From cf30e9441567e1bf315e9365e9f7ecb48acfd74c Mon Sep 17 00:00:00 2001
From: Shuguang Chen <54548843+nehcgs@users.noreply.github.com>
Date: Mon, 24 Mar 2025 16:53:10 -0700
Subject: [PATCH] Init update

---
 model_server/poetry.lock                   | 551 ++++++++++-----------
 model_server/src/commons/globals.py        |   5 -
 model_server/src/core/function_calling.py  | 481 +++++++-----------
 model_server/src/core/utils/model_utils.py |  10 +-
 model_server/src/main.py                   |  93 ++--
 5 files changed, 508 insertions(+), 632 deletions(-)

diff --git a/model_server/poetry.lock b/model_server/poetry.lock
index 60a07b5d..cb7c3407 100644
--- a/model_server/poetry.lock
+++ b/model_server/poetry.lock
@@ -2,13 +2,13 @@
 
 [[package]]
 name = "accelerate"
-version = "1.4.0"
+version = "1.5.2"
 description = "Accelerate"
 optional = false
 python-versions = ">=3.9.0"
 files = [
-    {file = "accelerate-1.4.0-py3-none-any.whl", hash = "sha256:f6e1e7dfaf9d799a20a1dc45efbf4b1546163eac133faa5acd0d89177c896e55"},
-    {file = "accelerate-1.4.0.tar.gz", hash = "sha256:37d413e1b64cb8681ccd2908ae211cf73e13e6e636a2f598a96eccaa538773a5"},
+    {file = "accelerate-1.5.2-py3-none-any.whl", hash = "sha256:68a3b272f6a6ffebb457bdc138581a2bf52efad6a5e0214dc46675f3edd98792"},
+    {file = "accelerate-1.5.2.tar.gz", hash = "sha256:a1cf39473edc0e42772a9d9a18c9eb1ce8ffd9e1719dc0ab80670f5c1fd4dc43"},
 ]
 
 [package.dependencies]
@@ -22,14 +22,14 @@ torch = ">=2.0.0"
 
 [package.extras]
 deepspeed = ["deepspeed"]
-dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.6.4,<0.7.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.6.4,<0.7.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
 quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.6.4,<0.7.0)"]
 rich = ["rich"]
 sagemaker = ["sagemaker"]
 test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
-test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist"]
+test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist"]
 test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
-testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
 
 [[package]]
 name = "annotated-types"
@@ -44,13 +44,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.8.0"
+version = "4.9.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
-    {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
+    {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
+    {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
 ]
 
 [package.dependencies]
@@ -60,8 +60,8 @@ sniffio = ">=1.1"
 typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -304,13 +304,13 @@ standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "htt
 
 [[package]]
 name = "filelock"
-version = "3.17.0"
+version = "3.18.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "filelock-3.17.0-py3-none-any.whl", hash = "sha256:533dc2f7ba78dc2f0f531fc6c4940addf7b70a481e269a5a3b93be94ffbe8338"},
-    {file = "filelock-3.17.0.tar.gz", hash = "sha256:ee4e77401ef576ebb38cd7f13b9b28893194acc20a8e68e18730ba9c0e54660e"},
+    {file = "filelock-3.18.0-py3-none-any.whl", hash = "sha256:c401f4f8377c4464e6db25fff06205fd89bdd83b65eb0488ed1b160f780e21de"},
+    {file = "filelock-3.18.0.tar.gz", hash = "sha256:adbc88eabb99d2fec8c9c1b229b171f18afa655400173ddc653d5d01501fb9f2"},
 ]
 
 [package.extras]
@@ -320,13 +320,13 @@ typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
 name = "fsspec"
-version = "2025.2.0"
+version = "2025.3.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2025.2.0-py3-none-any.whl", hash = "sha256:9de2ad9ce1f85e1931858535bc882543171d197001a0a5eb2ddc04f1781ab95b"},
-    {file = "fsspec-2025.2.0.tar.gz", hash = "sha256:1c24b16eaa0a1798afa0337aa0db9b256718ab2a89c425371f5628d22c3b6afd"},
+    {file = "fsspec-2025.3.0-py3-none-any.whl", hash = "sha256:efb87af3efa9103f94ca91a7f8cb7a4df91af9f74fc106c9c7ea0efd7277c1b3"},
+    {file = "fsspec-2025.3.0.tar.gz", hash = "sha256:a935fd1ea872591f2b5148907d103488fc523295e6c64b835cfad8c3eca44972"},
 ]
 
 [package.extras]
@@ -359,87 +359,83 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.69.0"
+version = "1.69.2"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "googleapis_common_protos-1.69.0-py2.py3-none-any.whl", hash = "sha256:17835fdc4fa8da1d61cfe2d4d5d57becf7c61d4112f8d81c67eaa9d7ce43042d"},
-    {file = "googleapis_common_protos-1.69.0.tar.gz", hash = "sha256:5a46d58af72846f59009b9c4710425b9af2139555c71837081706b213b298187"},
+    {file = "googleapis_common_protos-1.69.2-py3-none-any.whl", hash = "sha256:0b30452ff9c7a27d80bfc5718954063e8ab53dd3697093d3bc99581f5fd24212"},
+    {file = "googleapis_common_protos-1.69.2.tar.gz", hash = "sha256:3e1b904a27a33c821b4b749fd31d334c0c9c30e6113023d495e48979a3dc9c5f"},
 ]
 
 [package.dependencies]
-protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
+protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0"
 
 [package.extras]
-grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
+grpc = ["grpcio (>=1.44.0,<2.0.0)"]
 
 [[package]]
 name = "grpcio"
-version = "1.70.0"
+version = "1.71.0"
 description = "HTTP/2-based RPC framework"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 [[package]]
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@@ -500,13 +496,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
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 [package.dependencies]
@@ -571,24 +567,24 @@ type = ["pytest-mypy"]
 
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 ]
 
 [package.dependencies]
@@ -1977,13 +1974,13 @@ telegram = ["requests"]
 
 [[package]]
 name = "transformers"
-version = "4.49.0"
+version = "4.50.0"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.9.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1994,29 +1991,31 @@ packaging = ">=20.0"
 pyyaml = ">=5.1"
 regex = "!=2019.12.17"
 requests = "*"
-safetensors = ">=0.4.1"
+safetensors = ">=0.4.3"
 tokenizers = ">=0.21,<0.22"
 tqdm = ">=4.27"
 
 [package.extras]
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 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
-integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
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 modelcreation = ["cookiecutter (==1.7.3)"]
 natten = ["natten (>=0.14.6,<0.15.0)"]
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 onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
 onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
 optuna = ["optuna"]
@@ -2030,7 +2029,7 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
 tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
@@ -2079,24 +2078,24 @@ files = [
 
 [[package]]
 name = "tzdata"
-version = "2025.1"
+version = "2025.2"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
-    {file = "tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639"},
-    {file = "tzdata-2025.1.tar.gz", hash = "sha256:24894909e88cdb28bd1636c6887801df64cb485bd593f2fd83ef29075a81d694"},
+    {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"},
+    {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"},
 ]
 
 [[package]]
 name = "tzlocal"
-version = "5.3"
+version = "5.3.1"
 description = "tzinfo object for the local timezone"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "tzlocal-5.3-py3-none-any.whl", hash = "sha256:3814135a1bb29763c6e4f08fd6e41dbb435c7a60bfbb03270211bcc537187d8c"},
-    {file = "tzlocal-5.3.tar.gz", hash = "sha256:2fafbfc07e9d8b49ade18f898d6bcd37ae88ce3ad6486842a2e4f03af68323d2"},
+    {file = "tzlocal-5.3.1-py3-none-any.whl", hash = "sha256:eb1a66c3ef5847adf7a834f1be0800581b683b5608e74f86ecbcef8ab91bb85d"},
+    {file = "tzlocal-5.3.1.tar.gz", hash = "sha256:cceffc7edecefea1f595541dbd6e990cb1ea3d19bf01b2809f362a03dd7921fd"},
 ]
 
 [package.dependencies]
diff --git a/model_server/src/commons/globals.py b/model_server/src/commons/globals.py
index 49dce5e7..97da3e20 100644
--- a/model_server/src/commons/globals.py
+++ b/model_server/src/commons/globals.py
@@ -5,8 +5,6 @@ from src.core.guardrails import get_guardrail_handler
 from src.core.function_calling import (
     ArchAgentConfig,
     ArchAgentHandler,
-    ArchIntentConfig,
-    ArchIntentHandler,
     ArchFunctionConfig,
     ArchFunctionHandler,
 )
@@ -30,9 +28,6 @@ ARCH_GUARD_MODEL_ALIAS = "katanemo/Arch-Guard"
 
 # Define model handlers
 handler_map = {
-    "Arch-Intent": ArchIntentHandler(
-        ARCH_CLIENT, ARCH_INTENT_MODEL_ALIAS, ArchIntentConfig
-    ),
     "Arch-Function": ArchFunctionHandler(
         ARCH_CLIENT, ARCH_FUNCTION_MODEL_ALIAS, ArchFunctionConfig
     ),
diff --git a/model_server/src/core/function_calling.py b/model_server/src/core/function_calling.py
index 8c01493a..6714da24 100644
--- a/model_server/src/core/function_calling.py
+++ b/model_server/src/core/function_calling.py
@@ -3,7 +3,6 @@ import copy
 import json
 import random
 import builtins
-import textwrap
 import src.commons.utils as utils
 
 from openai import OpenAI
@@ -22,176 +21,23 @@ from src.core.utils.model_utils import (
 logger = utils.get_model_server_logger()
 
 
-class ArchIntentConfig:
-    TASK_PROMPT = textwrap.dedent(
-        """
-    You are a helpful assistant.
-    """
-    ).strip()
-
-    TOOL_PROMPT_TEMPLATE = textwrap.dedent(
-        """
-    You task is to check if there are any tools that can be used to help the last user message in conversations according to the available tools listed below.
-
-    <tools>
-    {tool_text}
-    </tools>
-    """
-    ).strip()
-
-    FORMAT_PROMPT = textwrap.dedent(
-        """
-    Provide your tool assessment for ONLY THE LAST USER MESSAGE in the above conversation:
-    - First line must read 'Yes' or 'No'.
-    - If yes, a second line must include a comma-separated list of tool indexes.
-    """
-    ).strip()
-
-    EXTRA_INSTRUCTION = "Are there any tools can help?"
-
-    GENERATION_PARAMS = {
-        "temperature": 0.01,
-        "max_tokens": 1,
-        "stop_token_ids": [151645],
-    }
-
-
-class ArchIntentHandler(ArchBaseHandler):
-    def __init__(self, client: OpenAI, model_name: str, config: ArchIntentConfig):
-        """
-        Initializes the intent handler.
-
-        Args:
-            client (OpenAI): An OpenAI client instance.
-            model_name (str): Name of the model to use.
-            config (ArchIntentConfig): The configuration for Arch-Intent.
-        """
-
-        super().__init__(
-            client,
-            model_name,
-            config.TASK_PROMPT,
-            config.TOOL_PROMPT_TEMPLATE,
-            config.FORMAT_PROMPT,
-            config.GENERATION_PARAMS,
-        )
-
-        self.extra_instruction = config.EXTRA_INSTRUCTION
-
-    @override
-    def _convert_tools(self, tools: List[Dict[str, Any]]) -> str:
-        """
-        Converts a list of tools into a JSON-like format with indexed keys.
-
-        Args:
-            tools (List[Dict[str, Any]]): A list of tools represented as dictionaries.
-
-        Returns:
-            str: A string representation of converted tools.
-        """
-
-        converted = [
-            json.dumps({"index": f"T{idx}"} | tool) for idx, tool in enumerate(tools)
-        ]
-        return "\n".join(converted)
-
-    def detect_intent(self, content: str) -> bool:
-        """
-        Detect if any intent match with prompts
-
-        Args:
-            content: str: Model response that contains intent detection results
-
-        Returns:
-            bool: A boolean value to indicate if any intent match with prompts or not
-        """
-        if hasattr(content.choices[0].message, "content"):
-            return content.choices[0].message.content == "Yes"
-        else:
-            return False
-
-    @override
-    async def chat_completion(self, req: ChatMessage) -> ChatCompletionResponse:
-        """
-        Generates a chat completion for a given request.
-
-        Args:
-            req (ChatMessage): A chat message request object.
-
-        Returns:
-            ChatCompletionResponse: The model's response to the chat request.
-
-        Note:
-            Currently only support vllm inference
-        """
-        logger.info("[Arch-Intent] - ChatCompletion")
-
-        # In the case that no tools are available, simply return `No` to avoid making a call
-        if len(req.tools) == 0:
-            model_response = Message(content="No", tool_calls=[])
-            logger.info("No tools found, return `No` as the model response.")
-        else:
-            messages = self._process_messages(
-                req.messages, req.tools, self.extra_instruction
-            )
-
-            logger.info(f"[request to arch-fc (intent)]: {json.dumps(messages)}")
-
-            model_response = self.client.chat.completions.create(
-                messages=messages,
-                model=self.model_name,
-                stream=False,
-                extra_body=self.generation_params,
-            )
-
-            logger.info(f"[response]: {json.dumps(model_response.model_dump())}")
-
-            model_response = Message(
-                content=model_response.choices[0].message.content, tool_calls=[]
-            )
-
-        chat_completion_response = ChatCompletionResponse(
-            choices=[Choice(message=model_response)], model=self.model_name
-        )
-
-        return chat_completion_response
-
-
-# =============================================================================================================
+# ==============================================================================================================================================
 
 
 class ArchFunctionConfig:
-    TASK_PROMPT = textwrap.dedent(
-        """
-    You are a helpful assistant.
+    TASK_PROMPT = (
+        "You are a helpful assistant designed to assist with the user query by making one or more function calls if needed."
+        "\nToday's date: {today_date}"
+        "\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>{tool_text}\n</tools>"
+        "\n\nYour task is to decide which functions are needed and collect missing parameters if necessary.\n\n"
+    )
 
-    Today's date: {}
-    """.format(
-            utils.get_today_date()
-        )
-    ).strip()
-
-    TOOL_PROMPT_TEMPLATE = textwrap.dedent(
-        """
-    # Tools
-
-    You may call one or more functions to assist with the user query.
-
-    You are provided with function signatures within <tools></tools> XML tags:
-    <tools>
-    {tool_text}
-    </tools>
-    """
-    ).strip()
-
-    FORMAT_PROMPT = textwrap.dedent(
-        """
-    For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
-    <tool_call>
-    {"name": <function-name>, "arguments": <args-json-object>}
-    </tool_call>
-    """
-    ).strip()
+    FORMAT_PROMPT = (
+        "Based on your analysis, provide your response in one of the following JSON formats:"
+        '\n1. If no functions are needed:\n```\n{"response": "Your response text here"}\n```'
+        '\n2. If functions are needed but some required parameters are missing:\n```\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```\n{"tool_calls": [{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},... (more tool calls as required)]}\n```'
+    )
 
     GENERATION_PARAMS = {
         "temperature": 0.6,
@@ -222,15 +68,6 @@ class ArchFunctionConfig:
     SUPPORT_DATA_TYPES = ["int", "float", "bool", "str", "list", "tuple", "set", "dict"]
 
 
-class ArchAgentConfig(ArchFunctionConfig):
-    GENERATION_PARAMS = {
-        "temperature": 0.01,
-        "stop_token_ids": [151645],
-        "logprobs": True,
-        "top_logprobs": 10,
-    }
-
-
 class ArchFunctionHandler(ArchBaseHandler):
     def __init__(
         self,
@@ -251,7 +88,6 @@ class ArchFunctionHandler(ArchBaseHandler):
             client,
             model_name,
             config.TASK_PROMPT,
-            config.TOOL_PROMPT_TEMPLATE,
             config.FORMAT_PROMPT,
             config.GENERATION_PARAMS,
         )
@@ -280,7 +116,7 @@ class ArchFunctionHandler(ArchBaseHandler):
             str: A string representation of converted tools.
         """
 
-        converted = [json.dumps(tool) for tool in 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:
@@ -331,7 +167,7 @@ class ArchFunctionHandler(ArchBaseHandler):
         # Attempt to parse the corrected string to ensure it’s valid JSON
         return fixed_str.replace("'", '"')
 
-    def _extract_tool_calls(self, content: str) -> Dict[str, any]:
+    def _parse_model_resonse(self, content: str) -> Dict[str, any]:
         """
         Extracts tool call information from a given string.
 
@@ -340,49 +176,47 @@ class ArchFunctionHandler(ArchBaseHandler):
 
         Returns:
             Dict: A dictionary of extraction, including:
-                - "result": A list of tool call dictionaries.
-                - "status": A boolean indicating if the extraction was valid.
-                - "message": An error message or exception if extraction failed.
+                - "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.
         """
 
-        tool_calls, is_valid, error_message = [], True, ""
+        response_dict = {
+            "response": [],
+            "required_functions": [],
+            "clarification": "",
+            "tool_calls": [],
+            "is_valid": True,
+            "error_message": "",
+        }
 
-        flag = False
-        for line in content.split("\n"):
-            if not is_valid:
-                break
+        try:
+            model_response = json.loads(self._fix_json_string(content))
 
-            if "<tool_call>" == line:
-                flag = True
-            elif "</tool_call>" == line:
-                flag = False
-            else:
-                if flag:
-                    try:
-                        tool_content = json.loads(line)
-                    except Exception as e:
-                        fixed_content = self._fix_json_string(line)
-                        try:
-                            tool_content = json.loads(fixed_content)
-                        except Exception:
-                            is_valid, error_message = False, e
-                            break
+            response_dict["response"] = model_response.get("response", "")
+            response_dict["required_functions"] = model_response.get(
+                "required_functions", ""
+            )
+            response_dict["clarification"] = model_response.get("clarification", "")
 
-                    tool = {
+            for tool_call in model_response.get("tool_calls", []):
+                response_dict["tool_call"].append(
+                    {
                         "id": f"call_{random.randint(1000, 10000)}",
                         "type": "function",
                         "function": {
-                            "name": tool_content["name"],
+                            "name": tool_call.get("name", ""),
+                            "arguments": tool_call.get("arguments", {}),
                         },
                     }
-                    if "arguments" in tool_content:
-                        tool["function"]["arguments"] = tool_content["arguments"]
+                )
+        except Exception as e:
+            response_dict["is_valid"] = False
+            response_dict["error_message"] = f"Fail to parse model responses: {e}"
 
-                    tool_calls.append(tool)
-
-                flag = False
-
-        return {"result": tool_calls, "status": is_valid, "message": error_message}
+        return response_dict
 
     def _convert_data_type(self, value: str, target_type: str):
         # TODO: Add more conversion rules as needed
@@ -414,7 +248,11 @@ class ArchFunctionHandler(ArchBaseHandler):
                 - "message": An error message.
         """
 
-        is_valid, invalid_tool_call, error_message = True, None, ""
+        verification_dict = {
+            "is_valid": True,
+            "invalid_tool_call": {},
+            "error_message": "",
+        }
 
         functions = {}
         for tool in tools:
@@ -422,28 +260,26 @@ class ArchFunctionHandler(ArchBaseHandler):
                 functions[tool["function"]["name"]] = tool["function"]["parameters"]
 
         for tool_call in tool_calls:
-            if not is_valid:
+            if not verification_dict["is_valid"]:
                 break
 
             func_name = tool_call["function"]["name"]
-            func_args = tool_call["function"].get("arguments")
-            if not func_args:
-                func_args = {}
+            func_args = tool_call["function"]["arguments"]
 
             # Check whether the function is available or not
             if func_name not in functions:
-                is_valid = False
-                invalid_tool_call = tool_call
-                error_message = f"{func_name} is not defined!"
-                break
-
+                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:
-                        is_valid = False
-                        invalid_tool_call = tool_call
-                        error_message = f"`{required_param}` is required by the function `{func_name}` but not found in the tool call!"
+                        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
@@ -453,9 +289,11 @@ class ArchFunctionHandler(ArchBaseHandler):
                 logger.info(func_args)
                 for param_name in func_args:
                     if param_name not in function_properties:
-                        is_valid = False
-                        invalid_tool_call = tool_call
-                        error_message = f"Parameter `{param_name}` is not defined in the function `{func_name}`."
+                        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]
@@ -469,20 +307,20 @@ class ArchFunctionHandler(ArchBaseHandler):
                                     param_value, data_type
                                 )
                                 if not isinstance(param_value, data_type):
-                                    is_valid = False
-                                    invalid_tool_call = tool_call
-                                    error_message = f"Parameter `{param_name}` is expected to have the data type `{data_type}`, got `{type(param_value)}`."
+                                    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:
-                            error_message = (
-                                f"Data type `{target_type}` is not supported."
-                            )
+                            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 {
-            "status": is_valid,
-            "invalid_tool_call": invalid_tool_call,
-            "message": error_message,
-        }
+        return verification_dict
 
     def _add_prefill_message(self, messages: List[Dict[str, str]]):
         """
@@ -558,72 +396,110 @@ class ArchFunctionHandler(ArchBaseHandler):
                     model_response += chunk.choices[0].delta.content
             logger.info(f"[Agent Orchestrator]: response received: {model_response}")
         else:
+            # *********************************************************************************************\
+            # Update the following logic for hallucination check
+            # 1. If the model response starts wtth `tool_calls`, continue halluciantion check:
+            #    - If hallucination detected, start prompt prefilling
+            #    - Otherwise, continue until the end
+            # 2. Otherwise, stop it
+            # *********************************************************************************************
+
             # 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 the first token is <tool_call>
-                if len(self.hallucination_state.tokens) > 0 and has_tool_calls is None:
-                    if self.hallucination_state.tokens[0] == "<tool_call>":
-                        has_tool_calls = True
-                    else:
-                        has_tool_calls = False
-                        break
+            # self.hallucination_state = HallucinationState(
+            #     response_iterator=response, function=req.tools
+            # )
 
-                # if the model is hallucinating, start parameter gathering
-                if self.hallucination_state.hallucination is True:
-                    has_hallucination = True
-                    break
+            # has_tool_calls, has_hallucination = None, False
+            # for _ in self.hallucination_state:
+            #     # check if the first token is <tool_call>
+            #     if len(self.hallucination_state.tokens) > 0 and has_tool_calls is None:
+            #         if self.hallucination_state.tokens[0] == "<tool_call>":
+            #             has_tool_calls = True
+            #         else:
+            #             has_tool_calls = False
+            #             break
 
-            if has_tool_calls:
-                if has_hallucination:
-                    # start prompt prefilling if hallcuination is found in tool calls
-                    logger.info(
-                        f"[Hallucination]: {self.hallucination_state.error_message}"
-                    )
-                    prefill_response = self._engage_parameter_gathering(messages)
-                    model_response = prefill_response.choices[0].message.content
-                else:
-                    model_response = "".join(self.hallucination_state.tokens)
-            else:
-                # start parameter gathering if the model is not generating tool calls
-                prefill_response = self._engage_parameter_gathering(messages)
-                model_response = prefill_response.choices[0].message.content
+            #     # if the model is hallucinating, start parameter gathering
+            #     if self.hallucination_state.hallucination is True:
+            #         has_hallucination = True
+            #         break
+
+            # if has_tool_calls:
+            #     if has_hallucination:
+            #         # start prompt prefilling if hallcuination is found in tool calls
+            #         logger.info(
+            #             f"[Hallucination]: {self.hallucination_state.error_message}"
+            #         )
+            #         prefill_response = self._engage_parameter_gathering(messages)
+            #         model_response = prefill_response.choices[0].message.content
+            #     else:
+            #         model_response = "".join(self.hallucination_state.tokens)
+            # else:
+            #     # start parameter gathering if the model is not generating tool calls
+            #     prefill_response = self._engage_parameter_gathering(messages)
+            #     model_response = prefill_response.choices[0].message.content
+
+            # *********************************************************************************************\
+            # Remove the following for loop after updating hallucination check
+            # *********************************************************************************************
+            for chunk in response:
+                if len(chunk.choices) > 0 and chunk.choices[0].delta.content:
+                    model_response += chunk.choices[0].delta.content
 
         # Extract tool calls from model response
-        extracted = self._extract_tool_calls(model_response)
+        response_dict = self._parse_model_resonse(model_response)
 
-        if extracted["status"]:
-            # Response with tool calls
-            if len(extracted["result"]):
-                verified = {}
-                if use_agent_orchestrator:
-                    # skip tool call verification if using agent orchestrator
-                    verified = {"status": True, "message": ""}
-                else:
-                    verified = self._verify_tool_calls(
-                        tools=req.tools, tool_calls=extracted["result"]
-                    )
-
-                if verified["status"]:
-                    logger.info(
-                        f"[Tool calls]: {json.dumps([tool_call['function'] for tool_call in extracted['result']])}"
-                    )
-                    model_response = Message(content="", tool_calls=extracted["result"])
-                else:
-                    logger.error(f"Invalid tool call - {verified['message']}")
-            # Response without tool calls
+        if response_dict.get("response", ""):
+            # General model response
+            model_message = Message(content="", tool_calls=[])
+        elif response_dict.get("required_functions", []):
+            # Model response for parameter gathering
+            if not use_agent_orchestrator:
+                clarification = response_dict.get("clarification", "")
+                model_message = Message(content=clarification, tool_calls=[])
             else:
-                model_response = Message(content=model_response, tool_calls=[])
-        # Response with tool calls but contain errors
+                model_message = Message(content="", tool_calls=[])
+        elif response_dict.get("tool_calls", []):
+            # Response with 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 contain errors
+                model_message = Message(content="", tool_calls=[])
         else:
-            logger.error(f"Tool call extraction error - {extracted['message']}")
+            logger.error(f"Invalid model response - {model_response}")
+
+            # Response with tool calls but contain errors
+            model_message = Message(content="", tool_calls=[])
 
         chat_completion_response = ChatCompletionResponse(
-            choices=[Choice(message=model_response)], model=self.model_name
+            choices=[Choice(message=model_message)], model=self.model_name
         )
 
         logger.info(f"[response]: {json.dumps(chat_completion_response.model_dump())}")
@@ -631,6 +507,21 @@ class ArchFunctionHandler(ArchBaseHandler):
         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)
@@ -657,7 +548,7 @@ class ArchAgentHandler(ArchFunctionHandler):
             ):
                 tool_copy = copy.deepcopy(tool)
                 del tool_copy["function"]["parameters"]
-                converted.append(json.dumps(tool_copy))
+                converted.append(json.dumps(tool_copy["function"], ensure_ascii=False))
             else:
-                converted.append(json.dumps(tool))
+                converted.append(json.dumps(tool["function"], ensure_ascii=False))
         return "\n".join(converted)
diff --git a/model_server/src/core/utils/model_utils.py b/model_server/src/core/utils/model_utils.py
index 73dc6fef..0c42c796 100644
--- a/model_server/src/core/utils/model_utils.py
+++ b/model_server/src/core/utils/model_utils.py
@@ -1,4 +1,5 @@
 import json
+import src.commons.utils as utils
 
 from openai import OpenAI
 from pydantic import BaseModel
@@ -56,7 +57,6 @@ class ArchBaseHandler:
         client: OpenAI,
         model_name: str,
         task_prompt: str,
-        tool_prompt_template: str,
         format_prompt: str,
         generation_params: Dict,
     ):
@@ -67,7 +67,6 @@ class ArchBaseHandler:
             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.
-            tool_prompt (str): A prompt to describe tools.
             format_prompt (str): A prompt specifying the desired output format.
             generation_params (Dict): Generation parameters for the model.
         """
@@ -75,7 +74,6 @@ class ArchBaseHandler:
         self.model_name = model_name
 
         self.task_prompt = task_prompt
-        self.tool_prompt_template = tool_prompt_template
         self.format_prompt = format_prompt
 
         self.generation_params = generation_params
@@ -105,13 +103,11 @@ class ArchBaseHandler:
             str: A formatted system prompt.
         """
 
+        today_date = utils.get_today_date()
         tool_text = self._convert_tools(tools)
 
         system_prompt = (
-            self.task_prompt
-            + "\n\n"
-            + self.tool_prompt_template.format(tool_text=tool_text)
-            + "\n\n"
+            self.task_prompt.format(today_date=today_date, tool_text=tool_text)
             + self.format_prompt
         )
 
diff --git a/model_server/src/main.py b/model_server/src/main.py
index 1763b015..b1d5150b 100644
--- a/model_server/src/main.py
+++ b/model_server/src/main.py
@@ -76,62 +76,57 @@ async def function_calling(req: ChatMessage, res: Response):
     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}")
+    # if not use_agent_orchestrator:
+    #     intent_start_time = time.perf_counter()
+    #     intent_response = await handler_map["Arch-Intent"].chat_completion(req)
+    #     intent_latency = time.perf_counter() - intent_start_time
+    #     intent_detected = handler_map["Arch-Intent"].detect_intent(intent_response)
+
     try:
-        intent_detected = False
-        use_agent_orchestrator = req.metadata.get("use_agent_orchestrator", False)
-        logger.info(f"Use agent orchestrator: {use_agent_orchestrator}")
-        if not use_agent_orchestrator:
-            intent_start_time = time.perf_counter()
-            intent_response = await handler_map["Arch-Intent"].chat_completion(req)
-            intent_latency = time.perf_counter() - intent_start_time
-            intent_detected = handler_map["Arch-Intent"].detect_intent(intent_response)
+        handler_name = "Arch-Agent" if use_agent_orchestrator else "Arch-Function"
+        model_handler: ArchFunctionHandler = handler_map[handler_name]
 
-        if use_agent_orchestrator or intent_detected:
-            # TODO: measure agreement between intent detection and function calling
-            try:
-                function_start_time = time.perf_counter()
-                handler_name = (
-                    "Arch-Agent" if use_agent_orchestrator else "Arch-Function"
-                )
-                function_calling_handler: ArchFunctionHandler = handler_map[
-                    handler_name
-                ]
-                final_response = await function_calling_handler.chat_completion(req)
-                function_latency = time.perf_counter() - function_start_time
+        start_time = time.perf_counter()
+        final_response = await model_handler.chat_completion(req)
+        latency = time.perf_counter() - start_time
 
-                final_response.metadata = {
-                    "function_latency": str(round(function_latency * 1000, 3)),
-                }
-
-                if not use_agent_orchestrator:
-                    final_response.metadata["intent_latency"] = str(
-                        round(intent_latency * 1000, 3)
-                    )
-                    final_response.metadata["hallucination"] = str(
-                        function_calling_handler.hallucination_state.hallucination
-                    )
-            except ValueError as e:
-                res.statuscode = 503
-                error_messages = (
-                    f"[{handler_name}] - Error in tool call extraction: {e}"
-                )
-            except StopIteration as e:
-                res.statuscode = 500
-                error_messages = f"[{handler_name}] - Error in hallucination check: {e}"
-            except Exception as e:
-                res.status_code = 500
-                error_messages = f"[{handler_name}] - Error in ChatCompletion: {e}"
-                raise
-        else:
-            # no intent matched
-            intent_response.metadata = {
-                "intent_latency": str(round(intent_latency * 1000, 3)),
+        # 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)),
             }
-            final_response = intent_response
 
+        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}"
+    except StopIteration as e:
+        res.statuscode = 500
+        error_messages = f"[{handler_name}] - Error in hallucination check: {e}"
     except Exception as e:
         res.status_code = 500
-        error_messages = f"[Arch-Intent] - Error in ChatCompletion: {e}"
+        error_messages = f"[{handler_name}] - Error in ChatCompletion: {e}"
         raise
 
     if error_messages is not None: