add preliminary support for llm agents

arch/Dockerfile

@@ -28,4 +28,5 @@ COPY arch/arch_config_schema.yaml .
 RUN pip install requests
 RUN touch /var/log/envoy.log
 
+# ENTRYPOINT ["sh","-c", "python config_generator.py && envsubst < /etc/envoy/envoy.yaml > /etc/envoy.env_sub.yaml && envoy -c /etc/envoy.env_sub.yaml --log-level trace 2>&1 | tee /var/log/envoy.log"]
 ENTRYPOINT ["sh","-c", "python config_generator.py && envsubst < /etc/envoy/envoy.yaml > /etc/envoy.env_sub.yaml && envoy -c /etc/envoy.env_sub.yaml --component-log-level wasm:debug 2>&1 | tee /var/log/envoy.log"]

arch/arch_config_schema.yaml

@@ -101,6 +101,8 @@ properties:
         type: number
       optimize_context_window:
         type: boolean
+      use_agent_orchestrator:
+        type: boolean
   system_prompt:
     type: string
   prompt_targets:
@@ -167,6 +169,8 @@ properties:
               type: object
               additionalProperties:
                 type: string
+            pass_context:
+              type: boolean
           additionalProperties: false
           required:
             - name

crates/common/src/api/open_ai.rs

@@ -189,7 +189,7 @@ pub struct ToolCall {
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct FunctionCallDetail {
     pub name: String,
-    pub arguments: HashMap<String, Value>,
+    pub arguments: Option<HashMap<String, Value>>,
 }
 
 #[derive(Debug, Deserialize, Serialize)]

crates/common/src/configuration.rs

@@ -25,6 +25,7 @@ pub struct Configuration {
 pub struct Overrides {
     pub prompt_target_intent_matching_threshold: Option<f64>,
     pub optimize_context_window: Option<bool>,
+    pub use_agent_orchestrator: Option<bool>,
 }
 
 #[derive(Debug, Clone, Serialize, Deserialize, Default)]
@@ -217,6 +218,7 @@ pub struct EndpointDetails {
     #[serde(rename = "http_method")]
     pub method: Option<HttpMethod>,
     pub http_headers: Option<HashMap<String, String>>,
+    pub pass_context: Option<bool>,
 }
 
 #[derive(Debug, Clone, Serialize, Deserialize)]

crates/llm_gateway/src/stream_context.rs

@@ -217,6 +217,12 @@ impl HttpContext for StreamContext {
     fn on_http_request_body(&mut self, body_size: usize, end_of_stream: bool) -> Action {
         // Let the client send the gateway all the data before sending to the LLM_provider.
         // TODO: consider a streaming API.
+        trace!(
+            "on_http_request_body [S={}] bytes={} end_stream={}",
+            self.context_id,
+            body_size,
+            end_of_stream
+        );
 
         if self.request_body_sent_time.is_none() {
             self.request_body_sent_time = Some(current_time_ns());
@@ -230,33 +236,37 @@ impl HttpContext for StreamContext {
             return Action::Continue;
         }
 
+        let body_bytes = match self.get_http_request_body(0, body_size) {
+            Some(body_bytes) => body_bytes,
+            None => {
+                self.send_server_error(
+                    ServerError::LogicError(format!(
+                        "Failed to obtain body bytes even though body_size is {}",
+                        body_size
+                    )),
+                    None,
+                );
+                return Action::Pause;
+            }
+        };
+
         // Deserialize body into spec.
         // Currently OpenAI API.
         let mut deserialized_body: ChatCompletionsRequest =
-            match self.get_http_request_body(0, body_size) {
-                Some(body_bytes) => match serde_json::from_slice(&body_bytes) {
-                    Ok(deserialized) => deserialized,
-                    Err(e) => {
-                        self.send_server_error(
-                            ServerError::Deserialization(e),
-                            Some(StatusCode::BAD_REQUEST),
-                        );
-                        return Action::Pause;
-                    }
-                },
-                None => {
+            match serde_json::from_slice(&body_bytes) {
+                Ok(deserialized) => deserialized,
+                Err(e) => {
+                    debug!("body str: {}", String::from_utf8_lossy(&body_bytes));
                     self.send_server_error(
-                        ServerError::LogicError(format!(
-                            "Failed to obtain body bytes even though body_size is {}",
-                            body_size
-                        )),
-                        None,
+                        ServerError::Deserialization(e),
+                        Some(StatusCode::BAD_REQUEST),
                     );
                     return Action::Pause;
                 }
             };
 
         // remove metadata from the request body
+        //TODO: move this to prompt gateway
         deserialized_body.metadata = None;
         // delete model key from message array
         for message in deserialized_body.messages.iter_mut() {

crates/prompt_gateway/src/http_context.rs

@@ -152,6 +152,18 @@ impl HttpContext for StreamContext {
             }
         }
 
+        if let Some(overrides) = self.overrides.as_ref() {
+            if overrides.use_agent_orchestrator.unwrap_or_default() {
+                if metadata.is_none() {
+                    metadata = Some(HashMap::new());
+                }
+                metadata
+                    .as_mut()
+                    .unwrap()
+                    .insert("use_agent_orchestrator".to_string(), "true".to_string());
+            }
+        }
+
         let arch_fc_chat_completion_request = ChatCompletionsRequest {
             messages: deserialized_body.messages.clone(),
             metadata,

crates/prompt_gateway/src/stream_context.rs

@@ -316,8 +316,10 @@ impl StreamContext {
     }
 
     fn schedule_api_call_request(&mut self, mut callout_context: StreamCallContext) {
+        // Construct messages early to avoid mutable borrow conflicts
+
         let tools_call_name = self.tool_calls.as_ref().unwrap()[0].function.name.clone();
-        let prompt_target = self.prompt_targets.get(&tools_call_name).unwrap();
+        let prompt_target = self.prompt_targets.get(&tools_call_name).unwrap().clone();
         let tool_params = &self.tool_calls.as_ref().unwrap()[0].function.arguments;
         let endpoint_details = prompt_target.endpoint.as_ref().unwrap();
         let endpoint_path: String = endpoint_details
@@ -361,6 +363,25 @@ impl StreamContext {
         .into_iter()
         .collect();
 
+        let api_call_body = match endpoint_details.pass_context.unwrap_or_default() {
+            true => {
+                let messages = self.construct_llm_messages(&callout_context);
+
+                let chat_completion_request = ChatCompletionsRequest {
+                    model: callout_context.request_body.model.clone(),
+                    messages,
+                    tools: None,
+                    stream: callout_context.request_body.stream,
+                    stream_options: callout_context.request_body.stream_options.clone(),
+                    metadata: None,
+                };
+
+                let body_str = serde_json::to_string(&chat_completion_request).unwrap();
+                Some(body_str)
+            }
+            false => body,
+        };
+
         if self.request_id.is_some() {
             headers.insert(REQUEST_ID_HEADER, self.request_id.as_ref().unwrap());
         }
@@ -375,11 +396,13 @@ impl StreamContext {
             headers.insert(key.as_str(), value.as_str());
         }
 
+        debug!("api call body string: {}", api_call_body.as_ref().unwrap());
+
         let call_args = CallArgs::new(
             ARCH_INTERNAL_CLUSTER_NAME,
             &path,
             headers.into_iter().collect(),
-            body.as_deref().map(|s| s.as_bytes()),
+            api_call_body.as_deref().map(|s| s.as_bytes()),
             vec![],
             Duration::from_secs(5),
         );
@@ -406,6 +429,11 @@ impl StreamContext {
             "developer api call response received: status code: {}",
             http_status
         );
+        let prompt_target = self
+            .prompt_targets
+            .get(callout_context.prompt_target_name.as_ref().unwrap())
+            .unwrap()
+            .clone();
         if http_status != StatusCode::OK.as_str() {
             warn!(
                 "api server responded with non 2xx status code: {}",
@@ -441,6 +469,40 @@ impl StreamContext {
             }
         };
 
+        if !prompt_target
+            .auto_llm_dispatch_on_response
+            .unwrap_or_default()
+        {
+            let tool_call_response = self.tool_call_response.as_ref().unwrap().clone();
+
+            let direct_response_str = if self.streaming_response {
+                let chunks = vec![
+                    ChatCompletionStreamResponse::new(
+                        None,
+                        Some(ASSISTANT_ROLE.to_string()),
+                        Some(ARCH_FC_MODEL_NAME.to_owned()),
+                        None,
+                    ),
+                    ChatCompletionStreamResponse::new(
+                        Some(tool_call_response.clone()),
+                        None,
+                        Some(ARCH_FC_MODEL_NAME.to_owned()),
+                        None,
+                    ),
+                ];
+
+                to_server_events(chunks)
+            } else {
+                tool_call_response
+            };
+
+            return self.send_http_response(
+                StatusCode::OK.as_u16().into(),
+                vec![],
+                Some(direct_response_str.as_bytes()),
+            );
+        }
+
         let final_prompt = format!(
             "{}\ncontext: {}",
             user_message.content.unwrap(),

crates/prompt_gateway/src/tools.rs

@@ -4,8 +4,11 @@ use std::collections::HashMap;
 use serde_yaml::Value;
 
 // only add params that are of string, number and bool type
-pub fn filter_tool_params(tool_params: &HashMap<String, Value>) -> HashMap<String, String> {
-    tool_params
+pub fn filter_tool_params(tool_params: &Option<HashMap<String, Value>>) -> HashMap<String, String> {
+    if tool_params.is_none() {
+        return HashMap::new();
+    }
+    tool_params.as_ref().unwrap()
         .iter()
         .filter(|(_, value)| value.is_number() || value.is_string() || value.is_bool())
         .map(|(key, value)| match value {
@@ -22,7 +25,7 @@ pub fn filter_tool_params(tool_params: &HashMap<String, Value>) -> HashMap<Strin
 
 pub fn compute_request_path_body(
     endpoint_path: &str,
-    tool_params: &HashMap<String, Value>,
+    tool_params: &Option<HashMap<String, Value>>,
     prompt_target_params: &[Parameter],
     http_method: &HttpMethod,
 ) -> Result<(String, Option<String>), String> {

demos/use_cases/orchestrating_agents/Dockerfile

@@ -0,0 +1,41 @@
+# took inspiration from https://medium.com/@albertazzir/blazing-fast-python-docker-builds-with-poetry-a78a66f5aed0
+
+# The builder image, used to build the virtual environment
+FROM python:3.10 as builder
+
+RUN pip install poetry==1.8.3
+
+ENV POETRY_NO_INTERACTION=1 \
+    POETRY_VIRTUALENVS_IN_PROJECT=1 \
+    POETRY_VIRTUALENVS_CREATE=1 \
+    POETRY_CACHE_DIR=/tmp/poetry_cache
+
+WORKDIR /code
+
+COPY pyproject.toml poetry.lock ./
+RUN touch README.md
+
+RUN poetry install --no-root && rm -rf $POETRY_CACHE_DIR
+
+# The runtime image, used to just run the code provided its virtual environment
+FROM python:3.10-slim as runtime
+
+RUN apt-get update && apt-get install -y curl
+
+WORKDIR /code
+
+ENV VIRTUAL_ENV=/code/.venv \
+    PATH="/code/.venv/bin:$PATH"
+
+COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
+
+COPY main.py ./
+
+HEALTHCHECK \
+    --interval=5s \
+    --timeout=1s \
+    --start-period=1s \
+    --retries=3 \
+    CMD curl http://localhost:80/healthz
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--log-level", "debug"]

demos/use_cases/orchestrating_agents/arch_config.yaml

@@ -0,0 +1,57 @@
+version: "0.1-beta"
+
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
+
+overrides:
+  use_agent_orchestrator: true
+
+endpoints:
+  triage_service:
+    endpoint: host.docker.internal:18083
+
+llm_providers:
+  - name: gpt-4o-mini
+    access_key: $OPENAI_API_KEY
+    provider_interface: openai
+    model: gpt-4o-mini
+    default: true
+
+system_prompt: |
+  You are a helpful assistant.
+
+prompt_targets:
+  - name: sales_agent
+    description: handles queries related to sales and purchases
+    endpoint:
+      name: triage_service
+      path: /sales
+      http_method: POST
+      pass_context: true
+    auto_llm_dispatch_on_response: false
+
+  - name: issues_and_repairs
+    description: handles issues, repairs, or refunds
+    endpoint:
+      name: triage_service
+      path: /issues
+      http_method: POST
+      pass_context: true
+    auto_llm_dispatch_on_response: false
+
+  - name: escalate_to_human
+    description: escalates to human agent
+    endpoint:
+      name: triage_service
+      path: /escalate
+      http_method: POST
+      pass_context: true
+    auto_llm_dispatch_on_response: false
+
+tracing:
+  random_sampling: 100
+  trace_arch_internal: true

demos/use_cases/orchestrating_agents/docker-compose.yaml

@@ -0,0 +1,29 @@
+services:
+  triage_service:
+    build:
+      context: ./
+    environment:
+      - OLTP_HOST=http://jaeger:4317
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    ports:
+      - "18083:80"
+
+  chatbot_ui:
+    build:
+      context: ../../shared/chatbot_ui
+    ports:
+      - "18080:8080"
+    environment:
+      # this is only because we are running the sample app in the same docker container environemtn as archgw
+      - CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+
+  jaeger:
+    build:
+      context: ../../shared/jaeger
+    ports:
+      - "16686:16686"
+      - "4317:4317"
+      - "4318:4318"

demos/use_cases/orchestrating_agents/main.py

@@ -0,0 +1,75 @@
+import json
+import os
+import random
+from typing import Any, Dict, List
+from fastapi import FastAPI, Response
+from datetime import datetime, date, timedelta, timezone
+import logging
+from pydantic import BaseModel, Field
+from opentelemetry import trace
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+from opentelemetry.sdk.resources import Resource
+
+
+resource = Resource.create(
+    {
+        "service.name": "llm-agents",
+    }
+)
+
+# Initialize the tracer provider
+trace.set_tracer_provider(TracerProvider(resource=resource))
+tracer = trace.get_tracer(__name__)
+
+logger = logging.getLogger("uvicorn.error")
+logger.setLevel(logging.INFO)
+
+app = FastAPI()
+FastAPIInstrumentor().instrument_app(app)
+
+# Configure the OTLP exporter (Jaeger, Zipkin, etc.)
+otlp_exporter = OTLPSpanExporter(
+    endpoint=os.getenv("OLTP_HOST", "http://localhost:4317")
+)
+trace.get_tracer_provider().add_span_processor(BatchSpanProcessor(otlp_exporter))
+
+
+@app.get("/healthz")
+async def healthz():
+    return {"status": "ok"}
+
+
+class Message(BaseModel):
+    role: str
+    content: str
+
+
+class ChatCompletionsRequest(BaseModel):
+    messages: List[Message]
+    model: str
+    metadata: Dict[str, Any] = None
+
+
+class Choice(BaseModel):
+    message: Message
+
+
+@app.post("/sales")
+async def sales_agent(req: ChatCompletionsRequest, res: Response):
+    logger.info(f"sales: received messages: {req.messages}")
+    return "I am a sales agent, how can I help you?"
+
+
+@app.post("/issues")
+async def issues_agent(req: ChatCompletionsRequest, res: Response):
+    logger.info(f"issues: received messages: {req.messages}")
+    return "I am a issues agent, how can I help you?"
+
+
+@app.post("/escalate")
+async def escalate_agent(req: ChatCompletionsRequest, res: Response):
+    logger.info(f"escalates: received messages: {req.messages}")
+    return "You're talking to a human, how can I help you?"

demos/use_cases/orchestrating_agents/poetry.lock

@@ -0,0 +1,999 @@
+# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
+[[package]]
+name = "anyio"
+version = "4.8.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
+    {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
+
+[package.extras]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+trio = ["trio (>=0.26.1)"]
+
+[[package]]
+name = "asgiref"
+version = "3.8.1"
+description = "ASGI specs, helper code, and adapters"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "asgiref-3.8.1-py3-none-any.whl", hash = "sha256:3e1e3ecc849832fe52ccf2cb6686b7a55f82bb1d6aee72a58826471390335e47"},
+    {file = "asgiref-3.8.1.tar.gz", hash = "sha256:c343bd80a0bec947a9860adb4c432ffa7db769836c64238fc34bdc3fec84d590"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4", markers = "python_version < \"3.11\""}
+
+[package.extras]
+tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
+
+[[package]]
+name = "certifi"
+version = "2025.1.31"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
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+    {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"},
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.4.1"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7"
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+    {file = "charset_normalizer-3.4.1-cp310-cp310-win32.whl", hash = "sha256:c0429126cf75e16c4f0ad00ee0eae4242dc652290f940152ca8c75c3a4b6ee8f"},
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+    {file = "wrapt-1.17.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:62c2caa1585c82b3f7a7ab56afef7b3602021d6da34fbc1cf234ff139fed3cd9"},
+    {file = "wrapt-1.17.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c958bcfd59bacc2d0249dcfe575e71da54f9dcf4a8bdf89c4cb9a68a1170d73f"},
+    {file = "wrapt-1.17.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc78a84e2dfbc27afe4b2bd7c80c8db9bca75cc5b85df52bfe634596a1da846b"},
+    {file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:ba0f0eb61ef00ea10e00eb53a9129501f52385c44853dbd6c4ad3f403603083f"},
+    {file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:1e1fe0e6ab7775fd842bc39e86f6dcfc4507ab0ffe206093e76d61cde37225c8"},
+    {file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:c86563182421896d73858e08e1db93afdd2b947a70064b813d515d66549e15f9"},
+    {file = "wrapt-1.17.2-cp39-cp39-win32.whl", hash = "sha256:f393cda562f79828f38a819f4788641ac7c4085f30f1ce1a68672baa686482bb"},
+    {file = "wrapt-1.17.2-cp39-cp39-win_amd64.whl", hash = "sha256:36ccae62f64235cf8ddb682073a60519426fdd4725524ae38874adf72b5f2aeb"},
+    {file = "wrapt-1.17.2-py3-none-any.whl", hash = "sha256:b18f2d1533a71f069c7f82d524a52599053d4c7166e9dd374ae2136b7f40f7c8"},
+    {file = "wrapt-1.17.2.tar.gz", hash = "sha256:41388e9d4d1522446fe79d3213196bd9e3b301a336965b9e27ca2788ebd122f3"},
+]
+
+[[package]]
+name = "zipp"
+version = "3.21.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
+    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.10"
+content-hash = "4f3c897c33f2fe2c1631228aa41d7cfb119779df02089ec40ee6e9c7cbe03172"

demos/use_cases/orchestrating_agents/pyproject.toml

@@ -0,0 +1,23 @@
+[tool.poetry]
+name = "api-server"
+version = "0.1.0"
+description = ""
+authors = ["Adil Hafeez <info@katanemo.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+opentelemetry-instrumentation-fastapi = "^0.49b0"
+fastapi = "^0.115.4"
+pyyaml = "^6.0.2"
+uvicorn = "^0.32.0"
+opentelemetry-api = "^1.28.0"
+opentelemetry-sdk = "^1.28.0"
+opentelemetry-exporter-otlp = "^1.28.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry.scripts]
+api-server = "api_server.main:app"

model_server/src/commons/globals.py

@@ -3,6 +3,8 @@ from openai import OpenAI
 from src.commons.utils import get_model_server_logger
 from src.core.guardrails import get_guardrail_handler
 from src.core.function_calling import (
+    ArchAgentConfig,
+    ArchAgentHandler,
     ArchIntentConfig,
     ArchIntentHandler,
     ArchFunctionConfig,
@@ -18,10 +20,14 @@ logger = get_model_server_logger()
 ARCH_ENDPOINT = os.getenv("ARCH_ENDPOINT", "https://archfc.katanemo.dev/v1")
 ARCH_API_KEY = "EMPTY"
 ARCH_CLIENT = OpenAI(base_url=ARCH_ENDPOINT, api_key=ARCH_API_KEY)
+ARCH_AGENT_CLIENT = ARCH_CLIENT
+# ARCH_AGENT_CLIENT = OpenAI(api_key=os.getenv("OPENAI_API_KEY", "EMPTY"))
 
 # Define model names
 ARCH_INTENT_MODEL_ALIAS = "Arch-Intent"
 ARCH_FUNCTION_MODEL_ALIAS = "Arch-Function"
+ARCH_AGENT_MODEL_ALIAS = ARCH_FUNCTION_MODEL_ALIAS
+# ARCH_AGENT_MODEL_ALIAS = "gpt-4o-mini"
 ARCH_GUARD_MODEL_ALIAS = "katanemo/Arch-Guard"
 
 # Define model handlers
@@ -32,5 +38,8 @@ handler_map = {
     "Arch-Function": ArchFunctionHandler(
         ARCH_CLIENT, ARCH_FUNCTION_MODEL_ALIAS, ArchFunctionConfig
     ),
+    "Arch-Agent": ArchAgentHandler(
+        ARCH_AGENT_CLIENT, ARCH_AGENT_MODEL_ALIAS, ArchAgentConfig
+    ),
     "Arch-Guard": get_guardrail_handler(ARCH_GUARD_MODEL_ALIAS),
 }

model_server/src/core/function_calling.py

@@ -1,4 +1,5 @@
 import ast
+import copy
 import json
 import random
 import builtins
@@ -221,6 +222,41 @@ class ArchFunctionConfig:
     SUPPORT_DATA_TYPES = ["int", "float", "bool", "str", "list", "tuple", "set", "dict"]
 
 
+class ArchAgentConfig(ArchFunctionConfig):
+    TASK_PROMPT = textwrap.dedent(
+        """
+        You will be given a list of tools and a user request. Your task is to match the user request with the most appropriate tool(s) based on the tool descriptions. Do not explain your reasoning, just provide the tool(s) that best match the user request.
+        """
+    ).strip()
+
+    TOOL_PROMPT_TEMPLATE = textwrap.dedent(
+        """
+    You will be presented with a list of tools and their descriptions:
+
+    <tools>
+    {tool_text}
+    </tools>
+    """
+    ).strip()
+
+    FORMAT_PROMPT = textwrap.dedent(
+        """
+        Provide your answer in the following format:
+        For each function call, return a json object with function name <tool_call></tool_call> XML tags:
+        <tool_call>
+        {"name": <function-name>}
+        </tool_call>
+    """
+    ).strip()
+
+    GENERATION_PARAMS = {
+        "temperature": 0.01,
+        "stop_token_ids": [151645],
+        "logprobs": True,
+        "top_logprobs": 10,
+    }
+
+
 class ArchFunctionHandler(ArchBaseHandler):
     def __init__(
         self,
@@ -358,16 +394,17 @@ class ArchFunctionHandler(ArchBaseHandler):
                             is_valid, error_message = False, e
                             break
 
-                    tool_calls.append(
-                        {
-                            "id": f"call_{random.randint(1000, 10000)}",
-                            "type": "function",
-                            "function": {
-                                "name": tool_content["name"],
-                                "arguments": tool_content["arguments"],
-                            },
-                        }
-                    )
+                    tool = {
+                        "id": f"call_{random.randint(1000, 10000)}",
+                        "type": "function",
+                        "function": {
+                            "name": tool_content["name"],
+                        },
+                    }
+                    if "arguments" in tool_content:
+                        tool["function"]["arguments"] = tool_content["arguments"]
+
+                    tool_calls.append(tool)
 
                 flag = False
 
@@ -415,7 +452,9 @@ class ArchFunctionHandler(ArchBaseHandler):
                 break
 
             func_name = tool_call["function"]["name"]
-            func_args = tool_call["function"]["arguments"]
+            func_args = tool_call["function"].get("arguments")
+            if not func_args:
+                func_args = {}
 
             # Check whether the function is available or not
             if func_name not in functions:
@@ -430,12 +469,14 @@ class ArchFunctionHandler(ArchBaseHandler):
                     if required_param not in func_args:
                         is_valid = False
                         invalid_tool_call = tool_call
-                        error_message = f"`{required_param}` is requiried by the function `{func_name}` but not found in the tool call!"
+                        error_message = f"`{required_param}` is required by the function `{func_name}` but not found in the tool call!"
                         break
 
                 # Verify the data type of each parameter in the tool calls
                 function_properties = functions[func_name]["properties"]
 
+                logger.info("== func_args ==")
+                logger.info(func_args)
                 for param_name in func_args:
                     if param_name not in function_properties:
                         is_valid = False
@@ -523,52 +564,60 @@ class ArchFunctionHandler(ArchBaseHandler):
             req.messages, req.tools, metadata=req.metadata
         )
 
-        logger.info(f"[request to arch-fc]: {json.dumps(messages)}")
+        logger.info(
+            f"[request to arch-fc]: model: {self.model_name}, extra_body: {self.generation_params}, body: {json.dumps(messages)}"
+        )
 
         # always enable `stream=True` to collect model responses
         response = self.client.chat.completions.create(
             messages=messages,
             model=self.model_name,
             stream=True,
-            extra_body=self.generation_params,
-        )
-
-        # initialize the hallucination handler, which is an iterator
-        self.hallucination_state = HallucinationState(
-            response_iterator=response, function=req.tools
+            extra_body={"temperature": 0.01, "logprobs": True},
         )
 
+        use_agent_orchestrator = req.metadata.get("use_agent_orchestrator", False)
         model_response = ""
+        if use_agent_orchestrator:
+            for chunk in response:
+                if len(chunk.choices) > 0 and chunk.choices[0].delta.content:
+                    model_response += chunk.choices[0].delta.content
+            logger.info(f"[Agent Orchestrator]: response received: {model_response}")
+        else:
+            # initialize the hallucination handler, which is an iterator
+            self.hallucination_state = HallucinationState(
+                response_iterator=response, function=req.tools
+            )
 
-        has_tool_calls, has_hallucination = None, False
-        for _ in self.hallucination_state:
-            # check if the first token is <tool_call>
-            if len(self.hallucination_state.tokens) > 0 and has_tool_calls is None:
-                if self.hallucination_state.tokens[0] == "<tool_call>":
-                    has_tool_calls = True
-                else:
-                    has_tool_calls = False
+            has_tool_calls, has_hallucination = None, False
+            for _ in self.hallucination_state:
+                # check if the first token is <tool_call>
+                if len(self.hallucination_state.tokens) > 0 and has_tool_calls is None:
+                    if self.hallucination_state.tokens[0] == "<tool_call>":
+                        has_tool_calls = True
+                    else:
+                        has_tool_calls = False
+                        break
+
+                # if the model is hallucinating, start parameter gathering
+                if self.hallucination_state.hallucination is True:
+                    has_hallucination = True
                     break
 
-            # if the model is hallucinating, start parameter gathering
-            if self.hallucination_state.hallucination is True:
-                has_hallucination = True
-                break
-
-        if has_tool_calls:
-            if has_hallucination:
-                # start prompt prefilling if hallcuination is found in tool calls
-                logger.info(
-                    f"[Hallucination]: {self.hallucination_state.error_message}"
-                )
+            if has_tool_calls:
+                if has_hallucination:
+                    # start prompt prefilling if hallcuination is found in tool calls
+                    logger.info(
+                        f"[Hallucination]: {self.hallucination_state.error_message}"
+                    )
+                    prefill_response = self._engage_parameter_gathering(messages)
+                    model_response = prefill_response.choices[0].message.content
+                else:
+                    model_response = "".join(self.hallucination_state.tokens)
+            else:
+                # start parameter gathering if the model is not generating tool calls
                 prefill_response = self._engage_parameter_gathering(messages)
                 model_response = prefill_response.choices[0].message.content
-            else:
-                model_response = "".join(self.hallucination_state.tokens)
-        else:
-            # start parameter gathering if the model is not generating tool calls
-            prefill_response = self._engage_parameter_gathering(messages)
-            model_response = prefill_response.choices[0].message.content
 
         # Extract tool calls from model response
         extracted = self._extract_tool_calls(model_response)
@@ -601,3 +650,36 @@ class ArchFunctionHandler(ArchBaseHandler):
         logger.info(f"[response]: {json.dumps(chat_completion_response.model_dump())}")
 
         return chat_completion_response
+
+
+# override ArchFunctionHandler
+class ArchAgentHandler(ArchFunctionHandler):
+    def __init__(self, client: OpenAI, model_name: str, config: ArchAgentConfig):
+        super().__init__(client, model_name, config)
+
+    @override
+    def _convert_tools(self, tools: List[Dict[str, Any]]) -> str:
+        """
+        Converts a list of tools into JSON format.
+
+        Args:
+            tools (List[Dict[str, Any]]): A list of tools represented as dictionaries.
+
+        Returns:
+            str: A string representation of converted tools.
+        """
+
+        converted = []
+        # delete parameters key if its empty in tool
+        for tool in tools:
+            if (
+                "parameters" in tool["function"]
+                and "properties" in tool["function"]["parameters"]
+                and not tool["function"]["parameters"]["properties"]
+            ):
+                tool_copy = copy.deepcopy(tool)
+                del tool_copy["function"]["parameters"]
+                converted.append(json.dumps(tool_copy))
+            else:
+                converted.append(json.dumps(tool))
+        return "\n".join(converted)

model_server/src/main.py

@@ -5,6 +5,7 @@ import logging
 import src.commons.utils as utils
 
 from src.commons.globals import ARCH_ENDPOINT, handler_map
+from src.core.function_calling import ArchFunctionHandler
 from src.core.utils.model_utils import (
     ChatMessage,
     ChatCompletionResponse,
@@ -76,33 +77,51 @@ async def function_calling(req: ChatMessage, res: Response):
     error_messages = None
 
     try:
-        intent_start_time = time.perf_counter()
-        intent_response = await handler_map["Arch-Intent"].chat_completion(req)
-        intent_latency = time.perf_counter() - intent_start_time
+        intent_detected = False
+        use_agent_orchestrator = req.metadata.get("use_agent_orchestrator", False)
+        logger.info(f"Use agent orchestrator: {use_agent_orchestrator}")
+        if not use_agent_orchestrator:
+            intent_start_time = time.perf_counter()
+            intent_response = await handler_map["Arch-Intent"].chat_completion(req)
+            intent_latency = time.perf_counter() - intent_start_time
+            intent_detected = handler_map["Arch-Intent"].detect_intent(intent_response)
 
-        if handler_map["Arch-Intent"].detect_intent(intent_response):
+        if use_agent_orchestrator or intent_detected:
             # TODO: measure agreement between intent detection and function calling
             try:
                 function_start_time = time.perf_counter()
-                final_response = await handler_map["Arch-Function"].chat_completion(req)
+                handler_name = (
+                    "Arch-Agent" if use_agent_orchestrator else "Arch-Function"
+                )
+                function_calling_handler: ArchFunctionHandler = handler_map[
+                    handler_name
+                ]
+                final_response = await function_calling_handler.chat_completion(req)
                 function_latency = time.perf_counter() - function_start_time
 
                 final_response.metadata = {
-                    "intent_latency": str(round(intent_latency * 1000, 3)),
                     "function_latency": str(round(function_latency * 1000, 3)),
-                    "hallucination": str(
-                        handler_map["Arch-Function"].hallucination_state.hallucination
-                    ),
                 }
+
+                if not use_agent_orchestrator:
+                    final_response.metadata["intent_latency"] = str(
+                        round(intent_latency * 1000, 3)
+                    )
+                    final_response.metadata["hallucination"] = str(
+                        function_calling_handler.hallucination_state.hallucination
+                    )
             except ValueError as e:
                 res.statuscode = 503
-                error_messages = f"[Arch-Function] - Error in tool call extraction: {e}"
+                error_messages = (
+                    f"[{handler_name}] - Error in tool call extraction: {e}"
+                )
             except StopIteration as e:
                 res.statuscode = 500
-                error_messages = f"[Arch-Function] - Error in hallucination check: {e}"
+                error_messages = f"[{handler_name}] - Error in hallucination check: {e}"
             except Exception as e:
                 res.status_code = 500
-                error_messages = f"[Arch-Function] - Error in ChatCompletion: {e}"
+                error_messages = f"[{handler_name}] - Error in ChatCompletion: {e}"
+                raise
         else:
             # no intent matched
             intent_response.metadata = {
@@ -113,6 +132,7 @@ async def function_calling(req: ChatMessage, res: Response):
     except Exception as e:
         res.status_code = 500
         error_messages = f"[Arch-Intent] - Error in ChatCompletion: {e}"
+        raise
 
     if error_messages is not None:
         logger.error(error_messages)