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refactor demos (#398)

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1
demos/samples_python/currency_exchange/README.md Normal file
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This demo shows how you can use a publicly hosted rest api and interact it using arch gateway.

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version: v0.1
listener:
address: 0.0.0.0
port: 10000
message_format: huggingface
connect_timeout: 0.005s
llm_providers:
- name: gpt-4o
access_key: $OPENAI_API_KEY
provider_interface: openai
model: gpt-4o
endpoints:
frankfurther_api:
endpoint: api.frankfurter.dev
protocol: https
system_prompt: |
You are a helpful assistant.
prompt_guards:
input_guards:
jailbreak:
on_exception:
message: Looks like you're curious about my abilities, but I can only provide assistance for currency exchange.
prompt_targets:
- name: currency_exchange
description: Get currency exchange rate from USD to other currencies
parameters:
- name: currency_symbol
description: currency symbol to convert from USD
required: true
type: str
in_path: true
endpoint:
name: frankfurther_api
path: /v1/latest?base=USD&symbols={currency_symbol}
system_prompt: |
You are a helpful assistant. Show me the currency symbol you want to convert from USD.
- name: get_supported_currencies
description: Get list of supported currencies for conversion
endpoint:
name: frankfurther_api
path: /v1/currencies
tracing:
random_sampling: 100
trace_arch_internal: true

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demos/samples_python/currency_exchange/docker-compose.yaml Normal file
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services:
chatbot_ui:
build:
context: ../../shared/chatbot_ui
ports:
- "18080:8080"
environment:
# this is only because we are running the sample app in the same docker container environemtn as archgw
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
extra_hosts:
- "host.docker.internal:host-gateway"
volumes:
- ./arch_config.yaml:/app/arch_config.yaml
jaeger:
build:
context: ../../shared/jaeger
ports:
- "16686:16686"
- "4317:4317"
- "4318:4318"

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demos/samples_python/currency_exchange/run_demo.sh Normal file
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#!/bin/bash
set -e
# Function to start the demo
start_demo() {
# Step 1: Check if .env file exists
if [ -f ".env" ]; then
echo ".env file already exists. Skipping creation."
else
# Step 2: Create `.env` file and set OpenAI key
if [ -z "$OPENAI_API_KEY" ]; then
echo "Error: OPENAI_API_KEY environment variable is not set for the demo."
exit 1
fi
echo "Creating .env file..."
echo "OPENAI_API_KEY=$OPENAI_API_KEY" > .env
echo ".env file created with OPENAI_API_KEY."
fi
# Step 3: Start Arch
echo "Starting Arch with arch_config.yaml..."
archgw up arch_config.yaml
# Step 4: Start developer services
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

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demos/samples_python/currency_exchange/test_data.yaml Normal file
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test_cases:
- id: "get exchange rate"
input:
messages:
- role: user
content: what is exchange rate for gbp
expected_tools:
- type: function
function:
name: currency_exchange
arguments:
currency_symbol: GBP
expected_output_contains: gbp

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demos/samples_python/human_resources_agent/Dockerfile Normal file
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FROM python:3.12 AS base
FROM base AS builder
WORKDIR /src
COPY requirements.txt /src/
RUN pip install --prefix=/runtime --force-reinstall -r requirements.txt
FROM python:3.12-slim AS output
COPY --from=builder /runtime /usr/local
WORKDIR /app
COPY . /app
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--log-level", "info"]

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demos/samples_python/human_resources_agent/README.md Normal file
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# HR Agent Demo
This demo showcases how the **Arch** can be used to build an HR agent to manage workforce-related inquiries, workforce planning, and communication via Slack. It intelligently routes incoming prompts to the correct targets, providing concise and useful responses tailored for HR and workforce decision-making.
## Available Functions:
- **HR Q/A**: Handles general Q&A related to insurance policies.
- **Endpoint**: `/agent/hr_qa`
- **Workforce Data Retrieval**: Retrieves data related to workforce metrics like headcount, satisfaction, and staffing.
- **Endpoint**: `/agent/workforce`
- Parameters:
- `staffing_type` (str, required): Type of staffing (e.g., `contract`, `fte`, `agency`).
- `region` (str, required): Region for which the data is requested (e.g., `asia`, `europe`, `americas`).
- `point_in_time` (int, optional): Time point for data retrieval (e.g., `0 days ago`, `30 days ago`).
- **Initiate Policy**: Sends messages to a Slack channel
- **Endpoint**: `/agent/slack_message`
- Parameters:
- `slack_message` (str, required): The message content to be sent
# Starting the demo
1. Please make sure the [pre-requisites](https://github.com/katanemo/arch/?tab=readme-ov-file#prerequisites) are installed correctly
2. Start Arch
```sh
sh run_demo.sh
```
3. Navigate to http://localhost:18080/agent/chat
4. "Can you give me workforce data for asia?"

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version: v0.1
listener:
address: 127.0.0.1
port: 8080 #If you configure port 443, you'll need to update the listener with tls_certificates
message_format: huggingface
# Centralized way to manage LLMs, manage keys, retry logic, failover and limits in a central way
llm_providers:
- name: OpenAI
provider_interface: openai
access_key: $OPENAI_API_KEY
model: gpt-4o-mini
default: true
# Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.
endpoints:
app_server:
# value could be ip address or a hostname with port
# this could also be a list of endpoints for load balancing
# for example endpoint: [ ip1:port, ip2:port ]
endpoint: host.docker.internal:18080
# max time to wait for a connection to be established
connect_timeout: 0.005s
# default system prompt used by all prompt targets
system_prompt: |
You are a Workforce assistant that helps on workforce planning and HR decision makers with reporting and workforce planning. Use following rules when responding,
- when you get data in json format, offer some summary but don't be too verbose
- be concise, to the point and do not over analyze the data
prompt_targets:
- name: workforce
description: Get workforce data like headcount and satisfaction levels by region and staffing type
endpoint:
name: app_server
path: /agent/workforce
http_method: POST
parameters:
- name: staffing_type
type: str
description: specific category or nature of employment used by an organization like fte, contract and agency
required: true
enum: [fte, contract, agency]
- name: region
type: str
required: true
description: Geographical region for which you want workforce data like asia, europe, americas.
- name: data_snapshot_days_ago
type: int
required: false
description: the snapshot day for which you want workforce data.
- name: slack_message
endpoint:
name: app_server
path: /agent/slack_message
http_method: POST
description: sends a slack message on a channel
parameters:
- name: slack_message
type: string
required: true
description: the message that should be sent to a slack channel

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demos/samples_python/human_resources_agent/docker-compose.yaml Normal file
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services:
api_server:
build:
context: .
environment:
- SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN:-None}
- OPENAI_API_KEY=${OPENAI_API_KEY:?error}
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
volumes:
- ./arch_config.yaml:/app/arch_config.yaml
ports:
- "18080:80"
healthcheck:
test: ["CMD", "curl" ,"http://localhost:80/healthz"]
interval: 5s
retries: 20
chatbot_ui:
build:
context: ../../shared/chatbot_ui
dockerfile: Dockerfile
ports:
- "18080:8080"
environment:
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
extra_hosts:
- "host.docker.internal:host-gateway"
volumes:
- ./arch_config.yaml:/app/arch_config.yaml

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import os
import json
import pandas as pd
import gradio as gr
import logging
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from enum import Enum
from typing import List, Optional, Tuple
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError
from openai import OpenAI
app = FastAPI()
workforce_data_df = None
with open("workforce_data.json") as file:
workforce_data = json.load(file)
workforce_data_df = pd.json_normalize(
workforce_data,
record_path=["regions"],
meta=["data_snapshot_days_ago", "satisfaction"],
)
# Define the request model
class WorkforceRequest(BaseModel):
region: str
staffing_type: str
data_snapshot_days_ago: Optional[int] = None
class SlackRequest(BaseModel):
slack_message: str
class WorkforceResponse(BaseModel):
region: str
staffing_type: str
headcount: int
satisfaction: float
@app.post("/agent/slack_message")
def send_slack_message(request: SlackRequest):
"""
Endpoint that sends slack message
"""
slack_message = request.slack_message
# Load the bot token from an environment variable or replace it directly
slack_token = os.getenv(
"SLACK_BOT_TOKEN"
) # Replace with your token if needed: 'xoxb-your-token'
if slack_token is None:
print(f"Message for slack: {slack_message}")
else:
client = WebClient(token=slack_token)
channel = "hr_agent_demo"
try:
# Send the message
response = client.chat_postMessage(channel=channel, text=slack_message)
return f"Message sent to {channel}: {response['message']['text']}"
except SlackApiError as e:
print(f"Error sending message: {e.response['error']}")
# Post method for device summary
@app.post("/agent/workforce")
def get_workforce(request: WorkforceRequest):
"""
Endpoint to workforce data by region, staffing type at a given point in time.
"""
region = request.region.lower()
staffing_type = request.staffing_type.lower()
data_snapshot_days_ago = (
request.data_snapshot_days_ago
if request.data_snapshot_days_ago
else 0 # this param is not required.
)
response = {
"region": region,
"staffing_type": f"Staffing agency: {staffing_type}",
"headcount": f"Headcount: {int(workforce_data_df[(workforce_data_df['region']==region) & (workforce_data_df['data_snapshot_days_ago']==data_snapshot_days_ago)][staffing_type].values[0])}",
"satisfaction": f"Satisfaction: {float(workforce_data_df[(workforce_data_df['region']==region) & (workforce_data_df['data_snapshot_days_ago']==data_snapshot_days_ago)]['satisfaction'].values[0])}",
}
return response
if __name__ == "__main__":
app.run(debug=True)

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demos/samples_python/human_resources_agent/requirements.txt Normal file
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fastapi
uvicorn
slack-sdk
typing
pandas
gradio==5.3.0
async_timeout==4.0.3
loguru==0.7.2
asyncio==3.4.3
httpx==0.27.0
python-dotenv==1.0.1
pydantic==2.8.2
openai==1.51.0

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demos/samples_python/human_resources_agent/run_demo.sh Normal file
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#!/bin/bash
set -e
# Function to start the demo
start_demo() {
# Step 1: Check if .env file exists
if [ -f ".env" ]; then
echo ".env file already exists. Skipping creation."
else
# Step 2: Create `.env` file and set OpenAI key
if [ -z "$OPENAI_API_KEY" ]; then
echo "Error: OPENAI_API_KEY environment variable is not set for the demo."
exit 1
fi
echo "Creating .env file..."
echo "OPENAI_API_KEY=$OPENAI_API_KEY" > .env
echo ".env file created with OPENAI_API_KEY."
fi
# Step 3: Start Arch
echo "Starting Arch with arch_config.yaml..."
archgw up arch_config.yaml
# Step 4: Start Network Agent
echo "Starting HR 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 HR 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

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test_cases:
- id: get workforce data
input:
messages:
- role: user
content: what is workforce data for asia for fte employees
expected_tools:
- type: function
function:
name: workforce
arguments:
staffing_type: fte
region: asia
expected_output_contains: asia

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demos/samples_python/human_resources_agent/workforce_data.json Normal file
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[
{
"data_snapshot_days_ago": 0,
"regions": [
{ "region": "asia", "contract": 100, "fte": 150, "agency": 2000 },
{ "region": "europe", "contract": 80, "fte": 120, "agency": 2500 },
{ "region": "americas", "contract": 90, "fte": 200, "agency": 3100 }
],
"satisfaction": 3.5
},
{
"data_snapshot_days_ago": 30,
"regions": [
{ "region": "asia", "contract": 110, "fte": 155, "agency": 1000 },
{ "region": "europe", "contract": 85, "fte": 130, "agency": 1600 },
{ "region": "americas", "contract": 95, "fte": 210, "agency": 3100 }
],
"satisfaction": 4.0
},
{
"data_snapshot_days_ago": 60,
"regions": [
{ "region": "asia", "contract": 115, "fte": 160, "agency": 500 },
{ "region": "europe", "contract": 90, "fte": 140, "agency": 700 },
{ "region": "americas", "contract": 100, "fte": 220, "agency": 1200 }
],
"satisfaction": 4.7
}
]

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FROM python:3.12 AS base
FROM base AS builder
WORKDIR /src
COPY requirements.txt /src/
RUN pip install --prefix=/runtime --force-reinstall -r requirements.txt
COPY . /src
FROM python:3.12-slim AS output
COPY --from=builder /runtime /usr/local
COPY . /app
WORKDIR /app
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--log-level", "info"]

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# Multi-Turn Agentic Demo (RAG)
This demo showcases how the **Arch** can be used to build accurate multi-turn RAG agent by just writing simple APIs.
![Example of Multi-turn Interaction](mutli-turn-example.png)
### Energy Source Q/A
Provides information about various energy sources and considerations.
- **Endpoint**: `/agent/energy_source`
- **Parameters**:
- `energy_source` (`str`, **required**): A source of energy (e.g., `renewable`, `fossil`).
- `consideration` (`str`, *optional*): A specific type of consideration for an energy source (e.g., `cost`, `economic`, `technology`).
# Starting the demo
1. Please make sure the [pre-requisites](https://github.com/katanemo/arch/?tab=readme-ov-file#prerequisites) are installed correctly
2. Start Arch
```sh
sh run_demo.sh
```
3. Navigate to http://localhost:18080
4. Ask "give me information about renewable energy sources"

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version: v0.1
listener:
address: 127.0.0.1
port: 10000
message_format: huggingface
connect_timeout: 0.005s
endpoints:
rag_energy_source_agent:
endpoint: host.docker.internal:18083
connect_timeout: 0.005s
llm_providers:
- name: gpt-4o-mini
access_key: $OPENAI_API_KEY
provider_interface: openai
model: gpt-4o-mini
default: true
system_prompt: |
You are a helpful assistant and can offer information about energy sources.
You will get a JSON object with energy_source and consideration fields. Focus on answering the querstion using those fields.
Keep your responses to just three main points to make it easy for the reader to digest the information
prompt_targets:
- name: get_info_for_energy_source
description: get information about an energy source
parameters:
- name: energy_source
type: str
description: a source of energy
required: true
enum: [renewable, fossil]
- name: consideration
type: str
description: a specific type of consideration for an energy source
enum: [cost, economic, technology]
endpoint:
name: rag_energy_source_agent
path: /agent/energy_source_info
http_method: POST
- name: default_target
default: true
description: This is the default target for all unmatched prompts.
endpoint:
name: rag_energy_source_agent
path: /default_target
http_method: POST
system_prompt: |
You are a helpful assistant! Summarize the user's request and provide a helpful response.
# if it is set to false arch will send response that it received from this prompt target to the user
# if true arch will forward the response to the default LLM
auto_llm_dispatch_on_response: false
tracing:
random_sampling: 100
trace_arch_internal: true

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services:
rag_energy_source_agent:
build:
context: .
dockerfile: Dockerfile
ports:
- "18083:80"
healthcheck:
test: ["CMD", "curl" ,"http://localhost:80/healthz"]
interval: 5s
retries: 20
chatbot_ui:
build:
context: ../../shared/chatbot_ui
dockerfile: Dockerfile
ports:
- "18080:8080"
environment:
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
extra_hosts:
- "host.docker.internal:host-gateway"
volumes:
- ./arch_config.yaml:/app/arch_config.yaml

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import os
import gradio as gr
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Optional
from openai import OpenAI
app = FastAPI()
# Define the request model
class EnergySourceRequest(BaseModel):
energy_source: str
consideration: Optional[str] = None
class EnergySourceResponse(BaseModel):
energy_source: str
consideration: Optional[str] = None
# Post method for device summary
@app.post("/agent/energy_source_info")
def get_workforce(request: EnergySourceRequest):
"""
Endpoint to get details about energy source
"""
considertion = "You don't have any specific consideration. Feel free to talk in a more open ended fashion"
if request.consideration is not None:
considertion = f"Add specific focus on the following consideration when you summarize the content for the energy source: {request.consideration}"
response = {
"energy_source": request.energy_source,
"consideration": considertion,
}
return response
if __name__ == "__main__":
app.run(debug=True)

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demos/samples_python/multi_turn_rag_agent/requirements.txt Normal file
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fastapi
uvicorn
typing
pandas
gradio==5.3.0
async_timeout==4.0.3
loguru==0.7.2
asyncio==3.4.3
httpx==0.27.0
python-dotenv==1.0.1
pydantic==2.8.2
openai==1.51.0

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demos/samples_python/multi_turn_rag_agent/run_demo.sh Normal file
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#!/bin/bash
set -e
# Function to start the demo
start_demo() {
# Step 1: Check if .env file exists
if [ -f ".env" ]; then
echo ".env file already exists. Skipping creation."
else
# Step 2: Create `.env` file and set OpenAI key
if [ -z "$OPENAI_API_KEY" ]; then
echo "Error: OPENAI_API_KEY environment variable is not set for the demo."
exit 1
fi
echo "Creating .env file..."
echo "OPENAI_API_KEY=$OPENAI_API_KEY" > .env
echo ".env file created with OPENAI_API_KEY."
fi
# Step 3: Start Arch
echo "Starting Arch with arch_config.yaml..."
archgw up arch_config.yaml
# Step 4: Start Network Agent
echo "Starting HR 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 HR Agent using Docker Compose..."
docker compose down -v
# 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

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FROM python:3.12 AS base
FROM base AS builder
WORKDIR /src
COPY requirements.txt /src/
RUN pip install --prefix=/runtime --force-reinstall -r requirements.txt
COPY ../. /src
FROM python:3.12-slim AS output
COPY --from=builder /runtime /usr/local
COPY ../. /app
WORKDIR /app
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--log-level", "info"]

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# Network Agent Demo
This demo illustrates how **Arch** can be used to perform function calling with network-related tasks. In this demo, you act as a **network assistant** that provides factual information, without offering advice on manufacturers or purchasing decisions.
The assistant can perform several key operations, including rebooting devices, answering general networking questions, and retrieving device statistics. By default, the system prompt ensures that the assistant's responses are factual and neutral.
## Available Functions:
- **Reboot Devices**: Allows rebooting specific devices or device groups, with an optional time range for scheduling the reboot.
- Parameters:
- `device_ids` (required): A list of device IDs to reboot.
- `time_range` (optional): Specifies the time range in days, defaulting to 7 days if not provided.
- **Network Q/A**: Handles general Q&A related to networking. This function is the default target for general networking queries.
- **Device Summary**: Retrieves statistics for specific devices within a given time range.
- Parameters:
- `device_ids` (required): A list of device IDs for which statistics will be retrieved.
- `time_range` (optional): Specifies the time range in days for gathering statistics, with a default of 7 days.
# Starting the demo
1. Please make sure the [pre-requisites](https://github.com/katanemo/arch/?tab=readme-ov-file#prerequisites) are installed correctly
2. Start Arch
```sh
sh run_demo.sh
```
3. Navigate to http://localhost:18080/agent/chat
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 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 "Intelligent Gateway Overview" to view arch gateway stats
Here is sample interaction
![alt text](image.png)

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version: v0.1
listener:
address: 127.0.0.1
port: 8080 #If you configure port 443, you'll need to update the listener with tls_certificates
message_format: huggingface
# Centralized way to manage LLMs, manage keys, retry logic, failover and limits in a central way
llm_providers:
- name: OpenAI
provider_interface: openai
access_key: $OPENAI_API_KEY
model: gpt-3.5-turbo
default: true
# default system prompt used by all prompt targets
system_prompt: |
You are a network assistant that helps operators with a better understanding of network traffic flow and perform actions on networking operations. No advice on manufacturers or purchasing decisions.
prompt_targets:
- name: device_summary
description: Retrieve network statistics for specific devices within a time range
endpoint:
name: app_server
path: /agent/device_summary
http_method: POST
parameters:
- name: device_ids
type: list
description: A list of device identifiers (IDs) to retrieve statistics for.
required: true # device_ids are required to get device statistics
- name: days
type: int
description: The number of days for which to gather device statistics.
default: "7"
- name: reboot_devices
description: Reboot a list of devices
endpoint:
name: app_server
path: /agent/device_reboot
http_method: POST
parameters:
- name: device_ids
type: list
description: A list of device identifiers (IDs).
required: true
# Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.
endpoints:
app_server:
# value could be ip address or a hostname with port
# this could also be a list of endpoints for load balancing
# for example endpoint: [ ip1:port, ip2:port ]
endpoint: host.docker.internal:18083
# max time to wait for a connection to be established
connect_timeout: 0.005s

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services:
api_server:
build:
context: .
dockerfile: Dockerfile
ports:
- "18083:80"
chatbot_ui:
build:
context: ../../shared/chatbot_ui
dockerfile: Dockerfile
ports:
- "18080:8080"
environment:
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
extra_hosts:
- "host.docker.internal:host-gateway"
volumes:
- ./arch_config.yaml:/app/arch_config.yaml

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import os
from typing import List, Optional
from fastapi import FastAPI, HTTPException
from openai import OpenAI
from pydantic import BaseModel, Field
app = FastAPI()
DEMO_DESCRIPTION = """This demo illustrates how **Arch** can be used to perform function calling
with network-related tasks. In this demo, you act as a **network assistant** that provides factual
information, without offering advice on manufacturers or purchasing decisions."""
# Define the request model
class DeviceSummaryRequest(BaseModel):
device_ids: List[int]
time_range: Optional[int] = Field(
default=7, description="Time range in days, defaults to 7"
)
# Define the response model
class DeviceStatistics(BaseModel):
device_id: int
time_range: str
data: str
class DeviceSummaryResponse(BaseModel):
statistics: List[DeviceStatistics]
# Request model for device reboot
class DeviceRebootRequest(BaseModel):
device_ids: List[int]
# Response model for the device reboot
class CoverageResponse(BaseModel):
status: str
summary: dict
@app.post("/agent/device_reboot", response_model=CoverageResponse)
def reboot_network_device(request_data: DeviceRebootRequest):
"""
Endpoint to reboot network devices based on device IDs and an optional time range.
"""
# Access data from the Pydantic model
device_ids = request_data.device_ids
# Validate 'device_ids'
# (This is already validated by Pydantic, but additional logic can be added if needed)
if not device_ids:
raise HTTPException(
status_code=400, detail="'device_ids' parameter is required"
)
# Simulate reboot operation and return the response
statistics = []
for device_id in device_ids:
# Placeholder for actual data retrieval or device reboot logic
stats = {"data": f"Device {device_id} has been successfully rebooted."}
statistics.append(stats)
# Return the response with a summary
return CoverageResponse(status="success", summary={"device_ids": device_ids})
# Post method for device summary
@app.post("/agent/device_summary", response_model=DeviceSummaryResponse)
def get_device_summary(request: DeviceSummaryRequest):
"""
Endpoint to retrieve device statistics based on device IDs and an optional time range.
"""
# Extract 'device_ids' and 'time_range' from the request
device_ids = request.device_ids
time_range = request.time_range
# Simulate retrieving statistics for the given device IDs and time range
statistics = []
minutes = 1
for device_id in device_ids:
stats = {
"device_id": device_id,
"time_range": f"Last {time_range} days",
"data": f"""Device {device_id} over the last {time_range} days experienced {minutes}
minutes of downtime.""",
}
minutes += 1
statistics.append(DeviceStatistics(**stats))
return DeviceSummaryResponse(statistics=statistics)
CHAT_COMPLETION_ENDPOINT = os.getenv("CHAT_COMPLETION_ENDPOINT")
client = OpenAI(
api_key="--",
base_url=CHAT_COMPLETION_ENDPOINT,
)

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fastapi
uvicorn
pydantic
typing
pandas
gradio==5.3.0
async_timeout==4.0.3
loguru==0.7.2
asyncio==3.4.3
httpx==0.27.0
python-dotenv==1.0.1
pydantic==2.8.2
openai==1.51.0

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#!/bin/bash
set -e
# Function to start the demo
start_demo() {
# Step 1: Check if .env file exists
if [ -f ".env" ]; then
echo ".env file already exists. Skipping creation."
else
# Step 2: Create `.env` file and set OpenAI key
if [ -z "$OPENAI_API_KEY" ]; then
echo "Error: OPENAI_API_KEY environment variable is not set for the demo."
exit 1
fi
echo "Creating .env file..."
echo "OPENAI_API_KEY=$OPENAI_API_KEY" > .env
echo ".env file created with OPENAI_API_KEY."
fi
# Step 3: 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..."
cd build
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

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This demo shows how you can use a publicly hosted rest api that is protected by an access key.
Before you start the demo make sure you set `OPENAI_API_KEY` and `TWELVEDATA_API_KEY`.
To get `TWELVEDATA_API_KEY` please head over to https://twelvedata.com/.
Following screenshot shows interaction with stock quote demo,
![alt text](stock_quote_demo.png)

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version: v0.1
listener:
address: 0.0.0.0
port: 10000
message_format: huggingface
connect_timeout: 0.005s
llm_providers:
- name: gpt-4o
access_key: $OPENAI_API_KEY
provider_interface: openai
model: gpt-4o
endpoints:
twelvedata_api:
endpoint: api.twelvedata.com
protocol: https
system_prompt: |
You are a helpful assistant.
prompt_guards:
input_guards:
jailbreak:
on_exception:
message: Looks like you're curious about my abilities, but I can only provide assistance for currency exchange.
prompt_targets:
- name: stock_quote
description: get current stock exchange rate for a given symbol
parameters:
- name: symbol
description: Stock symbol
required: true
type: str
endpoint:
name: twelvedata_api
path: /quote
http_headers:
Authorization: "apikey $TWELVEDATA_API_KEY"
system_prompt: |
You are a helpful stock exchange assistant. You are given stock symbol along with its exchange rate in json format. Your task is to parse the data and present it in a human-readable format. Keep the details to highlevel and be concise.
- name: stock_quote_time_series
description: get historical stock exchange rate for a given symbol
parameters:
- name: symbol
description: Stock symbol
required: true
type: str
- name: interval
description: Time interval
default: 1day
enum:
- 1h
- 1day
type: str
endpoint:
name: twelvedata_api
path: /time_series
http_headers:
Authorization: "apikey $TWELVEDATA_API_KEY"
system_prompt: |
You are a helpful stock exchange assistant. You are given stock symbol along with its historical data in json format. Your task is to parse the data and present it in a human-readable format. Keep the details to highlevel only and be concise.
tracing:
random_sampling: 100
trace_arch_internal: true

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services:
chatbot_ui:
build:
context: ../../shared/chatbot_ui
ports:
- "18080:8080"
environment:
# this is only because we are running the sample app in the same docker container environment as archgw
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
extra_hosts:
- "host.docker.internal:host-gateway"
volumes:
- ./arch_config.yaml:/app/arch_config.yaml
jaeger:
build:
context: ../../shared/jaeger
ports:
- "16686:16686"
- "4317:4317"
- "4318:4318"

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#!/bin/bash
set -e
# Function to start the demo
start_demo() {
# Step 1: Check if .env file exists
if [ -f ".env" ]; then
echo ".env file already exists. Skipping creation."
else
# Step 2: Create `.env` file and set OpenAI key
if [ -z "$OPENAI_API_KEY" ]; then
echo "Error: OPENAI_API_KEY environment variable is not set for the demo."
exit 1
fi
echo "Creating .env file..."
echo "OPENAI_API_KEY=$OPENAI_API_KEY" > .env
echo ".env file created with OPENAI_API_KEY."
fi
# Step 3: Start Arch
echo "Starting Arch with arch_config.yaml..."
archgw up arch_config.yaml
# Step 4: Start developer services
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

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# took inspiration from https://medium.com/@albertazzir/blazing-fast-python-docker-builds-with-poetry-a78a66f5aed0
# The builder image, used to build the virtual environment
FROM python:3.12 as builder
RUN pip install poetry==1.8.3
ENV POETRY_NO_INTERACTION=1 \
POETRY_VIRTUALENVS_IN_PROJECT=1 \
POETRY_VIRTUALENVS_CREATE=1 \
POETRY_CACHE_DIR=/tmp/poetry_cache
WORKDIR /code
COPY pyproject.toml poetry.lock ./
RUN touch README.md
RUN poetry install --no-root && rm -rf $POETRY_CACHE_DIR
# The runtime image, used to just run the code provided its virtual environment
FROM python:3.12-slim as runtime
RUN apt-get update && apt-get install -y curl
WORKDIR /code
ENV VIRTUAL_ENV=/code/.venv \
PATH="/code/.venv/bin:$PATH"
COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
COPY main.py ./
HEALTHCHECK \
--interval=5s \
--timeout=1s \
--start-period=1s \
--retries=3 \
CMD curl http://localhost:80/healthz
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--log-level", "debug"]

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# Function calling
This demo shows how you can use Arch's core function calling capabilites.
# Starting the demo
1. Please make sure the [pre-requisites](https://github.com/katanemo/arch/?tab=readme-ov-file#prerequisites) are installed correctly
2. Start Arch
3. ```sh
sh run_demo.sh
```
4. Navigate to http://localhost:18080/
5. 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,
1. Start grafana and prometheus using following command
```yaml
docker compose --profile monitoring up
```
2. Navigate to http://localhost:3000/ to open grafana UI (use admin/grafana as credentials)
3. From grafana left nav click on dashboards and select "Intelligent Gateway Overview" to view arch gateway stats
Here is a sample interaction,
<img width="575" alt="image" src="https://github.com/user-attachments/assets/e0929490-3eb2-4130-ae87-a732aea4d059">
## Tracing
To see a tracing dashboard follow instructions below,
1. For Jaeger, you can either use the default run_demo.sh script or run the following command:
```sh
sh run_demo.sh jaeger
```
2. For Logfire, first make sure to add a LOGFIRE_API_KEY to the .env file. You can either use the default run_demo.sh script or run the following command:
```sh
sh run_demo.sh logfire
```
3. For Signoz, you can either use the default run_demo.sh script or run the following command:
```sh
sh run_demo.sh signoz
```
If using Jaeger, navigate to http://localhost:16686/ to open Jaeger UI
If using Signoz, navigate to http://localhost:3301/ to open Signoz UI
If using Logfire, navigate to your logfire dashboard that you got the write key from to view the dashboard
### Stopping Demo
1. To end the demo, run the following command:
```sh
sh run_demo.sh down
```

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version: "0.1-beta"
listener:
address: 0.0.0.0
port: 10000
message_format: huggingface
connect_timeout: 0.005s
endpoints:
weather_forecast_service:
endpoint: host.docker.internal:18083
connect_timeout: 0.005s
overrides:
# confidence threshold for prompt target intent matching
prompt_target_intent_matching_threshold: 0.6
llm_providers:
- name: gpt-4o-mini
access_key: $OPENAI_API_KEY
provider_interface: openai
model: gpt-4o-mini
default: true
- name: gpt-3.5-turbo-0125
access_key: $OPENAI_API_KEY
provider_interface: openai
model: gpt-3.5-turbo-0125
- name: gpt-4o
access_key: $OPENAI_API_KEY
provider_interface: openai
model: gpt-4o
system_prompt: |
You are a helpful assistant.
prompt_guards:
input_guards:
jailbreak:
on_exception:
message: Looks like you're curious about my abilities, but I can only provide assistance for weather forecasting.
prompt_targets:
- name: get_current_weather
description: Get current weather at a location.
parameters:
- name: location
description: The location to get the weather for
required: true
type: string
format: City, State
- name: days
description: the number of days for the request
required: true
type: int
endpoint:
name: weather_forecast_service
path: /weather
http_method: POST
- name: default_target
default: true
description: This is the default target for all unmatched prompts.
endpoint:
name: weather_forecast_service
path: /default_target
http_method: POST
system_prompt: |
You are a helpful assistant! Summarize the user's request and provide a helpful response.
# if it is set to false arch will send response that it received from this prompt target to the user
# if true arch will forward the response to the default LLM
auto_llm_dispatch_on_response: false
tracing:
random_sampling: 100
trace_arch_internal: true

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services:
weather_forecast_service:
build:
context: ./
environment:
- OLTP_HOST=http://otel-collector:4317
extra_hosts:
- "host.docker.internal:host-gateway"
ports:
- "18083:80"
chatbot_ui:
build:
context: ../../shared/chatbot_ui
ports:
- "18080:8080"
environment:
# this is only because we are running the sample app in the same docker container environment as archgw
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
extra_hosts:
- "host.docker.internal:host-gateway"
volumes:
- ./arch_config.yaml:/app/arch_config.yaml
otel-collector:
build:
context: ../../shared/honeycomb/
ports:
- "4317:4317"
- "4318:4318"
volumes:
- ../../shared/honeycomb/otel-collector-config.yaml:/etc/otel-collector-config.yaml
env_file:
- .env
environment:
- HONEYCOMB_API_KEY
prometheus:
build:
context: ../../shared/prometheus
grafana:
build:
context: ../../shared/grafana
ports:
- "3000:3000"

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services:
weather_forecast_service:
build:
context: ./
environment:
- OLTP_HOST=http://jaeger:4317
extra_hosts:
- "host.docker.internal:host-gateway"
ports:
- "18083:80"
chatbot_ui:
build:
context: ../../shared/chatbot_ui
ports:
- "18080:8080"
environment:
# this is only because we are running the sample app in the same docker container environemtn as archgw
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
extra_hosts:
- "host.docker.internal:host-gateway"
volumes:
- ./arch_config.yaml:/app/arch_config.yaml
jaeger:
build:
context: ../../shared/jaeger
ports:
- "16686:16686"
- "4317:4317"
- "4318:4318"
prometheus:
build:
context: ../../shared/prometheus
grafana:
build:
context: ../../shared/grafana
ports:
- "3000:3000"

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services:
weather_forecast_service:
build:
context: ./
environment:
- OLTP_HOST=http://otel-collector:4317
extra_hosts:
- "host.docker.internal:host-gateway"
ports:
- "18083:80"
chatbot_ui:
build:
context: ../../shared/chatbot_ui
ports:
- "18080:8080"
environment:
# this is only because we are running the sample app in the same docker container environment as archgw
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
extra_hosts:
- "host.docker.internal:host-gateway"
volumes:
- ./arch_config.yaml:/app/arch_config.yaml
otel-collector:
build:
context: ../../shared/logfire/
ports:
- "4317:4317"
- "4318:4318"
volumes:
- ../../shared/logfire/otel-collector-config.yaml:/etc/otel-collector-config.yaml
env_file:
- .env
environment:
- LOGFIRE_API_KEY
prometheus:
build:
context: ../../shared/prometheus
grafana:
build:
context: ../../shared/grafana
ports:
- "3000:3000"

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include:
- ../../shared/signoz/docker-compose-minimal.yaml
services:
weather_forecast_service:
build:
context: .
environment:
- OLTP_HOST=http://otel-collector:4317
extra_hosts:
- "host.docker.internal:host-gateway"
ports:
- "18083:80"
chatbot_ui:
build:
context: ../../shared/chatbot_ui
ports:
- "18080:8080"
environment:
# this is only because we are running the sample app in the same docker container environemtn as archgw
- CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
extra_hosts:
- "host.docker.internal:host-gateway"
volumes:
- ./arch_config.yaml:/app/arch_config.yaml
prometheus:
build:
context: ../../shared/prometheus
grafana:
build:
context: ../../shared/grafana
ports:
- "3000:3000"

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

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import json
import os
import random
from fastapi import FastAPI, Response
from datetime import datetime, date, timedelta, timezone
import logging
from pydantic import BaseModel
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk.resources import Resource
resource = Resource.create(
{
"service.name": "weather-forecast-service",
}
)
# Initialize the tracer provider
trace.set_tracer_provider(TracerProvider(resource=resource))
tracer = trace.get_tracer(__name__)
logger = logging.getLogger("uvicorn.error")
logger.setLevel(logging.INFO)
app = FastAPI()
FastAPIInstrumentor().instrument_app(app)
# Configure the OTLP exporter (Jaeger, Zipkin, etc.)
otlp_exporter = OTLPSpanExporter(
endpoint=os.getenv("OLTP_HOST", "http://localhost:4317")
)
trace.get_tracer_provider().add_span_processor(BatchSpanProcessor(otlp_exporter))
@app.get("/healthz")
async def healthz():
return {"status": "ok"}
class WeatherRequest(BaseModel):
location: str
days: int = 7
units: str = "Farenheit"
@app.post("/weather")
async def weather(req: WeatherRequest, res: Response):
weather_forecast = {
"location": req.location,
"temperature": [],
"units": req.units,
}
for i in range(req.days):
min_temp = random.randrange(50, 90)
max_temp = random.randrange(min_temp + 5, min_temp + 20)
if req.units.lower() == "celsius" or req.units.lower() == "c":
min_temp = (min_temp - 32) * 5.0 / 9.0
max_temp = (max_temp - 32) * 5.0 / 9.0
weather_forecast["temperature"].append(
{
"date": str(date.today() + timedelta(days=i)),
"temperature": {"min": min_temp, "max": max_temp},
"units": req.units,
"query_time": str(datetime.now(timezone.utc)),
}
)
return weather_forecast
class DefaultTargetRequest(BaseModel):
messages: list
@app.post("/default_target")
async def default_target(req: DefaultTargetRequest, res: Response):
logger.info(f"Received messages: {req.messages}")
resp = {
"choices": [
{
"message": {
"role": "assistant",
"content": "I can help you with weather forecast",
},
"finish_reason": "completed",
"index": 0,
}
],
"model": "api_server",
"usage": {"completion_tokens": 0},
}
logger.info(f"sending response: {json.dumps(resp)}")
return resp

979
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# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
name = "annotated-types"
version = "0.7.0"
description = "Reusable constraint types to use with typing.Annotated"
optional = false
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[[package]]
name = "zipp"
version = "3.20.2"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
{file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
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]
[package.extras]
check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
cover = ["pytest-cov"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
enabler = ["pytest-enabler (>=2.2)"]
test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = "^3.12"
content-hash = "1e3ce9e6208a62a9ec46f47af8d0d865d0052a7ac85b5520d44d5c960202a5d4"

23
demos/samples_python/weather_forecast/pyproject.toml Normal file
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@ -0,0 +1,23 @@
[tool.poetry]
name = "api-server"
version = "0.1.0"
description = ""
authors = ["Adil Hafeez <info@katanemo.com>"]
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.12"
opentelemetry-instrumentation-fastapi = "^0.49b0"
fastapi = "^0.115.4"
pyyaml = "^6.0.2"
uvicorn = "^0.32.0"
opentelemetry-api = "^1.28.0"
opentelemetry-sdk = "^1.28.0"
opentelemetry-exporter-otlp = "^1.28.0"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.poetry.scripts]
api-server = "api_server.main:app"

106
demos/samples_python/weather_forecast/run_demo.sh Normal file
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#!/bin/bash
set -e
# Function to load environment variables from the .env file
load_env() {
if [ -f ".env" ]; then
export $(grep -v '^#' .env | xargs)
fi
}
# Function to determine the docker-compose file based on the argument
get_compose_file() {
case "$1" in
jaeger)
echo "docker-compose-jaeger.yaml"
;;
logfire)
echo "docker-compose-logfire.yaml"
;;
signoz)
echo "docker-compose-signoz.yaml"
;;
honeycomb)
echo "docker-compose-honeycomb.yaml"
;;
*)
echo "docker-compose.yaml"
;;
esac
}
# Function to start the demo
start_demo() {
# Step 1: Determine the docker-compose file
COMPOSE_FILE=$(get_compose_file "$1" 2>/dev/null)
# Step 2: Check if .env file exists
if [ -f ".env" ]; then
echo ".env file already exists. Skipping creation."
else
# Step 3: Check for required environment variables
if [ -z "$OPENAI_API_KEY" ]; then
echo "Error: OPENAI_API_KEY environment variable is not set for the demo."
exit 1
fi
if [ "$1" == "logfire" ] && [ -z "$LOGFIRE_API_KEY" ]; then
echo "Error: LOGFIRE_API_KEY environment variable is required for Logfire."
exit 1
fi
if [ "$1" == "honeycomb" ] && [ -z "$HONEYCOMB_API_KEY" ]; then
echo "Error: HONEYCOMB_API_KEY environment variable is required for Honeycomb."
exit 1
fi
# Create .env file
echo "Creating .env file..."
echo "OPENAI_API_KEY=$OPENAI_API_KEY" >.env
if [ "$1" == "logfire" ]; then
echo "LOGFIRE_API_KEY=$LOGFIRE_API_KEY" >>.env
fi
echo ".env file created with required API keys."
fi
load_env
if [ "$1" == "logfire" ] && [ -z "$LOGFIRE_API_KEY" ]; then
echo "Error: LOGFIRE_API_KEY environment variable is required for Logfire."
exit 1
fi
if [ "$1" == "honeycomb" ] && [ -z "$HONEYCOMB_API_KEY" ]; then
echo "Error: HONEYCOMB_API_KEY environment variable is required for Honeycomb."
exit 1
fi
# Step 4: Start Arch
echo "Starting Arch with arch_config.yaml..."
archgw up arch_config.yaml
# Step 5: Start Network Agent with the chosen Docker Compose file
echo "Starting Network Agent with $COMPOSE_FILE..."
docker compose -f "$COMPOSE_FILE" up -d # Run in detached mode
}
# Function to stop the demo
stop_demo() {
echo "Stopping all Docker Compose services..."
# Stop all services by iterating through all configurations
for compose_file in ./docker-compose*.yaml; do
echo "Stopping services in $compose_file..."
docker compose -f "$compose_file" down
done
# Stop Arch
echo "Stopping Arch..."
archgw down
}
# Main script logic
if [ "$1" == "down" ]; then
# Call stop_demo with the second argument as the demo to stop
stop_demo
else
# Use the argument (jaeger, logfire, signoz) to determine the compose file
start_demo "$1"
fi