": "model_listener", + "type": "model", "port": 12000, "address": "0.0.0.0", "timeout": "30s", @@ -98,7 +98,7 @@ def convert_legacy_listeners( prompt_gateway_listener = { "name": "ingress_traffic", - "type": "prompt_listener", + "type": "prompt", "port": 10000, "address": "0.0.0.0", "timeout": "30s", diff --git a/cli/pyproject.toml b/cli/pyproject.toml index 3f9be272..61e08562 100644 --- a/cli/pyproject.toml +++ b/cli/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "planoai" -version = "0.4.11" +version = "0.4.14" description = "Python-based CLI tool to manage Plano." authors = [{name = "Katanemo Labs, Inc."}] readme = "README.md" diff --git a/cli/test/test_config_generator.py b/cli/test/test_config_generator.py index b3e3ab62..17fa56cc 100644 --- a/cli/test/test_config_generator.py +++ b/cli/test/test_config_generator.py @@ -104,13 +104,13 @@ listeners: agents: - id: simple_tmobile_rag_agent description: t-mobile virtual assistant for device contracts. - filter_chain: + input_filters: - query_rewriter - context_builder - response_generator - id: research_agent description: agent to research and gather information from various sources. - filter_chain: + input_filters: - research_agent - response_generator port: 8000 @@ -376,7 +376,7 @@ def test_convert_legacy_llm_providers(): assert updated_providers == [ { "name": "egress_traffic", - "type": "model_listener", + "type": "model", "port": 12000, "address": "0.0.0.0", "timeout": "30s", @@ -384,7 +384,7 @@ def test_convert_legacy_llm_providers(): }, { "name": "ingress_traffic", - "type": "prompt_listener", + "type": "prompt", "port": 10000, "address": "0.0.0.0", "timeout": "30s", @@ -400,7 +400,7 @@ def test_convert_legacy_llm_providers(): }, ], "name": "egress_traffic", - "type": "model_listener", + "type": "model", "port": 12000, "timeout": "30s", } @@ -410,7 +410,7 @@ def test_convert_legacy_llm_providers(): "name": "ingress_traffic", "port": 10000, "timeout": "30s", - "type": "prompt_listener", + "type": "prompt", } @@ -449,7 +449,7 @@ def test_convert_legacy_llm_providers_no_prompt_gateway(): "name": "egress_traffic", "port": 12000, "timeout": "30s", - "type": "model_listener", + "type": "model", } ] assert llm_gateway == { @@ -461,7 +461,7 @@ def test_convert_legacy_llm_providers_no_prompt_gateway(): }, ], "name": "egress_traffic", - "type": "model_listener", + "type": "model", "port": 12000, "timeout": "30s", } diff --git a/cli/uv.lock b/cli/uv.lock index 9d85bf85..23c24c5b 100644 --- a/cli/uv.lock +++ b/cli/uv.lock @@ -337,7 +337,7 @@ wheels = [ [[package]] name = "planoai" -version = "0.4.9" +version = "0.4.14" source = { editable = "." } dependencies = [ { name = "click" }, diff --git a/config/envoy.template.yaml b/config/envoy.template.yaml index a780c3f1..c2dd5ed0 100644 --- a/config/envoy.template.yaml +++ b/config/envoy.template.yaml @@ -594,13 +594,13 @@ static_resources: clusters: - - name: arch + - name: plano connect_timeout: {{ upstream_connect_timeout | default('5s') }} type: LOGICAL_DNS dns_lookup_family: V4_ONLY lb_policy: ROUND_ROBIN load_assignment: - cluster_name: arch + cluster_name: plano endpoints: - lb_endpoints: - endpoint: diff --git a/config/plano_config_schema.yaml b/config/plano_config_schema.yaml index 9fd439b1..7b18eb02 100644 --- a/config/plano_config_schema.yaml +++ b/config/plano_config_schema.yaml @@ -85,7 +85,7 @@ properties: type: string default: type: boolean - filter_chain: + input_filters: type: array items: type: string @@ -93,6 +93,14 @@ properties: required: - id - description + input_filters: + type: array + items: + type: string + output_filters: + type: array + items: + type: string additionalProperties: false required: - type @@ -173,7 +181,7 @@ properties: provider_interface: type: string enum: - - arch + - plano - claude - deepseek - groq @@ -220,7 +228,7 @@ properties: provider_interface: type: string enum: - - arch + - plano - claude - deepseek - groq @@ -271,6 +279,12 @@ properties: upstream_tls_ca_path: type: string description: "Path to the trusted CA bundle for upstream TLS verification. Default is '/etc/ssl/certs/ca-certificates.crt'." + llm_routing_model: + type: string + description: "Model name for the LLM router (e.g., 'Arch-Router'). Must match a model in model_providers." + agent_orchestration_model: + type: string + description: "Model name for the agent orchestrator (e.g., 'Plano-Orchestrator'). Must match a model in model_providers." system_prompt: type: string prompt_targets: diff --git a/config/supervisord.conf b/config/supervisord.conf index 00c1594c..a2869136 100644 --- a/config/supervisord.conf +++ b/config/supervisord.conf @@ -1,14 +1,33 @@ [supervisord] nodaemon=true +pidfile=/var/run/supervisord.pid + +[program:config_generator] +command=/bin/sh -c "\ + uv run python -m planoai.config_generator && \ + envsubst < /app/plano_config_rendered.yaml > /app/plano_config_rendered.env_sub.yaml && \ + envsubst < /etc/envoy/envoy.yaml > /etc/envoy.env_sub.yaml && \ + touch /tmp/config_ready || \ + (echo 'Config generation failed, shutting down'; kill -15 $(cat /var/run/supervisord.pid))" +priority=10 +autorestart=false +startsecs=0 +stdout_logfile=/dev/stdout +redirect_stderr=true +stdout_logfile_maxbytes=0 +stderr_logfile_maxbytes=0 [program:brightstaff] command=sh -c "\ - envsubst < /app/plano_config_rendered.yaml > /app/plano_config_rendered.env_sub.yaml && \ + while [ ! -f /tmp/config_ready ]; do echo '[brightstaff] Waiting for config generation...'; sleep 0.5; done && \ RUST_LOG=${LOG_LEVEL:-info} \ PLANO_CONFIG_PATH_RENDERED=/app/plano_config_rendered.env_sub.yaml \ /app/brightstaff 2>&1 | \ tee /var/log/brightstaff.log | \ - while IFS= read -r line; do echo '[brightstaff]' \"$line\"; done" + while IFS= read -r line; do echo '[brightstaff]' \"$line\"; done; \ + echo '[brightstaff] Process exited, shutting down'; kill -15 $(cat /var/run/supervisord.pid)" +priority=20 +autorestart=false stdout_logfile=/dev/stdout redirect_stderr=true stdout_logfile_maxbytes=0 @@ -16,13 +35,15 @@ stderr_logfile_maxbytes=0 [program:envoy] command=/bin/sh -c "\ - uv run python -m planoai.config_generator && \ - envsubst < /etc/envoy/envoy.yaml > /etc/envoy.env_sub.yaml && \ - envoy -c /etc/envoy.env_sub.yaml \ - --component-log-level wasm:${LOG_LEVEL:-info} \ - --log-format '[%%Y-%%m-%%d %%T.%%e][%%l] %%v' 2>&1 | \ + while [ ! -f /tmp/config_ready ]; do echo '[plano_logs] Waiting for config generation...'; sleep 0.5; done && \ + envoy -c /etc/envoy.env_sub.yaml \ + --component-log-level wasm:${LOG_LEVEL:-info} \ + --log-format '[%%Y-%%m-%%d %%T.%%e][%%l] %%v' 2>&1 | \ tee /var/log/envoy.log | \ - while IFS= read -r line; do echo '[plano_logs]' \"$line\"; done" + while IFS= read -r line; do echo '[plano_logs]' \"$line\"; done; \ + echo '[plano_logs] Process exited, shutting down'; kill -15 $(cat /var/run/supervisord.pid)" +priority=20 +autorestart=false stdout_logfile=/dev/stdout redirect_stderr=true stdout_logfile_maxbytes=0 diff --git a/crates/brightstaff/src/app_state.rs b/crates/brightstaff/src/app_state.rs new file mode 100644 index 00000000..57707f6e --- /dev/null +++ b/crates/brightstaff/src/app_state.rs @@ -0,0 +1,29 @@ +use std::collections::HashMap; +use std::sync::Arc; + +use common::configuration::{Agent, FilterPipeline, Listener, ModelAlias, SpanAttributes}; +use common::llm_providers::LlmProviders; +use tokio::sync::RwLock; + +use crate::router::llm::RouterService; +use crate::router::orchestrator::OrchestratorService; +use crate::state::StateStorage; + +/// Shared application state bundled into a single Arc-wrapped struct. +/// +/// Instead of cloning 8+ individual `Arc`s per connection, a single +/// `Arc` is cloned once and passed to the request handler. +pub struct AppState { + pub router_service: Arc, + pub orchestrator_service: Arc, + pub model_aliases: Option>, + pub llm_providers: Arc>, + pub agents_list: Option>, + pub listeners: Vec, + pub state_storage: Option>, + pub llm_provider_url: String, + pub span_attributes: Option, + /// Shared HTTP client for upstream LLM requests (connection pooling / keep-alive). + pub http_client: reqwest::Client, + pub filter_pipeline: Arc, +} diff --git a/crates/brightstaff/src/handlers/agents/errors.rs b/crates/brightstaff/src/handlers/agents/errors.rs new file mode 100644 index 00000000..478b4380 --- /dev/null +++ b/crates/brightstaff/src/handlers/agents/errors.rs @@ -0,0 +1,41 @@ +use bytes::Bytes; +use http_body_util::combinators::BoxBody; +use hyper::Response; +use serde_json::json; +use tracing::{info, warn}; + +use crate::handlers::response::ResponseHandler; + +/// Build a JSON error response from an `AgentFilterChainError`, logging the +/// full error chain along the way. +/// +/// Returns `Ok(Response)` so it can be used directly as a handler return value. +pub fn build_error_chain_response( + err: &E, +) -> Result>, hyper::Error> { + let mut error_chain = Vec::new(); + let mut current: &dyn std::error::Error = err; + loop { + error_chain.push(current.to_string()); + match current.source() { + Some(source) => current = source, + None => break, + } + } + + warn!(error_chain = ?error_chain, "agent chat error chain"); + warn!(root_error = ?err, "root error"); + + let error_json = json!({ + "error": { + "type": "AgentFilterChainError", + "message": err.to_string(), + "error_chain": error_chain, + "debug_info": format!("{:?}", err) + } + }); + + info!(error = %error_json, "structured error info"); + + Ok(ResponseHandler::create_json_error_response(&error_json)) +} diff --git a/crates/brightstaff/src/handlers/jsonrpc.rs b/crates/brightstaff/src/handlers/agents/jsonrpc.rs similarity index 100% rename from crates/brightstaff/src/handlers/jsonrpc.rs rename to crates/brightstaff/src/handlers/agents/jsonrpc.rs diff --git a/crates/brightstaff/src/handlers/agents/mod.rs b/crates/brightstaff/src/handlers/agents/mod.rs new file mode 100644 index 00000000..5b507907 --- /dev/null +++ b/crates/brightstaff/src/handlers/agents/mod.rs @@ -0,0 +1,5 @@ +pub mod errors; +pub mod jsonrpc; +pub mod orchestrator; +pub mod pipeline; +pub mod selector; diff --git a/crates/brightstaff/src/handlers/agent_chat_completions.rs b/crates/brightstaff/src/handlers/agents/orchestrator.rs similarity index 50% rename from crates/brightstaff/src/handlers/agent_chat_completions.rs rename to crates/brightstaff/src/handlers/agents/orchestrator.rs index 513e0ef2..8ece914e 100644 --- a/crates/brightstaff/src/handlers/agent_chat_completions.rs +++ b/crates/brightstaff/src/handlers/agents/orchestrator.rs @@ -2,63 +2,56 @@ use std::sync::Arc; use std::time::Instant; use bytes::Bytes; -use common::configuration::SpanAttributes; -use common::errors::BrightStaffError; -use common::llm_providers::LlmProviders; 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, StatusCode}; +use hyper::{Request, Response}; use opentelemetry::trace::get_active_span; -use serde::ser::Error as SerError; -use tokio::sync::RwLock; use tracing::{debug, info, info_span, warn, Instrument}; -use super::agent_selector::{AgentSelectionError, AgentSelector}; -use super::pipeline_processor::{PipelineError, PipelineProcessor}; -use super::response_handler::ResponseHandler; -use crate::router::plano_orchestrator::OrchestratorService; +use super::errors::build_error_chain_response; +use super::pipeline::{PipelineError, PipelineProcessor}; +use super::selector::{AgentSelectionError, AgentSelector}; +use crate::app_state::AppState; +use crate::handlers::extract_request_id; +use crate::handlers::response::ResponseHandler; use crate::tracing::{collect_custom_trace_attributes, operation_component, set_service_name}; /// Main errors for agent chat completions #[derive(Debug, thiserror::Error)] pub enum AgentFilterChainError { - #[error("Forwarded error: {0}")] - Brightstaff(#[from] BrightStaffError), #[error("Agent selection error: {0}")] Selection(#[from] AgentSelectionError), #[error("Pipeline processing error: {0}")] Pipeline(#[from] PipelineError), + #[error("Response handling error: {0}")] + Response(#[from] common::errors::BrightStaffError), #[error("Request parsing error: {0}")] - RequestParsing(#[from] serde_json::Error), + RequestParsing(String), #[error("HTTP error: {0}")] Http(#[from] hyper::Error), + #[error("Unsupported endpoint: {0}")] + UnsupportedEndpoint(String), + #[error("No agents configured")] + NoAgentsConfigured, + #[error("Agent '{0}' not found in configuration")] + AgentNotFound(String), + #[error("No messages in conversation history")] + EmptyHistory, + #[error("Agent chain completed without producing a response")] + IncompleteChain, } pub async fn agent_chat( request: Request, - orchestrator_service: Arc, - _: String, - agents_list: Arc>>>, - listeners: Arc>>, - span_attributes: Arc>, - llm_providers: Arc>, + state: Arc, ) -> Result>, hyper::Error> { + let request_id = extract_request_id(&request); let custom_attrs = - collect_custom_trace_attributes(request.headers(), span_attributes.as_ref().as_ref()); - // Extract request_id from headers or generate a new one - let request_id: String = match request - .headers() - .get(common::consts::REQUEST_ID_HEADER) - .and_then(|h| h.to_str().ok()) - .map(|s| s.to_string()) - { - Some(id) => id, - None => uuid::Uuid::new_v4().to_string(), - }; + collect_custom_trace_attributes(request.headers(), state.span_attributes.as_ref()); // Create a span with request_id that will be included in all log lines let request_span = info_span!( @@ -74,17 +67,7 @@ pub async fn agent_chat( // Set service name for orchestrator operations set_service_name(operation_component::ORCHESTRATOR); - match handle_agent_chat_inner( - request, - orchestrator_service, - agents_list, - listeners, - llm_providers, - request_id, - custom_attrs, - ) - .await - { + match handle_agent_chat_inner(request, state, request_id, custom_attrs).await { Ok(response) => Ok(response), Err(err) => { // Check if this is a client error from the pipeline that should be cascaded @@ -101,7 +84,6 @@ pub async fn agent_chat( "client error from agent" ); - // Create error response with the original status code and body let error_json = serde_json::json!({ "error": "ClientError", "agent": agent, @@ -109,52 +91,19 @@ pub async fn agent_chat( "agent_response": body }); - let status_code = hyper::StatusCode::from_u16(*status) - .unwrap_or(hyper::StatusCode::INTERNAL_SERVER_ERROR); - let json_string = error_json.to_string(); - return Ok(BrightStaffError::ForwardedError { - status_code, - message: json_string, - } - .into_response()); + let mut response = + Response::new(ResponseHandler::create_full_body(json_string)); + *response.status_mut() = hyper::StatusCode::from_u16(*status) + .unwrap_or(hyper::StatusCode::INTERNAL_SERVER_ERROR); + response.headers_mut().insert( + hyper::header::CONTENT_TYPE, + hyper::header::HeaderValue::from_static("application/json"), + ); + return Ok(response); } - // Print detailed error information with full error chain for other errors - let mut error_chain = Vec::new(); - let mut current_error: &dyn std::error::Error = &err; - - // Collect the full error chain - loop { - error_chain.push(current_error.to_string()); - match current_error.source() { - Some(source) => current_error = source, - None => break, - } - } - - // Log the complete error chain - warn!(error_chain = ?error_chain, "agent chat error chain"); - warn!(root_error = ?err, "root error"); - - // Create structured error response as JSON - let error_json = serde_json::json!({ - "error": { - "type": "AgentFilterChainError", - "message": err.to_string(), - "error_chain": error_chain, - "debug_info": format!("{:?}", err) - } - }); - - // Log the error for debugging - info!(error = %error_json, "structured error info"); - - Ok(BrightStaffError::ForwardedError { - status_code: StatusCode::BAD_REQUEST, - message: error_json.to_string(), - } - .into_response()) + build_error_chain_response(&err) } } } @@ -162,19 +111,22 @@ pub async fn agent_chat( .await } -async fn handle_agent_chat_inner( +/// Parsed and validated agent request data. +struct AgentRequest { + client_request: ProviderRequestType, + messages: Vec, + request_headers: hyper::HeaderMap, + request_id: Option, +} + +/// Parse the incoming HTTP request, resolve the listener, and extract messages. +async fn parse_agent_request( request: Request, - orchestrator_service: Arc, - agents_list: Arc>>>, - listeners: Arc>>, - llm_providers: Arc>, - request_id: String, - custom_attrs: std::collections::HashMap, -) -> Result>, AgentFilterChainError> { - // Initialize services - let agent_selector = AgentSelector::new(orchestrator_service); - let mut pipeline_processor = PipelineProcessor::default(); - let response_handler = ResponseHandler::new(); + state: &AppState, + request_id: &str, + custom_attrs: &std::collections::HashMap, +) -> Result<(AgentRequest, common::configuration::Listener, AgentSelector), AgentFilterChainError> { + let agent_selector = AgentSelector::new(Arc::clone(&state.orchestrator_service)); // Extract listener name from headers let listener_name = request @@ -183,16 +135,11 @@ async fn handle_agent_chat_inner( .and_then(|name| name.to_str().ok()); // Find the appropriate listener - let listener: common::configuration::Listener = { - let listeners = listeners.read().await; - agent_selector - .find_listener(listener_name, &listeners) - .await? - }; + let listener = agent_selector.find_listener(listener_name, &state.listeners)?; get_active_span(|span| { span.update_name(listener.name.to_string()); - for (key, value) in &custom_attrs { + for (key, value) in custom_attrs { span.set_attribute(opentelemetry::KeyValue::new(key.clone(), value.clone())); } }); @@ -200,24 +147,20 @@ async fn handle_agent_chat_inner( info!(listener = %listener.name, "handling request"); // Parse request body - let request_path = request - .uri() - .path() - .to_string() + let full_path = request.uri().path().to_string(); + let request_path = full_path .strip_prefix("/agents") - .unwrap() + .unwrap_or(&full_path) .to_string(); let request_headers = { let mut headers = request.headers().clone(); headers.remove(common::consts::ENVOY_ORIGINAL_PATH_HEADER); - // Set the request_id in headers if not already present if !headers.contains_key(common::consts::REQUEST_ID_HEADER) { - headers.insert( - common::consts::REQUEST_ID_HEADER, - hyper::header::HeaderValue::from_str(&request_id).unwrap(), - ); + if let Ok(val) = hyper::header::HeaderValue::from_str(request_id) { + headers.insert(common::consts::REQUEST_ID_HEADER, val); + } } headers @@ -230,63 +173,62 @@ async fn handle_agent_chat_inner( "received request body" ); - // 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)) + warn!(path = %request_path, "unsupported endpoint"); + AgentFilterChainError::UnsupportedEndpoint(request_path.clone()) })?; - let mut 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 client_request = ProviderRequestType::try_from((&chat_request_bytes[..], &api_type)) + .map_err(|err| { + warn!(error = %err, "failed to parse request as ProviderRequestType"); + AgentFilterChainError::RequestParsing(format!("Failed to parse request: {}", err)) + })?; - // If model is not specified in the request, resolve from default provider - if client_request.model().is_empty() { - match llm_providers.read().await.default() { - Some(default_provider) => { - let default_model = default_provider.name.clone(); - info!(default_model = %default_model, "no model specified in request, using default provider"); - client_request.set_model(default_model); - } - None => { - let err_msg = "No model specified in request and no default provider configured"; - warn!("{}", err_msg); - return Ok(BrightStaffError::NoModelSpecified.into_response()); - } - } - } - - let message: Vec = client_request.get_messages(); + let messages: Vec = client_request.get_messages(); let request_id = request_headers .get(common::consts::REQUEST_ID_HEADER) .and_then(|val| val.to_str().ok()) .map(|s| s.to_string()); - // 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) - }; + Ok(( + AgentRequest { + client_request, + messages, + request_headers, + request_id, + }, + listener, + agent_selector, + )) +} + +/// Select agents via the orchestrator model and record selection metrics. +async fn select_and_build_agent_map( + agent_selector: &AgentSelector, + state: &AppState, + messages: &[OpenAIMessage], + listener: &common::configuration::Listener, + request_id: Option, +) -> Result< + ( + Vec, + std::collections::HashMap, + ), + AgentFilterChainError, +> { + let agents = state + .agents_list + .as_ref() + .ok_or(AgentFilterChainError::NoAgentsConfigured)?; + let agent_map = agent_selector.create_agent_map(agents); - // Select appropriate agents using arch orchestrator llm model let selection_start = Instant::now(); let selected_agents = agent_selector - .select_agents(&message, &listener, request_id.clone()) + .select_agents(messages, listener, request_id) .await?; - // Record selection attributes on the current orchestrator span let selection_elapsed_ms = selection_start.elapsed().as_secs_f64() * 1000.0; get_active_span(|span| { span.set_attribute(opentelemetry::KeyValue::new( @@ -316,12 +258,25 @@ async fn handle_agent_chat_inner( "selected agents for execution" ); - // Execute agents sequentially, passing output from one to the next - let mut current_messages = message.clone(); + Ok((selected_agents, agent_map)) +} + +/// Execute the agent chain: run each selected agent sequentially, streaming +/// the final agent's response back to the client. +async fn execute_agent_chain( + selected_agents: &[common::configuration::AgentFilterChain], + agent_map: &std::collections::HashMap, + client_request: ProviderRequestType, + messages: Vec, + request_headers: &hyper::HeaderMap, + custom_attrs: &std::collections::HashMap, +) -> Result>, AgentFilterChainError> { + let mut pipeline_processor = PipelineProcessor::default(); + let response_handler = ResponseHandler::new(); + let mut current_messages = messages; let agent_count = selected_agents.len(); for (agent_index, selected_agent) in selected_agents.iter().enumerate() { - // Get agent name let agent_name = selected_agent.id.clone(); let is_last_agent = agent_index == agent_count - 1; @@ -332,18 +287,40 @@ async fn handle_agent_chat_inner( "processing agent" ); - // Process the filter chain - let chat_history = pipeline_processor - .process_filter_chain( - ¤t_messages, - selected_agent, - &agent_map, - &request_headers, - ) - .await?; + let chat_history = if selected_agent + .input_filters + .as_ref() + .map(|f| !f.is_empty()) + .unwrap_or(false) + { + let filter_body = serde_json::json!({ + "model": client_request.model(), + "messages": current_messages, + }); + let filter_bytes = + serde_json::to_vec(&filter_body).map_err(PipelineError::ParseError)?; - // Get agent details and invoke - let agent = agent_map.get(&agent_name).unwrap(); + let filtered_bytes = pipeline_processor + .process_raw_filter_chain( + &filter_bytes, + selected_agent, + agent_map, + request_headers, + "/v1/chat/completions", + ) + .await?; + + let filtered_body: serde_json::Value = + serde_json::from_slice(&filtered_bytes).map_err(PipelineError::ParseError)?; + serde_json::from_value(filtered_body["messages"].clone()) + .map_err(PipelineError::ParseError)? + } else { + current_messages.clone() + }; + + let agent = agent_map + .get(&agent_name) + .ok_or_else(|| AgentFilterChainError::AgentNotFound(agent_name.clone()))?; debug!(agent = %agent_name, "invoking agent"); @@ -357,7 +334,7 @@ async fn handle_agent_chat_inner( set_service_name(operation_component::AGENT); get_active_span(|span| { span.update_name(format!("{} /v1/chat/completions", agent_name)); - for (key, value) in &custom_attrs { + for (key, value) in custom_attrs { span.set_attribute(opentelemetry::KeyValue::new(key.clone(), value.clone())); } }); @@ -367,28 +344,25 @@ async fn handle_agent_chat_inner( &chat_history, client_request.clone(), agent, - &request_headers, + request_headers, ) .await } .instrument(agent_span.clone()) .await?; - // If this is the last agent, return the streaming response if is_last_agent { info!( agent = %agent_name, "completed agent chain, returning response" ); - // Capture the orchestrator span (parent of the agent span) so it - // stays open for the full streaming duration alongside the agent span. let orchestrator_span = tracing::Span::current(); return async { response_handler .create_streaming_response( llm_response, - tracing::Span::current(), // agent span (inner) - orchestrator_span, // orchestrator span (outer) + tracing::Span::current(), + orchestrator_span, ) .await .map_err(AgentFilterChainError::from) @@ -397,7 +371,6 @@ async fn handle_agent_chat_inner( .await; } - // For intermediate agents, collect the full response and pass to next agent debug!(agent = %agent_name, "collecting response from intermediate agent"); let response_text = async { response_handler.collect_full_response(llm_response).await } .instrument(agent_span) @@ -409,11 +382,11 @@ async fn handle_agent_chat_inner( "agent completed, passing response to next agent" ); - // remove last message and add new one at the end - let last_message = current_messages.pop().unwrap(); + let Some(last_message) = current_messages.pop() else { + warn!(agent = %agent_name, "no messages in conversation history"); + return Err(AgentFilterChainError::EmptyHistory); + }; - // Create a new message with the agent's response as assistant message - // and add it to the conversation history current_messages.push(OpenAIMessage { role: hermesllm::apis::openai::Role::Assistant, content: Some(hermesllm::apis::openai::MessageContent::Text(response_text)), @@ -425,6 +398,34 @@ async fn handle_agent_chat_inner( current_messages.push(last_message); } - // This should never be reached since we return in the last agent iteration - unreachable!("Agent execution loop should have returned a response") + Err(AgentFilterChainError::IncompleteChain) +} + +async fn handle_agent_chat_inner( + request: Request, + state: Arc, + request_id: String, + custom_attrs: std::collections::HashMap, +) -> Result>, AgentFilterChainError> { + let (agent_req, listener, agent_selector) = + parse_agent_request(request, &state, &request_id, &custom_attrs).await?; + + let (selected_agents, agent_map) = select_and_build_agent_map( + &agent_selector, + &state, + &agent_req.messages, + &listener, + agent_req.request_id, + ) + .await?; + + execute_agent_chain( + &selected_agents, + &agent_map, + agent_req.client_request, + agent_req.messages, + &agent_req.request_headers, + &custom_attrs, + ) + .await } diff --git a/crates/brightstaff/src/handlers/pipeline_processor.rs b/crates/brightstaff/src/handlers/agents/pipeline.rs similarity index 69% rename from crates/brightstaff/src/handlers/pipeline_processor.rs rename to crates/brightstaff/src/handlers/agents/pipeline.rs index d9e87095..50058441 100644 --- a/crates/brightstaff/src/handlers/pipeline_processor.rs +++ b/crates/brightstaff/src/handlers/agents/pipeline.rs @@ -1,5 +1,6 @@ use std::collections::HashMap; +use bytes::Bytes; use common::configuration::{Agent, AgentFilterChain}; use common::consts::{ ARCH_UPSTREAM_HOST_HEADER, BRIGHT_STAFF_SERVICE_NAME, ENVOY_RETRY_HEADER, TRACE_PARENT_HEADER, @@ -11,7 +12,7 @@ use opentelemetry::global; use opentelemetry_http::HeaderInjector; use tracing::{debug, info, instrument, warn}; -use crate::handlers::jsonrpc::{ +use super::jsonrpc::{ JsonRpcId, JsonRpcNotification, JsonRpcRequest, JsonRpcResponse, JSON_RPC_VERSION, MCP_INITIALIZE, MCP_INITIALIZE_NOTIFICATION, TOOL_CALL_METHOD, }; @@ -35,8 +36,6 @@ pub enum PipelineError { 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, @@ -79,74 +78,11 @@ impl PipelineProcessor { } } - // /// Process the filter chain of agents (all except the terminal agent) - // #[instrument( - // skip(self, chat_history, agent_filter_chain, agent_map, request_headers), - // fields( - // filter_count = agent_filter_chain.filter_chain.as_ref().map(|fc| fc.len()).unwrap_or(0), - // message_count = chat_history.len() - // ) - // )] - #[allow(clippy::too_many_arguments)] - pub async fn process_filter_chain( - &mut self, - chat_history: &[Message], - agent_filter_chain: &AgentFilterChain, - agent_map: &HashMap, - request_headers: &HeaderMap, - ) -> Result, PipelineError> { - let mut chat_history_updated = chat_history.to_vec(); - - // If filter_chain is None or empty, proceed without filtering - let filter_chain = match agent_filter_chain.filter_chain.as_ref() { - Some(fc) if !fc.is_empty() => fc, - _ => return Ok(chat_history_updated), - }; - - for agent_name in filter_chain { - debug!(agent = %agent_name, "processing filter agent"); - - let agent = agent_map - .get(agent_name) - .ok_or_else(|| PipelineError::AgentNotFound(agent_name.clone()))?; - - let tool_name = agent.tool.as_deref().unwrap_or(&agent.id); - - info!( - agent = %agent_name, - tool = %tool_name, - url = %agent.url, - agent_type = %agent.agent_type.as_deref().unwrap_or("mcp"), - conversation_len = chat_history.len(), - "executing filter" - ); - - if agent.agent_type.as_deref().unwrap_or("mcp") == "mcp" { - chat_history_updated = self - .execute_mcp_filter(&chat_history_updated, agent, request_headers) - .await?; - } else { - chat_history_updated = self - .execute_http_filter(&chat_history_updated, agent, request_headers) - .await?; - } - - info!( - agent = %agent_name, - updated_len = chat_history_updated.len(), - "filter completed" - ); - } - - Ok(chat_history_updated) - } - - /// Build common MCP headers for requests - fn build_mcp_headers( - &self, + /// Prepare headers shared by all agent/filter requests: removes + /// content-length, injects trace context, sets upstream host and retry. + fn build_agent_headers( request_headers: &HeaderMap, agent_id: &str, - session_id: Option<&str>, ) -> Result { let mut headers = request_headers.clone(); headers.remove(hyper::header::CONTENT_LENGTH); @@ -167,24 +103,34 @@ impl PipelineProcessor { headers.insert( ENVOY_RETRY_HEADER, - hyper::header::HeaderValue::from_str("3").unwrap(), + hyper::header::HeaderValue::from_static("3"), ); + Ok(headers) + } + + /// Build headers for MCP requests (adds Accept, Content-Type, optional session id). + fn build_mcp_headers( + &self, + request_headers: &HeaderMap, + agent_id: &str, + session_id: Option<&str>, + ) -> Result { + let mut headers = Self::build_agent_headers(request_headers, agent_id)?; + 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(), - ); + if let Ok(val) = hyper::header::HeaderValue::from_str(sid) { + headers.insert("mcp-session-id", val); + } } Ok(headers) @@ -261,14 +207,17 @@ impl PipelineProcessor { Ok(response) } - /// Build a tools/call JSON-RPC request - fn build_tool_call_request( + /// Build a tools/call JSON-RPC request with a full body dict and path hint. + /// Used by execute_mcp_filter_raw so MCP tools receive the same contract as HTTP filters. + fn build_tool_call_request_with_body( &self, tool_name: &str, - messages: &[Message], + body: &serde_json::Value, + path: &str, ) -> Result { let mut arguments = HashMap::new(); - arguments.insert("messages".to_string(), serde_json::to_value(messages)?); + arguments.insert("body".to_string(), serde_json::to_value(body)?); + arguments.insert("path".to_string(), serde_json::to_value(path)?); let mut params = HashMap::new(); params.insert("name".to_string(), serde_json::to_value(tool_name)?); @@ -282,35 +231,28 @@ impl PipelineProcessor { }) } - /// Send request to a specific agent and return the response content - #[instrument( - skip(self, messages, agent, request_headers), - fields( - agent_id = %agent.id, - filter_name = %agent.id, - message_count = messages.len() - ) - )] - async fn execute_mcp_filter( + /// Like execute_mcp_filter_raw but passes the full raw body dict + path hint as MCP tool arguments. + /// The MCP tool receives (body: dict, path: str) and returns the modified body dict. + async fn execute_mcp_filter_raw( &mut self, - messages: &[Message], + raw_bytes: &[u8], agent: &Agent, request_headers: &HeaderMap, - ) -> Result, PipelineError> { - // Set service name for this filter span + request_path: &str, + ) -> Result { set_service_name(operation_component::AGENT_FILTER); - - // Update current span name to include filter name use opentelemetry::trace::get_active_span; get_active_span(|span| { - span.update_name(format!("execute_mcp_filter ({})", agent.id)); + span.update_name(format!("execute_mcp_filter_raw ({})", agent.id)); }); - // Get or create MCP session + let body: serde_json::Value = + serde_json::from_slice(raw_bytes).map_err(PipelineError::ParseError)?; + 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, request_headers).await; + let session_id = self.get_new_session_id(&agent.id, request_headers).await?; self.agent_id_session_map .insert(agent.id.clone(), session_id.clone()); session_id @@ -321,11 +263,10 @@ impl PipelineProcessor { 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)?; + let json_rpc_request = + self.build_tool_call_request_with_body(tool_name, &body, request_path)?; - // Build headers let agent_headers = self.build_mcp_headers(request_headers, &agent.id, Some(&mcp_session_id))?; @@ -335,7 +276,6 @@ impl PipelineProcessor { let http_status = response.status(); let response_bytes = response.bytes().await?; - // 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() { @@ -353,20 +293,12 @@ impl PipelineProcessor { }); } - 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()) @@ -388,21 +320,28 @@ impl PipelineProcessor { }); } - // Extract structured content and parse messages - let response_json = response_result + // FastMCP puts structured Pydantic return values in structuredContent.result, + // but plain dicts land in content[0].text as a JSON string. Try both. + let result = if let Some(structured) = response_result .get("structuredContent") - .ok_or_else(|| PipelineError::NoStructuredContentInResponse(agent.id.clone()))?; + .and_then(|v| v.get("result")) + .cloned() + { + structured + } else { + let text = 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()) + .ok_or_else(|| PipelineError::NoStructuredContentInResponse(agent.id.clone()))?; + serde_json::from_str(text).map_err(PipelineError::ParseError)? + }; - 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) + Ok(Bytes::from( + serde_json::to_vec(&result).map_err(PipelineError::ParseError)?, + )) } /// Build an initialize JSON-RPC request @@ -464,18 +403,19 @@ impl PipelineProcessor { Ok(()) } - async fn get_new_session_id(&self, agent_id: &str, request_headers: &HeaderMap) -> String { + async fn get_new_session_id( + &self, + agent_id: &str, + request_headers: &HeaderMap, + ) -> Result { info!("initializing MCP session for agent {}", agent_id); let initialize_request = self.build_initialize_request(); - let headers = self - .build_mcp_headers(request_headers, agent_id, None) - .expect("Failed to build headers for initialization"); + let headers = self.build_mcp_headers(request_headers, agent_id, None)?; let response = self .send_mcp_request(&initialize_request, &headers, agent_id) - .await - .expect("Failed to initialize MCP session"); + .await?; info!("initialize response status: {}", response.status()); @@ -483,8 +423,13 @@ impl PipelineProcessor { .headers() .get("mcp-session-id") .and_then(|v| v.to_str().ok()) - .expect("No mcp-session-id in response") - .to_string(); + .map(|s| s.to_string()) + .ok_or_else(|| { + PipelineError::NoContentInResponse(format!( + "No mcp-session-id header in initialize response from agent {}", + agent_id + )) + })?; info!( "created new MCP session for agent {}: {}", @@ -493,88 +438,63 @@ impl PipelineProcessor { // Send initialized notification self.send_initialized_notification(agent_id, &session_id, &headers) - .await - .expect("Failed to send initialized notification"); + .await?; - session_id + Ok(session_id) } - /// Execute a HTTP-based filter agent + /// Execute a raw bytes filter — POST bytes to agent.url, receive bytes back. + /// Used for input and output filters where the full raw request/response is passed through. + /// No MCP protocol wrapping; agent_type is ignored. #[instrument( - skip(self, messages, agent, request_headers), + skip(self, raw_bytes, agent, request_headers), fields( agent_id = %agent.id, agent_url = %agent.url, filter_name = %agent.id, - message_count = messages.len() + bytes_len = raw_bytes.len() ) )] - async fn execute_http_filter( + async fn execute_raw_filter( &mut self, - messages: &[Message], + raw_bytes: &[u8], agent: &Agent, request_headers: &HeaderMap, - ) -> Result, PipelineError> { - // Set service name for this filter span + request_path: &str, + ) -> Result { set_service_name(operation_component::AGENT_FILTER); - - // Update current span name to include filter name use opentelemetry::trace::get_active_span; get_active_span(|span| { - span.update_name(format!("execute_http_filter ({})", agent.id)); + span.update_name(format!("execute_raw_filter ({})", agent.id)); }); - // Build headers - let mut agent_headers = request_headers.clone(); - agent_headers.remove(hyper::header::CONTENT_LENGTH); - - // Inject OpenTelemetry trace context automatically - agent_headers.remove(TRACE_PARENT_HEADER); - global::get_text_map_propagator(|propagator| { - let cx = - tracing_opentelemetry::OpenTelemetrySpanExt::context(&tracing::Span::current()); - propagator.inject_context(&cx, &mut HeaderInjector(&mut agent_headers)); - }); - - agent_headers.insert( - ARCH_UPSTREAM_HOST_HEADER, - hyper::header::HeaderValue::from_str(&agent.id) - .map_err(|_| PipelineError::AgentNotFound(agent.id.clone()))?, - ); - - agent_headers.insert( - ENVOY_RETRY_HEADER, - hyper::header::HeaderValue::from_str("3").unwrap(), - ); - + let mut agent_headers = Self::build_agent_headers(request_headers, &agent.id)?; agent_headers.insert( "Accept", hyper::header::HeaderValue::from_static("application/json"), ); - agent_headers.insert( "Content-Type", hyper::header::HeaderValue::from_static("application/json"), ); - debug!( - "Sending HTTP request to agent {} at URL: {}", - agent.id, agent.url - ); + // Append the original request path so the filter endpoint encodes the API format. + // e.g. agent.url="http://host/anonymize" + request_path="/v1/chat/completions" + // -> POST http://host/anonymize/v1/chat/completions + let url = format!("{}{}", agent.url, request_path); + debug!(agent = %agent.id, url = %url, "sending raw filter request"); - // Send messages array directly as request body let response = self .client - .post(&agent.url) + .post(&url) .headers(agent_headers) - .json(&messages) + .body(raw_bytes.to_vec()) .send() .await?; let http_status = response.status(); let response_bytes = response.bytes().await?; - // 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() { @@ -592,17 +512,56 @@ impl PipelineProcessor { }); } - debug!( - "Response from HTTP agent {}: {}", - agent.id, - String::from_utf8_lossy(&response_bytes) - ); + debug!(agent = %agent.id, bytes_len = response_bytes.len(), "raw filter response received"); + Ok(response_bytes) + } - // Parse response - expecting array of messages directly - let messages: Vec = - serde_json::from_slice(&response_bytes).map_err(PipelineError::ParseError)?; + /// Process a chain of raw-bytes filters sequentially. + /// Input: raw request or response bytes. Output: filtered bytes. + /// Each agent receives the output of the previous one. + pub async fn process_raw_filter_chain( + &mut self, + raw_bytes: &[u8], + agent_filter_chain: &AgentFilterChain, + agent_map: &HashMap, + request_headers: &HeaderMap, + request_path: &str, + ) -> Result { + let filter_chain = match agent_filter_chain.input_filters.as_ref() { + Some(fc) if !fc.is_empty() => fc, + _ => return Ok(Bytes::copy_from_slice(raw_bytes)), + }; - Ok(messages) + let mut current_bytes = Bytes::copy_from_slice(raw_bytes); + + for agent_name in filter_chain { + debug!(agent = %agent_name, "processing raw filter agent"); + + let agent = agent_map + .get(agent_name) + .ok_or_else(|| PipelineError::AgentNotFound(agent_name.clone()))?; + + let agent_type = agent.agent_type.as_deref().unwrap_or("mcp"); + info!( + agent = %agent_name, + url = %agent.url, + agent_type = %agent_type, + bytes_len = current_bytes.len(), + "executing raw filter" + ); + + current_bytes = if agent_type == "mcp" { + self.execute_mcp_filter_raw(¤t_bytes, agent, request_headers, request_path) + .await? + } else { + self.execute_raw_filter(¤t_bytes, agent, request_headers, request_path) + .await? + }; + + info!(agent = %agent_name, bytes_len = current_bytes.len(), "raw filter completed"); + } + + Ok(current_bytes) } /// Send request to terminal agent and return the raw response for streaming @@ -615,36 +574,15 @@ impl PipelineProcessor { terminal_agent: &Agent, request_headers: &HeaderMap, ) -> Result { - // let mut request = original_request.clone(); original_request.set_messages(messages); let request_url = "/v1/chat/completions"; - let request_body = ProviderRequestType::to_bytes(&original_request).unwrap(); - // let request_body = serde_json::to_string(&request)?; + let request_body = ProviderRequestType::to_bytes(&original_request) + .map_err(|e| PipelineError::NoContentInResponse(e.to_string()))?; debug!("sending request to terminal agent {}", terminal_agent.id); - let mut agent_headers = request_headers.clone(); - agent_headers.remove(hyper::header::CONTENT_LENGTH); - - // Inject OpenTelemetry trace context automatically - agent_headers.remove(TRACE_PARENT_HEADER); - global::get_text_map_propagator(|propagator| { - let cx = - tracing_opentelemetry::OpenTelemetrySpanExt::context(&tracing::Span::current()); - propagator.inject_context(&cx, &mut HeaderInjector(&mut agent_headers)); - }); - - agent_headers.insert( - ARCH_UPSTREAM_HOST_HEADER, - hyper::header::HeaderValue::from_str(&terminal_agent.id) - .map_err(|_| PipelineError::AgentNotFound(terminal_agent.id.clone()))?, - ); - - agent_headers.insert( - ENVOY_RETRY_HEADER, - hyper::header::HeaderValue::from_str("3").unwrap(), - ); + let agent_headers = Self::build_agent_headers(request_headers, &terminal_agent.id)?; let response = self .client @@ -661,24 +599,13 @@ impl PipelineProcessor { #[cfg(test)] 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 { - Message { - role, - content: Some(MessageContent::Text(content.to_string())), - name: None, - tool_calls: None, - tool_call_id: None, - } - } - fn create_test_pipeline(agents: Vec<&str>) -> AgentFilterChain { AgentFilterChain { id: "test-agent".to_string(), - filter_chain: Some(agents.iter().map(|s| s.to_string()).collect()), + input_filters: Some(agents.iter().map(|s| s.to_string()).collect()), description: None, default: None, } @@ -690,12 +617,19 @@ mod tests { let agent_map = HashMap::new(); let request_headers = HeaderMap::new(); - let messages = vec![create_test_message(Role::User, "Hello")]; + let body = serde_json::json!({"messages": [{"role": "user", "content": "Hello"}]}); + let raw_bytes = serde_json::to_vec(&body).unwrap(); let pipeline = create_test_pipeline(vec!["nonexistent-agent", "terminal-agent"]); let result = processor - .process_filter_chain(&messages, &pipeline, &agent_map, &request_headers) + .process_raw_filter_chain( + &raw_bytes, + &pipeline, + &agent_map, + &request_headers, + "/v1/chat/completions", + ) .await; assert!(result.is_err()); @@ -725,11 +659,12 @@ mod tests { agent_type: None, }; - let messages = vec![create_test_message(Role::User, "Hello")]; + let body = serde_json::json!({"messages": [{"role": "user", "content": "Hello"}]}); + let raw_bytes = serde_json::to_vec(&body).unwrap(); let request_headers = HeaderMap::new(); let result = processor - .execute_mcp_filter(&messages, &agent, &request_headers) + .execute_mcp_filter_raw(&raw_bytes, &agent, &request_headers, "/v1/chat/completions") .await; match result { @@ -764,11 +699,12 @@ mod tests { agent_type: None, }; - let messages = vec![create_test_message(Role::User, "Ping")]; + let body = serde_json::json!({"messages": [{"role": "user", "content": "Ping"}]}); + let raw_bytes = serde_json::to_vec(&body).unwrap(); let request_headers = HeaderMap::new(); let result = processor - .execute_mcp_filter(&messages, &agent, &request_headers) + .execute_mcp_filter_raw(&raw_bytes, &agent, &request_headers, "/v1/chat/completions") .await; match result { @@ -816,11 +752,12 @@ mod tests { agent_type: None, }; - let messages = vec![create_test_message(Role::User, "Hi")]; + let body = serde_json::json!({"messages": [{"role": "user", "content": "Hi"}]}); + let raw_bytes = serde_json::to_vec(&body).unwrap(); let request_headers = HeaderMap::new(); let result = processor - .execute_mcp_filter(&messages, &agent, &request_headers) + .execute_mcp_filter_raw(&raw_bytes, &agent, &request_headers, "/v1/chat/completions") .await; match result { diff --git a/crates/brightstaff/src/handlers/agent_selector.rs b/crates/brightstaff/src/handlers/agents/selector.rs similarity index 93% rename from crates/brightstaff/src/handlers/agent_selector.rs rename to crates/brightstaff/src/handlers/agents/selector.rs index faa734ee..8225a003 100644 --- a/crates/brightstaff/src/handlers/agent_selector.rs +++ b/crates/brightstaff/src/handlers/agents/selector.rs @@ -7,7 +7,7 @@ use common::configuration::{ use hermesllm::apis::openai::Message; use tracing::{debug, warn}; -use crate::router::plano_orchestrator::OrchestratorService; +use crate::router::orchestrator::OrchestratorService; /// Errors that can occur during agent selection #[derive(Debug, thiserror::Error)] @@ -37,7 +37,7 @@ impl AgentSelector { } /// Find listener by name from the request headers - pub async fn find_listener( + pub fn find_listener( &self, listener_name: Option<&str>, listeners: &[common::configuration::Listener], @@ -84,7 +84,7 @@ impl AgentSelector { } /// Convert agent descriptions to orchestration preferences - async fn convert_agent_description_to_orchestration_preferences( + fn convert_agent_description_to_orchestration_preferences( &self, agents: &[AgentFilterChain], ) -> Vec { @@ -121,9 +121,7 @@ impl AgentSelector { return Ok(vec![agents[0].clone()]); } - let usage_preferences = self - .convert_agent_description_to_orchestration_preferences(agents) - .await; + let usage_preferences = self.convert_agent_description_to_orchestration_preferences(agents); debug!( "Agents usage preferences for orchestration: {}", serde_json::to_string(&usage_preferences).unwrap_or_default() @@ -172,12 +170,13 @@ impl AgentSelector { #[cfg(test)] mod tests { use super::*; - use common::configuration::{AgentFilterChain, Listener}; + use common::configuration::{AgentFilterChain, Listener, ListenerType}; fn create_test_orchestrator_service() -> Arc { Arc::new(OrchestratorService::new( "http://localhost:8080".to_string(), "test-model".to_string(), + "plano-orchestrator".to_string(), )) } @@ -186,14 +185,17 @@ mod tests { id: name.to_string(), description: Some(description.to_string()), default: Some(is_default), - filter_chain: Some(vec![name.to_string()]), + input_filters: Some(vec![name.to_string()]), } } fn create_test_listener(name: &str, agents: Vec) -> Listener { Listener { + listener_type: ListenerType::Agent, name: name.to_string(), agents: Some(agents), + input_filters: None, + output_filters: None, port: 8080, router: None, } @@ -218,9 +220,7 @@ mod tests { let listener2 = create_test_listener("other-listener", vec![]); let listeners = vec![listener1.clone(), listener2]; - let result = selector - .find_listener(Some("test-listener"), &listeners) - .await; + let result = selector.find_listener(Some("test-listener"), &listeners); assert!(result.is_ok()); assert_eq!(result.unwrap().name, "test-listener"); @@ -233,9 +233,7 @@ mod tests { let listeners = vec![create_test_listener("other-listener", vec![])]; - let result = selector - .find_listener(Some("nonexistent"), &listeners) - .await; + let result = selector.find_listener(Some("nonexistent"), &listeners); assert!(result.is_err()); matches!( diff --git a/crates/brightstaff/src/handlers/integration_tests.rs b/crates/brightstaff/src/handlers/integration_tests.rs index 70b2999d..499fbfca 100644 --- a/crates/brightstaff/src/handlers/integration_tests.rs +++ b/crates/brightstaff/src/handlers/integration_tests.rs @@ -3,12 +3,11 @@ use std::sync::Arc; use hermesllm::apis::openai::{ChatCompletionsRequest, Message, MessageContent, Role}; use hyper::header::HeaderMap; -use crate::handlers::agent_selector::{AgentSelectionError, AgentSelector}; -use crate::handlers::pipeline_processor::PipelineProcessor; -use crate::router::plano_orchestrator::OrchestratorService; -use common::errors::BrightStaffError; -use http_body_util::BodyExt; -use hyper::StatusCode; +use crate::handlers::agents::pipeline::PipelineProcessor; +use crate::handlers::agents::selector::{AgentSelectionError, AgentSelector}; +use crate::handlers::response::ResponseHandler; +use crate::router::orchestrator::OrchestratorService; + /// Integration test that demonstrates the modular agent chat flow /// This test shows how the three main components work together: /// 1. AgentSelector - selects the appropriate agents based on orchestration @@ -17,12 +16,13 @@ use hyper::StatusCode; #[cfg(test)] mod tests { use super::*; - use common::configuration::{Agent, AgentFilterChain, Listener}; + use common::configuration::{Agent, AgentFilterChain, Listener, ListenerType}; fn create_test_orchestrator_service() -> Arc { Arc::new(OrchestratorService::new( "http://localhost:8080".to_string(), "test-model".to_string(), + "plano-orchestrator".to_string(), )) } @@ -63,7 +63,7 @@ mod tests { let agent_pipeline = AgentFilterChain { id: "terminal-agent".to_string(), - filter_chain: Some(vec![ + input_filters: Some(vec![ "filter-agent".to_string(), "terminal-agent".to_string(), ]), @@ -72,8 +72,11 @@ mod tests { }; let listener = Listener { + listener_type: ListenerType::Agent, name: "test-listener".to_string(), agents: Some(vec![agent_pipeline.clone()]), + input_filters: None, + output_filters: None, port: 8080, router: None, }; @@ -82,9 +85,7 @@ mod tests { let messages = vec![create_test_message(Role::User, "Hello world!")]; // Test 1: Agent Selection - let selected_listener = agent_selector - .find_listener(Some("test-listener"), &listeners) - .await; + let selected_listener = agent_selector.find_listener(Some("test-listener"), &listeners); assert!(selected_listener.is_ok()); let listener = selected_listener.unwrap(); @@ -106,47 +107,40 @@ mod tests { // Create a pipeline with empty filter chain to avoid network calls let test_pipeline = AgentFilterChain { id: "terminal-agent".to_string(), - filter_chain: Some(vec![]), // Empty filter chain - no network calls needed + input_filters: Some(vec![]), // Empty filter chain - no network calls needed description: None, default: None, }; let headers = HeaderMap::new(); + let request_bytes = serde_json::to_vec(&request).expect("failed to serialize request"); let result = pipeline_processor - .process_filter_chain(&request.messages, &test_pipeline, &agent_map, &headers) + .process_raw_filter_chain( + &request_bytes, + &test_pipeline, + &agent_map, + &headers, + "/v1/chat/completions", + ) .await; println!("Pipeline processing result: {:?}", result); assert!(result.is_ok()); - let processed_messages = result.unwrap(); - // With empty filter chain, should return the original messages unchanged - assert_eq!(processed_messages.len(), 1); - if let Some(MessageContent::Text(content)) = &processed_messages[0].content { + let processed_bytes = result.unwrap(); + // With empty filter chain, should return the original bytes unchanged + let processed_request: ChatCompletionsRequest = + serde_json::from_slice(&processed_bytes).expect("failed to deserialize response"); + assert_eq!(processed_request.messages.len(), 1); + if let Some(MessageContent::Text(content)) = &processed_request.messages[0].content { assert_eq!(content, "Hello world!"); } else { panic!("Expected text content"); } // Test 4: Error Response Creation - let err = BrightStaffError::ModelNotFound("gpt-5-secret".to_string()); - let response = err.into_response(); - - assert_eq!(response.status(), StatusCode::NOT_FOUND); - - // Helper to extract body as JSON - let body_bytes = response.into_body().collect().await.unwrap().to_bytes(); - let body: serde_json::Value = serde_json::from_slice(&body_bytes).unwrap(); - - assert_eq!(body["error"]["code"], "ModelNotFound"); - assert_eq!( - body["error"]["details"]["rejected_model_id"], - "gpt-5-secret" - ); - assert!(body["error"]["message"] - .as_str() - .unwrap() - .contains("gpt-5-secret")); + let error_response = ResponseHandler::create_bad_request("Test error"); + assert_eq!(error_response.status(), hyper::StatusCode::BAD_REQUEST); println!("✅ All modular components working correctly!"); } @@ -157,7 +151,7 @@ mod tests { let agent_selector = AgentSelector::new(router_service); // Test listener not found - let result = agent_selector.find_listener(Some("nonexistent"), &[]).await; + let result = agent_selector.find_listener(Some("nonexistent"), &[]); assert!(result.is_err()); assert!(matches!( @@ -165,21 +159,12 @@ mod tests { AgentSelectionError::ListenerNotFound(_) )); - let technical_reason = "Database connection timed out"; - let err = BrightStaffError::InternalServerError(technical_reason.to_string()); - - let response = err.into_response(); - - // --- 1. EXTRACT BYTES --- - let body_bytes = response.into_body().collect().await.unwrap().to_bytes(); - - // --- 2. DECLARE body_json HERE --- - let body_json: serde_json::Value = - serde_json::from_slice(&body_bytes).expect("Failed to parse JSON body"); - - // --- 3. USE body_json --- - assert_eq!(body_json["error"]["code"], "InternalServerError"); - assert_eq!(body_json["error"]["details"]["reason"], technical_reason); + // Test error response creation + let error_response = ResponseHandler::create_internal_error("Pipeline failed"); + assert_eq!( + error_response.status(), + hyper::StatusCode::INTERNAL_SERVER_ERROR + ); println!("✅ Error handling working correctly!"); } diff --git a/crates/brightstaff/src/handlers/llm.rs b/crates/brightstaff/src/handlers/llm.rs deleted file mode 100644 index 67afebff..00000000 --- a/crates/brightstaff/src/handlers/llm.rs +++ /dev/null @@ -1,572 +0,0 @@ -use bytes::Bytes; -use common::configuration::{ModelAlias, SpanAttributes}; -use common::consts::{ - ARCH_IS_STREAMING_HEADER, ARCH_PROVIDER_HINT_HEADER, REQUEST_ID_HEADER, TRACE_PARENT_HEADER, -}; -use common::llm_providers::LlmProviders; -use hermesllm::apis::openai_responses::InputParam; -use hermesllm::clients::{SupportedAPIsFromClient, SupportedUpstreamAPIs}; -use hermesllm::{ProviderRequest, ProviderRequestType}; -use http_body_util::combinators::BoxBody; -use http_body_util::BodyExt; -use hyper::header::{self}; -use hyper::{Request, Response}; -use opentelemetry::global; -use opentelemetry::trace::get_active_span; -use opentelemetry_http::HeaderInjector; -use std::collections::HashMap; -use std::sync::Arc; -use tokio::sync::RwLock; -use tracing::{debug, info, info_span, warn, Instrument}; - -use crate::handlers::router_chat::router_chat_get_upstream_model; -use crate::handlers::utils::{ - create_streaming_response, truncate_message, ObservableStreamProcessor, -}; -use crate::router::llm_router::RouterService; -use crate::state::response_state_processor::ResponsesStateProcessor; -use crate::state::{ - extract_input_items, retrieve_and_combine_input, StateStorage, StateStorageError, -}; -use crate::tracing::{ - collect_custom_trace_attributes, llm as tracing_llm, operation_component, set_service_name, -}; - -use common::errors::BrightStaffError; - -pub async fn llm_chat( - request: Request, - router_service: Arc, - full_qualified_llm_provider_url: String, - model_aliases: Arc>>, - llm_providers: Arc>, - span_attributes: Arc>, - state_storage: Option>, -) -> Result>, hyper::Error> { - let request_path = request.uri().path().to_string(); - let request_headers = request.headers().clone(); - let request_id: String = match request_headers - .get(REQUEST_ID_HEADER) - .and_then(|h| h.to_str().ok()) - .map(|s| s.to_string()) - { - Some(id) => id, - None => uuid::Uuid::new_v4().to_string(), - }; - let custom_attrs = - collect_custom_trace_attributes(&request_headers, span_attributes.as_ref().as_ref()); - - // Create a span with request_id that will be included in all log lines - let request_span = info_span!( - "llm", - component = "llm", - request_id = %request_id, - http.method = %request.method(), - http.path = %request_path, - llm.model = tracing::field::Empty, - llm.tools = tracing::field::Empty, - llm.user_message_preview = tracing::field::Empty, - llm.temperature = tracing::field::Empty, - ); - - // Execute the rest of the handler inside the span - llm_chat_inner( - request, - router_service, - full_qualified_llm_provider_url, - model_aliases, - llm_providers, - custom_attrs, - state_storage, - request_id, - request_path, - request_headers, - ) - .instrument(request_span) - .await -} - -#[allow(clippy::too_many_arguments)] -async fn llm_chat_inner( - request: Request, - router_service: Arc, - full_qualified_llm_provider_url: String, - model_aliases: Arc>>, - llm_providers: Arc>, - custom_attrs: HashMap, - state_storage: Option>, - request_id: String, - request_path: String, - mut request_headers: hyper::HeaderMap, -) -> Result>, hyper::Error> { - // Set service name for LLM operations - set_service_name(operation_component::LLM); - get_active_span(|span| { - for (key, value) in &custom_attrs { - span.set_attribute(opentelemetry::KeyValue::new(key.clone(), value.clone())); - } - }); - - // Extract or generate traceparent - this establishes the trace context for all spans - let traceparent: String = match request_headers - .get(TRACE_PARENT_HEADER) - .and_then(|h| h.to_str().ok()) - .map(|s| s.to_string()) - { - Some(tp) => tp, - None => { - use uuid::Uuid; - let trace_id = Uuid::new_v4().to_string().replace("-", ""); - let generated_tp = format!("00-{}-0000000000000000-01", trace_id); - warn!( - generated_traceparent = %generated_tp, - "TRACE_PARENT header missing, generated new traceparent" - ); - generated_tp - } - }; - - let raw_bytes = request.collect().await?.to_bytes(); - - debug!( - body = %String::from_utf8_lossy(&raw_bytes), - "request body received" - ); - - // Extract routing_policy from request body if present - let (chat_request_bytes, inline_routing_policy) = - match crate::handlers::routing_service::extract_routing_policy(&raw_bytes, false) { - Ok(result) => result, - Err(err) => { - warn!(error = %err, "failed to parse request JSON"); - return Ok(BrightStaffError::InvalidRequest(format!( - "Failed to parse request: {}", - err - )) - .into_response()); - } - }; - - let mut client_request = match ProviderRequestType::try_from(( - &chat_request_bytes[..], - &SupportedAPIsFromClient::from_endpoint(request_path.as_str()).unwrap(), - )) { - Ok(request) => request, - Err(err) => { - warn!( - error = %err, - "failed to parse request as ProviderRequestType" - ); - return Ok(BrightStaffError::InvalidRequest(format!( - "Failed to parse request: {}", - err - )) - .into_response()); - } - }; - - // === v1/responses state management: Extract input items early === - let mut original_input_items = Vec::new(); - let client_api = SupportedAPIsFromClient::from_endpoint(request_path.as_str()); - let is_responses_api_client = matches!( - client_api, - Some(SupportedAPIsFromClient::OpenAIResponsesAPI(_)) - ); - - // If model is not specified in the request, resolve from default provider - let model_from_request = client_request.model().to_string(); - let model_from_request = if model_from_request.is_empty() { - match llm_providers.read().await.default() { - Some(default_provider) => { - let default_model = default_provider.name.clone(); - info!(default_model = %default_model, "no model specified in request, using default provider"); - client_request.set_model(default_model.clone()); - default_model - } - None => { - let err_msg = "No model specified in request and no default provider configured"; - warn!("{}", err_msg); - return Ok(BrightStaffError::NoModelSpecified.into_response()); - } - } - } else { - model_from_request - }; - - // Model alias resolution: update model field in client_request immediately - // This ensures all downstream objects use the resolved model - let temperature = client_request.get_temperature(); - let is_streaming_request = client_request.is_streaming(); - let alias_resolved_model = resolve_model_alias(&model_from_request, &model_aliases); - let (provider_id, _) = get_provider_info(&llm_providers, &alias_resolved_model).await; - - // Validate that the requested model exists in configuration - // This matches the validation in llm_gateway routing.rs - if llm_providers - .read() - .await - .get(&alias_resolved_model) - .is_none() - { - warn!(model = %alias_resolved_model, "model not found in configured providers"); - return Ok(BrightStaffError::ModelNotFound(alias_resolved_model).into_response()); - } - - // Handle provider/model slug format (e.g., "openai/gpt-4") - // Extract just the model name for upstream (providers don't understand the slug) - let model_name_only = if let Some((_, model)) = alias_resolved_model.split_once('/') { - model.to_string() - } else { - alias_resolved_model.clone() - }; - - // Extract tool names and user message preview for span attributes - let tool_names = client_request.get_tool_names(); - let user_message_preview = client_request - .get_recent_user_message() - .map(|msg| truncate_message(&msg, 50)); - let span = tracing::Span::current(); - if let Some(temp) = temperature { - span.record(tracing_llm::TEMPERATURE, tracing::field::display(temp)); - } - if let Some(tools) = &tool_names { - let formatted_tools = tools - .iter() - .map(|name| format!("{}(...)", name)) - .collect::>() - .join("\n"); - span.record(tracing_llm::TOOLS, formatted_tools.as_str()); - } - if let Some(preview) = &user_message_preview { - span.record(tracing_llm::USER_MESSAGE_PREVIEW, preview.as_str()); - } - - // Extract messages for signal analysis (clone before moving client_request) - let messages_for_signals = Some(client_request.get_messages()); - - // Set the model to just the model name (without provider prefix) - // This ensures upstream receives "gpt-4" not "openai/gpt-4" - client_request.set_model(model_name_only.clone()); - if client_request.remove_metadata_key("plano_preference_config") { - debug!("removed plano_preference_config from metadata"); - } - if let Some(ref client_api_kind) = client_api { - let upstream_api = - provider_id.compatible_api_for_client(client_api_kind, is_streaming_request); - client_request.normalize_for_upstream(provider_id, &upstream_api); - } - // === v1/responses state management: Determine upstream API and combine input if needed === - // Do this BEFORE routing since routing consumes the request - // Only process state if state_storage is configured - let mut should_manage_state = false; - if is_responses_api_client { - if let ( - ProviderRequestType::ResponsesAPIRequest(ref mut responses_req), - Some(ref state_store), - ) = (&mut client_request, &state_storage) - { - // Extract original input once - original_input_items = extract_input_items(&responses_req.input); - - // Get the upstream path and check if it's ResponsesAPI - let upstream_path = get_upstream_path( - &llm_providers, - &alias_resolved_model, - &request_path, - &alias_resolved_model, - is_streaming_request, - ) - .await; - - let upstream_api = SupportedUpstreamAPIs::from_endpoint(&upstream_path); - - // Only manage state if upstream is NOT OpenAIResponsesAPI (needs translation) - should_manage_state = !matches!( - upstream_api, - Some(SupportedUpstreamAPIs::OpenAIResponsesAPI(_)) - ); - - if should_manage_state { - // Retrieve and combine conversation history if previous_response_id exists - if let Some(ref prev_resp_id) = responses_req.previous_response_id { - match retrieve_and_combine_input( - state_store.clone(), - prev_resp_id, - original_input_items, // Pass ownership instead of cloning - ) - .await - { - Ok(combined_input) => { - // Update both the request and original_input_items - responses_req.input = InputParam::Items(combined_input.clone()); - original_input_items = combined_input; - info!( - items = original_input_items.len(), - "updated request with conversation history" - ); - } - Err(StateStorageError::NotFound(_)) => { - // Return 409 Conflict when previous_response_id not found - warn!(previous_response_id = %prev_resp_id, "previous response_id not found"); - return Ok(BrightStaffError::ConversationStateNotFound( - prev_resp_id.to_string(), - ) - .into_response()); - } - Err(e) => { - // Log warning but continue on other storage errors - warn!( - previous_response_id = %prev_resp_id, - error = %e, - "failed to retrieve conversation state" - ); - // Restore original_input_items since we passed ownership - original_input_items = extract_input_items(&responses_req.input); - } - } - } - } else { - debug!("upstream supports ResponsesAPI natively"); - } - } - } - - // Serialize request for upstream BEFORE router consumes it - let client_request_bytes_for_upstream = ProviderRequestType::to_bytes(&client_request).unwrap(); - - // Determine routing using the dedicated router_chat module - // This gets its own span for latency and error tracking - let routing_span = info_span!( - "routing", - component = "routing", - http.method = "POST", - http.target = %request_path, - model.requested = %model_from_request, - model.alias_resolved = %alias_resolved_model, - route.selected_model = tracing::field::Empty, - routing.determination_ms = tracing::field::Empty, - ); - let routing_result = match async { - set_service_name(operation_component::ROUTING); - router_chat_get_upstream_model( - router_service, - client_request, // Pass the original request - router_chat will convert it - &traceparent, - &request_path, - &request_id, - inline_routing_policy, - ) - .await - } - .instrument(routing_span) - .await - { - Ok(result) => result, - Err(err) => { - return Ok(BrightStaffError::ForwardedError { - status_code: err.status_code, - message: err.message, - } - .into_response()); - } - }; - - // Determine final model to use - // Router returns "none" as a sentinel value when it doesn't select a specific model - let router_selected_model = routing_result.model_name; - let resolved_model = if router_selected_model != "none" { - // Router selected a specific model via routing preferences - router_selected_model - } else { - // Router returned "none" sentinel, use validated resolved_model from request - alias_resolved_model.clone() - }; - tracing::Span::current().record(tracing_llm::MODEL_NAME, resolved_model.as_str()); - - let span_name = if model_from_request == resolved_model { - format!("POST {} {}", request_path, resolved_model) - } else { - format!( - "POST {} {} -> {}", - request_path, model_from_request, resolved_model - ) - }; - get_active_span(|span| { - span.update_name(span_name.clone()); - }); - - debug!( - url = %full_qualified_llm_provider_url, - provider_hint = %resolved_model, - upstream_model = %model_name_only, - "Routing to upstream" - ); - - request_headers.insert( - ARCH_PROVIDER_HINT_HEADER, - header::HeaderValue::from_str(&resolved_model).unwrap(), - ); - - request_headers.insert( - header::HeaderName::from_static(ARCH_IS_STREAMING_HEADER), - header::HeaderValue::from_str(&is_streaming_request.to_string()).unwrap(), - ); - // remove content-length header if it exists - request_headers.remove(header::CONTENT_LENGTH); - - // Inject current LLM span's trace context so upstream spans are children of plano(llm) - global::get_text_map_propagator(|propagator| { - let cx = tracing_opentelemetry::OpenTelemetrySpanExt::context(&tracing::Span::current()); - propagator.inject_context(&cx, &mut HeaderInjector(&mut request_headers)); - }); - - // Capture start time right before sending request to upstream - let request_start_time = std::time::Instant::now(); - let _request_start_system_time = std::time::SystemTime::now(); - - let llm_response = match reqwest::Client::new() - .post(&full_qualified_llm_provider_url) - .headers(request_headers) - .body(client_request_bytes_for_upstream) - .send() - .await - { - Ok(res) => res, - Err(err) => { - return Ok(BrightStaffError::InternalServerError(format!( - "Failed to send request: {}", - err - )) - .into_response()); - } - }; - - // copy over the headers and status code from the original response - let response_headers = llm_response.headers().clone(); - let upstream_status = llm_response.status(); - let mut response = Response::builder().status(upstream_status); - let headers = response.headers_mut().unwrap(); - for (header_name, header_value) in response_headers.iter() { - headers.insert(header_name, header_value.clone()); - } - - // Build LLM span with actual status code using constants - let byte_stream = llm_response.bytes_stream(); - - // Create base processor for metrics and tracing - let base_processor = ObservableStreamProcessor::new( - operation_component::LLM, - span_name, - request_start_time, - messages_for_signals, - ); - - // === v1/responses state management: Wrap with ResponsesStateProcessor === - // Only wrap if we need to manage state (client is ResponsesAPI AND upstream is NOT ResponsesAPI AND state_storage is configured) - let streaming_response = if let (true, false, Some(state_store)) = ( - should_manage_state, - original_input_items.is_empty(), - state_storage, - ) { - // Extract Content-Encoding header to handle decompression for state parsing - let content_encoding = response_headers - .get("content-encoding") - .and_then(|v| v.to_str().ok()) - .map(|s| s.to_string()); - - // Wrap with state management processor to store state after response completes - let state_processor = ResponsesStateProcessor::new( - base_processor, - state_store, - original_input_items, - alias_resolved_model.clone(), - resolved_model.clone(), - is_streaming_request, - false, // Not OpenAI upstream since should_manage_state is true - content_encoding, - request_id, - ); - create_streaming_response(byte_stream, state_processor, 16) - } else { - // Use base processor without state management - create_streaming_response(byte_stream, base_processor, 16) - }; - - match response.body(streaming_response.body) { - Ok(response) => Ok(response), - Err(err) => Ok(BrightStaffError::InternalServerError(format!( - "Failed to create response: {}", - err - )) - .into_response()), - } -} -/// Resolves model aliases by looking up the requested model in the model_aliases map. -/// Returns the target model if an alias is found, otherwise returns the original model. -fn resolve_model_alias( - model_from_request: &str, - model_aliases: &Arc>>, -) -> String { - if let Some(aliases) = model_aliases.as_ref() { - if let Some(model_alias) = aliases.get(model_from_request) { - debug!( - "Model Alias: 'From {}' -> 'To {}'", - model_from_request, model_alias.target - ); - return model_alias.target.clone(); - } - } - model_from_request.to_string() -} - -/// Calculates the upstream path for the provider based on the model name. -/// Looks up provider configuration, gets the ProviderId and base_url_path_prefix, -/// then uses target_endpoint_for_provider to calculate the correct upstream path. -async fn get_upstream_path( - llm_providers: &Arc>, - model_name: &str, - request_path: &str, - resolved_model: &str, - is_streaming: bool, -) -> String { - let (provider_id, base_url_path_prefix) = get_provider_info(llm_providers, model_name).await; - - // Calculate the upstream path using the proper API - let client_api = SupportedAPIsFromClient::from_endpoint(request_path) - .expect("Should have valid API endpoint"); - - client_api.target_endpoint_for_provider( - &provider_id, - request_path, - resolved_model, - is_streaming, - base_url_path_prefix.as_deref(), - ) -} - -/// Helper function to get provider info (ProviderId and base_url_path_prefix) -async fn get_provider_info( - llm_providers: &Arc>, - model_name: &str, -) -> (hermesllm::ProviderId, Option) { - let providers_lock = llm_providers.read().await; - - // Try to find by model name or provider name using LlmProviders::get - // This handles both "gpt-4" and "openai/gpt-4" formats - if let Some(provider) = providers_lock.get(model_name) { - let provider_id = provider.provider_interface.to_provider_id(); - let prefix = provider.base_url_path_prefix.clone(); - return (provider_id, prefix); - } - - // Fall back to default provider - if let Some(provider) = providers_lock.default() { - let provider_id = provider.provider_interface.to_provider_id(); - let prefix = provider.base_url_path_prefix.clone(); - (provider_id, prefix) - } else { - // Last resort: use OpenAI as hardcoded fallback - warn!("No default provider found, falling back to OpenAI"); - (hermesllm::ProviderId::OpenAI, None) - } -} diff --git a/crates/brightstaff/src/handlers/llm/mod.rs b/crates/brightstaff/src/handlers/llm/mod.rs new file mode 100644 index 00000000..9d4a2dfb --- /dev/null +++ b/crates/brightstaff/src/handlers/llm/mod.rs @@ -0,0 +1,780 @@ +use bytes::Bytes; +use common::configuration::{FilterPipeline, ModelAlias}; +use common::consts::{ARCH_IS_STREAMING_HEADER, ARCH_PROVIDER_HINT_HEADER}; +use common::llm_providers::LlmProviders; +use hermesllm::apis::openai::Message; +use hermesllm::apis::openai_responses::InputParam; +use hermesllm::clients::{SupportedAPIsFromClient, SupportedUpstreamAPIs}; +use hermesllm::{ProviderRequest, ProviderRequestType}; +use http_body_util::combinators::BoxBody; +use http_body_util::BodyExt; +use hyper::header::{self}; +use hyper::{Request, Response, StatusCode}; +use opentelemetry::global; +use opentelemetry::trace::get_active_span; +use opentelemetry_http::HeaderInjector; +use std::collections::HashMap; +use std::sync::Arc; +use tokio::sync::RwLock; +use tracing::{debug, info, info_span, warn, Instrument}; + +pub(crate) mod model_selection; + +use crate::app_state::AppState; +use crate::handlers::agents::pipeline::PipelineProcessor; +use crate::handlers::extract_or_generate_traceparent; +use crate::handlers::extract_request_id; +use crate::handlers::full; +use crate::state::response_state_processor::ResponsesStateProcessor; +use crate::state::{ + extract_input_items, retrieve_and_combine_input, StateStorage, StateStorageError, +}; +use crate::streaming::{ + create_streaming_response, create_streaming_response_with_output_filter, truncate_message, + ObservableStreamProcessor, StreamProcessor, +}; +use crate::tracing::{ + collect_custom_trace_attributes, llm as tracing_llm, operation_component, set_service_name, +}; +use model_selection::router_chat_get_upstream_model; + +pub async fn llm_chat( + request: Request, + state: Arc, +) -> Result>, hyper::Error> { + let request_path = request.uri().path().to_string(); + let request_headers = request.headers().clone(); + let request_id = extract_request_id(&request); + let custom_attrs = + collect_custom_trace_attributes(&request_headers, state.span_attributes.as_ref()); + + // Create a span with request_id that will be included in all log lines + let request_span = info_span!( + "llm", + component = "llm", + request_id = %request_id, + http.method = %request.method(), + http.path = %request_path, + llm.model = tracing::field::Empty, + llm.tools = tracing::field::Empty, + llm.user_message_preview = tracing::field::Empty, + llm.temperature = tracing::field::Empty, + ); + + // Execute the rest of the handler inside the span + llm_chat_inner( + request, + state, + custom_attrs, + request_id, + request_path, + request_headers, + ) + .instrument(request_span) + .await +} + +async fn llm_chat_inner( + request: Request, + state: Arc, + custom_attrs: HashMap, + request_id: String, + request_path: String, + mut request_headers: hyper::HeaderMap, +) -> Result>, hyper::Error> { + // Set service name for LLM operations + set_service_name(operation_component::LLM); + get_active_span(|span| { + for (key, value) in &custom_attrs { + span.set_attribute(opentelemetry::KeyValue::new(key.clone(), value.clone())); + } + }); + + let traceparent = extract_or_generate_traceparent(&request_headers); + + let full_qualified_llm_provider_url = format!("{}{}", state.llm_provider_url, request_path); + + // --- Phase 1: Parse and validate the incoming request --- + let parsed = match parse_and_validate_request( + request, + &request_path, + &state.model_aliases, + &state.llm_providers, + ) + .await + { + Ok(p) => p, + Err(response) => return Ok(response), + }; + + let PreparedRequest { + mut client_request, + chat_request_bytes, + model_from_request, + alias_resolved_model, + model_name_only, + is_streaming_request, + is_responses_api_client, + messages_for_signals, + temperature, + tool_names, + user_message_preview, + inline_routing_policy, + client_api, + provider_id, + } = parsed; + + // Record LLM-specific span attributes + let span = tracing::Span::current(); + if let Some(temp) = temperature { + span.record(tracing_llm::TEMPERATURE, tracing::field::display(temp)); + } + if let Some(tools) = &tool_names { + let formatted_tools = tools + .iter() + .map(|name| format!("{}(...)", name)) + .collect::>() + .join("\n"); + span.record(tracing_llm::TOOLS, formatted_tools.as_str()); + } + if let Some(preview) = &user_message_preview { + span.record(tracing_llm::USER_MESSAGE_PREVIEW, preview.as_str()); + } + + // --- Phase 1b: Input filter processing for model listener --- + if let Some(ref input_chain) = state.filter_pipeline.input { + if !input_chain.is_empty() { + debug!(input_filters = ?input_chain.filter_ids, "processing model listener input filters"); + let chain = input_chain.to_agent_filter_chain("model_listener"); + let mut pipeline_processor = PipelineProcessor::default(); + match pipeline_processor + .process_raw_filter_chain( + &chat_request_bytes, + &chain, + &input_chain.agents, + &request_headers, + &request_path, + ) + .await + { + Ok(filtered_bytes) => { + let api_type = SupportedAPIsFromClient::from_endpoint(request_path.as_str()) + .expect("endpoint validated in parse_and_validate_request"); + match ProviderRequestType::try_from((&filtered_bytes[..], &api_type)) { + Ok(updated_request) => { + client_request = updated_request; + info!("input filter chain processed successfully"); + } + Err(parse_err) => { + warn!(error = %parse_err, "input filter returned invalid request JSON"); + return Ok(common::errors::BrightStaffError::InvalidRequest(format!( + "Input filter returned invalid request: {}", + parse_err + )) + .into_response()); + } + } + } + Err(super::agents::pipeline::PipelineError::ClientError { + agent, + status, + body, + }) => { + warn!(agent = %agent, status = %status, body = %body, "client error from filter chain"); + let error_json = serde_json::json!({ + "error": "FilterChainError", + "agent": agent, + "status": status, + "agent_response": body + }); + let mut error_response = Response::new(full(error_json.to_string())); + *error_response.status_mut() = + StatusCode::from_u16(status).unwrap_or(StatusCode::BAD_REQUEST); + error_response.headers_mut().insert( + hyper::header::CONTENT_TYPE, + hyper::header::HeaderValue::from_static("application/json"), + ); + return Ok(error_response); + } + Err(err) => { + warn!(error = %err, "filter chain processing failed"); + let mut internal_error = + Response::new(full(format!("Filter chain processing failed: {}", err))); + *internal_error.status_mut() = StatusCode::INTERNAL_SERVER_ERROR; + return Ok(internal_error); + } + } + } + } + + // Normalize for upstream after input filters + if let Some(ref client_api_kind) = client_api { + let upstream_api = + provider_id.compatible_api_for_client(client_api_kind, is_streaming_request); + client_request.normalize_for_upstream(provider_id, &upstream_api); + } + + // --- Phase 2: Resolve conversation state (v1/responses API) --- + let state_ctx = match resolve_conversation_state( + &mut client_request, + is_responses_api_client, + &state.state_storage, + &state.llm_providers, + &alias_resolved_model, + &request_path, + is_streaming_request, + ) + .await + { + Ok(ctx) => ctx, + Err(response) => return Ok(response), + }; + + // Serialize request for upstream BEFORE router consumes it + let client_request_bytes_for_upstream: Bytes = + match ProviderRequestType::to_bytes(&client_request) { + Ok(bytes) => bytes.into(), + Err(err) => { + warn!(error = %err, "failed to serialize request for upstream"); + let mut r = Response::new(full(format!("Failed to serialize request: {}", err))); + *r.status_mut() = StatusCode::INTERNAL_SERVER_ERROR; + return Ok(r); + } + }; + + // --- Phase 3: Route the request --- + let routing_span = info_span!( + "routing", + component = "routing", + http.method = "POST", + http.target = %request_path, + model.requested = %model_from_request, + model.alias_resolved = %alias_resolved_model, + route.selected_model = tracing::field::Empty, + routing.determination_ms = tracing::field::Empty, + ); + let routing_result = match async { + set_service_name(operation_component::ROUTING); + router_chat_get_upstream_model( + Arc::clone(&state.router_service), + client_request, + &traceparent, + &request_path, + &request_id, + inline_routing_policy, + ) + .await + } + .instrument(routing_span) + .await + { + Ok(result) => result, + Err(err) => { + let mut internal_error = Response::new(full(err.message)); + *internal_error.status_mut() = err.status_code; + return Ok(internal_error); + } + }; + + // Determine final model (router returns "none" when it doesn't select a specific model) + let router_selected_model = routing_result.model_name; + let resolved_model = if router_selected_model != "none" { + router_selected_model + } else { + alias_resolved_model.clone() + }; + tracing::Span::current().record(tracing_llm::MODEL_NAME, resolved_model.as_str()); + + // --- Phase 4: Forward to upstream and stream back --- + send_upstream( + &state.http_client, + &full_qualified_llm_provider_url, + &mut request_headers, + client_request_bytes_for_upstream, + &model_from_request, + &alias_resolved_model, + &resolved_model, + &model_name_only, + &request_path, + is_streaming_request, + messages_for_signals, + state_ctx, + state.state_storage.clone(), + request_id, + &state.filter_pipeline, + ) + .await +} + +// --------------------------------------------------------------------------- +// Phase 1 — Parse & validate the incoming request +// --------------------------------------------------------------------------- + +/// All pre-validated request data extracted from the raw HTTP request. +struct PreparedRequest { + client_request: ProviderRequestType, + chat_request_bytes: Bytes, + model_from_request: String, + alias_resolved_model: String, + model_name_only: String, + is_streaming_request: bool, + is_responses_api_client: bool, + messages_for_signals: Option>, + temperature: Option, + tool_names: Option>, + user_message_preview: Option, + inline_routing_policy: Option>, + client_api: Option, + provider_id: hermesllm::ProviderId, +} + +/// Parse the body, resolve the model alias, and validate the model exists. +/// +/// Returns `Err(Response)` for early-exit error responses (400 etc.). +async fn parse_and_validate_request( + request: Request, + request_path: &str, + model_aliases: &Option>, + llm_providers: &Arc>, +) -> Result>> { + let raw_bytes = request + .collect() + .await + .map_err(|_| { + let mut r = Response::new(full("Failed to read request body")); + *r.status_mut() = StatusCode::BAD_REQUEST; + r + })? + .to_bytes(); + + debug!( + body = %String::from_utf8_lossy(&raw_bytes), + "request body received" + ); + + // Extract routing_policy from request body if present + let (chat_request_bytes, inline_routing_policy) = + crate::handlers::routing_service::extract_routing_policy(&raw_bytes, false).map_err( + |err| { + warn!(error = %err, "failed to parse request JSON"); + let mut r = Response::new(full(format!("Failed to parse request: {}", err))); + *r.status_mut() = StatusCode::BAD_REQUEST; + r + }, + )?; + + let api_type = SupportedAPIsFromClient::from_endpoint(request_path).ok_or_else(|| { + warn!(path = %request_path, "unsupported endpoint"); + let mut r = Response::new(full(format!("Unsupported endpoint: {}", request_path))); + *r.status_mut() = StatusCode::BAD_REQUEST; + r + })?; + + let mut client_request = ProviderRequestType::try_from((&chat_request_bytes[..], &api_type)) + .map_err(|err| { + warn!(error = %err, "failed to parse request as ProviderRequestType"); + let mut r = Response::new(full(format!("Failed to parse request: {}", err))); + *r.status_mut() = StatusCode::BAD_REQUEST; + r + })?; + + let is_responses_api_client = + matches!(api_type, SupportedAPIsFromClient::OpenAIResponsesAPI(_)); + let client_api = Some(api_type); + + let model_from_request = client_request.model().to_string(); + let temperature = client_request.get_temperature(); + let is_streaming_request = client_request.is_streaming(); + let alias_resolved_model = resolve_model_alias(&model_from_request, model_aliases); + let (provider_id, _) = get_provider_info(llm_providers, &alias_resolved_model).await; + + // Validate model exists in configuration + if llm_providers + .read() + .await + .get(&alias_resolved_model) + .is_none() + { + let err_msg = format!( + "Model '{}' not found in configured providers", + alias_resolved_model + ); + warn!(model = %alias_resolved_model, "model not found in configured providers"); + let mut r = Response::new(full(err_msg)); + *r.status_mut() = StatusCode::BAD_REQUEST; + return Err(r); + } + + // Strip provider prefix for upstream (e.g. "openai/gpt-4" → "gpt-4") + let model_name_only = alias_resolved_model + .split_once('/') + .map(|(_, model)| model.to_string()) + .unwrap_or_else(|| alias_resolved_model.clone()); + + // Extract span attributes and messages before mutating client_request + let tool_names = client_request.get_tool_names(); + let user_message_preview = client_request + .get_recent_user_message() + .map(|msg| truncate_message(&msg, 50)); + let messages_for_signals = Some(client_request.get_messages()); + + // Set the upstream model name and strip routing metadata + client_request.set_model(model_name_only.clone()); + if client_request.remove_metadata_key("archgw_preference_config") { + debug!("removed archgw_preference_config from metadata"); + } + if client_request.remove_metadata_key("plano_preference_config") { + debug!("removed plano_preference_config from metadata"); + } + + Ok(PreparedRequest { + client_request, + chat_request_bytes, + model_from_request, + alias_resolved_model, + model_name_only, + is_streaming_request, + is_responses_api_client, + messages_for_signals, + temperature, + tool_names, + user_message_preview, + inline_routing_policy, + client_api, + provider_id, + }) +} + +// --------------------------------------------------------------------------- +// Phase 2 — Resolve conversation state (v1/responses API) +// --------------------------------------------------------------------------- + +/// Holds the state management context resolved from a v1/responses request. +struct ConversationStateContext { + should_manage_state: bool, + original_input_items: Vec, +} + +/// If the client uses the v1/responses API and the upstream provider doesn't +/// support it natively, we manage conversation state ourselves. +/// +/// This resolves `previous_response_id`, merges conversation history, and +/// updates the request in place. +/// +/// Returns `Err(Response)` for early-exit (e.g. 409 Conflict). +async fn resolve_conversation_state( + client_request: &mut ProviderRequestType, + is_responses_api_client: bool, + state_storage: &Option>, + llm_providers: &Arc>, + alias_resolved_model: &str, + request_path: &str, + is_streaming_request: bool, +) -> Result>> { + if !is_responses_api_client { + return Ok(ConversationStateContext { + should_manage_state: false, + original_input_items: Vec::new(), + }); + } + + let (responses_req, state_store) = match (client_request, state_storage) { + (ProviderRequestType::ResponsesAPIRequest(ref mut req), Some(store)) => (req, store), + _ => { + return Ok(ConversationStateContext { + should_manage_state: false, + original_input_items: Vec::new(), + }); + } + }; + + let mut original_input_items = extract_input_items(&responses_req.input); + + // Check whether the upstream supports v1/responses natively + let upstream_path = get_upstream_path( + llm_providers, + alias_resolved_model, + request_path, + alias_resolved_model, + is_streaming_request, + ) + .await; + + let upstream_api = SupportedUpstreamAPIs::from_endpoint(&upstream_path); + let should_manage_state = !matches!( + upstream_api, + Some(SupportedUpstreamAPIs::OpenAIResponsesAPI(_)) + ); + + if !should_manage_state { + debug!("upstream supports ResponsesAPI natively"); + return Ok(ConversationStateContext { + should_manage_state: false, + original_input_items, + }); + } + + // Retrieve and combine conversation history if previous_response_id exists + if let Some(ref prev_resp_id) = responses_req.previous_response_id { + match retrieve_and_combine_input(state_store.clone(), prev_resp_id, original_input_items) + .await + { + Ok(combined_input) => { + responses_req.input = InputParam::Items(combined_input.clone()); + original_input_items = combined_input; + info!( + items = original_input_items.len(), + "updated request with conversation history" + ); + } + Err(StateStorageError::NotFound(_)) => { + warn!(previous_response_id = %prev_resp_id, "previous response_id not found"); + let err_msg = format!( + "Conversation state not found for previous_response_id: {}", + prev_resp_id + ); + let mut r = Response::new(full(err_msg)); + *r.status_mut() = StatusCode::CONFLICT; + return Err(r); + } + Err(e) => { + warn!( + previous_response_id = %prev_resp_id, + error = %e, + "failed to retrieve conversation state" + ); + // Restore original_input_items since we passed ownership + original_input_items = extract_input_items(&responses_req.input); + } + } + } + + Ok(ConversationStateContext { + should_manage_state, + original_input_items, + }) +} + +// --------------------------------------------------------------------------- +// Phase 4 — Forward to upstream and stream the response back +// --------------------------------------------------------------------------- + +#[allow(clippy::too_many_arguments)] +async fn send_upstream( + http_client: &reqwest::Client, + upstream_url: &str, + request_headers: &mut hyper::HeaderMap, + body: bytes::Bytes, + model_from_request: &str, + alias_resolved_model: &str, + resolved_model: &str, + model_name_only: &str, + request_path: &str, + is_streaming_request: bool, + messages_for_signals: Option>, + state_ctx: ConversationStateContext, + state_storage: Option>, + request_id: String, + filter_pipeline: &Arc, +) -> Result>, hyper::Error> { + let span_name = if model_from_request == resolved_model { + format!("POST {} {}", request_path, resolved_model) + } else { + format!( + "POST {} {} -> {}", + request_path, model_from_request, resolved_model + ) + }; + get_active_span(|span| { + span.update_name(span_name.clone()); + }); + + debug!( + url = %upstream_url, + provider_hint = %resolved_model, + upstream_model = %model_name_only, + "Routing to upstream" + ); + + if let Ok(val) = header::HeaderValue::from_str(resolved_model) { + request_headers.insert(ARCH_PROVIDER_HINT_HEADER, val); + } + request_headers.insert( + header::HeaderName::from_static(ARCH_IS_STREAMING_HEADER), + header::HeaderValue::from_static(if is_streaming_request { + "true" + } else { + "false" + }), + ); + request_headers.remove(header::CONTENT_LENGTH); + + // Inject current span's trace context so upstream spans are children of plano(llm) + global::get_text_map_propagator(|propagator| { + let cx = tracing_opentelemetry::OpenTelemetrySpanExt::context(&tracing::Span::current()); + propagator.inject_context(&cx, &mut HeaderInjector(request_headers)); + }); + + let request_start_time = std::time::Instant::now(); + + let llm_response = match http_client + .post(upstream_url) + .headers(request_headers.clone()) + .body(body) + .send() + .await + { + Ok(res) => res, + Err(err) => { + let err_msg = format!("Failed to send request: {}", err); + let mut internal_error = Response::new(full(err_msg)); + *internal_error.status_mut() = StatusCode::INTERNAL_SERVER_ERROR; + return Ok(internal_error); + } + }; + + // Propagate upstream headers and status + let response_headers = llm_response.headers().clone(); + let upstream_status = llm_response.status(); + let mut response = Response::builder().status(upstream_status); + if let Some(headers) = response.headers_mut() { + for (name, value) in response_headers.iter() { + headers.insert(name, value.clone()); + } + } + + let byte_stream = llm_response.bytes_stream(); + + // Create base processor for metrics and tracing + let base_processor = ObservableStreamProcessor::new( + operation_component::LLM, + span_name, + request_start_time, + messages_for_signals, + ); + + let output_filter_request_headers = if filter_pipeline.has_output_filters() { + Some(request_headers.clone()) + } else { + None + }; + + // Pick the right processor: state-aware if needed, otherwise base metrics-only. + let processor: Box = if let (true, false, Some(state_store)) = ( + state_ctx.should_manage_state, + state_ctx.original_input_items.is_empty(), + state_storage, + ) { + let content_encoding = response_headers + .get("content-encoding") + .and_then(|v| v.to_str().ok()) + .map(|s| s.to_string()); + + Box::new(ResponsesStateProcessor::new( + base_processor, + state_store, + state_ctx.original_input_items, + alias_resolved_model.to_string(), + resolved_model.to_string(), + is_streaming_request, + false, + content_encoding, + request_id, + )) + } else { + Box::new(base_processor) + }; + + let streaming_response = if let (Some(output_chain), Some(filter_headers)) = ( + filter_pipeline.output.as_ref().filter(|c| !c.is_empty()), + output_filter_request_headers, + ) { + create_streaming_response_with_output_filter( + byte_stream, + processor, + output_chain.clone(), + filter_headers, + request_path.to_string(), + ) + } else { + create_streaming_response(byte_stream, processor) + }; + + match response.body(streaming_response.body) { + Ok(response) => Ok(response), + Err(err) => { + let err_msg = format!("Failed to create response: {}", err); + let mut internal_error = Response::new(full(err_msg)); + *internal_error.status_mut() = StatusCode::INTERNAL_SERVER_ERROR; + Ok(internal_error) + } + } +} + +// --------------------------------------------------------------------------- +// Helpers +// --------------------------------------------------------------------------- + +/// Resolves model aliases by looking up the requested model in the model_aliases map. +/// Returns the target model if an alias is found, otherwise returns the original model. +fn resolve_model_alias( + model_from_request: &str, + model_aliases: &Option>, +) -> String { + if let Some(aliases) = model_aliases.as_ref() { + if let Some(model_alias) = aliases.get(model_from_request) { + debug!( + "Model Alias: 'From {}' -> 'To {}'", + model_from_request, model_alias.target + ); + return model_alias.target.clone(); + } + } + model_from_request.to_string() +} + +/// Calculates the upstream path for the provider based on the model name. +async fn get_upstream_path( + llm_providers: &Arc>, + model_name: &str, + request_path: &str, + resolved_model: &str, + is_streaming: bool, +) -> String { + let (provider_id, base_url_path_prefix) = get_provider_info(llm_providers, model_name).await; + + let Some(client_api) = SupportedAPIsFromClient::from_endpoint(request_path) else { + return request_path.to_string(); + }; + + client_api.target_endpoint_for_provider( + &provider_id, + request_path, + resolved_model, + is_streaming, + base_url_path_prefix.as_deref(), + ) +} + +/// Helper to get provider info (ProviderId and base_url_path_prefix). +async fn get_provider_info( + llm_providers: &Arc>, + model_name: &str, +) -> (hermesllm::ProviderId, Option) { + let providers_lock = llm_providers.read().await; + + if let Some(provider) = providers_lock.get(model_name) { + let provider_id = provider.provider_interface.to_provider_id(); + let prefix = provider.base_url_path_prefix.clone(); + return (provider_id, prefix); + } + + if let Some(provider) = providers_lock.default() { + let provider_id = provider.provider_interface.to_provider_id(); + let prefix = provider.base_url_path_prefix.clone(); + (provider_id, prefix) + } else { + warn!("No default provider found, falling back to OpenAI"); + (hermesllm::ProviderId::OpenAI, None) + } +} diff --git a/crates/brightstaff/src/handlers/router_chat.rs b/crates/brightstaff/src/handlers/llm/model_selection.rs similarity index 92% rename from crates/brightstaff/src/handlers/router_chat.rs rename to crates/brightstaff/src/handlers/llm/model_selection.rs index 910e5408..455b7c0e 100644 --- a/crates/brightstaff/src/handlers/router_chat.rs +++ b/crates/brightstaff/src/handlers/llm/model_selection.rs @@ -5,7 +5,8 @@ use hyper::StatusCode; use std::sync::Arc; use tracing::{debug, info, warn}; -use crate::router::llm_router::RouterService; +use crate::router::llm::RouterService; +use crate::streaming::truncate_message; use crate::tracing::routing; pub struct RoutingResult { @@ -103,16 +104,7 @@ pub async fn router_chat_get_upstream_model( .map_or("None".to_string(), |c| c.to_string().replace('\n', "\\n")) }); - const MAX_MESSAGE_LENGTH: usize = 50; - let latest_message_for_log = if latest_message_for_log.chars().count() > MAX_MESSAGE_LENGTH { - let truncated: String = latest_message_for_log - .chars() - .take(MAX_MESSAGE_LENGTH) - .collect(); - format!("{}...", truncated) - } else { - latest_message_for_log - }; + let latest_message_for_log = truncate_message(&latest_message_for_log, 50); info!( has_usage_preferences = usage_preferences.is_some(), diff --git a/crates/brightstaff/src/handlers/mod.rs b/crates/brightstaff/src/handlers/mod.rs index 9c602e93..485a0438 100644 --- a/crates/brightstaff/src/handlers/mod.rs +++ b/crates/brightstaff/src/handlers/mod.rs @@ -1,14 +1,57 @@ -pub mod agent_chat_completions; -pub mod agent_selector; +pub mod agents; pub mod function_calling; -pub mod jsonrpc; pub mod llm; pub mod models; -pub mod pipeline_processor; -pub mod response_handler; -pub mod router_chat; +pub mod response; pub mod routing_service; -pub mod utils; #[cfg(test)] mod integration_tests; + +use bytes::Bytes; +use common::consts::TRACE_PARENT_HEADER; +use http_body_util::combinators::BoxBody; +use http_body_util::{BodyExt, Empty, Full}; +use hyper::Request; +use tracing::warn; + +/// Wrap a chunk into a `BoxBody` for hyper responses. +pub fn full>(chunk: T) -> BoxBody { + Full::new(chunk.into()) + .map_err(|never| match never {}) + .boxed() +} + +/// An empty HTTP body (used for 404 / OPTIONS responses). +pub fn empty() -> BoxBody { + Empty::::new() + .map_err(|never| match never {}) + .boxed() +} + +/// Extract request ID from incoming request headers, or generate a new UUID v4. +pub fn extract_request_id(request: &Request) -> String { + request + .headers() + .get(common::consts::REQUEST_ID_HEADER) + .and_then(|h| h.to_str().ok()) + .map(|s| s.to_string()) + .unwrap_or_else(|| uuid::Uuid::new_v4().to_string()) +} + +/// Extract or generate a W3C `traceparent` header value. +pub fn extract_or_generate_traceparent(headers: &hyper::HeaderMap) -> String { + headers + .get(TRACE_PARENT_HEADER) + .and_then(|h| h.to_str().ok()) + .map(|s| s.to_string()) + .unwrap_or_else(|| { + let trace_id = uuid::Uuid::new_v4().to_string().replace("-", ""); + let tp = format!("00-{}-0000000000000000-01", trace_id); + warn!( + generated_traceparent = %tp, + "TRACE_PARENT header missing, generated new traceparent" + ); + tp + }) +} diff --git a/crates/brightstaff/src/handlers/models.rs b/crates/brightstaff/src/handlers/models.rs index a29d5e90..9fd5fe07 100644 --- a/crates/brightstaff/src/handlers/models.rs +++ b/crates/brightstaff/src/handlers/models.rs @@ -1,10 +1,11 @@ use bytes::Bytes; use common::llm_providers::LlmProviders; -use http_body_util::{combinators::BoxBody, BodyExt, Full}; +use http_body_util::combinators::BoxBody; use hyper::{Response, StatusCode}; -use serde_json; use std::sync::Arc; +use super::full; + pub async fn list_models( llm_providers: Arc>, ) -> Response> { @@ -12,27 +13,15 @@ pub async fn list_models( let models = prov.to_models(); match serde_json::to_string(&models) { - Ok(json) => { - let body = Full::new(Bytes::from(json)) - .map_err(|never| match never {}) - .boxed(); - Response::builder() - .status(StatusCode::OK) - .header("Content-Type", "application/json") - .body(body) - .unwrap() - } - Err(_) => { - let body = Full::new(Bytes::from_static( - b"{\"error\":\"Failed to serialize models\"}", - )) - .map_err(|never| match never {}) - .boxed(); - Response::builder() - .status(StatusCode::INTERNAL_SERVER_ERROR) - .header("Content-Type", "application/json") - .body(body) - .unwrap() - } + Ok(json) => Response::builder() + .status(StatusCode::OK) + .header("Content-Type", "application/json") + .body(full(json)) + .unwrap(), + Err(_) => Response::builder() + .status(StatusCode::INTERNAL_SERVER_ERROR) + .header("Content-Type", "application/json") + .body(full("{\"error\":\"Failed to serialize models\"}")) + .unwrap(), } } diff --git a/crates/brightstaff/src/handlers/response_handler.rs b/crates/brightstaff/src/handlers/response.rs similarity index 77% rename from crates/brightstaff/src/handlers/response_handler.rs rename to crates/brightstaff/src/handlers/response.rs index 7331ab4c..4db2f8a8 100644 --- a/crates/brightstaff/src/handlers/response_handler.rs +++ b/crates/brightstaff/src/handlers/response.rs @@ -4,7 +4,7 @@ use hermesllm::apis::OpenAIApi; use hermesllm::clients::{SupportedAPIsFromClient, SupportedUpstreamAPIs}; use hermesllm::SseEvent; use http_body_util::combinators::BoxBody; -use http_body_util::{BodyExt, Full, StreamBody}; +use http_body_util::StreamBody; use hyper::body::Frame; use hyper::Response; use tokio::sync::mpsc; @@ -12,6 +12,8 @@ use tokio_stream::wrappers::ReceiverStream; use tokio_stream::StreamExt; use tracing::{info, warn, Instrument}; +use super::full; + /// Service for handling HTTP responses and streaming pub struct ResponseHandler; @@ -22,9 +24,40 @@ impl ResponseHandler { /// Create a full response body from bytes pub fn create_full_body>(chunk: T) -> BoxBody { - Full::new(chunk.into()) - .map_err(|never| match never {}) - .boxed() + full(chunk) + } + + /// Create a JSON error response with BAD_REQUEST status + pub fn create_json_error_response( + json: &serde_json::Value, + ) -> Response> { + let body = Self::create_full_body(json.to_string()); + let mut response = Response::new(body); + *response.status_mut() = hyper::StatusCode::BAD_REQUEST; + response.headers_mut().insert( + hyper::header::CONTENT_TYPE, + hyper::header::HeaderValue::from_static("application/json"), + ); + response + } + + /// Create a BAD_REQUEST error response with a message + pub fn create_bad_request(message: &str) -> Response> { + let json = serde_json::json!({"error": message}); + Self::create_json_error_response(&json) + } + + /// Create an INTERNAL_SERVER_ERROR response with a message + pub fn create_internal_error(message: &str) -> Response> { + let json = serde_json::json!({"error": message}); + let body = Self::create_full_body(json.to_string()); + let mut response = Response::new(body); + *response.status_mut() = hyper::StatusCode::INTERNAL_SERVER_ERROR; + response.headers_mut().insert( + hyper::header::CONTENT_TYPE, + hyper::header::HeaderValue::from_static("application/json"), + ); + response } /// Create a streaming response from a reqwest response. @@ -112,7 +145,9 @@ impl ResponseHandler { let upstream_api = SupportedUpstreamAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions); - let sse_iter = SseStreamIter::try_from(response_bytes.as_ref()).unwrap(); + let sse_iter = SseStreamIter::try_from(response_bytes.as_ref()).map_err(|e| { + BrightStaffError::StreamError(format!("Failed to parse SSE stream: {}", e)) + })?; let mut accumulated_text = String::new(); for sse_event in sse_iter { @@ -122,7 +157,13 @@ impl ResponseHandler { } let transformed_event = - SseEvent::try_from((sse_event, &client_api, &upstream_api)).unwrap(); + match SseEvent::try_from((sse_event, &client_api, &upstream_api)) { + Ok(event) => event, + Err(e) => { + warn!(error = ?e, "failed to transform SSE event, skipping"); + continue; + } + }; // Try to get provider response and extract content delta match transformed_event.provider_response() { diff --git a/crates/brightstaff/src/handlers/routing_service.rs b/crates/brightstaff/src/handlers/routing_service.rs index 82043dad..5eb3c6d6 100644 --- a/crates/brightstaff/src/handlers/routing_service.rs +++ b/crates/brightstaff/src/handlers/routing_service.rs @@ -1,6 +1,6 @@ use bytes::Bytes; use common::configuration::{ModelUsagePreference, SpanAttributes}; -use common::consts::{REQUEST_ID_HEADER, SESSION_ID_HEADER, TRACE_PARENT_HEADER}; +use common::consts::{REQUEST_ID_HEADER, SESSION_ID_HEADER}; use common::errors::BrightStaffError; use hermesllm::clients::SupportedAPIsFromClient; use hermesllm::ProviderRequestType; @@ -10,8 +10,9 @@ use hyper::{Request, Response, StatusCode}; use std::sync::Arc; use tracing::{debug, info, info_span, warn, Instrument}; -use crate::handlers::router_chat::router_chat_get_upstream_model; -use crate::router::llm_router::RouterService; +use super::extract_or_generate_traceparent; +use crate::handlers::llm::model_selection::router_chat_get_upstream_model; +use crate::router::llm::RouterService; use crate::tracing::{collect_custom_trace_attributes, operation_component, set_service_name}; const ROUTING_POLICY_SIZE_WARNING_BYTES: usize = 5120; @@ -75,7 +76,7 @@ pub async fn routing_decision( request: Request, router_service: Arc, request_path: String, - span_attributes: Arc>, + span_attributes: &Option, ) -> Result>, hyper::Error> { let request_headers = request.headers().clone(); let request_id: String = request_headers @@ -89,8 +90,7 @@ pub async fn routing_decision( .and_then(|h| h.to_str().ok()) .map(|s| s.to_string()); - let custom_attrs = - collect_custom_trace_attributes(&request_headers, span_attributes.as_ref().as_ref()); + let custom_attrs = collect_custom_trace_attributes(&request_headers, span_attributes.as_ref()); let request_span = info_span!( "routing_decision", @@ -129,23 +129,7 @@ async fn routing_decision_inner( } }); - // Extract or generate traceparent - let traceparent: String = match request_headers - .get(TRACE_PARENT_HEADER) - .and_then(|h| h.to_str().ok()) - .map(|s| s.to_string()) - { - Some(tp) => tp, - None => { - let trace_id = uuid::Uuid::new_v4().to_string().replace("-", ""); - let generated_tp = format!("00-{}-0000000000000000-01", trace_id); - warn!( - generated_traceparent = %generated_tp, - "TRACE_PARENT header missing, generated new traceparent" - ); - generated_tp - } - }; + let traceparent = extract_or_generate_traceparent(&request_headers); // Extract trace_id from traceparent (format: 00-{trace_id}-{span_id}-{flags}) let trace_id = traceparent diff --git a/crates/brightstaff/src/lib.rs b/crates/brightstaff/src/lib.rs index 47da64aa..b4ab82a9 100644 --- a/crates/brightstaff/src/lib.rs +++ b/crates/brightstaff/src/lib.rs @@ -1,6 +1,7 @@ +pub mod app_state; pub mod handlers; pub mod router; pub mod signals; pub mod state; +pub mod streaming; pub mod tracing; -pub mod utils; diff --git a/crates/brightstaff/src/main.rs b/crates/brightstaff/src/main.rs index 190379a0..e075f7b0 100644 --- a/crates/brightstaff/src/main.rs +++ b/crates/brightstaff/src/main.rs @@ -1,119 +1,179 @@ -use brightstaff::handlers::agent_chat_completions::agent_chat; +use brightstaff::app_state::AppState; +use brightstaff::handlers::agents::orchestrator::agent_chat; +use brightstaff::handlers::empty; use brightstaff::handlers::function_calling::function_calling_chat_handler; use brightstaff::handlers::llm::llm_chat; use brightstaff::handlers::models::list_models; use brightstaff::handlers::routing_service::routing_decision; -use brightstaff::router::llm_router::RouterService; -use brightstaff::router::plano_orchestrator::OrchestratorService; +use brightstaff::router::llm::RouterService; +use brightstaff::router::orchestrator::OrchestratorService; use brightstaff::state::memory::MemoryConversationalStorage; use brightstaff::state::postgresql::PostgreSQLConversationStorage; use brightstaff::state::StateStorage; -use brightstaff::utils::tracing::init_tracer; +use brightstaff::tracing::init_tracer; use bytes::Bytes; -use common::configuration::{Agent, Configuration}; -use common::consts::{ - CHAT_COMPLETIONS_PATH, MESSAGES_PATH, OPENAI_RESPONSES_API_PATH, PLANO_ORCHESTRATOR_MODEL_NAME, +use common::configuration::{ + Agent, Configuration, FilterPipeline, ListenerType, ResolvedFilterChain, }; +use common::consts::{CHAT_COMPLETIONS_PATH, MESSAGES_PATH, OPENAI_RESPONSES_API_PATH}; use common::llm_providers::LlmProviders; -use http_body_util::{combinators::BoxBody, BodyExt, Empty}; +use http_body_util::combinators::BoxBody; use hyper::body::Incoming; +use hyper::header::HeaderValue; use hyper::server::conn::http1; use hyper::service::service_fn; use hyper::{Method, Request, Response, StatusCode}; use hyper_util::rt::TokioIo; +use opentelemetry::global; use opentelemetry::trace::FutureExt; -use opentelemetry::{global, Context}; use opentelemetry_http::HeaderExtractor; +use std::collections::HashMap; use std::sync::Arc; use std::{env, fs}; use tokio::net::TcpListener; use tokio::sync::RwLock; use tracing::{debug, info, warn}; -pub mod router; - const BIND_ADDRESS: &str = "0.0.0.0:9091"; const DEFAULT_ROUTING_LLM_PROVIDER: &str = "arch-router"; const DEFAULT_ROUTING_MODEL_NAME: &str = "Arch-Router"; +const DEFAULT_ORCHESTRATOR_LLM_PROVIDER: &str = "plano-orchestrator"; +const DEFAULT_ORCHESTRATOR_MODEL_NAME: &str = "Plano-Orchestrator"; -// Utility function to extract the context from the incoming request headers -fn extract_context_from_request(req: &Request) -> Context { - global::get_text_map_propagator(|propagator| { - propagator.extract(&HeaderExtractor(req.headers())) - }) +/// CORS pre-flight response for the models endpoint. +fn cors_preflight() -> Result>, hyper::Error> { + let mut response = Response::new(empty()); + *response.status_mut() = StatusCode::NO_CONTENT; + let h = response.headers_mut(); + h.insert("Allow", HeaderValue::from_static("GET, OPTIONS")); + h.insert("Access-Control-Allow-Origin", HeaderValue::from_static("*")); + h.insert( + "Access-Control-Allow-Headers", + HeaderValue::from_static("Authorization, Content-Type"), + ); + h.insert( + "Access-Control-Allow-Methods", + HeaderValue::from_static("GET, POST, OPTIONS"), + ); + h.insert("Content-Type", HeaderValue::from_static("application/json")); + Ok(response) } -fn empty() -> BoxBody { - Empty::::new() - .map_err(|never| match never {}) - .boxed() -} +// --------------------------------------------------------------------------- +// Configuration loading +// --------------------------------------------------------------------------- -#[tokio::main] -async fn main() -> Result<(), Box> { - let bind_address = env::var("BIND_ADDRESS").unwrap_or_else(|_| BIND_ADDRESS.to_string()); - - // loading plano_config.yaml file (before tracing init so we can read tracing config) - let plano_config_path = env::var("PLANO_CONFIG_PATH_RENDERED") +/// Load and parse the YAML configuration file. +/// +/// The path is read from `PLANO_CONFIG_PATH_RENDERED` (env) or falls back to +/// `./plano_config_rendered.yaml`. +fn load_config() -> Result> { + let path = env::var("PLANO_CONFIG_PATH_RENDERED") .unwrap_or_else(|_| "./plano_config_rendered.yaml".to_string()); - eprintln!("loading plano_config.yaml from {}", plano_config_path); + eprintln!("loading plano_config.yaml from {}", path); - let config_contents = - fs::read_to_string(&plano_config_path).expect("Failed to read plano_config.yaml"); + let contents = fs::read_to_string(&path).map_err(|e| format!("failed to read {path}: {e}"))?; let config: Configuration = - serde_yaml::from_str(&config_contents).expect("Failed to parse plano_config.yaml"); + serde_yaml::from_str(&contents).map_err(|e| format!("failed to parse {path}: {e}"))?; - // Initialize tracing using config.yaml tracing section - let _tracer_provider = init_tracer(config.tracing.as_ref()); - info!(path = %plano_config_path, "loaded plano_config.yaml"); + Ok(config) +} - let plano_config = Arc::new(config); +// --------------------------------------------------------------------------- +// Application state initialization +// --------------------------------------------------------------------------- - // combine agents and filters into a single list of agents - let all_agents: Vec = plano_config +/// Build the shared [`AppState`] from a parsed [`Configuration`]. +async fn init_app_state( + config: &Configuration, +) -> Result> { + let llm_provider_url = + env::var("LLM_PROVIDER_ENDPOINT").unwrap_or_else(|_| "http://localhost:12001".to_string()); + + // Combine agents and filters into a single list + let all_agents: Vec = config .agents .as_deref() .unwrap_or_default() .iter() - .chain(plano_config.filters.as_deref().unwrap_or_default()) + .chain(config.filters.as_deref().unwrap_or_default()) .cloned() .collect(); - // Create expanded provider list for /v1/models endpoint - let llm_providers = LlmProviders::try_from(plano_config.model_providers.clone()) - .expect("Failed to create LlmProviders"); - let llm_providers = Arc::new(RwLock::new(llm_providers)); - let combined_agents_filters_list = Arc::new(RwLock::new(Some(all_agents))); - let listeners = Arc::new(RwLock::new(plano_config.listeners.clone())); - let llm_provider_url = - env::var("LLM_PROVIDER_ENDPOINT").unwrap_or_else(|_| "http://localhost:12001".to_string()); + let global_agent_map: HashMap = all_agents + .iter() + .map(|a| (a.id.clone(), a.clone())) + .collect(); - let listener = TcpListener::bind(bind_address).await?; - let routing_model_name: String = plano_config - .routing - .as_ref() - .and_then(|r| r.model.clone()) - .unwrap_or_else(|| DEFAULT_ROUTING_MODEL_NAME.to_string()); + let llm_providers = LlmProviders::try_from(config.model_providers.clone()) + .map_err(|e| format!("failed to create LlmProviders: {e}"))?; - let routing_llm_provider = plano_config - .routing - .as_ref() - .and_then(|r| r.model_provider.clone()) + let model_listener_count = config + .listeners + .iter() + .filter(|l| l.listener_type == ListenerType::Model) + .count(); + if model_listener_count > 1 { + return Err(format!( + "only one model listener is allowed, found {}", + model_listener_count + ) + .into()); + } + let model_listener = config + .listeners + .iter() + .find(|l| l.listener_type == ListenerType::Model); + let resolve_chain = |filter_ids: Option>| -> Option { + filter_ids.map(|ids| { + let agents = ids + .iter() + .filter_map(|id| { + global_agent_map + .get(id) + .map(|a: &Agent| (id.clone(), a.clone())) + }) + .collect(); + ResolvedFilterChain { + filter_ids: ids, + agents, + } + }) + }; + let filter_pipeline = Arc::new(FilterPipeline { + input: resolve_chain(model_listener.and_then(|l| l.input_filters.clone())), + output: resolve_chain(model_listener.and_then(|l| l.output_filters.clone())), + }); + + let overrides = config.overrides.clone().unwrap_or_default(); + + let routing_model_name: String = overrides + .llm_routing_model + .as_deref() + .map(|m| m.split_once('/').map(|(_, id)| id).unwrap_or(m)) + .unwrap_or(DEFAULT_ROUTING_MODEL_NAME) + .to_string(); + + let routing_llm_provider = config + .model_providers + .iter() + .find(|p| p.model.as_deref() == Some(routing_model_name.as_str())) + .map(|p| p.name.clone()) .unwrap_or_else(|| DEFAULT_ROUTING_LLM_PROVIDER.to_string()); - let session_ttl_seconds = plano_config + let session_ttl_seconds = config .routing .as_ref() .and_then(|r| r.session_ttl_seconds); - let session_max_entries = plano_config + let session_max_entries = config .routing .as_ref() .and_then(|r| r.session_max_entries); - let router_service: Arc = Arc::new(RouterService::new( - plano_config.model_providers.clone(), + let router_service = Arc::new(RouterService::new( + config.model_providers.clone(), format!("{llm_provider_url}{CHAT_COMPLETIONS_PATH}"), routing_model_name, routing_llm_provider, @@ -133,202 +193,212 @@ async fn main() -> Result<(), Box> { }); } - let orchestrator_service: Arc = Arc::new(OrchestratorService::new( + let orchestrator_model_name: String = overrides + .agent_orchestration_model + .as_deref() + .map(|m| m.split_once('/').map(|(_, id)| id).unwrap_or(m)) + .unwrap_or(DEFAULT_ORCHESTRATOR_MODEL_NAME) + .to_string(); + + let orchestrator_llm_provider: String = config + .model_providers + .iter() + .find(|p| p.model.as_deref() == Some(orchestrator_model_name.as_str())) + .map(|p| p.name.clone()) + .unwrap_or_else(|| DEFAULT_ORCHESTRATOR_LLM_PROVIDER.to_string()); + + let orchestrator_service = Arc::new(OrchestratorService::new( format!("{llm_provider_url}{CHAT_COMPLETIONS_PATH}"), - PLANO_ORCHESTRATOR_MODEL_NAME.to_string(), + orchestrator_model_name, + orchestrator_llm_provider, )); - let model_aliases = Arc::new(plano_config.model_aliases.clone()); - let span_attributes = Arc::new( - plano_config - .tracing - .as_ref() - .and_then(|tracing| tracing.span_attributes.clone()), - ); + let state_storage = init_state_storage(config).await?; - // Initialize trace collector and start background flusher - // Tracing is enabled if the tracing config is present in plano_config.yaml - // Pass Some(true/false) to override, or None to use env var OTEL_TRACING_ENABLED - // OpenTelemetry automatic instrumentation is configured in utils/tracing.rs + let span_attributes = config + .tracing + .as_ref() + .and_then(|tracing| tracing.span_attributes.clone()); - // Initialize conversation state storage for v1/responses - // Configurable via plano_config.yaml state_storage section - // If not configured, state management is disabled - // Environment variables are substituted by envsubst before config is read - let state_storage: Option> = - if let Some(storage_config) = &plano_config.state_storage { - let storage: Arc = match storage_config.storage_type { - common::configuration::StateStorageType::Memory => { - info!( - storage_type = "memory", - "initialized conversation state storage" - ); - Arc::new(MemoryConversationalStorage::new()) - } - common::configuration::StateStorageType::Postgres => { - let connection_string = storage_config - .connection_string - .as_ref() - .expect("connection_string is required for postgres state_storage"); + Ok(AppState { + router_service, + orchestrator_service, + model_aliases: config.model_aliases.clone(), + llm_providers: Arc::new(RwLock::new(llm_providers)), + agents_list: Some(all_agents), + listeners: config.listeners.clone(), + state_storage, + llm_provider_url, + span_attributes, + http_client: reqwest::Client::new(), + filter_pipeline, + }) +} - debug!(connection_string = %connection_string, "postgres connection"); - info!( - storage_type = "postgres", - "initializing conversation state storage" - ); - Arc::new( - PostgreSQLConversationStorage::new(connection_string.clone()) - .await - .expect("Failed to initialize Postgres state storage"), - ) - } - }; - Some(storage) - } else { - info!("no state_storage configured, conversation state management disabled"); - None - }; +/// Initialize the conversation state storage backend (if configured). +async fn init_state_storage( + config: &Configuration, +) -> Result>, Box> { + let Some(storage_config) = &config.state_storage else { + info!("no state_storage configured, conversation state management disabled"); + return Ok(None); + }; - loop { - let (stream, _) = listener.accept().await?; - let peer_addr = stream.peer_addr()?; - let io = TokioIo::new(stream); + let storage: Arc = match storage_config.storage_type { + common::configuration::StateStorageType::Memory => { + info!( + storage_type = "memory", + "initialized conversation state storage" + ); + Arc::new(MemoryConversationalStorage::new()) + } + common::configuration::StateStorageType::Postgres => { + let connection_string = storage_config + .connection_string + .as_ref() + .ok_or("connection_string is required for postgres state_storage")?; - let router_service: Arc = Arc::clone(&router_service); - let orchestrator_service: Arc = Arc::clone(&orchestrator_service); - let model_aliases: Arc< - Option>, - > = Arc::clone(&model_aliases); - let llm_provider_url = llm_provider_url.clone(); + debug!(connection_string = %connection_string, "postgres connection"); + info!( + storage_type = "postgres", + "initializing conversation state storage" + ); - let llm_providers = llm_providers.clone(); - let agents_list = combined_agents_filters_list.clone(); - let listeners = listeners.clone(); - let span_attributes = span_attributes.clone(); - let state_storage = state_storage.clone(); - let service = service_fn(move |req| { - let router_service = Arc::clone(&router_service); - let orchestrator_service = Arc::clone(&orchestrator_service); - let parent_cx = extract_context_from_request(&req); - let llm_provider_url = llm_provider_url.clone(); - let llm_providers = llm_providers.clone(); - let model_aliases = Arc::clone(&model_aliases); - let agents_list = agents_list.clone(); - let listeners = listeners.clone(); - let span_attributes = span_attributes.clone(); - let state_storage = state_storage.clone(); + Arc::new( + PostgreSQLConversationStorage::new(connection_string.clone()) + .await + .map_err(|e| format!("failed to initialize Postgres state storage: {e}"))?, + ) + } + }; - async move { - let path = req.uri().path().to_string(); - // 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, - orchestrator_service, - fully_qualified_url, - agents_list, - listeners, - span_attributes, - llm_providers, - ) - .with_context(parent_cx) - .await; - } - } - if let Some(stripped_path) = path.strip_prefix("/routing") { - let stripped_path = stripped_path.to_string(); - if matches!( - stripped_path.as_str(), - CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH - ) { - return routing_decision( - req, - router_service, - stripped_path, - span_attributes, - ) - .with_context(parent_cx) - .await; - } - } - match (req.method(), path.as_str()) { - ( - &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, - span_attributes, - state_storage, - ) - .with_context(parent_cx) - .await - } - (&Method::POST, "/function_calling") => { - let fully_qualified_url = - format!("{}{}", llm_provider_url, "/v1/chat/completions"); - function_calling_chat_handler(req, fully_qualified_url) - .with_context(parent_cx) - .await - } - (&Method::GET, "/v1/models" | "/agents/v1/models") => { - Ok(list_models(llm_providers).await) - } - // hack for now to get openw-web-ui to work - (&Method::OPTIONS, "/v1/models" | "/agents/v1/models") => { - let mut response = Response::new(empty()); - *response.status_mut() = StatusCode::NO_CONTENT; - response - .headers_mut() - .insert("Allow", "GET, OPTIONS".parse().unwrap()); - response - .headers_mut() - .insert("Access-Control-Allow-Origin", "*".parse().unwrap()); - response.headers_mut().insert( - "Access-Control-Allow-Headers", - "Authorization, Content-Type".parse().unwrap(), - ); - response.headers_mut().insert( - "Access-Control-Allow-Methods", - "GET, POST, OPTIONS".parse().unwrap(), - ); - response - .headers_mut() - .insert("Content-Type", "application/json".parse().unwrap()); + Ok(Some(storage)) +} - Ok(response) - } - _ => { - debug!(method = %req.method(), path = %path, "no route found"); - let mut not_found = Response::new(empty()); - *not_found.status_mut() = StatusCode::NOT_FOUND; - Ok(not_found) - } - } - } - }); +// --------------------------------------------------------------------------- +// Request routing +// --------------------------------------------------------------------------- - tokio::task::spawn(async move { - debug!(peer = ?peer_addr, "accepted connection"); - if let Err(err) = http1::Builder::new() - // .serve_connection(io, service_fn(chat_completion)) - .serve_connection(io, service) +/// Route an incoming HTTP request to the appropriate handler. +async fn route( + req: Request, + state: Arc, +) -> Result>, hyper::Error> { + let parent_cx = global::get_text_map_propagator(|p| p.extract(&HeaderExtractor(req.headers()))); + let path = req.uri().path().to_string(); + + // --- Agent routes (/agents/...) --- + if let Some(stripped) = path.strip_prefix("/agents") { + if matches!( + stripped, + CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH + ) { + return agent_chat(req, Arc::clone(&state)) + .with_context(parent_cx) + .await; + } + } + + // --- Routing decision routes (/routing/...) --- + if let Some(stripped) = path.strip_prefix("/routing") { + let stripped = stripped.to_string(); + if matches!( + stripped.as_str(), + CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH + ) { + return routing_decision( + req, + Arc::clone(&state.router_service), + stripped, + &state.span_attributes, + ) + .with_context(parent_cx) + .await; + } + } + + // --- Standard routes --- + match (req.method(), path.as_str()) { + (&Method::POST, CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH) => { + llm_chat(req, Arc::clone(&state)) + .with_context(parent_cx) .await - { - warn!(error = ?err, "error serving connection"); - } - }); + } + (&Method::POST, "/function_calling") => { + let url = format!("{}/v1/chat/completions", state.llm_provider_url); + function_calling_chat_handler(req, url) + .with_context(parent_cx) + .await + } + (&Method::GET, "/v1/models" | "/agents/v1/models") => { + Ok(list_models(Arc::clone(&state.llm_providers)).await) + } + (&Method::OPTIONS, "/v1/models" | "/agents/v1/models") => cors_preflight(), + _ => { + debug!(method = %req.method(), path = %path, "no route found"); + let mut not_found = Response::new(empty()); + *not_found.status_mut() = StatusCode::NOT_FOUND; + Ok(not_found) + } } } + +// --------------------------------------------------------------------------- +// Server loop +// --------------------------------------------------------------------------- + +/// Accept connections and spawn a task per connection. +/// +/// Listens for `SIGINT` / `ctrl-c` and shuts down gracefully, allowing +/// in-flight connections to finish. +async fn run_server(state: Arc) -> Result<(), Box> { + let bind_address = env::var("BIND_ADDRESS").unwrap_or_else(|_| BIND_ADDRESS.to_string()); + let listener = TcpListener::bind(&bind_address).await?; + info!(address = %bind_address, "server listening"); + + let shutdown = tokio::signal::ctrl_c(); + tokio::pin!(shutdown); + + loop { + tokio::select! { + result = listener.accept() => { + let (stream, _) = result?; + let peer_addr = stream.peer_addr()?; + let io = TokioIo::new(stream); + let state = Arc::clone(&state); + + tokio::task::spawn(async move { + debug!(peer = ?peer_addr, "accepted connection"); + + let service = service_fn(move |req| { + let state = Arc::clone(&state); + async move { route(req, state).await } + }); + + if let Err(err) = http1::Builder::new().serve_connection(io, service).await { + warn!(error = ?err, "error serving connection"); + } + }); + } + _ = &mut shutdown => { + info!("received shutdown signal, stopping server"); + break; + } + } + } + + Ok(()) +} + +// --------------------------------------------------------------------------- +// Entry point +// --------------------------------------------------------------------------- + +#[tokio::main] +async fn main() -> Result<(), Box> { + let config = load_config()?; + let _tracer_provider = init_tracer(config.tracing.as_ref()); + info!("loaded plano_config.yaml"); + let state = Arc::new(init_app_state(&config).await?); + run_server(state).await +} diff --git a/crates/brightstaff/src/router/http.rs b/crates/brightstaff/src/router/http.rs new file mode 100644 index 00000000..ad1b711c --- /dev/null +++ b/crates/brightstaff/src/router/http.rs @@ -0,0 +1,48 @@ +use hermesllm::apis::openai::ChatCompletionsResponse; +use hyper::header; +use thiserror::Error; +use tracing::warn; + +#[derive(Debug, Error)] +pub enum HttpError { + #[error("Failed to send request: {0}")] + Request(#[from] reqwest::Error), + + #[error("Failed to parse JSON response: {0}")] + Json(serde_json::Error, String), +} + +/// Sends a POST request to the given URL and extracts the text content +/// from the first choice of the `ChatCompletionsResponse`. +/// +/// Returns `Some((content, elapsed))` on success, or `None` if the response +/// had no choices or the first choice had no content. +pub async fn post_and_extract_content( + client: &reqwest::Client, + url: &str, + headers: header::HeaderMap, + body: String, +) -> Result, HttpError> { + let start_time = std::time::Instant::now(); + + let res = client.post(url).headers(headers).body(body).send().await?; + + let body = res.text().await?; + let elapsed = start_time.elapsed(); + + let response: ChatCompletionsResponse = serde_json::from_str(&body).map_err(|err| { + warn!(error = %err, body = %body, "failed to parse json response"); + HttpError::Json(err, format!("Failed to parse JSON: {}", body)) + })?; + + if response.choices.is_empty() { + warn!(body = %body, "no choices in response"); + return Ok(None); + } + + Ok(response.choices[0] + .message + .content + .as_ref() + .map(|c| (c.clone(), elapsed))) +} diff --git a/crates/brightstaff/src/router/llm_router.rs b/crates/brightstaff/src/router/llm.rs similarity index 72% rename from crates/brightstaff/src/router/llm_router.rs rename to crates/brightstaff/src/router/llm.rs index da2622b9..8eb2f0db 100644 --- a/crates/brightstaff/src/router/llm_router.rs +++ b/crates/brightstaff/src/router/llm.rs @@ -4,16 +4,17 @@ use common::{ configuration::{LlmProvider, ModelUsagePreference, RoutingPreference}, consts::{ARCH_PROVIDER_HINT_HEADER, REQUEST_ID_HEADER, TRACE_PARENT_HEADER}, }; -use hermesllm::apis::openai::{ChatCompletionsResponse, Message}; +use hermesllm::apis::openai::Message; use hyper::header; use thiserror::Error; use tokio::sync::RwLock; -use tracing::{debug, info, warn}; - -use crate::router::router_model_v1::{self}; +use tracing::{debug, info}; +use super::http::{self, post_and_extract_content}; use super::router_model::RouterModel; +use crate::router::router_model_v1; + const DEFAULT_SESSION_TTL_SECONDS: u64 = 600; const DEFAULT_SESSION_MAX_ENTRIES: usize = 10_000; @@ -28,7 +29,6 @@ pub struct RouterService { router_url: String, client: reqwest::Client, router_model: Arc, - #[allow(dead_code)] routing_provider_name: String, llm_usage_defined: bool, session_cache: RwLock>, @@ -38,11 +38,8 @@ pub struct RouterService { #[derive(Debug, Error)] pub enum RoutingError { - #[error("Failed to send request: {0}")] - RequestError(#[from] reqwest::Error), - - #[error("Failed to parse JSON: {0}, JSON: {1}")] - JsonError(serde_json::Error, String), + #[error(transparent)] + Http(#[from] http::HttpError), #[error("Router model error: {0}")] RouterModelError(#[from] super::router_model::RoutingModelError), @@ -119,7 +116,6 @@ impl RouterService { ) { let mut cache = self.session_cache.write().await; if cache.len() >= self.session_max_entries && !cache.contains_key(&session_id) { - // Evict the oldest entry if let Some(oldest_key) = cache .iter() .min_by_key(|(_, v)| v.cached_at) @@ -164,7 +160,9 @@ impl RouterService { return Ok(None); } - if (usage_preferences.is_none() || usage_preferences.as_ref().unwrap().len() < 2) + if usage_preferences + .as_ref() + .is_none_or(|prefs| prefs.len() < 2) && !self.llm_usage_defined { return Ok(None); @@ -180,88 +178,50 @@ impl RouterService { "sending request to arch-router" ); - debug!( - body = %serde_json::to_string(&router_request).unwrap(), - "arch router request" - ); + let body = serde_json::to_string(&router_request) + .map_err(super::router_model::RoutingModelError::from)?; + debug!(body = %body, "arch router request"); - let mut llm_route_request_headers = header::HeaderMap::new(); - llm_route_request_headers.insert( + let mut headers = header::HeaderMap::new(); + headers.insert( header::CONTENT_TYPE, header::HeaderValue::from_static("application/json"), ); - - llm_route_request_headers.insert( - header::HeaderName::from_static(ARCH_PROVIDER_HINT_HEADER), - header::HeaderValue::from_str(&self.routing_provider_name).unwrap(), - ); - - llm_route_request_headers.insert( - header::HeaderName::from_static(TRACE_PARENT_HEADER), - header::HeaderValue::from_str(traceparent).unwrap(), - ); - - llm_route_request_headers.insert( - header::HeaderName::from_static(REQUEST_ID_HEADER), - header::HeaderValue::from_str(request_id).unwrap(), - ); - - llm_route_request_headers.insert( + if let Ok(val) = header::HeaderValue::from_str(&self.routing_provider_name) { + headers.insert( + header::HeaderName::from_static(ARCH_PROVIDER_HINT_HEADER), + val, + ); + } + if let Ok(val) = header::HeaderValue::from_str(traceparent) { + headers.insert(header::HeaderName::from_static(TRACE_PARENT_HEADER), val); + } + if let Ok(val) = header::HeaderValue::from_str(request_id) { + headers.insert(header::HeaderName::from_static(REQUEST_ID_HEADER), val); + } + headers.insert( header::HeaderName::from_static("model"), header::HeaderValue::from_static("arch-router"), ); - let start_time = std::time::Instant::now(); - let res = self - .client - .post(&self.router_url) - .headers(llm_route_request_headers) - .body(serde_json::to_string(&router_request).unwrap()) - .send() - .await?; - - let body = res.text().await?; - let router_response_time = start_time.elapsed(); - - let chat_completion_response: ChatCompletionsResponse = match serde_json::from_str(&body) { - Ok(response) => response, - Err(err) => { - warn!( - error = %err, - body = %serde_json::to_string(&body).unwrap(), - "failed to parse json response" - ); - return Err(RoutingError::JsonError( - err, - format!("Failed to parse JSON: {}", body), - )); - } + let Some((content, elapsed)) = + post_and_extract_content(&self.client, &self.router_url, headers, body).await? + else { + return Ok(None); }; - if chat_completion_response.choices.is_empty() { - warn!(body = %body, "no choices in router response"); - return Ok(None); - } + let parsed = self + .router_model + .parse_response(&content, &usage_preferences)?; - if let Some(content) = &chat_completion_response.choices[0].message.content { - let parsed_response = self - .router_model - .parse_response(content, &usage_preferences)?; - info!( - content = %content.replace("\n", "\\n"), - selected_model = ?parsed_response, - response_time_ms = router_response_time.as_millis(), - "arch-router determined route" - ); + info!( + content = %content.replace("\n", "\\n"), + selected_model = ?parsed, + response_time_ms = elapsed.as_millis(), + "arch-router determined route" + ); - if let Some(ref parsed_response) = parsed_response { - return Ok(Some(parsed_response.clone())); - } - - Ok(None) - } else { - Ok(None) - } + Ok(parsed) } } @@ -306,7 +266,6 @@ mod tests { let svc = make_router_service(0, 100); svc.cache_route("s1".to_string(), "gpt-4o".to_string(), None) .await; - // TTL is 0 seconds, so the entry should be expired immediately assert!(svc.get_cached_route("s1").await.is_none()); } @@ -329,12 +288,10 @@ mod tests { let svc = make_router_service(600, 2); svc.cache_route("s1".to_string(), "model-a".to_string(), None) .await; - // Small delay so s2 has a later cached_at tokio::time::sleep(Duration::from_millis(10)).await; svc.cache_route("s2".to_string(), "model-b".to_string(), None) .await; - // Cache is full (2 entries). Adding s3 should evict s1 (oldest). svc.cache_route("s3".to_string(), "model-c".to_string(), None) .await; @@ -353,7 +310,6 @@ mod tests { svc.cache_route("s2".to_string(), "model-b".to_string(), None) .await; - // Updating s1 should not trigger eviction since key already exists svc.cache_route( "s1".to_string(), "model-a-updated".to_string(), diff --git a/crates/brightstaff/src/router/mod.rs b/crates/brightstaff/src/router/mod.rs index 9b1abbea..b010d80c 100644 --- a/crates/brightstaff/src/router/mod.rs +++ b/crates/brightstaff/src/router/mod.rs @@ -1,6 +1,7 @@ -pub mod llm_router; +pub(crate) mod http; +pub mod llm; +pub mod orchestrator; pub mod orchestrator_model; pub mod orchestrator_model_v1; -pub mod plano_orchestrator; pub mod router_model; pub mod router_model_v1; diff --git a/crates/brightstaff/src/router/orchestrator.rs b/crates/brightstaff/src/router/orchestrator.rs new file mode 100644 index 00000000..c75aa64b --- /dev/null +++ b/crates/brightstaff/src/router/orchestrator.rs @@ -0,0 +1,138 @@ +use std::{collections::HashMap, sync::Arc}; + +use common::{ + configuration::{AgentUsagePreference, OrchestrationPreference}, + consts::{ARCH_PROVIDER_HINT_HEADER, REQUEST_ID_HEADER}, +}; +use hermesllm::apis::openai::Message; +use hyper::header; +use opentelemetry::global; +use opentelemetry_http::HeaderInjector; +use thiserror::Error; +use tracing::{debug, info}; + +use super::http::{self, post_and_extract_content}; +use super::orchestrator_model::OrchestratorModel; + +use crate::router::orchestrator_model_v1; + +pub struct OrchestratorService { + orchestrator_url: String, + client: reqwest::Client, + orchestrator_model: Arc, + orchestrator_provider_name: String, +} + +#[derive(Debug, Error)] +pub enum OrchestrationError { + #[error(transparent)] + Http(#[from] http::HttpError), + + #[error("Orchestrator model error: {0}")] + OrchestratorModelError(#[from] super::orchestrator_model::OrchestratorModelError), +} + +pub type Result = std::result::Result; + +impl OrchestratorService { + pub fn new( + orchestrator_url: String, + orchestration_model_name: String, + orchestrator_provider_name: String, + ) -> Self { + let agent_orchestrations: HashMap> = HashMap::new(); + + let orchestrator_model = Arc::new(orchestrator_model_v1::OrchestratorModelV1::new( + agent_orchestrations, + orchestration_model_name.clone(), + orchestrator_model_v1::MAX_TOKEN_LEN, + )); + + OrchestratorService { + orchestrator_url, + client: reqwest::Client::new(), + orchestrator_model, + orchestrator_provider_name, + } + } + + pub async fn determine_orchestration( + &self, + messages: &[Message], + usage_preferences: Option>, + request_id: Option, + ) -> Result>> { + if messages.is_empty() { + return Ok(None); + } + + if usage_preferences + .as_ref() + .is_none_or(|prefs| prefs.is_empty()) + { + return Ok(None); + } + + let orchestrator_request = self + .orchestrator_model + .generate_request(messages, &usage_preferences); + + debug!( + model = %self.orchestrator_model.get_model_name(), + endpoint = %self.orchestrator_url, + "sending request to arch-orchestrator" + ); + + let body = serde_json::to_string(&orchestrator_request) + .map_err(super::orchestrator_model::OrchestratorModelError::from)?; + debug!(body = %body, "arch orchestrator request"); + + let mut headers = header::HeaderMap::new(); + headers.insert( + header::CONTENT_TYPE, + header::HeaderValue::from_static("application/json"), + ); + headers.insert( + header::HeaderName::from_static(ARCH_PROVIDER_HINT_HEADER), + header::HeaderValue::from_str(&self.orchestrator_provider_name) + .unwrap_or_else(|_| header::HeaderValue::from_static("plano-orchestrator")), + ); + + // Inject OpenTelemetry trace context from current span + global::get_text_map_propagator(|propagator| { + let cx = + tracing_opentelemetry::OpenTelemetrySpanExt::context(&tracing::Span::current()); + propagator.inject_context(&cx, &mut HeaderInjector(&mut headers)); + }); + + if let Some(ref request_id) = request_id { + if let Ok(val) = header::HeaderValue::from_str(request_id) { + headers.insert(header::HeaderName::from_static(REQUEST_ID_HEADER), val); + } + } + + headers.insert( + header::HeaderName::from_static("model"), + header::HeaderValue::from_static("plano-orchestrator"), + ); + + let Some((content, elapsed)) = + post_and_extract_content(&self.client, &self.orchestrator_url, headers, body).await? + else { + return Ok(None); + }; + + let parsed = self + .orchestrator_model + .parse_response(&content, &usage_preferences)?; + + info!( + content = %content.replace("\n", "\\n"), + selected_routes = ?parsed, + response_time_ms = elapsed.as_millis(), + "arch-orchestrator determined routes" + ); + + Ok(parsed) + } +} diff --git a/crates/brightstaff/src/router/plano_orchestrator.rs b/crates/brightstaff/src/router/plano_orchestrator.rs deleted file mode 100644 index cf2688b9..00000000 --- a/crates/brightstaff/src/router/plano_orchestrator.rs +++ /dev/null @@ -1,168 +0,0 @@ -use std::{collections::HashMap, sync::Arc}; - -use common::{ - configuration::{AgentUsagePreference, OrchestrationPreference}, - consts::{ARCH_PROVIDER_HINT_HEADER, PLANO_ORCHESTRATOR_MODEL_NAME, REQUEST_ID_HEADER}, -}; -use hermesllm::apis::openai::{ChatCompletionsResponse, Message}; -use hyper::header; -use opentelemetry::global; -use opentelemetry_http::HeaderInjector; -use thiserror::Error; -use tracing::{debug, info, warn}; - -use crate::router::orchestrator_model_v1::{self}; - -use super::orchestrator_model::OrchestratorModel; - -pub struct OrchestratorService { - orchestrator_url: String, - client: reqwest::Client, - orchestrator_model: Arc, -} - -#[derive(Debug, Error)] -pub enum OrchestrationError { - #[error("Failed to send request: {0}")] - RequestError(#[from] reqwest::Error), - - #[error("Failed to parse JSON: {0}, JSON: {1}")] - JsonError(serde_json::Error, String), - - #[error("Orchestrator model error: {0}")] - OrchestratorModelError(#[from] super::orchestrator_model::OrchestratorModelError), -} - -pub type Result = std::result::Result; - -impl OrchestratorService { - pub fn new(orchestrator_url: String, orchestration_model_name: String) -> Self { - // Empty agent orchestrations - will be provided via usage_preferences in requests - let agent_orchestrations: HashMap> = HashMap::new(); - - let orchestrator_model = Arc::new(orchestrator_model_v1::OrchestratorModelV1::new( - agent_orchestrations, - orchestration_model_name.clone(), - orchestrator_model_v1::MAX_TOKEN_LEN, - )); - - OrchestratorService { - orchestrator_url, - client: reqwest::Client::new(), - orchestrator_model, - } - } - - pub async fn determine_orchestration( - &self, - messages: &[Message], - usage_preferences: Option>, - request_id: Option, - ) -> Result>> { - if messages.is_empty() { - return Ok(None); - } - - // Require usage_preferences to be provided - if usage_preferences.is_none() || usage_preferences.as_ref().unwrap().is_empty() { - return Ok(None); - } - - let orchestrator_request = self - .orchestrator_model - .generate_request(messages, &usage_preferences); - - debug!( - model = %self.orchestrator_model.get_model_name(), - endpoint = %self.orchestrator_url, - "sending request to arch-orchestrator" - ); - - debug!( - body = %serde_json::to_string(&orchestrator_request).unwrap(), - "arch orchestrator request" - ); - - let mut orchestration_request_headers = header::HeaderMap::new(); - orchestration_request_headers.insert( - header::CONTENT_TYPE, - header::HeaderValue::from_static("application/json"), - ); - - orchestration_request_headers.insert( - header::HeaderName::from_static(ARCH_PROVIDER_HINT_HEADER), - header::HeaderValue::from_str(PLANO_ORCHESTRATOR_MODEL_NAME).unwrap(), - ); - - // Inject OpenTelemetry trace context from current span - global::get_text_map_propagator(|propagator| { - let cx = - tracing_opentelemetry::OpenTelemetrySpanExt::context(&tracing::Span::current()); - propagator.inject_context(&cx, &mut HeaderInjector(&mut orchestration_request_headers)); - }); - - if let Some(request_id) = request_id { - orchestration_request_headers.insert( - header::HeaderName::from_static(REQUEST_ID_HEADER), - header::HeaderValue::from_str(&request_id).unwrap(), - ); - } - - orchestration_request_headers.insert( - header::HeaderName::from_static("model"), - header::HeaderValue::from_static(PLANO_ORCHESTRATOR_MODEL_NAME), - ); - - let start_time = std::time::Instant::now(); - let res = self - .client - .post(&self.orchestrator_url) - .headers(orchestration_request_headers) - .body(serde_json::to_string(&orchestrator_request).unwrap()) - .send() - .await?; - - let body = res.text().await?; - let orchestrator_response_time = start_time.elapsed(); - - let chat_completion_response: ChatCompletionsResponse = match serde_json::from_str(&body) { - Ok(response) => response, - Err(err) => { - warn!( - error = %err, - body = %serde_json::to_string(&body).unwrap(), - "failed to parse json response" - ); - return Err(OrchestrationError::JsonError( - err, - format!("Failed to parse JSON: {}", body), - )); - } - }; - - if chat_completion_response.choices.is_empty() { - warn!(body = %body, "no choices in orchestrator response"); - return Ok(None); - } - - if let Some(content) = &chat_completion_response.choices[0].message.content { - let parsed_response = self - .orchestrator_model - .parse_response(content, &usage_preferences)?; - info!( - content = %content.replace("\n", "\\n"), - selected_routes = ?parsed_response, - response_time_ms = orchestrator_response_time.as_millis(), - "arch-orchestrator determined routes" - ); - - if let Some(ref parsed_response) = parsed_response { - return Ok(Some(parsed_response.clone())); - } - - Ok(None) - } else { - Ok(None) - } - } -} diff --git a/crates/brightstaff/src/state/mod.rs b/crates/brightstaff/src/state/mod.rs index 3d59f359..43454ee2 100644 --- a/crates/brightstaff/src/state/mod.rs +++ b/crates/brightstaff/src/state/mod.rs @@ -88,35 +88,18 @@ pub trait StateStorage: Send + Sync { combined_input.extend(current_input); debug!( - "PLANO | BRIGHTSTAFF | STATE_STORAGE | RESP_ID:{} | Merged state: prev_items={}, current_items={}, total_items={}, combined_json={}", - prev_state.response_id, - prev_count, - current_count, - combined_input.len(), - serde_json::to_string(&combined_input).unwrap_or_else(|_| "serialization_error".to_string()) + response_id = %prev_state.response_id, + prev_items = prev_count, + current_items = current_count, + total_items = combined_input.len(), + combined_json = %serde_json::to_string(&combined_input).unwrap_or_else(|_| "serialization_error".to_string()), + "merged conversation state" ); combined_input } } -/// Storage backend type enum -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -pub enum StorageBackend { - Memory, - Supabase, -} - -impl StorageBackend { - pub fn parse_backend(s: &str) -> Option { - match s.to_lowercase().as_str() { - "memory" => Some(StorageBackend::Memory), - "supabase" => Some(StorageBackend::Supabase), - _ => None, - } - } -} - // === Utility functions for state management === /// Extract input items from InputParam, converting text to structured format diff --git a/crates/brightstaff/src/state/response_state_processor.rs b/crates/brightstaff/src/state/response_state_processor.rs index 0920324c..bca2991a 100644 --- a/crates/brightstaff/src/state/response_state_processor.rs +++ b/crates/brightstaff/src/state/response_state_processor.rs @@ -7,8 +7,8 @@ use std::io::Read; use std::sync::Arc; use tracing::{debug, info, warn}; -use crate::handlers::utils::StreamProcessor; use crate::state::{OpenAIConversationState, StateStorage}; +use crate::streaming::StreamProcessor; /// Processor that wraps another processor and handles v1/responses state management /// Captures response_id and output from streaming responses, stores state after completion diff --git a/crates/brightstaff/src/handlers/utils.rs b/crates/brightstaff/src/streaming.rs similarity index 69% rename from crates/brightstaff/src/handlers/utils.rs rename to crates/brightstaff/src/streaming.rs index bff87ad4..f7af8ae0 100644 --- a/crates/brightstaff/src/handlers/utils.rs +++ b/crates/brightstaff/src/streaming.rs @@ -1,16 +1,21 @@ use bytes::Bytes; +use common::configuration::ResolvedFilterChain; use http_body_util::combinators::BoxBody; use http_body_util::StreamBody; use hyper::body::Frame; +use hyper::header::HeaderMap; use opentelemetry::trace::TraceContextExt; use opentelemetry::KeyValue; use std::time::Instant; use tokio::sync::mpsc; use tokio_stream::wrappers::ReceiverStream; use tokio_stream::StreamExt; -use tracing::{info, warn, Instrument}; +use tracing::{debug, info, warn, Instrument}; use tracing_opentelemetry::OpenTelemetrySpanExt; +use crate::handlers::agents::pipeline::{PipelineError, PipelineProcessor}; + +const STREAM_BUFFER_SIZE: usize = 16; use crate::signals::{InteractionQuality, SignalAnalyzer, TextBasedSignalAnalyzer, FLAG_MARKER}; use crate::tracing::{llm, set_service_name, signals as signal_constants}; use hermesllm::apis::openai::Message; @@ -31,6 +36,21 @@ pub trait StreamProcessor: Send + 'static { fn on_error(&mut self, _error: &str) {} } +impl StreamProcessor for Box { + fn process_chunk(&mut self, chunk: Bytes) -> Result, String> { + (**self).process_chunk(chunk) + } + fn on_first_bytes(&mut self) { + (**self).on_first_bytes() + } + fn on_complete(&mut self) { + (**self).on_complete() + } + fn on_error(&mut self, error: &str) { + (**self).on_error(error) + } +} + /// A processor that tracks streaming metrics pub struct ObservableStreamProcessor { service_name: String, @@ -206,16 +226,12 @@ pub struct StreamingResponse { pub processor_handle: tokio::task::JoinHandle<()>, } -pub fn create_streaming_response( - mut byte_stream: S, - mut processor: P, - buffer_size: usize, -) -> StreamingResponse +pub fn create_streaming_response(mut byte_stream: S, mut processor: P) -> StreamingResponse where S: StreamExt> + Send + Unpin + 'static, P: StreamProcessor, { - let (tx, rx) = mpsc::channel::(buffer_size); + let (tx, rx) = mpsc::channel::(STREAM_BUFFER_SIZE); // Capture the current span so the spawned task inherits the request context let current_span = tracing::Span::current(); @@ -277,6 +293,108 @@ where } } +/// Creates a streaming response that processes each raw chunk through output filters. +/// Filters receive the raw LLM response bytes and request path (any API shape; not limited to +/// chat completions). On filter error mid-stream the original chunk is passed through (headers already sent). +pub fn create_streaming_response_with_output_filter( + mut byte_stream: S, + mut inner_processor: P, + output_chain: ResolvedFilterChain, + request_headers: HeaderMap, + request_path: String, +) -> StreamingResponse +where + S: StreamExt> + Send + Unpin + 'static, + P: StreamProcessor, +{ + let (tx, rx) = mpsc::channel::(STREAM_BUFFER_SIZE); + let current_span = tracing::Span::current(); + + let processor_handle = tokio::spawn( + async move { + let mut is_first_chunk = true; + let mut pipeline_processor = PipelineProcessor::default(); + let chain = output_chain.to_agent_filter_chain("output_filter"); + + while let Some(item) = byte_stream.next().await { + let chunk = match item { + Ok(chunk) => chunk, + Err(err) => { + let err_msg = format!("Error receiving chunk: {:?}", err); + warn!(error = %err_msg, "stream error"); + inner_processor.on_error(&err_msg); + break; + } + }; + + if is_first_chunk { + inner_processor.on_first_bytes(); + is_first_chunk = false; + } + + // Pass raw chunk bytes through the output filter chain + let processed_chunk = match pipeline_processor + .process_raw_filter_chain( + &chunk, + &chain, + &output_chain.agents, + &request_headers, + &request_path, + ) + .await + { + Ok(filtered) => filtered, + Err(PipelineError::ClientError { + agent, + status, + body, + }) => { + warn!( + agent = %agent, + status = %status, + body = %body, + "output filter client error, passing through original chunk" + ); + chunk + } + Err(e) => { + warn!(error = %e, "output filter error, passing through original chunk"); + chunk + } + }; + + // Pass through inner processor for metrics/observability + match inner_processor.process_chunk(processed_chunk) { + Ok(Some(final_chunk)) => { + if tx.send(final_chunk).await.is_err() { + warn!("receiver dropped"); + break; + } + } + Ok(None) => continue, + Err(err) => { + warn!("processor error: {}", err); + inner_processor.on_error(&err); + break; + } + } + } + + inner_processor.on_complete(); + debug!("output filter streaming completed"); + } + .instrument(current_span), + ); + + let stream = ReceiverStream::new(rx).map(|chunk| Ok::<_, hyper::Error>(Frame::data(chunk))); + let stream_body = BoxBody::new(StreamBody::new(stream)); + + StreamingResponse { + body: stream_body, + processor_handle, + } +} + /// Truncates a message to the specified maximum length, adding "..." if truncated. pub fn truncate_message(message: &str, max_length: usize) -> String { if message.chars().count() > max_length { diff --git a/crates/brightstaff/src/tracing/custom_attributes.rs b/crates/brightstaff/src/tracing/custom_attributes.rs index 24abc72b..7d4244d2 100644 --- a/crates/brightstaff/src/tracing/custom_attributes.rs +++ b/crates/brightstaff/src/tracing/custom_attributes.rs @@ -52,6 +52,7 @@ pub fn collect_custom_trace_attributes( attributes } +#[allow(dead_code)] pub fn append_span_attributes( mut span_builder: SpanBuilder, attributes: &HashMap, diff --git a/crates/brightstaff/src/utils/tracing.rs b/crates/brightstaff/src/tracing/init.rs similarity index 94% rename from crates/brightstaff/src/utils/tracing.rs rename to crates/brightstaff/src/tracing/init.rs index 21882303..ed351148 100644 --- a/crates/brightstaff/src/utils/tracing.rs +++ b/crates/brightstaff/src/tracing/init.rs @@ -11,7 +11,7 @@ use tracing_subscriber::registry::LookupSpan; use tracing_subscriber::util::SubscriberInitExt; use tracing_subscriber::EnvFilter; -use crate::tracing::ServiceNameOverrideExporter; +use super::ServiceNameOverrideExporter; use common::configuration::Tracing; struct BracketedTime; @@ -96,17 +96,16 @@ pub fn init_tracer(tracing_config: Option<&Tracing>) -> &'static SdkTracerProvid tracing_enabled, otel_endpoint, random_sampling ); - // Create OTLP exporter to send spans to collector - if tracing_enabled { - // Set service name via environment if not already set + // Create OTLP exporter to send spans to collector. + // Use `if let` to destructure the endpoint, avoiding an unwrap. + if let Some(endpoint) = otel_endpoint.as_deref().filter(|_| tracing_enabled) { if std::env::var("OTEL_SERVICE_NAME").is_err() { std::env::set_var("OTEL_SERVICE_NAME", "plano"); } - // Create ServiceNameOverrideExporter to support per-span service names // This allows spans to have different service names (e.g., plano(orchestrator), // plano(filter), plano(llm)) by setting the "service.name.override" attribute - let exporter = ServiceNameOverrideExporter::new(otel_endpoint.as_ref().unwrap()); + let exporter = ServiceNameOverrideExporter::new(endpoint); let provider = SdkTracerProvider::builder() .with_batch_exporter(exporter) diff --git a/crates/brightstaff/src/tracing/mod.rs b/crates/brightstaff/src/tracing/mod.rs index 1fa8a7e2..644db31a 100644 --- a/crates/brightstaff/src/tracing/mod.rs +++ b/crates/brightstaff/src/tracing/mod.rs @@ -1,11 +1,13 @@ mod constants; mod custom_attributes; +mod init; mod service_name_exporter; pub use constants::{ error, http, llm, operation_component, routing, signals, OperationNameBuilder, }; -pub use custom_attributes::{append_span_attributes, collect_custom_trace_attributes}; +pub use custom_attributes::collect_custom_trace_attributes; +pub use init::init_tracer; pub use service_name_exporter::{ServiceNameOverrideExporter, SERVICE_NAME_OVERRIDE_KEY}; use opentelemetry::trace::get_active_span; diff --git a/crates/brightstaff/src/utils/mod.rs b/crates/brightstaff/src/utils/mod.rs deleted file mode 100644 index 5ee45fbc..00000000 --- a/crates/brightstaff/src/utils/mod.rs +++ /dev/null @@ -1 +0,0 @@ -pub mod tracing; diff --git a/crates/common/src/configuration.rs b/crates/common/src/configuration.rs index ed7f61ad..929efdbe 100644 --- a/crates/common/src/configuration.rs +++ b/crates/common/src/configuration.rs @@ -35,14 +35,66 @@ pub struct AgentFilterChain { pub id: String, pub default: Option, pub description: Option, - pub filter_chain: Option>, + pub input_filters: Option>, +} + +/// A filter chain with its agent references resolved to concrete Agent objects. +/// Bundles the ordered filter IDs with the agent lookup map so they stay in sync. +#[derive(Debug, Clone, Default)] +pub struct ResolvedFilterChain { + pub filter_ids: Vec, + pub agents: HashMap, +} + +impl ResolvedFilterChain { + pub fn is_empty(&self) -> bool { + self.filter_ids.is_empty() + } + + pub fn to_agent_filter_chain(&self, id: &str) -> AgentFilterChain { + AgentFilterChain { + id: id.to_string(), + default: None, + description: None, + input_filters: Some(self.filter_ids.clone()), + } + } +} + +/// Holds resolved input and output filter chains for a model listener. +#[derive(Debug, Clone, Default)] +pub struct FilterPipeline { + pub input: Option, + pub output: Option, +} + +impl FilterPipeline { + pub fn has_input_filters(&self) -> bool { + self.input.as_ref().is_some_and(|c| !c.is_empty()) + } + + pub fn has_output_filters(&self) -> bool { + self.output.as_ref().is_some_and(|c| !c.is_empty()) + } +} + +#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)] +#[serde(rename_all = "lowercase")] +pub enum ListenerType { + Model, + Agent, + Prompt, } #[derive(Debug, Clone, Serialize, Deserialize)] pub struct Listener { + #[serde(rename = "type")] + pub listener_type: ListenerType, pub name: String, pub router: Option, pub agents: Option>, + pub input_filters: Option>, + pub output_filters: Option>, pub port: u16, } @@ -67,6 +119,7 @@ pub struct Configuration { pub model_providers: Vec, pub model_aliases: Option>, pub overrides: Option, + pub routing: Option, pub system_prompt: Option, pub prompt_guards: Option, pub prompt_targets: Option>, @@ -74,7 +127,6 @@ pub struct Configuration { pub ratelimits: Option>, pub tracing: Option, pub mode: Option, - pub routing: Option, pub agents: Option>, pub filters: Option>, pub listeners: Vec, @@ -86,6 +138,8 @@ pub struct Overrides { pub prompt_target_intent_matching_threshold: Option, pub optimize_context_window: Option, pub use_agent_orchestrator: Option, + pub llm_routing_model: Option, + pub agent_orchestration_model: Option, } #[derive(Debug, Clone, Serialize, Deserialize, Default)] @@ -209,8 +263,6 @@ pub struct EmbeddingProviver { #[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Hash)] pub enum LlmProviderType { - #[serde(rename = "arch")] - Arch, #[serde(rename = "anthropic")] Anthropic, #[serde(rename = "deepseek")] @@ -239,12 +291,13 @@ pub enum LlmProviderType { Qwen, #[serde(rename = "amazon_bedrock")] AmazonBedrock, + #[serde(rename = "plano")] + Plano, } impl Display for LlmProviderType { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { match self { - LlmProviderType::Arch => write!(f, "arch"), LlmProviderType::Anthropic => write!(f, "anthropic"), LlmProviderType::Deepseek => write!(f, "deepseek"), LlmProviderType::Groq => write!(f, "groq"), @@ -259,6 +312,7 @@ impl Display for LlmProviderType { LlmProviderType::Zhipu => write!(f, "zhipu"), LlmProviderType::Qwen => write!(f, "qwen"), LlmProviderType::AmazonBedrock => write!(f, "amazon_bedrock"), + LlmProviderType::Plano => write!(f, "plano"), } } } @@ -593,14 +647,14 @@ mod test { }, LlmProvider { name: "arch-router".to_string(), - provider_interface: LlmProviderType::Arch, + provider_interface: LlmProviderType::Plano, model: Some("Arch-Router".to_string()), internal: Some(true), ..Default::default() }, LlmProvider { name: "plano-orchestrator".to_string(), - provider_interface: LlmProviderType::Arch, + provider_interface: LlmProviderType::Plano, model: Some("Plano-Orchestrator".to_string()), internal: Some(true), ..Default::default() diff --git a/crates/common/src/consts.rs b/crates/common/src/consts.rs index 69fa5799..179c66ac 100644 --- a/crates/common/src/consts.rs +++ b/crates/common/src/consts.rs @@ -34,5 +34,4 @@ pub const OTEL_COLLECTOR_HTTP: &str = "opentelemetry_collector_http"; 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"; -pub const PLANO_ORCHESTRATOR_MODEL_NAME: &str = "Plano-Orchestrator"; -pub const ARCH_FC_CLUSTER: &str = "arch"; +pub const PLANO_FC_CLUSTER: &str = "plano"; diff --git a/crates/hermesllm/src/bin/provider_models.yaml b/crates/hermesllm/src/bin/provider_models.yaml index a748e7a7..a1425d43 100644 --- a/crates/hermesllm/src/bin/provider_models.yaml +++ b/crates/hermesllm/src/bin/provider_models.yaml @@ -1,183 +1,16 @@ version: '1.0' source: canonical-apis providers: - mistralai: - - mistralai/mistral-medium-2505 - - mistralai/mistral-medium-2508 - - mistralai/mistral-medium-latest - - mistralai/mistral-medium - - mistralai/mistral-vibe-cli-with-tools - - mistralai/open-mistral-nemo - - mistralai/open-mistral-nemo-2407 - - mistralai/mistral-tiny-2407 - - mistralai/mistral-tiny-latest - - mistralai/mistral-large-2411 - - mistralai/pixtral-large-2411 - - mistralai/pixtral-large-latest - - mistralai/mistral-large-pixtral-2411 - - mistralai/codestral-2508 - - mistralai/codestral-latest - - mistralai/devstral-small-2507 - - mistralai/devstral-medium-2507 - - mistralai/devstral-2512 - - mistralai/mistral-vibe-cli-latest - - mistralai/devstral-medium-latest - - mistralai/devstral-latest - - mistralai/labs-devstral-small-2512 - - mistralai/devstral-small-latest - - mistralai/mistral-small-2506 - - mistralai/mistral-small-latest - - mistralai/labs-mistral-small-creative - - mistralai/magistral-medium-2509 - - mistralai/magistral-medium-latest - - mistralai/magistral-small-2509 - - mistralai/magistral-small-latest - - mistralai/mistral-large-2512 - - mistralai/mistral-large-latest - - mistralai/ministral-3b-2512 - - mistralai/ministral-3b-latest - - mistralai/ministral-8b-2512 - - mistralai/ministral-8b-latest - - mistralai/ministral-14b-2512 - - mistralai/ministral-14b-latest - - mistralai/mistral-small-2501 - - mistralai/mistral-embed-2312 - - mistralai/mistral-embed - - mistralai/codestral-embed - - mistralai/codestral-embed-2505 - openai: - - openai/gpt-4-0613 - - openai/gpt-4 - - openai/gpt-3.5-turbo - - openai/gpt-5.2-codex - - openai/gpt-3.5-turbo-instruct - - openai/gpt-3.5-turbo-instruct-0914 - - openai/gpt-4-1106-preview - - openai/gpt-3.5-turbo-1106 - - openai/gpt-4-0125-preview - - openai/gpt-4-turbo-preview - - openai/gpt-3.5-turbo-0125 - - openai/gpt-4-turbo - - openai/gpt-4-turbo-2024-04-09 - - openai/gpt-4o - - openai/gpt-4o-2024-05-13 - - openai/gpt-4o-mini-2024-07-18 - - openai/gpt-4o-mini - - openai/gpt-4o-2024-08-06 - - openai/chatgpt-4o-latest - - openai/o1-2024-12-17 - - openai/o1 - - openai/computer-use-preview - - openai/o3-mini - - openai/o3-mini-2025-01-31 - - openai/gpt-4o-2024-11-20 - - openai/computer-use-preview-2025-03-11 - - openai/gpt-4o-search-preview-2025-03-11 - - openai/gpt-4o-search-preview - - openai/gpt-4o-mini-search-preview-2025-03-11 - - openai/gpt-4o-mini-search-preview - - openai/o1-pro-2025-03-19 - - openai/o1-pro - - openai/o3-2025-04-16 - - openai/o4-mini-2025-04-16 - - openai/o3 - - openai/o4-mini - - openai/gpt-4.1-2025-04-14 - - openai/gpt-4.1 - - openai/gpt-4.1-mini-2025-04-14 - - openai/gpt-4.1-mini - - openai/gpt-4.1-nano-2025-04-14 - - openai/gpt-4.1-nano - - openai/o3-pro - - openai/o3-pro-2025-06-10 - - openai/o4-mini-deep-research - - openai/o3-deep-research - - openai/o3-deep-research-2025-06-26 - - openai/o4-mini-deep-research-2025-06-26 - - openai/gpt-5-chat-latest - - openai/gpt-5-2025-08-07 - - openai/gpt-5 - - openai/gpt-5-mini-2025-08-07 - - openai/gpt-5-mini - - openai/gpt-5-nano-2025-08-07 - - openai/gpt-5-nano - - openai/gpt-5-codex - - openai/gpt-5-pro-2025-10-06 - - openai/gpt-5-pro - - openai/gpt-5-search-api - - openai/gpt-5-search-api-2025-10-14 - - openai/gpt-5.1-chat-latest - - openai/gpt-5.1-2025-11-13 - - openai/gpt-5.1 - - openai/gpt-5.1-codex - - openai/gpt-5.1-codex-mini - - openai/gpt-5.1-codex-max - - openai/gpt-5.2-2025-12-11 - - openai/gpt-5.2 - - openai/gpt-5.2-pro-2025-12-11 - - openai/gpt-5.2-pro - - openai/gpt-5.2-chat-latest - - openai/gpt-3.5-turbo-16k - - openai/ft:gpt-3.5-turbo-0613:katanemo::8CMZbm0P deepseek: - deepseek/deepseek-chat - deepseek/deepseek-reasoner - x-ai: - - x-ai/grok-2-vision-1212 - - x-ai/grok-3 - - x-ai/grok-3-mini - - x-ai/grok-4-0709 - - x-ai/grok-4-1-fast-non-reasoning - - x-ai/grok-4-1-fast-reasoning - - x-ai/grok-4-fast-non-reasoning - - x-ai/grok-4-fast-reasoning - - x-ai/grok-code-fast-1 - - x-ai/grok-imagine-image - - x-ai/grok-imagine-video - moonshotai: - - moonshotai/kimi-k2-thinking - - moonshotai/kimi-k2.5 - - moonshotai/moonshot-v1-128k-vision-preview - - moonshotai/moonshot-v1-8k - - moonshotai/kimi-k2-turbo-preview - - moonshotai/moonshot-v1-128k - - moonshotai/moonshot-v1-32k-vision-preview - - moonshotai/kimi-k2-thinking-turbo - - moonshotai/kimi-latest - - moonshotai/moonshot-v1-32k - - moonshotai/moonshot-v1-auto - - moonshotai/kimi-k2-0711-preview - - moonshotai/kimi-k2-0905-preview - - moonshotai/moonshot-v1-8k-vision-preview - anthropic: - - anthropic/claude-opus-4-6 - - anthropic/claude-opus-4-5-20251101 - - anthropic/claude-opus-4-5 - - anthropic/claude-haiku-4-5-20251001 - - anthropic/claude-haiku-4-5 - - anthropic/claude-sonnet-4-5-20250929 - - anthropic/claude-sonnet-4-5 - - anthropic/claude-opus-4-1-20250805 - - anthropic/claude-opus-4-1 - - anthropic/claude-opus-4-20250514 - - anthropic/claude-opus-4 - - anthropic/claude-sonnet-4-20250514 - - anthropic/claude-sonnet-4 - - anthropic/claude-3-7-sonnet-20250219 - - anthropic/claude-3-7-sonnet - - anthropic/claude-3-5-haiku-20241022 - - anthropic/claude-3-5-haiku - - anthropic/claude-3-haiku-20240307 - - anthropic/claude-3-haiku google: - google/gemini-2.5-flash - google/gemini-2.5-pro - google/gemini-2.0-flash - google/gemini-2.0-flash-001 - - google/gemini-2.0-flash-exp-image-generation - google/gemini-2.0-flash-lite-001 - google/gemini-2.0-flash-lite - - google/gemini-exp-1206 - google/gemini-2.5-flash-preview-tts - google/gemini-2.5-pro-preview-tts - google/gemma-3-1b-it @@ -191,12 +24,15 @@ providers: - google/gemini-pro-latest - google/gemini-2.5-flash-lite - google/gemini-2.5-flash-image - - google/gemini-2.5-flash-preview-09-2025 - google/gemini-2.5-flash-lite-preview-09-2025 - google/gemini-3-pro-preview - google/gemini-3-flash-preview + - google/gemini-3.1-pro-preview + - google/gemini-3.1-pro-preview-customtools + - google/gemini-3.1-flash-lite-preview - google/gemini-3-pro-image-preview - google/nano-banana-pro-preview + - google/gemini-3.1-flash-image-preview - google/gemini-robotics-er-1.5-preview - google/gemini-2.5-computer-use-preview-10-2025 - google/deep-research-pro-preview-12-2025 @@ -212,7 +48,37 @@ providers: - amazon/amazon.nova-premier-v1:0 - amazon/amazon.nova-lite-v1:0 - amazon/amazon.nova-micro-v1:0 + x-ai: + - x-ai/grok-3 + - x-ai/grok-3-mini + - x-ai/grok-4-0709 + - x-ai/grok-4-1-fast-non-reasoning + - x-ai/grok-4-1-fast-reasoning + - x-ai/grok-4-fast-non-reasoning + - x-ai/grok-4-fast-reasoning + - x-ai/grok-4.20-beta-0309-non-reasoning + - x-ai/grok-4.20-beta-0309-reasoning + - x-ai/grok-4.20-multi-agent-beta-0309 + - x-ai/grok-code-fast-1 + - x-ai/grok-imagine-image + - x-ai/grok-imagine-video + z-ai: + - z-ai/glm-4.5 + - z-ai/glm-4.5-air + - z-ai/glm-4.6 + - z-ai/glm-4.7 + - z-ai/glm-5 qwen: + - qwen/qwen3-asr-flash-2026-02-10 + - qwen/qwen3.5-flash-2026-02-23 + - qwen/qwen3.5-flash + - qwen/qwen3.5-122b-a10b + - qwen/qwen3.5-35b-a3b + - qwen/qwen3.5-27b + - qwen/qwen3-coder-next + - qwen/qwen3.5-397b-a17b + - qwen/qwen3.5-plus-2026-02-15 + - qwen/qwen3.5-plus - qwen/qwen3-vl-flash-2026-01-22 - qwen/qwen3-max-2026-01-23 - qwen/qwen-plus-character @@ -294,13 +160,161 @@ providers: - qwen/qwen-max - qwen/qwen-plus - qwen/qwen-turbo - z-ai: - - z-ai/glm-4.5 - - z-ai/glm-4.5-air - - z-ai/glm-4.6 - - z-ai/glm-4.7 - - z-ai/glm-5 + mistralai: + - mistralai/mistral-medium-2505 + - mistralai/mistral-medium-2508 + - mistralai/mistral-medium-latest + - mistralai/mistral-medium + - mistralai/mistral-vibe-cli-with-tools + - mistralai/open-mistral-nemo + - mistralai/open-mistral-nemo-2407 + - mistralai/mistral-tiny-2407 + - mistralai/mistral-tiny-latest + - mistralai/codestral-2508 + - mistralai/codestral-latest + - mistralai/devstral-2512 + - mistralai/mistral-vibe-cli-latest + - mistralai/devstral-medium-latest + - mistralai/devstral-latest + - mistralai/mistral-small-2506 + - mistralai/mistral-small-latest + - mistralai/labs-mistral-small-creative + - mistralai/magistral-medium-2509 + - mistralai/magistral-medium-latest + - mistralai/magistral-small-2509 + - mistralai/magistral-small-latest + - mistralai/mistral-large-2512 + - mistralai/mistral-large-latest + - mistralai/ministral-3b-2512 + - mistralai/ministral-3b-latest + - mistralai/ministral-8b-2512 + - mistralai/ministral-8b-latest + - mistralai/ministral-14b-2512 + - mistralai/ministral-14b-latest + - mistralai/mistral-large-2411 + - mistralai/pixtral-large-2411 + - mistralai/pixtral-large-latest + - mistralai/mistral-large-pixtral-2411 + - mistralai/devstral-small-2507 + - mistralai/devstral-medium-2507 + - mistralai/labs-devstral-small-2512 + - mistralai/devstral-small-latest + - mistralai/mistral-squarepoint-2602 + - mistralai/mistral-embed-2312 + - mistralai/mistral-embed + - mistralai/codestral-embed + - mistralai/codestral-embed-2505 + moonshotai: + - moonshotai/kimi-k2.5 + - moonshotai/kimi-k2-0905-preview + - moonshotai/moonshot-v1-32k + - moonshotai/moonshot-v1-128k + - moonshotai/kimi-k2-thinking-turbo + - moonshotai/moonshot-v1-8k-vision-preview + - moonshotai/kimi-k2-0711-preview + - moonshotai/moonshot-v1-auto + - moonshotai/kimi-k2-thinking + - moonshotai/moonshot-v1-128k-vision-preview + - moonshotai/kimi-k2-turbo-preview + - moonshotai/moonshot-v1-32k-vision-preview + - moonshotai/moonshot-v1-8k + anthropic: + - anthropic/claude-sonnet-4-6 + - anthropic/claude-opus-4-6 + - anthropic/claude-opus-4-5-20251101 + - anthropic/claude-opus-4-5 + - anthropic/claude-haiku-4-5-20251001 + - anthropic/claude-haiku-4-5 + - anthropic/claude-sonnet-4-5-20250929 + - anthropic/claude-sonnet-4-5 + - anthropic/claude-opus-4-1-20250805 + - anthropic/claude-opus-4-1 + - anthropic/claude-opus-4-20250514 + - anthropic/claude-opus-4 + - anthropic/claude-sonnet-4-20250514 + - anthropic/claude-sonnet-4 + - anthropic/claude-3-haiku-20240307 + - anthropic/claude-3-haiku + openai: + - openai/gpt-4-0613 + - openai/gpt-4 + - openai/gpt-3.5-turbo + - openai/gpt-5.4 + - openai/gpt-5.3-chat-latest + - openai/gpt-5.4-2026-03-05 + - openai/gpt-5.4-pro + - openai/gpt-5.4-pro-2026-03-05 + - openai/gpt-3.5-turbo-instruct + - openai/gpt-3.5-turbo-instruct-0914 + - openai/gpt-4-1106-preview + - openai/gpt-3.5-turbo-1106 + - openai/gpt-4-0125-preview + - openai/gpt-4-turbo-preview + - openai/gpt-3.5-turbo-0125 + - openai/gpt-4-turbo + - openai/gpt-4-turbo-2024-04-09 + - openai/gpt-4o + - openai/gpt-4o-2024-05-13 + - openai/gpt-4o-mini-2024-07-18 + - openai/gpt-4o-mini + - openai/gpt-4o-2024-08-06 + - openai/o1-2024-12-17 + - openai/o1 + - openai/computer-use-preview + - openai/o3-mini + - openai/o3-mini-2025-01-31 + - openai/gpt-4o-2024-11-20 + - openai/computer-use-preview-2025-03-11 + - openai/gpt-4o-mini-search-preview-2025-03-11 + - openai/gpt-4o-mini-search-preview + - openai/o1-pro-2025-03-19 + - openai/o1-pro + - openai/o3-2025-04-16 + - openai/o4-mini-2025-04-16 + - openai/o3 + - openai/o4-mini + - openai/gpt-4.1-2025-04-14 + - openai/gpt-4.1 + - openai/gpt-4.1-mini-2025-04-14 + - openai/gpt-4.1-mini + - openai/gpt-4.1-nano-2025-04-14 + - openai/gpt-4.1-nano + - openai/o3-pro + - openai/o3-pro-2025-06-10 + - openai/o4-mini-deep-research + - openai/o3-deep-research + - openai/o3-deep-research-2025-06-26 + - openai/o4-mini-deep-research-2025-06-26 + - openai/gpt-5-chat-latest + - openai/gpt-5-2025-08-07 + - openai/gpt-5 + - openai/gpt-5-mini-2025-08-07 + - openai/gpt-5-mini + - openai/gpt-5-nano-2025-08-07 + - openai/gpt-5-nano + - openai/gpt-5-codex + - openai/gpt-5-pro-2025-10-06 + - openai/gpt-5-pro + - openai/gpt-5-search-api + - openai/gpt-5-search-api-2025-10-14 + - openai/gpt-5.1-chat-latest + - openai/gpt-5.1-2025-11-13 + - openai/gpt-5.1 + - openai/gpt-5.1-codex + - openai/gpt-5.1-codex-mini + - openai/gpt-5.1-codex-max + - openai/gpt-5.2-2025-12-11 + - openai/gpt-5.2 + - openai/gpt-5.2-pro-2025-12-11 + - openai/gpt-5.2-pro + - openai/gpt-5.2-chat-latest + - openai/gpt-5.2-codex + - openai/gpt-5.3-codex + - openai/gpt-4o-search-preview + - openai/gpt-4o-search-preview-2025-03-11 + - openai/gpt-3.5-turbo-16k + - openai/ft:gpt-3.5-turbo-0613:katanemo::8CMZbm0P metadata: total_providers: 10 - total_models: 289 - last_updated: 2026-02-13T22:44:30.413065+00:00 + total_models: 303 + last_updated: 2026-03-15T16:47:22.207197+00:00 diff --git a/crates/hermesllm/src/lib.rs b/crates/hermesllm/src/lib.rs index 997fc72a..3b9611e0 100644 --- a/crates/hermesllm/src/lib.rs +++ b/crates/hermesllm/src/lib.rs @@ -35,7 +35,7 @@ mod tests { ProviderId::Mistral ); assert_eq!(ProviderId::try_from("groq").unwrap(), ProviderId::Groq); - assert_eq!(ProviderId::try_from("arch").unwrap(), ProviderId::Arch); + assert_eq!(ProviderId::try_from("plano").unwrap(), ProviderId::Plano); // Test aliases assert_eq!(ProviderId::try_from("google").unwrap(), ProviderId::Gemini); diff --git a/crates/hermesllm/src/providers/id.rs b/crates/hermesllm/src/providers/id.rs index 11008711..9f5f42c9 100644 --- a/crates/hermesllm/src/providers/id.rs +++ b/crates/hermesllm/src/providers/id.rs @@ -34,7 +34,7 @@ pub enum ProviderId { Gemini, Anthropic, GitHub, - Arch, + Plano, AzureOpenAI, XAI, TogetherAI, @@ -58,7 +58,7 @@ impl TryFrom<&str> for ProviderId { "google" => Ok(ProviderId::Gemini), // alias "anthropic" => Ok(ProviderId::Anthropic), "github" => Ok(ProviderId::GitHub), - "arch" => Ok(ProviderId::Arch), + "plano" => Ok(ProviderId::Plano), "azure_openai" => Ok(ProviderId::AzureOpenAI), "xai" => Ok(ProviderId::XAI), "together_ai" => Ok(ProviderId::TogetherAI), @@ -135,7 +135,7 @@ impl ProviderId { | ProviderId::Groq | ProviderId::Mistral | ProviderId::Deepseek - | ProviderId::Arch + | ProviderId::Plano | ProviderId::Gemini | ProviderId::GitHub | ProviderId::AzureOpenAI @@ -153,7 +153,7 @@ impl ProviderId { | ProviderId::Groq | ProviderId::Mistral | ProviderId::Deepseek - | ProviderId::Arch + | ProviderId::Plano | ProviderId::Gemini | ProviderId::GitHub | ProviderId::AzureOpenAI @@ -219,7 +219,7 @@ impl Display for ProviderId { ProviderId::Gemini => write!(f, "Gemini"), ProviderId::Anthropic => write!(f, "Anthropic"), ProviderId::GitHub => write!(f, "GitHub"), - ProviderId::Arch => write!(f, "Arch"), + ProviderId::Plano => write!(f, "Plano"), ProviderId::AzureOpenAI => write!(f, "azure_openai"), ProviderId::XAI => write!(f, "xai"), ProviderId::TogetherAI => write!(f, "together_ai"), diff --git a/crates/llm_gateway/src/stream_context.rs b/crates/llm_gateway/src/stream_context.rs index 7a353bcb..f62631fa 100644 --- a/crates/llm_gateway/src/stream_context.rs +++ b/crates/llm_gateway/src/stream_context.rs @@ -873,7 +873,7 @@ impl HttpContext for StreamContext { // ensure that the provider has an endpoint if the access key is missing else return a bad request if self.llm_provider.as_ref().unwrap().endpoint.is_none() && self.llm_provider.as_ref().unwrap().provider_interface - != LlmProviderType::Arch + != LlmProviderType::Plano { self.send_server_error(error, Some(StatusCode::BAD_REQUEST)); } diff --git a/demos/advanced/model_choice_test_harness/pyproject.toml b/demos/advanced/model_choice_test_harness/pyproject.toml index fc930434..b34de224 100644 --- a/demos/advanced/model_choice_test_harness/pyproject.toml +++ b/demos/advanced/model_choice_test_harness/pyproject.toml @@ -5,7 +5,7 @@ description = "Benchmarking model alias routing with Plano." authors = [{name = "Your Name", email = "your@email.com"}] license = {text = "Apache 2.0"} readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.10,<3.14" dependencies = [ "pydantic>=2.0", "openai>=1.0", diff --git a/demos/advanced/model_choice_test_harness/uv.lock b/demos/advanced/model_choice_test_harness/uv.lock index 9563914d..8d671845 100644 --- a/demos/advanced/model_choice_test_harness/uv.lock +++ b/demos/advanced/model_choice_test_harness/uv.lock @@ -1,6 +1,6 @@ version = 1 revision = 3 -requires-python = ">=3.10" +requires-python = ">=3.10, <3.14" [[package]] name = "annotated-types" @@ -113,22 +113,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/89/66/c7a9e1b7429be72123441bfdbaf2bc13faab3f90b933f664db506dea5915/charset_normalizer-3.4.4-cp313-cp313-win32.whl", hash = "sha256:9b35f4c90079ff2e2edc5b26c0c77925e5d2d255c42c74fdb70fb49b172726ac", size = 99404, upload-time = "2025-10-14T04:41:29.95Z" }, { url = "https://files.pythonhosted.org/packages/c4/26/b9924fa27db384bdcd97ab83b4f0a8058d96ad9626ead570674d5e737d90/charset_normalizer-3.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:b435cba5f4f750aa6c0a0d92c541fb79f69a387c91e61f1795227e4ed9cece14", size = 107092, upload-time = "2025-10-14T04:41:31.188Z" }, { url = "https://files.pythonhosted.org/packages/af/8f/3ed4bfa0c0c72a7ca17f0380cd9e4dd842b09f664e780c13cff1dcf2ef1b/charset_normalizer-3.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:542d2cee80be6f80247095cc36c418f7bddd14f4a6de45af91dfad36d817bba2", size = 100408, upload-time = "2025-10-14T04:41:32.624Z" }, - 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To self-host the orchestrator model locally using vLLM on a server with an NVIDIA GPU: + +1. Install vLLM and download the model: +```bash +pip install vllm +``` + +2. Start the vLLM server with the 4B model: +```bash +vllm serve katanemo/Plano-Orchestrator-4B \ + --host 0.0.0.0 \ + --port 8000 \ + --tensor-parallel-size 1 \ + --gpu-memory-utilization 0.3 \ + --tokenizer katanemo/Plano-Orchestrator-4B \ + --chat-template chat_template.jinja \ + --served-model-name katanemo/Plano-Orchestrator-4B \ + --enable-prefix-caching +``` + +3. Start the demo with the local orchestrator config: +```bash +./run_demo.sh --local-orchestrator +``` + +4. Test with curl: +```bash +curl -X POST http://localhost:8001/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{"model": "gpt-5.2", "messages": [{"role": "user", "content": "What is the weather in Istanbul?"}]}' +``` + +You should see Plano use your local orchestrator to route the request to the weather agent. + ## Observability This demo includes full OpenTelemetry (OTel) compatible distributed tracing to monitor and debug agent interactions: diff --git a/demos/agent_orchestration/travel_agents/config_local_orchestrator.yaml b/demos/agent_orchestration/travel_agents/config_local_orchestrator.yaml new file mode 100644 index 00000000..1d3a0be8 --- /dev/null +++ b/demos/agent_orchestration/travel_agents/config_local_orchestrator.yaml @@ -0,0 +1,66 @@ +version: v0.3.0 + +overrides: + agent_orchestration_model: plano/katanemo/Plano-Orchestrator-4B + +agents: + - id: weather_agent + url: http://localhost:10510 + - id: flight_agent + url: http://localhost:10520 + +model_providers: + - model: plano/katanemo/Plano-Orchestrator-4B + base_url: http://localhost:8000 + + - model: openai/gpt-5.2 + access_key: $OPENAI_API_KEY + default: true + - model: openai/gpt-4o-mini + access_key: $OPENAI_API_KEY # smaller, faster, cheaper model for extracting entities like location + +listeners: + - type: agent + name: travel_booking_service + port: 8001 + router: plano_orchestrator_v1 + agents: + - id: weather_agent + description: | + + WeatherAgent is a specialized AI assistant for real-time weather information and forecasts. It provides accurate weather data for any city worldwide using the Open-Meteo API, helping travelers plan their trips with up-to-date weather conditions. + + Capabilities: + * Get real-time weather conditions and multi-day forecasts for any city worldwide using Open-Meteo API (free, no API key needed) + * Provides current temperature + * Provides multi-day forecasts + * Provides weather conditions + * Provides sunrise/sunset times + * Provides detailed weather information + * Understands conversation context to resolve location references from previous messages + * Handles weather-related questions including "What's the weather in [city]?", "What's the forecast for [city]?", "How's the weather in [city]?" + * When queries include both weather and other travel questions (e.g., flights, currency), this agent answers ONLY the weather part + + - id: flight_agent + description: | + + FlightAgent is an AI-powered tool specialized in providing live flight information between airports. It leverages the FlightAware AeroAPI to deliver real-time flight status, gate information, and delay updates. + + Capabilities: + * Get live flight information between airports using FlightAware AeroAPI + * Shows real-time flight status + * Shows scheduled/estimated/actual departure and arrival times + * Shows gate and terminal information + * Shows delays + * Shows aircraft type + * Shows flight status + * Automatically resolves city names to airport codes (IATA/ICAO) + * Understands conversation context to infer origin/destination from follow-up questions + * Handles flight-related questions including "What flights go from [city] to [city]?", "Do flights go to [city]?", "Are there direct flights from [city]?" + * When queries include both flight and other travel questions (e.g., weather, currency), this agent answers ONLY the flight part + +tracing: + random_sampling: 100 + span_attributes: + header_prefixes: + - x-acme- diff --git a/demos/agent_orchestration/travel_agents/pyproject.toml b/demos/agent_orchestration/travel_agents/pyproject.toml index 7f37b26b..9e7fe76d 100644 --- a/demos/agent_orchestration/travel_agents/pyproject.toml +++ b/demos/agent_orchestration/travel_agents/pyproject.toml @@ -3,7 +3,7 @@ name = "travel-agents" version = "0.1.0" description = "Travel Booking Agents - Weather, Flight, and Currency" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.10,<3.14" dependencies = [ "click>=8.2.1", "pydantic>=2.11.7", diff --git a/demos/agent_orchestration/travel_agents/run_demo.sh b/demos/agent_orchestration/travel_agents/run_demo.sh index 643a0aa2..35166b85 100755 --- a/demos/agent_orchestration/travel_agents/run_demo.sh +++ b/demos/agent_orchestration/travel_agents/run_demo.sh @@ -31,8 +31,13 @@ start_demo() { fi # Step 4: Start Plano - echo "Starting Plano with config.yaml..." - planoai up config.yaml + PLANO_CONFIG="config.yaml" + if [ "$1" == "--local-orchestrator" ]; then + PLANO_CONFIG="config_local_orchestrator.yaml" + echo "Using local orchestrator config..." + fi + echo "Starting Plano with $PLANO_CONFIG..." + planoai up "$PLANO_CONFIG" # Step 5: Start agents natively echo "Starting agents..." diff --git a/demos/agent_orchestration/travel_agents/uv.lock b/demos/agent_orchestration/travel_agents/uv.lock index 985c19e7..ef07473b 100644 --- a/demos/agent_orchestration/travel_agents/uv.lock +++ b/demos/agent_orchestration/travel_agents/uv.lock @@ -1,6 +1,6 @@ version = 1 revision = 3 -requires-python = ">=3.10" +requires-python = ">=3.10, <3.14" [[package]] name = "annotated-doc" @@ -220,31 +220,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/05/17/710bf8472d1dff0d3caf4ced6031060091c1320f84ee7d5dcbed1f352417/jiter-0.12.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:351d54f2b09a41600ffea43d081522d792e81dcfb915f6d2d242744c1cc48beb", size = 361272, upload-time = "2025-11-09T20:48:06.951Z" }, { url = "https://files.pythonhosted.org/packages/fb/f1/1dcc4618b59761fef92d10bcbb0b038b5160be653b003651566a185f1a5c/jiter-0.12.0-cp313-cp313t-win_amd64.whl", hash = "sha256:2a5e90604620f94bf62264e7c2c038704d38217b7465b863896c6d7c902b06c7", size = 204604, upload-time = "2025-11-09T20:48:08.328Z" }, { url = "https://files.pythonhosted.org/packages/d9/32/63cb1d9f1c5c6632a783c0052cde9ef7ba82688f7065e2f0d5f10a7e3edb/jiter-0.12.0-cp313-cp313t-win_arm64.whl", hash = "sha256:88ef757017e78d2860f96250f9393b7b577b06a956ad102c29c8237554380db3", size = 185628, upload-time = "2025-11-09T20:48:09.572Z" }, - 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Validates queries are within TechCorp's domain -2. **Query Rewriter** (MCP filter) - Rewrites user queries for better retrieval -3. **Context Builder** (MCP filter) - Retrieves relevant context from knowledge base +1. **Input Guards** (HTTP filter) - Validates queries are within TechCorp's domain +2. **Query Rewriter** (HTTP filter) - Rewrites user queries for better retrieval +3. **Context Builder** (HTTP filter) - Retrieves relevant context from knowledge base 4. **RAG Agent** (REST) - Generates final responses based on augmented context ## Components -### Input Guards Filter (MCP) +### Input Guards Filter (HTTP) - **Port**: 10500 -- **Tool**: `input_guards` - Validates queries are within TechCorp's domain - Rejects queries about other companies or unrelated topics -### Query Rewrit3r Filter (MCP) +### Query Rewriter Filter (HTTP) - **Port**: 10501 -- **Tool**: `query_rewriter` - Improves queries using LLM before retrieval -### Context Builder Filter (MCP) +### Context Builder Filter (HTTP) - **Port**: 10502 -- **Tool**: `context_builder` - Augments queries with relevant passages from knowledge base ### RAG Agent (REST/OpenAI) @@ -93,7 +90,7 @@ filters: ## How It Works 1. User sends request to plano listener on port 8001 -2. Request passes through MCP filter chain: +2. Request passes through HTTP filter chain: - **Input Guards** validates the query is within TechCorp's domain - **Query Rewriter** rewrites the query for better retrieval - **Context Builder** augments query with relevant knowledge base passages @@ -102,10 +99,7 @@ filters: ## Additional Configuration -See `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 +See `config.yaml` for the complete filter chain setup. The HTTP filters use `type: http` to indicate they communicate over plain HTTP POST rather than MCP. See `sample_queries.md` for example queries to test the RAG system. @@ -123,7 +117,7 @@ curl -X POST http://localhost:8001/v1/chat/completions \ ] }' ``` -- `LLM_GATEWAY_ENDPOINT` - lpano endpoint (default: `http://localhost:12000/v1`) +- `LLM_GATEWAY_ENDPOINT` - Plano endpoint (default: `http://localhost:12000/v1`) - `OPENAI_API_KEY` - OpenAI API key for model providers ## Additional Resources diff --git a/demos/filter_chains/http_filter/config.yaml b/demos/filter_chains/http_filter/config.yaml index 014a141a..5dec44c6 100644 --- a/demos/filter_chains/http_filter/config.yaml +++ b/demos/filter_chains/http_filter/config.yaml @@ -8,15 +8,9 @@ filters: - id: input_guards url: http://localhost:10500 type: http - # type: mcp (default) - # transport: streamable-http (default) - # tool: input_guards (default - same as filter id) - id: query_rewriter url: http://localhost:10501 type: http - # type: mcp (default) - # transport: streamable-http (default) - # tool: query_rewriter (default - same as filter id) - id: context_builder url: http://localhost:10502 type: http @@ -42,7 +36,7 @@ listeners: agents: - id: rag_agent description: virtual assistant for retrieval augmented generation tasks - filter_chain: + input_filters: - input_guards - query_rewriter - context_builder diff --git a/demos/filter_chains/http_filter/pyproject.toml b/demos/filter_chains/http_filter/pyproject.toml index 7cd9411b..47bad6aa 100644 --- a/demos/filter_chains/http_filter/pyproject.toml +++ b/demos/filter_chains/http_filter/pyproject.toml @@ -3,7 +3,7 @@ name = "rag_agent" version = "0.1.0" description = "RAG Agent" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.10,<3.14" dependencies = [ "click>=8.2.1", "mcp>=1.13.1", diff --git a/demos/filter_chains/http_filter/src/rag_agent/context_builder.py b/demos/filter_chains/http_filter/src/rag_agent/context_builder.py index 5da1c43d..be67eb10 100644 --- a/demos/filter_chains/http_filter/src/rag_agent/context_builder.py +++ b/demos/filter_chains/http_filter/src/rag_agent/context_builder.py @@ -195,11 +195,11 @@ async def augment_query_with_context( load_knowledge_base() -@app.post("/") -async def context_builder( - messages: List[ChatMessage], request: Request -) -> List[ChatMessage]: +@app.post("/{path:path}") +async def context_builder(path: str, request: Request) -> dict: """MCP tool that augments user queries with relevant context from the knowledge base.""" + body = await request.json() + messages = [ChatMessage(**m) for m in body.get("messages", [])] logger.info(f"Received chat completion request with {len(messages)} messages") # Get traceparent header from MCP request @@ -219,8 +219,12 @@ async def context_builder( messages, traceparent_header, request_id ) - # Return as dict to minimize text serialization - return [{"role": msg.role, "content": msg.content} for msg in updated_messages] + return { + **body, + "messages": [ + {"role": msg.role, "content": msg.content} for msg in updated_messages + ], + } # Register MCP tool only if mcp is available diff --git a/demos/filter_chains/http_filter/src/rag_agent/input_guards.py b/demos/filter_chains/http_filter/src/rag_agent/input_guards.py index 3b2414ad..98d481d6 100644 --- a/demos/filter_chains/http_filter/src/rag_agent/input_guards.py +++ b/demos/filter_chains/http_filter/src/rag_agent/input_guards.py @@ -126,14 +126,14 @@ Respond in JSON format: # @mcp.tool -@app.post("/") -async def input_guards( - messages: List[ChatMessage], request: Request -) -> List[ChatMessage]: +@app.post("/{path:path}") +async def input_guards(path: str, request: Request) -> dict: """Input guard that validates queries are within TechCorp's domain. If the query is out of scope, replaces the user message with a rejection notice. """ + body = await request.json() + messages = [ChatMessage(**m) for m in body.get("messages", [])] logger.info(f"Received request with {len(messages)} messages") # Get traceparent header from HTTP request using FastMCP's dependency function @@ -164,7 +164,7 @@ async def input_guards( ) logger.info("Query validation passed - forwarding to next filter") - return messages + return body @app.get("/health") diff --git a/demos/filter_chains/http_filter/src/rag_agent/query_rewriter.py b/demos/filter_chains/http_filter/src/rag_agent/query_rewriter.py index 3e05836c..43824473 100644 --- a/demos/filter_chains/http_filter/src/rag_agent/query_rewriter.py +++ b/demos/filter_chains/http_filter/src/rag_agent/query_rewriter.py @@ -81,11 +81,11 @@ Return only the rewritten query, nothing else.""" return "" -@app.post("/") -async def query_rewriter_http( - messages: List[ChatMessage], request: Request -) -> List[ChatMessage]: +@app.post("/{path:path}") +async def query_rewriter_http(path: str, request: Request) -> dict: """HTTP filter endpoint used by Plano (type: http).""" + body = await request.json() + messages = [ChatMessage(**m) for m in body.get("messages", [])] logger.info(f"Received request with {len(messages)} messages") traceparent_header = request.headers.get("traceparent") @@ -99,25 +99,20 @@ async def query_rewriter_http( rewritten_query = await rewrite_query_with_plano( messages, traceparent_header, request_id ) - 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**Port**: 10501 - **Tool**: `query_rewriter` - Improves queries using LLM before retrieval @@ -123,7 +123,7 @@ curl -X POST http://localhost:8001/v1/chat/completions \ ] }' ``` -- `LLM_GATEWAY_ENDPOINT` - lpano endpoint (default: `http://localhost:12000/v1`) +- `LLM_GATEWAY_ENDPOINT` - Plano endpoint (default: `http://localhost:12000/v1`) - `OPENAI_API_KEY` - OpenAI API key for model providers ## Additional Resources diff --git a/demos/filter_chains/mcp_filter/config.yaml b/demos/filter_chains/mcp_filter/config.yaml index e07a49dc..1270a2f3 100644 --- a/demos/filter_chains/mcp_filter/config.yaml +++ b/demos/filter_chains/mcp_filter/config.yaml @@ -39,7 +39,7 @@ listeners: agents: - id: rag_agent description: virtual assistant for retrieval augmented generation tasks - filter_chain: + input_filters: - input_guards - query_rewriter - context_builder diff --git a/demos/filter_chains/mcp_filter/pyproject.toml b/demos/filter_chains/mcp_filter/pyproject.toml index 7cd9411b..47bad6aa 100644 --- a/demos/filter_chains/mcp_filter/pyproject.toml +++ b/demos/filter_chains/mcp_filter/pyproject.toml @@ -3,7 +3,7 @@ name = "rag_agent" version = "0.1.0" description = "RAG Agent" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.10,<3.14" dependencies = [ "click>=8.2.1", "mcp>=1.13.1", diff --git a/demos/filter_chains/mcp_filter/src/rag_agent/context_builder.py b/demos/filter_chains/mcp_filter/src/rag_agent/context_builder.py index e50bb76c..f46d3f19 100644 --- a/demos/filter_chains/mcp_filter/src/rag_agent/context_builder.py +++ b/demos/filter_chains/mcp_filter/src/rag_agent/context_builder.py @@ -195,9 +195,14 @@ async def augment_query_with_context( load_knowledge_base() -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") +async def context_builder(body: dict, path: str) -> dict: + """MCP tool that augments user queries with relevant context from the knowledge base. + + Receives the full request body dict and the API path hint (e.g. /v1/chat/completions). + Returns the body with the last user message augmented with retrieved context. + """ + messages = [ChatMessage(**m) for m in body.get("messages", [])] + logger.info(f"Received request with {len(messages)} messages at path {path}") # Get traceparent header from MCP request headers = get_http_headers() @@ -215,8 +220,12 @@ async def context_builder(messages: List[ChatMessage]) -> List[ChatMessage]: messages, traceparent_header, request_id ) - # Return as dict to minimize text serialization - return [{"role": msg.role, "content": msg.content} for msg in updated_messages] + return { + **body, + "messages": [ + {"role": msg.role, "content": msg.content} for msg in updated_messages + ], + } # Register MCP tool only if mcp is available diff --git a/demos/filter_chains/mcp_filter/src/rag_agent/input_guards.py b/demos/filter_chains/mcp_filter/src/rag_agent/input_guards.py index 607ff035..7ae4eb86 100644 --- a/demos/filter_chains/mcp_filter/src/rag_agent/input_guards.py +++ b/demos/filter_chains/mcp_filter/src/rag_agent/input_guards.py @@ -1,15 +1,11 @@ -import asyncio import json -import time from typing import List, Optional, Dict, Any -import uuid -from fastapi import FastAPI, Depends, Request from fastmcp.exceptions import ToolError from openai import AsyncOpenAI import os import logging -from .api import ChatCompletionRequest, ChatCompletionResponse, ChatMessage +from .api import ChatMessage from . import mcp from fastmcp.server.dependencies import get_http_headers @@ -30,8 +26,6 @@ plano_client = AsyncOpenAI( api_key="EMPTY", # Plano doesn't require a real API key ) -app = FastAPI() - async def validate_query_scope( messages: List[ChatMessage], @@ -127,12 +121,14 @@ Respond in JSON format: @mcp.tool -async def input_guards(messages: List[ChatMessage]) -> List[ChatMessage]: +async def input_guards(body: dict, path: str) -> dict: """Input guard that validates queries are within TechCorp's domain. - If the query is out of scope, replaces the user message with a rejection notice. + Receives the full request body dict and the API path hint (e.g. /v1/chat/completions). + If the query is out of scope, raises a ToolError to block the request. """ - logger.info(f"Received request with {len(messages)} messages") + messages = [ChatMessage(**m) for m in body.get("messages", [])] + logger.info(f"Received request with {len(messages)} messages at path {path}") # Get traceparent header from HTTP request using FastMCP's dependency function headers = get_http_headers() @@ -153,9 +149,8 @@ async def input_guards(messages: List[ChatMessage]) -> List[ChatMessage]: reason = validation_result.get("reason", "Query is outside TechCorp's domain") logger.warning(f"Query rejected: {reason}") - # Throw ToolError error_message = f"I apologize, but I can only assist with questions related to TechCorp and its services. Your query appears to be outside this scope. {reason}\n\nPlease ask me about TechCorp's products, services, pricing, SLAs, or technical support." raise ToolError(error_message) logger.info("Query validation passed - forwarding to next filter") - return messages + return body diff --git a/demos/filter_chains/mcp_filter/src/rag_agent/query_rewriter.py b/demos/filter_chains/mcp_filter/src/rag_agent/query_rewriter.py index 5939170e..dedf5359 100644 --- a/demos/filter_chains/mcp_filter/src/rag_agent/query_rewriter.py +++ b/demos/filter_chains/mcp_filter/src/rag_agent/query_rewriter.py @@ -1,14 +1,9 @@ -import asyncio -import json -import time -from typing import List, Optional, Dict, Any -import uuid -from fastapi import FastAPI, Depends, Request +from typing import List, Optional from openai import AsyncOpenAI import os import logging -from .api import ChatCompletionRequest, ChatCompletionResponse, ChatMessage +from .api import ChatMessage from . import mcp from fastmcp.server.dependencies import get_http_headers @@ -29,8 +24,6 @@ plano_client = AsyncOpenAI( api_key="EMPTY", # Plano doesn't require a real API key ) -app = FastAPI() - async def rewrite_query_with_plano( messages: List[ChatMessage], @@ -87,12 +80,14 @@ async def rewrite_query_with_plano( return "" -async def query_rewriter(messages: List[ChatMessage]) -> List[ChatMessage]: - """Chat completions endpoint that rewrites the last user query using Plano. +async def query_rewriter(body: dict, path: str) -> dict: + """Rewrites the last user query in the request body using Plano. - Returns a dict with a 'messages' key containing the updated message list. + Receives the full request body dict and the API path hint (e.g. /v1/chat/completions). + Returns the body with the last user message rewritten for better retrieval. """ - logger.info(f"Received chat completion request with {len(messages)} messages") + messages = [ChatMessage(**m) for m in body.get("messages", [])] + logger.info(f"Received request with {len(messages)} messages at path {path}") # Get traceparent header from HTTP request using FastMCP's dependency function headers = get_http_headers() @@ -109,57 +104,20 @@ async def query_rewriter(messages: List[ChatMessage]) -> List[ChatMessage]: messages, traceparent_header, request_id ) - # Create updated messages with the rewritten query - updated_messages = messages.copy() - # Find and update the last user message with the rewritten query + updated_messages = [m.model_dump() for m in messages] 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) + if updated_messages[i]["role"] == "user": logger.info( - f"Updated user query from '{original_query}' to '{rewritten_query}'" + f"Updated user query from '{updated_messages[i]['content']}' to '{rewritten_query}'" ) + updated_messages[i]["content"] = rewritten_query break - # Return as dict to minimize text serialization - return [{"role": msg.role, "content": msg.content} for msg in updated_messages] + logger.info("Returning rewritten chat completion response") + return {**body, "messages": updated_messages} # Register MCP tool only if mcp is available if mcp is not None: mcp.tool()(query_rewriter) - - -@app.post("/") -async def chat_completions_endpoint( - request_messages: List[ChatMessage], request: Request -) -> List[ChatMessage]: - """FastAPI endpoint for chat completions with query rewriting.""" - logger.info( - f"Received /v1/chat/completions request with {len(request_messages)} messages" - ) - - # Extract traceparent header - traceparent_header = request.headers.get("traceparent") - if traceparent_header: - logger.info(f"Received traceparent header: 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b/demos/filter_chains/model_listener_filter/Dockerfile @@ -0,0 +1,11 @@ +FROM python:3.14-slim + +WORKDIR /app + +RUN pip install --no-cache-dir fastapi uvicorn pydantic + +COPY content_guard.py . + +EXPOSE 10500 + +CMD ["uvicorn", "content_guard:app", "--host", "0.0.0.0", "--port", "10500"] diff --git a/demos/filter_chains/model_listener_filter/README.md b/demos/filter_chains/model_listener_filter/README.md new file mode 100644 index 00000000..fb49ee1e --- /dev/null +++ b/demos/filter_chains/model_listener_filter/README.md @@ -0,0 +1,63 @@ +# Model Listener Filter Chain Demo + +Run content-safety filters on direct LLM requests — no agent layer required. + +This demo uses the `input_filters` feature on a **model-type listener** to intercept +requests and block unsafe content before they reach the LLM provider. Works with all +request types: `/v1/chat/completions`, `/v1/responses`, and Anthropic `/v1/messages`. + +The filter receives the **full raw request body** and returns it unchanged (or raises 400 +to block). No message extraction — the complete JSON payload flows through as-is. + +## Architecture + +``` +Client ──► Plano (model listener :12000) + │ + ├─ input_filters: content_guard ──► Block / Allow + │ + └─ model_provider: openai/gpt-4o-mini +``` + +## Quick Start + +```bash +# 1. Export your API key +export OPENAI_API_KEY=sk-... + +# 2. Start services +docker compose up --build + +# 3. Run tests (in another terminal) +bash test.sh +``` + +## Try It + +**Allowed request:** + +```bash +curl http://localhost:12000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "What is 2+2?"}], + "stream": false + }' +``` + +**Blocked request:** + +```bash +curl http://localhost:12000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "How to hack into a system"}], + "stream": false + }' +``` + +## Tracing + +Open [Jaeger UI](http://localhost:16686) to see distributed traces for both allowed and blocked requests. diff --git a/demos/filter_chains/model_listener_filter/config.yaml b/demos/filter_chains/model_listener_filter/config.yaml new file mode 100644 index 00000000..2eb1d0f2 --- /dev/null +++ b/demos/filter_chains/model_listener_filter/config.yaml @@ -0,0 +1,21 @@ +version: v0.3.0 + +filters: + - id: content_guard + url: http://content-guard:10500 + type: http + +model_providers: + - model: openai/gpt-4o-mini + access_key: $OPENAI_API_KEY + default: true + +listeners: + - type: model + name: llm_gateway + port: 12000 + input_filters: + - content_guard + +tracing: + random_sampling: 100 diff --git a/demos/filter_chains/model_listener_filter/content_guard.py b/demos/filter_chains/model_listener_filter/content_guard.py new file mode 100644 index 00000000..2bd35b63 --- /dev/null +++ b/demos/filter_chains/model_listener_filter/content_guard.py @@ -0,0 +1,120 @@ +""" +Content guard filter — keyword-based content safety for model listeners. + +A minimal HTTP filter that blocks requests containing unsafe keywords. +No LLM calls required — keeps the demo self-contained and fast. + +Receives the full raw request body (any API format: /v1/chat/completions, +/v1/responses, /v1/messages) and returns it unchanged or raises 400. +""" + +import logging +from typing import Any + +from fastapi import FastAPI, Request, HTTPException + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - [CONTENT_GUARD] - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + +app = FastAPI(title="Content Guard", version="1.0.0") + +BLOCKED_KEYWORDS = [ + "hack", + "exploit", + "attack", + "malware", + "phishing", + "ransomware", + "ddos", + "injection", + "brute force", + "keylogger", + "bypass security", + "steal credentials", + "social engineering", +] + + +def check_content(text: str) -> str | None: + """Return the matched keyword if blocked, else None.""" + lower = text.lower() + for kw in BLOCKED_KEYWORDS: + if kw in lower: + return kw + return None + + +def extract_last_user_text(body: dict[str, Any]) -> str | None: + """Extract the most recent user message text from any supported request format.""" + messages = body.get("messages", []) + # Anthropic /v1/messages and OpenAI /v1/chat/completions both use "messages" + for msg in reversed(messages): + if msg.get("role") == "user": + content = msg.get("content", "") + if isinstance(content, str): + return content + if isinstance(content, list): + # Multimodal content parts + return " ".join( + part.get("text", "") + for part in content + if isinstance(part, dict) and part.get("type") == "text" + ) + + # OpenAI /v1/responses uses "input" instead of "messages" + input_val = body.get("input") + if isinstance(input_val, str): + return input_val + if isinstance(input_val, list): + for item in reversed(input_val): + if isinstance(item, dict) and item.get("role") == "user": + content = item.get("content", "") + if isinstance(content, str): + return content + + return None + + +@app.post("/{path:path}") +async def content_guard(path: str, request: Request) -> dict[str, Any]: + """Block requests containing unsafe keywords. Returns the full request body unchanged. + + The endpoint path encodes the API format: + /v1/chat/completions — check body["messages"] + /v1/responses — check body["input"] + /v1/messages — check body["messages"] (Anthropic format) + """ + endpoint = f"/{path}" + body = await request.json() + + # /v1/responses uses "input" instead of "messages" + if endpoint == "/v1/responses": + input_val = body.get("input", "") + last_user_msg = input_val if isinstance(input_val, str) else None + else: + last_user_msg = extract_last_user_text(body) + + if last_user_msg is None: + return body + + matched = check_content(last_user_msg) + if matched: + logger.warning(f"Blocked request — matched keyword: '{matched}'") + raise HTTPException( + status_code=400, + detail={ + "error": "content_blocked", + "message": f"Request blocked by content safety filter (matched: '{matched}').", + }, + ) + + logger.info("Content check passed — forwarding request") + return body + + +@app.get("/health") +async def health(): + return {"status": "healthy"} diff --git a/demos/filter_chains/model_listener_filter/docker-compose.yaml b/demos/filter_chains/model_listener_filter/docker-compose.yaml new file mode 100644 index 00000000..48f32c3f --- /dev/null +++ b/demos/filter_chains/model_listener_filter/docker-compose.yaml @@ -0,0 +1,25 @@ +services: + content-guard: + build: + context: . + dockerfile: Dockerfile + ports: + - "10500:10500" + plano: + build: + context: ../../../ + dockerfile: Dockerfile + ports: + - "12000:12000" + environment: + - OPENAI_API_KEY=${OPENAI_API_KEY:?OPENAI_API_KEY environment variable is required but not set} + volumes: + - ./config.yaml:/app/plano_config.yaml + - /etc/ssl/cert.pem:/etc/ssl/cert.pem + jaeger: + build: + context: ../../shared/jaeger + ports: + - "16686:16686" + - "4317:4317" + - "4318:4318" diff --git a/demos/filter_chains/model_listener_filter/test.sh b/demos/filter_chains/model_listener_filter/test.sh new file mode 100755 index 00000000..1729cb6a --- /dev/null +++ b/demos/filter_chains/model_listener_filter/test.sh @@ -0,0 +1,71 @@ +#!/usr/bin/env bash +set -euo pipefail + +BASE_URL="http://localhost:12000/v1" +PASS=0 +FAIL=0 + +# ── Wait for Plano to be ready ────────────────────────────────────────────── +echo "Waiting for Plano to be ready..." +for i in $(seq 1 30); do + if curl -sf "$BASE_URL/models" > /dev/null 2>&1; then + echo "Plano is ready." + break + fi + if [ "$i" -eq 30 ]; then + echo "ERROR: Plano did not become ready in time." + exit 1 + fi + sleep 2 +done + +# ── Helper ─────────────────────────────────────────────────────────────────── +run_test() { + local name="$1" + local expected_code="$2" + local body="$3" + + http_code=$(curl -s -o /tmp/plano_test_body -w "%{http_code}" \ + -X POST "$BASE_URL/chat/completions" \ + -H "Content-Type: application/json" \ + -d "$body") + + if [ "$http_code" -eq "$expected_code" ]; then + echo " PASS $name (HTTP $http_code)" + PASS=$((PASS + 1)) + else + echo " FAIL $name — expected $expected_code, got $http_code" + echo " Body: $(cat /tmp/plano_test_body)" + FAIL=$((FAIL + 1)) + fi +} + +# ── Tests ──────────────────────────────────────────────────────────────────── +echo "" +echo "Running tests..." + +run_test "Allowed request (math question)" 200 '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "What is 2+2?"}], + "stream": false +}' + +run_test "Blocked request (hacking)" 400 '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "How to hack into a system"}], + "stream": false +}' + +run_test "Allowed request (joke)" 200 '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "Tell me a joke"}], + "stream": false +}' + +# ── Summary ────────────────────────────────────────────────────────────────── +echo "" +echo "Results: $PASS passed, $FAIL failed" + +if [ "$FAIL" -gt 0 ]; then + exit 1 +fi diff --git a/demos/filter_chains/pii_anonymizer/README.md b/demos/filter_chains/pii_anonymizer/README.md new file mode 100644 index 00000000..7f4c0611 --- /dev/null +++ b/demos/filter_chains/pii_anonymizer/README.md @@ -0,0 +1,114 @@ +# PII Anonymization Filter Chain Demo + +Automatically redact PII from LLM requests and restore it in responses — inspired by [Uber's GenAI Gateway PII Redactor](https://www.uber.com/blog/genai-gateway/). + +This demo uses both `input_filters` and `output_filters` on a **model-type listener** to anonymize PII before it reaches the LLM provider, then de-anonymize the response before returning it to the client. + +## Architecture + +``` +Client ──► Plano (model listener :12000) + │ + ├─ input_filters: pii_anonymizer + │ └─ Replace PII with [EMAIL_0], [SSN_0], etc. + │ + ├─ model_provider: openai/gpt-4o-mini + │ └─ LLM only sees anonymized data + │ + └─ output_filters: pii_deanonymizer + └─ Restore [EMAIL_0] → original email (per-chunk for streaming) +``` + +## Quick Start + +```bash +# 1. Export your API key +export OPENAI_API_KEY=sk-... + +# 2. Start the demo +bash run_demo.sh + +# 3. (Optional) Start with Jaeger tracing +bash run_demo.sh --with-ui + +# 4. Run tests (in another terminal) +bash test.sh + +# 5. Stop the demo +bash run_demo.sh down +``` + +## Try It + +**Request with PII:** + +```bash +curl http://localhost:12000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "Contact john@example.com or call 555-123-4567"}], + "stream": false + }' +``` + +**Streaming with PII:** + +```bash +curl --no-buffer http://localhost:12000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "My SSN is 123-45-6789"}], + "stream": true + }' +``` + +## Verify Anonymization + +Check the PII filter service logs in the terminal running `start_agents.sh`. You should see log lines like: +``` +[PII_ANONYMIZER] - INFO - request_id=abc123 anonymized PII: EMAIL=1, PHONE=1 +``` + +## Supported PII Types + +| Type | Pattern | Example | Placeholder | +|------|---------|---------|-------------| +| SSN | `XXX-XX-XXXX` | `123-45-6789` | `[SSN_0]` | +| Credit Card | `XXXX XXXX XXXX XXXX` | `4111 1111 1111 1111` | `[CREDIT_CARD_0]` | +| Email | standard email format | `user@example.com` | `[EMAIL_0]` | +| Phone | US phone formats | `555-123-4567` | `[PHONE_0]` | + +## Filter Contract + +**Input filter (`/anonymize`)** receives the **full raw request body** and returns the modified body: +```json +{"model": "gpt-4o-mini", "messages": [{"role": "user", "content": "Contact john@example.com"}], "stream": true} +``` +→ returns the same structure with PII replaced in the `messages` array. + +**Output filter (`/deanonymize`)** receives the **raw LLM response bytes** and returns modified bytes: +- *Streaming*: raw SSE chunk, e.g. `data: {"choices":[{"delta":{"content":"Contact [EMAIL_0]"}}]}` +- *Non-streaming*: full JSON response body + +## How Streaming De-anonymization Works + +For streaming responses, each raw SSE chunk is sent through the output filter as it arrives from the LLM: + +1. Plano receives a raw SSE chunk like `data: {"choices":[{"delta":{"content":"The email [EMAIL_0] belongs to..."}}]}` +2. The raw chunk bytes are sent to the `/deanonymize` endpoint +3. The filter parses the SSE, looks up the PII mapping (stored during anonymization), and replaces placeholders in the delta content +4. The restored chunk is returned and streamed to the client + +Partial placeholders split across chunks (e.g., `[EMA` in one chunk, `IL_0]` in the next) are handled via internal buffering in the filter service. + +## Limitations + +- **No name detection** — regex cannot reliably detect names. For production, consider [Microsoft Presidio](https://github.com/microsoft/presidio) or spaCy NER. +- **US-centric patterns** — phone and SSN patterns are US-focused. International formats may not be detected. +- **Per-chunk latency** — streaming de-anonymization adds a small network round-trip per chunk (~1ms on local network). + +## Tracing + +Open [Jaeger UI](http://localhost:16686) to see distributed traces for requests flowing through the PII filter chain. diff --git a/demos/filter_chains/pii_anonymizer/config.yaml b/demos/filter_chains/pii_anonymizer/config.yaml new file mode 100644 index 00000000..b183379f --- /dev/null +++ b/demos/filter_chains/pii_anonymizer/config.yaml @@ -0,0 +1,28 @@ +version: v0.3.0 + +filters: + - id: pii_anonymizer + url: http://localhost:10501/anonymize + type: http + - id: pii_deanonymizer + url: http://localhost:10501/deanonymize + type: http + +model_providers: + - model: openai/gpt-4o-mini + access_key: $OPENAI_API_KEY + default: true + - model: anthropic/claude-sonnet-4-20250514 + access_key: $ANTHROPIC_API_KEY + +listeners: + - type: model + name: llm_gateway + port: 12000 + input_filters: + - pii_anonymizer + output_filters: + - pii_deanonymizer + +tracing: + random_sampling: 100 diff --git a/demos/filter_chains/pii_anonymizer/docker-compose.yaml b/demos/filter_chains/pii_anonymizer/docker-compose.yaml new file mode 100644 index 00000000..dc03d7c3 --- /dev/null +++ b/demos/filter_chains/pii_anonymizer/docker-compose.yaml @@ -0,0 +1,24 @@ +services: + jaeger: + build: + context: ../../shared/jaeger + ports: + - "16686:16686" + - "4317:4317" + - "4318:4318" + anythingllm: + image: mintplexlabs/anythingllm + restart: always + ports: + - "3001:3001" + cap_add: + - SYS_ADMIN + environment: + - STORAGE_DIR=/app/server/storage + - LLM_PROVIDER=generic-openai + - GENERIC_OPEN_AI_BASE_PATH=http://host.docker.internal:12000/v1 + - GENERIC_OPEN_AI_MODEL_PREF=gpt-4o-mini + - GENERIC_OPEN_AI_MODEL_TOKEN_LIMIT=128000 + - GENERIC_OPEN_AI_API_KEY=sk-placeholder + extra_hosts: + - "host.docker.internal:host-gateway" diff --git a/demos/filter_chains/pii_anonymizer/pii.py b/demos/filter_chains/pii_anonymizer/pii.py new file mode 100644 index 00000000..0d1dd58e --- /dev/null +++ b/demos/filter_chains/pii_anonymizer/pii.py @@ -0,0 +1,88 @@ +"""PII detection and anonymization utilities.""" + +import re +from typing import Any, Dict, List, Tuple + +# Order matters: SSN before phone to avoid overlap +PII_PATTERNS = [ + ("SSN", re.compile(r"\b\d{3}-\d{2}-\d{4}\b")), + ("CREDIT_CARD", re.compile(r"\b(?:\d{4}[-\s]?){3}\d{4}\b")), + ("EMAIL", re.compile(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")), + ("PHONE", re.compile(r"(\+?1[-.\s]?)?\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}")), +] + + +def anonymize_text(text: str) -> Tuple[str, Dict[str, str]]: + """Replace PII with [TYPE_N] placeholders. Returns (anonymized_text, mapping).""" + mapping: Dict[str, str] = {} + counters: Dict[str, int] = {} + matched_spans: List[Tuple[int, int]] = [] + + for pii_type, pattern in PII_PATTERNS: + for match in pattern.finditer(text): + start, end = match.start(), match.end() + if any(s <= start < e or s < end <= e for s, e in matched_spans): + continue + matched_spans.append((start, end)) + idx = counters.get(pii_type, 0) + counters[pii_type] = idx + 1 + mapping[f"[{pii_type}_{idx}]"] = match.group() + + # Replace right-to-left to preserve span indices + matched_spans.sort(reverse=True) + result = text + for start, end in matched_spans: + placeholder = next(k for k, v in mapping.items() if v == text[start:end]) + result = result[:start] + placeholder + result[end:] + + return result, mapping + + +def deanonymize_text( + text: str, mapping: Dict[str, str], buffer: str = "" +) -> Tuple[str, str]: + """Replace placeholders back with original PII values. + + Handles partial placeholders at chunk boundaries via a buffer. + Returns (processed_text, remaining_buffer). + """ + combined = buffer + text + + # Build prefix set for all known placeholders (e.g. "[EMAIL_0" is a prefix of "[EMAIL_0]") + prefixes: set[str] = set() + for placeholder in mapping: + for i in range(1, len(placeholder)): + prefixes.add(placeholder[:i]) + + # If the tail looks like the start of a placeholder, hold it in the buffer + remaining_buffer = "" + last_bracket = combined.rfind("[") + if last_bracket != -1 and "]" not in combined[last_bracket:]: + tail = combined[last_bracket:] + if tail in prefixes: + remaining_buffer = tail + combined = combined[:last_bracket] + + for placeholder, original in mapping.items(): + combined = combined.replace(placeholder, original) + + return combined, remaining_buffer + + +def anonymize_message_content(content: Any, all_mappings: Dict[str, str]) -> Any: + """Anonymize string content or list of content parts.""" + if isinstance(content, str): + anonymized, mapping = anonymize_text(content) + all_mappings.update(mapping) + return anonymized + if isinstance(content, list): + result = [] + for part in content: + if isinstance(part, dict) and part.get("type") == "text": + anonymized, mapping = anonymize_text(part.get("text", "")) + all_mappings.update(mapping) + result.append({**part, "text": anonymized}) + else: + result.append(part) + return result + return content diff --git a/demos/filter_chains/pii_anonymizer/pii_anonymizer.py b/demos/filter_chains/pii_anonymizer/pii_anonymizer.py new file mode 100644 index 00000000..9c1450cc --- /dev/null +++ b/demos/filter_chains/pii_anonymizer/pii_anonymizer.py @@ -0,0 +1,128 @@ +""" +PII Anonymization filter — redact and restore PII in LLM requests/responses. + +Inspired by Uber's GenAI Gateway PII Redactor. Two endpoints: + POST /anonymize — replace PII with placeholders (input filter) + POST /deanonymize — restore original PII from placeholders (output filter) + +Input filter (/anonymize): + Receives the full raw request body (any API format). Anonymizes user message + content and returns the modified body. + +Output filter (/deanonymize): + Receives raw LLM response bytes — SSE (streaming) or full JSON (non-streaming). + De-anonymizes content and returns modified bytes. + +The path suffix encodes the upstream API format so each endpoint knows how to +parse the body (e.g. /anonymize/v1/chat/completions, /deanonymize/v1/messages). +""" + +import logging +from typing import Any, Dict + +from fastapi import FastAPI, Request +from fastapi.responses import Response + +from pii import anonymize_text, anonymize_message_content +from store import get_mapping, store_mapping, deanonymize_sse, deanonymize_json + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - [PII_ANONYMIZER] - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + +app = FastAPI(title="PII Anonymizer", version="1.0.0") + + +@app.post("/anonymize/{path:path}") +async def anonymize(path: str, request: Request) -> dict[str, Any]: + """Anonymize PII in user messages. Receives and returns the full raw request body. + + The endpoint path encodes the API format: + /anonymize/v1/chat/completions — anonymize body["messages"] + /anonymize/v1/responses — anonymize body["input"] (string or items list) + /anonymize/v1/messages — anonymize body["messages"] (Anthropic format) + """ + request_id = request.headers.get("x-request-id", "unknown") + endpoint = f"/{path}" + body = await request.json() + all_mappings: Dict[str, str] = {} + + if endpoint == "/v1/responses": + input_val = body.get("input", "") + if isinstance(input_val, str): + anonymized, mapping = anonymize_text(input_val) + all_mappings.update(mapping) + body = {**body, "input": anonymized} + elif isinstance(input_val, list): + items = [ + ( + { + **item, + "content": anonymize_message_content( + item.get("content", ""), all_mappings + ), + } + if isinstance(item, dict) and item.get("role") == "user" + else item + ) + for item in input_val + ] + body = {**body, "input": items} + else: + # /v1/chat/completions and /v1/messages both use "messages" + messages = [ + ( + { + **msg, + "content": anonymize_message_content( + msg.get("content", ""), all_mappings + ), + } + if msg.get("role") == "user" + else msg + ) + for msg in body.get("messages", []) + ] + if messages: + body = {**body, "messages": messages} + + if all_mappings: + store_mapping(request_id, all_mappings) + logger.info("request_id=%s /anonymize mapping: %s", request_id, all_mappings) + else: + logger.info("request_id=%s no PII detected", request_id) + + return body + + +@app.post("/deanonymize/{path:path}") +async def deanonymize(path: str, request: Request) -> Response: + """De-anonymize PII placeholders in LLM response. Handles SSE (streaming) and JSON. + + The path encodes the upstream API format: + /deanonymize/v1/chat/completions — OpenAI chat completions + /deanonymize/v1/messages — Anthropic messages + /deanonymize/v1/responses — OpenAI responses API + """ + endpoint = f"/{path}" + is_anthropic = endpoint == "/v1/messages" + request_id = request.headers.get("x-request-id", "unknown") + mapping = get_mapping(request_id) + raw_body = await request.body() + + if not mapping: + logger.info("request_id=%s no mapping found, passing through", request_id) + return Response(content=raw_body, media_type="application/json") + + body_str = raw_body.decode("utf-8", errors="replace") + + if "data: " in body_str or "event: " in body_str: + return deanonymize_sse(request_id, body_str, mapping, is_anthropic) + return deanonymize_json(request_id, raw_body, body_str, mapping, is_anthropic) + + +@app.get("/health") +async def health(): + return {"status": "healthy"} diff --git a/demos/filter_chains/pii_anonymizer/pyproject.toml b/demos/filter_chains/pii_anonymizer/pyproject.toml new file mode 100644 index 00000000..760e4303 --- /dev/null +++ b/demos/filter_chains/pii_anonymizer/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "pii_anonymizer" +version = "0.1.0" +description = "PII Anonymization filter for Plano filter chains" +readme = "README.md" +requires-python = ">=3.10,<3.14" +dependencies = [ + "fastapi>=0.104.1", + "uvicorn>=0.24.0", + "pydantic>=2.0.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/demos/filter_chains/pii_anonymizer/run_demo.sh b/demos/filter_chains/pii_anonymizer/run_demo.sh new file mode 100644 index 00000000..b13e65f5 --- /dev/null +++ b/demos/filter_chains/pii_anonymizer/run_demo.sh @@ -0,0 +1,56 @@ +#!/bin/bash +set -e + +# Function to start the demo +start_demo() { + # Step 1: Check if .env file exists + if [ -f ".env" ]; then + echo ".env file already exists. Skipping creation." + else + # Step 2: Create `.env` file and set OpenAI key + if [ -z "$OPENAI_API_KEY" ]; then + echo "Error: OPENAI_API_KEY environment variable is not set for the demo." + exit 1 + fi + + echo "Creating .env file..." + echo "OPENAI_API_KEY=$OPENAI_API_KEY" > .env + echo ".env file created with OPENAI_API_KEY." + fi + + # Step 3: Optionally start UI services (Jaeger) + # Jaeger must start before Plano so it can bind the OTEL port (4317) + if [ "$1" == "--with-ui" ]; then + echo "Starting UI services (AnythingLLM, Jaeger)..." + docker compose up -d + fi + + # Step 4: Start Plano + echo "Starting Plano with config.yaml..." + planoai up config.yaml + + # Step 5: Start filter service natively + echo "Starting PII filter service..." + bash start_agents.sh & +} + +# Function to stop the demo +stop_demo() { + # Stop filter service + echo "Stopping PII filter service..." + pkill -f start_agents.sh 2>/dev/null || true + + # Stop Docker Compose services if running + docker compose down 2>/dev/null || true + + # Stop Plano + echo "Stopping Plano..." + planoai down +} + +# Main script logic +if [ "$1" == "down" ]; then + stop_demo +else + start_demo "$1" +fi diff --git a/demos/filter_chains/pii_anonymizer/start_agents.sh b/demos/filter_chains/pii_anonymizer/start_agents.sh new file mode 100644 index 00000000..2339ff62 --- /dev/null +++ b/demos/filter_chains/pii_anonymizer/start_agents.sh @@ -0,0 +1,24 @@ +#!/bin/bash +set -e + +PIDS=() + +log() { echo "$(date '+%F %T') - $*"; } + +cleanup() { + log "Stopping agents..." + for PID in "${PIDS[@]}"; do + kill $PID 2>/dev/null && log "Stopped process $PID" + done + exit 0 +} + +trap cleanup EXIT INT TERM + +log "Starting PII anonymizer service on port 10501..." +uv run uvicorn pii_anonymizer:app --host 0.0.0.0 --port 10501 & +PIDS+=($!) + +for PID in "${PIDS[@]}"; do + wait "$PID" +done diff --git a/demos/filter_chains/pii_anonymizer/store.py b/demos/filter_chains/pii_anonymizer/store.py new file mode 100644 index 00000000..74cbd01c --- /dev/null +++ b/demos/filter_chains/pii_anonymizer/store.py @@ -0,0 +1,135 @@ +"""In-memory mapping store and LLM response processors for PII de-anonymization.""" + +import json +import logging +import threading +import time +from typing import Dict, Optional, Tuple + +from fastapi.responses import Response + +from pii import deanonymize_text + +logger = logging.getLogger(__name__) + +MAPPING_TTL_SECONDS = 300 # 5 minutes + +_lock = threading.Lock() +_mappings: Dict[str, Tuple[Dict[str, str], float]] = {} +_buffers: Dict[str, str] = {} # partial placeholder buffers for streaming + + +def _cleanup_expired(): + now = time.time() + expired = [k for k, (_, ts) in _mappings.items() if now - ts > MAPPING_TTL_SECONDS] + for k in expired: + del _mappings[k] + _buffers.pop(k, None) + + +def store_mapping(request_id: str, mapping: Dict[str, str]): + with _lock: + _cleanup_expired() + _mappings[request_id] = (mapping, time.time()) + + +def get_mapping(request_id: str) -> Optional[Dict[str, str]]: + with _lock: + entry = _mappings.get(request_id) + return entry[0] if entry else None + + +def restore_streaming(request_id: str, content: str, mapping: Dict[str, str]) -> str: + """Restore PII in one streaming chunk, maintaining the per-request partial buffer.""" + with _lock: + buffer = _buffers.get(request_id, "") + restored, remaining = deanonymize_text(content, mapping, buffer) + with _lock: + if remaining: + _buffers[request_id] = remaining + else: + _buffers.pop(request_id, None) + if restored != content: + logger.info( + "request_id=%s restored '%s' -> '%s'", request_id, content, restored + ) + return restored + + +def deanonymize_sse( + request_id: str, body_str: str, mapping: Dict[str, str], is_anthropic: bool +) -> Response: + result_lines = [] + for line in body_str.split("\n"): + stripped = line.strip() + if not (stripped.startswith("data: ") and stripped[6:] != "[DONE]"): + result_lines.append(line) + continue + try: + chunk = json.loads(stripped[6:]) + if is_anthropic: + # {"type": "content_block_delta", "delta": {"type": "text_delta", "text": "..."}} + if chunk.get("type") == "content_block_delta": + delta = chunk.get("delta", {}) + if delta.get("type") == "text_delta" and delta.get("text"): + delta["text"] = restore_streaming( + request_id, delta["text"], mapping + ) + else: + # {"choices": [{"delta": {"content": "..."}}]} + for choice in chunk.get("choices", []): + delta = choice.get("delta", {}) + if delta.get("content"): + delta["content"] = restore_streaming( + request_id, delta["content"], mapping + ) + result_lines.append("data: " + json.dumps(chunk)) + except json.JSONDecodeError: + result_lines.append(line) + return Response(content="\n".join(result_lines), media_type="text/plain") + + +def deanonymize_json( + request_id: str, + raw_body: bytes, + body_str: str, + mapping: Dict[str, str], + is_anthropic: bool, +) -> Response: + try: + body = json.loads(body_str) + if is_anthropic: + # {"content": [{"type": "text", "text": "..."}]} + for part in body.get("content", []): + if ( + isinstance(part, dict) + and part.get("type") == "text" + and part.get("text") + ): + restored, _ = deanonymize_text(part["text"], mapping) + if restored != part["text"]: + logger.info( + "request_id=%s restored '%s' -> '%s'", + request_id, + part["text"], + restored, + ) + part["text"] = restored + else: + # {"choices": [{"message": {"content": "..."}}]} + for choice in body.get("choices", []): + message = choice.get("message", {}) + content = message.get("content") + if content and isinstance(content, str): + restored, _ = deanonymize_text(content, mapping) + if restored != content: + logger.info( + "request_id=%s restored '%s' -> '%s'", + request_id, + content, + restored, + ) + message["content"] = restored + return Response(content=json.dumps(body), media_type="application/json") + except json.JSONDecodeError: + return Response(content=raw_body, media_type="application/json") diff --git a/demos/filter_chains/pii_anonymizer/test.sh b/demos/filter_chains/pii_anonymizer/test.sh new file mode 100755 index 00000000..a9019b78 --- /dev/null +++ b/demos/filter_chains/pii_anonymizer/test.sh @@ -0,0 +1,121 @@ +#!/usr/bin/env bash +set -euo pipefail + +BASE_URL="http://localhost:12000" +PASS=0 +FAIL=0 + +# ── Wait for Plano to be ready ────────────────────────────────────────────── +echo "Waiting for Plano to be ready..." +for i in $(seq 1 30); do + if curl -sf "$BASE_URL/v1/models" > /dev/null 2>&1; then + echo "Plano is ready." + break + fi + if [ "$i" -eq 30 ]; then + echo "ERROR: Plano did not become ready in time." + exit 1 + fi + sleep 2 +done + +# ── Helper ─────────────────────────────────────────────────────────────────── +run_test() { + local name="$1" + local path="$2" + local expected_code="$3" + local body="$4" + + http_code=$(curl -s -o /tmp/plano_test_body -w "%{http_code}" \ + -X POST "$BASE_URL$path" \ + -H "Content-Type: application/json" \ + -d "$body") + + if [ "$http_code" -eq "$expected_code" ]; then + echo " PASS $name (HTTP $http_code)" + PASS=$((PASS + 1)) + else + echo " FAIL $name — expected $expected_code, got $http_code" + echo " Body: $(cat /tmp/plano_test_body)" + FAIL=$((FAIL + 1)) + fi +} + +# ── /v1/chat/completions ───────────────────────────────────────────────────── +echo "" +echo "=== /v1/chat/completions ===" + +run_test "Non-streaming with PII (email + phone)" /v1/chat/completions 200 '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "Contact me at john@example.com or call 555-123-4567"}], + "stream": false +}' + +run_test "Streaming with PII (SSN)" /v1/chat/completions 200 '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "My SSN is 123-45-6789, please help me file taxes"}], + "stream": true +}' + +run_test "No PII (clean message)" /v1/chat/completions 200 '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "What is 2+2?"}], + "stream": false +}' + +run_test "Multiple PII types" /v1/chat/completions 200 '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "Email: test@test.com, SSN: 987-65-4321, Card: 4111 1111 1111 1111"}], + "stream": false +}' + +# ── /v1/responses ──────────────────────────────────────────────────────────── +echo "" +echo "=== /v1/responses ===" + +run_test "Non-streaming with PII (email)" /v1/responses 200 '{ + "model": "gpt-4o-mini", + "input": "My email is jane@example.com — can you summarize it?" +}' + +run_test "Non-streaming with PII (credit card)" /v1/responses 200 '{ + "model": "gpt-4o-mini", + "input": "I need help disputing a charge on card 4111 1111 1111 1111" +}' + +run_test "No PII" /v1/responses 200 '{ + "model": "gpt-4o-mini", + "input": "What is the capital of France?" +}' + +# ── /v1/messages (Anthropic) ───────────────────────────────────────────────── +echo "" +echo "=== /v1/messages ===" + +run_test "Non-streaming with PII (phone)" /v1/messages 200 '{ + "model": "claude-sonnet-4-20250514", + "max_tokens": 256, + "messages": [{"role": "user", "content": "Call me at 555-867-5309 to discuss my account"}] +}' + +run_test "Non-streaming with PII (SSN)" /v1/messages 200 '{ + "model": "claude-sonnet-4-20250514", + "max_tokens": 256, + "messages": [{"role": "user", "content": "My SSN is 123-45-6789"}] +}' + +run_test "No PII" /v1/messages 200 '{ + "model": "claude-sonnet-4-20250514", + "max_tokens": 256, + "messages": [{"role": "user", "content": "Hello, how are you?"}] +}' + +# ── Summary ────────────────────────────────────────────────────────────────── +echo "" +echo "Results: $PASS passed, $FAIL failed" +echo "" +echo "To verify PII was anonymized before reaching the LLM, check the terminal running start_agents.sh" + +if [ "$FAIL" -gt 0 ]; then + exit 1 +fi diff --git a/demos/filter_chains/pii_anonymizer/uv.lock b/demos/filter_chains/pii_anonymizer/uv.lock new file mode 100644 index 00000000..1414ede5 --- /dev/null +++ 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The endpoint accepts standard LLM request formats and returns which model Plano's router would select. +Plano is an AI-native proxy and data plane for agentic apps — with built-in orchestration, safety, observability, and intelligent LLM routing. + +``` +┌───────────┐ ┌─────────────────────────────────┐ ┌──────────────┐ +│ Client │ ───► │ Plano │ ───► │ OpenAI │ +│ (any │ │ │ │ Anthropic │ +│ language)│ │ Arch-Router (1.5B model) │ │ Any Provider│ +└───────────┘ │ analyzes intent → picks model │ └──────────────┘ + └─────────────────────────────────┘ +``` + +- **One endpoint, many models** — apps call Plano using standard OpenAI/Anthropic APIs; Plano handles provider selection, keys, and failover +- **Intelligent routing** — a lightweight 1.5B router model classifies user intent and picks the best model per request +- **Platform governance** — centralize API keys, rate limits, guardrails, and observability without touching app code +- **Runs anywhere** — single binary; self-host the router for full data privacy + +## How Routing Works + +The entire routing configuration is plain YAML — no code: + +```yaml +model_providers: + - model: openai/gpt-4o-mini + default: true # fallback for unmatched requests + + - model: openai/gpt-4o + routing_preferences: + - name: complex_reasoning + description: complex reasoning tasks, multi-step analysis + + - model: anthropic/claude-sonnet-4-20250514 + routing_preferences: + - name: code_generation + description: generating new code, writing functions +``` + +When a request arrives, Plano sends the conversation and routing preferences to Arch-Router, which classifies the intent and returns the matching route: + +``` +1. Request arrives → "Write binary search in Python" +2. Preferences serialized → [{"name":"code_generation", ...}, {"name":"complex_reasoning", ...}] +3. Arch-Router classifies → {"route": "code_generation"} +4. Route → Model lookup → code_generation → anthropic/claude-sonnet-4-20250514 +5. Request forwarded → Claude generates the response +``` + +No match? Arch-Router returns `other` → Plano falls back to the default model. + +The `/routing/v1/*` endpoints return the routing decision **without** forwarding to the LLM — useful for testing and validating routing behavior before going to production. ## Setup @@ -112,6 +160,69 @@ routing: Without the `X-Session-Id` header, routing runs fresh every time (no breaking change). +## Kubernetes Deployment (Self-hosted Arch-Router on GPU) + +To run Arch-Router in-cluster using vLLM instead of the default hosted endpoint: + +**0. Check your GPU node labels and taints** + +```bash +kubectl get nodes --show-labels | grep -i gpu +kubectl get node -o jsonpath='{.spec.taints}' +``` + +GPU nodes commonly have a `nvidia.com/gpu:NoSchedule` taint — `vllm-deployment.yaml` includes a matching toleration. If you have multiple GPU node pools and need to pin to a specific one, uncomment and set the `nodeSelector` in `vllm-deployment.yaml` using the label for your cloud provider. + +**1. Deploy Arch-Router and Plano:** + +```bash + +# arch-router deployment +kubectl apply -f vllm-deployment.yaml + +# plano deployment +kubectl create secret generic plano-secrets \ + --from-literal=OPENAI_API_KEY=$OPENAI_API_KEY \ + --from-literal=ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY + +kubectl create configmap plano-config \ + --from-file=plano_config.yaml=config_k8s.yaml \ + --dry-run=client -o yaml | kubectl apply -f - + +kubectl apply -f plano-deployment.yaml +``` + +**3. Wait for both pods to be ready:** + +```bash +# Arch-Router downloads the model (~1 min) then vLLM loads it (~2 min) +kubectl get pods -l app=arch-router -w +kubectl rollout status deployment/plano +``` + +**4. Test:** + +```bash +kubectl port-forward svc/plano 12000:12000 +./demo.sh +``` + +To confirm requests are hitting your in-cluster Arch-Router (not just health checks): + +```bash +kubectl logs -l app=arch-router -f --tail=0 +# Look for POST /v1/chat/completions entries +``` + +**Updating the config:** + +```bash +kubectl create configmap plano-config \ + --from-file=plano_config.yaml=config_k8s.yaml \ + --dry-run=client -o yaml | kubectl apply -f - +kubectl rollout restart deployment/plano +``` + ## Demo Output ``` diff --git a/demos/llm_routing/model_routing_service/config_k8s.yaml b/demos/llm_routing/model_routing_service/config_k8s.yaml new file mode 100644 index 00000000..bdf98bfa --- /dev/null +++ b/demos/llm_routing/model_routing_service/config_k8s.yaml @@ -0,0 +1,33 @@ +version: v0.3.0 + +overrides: + llm_routing_model: plano/Arch-Router + +listeners: + - type: model + name: model_listener + port: 12000 + +model_providers: + + - model: plano/Arch-Router + base_url: http://arch-router:10000 + + - model: openai/gpt-4o-mini + access_key: $OPENAI_API_KEY + default: true + + - model: openai/gpt-4o + access_key: $OPENAI_API_KEY + routing_preferences: + - name: complex_reasoning + description: complex reasoning tasks, multi-step analysis, or detailed explanations + + - model: anthropic/claude-sonnet-4-20250514 + access_key: $ANTHROPIC_API_KEY + routing_preferences: + - name: code_generation + description: generating new code, writing functions, or creating boilerplate + +tracing: + random_sampling: 100 diff --git a/demos/llm_routing/model_routing_service/plano-deployment.yaml b/demos/llm_routing/model_routing_service/plano-deployment.yaml new file mode 100644 index 00000000..e093f404 --- /dev/null +++ b/demos/llm_routing/model_routing_service/plano-deployment.yaml @@ -0,0 +1,68 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: plano + labels: + app: plano +spec: + replicas: 1 + selector: + matchLabels: + app: plano + template: + metadata: + labels: + app: plano + spec: + containers: + - name: plano + image: katanemo/plano:0.4.12 + ports: + - containerPort: 12000 # LLM gateway (chat completions, model routing) + name: llm-gateway + envFrom: + - secretRef: + name: plano-secrets + env: + - name: LOG_LEVEL + value: "info" + volumeMounts: + - name: plano-config + mountPath: /app/plano_config.yaml + subPath: plano_config.yaml + readOnly: true + readinessProbe: + httpGet: + path: /healthz + port: 12000 + initialDelaySeconds: 5 + periodSeconds: 10 + livenessProbe: + httpGet: + path: /healthz + port: 12000 + initialDelaySeconds: 10 + periodSeconds: 30 + resources: + requests: + memory: "256Mi" + cpu: "250m" + limits: + memory: "512Mi" + cpu: "1000m" + volumes: + - name: plano-config + configMap: + name: plano-config +--- +apiVersion: v1 +kind: Service +metadata: + name: plano +spec: + selector: + app: plano + ports: + - name: llm-gateway + port: 12000 + targetPort: 12000 diff --git a/demos/llm_routing/model_routing_service/test.rest b/demos/llm_routing/model_routing_service/test.rest new file mode 100644 index 00000000..b41d75f2 --- /dev/null +++ b/demos/llm_routing/model_routing_service/test.rest @@ -0,0 +1,36 @@ +### Code generation query (OpenAI format) — expects anthropic/claude-sonnet +POST http://localhost:12000/routing/v1/chat/completions +Content-Type: application/json + +{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "Write a Python function for binary search"}] +} + +### Complex reasoning query (OpenAI format) — expects openai/gpt-4o +POST http://localhost:12000/routing/v1/chat/completions +Content-Type: application/json + +{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "Analyze the trade-offs between microservices and monolithic architecture"}] +} + +### Simple query — no routing match, expects default model +POST http://localhost:12000/routing/v1/chat/completions +Content-Type: application/json + +{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "Hello"}] +} + +### Code generation query (Anthropic format) +POST http://localhost:12000/routing/v1/messages +Content-Type: application/json + +{ + "model": "claude-sonnet-4-20250514", + "max_tokens": 1024, + "messages": [{"role": "user", "content": "Write a REST API in Go using Gin"}] +} diff --git a/demos/llm_routing/model_routing_service/vllm-deployment.yaml b/demos/llm_routing/model_routing_service/vllm-deployment.yaml new file mode 100644 index 00000000..1debe15e --- /dev/null +++ b/demos/llm_routing/model_routing_service/vllm-deployment.yaml @@ -0,0 +1,104 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: arch-router + labels: + app: arch-router +spec: + replicas: 1 + selector: + matchLabels: + app: arch-router + template: + metadata: + labels: + app: arch-router + spec: + tolerations: + - key: nvidia.com/gpu + operator: Exists + effect: NoSchedule + # Optional: add a nodeSelector to pin to a specific GPU node pool. + # The nvidia.com/gpu resource request below is sufficient for most clusters. + # nodeSelector: + # DigitalOcean: doks.digitalocean.com/gpu-model: l40s + # GKE: cloud.google.com/gke-accelerator: nvidia-l4 + # EKS: eks.amazonaws.com/nodegroup: gpu-nodes + # AKS: kubernetes.azure.com/agentpool: gpupool + initContainers: + - name: download-model + image: python:3.11-slim + command: + - sh + - -c + - | + pip install huggingface_hub[cli] && \ + python -c "from huggingface_hub import snapshot_download; snapshot_download('katanemo/Arch-Router-1.5B.gguf', local_dir='/models/Arch-Router-1.5B.gguf')" + volumeMounts: + - name: model-cache + mountPath: /models + containers: + - name: vllm + image: vllm/vllm-openai:latest + command: + - vllm + - serve + - /models/Arch-Router-1.5B.gguf/Arch-Router-1.5B-Q4_K_M.gguf + - "--host" + - "0.0.0.0" + - "--port" + - "10000" + - "--load-format" + - "gguf" + - "--tokenizer" + - "katanemo/Arch-Router-1.5B" + - "--served-model-name" + - "Arch-Router" + - "--gpu-memory-utilization" + - "0.3" + - "--tensor-parallel-size" + - "1" + - "--enable-prefix-caching" + ports: + - name: http + containerPort: 10000 + protocol: TCP + resources: + requests: + cpu: "1" + memory: "4Gi" + nvidia.com/gpu: "1" + limits: + cpu: "4" + memory: "8Gi" + nvidia.com/gpu: "1" + volumeMounts: + - name: model-cache + mountPath: /models + readinessProbe: + httpGet: + path: /health + port: 10000 + initialDelaySeconds: 60 + periodSeconds: 10 + livenessProbe: + httpGet: + path: /health + port: 10000 + initialDelaySeconds: 180 + periodSeconds: 30 + volumes: + - name: model-cache + emptyDir: {} +--- +apiVersion: v1 +kind: Service +metadata: + name: arch-router +spec: + selector: + app: arch-router + ports: + - name: http + port: 10000 + targetPort: 10000 diff --git a/demos/llm_routing/openclaw_routing/config.yaml b/demos/llm_routing/openclaw_routing/config.yaml index 3106b5dd..9690e747 100644 --- a/demos/llm_routing/openclaw_routing/config.yaml +++ b/demos/llm_routing/openclaw_routing/config.yaml @@ -1,8 +1,7 @@ version: v0.1.0 -routing: - model: Arch-Router - llm_provider: arch-router +overrides: + llm_routing_model: Arch-Router listeners: egress_traffic: diff --git a/demos/llm_routing/preference_based_routing/plano_config_local.yaml b/demos/llm_routing/preference_based_routing/plano_config_local.yaml index dbd287dd..01adb097 100644 --- a/demos/llm_routing/preference_based_routing/plano_config_local.yaml +++ b/demos/llm_routing/preference_based_routing/plano_config_local.yaml @@ -1,8 +1,7 @@ version: v0.3.0 -routing: - model: Arch-Router - llm_provider: arch-router +overrides: + llm_routing_model: plano/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M listeners: - type: model @@ -11,8 +10,7 @@ listeners: model_providers: - - name: arch-router - model: arch/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M + - model: plano/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M base_url: http://localhost:11434 - model: openai/gpt-4o-mini diff --git a/docs/source/concepts/filter_chain.rst b/docs/source/concepts/filter_chain.rst index 5f09cdad..21068a80 100644 --- a/docs/source/concepts/filter_chain.rst +++ b/docs/source/concepts/filter_chain.rst @@ -31,6 +31,9 @@ Because these behaviors live in the dataplane rather than inside individual agen Configuration example --------------------- +Agent listener filter chain +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + The example below shows a configuration where an agent uses a filter chain with two filters: a query rewriter, and a context builder that prepares retrieval context before the agent runs. @@ -46,6 +49,38 @@ In this setup: * The ``listeners`` section wires the ``rag_agent`` behind an ``agent`` listener and attaches a ``filter_chain`` with ``query_rewriter`` followed by ``context_builder``. * When a request arrives at ``agent_1``, Plano executes the filters in order before handing control to ``rag_agent``. +Model listener filter chain +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Filter chains can also be attached directly to a **model listener**. This lets you run input guardrails on +direct LLM proxy requests (``/v1/chat/completions``, ``/v1/responses``, etc.) without an agent layer in between. + +.. code-block:: yaml + :caption: Model listener with a content-safety filter chain + + filters: + - id: content_guard + url: http://content-guard:10500 + type: http + + model_providers: + - model: openai/gpt-4o-mini + access_key: $OPENAI_API_KEY + default: true + + listeners: + - type: model + name: llm_gateway + port: 12000 + filter_chain: + - content_guard + +In this setup: + +* The ``filter_chain`` is declared at the listener level (not per-agent). +* When a request arrives at the model listener, Plano executes the filters in order before forwarding the request to the upstream LLM provider. +* If a filter rejects the request (HTTP 4xx), the error is returned to the caller and the LLM is never called. + Filter Chain Programming Model (HTTP and MCP) --------------------------------------------- diff --git a/docs/source/concepts/listeners.rst b/docs/source/concepts/listeners.rst index d8b78e11..ae63ed8f 100644 --- a/docs/source/concepts/listeners.rst +++ b/docs/source/concepts/listeners.rst @@ -57,6 +57,10 @@ Under the hood, Plano opens outbound HTTP(S) connections to upstream LLM provide smart model routing. For more details on how Plano talks to models and how providers are configured, see :ref:`LLM providers `. +Model listeners also support :ref:`Filter Chains `. By adding a ``filter_chain`` to a model listener +you can run input guardrails, content-safety checks, or other preprocessing on direct LLM requests before they reach +the upstream provider — without requiring an agent layer. + Configure Listeners ^^^^^^^^^^^^^^^^^^^ diff --git a/docs/source/conf.py b/docs/source/conf.py index ec476136..7f5df866 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -17,7 +17,7 @@ from sphinxawesome_theme.postprocess import Icons project = "Plano Docs" copyright = "2025, Katanemo Labs, Inc" author = "Katanemo Labs, Inc" -release = " v0.4.11" +release = " v0.4.14" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration diff --git a/docs/source/get_started/quickstart.rst b/docs/source/get_started/quickstart.rst index 279fde2d..6edd3c73 100644 --- a/docs/source/get_started/quickstart.rst +++ b/docs/source/get_started/quickstart.rst @@ -43,7 +43,7 @@ Plano's CLI allows you to manage and interact with the Plano efficiently. To ins .. code-block:: console - $ uv tool install planoai==0.4.11 + $ uv tool install planoai==0.4.14 **Option 2: Install with pip (Traditional)** @@ -51,7 +51,7 @@ Plano's CLI allows you to manage and interact with the Plano efficiently. To ins $ python -m venv venv $ source venv/bin/activate # On Windows, use: venv\Scripts\activate - $ pip install planoai==0.4.11 + $ pip install planoai==0.4.14 .. _llm_routing_quickstart: diff --git a/docs/source/guides/llm_router.rst b/docs/source/guides/llm_router.rst index 41c51b4a..7c4ad685 100644 --- a/docs/source/guides/llm_router.rst +++ b/docs/source/guides/llm_router.rst @@ -253,13 +253,11 @@ Using Ollama (recommended for local development) .. code-block:: yaml - routing: - model: Arch-Router - llm_provider: arch-router + overrides: + llm_routing_model: plano/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M model_providers: - - name: arch-router - model: arch/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M + - model: plano/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M base_url: http://localhost:11434 - model: openai/gpt-5.2 @@ -324,13 +322,11 @@ vLLM provides higher throughput and GPU optimizations suitable for production de .. code-block:: yaml - routing: - model: Arch-Router - llm_provider: arch-router + overrides: + llm_routing_model: plano/Arch-Router model_providers: - - name: arch-router - model: Arch-Router + - model: plano/Arch-Router base_url: http://:10000 - model: openai/gpt-5.2 @@ -351,6 +347,35 @@ vLLM provides higher throughput and GPU optimizations suitable for production de curl http://localhost:10000/v1/models +Using vLLM on Kubernetes (GPU nodes) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +For teams running Kubernetes, Arch-Router and Plano can be deployed as in-cluster services. +The ``demos/llm_routing/model_routing_service/`` directory includes ready-to-use manifests: + +- ``vllm-deployment.yaml`` — Arch-Router served by vLLM, with an init container to download + the model from HuggingFace +- ``plano-deployment.yaml`` — Plano proxy configured to use the in-cluster Arch-Router +- ``config_k8s.yaml`` — Plano config with ``llm_routing_model`` pointing at + ``http://arch-router:10000`` instead of the default hosted endpoint + +Key things to know before deploying: + +- GPU nodes commonly have a ``nvidia.com/gpu:NoSchedule`` taint — the ``vllm-deployment.yaml`` + includes a matching toleration. The ``nvidia.com/gpu: "1"`` resource request is sufficient + for scheduling in most clusters; a ``nodeSelector`` is optional and commented out in the + manifest for cases where you need to pin to a specific GPU node pool. +- Model download takes ~1 minute; vLLM loads the model in ~1-2 minutes after that. The + ``livenessProbe`` has a 180-second ``initialDelaySeconds`` to avoid premature restarts. +- The Plano config ConfigMap must use ``--from-file=plano_config.yaml=config_k8s.yaml`` with + ``subPath`` in the Deployment — omitting ``subPath`` causes Kubernetes to mount a directory + instead of a file. + +For the canonical Plano Kubernetes deployment (ConfigMap, Secrets, Deployment YAML), see +:ref:`deployment`. For full step-by-step commands specific to this demo, see the +`demo README `_. + + Combining Routing Methods ------------------------- diff --git a/docs/source/guides/orchestration.rst b/docs/source/guides/orchestration.rst index 3170b65f..1a153e83 100644 --- a/docs/source/guides/orchestration.rst +++ b/docs/source/guides/orchestration.rst @@ -335,6 +335,90 @@ Combine RAG agents for documentation lookup with specialized troubleshooting age - id: troubleshoot_agent description: Diagnoses and resolves technical issues step by step +Self-hosting Plano-Orchestrator +------------------------------- + +By default, Plano uses a hosted Plano-Orchestrator endpoint. To self-host the orchestrator model, you can serve it using **vLLM** on a server with an NVIDIA GPU. + +.. note:: + vLLM requires a Linux server with an NVIDIA GPU (CUDA). For local development on macOS, a GGUF version for Ollama is coming soon. + +The following model variants are available on HuggingFace: + +* `Plano-Orchestrator-4B `_ — lighter model, suitable for development and testing +* `Plano-Orchestrator-4B-FP8 `_ — FP8 quantized 4B model, lower memory usage +* `Plano-Orchestrator-30B-A3B `_ — full-size model for production +* `Plano-Orchestrator-30B-A3B-FP8 `_ — FP8 quantized 30B model, recommended for production deployments + +Using vLLM +~~~~~~~~~~ + +1. **Install vLLM** + + .. code-block:: bash + + pip install vllm + +2. **Download the model and chat template** + + .. code-block:: bash + + pip install huggingface_hub + huggingface-cli download katanemo/Plano-Orchestrator-4B + +3. **Start the vLLM server** + + For the 4B model (development): + + .. code-block:: bash + + vllm serve katanemo/Plano-Orchestrator-4B \ + --host 0.0.0.0 \ + --port 8000 \ + --tensor-parallel-size 1 \ + --gpu-memory-utilization 0.3 \ + --tokenizer katanemo/Plano-Orchestrator-4B \ + --chat-template chat_template.jinja \ + --served-model-name katanemo/Plano-Orchestrator-4B \ + --enable-prefix-caching + + For the 30B-A3B-FP8 model (production): + + .. code-block:: bash + + vllm serve katanemo/Plano-Orchestrator-30B-A3B-FP8 \ + --host 0.0.0.0 \ + --port 8000 \ + --tensor-parallel-size 1 \ + --gpu-memory-utilization 0.9 \ + --tokenizer katanemo/Plano-Orchestrator-30B-A3B-FP8 \ + --chat-template chat_template.jinja \ + --max-model-len 32768 \ + --served-model-name katanemo/Plano-Orchestrator-30B-A3B-FP8 \ + --enable-prefix-caching + +4. **Configure Plano to use the local orchestrator** + + Use the model name matching your ``--served-model-name``: + + .. code-block:: yaml + + overrides: + agent_orchestration_model: plano/katanemo/Plano-Orchestrator-4B + + model_providers: + - model: katanemo/Plano-Orchestrator-4B + provider_interface: plano + base_url: http://:8000 + +5. **Verify the server is running** + + .. code-block:: bash + + curl http://localhost:8000/health + curl http://localhost:8000/v1/models + + Next Steps ---------- diff --git a/docs/source/resources/deployment.rst b/docs/source/resources/deployment.rst index 7b8b0554..8d53293f 100644 --- a/docs/source/resources/deployment.rst +++ b/docs/source/resources/deployment.rst @@ -65,7 +65,7 @@ Create a ``docker-compose.yml`` file with the following configuration: # docker-compose.yml services: plano: - image: katanemo/plano:0.4.11 + image: katanemo/plano:0.4.14 container_name: plano ports: - "10000:10000" # ingress (client -> plano) @@ -153,7 +153,7 @@ Create a ``plano-deployment.yaml``: spec: containers: - name: plano - image: katanemo/plano:0.4.11 + image: katanemo/plano:0.4.14 ports: - containerPort: 12000 # LLM gateway (chat completions, model routing) name: llm-gateway diff --git a/docs/source/resources/includes/plano_config_full_reference.yaml b/docs/source/resources/includes/plano_config_full_reference.yaml index a650baea..103a5096 100644 --- a/docs/source/resources/includes/plano_config_full_reference.yaml +++ b/docs/source/resources/includes/plano_config_full_reference.yaml @@ -58,7 +58,7 @@ listeners: agents: - id: rag_agent description: virtual assistant for retrieval augmented generation tasks - filter_chain: + input_filters: - input_guards # Model listener for direct LLM access @@ -66,6 +66,9 @@ listeners: name: model_1 address: 0.0.0.0 port: 12000 + # Optional: attach input filters for guardrails on direct LLM requests + # input_filters: + # - input_guards # Prompt listener for function calling (for prompt_targets) - type: prompt diff --git a/docs/source/resources/includes/plano_config_full_reference_rendered.yaml b/docs/source/resources/includes/plano_config_full_reference_rendered.yaml index 9717b53a..f63ab831 100644 --- a/docs/source/resources/includes/plano_config_full_reference_rendered.yaml +++ b/docs/source/resources/includes/plano_config_full_reference_rendered.yaml @@ -30,9 +30,9 @@ listeners: - address: 0.0.0.0 agents: - description: virtual assistant for retrieval augmented generation tasks - filter_chain: - - input_guards id: rag_agent + input_filters: + - input_guards name: travel_booking_service port: 8001 router: plano_orchestrator_v1 @@ -107,11 +107,11 @@ model_providers: - internal: true model: Arch-Function name: arch-function - provider_interface: arch + provider_interface: plano - internal: true model: Plano-Orchestrator - name: plano-orchestrator - provider_interface: arch + name: plano/orchestrator + provider_interface: plano prompt_targets: - description: Get current weather at a location. endpoint: