diff --git a/fieldExtraction/poetry.lock b/fieldExtraction/poetry.lock
index 1695f3f..5286d01 100644
--- a/fieldExtraction/poetry.lock
+++ b/fieldExtraction/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -118,7 +118,7 @@ propcache = ">=0.2.0"
 yarl = ">=1.17.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.3.0)", "brotlicffi ; platform_python_implementation != \"CPython\""]
+speedups = ["Brotli", "aiodns (>=3.3.0)", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
@@ -222,45 +222,6 @@ files = [
 [package.dependencies]
 argon2-cffi-bindings = "*"
 
-[[package]]
-name = "argon2-cffi-bindings"
-version = "21.2.0"
-description = "Low-level CFFI bindings for Argon2"
-optional = false
-python-versions = ">=3.6"
-groups = ["dev"]
-markers = "python_version >= \"3.14\""
-files = [
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-    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367"},
-    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9524464572e12979364b7d600abf96181d3541da11e23ddf565a32e70bd4dc0d"},
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-]
-
-[package.dependencies]
-cffi = ">=1.0.1"
-
-[package.extras]
-dev = ["cogapp", "pre-commit", "pytest", "wheel"]
-tests = ["pytest"]
-
 [[package]]
 name = "argon2-cffi-bindings"
 version = "25.1.0"
@@ -268,7 +229,6 @@ description = "Low-level CFFI bindings for Argon2"
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
-markers = "python_version < \"3.14\""
 files = [
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@@ -299,7 +259,10 @@ files = [
 ]
 
 [package.dependencies]
-cffi = {version = ">=1.0.1", markers = "python_version < \"3.14\""}
+cffi = [
+    {version = ">=1.0.1", markers = "python_version < \"3.14\""},
+    {version = ">=2.0.0b1", markers = "python_version >= \"3.14\""},
+]
 
 [[package]]
 name = "arrow"
@@ -362,12 +325,12 @@ files = [
 ]
 
 [package.extras]
-benchmark = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
-cov = ["cloudpickle ; platform_python_implementation == \"CPython\"", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
-dev = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
+benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
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-tests = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
-tests-mypy = ["mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\""]
+tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "babel"
@@ -382,7 +345,7 @@ files = [
 ]
 
 [package.extras]
-dev = ["backports.zoneinfo ; python_version < \"3.9\"", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata ; sys_platform == \"win32\""]
+dev = ["backports.zoneinfo", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata"]
 
 [[package]]
 name = "beautifulsoup4"
@@ -530,6 +493,7 @@ description = "Foreign Function Interface for Python calling C code."
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
+markers = "python_version < \"3.14\""
 files = [
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@@ -603,6 +567,104 @@ files = [
 [package.dependencies]
 pycparser = "*"
 
+[[package]]
+name = "cffi"
+version = "2.0.0"
+description = "Foreign Function Interface for Python calling C code."
+optional = false
+python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.14\""
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 [package.extras]
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 [[package]]
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 name = "faiss-cpu"
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-]
-
-[package.dependencies]
-numpy = ">=1.25.0,<3.0"
-packaging = "*"
-
 [[package]]
 name = "fastjsonschema"
 version = "2.21.2"
@@ -1265,7 +1256,7 @@ smb = ["smbprotocol"]
 ssh = ["paramiko"]
 test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
 test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
-test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard ; python_version < \"3.14\""]
+test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
 tqdm = ["tqdm"]
 
 [[package]]
@@ -1412,7 +1403,7 @@ httpcore = "==1.*"
 idna = "*"
 
 [package.extras]
-brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
+brotli = ["brotli", "brotlicffi"]
 cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
@@ -1453,16 +1444,16 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 hf-transfer = ["hf-transfer (>=0.1.4)"]
 hf-xet = ["hf-xet (>=1.1.2,<2.0.0)"]
 inference = ["aiohttp"]
 mcp = ["aiohttp", "mcp (>=1.8.0)", "typer"]
 oauth = ["authlib (>=1.3.2)", "fastapi", "httpx", "itsdangerous"]
-quality = ["libcst (>=1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "ruff (>=0.9.0)"]
+quality = ["libcst (>=1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "ruff (>=0.9.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 tensorflow-testing = ["keras (<3.0)", "tensorflow"]
 testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
@@ -1791,7 +1782,7 @@ files = [
 ]
 
 [package.extras]
-dev = ["build (==1.2.2.post1)", "coverage (==7.5.4) ; python_version < \"3.9\"", "coverage (==7.8.0) ; python_version >= \"3.9\"", "mypy (==1.14.1) ; python_version < \"3.9\"", "mypy (==1.15.0) ; python_version >= \"3.9\"", "pip (==25.0.1)", "pylint (==3.2.7) ; python_version < \"3.9\"", "pylint (==3.3.6) ; python_version >= \"3.9\"", "ruff (==0.11.2)", "twine (==6.1.0)", "uv (==0.6.11)"]
+dev = ["build (==1.2.2.post1)", "coverage (==7.5.4)", "coverage (==7.8.0)", "mypy (==1.14.1)", "mypy (==1.15.0)", "pip (==25.0.1)", "pylint (==3.2.7)", "pylint (==3.3.6)", "ruff (==0.11.2)", "twine (==6.1.0)", "uv (==0.6.11)"]
 
 [[package]]
 name = "jsonpatch"
@@ -1907,7 +1898,7 @@ traitlets = ">=5.3"
 
 [package.extras]
 docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
-test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko ; sys_platform == \"win32\"", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
+test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-console"
@@ -2196,7 +2187,7 @@ numpy = {version = ">=1.26.0,<3", markers = "python_version >= \"3.12\""}
 pydantic = ">=2.10.0,<3"
 
 [package.extras]
-tools = ["beautifulsoup4 (>=4.13.4)", "bedrock-agentcore (>=0.1.0) ; python_version >= \"3.10\"", "playwright (>=1.53.0)"]
+tools = ["beautifulsoup4 (>=4.13.4)", "bedrock-agentcore (>=0.1.0)", "playwright (>=1.53.0)"]
 
 [[package]]
 name = "langchain-community"
@@ -2459,7 +2450,7 @@ files = [
 [package.extras]
 develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
 docs = ["sphinx"]
-gmpy = ["gmpy2 (>=2.1.0a4) ; platform_python_implementation != \"PyPy\""]
+gmpy = ["gmpy2 (>=2.1.0a4)"]
 tests = ["pytest (>=4.6)"]
 
 [[package]]
@@ -2817,7 +2808,7 @@ tornado = ">=6.2.0"
 [package.extras]
 dev = ["hatch", "pre-commit"]
 docs = ["myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
-test = ["importlib-resources (>=5.0) ; python_version < \"3.10\"", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.27.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
+test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.27.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
 
 [[package]]
 name = "notebook-shim"
@@ -2928,27 +2919,13 @@ description = "CUBLAS native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:08ed2686e9875d01b58e3cb379c6896df8e76c75e0d4a7f7dace3d7b6d9ef8eb"},
     {file = "nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:235f728d6e2a409eddf1df58d5b0921cf80cfa9e72b9f2775ccb7b4a87984668"},
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 ]
 
-[[package]]
-name = "nvidia-cublas-cu12"
-version = "12.8.4.1"
-description = "CUBLAS native runtime libraries"
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
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-    {file = "nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:8ac4e771d5a348c551b2a426eda6193c19aa630236b418086020df5ba9667142"},
-    {file = "nvidia_cublas_cu12-12.8.4.1-py3-none-win_amd64.whl", hash = "sha256:47e9b82132fa8d2b4944e708049229601448aaad7e6f296f630f2d1a32de35af"},
-]
-
 [[package]]
 name = "nvidia-cuda-cupti-cu12"
 version = "12.6.80"
@@ -2956,7 +2933,7 @@ description = "CUDA profiling tools runtime libs."
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:166ee35a3ff1587f2490364f90eeeb8da06cd867bd5b701bf7f9a02b78bc63fc"},
     {file = "nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_aarch64.whl", hash = "sha256:358b4a1d35370353d52e12f0a7d1769fc01ff74a191689d3870b2123156184c4"},
@@ -2965,20 +2942,6 @@ files = [
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 ]
 
-[[package]]
-name = "nvidia-cuda-cupti-cu12"
-version = "12.8.90"
-description = "CUDA profiling tools runtime libs."
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:4412396548808ddfed3f17a467b104ba7751e6b58678a4b840675c56d21cf7ed"},
-    {file = "nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ea0cb07ebda26bb9b29ba82cda34849e73c166c18162d3913575b0c9db9a6182"},
-    {file = "nvidia_cuda_cupti_cu12-12.8.90-py3-none-win_amd64.whl", hash = "sha256:bb479dcdf7e6d4f8b0b01b115260399bf34154a1a2e9fe11c85c517d87efd98e"},
-]
-
 [[package]]
 name = "nvidia-cuda-nvrtc-cu12"
 version = "12.6.77"
@@ -2986,27 +2949,13 @@ description = "NVRTC native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-manylinux2014_aarch64.whl", hash = "sha256:5847f1d6e5b757f1d2b3991a01082a44aad6f10ab3c5c0213fa3e25bddc25a13"},
     {file = "nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl", hash = "sha256:35b0cc6ee3a9636d5409133e79273ce1f3fd087abb0532d2d2e8fff1fe9efc53"},
     {file = "nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-win_amd64.whl", hash = "sha256:f7007dbd914c56bd80ea31bc43e8e149da38f68158f423ba845fc3292684e45a"},
 ]
 
-[[package]]
-name = "nvidia-cuda-nvrtc-cu12"
-version = "12.8.93"
-description = "NVRTC native runtime libraries"
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994"},
-    {file = "nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fc1fec1e1637854b4c0a65fb9a8346b51dd9ee69e61ebaccc82058441f15bce8"},
-    {file = "nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-win_amd64.whl", hash = "sha256:7a4b6b2904850fe78e0bd179c4b655c404d4bb799ef03ddc60804247099ae909"},
-]
-
 [[package]]
 name = "nvidia-cuda-runtime-cu12"
 version = "12.6.77"
@@ -3014,7 +2963,7 @@ description = "CUDA Runtime native Libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6116fad3e049e04791c0256a9778c16237837c08b27ed8c8401e2e45de8d60cd"},
     {file = "nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_aarch64.whl", hash = "sha256:d461264ecb429c84c8879a7153499ddc7b19b5f8d84c204307491989a365588e"},
@@ -3023,20 +2972,6 @@ files = [
     {file = "nvidia_cuda_runtime_cu12-12.6.77-py3-none-win_amd64.whl", hash = "sha256:86c58044c824bf3c173c49a2dbc7a6c8b53cb4e4dca50068be0bf64e9dab3f7f"},
 ]
 
-[[package]]
-name = "nvidia-cuda-runtime-cu12"
-version = "12.8.90"
-description = "CUDA Runtime native Libraries"
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:52bf7bbee900262ffefe5e9d5a2a69a30d97e2bc5bb6cc866688caa976966e3d"},
-    {file = "nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adade8dcbd0edf427b7204d480d6066d33902cab2a4707dcfc48a2d0fd44ab90"},
-    {file = "nvidia_cuda_runtime_cu12-12.8.90-py3-none-win_amd64.whl", hash = "sha256:c0c6027f01505bfed6c3b21ec546f69c687689aad5f1a377554bc6ca4aa993a8"},
-]
-
 [[package]]
 name = "nvidia-cudnn-cu12"
 version = "9.5.1.17"
@@ -3044,7 +2979,7 @@ description = "cuDNN runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:9fd4584468533c61873e5fda8ca41bac3a38bcb2d12350830c69b0a96a7e4def"},
     {file = "nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:30ac3869f6db17d170e0e556dd6cc5eee02647abc31ca856634d5a40f82c15b2"},
@@ -3054,23 +2989,6 @@ files = [
 [package.dependencies]
 nvidia-cublas-cu12 = "*"
 
-[[package]]
-name = "nvidia-cudnn-cu12"
-version = "9.10.2.21"
-description = "cuDNN runtime libraries"
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:c9132cc3f8958447b4910a1720036d9eff5928cc3179b0a51fb6d167c6cc87d8"},
-    {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8"},
-    {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-win_amd64.whl", hash = "sha256:c6288de7d63e6cf62988f0923f96dc339cea362decb1bf5b3141883392a7d65e"},
-]
-
-[package.dependencies]
-nvidia-cublas-cu12 = "*"
-
 [[package]]
 name = "nvidia-cufft-cu12"
 version = "11.3.0.4"
@@ -3078,7 +2996,7 @@ description = "CUFFT native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d16079550df460376455cba121db6564089176d9bac9e4f360493ca4741b22a6"},
     {file = "nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8510990de9f96c803a051822618d42bf6cb8f069ff3f48d93a8486efdacb48fb"},
@@ -3090,23 +3008,6 @@ files = [
 [package.dependencies]
 nvidia-nvjitlink-cu12 = "*"
 
-[[package]]
-name = "nvidia-cufft-cu12"
-version = "11.3.3.83"
-description = "CUFFT native runtime libraries"
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:848ef7224d6305cdb2a4df928759dca7b1201874787083b6e7550dd6765ce69a"},
-    {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74"},
-    {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-win_amd64.whl", hash = "sha256:7a64a98ef2a7c47f905aaf8931b69a3a43f27c55530c698bb2ed7c75c0b42cb7"},
-]
-
-[package.dependencies]
-nvidia-nvjitlink-cu12 = "*"
-
 [[package]]
 name = "nvidia-cufile-cu12"
 version = "1.11.1.6"
@@ -3114,25 +3015,12 @@ description = "cuFile GPUDirect libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cc23469d1c7e52ce6c1d55253273d32c565dd22068647f3aa59b3c6b005bf159"},
     {file = "nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:8f57a0051dcf2543f6dc2b98a98cb2719c37d3cee1baba8965d57f3bbc90d4db"},
 ]
 
-[[package]]
-name = "nvidia-cufile-cu12"
-version = "1.13.1.3"
-description = "cuFile GPUDirect libraries"
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc"},
-    {file = "nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:4beb6d4cce47c1a0f1013d72e02b0994730359e17801d395bdcbf20cfb3bb00a"},
-]
-
 [[package]]
 name = "nvidia-curand-cu12"
 version = "10.3.7.77"
@@ -3140,7 +3028,7 @@ description = "CURAND native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_aarch64.whl", hash = "sha256:6e82df077060ea28e37f48a3ec442a8f47690c7499bff392a5938614b56c98d8"},
     {file = "nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a42cd1344297f70b9e39a1e4f467a4e1c10f1da54ff7a85c12197f6c652c8bdf"},
@@ -3149,20 +3037,6 @@ files = [
     {file = "nvidia_curand_cu12-10.3.7.77-py3-none-win_amd64.whl", hash = "sha256:6d6d935ffba0f3d439b7cd968192ff068fafd9018dbf1b85b37261b13cfc9905"},
 ]
 
-[[package]]
-name = "nvidia-curand-cu12"
-version = "10.3.9.90"
-description = "CURAND native runtime libraries"
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:dfab99248034673b779bc6decafdc3404a8a6f502462201f2f31f11354204acd"},
-    {file = "nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:b32331d4f4df5d6eefa0554c565b626c7216f87a06a4f56fab27c3b68a830ec9"},
-    {file = "nvidia_curand_cu12-10.3.9.90-py3-none-win_amd64.whl", hash = "sha256:f149a8ca457277da854f89cf282d6ef43176861926c7ac85b2a0fbd237c587ec"},
-]
-
 [[package]]
 name = "nvidia-cusolver-cu12"
 version = "11.7.1.2"
@@ -3170,7 +3044,7 @@ description = "CUDA solver native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0ce237ef60acde1efc457335a2ddadfd7610b892d94efee7b776c64bb1cac9e0"},
     {file = "nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e9e49843a7707e42022babb9bcfa33c29857a93b88020c4e4434656a655b698c"},
@@ -3184,25 +3058,6 @@ nvidia-cublas-cu12 = "*"
 nvidia-cusparse-cu12 = "*"
 nvidia-nvjitlink-cu12 = "*"
 
-[[package]]
-name = "nvidia-cusolver-cu12"
-version = "11.7.3.90"
-description = "CUDA solver native runtime libraries"
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:db9ed69dbef9715071232caa9b69c52ac7de3a95773c2db65bdba85916e4e5c0"},
-    {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450"},
-    {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-win_amd64.whl", hash = "sha256:4a550db115fcabc4d495eb7d39ac8b58d4ab5d8e63274d3754df1c0ad6a22d34"},
-]
-
-[package.dependencies]
-nvidia-cublas-cu12 = "*"
-nvidia-cusparse-cu12 = "*"
-nvidia-nvjitlink-cu12 = "*"
-
 [[package]]
 name = "nvidia-cusparse-cu12"
 version = "12.5.4.2"
@@ -3210,7 +3065,7 @@ description = "CUSPARSE native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d25b62fb18751758fe3c93a4a08eff08effedfe4edf1c6bb5afd0890fe88f887"},
     {file = "nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7aa32fa5470cf754f72d1116c7cbc300b4e638d3ae5304cfa4a638a5b87161b1"},
@@ -3222,23 +3077,6 @@ files = [
 [package.dependencies]
 nvidia-nvjitlink-cu12 = "*"
 
-[[package]]
-name = "nvidia-cusparse-cu12"
-version = "12.5.8.93"
-description = "CUSPARSE native runtime libraries"
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9b6c161cb130be1a07a27ea6923df8141f3c295852f4b260c65f18f3e0a091dc"},
-    {file = "nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b"},
-    {file = "nvidia_cusparse_cu12-12.5.8.93-py3-none-win_amd64.whl", hash = "sha256:9a33604331cb2cac199f2e7f5104dfbb8a5a898c367a53dfda9ff2acb6b6b4dd"},
-]
-
-[package.dependencies]
-nvidia-nvjitlink-cu12 = "*"
-
 [[package]]
 name = "nvidia-cusparselt-cu12"
 version = "0.6.3"
@@ -3246,27 +3084,13 @@ description = "NVIDIA cuSPARSELt"
 optional = false
 python-versions = "*"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8371549623ba601a06322af2133c4a44350575f5a3108fb75f3ef20b822ad5f1"},
     {file = "nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:e5c8a26c36445dd2e6812f1177978a24e2d37cacce7e090f297a688d1ec44f46"},
     {file = "nvidia_cusparselt_cu12-0.6.3-py3-none-win_amd64.whl", hash = "sha256:3b325bcbd9b754ba43df5a311488fca11a6b5dc3d11df4d190c000cf1a0765c7"},
 ]
 
-[[package]]
-name = "nvidia-cusparselt-cu12"
-version = "0.7.1"
-description = "NVIDIA cuSPARSELt"
-optional = false
-python-versions = "*"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8878dce784d0fac90131b6817b607e803c36e629ba34dc5b433471382196b6a5"},
-    {file = "nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f1bb701d6b930d5a7cea44c19ceb973311500847f81b634d802b7b539dc55623"},
-    {file = "nvidia_cusparselt_cu12-0.7.1-py3-none-win_amd64.whl", hash = "sha256:f67fbb5831940ec829c9117b7f33807db9f9678dc2a617fbe781cac17b4e1075"},
-]
-
 [[package]]
 name = "nvidia-nccl-cu12"
 version = "2.26.2"
@@ -3274,25 +3098,12 @@ description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_nccl_cu12-2.26.2-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5c196e95e832ad30fbbb50381eb3cbd1fadd5675e587a548563993609af19522"},
     {file = "nvidia_nccl_cu12-2.26.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:694cf3879a206553cc9d7dbda76b13efaf610fdb70a50cba303de1b0d1530ac6"},
 ]
 
-[[package]]
-name = "nvidia-nccl-cu12"
-version = "2.27.3"
-description = "NVIDIA Collective Communication Library (NCCL) Runtime"
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9ddf1a245abc36c550870f26d537a9b6087fb2e2e3d6e0ef03374c6fd19d984f"},
-    {file = "nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adf27ccf4238253e0b826bce3ff5fa532d65fc42322c8bfdfaf28024c0fbe039"},
-]
-
 [[package]]
 name = "nvidia-nvjitlink-cu12"
 version = "12.6.85"
@@ -3300,27 +3111,13 @@ description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:eedc36df9e88b682efe4309aa16b5b4e78c2407eac59e8c10a6a47535164369a"},
     {file = "nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cf4eaa7d4b6b543ffd69d6abfb11efdeb2db48270d94dfd3a452c24150829e41"},
     {file = "nvidia_nvjitlink_cu12-12.6.85-py3-none-win_amd64.whl", hash = "sha256:e61120e52ed675747825cdd16febc6a0730537451d867ee58bee3853b1b13d1c"},
 ]
 
-[[package]]
-name = "nvidia-nvjitlink-cu12"
-version = "12.8.93"
-description = "Nvidia JIT LTO Library"
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88"},
-    {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:adccd7161ace7261e01bb91e44e88da350895c270d23f744f0820c818b7229e7"},
-    {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-win_amd64.whl", hash = "sha256:bd93fbeeee850917903583587f4fc3a4eafa022e34572251368238ab5e6bd67f"},
-]
-
 [[package]]
 name = "nvidia-nvtx-cu12"
 version = "12.6.77"
@@ -3328,7 +3125,7 @@ description = "NVIDIA Tools Extension"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f44f8d86bb7d5629988d61c8d3ae61dddb2015dee142740536bc7481b022fe4b"},
     {file = "nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_aarch64.whl", hash = "sha256:adcaabb9d436c9761fca2b13959a2d237c5f9fd406c8e4b723c695409ff88059"},
@@ -3337,20 +3134,6 @@ files = [
     {file = "nvidia_nvtx_cu12-12.6.77-py3-none-win_amd64.whl", hash = "sha256:2fb11a4af04a5e6c84073e6404d26588a34afd35379f0855a99797897efa75c0"},
 ]
 
-[[package]]
-name = "nvidia-nvtx-cu12"
-version = "12.8.90"
-description = "NVIDIA Tools Extension"
-optional = false
-python-versions = ">=3"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d7ad891da111ebafbf7e015d34879f7112832fc239ff0d7d776b6cb685274615"},
-    {file = "nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5b17e2001cc0d751a5bc2c6ec6d26ad95913324a4adb86788c944f8ce9ba441f"},
-    {file = "nvidia_nvtx_cu12-12.8.90-py3-none-win_amd64.whl", hash = "sha256:619c8304aedc69f02ea82dd244541a83c3d9d40993381b3b590f1adaed3db41e"},
-]
-
 [[package]]
 name = "openpyxl"
 version = "3.1.5"
@@ -3732,7 +3515,7 @@ fpx = ["olefile"]
 mic = ["olefile"]
 test-arrow = ["pyarrow"]
 tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "trove-classifiers (>=2024.10.12)"]
-typing = ["typing-extensions ; python_version < \"3.10\""]
+typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
 [[package]]
@@ -3972,6 +3755,7 @@ description = "C parser in Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
+markers = "python_version < \"3.14\" or implementation_name != \"PyPy\""
 files = [
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@@ -3997,7 +3781,7 @@ typing-inspection = ">=0.4.0"
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
-timezone = ["tzdata ; python_version >= \"3.9\" and platform_system == \"Windows\""]
+timezone = ["tzdata"]
 
 [[package]]
 name = "pydantic-core"
@@ -4271,7 +4055,7 @@ files = [
 ]
 
 [package.extras]
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+dev = ["backports.zoneinfo", "black", "build", "freezegun", "mdx_truly_sane_lists", "mike", "mkdocs", "mkdocs-awesome-pages-plugin", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-material (>=8.5)", "mkdocstrings[python]", "msgspec", "mypy", "orjson", "pylint", "pytest", "tzdata", "validate-pyproject[all]"]
 
 [[package]]
 name = "pytz"
@@ -5179,7 +4963,7 @@ numpy = ">=1.25.2,<2.6"
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 [[package]]
 name = "send2trash"
@@ -5194,9 +4978,9 @@ files = [
 ]
 
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-objc = ["pyobjc-framework-Cocoa ; sys_platform == \"darwin\""]
-win32 = ["pywin32 ; sys_platform == \"win32\""]
+nativelib = ["pyobjc-framework-Cocoa", "pywin32"]
+objc = ["pyobjc-framework-Cocoa"]
+win32 = ["pywin32"]
 
 [[package]]
 name = "sentence-transformers"
@@ -5239,13 +5023,13 @@ files = [
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+type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.14.*)", "pytest-mypy"]
 
 [[package]]
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@@ -5516,7 +5310,6 @@ description = "Tensors and Dynamic neural networks in Python with strong GPU acc
 optional = false
 python-versions = ">=3.9.0"
 groups = ["main"]
-markers = "python_version >= \"3.14\""
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@@ -5572,70 +5365,6 @@ typing-extensions = ">=4.10.0"
 opt-einsum = ["opt-einsum (>=3.3)"]
 optree = ["optree (>=0.13.0)"]
 
-[[package]]
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-nvidia-nccl-cu12 = {version = "2.27.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nvjitlink-cu12 = {version = "12.8.93", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nvtx-cu12 = {version = "12.8.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-setuptools = {version = "*", markers = "python_version >= \"3.12\""}
-sympy = ">=1.13.3"
-triton = {version = "3.4.0", 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)"]
-pyyaml = ["pyyaml"]
-
 [[package]]
 name = "tornado"
 version = "6.5.2"
@@ -5777,7 +5506,7 @@ description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.14\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "triton-3.3.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b74db445b1c562844d3cfad6e9679c72e93fdfb1a90a24052b03bb5c49d1242e"},
     {file = "triton-3.3.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b31e3aa26f8cb3cc5bf4e187bf737cbacf17311e1112b781d4a059353dfd731b"},
@@ -5795,31 +5524,6 @@ build = ["cmake (>=3.20)", "lit"]
 tests = ["autopep8", "isort", "llnl-hatchet", "numpy", "pytest", "pytest-forked", "pytest-xdist", "scipy (>=1.7.1)"]
 tutorials = ["matplotlib", "pandas", "tabulate"]
 
-[[package]]
-name = "triton"
-version = "3.4.0"
-description = "A language and compiler for custom Deep Learning operations"
-optional = false
-python-versions = "<3.14,>=3.9"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.14\""
-files = [
-    {file = "triton-3.4.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7ff2785de9bc02f500e085420273bb5cc9c9bb767584a4aa28d6e360cec70128"},
-    {file = "triton-3.4.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7b70f5e6a41e52e48cfc087436c8a28c17ff98db369447bcaff3b887a3ab4467"},
-    {file = "triton-3.4.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:31c1d84a5c0ec2c0f8e8a072d7fd150cab84a9c239eaddc6706c081bfae4eb04"},
-    {file = "triton-3.4.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00be2964616f4c619193cb0d1b29a99bd4b001d7dc333816073f92cf2a8ccdeb"},
-    {file = "triton-3.4.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7936b18a3499ed62059414d7df563e6c163c5e16c3773678a3ee3d417865035d"},
-    {file = "triton-3.4.0-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:98e5c1442eaeabae2e2452ae765801bd53cd4ce873cab0d1bdd59a32ab2d9397"},
-]
-
-[package.dependencies]
-setuptools = ">=40.8.0"
-
-[package.extras]
-build = ["cmake (>=3.20,<4.0)", "lit"]
-tests = ["autopep8", "isort", "llnl-hatchet", "numpy", "pytest", "pytest-forked", "pytest-xdist", "scipy (>=1.7.1)"]
-tutorials = ["matplotlib", "pandas", "tabulate"]
-
 [[package]]
 name = "types-python-dateutil"
 version = "2.9.0.20250822"
@@ -5930,7 +5634,7 @@ files = [
 ]
 
 [package.extras]
-brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
 h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
@@ -6011,6 +5715,18 @@ files = [
     {file = "word2number-1.1.zip", hash = "sha256:70e27a5d387f67b04c71fbb7621c05930b19bfd26efd6851e6e0f9969dcde7d0"},
 ]
 
+[[package]]
+name = "xlsxwriter"
+version = "3.2.9"
+description = "A Python module for creating Excel XLSX files."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "xlsxwriter-3.2.9-py3-none-any.whl", hash = "sha256:9a5db42bc5dff014806c58a20b9eae7322a134abb6fce3c92c181bfb275ec5b3"},
+    {file = "xlsxwriter-3.2.9.tar.gz", hash = "sha256:254b1c37a368c444eac6e2f867405cc9e461b0ed97a3233b2ac1e574efb4140c"},
+]
+
 [[package]]
 name = "yarl"
 version = "1.20.1"
@@ -6240,9 +5956,9 @@ files = [
 ]
 
 [package.extras]
-cffi = ["cffi (>=1.17) ; python_version >= \"3.13\" and platform_python_implementation != \"PyPy\""]
+cffi = ["cffi (>=1.17)"]
 
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.12"
-content-hash = "70ce2a6a771ecefd5e6b22ebc37180593311f4cbad2543b7aa0ceaf17a03f743"
+content-hash = "3f82f89f27294f328c93d31edd8d64df332af6ac7f390b379d1a8f4a00cc9258"
diff --git a/fieldExtraction/pyproject.toml b/fieldExtraction/pyproject.toml
index 65e3245..bceaf7f 100644
--- a/fieldExtraction/pyproject.toml
+++ b/fieldExtraction/pyproject.toml
@@ -30,6 +30,7 @@ word2number = "^1.1"
 orjson = "^3.10.16"
 pymupdf = "^1.25.5"
 pillow = "^11.2.1"
+xlsxwriter = "^3.2.9"
 
 [tool.poetry.group.dev.dependencies]
 black = "^24.10.0"
diff --git a/fieldExtraction/src/config.py b/fieldExtraction/src/config.py
index e18d90e..3250796 100644
--- a/fieldExtraction/src/config.py
+++ b/fieldExtraction/src/config.py
@@ -230,11 +230,15 @@ MODEL_ID_CLAUDE3_HAIKU = "anthropic.claude-3-haiku-20240307-v1:0"
 MODEL_ID_CLAUDE35_SONNET = "anthropic.claude-3-5-sonnet-20240620-v1:0"
 MODEL_ID_CLAUDE2 = "anthropic.claude-instant-v1"
 MODEL_ID_CLAUDE35_SONNET_V2 = "anthropic.claude-3-5-sonnet-20241022-v2:0"
-MODEL_ID_CLAUDE37_SONNET = "anthropic.claude-3-7-sonnet-20250219-v1:0"
+MODEL_ID_CLAUDE37_SONNET = "us.anthropic.claude-3-7-sonnet-20250219-v1:0"
 MODEL_ID_CLAUDE4_SONNET = "us.anthropic.claude-sonnet-4-20250514-v1:0"
+MODEL_ID_CLAUDE45_SONNET = "us.anthropic.claude-sonnet-4-5-20250929-v1:0"
 
 # Model aliases for easy upgrades
 MODEL_ALIASES = {
+    "sonnet_37" : MODEL_ID_CLAUDE37_SONNET,
+    "sonnet_4" : MODEL_ID_CLAUDE4_SONNET,
+    "sonnet_45": MODEL_ID_CLAUDE45_SONNET,
     "sonnet_latest": MODEL_ID_CLAUDE4_SONNET,
     "haiku_latest": MODEL_ID_CLAUDE3_HAIKU,
     "legacy_sonnet": MODEL_ID_CLAUDE35_SONNET,
diff --git a/fieldExtraction/src/investment/file_processing.py b/fieldExtraction/src/investment/file_processing.py
index b44b122..0f5b971 100644
--- a/fieldExtraction/src/investment/file_processing.py
+++ b/fieldExtraction/src/investment/file_processing.py
@@ -39,7 +39,6 @@ def process_file(file_object, constants: Constants, run_timestamp):
 
     # Set default values
     dynamic_one_to_one_fields = FieldSet()
-
     process_one_to_n = config.FIELDS in ['all', 'one_to_n']
     process_one_to_one = config.FIELDS in ['all', 'one_to_one']
 
diff --git a/fieldExtraction/src/investment/main.py b/fieldExtraction/src/investment/main.py
index 02a2caa..50627b5 100644
--- a/fieldExtraction/src/investment/main.py
+++ b/fieldExtraction/src/investment/main.py
@@ -100,7 +100,7 @@ def main(testing=False, test_params={}):
 
     # Warm prompt caches before parallel processing
     # This ensures the cache is registered before multiple threads try to use it
-    logging.info("Warming prompt caches before parallel processing...")
+    logging.debug("Warming prompt caches before parallel processing...")
     try:
         cacheable_instructions = prompt_templates.get_cacheable_instructions(constants)
         for cache_key, instruction in cacheable_instructions.items():
@@ -109,7 +109,7 @@ def main(testing=False, test_params={}):
                 cache_key=cache_key,
                 model_id="sonnet_latest"
             )
-        logging.info(f"Successfully warmed {len(cacheable_instructions)} prompt caches")
+        logging.debug(f"Successfully warmed {len(cacheable_instructions)} prompt caches")
     except Exception as e:
         logging.warning(f"Error warming caches (will proceed anyway): {str(e)}")
 
diff --git a/fieldExtraction/src/investment/postprocessing_funcs.py b/fieldExtraction/src/investment/postprocessing_funcs.py
index 841a380..b717ec0 100644
--- a/fieldExtraction/src/investment/postprocessing_funcs.py
+++ b/fieldExtraction/src/investment/postprocessing_funcs.py
@@ -186,7 +186,7 @@ def date_postprocess(df, field_json_path):
         )
     else:
         missing = [col for col in required_cols if col not in df.columns]
-        logging.info(f"Skipping AARETE_DERIVED_TERMINATION_DT creation. Missing: {missing}")
+        logging.debug(f"Skipping AARETE_DERIVED_TERMINATION_DT creation. Missing: {missing}")
         df["AARETE_DERIVED_TERMINATION_DT"] = pd.NA
 
     return df
diff --git a/fieldExtraction/src/prompts/fieldset.py b/fieldExtraction/src/prompts/fieldset.py
index 1ea3b6b..dbb15b6 100644
--- a/fieldExtraction/src/prompts/fieldset.py
+++ b/fieldExtraction/src/prompts/fieldset.py
@@ -90,7 +90,6 @@ class Field:
         - Subsequent calls with same file_path: Uses cached data (no file I/O)
         - Thread-safe: Multiple threads can safely call this simultaneously
 
-
         :param file_path: Path to the JSON file.
         :param field_name: The field name to look for in the JSON data.
         :return: A Field object if found, otherwise raises a ValueError.
@@ -210,10 +209,15 @@ class FieldSet:
         FieldSet(file_path="investment_prompts.json", relationship="one_to_one")
         FieldSet(file_path="investment_prompts.json", relationship="one_to_n", field_type="exhibit_level")
         FieldSet(file_path="investment_prompts.json", field_type="reimbursement_level")
+        
+        # Now supports lists for any attribute:
+        FieldSet(file_path="investment_prompts.json", field_name=["LOB", "PROGRAM"])
+        FieldSet(file_path="investment_prompts.json", field_type=["exhibit_level", "reimbursement_level"])
 
         :param file_path: Path to a JSON file to load fields from (default: None).
         :param fields: List of Field objects to initialize the FieldSet instance with (default: None).
         :param filters: Key-value pairs for filtering fields based on their attributes.
+                        Each value can be a single value or a list of values.
         """
         self.fields = fields if fields is not None else []
         if file_path:
@@ -228,6 +232,11 @@ class FieldSet:
         - Each FieldSet can still apply different filters to the same cached data
         - Thread-safe: Multiple threads can safely call this simultaneously
 
+        FILTERING BEHAVIOR:
+        - Single values: Field attribute must match exactly
+        - Lists: Field attribute must match any value in the list
+        - True: Field attribute must exist (not None)
+
         PERFORMANCE IMPACT:
         - Before caching: 1000+ file reads for the same JSON → "too many open files"
         - After caching: 1 file read per unique path → fast and efficient
@@ -243,21 +252,33 @@ class FieldSet:
             # CACHE HIT: Use cached data
             data = _field_data_cache[file_path]
 
-        # Apply filters to cached data - this allows different FieldSets
-        # to get different subsets from the same cached JSON
-        self.fields = [
-            Field(field_dict)
-            for field_dict in data
-            if all(
-                (
-                    value is True and field_dict.get(attr) is not None
-                )  # Check for "not None" values
-                or (
-                    value is not True and field_dict.get(attr) == value
-                )  # Check for exact match
-                for attr, value in filters.items()
-            )
-        ]
+        # Apply filters to cached data with enhanced list support
+        self.fields = []
+        for field_dict in data:
+            # Check if this field_dict matches all filters
+            matches_all_filters = True
+            
+            for attr, filter_value in filters.items():
+                field_value = field_dict.get(attr)
+                
+                # Handle special case for True filter (check if attribute exists)
+                if filter_value is True:
+                    if field_value is None:
+                        matches_all_filters = False
+                        break
+                # Handle list-based filters (match any value in the list)
+                elif isinstance(filter_value, list):
+                    if field_value not in filter_value:
+                        matches_all_filters = False
+                        break
+                # Handle single value filters (exact match)
+                elif field_value != filter_value:
+                    matches_all_filters = False
+                    break
+            
+            # If all filters matched, add this field
+            if matches_all_filters:
+                self.fields.append(Field(field_dict))
 
     def add_field(self, field):
         """Adds a new field manually to the FieldSet instance.
diff --git a/fieldExtraction/src/testbed/model_evaluation.py b/fieldExtraction/src/testbed/model_evaluation.py
new file mode 100644
index 0000000..61c07b3
--- /dev/null
+++ b/fieldExtraction/src/testbed/model_evaluation.py
@@ -0,0 +1,205 @@
+import pandas as pd
+import concurrent.futures
+import threading
+from src.utils import io_utils, llm_utils
+from src.investment import preprocess
+from constants.constants import Constants
+from src.testbed import model_evaluation_utils
+from src import config
+from src.prompts.fieldset import FieldSet
+
+prompt = "This is a test prompt. Write 'test', nothing else."
+
+def process_file(filename, contract_text, model_id_dict, testbed_df, dynamic_primary_fields, reimbursement_primary_fields, constants):
+    """Process a single file and return its statistics."""
+    print(f"Processing: {filename}")
+
+    testbed_df = testbed_df[testbed_df["FILE_NAME"] == filename] 
+    
+    # Preprocess the contract
+    contract_text = preprocess.clean_text(contract_text)
+    text_dict, top_sheet_dict = preprocess.split_text(contract_text)
+    text_dict, header, footer = preprocess.find_headers_and_footers(text_dict)
+    text_dict = preprocess.clean_tables(
+        text_dict, constants.EXHIBIT_HEADER_MARKERS, filename
+    )
+    exhibit_dict, all_exhibit_headers = preprocess.one_to_n_exhibit_chunking(
+        text_dict, constants.EXHIBIT_HEADER_MARKERS, filename
+    )
+
+    # Evaluate Dynamic Primary
+    dynamic_primary_stats_dicts = model_evaluation_utils.evaluate_dynamic_primary(
+        exhibit_dict,
+        model_id_dict,
+        testbed_df,
+        dynamic_primary_fields,
+        constants,
+        filename
+    ) # list of dicts
+
+    # Evaluate Reimbursement Primary
+    reimbursement_primary_stats_dict = model_evaluation_utils.evaluate_reimbursement_primary(
+        exhibit_dict,
+        model_id_dict,
+        testbed_df,
+        reimbursement_primary_fields,
+        constants,
+        filename
+    ) # dict
+
+    return dynamic_primary_stats_dicts, reimbursement_primary_stats_dict
+
+
+# Initialize variables
+dynamic_primary_fields = FieldSet(
+    config.FIELD_JSON_PATH, field_name=["LOB", "PROGRAM", "NETWORK"]
+)
+reimbursement_primary_fields = FieldSet(
+    config.FIELD_JSON_PATH, field_type="reimbursement_level"
+)
+
+model_id_dict = {
+    "SONNET_37" : "sonnet_37",
+    "SONNET_4" : "sonnet_4",
+    "SONNET_45" : "sonnet_45"
+}
+input_dict = io_utils.read_input()
+testbed_df = pd.read_excel("Doczy-Testbed.xlsx")
+testbed_files = list(testbed_df["FILE_NAME"])
+constants = Constants()
+
+# Initialize dynamic primary stats for each model
+dynamic_primary = [{"Model": model_name, "TP": 0, "TN": 0, "FP": 0, "FN": 0} for model_name in model_id_dict.keys()]
+
+# Initialize list to collect all reimbursement primary stats
+reimbursement_primary_stats_list = []
+
+# Lock for thread-safe updates to stats
+stats_lock = threading.Lock()
+
+# Function to update global stats with results from one file
+def update_stats(results):
+    dynamic_primary_stats_dicts, reimbursement_primary_stats_dict = results
+    
+    with stats_lock:
+        # Update dynamic primary stats
+        for file_stats_dict in dynamic_primary_stats_dicts:
+            model_name = file_stats_dict["Model"]
+            for model_stats in dynamic_primary:
+                if model_stats["Model"] == model_name:
+                    for metric in ["TP", "TN", "FP", "FN"]:
+                        model_stats[metric] += file_stats_dict.get(metric, 0)
+                    break
+        
+        # Add reimbursement primary stats to the list
+        if reimbursement_primary_stats_dict:
+            reimbursement_primary_stats_list.append(reimbursement_primary_stats_dict)
+
+# Process files in parallel
+filtered_input = {filename: text for filename, text in input_dict.items() if filename in testbed_files}
+
+# Use ThreadPoolExecutor for parallel processing
+with concurrent.futures.ThreadPoolExecutor(max_workers=config.MAX_WORKERS) as executor:
+    # Submit all file processing tasks
+    future_to_file = {
+        executor.submit(
+            process_file, 
+            filename, 
+            contract_text, 
+            model_id_dict, 
+            testbed_df, 
+            dynamic_primary_fields, 
+            reimbursement_primary_fields,
+            constants
+        ): filename for filename, contract_text in filtered_input.items()
+    }
+    
+    # Process results as they complete
+    for future in concurrent.futures.as_completed(future_to_file):
+        filename = future_to_file[future]
+        try:
+            results = future.result()
+            update_stats(results)
+            print(f"Completed processing: {filename}")
+        except Exception as e:
+            print(f"Error processing {filename}: {e}")
+
+# Generate Excel report with two sheets
+def generate_excel_report(output_path="model_evaluation_results.xlsx"):
+    # Create Excel writer
+    with pd.ExcelWriter(output_path, engine='xlsxwriter') as writer:
+        
+        # Sheet 1: Dynamic Primary Stats
+        dynamic_primary_df = pd.DataFrame(dynamic_primary)
+        
+        # Calculate additional metrics for each model
+        for idx, row in dynamic_primary_df.iterrows():
+            tp = row["TP"]
+            tn = 0
+            fp = row["FP"]
+            fn = row["FN"]
+            
+            # Add calculated metrics
+            dynamic_primary_df.loc[idx, "Accuracy"] = (tp + tn) / (tp + tn + fp + fn) if (tp + tn + fp + fn) > 0 else 0
+            dynamic_primary_df.loc[idx, "Precision"] = tp / (tp + fp) if (tp + fp) > 0 else 0
+            dynamic_primary_df.loc[idx, "Recall"] = tp / (tp + fn) if (tp + fn) > 0 else 0
+            precision = dynamic_primary_df.loc[idx, "Precision"]
+            recall = dynamic_primary_df.loc[idx, "Recall"]
+            dynamic_primary_df.loc[idx, "F1"] = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
+        
+        # Write to Excel
+        dynamic_primary_df.to_excel(writer, sheet_name='Dynamic Primary Stats', index=False)
+        
+        # Sheet 2: Reimbursement Primary Stats
+        if reimbursement_primary_stats_list:
+            # Combine all reimbursement stats into a DataFrame
+            reimbursement_df = pd.DataFrame(reimbursement_primary_stats_list)
+            
+            # Write individual file stats
+            reimbursement_df.to_excel(writer, sheet_name='Reimbursement Stats', index=False)
+            
+            # Get a reference to the worksheet to add summary statistics
+            worksheet = writer.sheets['Reimbursement Stats']
+            
+            # Add a summary section
+            summary_row = len(reimbursement_df) + 3  # Leave a gap of 2 rows
+            
+            # Add headers for the summary section
+            worksheet.write(summary_row, 0, "Summary Statistics")
+            worksheet.write(summary_row + 1, 0, "Model")
+            worksheet.write(summary_row + 1, 1, "Average Row Count Diff")
+            worksheet.write(summary_row + 1, 2, "Average Row Count Pct Diff")
+            
+            # Add summary data
+            row_offset = summary_row + 2
+            
+            # Add model stats - only proceed if we have testbed data
+            if 'Testbed' in reimbursement_df.columns:
+                for model_name in model_id_dict.keys():
+                    if model_name in reimbursement_df.columns:
+                        # Calculate the absolute difference between model and testbed row counts
+                        reimbursement_df['Abs_Diff'] = abs(reimbursement_df[model_name] - reimbursement_df['Testbed'])
+                        
+                        # Calculate the percentage difference
+                        reimbursement_df['Pct_Diff'] = reimbursement_df['Abs_Diff'] / reimbursement_df['Testbed'] * 100
+                        reimbursement_df['Pct_Diff'] = reimbursement_df['Pct_Diff'].fillna(0)  # Handle division by zero
+                        
+                        # Calculate averages
+                        avg_abs_diff = reimbursement_df['Abs_Diff'].mean()
+                        avg_pct_diff = reimbursement_df['Pct_Diff'].mean()
+                        
+                        # Write to summary
+                        worksheet.write(row_offset, 0, model_name)
+                        worksheet.write(row_offset, 1, avg_abs_diff)
+                        worksheet.write(row_offset, 2, f"{avg_pct_diff:.2f}%")
+                        
+                        row_offset += 1
+                
+                # Remove the temporary columns from the DataFrame before writing
+                if 'Abs_Diff' in reimbursement_df.columns:
+                    reimbursement_df = reimbursement_df.drop(['Abs_Diff', 'Pct_Diff'], axis=1)
+                    # We need to write the dataframe again since we modified it
+                    reimbursement_df.to_excel(writer, sheet_name='Reimbursement Stats', index=False)
+
+# Call the report generation function after all processing is complete
+generate_excel_report()
diff --git a/fieldExtraction/src/testbed/model_evaluation_utils.py b/fieldExtraction/src/testbed/model_evaluation_utils.py
new file mode 100644
index 0000000..e19e5d8
--- /dev/null
+++ b/fieldExtraction/src/testbed/model_evaluation_utils.py
@@ -0,0 +1,183 @@
+
+import pandas as pd
+from src.prompts.fieldset import FieldSet
+from src.utils import llm_utils, string_utils
+from src.prompts import prompt_templates
+from constants.constants import Constants
+from src.investment import aarete_derived, one_to_n_funcs
+from src import config
+
+
+def prompt_dynamic_primary(exhibit_dict: dict[str, str], model_id_dict: dict[str, str], dynamic_primary_fields: FieldSet, constants: Constants, filename: str):
+    model_answer_dicts = {}
+    for model_name, model_id in model_id_dict.items():
+        answer_dicts = []
+        for exhibit_page, exhibit_text in exhibit_dict.items():
+            answer_dict = {}
+            for field in dynamic_primary_fields.fields:
+                prompt = prompt_templates.DYNAMIC_PRIMARY_TEXT(exhibit_text, field.field_name, field.get_prompt(constants))
+                llm_answer_raw = llm_utils.invoke_claude(
+                    prompt=prompt,
+                    model_id=model_id,
+                    filename=filename
+                )
+                llm_answer_final = string_utils.extract_text_from_delimiters(
+                    llm_answer_raw, string_utils.Delimiter.PIPE
+                )
+                answer_dict[field.field_name] = llm_answer_final
+            answer_dicts.append(answer_dict)
+            
+        answer_dicts = aarete_derived.get_crosswalk_fields(answer_dicts, constants)
+        model_answer_dicts[model_name] = answer_dicts
+    return model_answer_dicts
+
+def consolidate_dynamic_primary(model_answer_dicts: dict):
+    consolidated_answer_dicts = {}
+    for model_name, answer_dicts in model_answer_dicts.items():
+        unique_values = {}
+        for answer_dict in answer_dicts:
+            for field_name, value in answer_dict.items():
+                if "AARETE_DERIVED" not in field_name:
+                    continue
+                if field_name not in unique_values:
+                    unique_values[field_name] = set()
+                if not string_utils.is_empty(value):
+                    # Split pipe-separated values and add each individually
+                    if isinstance(value, str) and "|" in value:
+                        for split_value in value.split("|"):
+                            split_value = split_value.strip()  # Remove any extra whitespace
+                            if not string_utils.is_empty(split_value):
+                                unique_values[field_name].add(split_value)
+                    else:
+                        unique_values[field_name].add(value)
+        consolidated_answer_dicts[model_name] = {k: list(v) for k, v in unique_values.items()}
+    
+    return consolidated_answer_dicts
+
+def generate_dynamic_primary_stats(consolidated_answer_dicts: dict[str, dict], testbed_df: pd.DataFrame, filename: str):
+    """
+    Generate confusion matrix statistics for dynamic primary field extraction.
+    
+    Args:
+        consolidated_answer_dicts: Dictionary with model names as keys and dictionaries of field values as values
+        testbed_df: DataFrame containing the ground truth data
+        filename: Name of the file being evaluated
+    
+    Returns:
+        List of dictionaries with confusion matrix statistics for each model
+    """
+
+    # Extract all unique ground truth values from testbed_df
+    ground_truth_values = set()
+    aarete_cols = ["AARETE_DERIVED_LOB", "AARETE_DERIVED_PROGRAM", "AARETE_DERIVED_NETWORK"]
+    
+    # Only include columns that actually exist in the DataFrame
+    aarete_cols = [col for col in aarete_cols if col in testbed_df.columns]
+    
+    # Collect unique values from each column
+    for col in aarete_cols:
+        # Handle potential pipe-separated values
+        for value in testbed_df[col].dropna():
+            if isinstance(value, str):
+                if "|" in value:
+                    for split_value in value.split("|"):
+                        split_value = split_value.strip()
+                        if not string_utils.is_empty(split_value):
+                            ground_truth_values.add(split_value)
+                else:
+                    if not string_utils.is_empty(value):
+                        ground_truth_values.add(value)
+    
+    stats_dicts = []
+    for model_name, field_dict in consolidated_answer_dicts.items():
+        # Collect all unique values predicted by this model
+        model_values = set()
+        for field_name, values_list in field_dict.items():
+            for value in values_list:
+                if not string_utils.is_empty(value):
+                    model_values.add(value)
+        TP = len([v for v in model_values if v in ground_truth_values])
+        FP = len([v for v in model_values if v not in ground_truth_values])
+        FN = len([v for v in ground_truth_values if v not in model_values])
+        stats_dicts.append({"Model" : model_name, "TP" : TP, "FP" : FP, "FN" : FN})
+
+    return stats_dicts
+    
+def evaluate_dynamic_primary(exhibit_dict: dict[str, str], model_id_dict: dict[str, str], testbed_df: pd.DataFrame, dynamic_primary_fields: FieldSet, constants: Constants, filename: str):
+
+    ## Prompt for model_answer_dicts
+    dynamic_answer_dicts = prompt_dynamic_primary(
+        exhibit_dict,
+        model_id_dict,
+        dynamic_primary_fields,
+        constants,
+        filename
+    )
+
+    # Consolidate model_answer_dicts
+    consolidated_answer_dicts = consolidate_dynamic_primary(dynamic_answer_dicts)
+
+    # Generate stats
+    stats_dicts = generate_dynamic_primary_stats(consolidated_answer_dicts, testbed_df, filename)
+    
+    return stats_dicts
+    
+def evaluate_reimbursement_primary(exhibit_dict: dict[str, str], model_id_dict: dict[str, str], testbed_df: pd.DataFrame, reimbursement_primary_fields: FieldSet, constants: Constants, filename: str) -> dict[str, object]:
+    # Create two separate dictionaries and merge them at the end
+    metadata: dict[str, str] = {"FILE_NAME": filename}
+    
+    # Create a numeric counts dictionary with explicit types
+    counts: dict[str, int] = {}
+    num_lesser = len(testbed_df[testbed_df['LESSER_OF_IND'] == 'Y'])
+    counts["Testbed"] = int(num_lesser/2) + (len(testbed_df)-num_lesser)
+    
+    # Initialize model counts
+    for model_name in model_id_dict:
+        counts[model_name] = 0
+    
+    # Process each model
+    for model_name, model_id in model_id_dict.items():
+        seen_pairs = set()
+        for exhibit_page, exhibit_text in exhibit_dict.items():
+            field_prompts = reimbursement_primary_fields.print_prompt_dict(constants)
+            instruction, prompt = prompt_templates.REIMBURSEMENT_PRIMARY(exhibit_text, field_prompts)
+            llm_answer_raw = llm_utils.invoke_claude(
+                prompt,
+                model_id,
+                filename,
+                max_tokens=25000,
+                cache=True,
+                instruction=instruction,
+                usage_label="REIMBURSEMENT_PRIMARY",
+            )
+            # Check for the special "no results" case
+            if "NO_REIMBURSEMENT_TERMS_FOUND" in llm_answer_raw:
+                continue
+            
+            try:
+                llm_answer_final = string_utils.universal_json_load(llm_answer_raw)
+
+                reimbursement_primary_answers = one_to_n_funcs.clean_reimbursement_primary(
+                    llm_answer_final,
+                    "",
+                    seen_pairs,
+                    exhibit_page,
+                    constants,
+                    filename
+                )
+                counts[model_name] += len(reimbursement_primary_answers)
+            except Exception as e:
+                pass
+    
+    # Merge the dictionaries before returning
+    result: dict[str, object] = {}
+    result.update(metadata)
+    result.update(counts)
+    
+    return result
+
+
+
+
+
+
diff --git a/fieldExtraction/src/testbed/test.py b/fieldExtraction/src/testbed/testbed_metrics.py
similarity index 100%
rename from fieldExtraction/src/testbed/test.py
rename to fieldExtraction/src/testbed/testbed_metrics.py
diff --git a/fieldExtraction/src/utils/llm_utils.py b/fieldExtraction/src/utils/llm_utils.py
index 16f1c02..36345d0 100644
--- a/fieldExtraction/src/utils/llm_utils.py
+++ b/fieldExtraction/src/utils/llm_utils.py
@@ -194,6 +194,18 @@ def invoke_claude(
             )
             input_cost_per_1k, output_cost_per_1k = 0.003, 0.015
             cache_creation_cost_per_1k, cache_read_cost_per_1k = 0.00375, 0.0003
+        elif resolved_model_id == config.MODEL_ID_CLAUDE45_SONNET:
+            response = local_claude_3_and_up(
+                prompt,
+                resolved_model_id,
+                filename,
+                max_tokens,
+                cache=cache,
+                instruction=instruction,
+                usage_label=usage_label,
+            )
+            input_cost_per_1k, output_cost_per_1k = 0.003, 0.015
+            cache_creation_cost_per_1k, cache_read_cost_per_1k = 0.00375, 0.0003
         else:
             raise ValueError(f"Unsupported model_id: {model_id}")
     elif config.RUN_MODE == "ec2":
@@ -247,6 +259,16 @@ def invoke_claude(
                 instruction=instruction,
                 usage_label=usage_label,
             )
+        elif resolved_model_id == config.MODEL_ID_CLAUDE45_SONNET:
+            response = ec2_claude_3_and_up(
+                prompt,
+                resolved_model_id,
+                filename,
+                max_tokens,
+                cache=cache,
+                instruction=instruction,
+                usage_label=usage_label,
+            )
             input_cost_per_1k, output_cost_per_1k = 0.003, 0.015
             cache_creation_cost_per_1k, cache_read_cost_per_1k = 0.00375, 0.0003
         else: