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fixed cli to use poetry as well. this way we make it easy to have the… (#160)

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37
README.md
View file

@ -2,46 +2,43 @@
<img src="docs/source/_static/img/arch-logo.png" alt="Arch Gateway Logo" title="Arch Gateway Logo">
</p>
<h2>Build fast, robust, and personalized GenAI apps (agents, assistants, etc.)</h2>
## Build fast, robust, and personalized GenAI apps (agents, assistants, etc.)
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 built on (and by the core contributors of) the wildly popular and robust [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.*
**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 egreess traffic realted to prompts and LLMs
- Build fast Agentic and RAG applications. Engineered with purpose-built [(fast) LLMs](https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68) to handle fast, cost-effective, and accurate prompt-based tasks like function/API calling.
- Prompt [Guardrails](https://huggingface.co/collections/katanemo/arch-guard-6702bdc08b889e4bce8f446d): Arch centralizes prompt guardrails to prevent jailbreak attempts and ensure safe user interactions without writing extra code.
- Enables fast Agentic and RAG apps. Engineered with purpose-built [(fast) 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 [Guardrails](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.
- Open Observability: Arch uses the W3C Trace Context standard to enable complete request tracing across applications, ensuring compatibility with observability tools, and provides metrics to monitor latency, token usage, and error rates, helping optimize AI application performance.
- Standards-based Observability: Arch uses the W3C Trace Context standard to enable complete request tracing across applications, ensuring compatibility with observability tools, and provides metrics to monitor latency, token usage, and error rates, helping optimize AI application performance.
**Jump to our [docs](https://docs.archgw.com)** to learn more about how you can use Arch to improve the speed, robustneess 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.
# Demos
* [Function Calling](demos/function_calling/README.md) -Showcases critical function calling cabaility
* [Insurance Agent](demos/insurance_agent/README.md) -Build a full insurance agent with arch
## Demos
* [Function Calling](demos/function_calling/README.md) - Walk through of critical function calling capabilities
* [Insurance Agent](demos/insurance_agent/README.md) - Build a full insurance agent with arch
* [Network Agent](demos/network_agent/README.md) - Build a networking co-pilot/agent agent with arch
# Quickstart
## Quickstart
Follow this guide to learn how to quickly set up Arch and integrate it into your generative AI applications.
## Prerequisites
### Prerequisites
Before you begin, ensure you have the following:
- `Docker` & `Python` installed on your system
- `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.
You can also build it from source.
## Step 1: Install Arch
### Step 1: Install Arch
Arch's CLI allows you to manage and interact with the Arch gateway efficiently. To install the CLI, simply run the following command:
Tip: We recommend that developers create a new Python virtual environment to isolate dependencies before installing Arch. This ensures that archgw and its dependencies do not interfere with other packages on your system.
@ -53,7 +50,7 @@ $ source venv/bin/activate # On Windows, use: venv\Scripts\activate
$ pip install archgw
```
## Step 2: Configure Arch with your application
### Step 2: Configure Arch with your application
Arch operates based on a configuration file where you can define LLM providers, prompt targets, guardrails, etc.
Below is an example configuration to get you started:
@ -104,7 +101,7 @@ endpoints:
# max time to wait for a connection to be established
connect_timeout: 0.005s
```
## Step 3: Using OpenAI Client with Arch as an Egress Gateway
### Step 3: Using OpenAI Client with Arch as an Egress Gateway
Make outbound calls via Arch
@ -126,10 +123,10 @@ print("OpenAI Response:", response.choices[0].text.strip())
```
## [Observability](https://docs.archgw.com/guides/observability/observability.html)
### [Observability](https://docs.archgw.com/guides/observability/observability.html)
Arch is designed to support best-in class observability by supporting open standards. Please read our [docs](https://docs.archgw.com/guides/observability/observability.html) on observability for more details on tracing, metrics, and logs
## Contribution
### Contribution
We would love feedback on our [Roadmap](https://github.com/orgs/katanemo/projects/1) and we welcome contributions to **Arch**!
Whether you're fixing bugs, adding new features, improving documentation, or creating tutorials, your help is much appreciated.
Please vist our [Contribution Guide](arch/CONTRIBUTING.md) for more details

2
arch/Dockerfile
View file

@ -19,7 +19,7 @@ COPY --from=envoy /usr/local/bin/envoy /usr/local/bin/envoy
WORKDIR /config
COPY arch/requirements.txt .
RUN pip install -r requirements.txt
COPY arch/tools/config_generator.py .
COPY arch/tools/cli/config_generator.py .
COPY arch/envoy.template.yaml .
COPY arch/arch_config_schema.yaml .

38
arch/tools/README.md
View file

@ -1,4 +1,4 @@
## Setup Instructions: archgw CLI
## Setup Instructions(User): archgw CLI
This guide will walk you through the steps to set up the archgw cli on your local machine
@ -17,12 +17,46 @@ python -m venv venv
source venv/bin/activate
```
### Step 3: Run the build script
```bash
pip install archgw
```
## Uninstall Instructions: archgw CLI
```bash
pip uninstall archgw
```
## Setup Instructions (Dev): archgw CLI
This guide will walk you through the steps to set up the archgw cli on your local machine when you want to develop the Archgw CLI
### Step 1: Create a Python virtual environment
In the tools directory, create a Python virtual environment by running:
```bash
python -m venv venv
```
### Step 2: Activate the virtual environment
* On Linux/MacOS:
```bash
source venv/bin/activate
```
### Step 3: Run the build script
```bash
sh build-cli.sh
```
### Step 4: build Arch
```bash
archgw build
```
## Uninstall Instructions: archgw CLI
```bash
pip uninstall archgw
```

2
arch/tools/build-cli.sh
View file

@ -21,4 +21,4 @@ cp "$source_stage_env" "$destination_dir/stage.env"
echo "Files copied successfully!"
echo "Building the cli"
pip install -e .
poetry install

0
arch/tools/cli/__init__.py Normal file
View file

0
arch/tools/config_generator.py → arch/tools/cli/config_generator.py
View file

6
arch/tools/core.py → arch/tools/cli/core.py
View file

@ -3,7 +3,7 @@ import os
import time
import pkg_resources
import select
from 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
def start_arch(arch_config_file, env, log_timeout=120):
@ -16,7 +16,7 @@ def start_arch(arch_config_file, env, log_timeout=120):
"""
compose_file = pkg_resources.resource_filename(
__name__, "config/docker-compose.yaml"
__name__, "../config/docker-compose.yaml"
)
try:
@ -112,7 +112,7 @@ def stop_arch():
path (str): The path where the docker-compose.yml file is located.
"""
compose_file = pkg_resources.resource_filename(
__name__, "config/docker-compose.yaml"
__name__, "../config/docker-compose.yaml"
)
try:

23
arch/tools/cli.py → arch/tools/cli/main.py
View file

@ -1,12 +1,17 @@
import click
import targets
import os
import config_generator
import pkg_resources
import sys
import subprocess
from core import start_arch_modelserver, stop_arch_modelserver, start_arch, stop_arch
from utils import get_llm_provider_access_keys, load_env_file_to_dict
from cli import targets
from cli import config_generator
from cli.core import (
start_arch_modelserver,
stop_arch_modelserver,
start_arch,
stop_arch,
)
from cli.utils import get_llm_provider_access_keys, load_env_file_to_dict
logo = r"""
_ _
@ -83,7 +88,7 @@ def build():
@click.command()
@click.argument("file", required=False) # Optional file argument
@click.option(
"-path", default=".", help="Path to the directory containing arch_config.yml"
"--path", default=".", help="Path to the directory containing arch_config.yml"
)
def up(file, path):
"""Starts Arch."""
@ -101,7 +106,7 @@ def up(file, path):
print(f"Validating {arch_config_file}")
arch_schema_config = pkg_resources.resource_filename(
__name__, "config/arch_config_schema.yaml"
__name__, "../config/arch_config_schema.yaml"
)
try:
@ -110,7 +115,7 @@ def up(file, path):
arch_config_schema_file=arch_schema_config,
)
except Exception as e:
print("Exiting archgw up")
print(f"Exiting archgw up: {e}")
sys.exit(1)
print("Starting Arch gateway and Arch model server services via docker ")
@ -147,7 +152,7 @@ def up(file, path):
env_stage[access_key] = env_file_dict[access_key]
with open(
pkg_resources.resource_filename(__name__, "config/stage.env"), "w"
pkg_resources.resource_filename(__name__, "../config/stage.env"), "w"
) as file:
for key, value in env_stage.items():
file.write(f"{key}={value}\n")
@ -168,7 +173,7 @@ def down():
@click.command()
@click.option(
"-f",
"--f",
"--file",
type=click.Path(exists=True),
required=True,

0
arch/tools/targets.py → arch/tools/cli/targets.py
View file

0
arch/tools/utils.py → arch/tools/cli/utils.py
View file

507
arch/tools/poetry.lock generated Normal file
View file

@ -0,0 +1,507 @@
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
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[package.extras]
benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
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[[package]]
name = "typing-extensions"
version = "4.12.2"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.8"
files = [
{file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
]
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "ad84828eb5709cd7fa0623650d36c00df54a95420c9ec0e6d369942837982fac"

30
arch/tools/pyproject.toml Normal file
View file

@ -0,0 +1,30 @@
[tool.poetry]
name = "archgw"
version = "0.1"
description = "Python-based CLI tool to manage Arch Gateway."
authors = ["Katanemo Labs, Inc."]
packages = [
{ include = "cli" }
]
include = [
# Include package data (docker-compose.yaml and other files)[
"config/docker-compose.yaml",
"config/arch_config_schema.yaml",
"config/stage.env"
]
[tool.poetry.dependencies]
python = "^3.10"
pyyaml = "^6.0.2"
pydantic = "^2.9.2"
click = "^8.1.7"
jinja2 = "^3.1.4"
jsonschema = "^4.23.0"
[tool.poetry.scripts]
archgw = "cli.main:main"
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

18
demos/function_calling/README.md
View file

@ -1,22 +1,14 @@
# Function calling
This demo shows how you can use Arch's function calling capabilites.
This demo shows how you can use Arch's core function calling capabilites.
# Starting the demo
1. Create `.env` file and set OpenAI key using env var `OPENAI_API_KEY`
1. Please make sure the [pre-requisites](../../../README.md?tab=readme-ov-file#prerequisites) are installed correctly
2. Start Arch
```sh
archgw up arch_config.yaml
sh run_demo.sh
```
3. Start Network Agent
```sh
docker compose up
```
4. Navigate to http://localhost:18080/
4. You can type in queries like "how is the weather in Seattle"
- You can also ask follow up questions like "show me sunny days"
6. To see metrics navigate to "http://localhost:3000/" (use admin/grafana for login)
- Open up dahsboard named "Intelligent Gateway Overview"
- On this dashboard you can see reuqest latency and number of requests
3. Navigate to http://localhost:18080/
4. You can type in queries like "how is the weather?"
# 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,

46
demos/function_calling/run_demo.sh Normal file
View file

@ -0,0 +1,46 @@
#!/bin/bash
# Function to start the demo
start_demo() {
# Step 1: Check if .env file exists
if [ -f ".env" ]; then
echo ".env file already exists. Skipping creation."
else
# Step 2: Create `.env` file and set OpenAI key
if [ -z "$OPENAI_API_KEY" ]; then
echo "Error: OPENAI_API_KEY environment variable is not set for the demo."
exit 1
fi
echo "Creating .env file..."
echo "OPENAI_API_KEY=$OPENAI_API_KEY" > .env
echo ".env file created with OPENAI_API_KEY."
fi
# Step 3: Start Arch
echo "Starting Arch with arch_config.yaml..."
archgw up arch_config.yaml
# Step 4: Start Network Agent
echo "Starting Network Agent using Docker Compose..."
docker compose up -d # Run in detached mode
}
# Function to stop the demo
stop_demo() {
# Step 1: Stop Docker Compose services
echo "Stopping Network Agent using Docker Compose..."
docker compose down
# Step 2: Stop Arch
echo "Stopping Arch..."
archgw down
}
# Main script logic
if [ "$1" == "down" ]; then
stop_demo
else
# Default action is to bring the demo up
start_demo
fi

16
demos/insurance_agent/README.md
View file

@ -36,29 +36,23 @@ 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.
# Starting the demo
1. Create `.env` file and set OpenAI key using env var `OPENAI_API_KEY`
1. Please make sure the [pre-requisites](../../../README.md?tab=readme-ov-file#prerequisites) are installed correctly
2. Start Arch
```sh
archgw up [path to arch_config.yaml]
```
3. Start Network Agent
```sh
docker compose up
sh run_demo.sh
```
3. Navigate to http://localhost:18080/
4. You can type in queries like "show me device statics for the past 7 days"
4. Tell me what can you do for me?"
# Observability
Arch gateway publishes stats endpoint at http://localhost:19901/stats. In this demo we are using grafana 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,
1. Start grafana and prometheus using following command
```yaml
docker compose --profile monitoring up
```
1. Navigate to http://localhost:3000/ to open grafana UI (use admin/grafana as credentials)
1. From grafana left nav click on dashboards and select "Arch" to view the arch gateway stats
1. From grafana left nav click on dashboards and select "Intelligent Gateway Overview" to view arch gateway stats
Here is sample interaction,
<img width="575" alt="image" src="https://github.com/user-attachments/assets/25d40f46-616e-41ea-be8e-1623055c84ec">

0
demos/insurance_agent/arch_confirg.yaml → demos/insurance_agent/arch_config.yaml
View file

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

@ -17,4 +17,6 @@ services:
ports:
- "18090:8080"
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY:?error}
- MISTRAL_API_KEY=${MISTRAL_API_KEY:?error}
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1

46
demos/insurance_agent/run_demo.sh Normal file
View file

@ -0,0 +1,46 @@
#!/bin/bash
# Function to start the demo
start_demo() {
# Step 1: Check if .env file exists
if [ -f ".env" ]; then
echo ".env file already exists. Skipping creation."
else
# Step 2: Create `.env` file and set OpenAI key
if [ -z "$OPENAI_API_KEY" ]; then
echo "Error: OPENAI_API_KEY environment variable is not set for the demo."
exit 1
fi
echo "Creating .env file..."
echo "OPENAI_API_KEY=$OPENAI_API_KEY" > .env
echo ".env file created with OPENAI_API_KEY."
fi
# Step 3: Start Arch
echo "Starting Arch with arch_config.yaml..."
archgw up arch_config.yaml
# Step 4: Start Network Agent
echo "Starting Network Agent using Docker Compose..."
docker compose up -d # Run in detached mode
}
# Function to stop the demo
stop_demo() {
# Step 1: Stop Docker Compose services
echo "Stopping Network Agent using Docker Compose..."
docker compose down
# Step 2: Stop Arch
echo "Stopping Arch..."
archgw down
}
# Main script logic
if [ "$1" == "down" ]; then
stop_demo
else
# Default action is to bring the demo up
start_demo
fi

19
demos/network_agent/README.md
View file

@ -19,29 +19,24 @@ The assistant can perform several key operations, including rebooting devices, a
# Starting the demo
1. Create `.env` file and set OpenAI key using env var `OPENAI_API_KEY`
1. Please make sure the [pre-requisites](../../../README.md?tab=readme-ov-file#prerequisites) are installed correctly
2. Start Arch
```sh
archgw up [path to arch_config.yaml]
```
3. Start Network Agent
```sh
docker compose up
sh run_demo.sh
```
3. Navigate to http://localhost:18080/
4. You can type in queries like "show me device statics for the past 7 days"
4. Tell me what can you do for me?"
# Observability
Arch gateway publishes stats endpoint at http://localhost:19901/stats. In this demo we are using grafana 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,
1. Start grafana and prometheus using following command
```yaml
docker compose --profile monitoring up
```
1. Navigate to http://localhost:3000/ to open grafana UI (use admin/grafana as credentials)
1. From grafana left nav click on dashboards and select "Arch" to view the arch gateway stats
1. From grafana left nav click on dashboards and select "Intelligent Gateway Overview" to view arch gateway stats
Here is sample interaction
Here is sample interaction,
<img width="575" alt="image" src="https://github.com/user-attachments/assets/25d40f46-616e-41ea-be8e-1623055c84ec">

46
demos/network_agent/run_demo.sh Normal file
View file

@ -0,0 +1,46 @@
#!/bin/bash
# Function to start the demo
start_demo() {
# Step 1: Check if .env file exists
if [ -f ".env" ]; then
echo ".env file already exists. Skipping creation."
else
# Step 2: Create `.env` file and set OpenAI key
if [ -z "$OPENAI_API_KEY" ]; then
echo "Error: OPENAI_API_KEY environment variable is not set for the demo."
exit 1
fi
echo "Creating .env file..."
echo "OPENAI_API_KEY=$OPENAI_API_KEY" > .env
echo ".env file created with OPENAI_API_KEY."
fi
# Step 3: Start Arch
echo "Starting Arch with arch_config.yaml..."
archgw up arch_config.yaml
# Step 4: Start Network Agent
echo "Starting Network Agent using Docker Compose..."
docker compose up -d # Run in detached mode
}
# Function to stop the demo
stop_demo() {
# Step 1: Stop Docker Compose services
echo "Stopping Network Agent using Docker Compose..."
docker compose down
# Step 2: Stop Arch
echo "Stopping Arch..."
archgw down
}
# Main script logic
if [ "$1" == "down" ]; then
stop_demo
else
# Default action is to bring the demo up
start_demo
fi

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

@ -22,7 +22,7 @@ Below is an example of how you can configure ``llm_providers`` with an instance
.. Note::
When you start Arch, it creates a listener port for egress traffic based on the presence of ``llm_providers``
configuration section in the ``prompt_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``.
Arch also offers vendor-agnostic SDKs and libraries to make LLM calls to API-based LLM providers (like OpenAI,

10
docs/source/concepts/tech_overview/model_serving.rst
View file

@ -29,16 +29,6 @@ might not be available.
$ archgw up --local-cpu
Local Serving (GPU - Fast)
--------------------------
The following bash commands enable you to configure the model server subsystem in Arch to run locally on the
machine and utilize the GPU available for fast inference across all model use cases, including function calling
guardails, etc.
.. code-block:: console
$ archgw up --local-gpu
Cloud Serving (GPU - Blazing Fast)
----------------------------------
The command below instructs Arch to intelligently use GPUs locally for fast intent detection, but default to

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

@ -107,7 +107,7 @@ traffic, apply rate limits, and utilize a large set of traffic management capabi
.. Attention::
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 ``prompt_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.

3
model_server/README.md
View file

@ -1 +1,2 @@
## Model Server Package ##
# Model Server Package #
This model server package is a dependency of the Arch intelligent prompt gateway. It should not be used alone. Please refer to the [quickstart-guide](https://github.com/katanemo/arch?tab=readme-ov-file#quickstart) for more details on how to get start with Arch.

2
model_server/pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "archgw_modelserver"
version = "0.1.0"
version = "0.0.1"
description = "A model server for serving models"
authors = ["Katanemo Labs, Inc <archgw@katanemo.com>"]
license = "Apache 2.0"

30
model_server/setup.py
View file

@ -1,30 +0,0 @@
from setuptools import setup, find_packages
# Function to read requirements.txt
def parse_requirements(filename):
with open(filename, "r") as file:
return [
line.strip() for line in file if line.strip() and not line.startswith("#")
]
# Call the parse_requirements function to get the list of dependencies
requirements = parse_requirements("requirements.txt")
print(f"packages to install: {find_packages()}")
setup(
name="model_server",
version="0.1",
packages=find_packages(),
install_requires=requirements,
package_data={
# Specify the package and the data files you want to include
"app": ["/*.yaml"], # Includes all .yaml files in the config/ folder
},
entry_points={
"console_scripts": [
"model_server=app:run_server",
],
},
)