diff --git a/.github/workflows/e2e_model_server.yml b/.github/workflows/e2e_model_server.yml deleted file mode 100644 index 2c4a3c73..00000000 --- a/.github/workflows/e2e_model_server.yml +++ /dev/null @@ -1,46 +0,0 @@ -name: e2e model server tests - -on: - push: - branches: - - main - pull_request: - -jobs: - e2e_model_server_tests: - runs-on: ubuntu-latest-m - strategy: - fail-fast: false - matrix: - python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"] - defaults: - run: - working-directory: ./tests/modelserver - - steps: - - name: Checkout code - uses: actions/checkout@v3 - - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - cache: "pip" # auto-caches based on requirements files - - - name: install poetry - run: | - export POETRY_VERSION=2.2.1 - curl -sSL https://install.python-poetry.org | python3 - - export PATH="$HOME/.local/bin:$PATH" - - - name: install model server and start it - run: | - cd ../../model_server/ && poetry install && poetry run archgw_modelserver start - - - name: install test dependencies - run: | - poetry install - - - name: run tests - run: | - poetry run pytest diff --git a/.github/workflows/e2e_test_currency_convert.yml b/.github/workflows/e2e_test_currency_convert.yml index 8b15cb7e..93377f3c 100644 --- a/.github/workflows/e2e_test_currency_convert.yml +++ b/.github/workflows/e2e_test_currency_convert.yml @@ -40,11 +40,10 @@ jobs: curl --location --remote-name https://github.com/Orange-OpenSource/hurl/releases/download/4.0.0/hurl_4.0.0_amd64.deb sudo dpkg -i hurl_4.0.0_amd64.deb - - name: install model server, arch gateway and test dependencies + - name: install arch gateway and test dependencies run: | source venv/bin/activate - cd model_server/ && echo "installing model server" && poetry install - cd ../arch/tools && echo "installing archgw cli" && poetry install + cd arch/tools && echo "installing archgw cli" && poetry install cd ../../demos/shared/test_runner && echo "installing test dependencies" && poetry install - name: run demo tests diff --git a/.github/workflows/e2e_test_preference_based_routing.yml b/.github/workflows/e2e_test_preference_based_routing.yml index 9e0bcb13..6c3652f7 100644 --- a/.github/workflows/e2e_test_preference_based_routing.yml +++ b/.github/workflows/e2e_test_preference_based_routing.yml @@ -40,11 +40,10 @@ jobs: curl --location --remote-name https://github.com/Orange-OpenSource/hurl/releases/download/4.0.0/hurl_4.0.0_amd64.deb sudo dpkg -i hurl_4.0.0_amd64.deb - - name: install model server, arch gateway and test dependencies + - name: install arch gateway and test dependencies run: | source venv/bin/activate - cd model_server/ && echo "installing model server" && poetry install - cd ../arch/tools && echo "installing archgw cli" && poetry install + cd arch/tools && echo "installing archgw cli" && poetry install cd ../../demos/shared/test_runner && echo "installing test dependencies" && poetry install - name: run demo tests diff --git a/.github/workflows/model-server-tests.yml b/.github/workflows/model-server-tests.yml deleted file mode 100644 index 976bea4c..00000000 --- a/.github/workflows/model-server-tests.yml +++ /dev/null @@ -1,45 +0,0 @@ -name: model server tests - -on: - push: - branches: - - main # Run tests on pushes to the main branch - pull_request: - branches: - - main # Run tests on pull requests to the main branch - -jobs: - test: - runs-on: ubuntu-latest - - steps: - # Step 1: Check out the code from your repository - - name: Checkout code - uses: actions/checkout@v3 - - # Step 2: Set up Python (specify the version) - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: "3.12" - - # Step 3: Install Poetry - - name: Install Poetry - run: | - export POETRY_VERSION=2.2.1 - curl -sSL https://install.python-poetry.org | python3 - - export PATH="$HOME/.local/bin:$PATH" - - # Step 4: Install dependencies using Poetry - - name: Install dependencies - run: | - cd model_server - poetry install - - # Step 5: Set PYTHONPATH and run tests - - name: Run model server tests with pytest - env: - PYTHONPATH: model_server # Ensure the app's path is available - run: | - cd model_server - poetry run pytest diff --git a/.gitignore b/.gitignore index f155cd1a..6254ad18 100644 --- a/.gitignore +++ b/.gitignore @@ -113,11 +113,6 @@ venv.bak/ arch/tools/config arch/tools/build -# Archgw - model_server -model_server/venv_model_server -model_server/build -model_server/dist - # Archgw - Docs docs/build/ diff --git a/arch/docker-compose.dev.yaml b/arch/docker-compose.dev.yaml index 0b52d057..118b5ac3 100644 --- a/arch/docker-compose.dev.yaml +++ b/arch/docker-compose.dev.yaml @@ -22,4 +22,3 @@ services: - OPENAI_API_KEY=${OPENAI_API_KEY:?error} - MISTRAL_API_KEY=${MISTRAL_API_KEY:?error} - OTEL_TRACING_HTTP_ENDPOINT=http://host.docker.internal:4318/v1/traces - - MODEL_SERVER_PORT=${MODEL_SERVER_PORT:-51000} diff --git a/arch/tools/README.md b/arch/tools/README.md index 5efd5e40..29624295 100644 --- a/arch/tools/README.md +++ b/arch/tools/README.md @@ -56,15 +56,8 @@ poetry install archgw build ``` -### Step 5: download models -This will help download models so model_server can load faster. This should be done once. - -```bash -archgw download-models -``` - ### Logs -`archgw` command can also view logs from gateway and model_server. Use following command to view logs, +`archgw` command can also view logs from the gateway. Use following command to view logs, ```bash archgw logs --follow diff --git a/arch/tools/cli/consts.py b/arch/tools/cli/consts.py index ffff31aa..6b5c7447 100644 --- a/arch/tools/cli/consts.py +++ b/arch/tools/cli/consts.py @@ -1,13 +1,5 @@ import os - -KATANEMO_DOCKERHUB_REPO = "katanemo/archgw" -KATANEMO_LOCAL_MODEL_LIST = [ - "katanemo/Arch-Guard", -] SERVICE_NAME_ARCHGW = "archgw" -SERVICE_NAME_MODEL_SERVER = "model_server" -SERVICE_ALL = "all" -MODEL_SERVER_LOG_FILE = "~/archgw_logs/modelserver.log" ARCHGW_DOCKER_NAME = "archgw" ARCHGW_DOCKER_IMAGE = os.getenv("ARCHGW_DOCKER_IMAGE", "katanemo/archgw:0.3.18") diff --git a/arch/tools/cli/core.py b/arch/tools/cli/core.py index 94b5adf7..ec1d354a 100644 --- a/arch/tools/cli/core.py +++ b/arch/tools/cli/core.py @@ -9,9 +9,7 @@ from cli.utils import convert_legacy_listeners, getLogger from cli.consts import ( ARCHGW_DOCKER_IMAGE, ARCHGW_DOCKER_NAME, - KATANEMO_LOCAL_MODEL_LIST, ) -from huggingface_hub import snapshot_download import subprocess from cli.docker_cli import ( docker_container_status, @@ -144,49 +142,6 @@ def stop_docker_container(service=ARCHGW_DOCKER_NAME): log.info(f"Failed to shut down services: {str(e)}") -def download_models_from_hf(): - for model in KATANEMO_LOCAL_MODEL_LIST: - log.info(f"Downloading model: {model}") - snapshot_download(repo_id=model) - - -def start_arch_modelserver(foreground): - """ - Start the model server. This assumes that the archgw_modelserver package is installed locally - - """ - try: - log.info("archgw_modelserver restart") - if foreground: - subprocess.run( - ["archgw_modelserver", "start", "--foreground"], - check=True, - ) - else: - subprocess.run( - ["archgw_modelserver", "start"], - check=True, - ) - except subprocess.CalledProcessError as e: - log.info(f"Failed to start model_server. Please check archgw_modelserver logs") - sys.exit(1) - - -def stop_arch_modelserver(): - """ - Stop the model server. This assumes that the archgw_modelserver package is installed locally - - """ - try: - subprocess.run( - ["archgw_modelserver", "stop"], - check=True, - ) - except subprocess.CalledProcessError as e: - log.info(f"Failed to start model_server. Please check archgw_modelserver logs") - sys.exit(1) - - def start_cli_agent(arch_config_file=None, settings_json="{}"): """Start a CLI client connected to Arch.""" diff --git a/arch/tools/cli/main.py b/arch/tools/cli/main.py index de3ed20f..35bb34eb 100644 --- a/arch/tools/cli/main.py +++ b/arch/tools/cli/main.py @@ -20,20 +20,14 @@ from cli.utils import ( find_config_file, ) from cli.core import ( - start_arch_modelserver, - stop_arch_modelserver, start_arch, stop_docker_container, - download_models_from_hf, start_cli_agent, ) from cli.consts import ( ARCHGW_DOCKER_IMAGE, ARCHGW_DOCKER_NAME, - KATANEMO_DOCKERHUB_REPO, SERVICE_NAME_ARCHGW, - SERVICE_NAME_MODEL_SERVER, - SERVICE_ALL, ) log = getLogger(__name__) @@ -47,9 +41,8 @@ logo = r""" """ -# Command to build archgw and model_server Docker images +# Command to build archgw Docker images ARCHGW_DOCKERFILE = "./arch/Dockerfile" -MODEL_SERVER_BUILD_FILE = "./model_server/pyproject.toml" def get_version(): @@ -60,18 +53,6 @@ def get_version(): return "version not found" -def verify_service_name(service): - """Verify if the service name is valid.""" - if service not in [ - SERVICE_NAME_ARCHGW, - SERVICE_NAME_MODEL_SERVER, - SERVICE_ALL, - ]: - print(f"Error: Invalid service {service}. Exiting") - sys.exit(1) - return True - - @click.group(invoke_without_command=True) @click.option("--version", is_flag=True, help="Show the archgw cli version and exit.") @click.pass_context @@ -89,17 +70,11 @@ def main(ctx, version): @click.command() -@click.option( - "--service", - default=SERVICE_ALL, - help="Optional parameter to specify which service to build. Options are model_server, archgw", -) -def build(service): +def build(): """Build Arch from source. Must be in root of cloned repo.""" - verify_service_name(service) # Check if /arch/Dockerfile exists - if service == SERVICE_NAME_ARCHGW or service == SERVICE_ALL: + if os.path.exists(ARCHGW_DOCKERFILE): if os.path.exists(ARCHGW_DOCKERFILE): click.echo("Building archgw image...") try: @@ -110,8 +85,6 @@ def build(service): "-f", ARCHGW_DOCKERFILE, "-t", - f"{KATANEMO_DOCKERHUB_REPO}:latest", - "-t", f"{ARCHGW_DOCKER_IMAGE}", ".", "--add-host=host.docker.internal:host-gateway", @@ -128,57 +101,20 @@ def build(service): click.echo("archgw image built successfully.") - """Install the model server dependencies using Poetry.""" - if service == SERVICE_NAME_MODEL_SERVER or service == SERVICE_ALL: - # Check if pyproject.toml exists - if os.path.exists(MODEL_SERVER_BUILD_FILE): - click.echo("Installing model server dependencies with Poetry...") - try: - subprocess.run( - ["poetry", "install", "--no-cache"], - cwd=os.path.dirname(MODEL_SERVER_BUILD_FILE), - check=True, - ) - click.echo("Model server dependencies installed successfully.") - except subprocess.CalledProcessError as e: - click.echo(f"Error installing model server dependencies: {e}") - sys.exit(1) - else: - click.echo(f"Error: pyproject.toml not found in {MODEL_SERVER_BUILD_FILE}") - sys.exit(1) - @click.command() @click.argument("file", required=False) # Optional file argument @click.option( "--path", default=".", help="Path to the directory containing arch_config.yaml" ) -@click.option( - "--service", - default=SERVICE_ALL, - help="Service to start. Options are model_server, archgw.", -) @click.option( "--foreground", default=False, help="Run Arch in the foreground. Default is False", is_flag=True, ) -def up(file, path, service, foreground): +def up(file, path, foreground): """Starts Arch.""" - verify_service_name(service) - - if service == SERVICE_ALL and foreground: - # foreground can only be specified when starting individual services - log.info("foreground flag is only supported for individual services. Exiting.") - sys.exit(1) - - if service == SERVICE_NAME_MODEL_SERVER: - log.info("Download models from HuggingFace...") - download_models_from_hf() - start_arch_modelserver(foreground) - return - # Use the utility function to find config file arch_config_file = find_config_file(path, file) @@ -202,7 +138,6 @@ def up(file, path, service, foreground): # Set the ARCH_CONFIG_FILE environment variable env_stage = { "OTEL_TRACING_HTTP_ENDPOINT": "http://host.docker.internal:4318/v1/traces", - "MODEL_SERVER_PORT": os.getenv("MODEL_SERVER_PORT", "51000"), } env = os.environ.copy() # Remove PATH variable if present @@ -242,40 +177,13 @@ def up(file, path, service, foreground): env_stage[access_key] = env_file_dict[access_key] env.update(env_stage) - - if service == SERVICE_NAME_ARCHGW: - start_arch(arch_config_file, env, foreground=foreground) - else: - # Check if ingress_traffic listener is configured before starting model_server - if has_ingress_listener(arch_config_file): - download_models_from_hf() - start_arch_modelserver(foreground) - else: - log.info( - "Skipping model_server startup: no ingress_traffic listener configured in arch_config.yaml" - ) - - start_arch(arch_config_file, env, foreground=foreground) + start_arch(arch_config_file, env, foreground=foreground) @click.command() -@click.option( - "--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) - - if service == SERVICE_NAME_MODEL_SERVER: - stop_arch_modelserver() - elif service == SERVICE_NAME_ARCHGW: - 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) diff --git a/arch/tools/poetry.lock b/arch/tools/poetry.lock index d04e6209..bf08343b 100644 --- a/arch/tools/poetry.lock +++ b/arch/tools/poetry.lock @@ -1,118 +1,4 @@ -# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. - -[[package]] -name = "accelerate" -version = "1.11.0" -description = "Accelerate" -optional = false -python-versions = ">=3.10.0" -groups = ["main"] -files = [ - 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[[package]] name = "attrs" @@ -128,14 +14,14 @@ files = [ [[package]] name = "certifi" -version = "2025.10.5" +version = "2025.11.12" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "certifi-2025.10.5-py3-none-any.whl", hash = "sha256:0f212c2744a9bb6de0c56639a6f68afe01ecd92d91f14ae897c4fe7bbeeef0de"}, - {file = "certifi-2025.10.5.tar.gz", hash = "sha256:47c09d31ccf2acf0be3f701ea53595ee7e0b8fa08801c6624be771df09ae7b43"}, + {file = "certifi-2025.11.12-py3-none-any.whl", hash = "sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b"}, + {file = "certifi-2025.11.12.tar.gz", hash = "sha256:d8ab5478f2ecd78af242878415affce761ca6bc54a22a27e026d7c25357c3316"}, ] [[package]] @@ -289,52 +175,17 @@ files = [ ] markers = {main = "platform_system == \"Windows\"", dev = "sys_platform == \"win32\""} -[[package]] -name = "dateparser" -version = "1.2.2" -description = "Date parsing 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a/arch/tools/pyproject.toml +++ b/arch/tools/pyproject.toml @@ -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"] diff --git a/archgw.code-workspace b/archgw.code-workspace index 840bd2dd..f94e67b0 100644 --- a/archgw.code-workspace +++ b/archgw.code-workspace @@ -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" diff --git a/demos/use_cases/model_choice_with_test_harness/poetry.lock b/demos/use_cases/model_choice_with_test_harness/poetry.lock index 8f7a71b7..779c5b11 100644 --- a/demos/use_cases/model_choice_with_test_harness/poetry.lock +++ b/demos/use_cases/model_choice_with_test_harness/poetry.lock @@ -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 = "*" diff --git a/docs/source/resources/deployment.rst b/docs/source/resources/deployment.rst index 29006672..6f60df9a 100644 --- a/docs/source/resources/deployment.rst +++ b/docs/source/resources/deployment.rst @@ -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 ~~~~~~~~~~~~~~~~~~ diff --git a/model_server/.vscode/launch.json b/model_server/.vscode/launch.json deleted file mode 100644 index 19ed7342..00000000 --- a/model_server/.vscode/launch.json +++ /dev/null @@ -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"] - } - ] -} diff --git a/model_server/.vscode/settings.json b/model_server/.vscode/settings.json deleted file mode 100644 index 98ba633e..00000000 --- a/model_server/.vscode/settings.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "python.testing.pytestArgs": [ - "." - ], - "python.testing.unittestEnabled": false, - "python.testing.pytestEnabled": true -} diff --git a/model_server/Dockerfile b/model_server/Dockerfile deleted file mode 100644 index 1c9d5ff4..00000000 --- a/model_server/Dockerfile +++ /dev/null @@ -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"] diff --git a/model_server/Dockerfile.gpu b/model_server/Dockerfile.gpu deleted file mode 100644 index b1b29c06..00000000 --- a/model_server/Dockerfile.gpu +++ /dev/null @@ -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"] diff --git a/model_server/README.md b/model_server/README.md deleted file mode 100644 index 93248579..00000000 --- a/model_server/README.md +++ /dev/null @@ -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. diff --git a/model_server/__init__.py b/model_server/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/model_server/poetry.lock b/model_server/poetry.lock deleted file mode 100644 index 9710e47b..00000000 --- a/model_server/poetry.lock +++ /dev/null @@ -1,3102 +0,0 @@ -# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. - -[[package]] -name = "accelerate" -version = "1.11.0" -description = "Accelerate" -optional = false -python-versions = ">=3.10.0" -groups = ["main"] -files = [ - {file = "accelerate-1.11.0-py3-none-any.whl", hash = "sha256:a628fa6beb069b8e549460fc449135d5bd8d73e7a11fd09f0bc9fc4ace7f06f1"}, - {file = "accelerate-1.11.0.tar.gz", hash = "sha256:bb1caf2597b4cd632b917b5000c591d10730bb024a79746f1ee205bba80bd229"}, -] - -[package.dependencies] -huggingface_hub = ">=0.21.0" -numpy = ">=1.17" -packaging = ">=20.0" -psutil = "*" -pyyaml = "*" -safetensors = ">=0.4.3" -torch = ">=2.0.0" - 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"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 diff --git a/model_server/src/__init__.py b/model_server/src/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/model_server/src/cli.py b/model_server/src/cli.py deleted file mode 100644 index d0d9e9e1..00000000 --- a/model_server/src/cli.py +++ /dev/null @@ -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) diff --git a/model_server/src/commons/__init__.py b/model_server/src/commons/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/model_server/src/commons/globals.py b/model_server/src/commons/globals.py deleted file mode 100644 index 97da3e20..00000000 --- a/model_server/src/commons/globals.py +++ /dev/null @@ -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), -} diff --git a/model_server/src/commons/utils.py b/model_server/src/commons/utils.py deleted file mode 100644 index 76e46d13..00000000 --- a/model_server/src/commons/utils.py +++ /dev/null @@ -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 diff --git a/model_server/src/core/__init__.py b/model_server/src/core/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/model_server/src/core/function_calling.py b/model_server/src/core/function_calling.py deleted file mode 100644 index bec5dfdf..00000000 --- a/model_server/src/core/function_calling.py +++ /dev/null @@ -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 XML tags:\n\n{tools}\n" - "\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) diff --git a/model_server/src/core/guardrails.py b/model_server/src/core/guardrails.py deleted file mode 100644 index fae4e5ba..00000000 --- a/model_server/src/core/guardrails.py +++ /dev/null @@ -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) diff --git a/model_server/src/core/utils/__init__.py b/model_server/src/core/utils/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/model_server/src/core/utils/hallucination_utils.py b/model_server/src/core/utils/hallucination_utils.py deleted file mode 100644 index 992f8aa4..00000000 --- a/model_server/src/core/utils/hallucination_utils.py +++ /dev/null @@ -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:]) diff --git a/model_server/src/core/utils/model_utils.py b/model_server/src/core/utils/model_utils.py deleted file mode 100644 index 9dfac528..00000000 --- a/model_server/src/core/utils/model_utils.py +++ /dev/null @@ -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"\n{json.dumps(tool_calls[0]['function'])}\n" - elif role == "tool": - role = "user" - if metadata.get("optimize_context_window", "false").lower() == "true": - content = f"\n\n" - else: - # sample response below - # "content": "\n{'name': 'get_stock_price', 'result': '$196.66'}\n" - # 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"\n{json.dumps(tool_response)}\n" - - 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() diff --git a/model_server/src/main.py b/model_server/src/main.py deleted file mode 100644 index 34856498..00000000 --- a/model_server/src/main.py +++ /dev/null @@ -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 diff --git a/model_server/tests/__init__.py b/model_server/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/model_server/tests/core/__init__.py b/model_server/tests/core/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/model_server/tests/core/test_function_calling.py b/model_server/tests/core/test_function_calling.py deleted file mode 100644 index 6005d5e6..00000000 --- a/model_server/tests/core/test_function_calling.py +++ /dev/null @@ -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 diff --git a/model_server/tests/core/test_guardrails.py b/model_server/tests/core/test_guardrails.py deleted file mode 100644 index 74d2dc97..00000000 --- a/model_server/tests/core/test_guardrails.py +++ /dev/null @@ -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, - ) diff --git a/model_server/tests/core/test_state.py b/model_server/tests/core/test_state.py deleted file mode 100644 index 331b8d3f..00000000 --- a/model_server/tests/core/test_state.py +++ /dev/null @@ -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]) diff --git a/model_server/tests/test_app.py b/model_server/tests/test_app.py deleted file mode 100644 index 0029e4b5..00000000 --- a/model_server/tests/test_app.py +++ /dev/null @@ -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 diff --git a/tests/archgw/docker-compose.yaml b/tests/archgw/docker-compose.yaml index 3cafd86e..cd317b38 100644 --- a/tests/archgw/docker-compose.yaml +++ b/tests/archgw/docker-compose.yaml @@ -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} diff --git a/tests/e2e/docker-compose.yaml b/tests/e2e/docker-compose.yaml index a78c5632..18bd9c68 100644 --- a/tests/e2e/docker-compose.yaml +++ b/tests/e2e/docker-compose.yaml @@ -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=${MODEL_SERVER_PORT:-51000} diff --git a/tests/e2e/run_e2e_tests.sh b/tests/e2e/run_e2e_tests.sh index 54a53b1f..856b16ab 100644 --- a/tests/e2e/run_e2e_tests.sh +++ b/tests/e2e/run_e2e_tests.sh @@ -11,9 +11,6 @@ touch ~/archgw_logs/modelserver.log print_debug() { log "Received signal to stop" - log "Printing debug logs for model_server" - log "====================================" - tail -n 100 ~/archgw_logs/modelserver.log log "Printing debug logs for docker" log "====================================" tail -n 100 ../build.log @@ -30,12 +27,6 @@ cd ../../demos/samples_python/weather_forecast/ docker compose up weather_forecast_service --build -d cd - -log building and install model server -log ================================= -cd ../../model_server -poetry install -cd - - log building and installing archgw cli log ================================== cd ../../arch/tools @@ -53,11 +44,8 @@ poetry install log startup arch gateway with function calling demo cd ../../ -tail -F ~/archgw_logs/modelserver.log & -model_server_tail_pid=$! archgw down archgw up demos/samples_python/weather_forecast/arch_config.yaml -kill $model_server_tail_pid cd - log running e2e tests for prompt gateway diff --git a/tests/modelserver/.vscode/launch.json b/tests/modelserver/.vscode/launch.json deleted file mode 100644 index 6a211d8e..00000000 --- a/tests/modelserver/.vscode/launch.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - // Use IntelliSense to learn about possible attributes. - // Hover to view descriptions of existing attributes. - // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 - "version": "0.2.0", - "configurations": [ - { - "name": "Python Debugger: Current File", - "type": "debugpy", - "request": "launch", - "program": "${file}", - "console": "integratedTerminal" - } - ] -} diff --git a/tests/modelserver/.vscode/settings.json b/tests/modelserver/.vscode/settings.json deleted file mode 100644 index 98ba633e..00000000 --- a/tests/modelserver/.vscode/settings.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "python.testing.pytestArgs": [ - "." - ], - "python.testing.unittestEnabled": false, - "python.testing.pytestEnabled": true -} diff --git a/tests/modelserver/poetry.lock b/tests/modelserver/poetry.lock deleted file mode 100644 index d4a0640f..00000000 --- a/tests/modelserver/poetry.lock +++ /dev/null @@ -1,899 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. - 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-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 diff --git a/tests/modelserver/test_success_data.yaml b/tests/modelserver/test_success_data.yaml deleted file mode 100644 index 36e3dcfe..00000000 --- a/tests/modelserver/test_success_data.yaml +++ /dev/null @@ -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