Merge branch 'main' into debug-streaming-v2

.github/workflows/model-server-tests.yml

@@ -23,16 +23,22 @@ jobs:
         with:
           python-version: "3.10"  # Adjust to your Python version
 
-      # Step 3: Install dependencies (from requirements.txt or Pipfile)
+      # Step 3: Install Poetry
+      - name: Install Poetry
+        run: |
+          curl -sSL https://install.python-poetry.org | python3 -
+          export PATH="$HOME/.local/bin:$PATH"
+
+      # Step 4: Install dependencies using Poetry
       - name: Install dependencies
         run: |
           cd model_server
-          pip install --upgrade pip
+          poetry install
-          pip install -r requirements.txt  # Or use pipenv install
-          pip install pytest
 
-      # Step 4: Set PYTHONPATH and run tests
+      # Step 5: Set PYTHONPATH and run tests
       - name: Run model server tests with pytest
+        env:
+          PYTHONPATH: model_server  # Ensure the app's path is available
         run: |
           cd model_server
-          PYTHONPATH=. pytest --maxfail=5 --disable-warnings
+          poetry run pytest --maxfail=5 --disable-warnings

README.md

@@ -22,7 +22,7 @@ Engineered with purpose-built LLMs, Arch handles the critical but undifferentiat
 **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
-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.
+To get in touch with us, please join our [discord server](https://discord.gg/pGZf2gcwEc). We will be monitoring that actively and offering support there.
 
 ## Demos
 * [Function Calling](demos/function_calling/README.md) - Walk through of the core function calling capabilities Arch offers
@@ -59,11 +59,9 @@ Below is an example configuration to get you started:
 
 ```yaml
 version: v0.1
-
+listener:
-listen:
+  address: 127.0.0.1
-  address: 0.0.0.0 # or 127.0.0.1
+  port: 8080 #If you configure port 443, you'll need to update the listener with tls_certificates
-  port: 10000
-  # Defines how Arch should parse the content from application/json or text/pain Content-type in the http request
   message_format: huggingface
 
 # Centralized way to manage LLMs, manage keys, retry logic, failover and limits in a central way
@@ -73,10 +71,10 @@ llm_providers:
     access_key: OPENAI_API_KEY
     model: gpt-4o
     default: true
-    stream: true
 
 # default system prompt used by all prompt targets
-system_prompt: You are a network assistant that just offers facts; not advice on manufacturers or purchasing decisions.
+system_prompt: |
+   You are a network assistant that just offers facts; not advice on manufacturers or purchasing decisions.
 
 prompt_targets:
   - name: reboot_devices
@@ -131,4 +129,4 @@ Arch is designed to support best-in class observability by supporting open stand
 ### 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
+Please visit our [Contribution Guide](CONTRIBUTING.md) for more details

arch/tools/.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-  "python.defaultInterpreterPath": "${workspaceFolder}/venv/bin/python",
-}

arch/tools/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "archgw"
-version = "0.0.4"
+version = "0.0.5"
 description = "Python-based CLI tool to manage Arch Gateway."
 authors = ["Katanemo Labs, Inc."]
 packages = [
@@ -22,9 +22,9 @@ pydantic = "^2.9.2"
 click = "^8.1.7"
 jinja2 = "^3.1.4"
 jsonschema = "^4.23.0"
-setuptools = "75.1.0"
+setuptools = "75.2.0"
-archgw_modelserver= "0.0.3"
+archgw_modelserver = "0.0.4"
-huggingface_hub = "^0.25.2"
+huggingface_hub = "^0.26.0"
 
 [tool.poetry.scripts]
 archgw = "cli.main:main"

chatbot_ui/.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-  "python.defaultInterpreterPath": "${workspaceFolder}/venv/bin/python",
-}

chatbot_ui/app/run.py

@@ -1,8 +1,11 @@
 import json
 import os
-from openai import OpenAI, DefaultHttpxClient
-import gradio as gr
 import logging
+import yaml
+import gradio as gr
+
+from typing import List, Optional, Tuple
+from openai import OpenAI, DefaultHttpxClient
 from dotenv import load_dotenv
 
 load_dotenv()
@@ -15,9 +18,22 @@ logging.basicConfig(
 log = logging.getLogger(__name__)
 
 CHAT_COMPLETION_ENDPOINT = os.getenv("CHAT_COMPLETION_ENDPOINT")
-ARCH_STATE_HEADER = "x-arch-state"
 log.info(f"CHAT_COMPLETION_ENDPOINT: {CHAT_COMPLETION_ENDPOINT}")
 
+ARCH_STATE_HEADER = "x-arch-state"
+
+CSS_STYLE = """
+.json-container {
+    height: 95vh !important;
+    overflow-y: auto !important;
+}
+.chatbot {
+    height: calc(95vh - 100px) !important;
+    overflow-y: auto !important;
+}
+footer {visibility: hidden}
+"""
+
 client = OpenAI(
     api_key="--",
     base_url=CHAT_COMPLETION_ENDPOINT,
@@ -25,11 +41,56 @@ client = OpenAI(
 )
 
 
-def predict(message, state):
+def convert_prompt_target_to_openai_format(target):
+    tool = {
+        "description": target["description"],
+        "parameters": {"type": "object", "properties": {}, "required": []},
+    }
+
+    if "parameters" in target:
+        for param_info in target["parameters"]:
+            parameter = {
+                "type": param_info["type"],
+                "description": param_info["description"],
+            }
+
+            for key in ["default", "format", "enum", "items", "minimum", "maximum"]:
+                if key in param_info:
+                    parameter[key] = param_info[key]
+
+            tool["parameters"]["properties"][param_info["name"]] = parameter
+
+            required = param_info.get("required", False)
+            if required:
+                tool["parameters"]["required"].append(param_info["name"])
+
+    return {"name": target["name"], "info": tool}
+
+
+def get_prompt_targets():
+    try:
+        with open("arch_config.yaml", "r") as file:
+            config = yaml.safe_load(file)
+
+            available_tools = []
+            for target in config["prompt_targets"]:
+                if not target.get("default", False):
+                    available_tools.append(
+                        convert_prompt_target_to_openai_format(target)
+                    )
+
+            return {tool["name"]: tool["info"] for tool in available_tools}
+    except Exception as e:
+        log.info(e)
+        return None
+
+
+def chat(query: Optional[str], conversation: Optional[List[Tuple[str, str]]], state):
     if "history" not in state:
         state["history"] = []
+
     history = state.get("history")
-    history.append({"role": "user", "content": message})
+    history.append({"role": "user", "content": query})
     log.info(f"history: {history}")
 
     # Custom headers
@@ -58,7 +119,8 @@ def predict(message, state):
     # extract arch_state from metadata and store it in gradio session state
     # this state must be passed back to the gateway in the next request
     response_json = json.loads(raw_response.text)
-    if response_json:
+    log.info(response_json)
+    if response_json and "metadata" in response_json:
         # load arch_state from metadata
         arch_state_str = response_json.get("metadata", {}).get(ARCH_STATE_HEADER, "{}")
         # parse arch_state into json object
@@ -78,25 +140,53 @@ def predict(message, state):
     # for gradio UI we don't want to show raw tool calls and messages from developer application
     # so we're filtering those out
     history_view = [h for h in history if h["role"] != "tool" and "content" in h]
+
     messages = [
         (history_view[i]["content"], history_view[i + 1]["content"])
         for i in range(0, len(history_view) - 1, 2)
     ]
-    return messages, state
+
+    return "", messages, state
 
 
-with gr.Blocks(fill_height=True, css="footer {visibility: hidden}") as demo:
+def main():
-    print("Starting Demo...")
+    with gr.Blocks(
-    chatbot = gr.Chatbot(label="Arch Chatbot", scale=1)
+        theme=gr.themes.Default(
-    state = gr.State({})
+            font_mono=[gr.themes.GoogleFont("IBM Plex Mono"), "Arial", "sans-serif"]
-    with gr.Row():
+        ),
-        txt = gr.Textbox(
+        fill_height=True,
-            show_label=False,
+        css=CSS_STYLE,
-            placeholder="Enter text and press enter",
+    ) as demo:
-            scale=1,
+        with gr.Row(equal_height=True):
-            autofocus=True,
+            state = gr.State({})
-        )
 
-    txt.submit(predict, [txt, state], [chatbot, state])
+            with gr.Column(scale=4):
+                gr.JSON(
+                    value=get_prompt_targets(),
+                    open=True,
+                    show_indices=False,
+                    label="Available Tools",
+                    scale=1,
+                    min_height="95vh",
+                    elem_classes="json-container",
+                )
+            with gr.Column(scale=6):
+                chatbot = gr.Chatbot(
+                    label="Arch Chatbot",
+                    scale=1,
+                    elem_classes="chatbot",
+                )
+                textbox = gr.Textbox(
+                    show_label=False,
+                    placeholder="Enter text and press enter",
+                    scale=1,
+                    autofocus=True,
+                )
 
-demo.launch(server_name="0.0.0.0", server_port=8080, show_error=True, debug=True)
+            textbox.submit(chat, [textbox, chatbot, state], [textbox, chatbot, state])
+
+    demo.launch(server_name="0.0.0.0", server_port=8080, show_error=True, debug=True)
+
+
+if __name__ == "__main__":
+    main()

chatbot_ui/requirements.txt

@@ -1,4 +1,4 @@
-gradio==4.44.1
+gradio==5.3.0
 async_timeout==4.0.3
 loguru==0.7.2
 asyncio==3.4.3

crates/common/src/consts.rs

@@ -15,7 +15,7 @@ pub const HALLUCINATION_INTERNAL_HOST: &str = "hallucination";
 pub const EMBEDDINGS_INTERNAL_HOST: &str = "embeddings";
 pub const GUARD_INTERNAL_HOST: &str = "guard";
 pub const ARCH_ROUTING_HEADER: &str = "x-arch-llm-provider";
-pub const ARCH_MESSAGES_KEY: &str = "arch_messages";
+pub const MESSAGES_KEY: &str = "messages";
 pub const ARCH_PROVIDER_HINT_HEADER: &str = "x-arch-llm-provider-hint";
 pub const CHAT_COMPLETIONS_PATH: &str = "/v1/chat/completions";
 pub const ARCH_STATE_HEADER: &str = "x-arch-state";
@@ -25,3 +25,4 @@ pub const ARCH_INTERNAL_CLUSTER_NAME: &str = "arch_internal";
 pub const ARCH_UPSTREAM_HOST_HEADER: &str = "x-arch-upstream";
 pub const ARCH_LLM_UPSTREAM_LISTENER: &str = "arch_llm_listener";
 pub const ARCH_MODEL_PREFIX: &str = "Arch";
+pub const HALLUCINATION_TEMPLATE: &str = "It seems I’m missing some information. Could you provide the following details ";

crates/prompt_gateway/src/hallucination.rs

@@ -1,6 +1,6 @@
 use common::{
     common_types::open_ai::Message,
-    consts::{ARCH_MODEL_PREFIX, ASSISTANT_ROLE, USER_ROLE},
+    consts::{ARCH_MODEL_PREFIX, USER_ROLE, HALLUCINATION_TEMPLATE},
 };
 
 pub fn extract_messages_for_hallucination(messages: &Vec<Message>) -> Vec<String> {
@@ -18,9 +18,11 @@ pub fn extract_messages_for_hallucination(messages: &Vec<Message>) -> Vec<String
         for message in messages.iter().rev() {
             if let Some(model) = message.model.as_ref() {
                 if !model.starts_with(ARCH_MODEL_PREFIX) {
-                    if message.role == ASSISTANT_ROLE {
+                  if let Some(content) = &message.content {
-                        break;
+                      if !content.starts_with(HALLUCINATION_TEMPLATE) {
-                    }
+                          break;
+                      }
+                  }
                 }
             }
             if message.role == USER_ROLE {

crates/prompt_gateway/src/stream_context.rs

@@ -12,12 +12,7 @@ use common::common_types::{
 };
 use common::configuration::{Overrides, PromptGuards, PromptTarget};
 use common::consts::{
-    ARCH_FC_INTERNAL_HOST, ARCH_FC_MODEL_NAME, ARCH_FC_REQUEST_TIMEOUT_MS,
+    ARCH_FC_INTERNAL_HOST, ARCH_FC_MODEL_NAME, ARCH_FC_REQUEST_TIMEOUT_MS, ARCH_INTERNAL_CLUSTER_NAME, MESSAGES_KEY, ARCH_MODEL_PREFIX, ARCH_STATE_HEADER, ARCH_UPSTREAM_HOST_HEADER, ASSISTANT_ROLE, DEFAULT_EMBEDDING_MODEL, HALLUCINATION_TEMPLATE, DEFAULT_HALLUCINATED_THRESHOLD, DEFAULT_INTENT_MODEL, DEFAULT_PROMPT_TARGET_THRESHOLD, EMBEDDINGS_INTERNAL_HOST, HALLUCINATION_INTERNAL_HOST, REQUEST_ID_HEADER, SYSTEM_ROLE, TOOL_ROLE, USER_ROLE, ZEROSHOT_INTERNAL_HOST
-    ARCH_INTERNAL_CLUSTER_NAME, ARCH_MESSAGES_KEY, ARCH_MODEL_PREFIX, ARCH_STATE_HEADER,
-    ARCH_UPSTREAM_HOST_HEADER, DEFAULT_EMBEDDING_MODEL, DEFAULT_HALLUCINATED_THRESHOLD,
-    DEFAULT_INTENT_MODEL, DEFAULT_PROMPT_TARGET_THRESHOLD, EMBEDDINGS_INTERNAL_HOST,
-    HALLUCINATION_INTERNAL_HOST, REQUEST_ID_HEADER, SYSTEM_ROLE, TOOL_ROLE, USER_ROLE,
-    ZEROSHOT_INTERNAL_HOST,
 };
 use common::embeddings::{
     CreateEmbeddingRequest, CreateEmbeddingRequestInput, CreateEmbeddingResponse,
@@ -328,12 +323,11 @@ impl StreamContext {
 
         if !keys_with_low_score.is_empty() {
             let response =
-                "It seems I’m missing some information. Could you provide the following details: "
+                    HALLUCINATION_TEMPLATE.to_string()
-                    .to_string()
                     + &keys_with_low_score.join(", ")
                     + " ?";
             let message = Message {
-                role: SYSTEM_ROLE.to_string(),
+                role: ASSISTANT_ROLE.to_string(),
                 content: Some(response),
                 model: Some(ARCH_FC_MODEL_NAME.to_string()),
                 tool_calls: None,
@@ -461,7 +455,7 @@ impl StreamContext {
                     let upstream_endpoint = endpoint.name;
                     let mut params = HashMap::new();
                     params.insert(
-                        ARCH_MESSAGES_KEY.to_string(),
+                        MESSAGES_KEY.to_string(),
                         callout_context.request_body.messages.clone(),
                     );
                     let arch_messages_json = serde_json::to_string(&params).unwrap();
@@ -688,7 +682,7 @@ impl StreamContext {
             tool_params_json_str
         );
         tool_params.insert(
-            String::from(ARCH_MESSAGES_KEY),
+            String::from(MESSAGES_KEY),
             serde_yaml::to_value(&callout_context.request_body.messages).unwrap(),
         );
         let tool_params_json_str = serde_json::to_string(&tool_params).unwrap();
@@ -772,7 +766,7 @@ impl StreamContext {
             .arguments
             .clone();
         tool_params.insert(
-            String::from(ARCH_MESSAGES_KEY),
+            String::from(MESSAGES_KEY),
             serde_yaml::to_value(&callout_context.request_body.messages).unwrap(),
         );
 

demos/function_calling/docker-compose.yaml

@@ -20,6 +20,8 @@ services:
       - 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"
+    volumes:
+      - ./arch_config.yaml:/app/arch_config.yaml
 
   opentelemetry:
     build:

demos/hr_agent/Dockerfile

@@ -0,0 +1,20 @@
+FROM python:3.10 AS base
+
+FROM base AS builder
+
+WORKDIR /src
+
+COPY requirements.txt /src/
+COPY workforce_data.json /src/
+RUN pip install --prefix=/runtime --force-reinstall -r requirements.txt
+
+COPY . /src
+
+FROM python:3.10-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"]

demos/hr_agent/arch_config.yaml

@@ -0,0 +1,63 @@
+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: openai
+    access_key: OPENAI_API_KEY
+    model: gpt-4o
+    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:18083
+    # 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 workfoce planning. NOTHING ELSE. When you get data in json format, offer some summary but don't be too verbose.
+
+prompt_targets:
+    - name: hr_qa
+      endpoint:
+        name: app_server
+        path: /agent/hr_qa
+      description: Handle general Q/A related to HR.
+      default: true
+    - name: workforce
+      description: Get workforce data like headcount and satisfacton levels by region and staffing type
+      endpoint:
+        name: app_server
+        path: /agent/workforce
+      parameters:
+        - name: staffing_type
+          type: str
+          description: Staffing type like contract, fte or agency
+          required: true
+        - name: region
+          type: str
+          required: true
+          description: Geographical region for which you want workforce data like asia, europe, americas.
+        - name: point_in_time
+          type: int
+          required: false
+          description: the point in time for which to retrieve data. For e.g 0 days ago, 30 days ago, etc.
+    - name: slack_message
+      endpoint:
+        name: app_server
+        path: /agent/slack_message
+      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

demos/hr_agent/docker-compose.yaml

@@ -0,0 +1,25 @@
+services:
+  api_server:
+    build:
+      context: .
+    environment:
+      - SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN:-None}
+    ports:
+      - "18083:80"
+    healthcheck:
+        test: ["CMD", "curl" ,"http://localhost:80/healthz"]
+        interval: 5s
+        retries: 20
+
+  chatbot_ui:
+    build:
+      context: ../../chatbot_ui
+    ports:
+      - "18080:8080"
+    environment:
+      - OPENAI_API_KEY=${OPENAI_API_KEY:?error}
+      - CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    volumes:
+      - ./arch_config.yaml:/app/arch_config.yaml

demos/hr_agent/main.py

@@ -0,0 +1,106 @@
+import os
+import json
+import pandas as pd
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, Field
+from typing import Optional
+from enum import Enum
+from slack_sdk import WebClient
+from slack_sdk.errors import SlackApiError
+
+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=["point_in_time", "satisfaction"]
+    )
+
+
+# Define the request model
+class WorkforceRequset(BaseModel):
+    region: str
+    staffing_type: str
+    point_in_time: Optional[int] = None
+
+
+class SlackRequest(BaseModel):
+    slack_message: str
+
+
+class WorkforceResponse(BaseModel):
+    region: str
+    staffing_type: str
+    headcount: int
+    satisfaction: float
+
+
+# Post method for device summary
+@app.post("/agent/workforce")
+def get_workforce(request: WorkforceRequset):
+    """
+    Endpoint to workforce data by region, staffing type at a given point in time.
+    """
+    region = request.region.lower()
+    staffing_type = request.staffing_type.lower()
+    point_in_time = request.point_in_time if request.point_in_time else 0
+
+    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['point_in_time']==point_in_time)][staffing_type].values[0])}",
+        "satisfaction": f"Satisifaction: {float(workforce_data_df[(workforce_data_df['region']==region) & (workforce_data_df['point_in_time']==point_in_time)]['satisfaction'].values[0])}",
+    }
+    return response
+
+
+@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']}")
+
+
+@app.post("/agent/hr_qa")
+async def general_hr_qa():
+    """
+    This method handles Q/A related to general issues in HR.
+    It forwards the conversation to the OpenAI client via a local proxy and returns the response.
+    """
+    return {
+        "choices": [
+            {
+                "message": {
+                    "role": "assistant",
+                    "content": "I am a helpful HR agent, and I can help you plan for workforce related questions",
+                },
+                "finish_reason": "completed",
+                "index": 0,
+            }
+        ],
+        "model": "hr_agent",
+        "usage": {"completion_tokens": 0},
+    }
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

demos/hr_agent/requirements.txt

@@ -0,0 +1,6 @@
+fastapi
+uvicorn
+pydantic
+slack-sdk
+typing
+pandas

demos/hr_agent/run_demo.sh

@@ -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 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

demos/hr_agent/workforce_data.json

@@ -0,0 +1,29 @@
+[
+    {
+        "point_in_time": 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
+    },
+    {
+        "point_in_time": 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
+    },
+    {
+        "point_in_time": 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
+    }
+]

demos/insurance_agent/docker-compose.yaml

@@ -20,3 +20,5 @@ services:
       - CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
     extra_hosts:
       - "host.docker.internal:host-gateway"
+    volumes:
+      - ./arch_config.yaml:/app/arch_config.yaml

demos/network_agent/docker-compose.yaml

@@ -21,3 +21,5 @@ services:
       - CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
     extra_hosts:
       - "host.docker.internal:host-gateway"
+    volumes:
+      - ./arch_config.yaml:/app/arch_config.yaml

model_server/.vscode/launch.json

@@ -9,10 +9,7 @@
       "type": "debugpy",
       "request": "launch",
       "module": "uvicorn",
-      "args": ["app.main:app","--reload", "--port", "8000"],
+      "args": ["app.main:app","--reload", "--port", "8000"]
-      "env": {
-        "MODE": "local-cpu",
-      }
     }
   ]
 }

model_server/.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-  "python.defaultInterpreterPath": "${workspaceFolder}/venv/bin/python",
-}

model_server/app/cli.py

@@ -47,7 +47,16 @@ def start_server():
         "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"],
+        [
+            "python",
+            "-m",
+            "uvicorn",
+            "app.main:app",
+            "--host",
+            "0.0.0.0",
+            "--port",
+            "51000",
+        ],
         start_new_session=True,
         bufsize=1,
         universal_newlines=True,

model_server/app/function_calling/model_utils.py

@@ -28,7 +28,9 @@ def process_messages(history: list[Message]):
     for hist in history:
         if hist.tool_calls:
             if len(hist.tool_calls) > 1:
-                raise ValueError("Only one tool call is supported")
+                error_msg = f"Only one tool call is supported, tools counts: {len(hist.tool_calls)}"
+                logger.error(error_msg)
+                raise ValueError(error_msg)
             tool_call_str = json.dumps(hist.tool_calls[0]["function"])
             updated_history.append(
                 {

model_server/poetry.lock

@@ -636,13 +636,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2024.9.0"
+version = "2024.10.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2024.9.0-py3-none-any.whl", hash = "sha256:a0947d552d8a6efa72cc2c730b12c41d043509156966cca4fb157b0f2a0c574b"},
+    {file = "fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871"},
-    {file = "fsspec-2024.9.0.tar.gz", hash = "sha256:4b0afb90c2f21832df142f292649035d80b421f60a9e1c027802e5a0da2b04e8"},
+    {file = "fsspec-2024.10.0.tar.gz", hash = "sha256:eda2d8a4116d4f2429db8550f2457da57279247dd930bb12f821b58391359493"},
 ]
 
 [package.dependencies]
@@ -735,13 +735,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.25.2"
+version = "0.26.1"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.25.2-py3-none-any.whl", hash = "sha256:1897caf88ce7f97fe0110603d8f66ac264e3ba6accdf30cd66cc0fed5282ad25"},
+    {file = "huggingface_hub-0.26.1-py3-none-any.whl", hash = "sha256:5927a8fc64ae68859cd954b7cc29d1c8390a5e15caba6d3d349c973be8fdacf3"},
-    {file = "huggingface_hub-0.25.2.tar.gz", hash = "sha256:a1014ea111a5f40ccd23f7f7ba8ac46e20fa3b658ced1f86a00c75c06ec6423c"},
+    {file = "huggingface_hub-0.26.1.tar.gz", hash = "sha256:414c0d9b769eecc86c70f9d939d0f48bb28e8461dd1130021542eff0212db890"},
 ]
 
 [package.dependencies]
@@ -754,16 +754,16 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 hf-transfer = ["hf-transfer (>=0.1.4)"]
-inference = ["aiohttp", "minijinja (>=1.0)"]
+inference = ["aiohttp"]
-quality = ["mypy (==1.5.1)", "ruff (>=0.5.0)"]
+quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.5.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 tensorflow-testing = ["keras (<3.0)", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
 torch = ["safetensors[torch]", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
@@ -918,72 +918,72 @@ files = [
 
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 name = "markupsafe"
-version = "3.0.1"
+version = "3.0.2"
 description = "Safely add untrusted strings to HTML/XML markup."
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 python-versions = ">=3.9"
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 [[package]]
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 [[package]]
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-version = "3.4.1"
+version = "3.4.2"
 description = "Python package for creating and manipulating graphs and networks"
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 python-versions = ">=3.10"
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+version = "1.23.2"
 description = "Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality."
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 python-versions = ">=3.7.0"
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 ]
 
 [package.dependencies]
@@ -1542,7 +1542,7 @@ exporters-gpu = ["onnx", "onnxruntime-gpu", "timm", "transformers (<4.46.0)"]
 exporters-tf = ["datasets (<=2.16)", "h5py", "numpy (<1.24.0)", "onnx", "onnxruntime", "tensorflow (>=2.4,<=2.12.1)", "tf2onnx", "timm", "transformers[sentencepiece] (>=4.26,<4.38)"]
 furiosa = ["optimum-furiosa"]
 graphcore = ["optimum-graphcore"]
-habana = ["optimum-habana", "transformers (>=4.43.0,<4.44.0)"]
+habana = ["optimum-habana", "transformers (>=4.45.0,<4.46.0)"]
 intel = ["optimum-intel (>=1.18.0)"]
 ipex = ["optimum-intel[ipex] (>=1.18.0)"]
 neural-compressor = ["optimum-intel[neural-compressor] (>=1.18.0)"]
@@ -1899,22 +1899,22 @@ files = [
 
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 name = "protobuf"
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 description = ""
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]

model_server/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "archgw_modelserver"
-version = "0.0.3"
+version = "0.0.4"
 description = "A model server for serving models"
 authors = ["Katanemo Labs, Inc <archgw@katanemo.com>"]
 license = "Apache 2.0"

model_server/requirements.txt

@@ -1,23 +0,0 @@
-#TOOD: pin versions
-fastapi==0.115.0
-sentence-transformers==3.1.1
-torch==2.4.1
-uvicorn==0.31.0
-transformers
-pyyaml==6.0.2
-accelerate
-psutil==6.0.0
-# guard inference packages
-optimum-intel==1.19.0
-openvino==2024.4.0
-psutil
-dateparser
-openai==1.50.2
-httpx
-pytest-asyncio
-pytest
-pandas
-tf-keras
-onnx==1.17.0
-onnxruntime==1.19.2
-pytest