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Merge branch 'main' into adil/fix_prompt_target_name

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Adil Hafeez 2025-01-13 15:53:40 -08:00
commit b8474f42c9
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9
README.md
View file

@ -19,11 +19,14 @@ Engineered with purpose-built LLMs, Arch handles the critical but undifferentiat
**Core Features**:
- Built on [Envoy](https://envoyproxy.io): Arch runs alongside application servers as a separate containerized process, and builds on top of Envoy's proven HTTP management and scalability features to handle ingress and egress traffic related to prompts and LLMs.
- Function Calling for fast Agents and RAG apps. Engineered with purpose-built [LLMs](https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68) to handle fast, cost-effective, and accurate prompt-based tasks like function/API calling, and parameter extraction from prompts.
- Intent Routing & Fast Function Calling. Engineered with purpose-built [LLMs](https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68) to handle fast, cost-effective, and accurate prompt-based tasks like function/API calling, and parameter extraction from prompts to build smarter more accurate agentic applications.
- Prompt [Guard](https://huggingface.co/collections/katanemo/arch-guard-6702bdc08b889e4bce8f446d): Arch centralizes guardrails to prevent jailbreak attempts and ensure safe user interactions without writing a single line of code.
- Routing & Traffic Management: Arch manages LLM calls, offering smart retries, automatic cutover, and resilient upstream connections for continuous availability.
- Routing & Traffic Management: Arch centralizes calls to LLMs used by your applications, offering smart retries, automatic cutover, and resilient upstream connections for continuous availability.
- Observability: Arch uses the W3C Trace Context standard to enable complete request tracing across applications, ensuring compatibility with observability tools, and provides metrics to monitor latency, token usage, and error rates, helping optimize AI application performance.
**High-Level Network Flow**:
![alt text](docs/source/_static/img/arch_network_diagram_high_level.png)
**Jump to our [docs](https://docs.archgw.com)** to learn how you can use Arch to improve the speed, security and personalization of your GenAI apps.
> [!IMPORTANT]
@ -57,7 +60,7 @@ Arch's CLI allows you to manage and interact with the Arch gateway efficiently.
```console
$ python -m venv venv
$ source venv/bin/activate # On Windows, use: venv\Scripts\activate
$ pip install archgw==0.1.7
$ pip install archgw==0.1.8
```
### Build AI Agent with Arch Gateway

2
arch/tools/README.md
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@ -19,7 +19,7 @@ source venv/bin/activate
### Step 3: Run the build script
```bash
pip install archgw==0.1.7
pip install archgw==0.1.8
```
## Uninstall Instructions: archgw CLI

566
arch/tools/poetry.lock generated
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@ -1,19 +1,8 @@
# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
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description = "Reusable constraint types to use with typing.Annotated"
optional = false
python-versions = ">=3.8"
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# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
[[package]]
name = "archgw_modelserver"
version = "0.1.7"
version = "0.1.8"
description = "A model server for serving models"
optional = false
python-versions = "*"
@ -56,127 +45,114 @@ files = [
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{file = "pyyaml-6.0.2.tar.gz", hash = "sha256:d584d9ec91ad65861cc08d42e834324ef890a082e591037abe114850ff7bbc3e"},
]
[[package]]
name = "referencing"
version = "0.35.1"
@ -854,47 +536,15 @@ enabler = ["pytest-enabler (>=2.2)"]
test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (>=1.12,<1.14)", "pytest-mypy"]
[[package]]
name = "tqdm"
version = "4.67.1"
description = "Fast, Extensible Progress Meter"
optional = false
python-versions = ">=3.7"
files = [
{file = "tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2"},
{file = "tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2"},
]
[package.dependencies]
colorama = {version = "*", markers = "platform_system == \"Windows\""}
[package.extras]
dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"]
discord = ["requests"]
notebook = ["ipywidgets (>=6)"]
slack = ["slack-sdk"]
telegram = ["requests"]
[[package]]
name = "typing-extensions"
version = "4.12.2"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.8"
files = [
{file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
]
[[package]]
name = "urllib3"
version = "2.2.3"
version = "2.3.0"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.8"
python-versions = ">=3.9"
files = [
{file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
{file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
{file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
{file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
]
[package.extras]
@ -905,5 +555,5 @@ zstd = ["zstandard (>=0.18.0)"]
[metadata]
lock-version = "2.0"
python-versions = "^3.12"
content-hash = "6016abef181ad0b4053e51b3b7de2ce5f1b852e8f4dd950eda8cde7294e70026"
python-versions = "^3.10"
content-hash = "ab027b62af1cbf96ef77b4db808824d66b09b065f1b18d215bdc6110eb766d10"

9
arch/tools/pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "archgw"
version = "0.1.7"
version = "0.1.8"
description = "Python-based CLI tool to manage Arch Gateway."
authors = ["Katanemo Labs, Inc."]
packages = [
@ -9,15 +9,12 @@ packages = [
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.12"
archgw_modelserver = "0.1.7"
pyyaml = "^6.0.2"
pydantic = "^2.10.1"
python = "^3.10"
archgw_modelserver = "^0.1.8"
click = "^8.1.7"
jinja2 = "^3.1.4"
jsonschema = "^4.23.0"
setuptools = "75.5.0"
huggingface_hub = "^0.26.0"
docker = "^7.1.0"
python-dotenv = "^1.0.1"

6
archgw.code-workspace
View file

@ -1,9 +1,9 @@
{
"folders": [
{
{
"name": "root",
"path": "."
},
"path": "."
},
{
"name": "crates",
"path": "crates"

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4
docs/source/conf.py
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@ -13,9 +13,9 @@ from sphinxawesome_theme import ThemeOptions
from sphinxawesome_theme.postprocess import Icons
project = "Arch Docs"
copyright = "2024, Katanemo Labs, Inc"
copyright = "2025, Katanemo Labs, Inc"
author = "Katanemo Labs, Inc"
release = " v0.1.7"
release = " v0.1.8"
# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

5
docs/source/get_started/intro_to_arch.rst
View file

@ -8,10 +8,13 @@ Engineered with purpose-built large language models (LLMs), Arch handles all the
detecting and rejecting jailbreak attempts, intelligently calling “backend” APIs to fulfill the user's request represented in a prompt, routing to and offering disaster recovery
between upstream LLMs, and managing the observability of prompts and LLM interactions in a centralized way.
.. image:: /_static/img/arch-logo.png
.. figure:: /_static/img/arch_network_diagram_high_level.png
:width: 100%
:align: center
High-level network flow of where Arch Gateway sits in your agentic stack. Designed for both ingress and egress prompt traffic.
**The project was born out of the belief that:**
*Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests

6
docs/source/get_started/overview.rst
View file

@ -7,6 +7,12 @@ Welcome to Arch, the intelligent prompt gateway designed to help developers buil
In this documentation, you will learn how to quickly set up Arch to trigger API calls via prompts, apply prompt guardrails without writing any application-level logic,
simplify the interaction with upstream LLMs, and improve observability all while simplifying your application development process.
.. figure:: /_static/img/arch_network_diagram_high_level.png
:width: 100%
:align: center
High-level network flow of where Arch Gateway sits in your agentic stack. Designed for both ingress and egress prompt traffic.
Get Started
-----------

2
docs/source/get_started/quickstart.rst
View file

@ -25,7 +25,7 @@ Arch's CLI allows you to manage and interact with the Arch gateway efficiently.
$ python -m venv venv
$ source venv/bin/activate # On Windows, use: venv\Scripts\activate
$ pip install archgw==0.1.7
$ pip install archgw==0.1.8
Build AI Agent with Arch Gateway

80
docs/source/guides/function_calling.rst
View file

@ -61,21 +61,6 @@ Key Features
Parallel & Multiple Perform both parallel and multiple function calling
========================= ===============================================================
Supported Languages
~~~~~~~~~~~~~~~~~~~
.. table::
:width: 100%
========================= ===========================================================================================================================================
**Language** **Data Type**
========================= ===========================================================================================================================================
Python ``int``, ``str``, ``float``, ``bool``, ``list``, ``set``, ``dict``, ``tuple``
Java ``byte``, ``short``, ``int``, ``long``, ``float``, ``double``, ``boolean``, ``char``, ``Array``, ``ArrayList``, ``Set``, ``HashMap``, ``Hashtable``, ``Queue``, ``Stack``
Javascript ``Number``, ``Bigint``, ``String``, ``Boolean``, ``Object``, ``Array``, ``Date``
========================= ===========================================================================================================================================
Implementing Function Calling
-----------------------------
@ -86,7 +71,6 @@ Step 1: Define the Function
First, create or identify the backend function you want Arch to call. This could be an API endpoint, a script, or any other executable backend logic.
.. code-block:: python
:caption: Example Function
import requests
@ -136,63 +120,21 @@ Specify the parameters your function needs and how Arch should interpret these.
Step 3: Arch Takes Over
~~~~~~~~~~~~~~~~~~~~~~~
Once you have defined the functions and configured the prompt targets, Arch takes care of the remaining work.
It will automatically validate parameters validate parameters and ensure that the required parameters (e.g., location) are present in the prompt, and add validation rules if necessary.
Here is ane example validation schema using the `jsonschema <https://json-schema.org/docs>`_ library
Once you have defined the functions and configured the prompt targets, Arch Gateway takes care of the remaining work.
It will automatically validate parameters, and ensure that the required parameters (e.g., location) are present in the prompt, and add validation rules if necessary.
.. code-block:: python
:caption: Example Validation Schema
.. figure:: /_static/img/arch_network_diagram_high_level.png
:width: 100%
:align: center
import requests
from jsonschema import validate, ValidationError
# Define the JSON Schema for parameter validation
weather_validation_schema = {
"type": "object",
"properties": {
"location": {
"type": "string",
"minLength": 1,
"description": "The city and state, e.g. 'San Francisco, New York'"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The unit of temperature to return"
}
},
"required": ["location"],
"additionalProperties": False
}
def get_weather(location: str, unit: str = "fahrenheit"):
# Create the data object for validation
params = {
"location": location,
"unit": unit
}
# Validate parameters using JSON Schema
try:
validate(instance=params, schema=weather_validation_schema)
except ValidationError as e:
raise ValueError(f"Invalid input: {e.message}")
# Prepare the API request
api_server = "https://api.yourweatherapp.com"
endpoint = f"{api_server}/weather"
# Make the API request
response = requests.get(endpoint, params=params)
return response.json()
# Example usage
weather_info = get_weather("Seattle, WA", "celsius")
print(weather_info)
High-level network flow of where Arch Gateway sits in your agentic stack. Managing incoming and outgoing prompt traffic
Once the functions are called, Arch formats the response and deliver back to users.
By completing these setup steps, you enable Arch to manage the process from validation to response, ensuring users receive consistent, reliable results.
Once a downstream function (API) is called, Arch Gateway takes the response and sends it an upstream LLM to complete the request (for summarization, Q/A, text generation tasks).
For more details on how Arch Gateway enables you to centralize usage of LLMs, please read :ref:`LLM providers <llm_provider>`.
By completing these steps, you enable Arch to manage the process from validation to response, ensuring users receive consistent, reliable results - and that you are focused
on the stuff that matters most.
Example Use Cases
-----------------

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16
model_server/pyproject.toml
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@ -1,6 +1,6 @@
[tool.poetry]
name = "archgw_modelserver"
version = "0.1.7"
version = "0.1.8"
description = "A model server for serving models"
authors = ["Katanemo Labs, Inc <info@katanemo.com>"]
license = "Apache 2.0"
@ -10,17 +10,15 @@ packages = [
]
[tool.poetry.dependencies]
python = "^3.12"
python = "^3.10"
fastapi = "0.115.0"
torch = "2.4.1"
uvicorn = "0.31.0"
transformers = "*"
pyyaml = "6.0.2"
accelerate = "*"
psutil = "6.0.0"
pandas = "*"
transformers = "^4.37.0"
accelerate = "^1.0.0"
pydantic = "^2.10.1"
dateparser = "*"
openai = "1.50.2"
openai = "^1.50.2"
httpx = "0.27.2" # https://community.openai.com/t/typeerror-asyncclient-init-got-an-unexpected-keyword-argument-proxies/1040287
pytest-asyncio = "*"
pytest = "*"
@ -33,7 +31,7 @@ pytest-retry = "^1.6.3"
pytest-httpserver = "^1.1.0"
[tool.poetry.scripts]
archgw_modelserver = "src.cli:run_server"
archgw_modelserver = "src.cli:main"
[build-system]
requires = ["poetry-core>=1.0.0"]

140
model_server/src/cli.py
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@ -1,22 +1,17 @@
import importlib
import logging
from os import path
import os
from signal import SIGKILL
import sys
import subprocess
import argparse
import signal
import tempfile
import time
import requests
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
import src.commons.utils as utils
logger = logging.getLogger(__name__)
logger = utils.get_model_server_logger()
def get_version():
@ -42,76 +37,9 @@ def wait_for_health_check(url, timeout=300):
return False
def parse_args():
parser = argparse.ArgumentParser(description="Manage the Uvicorn server.")
parser.add_argument(
"action",
choices=["start", "stop", "restart"],
default="start",
nargs="?",
help="Action to perform on the server (default: start).",
)
parser.add_argument(
"--port",
type=int,
default=51000,
help="Port number for the server (default: 51000).",
)
parser.add_argument(
"--foreground",
default=False,
action="store_true",
help="Run the server in the foreground (default: False).",
)
return parser.parse_args()
def get_pid_file():
temp_dir = tempfile.gettempdir()
return path.join(temp_dir, "model_server.pid")
def stop_server():
"""Stop the Uvicorn server."""
pid_file = get_pid_file()
if os.path.exists(pid_file):
logger.info(f"PID file found, shutting down the server.")
# read pid from file
with open(pid_file, "r") as f:
pid = int(f.read())
logger.info(f"Killing model server {pid}")
try:
os.kill(pid, SIGKILL)
except ProcessLookupError:
logger.info(f"Process {pid} not found")
os.remove(pid_file)
else:
logger.info("No PID file found, server is not running.")
def restart_server(port=51000, foreground=False):
"""Restart the Uvicorn server."""
stop_server()
start_server(port, foreground)
def run_server():
"""Start, stop, or restart the Uvicorn server based on command-line arguments."""
args = parse_args()
action = args.action
if action == "start":
start_server(args.port, args.foreground)
elif action == "stop":
stop_server()
elif action == "restart":
restart_server(args.port, args.foreground)
else:
logger.info(f"Unknown action: {action}")
sys.exit(1)
return os.path.join(temp_dir, "model_server.pid")
def ensure_killed(process):
@ -131,7 +59,7 @@ def ensure_killed(process):
def start_server(port=51000, foreground=False):
"""Start the Uvicorn server."""
logging.info("model server version: %s", get_version())
logger.info("model server version: %s", get_version())
stop_server()
@ -196,6 +124,57 @@ def start_server(port=51000, foreground=False):
ensure_killed(process)
def stop_server():
"""Stop the Uvicorn server."""
pid_file = get_pid_file()
if os.path.exists(pid_file):
logger.info("PID file found, shutting down the server.")
# read pid from file
with open(pid_file, "r") as f:
pid = int(f.read())
logger.info(f"Killing model server {pid}")
try:
os.kill(pid, signal.SIGKILL)
except ProcessLookupError:
logger.info(f"Process {pid} not found")
os.remove(pid_file)
else:
logger.info("No PID file found, server is not running.")
def restart_server(port=51000, foreground=False):
"""Restart the Uvicorn server."""
stop_server()
start_server(port, foreground)
def parse_args():
parser = argparse.ArgumentParser(description="Manage the Uvicorn server.")
parser.add_argument(
"action",
choices=["start", "stop", "restart"],
default="start",
nargs="?",
help="Action to perform on the server (default: start).",
)
parser.add_argument(
"--port",
type=int,
default=51000,
help="Port number for the server (default: 51000).",
)
parser.add_argument(
"--foreground",
default=False,
action="store_true",
help="Run the server in the foreground (default: False).",
)
return parser.parse_args()
def main():
"""
Start, stop, or restart the Uvicorn server based on command-line arguments.
@ -204,11 +183,14 @@ def main():
args = parse_args()
if args.action == "start":
logger.info("[CLI] - Starting server")
start_server(args.port, args.foreground)
elif args.action == "stop":
logger.info("[CLI] - Stopping server")
stop_server()
elif args.action == "restart":
logger.info("[CLI] - Restarting server")
restart_server(args.port)
else:
logger.error(f"Unknown action: {args.action}")
logger.error(f"[CLI] - Unknown action: {args.action}")
sys.exit(1)

6
model_server/src/commons/globals.py
View file

@ -22,9 +22,7 @@ ARCH_CLIENT = OpenAI(base_url=ARCH_ENDPOINT, api_key=ARCH_API_KEY)
# Define model names
ARCH_INTENT_MODEL_ALIAS = "Arch-Intent"
ARCH_FUNCTION_MODEL_ALIAS = "Arch-Function"
logger.info("loading prompt guard model ...")
arch_guard_model = get_guardrail_handler()
ARCH_GUARD_MODEL_ALIAS = "katanemo/Arch-Guard"
# Define model handlers
handler_map = {
@ -34,5 +32,5 @@ handler_map = {
"Arch-Function": ArchFunctionHandler(
ARCH_CLIENT, ARCH_FUNCTION_MODEL_ALIAS, ArchFunctionConfig
),
"Arch-Guard": arch_guard_model,
"Arch-Guard": get_guardrail_handler(ARCH_GUARD_MODEL_ALIAS),
}

81
model_server/src/commons/utils.py
View file

@ -1,87 +1,50 @@
import os
import sys
import time
import torch
import logging
import requests
import subprocess
import importlib
from datetime import datetime
PROJ_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
# Default log directory and file
DEFAULT_LOG_DIR = os.path.join(PROJ_DIR, ".logs")
DEFAULT_LOG_FILE = "modelserver.log"
def get_model_server_logger(log_dir=None, log_file=None):
def get_model_server_logger():
"""
Get or initialize the logger instance for the model server.
Parameters:
- log_dir (str): Custom directory to store the log file. Defaults to `./.logs`.
- log_file (str): Custom log file name. Defaults to `modelserver.log`.
Returns:
- logging.Logger: Configured logger instance.
"""
log_dir = log_dir or DEFAULT_LOG_DIR
log_file = log_file or DEFAULT_LOG_FILE
log_file_path = os.path.join(log_dir, log_file)
# Check if the logger is already configured
logger = logging.getLogger("model_server_logger")
logger = logging.getLogger("model_server")
# Return existing logger instance if already configured
if logger.hasHandlers():
# Return existing logger instance if already configured
return logger
# Ensure the log directory exists, create it if necessary
try:
# Create directory if it doesn't exist
os.makedirs(log_dir, exist_ok=True)
# Check for write permissions
if not os.access(log_dir, os.W_OK):
raise PermissionError(f"No write permission for the directory: {log_dir}")
except (PermissionError, OSError) as e:
raise RuntimeError(f"Failed to initialize logger: {e}")
# Configure logging to file
# Configure logging to only log to console
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
handlers=[
# logging.FileHandler(log_file_path, mode="w"), # Overwrite logs in the file
logging.StreamHandler(), # Also log to console
],
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
handlers=[logging.StreamHandler()],
)
return logger
logger = get_model_server_logger()
logging.info("initializing torch device ...")
import torch
def get_device():
available_device = {
"cpu": True,
"cuda": torch.cuda.is_available(),
"mps": (
torch.backends.mps.is_available()
if hasattr(torch.backends, "mps")
else False
),
}
if available_device["cuda"]:
if torch.cuda.is_available():
device = "cuda"
elif available_device["mps"]:
elif torch.backends.mps.is_available():
device = "mps"
else:
device = "cpu"
return device
def get_today_date():
# Get today's date
today = datetime.now()
# Get full date with day of week
full_date = today.strftime("%Y-%m-%d")
return full_date

204
model_server/src/core/function_calling.py
View file

@ -1,22 +1,24 @@
import ast
import json
import random
import builtins
import textwrap
import src.commons.utils as utils
from openai import OpenAI
from typing import Any, Dict, List
from overrides import override
from src.commons.utils import get_model_server_logger
from src.core.model_utils import (
from src.core.utils.hallucination_utils import HallucinationState
from src.core.utils.model_utils import (
Message,
ChatMessage,
Choice,
ChatCompletionResponse,
ArchBaseHandler,
)
from src.core.hallucination import HallucinationStateHandler
logger = get_model_server_logger()
logger = utils.get_model_server_logger()
class ArchIntentConfig:
@ -74,7 +76,6 @@ class ArchIntentHandler(ArchBaseHandler):
)
self.extra_instruction = config.EXTRA_INSTRUCTION
self.prompt_prefilling = False
@override
def _convert_tools(self, tools: List[Dict[str, Any]]) -> str:
@ -122,15 +123,19 @@ class ArchIntentHandler(ArchBaseHandler):
Note:
Currently only support vllm inference
"""
logger.info("[Arch-Intent] - ChatCompletion")
# In the case that no tools are available, simply return `No` to avoid making a call
if len(req.tools) == 0:
model_response = Message(content="No", tool_calls=[])
logger.info("No tools found, return `No` as the model response.")
else:
messages = self._process_messages(
req.messages, req.tools, self.extra_instruction
)
logger.info(f"[request]: {json.dumps(messages)}")
model_response = self.client.chat.completions.create(
messages=messages,
model=self.model_name,
@ -138,9 +143,7 @@ class ArchIntentHandler(ArchBaseHandler):
extra_body=self.generation_params,
)
logger.info(
"arch_intent response: %s", json.dumps(model_response.model_dump())
)
logger.info(f"[response]: {json.dumps(model_response.model_dump())}")
model_response = Message(
content=model_response.choices[0].message.content, tool_calls=[]
@ -160,7 +163,11 @@ class ArchFunctionConfig:
TASK_PROMPT = textwrap.dedent(
"""
You are a helpful assistant.
"""
Today's date: {}
""".format(
utils.get_today_date()
)
).strip()
TOOL_PROMPT_TEMPLATE = textwrap.dedent(
@ -189,7 +196,7 @@ class ArchFunctionConfig:
"temperature": 0.6,
"top_p": 1.0,
"top_k": 10,
"max_tokens": 512,
"max_tokens": 1024,
"stop_token_ids": [151645],
"logprobs": True,
"top_logprobs": 10,
@ -241,10 +248,11 @@ class ArchFunctionHandler(ArchBaseHandler):
self.prefill_params = config.PREFILL_CONFIG["prefill_params"]
self.prefill_prefix = config.PREFILL_CONFIG["prefill_prefix"]
self.prompt_prefilling = False
self.hallucination_state = None
# Predefine data types for verification. Only support Python for now.
# [TODO] Extend the list of support data types
# TODO: Extend the list of support data types
self.support_data_types = {
type_name: getattr(builtins, type_name)
for type_name in config.SUPPORT_DATA_TYPES
@ -365,15 +373,15 @@ class ArchFunctionHandler(ArchBaseHandler):
return {"result": tool_calls, "status": is_valid, "message": error_message}
def _correcting_type(self, value, target_type):
def _convert_data_type(self, value: str, target_type: str):
# TODO: Add more conversion rules as needed
try:
if target_type == float and isinstance(value, int):
if target_type is float and isinstance(value, int):
return float(value)
elif target_type == list and isinstance(value, str):
elif target_type is list and isinstance(value, str):
return ast.literal_eval(value)
elif target_type == str and not isinstance(value, str):
elif target_type is str and not isinstance(value, str):
return str(value)
# Add more conversion rules as needed
except (ValueError, TypeError, json.JSONDecodeError):
pass
return value
@ -426,32 +434,34 @@ class ArchFunctionHandler(ArchBaseHandler):
break
# Verify the data type of each parameter in the tool calls
function_properties = functions[func_name]["properties"]
for param_name in func_args:
if param_name not in functions[func_name]["properties"]:
if param_name not in function_properties:
is_valid = False
invalid_tool_call = tool_call
error_message = f"Parameter `{param_name}` is not defined in the function `{func_name}`."
break
else:
param_value = func_args[param_name]
data_type = functions[func_name]["properties"][param_name][
"type"
]
target_type = function_properties[param_name]["type"]
if data_type in self.support_data_types:
if not isinstance(
param_value,
self.support_data_types[data_type],
) and not isinstance(
self._correcting_type(
param_value, self.support_data_types[data_type]
),
self.support_data_types[data_type],
):
is_valid = False
invalid_tool_call = tool_call
error_message = f"Parameter `{param_name}` is expected to have the data type `{self.support_data_types[data_type]}`, but got `{type(param_value)}`."
break
if target_type in self.support_data_types:
data_type = self.support_data_types[target_type]
if not isinstance(param_value, data_type):
param_value = self._convert_data_type(
param_value, data_type
)
if not isinstance(param_value, data_type):
is_valid = False
invalid_tool_call = tool_call
error_message = f"Parameter `{param_name}` is expected to have the data type `{data_type}`, got `{type(param_value)}`."
break
else:
error_message = (
f"Data type `{target_type}` is not supported."
)
return {
"status": is_valid,
@ -491,51 +501,8 @@ class ArchFunctionHandler(ArchBaseHandler):
**self.prefill_params,
},
)
self.prompt_prefilling = True
return prefill_response
def _check_length_and_pop_messages(self, messages, max_tokens=4096):
"""
Trims the `messages` list to ensure the total token count does not exceed `max_tokens`.
Args:
messages (list): List of message dictionaries.
max_tokens (int): Maximum allowed token count.
Returns:
list: Trimmed list of messages.
"""
def estimate_token_length(messages):
"""Estimate the total token length of the messages."""
total_tokens = 0
for message in messages:
# Approximate token length: assuming ~4 characters per token on average
total_tokens += len(message["content"]) // 4
return total_tokens
# Calculate initial token length
total_tokens = estimate_token_length(messages)
# Trim messages if token count exceeds the limit
while total_tokens > max_tokens and len(messages) >= 3:
# Find the first non-system message pair
for i in range(len(messages)):
if messages[i]["role"] != "system":
# Remove the 'user'/'assistant' pair
if i + 1 < len(messages) and messages[i + 1]["role"] in [
"user",
"assistant",
]:
del messages[i : i + 2]
else:
del messages[i]
break
# Recalculate token length
total_tokens = estimate_token_length(messages)
return messages
@override
async def chat_completion(self, req: ChatMessage) -> ChatCompletionResponse:
"""
@ -550,13 +517,11 @@ class ArchFunctionHandler(ArchBaseHandler):
Note:
Currently only support vllm inference
"""
logger.info(
f"model_server => arch_function: request body: {json.dumps(req.model_dump())}"
)
logger.info("[Arch-Function] - ChatCompletion")
messages = self._process_messages(req.messages, req.tools)
messages = self._check_length_and_pop_messages(messages)
logger.info(f"[request]: {json.dumps(messages)}")
# always enable `stream=True` to collect model responses
response = self.client.chat.completions.create(
@ -567,45 +532,39 @@ class ArchFunctionHandler(ArchBaseHandler):
)
# initialize the hallucination handler, which is an iterator
self.hallu_handler = HallucinationStateHandler(
self.hallucination_state = HallucinationState(
response_iterator=response, function=req.tools
)
model_response, self.has_tool_call = "", None
self.hallucination = False
for _ in self.hallu_handler:
model_response = ""
has_tool_calls, has_hallucination = None, False
for _ in self.hallucination_state:
# check if the first token is <tool_call>
if len(self.hallu_handler.tokens) > 0 and self.has_tool_call is None:
if self.hallu_handler.tokens[0] == "<tool_call>":
self.has_tool_call = True
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:
self.has_tool_call = False
has_tool_calls = False
break
# if the model is hallucinating, start parameter gathering
if self.hallu_handler.hallucination is True:
self.hallucination = True
logger.info(
f"{self.hallu_handler.error_message} - start parameter gathering"
)
logger.info(
f"Hallucinated response : {''.join(self.hallu_handler.tokens)}"
)
# [TODO] - add break when hallucination is detected
if self.hallucination_state.hallucination is True:
has_hallucination = True
break
if self.hallucination is True:
prefill_response = self._engage_parameter_gathering(messages)
model_response = prefill_response.choices[0].message.content
if self.has_tool_call and self.hallucination is False:
# [TODO] - Review: remove the following code
model_response = "".join(self.hallu_handler.tokens)
logger.info(f"Tool call found, no hallucination detected {model_response}!")
# start parameter gathering if the model is not generating tool calls
if self.has_tool_call is False:
# [TODO] - Review: remove the following code
logger.info("No tool call found, start parameter gathering")
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
@ -613,21 +572,20 @@ class ArchFunctionHandler(ArchBaseHandler):
extracted = self._extract_tool_calls(model_response)
if len(extracted["result"]) and extracted["status"]:
# [TODO] Review: define the behavior in the case that tool call extraction fails
# if not extracted["status"]:
verified = self._verify_tool_calls(
tools=req.tools, tool_calls=extracted["result"]
)
# [TODO] - Review: remvoe the following code
# print(f"[Verified] - {verified}")
# [TODO] Review: In the case that tool calls are invalid, define the protocol to collect debugging output and the behavior to handle it appropriately
if verified["status"]:
logger.info(
f"[Tool calls]: {json.dumps([tool_call['function'] for tool_call in extracted['result']])}"
)
model_response = Message(content="", tool_calls=extracted["result"])
log_message = f"model_server <= arch_function: (tool_calls): {json.dumps([tool_call['function'] for tool_call in extracted['result']])}"
logger.info(log_message)
else:
raise ValueError(f"Invalid tool call: {verified['message']}")
logger.error(f"Invalid tool call - {verified['message']}")
# raise ValueError(
# f"[Arch-Function]: Invalid tool call - {verified['message']}"
# )
else:
model_response = Message(content=model_response, tool_calls=[])
@ -635,10 +593,6 @@ class ArchFunctionHandler(ArchBaseHandler):
choices=[Choice(message=model_response)], model=self.model_name
)
# [TODO] Review: define the protocol to collect debugging output
logger.info(
f"model_server <= arch_function: response body: {json.dumps(chat_completion_response.model_dump())}"
)
logger.info(f"[response]: {json.dumps(chat_completion_response.model_dump())}")
return chat_completion_response

63
model_server/src/core/guardrails.py
View file

@ -1,12 +1,12 @@
import time
import torch
import numpy as np
import src.commons.utils as utils
from transformers import AutoTokenizer
from src.core.model_utils import GuardRequest, GuardResponse
# from optimum.intel import OVModelForSequenceClassification
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from src.core.utils.model_utils import GuardRequest, GuardResponse
logger = utils.get_model_server_logger()
class ArchGuardHanlder:
@ -76,26 +76,15 @@ class ArchGuardHanlder:
text, truncation=True, max_length=max_length, return_tensors="pt"
).to(self.device)
start_time = time.perf_counter()
with torch.no_grad():
logits = self.model(**inputs).logits.cpu().detach().numpy()[0]
prob = ArchGuardHanlder.softmax(logits)[
self.support_tasks[task]["positive_class"]
]
].item()
latency = time.perf_counter() - start_time
verdict = prob > self.support_tasks[task]["threshold"]
if prob > self.support_tasks[task]["threshold"]:
verdict = True
sentence = text
else:
verdict = False
sentence = None
return GuardResponse(
prob=[prob.item()], verdict=verdict, sentence=[sentence], latency=latency
)
return GuardResponse(task=task, input=text, prob=prob, verdict=verdict)
def predict(self, req: GuardRequest, max_num_words=300) -> GuardResponse:
"""
@ -115,29 +104,37 @@ class ArchGuardHanlder:
if req.task not in self.support_tasks:
raise NotImplementedError(f"{req.task} is not supported!")
logger.info("[Arch-Guard] - Prediction")
logger.info(f"[request]: {req.input}")
if len(req.input.split()) < max_num_words:
return self._predict_text(req.task, req.input)
result = self._predict_text(req.task, req.input)
else:
prob, verdict = 0.0, False
# split into chunks if text is long
text_chunks = self._split_text_into_chunks(req.input)
prob, verdict, sentence, latency = [], False, [], 0
for chunk in text_chunks:
chunk_result = self._predict_text(req.task, chunk)
if chunk_result.verdict:
prob.append(chunk_result.prob[0])
prob = chunk_result.prob
verdict = True
sentence.append(chunk_result.sentence[0])
latency += chunk_result.latency
break
return GuardResponse(
prob=prob, verdict=verdict, sentence=sentence, latency=latency
result = GuardResponse(
task=req.task, input=req.input, prob=prob, verdict=verdict
)
logger.info(
f"[response]: {req.task}: {'True' if result.verdict else 'False'} (prob: {result.prob:.2f})"
)
def get_guardrail_handler(device: str = None):
return result
def get_guardrail_handler(model_name: str = "katanemo/Arch-Guard", device: str = None):
"""
Initializes and returns an instance of ArchGuardHanlder based on the specified device.
@ -151,19 +148,11 @@ def get_guardrail_handler(device: str = None):
if device is None:
device = utils.get_device()
model_class, model_name = None, None
# if device == "cpu":
# model_class = OVModelForSequenceClassification
# model_name = "katanemo/Arch-Guard-cpu"
# else:
model_class = AutoModelForSequenceClassification
model_name = "katanemo/Arch-Guard"
guardrail_dict = {
"device": device,
"model_name": model_name,
"tokenizer": AutoTokenizer.from_pretrained(model_name, trust_remote_code=True),
"model": model_class.from_pretrained(
"model": AutoModelForSequenceClassification.from_pretrained(
model_name, device_map=device, low_cpu_mem_usage=True
),
}

0
model_server/src/core/utils/__init__.py Normal file
View file

12
model_server/src/core/hallucination.py → model_server/src/core/utils/hallucination_utils.py
View file

@ -127,7 +127,7 @@ def is_parameter_property(
return property_name in parameter_info
class HallucinationStateHandler:
class HallucinationState:
"""
A class to handle the state of hallucination detection in token processing.
@ -145,7 +145,7 @@ class HallucinationStateHandler:
def __init__(self, response_iterator=None, function=None):
"""
Initializes the HallucinationStateHandler with default values.
Initializes the HallucinationState with default values.
"""
self.tokens: List[str] = []
self.logprobs: List[float] = []
@ -173,7 +173,7 @@ class HallucinationStateHandler:
def _reset_parameters(self):
"""
Resets all parameters in the HallucinationStateHandler to their default values.
Resets all parameters in the HallucinationState to their default values.
"""
self.state = None
self.parameter_name_done = False
@ -268,7 +268,7 @@ class HallucinationStateHandler:
# if the parameter name is done and the token is a parameter name start token, change the state
elif (
self.parameter_name_done
and self.open_bracket == False
and not self.open_bracket
and content.endswith(PARAMETER_NAME_START_PATTERN)
):
self.state = "parameter_name"
@ -324,7 +324,7 @@ class HallucinationStateHandler:
# if the state is parameter value and the token is an end token, change the state
elif (
self.state == "parameter_value"
and self.open_bracket == False
and not self.open_bracket
and content.endswith(PARAMETER_VALUE_END_TOKEN)
):
self.state = None
@ -354,7 +354,7 @@ class HallucinationStateHandler:
self.HALLUCINATION_THRESHOLD_DICT[self.mask[-1].value],
):
self.hallucination = True
self.error_message = f"Hallucination: token '{self.tokens[-1]}' is uncertain. {self.token_probs_map}"
self.error_message = f"token '{self.tokens[-1]}' is uncertain. Generated response:\n{''.join(self.tokens)}"
def _count_consecutive_token(self, token=MaskToken.PARAMETER_VALUE) -> int:
"""

44
model_server/src/core/model_utils.py → model_server/src/core/utils/model_utils.py
View file

@ -14,8 +14,8 @@ class Message(BaseModel):
class ChatMessage(BaseModel):
messages: list[Message]
tools: List[Dict[str, Any]]
messages: List[Message] = []
tools: List[Dict[str, Any]] = []
class Choice(BaseModel):
@ -28,8 +28,8 @@ class ChatCompletionResponse(BaseModel):
id: Optional[int] = 0
object: Optional[str] = "chat_completion"
created: Optional[str] = ""
choices: List[Choice]
model: str
choices: List[Choice] = []
model: str = ""
metadata: Optional[Dict[str, str]] = {}
@ -39,10 +39,11 @@ class GuardRequest(BaseModel):
class GuardResponse(BaseModel):
prob: List
verdict: bool
sentence: List
latency: float = 0
task: str = ""
input: str = ""
prob: float = 0.0
verdict: bool = False
metadata: Optional[Dict[str, str]] = {}
# ================================================================================================
@ -121,6 +122,7 @@ class ArchBaseHandler:
messages: List[Message],
tools: List[Dict[str, Any]] = None,
extra_instruction: str = None,
max_tokens=4096,
):
"""
Processes a list of messages and formats them appropriately.
@ -129,6 +131,7 @@ class ArchBaseHandler:
messages (List[Message]): A list of message objects.
tools (List[Dict[str, Any]], optional): A list of tools to include in the system prompt.
extra_instruction (str, optional): Additional instructions to append to the last user message.
max_tokens (int): Maximum allowed token count, assuming ~4 characters per token on average.
Returns:
List[Dict[str, Any]]: A list of processed message dictionaries.
@ -149,14 +152,12 @@ class ArchBaseHandler:
)
if tool_calls:
# [TODO] Extend to support multiple function calls
# TODO: Extend to support multiple function calls
role = "assistant"
content = f"<tool_call>\n{json.dumps(tool_calls[0]['function'])}\n</tool_call>"
elif message.role == "tool":
elif role == "tool":
role = "user"
content = (
f"<tool_response>\n{json.dumps(message.content)}\n</tool_response>"
)
content = f"<tool_response>\n{json.dumps(content)}\n</tool_response>"
processed_messages.append({"role": role, "content": content})
@ -165,6 +166,23 @@ class ArchBaseHandler:
if extra_instruction:
processed_messages[-1]["content"] += extra_instruction
# keep the first system message and shift conversation if the total token length exceeds the limit
def truncate_messages(messages: List[Dict[str, Any]]):
num_tokens, conversation_idx = 0, 0
if messages[0]["role"] == "system":
num_tokens += len(messages[0]["content"]) // 4
conversation_idx = 1
for message_idx in range(len(messages) - 1, conversation_idx - 1, -1):
num_tokens += len(messages[message_idx]["content"]) // 4
if num_tokens >= max_tokens:
if messages[message_idx]["role"] == "user":
break
return messages[:conversation_idx] + messages[message_idx:]
processed_messages = truncate_messages(processed_messages)
return processed_messages
async def chat_completion(self, req: ChatMessage) -> ChatCompletionResponse:

110
model_server/src/main.py
View file

@ -1,25 +1,25 @@
import json
import logging
import os
import time
import logging
import src.commons.utils as utils
from src.commons.globals import handler_map
from src.core.model_utils import ChatMessage, GuardRequest
from src.core.utils.model_utils import (
ChatMessage,
ChatCompletionResponse,
GuardRequest,
GuardResponse,
)
from fastapi import FastAPI, Response
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
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
logger = logging.getLogger(__name__)
resource = Resource.create(
{
@ -31,11 +31,6 @@ resource = Resource.create(
trace.set_tracer_provider(TracerProvider(resource=resource))
tracer = trace.get_tracer(__name__)
app = FastAPI()
FastAPIInstrumentor().instrument_app(app)
# DEFAULT_OTLP_HOST = "http://localhost:4317"
DEFAULT_OTLP_HOST = "none"
@ -47,6 +42,16 @@ otlp_exporter = OTLPSpanExporter(
trace.get_tracer_provider().add_span_processor(BatchSpanProcessor(otlp_exporter))
logger = utils.get_model_server_logger()
logging.getLogger("httpx").setLevel(logging.ERROR)
logging.getLogger("opentelemetry.exporter.otlp.proto.grpc.exporter").setLevel(
logging.ERROR
)
app = FastAPI()
FastAPIInstrumentor().instrument_app(app)
@app.get("/healthz")
async def healthz():
return {"status": "ok"}
@ -62,73 +67,78 @@ async def models():
@app.post("/function_calling")
async def function_calling(req: ChatMessage, res: Response):
logger.info("[Endpoint: /function_calling]")
logger.info(f"[request body]: {json.dumps(req.model_dump())}")
final_response: ChatCompletionResponse = None
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
if handler_map["Arch-Intent"].detect_intent(intent_response):
# [TODO] measure agreement between intent detection and function calling
# TODO: measure agreement between intent detection and function calling
try:
function_start_time = time.perf_counter()
function_calling_response = await handler_map[
"Arch-Function"
].chat_completion(req)
final_response = await handler_map["Arch-Function"].chat_completion(req)
function_latency = time.perf_counter() - function_start_time
function_calling_response.metadata = {
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),
"tokens_uncertainty": json.dumps(
handler_map["Arch-Function"].hallu_handler.token_probs_map
),
"prompt_prefilling": str(
handler_map["Arch-Function"].prompt_prefilling
"hallucination": str(
handler_map["Arch-Function"].hallucination_state.hallucination
),
}
return function_calling_response
except ValueError as e:
res.statuscode = 503
error_message = "Tool call extraction error"
logger.error(f" {error_message}: {e}")
return {"error": f"[Arch-Function] - {error_message} - {e}"}
error_messages = f"[Arch-Function] - Error in tool call extraction: {e}"
except StopIteration as e:
res.statuscode = 500
error_message = "Hallucination iterator error"
logger.error(f" {error_message}: {e}")
return {"error": f"[Arch-Function] - {error_message} - {e}"}
error_messages = f"[Arch-Function] - Error in hallucination check: {e}"
except Exception as e:
# [TODO] Review: update how to collect debugging outputs
logger.error(f"Error in chat_completion from `Arch-Function`: {e}")
res.status_code = 500
return {"error": f"[Arch-Function] - {e}"}
# [TODO] Review: define the behavior if `Arch-Intent` doesn't detect an intent
error_messages = f"[Arch-Function] - Error in ChatCompletion: {e}"
else:
return {
"result": "No intent matched",
"intent_latency": round(intent_latency * 1000, 3),
intent_response.metadata = {
"intent_latency": str(round(intent_latency * 1000, 3)),
}
final_response = intent_response
except Exception as e:
# [TODO] Review: update how to collect debugging outputs
# logger.error(f"Error in chat_completion from `Arch-Intent`: {e}")
logger.error(f"Error in chat_completion /function_calling: {e}")
res.status_code = 500
return {"error": f"[Arch-Intent] - {e}"}
error_messages = f"[Arch-Intent] - Error in ChatCompletion: {e}"
if error_messages is not None:
logger.error(error_messages)
final_response = ChatCompletionResponse(metadata={"error": error_messages})
return final_response
@app.post("/guardrails")
async def guardrails(req: GuardRequest, res: Response, max_num_words=300):
logger.info("[Endpoint: /guardrails] - Gateway")
logger.info(f"[request body]: {json.dumps(req.model_dump())}")
final_response: GuardResponse = None
error_messages = None
try:
guard_start_time = time.perf_counter()
guard_result = handler_map["Arch-Guard"].predict(req)
final_response = handler_map["Arch-Guard"].predict(req)
guard_latency = time.perf_counter() - guard_start_time
return {
"response": guard_result,
final_response.metadata = {
"guard_latency": round(guard_latency * 1000, 3),
}
except Exception as e:
# [TODO] Review: update how to collect debugging outputs
res.status_code = 500
return {"error": f"[Arch-Guard] - {e}"}
error_messages = f"[Arch-Guard]: {e}"
if error_messages is not None:
logger.error(error_messages)
final_response = GuardResponse(metadata={"error": error_messages})
return final_response

15
model_server/tests/core/test_function_calling.py
View file

@ -1,12 +1,8 @@
import os
import pytest
from src.commons.globals import handler_map
from src.core.model_utils import ChatMessage, Message
import pytest
from fastapi.testclient import TestClient
from unittest.mock import AsyncMock, patch
from src.main import app
from src.commons.globals import handler_map
from src.core.utils.model_utils import ChatMessage, Message
# define function
get_weather_api = {
@ -163,7 +159,10 @@ async def test_function_calling(get_data_func):
function_calling_response = await handler_map["Arch-Function"].chat_completion(
req
)
assert handler_map["Arch-Function"].hallu_handler.hallucination == hallucination
assert (
handler_map["Arch-Function"].hallucination_state.hallucination
== hallucination
)
response_txt = function_calling_response.choices[0].message.content
if parameter_gathering:

27
model_server/tests/core/test_guardrails.py
View file

@ -1,33 +1,6 @@
from unittest.mock import patch, MagicMock
from src.core.guardrails import get_guardrail_handler
# Mock constants
arch_guard_model_type = {
"cpu": "katanemo/Arch-Guard-cpu",
"cuda": "katanemo/Arch-Guard",
"mps": "katanemo/Arch-Guard",
}
# [TODO] Review: check the following code to test under `cpu`, `cuda`, and `mps`
# Test for `get_guardrail_handler()` function on `cpu`
@patch("src.core.guardrails.AutoTokenizer.from_pretrained")
@patch("src.core.guardrails.AutoModelForSequenceClassification.from_pretrained")
def test_guardrail_handler_on_cpu(mock_auto_model, mock_tokenizer):
device = "cpu"
mock_tokenizer.return_value = MagicMock()
guardrail = get_guardrail_handler(device=device)
mock_tokenizer.assert_called_once_with(guardrail.model_name, trust_remote_code=True)
mock_auto_model.assert_called_once_with(
guardrail.model_name,
device_map=device,
low_cpu_mem_usage=True,
)
# Test for `get_guardrail_handler()` function on `cuda`
@patch("src.core.guardrails.AutoTokenizer.from_pretrained")

6
model_server/tests/test_app.py
View file

@ -8,7 +8,6 @@ from src.main import app
client = TestClient(app)
# [TODO] Review: check the following code. Seems something wrong with asyncio package❗
# Unit tests for the health check endpoint
@pytest.mark.asyncio
async def test_healthz():
@ -17,7 +16,6 @@ async def test_healthz():
assert response.json() == {"status": "ok"}
# [TODO] Review: check the following code. Seems something wrong with asyncio package❗
# Unit test for the models endpoint
@pytest.mark.asyncio
async def test_models():
@ -27,17 +25,14 @@ async def test_models():
assert len(response.json()["data"]) > 0
# [TODO] Review: check the following code. Seems something wrong with asyncio package❗
# Unit test for the guardrail endpoint
@pytest.mark.asyncio
async def test_guardrail_endpoint():
request_data = {"input": "Test for jailbreak and toxicity", "task": "jailbreak"}
response = client.post("/guardrails", json=request_data)
assert response.status_code == 200
assert "response" in response.json()
# [TODO] Review: check the following code. Seems something wrong with asyncio package❗
# Unit test for the function calling endpoint
@pytest.mark.asyncio
async def test_function_calling_endpoint():
@ -50,4 +45,3 @@ async def test_function_calling_endpoint():
}
response = await client.post("/function_calling", json=request_data)
assert response.status_code == 200
assert "result" in response.json()

413
tests/modelserver/poetry.lock generated
View file

@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
[[package]]
name = "attrs"
@ -111,116 +111,103 @@ pycparser = "*"
[[package]]
name = "charset-normalizer"
version = "3.4.0"
version = "3.4.1"
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
optional = false
python-versions = ">=3.7.0"
python-versions = ">=3.7"
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]
[package.dependencies]
tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""}
[package.extras]
toml = ["tomli"]
@ -326,6 +316,20 @@ orderly-set = ">=5.2.3,<6"
cli = ["click (==8.1.7)", "pyyaml (==6.0.2)"]
optimize = ["orjson"]
[[package]]
name = "exceptiongroup"
version = "1.2.2"
description = "Backport of PEP 654 (exception groups)"
optional = false
python-versions = ">=3.7"
files = [
{file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
{file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
]
[package.extras]
test = ["pytest (>=6)"]
[[package]]
name = "h11"
version = "0.14.0"
@ -519,9 +523,11 @@ files = [
[package.dependencies]
colorama = {version = "*", markers = "sys_platform == \"win32\""}
exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
iniconfig = "*"
packaging = "*"
pluggy = ">=1.5,<2"
tomli = {version = ">=1", markers = "python_version < \"3.11\""}
[package.extras]
dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
@ -733,19 +739,61 @@ files = [
tests = ["pytest", "pytest-cov"]
[[package]]
name = "trio"
version = "0.27.0"
description = "A friendly Python library for async concurrency and I/O"
name = "tomli"
version = "2.2.1"
description = "A lil' TOML parser"
optional = false
python-versions = ">=3.8"
files = [
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{file = "trio-0.27.0.tar.gz", hash = "sha256:1dcc95ab1726b2da054afea8fd761af74bad79bd52381b84eae408e983c76831"},
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{file = "tomli-2.2.1.tar.gz", hash = "sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff"},
]
[[package]]
name = "trio"
version = "0.28.0"
description = "A friendly Python library for async concurrency and I/O"
optional = false
python-versions = ">=3.9"
files = [
{file = "trio-0.28.0-py3-none-any.whl", hash = "sha256:56d58977acc1635735a96581ec70513cc781b8b6decd299c487d3be2a721cd94"},
{file = "trio-0.28.0.tar.gz", hash = "sha256:4e547896fe9e8a5658e54e4c7c5fa1db748cbbbaa7c965e7d40505b928c73c05"},
]
[package.dependencies]
attrs = ">=23.2.0"
cffi = {version = ">=1.14", markers = "os_name == \"nt\" and implementation_name != \"pypy\""}
exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
idna = "*"
outcome = "*"
sniffio = ">=1.3.0"
@ -763,6 +811,7 @@ files = [
]
[package.dependencies]
exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
trio = ">=0.11"
wsproto = ">=0.14"
@ -779,13 +828,13 @@ files = [
[[package]]
name = "urllib3"
version = "2.2.3"
version = "2.3.0"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.8"
python-versions = ">=3.9"
files = [
{file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
{file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
{file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
{file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
]
[package.dependencies]
@ -846,5 +895,5 @@ h11 = ">=0.9.0,<1"
[metadata]
lock-version = "2.0"
python-versions = "^3.12"
content-hash = "56e809b45f1e4d0ab865523472c7ea4587d1c4b2408ee62ec16d1507aecf0e1d"
python-versions = "^3.10"
content-hash = "d0ba3f1875425991d473c9b6fee9f9cb35de5df5835fd7f7e27e3863ff7d37fe"

2
tests/modelserver/pyproject.toml
View file

@ -8,7 +8,7 @@ readme = "README.md"
package-mode = false
[tool.poetry.dependencies]
python = "^3.12"
python = "^3.10"
pytest = "^8.3.3"
requests = "^2.29.0"
selenium = "^4.11.2"

12
tests/modelserver/test_hallucination.py
View file

@ -1,13 +1,9 @@
import json
import os
import pytest
import requests
from deepdiff import DeepDiff
import logging
import yaml
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
MODEL_SERVER_ENDPOINT = os.getenv(
"MODEL_SERVER_ENDPOINT", "http://localhost:51000/function_calling"
@ -37,13 +33,9 @@ def test_model_server(test_data):
response = requests.post(MODEL_SERVER_ENDPOINT, json=input)
assert response.status_code == 200
# print(json.dumps(response.json()))
# ensure that response is json
assert response.headers["content-type"] == "application/json"
response_json = response.json()
assert response_json
metadata = response_json.get("metadata", [])
metadata = response_json.get("metadata", {})
assert (metadata["hallucination"].lower() == "true") == expected[0]["hallucination"]
assert (metadata["prompt_prefilling"].lower() == "true") == expected[0][
"prompt_prefilling"
]

29
tests/modelserver/test_hallucination_data.yaml
View file

@ -5,8 +5,7 @@ test_cases:
- role: "user"
content: "what is the weather forecast for seattle?"
tools:
- id: "weather-112"
type: "function"
- type: "function"
function:
name: "get_current_weather"
description: "Get current weather at a location."
@ -14,17 +13,18 @@ test_cases:
type: "object"
properties:
location:
type: "str"
type: "string"
description: "The location to get the weather for"
format: "City, State"
days:
type: "int"
description: "the number of days for the request."
required: ["location", "days"]
type: "integer"
description: "The number of days for the request."
required:
- location
- days
expected:
- type: "metadata"
hallucination: false
prompt_prefilling: true
- type: "metadata"
hallucination: false
- id: "[WEATHER AGENT] - single turn, single tool, hallucination"
input:
@ -32,8 +32,7 @@ test_cases:
- role: "user"
content: "what is the weather in Seattle in days?"
tools:
- id: "weather-112"
type: "function"
- type: "function"
function:
name: "get_current_weather"
description: "Get current weather at a location."
@ -51,7 +50,6 @@ test_cases:
expected:
- type: "metadata"
hallucination: true
prompt_prefilling: true
- id: "[WEATHER AGENT] - multi turn, single tool, all params passed"
input:
@ -85,8 +83,6 @@ test_cases:
expected:
- type: "metadata"
hallucination: true
prompt_prefilling: true
- id: "[WEATHER AGENT] - multi turn, single tool, clarification"
input:
@ -120,7 +116,7 @@ test_cases:
expected:
- type: "metadata"
hallucination: false
prompt_prefilling: true
- id: "[SALE AGENT] - single turn, single tool, hallucination region"
input:
messages:
@ -148,7 +144,6 @@ test_cases:
expected:
- type: "metadata"
hallucination: true
prompt_prefilling: true
- id: "[SALE AGENT] - single turn, single tool, hallucination industry"
input:
@ -177,7 +172,6 @@ test_cases:
expected:
- type: "metadata"
hallucination: true
prompt_prefilling: true
- id: "[PRODUCT AGENT] - single turn, single tool, hallucination industry"
input:
@ -261,4 +255,3 @@ test_cases:
expected:
- type: "metadata"
hallucination: true
prompt_prefilling: true

7
tests/modelserver/test_modelserver.py
View file

@ -1,13 +1,10 @@
import json
import os
import pytest
import requests
from deepdiff import DeepDiff
import logging
import yaml
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
from deepdiff import DeepDiff
MODEL_SERVER_ENDPOINT = os.getenv(
"MODEL_SERVER_ENDPOINT", "http://localhost:51000/function_calling"

89
tests/modelserver/test_success_data.yaml
View file

@ -107,50 +107,51 @@ test_cases:
location: "Los Angeles, CA"
days: 5
- id: "[WEATHER AGENT] - multi turn, single tool, infer param from context"
input:
messages:
- role: "user"
content: "how is the weather in chicago for next 5 days?"
- role: "assistant"
tool_calls:
- id: "call_3394"
type: "function"
function:
name: "get_current_weather"
arguments:
city: "Chicago, IL"
days: 5
- role: "tool"
content: "--"
tool_call_id: "call_3394"
- role: "assistant"
content: "--"
- role: "user"
content: "how is the weather in LA?"
tools:
- type: "function"
function:
name: "get_current_weather"
description: "Get current weather at a location."
parameters:
type: "object"
properties:
location:
type: "str"
description: "The location to get the weather for"
format: "City, State"
days:
type: "int"
description: "the number of days for the request."
required: ["location", "days"]
expected:
- type: "function"
function:
name: "get_current_weather"
arguments:
location: "Los Angeles, CA"
days: 5
# Skip!
# - id: "[WEATHER AGENT] - multi turn, single tool, infer param from context"
# input:
# messages:
# - role: "user"
# content: "how is the weather in chicago for next 5 days?"
# - role: "assistant"
# tool_calls:
# - id: "call_3394"
# type: "function"
# function:
# name: "get_current_weather"
# arguments:
# location: "Chicago, IL"
# days: 5
# - role: "tool"
# content: "--"
# tool_call_id: "call_3394"
# - role: "assistant"
# content: "--"
# - role: "user"
# content: "how is the weather in LA?"
# tools:
# - type: "function"
# function:
# name: "get_current_weather"
# description: "Get current weather at a location."
# parameters:
# type: "object"
# properties:
# location:
# type: "str"
# description: "The location to get the weather for"
# format: "City, State"
# days:
# type: "int"
# description: "the number of days for the request."
# required: ["location", "days"]
# expected:
# - type: "function"
# function:
# name: "get_current_weather"
# arguments:
# location: "Los Angeles, CA"
# days: 5
- id: "[WEATHER AGENT] - multi turn, single tool, infer param from context 2nd try"
input:

53
tests/rest/api_model_server.rest
View file

@ -15,7 +15,6 @@ Content-Type: application/json
],
"tools": [
{
"id": "weather-112",
"type": "function",
"function": {
"name": "get_current_weather",
@ -53,32 +52,30 @@ Content-Type: application/json
],
"tools": [
{
"id": "weather-112",
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get current weather at a location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "str",
"description": "The location to get the weather for",
"format": "City, State"
},
"unit": {
"type": "str",
"description": "The unit to return the weather in.",
"enum": ["celsius", "fahrenheit"],
"default": "celsius"
},
"days": {
"type": "str",
"description": "the number of days for the request."
}
},
"required": ["location", "days"]
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get current weather at a location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The location to get the weather for",
"format": "City, State"
},
"unit": {
"type": "string",
"description": "The unit to return the weather in.",
"enum": ["celsius", "fahrenheit"],
"default": "celsius"
},
"days": {
"type": "string",
"description": "The number of days for the request."
}
},
"required": ["location", "days"]
}
}
}
@ -87,6 +84,7 @@ Content-Type: application/json
### talk to function calling endpoint
POST {{model_server_endpoint}}/function_calling HTTP/1.1
Content-Type: application/json
@ -121,7 +119,7 @@ Content-Type: application/json
}
### talk to Arch-Intent directly for completion
POST https://api.fc.archgw.com/v1/chat/completions HTTP/1.1
POST {{archfc_endpoint}}/v1/chat/completions HTTP/1.1
Content-Type: application/json
{
@ -212,7 +210,6 @@ Content-Type: application/json
],
"tools": [
{
"id": "weather-112",
"type": "function",
"function": {
"name": "weather_forecast",

4
tests/rest/insurance_agent.rest
View file

@ -31,7 +31,6 @@ Content-Type: application/json
],
"tools": [
{
"id": "weather-112",
"type": "function",
"function": {
"name": "get_current_weather",
@ -103,7 +102,6 @@ Content-Type: application/json
],
"tools": [
{
"id": "weather-112",
"type": "function",
"function": {
"name": "get_current_weather",
@ -172,7 +170,6 @@ Content-Type: application/json
],
"tools": [
{
"id": "weather-112",
"type": "function",
"function":
{
@ -374,7 +371,6 @@ Content-Type: application/json
],
"tools": [
{
"id": "weather-112",
"type": "function",
"function":
{