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Merge branch 'main' of https://github.com/katanemo/arch into cotran/hallu-fix

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cotran 2024-10-15 11:25:58 -07:00
commit b8c6bd73af
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5
.github/workflows/checks.yml vendored
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@ -1,6 +1,9 @@
name: Checks name: Checks
on: pull_request on:
pull_request:
push:
branches: [main]
jobs: jobs:
test: test:

1
.gitignore vendored
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@ -30,3 +30,4 @@ model_server/venv_model_server
model_server/build model_server/build
model_server/dist model_server/dist
arch_logs/ arch_logs/
dist/

17
README.md
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@ -2,16 +2,18 @@
<img src="docs/source/_static/img/arch-logo.png" alt="Arch Gateway Logo" title="Arch Gateway Logo"> <img src="docs/source/_static/img/arch-logo.png" alt="Arch Gateway Logo" title="Arch Gateway Logo">
</p> </p>
## Build fast, robust, and personalized GenAI apps (agents, assistants, etc.) ## Build fast, robust, and personalized AI agents.
Arch is an intelligent [Layer 7](https://www.cloudflare.com/learning/ddos/what-is-layer-7/) gateway designed for generative AI apps, AI agents, and co-pilots that work with prompts. Engineered with purpose-built LLMs, Arch handles the critical but undifferentiated tasks related to the handling and processing of prompts, including detecting and rejecting [jailbreak](https://github.com/verazuo/jailbreak_llms) 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. Arch is an intelligent [Layer 7](https://www.cloudflare.com/learning/ddos/what-is-layer-7/) gateway designed to protect, observe, and personalize LLM applications (agents, assistants, co-pilots) with your APIs.
Engineered with purpose-built LLMs, Arch handles the critical but undifferentiated tasks related to the handling and processing of prompts, including detecting and rejecting [jailbreak](https://github.com/verazuo/jailbreak_llms) 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.
Arch is built on (and by the core contributors of) [Envoy Proxy](https://www.envoyproxy.io/) with the belief that: Arch is built on (and by the core contributors of) [Envoy Proxy](https://www.envoyproxy.io/) with the belief that:
>Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests including secure handling, intelligent routing, robust observability, and integration with backend (API) systems for personalization – all outside business logic.* >Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests including secure handling, intelligent routing, robust observability, and integration with backend (API) systems for personalization – all outside business logic.*
**Core Features**: **Core Features**:
- Built on [Envoy](https://envoyproxy.io): Arch runs alongside application servers, and builds on top of Envoy's proven HTTP management and scalability features to handle ingress and egress traffic related to prompts and LLMs - Built on [Envoy](https://envoyproxy.io): Arch runs alongside application servers, 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 Agentic 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. - Function Calling for fast Agentic 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.
- Prompt [Guard](https://huggingface.co/collections/katanemo/arch-guard-6702bdc08b889e4bce8f446d): Arch centralizes prompt guardrails to prevent jailbreak attempts and ensure safe user interactions without writing a single line of code. - Prompt [Guard](https://huggingface.co/collections/katanemo/arch-guard-6702bdc08b889e4bce8f446d): Arch centralizes prompt guardrails to prevent jailbreak attempts and ensure safe user interactions without writing a single line of code.
- Traffic Management: Arch manages LLM calls, offering smart retries, automatic cutover, and resilient upstream connections for continuous availability. - Traffic Management: Arch manages LLM calls, offering smart retries, automatic cutover, and resilient upstream connections for continuous availability.
@ -20,7 +22,7 @@ Arch is an intelligent [Layer 7](https://www.cloudflare.com/learning/ddos/what-i
**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. **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.
## Contact ## Contact
To get in touch with us, please join our [discord server](https://discord.gg/rbjqVbpa). We will be monitoring that actively and offering support there. To get in touch with us, please join our [discord server](https://discord.gg/rSRQ9fv7). We will be monitoring that actively and offering support there.
## Demos ## Demos
* [Function Calling](demos/function_calling/README.md) - Walk through of critical function calling capabilities * [Function Calling](demos/function_calling/README.md) - Walk through of critical function calling capabilities
@ -35,7 +37,7 @@ Follow this guide to learn how to quickly set up Arch and integrate it into your
Before you begin, ensure you have the following: Before you begin, ensure you have the following:
- `Docker` & `Python` verion 3.10 installed on your system - `Docker` & `Python` installed on your system
- `API Keys` for LLM providers (if using external LLMs) - `API Keys` for LLM providers (if using external LLMs)
### Step 1: Install Arch ### Step 1: Install Arch
@ -109,15 +111,12 @@ Make outbound calls via Arch
import openai import openai
# Set the OpenAI API base URL to the Arch gateway endpoint # Set the OpenAI API base URL to the Arch gateway endpoint
openai.api_base = "http://127.0.0.1:12000/" openai.api_base = "http://127.0.0.1:51001/v1"
# No need to set openai.api_key since it's configured in Arch's gateway # No need to set openai.api_key since it's configured in Arch's gateway
# Use the OpenAI client as usual # Use the OpenAI client as usual
# we set api_key to '--' becasue openai client would fail to initiate request without it. Just pass any
# dummy value here since arch gateway will properly pass access key before making outbound call.
response = openai.Completion.create( response = openai.Completion.create(
api_key="--",
model="text-davinci-003", model="text-davinci-003",
prompt="What is the capital of France?" prompt="What is the capital of France?"
) )

2
arch/docker-compose.yaml
View file

@ -12,3 +12,5 @@ services:
- ~/archgw_logs:/var/log/ - ~/archgw_logs:/var/log/
env_file: env_file:
- stage.env - stage.env
extra_hosts:
- "host.docker.internal:host-gateway"

13
arch/tools/README.md
View file

@ -56,9 +56,18 @@ sh build_cli.sh
archgw build archgw build
``` ```
## Step 5: start model server in the background ### Step 5: download models
This will help download models so model_server can load faster. This should be done once.
```bash
archgw download-models
``` ```
archgw up --services model_server
### Logs
`archgw` command can also view logs from gateway and model_server. Use following command to view logs,
```bash
archgw logs --follow
``` ```
## Uninstall Instructions: archgw CLI ## Uninstall Instructions: archgw CLI

1
arch/tools/cli/consts.py Normal file
View file

@ -0,0 +1 @@
KATANEMO_DOCKERHUB_REPO = "katanemo/archgw"

67
arch/tools/cli/core.py
View file

@ -4,6 +4,37 @@ import time
import pkg_resources import pkg_resources
import select import select
from cli.utils import run_docker_compose_ps, print_service_status, check_services_state from cli.utils import run_docker_compose_ps, print_service_status, check_services_state
from cli.utils import getLogger
import sys
log = getLogger(__name__)
def stream_gateway_logs(follow):
"""
Stream logs from the arch gateway service.
"""
compose_file = pkg_resources.resource_filename(
__name__, "../config/docker-compose.yaml"
)
log.info("Logs from arch gateway service.")
options = ["docker", "compose", "-p", "arch", "logs"]
if follow:
options.append("-f")
try:
# Run `docker-compose logs` to stream logs from the gateway service
subprocess.run(
options,
cwd=os.path.dirname(compose_file),
check=True,
stdout=sys.stdout,
stderr=sys.stderr,
)
except subprocess.CalledProcessError as e:
log.info(f"Failed to stream logs: {str(e)}")
def start_arch(arch_config_file, env, log_timeout=120): def start_arch(arch_config_file, env, log_timeout=120):
@ -14,7 +45,7 @@ def start_arch(arch_config_file, env, log_timeout=120):
path (str): The path where the prompt_confi.yml file is located. path (str): The path where the prompt_confi.yml file is located.
log_timeout (int): Time in seconds to show logs before checking for healthy state. log_timeout (int): Time in seconds to show logs before checking for healthy state.
""" """
log.info("Starting arch gateway")
compose_file = pkg_resources.resource_filename( compose_file = pkg_resources.resource_filename(
__name__, "../config/docker-compose.yaml" __name__, "../config/docker-compose.yaml"
) )
@ -35,9 +66,10 @@ def start_arch(arch_config_file, env, log_timeout=120):
), # Ensure the Docker command runs in the correct path ), # Ensure the Docker command runs in the correct path
env=env, # Pass the modified environment env=env, # Pass the modified environment
check=True, # Raise an exception if the command fails check=True, # Raise an exception if the command fails
stderr=subprocess.PIPE,
stdout=subprocess.PIPE,
) )
print(f"Arch docker-compose started in detached.") log.info(f"Arch docker-compose started in detached.")
print("Monitoring `docker-compose ps` logs...")
start_time = time.time() start_time = time.time()
services_status = {} services_status = {}
@ -51,14 +83,14 @@ def start_arch(arch_config_file, env, log_timeout=120):
# Check if timeout is reached # Check if timeout is reached
if elapsed_time > log_timeout: if elapsed_time > log_timeout:
print(f"Stopping log monitoring after {log_timeout} seconds.") log.info(f"Stopping log monitoring after {log_timeout} seconds.")
break break
current_services_status = run_docker_compose_ps( current_services_status = run_docker_compose_ps(
compose_file=compose_file, env=env compose_file=compose_file, env=env
) )
if not current_services_status: if not current_services_status:
print( log.info(
"Status for the services could not be detected. Something went wrong. Please run docker logs" "Status for the services could not be detected. Something went wrong. Please run docker logs"
) )
break break
@ -74,11 +106,11 @@ def start_arch(arch_config_file, env, log_timeout=120):
running_states = ["running", "up"] running_states = ["running", "up"]
if check_services_state(current_services_status, running_states): if check_services_state(current_services_status, running_states):
print("Arch is up and running!") log.info("Arch gateway is up and running!")
break break
if check_services_state(current_services_status, unhealthy_states): if check_services_state(current_services_status, unhealthy_states):
print( log.info(
"One or more Arch services are unhealthy. Please run `docker logs` for more information" "One or more Arch services are unhealthy. Please run `docker logs` for more information"
) )
print_service_status( print_service_status(
@ -92,7 +124,7 @@ def start_arch(arch_config_file, env, log_timeout=120):
services_status[service_name]["State"] services_status[service_name]["State"]
!= current_services_status[service_name]["State"] != current_services_status[service_name]["State"]
): ):
print( log.info(
"One or more Arch services have changed state. Printing current state" "One or more Arch services have changed state. Printing current state"
) )
print_service_status(current_services_status) print_service_status(current_services_status)
@ -101,7 +133,7 @@ def start_arch(arch_config_file, env, log_timeout=120):
services_status = current_services_status services_status = current_services_status
except subprocess.CalledProcessError as e: except subprocess.CalledProcessError as e:
print(f"Failed to start Arch: {str(e)}") log.info(f"Failed to start Arch: {str(e)}")
def stop_arch(): def stop_arch():
@ -115,17 +147,21 @@ def stop_arch():
__name__, "../config/docker-compose.yaml" __name__, "../config/docker-compose.yaml"
) )
log.info("Shutting down arch gateway service.")
try: try:
# Run `docker-compose down` to shut down all services # Run `docker-compose down` to shut down all services
subprocess.run( subprocess.run(
["docker", "compose", "-p", "arch", "down"], ["docker", "compose", "-p", "arch", "down"],
cwd=os.path.dirname(compose_file), cwd=os.path.dirname(compose_file),
check=True, check=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
) )
print("Successfully shut down all services.") log.info("Successfully shut down arch gateway service.")
except subprocess.CalledProcessError as e: except subprocess.CalledProcessError as e:
print(f"Failed to shut down services: {str(e)}") log.info(f"Failed to shut down services: {str(e)}")
def start_arch_modelserver(): def start_arch_modelserver():
@ -134,12 +170,13 @@ def start_arch_modelserver():
""" """
try: try:
log.info("archgw_modelserver restart")
subprocess.run( subprocess.run(
["archgw_modelserver", "restart"], check=True, start_new_session=True ["archgw_modelserver", "restart"], check=True, start_new_session=True
) )
print("Successfull run the archgw model_server") log.info("Successfull ran model_server")
except subprocess.CalledProcessError as e: except subprocess.CalledProcessError as e:
print(f"Failed to start model_server. Please check archgw_modelserver logs") log.info(f"Failed to start model_server. Please check archgw_modelserver logs")
sys.exit(1) sys.exit(1)
@ -153,7 +190,7 @@ def stop_arch_modelserver():
["archgw_modelserver", "stop"], ["archgw_modelserver", "stop"],
check=True, check=True,
) )
print("Successfull stopped the archgw model_server") log.info("Successfull stopped the archgw model_server")
except subprocess.CalledProcessError as e: except subprocess.CalledProcessError as e:
print(f"Failed to start model_server. Please check archgw_modelserver logs") log.info(f"Failed to start model_server. Please check archgw_modelserver logs")
sys.exit(1) sys.exit(1)

121
arch/tools/cli/main.py
View file

@ -10,8 +10,17 @@ from cli.core import (
stop_arch_modelserver, stop_arch_modelserver,
start_arch, start_arch,
stop_arch, stop_arch,
stream_gateway_logs,
) )
from cli.utils import get_llm_provider_access_keys, load_env_file_to_dict from cli.utils import get_llm_provider_access_keys, load_env_file_to_dict
from cli.consts import KATANEMO_DOCKERHUB_REPO
from cli.utils import getLogger
import multiprocessing
from huggingface_hub import snapshot_download
import joblib
log = getLogger(__name__)
logo = r""" logo = r"""
_ _ _ _
@ -39,17 +48,17 @@ MODEL_SERVER_BUILD_FILE = "./model_server/pyproject.toml"
@click.command() @click.command()
@click.option( @click.option(
"--services", "--service",
default="all", default="all",
help="Services to build. Options are all, model_server, archgw. Default is all", help="Optioanl parameter to specify which service to build. Options are model_server, archgw",
) )
def build(services): def build(service):
"""Build Arch from source. Must be in root of cloned repo.""" """Build Arch from source. Must be in root of cloned repo."""
if services not in ["all", "model_server", "archgw"]: if service not in ["model_server", "archgw", "all"]:
print(f"Error: Invalid service {services}. Exiting") print(f"Error: Invalid service {service}. Exiting")
sys.exit(1) sys.exit(1)
# Check if /arch/Dockerfile exists # Check if /arch/Dockerfile exists
if services == "archgw" or services == "all": if service == "archgw" or service == "all":
if os.path.exists(ARCHGW_DOCKERFILE): if os.path.exists(ARCHGW_DOCKERFILE):
click.echo("Building archgw image...") click.echo("Building archgw image...")
try: try:
@ -60,8 +69,9 @@ def build(services):
"-f", "-f",
ARCHGW_DOCKERFILE, ARCHGW_DOCKERFILE,
"-t", "-t",
"archgw:latest", f"{KATANEMO_DOCKERHUB_REPO}:latest",
".", ".",
"--add-host=host.docker.internal:host-gateway",
], ],
check=True, check=True,
) )
@ -76,7 +86,7 @@ def build(services):
click.echo("archgw image built successfully.") click.echo("archgw image built successfully.")
"""Install the model server dependencies using Poetry.""" """Install the model server dependencies using Poetry."""
if services == "model_server" or services == "all": if service == "model_server" or service == "all":
# Check if pyproject.toml exists # Check if pyproject.toml exists
if os.path.exists(MODEL_SERVER_BUILD_FILE): if os.path.exists(MODEL_SERVER_BUILD_FILE):
click.echo("Installing model server dependencies with Poetry...") click.echo("Installing model server dependencies with Poetry...")
@ -101,17 +111,17 @@ def build(services):
"--path", default=".", help="Path to the directory containing arch_config.yaml" "--path", default=".", help="Path to the directory containing arch_config.yaml"
) )
@click.option( @click.option(
"--services", "--service",
default="all", default="all",
help="Services to start. Options are all, model_server, archgw. Default is all", help="Service to start. Options are model_server, archgw.",
) )
def up(file, path, services): def up(file, path, service):
"""Starts Arch.""" """Starts Arch."""
if services not in ["all", "model_server", "archgw"]: if service not in ["all", "model_server", "archgw"]:
print(f"Error: Invalid service {services}. Exiting") print(f"Error: Invalid service {service}. Exiting")
sys.exit(1) sys.exit(1)
if services == "model_server": if service == "model_server":
start_arch_modelserver() start_arch_modelserver()
return return
@ -141,7 +151,7 @@ def up(file, path, services):
print(f"Exiting archgw up: {e}") print(f"Exiting archgw up: {e}")
sys.exit(1) sys.exit(1)
print("Starting Arch gateway and Arch model server services via docker ") log.info("Starging arch model server and arch gateway")
# Set the ARCH_CONFIG_FILE environment variable # Set the ARCH_CONFIG_FILE environment variable
env_stage = {} env_stage = {}
@ -183,7 +193,7 @@ def up(file, path, services):
env.update(env_stage) env.update(env_stage)
env["ARCH_CONFIG_FILE"] = arch_config_file env["ARCH_CONFIG_FILE"] = arch_config_file
if services == "archgw": if service == "archgw":
start_arch(arch_config_file, env) start_arch(arch_config_file, env)
else: else:
start_arch_modelserver() start_arch_modelserver()
@ -192,19 +202,19 @@ def up(file, path, services):
@click.command() @click.command()
@click.option( @click.option(
"--services", "--service",
default="all", default="all",
help="Services to down. Options are all, model_server, archgw. Default is all", help="Service to down. Options are all, model_server, archgw. Default is all",
) )
def down(services): def down(service):
"""Stops Arch.""" """Stops Arch."""
if services not in ["all", "model_server", "archgw"]: if service not in ["all", "model_server", "archgw"]:
print(f"Error: Invalid service {services}. Exiting") print(f"Error: Invalid service {service}. Exiting")
sys.exit(1) sys.exit(1)
if services == "model_server": if service == "model_server":
stop_arch_modelserver() stop_arch_modelserver()
elif services == "archgw": elif service == "archgw":
stop_arch() stop_arch()
else: else:
stop_arch_modelserver() stop_arch_modelserver()
@ -233,9 +243,74 @@ def generate_prompt_targets(file):
targets.generate_prompt_targets(file) targets.generate_prompt_targets(file)
def stream_model_server_logs(follow):
log_file = "~/archgw_logs/modelserver.log"
log_file_expanded = os.path.expanduser(log_file)
stream_command = ["tail"]
if follow:
stream_command.append("-f")
stream_command.append(log_file_expanded)
subprocess.run(
stream_command,
check=True,
stdout=sys.stdout,
stderr=sys.stderr,
)
@click.command()
@click.option(
"--service",
default="all",
help="Service to monitor. By default it will monitor both gateway and model_serve",
)
@click.option("--follow", help="Follow the logs", is_flag=True)
def logs(service, follow):
"""Stream logs from arch services."""
if service not in ["all", "model_server", "archgw"]:
print(f"Error: Invalid service {service}. Exiting")
sys.exit(1)
archgw_process = None
if service == "archgw" or service == "all":
archgw_process = multiprocessing.Process(
target=stream_gateway_logs, args=(follow,)
)
archgw_process.start()
model_server_process = None
if service == "model_server" or service == "all":
model_server_process = multiprocessing.Process(
target=stream_model_server_logs, args=(follow,)
)
model_server_process.start()
if archgw_process:
archgw_process.join()
if model_server_process:
model_server_process.join()
model_list = [
"katanemo/Arch-Guard-cpu",
"katanemo/Arch-Guard",
"katanemo/bge-large-en-v1.5",
]
@click.command()
def download_models():
"""Download required models from Hugging Face Hub in the cache directory"""
for model in model_list:
log.info(f"Downloading model: {model}")
snapshot_download(repo_id=model)
main.add_command(up) main.add_command(up)
main.add_command(down) main.add_command(down)
main.add_command(build) main.add_command(build)
main.add_command(logs)
main.add_command(download_models)
main.add_command(generate_prompt_targets) main.add_command(generate_prompt_targets)
if __name__ == "__main__": if __name__ == "__main__":

29
arch/tools/cli/utils.py
View file

@ -5,6 +5,23 @@ import select
import shlex import shlex
import yaml import yaml
import json import json
import logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
def getLogger(name="cli"):
import logging
logger = logging.getLogger(name)
logger.setLevel(logging.INFO)
return logger
log = getLogger(__name__)
def run_docker_compose_ps(compose_file, env): def run_docker_compose_ps(compose_file, env):
@ -15,7 +32,7 @@ def run_docker_compose_ps(compose_file, env):
path (str): The path where the docker-compose.yml file is located. path (str): The path where the docker-compose.yml file is located.
""" """
try: try:
# Run `docker-compose ps` to get the health status of each service # Run `docker compose ps` to get the health status of each service
ps_process = subprocess.Popen( ps_process = subprocess.Popen(
[ [
"docker", "docker",
@ -38,7 +55,7 @@ def run_docker_compose_ps(compose_file, env):
# Check if there is any error output # Check if there is any error output
if error_output: if error_output:
print( log.info(
f"Error while checking service status:\n{error_output}", f"Error while checking service status:\n{error_output}",
file=os.sys.stderr, file=os.sys.stderr,
) )
@ -48,18 +65,18 @@ def run_docker_compose_ps(compose_file, env):
return services return services
except subprocess.CalledProcessError as e: except subprocess.CalledProcessError as e:
print(f"Failed to check service status. Error:\n{e.stderr}") log.info(f"Failed to check service status. Error:\n{e.stderr}")
return e return e
# Helper method to print service status # Helper method to print service status
def print_service_status(services): def print_service_status(services):
print(f"{'Service Name':<25} {'State':<20} {'Ports'}") log.info(f"{'Service Name':<25} {'State':<20} {'Ports'}")
print("=" * 72) log.info("=" * 72)
for service_name, info in services.items(): for service_name, info in services.items():
status = info["STATE"] status = info["STATE"]
ports = info["PORTS"] ports = info["PORTS"]
print(f"{service_name:<25} {status:<20} {ports}") log.info(f"{service_name:<25} {status:<20} {ports}")
# check for states based on the states passed in # check for states based on the states passed in

561
arch/tools/poetry.lock generated
View file

@ -44,102 +44,102 @@ files = [
[[package]] [[package]]
name = "aiohttp" name = "aiohttp"
version = "3.10.9" version = "3.10.10"
description = "Async http client/server framework (asyncio)" description = "Async http client/server framework (asyncio)"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
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dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"] dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.20,<0.21)", "urllib3 (<2.0.0)"]
dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"] flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
ftfy = ["ftfy"] ftfy = ["ftfy"]
@ -3160,7 +3170,7 @@ natten = ["natten (>=0.14.6,<0.15.0)"]
onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"] onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"] onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
optuna = ["optuna"] optuna = ["optuna"]
quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.5.1)", "urllib3 (<2.0.0)"] quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "libcst", "rich", "ruff (==0.5.1)", "urllib3 (<2.0.0)"]
ray = ["ray[tune] (>=2.7.0)"] ray = ["ray[tune] (>=2.7.0)"]
retrieval = ["datasets (!=2.5.0)", "faiss-cpu"] retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
ruff = ["ruff (==0.5.1)"] ruff = ["ruff (==0.5.1)"]
@ -3170,16 +3180,17 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
sigopt = ["sigopt"] sigopt = ["sigopt"]
sklearn = ["scikit-learn"] sklearn = ["scikit-learn"]
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"] tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
tiktoken = ["blobfile", "tiktoken"]
timm = ["timm (<=0.9.16)"] timm = ["timm (<=0.9.16)"]
tokenizers = ["tokenizers (>=0.19,<0.20)"] tokenizers = ["tokenizers (>=0.20,<0.21)"]
torch = ["accelerate (>=0.21.0)", "torch"] torch = ["accelerate (>=0.26.0)", "torch"]
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.20,<0.21)", "torch", "tqdm (>=4.27)"]
video = ["av (==9.2.0)", "decord (==0.6.0)"] video = ["av (==9.2.0)", "decord (==0.6.0)"]
vision = ["Pillow (>=10.0.1,<=15.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"]
@ -3521,4 +3532,4 @@ propcache = ">=0.2.0"
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = "^3.10" python-versions = "^3.10"
content-hash = "11509af5007ffed46d1a84a53de8fb2f816eca24247064b2d85560e80d5eaf24" content-hash = "35ffc7511fb162b24cc24f3d2884128b8e34b68c42811eba4f878479d98b55b7"

5
arch/tools/pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry] [tool.poetry]
name = "archgw" name = "archgw"
version = "0.0.1" version = "0.0.3"
description = "Python-based CLI tool to manage Arch Gateway." description = "Python-based CLI tool to manage Arch Gateway."
authors = ["Katanemo Labs, Inc."] authors = ["Katanemo Labs, Inc."]
packages = [ packages = [
@ -22,7 +22,8 @@ click = "^8.1.7"
jinja2 = "^3.1.4" jinja2 = "^3.1.4"
jsonschema = "^4.23.0" jsonschema = "^4.23.0"
setuptools = "75.1.0" setuptools = "75.1.0"
archgw_modelserver= "0.0.2" archgw_modelserver= "0.0.3"
huggingface_hub = "^0.25.2"
[tool.poetry.scripts] [tool.poetry.scripts]
archgw = "cli.main:main" archgw = "cli.main:main"

8
demos/function_calling/README.md
View file

@ -2,13 +2,15 @@
This demo shows how you can use Arch's core function calling capabilites. This demo shows how you can use Arch's core function calling capabilites.
# Starting the demo # Starting the demo
1. Please make sure the [pre-requisites](../../../README.md?tab=readme-ov-file#prerequisites) are installed correctly 1. Please make sure the [pre-requisites](https://github.com/katanemo/arch/?tab=readme-ov-file#prerequisites) are installed correctly
2. Start Arch 2. Start Arch
3.
```sh ```sh
sh run_demo.sh sh run_demo.sh
``` ```
3. Navigate to http://localhost:18080/ 4. Navigate to http://localhost:18080/
4. You can type in queries like "how is the weather?" 5. You can type in queries like "how is the weather?"
# Observability # Observability
Arch gateway publishes stats endpoint at http://localhost:19901/stats. In this demo we are using prometheus to pull stats from arch and we are using grafana to visalize the stats in dashboard. To see grafana dashboard follow instructions below, Arch gateway publishes stats endpoint at http://localhost:19901/stats. In this demo we are using prometheus to pull stats from arch and we are using grafana to visalize the stats in dashboard. To see grafana dashboard follow instructions below,

4
demos/function_calling/arch_config.yaml
View file

@ -16,10 +16,10 @@ overrides:
prompt_target_intent_matching_threshold: 0.6 prompt_target_intent_matching_threshold: 0.6
llm_providers: llm_providers:
- name: gpt-4o - name: gpt
access_key: OPENAI_API_KEY access_key: OPENAI_API_KEY
provider: openai provider: openai
model: gpt-4o model: gpt-3.5-turbo
default: true default: true
system_prompt: | system_prompt: |

2
demos/function_calling/docker-compose.yaml
View file

@ -18,6 +18,8 @@ services:
- "18080:8080" - "18080:8080"
environment: environment:
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1 #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 #this is only because we are running the sample app in the same docker container environemtn as archgw
extra_hosts:
- "host.docker.internal:host-gateway"
opentelemetry: opentelemetry:
build: build:

210
demos/function_calling/grafana/dashboards/envoy_overview.json
View file

@ -22,6 +22,7 @@
"panels": [ "panels": [
{ {
"datasource": { "datasource": {
"default": true,
"type": "prometheus", "type": "prometheus",
"uid": "PBFA97CFB590B2093" "uid": "PBFA97CFB590B2093"
}, },
@ -37,6 +38,7 @@
"axisLabel": "", "axisLabel": "",
"axisPlacement": "auto", "axisPlacement": "auto",
"barAlignment": 0, "barAlignment": 0,
"barWidthFactor": 0.6,
"drawStyle": "line", "drawStyle": "line",
"fillOpacity": 0, "fillOpacity": 0,
"gradientMode": "none", "gradientMode": "none",
@ -77,7 +79,32 @@
] ]
} }
}, },
"overrides": [] "overrides": [
{
"__systemRef": "hideSeriesFrom",
"matcher": {
"id": "byNames",
"options": {
"mode": "exclude",
"names": [
"api_server"
],
"prefix": "All except:",
"readOnly": true
}
},
"properties": [
{
"id": "custom.hideFrom",
"value": {
"legend": false,
"tooltip": false,
"viz": true
}
}
]
}
]
}, },
"gridPos": { "gridPos": {
"h": 8, "h": 8,
@ -85,7 +112,7 @@
"x": 0, "x": 0,
"y": 0 "y": 0
}, },
"id": 2, "id": 3,
"options": { "options": {
"legend": { "legend": {
"calcs": [], "calcs": [],
@ -106,22 +133,39 @@
}, },
"disableTextWrap": false, "disableTextWrap": false,
"editorMode": "code", "editorMode": "code",
"expr": "avg(rate(envoy_cluster_internal_upstream_rq_time_sum[1m]) / rate(envoy_cluster_internal_upstream_rq_time_count[1m])) by (envoy_cluster_name)", "expr": "avg(rate(envoy_cluster_internal_upstream_rq_completed{envoy_cluster_name=~\"api_server|openai\"}[1m])) by (envoy_cluster_name)",
"fullMetaSearch": false, "fullMetaSearch": false,
"hide": false,
"includeNullMetadata": true, "includeNullMetadata": true,
"instant": false, "instant": false,
"legendFormat": "__auto", "legendFormat": "__auto",
"range": true, "range": true,
"refId": "A", "refId": "A",
"useBackend": false "useBackend": false
},
{
"datasource": {
"type": "prometheus",
"uid": "PBFA97CFB590B2093"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "avg(rate(envoy_cluster_external_upstream_rq_completed{envoy_cluster_name=~\"api_server|openai\"}[1m])) by (envoy_cluster_name)",
"fullMetaSearch": false,
"hide": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "__auto",
"range": true,
"refId": "B",
"useBackend": false
} }
], ],
"title": "request latency - internal (ms)", "title": "Upstream request rate",
"type": "timeseries" "type": "timeseries"
}, },
{ {
"datasource": { "datasource": {
"default": true,
"type": "prometheus", "type": "prometheus",
"uid": "PBFA97CFB590B2093" "uid": "PBFA97CFB590B2093"
}, },
@ -137,6 +181,7 @@
"axisLabel": "", "axisLabel": "",
"axisPlacement": "auto", "axisPlacement": "auto",
"barAlignment": 0, "barAlignment": 0,
"barWidthFactor": 0.6,
"drawStyle": "line", "drawStyle": "line",
"fillOpacity": 0, "fillOpacity": 0,
"gradientMode": "none", "gradientMode": "none",
@ -206,7 +251,7 @@
}, },
"disableTextWrap": false, "disableTextWrap": false,
"editorMode": "code", "editorMode": "code",
"expr": "avg(rate(envoy_cluster_external_upstream_rq_time_sum[1m]) / rate(envoy_cluster_external_upstream_rq_time_count[1m])) by (envoy_cluster_name)", "expr": "avg(rate (envoy_cluster_external_upstream_rq_time_sum{envoy_cluster_name=~\"api_server|openai\"}[1m])/ rate(envoy_cluster_external_upstream_rq_time_count{envoy_cluster_name=~\"api_server|openai\"}[1m])) by (envoy_cluster_name)",
"fullMetaSearch": false, "fullMetaSearch": false,
"hide": false, "hide": false,
"includeNullMetadata": true, "includeNullMetadata": true,
@ -222,45 +267,14 @@
}, },
{ {
"datasource": { "datasource": {
"default": true,
"type": "prometheus", "type": "prometheus",
"uid": "PBFA97CFB590B2093" "uid": "PBFA97CFB590B2093"
}, },
"fieldConfig": { "fieldConfig": {
"defaults": { "defaults": {
"color": { "color": {
"mode": "palette-classic" "mode": "thresholds"
},
"custom": {
"axisBorderShow": false,
"axisCenteredZero": false,
"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"insertNulls": false,
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
}, },
"mappings": [], "mappings": [],
"thresholds": { "thresholds": {
@ -285,19 +299,25 @@
"x": 0, "x": 0,
"y": 8 "y": 8
}, },
"id": 3, "id": 4,
"options": { "options": {
"legend": { "colorMode": "value",
"calcs": [], "graphMode": "area",
"displayMode": "list", "justifyMode": "auto",
"placement": "bottom", "orientation": "auto",
"showLegend": true "percentChangeColorMode": "standard",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
}, },
"tooltip": { "showPercentChange": false,
"mode": "single", "textMode": "auto",
"sort": "none" "wideLayout": true
}
}, },
"pluginVersion": "11.2.0",
"targets": [ "targets": [
{ {
"datasource": { "datasource": {
@ -305,38 +325,98 @@
"uid": "PBFA97CFB590B2093" "uid": "PBFA97CFB590B2093"
}, },
"disableTextWrap": false, "disableTextWrap": false,
"editorMode": "code", "editorMode": "builder",
"expr": "avg(rate(envoy_cluster_internal_upstream_rq_completed[1m])) by (envoy_cluster_name)", "exemplar": false,
"expr": "envoy_cluster_upstream_rq_completed{envoy_cluster_name=~\"openai|api_server\"}",
"fullMetaSearch": false, "fullMetaSearch": false,
"includeNullMetadata": true, "includeNullMetadata": true,
"instant": false, "instant": true,
"legendFormat": "__auto", "legendFormat": "{{envoy_cluster_name}}",
"range": true, "range": false,
"refId": "A", "refId": "A",
"useBackend": false "useBackend": false
}
],
"title": "# of Completd Requests",
"type": "stat"
},
{
"datasource": {
"default": true,
"type": "prometheus",
"uid": "PBFA97CFB590B2093"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
}
}, },
"overrides": []
},
"gridPos": {
"h": 8,
"w": 12,
"x": 12,
"y": 8
},
"id": 5,
"options": {
"colorMode": "value",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"percentChangeColorMode": "standard",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": false,
"textMode": "auto",
"wideLayout": true
},
"pluginVersion": "11.2.0",
"targets": [
{ {
"datasource": { "datasource": {
"type": "prometheus", "type": "prometheus",
"uid": "PBFA97CFB590B2093" "uid": "PBFA97CFB590B2093"
}, },
"disableTextWrap": false, "disableTextWrap": false,
"editorMode": "code", "editorMode": "builder",
"expr": "avg(rate(envoy_cluster_external_upstream_rq_completed[1m])) by (envoy_cluster_name)", "exemplar": false,
"expr": "envoy_cluster_upstream_rq_cancelled{envoy_cluster_name=~\"api_server|openai\"} + envoy_cluster_upstream_rq_pending_failure_eject{envoy_cluster_name=~\"api_server|openai\"} + envoy_cluster_upstream_rq_pending_overflow{envoy_cluster_name=~\"api_server|openai\"}",
"fullMetaSearch": false, "fullMetaSearch": false,
"hide": false,
"includeNullMetadata": true, "includeNullMetadata": true,
"instant": false, "instant": true,
"legendFormat": "__auto", "legendFormat": "{{envoy_cluster_name}}",
"range": true, "range": false,
"refId": "B", "refId": "A",
"useBackend": false "useBackend": false
} }
], ],
"title": "Upstream request count", "title": "# of Failed or Cancelled Requests",
"type": "timeseries" "type": "stat"
} }
], ],
"refresh": "",
"schemaVersion": 39, "schemaVersion": 39,
"tags": [], "tags": [],
"templating": { "templating": {
@ -348,8 +428,8 @@
}, },
"timepicker": {}, "timepicker": {},
"timezone": "browser", "timezone": "browser",
"title": "Intelligent Gateway Overview", "title": "Arch Gateway Dashboard",
"uid": "adt6uhx5lk8aob", "uid": "adt6uhx5lk8aob",
"version": 3, "version": 1,
"weekStart": "" "weekStart": ""
} }

2
demos/function_calling/prometheus/prometheus.yaml
View file

@ -18,6 +18,6 @@ scrape_configs:
scheme: http scheme: http
static_configs: static_configs:
- targets: - targets:
- arch:9901 - host.docker.internal:19901
params: params:
format: ['prometheus'] format: ['prometheus']

2
demos/insurance_agent/README.md
View file

@ -36,7 +36,7 @@ The system can perform a variety of tasks, such as answering insurance-related q
**Arch** is designed to intelligently routes prompts to the appropriate functions based on the target, allowing for seamless interaction with various insurance-related services. **Arch** is designed to intelligently routes prompts to the appropriate functions based on the target, allowing for seamless interaction with various insurance-related services.
# Starting the demo # Starting the demo
1. Please make sure the [pre-requisites](../../../README.md?tab=readme-ov-file#prerequisites) are installed correctly 1. Please make sure the [pre-requisites](https://github.com/katanemo/arch/?tab=readme-ov-file#prerequisites) are installed correctly
2. Start Arch 2. Start Arch
```sh ```sh
sh run_demo.sh sh run_demo.sh

2
demos/insurance_agent/docker-compose.yaml
View file

@ -18,3 +18,5 @@ services:
- "18080:8080" - "18080:8080"
environment: environment:
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1 - CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
extra_hosts:
- "host.docker.internal:host-gateway"

2
demos/network_agent/README.md
View file

@ -19,7 +19,7 @@ The assistant can perform several key operations, including rebooting devices, a
# Starting the demo # Starting the demo
1. Please make sure the [pre-requisites](../../../README.md?tab=readme-ov-file#prerequisites) are installed correctly 1. Please make sure the [pre-requisites](https://github.com/katanemo/arch/?tab=readme-ov-file#prerequisites) are installed correctly
2. Start Arch 2. Start Arch
```sh ```sh
sh run_demo.sh sh run_demo.sh

2
demos/network_agent/arch_config.yaml
View file

@ -9,7 +9,7 @@ llm_providers:
- name: OpenAI - name: OpenAI
provider: openai provider: openai
access_key: OPENAI_API_KEY access_key: OPENAI_API_KEY
model: gpt-4o model: gpt-3.5-turbo
default: true default: true
# default system prompt used by all prompt targets # default system prompt used by all prompt targets

2
demos/network_agent/docker-compose.yaml
View file

@ -19,3 +19,5 @@ services:
environment: environment:
- OPENAI_API_KEY=${OPENAI_API_KEY:?error} - OPENAI_API_KEY=${OPENAI_API_KEY:?error}
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1 - CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
extra_hosts:
- "host.docker.internal:host-gateway"

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10
docs/source/build_with_arch/agent.rst
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@ -5,14 +5,14 @@ Agentic Workflow
Arch helps you easily personalize your applications by calling application-specific (API) functions Arch helps you easily personalize your applications by calling application-specific (API) functions
via user prompts. This involves any predefined functions or APIs you want to expose to users to perform tasks, via user prompts. This involves any predefined functions or APIs you want to expose to users to perform tasks,
gather information, or manipulate data. This capability is generally referred to as **function calling**, where gather information, or manipulate data. This capability is generally referred to as :ref:`function calling <function_calling>`, where
you have the flexibility to support “agentic” apps tailored to specific use cases - from updating insurance you have the flexibility to support “agentic” apps tailored to specific use cases - from updating insurance
claims to creating ad campaigns - via prompts. claims to creating ad campaigns - via prompts.
Arch analyzes prompts, extracts critical information from prompts, engages in lightweight conversation with Arch analyzes prompts, extracts critical information from prompts, engages in lightweight conversation with
the user to gather any missing parameters and makes API calls so that you can focus on writing business logic. the user to gather any missing parameters and makes API calls so that you can focus on writing business logic.
Arch does this via its purpose-built :ref:`Arch-Function <function_calling>` - the fastest (200ms p90 - 10x faser than GPT-4o) Arch does this via its purpose-built `Arch-Function <https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68>`_ - the fastest (200ms p90 - 10x faser than GPT-4o)
and cheapest (100x than GPT-40) function-calling LLM that matches performance with frontier models. and cheapest (100x than GPT-4o) function calling LLM that matches performance with frontier models.
.. image:: includes/agent/function-calling-flow.jpg .. image:: includes/agent/function-calling-flow.jpg
:width: 100% :width: 100%
@ -31,7 +31,7 @@ Step 1: Define Prompt Targets
.. literalinclude:: includes/agent/function-calling-agent.yaml .. literalinclude:: includes/agent/function-calling-agent.yaml
:language: yaml :language: yaml
:linenos: :linenos:
:emphasize-lines: 21-34 :emphasize-lines: 19-49
:caption: Prompt Target Example Configuration :caption: Prompt Target Example Configuration
Step 2: Process Request Parameters Step 2: Process Request Parameters
@ -66,5 +66,5 @@ Example of Multiple Prompt Targets in YAML:
.. literalinclude:: includes/agent/function-calling-agent.yaml .. literalinclude:: includes/agent/function-calling-agent.yaml
:language: yaml :language: yaml
:linenos: :linenos:
:emphasize-lines: 21-34 :emphasize-lines: 19-49
:caption: Prompt Target Example Configuration :caption: Prompt Target Example Configuration

4
docs/source/build_with_arch/includes/agent/function-calling-agent.yaml
View file

@ -9,7 +9,7 @@ llm_providers:
- name: OpenAI - name: OpenAI
provider: openai provider: openai
access_key: OPENAI_API_KEY access_key: OPENAI_API_KEY
model: gpt-4o model: gpt-3.5-turbo
default: true default: true
# default system prompt used by all prompt targets # default system prompt used by all prompt targets
@ -46,7 +46,7 @@ prompt_targets:
- name: time_range - name: time_range
type: int type: int
description: Time range in days for which to gather device statistics. Defaults to 7. description: Time range in days for which to gather device statistics. Defaults to 7.
default: "7" default: 7
# Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem. # Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.
endpoints: endpoints:

12
docs/source/build_with_arch/rag.rst
View file

@ -9,7 +9,7 @@ Retrieval-Augmented Generation (RAG) applications.
Parameter Extraction for RAG Parameter Extraction for RAG
---------------------------- ----------------------------
To build RAG (Retrieval-Augmented Generation) applications, you can configure prompt targets with parameters, To build RAG (Retrieval Augmented Generation) applications, you can configure prompt targets with parameters,
enabling Arch to retrieve critical information in a structured way for processing. This approach improves the enabling Arch to retrieve critical information in a structured way for processing. This approach improves the
retrieval quality and speed of your application. By extracting parameters from the conversation, you can pull retrieval quality and speed of your application. By extracting parameters from the conversation, you can pull
the appropriate chunks from a vector database or SQL-like data store to enhance accuracy. With Arch, you can the appropriate chunks from a vector database or SQL-like data store to enhance accuracy. With Arch, you can
@ -37,12 +37,12 @@ Once the prompt targets are configured as above, handling those parameters is
----------------------------------------------------------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------------------------------------------------------------
Developers struggle to efficiently handle ``follow-up`` or ``clarification`` questions. Specifically, when users ask for Developers struggle to efficiently handle ``follow-up`` or ``clarification`` questions. Specifically, when users ask for
changes or additions to previous responses their AI applications often generate entirely new responses instead of adjusting changes or additions to previous responses their AI applications often generate entirely new responses instead of adjusting
previous ones.Arch offers **intent** tracking as a feature so that developers can know when the user has shifted away from a previous ones. Arch offers ``intent tracking`` as a feature so that developers can know when the user has shifted away from a
previous intent so that they can dramatically improve retrieval accuracy, lower overall token cost and improve the speed of previous intent so that they can dramatically improve retrieval accuracy, lower overall token cost and improve the speed of
their responses back to users. their responses back to users.
Arch uses its built-in lightweight NLI and embedding models to know if the user has steered away from an active intent. Arch uses its built-in lightweight NLI and embedding models to know if the user has steered away from an active intent.
Arch's intent-drift detection mechanism is based on its' :ref:`prompt_targets <prompt_target>` primtive. Arch tries to match an incoming Arch's intent-drift detection mechanism is based on its :ref:`prompt target <prompt_target>` primtive. Arch tries to match an incoming
prompt to one of the prompt_targets configured in the gateway. Once it detects that the user has moved away from an active prompt to one of the prompt_targets configured in the gateway. Once it detects that the user has moved away from an active
active intent, Arch adds the ``x-arch-intent-marker`` headers to the request before sending it your application servers. active intent, Arch adds the ``x-arch-intent-marker`` headers to the request before sending it your application servers.
@ -50,15 +50,15 @@ active intent, Arch adds the ``x-arch-intent-marker`` headers to the request bef
:language: python :language: python
:linenos: :linenos:
:lines: 101-157 :lines: 101-157
:emphasize-lines: 14-24 :emphasize-lines: 14-25
:caption: Intent Detection Example :caption: Intent Detection Example
.. Note:: .. Note::
Arch is (mostly) stateless so that it can scale in an embarrassingly parrallel fashion. So, while Arch offers Arch is (mostly) stateless so that it can scale in an embarrassingly parrallel fashion. So, while Arch offers
intent-drift detetction, you still have to maintain converational state with intent drift as meta-data. The intent-drift detetction, you still have to maintain converational state with intent drift as metadata. The
following code snippets show how easily you can build and enrich conversational history with Langchain (in python), following code snippets show how easily you can build and enrich conversational history with Langchain (in Python),
so that you can use the most relevant prompts for your retrieval and for prompting upstream LLMs. so that you can use the most relevant prompts for your retrieval and for prompting upstream LLMs.

4
docs/source/concepts/llm_provider.rst
View file

@ -23,7 +23,7 @@ Below is an example of how you can configure ``llm_providers`` with an instance
.. Note:: .. Note::
When you start Arch, it creates a listener port for egress traffic based on the presence of ``llm_providers`` When you start Arch, it creates a listener port for egress traffic based on the presence of ``llm_providers``
configuration section in the ``arch_config.yml`` file. Arch binds itself to a local address such as configuration section in the ``arch_config.yml`` file. Arch binds itself to a local address such as
``127.0.0.1:51001/v1``. ``127.0.0.1:12000``.
Arch also offers vendor-agnostic SDKs and libraries to make LLM calls to API-based LLM providers (like OpenAI, Arch also offers vendor-agnostic SDKs and libraries to make LLM calls to API-based LLM providers (like OpenAI,
Anthropic, Mistral, Cohere, etc.) and supports calls to OSS LLMs that are hosted on your infrastructure. Arch Anthropic, Mistral, Cohere, etc.) and supports calls to OSS LLMs that are hosted on your infrastructure. Arch
@ -40,7 +40,7 @@ Example: Using the OpenAI Python SDK
from openai import OpenAI from openai import OpenAI
# Initialize the Arch client # Initialize the Arch client
client = OpenAI(base_url="http://127.0.0.1:51001/v1") client = OpenAI(base_url="http://127.0.0.12000/")
# Define your LLM provider and prompt # Define your LLM provider and prompt
llm_provider = "openai" llm_provider = "openai"

2
docs/source/concepts/prompt_target.rst
View file

@ -80,7 +80,7 @@ Here is a full list of parameter attributes that Arch can support:
Example Configuration Example Configuration
~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~
.. code-block:: python .. code-block:: yaml
prompt_targets: prompt_targets:
- name: get_weather - name: get_weather

2
docs/source/concepts/tech_overview/error_target.rst
View file

@ -16,8 +16,6 @@ Key Concepts
- **Error Message**: A clear, human-readable message describing the error. This should provide enough detail to inform users or developers of the root cause or required action. - **Error Message**: A clear, human-readable message describing the error. This should provide enough detail to inform users or developers of the root cause or required action.
- **Target Prompt**: The specific prompt or operation where the error occurred. Understanding where the error happened helps with debugging and pinpointing the source of the problem.
- **Parameter-Specific Errors**: Errors that arise due to invalid or missing parameters when invoking a function. These errors are critical for ensuring the correctness of inputs. - **Parameter-Specific Errors**: Errors that arise due to invalid or missing parameters when invoking a function. These errors are critical for ensuring the correctness of inputs.

51
docs/source/concepts/tech_overview/prompt.rst
View file

@ -27,7 +27,7 @@ containing two key-value pairs:
Prompt Guard Prompt Guard
----------------- -----------------
Arch is engineered with :ref:`Arch-Guard <prompt_guard>`, an industry leading safety layer, powered by a Arch is engineered with `Arch-Guard <https://huggingface.co/collections/katanemo/arch-guard-6702bdc08b889e4bce8f446d>`_, an industry leading safety layer, powered by a
compact and high-performimg LLM that monitors incoming prompts to detect and reject jailbreak attempts - compact and high-performimg LLM that monitors incoming prompts to detect and reject jailbreak attempts -
ensuring that unauthorized or harmful behaviors are intercepted early in the process. ensuring that unauthorized or harmful behaviors are intercepted early in the process.
@ -50,7 +50,7 @@ Prompt Targets
-------------- --------------
Once a prompt passes any configured guardrail checks, Arch processes the contents of the incoming conversation Once a prompt passes any configured guardrail checks, Arch processes the contents of the incoming conversation
and identifies where to forwad the conversation to via its ``prompt_targets`` primitve. Prompt targets are endpoints and identifies where to forwad the conversation to via its ``prompt target`` primitve. Prompt targets are endpoints
that receive prompts that are processed by Arch. For example, Arch enriches incoming prompts with metadata like knowing that receive prompts that are processed by Arch. For example, Arch enriches incoming prompts with metadata like knowing
when a user's intent has changed so that you can build faster, more accurate RAG apps. when a user's intent has changed so that you can build faster, more accurate RAG apps.
@ -67,48 +67,39 @@ Configuring ``prompt_targets`` is simple. See example below:
Check :ref:`Prompt Target <prompt_target>` for more details! Check :ref:`Prompt Target <prompt_target>` for more details!
Intent Detection and Prompt Matching: Intent Matching
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^
Arch uses fast Natural Language Inference (NLI) and embedding approaches to first detect the intent of each Arch uses fast text embedding and intent recognition approaches to first detect the intent of each incoming prompt.
incoming prompt. This intent detection phase analyzes the prompt's content and matches it against predefined This intent matching phase analyzes the prompt's content and matches it against predefined prompt targets, ensuring that each prompt is forwarded to the most appropriate endpoint.
prompt targets, ensuring that each prompt is forwarded to the most appropriate endpoint. Arch’s intent Arch’s intent matching framework considers both the name and description of each prompt target, and uses a composite matching score between embedding similarity and intent classification scores to enchance accuracy in forwarding decisions.
detection framework considers both the name and description of each prompt target, and uses a composite matching
score between an NLI and cosine similarity to enchance accuracy in forwarding decisions.
- **Embeddings**: By embedding the prompt and comparing it to known target vectors, Arch effectively identifies - **Intent Recognition**: NLI techniques further refine the matching process by evaluating the semantic alignment between the prompt and potential targets.
the closest match, ensuring that the prompt is handled by the correct downstream service.
- **NLI**: NLI techniques further refine the matching process by evaluating the semantic alignment between the - **Text Embedding**: By embedding the prompt and comparing it to known target vectors, Arch effectively identifies the closest match, ensuring that the prompt is handled by the correct downstream service.
prompt and potential targets.
Agentic Apps via Prompt Targets Agentic Apps via Prompt Targets
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
To support agentic apps, like scheduling travel plans or sharing comments on a document - via prompts, Arch uses To support agentic apps, like scheduling travel plans or sharing comments on a document - via prompts, Arch uses its function calling abilities to extract critical information from the incoming prompt (or a set of prompts) needed by a downstream backend API or function call before calling it directly.
its function calling abilities to extract critical information from the incoming prompt (or a set of prompts) For more details on how you can build agentic applications using Arch, see our full guide :ref:`here <arch_agent_guide>`:
needed by a downstream backend API or function call before calling it directly. For more details on how you can
build agentic applications using Arch, see our full guide :ref:`here <arch_agent_guide>`:
.. Note:: .. Note::
Arch :ref:`Arch-Function <function_calling>` is the dedicated agentic model engineered in Arch to extract information from `Arch-Function <https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68>`_ is a collection of dedicated agentic models engineered in Arch to extract information from a (set of) prompts and executes necessary backend API calls.
a (set of) prompts and executes necessary backend API calls. This allows for efficient handling of agentic tasks, This allows for efficient handling of agentic tasks, such as scheduling data retrieval, by dynamically interacting with backend services.
such as scheduling data retrieval, by dynamically interacting with backend services. Arch-Function is a flagship 1.3 Arch-Function achieves state-of-the-art performance, comparable with frontier models like Claude Sonnet 3.5 ang GPT-4, while being 100x cheaper ($0.05M/token hosted) and 10x faster (p50 latencies of 200ms).
billion parameter model that matches performance with frontier models like Claude Sonnet 3.5 ang GPT-4, while
being 100x cheaper ($0.05M/token hosted) and 10x faster (p50 latencies of 200ms).
Prompting LLMs Prompting LLMs
-------------- --------------
Arch is a single piece of software that is designed to manage both ingress and egress prompt traffic, drawing its Arch is a single piece of software that is designed to manage both ingress and egress prompt traffic, drawing its distributed proxy nature from the robust `Envoy <https://envoyproxy.io>`_.
distributed proxy nature from the robust `Envoy <https://envoyproxy.io>`_. This makes it extremely efficient and capable This makes it extremely efficient and capable of handling upstream connections to LLMs.
of handling upstream connections to LLMs. If your application is originating code to an API-based LLM, simply use If your application is originating code to an API-based LLM, simply use the OpenAI client and configure it with Arch.
the OpenAI client and configure it with Arch. By sending traffic through Arch, you can propagate traces, manage and monitor By sending traffic through Arch, you can propagate traces, manage and monitor traffic, apply rate limits, and utilize a large set of traffic management capabilities in a centralized way.
traffic, apply rate limits, and utilize a large set of traffic management capabilities in a centralized way.
.. Attention:: .. Attention::
When you start Arch, it automatically creates a listener port for egress calls to upstream LLMs. This is based on the When you start Arch, it automatically creates a listener port for egress calls to upstream LLMs. This is based on the
``llm_providers`` configuration section in the ``arch_config.yml`` file. Arch binds itself to a local address such as ``llm_providers`` configuration section in the ``arch_config.yml`` file. Arch binds itself to a local address such as
127.0.0.1:12000/v1. ``127.0.0.1:12000``.
Example: Using OpenAI Client with Arch as an Egress Gateway Example: Using OpenAI Client with Arch as an Egress Gateway
@ -119,7 +110,7 @@ Example: Using OpenAI Client with Arch as an Egress Gateway
import openai import openai
# Set the OpenAI API base URL to the Arch gateway endpoint # Set the OpenAI API base URL to the Arch gateway endpoint
openai.api_base = "http://127.0.0.1:12000/v1" openai.api_base = "http://127.0.0.1:12000"
# No need to set openai.api_key since it's configured in Arch's gateway # No need to set openai.api_key since it's configured in Arch's gateway
@ -132,5 +123,5 @@ Example: Using OpenAI Client with Arch as an Egress Gateway
print("OpenAI Response:", response.choices[0].text.strip()) print("OpenAI Response:", response.choices[0].text.strip())
In these examples, the OpenAI client is used to send traffic directly through the Arch egress proxy to the LLM of your choice, such as OpenAI. In these examples, the OpenAI client is used to send traffic directly through the Arch egress proxy to the LLM of your choice, such as OpenAI.
The OpenAI client is configured to route traffic via Arch by setting the proxy to ``127.0.0.1:51001``, assuming Arch is running locally and bound to that address and port. The OpenAI client is configured to route traffic via Arch by setting the proxy to ``127.0.0.1:12000``, assuming Arch is running locally and bound to that address and port.
This setup allows you to take advantage of Arch's advanced traffic management features while interacting with LLM APIs like OpenAI. This setup allows you to take advantage of Arch's advanced traffic management features while interacting with LLM APIs like OpenAI.

12
docs/source/concepts/tech_overview/request_lifecycle.rst
View file

@ -87,8 +87,6 @@ Today, only support a static bootstrap configuration file for simplicity today:
Request Flow (Ingress) Request Flow (Ingress)
---------------------- ----------------------
Overview
^^^^^^^^
A brief outline of the lifecycle of a request and response using the example configuration above: A brief outline of the lifecycle of a request and response using the example configuration above:
1. **TCP Connection Establishment**: 1. **TCP Connection Establishment**:
@ -105,7 +103,7 @@ A brief outline of the lifecycle of a request and response using the example con
intent matching via is **prompt-handler** subsystem using the name and description of the defined prompt targets, intent matching via is **prompt-handler** subsystem using the name and description of the defined prompt targets,
determining which endpoint should handle the prompt. determining which endpoint should handle the prompt.
4. **Parameter Gathering with Arch-FC**: 4. **Parameter Gathering with Arch-Function**:
If a prompt target requires specific parameters, Arch engages Arch-FC to extract the necessary details If a prompt target requires specific parameters, Arch engages Arch-FC to extract the necessary details
from the incoming prompt(s). This process gathers the critical information needed for downstream API calls. from the incoming prompt(s). This process gathers the critical information needed for downstream API calls.
@ -115,7 +113,7 @@ A brief outline of the lifecycle of a request and response using the example con
6. **Default Summarization by Upstream LLM**: 6. **Default Summarization by Upstream LLM**:
By default, if no specific endpoint processing is needed, the prompt is sent to an upstream LLM for summarization. By default, if no specific endpoint processing is needed, the prompt is sent to an upstream LLM for summarization.
This ensures that responses are concise and relevant, enhancing user experience in RAG (Retrieval-Augmented Generation) This ensures that responses are concise and relevant, enhancing user experience in RAG (Retrieval Augmented Generation)
and agentic applications. and agentic applications.
7. **Error Handling and Forwarding**: 7. **Error Handling and Forwarding**:
@ -134,11 +132,7 @@ A brief outline of the lifecycle of a request and response using the example con
Request Flow (Egress) Request Flow (Egress)
--------------------- ---------------------
Overview A brief outline of the lifecycle of a request and response in the context of egress traffic from an application to Large Language Models (LLMs) via Arch:
--------
A brief outline of the lifecycle of a request and response in the context of egress traffic from an application
to Large Language Models (LLMs) via Arch:
1. **HTTP Connection Establishment to LLM**: 1. **HTTP Connection Establishment to LLM**:
Arch initiates an HTTP connection to the upstream LLM service. This connection is handled by Arch’s egress listener Arch initiates an HTTP connection to the upstream LLM service. This connection is handled by Arch’s egress listener

2
docs/source/concepts/tech_overview/terminology.rst
View file

@ -29,7 +29,7 @@ networking operations (auth, tls, observability, etc) and the second process to
decisions on how to accept, handle and forward prompts. The second process is optional, as the model serving sevice could be decisions on how to accept, handle and forward prompts. The second process is optional, as the model serving sevice could be
hosted on a different network (an API call). But these two processes are considered a single instance of Arch. hosted on a different network (an API call). But these two processes are considered a single instance of Arch.
**Prompt Target**: Arch offers a primitive called :ref:`prompt_target <prompt_target>` to help separate business logic from undifferentiated **Prompt Target**: Arch offers a primitive called :ref:`prompt target <prompt_target>` to help separate business logic from undifferentiated
work in building generative AI apps. Prompt targets are endpoints that receive prompts that are processed by Arch. work in building generative AI apps. Prompt targets are endpoints that receive prompts that are processed by Arch.
For example, Arch enriches incoming prompts with metadata like knowing when a request is a follow-up or clarifying prompt For example, Arch enriches incoming prompts with metadata like knowing when a request is a follow-up or clarifying prompt
so that you can build faster, more accurate retrieval (RAG) apps. To support agentic apps, like scheduling travel plans or so that you can build faster, more accurate retrieval (RAG) apps. To support agentic apps, like scheduling travel plans or

92
docs/source/get_started/intro_to_arch.rst
View file

@ -3,13 +3,8 @@
Intro to Arch Intro to Arch
============= =============
Arch is an intelligent `(Layer 7) <https://www.cloudflare.com/learning/ddos/what-is-layer-7/>`_ gateway Arch is an intelligent `(Layer 7) <https://www.cloudflare.com/learning/ddos/what-is-layer-7/>`_ gateway designed for generative AI apps, AI agents, and AI copilots that work with prompts.
designed for generative AI apps, AI agents, and Co-pilots that work with prompts. Engineered with purpose-built Engineered with purpose-built large language models (LLMs), Arch handles all the critical but undifferentiated tasks related to the handling and processing of prompts, including 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.
large language models (LLMs), Arch handles all the critical but undifferentiated tasks related to the handling and
processing of prompts, including detecting and rejecting `jailbreak <https://github.com/verazuo/jailbreak_llms>`_
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 .. image:: /_static/img/arch-logo.png
:width: 100% :width: 100%
@ -21,70 +16,51 @@ in a centralized way.
including secure handling, intelligent routing, robust observability, and integration with backend (API) including secure handling, intelligent routing, robust observability, and integration with backend (API)
systems for personalization - all outside business logic.* systems for personalization - all outside business logic.*
In practice, achieving the above goal is incredibly difficult.
Arch attempts to do so by providing the following high level features:
In practice, achieving the above goal is incredibly difficult. Arch attempts to do so by providing the **Out-of-process architecture, built on** `Envoy <http://envoyproxy.io/>`_:
following high level features: Arch is takes a dependency on Envoy and is a self-contained process that is designed to run alongside your application servers.
Arch uses Envoy's HTTP connection management subsystem, HTTP L7 filtering and telemetry capabilities to extend the functionality exclusively for prompts and LLMs.
This gives Arch several advantages:
_____________________________________________________________________________________________________________ * Arch builds on Envoy's proven success. Envoy is used at masssive sacle by the leading technology companies of our time including `AirBnB <https://www.airbnb.com>`_, `Dropbox <https://www.dropbox.com>`_, `Google <https://www.google.com>`_, `Reddit <https://www.reddit.com>`_, `Stripe <https://www.stripe.com>`_, etc. Its battle tested and scales linearly with usage and enables developers to focus on what really matters: application features and business logic.
**Out-of-process architecture, built on** `Envoy <http://envoyproxy.io/>`_: Arch is takes a dependency on * Arch works with any application language. A single Arch deployment can act as gateway for AI applications written in Python, Java, C++, Go, Php, etc.
Envoy and is a self-contained process that is designed to run alongside your application servers. Arch uses
Envoy's HTTP connection management subsystem, HTTP L7 filtering and telemetry capabilities to extend the
functionality exclusively for prompts and LLMs. This gives Arch several advantages:
* Arch builds on Envoy's proven success. Envoy is used at masssive sacle by the leading technology companies of * Arch can be deployed and upgraded quickly across your infrastructure transparently without the horrid pain of deploying library upgrades in your applications.
our time including `AirBnB <https://www.airbnb.com>`_, `Dropbox <https://www.dropbox.com>`_,
`Google <https://www.google.com>`_, `Reddit <https://www.reddit.com>`_, `Stripe <https://www.stripe.com>`_,
etc. Its battle tested and scales linearly with usage and enables developers to focus on what really matters:
application features and business logic.
* Arch works with any application language. A single Arch deployment can act as gateway for AI applications **Engineered with Fast LLMs:** Arch is engineered with specialized tiny LLMs that are desgined for fast, cost-effective and acurrate handling of prompts.
written in Python, Java, C++, Go, Php, etc. These LLMs are designed to be best-in-class for critcal prompt-related tasks like:
* Arch can be deployed and upgraded quickly across your infrastructure transparently without the horrid pain * **Function Calling:** Arch helps you easily personalize your applications by enabling calls to application-specific (API) operations via user prompts.
of deploying library upgrades in your applications. This involves any predefined functions or APIs you want to expose to users to perform tasks, gather information, or manipulate data.
With function calling, you have flexibility to support "agentic" experiences tailored to specific use cases - from updating insurance claims to creating ad campaigns - via prompts.
Arch analyzes prompts, extracts critical information from prompts, engages in lightweight conversation to gather any missing parameters and makes API calls so that you can focus on writing business logic.
For more details, read :ref:`Function Calling <function_calling>`.
**Engineered with Fast LLMs:** Arch is engineered with specialized (sub-billion) LLMs that are desgined for * **Prompt Guard:** Arch helps you improve the safety of your application by applying prompt guardrails in a centralized way for better governance hygiene.
fast, cost-effective and acurrate handling of prompts. These LLMs are designed to be With prompt guardrails you can prevent ``jailbreak attempts`` present in user's prompts without having to write a single line of code.
best-in-class for critcal prompt-related tasks like: To learn more about how to configure guardrails available in Arch, read :ref:`Prompt Guard <prompt_guard>`.
* **Function/API Calling:** Arch helps you easily personalize your applications by enabling calls to * **[Coming Soon] Intent-Markers:** Developers struggle to handle ``follow-up`` or ``clarifying`` questions.
application-specific (API) operations via user prompts. This involves any predefined functions or APIs Specifically, when users ask for modifications or additions to previous responses their AI applications often generate entirely new responses instead of adjusting the previous ones.
you want to expose to users to perform tasks, gather information, or manipulate data. With function calling, Arch offers intent-markers as a feature so that developers know when the user has shifted away from the previous intent so that they can improve their retrieval, lower overall token cost and dramatically improve the speed and accuracy of their responses back to users.
you have flexibility to support "agentic" experiences tailored to specific use cases - from updating insurance For more details :ref:`intent markers <arch_rag_guide>`.
claims to creating ad campaigns - via prompts. Arch analyzes prompts, extracts critical information from
prompts, engages in lightweight conversation to gather any missing parameters and makes API calls so that you can
focus on writing business logic. For more details, read :ref:`prompt processing <arch_overview_prompt_handling>`.
* **Prompt Guardrails:** Arch helps you improve the safety of your application by applying prompt guardrails in **Traffic Management:** Arch offers several capabilities for LLM calls originating from your applications, including smart retries on errors from upstream LLMs, and automatic cutover to other LLMs configured in Arch for continuous availability and disaster recovery scenarios.
a centralized way for better governance hygiene. With prompt guardrails you can prevent `jailbreak <https://github.com/verazuo/jailbreak_llms>`_ Arch extends Envoy's `cluster subsystem <https://www.envoyproxy.io/docs/envoy/latest/intro/arch_overview/upstream/cluster_manager>`_ to manage upstream connections to LLMs so that you can build resilient AI applications.
attempts or toxicity present in user's prompts without having to write a single line of code. To learn more
about how to configure guardrails available in Arch, read :ref:`prompt processing <arch_overview_prompt_handling>`.
* **[Coming Soon] Intent-Markers:** Developers struggle to handle `follow-up <https://www.reddit.com/r/ChatGPTPromptGenius/comments/17dzmpy/how_to_use_rag_with_conversation_history_for/?>`_, **Front/edge Gateway:** There is substantial benefit in using the same software at the edge (observability, traffic shaping alogirithms, applying guardrails, etc.) as for outbound LLM inference use cases.
or `clarifying <https://www.reddit.com/r/LocalLLaMA/comments/18mqwg6/best_practice_for_rag_with_followup_chat/>`_ Arch has the feature set that makes it exceptionally well suited as an edge gateway for AI applications.
questions. Specifically, when users ask for modifications or additions to previous responses their AI applications This includes TLS termination, applying guardrail early in the pricess, intelligent parameter gathering from prompts, and prompt-based routing to backend APIs.
often generate entirely new responses instead of adjusting the previous ones. Arch offers intent-markers as a
feature so that developers know when the user has shifted away from the previous intent so that they can improve
their retrieval, lower overall token cost and dramatically improve the speed and accuracy of their responses back
to users. For more details :ref:`intent markers<arch_rag_guide>`
**Traffic Management:** Arch offers several capabilities for LLM calls originating from your applications, including smart
retries on errors from upstream LLMs, and automatic cutover to other LLMs configured in Arch for continuous availability
and disaster recovery scenarios. Arch extends Envoy's `cluster subsystem <https://www.envoyproxy.io/docs/envoy/latest/intro/arch_overview/upstream/cluster_manager>`_
to manage upstream connections to LLMs so that you can build resilient AI applications.
**Front/edge Gateway:** There is substantial benefit in using the same software at the edge (observability,
traffic shaping alogirithms, applying guardrails, etc.) as for outbound LLM inference use cases. Arch has the feature set
that makes it exceptionally well suited as an edge gateway for AI applications. This includes TLS termination, applying
guardrail early in the pricess, intelligent parameter gathering from prompts, and prompt-based routing to backend APIs.
**Best-In Class Monitoring:** Arch offers several monitoring metrics that help you understand three critical aspects of **Best-In Class Monitoring:** Arch offers several monitoring metrics that help you understand three critical aspects of
your application: latency, token usage, and error rates by an upstream LLM provider. Latency measures the speed at which your application: latency, token usage, and error rates by an upstream LLM provider. Latency measures the speed at which
your application is responding to users, which includes metrics like time to first token (TFT), time per output token (TOT) your application is responding to users, which includes metrics like time to first token (TFT), time per output token (TOT)
metrics, and the total latency as perceived by users. metrics, and the total latency as perceived by users.
**End-to-End Tracing:** Arch propagates trace context using the W3C Trace Context standard, specifically through the **End-to-End Tracing:** Arch propagates trace context using the W3C Trace Context standard, specifically through the ``traceparent`` header.
``traceparent`` header. This allows each component in the system to record its part of the request flow, enabling **end-to-end tracing** This allows each component in the system to record its part of the request flow, enabling end-to-end tracing across the entire application.
across the entire application. By using OpenTelemetry, Arch ensures that developers can capture this trace data consistently and By using OpenTelemetry, Arch ensures that developers can capture this trace data consistently and in a format compatible with various observability tools.
in a format compatible with various observability tools. For more details, read :ref:`tracing <arch_overview_tracing>`. For more details, read :ref:`Tracing <arch_overview_tracing>`.

2
docs/source/get_started/quickstart.rst
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@ -16,7 +16,7 @@ Before you begin, ensure you have the following:
- ``Docker`` & ``Python`` installed on your system - ``Docker`` & ``Python`` installed on your system
- ``API Keys`` for LLM providers (if using external LLMs) - ``API Keys`` for LLM providers (if using external LLMs)
The fastest way to get started using Arch is to use `katanemo/arch <https://hub.docker.com/r/katanemo/arch>`_ pre-built binaries. The fastest way to get started using Arch is to use `katanemo/archgw <https://hub.docker.com/r/katanemo/archgw>`_ pre-built binaries.
You can also build it from source. You can also build it from source.

2
docs/source/guides/function_calling.rst
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@ -35,7 +35,7 @@ Function Calling Workflow
Arch-Function Arch-Function
------------------------- -------------------------
The `Arch-Function <https://huggingface.co/collections/katanemolabs/arch-function-66f209a693ea8df14317ad68>`_ collection of large language models (LLMs) is a collection state-of-the-art (SOTA) LLMs specifically designed for **function calling** tasks. The `Arch-Function <https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68>`_ collection of large language models (LLMs) is a collection state-of-the-art (SOTA) LLMs specifically designed for **function calling** tasks.
The models are designed to understand complex function signatures, identify required parameters, and produce accurate function call outputs based on natural language prompts. The models are designed to understand complex function signatures, identify required parameters, and produce accurate function call outputs based on natural language prompts.
Achieving performance on par with GPT-4, these models set a new benchmark in the domain of function-oriented tasks, making them suitable for scenarios where automated API interaction and function execution is crucial. Achieving performance on par with GPT-4, these models set a new benchmark in the domain of function-oriented tasks, making them suitable for scenarios where automated API interaction and function execution is crucial.

2
docs/source/guides/prompt_guard.rst
View file

@ -39,7 +39,7 @@ Arch-Guard is designed to address this challenge.
What Is Arch-Guard What Is Arch-Guard
~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~
`Arch-Guard <https://huggingface.co/collections/katanemolabs/arch-guard-6702bdc08b889e4bce8f446d>`_ is a robust classifier model specifically trained on a diverse corpus of prompt attacks. `Arch-Guard <https://huggingface.co/collections/katanemo/arch-guard-6702bdc08b889e4bce8f446d>`_ is a robust classifier model specifically trained on a diverse corpus of prompt attacks.
It excels at detecting explicitly malicious prompts, providing an essential layer of security for LLM applications. It excels at detecting explicitly malicious prompts, providing an essential layer of security for LLM applications.
By embedding Arch-Guard within the Arch architecture, we empower developers to build robust, LLM-powered applications while prioritizing security and safety. With Arch-Guard, you can navigate the complexities of prompt management with confidence, knowing you have a reliable defense against malicious input. By embedding Arch-Guard within the Arch architecture, we empower developers to build robust, LLM-powered applications while prioritizing security and safety. With Arch-Guard, you can navigate the complexities of prompt management with confidence, knowing you have a reliable defense against malicious input.

7
docs/source/index.rst
View file

@ -2,7 +2,7 @@ Welcome to Arch!
================ ================
.. image:: /_static/img/arch-logo.png .. image:: /_static/img/arch-logo.png
:width: 80% :width: 100%
:align: center :align: center
.. raw:: html .. raw:: html
@ -23,6 +23,7 @@ Arch (built by the contributors of `Envoy <https://www.envoyproxy.io/>`_ ) was b
.. toctree:: .. toctree::
:caption: Get Started :caption: Get Started
:titlesonly: :titlesonly:
:maxdepth: 2
get_started/overview get_started/overview
get_started/intro_to_arch get_started/intro_to_arch
@ -33,6 +34,7 @@ Arch (built by the contributors of `Envoy <https://www.envoyproxy.io/>`_ ) was b
.. toctree:: .. toctree::
:caption: Concepts :caption: Concepts
:titlesonly: :titlesonly:
:maxdepth: 2
concepts/tech_overview/tech_overview concepts/tech_overview/tech_overview
concepts/llm_provider concepts/llm_provider
@ -43,6 +45,7 @@ Arch (built by the contributors of `Envoy <https://www.envoyproxy.io/>`_ ) was b
.. toctree:: .. toctree::
:caption: Guides :caption: Guides
:titlesonly: :titlesonly:
:maxdepth: 2
guides/prompt_guard guides/prompt_guard
guides/function_calling guides/function_calling
@ -53,6 +56,7 @@ Arch (built by the contributors of `Envoy <https://www.envoyproxy.io/>`_ ) was b
.. toctree:: .. toctree::
:caption: Build with Arch :caption: Build with Arch
:titlesonly: :titlesonly:
:maxdepth: 2
build_with_arch/agent build_with_arch/agent
build_with_arch/rag build_with_arch/rag
@ -62,5 +66,6 @@ Arch (built by the contributors of `Envoy <https://www.envoyproxy.io/>`_ ) was b
.. toctree:: .. toctree::
:caption: Resources :caption: Resources
:titlesonly: :titlesonly:
:maxdepth: 2
resources/configuration_reference resources/configuration_reference

107
model_server/app/__init__.py
View file

@ -1,107 +0,0 @@
import sys
import os
import time
import requests
import psutil
import tempfile
import subprocess
# Path to the file where the server process ID will be stored
PID_FILE = os.path.join(tempfile.gettempdir(), "model_server.pid")
def run_server():
"""Start, stop, or restart the Uvicorn server based on command-line arguments."""
if len(sys.argv) > 1:
action = sys.argv[1]
else:
action = "start"
if action == "start":
start_server()
elif action == "stop":
stop_server()
elif action == "restart":
restart_server()
else:
print(f"Unknown action: {action}")
sys.exit(1)
def start_server():
"""Start the Uvicorn server and save the process ID."""
if os.path.exists(PID_FILE):
print("Server is already running. Use 'model_server restart' to restart it.")
sys.exit(1)
print(
"Starting Archgw Model Server - Loading some awesomeness, this may take a little time.)"
)
process = subprocess.Popen(
["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "51000"],
start_new_session=True,
stdout=subprocess.DEVNULL, # Suppress standard output. There is a logger that model_server prints to
stderr=subprocess.DEVNULL, # Suppress standard error. There is a logger that model_server prints to
)
if wait_for_health_check("http://0.0.0.0:51000/healthz"):
# Write the process ID to the PID file
with open(PID_FILE, "w") as f:
f.write(str(process.pid))
print(f"Archgw Model Server started with PID {process.pid}")
else:
# Add model_server boot-up logs
print("Archgw Model Server - Didn't Sart In Time. Shutting Down")
process.terminate()
def wait_for_health_check(url, timeout=180):
"""Wait for the Uvicorn server to respond to health-check requests."""
start_time = time.time()
while time.time() - start_time < timeout:
try:
response = requests.get(url)
if response.status_code == 200:
return True
except requests.ConnectionError:
time.sleep(1)
print("Timed out waiting for Archgw Model Server to respond.")
return False
def stop_server():
"""Stop the running Uvicorn server."""
if not os.path.exists(PID_FILE):
print("Status: Archgw Model Server not running")
return
# Read the process ID from the PID file
with open(PID_FILE, "r") as f:
pid = int(f.read())
try:
# Get process by PID
process = psutil.Process(pid)
# Gracefully terminate the process
process.terminate() # Sends SIGTERM by default
process.wait(timeout=10) # Wait for up to 10 seconds for the process to exit
print(f"Server with PID {pid} stopped.")
os.remove(PID_FILE)
except psutil.NoSuchProcess:
print(f"Process with PID {pid} not found. Cleaning up PID file.")
os.remove(PID_FILE)
except psutil.TimeoutExpired:
print(f"Process with PID {pid} did not terminate in time. Forcing shutdown.")
process.kill() # Forcefully kill the process
os.remove(PID_FILE)
def restart_server():
"""Restart the Uvicorn server."""
print("Check: Is Archgw Model Server running?")
stop_server()
start_server()

119
model_server/app/cli.py Normal file
View file

@ -0,0 +1,119 @@
import sys
import os
import time
import requests
import psutil
import tempfile
import subprocess
import logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
log = logging.getLogger("model_server.cli")
log.setLevel(logging.INFO)
# Path to the file where the server process ID will be stored
PID_FILE = os.path.join(tempfile.gettempdir(), "model_server.pid")
def run_server():
"""Start, stop, or restart the Uvicorn server based on command-line arguments."""
if len(sys.argv) > 1:
action = sys.argv[1]
else:
action = "start"
if action == "start":
start_server()
elif action == "stop":
stop_server()
elif action == "restart":
restart_server()
else:
log.info(f"Unknown action: {action}")
sys.exit(1)
def start_server():
"""Start the Uvicorn server and save the process ID."""
if os.path.exists(PID_FILE):
log.info("Server is already running. Use 'model_server restart' to restart it.")
sys.exit(1)
log.info(
"Starting model server - loading some awesomeness, this may take some time :)"
)
process = subprocess.Popen(
["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "51000"],
start_new_session=True,
bufsize=1,
universal_newlines=True,
stdout=subprocess.PIPE, # Suppress standard output. There is a logger that model_server prints to
stderr=subprocess.PIPE, # Suppress standard error. There is a logger that model_server prints to
)
if wait_for_health_check("http://0.0.0.0:51000/healthz"):
# Write the process ID to the PID file
with open(PID_FILE, "w") as f:
f.write(str(process.pid))
log.info(f"Model server started with PID {process.pid}")
else:
# Add model_server boot-up logs
log.info("Model server - Didn't Sart In Time. Shutting Down")
process.terminate()
def wait_for_health_check(url, timeout=180):
"""Wait for the Uvicorn server to respond to health-check requests."""
start_time = time.time()
while time.time() - start_time < timeout:
try:
response = requests.get(url)
if response.status_code == 200:
return True
except requests.ConnectionError:
time.sleep(1)
print("Timed out waiting for model server to respond.")
return False
def stop_server():
"""Stop the running Uvicorn server."""
log.info("Stopping model server")
if not os.path.exists(PID_FILE):
log.info("Process id file not found, seems like model server was not running")
return
# Read the process ID from the PID file
with open(PID_FILE, "r") as f:
pid = int(f.read())
try:
# Get process by PID
process = psutil.Process(pid)
# Gracefully terminate the process
process.terminate() # Sends SIGTERM by default
process.wait(timeout=10) # Wait for up to 10 seconds for the process to exit
log.info(f"Model server with PID {pid} stopped.")
os.remove(PID_FILE)
except psutil.NoSuchProcess:
log.info(f"Model server with PID {pid} not found. Cleaning up PID file.")
os.remove(PID_FILE)
except psutil.TimeoutExpired:
log.info(
f"Model server with PID {pid} did not terminate in time. Forcing shutdown."
)
process.kill() # Forcefully kill the process
os.remove(PID_FILE)
def restart_server():
"""Restart the Uvicorn server."""
stop_server()
start_server()

2
model_server/app/commons/constants.py
View file

@ -5,6 +5,7 @@ import app.loader as loader
from app.function_calling.model_handler import ArchFunctionHandler from app.function_calling.model_handler import ArchFunctionHandler
from app.prompt_guard.model_handler import ArchGuardHanlder from app.prompt_guard.model_handler import ArchGuardHanlder
logger = utils.get_model_server_logger()
arch_function_hanlder = ArchFunctionHandler() arch_function_hanlder = ArchFunctionHandler()
arch_function_endpoint = "https://api.fc.archgw.com/v1" arch_function_endpoint = "https://api.fc.archgw.com/v1"
@ -19,7 +20,6 @@ arch_function_generation_params = {
arch_guard_model_type = {"cpu": "katanemo/Arch-Guard-cpu", "gpu": "katanemo/Arch-Guard"} arch_guard_model_type = {"cpu": "katanemo/Arch-Guard-cpu", "gpu": "katanemo/Arch-Guard"}
# Model definition # Model definition
embedding_model = loader.get_embedding_model() embedding_model = loader.get_embedding_model()
zero_shot_model = loader.get_zero_shot_model() zero_shot_model = loader.get_zero_shot_model()

9
model_server/app/loader.py
View file

@ -6,12 +6,15 @@ from optimum.onnxruntime import (
ORTModelForFeatureExtraction, ORTModelForFeatureExtraction,
ORTModelForSequenceClassification, ORTModelForSequenceClassification,
) )
import app.commons.utilities as utils
logger = utils.get_model_server_logger()
def get_embedding_model( def get_embedding_model(
model_name=os.getenv("MODELS", "katanemo/bge-large-en-v1.5"), model_name=os.getenv("MODELS", "katanemo/bge-large-en-v1.5"),
): ):
print("Loading Embedding Model...") logger.info("Loading Embedding Model...")
if glb.DEVICE != "cuda": if glb.DEVICE != "cuda":
model = ORTModelForFeatureExtraction.from_pretrained( model = ORTModelForFeatureExtraction.from_pretrained(
@ -32,7 +35,7 @@ def get_embedding_model(
def get_zero_shot_model( def get_zero_shot_model(
model_name=os.getenv("ZERO_SHOT_MODELS", "katanemo/bart-large-mnli"), model_name=os.getenv("ZERO_SHOT_MODELS", "katanemo/bart-large-mnli"),
): ):
print("Loading Zero-shot Model...") logger.info("Loading Zero-shot Model...")
if glb.DEVICE != "cuda": if glb.DEVICE != "cuda":
model = ORTModelForSequenceClassification.from_pretrained( model = ORTModelForSequenceClassification.from_pretrained(
@ -58,7 +61,7 @@ def get_zero_shot_model(
def get_prompt_guard(model_name, hardware_config="cpu"): def get_prompt_guard(model_name, hardware_config="cpu"):
print("Loading Guard Model...") logger.info("Loading Guard Model...")
if hardware_config == "cpu": if hardware_config == "cpu":
from optimum.intel import OVModelForSequenceClassification from optimum.intel import OVModelForSequenceClassification

4
model_server/app/main.py
View file

@ -4,7 +4,6 @@ import app.commons.utilities as utils
import app.commons.globals as glb import app.commons.globals as glb
import app.prompt_guard.model_utils as guard_utils import app.prompt_guard.model_utils as guard_utils
from typing import List, Dict from typing import List, Dict
from pydantic import BaseModel from pydantic import BaseModel
from fastapi import FastAPI, Response, HTTPException from fastapi import FastAPI, Response, HTTPException
@ -17,8 +16,7 @@ from app.function_calling.model_utils import (
logger = utils.get_model_server_logger() logger = utils.get_model_server_logger()
logger.info(f"Devices Avialble: {glb.DEVICE}") logger.info(f"Ready to serve traffic. available device: {glb.DEVICE}")
app = FastAPI() app = FastAPI()

4
model_server/pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry] [tool.poetry]
name = "archgw_modelserver" name = "archgw_modelserver"
version = "0.0.2" version = "0.0.3"
description = "A model server for serving models" description = "A model server for serving models"
authors = ["Katanemo Labs, Inc <archgw@katanemo.com>"] authors = ["Katanemo Labs, Inc <archgw@katanemo.com>"]
license = "Apache 2.0" license = "Apache 2.0"
@ -31,7 +31,7 @@ onnx = "1.17.0"
onnxruntime = "1.19.2" onnxruntime = "1.19.2"
[tool.poetry.scripts] [tool.poetry.scripts]
archgw_modelserver = "app:run_server" archgw_modelserver = "app.cli:run_server"
[build-system] [build-system]
requires = ["poetry-core>=1.0.0"] requires = ["poetry-core>=1.0.0"]

2
www/index.html
View file

@ -174,7 +174,7 @@
<header> <header>
<a href="https://github.com/katanemo/arch">GitHub</a> <a href="https://github.com/katanemo/arch">GitHub</a>
<a href="https://docs.archgw.com">Docs</a> <a href="https://docs.archgw.com">Docs</a>
<a href="https://discord.gg/rbjqVbpa">Discord</a> <a href="https://discord.gg/rSRQ9fv7">Discord</a>
<a href="https://github.com/katanemo/arch?tab=readme-ov-file#contact">Contact</a> <a href="https://github.com/katanemo/arch?tab=readme-ov-file#contact">Contact</a>
</header> </header>
<div class="container"> <div class="container">