Merge branch 'main' into huggingface

poetry.lock

@@ -238,7 +238,7 @@ version = "2024.12.14"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56"},
@@ -251,7 +251,7 @@ version = "3.4.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "charset_normalizer-3.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:91b36a978b5ae0ee86c394f5a54d6ef44db1de0815eb43de826d41d21e4af3de"},
@@ -480,12 +480,12 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
-groups = ["main", "dev"]
+groups = ["main", "dev", "huggingface"]
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
-markers = {main = "python_version <= \"3.11\" or python_version >= \"3.12\"", dev = "python_version <= \"3.11\" and sys_platform == \"win32\" or python_version >= \"3.12\" and sys_platform == \"win32\""}
+markers = {main = "python_version <= \"3.11\" or python_version >= \"3.12\"", dev = "python_version <= \"3.11\" and sys_platform == \"win32\" or python_version >= \"3.12\" and sys_platform == \"win32\"", huggingface = "python_version <= \"3.11\" and platform_system == \"Windows\" or python_version >= \"3.12\" and platform_system == \"Windows\""}
 
 [[package]]
 name = "coloredlogs"
@@ -688,7 +688,7 @@ version = "3.17.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.9"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "filelock-3.17.0-py3-none-any.whl", hash = "sha256:533dc2f7ba78dc2f0f531fc6c4940addf7b70a481e269a5a3b93be94ffbe8338"},
@@ -719,7 +719,7 @@ version = "2024.12.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "fsspec-2024.12.0-py3-none-any.whl", hash = "sha256:b520aed47ad9804237ff878b504267a3b0b441e97508bd6d2d8774e3db85cee2"},
@@ -1145,7 +1145,7 @@ version = "0.27.1"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "huggingface_hub-0.27.1-py3-none-any.whl", hash = "sha256:1c5155ca7d60b60c2e2fc38cbb3ffb7f7c3adf48f824015b219af9061771daec"},
@@ -1197,7 +1197,7 @@ version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.6"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
@@ -1272,7 +1272,7 @@ version = "3.1.5"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "jinja2-3.1.5-py3-none-any.whl", hash = "sha256:aba0f4dc9ed8013c424088f68a5c226f7d6097ed89b246d7749c2ec4175c6adb"},
@@ -1504,7 +1504,7 @@ version = "2.1.5"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = false
 python-versions = ">=3.7"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a17a92de5231666cfbe003f0e4b9b3a7ae3afb1ec2845aadc2bacc93ff85febc"},
@@ -1725,7 +1725,7 @@ version = "1.3.0"
 description = "Python library for arbitrary-precision floating-point arithmetic"
 optional = false
 python-versions = "*"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
@@ -1751,13 +1751,34 @@ files = [
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
 ]
 
+[[package]]
+name = "networkx"
+version = "3.4.2"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.10"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
+files = [
+    {file = "networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f"},
+    {file = "networkx-3.4.2.tar.gz", hash = "sha256:307c3669428c5362aab27c8a1260aa8f47c4e91d3891f48be0141738d8d053e1"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.7)", "numpy (>=1.24)", "pandas (>=2.0)", "scipy (>=1.10,!=1.11.0,!=1.11.1)"]
+developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
+doc = ["intersphinx-registry", "myst-nb (>=1.1)", "numpydoc (>=1.8.0)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.15)", "sphinx (>=7.3)", "sphinx-gallery (>=0.16)", "texext (>=0.6.7)"]
+example = ["cairocffi (>=1.7)", "contextily (>=1.6)", "igraph (>=0.11)", "momepy (>=0.7.2)", "osmnx (>=1.9)", "scikit-learn (>=1.5)", "seaborn (>=0.13)"]
+extra = ["lxml (>=4.6)", "pydot (>=3.0.1)", "pygraphviz (>=1.14)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
 [[package]]
 name = "numpy"
 version = "2.2.2"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.10"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "numpy-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7079129b64cb78bdc8d611d1fd7e8002c0a2565da6a47c4df8062349fee90e3e"},
@@ -1817,6 +1838,198 @@ files = [
     {file = "numpy-2.2.2.tar.gz", hash = "sha256:ed6906f61834d687738d25988ae117683705636936cc605be0bb208b23df4d8f"},
 ]
 
+[[package]]
+name = "nvidia-cublas-cu12"
+version = "12.4.5.8"
+description = "CUBLAS native runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0f8aa1706812e00b9f19dfe0cdb3999b092ccb8ca168c0db5b8ea712456fd9b3"},
+    {file = "nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl", hash = "sha256:2fc8da60df463fdefa81e323eef2e36489e1c94335b5358bcb38360adf75ac9b"},
+    {file = "nvidia_cublas_cu12-12.4.5.8-py3-none-win_amd64.whl", hash = "sha256:5a796786da89203a0657eda402bcdcec6180254a8ac22d72213abc42069522dc"},
+]
+
+[[package]]
+name = "nvidia-cuda-cupti-cu12"
+version = "12.4.127"
+description = "CUDA profiling tools runtime libs."
+optional = false
+python-versions = ">=3"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:79279b35cf6f91da114182a5ce1864997fd52294a87a16179ce275773799458a"},
+    {file = "nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:9dec60f5ac126f7bb551c055072b69d85392b13311fcc1bcda2202d172df30fb"},
+    {file = "nvidia_cuda_cupti_cu12-12.4.127-py3-none-win_amd64.whl", hash = "sha256:5688d203301ab051449a2b1cb6690fbe90d2b372f411521c86018b950f3d7922"},
+]
+
+[[package]]
+name = "nvidia-cuda-nvrtc-cu12"
+version = "12.4.127"
+description = "NVRTC native runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0eedf14185e04b76aa05b1fea04133e59f465b6f960c0cbf4e37c3cb6b0ea198"},
+    {file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a178759ebb095827bd30ef56598ec182b85547f1508941a3d560eb7ea1fbf338"},
+    {file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-win_amd64.whl", hash = "sha256:a961b2f1d5f17b14867c619ceb99ef6fcec12e46612711bcec78eb05068a60ec"},
+]
+
+[[package]]
+name = "nvidia-cuda-runtime-cu12"
+version = "12.4.127"
+description = "CUDA Runtime native Libraries"
+optional = false
+python-versions = ">=3"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:961fe0e2e716a2a1d967aab7caee97512f71767f852f67432d572e36cb3a11f3"},
+    {file = "nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:64403288fa2136ee8e467cdc9c9427e0434110899d07c779f25b5c068934faa5"},
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+]
+
+[[package]]
+name = "nvidia-cudnn-cu12"
+version = "9.1.0.70"
+description = "cuDNN runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f"},
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+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+
+[[package]]
+name = "nvidia-cufft-cu12"
+version = "11.2.1.3"
+description = "CUFFT native runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:5dad8008fc7f92f5ddfa2101430917ce2ffacd86824914c82e28990ad7f00399"},
+    {file = "nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f083fc24912aa410be21fa16d157fed2055dab1cc4b6934a0e03cba69eb242b9"},
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+]
+
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-curand-cu12"
+version = "10.3.5.147"
+description = "CURAND native runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
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+]
+
+[[package]]
+name = "nvidia-cusolver-cu12"
+version = "11.6.1.9"
+description = "CUDA solver native runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_aarch64.whl", hash = "sha256:d338f155f174f90724bbde3758b7ac375a70ce8e706d70b018dd3375545fc84e"},
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+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+nvidia-cusparse-cu12 = "*"
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cusparse-cu12"
+version = "12.3.1.170"
+description = "CUSPARSE native runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
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+
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cusparselt-cu12"
+version = "0.6.2"
+description = "NVIDIA cuSPARSELt"
+optional = false
+python-versions = "*"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
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+    {file = "nvidia_cusparselt_cu12-0.6.2-py3-none-win_amd64.whl", hash = "sha256:0057c91d230703924c0422feabe4ce768841f9b4b44d28586b6f6d2eb86fbe70"},
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+[[package]]
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+version = "2.21.5"
+description = "NVIDIA Collective Communication Library (NCCL) Runtime"
+optional = false
+python-versions = ">=3"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
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+]
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+[[package]]
+name = "nvidia-nvjitlink-cu12"
+version = "12.4.127"
+description = "Nvidia JIT LTO Library"
+optional = false
+python-versions = ">=3"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
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+]
+
+[[package]]
+name = "nvidia-nvtx-cu12"
+version = "12.4.127"
+description = "NVIDIA Tools Extension"
+optional = false
+python-versions = ">=3"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
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+
 [[package]]
 name = "oauthlib"
 version = "3.2.2"
@@ -2234,7 +2447,7 @@ version = "24.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["main", "dev", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
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@@ -2926,7 +3139,7 @@ version = "6.0.2"
 description = "YAML parser and emitter for Python"
 optional = false
 python-versions = ">=3.8"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
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@@ -2984,13 +3197,118 @@ files = [
     {file = "pyyaml-6.0.2.tar.gz", hash = "sha256:d584d9ec91ad65861cc08d42e834324ef890a082e591037abe114850ff7bbc3e"},
 ]
 
+[[package]]
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+groups = ["huggingface"]
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+]
+
 [[package]]
 name = "requests"
 version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
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@@ -3112,6 +3430,45 @@ httpx = "*"
 [package.extras]
 dev = ["pytest"]
 
+[[package]]
+name = "safetensors"
+version = "0.5.2"
+description = ""
+optional = false
+python-versions = ">=3.7"
+groups = ["huggingface"]
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+]
+
+[package.extras]
+all = ["safetensors[jax]", "safetensors[numpy]", "safetensors[paddlepaddle]", "safetensors[pinned-tf]", "safetensors[quality]", "safetensors[testing]", "safetensors[torch]"]
+dev = ["safetensors[all]"]
+jax = ["flax (>=0.6.3)", "jax (>=0.3.25)", "jaxlib (>=0.3.25)", "safetensors[numpy]"]
+mlx = ["mlx (>=0.0.9)"]
+numpy = ["numpy (>=1.21.6)"]
+paddlepaddle = ["paddlepaddle (>=2.4.1)", "safetensors[numpy]"]
+pinned-tf = ["safetensors[numpy]", "tensorflow (==2.18.0)"]
+quality = ["black (==22.3)", "click (==8.0.4)", "flake8 (>=3.8.3)", "isort (>=5.5.4)"]
+tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
+testing = ["h5py (>=3.7.0)", "huggingface-hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools-rust (>=1.5.2)"]
+torch = ["safetensors[numpy]", "torch (>=1.10)"]
+
 [[package]]
 name = "semantic-version"
 version = "2.10.0"
@@ -3129,6 +3486,28 @@ files = [
 dev = ["Django (>=1.11)", "check-manifest", "colorama (<=0.4.1)", "coverage", "flake8", "nose2", "readme-renderer (<25.0)", "tox", "wheel", "zest.releaser[recommended]"]
 doc = ["Sphinx", "sphinx-rtd-theme"]
 
+[[package]]
+name = "setuptools"
+version = "75.8.0"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+optional = false
+python-versions = ">=3.9"
+groups = ["huggingface"]
+markers = "python_version >= \"3.12\""
+files = [
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+
+[package.extras]
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+core = ["importlib_metadata (>=6)", "jaraco.collections", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.14.*)", "pytest-mypy"]
+
 [[package]]
 name = "shellingham"
 version = "1.5.4"
@@ -3202,15 +3581,15 @@ full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart
 
 [[package]]
 name = "sympy"
-version = "1.13.3"
+version = "1.13.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "sympy-1.13.3-py3-none-any.whl", hash = "sha256:54612cf55a62755ee71824ce692986f23c88ffa77207b30c1368eda4a7060f73"},
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+    {file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"},
 ]
 
 [package.dependencies]
@@ -3242,7 +3621,7 @@ version = "0.21.0"
 description = ""
 optional = false
 python-versions = ">=3.7"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "tokenizers-0.21.0-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:3c4c93eae637e7d2aaae3d376f06085164e1660f89304c0ab2b1d08a406636b2"},
@@ -3326,13 +3705,71 @@ files = [
     {file = "tomlkit-0.13.2.tar.gz", hash = "sha256:fff5fe59a87295b278abd31bec92c15d9bc4a06885ab12bcea52c71119392e79"},
 ]
 
+[[package]]
+name = "torch"
+version = "2.6.0"
+description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
+optional = false
+python-versions = ">=3.9.0"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
+files = [
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+]
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+fsspec = "*"
+jinja2 = "*"
+networkx = "*"
+nvidia-cublas-cu12 = {version = "12.4.5.8", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-cupti-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-nvrtc-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-runtime-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cudnn-cu12 = {version = "9.1.0.70", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufft-cu12 = {version = "11.2.1.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-curand-cu12 = {version = "10.3.5.147", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusolver-cu12 = {version = "11.6.1.9", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparse-cu12 = {version = "12.3.1.170", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparselt-cu12 = {version = "0.6.2", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.21.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvjitlink-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvtx-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+setuptools = {version = "*", markers = "python_version >= \"3.12\""}
+sympy = {version = "1.13.1", markers = "python_version >= \"3.9\""}
+triton = {version = "3.2.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+typing-extensions = ">=4.10.0"
+
+[package.extras]
+opt-einsum = ["opt-einsum (>=3.3)"]
+optree = ["optree (>=0.13.0)"]
+
 [[package]]
 name = "tqdm"
 version = "4.67.1"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
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@@ -3349,6 +3786,77 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "transformers"
+version = "4.48.2"
+description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
+optional = false
+python-versions = ">=3.9.0"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
+files = [
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+    {file = "transformers-4.48.2.tar.gz", hash = "sha256:dcfb73473e61f22fb3366fe2471ed2e42779ecdd49527a1bdf1937574855d516"},
+]
+
+[package.dependencies]
+filelock = "*"
+huggingface-hub = ">=0.24.0,<1.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+pyyaml = ">=5.1"
+regex = "!=2019.12.17"
+requests = "*"
+safetensors = ">=0.4.1"
+tokenizers = ">=0.21,<0.22"
+tqdm = ">=4.27"
+
+[package.extras]
+accelerate = ["accelerate (>=0.26.0)"]
+agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=2.0)"]
+all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "codecarbon (>=2.8.1)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "torchaudio", "torchvision"]
+audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+benchmark = ["optimum-benchmark (>=0.3.0)"]
+codecarbon = ["codecarbon (>=2.8.1)"]
+deepspeed = ["accelerate (>=0.26.0)", "deepspeed (>=0.9.3)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "optuna", "parameterized", "protobuf", "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"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "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", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "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", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.21,<0.22)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "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", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.6,<0.15.0)"]
+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"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "libcst", "rich", "ruff (==0.5.1)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune] (>=2.7.0)"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+ruff = ["ruff (==0.5.1)"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+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"]
+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)"]
+tiktoken = ["blobfile", "tiktoken"]
+timm = ["timm (<=1.0.11)"]
+tokenizers = ["tokenizers (>=0.21,<0.22)"]
+torch = ["accelerate (>=0.26.0)", "torch (>=2.0)"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.24.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
 [[package]]
 name = "tree-sitter"
 version = "0.24.0"
@@ -3415,6 +3923,27 @@ files = [
 [package.extras]
 core = ["tree-sitter (>=0.22,<1.0)"]
 
+[[package]]
+name = "triton"
+version = "3.2.0"
+description = "A language and compiler for custom Deep Learning operations"
+optional = false
+python-versions = "*"
+groups = ["huggingface"]
+markers = "python_version <= \"3.11\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\" and platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "triton-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3e54983cd51875855da7c68ec05c05cf8bb08df361b1d5b69e05e40b0c9bd62"},
+    {file = "triton-3.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8009a1fb093ee8546495e96731336a33fb8856a38e45bb4ab6affd6dbc3ba220"},
+    {file = "triton-3.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8d9b215efc1c26fa7eefb9a157915c92d52e000d2bf83e5f69704047e63f125c"},
+    {file = "triton-3.2.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e5dfa23ba84541d7c0a531dfce76d8bcd19159d50a4a8b14ad01e91734a5c1b0"},
+    {file = "triton-3.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:30ceed0eff2c4a73b14eb63e052992f44bbdf175f3fad21e1ac8097a772de7ee"},
+]
+
+[package.extras]
+build = ["cmake (>=3.20)", "lit"]
+tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
+
 [[package]]
 name = "typer"
 version = "0.15.1"
@@ -3456,7 +3985,7 @@ version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
@@ -3499,7 +4028,7 @@ version = "2.3.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.9"
-groups = ["main"]
+groups = ["main", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
@@ -3890,4 +4419,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10, <4.0"
-content-hash = "05c5a583a77ba47659d1f11de3757d44a84b1fdb0491e6cfff74e6c9fbe500ec"
+content-hash = "ed37bda9891fbefaa9fa3eb73382a1887aec8cfdec3506df79da7bcceaadc63b"

pyproject.toml

@@ -32,6 +32,13 @@ packages = [
 pytest = "^8.3.4"
 pytest-asyncio = "^0.25.2"
 
+[tool.poetry.group.huggingface]
+optional=true
+
+[tool.poetry.group.huggingface.dependencies]
+torch = "^2.6.0"
+transformers = "^4.48.2"
+
 [build-system]
 requires = ["poetry-core>=2.0.0,<3.0.0"]
 build-backend = "poetry.core.masonry.api"

src/know_lang_bot/chat_bot/__main__.py

@@ -19,7 +19,6 @@ async def test_chat_processing():
     )
     
     print(f"Answer: {result.answer}")
-    print(f"References: {result.references_md}")
 
 if __name__ == "__main__":
     asyncio.run(test_chat_processing())

src/know_lang_bot/chat_bot/chat_graph.py

@@ -12,8 +12,11 @@ from pydantic_ai import Agent
 import logfire
 from pprint import pformat
 from enum import Enum
+from rich.console import Console
+from know_lang_bot.utils.model_provider import create_pydantic_model
 
 LOG = FancyLogger(__name__)
+console = Console()
 
 class ChatStatus(str, Enum):
     """Enum for tracking chat progress status"""
@@ -128,7 +131,10 @@ Example Output: "Where is the configuration file or configuration settings store
     async def run(self, ctx: GraphRunContext[ChatGraphState, ChatGraphDeps]) -> RetrieveContextNode:
         # Create an agent for question polishing
         polish_agent = Agent(
-            f"{ctx.deps.config.llm.model_provider}:{ctx.deps.config.llm.model_name}",
+            create_pydantic_model(
+                model_provider=ctx.deps.config.llm.model_provider,
+                model_name=ctx.deps.config.llm.model_name
+            ),
             system_prompt=self.system_prompt
         )
         prompt = f"""Original question: "{ctx.state.original_question}"
@@ -203,7 +209,10 @@ Remember: Your primary goal is answering the user's specific question, not expla
 
     async def run(self, ctx: GraphRunContext[ChatGraphState, ChatGraphDeps]) -> End[ChatResult]:
         answer_agent = Agent(
-            f"{ctx.deps.config.llm.model_provider}:{ctx.deps.config.llm.model_name}",
+            create_pydantic_model(
+                model_provider=ctx.deps.config.llm.model_provider,
+                model_name=ctx.deps.config.llm.model_name
+            ),
             system_prompt=self.system_prompt
         )
         
@@ -267,6 +276,8 @@ async def process_chat(
         )
     except Exception as e:
         LOG.error(f"Error processing chat in graph: {e}")
+        console.print_exception()
+        
         result = ChatResult(
             answer="I encountered an error processing your question. Please try again."
         )

src/know_lang_bot/models/huggingface.py

@@ -0,0 +1,193 @@
+from __future__ import annotations
+
+import asyncio
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from typing import AsyncIterator, Optional
+from contextlib import asynccontextmanager
+
+from pydantic_ai.models import (
+    AgentModel, 
+    ModelMessage, 
+    ModelResponse, 
+    StreamedResponse, 
+    Usage, 
+    Model,
+    check_allow_model_requests
+)
+from pydantic_ai.tools import ToolDefinition
+from pydantic_ai.messages import (
+    ModelResponsePart, TextPart,  SystemPromptPart,
+    UserPromptPart, ToolReturnPart, ModelResponseStreamEvent
+)
+from pydantic_ai.settings import ModelSettings
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import torch
+
+
+@dataclass(init=False)
+class HuggingFaceModel(Model):
+    """A model that uses HuggingFace models locally.
+    
+    For MVP, this implements basic functionality without streaming or advanced features.
+    """
+    
+    model_name: str
+    model: AutoModelForCausalLM = field(repr=False)
+    tokenizer: AutoTokenizer = field(repr=False)
+    device: str = field(default="cuda" if torch.cuda.is_available() else "cpu")
+    
+    def __init__(
+        self,
+        model_name: str,
+        *,
+        device: Optional[str] = None,
+        max_new_tokens: int = 512,
+    ):
+        """Initialize a HuggingFace model.
+        
+        Args:
+            model_name: Name of the model on HuggingFace Hub
+            device: Device to run model on ('cuda' or 'cpu')
+            max_new_tokens: Maximum number of tokens to generate
+        """
+        self.model_name = model_name
+        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        self.max_new_tokens = max_new_tokens
+        
+        # Load model and tokenizer
+        self.model = AutoModelForCausalLM.from_pretrained(model_name)
+        self.model.to(self.device)
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        if not self.tokenizer.pad_token:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+
+    async def agent_model(
+        self,
+        *,
+        function_tools: list[ToolDefinition],
+        allow_text_result: bool,
+        result_tools: list[ToolDefinition],
+    ) -> AgentModel:
+        """Create an agent model for each step of an agent run."""
+        check_allow_model_requests()
+        
+        return HuggingFaceAgentModel(
+            model=self.model,
+            tokenizer=self.tokenizer,
+            model_name=self.model_name,
+            device=self.device,
+            max_new_tokens=self.max_new_tokens,
+            tools=function_tools if function_tools else None
+        )
+
+    def name(self) -> str:
+        return f"huggingface:{self.model_name}"
+
+@dataclass
+class HuggingFaceAgentModel(AgentModel):
+    """Implementation of AgentModel for HuggingFace models."""
+    
+    model: AutoModelForCausalLM
+    tokenizer: AutoTokenizer
+    model_name: str
+    device: str
+    max_new_tokens: int
+    tools: Optional[list[ToolDefinition]] = None
+    
+    def _format_messages(self, messages: list[ModelMessage]) -> str:
+        """Format messages into a prompt the model can understand."""
+        formatted = []
+        for message in messages:
+            for part in message.parts:
+                if isinstance(part, SystemPromptPart):
+                    formatted.append(f"<|system|>{part.content}")
+                elif isinstance(part, UserPromptPart):
+                    formatted.append(f"<|user|>{part.content}")
+                else:
+                    # For MVP, we'll just pass through other message types
+                    formatted.append(str(part.content))
+        formatted.append("<|assistant|>")
+        return "\n".join(formatted)
+
+    async def request(
+        self, messages: list[ModelMessage], model_settings: ModelSettings | None
+    ) -> tuple[ModelResponse, Usage]:
+        """Make a request to the model."""
+        prompt = self._format_messages(messages)
+        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
+        
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                max_new_tokens=self.max_new_tokens,
+                pad_token_id=self.tokenizer.pad_token_id,
+            )
+        
+        response_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        response_text = response_text[len(prompt):]  # Remove the prompt
+        
+        timestamp = datetime.now(timezone.utc)
+        response = ModelResponse(
+            parts=[TextPart(response_text)],
+            model_name=self.model_name,
+            timestamp=timestamp
+        )
+        
+        usage_stats = Usage(
+            requests=1,
+            request_tokens=inputs.input_ids.shape[1],
+            response_tokens=len(outputs[0]) - inputs.input_ids.shape[1],
+            total_tokens=len(outputs[0])
+        )
+        
+        return response, usage_stats
+
+    @asynccontextmanager
+    async def request_stream(
+        self, messages: list[ModelMessage], model_settings: ModelSettings | None
+    ) -> AsyncIterator[StreamedResponse]:
+        """Make a streaming request to the model."""
+        prompt = self._format_messages(messages)
+        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
+        
+        streamer = TextIteratorStreamer(self.tokenizer, skip_special_tokens=True)
+        
+        generation_kwargs = dict(
+            input_ids=inputs.input_ids,
+            max_new_tokens=self.max_new_tokens,
+            pad_token_id=self.tokenizer.pad_token_id,
+            streamer=streamer,
+        )
+        
+        thread = torch.jit.fork(self.model.generate, **generation_kwargs)
+        
+        try:
+            yield HuggingFaceStreamedResponse(
+                _model_name=self.model_name,
+                _streamer=streamer,
+                _timestamp=datetime.now(timezone.utc),
+            )
+        finally:
+            torch.jit.wait(thread)
+
+@dataclass 
+class HuggingFaceStreamedResponse(StreamedResponse):
+    """Implementation of StreamedResponse for HuggingFace models."""
+    
+    _streamer: TextIteratorStreamer
+    _timestamp: datetime
+
+    async def _get_event_iterator(self) -> AsyncIterator[ModelResponseStreamEvent]:
+        """Stream tokens from the model."""
+        for new_text in self._streamer:
+            self._usage.response_tokens += len(new_text)
+            self._usage.total_tokens = self._usage.request_tokens + self._usage.response_tokens
+            
+            yield self._parts_manager.handle_text_delta(
+                vendor_part_id='content',
+                content=new_text
+            )
+
+    def timestamp(self) -> datetime:
+        return self._timestamp

src/know_lang_bot/summarizer/summarizer.py

@@ -10,6 +10,7 @@ from rich.progress import Progress
 from know_lang_bot.config import AppConfig
 from know_lang_bot.core.types import CodeChunk, ModelProvider
 from know_lang_bot.utils.fancy_log import FancyLogger
+from know_lang_bot.utils.model_provider import create_pydantic_model
 
 LOG = FancyLogger(__name__)
 
@@ -50,7 +51,10 @@ Provide a clean, concise and focused summary. Don't include unnecessary nor gene
 """
         
         self.agent = Agent(
-            f"{self.config.llm.model_provider}:{self.config.llm.model_name}",
+            create_pydantic_model(
+                model_provider=self.config.llm.model_provider,
+                model_name=self.config.llm.model_name
+            ),
             system_prompt=system_prompt,
             model_settings=self.config.llm.model_settings
         )

src/know_lang_bot/utils/model_provider.py

@@ -0,0 +1,17 @@
+from pydantic_ai.models import Model, KnownModelName
+from know_lang_bot.core.types import ModelProvider
+from know_lang_bot.models.huggingface import HuggingFaceModel
+from typing import get_args
+
+def create_pydantic_model(
+    model_provider: ModelProvider,
+    model_name: str,
+) -> Model | KnownModelName:
+    model_str = f"{model_provider}:{model_name}"
+
+    if model_str in get_args(KnownModelName):
+        return model_str
+    elif model_provider == ModelProvider.HUGGINGFACE:
+        return HuggingFaceModel(model_name=model_name)
+    else:
+        raise NotImplementedError(f"Model {model_provider}:{model_name} is not supported")