mirror of
https://github.com/katanemo/plano.git
synced 2026-04-25 00:36:34 +02:00
removing model_server. buh bye (#619)
This commit is contained in:
Salman Paracha
GitHub
parent
88c2bd1851
commit
d37af7605c
50 changed files with 40 additions and 9597 deletions
46
.github/workflows/e2e_model_server.yml
vendored
46
.github/workflows/e2e_model_server.yml
vendored
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@ -1,46 +0,0 @@
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name: e2e model server tests
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on:
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push:
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branches:
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- main
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pull_request:
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jobs:
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e2e_model_server_tests:
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runs-on: ubuntu-latest-m
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
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defaults:
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run:
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working-directory: ./tests/modelserver
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steps:
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- name: Checkout code
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uses: actions/checkout@v3
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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cache: "pip" # auto-caches based on requirements files
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- name: install poetry
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run: |
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export POETRY_VERSION=2.2.1
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curl -sSL https://install.python-poetry.org | python3 -
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export PATH="$HOME/.local/bin:$PATH"
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- name: install model server and start it
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run: |
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cd ../../model_server/ && poetry install && poetry run archgw_modelserver start
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- name: install test dependencies
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run: |
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poetry install
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- name: run tests
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run: |
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poetry run pytest
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@ -40,11 +40,10 @@ jobs:
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curl --location --remote-name https://github.com/Orange-OpenSource/hurl/releases/download/4.0.0/hurl_4.0.0_amd64.deb
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sudo dpkg -i hurl_4.0.0_amd64.deb
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- name: install model server, arch gateway and test dependencies
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- name: install arch gateway and test dependencies
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run: |
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source venv/bin/activate
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cd model_server/ && echo "installing model server" && poetry install
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cd ../arch/tools && echo "installing archgw cli" && poetry install
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cd arch/tools && echo "installing archgw cli" && poetry install
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cd ../../demos/shared/test_runner && echo "installing test dependencies" && poetry install
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- name: run demo tests
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|
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@ -40,11 +40,10 @@ jobs:
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curl --location --remote-name https://github.com/Orange-OpenSource/hurl/releases/download/4.0.0/hurl_4.0.0_amd64.deb
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sudo dpkg -i hurl_4.0.0_amd64.deb
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- name: install model server, arch gateway and test dependencies
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- name: install arch gateway and test dependencies
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run: |
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source venv/bin/activate
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cd model_server/ && echo "installing model server" && poetry install
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cd ../arch/tools && echo "installing archgw cli" && poetry install
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cd arch/tools && echo "installing archgw cli" && poetry install
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cd ../../demos/shared/test_runner && echo "installing test dependencies" && poetry install
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- name: run demo tests
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|
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45
.github/workflows/model-server-tests.yml
vendored
45
.github/workflows/model-server-tests.yml
vendored
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@ -1,45 +0,0 @@
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name: model server tests
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on:
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push:
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branches:
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- main # Run tests on pushes to the main branch
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pull_request:
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branches:
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- main # Run tests on pull requests to the main branch
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jobs:
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test:
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runs-on: ubuntu-latest
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steps:
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# Step 1: Check out the code from your repository
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- name: Checkout code
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uses: actions/checkout@v3
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# Step 2: Set up Python (specify the version)
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: "3.12"
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# Step 3: Install Poetry
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- name: Install Poetry
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run: |
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export POETRY_VERSION=2.2.1
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curl -sSL https://install.python-poetry.org | python3 -
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export PATH="$HOME/.local/bin:$PATH"
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# Step 4: Install dependencies using Poetry
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- name: Install dependencies
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run: |
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cd model_server
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poetry install
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# Step 5: Set PYTHONPATH and run tests
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- name: Run model server tests with pytest
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env:
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PYTHONPATH: model_server # Ensure the app's path is available
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run: |
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cd model_server
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poetry run pytest
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5
.gitignore
vendored
5
.gitignore
vendored
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@ -113,11 +113,6 @@ venv.bak/
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arch/tools/config
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arch/tools/build
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# Archgw - model_server
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model_server/venv_model_server
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model_server/build
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model_server/dist
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# Archgw - Docs
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docs/build/
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|
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@ -22,4 +22,3 @@ services:
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- OPENAI_API_KEY=${OPENAI_API_KEY:?error}
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- MISTRAL_API_KEY=${MISTRAL_API_KEY:?error}
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- OTEL_TRACING_HTTP_ENDPOINT=http://host.docker.internal:4318/v1/traces
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- MODEL_SERVER_PORT=${MODEL_SERVER_PORT:-51000}
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|
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@ -56,15 +56,8 @@ poetry install
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archgw build
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```
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### Step 5: download models
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This will help download models so model_server can load faster. This should be done once.
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```bash
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archgw download-models
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```
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### Logs
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`archgw` command can also view logs from gateway and model_server. Use following command to view logs,
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`archgw` command can also view logs from the gateway. Use following command to view logs,
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```bash
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archgw logs --follow
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@ -1,13 +1,5 @@
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import os
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KATANEMO_DOCKERHUB_REPO = "katanemo/archgw"
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KATANEMO_LOCAL_MODEL_LIST = [
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"katanemo/Arch-Guard",
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]
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SERVICE_NAME_ARCHGW = "archgw"
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SERVICE_NAME_MODEL_SERVER = "model_server"
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SERVICE_ALL = "all"
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MODEL_SERVER_LOG_FILE = "~/archgw_logs/modelserver.log"
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ARCHGW_DOCKER_NAME = "archgw"
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ARCHGW_DOCKER_IMAGE = os.getenv("ARCHGW_DOCKER_IMAGE", "katanemo/archgw:0.3.18")
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@ -9,9 +9,7 @@ from cli.utils import convert_legacy_listeners, getLogger
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from cli.consts import (
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ARCHGW_DOCKER_IMAGE,
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ARCHGW_DOCKER_NAME,
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KATANEMO_LOCAL_MODEL_LIST,
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)
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from huggingface_hub import snapshot_download
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import subprocess
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from cli.docker_cli import (
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docker_container_status,
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@ -144,49 +142,6 @@ def stop_docker_container(service=ARCHGW_DOCKER_NAME):
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log.info(f"Failed to shut down services: {str(e)}")
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def download_models_from_hf():
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for model in KATANEMO_LOCAL_MODEL_LIST:
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log.info(f"Downloading model: {model}")
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snapshot_download(repo_id=model)
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def start_arch_modelserver(foreground):
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"""
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Start the model server. This assumes that the archgw_modelserver package is installed locally
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"""
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try:
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log.info("archgw_modelserver restart")
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if foreground:
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subprocess.run(
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["archgw_modelserver", "start", "--foreground"],
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check=True,
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)
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else:
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subprocess.run(
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["archgw_modelserver", "start"],
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check=True,
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)
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except subprocess.CalledProcessError as e:
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log.info(f"Failed to start model_server. Please check archgw_modelserver logs")
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sys.exit(1)
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def stop_arch_modelserver():
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"""
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Stop the model server. This assumes that the archgw_modelserver package is installed locally
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"""
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try:
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subprocess.run(
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["archgw_modelserver", "stop"],
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check=True,
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)
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except subprocess.CalledProcessError as e:
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log.info(f"Failed to start model_server. Please check archgw_modelserver logs")
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sys.exit(1)
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def start_cli_agent(arch_config_file=None, settings_json="{}"):
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"""Start a CLI client connected to Arch."""
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|
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@ -20,20 +20,14 @@ from cli.utils import (
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find_config_file,
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)
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from cli.core import (
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start_arch_modelserver,
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stop_arch_modelserver,
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start_arch,
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stop_docker_container,
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download_models_from_hf,
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start_cli_agent,
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)
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from cli.consts import (
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ARCHGW_DOCKER_IMAGE,
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ARCHGW_DOCKER_NAME,
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KATANEMO_DOCKERHUB_REPO,
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SERVICE_NAME_ARCHGW,
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SERVICE_NAME_MODEL_SERVER,
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SERVICE_ALL,
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)
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log = getLogger(__name__)
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@ -47,9 +41,8 @@ logo = r"""
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"""
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# Command to build archgw and model_server Docker images
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# Command to build archgw Docker images
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ARCHGW_DOCKERFILE = "./arch/Dockerfile"
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MODEL_SERVER_BUILD_FILE = "./model_server/pyproject.toml"
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def get_version():
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@ -60,18 +53,6 @@ def get_version():
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return "version not found"
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def verify_service_name(service):
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"""Verify if the service name is valid."""
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if service not in [
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SERVICE_NAME_ARCHGW,
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SERVICE_NAME_MODEL_SERVER,
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SERVICE_ALL,
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]:
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print(f"Error: Invalid service {service}. Exiting")
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sys.exit(1)
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return True
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@click.group(invoke_without_command=True)
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@click.option("--version", is_flag=True, help="Show the archgw cli version and exit.")
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@click.pass_context
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@ -89,17 +70,11 @@ def main(ctx, version):
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@click.command()
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@click.option(
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"--service",
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default=SERVICE_ALL,
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help="Optional parameter to specify which service to build. Options are model_server, archgw",
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)
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def build(service):
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def build():
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"""Build Arch from source. Must be in root of cloned repo."""
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verify_service_name(service)
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# Check if /arch/Dockerfile exists
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if service == SERVICE_NAME_ARCHGW or service == SERVICE_ALL:
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if os.path.exists(ARCHGW_DOCKERFILE):
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if os.path.exists(ARCHGW_DOCKERFILE):
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click.echo("Building archgw image...")
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try:
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|
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@ -110,8 +85,6 @@ def build(service):
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"-f",
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ARCHGW_DOCKERFILE,
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"-t",
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f"{KATANEMO_DOCKERHUB_REPO}:latest",
|
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"-t",
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f"{ARCHGW_DOCKER_IMAGE}",
|
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".",
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"--add-host=host.docker.internal:host-gateway",
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|
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@ -128,57 +101,20 @@ def build(service):
|
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click.echo("archgw image built successfully.")
|
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|
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"""Install the model server dependencies using Poetry."""
|
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if service == SERVICE_NAME_MODEL_SERVER or service == SERVICE_ALL:
|
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# Check if pyproject.toml exists
|
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if os.path.exists(MODEL_SERVER_BUILD_FILE):
|
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click.echo("Installing model server dependencies with Poetry...")
|
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try:
|
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subprocess.run(
|
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["poetry", "install", "--no-cache"],
|
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cwd=os.path.dirname(MODEL_SERVER_BUILD_FILE),
|
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check=True,
|
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)
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click.echo("Model server dependencies installed successfully.")
|
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except subprocess.CalledProcessError as e:
|
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click.echo(f"Error installing model server dependencies: {e}")
|
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sys.exit(1)
|
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else:
|
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click.echo(f"Error: pyproject.toml not found in {MODEL_SERVER_BUILD_FILE}")
|
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sys.exit(1)
|
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|
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|
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@click.command()
|
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@click.argument("file", required=False) # Optional file argument
|
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@click.option(
|
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"--path", default=".", help="Path to the directory containing arch_config.yaml"
|
||||
)
|
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@click.option(
|
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"--service",
|
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default=SERVICE_ALL,
|
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help="Service to start. Options are model_server, archgw.",
|
||||
)
|
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@click.option(
|
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"--foreground",
|
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default=False,
|
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help="Run Arch in the foreground. Default is False",
|
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is_flag=True,
|
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)
|
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def up(file, path, service, foreground):
|
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def up(file, path, foreground):
|
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"""Starts Arch."""
|
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verify_service_name(service)
|
||||
|
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if service == SERVICE_ALL and foreground:
|
||||
# foreground can only be specified when starting individual services
|
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log.info("foreground flag is only supported for individual services. Exiting.")
|
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sys.exit(1)
|
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|
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if service == SERVICE_NAME_MODEL_SERVER:
|
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log.info("Download models from HuggingFace...")
|
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download_models_from_hf()
|
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start_arch_modelserver(foreground)
|
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return
|
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|
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# Use the utility function to find config file
|
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arch_config_file = find_config_file(path, file)
|
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|
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|
|
@ -202,7 +138,6 @@ def up(file, path, service, foreground):
|
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# Set the ARCH_CONFIG_FILE environment variable
|
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env_stage = {
|
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"OTEL_TRACING_HTTP_ENDPOINT": "http://host.docker.internal:4318/v1/traces",
|
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"MODEL_SERVER_PORT": os.getenv("MODEL_SERVER_PORT", "51000"),
|
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}
|
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env = os.environ.copy()
|
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# Remove PATH variable if present
|
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|
|
@ -242,40 +177,13 @@ def up(file, path, service, foreground):
|
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env_stage[access_key] = env_file_dict[access_key]
|
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|
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env.update(env_stage)
|
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|
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if service == SERVICE_NAME_ARCHGW:
|
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start_arch(arch_config_file, env, foreground=foreground)
|
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else:
|
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# Check if ingress_traffic listener is configured before starting model_server
|
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if has_ingress_listener(arch_config_file):
|
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download_models_from_hf()
|
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start_arch_modelserver(foreground)
|
||||
else:
|
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log.info(
|
||||
"Skipping model_server startup: no ingress_traffic listener configured in arch_config.yaml"
|
||||
)
|
||||
|
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start_arch(arch_config_file, env, foreground=foreground)
|
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start_arch(arch_config_file, env, foreground=foreground)
|
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|
||||
|
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@click.command()
|
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@click.option(
|
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"--service",
|
||||
default=SERVICE_ALL,
|
||||
help="Service to down. Options are all, model_server, archgw. Default is all",
|
||||
)
|
||||
def down(service):
|
||||
def down():
|
||||
"""Stops Arch."""
|
||||
|
||||
verify_service_name(service)
|
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|
||||
if service == SERVICE_NAME_MODEL_SERVER:
|
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stop_arch_modelserver()
|
||||
elif service == SERVICE_NAME_ARCHGW:
|
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stop_docker_container()
|
||||
else:
|
||||
stop_arch_modelserver()
|
||||
stop_docker_container(SERVICE_NAME_ARCHGW)
|
||||
stop_docker_container()
|
||||
|
||||
|
||||
@click.command()
|
||||
|
|
@ -303,7 +211,7 @@ def generate_prompt_targets(file):
|
|||
@click.command()
|
||||
@click.option(
|
||||
"--debug",
|
||||
help="For detailed debug logs to trace calls from archgw <> model_server <> api_server, etc",
|
||||
help="For detailed debug logs to trace calls from archgw <> api_server, etc",
|
||||
is_flag=True,
|
||||
)
|
||||
@click.option("--follow", help="Follow the logs", is_flag=True)
|
||||
|
|
|
|||
2481
arch/tools/poetry.lock
generated
2481
arch/tools/poetry.lock
generated
File diff suppressed because it is too large
Load diff
|
|
@ -1,34 +1,28 @@
|
|||
[project]
|
||||
[tool.poetry]
|
||||
name = "archgw"
|
||||
version = "0.3.18"
|
||||
description = "Python-based CLI tool to manage Arch Gateway."
|
||||
authors = [{ name = "Katanemo Labs, Inc." }]
|
||||
authors = ["Katanemo Labs, Inc."]
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
"archgw_modelserver==0.3.18",
|
||||
"click>=8.1.7,<9.0.0",
|
||||
"jinja2>=3.1.4,<4.0.0",
|
||||
"jsonschema>=4.23.0,<5.0.0",
|
||||
"pyyaml>=6.0.2,<7.0.0",
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
archgw = "cli.main:main"
|
||||
|
||||
[dependency-groups]
|
||||
dev = [
|
||||
"pytest>=8.4.1,<9.0.0",
|
||||
]
|
||||
|
||||
[tool.poetry]
|
||||
packages = [{ include = "cli" }]
|
||||
dependencies = { archgw_modelserver = { path = "../../model_server", develop = true } }
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.10"
|
||||
click = ">=8.1.7,<9.0.0"
|
||||
jinja2 = ">=3.1.4,<4.0.0"
|
||||
jsonschema = ">=4.23.0,<5.0.0"
|
||||
pyyaml = ">=6.0.2,<7.0.0"
|
||||
requests = ">=2.31.0,<3.0.0"
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pytest = ">=8.4.1,<9.0.0"
|
||||
|
||||
[tool.poetry.scripts]
|
||||
archgw = "cli.main:main"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core>=2.0.0"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
addopts = ["-v"]
|
||||
|
|
|
|||
|
|
@ -12,10 +12,6 @@
|
|||
"name": "archgw_cli",
|
||||
"path": "arch/tools"
|
||||
},
|
||||
{
|
||||
"name": "model_server",
|
||||
"path": "model_server"
|
||||
},
|
||||
{
|
||||
"name": "tests_e2e",
|
||||
"path": "tests/e2e"
|
||||
|
|
@ -24,10 +20,6 @@
|
|||
"name": "tests_archgw",
|
||||
"path": "tests/archgw"
|
||||
},
|
||||
{
|
||||
"name": "tests_modelserver",
|
||||
"path": "tests/modelserver"
|
||||
},
|
||||
{
|
||||
"name": "chatbot_ui",
|
||||
"path": "demos/shared/chatbot_ui"
|
||||
|
|
|
|||
|
|
@ -79,7 +79,6 @@ files = [
|
|||
]
|
||||
|
||||
[package.dependencies]
|
||||
archgw_modelserver = ">=0.3.12,<0.4.0"
|
||||
click = ">=8.1.7,<9.0.0"
|
||||
jinja2 = ">=3.1.4,<4.0.0"
|
||||
jsonschema = ">=4.23.0,<5.0.0"
|
||||
|
|
@ -93,11 +92,6 @@ description = "A model server for serving models"
|
|||
optional = false
|
||||
python-versions = "<4.0,>=3.10"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "archgw_modelserver-0.3.12-py3-none-any.whl", hash = "sha256:ddc4536108e74bee637b41e35f80ad5cdf1708a32f670f33b2eef3526eb6d4a1"},
|
||||
{file = "archgw_modelserver-0.3.12.tar.gz", hash = "sha256:b749edd0b3d4e6f01e94bcf6b363bf52b8ca515e8191a0951b99152f45745212"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
accelerate = ">=1.0.0,<2.0.0"
|
||||
dateparser = "*"
|
||||
|
|
|
|||
|
|
@ -35,7 +35,6 @@ Create a ``docker-compose.yml`` file with the following configuration:
|
|||
environment:
|
||||
- OPENAI_API_KEY=${OPENAI_API_KEY:?error}
|
||||
- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:?error}
|
||||
- MODEL_SERVER_PORT=51000
|
||||
|
||||
Starting the Stack
|
||||
~~~~~~~~~~~~~~~~~~
|
||||
|
|
|
|||
16
model_server/.vscode/launch.json
vendored
16
model_server/.vscode/launch.json
vendored
|
|
@ -1,16 +0,0 @@
|
|||
{
|
||||
// Use IntelliSense to learn about possible attributes.
|
||||
// Hover to view descriptions of existing attributes.
|
||||
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
|
||||
"version": "0.2.0",
|
||||
"configurations": [
|
||||
|
||||
{
|
||||
"name": "model server",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"module": "uvicorn",
|
||||
"args": ["src.main:app","--reload", "--port", "51000"]
|
||||
}
|
||||
]
|
||||
}
|
||||
7
model_server/.vscode/settings.json
vendored
7
model_server/.vscode/settings.json
vendored
|
|
@ -1,7 +0,0 @@
|
|||
{
|
||||
"python.testing.pytestArgs": [
|
||||
"."
|
||||
],
|
||||
"python.testing.unittestEnabled": false,
|
||||
"python.testing.pytestEnabled": true
|
||||
}
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
FROM python:3.12 AS builder
|
||||
|
||||
COPY requirements.txt .
|
||||
RUN pip install --prefix=/runtime -r requirements.txt
|
||||
|
||||
FROM python:3.12-slim AS output
|
||||
|
||||
# curl is needed for health check in docker-compose
|
||||
RUN apt-get update && apt-get install -y curl && apt-get clean && rm -rf /var/lib/apt/lists/*
|
||||
|
||||
COPY --from=builder /runtime /usr/local
|
||||
|
||||
WORKDIR /src
|
||||
|
||||
# specify list of models that will go into the image as a comma separated list
|
||||
# following models have been tested to work with this image
|
||||
# "sentence-transformers/all-MiniLM-L6-v2,sentence-transformers/all-mpnet-base-v2,thenlper/gte-base,thenlper/gte-large,thenlper/gte-small"
|
||||
ENV MODELS=""
|
||||
|
||||
COPY ./app ./app
|
||||
COPY ./app/guard_model_config.yaml .
|
||||
COPY ./app/openai_params.yaml .
|
||||
|
||||
# comment it out for now as we don't want to download the model every time we build the image
|
||||
# we will mount host cache to docker image to avoid downloading the model every time
|
||||
# see docker-compose file for more details
|
||||
|
||||
# RUN python install.py && \
|
||||
# find /root/.cache/torch/sentence_transformers/ -name onnx -exec rm -rf {} +
|
||||
|
||||
CMD ["uvicorn", "src.app.main:app", "--host", "0.0.0.0", "--port", "80"]
|
||||
|
|
@ -1,64 +0,0 @@
|
|||
# Use NVIDIA CUDA base image to enable GPU support
|
||||
FROM nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04 as base
|
||||
ENV DEBIAN_FRONTEND=noninteractive
|
||||
|
||||
# Install Python 3.10
|
||||
RUN apt-get update && \
|
||||
apt-get install -y python3.10 python3-pip python3-dev python-is-python3 && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
|
||||
|
||||
#
|
||||
# builder
|
||||
#
|
||||
FROM base AS builder
|
||||
|
||||
WORKDIR /src
|
||||
|
||||
# Upgrade pip
|
||||
RUN pip install --upgrade pip
|
||||
|
||||
# Install git for cloning repositories
|
||||
RUN apt-get update && apt-get install -y git && apt-get clean
|
||||
|
||||
# Copy requirements.txt
|
||||
COPY requirements.txt /src/
|
||||
|
||||
# Install Python dependencies
|
||||
RUN pip install --force-reinstall -r requirements.txt
|
||||
|
||||
RUN apt-get update && \
|
||||
apt-get install -y cuda-toolkit-12-2
|
||||
|
||||
# Check for NVIDIA GPU and CUDA support and install EETQ if detected
|
||||
RUN if command -v nvcc >/dev/null 2>&1; then \
|
||||
echo "CUDA and NVIDIA GPU detected, installing EETQ..." && \
|
||||
git clone https://github.com/NetEase-FuXi/EETQ.git && \
|
||||
cd EETQ && \
|
||||
git submodule update --init --recursive && \
|
||||
pip install .; \
|
||||
else \
|
||||
echo "CUDA or NVIDIA GPU not detected, skipping EETQ installation."; \
|
||||
fi
|
||||
|
||||
COPY . /src
|
||||
|
||||
# Specify list of models that will go into the image as a comma separated list
|
||||
ENV MODELS=""
|
||||
ENV DEBIAN_FRONTEND=noninteractive
|
||||
|
||||
COPY /app /app
|
||||
WORKDIR /app
|
||||
|
||||
# Install required tools
|
||||
RUN apt-get update && apt-get install -y \
|
||||
curl \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Uncomment if you want to install the model during the image build
|
||||
# RUN python install.py && \
|
||||
# find /root/.cache/torch/sentence_transformers/ -name onnx -exec rm -rf {} +
|
||||
|
||||
# Set the default command to run the application
|
||||
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80"]
|
||||
|
|
@ -1,2 +0,0 @@
|
|||
# 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.
|
||||
3102
model_server/poetry.lock
generated
3102
model_server/poetry.lock
generated
File diff suppressed because it is too large
Load diff
|
|
@ -1,49 +0,0 @@
|
|||
[project]
|
||||
name = "archgw_modelserver"
|
||||
version = "0.3.18"
|
||||
description = "A model server for serving models"
|
||||
authors = [{name = "Katanemo Labs, Inc", email = "info@katanemo.com"}]
|
||||
license = "Apache 2.0"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
"fastapi==0.115.0",
|
||||
"torch>=2.6.0",
|
||||
"uvicorn==0.31.0",
|
||||
"transformers>=4.37.0,<5.0.0",
|
||||
"accelerate>=1.0.0,<2.0.0",
|
||||
"pydantic>=2.10.1,<3.0.0",
|
||||
"dateparser",
|
||||
"openai>=1.50.2,<2.0.0",
|
||||
"httpx==0.27.2", # https://community.openai.com/t/typeerror-asyncclient-init-got-an-unexpected-keyword-argument-proxies/1040287
|
||||
"opentelemetry-api>=1.28.0,<2.0.0",
|
||||
"opentelemetry-sdk>=1.28.0,<2.0.0",
|
||||
"opentelemetry-exporter-otlp>=1.28.0,<2.0.0",
|
||||
"opentelemetry-instrumentation-fastapi>=0.49b0,<1.0",
|
||||
"overrides>=7.7.0,<8.0.0",
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
archgw_modelserver = "src.cli:main"
|
||||
|
||||
[dependency-groups]
|
||||
dev = [
|
||||
"pytest",
|
||||
"pytest-asyncio",
|
||||
"pytest-httpserver>=1.1.0,<2.0.0",
|
||||
"pytest-retry>=1.6.3,<2.0.0",
|
||||
]
|
||||
|
||||
[tool.poetry]
|
||||
packages = [{ include = "src" }]
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core>=2.0.0"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
python_files = ["test*.py"]
|
||||
addopts = ["-v", "-s"]
|
||||
retries = 2
|
||||
retry_delay = 0.5
|
||||
cumulative_timing = false
|
||||
|
|
@ -1,196 +0,0 @@
|
|||
import importlib
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
import argparse
|
||||
import signal
|
||||
import tempfile
|
||||
import time
|
||||
import requests
|
||||
|
||||
import src.commons.utils as utils
|
||||
|
||||
|
||||
logger = utils.get_model_server_logger()
|
||||
|
||||
|
||||
def get_version():
|
||||
try:
|
||||
version = importlib.metadata.version("archgw_modelserver")
|
||||
return version
|
||||
except importlib.metadata.PackageNotFoundError:
|
||||
return "version not found"
|
||||
|
||||
|
||||
def wait_for_health_check(url, timeout=300):
|
||||
"""Wait for the Uvicorn server to respond to health-check requests."""
|
||||
|
||||
start_time = time.time()
|
||||
while time.time() - start_time < timeout:
|
||||
try:
|
||||
response = requests.get(url)
|
||||
if response.status_code == 200:
|
||||
return True
|
||||
except requests.ConnectionError:
|
||||
time.sleep(1)
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def get_pid_file():
|
||||
temp_dir = tempfile.gettempdir()
|
||||
return os.path.join(temp_dir, "model_server.pid")
|
||||
|
||||
|
||||
def ensure_killed(process):
|
||||
process.terminate()
|
||||
# if the process is not terminated, kill it
|
||||
now = time.time()
|
||||
# wait for 5 seconds
|
||||
while time.time() - now < 5:
|
||||
if process.poll() is not None:
|
||||
break
|
||||
time.sleep(1)
|
||||
if process.poll() is None:
|
||||
logger.info("Killing model server")
|
||||
process.kill()
|
||||
|
||||
|
||||
def start_server(port=51000, foreground=False):
|
||||
"""Start the Uvicorn server."""
|
||||
|
||||
logger.info("model server version: %s", get_version())
|
||||
|
||||
stop_server()
|
||||
|
||||
logger.info(
|
||||
"starting model server, port: %s, foreground: %s. Please wait ...",
|
||||
port,
|
||||
foreground,
|
||||
)
|
||||
|
||||
if foreground:
|
||||
process = subprocess.Popen(
|
||||
[
|
||||
sys.executable,
|
||||
"-m",
|
||||
"uvicorn",
|
||||
"src.main:app",
|
||||
"--host",
|
||||
"0.0.0.0",
|
||||
"--port",
|
||||
str(port),
|
||||
],
|
||||
)
|
||||
else:
|
||||
process = subprocess.Popen(
|
||||
[
|
||||
sys.executable,
|
||||
"-m",
|
||||
"uvicorn",
|
||||
"src.main:app",
|
||||
"--host",
|
||||
"0.0.0.0",
|
||||
"--port",
|
||||
str(port),
|
||||
],
|
||||
stderr=subprocess.PIPE,
|
||||
stdout=subprocess.PIPE,
|
||||
)
|
||||
|
||||
try:
|
||||
if wait_for_health_check(f"http://0.0.0.0:{port}/healthz"):
|
||||
logger.info(
|
||||
f"model server health check passed, port {port}, pid: {process.pid}"
|
||||
)
|
||||
else:
|
||||
logger.error("health check failed, shutting it down.")
|
||||
process.terminate()
|
||||
except KeyboardInterrupt:
|
||||
logger.info("model server stopped by user during initialization.")
|
||||
ensure_killed(process)
|
||||
|
||||
# write process id to temp file in temp folder
|
||||
pid_file = get_pid_file()
|
||||
logger.info(f"writing pid {process.pid} to {pid_file}")
|
||||
with open(pid_file, "w") as f:
|
||||
f.write(str(process.pid))
|
||||
|
||||
if foreground:
|
||||
try:
|
||||
process.wait()
|
||||
except KeyboardInterrupt:
|
||||
logger.info("model server stopped by user.")
|
||||
ensure_killed(process)
|
||||
|
||||
|
||||
def stop_server():
|
||||
"""Stop the Uvicorn server."""
|
||||
|
||||
pid_file = get_pid_file()
|
||||
if os.path.exists(pid_file):
|
||||
logger.info("PID file found, shutting down the server.")
|
||||
# read pid from file
|
||||
with open(pid_file, "r") as f:
|
||||
pid = int(f.read())
|
||||
logger.info(f"Killing model server {pid}")
|
||||
try:
|
||||
os.kill(pid, signal.SIGKILL)
|
||||
except ProcessLookupError:
|
||||
logger.info(f"Process {pid} not found")
|
||||
os.remove(pid_file)
|
||||
else:
|
||||
logger.info("No PID file found, server is not running.")
|
||||
|
||||
|
||||
def restart_server(port=51000, foreground=False):
|
||||
"""Restart the Uvicorn server."""
|
||||
stop_server()
|
||||
start_server(port, foreground)
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description="Manage the Uvicorn server.")
|
||||
parser.add_argument(
|
||||
"action",
|
||||
choices=["start", "stop", "restart"],
|
||||
default="start",
|
||||
nargs="?",
|
||||
help="Action to perform on the server (default: start).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--port",
|
||||
type=int,
|
||||
default=51000,
|
||||
help="Port number for the server (default: 51000).",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--foreground",
|
||||
default=False,
|
||||
action="store_true",
|
||||
help="Run the server in the foreground (default: False).",
|
||||
)
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
Start, stop, or restart the Uvicorn server based on command-line arguments.
|
||||
"""
|
||||
|
||||
args = parse_args()
|
||||
|
||||
if args.action == "start":
|
||||
logger.info("[CLI] - Starting server")
|
||||
start_server(args.port, args.foreground)
|
||||
elif args.action == "stop":
|
||||
logger.info("[CLI] - Stopping server")
|
||||
stop_server()
|
||||
elif args.action == "restart":
|
||||
logger.info("[CLI] - Restarting server")
|
||||
restart_server(args.port)
|
||||
else:
|
||||
logger.error(f"[CLI] - Unknown action: {args.action}")
|
||||
sys.exit(1)
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
import os
|
||||
from openai import OpenAI
|
||||
from src.commons.utils import get_model_server_logger
|
||||
from src.core.guardrails import get_guardrail_handler
|
||||
from src.core.function_calling import (
|
||||
ArchAgentConfig,
|
||||
ArchAgentHandler,
|
||||
ArchFunctionConfig,
|
||||
ArchFunctionHandler,
|
||||
)
|
||||
|
||||
|
||||
# Define logger
|
||||
logger = get_model_server_logger()
|
||||
|
||||
|
||||
# Define the client
|
||||
ARCH_ENDPOINT = os.getenv("ARCH_ENDPOINT", "https://archfc.katanemo.dev/v1")
|
||||
ARCH_API_KEY = "EMPTY"
|
||||
ARCH_CLIENT = OpenAI(base_url=ARCH_ENDPOINT, api_key=ARCH_API_KEY)
|
||||
ARCH_AGENT_CLIENT = ARCH_CLIENT
|
||||
|
||||
# Define model names
|
||||
ARCH_INTENT_MODEL_ALIAS = "Arch-Intent"
|
||||
ARCH_FUNCTION_MODEL_ALIAS = "Arch-Function"
|
||||
ARCH_AGENT_MODEL_ALIAS = ARCH_FUNCTION_MODEL_ALIAS
|
||||
ARCH_GUARD_MODEL_ALIAS = "katanemo/Arch-Guard"
|
||||
|
||||
# Define model handlers
|
||||
handler_map = {
|
||||
"Arch-Function": ArchFunctionHandler(
|
||||
ARCH_CLIENT, ARCH_FUNCTION_MODEL_ALIAS, ArchFunctionConfig
|
||||
),
|
||||
"Arch-Agent": ArchAgentHandler(
|
||||
ARCH_AGENT_CLIENT, ARCH_AGENT_MODEL_ALIAS, ArchAgentConfig
|
||||
),
|
||||
"Arch-Guard": get_guardrail_handler(ARCH_GUARD_MODEL_ALIAS),
|
||||
}
|
||||
|
|
@ -1,50 +0,0 @@
|
|||
import torch
|
||||
import logging
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
def get_model_server_logger():
|
||||
"""
|
||||
Get or initialize the logger instance for the model server.
|
||||
|
||||
Returns:
|
||||
- logging.Logger: Configured logger instance.
|
||||
"""
|
||||
|
||||
# Check if the logger is already configured
|
||||
logger = logging.getLogger("model_server")
|
||||
|
||||
# Return existing logger instance if already configured
|
||||
if logger.hasHandlers():
|
||||
return logger
|
||||
|
||||
# Configure logging to only log to console
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
|
||||
handlers=[logging.StreamHandler()],
|
||||
)
|
||||
|
||||
return logger
|
||||
|
||||
|
||||
def get_device():
|
||||
if torch.cuda.is_available():
|
||||
device = "cuda"
|
||||
elif torch.backends.mps.is_available():
|
||||
device = "mps"
|
||||
else:
|
||||
device = "cpu"
|
||||
|
||||
return device
|
||||
|
||||
|
||||
def get_today_date():
|
||||
# Get today's date
|
||||
today = datetime.now()
|
||||
|
||||
# Get full date with day of week
|
||||
full_date = today.strftime("%Y-%m-%d")
|
||||
|
||||
return full_date
|
||||
|
|
@ -1,520 +0,0 @@
|
|||
import ast
|
||||
import copy
|
||||
import json
|
||||
import random
|
||||
import builtins
|
||||
import src.commons.utils as utils
|
||||
|
||||
from openai import OpenAI
|
||||
from typing import Any, Dict, List
|
||||
from overrides import override
|
||||
from src.core.utils.hallucination_utils import HallucinationState
|
||||
from src.core.utils.model_utils import (
|
||||
Message,
|
||||
ChatMessage,
|
||||
Choice,
|
||||
ChatCompletionResponse,
|
||||
ArchBaseHandler,
|
||||
)
|
||||
|
||||
|
||||
logger = utils.get_model_server_logger()
|
||||
|
||||
|
||||
# ==============================================================================================================================================
|
||||
|
||||
|
||||
class ArchFunctionConfig:
|
||||
TASK_PROMPT = (
|
||||
"You are a helpful assistant designed to assist with the user query by making one or more function calls if needed."
|
||||
"\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{tools}\n</tools>"
|
||||
"\n\nYour task is to decide which functions are needed and collect missing parameters if necessary."
|
||||
)
|
||||
|
||||
FORMAT_PROMPT = (
|
||||
"\n\nBased on your analysis, provide your response in one of the following JSON formats:"
|
||||
'\n1. If no functions are needed:\n```json\n{"response": "Your response text here"}\n```'
|
||||
'\n2. If functions are needed but some required parameters are missing:\n```json\n{"required_functions": ["func_name1", "func_name2", ...], "clarification": "Text asking for missing parameters"}\n```'
|
||||
'\n3. If functions are needed and all required parameters are available:\n```json\n{"tool_calls": [{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},... (more tool calls as required)]}\n```'
|
||||
)
|
||||
|
||||
GENERATION_PARAMS = {
|
||||
"temperature": 0.1,
|
||||
"top_p": 1.0,
|
||||
"top_k": 10,
|
||||
"max_tokens": 1024,
|
||||
"stop_token_ids": [151645],
|
||||
"logprobs": True,
|
||||
"top_logprobs": 10,
|
||||
}
|
||||
|
||||
SUPPORT_DATA_TYPES = ["int", "float", "bool", "str", "list", "tuple", "set", "dict"]
|
||||
|
||||
|
||||
class ArchFunctionHandler(ArchBaseHandler):
|
||||
def __init__(
|
||||
self,
|
||||
client: OpenAI,
|
||||
model_name: str,
|
||||
config: ArchFunctionConfig,
|
||||
):
|
||||
"""
|
||||
Initializes the function handler.
|
||||
|
||||
Args:
|
||||
client (OpenAI): An OpenAI client instance.
|
||||
model_name (str): Name of the model to use.
|
||||
config (ArchFunctionConfig): The configuration for Arch-Function
|
||||
"""
|
||||
|
||||
super().__init__(
|
||||
client,
|
||||
model_name,
|
||||
config.TASK_PROMPT,
|
||||
config.FORMAT_PROMPT,
|
||||
config.GENERATION_PARAMS,
|
||||
)
|
||||
|
||||
self.generation_params = self.generation_params | {
|
||||
"continue_final_message": True,
|
||||
"add_generation_prompt": False,
|
||||
}
|
||||
|
||||
self.default_prefix = '```json\n{"'
|
||||
self.clarify_prefix = '```json\n{"required_functions":'
|
||||
|
||||
self.hallucination_state = None
|
||||
|
||||
# Predefine data types for verification. Only support Python for now.
|
||||
# TODO: Extend the list of support data types
|
||||
self.support_data_types = {
|
||||
type_name: getattr(builtins, type_name)
|
||||
for type_name in config.SUPPORT_DATA_TYPES
|
||||
}
|
||||
|
||||
@override
|
||||
def _convert_tools(self, tools: List[Dict[str, Any]]) -> str:
|
||||
"""
|
||||
Converts a list of tools into JSON format.
|
||||
|
||||
Args:
|
||||
tools (List[Dict[str, Any]]): A list of tools represented as dictionaries.
|
||||
|
||||
Returns:
|
||||
str: A string representation of converted tools.
|
||||
"""
|
||||
|
||||
converted = [json.dumps(tool["function"], ensure_ascii=False) for tool in tools]
|
||||
return "\n".join(converted)
|
||||
|
||||
def _fix_json_string(self, json_str: str) -> str:
|
||||
"""
|
||||
Fixes malformed JSON strings by ensuring proper bracket matching.
|
||||
|
||||
Args:
|
||||
json_str (str): A JSON string that might be malformed.
|
||||
|
||||
Returns:
|
||||
str: A corrected JSON string.
|
||||
"""
|
||||
|
||||
# Remove any leading or trailing whitespace or newline characters
|
||||
json_str = json_str.strip()
|
||||
|
||||
# Stack to keep track of brackets
|
||||
stack = []
|
||||
|
||||
# Clean string to collect valid characters
|
||||
fixed_str = ""
|
||||
|
||||
# Dictionary for matching brackets
|
||||
matching_bracket = {")": "(", "}": "{", "]": "["}
|
||||
|
||||
# Dictionary for the opposite of matching_bracket
|
||||
opening_bracket = {v: k for k, v in matching_bracket.items()}
|
||||
|
||||
for char in json_str:
|
||||
if char in "{[(":
|
||||
stack.append(char)
|
||||
fixed_str += char
|
||||
elif char in "}])":
|
||||
if stack and stack[-1] == matching_bracket[char]:
|
||||
stack.pop()
|
||||
fixed_str += char
|
||||
else:
|
||||
# Ignore the unmatched closing brackets
|
||||
continue
|
||||
else:
|
||||
fixed_str += char
|
||||
|
||||
# If there are unmatched opening brackets left in the stack, add corresponding closing brackets
|
||||
while stack:
|
||||
unmatched_opening = stack.pop()
|
||||
fixed_str += opening_bracket[unmatched_opening]
|
||||
|
||||
try:
|
||||
fixed_str = json.loads(fixed_str)
|
||||
except Exception:
|
||||
fixed_str = json.loads(fixed_str.replace("'", '"'))
|
||||
|
||||
return json.dumps(fixed_str)
|
||||
|
||||
def _parse_model_response(self, content: str) -> Dict[str, any]:
|
||||
"""
|
||||
Extracts tool call information from a given string.
|
||||
|
||||
Args:
|
||||
content (str): The content string containing potential tool call information.
|
||||
|
||||
Returns:
|
||||
Dict: A dictionary of extraction, including:
|
||||
- "required_functions": A list of detected intents.
|
||||
- "clarification": Text to collect missing parameters
|
||||
- "tool_calls": A list of tool call dictionaries.
|
||||
- "is_valid": A boolean indicating if the extraction was valid.
|
||||
- "error_message": An error message or exception if parsing failed.
|
||||
"""
|
||||
|
||||
response_dict = {
|
||||
"raw_response": [],
|
||||
"response": [],
|
||||
"required_functions": [],
|
||||
"clarification": "",
|
||||
"tool_calls": [],
|
||||
"is_valid": True,
|
||||
"error_message": "",
|
||||
}
|
||||
|
||||
try:
|
||||
if content.startswith("```") and content.endswith("```"):
|
||||
content = content.strip("```").strip()
|
||||
if content.startswith("json"):
|
||||
content = content[4:].strip()
|
||||
|
||||
content = self._fix_json_string(content)
|
||||
response_dict["raw_response"] = f"```json\n{content}\n```"
|
||||
|
||||
model_response = json.loads(content)
|
||||
response_dict["response"] = model_response.get("response", "")
|
||||
response_dict["required_functions"] = model_response.get(
|
||||
"required_functions", []
|
||||
)
|
||||
response_dict["clarification"] = model_response.get("clarification", "")
|
||||
|
||||
for tool_call in model_response.get("tool_calls", []):
|
||||
response_dict["tool_calls"].append(
|
||||
{
|
||||
"id": f"call_{random.randint(1000, 10000)}",
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": tool_call.get("name", ""),
|
||||
"arguments": tool_call.get("arguments", {}),
|
||||
},
|
||||
}
|
||||
)
|
||||
except Exception as e:
|
||||
response_dict["is_valid"] = False
|
||||
response_dict["error_message"] = f"Fail to parse model responses: {e}"
|
||||
|
||||
return response_dict
|
||||
|
||||
def _convert_data_type(self, value: str, target_type: str):
|
||||
# TODO: Add more conversion rules as needed
|
||||
try:
|
||||
if target_type is float and isinstance(value, int):
|
||||
return float(value)
|
||||
elif target_type is list and isinstance(value, str):
|
||||
return ast.literal_eval(value)
|
||||
elif target_type is str and not isinstance(value, str):
|
||||
return str(value)
|
||||
except (ValueError, TypeError, json.JSONDecodeError):
|
||||
pass
|
||||
return value
|
||||
|
||||
def _verify_tool_calls(
|
||||
self, tools: List[Dict[str, Any]], tool_calls: List[Dict[str, Any]]
|
||||
) -> Dict[str, any]:
|
||||
"""
|
||||
Verifies the validity of extracted tool calls against the provided tools.
|
||||
|
||||
Args:
|
||||
tools (List[Dict[str, Any]]): A list of available tools.
|
||||
tool_calls (List[Dict[str, Any]]): A list of tool calls to verify.
|
||||
|
||||
Returns:
|
||||
Dict: A dictionary of verification, including:
|
||||
- "status": A boolean indicating if the tool calls are valid.
|
||||
- "invalid_tool_call": A dictionary of the invalid tool call if any.
|
||||
- "message": An error message.
|
||||
"""
|
||||
|
||||
verification_dict = {
|
||||
"is_valid": True,
|
||||
"invalid_tool_call": {},
|
||||
"error_message": "",
|
||||
}
|
||||
|
||||
functions = {}
|
||||
for tool in tools:
|
||||
functions[tool["function"]["name"]] = tool["function"]["parameters"]
|
||||
|
||||
for tool_call in tool_calls:
|
||||
if not verification_dict["is_valid"]:
|
||||
break
|
||||
|
||||
func_name = tool_call["function"]["name"]
|
||||
func_args = tool_call["function"]["arguments"]
|
||||
|
||||
# Check whether the function is available or not
|
||||
if func_name not in functions:
|
||||
verification_dict["is_valid"] = False
|
||||
verification_dict["invalid_tool_call"] = tool_call
|
||||
verification_dict["error_message"] = f"{func_name} is not available!"
|
||||
else:
|
||||
# Check if all the requried parameters can be found in the tool calls
|
||||
for required_param in functions[func_name].get("required", []):
|
||||
if required_param not in func_args:
|
||||
verification_dict["is_valid"] = False
|
||||
verification_dict["invalid_tool_call"] = tool_call
|
||||
verification_dict[
|
||||
"error_message"
|
||||
] = f"`{required_param}` is required by the function `{func_name}` but not found in the tool call!"
|
||||
break
|
||||
|
||||
# Verify the data type of each parameter in the tool calls
|
||||
function_properties = functions[func_name]["properties"]
|
||||
|
||||
logger.info("== func_args ==")
|
||||
logger.info(func_args)
|
||||
for param_name in func_args:
|
||||
if param_name not in function_properties:
|
||||
verification_dict["is_valid"] = False
|
||||
verification_dict["invalid_tool_call"] = tool_call
|
||||
verification_dict[
|
||||
"error_message"
|
||||
] = f"Parameter `{param_name}` is not defined in the function `{func_name}`."
|
||||
break
|
||||
else:
|
||||
param_value = func_args[param_name]
|
||||
target_type = function_properties[param_name]["type"]
|
||||
|
||||
if target_type in self.support_data_types:
|
||||
data_type = self.support_data_types[target_type]
|
||||
|
||||
if not isinstance(param_value, data_type):
|
||||
param_value = self._convert_data_type(
|
||||
param_value, data_type
|
||||
)
|
||||
if not isinstance(param_value, data_type):
|
||||
verification_dict["is_valid"] = False
|
||||
verification_dict["invalid_tool_call"] = tool_call
|
||||
verification_dict[
|
||||
"error_message"
|
||||
] = f"Parameter `{param_name}` is expected to have the data type `{data_type}`, got `{type(param_value)}`."
|
||||
break
|
||||
else:
|
||||
verification_dict["is_valid"] = False
|
||||
verification_dict["invalid_tool_call"] = tool_call
|
||||
verification_dict[
|
||||
"error_message"
|
||||
] = f"Data type `{target_type}` is not supported."
|
||||
|
||||
return verification_dict
|
||||
|
||||
def _prefill_message(self, messages: List[Dict[str, str]], prefill_message):
|
||||
"""
|
||||
Update messages and generation params for prompt prefilling
|
||||
|
||||
Args:
|
||||
messages (List[Dict[str, str]]): A list of messages.
|
||||
|
||||
Returns:
|
||||
prefill_messages (List[Dict[str, str]]): A list of messages.
|
||||
"""
|
||||
return messages + [{"role": "assistant", "content": prefill_message}]
|
||||
|
||||
@override
|
||||
async def chat_completion(self, req: ChatMessage) -> ChatCompletionResponse:
|
||||
"""
|
||||
Generates a chat completion response for a given request.
|
||||
|
||||
Args:
|
||||
req (ChatMessage): A chat message request object.
|
||||
enable_prefilling (bool, optional): Whether to enable prefill responses. Defaults to True.
|
||||
Returns:
|
||||
ChatCompletionResponse: The model's response to the chat request.
|
||||
|
||||
Note:
|
||||
Currently only support vllm inference
|
||||
"""
|
||||
logger.info("[Arch-Function] - ChatCompletion")
|
||||
|
||||
messages = self._process_messages(
|
||||
req.messages, req.tools, metadata=req.metadata
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"[request to arch-fc]: model: {self.model_name}, extra_body: {self.generation_params}, body: {json.dumps(messages)}"
|
||||
)
|
||||
|
||||
# always enable `stream=True` to collect model responses
|
||||
response = self.client.chat.completions.create(
|
||||
messages=self._prefill_message(messages, self.default_prefix),
|
||||
model=self.model_name,
|
||||
stream=True,
|
||||
extra_body=self.generation_params,
|
||||
)
|
||||
|
||||
use_agent_orchestrator = req.metadata.get("use_agent_orchestrator", False)
|
||||
model_response = ""
|
||||
if use_agent_orchestrator:
|
||||
for chunk in response:
|
||||
if len(chunk.choices) > 0 and chunk.choices[0].delta.content:
|
||||
model_response += chunk.choices[0].delta.content
|
||||
logger.info(f"[Agent Orchestrator]: response received: {model_response}")
|
||||
else:
|
||||
# initialize the hallucination handler, which is an iterator
|
||||
self.hallucination_state = HallucinationState(
|
||||
response_iterator=response, function=req.tools
|
||||
)
|
||||
|
||||
has_tool_calls, has_hallucination = None, False
|
||||
for _ in self.hallucination_state:
|
||||
# check if moodel response starts with tool calls, we do it after 5 tokens because we only check the first part of the response.
|
||||
if len(self.hallucination_state.tokens) > 5 and has_tool_calls is None:
|
||||
content = "".join(self.hallucination_state.tokens)
|
||||
if "tool_calls" in content:
|
||||
has_tool_calls = True
|
||||
else:
|
||||
has_tool_calls = False
|
||||
|
||||
# if the model is hallucinating, start parameter gathering
|
||||
if self.hallucination_state.hallucination is True:
|
||||
has_hallucination = True
|
||||
break
|
||||
|
||||
if has_tool_calls and has_hallucination:
|
||||
# start prompt prefilling if hallcuination is found in tool calls
|
||||
logger.info(
|
||||
f"[Hallucination]: {self.hallucination_state.error_message}"
|
||||
)
|
||||
response = self.client.chat.completions.create(
|
||||
messages=self._prefill_message(messages, self.clarify_prefix),
|
||||
model=self.model_name,
|
||||
stream=False,
|
||||
extra_body=self.generation_params,
|
||||
)
|
||||
model_response = response.choices[0].message.content
|
||||
else:
|
||||
model_response = "".join(self.hallucination_state.tokens)
|
||||
|
||||
# Extract tool calls from model response
|
||||
response_dict = self._parse_model_response(model_response)
|
||||
logger.info(f"[arch-fc]: raw model response: {response_dict['raw_response']}")
|
||||
|
||||
# General model response
|
||||
if response_dict.get("response", ""):
|
||||
model_message = Message(content="", tool_calls=[])
|
||||
# Parameter gathering
|
||||
elif response_dict.get("required_functions", []):
|
||||
if not use_agent_orchestrator:
|
||||
clarification = response_dict.get("clarification", "")
|
||||
model_message = Message(content=clarification, tool_calls=[])
|
||||
else:
|
||||
model_message = Message(content="", tool_calls=[])
|
||||
# Function Calling
|
||||
elif response_dict.get("tool_calls", []):
|
||||
if response_dict["is_valid"]:
|
||||
if not use_agent_orchestrator:
|
||||
verification_dict = self._verify_tool_calls(
|
||||
tools=req.tools, tool_calls=response_dict["tool_calls"]
|
||||
)
|
||||
|
||||
if verification_dict["is_valid"]:
|
||||
logger.info(
|
||||
f"[Tool calls]: {json.dumps([tool_call['function'] for tool_call in response_dict['tool_calls']])}"
|
||||
)
|
||||
model_message = Message(
|
||||
content="", tool_calls=response_dict["tool_calls"]
|
||||
)
|
||||
else:
|
||||
logger.error(
|
||||
f"Invalid tool call - {verification_dict['error_message']}"
|
||||
)
|
||||
model_message = Message(content="", tool_calls=[])
|
||||
else:
|
||||
# skip tool call verification if using agent orchestrator
|
||||
logger.info(
|
||||
f"[Tool calls]: {json.dumps([tool_call['function'] for tool_call in response_dict['tool_calls']])}"
|
||||
)
|
||||
model_message = Message(
|
||||
content="", tool_calls=response_dict["tool_calls"]
|
||||
)
|
||||
|
||||
else:
|
||||
# Response with tool calls but invalid
|
||||
model_message = Message(content="", tool_calls=[])
|
||||
# Response not in the desired format
|
||||
else:
|
||||
logger.error(f"Invalid model response - {model_response}")
|
||||
model_message = Message(content="", tool_calls=[])
|
||||
|
||||
chat_completion_response = ChatCompletionResponse(
|
||||
choices=[Choice(message=model_message)],
|
||||
model=self.model_name,
|
||||
metadata={"x-arch-fc-model-response": response_dict["raw_response"]},
|
||||
role="assistant",
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"[response arch-fc]: {json.dumps(chat_completion_response.model_dump(exclude_none=True))}"
|
||||
)
|
||||
|
||||
return chat_completion_response
|
||||
|
||||
|
||||
# ==============================================================================================================================================
|
||||
|
||||
|
||||
class ArchAgentConfig(ArchFunctionConfig):
|
||||
GENERATION_PARAMS = {
|
||||
"temperature": 0.01,
|
||||
"top_p": 1.0,
|
||||
"top_k": 10,
|
||||
"max_tokens": 1024,
|
||||
"stop_token_ids": [151645],
|
||||
"logprobs": True,
|
||||
"top_logprobs": 10,
|
||||
}
|
||||
|
||||
|
||||
class ArchAgentHandler(ArchFunctionHandler):
|
||||
def __init__(self, client: OpenAI, model_name: str, config: ArchAgentConfig):
|
||||
super().__init__(client, model_name, config)
|
||||
|
||||
@override
|
||||
def _convert_tools(self, tools: List[Dict[str, Any]]) -> str:
|
||||
"""
|
||||
Converts a list of tools into JSON format.
|
||||
|
||||
Args:
|
||||
tools (List[Dict[str, Any]]): A list of tools represented as dictionaries.
|
||||
|
||||
Returns:
|
||||
str: A string representation of converted tools.
|
||||
"""
|
||||
|
||||
converted = []
|
||||
# delete parameters key if its empty in tool
|
||||
for tool in tools:
|
||||
if (
|
||||
"parameters" in tool["function"]
|
||||
and "properties" in tool["function"]["parameters"]
|
||||
and not tool["function"]["parameters"]["properties"]
|
||||
):
|
||||
tool_copy = copy.deepcopy(tool)
|
||||
del tool_copy["function"]["parameters"]
|
||||
converted.append(json.dumps(tool_copy["function"], ensure_ascii=False))
|
||||
else:
|
||||
converted.append(json.dumps(tool["function"], ensure_ascii=False))
|
||||
return "\n".join(converted)
|
||||
|
|
@ -1,160 +0,0 @@
|
|||
import torch
|
||||
import numpy as np
|
||||
import src.commons.utils as utils
|
||||
|
||||
from transformers import AutoTokenizer, AutoModelForSequenceClassification
|
||||
from src.core.utils.model_utils import GuardRequest, GuardResponse
|
||||
|
||||
|
||||
logger = utils.get_model_server_logger()
|
||||
|
||||
|
||||
class ArchGuardHanlder:
|
||||
def __init__(self, model_dict):
|
||||
"""
|
||||
Initializes the ArchGuardHanlder with the given model dictionary.
|
||||
|
||||
Args:
|
||||
model_dict (dict): A dictionary containing the model, tokenizer, and device information.
|
||||
"""
|
||||
|
||||
self.model = model_dict["model"]
|
||||
self.model_name = model_dict["model_name"]
|
||||
self.tokenizer = model_dict["tokenizer"]
|
||||
self.device = model_dict["device"]
|
||||
|
||||
self.support_tasks = {"jailbreak": {"positive_class": 2, "threshold": 0.5}}
|
||||
|
||||
def _split_text_into_chunks(self, text, max_num_words=300):
|
||||
"""
|
||||
Splits the input text into chunks of up to `max_num_words` words.
|
||||
|
||||
Args:
|
||||
text (str): The input text to be split.
|
||||
max_num_words (int, optional): The maximum number of words in each chunk. Defaults to 300.
|
||||
|
||||
Returns:
|
||||
List[str]: A list of text chunks.
|
||||
"""
|
||||
|
||||
words = text.split()
|
||||
|
||||
chunks = [
|
||||
" ".join(words[i : i + max_num_words])
|
||||
for i in range(0, len(words), max_num_words)
|
||||
]
|
||||
|
||||
return chunks
|
||||
|
||||
@staticmethod
|
||||
def softmax(x):
|
||||
"""
|
||||
Computes the softmax of the input array.
|
||||
|
||||
Args:
|
||||
x (np.ndarray): The input array.
|
||||
|
||||
Returns:
|
||||
np.ndarray: The softmax of the input.
|
||||
"""
|
||||
return np.exp(x) / np.exp(x).sum(axis=0)
|
||||
|
||||
def _predict_text(self, task, text, max_length=512) -> GuardResponse:
|
||||
"""
|
||||
Predicts the result for the provided text for a specific task.
|
||||
|
||||
Args:
|
||||
task (str): The task to perform (e.g., "jailbreak").
|
||||
text (str): The input text to classify.
|
||||
max_length (int, optional): The maximum length for tokenization. Defaults to 512.
|
||||
|
||||
Returns:
|
||||
GuardResponse: A GuardResponse object containing the prediction.
|
||||
"""
|
||||
|
||||
inputs = self.tokenizer(
|
||||
text, truncation=True, max_length=max_length, return_tensors="pt"
|
||||
).to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
logits = self.model(**inputs).logits.cpu().detach().numpy()[0]
|
||||
prob = ArchGuardHanlder.softmax(logits)[
|
||||
self.support_tasks[task]["positive_class"]
|
||||
].item()
|
||||
|
||||
verdict = prob > self.support_tasks[task]["threshold"]
|
||||
|
||||
return GuardResponse(task=task, input=text, prob=prob, verdict=verdict)
|
||||
|
||||
def predict(self, req: GuardRequest, max_num_words=300) -> GuardResponse:
|
||||
"""
|
||||
Makes a prediction based on the GuardRequest input.
|
||||
|
||||
Args:
|
||||
req (GuardRequest): The GuardRequest object containing the input text and task.
|
||||
max_num_words (int, optional): The maximum number of words in each chunk if splitting is needed. Defaults to 300.
|
||||
|
||||
Returns:
|
||||
GuardResponse: A GuardResponse object containing the prediction.
|
||||
|
||||
Note:
|
||||
currently only support jailbreak check
|
||||
"""
|
||||
|
||||
if req.task not in self.support_tasks:
|
||||
raise NotImplementedError(f"{req.task} is not supported!")
|
||||
|
||||
logger.info("[Arch-Guard] - Prediction")
|
||||
logger.info(f"[request arch-guard]: {req.input}")
|
||||
|
||||
if len(req.input.split()) < max_num_words:
|
||||
result = self._predict_text(req.task, req.input)
|
||||
else:
|
||||
prob, verdict = 0.0, False
|
||||
|
||||
# split into chunks if text is long
|
||||
text_chunks = self._split_text_into_chunks(req.input)
|
||||
|
||||
for chunk in text_chunks:
|
||||
chunk_result = self._predict_text(req.task, chunk)
|
||||
|
||||
if chunk_result.verdict:
|
||||
prob = chunk_result.prob
|
||||
verdict = True
|
||||
break
|
||||
|
||||
result = GuardResponse(
|
||||
task=req.task, input=req.input, prob=prob, verdict=verdict
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"[response]: {req.task}: {'True' if result.verdict else 'False'} (prob: {result.prob:.2f})"
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def get_guardrail_handler(model_name: str = "katanemo/Arch-Guard", device: str = None):
|
||||
"""
|
||||
Initializes and returns an instance of ArchGuardHanlder based on the specified device.
|
||||
|
||||
Args:
|
||||
device (str, optional): The device to use for model inference (e.g., "cpu" or "cuda"). Defaults to None.
|
||||
|
||||
Returns:
|
||||
ArchGuardHanlder: An instance of ArchGuardHanlder configured for the specified device.
|
||||
"""
|
||||
|
||||
if device is None:
|
||||
device = utils.get_device()
|
||||
|
||||
guardrail_dict = {
|
||||
"device": device,
|
||||
"model_name": model_name,
|
||||
"tokenizer": AutoTokenizer.from_pretrained(model_name, trust_remote_code=True),
|
||||
"model": AutoModelForSequenceClassification.from_pretrained(
|
||||
model_name, device_map=device, low_cpu_mem_usage=True
|
||||
),
|
||||
}
|
||||
|
||||
return ArchGuardHanlder(model_dict=guardrail_dict)
|
||||
|
|
@ -1,393 +0,0 @@
|
|||
import json
|
||||
import math
|
||||
import torch
|
||||
import itertools
|
||||
|
||||
|
||||
from typing import Dict, List, Tuple
|
||||
from enum import Enum
|
||||
import string
|
||||
|
||||
from src.commons.utils import get_model_server_logger
|
||||
|
||||
logger = get_model_server_logger()
|
||||
|
||||
# constants
|
||||
FUNC_NAME_START_PATTERN = ('{"name":"', "{'name':'")
|
||||
FUNC_NAME_END_TOKEN = ('",', "',")
|
||||
END_TOOL_CALL_TOKEN = "}}"
|
||||
|
||||
FIRST_PARAM_NAME_START_PATTERN = ('"arguments":{"', "'arguments':{'")
|
||||
PARAMETER_NAME_END_TOKENS = ('":', ':"', "':", ":'", '":"', "':'")
|
||||
PARAMETER_NAME_START_PATTERN = ('","', "','")
|
||||
PARAMETER_VALUE_START_PATTERN = ('":', "':")
|
||||
PARAMETER_VALUE_END_TOKEN = ('",', '"}')
|
||||
|
||||
BRACKETS = {"(": ")", "{": "}", "[": "]"}
|
||||
|
||||
|
||||
# Thresholds
|
||||
class MaskToken(Enum):
|
||||
FUNCTION_NAME = "f"
|
||||
PARAMETER_VALUE = "v"
|
||||
PARAMETER_NAME = "p"
|
||||
NOT_USED = "e"
|
||||
TOOL_CALL = "t"
|
||||
|
||||
|
||||
HALLUCINATION_THRESHOLD_DICT = {
|
||||
"entropy": 0.0001,
|
||||
"varentropy": 0.0001,
|
||||
"probability": 0.8,
|
||||
}
|
||||
|
||||
|
||||
def check_threshold(entropy: float, varentropy: float, thd: Dict) -> bool:
|
||||
"""
|
||||
Check if the given entropy or variance of entropy exceeds the specified thresholds.
|
||||
|
||||
Args:
|
||||
entropy (float): The entropy value to check.
|
||||
varentropy (float): The variance of entropy value to check.
|
||||
thd (dict): A dictionary containing the threshold values with keys 'entropy' and 'varentropy'.
|
||||
|
||||
Returns:
|
||||
bool: True if both the entropy and varentropy exceeds their respective thresholds, False otherwise.
|
||||
"""
|
||||
return entropy > thd["entropy"] and varentropy > thd["varentropy"]
|
||||
|
||||
|
||||
def calculate_uncertainty(log_probs: List[float]) -> Tuple[float, float]:
|
||||
"""
|
||||
Calculate the entropy and variance of entropy (varentropy) from log probabilities.
|
||||
|
||||
Args:
|
||||
log_probs (list of float): A list of log probabilities.
|
||||
|
||||
Returns:
|
||||
tuple: A tuple containing:
|
||||
- log_probs (list of float): The input log probabilities as a list.
|
||||
- entropy (float): The calculated entropy.
|
||||
- varentropy (float): The calculated variance of entropy.
|
||||
"""
|
||||
log_probs = torch.tensor(log_probs)
|
||||
token_probs = torch.exp(log_probs)
|
||||
entropy = -torch.sum(log_probs * token_probs, dim=-1) / math.log(2, math.e)
|
||||
varentropy = torch.sum(
|
||||
token_probs * (log_probs / math.log(2, math.e) + entropy.unsqueeze(-1)) ** 2,
|
||||
dim=-1,
|
||||
)
|
||||
return entropy.item(), varentropy.item(), token_probs[0].item()
|
||||
|
||||
|
||||
def is_parameter_required(
|
||||
function_description: Dict,
|
||||
parameter_name: str,
|
||||
) -> bool:
|
||||
"""
|
||||
Check if a parameter in required list
|
||||
|
||||
Args:
|
||||
function_description (dict): The API description in JSON format.
|
||||
parameter_name (str): The name of the parameter to check.
|
||||
|
||||
Returns:
|
||||
bool: True if the parameter has the specified property, False otherwise.
|
||||
"""
|
||||
required_parameters = function_description.get("required", {})
|
||||
|
||||
return parameter_name in required_parameters
|
||||
|
||||
|
||||
def is_parameter_property(
|
||||
function_description: Dict, parameter_name: str, property_name: str
|
||||
) -> bool:
|
||||
"""
|
||||
Check if a parameter in an API description has a specific property.
|
||||
|
||||
Args:
|
||||
function_description (dict): The API description in JSON format.
|
||||
parameter_name (str): The name of the parameter to check.
|
||||
property_name (str): The property to look for (e.g., 'format', 'default').
|
||||
|
||||
Returns:
|
||||
bool: True if the parameter has the specified property, False otherwise.
|
||||
"""
|
||||
parameters = function_description.get("properties", {})
|
||||
parameter_info = parameters.get(parameter_name, {})
|
||||
|
||||
return property_name in parameter_info
|
||||
|
||||
|
||||
class HallucinationState:
|
||||
"""
|
||||
A class to handle the state of hallucination detection in token processing.
|
||||
|
||||
Attributes:
|
||||
tokens (list): List of tokens processed.
|
||||
logprobs (list): List of log probabilities for each token.
|
||||
state (str): Current state of the handler.
|
||||
mask (list): List of masks indicating the type of each token.
|
||||
parameter_name_done (bool): Flag indicating if parameter name extraction is done.
|
||||
hallucination (bool): Flag indicating if a hallucination is detected.
|
||||
hallucination_message (str): Message describing the hallucination.
|
||||
parameter_name (list): List of extracted parameter names.
|
||||
token_probs_map (list): List mapping tokens to their entropy and variance of entropy.
|
||||
"""
|
||||
|
||||
def __init__(self, response_iterator=None, function=None):
|
||||
"""
|
||||
Initializes the HallucinationState with default values.
|
||||
"""
|
||||
self.tokens: List[str] = []
|
||||
self.logprobs: List[float] = []
|
||||
self.state: str = None
|
||||
self.mask: List[str] = []
|
||||
self.parameter_name_done: bool = False
|
||||
self.hallucination: bool = False
|
||||
self.error_message: str = ""
|
||||
self.parameter_name: List[str] = []
|
||||
self.token_probs_map: List[Tuple[str, float, float]] = []
|
||||
self.response_iterator = response_iterator
|
||||
self._process_function(function)
|
||||
self.open_bracket = False
|
||||
self.bracket = None
|
||||
self.function_name = ""
|
||||
self.check_parameter_name = {}
|
||||
self.HALLUCINATION_THRESHOLD_DICT = HALLUCINATION_THRESHOLD_DICT
|
||||
|
||||
def _process_function(self, function):
|
||||
self.function = function
|
||||
if self.function is None:
|
||||
raise ValueError("API descriptions not set.")
|
||||
self.function_properties = {
|
||||
x["function"]["name"]: x["function"]["parameters"] for x in self.function
|
||||
}
|
||||
|
||||
def _reset_parameters(self):
|
||||
"""
|
||||
Resets all parameters in the HallucinationState to their default values.
|
||||
"""
|
||||
self.state = None
|
||||
self.parameter_name_done = False
|
||||
self.hallucination = False
|
||||
self.error_message = ""
|
||||
self.open_bracket = False
|
||||
self.bracket = None
|
||||
self.check_parameter_name = {}
|
||||
|
||||
def append_and_check_token_hallucination(self, token, logprob):
|
||||
"""
|
||||
Check if the given token is hallucinated based on the log probability.
|
||||
|
||||
Args:
|
||||
token (str): The token to check.
|
||||
logprob (float): The log probability of the token.
|
||||
|
||||
Returns:
|
||||
bool: True if the token is hallucinated, False otherwise.
|
||||
"""
|
||||
self.tokens.append(token)
|
||||
self.logprobs.append(logprob)
|
||||
self._process_token()
|
||||
return self.hallucination
|
||||
|
||||
def __iter__(self):
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
if self.response_iterator is not None:
|
||||
try:
|
||||
r = next(self.response_iterator)
|
||||
if hasattr(r.choices[0].delta, "content"):
|
||||
token_content = r.choices[0].delta.content
|
||||
if token_content != "":
|
||||
try:
|
||||
logprobs = [
|
||||
p.logprob
|
||||
for p in r.choices[0].logprobs.content[0].top_logprobs
|
||||
]
|
||||
self.append_and_check_token_hallucination(
|
||||
token_content, logprobs
|
||||
)
|
||||
except Exception as e:
|
||||
self.append_and_check_token_hallucination(
|
||||
token_content, [None]
|
||||
)
|
||||
|
||||
return token_content
|
||||
except StopIteration:
|
||||
raise StopIteration
|
||||
|
||||
def _process_token(self):
|
||||
"""
|
||||
Processes the current token and updates the state and mask accordingly.
|
||||
Detects hallucinations based on the token type and log probabilities.
|
||||
"""
|
||||
content = "".join(self.tokens).replace(" ", "")
|
||||
|
||||
# Function name extraction logic
|
||||
# If the state is function name and the token is not an end token, add to the mask
|
||||
if content.endswith(END_TOOL_CALL_TOKEN):
|
||||
self._reset_parameters()
|
||||
|
||||
if self.state == "function_name":
|
||||
if self.tokens[-1] not in FUNC_NAME_END_TOKEN:
|
||||
self.mask.append(MaskToken.FUNCTION_NAME)
|
||||
else:
|
||||
self.state = None
|
||||
self._get_function_name()
|
||||
|
||||
# Check if the token is a function name start token, change the state
|
||||
if content.endswith(FUNC_NAME_START_PATTERN):
|
||||
self.state = "function_name"
|
||||
|
||||
# Parameter name extraction logic
|
||||
# if the state is parameter name and the token is not an end token, add to the mask
|
||||
if self.state == "parameter_name" and not content.endswith(
|
||||
PARAMETER_NAME_END_TOKENS
|
||||
):
|
||||
self.mask.append(MaskToken.PARAMETER_NAME)
|
||||
# if the state is parameter name and the token is an end token, change the state, check hallucination and set the flag parameter name done
|
||||
# The need for parameter name done is to allow the check of parameter value pattern
|
||||
elif self.state == "parameter_name" and content.endswith(
|
||||
PARAMETER_NAME_END_TOKENS
|
||||
):
|
||||
self.state = None
|
||||
self.parameter_name_done = True
|
||||
self._get_parameter_name()
|
||||
# if the parameter name is done and the token is a parameter name start token, change the state
|
||||
elif (
|
||||
self.parameter_name_done
|
||||
and not self.open_bracket
|
||||
and content.endswith(PARAMETER_NAME_START_PATTERN)
|
||||
):
|
||||
self.state = "parameter_name"
|
||||
|
||||
# if token is a first parameter value start token, change the state
|
||||
if content.endswith(FIRST_PARAM_NAME_START_PATTERN):
|
||||
self.state = "parameter_name"
|
||||
|
||||
# Parameter value extraction logic
|
||||
# if the state is parameter value and the token is not an end token, add to the mask
|
||||
if self.state == "parameter_value" and not content.endswith(
|
||||
PARAMETER_VALUE_END_TOKEN
|
||||
):
|
||||
# checking if the token is a value token and is not empty
|
||||
open_brackets = [
|
||||
char for char in self.tokens[-1].strip() if char in BRACKETS
|
||||
]
|
||||
if open_brackets:
|
||||
self.open_bracket = True
|
||||
self.bracket = open_brackets[0]
|
||||
|
||||
if self.open_bracket and BRACKETS[self.bracket] in self.tokens[-1].strip():
|
||||
self.open_bracket = False
|
||||
self.bracket = None
|
||||
|
||||
if (
|
||||
not all(
|
||||
char in set(string.punctuation) for char in self.tokens[-1].strip()
|
||||
)
|
||||
and self.tokens[-1].strip() != ""
|
||||
):
|
||||
self.mask.append(MaskToken.PARAMETER_VALUE)
|
||||
|
||||
# checking if the parameter doesn't have enum and the token is the first parameter value token
|
||||
# check if function name is in function properties
|
||||
if self.function_name in self.function_properties:
|
||||
if (
|
||||
len(self.mask) > 1
|
||||
and self.mask[-2] != MaskToken.PARAMETER_VALUE
|
||||
and is_parameter_required(
|
||||
self.function_properties[self.function_name],
|
||||
self.parameter_name[-1],
|
||||
)
|
||||
and not is_parameter_property(
|
||||
self.function_properties[self.function_name],
|
||||
self.parameter_name[-1],
|
||||
"enum",
|
||||
)
|
||||
):
|
||||
if self.parameter_name[-1] not in self.check_parameter_name:
|
||||
self._check_logprob()
|
||||
self.check_parameter_name[self.parameter_name[-1]] = True
|
||||
else:
|
||||
self._check_logprob()
|
||||
self.error_message = f"Function name {self.function_name} not found in function properties"
|
||||
logger.warning(
|
||||
f"Function name {self.function_name} not found in function properties"
|
||||
)
|
||||
else:
|
||||
self.mask.append(MaskToken.NOT_USED)
|
||||
# if the state is parameter value and the token is an end token, change the state
|
||||
elif (
|
||||
self.state == "parameter_value"
|
||||
and not self.open_bracket
|
||||
and content.endswith(PARAMETER_VALUE_END_TOKEN)
|
||||
):
|
||||
self.state = None
|
||||
# if the parameter name is done and the token is a parameter value start token, change the state
|
||||
elif self.parameter_name_done and content.endswith(
|
||||
PARAMETER_VALUE_START_PATTERN
|
||||
):
|
||||
self.state = "parameter_value"
|
||||
|
||||
# Maintain consistency between stack and mask
|
||||
# If the mask length is less than tokens, add an not used (e) token to the mask
|
||||
if len(self.mask) != len(self.tokens):
|
||||
self.mask.append(MaskToken.NOT_USED)
|
||||
|
||||
def _check_logprob(self):
|
||||
"""
|
||||
Checks the log probability of the current token and updates the token probability map.
|
||||
Detects hallucinations based on entropy and variance of entropy.
|
||||
"""
|
||||
probs = self.logprobs[-1]
|
||||
entropy, varentropy, probability = calculate_uncertainty(probs)
|
||||
self.token_probs_map.append((self.tokens[-1], entropy, varentropy, probability))
|
||||
|
||||
if check_threshold(
|
||||
entropy,
|
||||
varentropy,
|
||||
self.HALLUCINATION_THRESHOLD_DICT,
|
||||
):
|
||||
self.hallucination = True
|
||||
self.error_message = f"token '{self.tokens[-1]}' is uncertain. Generated response:\n{''.join(self.tokens)}"
|
||||
|
||||
def _count_consecutive_token(self, token=MaskToken.PARAMETER_VALUE) -> int:
|
||||
"""
|
||||
Counts the number of consecutive occurrences of a given token in the mask.
|
||||
|
||||
Args:
|
||||
token (str): The token to count in the mask.
|
||||
|
||||
Returns:
|
||||
int: The number of consecutive occurrences of the token.
|
||||
"""
|
||||
return (
|
||||
len(list(itertools.takewhile(lambda x: x == token, reversed(self.mask))))
|
||||
if self.mask and self.mask[-1] == token
|
||||
else 0
|
||||
)
|
||||
|
||||
def _get_parameter_name(self):
|
||||
"""
|
||||
Get the parameter name from the tokens.
|
||||
|
||||
Returns:
|
||||
str: The extracted parameter name.
|
||||
"""
|
||||
p_len = self._count_consecutive_token(MaskToken.PARAMETER_NAME)
|
||||
parameter_name = "".join(self.tokens[:-1][-p_len:])
|
||||
self.parameter_name.append(parameter_name)
|
||||
|
||||
def _get_function_name(self):
|
||||
"""
|
||||
Get the function name from the tokens.
|
||||
|
||||
Returns:
|
||||
str: The extracted function name.
|
||||
"""
|
||||
f_len = self._count_consecutive_token(MaskToken.FUNCTION_NAME)
|
||||
self.function_name = "".join(self.tokens[:-1][-f_len:])
|
||||
|
|
@ -1,217 +0,0 @@
|
|||
import json
|
||||
import src.commons.utils as utils
|
||||
|
||||
from openai import OpenAI
|
||||
from pydantic import BaseModel
|
||||
from typing import Any, Dict, List, Optional
|
||||
from overrides import final
|
||||
|
||||
|
||||
class Message(BaseModel):
|
||||
role: Optional[str] = ""
|
||||
content: Optional[str] = ""
|
||||
tool_call_id: Optional[str] = ""
|
||||
tool_calls: Optional[List[Dict[str, Any]]] = []
|
||||
|
||||
|
||||
class ChatMessage(BaseModel):
|
||||
messages: List[Message] = []
|
||||
tools: List[Dict[str, Any]] = []
|
||||
metadata: Optional[Dict[str, str]] = {}
|
||||
|
||||
|
||||
class Choice(BaseModel):
|
||||
id: Optional[int] = 0
|
||||
message: Message
|
||||
finish_reason: Optional[str] = "stop"
|
||||
|
||||
|
||||
class ChatCompletionResponse(BaseModel):
|
||||
id: Optional[int] = 0
|
||||
object: Optional[str] = "chat_completion"
|
||||
created: Optional[str] = ""
|
||||
choices: List[Choice] = []
|
||||
model: str = ""
|
||||
metadata: Optional[Dict[str, str]] = {}
|
||||
|
||||
|
||||
class GuardRequest(BaseModel):
|
||||
input: str
|
||||
task: str
|
||||
|
||||
|
||||
class GuardResponse(BaseModel):
|
||||
task: str = ""
|
||||
input: str = ""
|
||||
prob: float = 0.0
|
||||
verdict: bool = False
|
||||
metadata: Optional[Dict[str, str]] = {}
|
||||
|
||||
|
||||
# ================================================================================================
|
||||
|
||||
|
||||
class ArchBaseHandler:
|
||||
def __init__(
|
||||
self,
|
||||
client: OpenAI,
|
||||
model_name: str,
|
||||
task_prompt: str,
|
||||
format_prompt: str,
|
||||
generation_params: Dict,
|
||||
):
|
||||
"""
|
||||
Initializes the base handler.
|
||||
|
||||
Args:
|
||||
client (OpenAI): An OpenAI client instance.
|
||||
model_name (str): Name of the model to use.
|
||||
task_prompt (str): The main task prompt for the system.
|
||||
format_prompt (str): A prompt specifying the desired output format.
|
||||
generation_params (Dict): Generation parameters for the model.
|
||||
"""
|
||||
self.client = client
|
||||
self.model_name = model_name
|
||||
|
||||
self.task_prompt = task_prompt
|
||||
self.format_prompt = format_prompt
|
||||
|
||||
self.generation_params = generation_params
|
||||
|
||||
def _convert_tools(self, tools: List[Dict[str, Any]]) -> str:
|
||||
"""
|
||||
Converts a list of tools into the desired internal representation.
|
||||
|
||||
Args:
|
||||
tools (List[Dict[str, Any]]): A list of tools represented as dictionaries.
|
||||
|
||||
Raises:
|
||||
NotImplementedError: Method should be overridden in subclasses.
|
||||
"""
|
||||
|
||||
raise NotImplementedError()
|
||||
|
||||
@final
|
||||
def _format_system_prompt(self, tools: List[Dict[str, Any]]) -> str:
|
||||
"""
|
||||
Formats the system prompt using provided tools.
|
||||
|
||||
Args:
|
||||
tools (List[Dict[str, Any]]): A list of tools represented as dictionaries.
|
||||
|
||||
Returns:
|
||||
str: A formatted system prompt.
|
||||
"""
|
||||
|
||||
today_date = utils.get_today_date()
|
||||
tools = self._convert_tools(tools)
|
||||
|
||||
system_prompt = (
|
||||
self.task_prompt.format(today_date=today_date, tools=tools)
|
||||
+ self.format_prompt
|
||||
)
|
||||
|
||||
return system_prompt
|
||||
|
||||
@final
|
||||
def _process_messages(
|
||||
self,
|
||||
messages: List[Message],
|
||||
tools: List[Dict[str, Any]] = None,
|
||||
extra_instruction: str = None,
|
||||
max_tokens=4096,
|
||||
metadata: Dict[str, str] = {},
|
||||
):
|
||||
"""
|
||||
Processes a list of messages and formats them appropriately.
|
||||
|
||||
Args:
|
||||
messages (List[Message]): A list of message objects.
|
||||
tools (List[Dict[str, Any]], optional): A list of tools to include in the system prompt.
|
||||
extra_instruction (str, optional): Additional instructions to append to the last user message.
|
||||
max_tokens (int): Maximum allowed token count, assuming ~4 characters per token on average.
|
||||
|
||||
Returns:
|
||||
List[Dict[str, Any]]: A list of processed message dictionaries.
|
||||
"""
|
||||
|
||||
processed_messages = []
|
||||
|
||||
if tools:
|
||||
processed_messages.append(
|
||||
{"role": "system", "content": self._format_system_prompt(tools)}
|
||||
)
|
||||
|
||||
for idx, message in enumerate(messages):
|
||||
role, content, tool_calls = (
|
||||
message.role,
|
||||
message.content,
|
||||
message.tool_calls,
|
||||
)
|
||||
|
||||
if tool_calls:
|
||||
# TODO: Extend to support multiple function calls
|
||||
role = "assistant"
|
||||
content = f"<tool_call>\n{json.dumps(tool_calls[0]['function'])}\n</tool_call>"
|
||||
elif role == "tool":
|
||||
role = "user"
|
||||
if metadata.get("optimize_context_window", "false").lower() == "true":
|
||||
content = f"<tool_response>\n\n</tool_response>"
|
||||
else:
|
||||
# sample response below
|
||||
# "content": "<tool_response>\n{'name': 'get_stock_price', 'result': '$196.66'}\n</tool_response>"
|
||||
# msg[idx-1] contains tool call = '{"tool_calls": [{"name": "currency_exchange", "arguments": {"currency_symbol": "NZD"}}]}'
|
||||
tool_call_msg = messages[idx - 1].content
|
||||
if tool_call_msg.startswith("```") and tool_call_msg.endswith(
|
||||
"```"
|
||||
):
|
||||
tool_call_msg = tool_call_msg.strip("```").strip()
|
||||
if tool_call_msg.startswith("json"):
|
||||
tool_call_msg = tool_call_msg[4:].strip()
|
||||
func_name = json.loads(tool_call_msg)["tool_calls"][0].get(
|
||||
"name", "no_name"
|
||||
)
|
||||
tool_response = {
|
||||
"name": func_name,
|
||||
"result": content,
|
||||
}
|
||||
content = f"<tool_response>\n{json.dumps(tool_response)}\n</tool_response>"
|
||||
|
||||
processed_messages.append({"role": role, "content": content})
|
||||
|
||||
assert processed_messages[-1]["role"] == "user"
|
||||
|
||||
if extra_instruction:
|
||||
processed_messages[-1]["content"] += "\n" + extra_instruction
|
||||
|
||||
# keep the first system message and shift conversation if the total token length exceeds the limit
|
||||
def truncate_messages(messages: List[Dict[str, Any]]):
|
||||
num_tokens, conversation_idx = 0, 0
|
||||
if messages[0]["role"] == "system":
|
||||
num_tokens += len(messages[0]["content"]) // 4
|
||||
conversation_idx = 1
|
||||
|
||||
for message_idx in range(len(messages) - 1, conversation_idx - 1, -1):
|
||||
num_tokens += len(messages[message_idx]["content"]) // 4
|
||||
if num_tokens >= max_tokens:
|
||||
if messages[message_idx]["role"] == "user":
|
||||
break
|
||||
|
||||
return messages[:conversation_idx] + messages[message_idx:]
|
||||
|
||||
processed_messages = truncate_messages(processed_messages)
|
||||
|
||||
return processed_messages
|
||||
|
||||
async def chat_completion(self, req: ChatMessage) -> ChatCompletionResponse:
|
||||
"""
|
||||
Abstract method for generating chat completions.
|
||||
|
||||
Args:
|
||||
req (ChatMessage): A chat message request object.
|
||||
|
||||
Raises:
|
||||
NotImplementedError: Method should be overridden in subclasses.
|
||||
"""
|
||||
|
||||
raise NotImplementedError()
|
||||
|
|
@ -1,158 +0,0 @@
|
|||
import json
|
||||
import os
|
||||
import time
|
||||
import logging
|
||||
import src.commons.utils as utils
|
||||
|
||||
from src.commons.globals import ARCH_ENDPOINT, handler_map
|
||||
from src.core.function_calling import ArchFunctionHandler
|
||||
from src.core.utils.model_utils import (
|
||||
ChatMessage,
|
||||
ChatCompletionResponse,
|
||||
GuardRequest,
|
||||
GuardResponse,
|
||||
)
|
||||
|
||||
from fastapi import FastAPI, Response
|
||||
from opentelemetry import trace
|
||||
from opentelemetry.sdk.trace import TracerProvider
|
||||
from opentelemetry.sdk.trace.export import BatchSpanProcessor
|
||||
from opentelemetry.sdk.resources import Resource
|
||||
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
|
||||
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
|
||||
|
||||
|
||||
resource = Resource.create(
|
||||
{
|
||||
"service.name": "model-server",
|
||||
}
|
||||
)
|
||||
|
||||
# Initialize the tracer provider
|
||||
trace.set_tracer_provider(TracerProvider(resource=resource))
|
||||
tracer = trace.get_tracer(__name__)
|
||||
|
||||
# DEFAULT_OTLP_HOST = "http://localhost:4317"
|
||||
DEFAULT_OTLP_HOST = "none"
|
||||
|
||||
# Configure the OTLP exporter (Jaeger, Zipkin, etc.)
|
||||
otlp_exporter = OTLPSpanExporter(
|
||||
endpoint=os.getenv("OTLP_HOST", DEFAULT_OTLP_HOST) # noqa: F821
|
||||
)
|
||||
|
||||
trace.get_tracer_provider().add_span_processor(BatchSpanProcessor(otlp_exporter))
|
||||
|
||||
|
||||
logger = utils.get_model_server_logger()
|
||||
logging.getLogger("httpx").setLevel(logging.ERROR)
|
||||
logging.getLogger("opentelemetry.exporter.otlp.proto.grpc.exporter").setLevel(
|
||||
logging.ERROR
|
||||
)
|
||||
|
||||
app = FastAPI()
|
||||
FastAPIInstrumentor().instrument_app(app)
|
||||
|
||||
logger.info(f"using archfc endpoint: {ARCH_ENDPOINT}")
|
||||
|
||||
|
||||
@app.get("/healthz")
|
||||
async def healthz():
|
||||
return {"status": "ok"}
|
||||
|
||||
|
||||
@app.get("/models")
|
||||
async def models():
|
||||
return {
|
||||
"object": "list",
|
||||
"data": [{"id": model_name, "object": "model"} for model_name in handler_map],
|
||||
}
|
||||
|
||||
|
||||
@app.post("/function_calling")
|
||||
async def function_calling(req: ChatMessage, res: Response):
|
||||
logger.info("[Endpoint: /function_calling]")
|
||||
logger.info(f"[request body]: {json.dumps(req.model_dump(exclude_none=True))}")
|
||||
|
||||
final_response: ChatCompletionResponse = None
|
||||
error_messages = None
|
||||
|
||||
use_agent_orchestrator = req.metadata.get("use_agent_orchestrator", False)
|
||||
logger.info(f"Use agent orchestrator: {use_agent_orchestrator}")
|
||||
|
||||
try:
|
||||
handler_name = "Arch-Agent" if use_agent_orchestrator else "Arch-Function"
|
||||
model_handler: ArchFunctionHandler = handler_map[handler_name]
|
||||
|
||||
start_time = time.perf_counter()
|
||||
final_response = await model_handler.chat_completion(req)
|
||||
latency = time.perf_counter() - start_time
|
||||
|
||||
if not final_response.metadata:
|
||||
final_response.metadata = {}
|
||||
|
||||
# Parameter gathering for detected intents
|
||||
if final_response.choices[0].message.content:
|
||||
final_response.metadata["function_latency"] = str(round(latency * 1000, 3))
|
||||
# Function Calling
|
||||
elif final_response.choices[0].message.tool_calls:
|
||||
final_response.metadata["function_latency"] = str(round(latency * 1000, 3))
|
||||
|
||||
if not use_agent_orchestrator:
|
||||
final_response.metadata["hallucination"] = str(
|
||||
model_handler.hallucination_state.hallucination
|
||||
)
|
||||
# No intent detected
|
||||
else:
|
||||
final_response.metadata["intent_latency"] = str(round(latency * 1000, 3))
|
||||
|
||||
if not use_agent_orchestrator:
|
||||
final_response.metadata["intent_latency"] = str(round(latency * 1000, 3))
|
||||
|
||||
final_response.metadata["hallucination"] = str(
|
||||
model_handler.hallucination_state.hallucination
|
||||
)
|
||||
|
||||
except ValueError as e:
|
||||
res.statuscode = 503
|
||||
error_messages = f"[{handler_name}] - Error in tool call extraction: {e}"
|
||||
raise
|
||||
except StopIteration as e:
|
||||
res.statuscode = 500
|
||||
error_messages = f"[{handler_name}] - Error in hallucination check: {e}"
|
||||
raise
|
||||
except Exception as e:
|
||||
res.status_code = 500
|
||||
error_messages = f"[{handler_name}] - Error in ChatCompletion: {e}"
|
||||
raise
|
||||
|
||||
if error_messages is not None:
|
||||
logger.error(error_messages)
|
||||
final_response = ChatCompletionResponse(metadata={"error": error_messages})
|
||||
|
||||
return final_response
|
||||
|
||||
|
||||
@app.post("/guardrails")
|
||||
async def guardrails(req: GuardRequest, res: Response, max_num_words=300):
|
||||
logger.info("[Endpoint: /guardrails] - Gateway")
|
||||
logger.info(f"[request body]: {json.dumps(req.model_dump(exclude_none=True))}")
|
||||
|
||||
final_response: GuardResponse = None
|
||||
error_messages = None
|
||||
|
||||
try:
|
||||
guard_start_time = time.perf_counter()
|
||||
final_response = handler_map["Arch-Guard"].predict(req)
|
||||
guard_latency = time.perf_counter() - guard_start_time
|
||||
final_response.metadata = {
|
||||
"guard_latency": round(guard_latency * 1000, 3),
|
||||
}
|
||||
except Exception as e:
|
||||
res.status_code = 500
|
||||
error_messages = f"[Arch-Guard]: {e}"
|
||||
|
||||
if error_messages is not None:
|
||||
logger.error(error_messages)
|
||||
final_response = GuardResponse(metadata={"error": error_messages})
|
||||
|
||||
return final_response
|
||||
|
|
@ -1,115 +0,0 @@
|
|||
import pytest
|
||||
import time
|
||||
from src.commons.globals import handler_map
|
||||
from src.core.utils.model_utils import ChatMessage, Message
|
||||
|
||||
|
||||
# define function
|
||||
get_weather_api = {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_current_weather",
|
||||
"description": "Get current weather at a location.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "str",
|
||||
"description": "The location to get the weather for",
|
||||
"format": "City, State",
|
||||
},
|
||||
"unit": {
|
||||
"type": "str",
|
||||
"description": "The unit to return the weather in.",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"default": "celsius",
|
||||
},
|
||||
"days": {
|
||||
"type": "str",
|
||||
"description": "the number of days for the request.",
|
||||
},
|
||||
},
|
||||
"required": ["location", "days"],
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
# get_data class return request, intent, hallucination, parameter_gathering
|
||||
|
||||
|
||||
def get_hallucination_data():
|
||||
# Create instances of the Message class
|
||||
message1 = Message(role="user", content="How is the weather in Seattle in days?")
|
||||
|
||||
# Create a list of tools
|
||||
tools = [get_weather_api]
|
||||
|
||||
# Create an instance of the ChatMessage class
|
||||
req = ChatMessage(messages=[message1], tools=tools)
|
||||
|
||||
# first token will not be tool call
|
||||
return req, False, True
|
||||
|
||||
|
||||
def get_success_tool_call_data():
|
||||
# Create instances of the Message class
|
||||
message1 = Message(role="user", content="How is the weather in Seattle in 7 days?")
|
||||
|
||||
# Create a list of tools
|
||||
tools = [get_weather_api]
|
||||
|
||||
# Create an instance of the ChatMessage class
|
||||
req = ChatMessage(messages=[message1], tools=tools)
|
||||
|
||||
return req, True, False
|
||||
|
||||
|
||||
def get_irrelevant_data():
|
||||
# Create instances of the Message class
|
||||
message1 = Message(role="user", content="What is 1+1?")
|
||||
|
||||
# Create a list of tools
|
||||
tools = [get_weather_api]
|
||||
|
||||
# Create an instance of the ChatMessage class
|
||||
req = ChatMessage(messages=[message1], tools=tools)
|
||||
|
||||
return req, False, False
|
||||
|
||||
|
||||
def get_greeting_data():
|
||||
# Create instances of the Message class
|
||||
message1 = Message(role="user", content="Hello how are you?")
|
||||
|
||||
# Create a list of tools
|
||||
tools = [get_weather_api]
|
||||
|
||||
# Create an instance of the ChatMessage class
|
||||
req = ChatMessage(messages=[message1], tools=tools)
|
||||
|
||||
return req, False, False
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(
|
||||
"get_data_func",
|
||||
[
|
||||
get_hallucination_data,
|
||||
get_greeting_data,
|
||||
get_irrelevant_data,
|
||||
get_success_tool_call_data,
|
||||
],
|
||||
)
|
||||
async def test_function_calling(get_data_func):
|
||||
req, intent, hallucination = get_data_func()
|
||||
handler_name = "Arch-Function"
|
||||
use_agent_orchestrator = False
|
||||
model_handler: ArchFunctionHandler = handler_map[handler_name]
|
||||
|
||||
start_time = time.perf_counter()
|
||||
final_response = await model_handler.chat_completion(req)
|
||||
latency = time.perf_counter() - start_time
|
||||
|
||||
assert intent == (len(final_response.choices[0].message.tool_calls) >= 1)
|
||||
|
||||
assert hallucination == model_handler.hallucination_state.hallucination
|
||||
|
|
@ -1,42 +0,0 @@
|
|||
from unittest.mock import patch, MagicMock
|
||||
from src.core.guardrails import get_guardrail_handler
|
||||
|
||||
|
||||
# Test for `get_guardrail_handler()` function on `cuda`
|
||||
@patch("src.core.guardrails.AutoTokenizer.from_pretrained")
|
||||
@patch("src.core.guardrails.AutoModelForSequenceClassification.from_pretrained")
|
||||
def test_guardrail_handler_on_cuda(mock_auto_model, mock_tokenizer):
|
||||
device = "cuda"
|
||||
|
||||
mock_auto_model.return_value = MagicMock()
|
||||
mock_tokenizer.return_value = MagicMock()
|
||||
|
||||
guardrail = get_guardrail_handler(device=device)
|
||||
|
||||
mock_tokenizer.assert_called_once_with(guardrail.model_name, trust_remote_code=True)
|
||||
|
||||
mock_auto_model.assert_called_once_with(
|
||||
guardrail.model_name,
|
||||
device_map=device,
|
||||
low_cpu_mem_usage=True,
|
||||
)
|
||||
|
||||
|
||||
# Test for `get_guardrail_handler()` function on `mps`
|
||||
@patch("src.core.guardrails.AutoTokenizer.from_pretrained")
|
||||
@patch("src.core.guardrails.AutoModelForSequenceClassification.from_pretrained")
|
||||
def test_guardrail_handler_on_mps(mock_auto_model, mock_tokenizer):
|
||||
device = "mps"
|
||||
|
||||
mock_auto_model.return_value = MagicMock()
|
||||
mock_tokenizer.return_value = MagicMock()
|
||||
|
||||
guardrail = get_guardrail_handler(device=device)
|
||||
|
||||
mock_tokenizer.assert_called_once_with(guardrail.model_name, trust_remote_code=True)
|
||||
|
||||
mock_auto_model.assert_called_once_with(
|
||||
guardrail.model_name,
|
||||
device_map=device,
|
||||
low_cpu_mem_usage=True,
|
||||
)
|
||||
|
|
@ -1,36 +0,0 @@
|
|||
from src.commons.globals import handler_map
|
||||
from src.core.function_calling import ArchFunctionHandler, Message
|
||||
|
||||
|
||||
test_input_history = [
|
||||
{"role": "user", "content": "how is the weather in chicago for next 5 days?"},
|
||||
{
|
||||
"role": "assistant",
|
||||
"model": "Arch-Function",
|
||||
"content": '```json\n{"tool_calls": [{"name": "get_current_weather", "arguments": {"days": 5, "location": "Chicago, Illinois"}}]}\n```',
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"model": "Arch-Function",
|
||||
"content": '{"location":"Chicago%2C%20Illinois","temperature":[{"date":"2025-04-14","temperature":{"min":53,"max":65},"units":"Farenheit","query_time":"2025-04-14 17:01:52.432817+00:00"},{"date":"2025-04-15","temperature":{"min":85,"max":97},"units":"Farenheit","query_time":"2025-04-14 17:01:52.432830+00:00"},{"date":"2025-04-16","temperature":{"min":62,"max":78},"units":"Farenheit","query_time":"2025-04-14 17:01:52.432835+00:00"},{"date":"2025-04-17","temperature":{"min":89,"max":101},"units":"Farenheit","query_time":"2025-04-14 17:01:52.432839+00:00"},{"date":"2025-04-18","temperature":{"min":86,"max":104},"units":"Farenheit","query_time":"2025-04-14 17:01:52.432843+00:00"}],"units":"Farenheit"}',
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"content": '{"response": "Based on the forecast data you provided, here is the weather for the next 5 days in Chicago:\\n\\n- **April 14, 2025**: The temperature will range between 53\\u00b0F and 65\\u00b0F. \\n- **April 15, 2025**: The temperature will range between 85\\u00b0F and 97\\u00b0F.\\n- **April 16, 2025**: The temperature will range between 62\\u00b0F and 78\\u00b0F.\\n- **April 17, 2025**: The temperature will range between 89\\u00b0F and 101\\u00b0F.\\n- **April 18, 2025**: The temperature will range between 86\\u00b0F and 104\\u00b0F.\\n\\nPlease note that the temperatures are given in Fahrenheit."}',
|
||||
},
|
||||
{"role": "user", "content": "what about seattle?"},
|
||||
]
|
||||
|
||||
|
||||
def test_update_fc_history():
|
||||
message_history = []
|
||||
|
||||
for h in test_input_history:
|
||||
message_history.append(Message(**h))
|
||||
|
||||
handler: ArchFunctionHandler = handler_map["Arch-Function"]
|
||||
updated_history = handler._process_messages(message_history)
|
||||
assert len(updated_history) == 5
|
||||
# ensure that tool role does not exist anymore
|
||||
assert all([h["role"] != "tool" for h in updated_history])
|
||||
|
|
@ -1,47 +0,0 @@
|
|||
import pytest
|
||||
import httpx
|
||||
|
||||
from fastapi.testclient import TestClient
|
||||
from src.main import app
|
||||
|
||||
|
||||
client = TestClient(app)
|
||||
|
||||
|
||||
# Unit tests for the health check endpoint
|
||||
@pytest.mark.asyncio
|
||||
async def test_healthz():
|
||||
response = client.get("/healthz")
|
||||
assert response.status_code == 200
|
||||
assert response.json() == {"status": "ok"}
|
||||
|
||||
|
||||
# Unit test for the models endpoint
|
||||
@pytest.mark.asyncio
|
||||
async def test_models():
|
||||
response = client.get("/models")
|
||||
assert response.status_code == 200
|
||||
assert response.json()["object"] == "list"
|
||||
assert len(response.json()["data"]) > 0
|
||||
|
||||
|
||||
# Unit test for the guardrail endpoint
|
||||
@pytest.mark.asyncio
|
||||
async def test_guardrail_endpoint():
|
||||
request_data = {"input": "Test for jailbreak and toxicity", "task": "jailbreak"}
|
||||
response = client.post("/guardrails", json=request_data)
|
||||
assert response.status_code == 200
|
||||
|
||||
|
||||
# Unit test for the function calling endpoint
|
||||
@pytest.mark.asyncio
|
||||
async def test_function_calling_endpoint():
|
||||
async with httpx.AsyncClient(app=app, base_url="http://test") as client:
|
||||
request_data = {
|
||||
"messages": [{"role": "user", "content": "Hello!"}],
|
||||
"model": "Arch-Function",
|
||||
"tools": [],
|
||||
"metadata": {"x-arch-state": "[]"},
|
||||
}
|
||||
response = await client.post("/function_calling", json=request_data)
|
||||
assert response.status_code == 200
|
||||
|
|
@ -12,4 +12,3 @@ services:
|
|||
environment:
|
||||
- OPENAI_API_KEY=${OPENAI_API_KEY:?error}
|
||||
- MISTRAL_API_KEY=${MISTRAL_API_KEY:?error}
|
||||
- MODEL_SERVER_PORT=${MODEL_SERVER_PORT:-51001}
|
||||
|
|
|
|||
|
|
@ -18,4 +18,3 @@ services:
|
|||
- MISTRAL_API_KEY=${MISTRAL_API_KEY:?error}
|
||||
- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:?error}
|
||||
- OTEL_TRACING_HTTP_ENDPOINT=http://host.docker.internal:4318/v1/traces
|
||||
- MODEL_SERVER_PORT=${MODEL_SERVER_PORT:-51000}
|
||||
|
|
|
|||
|
|
@ -11,9 +11,6 @@ touch ~/archgw_logs/modelserver.log
|
|||
|
||||
print_debug() {
|
||||
log "Received signal to stop"
|
||||
log "Printing debug logs for model_server"
|
||||
log "===================================="
|
||||
tail -n 100 ~/archgw_logs/modelserver.log
|
||||
log "Printing debug logs for docker"
|
||||
log "===================================="
|
||||
tail -n 100 ../build.log
|
||||
|
|
@ -30,12 +27,6 @@ cd ../../demos/samples_python/weather_forecast/
|
|||
docker compose up weather_forecast_service --build -d
|
||||
cd -
|
||||
|
||||
log building and install model server
|
||||
log =================================
|
||||
cd ../../model_server
|
||||
poetry install
|
||||
cd -
|
||||
|
||||
log building and installing archgw cli
|
||||
log ==================================
|
||||
cd ../../arch/tools
|
||||
|
|
@ -53,11 +44,8 @@ poetry install
|
|||
|
||||
log startup arch gateway with function calling demo
|
||||
cd ../../
|
||||
tail -F ~/archgw_logs/modelserver.log &
|
||||
model_server_tail_pid=$!
|
||||
archgw down
|
||||
archgw up demos/samples_python/weather_forecast/arch_config.yaml
|
||||
kill $model_server_tail_pid
|
||||
cd -
|
||||
|
||||
log running e2e tests for prompt gateway
|
||||
|
|
|
|||
15
tests/modelserver/.vscode/launch.json
vendored
15
tests/modelserver/.vscode/launch.json
vendored
|
|
@ -1,15 +0,0 @@
|
|||
{
|
||||
// Use IntelliSense to learn about possible attributes.
|
||||
// Hover to view descriptions of existing attributes.
|
||||
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
|
||||
"version": "0.2.0",
|
||||
"configurations": [
|
||||
{
|
||||
"name": "Python Debugger: Current File",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"program": "${file}",
|
||||
"console": "integratedTerminal"
|
||||
}
|
||||
]
|
||||
}
|
||||
7
tests/modelserver/.vscode/settings.json
vendored
7
tests/modelserver/.vscode/settings.json
vendored
|
|
@ -1,7 +0,0 @@
|
|||
{
|
||||
"python.testing.pytestArgs": [
|
||||
"."
|
||||
],
|
||||
"python.testing.unittestEnabled": false,
|
||||
"python.testing.pytestEnabled": true
|
||||
}
|
||||
899
tests/modelserver/poetry.lock
generated
899
tests/modelserver/poetry.lock
generated
|
|
@ -1,899 +0,0 @@
|
|||
# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "attrs"
|
||||
version = "24.3.0"
|
||||
description = "Classes Without Boilerplate"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "attrs-24.3.0-py3-none-any.whl", hash = "sha256:ac96cd038792094f438ad1f6ff80837353805ac950cd2aa0e0625ef19850c308"},
|
||||
{file = "attrs-24.3.0.tar.gz", hash = "sha256:8f5c07333d543103541ba7be0e2ce16eeee8130cb0b3f9238ab904ce1e85baff"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
|
||||
cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
|
||||
dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
|
||||
docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
|
||||
tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
|
||||
tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2024.12.14"
|
||||
description = "Python package for providing Mozilla's CA Bundle."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56"},
|
||||
{file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cffi"
|
||||
version = "1.17.1"
|
||||
description = "Foreign Function Interface for Python calling C code."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "cffi-1.17.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df8b1c11f177bc2313ec4b2d46baec87a5f3e71fc8b45dab2ee7cae86d9aba14"},
|
||||
{file = "cffi-1.17.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8f2cdc858323644ab277e9bb925ad72ae0e67f69e804f4898c070998d50b1a67"},
|
||||
{file = "cffi-1.17.1-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:edae79245293e15384b51f88b00613ba9f7198016a5948b5dddf4917d4d26382"},
|
||||
{file = "cffi-1.17.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:45398b671ac6d70e67da8e4224a065cec6a93541bb7aebe1b198a61b58c7b702"},
|
||||
{file = "cffi-1.17.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ad9413ccdeda48c5afdae7e4fa2192157e991ff761e7ab8fdd8926f40b160cc3"},
|
||||
{file = "cffi-1.17.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5da5719280082ac6bd9aa7becb3938dc9f9cbd57fac7d2871717b1feb0902ab6"},
|
||||
{file = "cffi-1.17.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2bb1a08b8008b281856e5971307cc386a8e9c5b625ac297e853d36da6efe9c17"},
|
||||
{file = "cffi-1.17.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:045d61c734659cc045141be4bae381a41d89b741f795af1dd018bfb532fd0df8"},
|
||||
{file = "cffi-1.17.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:6883e737d7d9e4899a8a695e00ec36bd4e5e4f18fabe0aca0efe0a4b44cdb13e"},
|
||||
{file = "cffi-1.17.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:6b8b4a92e1c65048ff98cfe1f735ef8f1ceb72e3d5f0c25fdb12087a23da22be"},
|
||||
{file = "cffi-1.17.1-cp310-cp310-win32.whl", hash = "sha256:c9c3d058ebabb74db66e431095118094d06abf53284d9c81f27300d0e0d8bc7c"},
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[package.dependencies]
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||||
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||||
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||||
[[package]]
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||||
name = "charset-normalizer"
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||||
version = "3.4.1"
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||||
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optional = false
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|
||||
{file = "tomli-2.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a92ef1a44547e894e2a17d24e7557a5e85a9e1d0048b0b5e7541f76c5032cb13"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9316dc65bed1684c9a98ee68759ceaed29d229e985297003e494aa825ebb0281"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e85e99945e688e32d5a35c1ff38ed0b3f41f43fad8df0bdf79f72b2ba7bc5272"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ac065718db92ca818f8d6141b5f66369833d4a80a9d74435a268c52bdfa73140"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:d920f33822747519673ee656a4b6ac33e382eca9d331c87770faa3eef562aeb2"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a198f10c4d1b1375d7687bc25294306e551bf1abfa4eace6650070a5c1ae2744"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-win32.whl", hash = "sha256:d3f5614314d758649ab2ab3a62d4f2004c825922f9e370b29416484086b264ec"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:a38aa0308e754b0e3c67e344754dff64999ff9b513e691d0e786265c93583c69"},
|
||||
{file = "tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc"},
|
||||
{file = "tomli-2.2.1.tar.gz", hash = "sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "trio"
|
||||
version = "0.28.0"
|
||||
description = "A friendly Python library for async concurrency and I/O"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "trio-0.28.0-py3-none-any.whl", hash = "sha256:56d58977acc1635735a96581ec70513cc781b8b6decd299c487d3be2a721cd94"},
|
||||
{file = "trio-0.28.0.tar.gz", hash = "sha256:4e547896fe9e8a5658e54e4c7c5fa1db748cbbbaa7c965e7d40505b928c73c05"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
attrs = ">=23.2.0"
|
||||
cffi = {version = ">=1.14", markers = "os_name == \"nt\" and implementation_name != \"pypy\""}
|
||||
exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
|
||||
idna = "*"
|
||||
outcome = "*"
|
||||
sniffio = ">=1.3.0"
|
||||
sortedcontainers = "*"
|
||||
|
||||
[[package]]
|
||||
name = "trio-websocket"
|
||||
version = "0.11.1"
|
||||
description = "WebSocket library for Trio"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "trio-websocket-0.11.1.tar.gz", hash = "sha256:18c11793647703c158b1f6e62de638acada927344d534e3c7628eedcb746839f"},
|
||||
{file = "trio_websocket-0.11.1-py3-none-any.whl", hash = "sha256:520d046b0d030cf970b8b2b2e00c4c2245b3807853ecd44214acd33d74581638"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
|
||||
trio = ">=0.11"
|
||||
wsproto = ">=0.14"
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.12.2"
|
||||
description = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
|
||||
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.3.0"
|
||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
|
||||
{file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pysocks = {version = ">=1.5.6,<1.5.7 || >1.5.7,<2.0", optional = true, markers = "extra == \"socks\""}
|
||||
|
||||
[package.extras]
|
||||
brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
|
||||
h2 = ["h2 (>=4,<5)"]
|
||||
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
|
||||
zstd = ["zstandard (>=0.18.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "websocket-client"
|
||||
version = "1.8.0"
|
||||
description = "WebSocket client for Python with low level API options"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"},
|
||||
{file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
|
||||
optional = ["python-socks", "wsaccel"]
|
||||
test = ["websockets"]
|
||||
|
||||
[[package]]
|
||||
name = "werkzeug"
|
||||
version = "3.1.3"
|
||||
description = "The comprehensive WSGI web application library."
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "werkzeug-3.1.3-py3-none-any.whl", hash = "sha256:54b78bf3716d19a65be4fceccc0d1d7b89e608834989dfae50ea87564639213e"},
|
||||
{file = "werkzeug-3.1.3.tar.gz", hash = "sha256:60723ce945c19328679790e3282cc758aa4a6040e4bb330f53d30fa546d44746"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
MarkupSafe = ">=2.1.1"
|
||||
|
||||
[package.extras]
|
||||
watchdog = ["watchdog (>=2.3)"]
|
||||
|
||||
[[package]]
|
||||
name = "wsproto"
|
||||
version = "1.2.0"
|
||||
description = "WebSockets state-machine based protocol implementation"
|
||||
optional = false
|
||||
python-versions = ">=3.7.0"
|
||||
files = [
|
||||
{file = "wsproto-1.2.0-py3-none-any.whl", hash = "sha256:b9acddd652b585d75b20477888c56642fdade28bdfd3579aa24a4d2c037dd736"},
|
||||
{file = "wsproto-1.2.0.tar.gz", hash = "sha256:ad565f26ecb92588a3e43bc3d96164de84cd9902482b130d0ddbaa9664a85065"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
h11 = ">=0.9.0,<1"
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "d0ba3f1875425991d473c9b6fee9f9cb35de5df5835fd7f7e27e3863ff7d37fe"
|
||||
|
|
@ -1,29 +0,0 @@
|
|||
[tool.poetry]
|
||||
name = "modelserver_mock_tests"
|
||||
version = "0.0.1"
|
||||
description = "modelserver tests"
|
||||
authors = ["Katanemo Labs, Inc <info@katanemo.com>"]
|
||||
license = "Apache 2.0"
|
||||
readme = "README.md"
|
||||
package-mode = false
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.10"
|
||||
pytest = "^8.3.3"
|
||||
requests = "^2.29.0"
|
||||
selenium = "^4.11.2"
|
||||
pytest-sugar = "^1.0.0"
|
||||
deepdiff = "^8.0.1"
|
||||
pytest-retry = "^1.6.3"
|
||||
pytest-httpserver = "^1.1.0"
|
||||
pyyaml = "*"
|
||||
|
||||
[tool.poetry.dev-dependencies]
|
||||
pytest-cov = "^4.1.0"
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
python_files = ["test*.py"]
|
||||
addopts = ["-v", "-s"]
|
||||
retries = 2
|
||||
retry_delay = 0.5
|
||||
cumulative_timing = false
|
||||
|
|
@ -1,58 +0,0 @@
|
|||
import os
|
||||
import pytest
|
||||
import requests
|
||||
import yaml
|
||||
|
||||
from deepdiff import DeepDiff
|
||||
|
||||
pytestmark = pytest.mark.skip(
|
||||
reason="Skipping entire test file as this these tests are heavily dependent on model output"
|
||||
)
|
||||
|
||||
MODEL_SERVER_ENDPOINT = os.getenv(
|
||||
"MODEL_SERVER_ENDPOINT", "http://localhost:12000/function_calling"
|
||||
)
|
||||
|
||||
# Load test data from YAML file
|
||||
script_dir = os.path.dirname(__file__)
|
||||
|
||||
# Construct the full path to the YAML file
|
||||
yaml_file_path = os.path.join(script_dir, "test_success_data.yaml")
|
||||
|
||||
# Load test data from YAML file
|
||||
with open(yaml_file_path, "r") as file:
|
||||
test_data_yaml = yaml.safe_load(file)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"test_data",
|
||||
[
|
||||
pytest.param(test_case, id=test_case["id"])
|
||||
for test_case in test_data_yaml["test_cases"]
|
||||
],
|
||||
)
|
||||
def test_model_server(test_data):
|
||||
input = test_data["input"]
|
||||
expected = test_data["expected"]
|
||||
|
||||
response = requests.post(MODEL_SERVER_ENDPOINT, json=input)
|
||||
assert response.status_code == 200
|
||||
# ensure that response is json
|
||||
assert response.headers["content-type"] == "application/json"
|
||||
response_json = response.json()
|
||||
assert response_json
|
||||
choices = response_json.get("choices", [])
|
||||
assert len(choices) == 1
|
||||
choice = choices[0]
|
||||
assert "message" in choice
|
||||
message = choice["message"]
|
||||
assert "tool_calls" in message
|
||||
tool_calls = message["tool_calls"]
|
||||
assert len(tool_calls) == len(expected)
|
||||
|
||||
for tool_call, expected_tool_call in zip(tool_calls, expected):
|
||||
assert "id" in tool_call
|
||||
del tool_call["id"]
|
||||
# ensure that the tool call matches the expected tool call
|
||||
diff = DeepDiff(expected_tool_call, tool_call, ignore_string_case=True)
|
||||
assert not diff
|
||||
|
|
@ -1,561 +0,0 @@
|
|||
test_cases:
|
||||
- id: "[WEATHER AGENT] - single turn, single tool, all parameters"
|
||||
input:
|
||||
messages:
|
||||
- role: "user"
|
||||
content: "what is the weather forecast for Seattle, WA in the next 10 days?"
|
||||
tools:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_current_weather"
|
||||
description: "Get current weather at a location."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
location:
|
||||
type: "str"
|
||||
description: "The location to get the weather for"
|
||||
format: "City, State"
|
||||
days:
|
||||
type: "int"
|
||||
description: "the number of days for the request."
|
||||
required: ["location", "days"]
|
||||
expected:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_current_weather"
|
||||
arguments:
|
||||
location: "Seattle, WA"
|
||||
days: 10
|
||||
|
||||
- id: "[WEATHER AGENT] - single turn, single tool, param gathering"
|
||||
input:
|
||||
messages:
|
||||
- role: "user"
|
||||
content: "what is the weather in Seattle?"
|
||||
- role: "assistant"
|
||||
content: "May I know the location and number of days you want to get the weather for?"
|
||||
model: "Arch-Function"
|
||||
- role: "user"
|
||||
content: "5 days"
|
||||
tools:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_current_weather"
|
||||
description: "Get current weather at a location."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
location:
|
||||
type: "str"
|
||||
description: "The location to get the weather for"
|
||||
format: "City, State"
|
||||
days:
|
||||
type: "int"
|
||||
description: "the number of days for the request."
|
||||
required: ["location", "days"]
|
||||
expected:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_current_weather"
|
||||
arguments:
|
||||
location: "Seattle, WA"
|
||||
days: 5
|
||||
|
||||
- id: "[WEATHER AGENT] - multi turn, single tool, all params passed"
|
||||
input:
|
||||
messages:
|
||||
- role: "user"
|
||||
content: "how is the weather in chicago for next 5 days?"
|
||||
- role: "assistant"
|
||||
tool_calls:
|
||||
- id: "call_3394"
|
||||
type: "function"
|
||||
function:
|
||||
name: "get_current_weather"
|
||||
arguments:
|
||||
location: "Chicago, IL"
|
||||
days: 5
|
||||
- role: "tool"
|
||||
content: "--"
|
||||
tool_call_id: "call_3394"
|
||||
- role: "assistant"
|
||||
content: "--"
|
||||
- role: "user"
|
||||
content: "how is the weather in LA for next 5 days?"
|
||||
tools:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_current_weather"
|
||||
description: "Get current weather at a location."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
location:
|
||||
type: "str"
|
||||
description: "The location to get the weather for"
|
||||
format: "City, State"
|
||||
days:
|
||||
type: "int"
|
||||
description: "the number of days for the request."
|
||||
required: ["location", "days"]
|
||||
expected:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_current_weather"
|
||||
arguments:
|
||||
location: "Los Angeles, CA"
|
||||
days: 5
|
||||
|
||||
# Skip!
|
||||
# - id: "[WEATHER AGENT] - multi turn, single tool, infer param from context"
|
||||
# input:
|
||||
# messages:
|
||||
# - role: "user"
|
||||
# content: "how is the weather in chicago for next 5 days?"
|
||||
# - role: "assistant"
|
||||
# tool_calls:
|
||||
# - id: "call_3394"
|
||||
# type: "function"
|
||||
# function:
|
||||
# name: "get_current_weather"
|
||||
# arguments:
|
||||
# location: "Chicago, IL"
|
||||
# days: 5
|
||||
# - role: "tool"
|
||||
# content: "--"
|
||||
# tool_call_id: "call_3394"
|
||||
# - role: "assistant"
|
||||
# content: "--"
|
||||
# - role: "user"
|
||||
# content: "how is the weather in LA?"
|
||||
# tools:
|
||||
# - type: "function"
|
||||
# function:
|
||||
# name: "get_current_weather"
|
||||
# description: "Get current weather at a location."
|
||||
# parameters:
|
||||
# type: "object"
|
||||
# properties:
|
||||
# location:
|
||||
# type: "str"
|
||||
# description: "The location to get the weather for"
|
||||
# format: "City, State"
|
||||
# days:
|
||||
# type: "int"
|
||||
# description: "the number of days for the request."
|
||||
# required: ["location", "days"]
|
||||
# expected:
|
||||
# - type: "function"
|
||||
# function:
|
||||
# name: "get_current_weather"
|
||||
# arguments:
|
||||
# location: "Los Angeles, CA"
|
||||
# days: 5
|
||||
|
||||
- id: "[WEATHER AGENT] - multi turn, single tool, infer param from context 2nd try"
|
||||
input:
|
||||
messages:
|
||||
- role: "user"
|
||||
content: "how is the weather in seattle for 5 days?"
|
||||
tool_call_id: ""
|
||||
- role: "assistant"
|
||||
content: ""
|
||||
tool_call_id: ""
|
||||
tool_calls:
|
||||
- id: "call_7134"
|
||||
type: "function"
|
||||
function:
|
||||
name: "get_current_weather"
|
||||
arguments:
|
||||
location: "Seattle, WA"
|
||||
days: 5
|
||||
- role: "tool"
|
||||
content: "{\"location\":\"Seattle, WA\",\"temperature\":[{\"date\":\"2024-12-19\",\"temperature\":{\"min\":74,\"max\":90},\"units\":\"Farenheit\",\"query_time\":\"2024-12-19 00:14:35.853372+00:00\"},{\"date\":\"2024-12-20\",\"temperature\":{\"min\":79,\"max\":88},\"units\":\"Farenheit\",\"query_time\":\"2024-12-19 00:14:35.853402+00:00\"}],\"units\":\"Farenheit\"}"
|
||||
tool_call_id: ""
|
||||
- role: "assistant"
|
||||
content: "The weather in Seattle for the next two days is as follows:\n\n- **December 19, 2024**: The temperature will range from a minimum of 74°F to a maximum of 90°F.\n- **December 20, 2024**: The temperature will range from a minimum of 79°F to a maximum of 88°F.\n\nIt seems to be quite warm for Seattle during these dates!"
|
||||
tool_call_id: ""
|
||||
- role: "user"
|
||||
content: "what about weather in chicago?"
|
||||
tool_call_id: ""
|
||||
tools:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_current_weather"
|
||||
description: "Get current weather at a location."
|
||||
parameters:
|
||||
properties:
|
||||
days:
|
||||
type: "int"
|
||||
description: "the number of days for the request"
|
||||
location:
|
||||
type: "str"
|
||||
description: "The location to get the weather for"
|
||||
format: "city, state"
|
||||
required: ["days", "location"]
|
||||
- type: "function"
|
||||
function:
|
||||
name: "default_target"
|
||||
description: "This is the default target for all unmatched prompts."
|
||||
parameters:
|
||||
properties: {}
|
||||
expected:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_current_weather"
|
||||
arguments:
|
||||
location: "Chicago, IL"
|
||||
days: 5
|
||||
- id: "[HR AGENT] - single turn, single tool, all parameters"
|
||||
input:
|
||||
messages:
|
||||
- role: "user"
|
||||
content: "Can you show the workforce data for agency staff in america?"
|
||||
tools:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_hr_data"
|
||||
description: "Get workforce data like headcount and satisfacton levels by region and staffing type."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
staffing_type:
|
||||
type: "str"
|
||||
description: "Staffing type of employees"
|
||||
region:
|
||||
type: "str"
|
||||
description: "Geographical region for which you want workforce data."
|
||||
enum: ["america", "emea", "apac"]
|
||||
point_in_time:
|
||||
type: "str"
|
||||
description: "the point in time for which to retrieve data."
|
||||
default: "1"
|
||||
required: ["staffing_type", "region"]
|
||||
expected:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_hr_data"
|
||||
arguments:
|
||||
region: "america"
|
||||
staffing_type: "agency"
|
||||
- id: "[HR AGENT] - multi turn, single tool, all parameters, enum"
|
||||
input:
|
||||
messages:
|
||||
- role: "user"
|
||||
content: "Can you show the workforce data for agency staff?"
|
||||
- role: "assistant"
|
||||
content: "Of course, I can help with that. However, I need the region and staffing type to provide the workforce data. Could you please provide that information?"
|
||||
- role: "user"
|
||||
content: "ameriza"
|
||||
tools:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_hr_data"
|
||||
description: "Get workforce data like headcount and satisfacton levels by region and staffing type."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
staffing_type:
|
||||
type: "str"
|
||||
description: "Staffing type of employees"
|
||||
region:
|
||||
type: "str"
|
||||
description: "Geographical region for which you want workforce data."
|
||||
enum: ["america", "emea", "apac"]
|
||||
point_in_time:
|
||||
type: "str"
|
||||
description: "the point in time for which to retrieve data."
|
||||
default: "1"
|
||||
required: ["staffing_type", "region"]
|
||||
expected:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_hr_data"
|
||||
arguments:
|
||||
region: "america"
|
||||
staffing_type: "agency"
|
||||
- id: "[HR AGENT] - multi turn, multi tool, all parameters, enum"
|
||||
input:
|
||||
messages:
|
||||
- role: "user"
|
||||
content: "Can you show the workforce data for agency staff?"
|
||||
- role: "assistant"
|
||||
content: "Of course, I can help with that. However, I need the region and staffing type to provide the workforce data. Could you please provide that information?"
|
||||
- role: "user"
|
||||
content: "america. Also, please get the satisfaction levels for the full_time staff in emea"
|
||||
tools:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_hr_data"
|
||||
description: "Get workforce data like headcount and satisfacton levels by region and staffing type."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
staffing_type:
|
||||
type: "str"
|
||||
description: "Staffing type of employees"
|
||||
region:
|
||||
type: "str"
|
||||
description: "Geographical region for which you want workforce data."
|
||||
enum: ["america", "emea", "apac"]
|
||||
point_in_time:
|
||||
type: "str"
|
||||
description: "the point in time for which to retrieve data."
|
||||
default: "1"
|
||||
required: ["staffing_type", "region"]
|
||||
expected:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_hr_data"
|
||||
arguments:
|
||||
region: "america"
|
||||
staffing_type: "agency"
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_hr_data"
|
||||
arguments:
|
||||
region: "emea"
|
||||
staffing_type: "full_time"
|
||||
- id: "[INSURANCE AGENT] - single turn, multi tool, all parameters"
|
||||
input:
|
||||
messages:
|
||||
- role: "user"
|
||||
content: " i want to start an insurance policy with 500 deductible for car and update deductible my boat insurance policy with id boawd123 to 1000"
|
||||
tools:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "policy_qa"
|
||||
description: "Handle general Q/A related to insurance."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties: {}
|
||||
required: []
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_policy_coverage"
|
||||
description: "Retrieve the coverage details for an insurance policy."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
policy_type:
|
||||
type: "str"
|
||||
description: "The type of insurance policy."
|
||||
required: ["policy_type"]
|
||||
- type: "function"
|
||||
function:
|
||||
name: "initiate_policy"
|
||||
description: "Start a policy coverage for an insurance policy."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
policy_type:
|
||||
type: "str"
|
||||
description: "The type of insurance policy."
|
||||
deductible:
|
||||
type: "float"
|
||||
description: "The deductible amount set for the policy."
|
||||
required: ["policy_type", "deductible"]
|
||||
- type: "function"
|
||||
function:
|
||||
name: "update_claim"
|
||||
description: "Update the notes on the claim."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
claim_id:
|
||||
type: "str"
|
||||
description: "The claim number."
|
||||
notes:
|
||||
type: "str"
|
||||
description: "Notes about the claim number for your adjustor to see."
|
||||
required: ["claim_id"]
|
||||
- type: "function"
|
||||
function:
|
||||
name: "update_deductible"
|
||||
description: "Update the deductible amount for a specific insurance policy coverage."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
policy_id:
|
||||
type: "str"
|
||||
description: "The ID of the insurance policy."
|
||||
deductible:
|
||||
type: "float"
|
||||
description: "The deductible amount set for the policy."
|
||||
required: ["policy_id", "deductible"]
|
||||
expected:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "initiate_policy"
|
||||
arguments:
|
||||
policy_type: "car"
|
||||
deductible: 500
|
||||
- type: "function"
|
||||
function:
|
||||
name: "update_deductible"
|
||||
arguments:
|
||||
policy_id: "boawd123"
|
||||
deductible: 1000
|
||||
- id: "[INSURANCE AGENT] - multi turn, multi tool, all parameters"
|
||||
input:
|
||||
messages:
|
||||
- role: "user"
|
||||
content: "hi what can you do?"
|
||||
- role: "assistant"
|
||||
content: "Certainly! I'm here to assist you with various questions and tasks. Whether it's answering specific questions, providing information, or helping with something else, feel free to let me know how I can assist you."
|
||||
- role: "user"
|
||||
content: "i want to start a new insurance policy"
|
||||
- role: "assistant"
|
||||
content: "Certainly! To start a new insurance policy, I'll need the type of insurance policy you're interested in and the deductible amount you'd like to set for that policy. Could you please provide this information?"
|
||||
- role: "user"
|
||||
content: "car insurance, 500. Also, please get me the coverage details for a house insurance"
|
||||
tools:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "policy_qa"
|
||||
description: "Handle general Q/A related to insurance."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties: {}
|
||||
required: []
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_policy_coverage"
|
||||
description: "Retrieve the coverage details for an insurance policy."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
policy_type:
|
||||
type: "str"
|
||||
description: "The type of insurance policy."
|
||||
required: ["policy_type"]
|
||||
- type: "function"
|
||||
function:
|
||||
name: "initiate_policy"
|
||||
description: "Start a policy coverage for an insurance policy."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
policy_type:
|
||||
type: "str"
|
||||
description: "The type of insurance policy."
|
||||
deductible:
|
||||
type: "float"
|
||||
description: "The deductible amount set for the policy."
|
||||
required: ["policy_type", "deductible"]
|
||||
- type: "function"
|
||||
function:
|
||||
name: "update_claim"
|
||||
description: "Update the notes on the claim."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
claim_id:
|
||||
type: "str"
|
||||
description: "The claim number."
|
||||
notes:
|
||||
type: "str"
|
||||
description: "Notes about the claim number for your adjustor to see."
|
||||
required: ["claim_id"]
|
||||
- type: "function"
|
||||
function:
|
||||
name: "update_deductible"
|
||||
description: "Update the deductible amount for a specific insurance policy coverage."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
policy_id:
|
||||
type: "str"
|
||||
description: "The ID of the insurance policy."
|
||||
deductible:
|
||||
type: "float"
|
||||
description: "The deductible amount set for the policy."
|
||||
required: ["policy_id", "deductible"]
|
||||
expected:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "initiate_policy"
|
||||
arguments:
|
||||
policy_type: "car insurance"
|
||||
deductible: 500
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_policy_coverage"
|
||||
arguments:
|
||||
policy_type: "house insurance"
|
||||
|
||||
- id: "[INSURANCE AGENT] - single turn, single tool, all parameters"
|
||||
input:
|
||||
messages:
|
||||
- role: "user"
|
||||
content: "i want to start a insurance policy for car with 500 deductible"
|
||||
tools:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "policy_qa"
|
||||
description: "Handle general Q/A related to insurance."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties: {}
|
||||
required: []
|
||||
- type: "function"
|
||||
function:
|
||||
name: "get_policy_coverage"
|
||||
description: "Retrieve the coverage details for an insurance policy."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
policy_type:
|
||||
type: "str"
|
||||
description: "The type of insurance policy."
|
||||
required: ["policy_type"]
|
||||
- type: "function"
|
||||
function:
|
||||
name: "initiate_policy"
|
||||
description: "Start a policy coverage for an insurance policy."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
policy_type:
|
||||
type: "str"
|
||||
description: "The type of insurance policy."
|
||||
deductible:
|
||||
type: "float"
|
||||
description: "The deductible amount set for the policy."
|
||||
required: ["policy_type", "deductible"]
|
||||
- type: "function"
|
||||
function:
|
||||
name: "update_claim"
|
||||
description: "Update the notes on the claim."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
claim_id:
|
||||
type: "str"
|
||||
description: "The claim number."
|
||||
notes:
|
||||
type: "str"
|
||||
description: "Notes about the claim number for your adjustor to see."
|
||||
required: ["claim_id"]
|
||||
- type: "function"
|
||||
function:
|
||||
name: "update_deductible"
|
||||
description: "Update the deductible amount for a specific insurance policy coverage."
|
||||
parameters:
|
||||
type: "object"
|
||||
properties:
|
||||
policy_id:
|
||||
type: "str"
|
||||
description: "The ID of the insurance policy."
|
||||
deductible:
|
||||
type: "float"
|
||||
description: "The deductible amount set for the policy."
|
||||
required: ["policy_id", "deductible"]
|
||||
expected:
|
||||
- type: "function"
|
||||
function:
|
||||
name: "initiate_policy"
|
||||
arguments:
|
||||
policy_type: "car"
|
||||
deductible: 500
|
||||
Loading…
Add table
Add a link
Reference in a new issue