diff --git a/fieldExtraction/poetry.lock b/fieldExtraction/poetry.lock
index 52457a9..5470100 100644
--- a/fieldExtraction/poetry.lock
+++ b/fieldExtraction/poetry.lock
@@ -2,100 +2,105 @@
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.4"
+version = "2.4.6"
 description = "Happy Eyeballs for asyncio"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 ]
 
 [[package]]
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+version = "3.11.12"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
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 [package.dependencies]
@@ -342,18 +347,18 @@ tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "babel"
-version = "2.16.0"
+version = "2.17.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
 files = [
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 ]
 
 [package.extras]
-dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
+dev = ["backports.zoneinfo", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata"]
 
 [[package]]
 name = "backoff"
@@ -408,18 +413,19 @@ typecheck = ["mypy"]
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.12.3"
+version = "4.13.3"
 description = "Screen-scraping library"
 optional = false
-python-versions = ">=3.6.0"
+python-versions = ">=3.7.0"
 groups = ["dev"]
 files = [
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 ]
 
 [package.dependencies]
 soupsieve = ">1.2"
+typing-extensions = ">=4.0.0"
 
 [package.extras]
 cchardet = ["cchardet"]
@@ -494,18 +500,18 @@ css = ["tinycss2 (>=1.1.0,<1.5)"]
 
 [[package]]
 name = "boto3"
-version = "1.36.6"
+version = "1.36.24"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
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+botocore = ">=1.36.24,<1.37.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.11.0,<0.12.0"
 
@@ -514,14 +520,14 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.36.6"
+version = "1.36.24"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
@@ -530,7 +536,7 @@ python-dateutil = ">=2.1,<3.0.0"
 urllib3 = {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""}
 
 [package.extras]
-crt = ["awscrt (==0.23.4)"]
+crt = ["awscrt (==0.23.8)"]
 
 [[package]]
 name = "build"
@@ -570,14 +576,14 @@ files = [
 
 [[package]]
 name = "certifi"
-version = "2024.12.14"
+version = "2025.1.31"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 groups = ["main", "dev"]
 files = [
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 ]
 
 [[package]]
@@ -807,14 +813,14 @@ numpy = "*"
 
 [[package]]
 name = "chromadb"
-version = "0.5.23"
+version = "0.6.3"
 description = "Chroma."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 [package.dependencies]
@@ -841,7 +847,7 @@ pypika = ">=0.48.9"
 PyYAML = ">=6.0.0"
 rich = ">=10.11.0"
 tenacity = ">=8.2.3"
-tokenizers = ">=0.13.2,<=0.20.3"
+tokenizers = ">=0.13.2"
 tqdm = ">=4.65.0"
 typer = ">=0.9.0"
 typing_extensions = ">=4.5.0"
@@ -913,74 +919,75 @@ test = ["pytest"]
 
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-version = "7.6.10"
+version = "7.6.12"
 description = "Code coverage measurement for Python"
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 python-versions = ">=3.9"
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 ]
 
 [package.extras]
@@ -1173,14 +1180,14 @@ packaging = "*"
 
 [[package]]
 name = "fastapi"
-version = "0.115.7"
+version = "0.115.8"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
@@ -1226,14 +1233,14 @@ typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
 name = "flatbuffers"
-version = "25.1.24"
+version = "25.2.10"
 description = "The FlatBuffers serialization format for Python"
 optional = false
 python-versions = "*"
 groups = ["main"]
 files = [
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 ]
 
 [[package]]
@@ -1352,14 +1359,14 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2024.12.0"
+version = "2025.2.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 ]
 
 [package.extras]
@@ -1386,7 +1393,7 @@ sftp = ["paramiko"]
 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"]
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+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"]
 tqdm = ["tqdm"]
 
@@ -1417,14 +1424,14 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.66.0"
+version = "1.67.0"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
@@ -1718,14 +1725,14 @@ files = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.27.1"
+version = "0.29.1"
 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"]
 files = [
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 ]
 
 [package.dependencies]
@@ -1738,13 +1745,13 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "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", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "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", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "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", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "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)"]
 inference = ["aiohttp"]
-quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.5.0)"]
+quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.9.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 tensorflow-testing = ["keras (<3.0)", "tensorflow"]
 testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "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)"]
@@ -1873,14 +1880,14 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.31.0"
+version = "8.32.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 groups = ["dev"]
 files = [
-    {file = "ipython-8.31.0-py3-none-any.whl", hash = "sha256:46ec58f8d3d076a61d128fe517a51eb730e3aaf0c184ea8c17d16e366660c6a6"},
-    {file = "ipython-8.31.0.tar.gz", hash = "sha256:b6a2274606bec6166405ff05e54932ed6e5cfecaca1fc05f2cacde7bb074d70b"},
+    {file = "ipython-8.32.0-py3-none-any.whl", hash = "sha256:cae85b0c61eff1fc48b0a8002de5958b6528fa9c8defb1894da63f42613708aa"},
+    {file = "ipython-8.32.0.tar.gz", hash = "sha256:be2c91895b0b9ea7ba49d33b23e2040c352b33eb6a519cca7ce6e0c743444251"},
 ]
 
 [package.dependencies]
@@ -2286,18 +2293,19 @@ test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"
 
 [[package]]
 name = "jupyter-events"
-version = "0.11.0"
+version = "0.12.0"
 description = "Jupyter Event System library"
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "jupyter_events-0.11.0-py3-none-any.whl", hash = "sha256:36399b41ce1ca45fe8b8271067d6a140ffa54cec4028e95491c93b78a855cacf"},
-    {file = "jupyter_events-0.11.0.tar.gz", hash = "sha256:c0bc56a37aac29c1fbc3bcfbddb8c8c49533f9cf11f1c4e6adadba936574ab90"},
+    {file = "jupyter_events-0.12.0-py3-none-any.whl", hash = "sha256:6464b2fa5ad10451c3d35fabc75eab39556ae1e2853ad0c0cc31b656731a97fb"},
+    {file = "jupyter_events-0.12.0.tar.gz", hash = "sha256:fc3fce98865f6784c9cd0a56a20644fc6098f21c8c33834a8d9fe383c17e554b"},
 ]
 
 [package.dependencies]
 jsonschema = {version = ">=4.18.0", extras = ["format-nongpl"]}
+packaging = "*"
 python-json-logger = ">=2.0.4"
 pyyaml = ">=5.3"
 referencing = "*"
@@ -2384,14 +2392,14 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.3.4"
+version = "4.3.5"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
 files = [
-    {file = "jupyterlab-4.3.4-py3-none-any.whl", hash = "sha256:b754c2601c5be6adf87cb5a1d8495d653ffb945f021939f77776acaa94dae952"},
-    {file = "jupyterlab-4.3.4.tar.gz", hash = "sha256:f0bb9b09a04766e3423cccc2fc23169aa2ffedcdf8713e9e0fb33cac0b6859d0"},
+    {file = "jupyterlab-4.3.5-py3-none-any.whl", hash = "sha256:571bbdee20e4c5321ab5195bc41cf92a75a5cff886be5e57ce78dfa37a5e9fdb"},
+    {file = "jupyterlab-4.3.5.tar.gz", hash = "sha256:c779bf72ced007d7d29d5bcef128e7fdda96ea69299e19b04a43635a7d641f9d"},
 ]
 
 [package.dependencies]
@@ -2468,14 +2476,14 @@ files = [
 
 [[package]]
 name = "kubernetes"
-version = "32.0.0"
+version = "32.0.1"
 description = "Kubernetes python client"
 optional = false
 python-versions = ">=3.6"
 groups = ["main"]
 files = [
-    {file = "kubernetes-32.0.0-py2.py3-none-any.whl", hash = "sha256:60fd8c29e8e43d9c553ca4811895a687426717deba9c0a66fb2dcc3f5ef96692"},
-    {file = "kubernetes-32.0.0.tar.gz", hash = "sha256:319fa840345a482001ac5d6062222daeb66ec4d1bcb3087402aed685adf0aecb"},
+    {file = "kubernetes-32.0.1-py2.py3-none-any.whl", hash = "sha256:35282ab8493b938b08ab5526c7ce66588232df00ef5e1dbe88a419107dc10998"},
+    {file = "kubernetes-32.0.1.tar.gz", hash = "sha256:42f43d49abd437ada79a79a16bd48a604d3471a117a8347e87db693f2ba0ba28"},
 ]
 
 [package.dependencies]
@@ -2496,20 +2504,20 @@ adal = ["adal (>=1.0.2)"]
 
 [[package]]
 name = "langchain"
-version = "0.3.15"
+version = "0.3.19"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
-    {file = "langchain-0.3.15-py3-none-any.whl", hash = "sha256:2657735184054cae8181ac43fce6cbc9ee64ca81a2ad2aed3ccd6e5d6fe1f19f"},
-    {file = "langchain-0.3.15.tar.gz", hash = "sha256:1204d67f8469cd8da5621d2b39501650a824d4c0d5a74264dfe3df9a7528897e"},
+    {file = "langchain-0.3.19-py3-none-any.whl", hash = "sha256:1e16d97db9106640b7de4c69f8f5ed22eeda56b45b9241279e83f111640eff16"},
+    {file = "langchain-0.3.19.tar.gz", hash = "sha256:b96f8a445f01d15d522129ffe77cc89c8468dbd65830d153a676de8f6b899e7b"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
-langchain-core = ">=0.3.31,<0.4.0"
-langchain-text-splitters = ">=0.3.3,<0.4.0"
+langchain-core = ">=0.3.35,<1.0.0"
+langchain-text-splitters = ">=0.3.6,<1.0.0"
 langsmith = ">=0.1.17,<0.4"
 numpy = {version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""}
 pydantic = ">=2.7.4,<3.0.0"
@@ -2518,16 +2526,33 @@ requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
 tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
 
+[package.extras]
+anthropic = ["langchain-anthropic"]
+aws = ["langchain-aws"]
+cohere = ["langchain-cohere"]
+community = ["langchain-community"]
+deepseek = ["langchain-deepseek"]
+fireworks = ["langchain-fireworks"]
+google-genai = ["langchain-google-genai"]
+google-vertexai = ["langchain-google-vertexai"]
+groq = ["langchain-groq"]
+huggingface = ["langchain-huggingface"]
+mistralai = ["langchain-mistralai"]
+ollama = ["langchain-ollama"]
+openai = ["langchain-openai"]
+together = ["langchain-together"]
+xai = ["langchain-xai"]
+
 [[package]]
 name = "langchain-aws"
-version = "0.2.11"
+version = "0.2.13"
 description = "An integration package connecting AWS and LangChain"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
-    {file = "langchain_aws-0.2.11-py3-none-any.whl", hash = "sha256:8351f5ea49da16ad67fd54801629d67ff71a204d0c6d80bd056fa451bcb43824"},
-    {file = "langchain_aws-0.2.11.tar.gz", hash = "sha256:1ceb58e44d01bc4e90e05e340ae7f90331c7dda012bcf8e4a89d35d864994835"},
+    {file = "langchain_aws-0.2.13-py3-none-any.whl", hash = "sha256:f914c046d8e92b7e721f18916b2fe3dc1a1715c2abe9f60ecd41f844942a123b"},
+    {file = "langchain_aws-0.2.13.tar.gz", hash = "sha256:2dc41928ff35f0e37cf521e976f6e87f8263b0a27cfb5007394241f56d1d6644"},
 ]
 
 [package.dependencies]
@@ -2538,40 +2563,39 @@ pydantic = ">=2,<3"
 
 [[package]]
 name = "langchain-chroma"
-version = "0.2.0"
+version = "0.2.2"
 description = "An integration package connecting Chroma and LangChain"
 optional = false
 python-versions = "<4,>=3.9"
 groups = ["main"]
 files = [
-    {file = "langchain_chroma-0.2.0-py3-none-any.whl", hash = "sha256:2c405e8fe79267d2298342e7c80b4804ceae6b81d80b24b83ae5fbdf86676072"},
-    {file = "langchain_chroma-0.2.0.tar.gz", hash = "sha256:611dd5d844388cca0acd99dab230ac8512d79ac8d86ff80a6f82607f497d5930"},
+    {file = "langchain_chroma-0.2.2-py3-none-any.whl", hash = "sha256:7766335f16975c2059bb6e8ea75a59a4082c52e6c9d66827681d1bce2c2756a2"},
+    {file = "langchain_chroma-0.2.2.tar.gz", hash = "sha256:11225ca6077b2bf919b84d74e4d343121e077c0fa3274db1929a270fef9d1002"},
 ]
 
 [package.dependencies]
-chromadb = ">=0.4.0,<0.5.4 || >0.5.4,<0.5.5 || >0.5.5,<0.5.7 || >0.5.7,<0.5.9 || >0.5.9,<0.5.10 || >0.5.10,<0.5.11 || >0.5.11,<0.5.12 || >0.5.12,<0.6.0"
-fastapi = ">=0.95.2,<1"
+chromadb = ">=0.4.0,<0.5.4 || >0.5.4,<0.5.5 || >0.5.5,<0.5.7 || >0.5.7,<0.5.9 || >0.5.9,<0.5.10 || >0.5.10,<0.5.11 || >0.5.11,<0.5.12 || >0.5.12,<0.7.0"
 langchain-core = ">=0.2.43,<0.3.0 || >0.3.0,<0.3.1 || >0.3.1,<0.3.2 || >0.3.2,<0.3.3 || >0.3.3,<0.3.4 || >0.3.4,<0.3.5 || >0.3.5,<0.3.6 || >0.3.6,<0.3.7 || >0.3.7,<0.3.8 || >0.3.8,<0.3.9 || >0.3.9,<0.3.10 || >0.3.10,<0.3.11 || >0.3.11,<0.3.12 || >0.3.12,<0.3.13 || >0.3.13,<0.3.14 || >0.3.14,<0.4.0"
 numpy = {version = ">=1.26.2,<2.0.0", markers = "python_version >= \"3.12\""}
 
 [[package]]
 name = "langchain-community"
-version = "0.3.15"
+version = "0.3.18"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
-    {file = "langchain_community-0.3.15-py3-none-any.whl", hash = "sha256:5b6ac359f75922a826566f94eb9a9b5c763cc78f395f0baf2f5638e62fdae1dd"},
-    {file = "langchain_community-0.3.15.tar.gz", hash = "sha256:c2fee46a0ea1b94c475bd4263edb53d5615dbe37c5263480bf55cb8e465ac235"},
+    {file = "langchain_community-0.3.18-py3-none-any.whl", hash = "sha256:0d4a70144a1750045c4f726f9a43379ed2484178f76e4b8295bcef3a7fdf41d5"},
+    {file = "langchain_community-0.3.18.tar.gz", hash = "sha256:fa2889a8f0b2d22b5c306fd1b070c0970e1f11b604bf55fad2f4a1d0bf68a077"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-httpx-sse = ">=0.4.0,<0.5.0"
-langchain = ">=0.3.15,<0.4.0"
-langchain-core = ">=0.3.31,<0.4.0"
+httpx-sse = ">=0.4.0,<1.0.0"
+langchain = ">=0.3.19,<1.0.0"
+langchain-core = ">=0.3.37,<1.0.0"
 langsmith = ">=0.1.125,<0.4"
 numpy = {version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""}
 pydantic-settings = ">=2.4.0,<3.0.0"
@@ -2582,14 +2606,14 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.31"
+version = "0.3.37"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
-    {file = "langchain_core-0.3.31-py3-none-any.whl", hash = "sha256:882e64ad95887c951dce8e835889e43263b11848c394af3b73e06912624bd743"},
-    {file = "langchain_core-0.3.31.tar.gz", hash = "sha256:5ffa56354c07de9efaa4139609659c63e7d9b29da2c825f6bab9392ec98300df"},
+    {file = "langchain_core-0.3.37-py3-none-any.whl", hash = "sha256:8202fd6506ce139a3a1b1c4c3006216b1c7fffa40bdd1779f7d2c67f75eb5f79"},
+    {file = "langchain_core-0.3.37.tar.gz", hash = "sha256:cda8786e616caa2f68f7cc9e811b9b50e3b63fb2094333318b348e5961a7ea01"},
 ]
 
 [package.dependencies]
@@ -2606,18 +2630,18 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langchain-text-splitters"
-version = "0.3.5"
+version = "0.3.6"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
-    {file = "langchain_text_splitters-0.3.5-py3-none-any.whl", hash = "sha256:8c9b059827438c5fa8f327b4df857e307828a5ec815163c9b5c9569a3e82c8ee"},
-    {file = "langchain_text_splitters-0.3.5.tar.gz", hash = "sha256:11cb7ca3694e5bdd342bc16d3875b7f7381651d4a53cbb91d34f22412ae16443"},
+    {file = "langchain_text_splitters-0.3.6-py3-none-any.whl", hash = "sha256:e5d7b850f6c14259ea930be4a964a65fa95d9df7e1dbdd8bad8416db72292f4e"},
+    {file = "langchain_text_splitters-0.3.6.tar.gz", hash = "sha256:c537972f4b7c07451df431353a538019ad9dadff7a1073ea363946cea97e1bee"},
 ]
 
 [package.dependencies]
-langchain-core = ">=0.3.29,<0.4.0"
+langchain-core = ">=0.3.34,<1.0.0"
 
 [[package]]
 name = "langchainhub"
@@ -2638,14 +2662,14 @@ types-requests = ">=2.31.0.2,<3.0.0.0"
 
 [[package]]
 name = "langsmith"
-version = "0.3.2"
+version = "0.3.8"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
-    {file = "langsmith-0.3.2-py3-none-any.whl", hash = "sha256:48ff6bc5eda62f4729596bb68d4f96166d2654728ac32970b69b1be874c61925"},
-    {file = "langsmith-0.3.2.tar.gz", hash = "sha256:7724668e9705734ab25a7977fc34a9ee15a40ba4108987926c69293a05d40229"},
+    {file = "langsmith-0.3.8-py3-none-any.whl", hash = "sha256:fbb9dd97b0f090219447fca9362698d07abaeda1da85aa7cc6ec6517b36581b1"},
+    {file = "langsmith-0.3.8.tar.gz", hash = "sha256:97f9bebe0b7cb0a4f278e6ff30ae7d5ededff3883b014442ec6d7d575b02a0f1"},
 ]
 
 [package.dependencies]
@@ -2761,14 +2785,14 @@ files = [
 
 [[package]]
 name = "marshmallow"
-version = "3.26.0"
+version = "3.26.1"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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-    {file = "marshmallow-3.26.0.tar.gz", hash = "sha256:eb36762a1cc76d7abf831e18a3a1b26d3d481bbc74581b8e532a3d3a8115e1cb"},
+    {file = "marshmallow-3.26.1-py3-none-any.whl", hash = "sha256:3350409f20a70a7e4e11a27661187b77cdcaeb20abca41c1454fe33636bea09c"},
+    {file = "marshmallow-3.26.1.tar.gz", hash = "sha256:e6d8affb6cb61d39d26402096dc0aee12d5a26d490a121f118d2e81dc0719dc6"},
 ]
 
 [package.dependencies]
@@ -2808,14 +2832,14 @@ files = [
 
 [[package]]
 name = "mistune"
-version = "3.1.0"
+version = "3.1.2"
 description = "A sane and fast Markdown parser with useful plugins and renderers"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
 files = [
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+    {file = "mistune-3.1.2.tar.gz", hash = "sha256:733bf018ba007e8b5f2d3a9eb624034f6ee26c4ea769a98ec533ee111d504dff"},
 ]
 
 [[package]]
@@ -3051,50 +3075,44 @@ files = [
 
 [[package]]
 name = "mypy"
-version = "1.14.1"
+version = "1.15.0"
 description = "Optional static typing for Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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 [package.dependencies]
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 [[package]]
@@ -3418,41 +3440,47 @@ nvidia-cublas-cu12 = "*"
 
 [[package]]
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+    {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"},
+    {file = "nvidia_cufft_cu12-11.2.1.3-py3-none-win_amd64.whl", hash = "sha256:d802f4954291101186078ccbe22fc285a902136f974d369540fd4a5333d1440b"},
 ]
 
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
 [[package]]
 name = "nvidia-curand-cu12"
-version = "10.3.2.106"
+version = "10.3.5.147"
 description = "CURAND native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"},
-    {file = "nvidia_curand_cu12-10.3.2.106-py3-none-win_amd64.whl", hash = "sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a"},
+    {file = "nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1f173f09e3e3c76ab084aba0de819c49e56614feae5c12f69883f4ae9bb5fad9"},
+    {file = "nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a88f583d4e0bb643c49743469964103aa59f7f708d862c3ddb0fc07f851e3b8b"},
+    {file = "nvidia_curand_cu12-10.3.5.147-py3-none-win_amd64.whl", hash = "sha256:f307cc191f96efe9e8f05a87096abc20d08845a841889ef78cb06924437f6771"},
 ]
 
 [[package]]
 name = "nvidia-cusolver-cu12"
-version = "11.4.5.107"
+version = "11.6.1.9"
 description = "CUDA solver native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
-    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"},
+    {file = "nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_aarch64.whl", hash = "sha256:d338f155f174f90724bbde3758b7ac375a70ce8e706d70b018dd3375545fc84e"},
+    {file = "nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl", hash = "sha256:19e33fa442bcfd085b3086c4ebf7e8debc07cfe01e11513cc6d332fd918ac260"},
+    {file = "nvidia_cusolver_cu12-11.6.1.9-py3-none-win_amd64.whl", hash = "sha256:e77314c9d7b694fcebc84f58989f3aa4fb4cb442f12ca1a9bde50f5e8f6d1b9c"},
 ]
 
 [package.dependencies]
@@ -3462,58 +3490,73 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
 name = "nvidia-cusparse-cu12"
-version = "12.1.0.106"
+version = "12.3.1.170"
 description = "CUSPARSE native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"},
-    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"},
+    {file = "nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_aarch64.whl", hash = "sha256:9d32f62896231ebe0480efd8a7f702e143c98cfaa0e8a76df3386c1ba2b54df3"},
+    {file = "nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ea4f11a2904e2a8dc4b1833cc1b5181cde564edd0d5cd33e3c168eff2d1863f1"},
+    {file = "nvidia_cusparse_cu12-12.3.1.170-py3-none-win_amd64.whl", hash = "sha256:9bc90fb087bc7b4c15641521f31c0371e9a612fc2ba12c338d3ae032e6b6797f"},
 ]
 
 [package.dependencies]
 nvidia-nvjitlink-cu12 = "*"
 
+[[package]]
+name = "nvidia-cusparselt-cu12"
+version = "0.6.2"
+description = "NVIDIA cuSPARSELt"
+optional = false
+python-versions = "*"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:067a7f6d03ea0d4841c85f0c6f1991c5dda98211f6302cb83a4ab234ee95bef8"},
+    {file = "nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:df2c24502fd76ebafe7457dbc4716b2fec071aabaed4fb7691a201cde03704d9"},
+    {file = "nvidia_cusparselt_cu12-0.6.2-py3-none-win_amd64.whl", hash = "sha256:0057c91d230703924c0422feabe4ce768841f9b4b44d28586b6f6d2eb86fbe70"},
+]
+
 [[package]]
 name = "nvidia-nccl-cu12"
-version = "2.20.5"
+version = "2.21.5"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"},
-    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56"},
+    {file = "nvidia_nccl_cu12-2.21.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:8579076d30a8c24988834445f8d633c697d42397e92ffc3f63fa26766d25e0a0"},
 ]
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.8.61"
+version = "12.4.127"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_nvjitlink_cu12-12.8.61-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:45fd79f2ae20bd67e8bc411055939049873bfd8fac70ff13bd4865e0b9bdab17"},
-    {file = "nvidia_nvjitlink_cu12-12.8.61-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9b80ecab31085dda3ce3b41d043be0ec739216c3fc633b8abe212d5a30026df0"},
-    {file = "nvidia_nvjitlink_cu12-12.8.61-py3-none-win_amd64.whl", hash = "sha256:1166a964d25fdc0eae497574d38824305195a5283324a21ccb0ce0c802cbf41c"},
+    {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:4abe7fef64914ccfa909bc2ba39739670ecc9e820c83ccc7a6ed414122599b83"},
+    {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:06b3b9b25bf3f8af351d664978ca26a16d2c5127dbd53c0497e28d1fb9611d57"},
+    {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-win_amd64.whl", hash = "sha256:fd9020c501d27d135f983c6d3e244b197a7ccad769e34df53a42e276b0e25fa1"},
 ]
 
 [[package]]
 name = "nvidia-nvtx-cu12"
-version = "12.1.105"
+version = "12.4.127"
 description = "NVIDIA Tools Extension"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
-    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
+    {file = "nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7959ad635db13edf4fc65c06a6e9f9e55fc2f92596db928d169c0bb031e88ef3"},
+    {file = "nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:781e950d9b9f60d8241ccea575b32f5105a5baf4c2351cab5256a24869f12a1a"},
+    {file = "nvidia_nvtx_cu12-12.4.127-py3-none-win_amd64.whl", hash = "sha256:641dccaaa1139f3ffb0d3164b4b84f9d253397e38246a4f2f36728b48566d485"},
 ]
 
 [[package]]
@@ -3589,14 +3632,14 @@ et-xmlfile = "*"
 
 [[package]]
 name = "opentelemetry-api"
-version = "1.29.0"
+version = "1.30.0"
 description = "OpenTelemetry Python API"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "opentelemetry_api-1.29.0-py3-none-any.whl", hash = "sha256:5fcd94c4141cc49c736271f3e1efb777bebe9cc535759c54c936cca4f1b312b8"},
-    {file = "opentelemetry_api-1.29.0.tar.gz", hash = "sha256:d04a6cf78aad09614f52964ecb38021e248f5714dc32c2e0d8fd99517b4d69cf"},
+    {file = "opentelemetry_api-1.30.0-py3-none-any.whl", hash = "sha256:d5f5284890d73fdf47f843dda3210edf37a38d66f44f2b5aedc1e89ed455dc09"},
+    {file = "opentelemetry_api-1.30.0.tar.gz", hash = "sha256:375893400c1435bf623f7dfb3bcd44825fe6b56c34d0667c542ea8257b1a1240"},
 ]
 
 [package.dependencies]
@@ -3605,29 +3648,29 @@ importlib-metadata = ">=6.0,<=8.5.0"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-common"
-version = "1.29.0"
+version = "1.30.0"
 description = "OpenTelemetry Protobuf encoding"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "opentelemetry_exporter_otlp_proto_common-1.29.0-py3-none-any.whl", hash = "sha256:a9d7376c06b4da9cf350677bcddb9618ed4b8255c3f6476975f5e38274ecd3aa"},
-    {file = "opentelemetry_exporter_otlp_proto_common-1.29.0.tar.gz", hash = "sha256:e7c39b5dbd1b78fe199e40ddfe477e6983cb61aa74ba836df09c3869a3e3e163"},
+    {file = "opentelemetry_exporter_otlp_proto_common-1.30.0-py3-none-any.whl", hash = "sha256:5468007c81aa9c44dc961ab2cf368a29d3475977df83b4e30aeed42aa7bc3b38"},
+    {file = "opentelemetry_exporter_otlp_proto_common-1.30.0.tar.gz", hash = "sha256:ddbfbf797e518411857d0ca062c957080279320d6235a279f7b64ced73c13897"},
 ]
 
 [package.dependencies]
-opentelemetry-proto = "1.29.0"
+opentelemetry-proto = "1.30.0"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-grpc"
-version = "1.29.0"
+version = "1.30.0"
 description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.29.0-py3-none-any.whl", hash = "sha256:5a2a3a741a2543ed162676cf3eefc2b4150e6f4f0a193187afb0d0e65039c69c"},
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.29.0.tar.gz", hash = "sha256:3d324d07d64574d72ed178698de3d717f62a059a93b6b7685ee3e303384e73ea"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.30.0-py3-none-any.whl", hash = "sha256:2906bcae3d80acc54fd1ffcb9e44d324e8631058b502ebe4643ca71d1ff30830"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.30.0.tar.gz", hash = "sha256:d0f10f0b9b9a383b7d04a144d01cb280e70362cccc613987e234183fd1f01177"},
 ]
 
 [package.dependencies]
@@ -3635,82 +3678,82 @@ deprecated = ">=1.2.6"
 googleapis-common-protos = ">=1.52,<2.0"
 grpcio = ">=1.63.2,<2.0.0"
 opentelemetry-api = ">=1.15,<2.0"
-opentelemetry-exporter-otlp-proto-common = "1.29.0"
-opentelemetry-proto = "1.29.0"
-opentelemetry-sdk = ">=1.29.0,<1.30.0"
+opentelemetry-exporter-otlp-proto-common = "1.30.0"
+opentelemetry-proto = "1.30.0"
+opentelemetry-sdk = ">=1.30.0,<1.31.0"
 
 [[package]]
 name = "opentelemetry-instrumentation"
-version = "0.50b0"
+version = "0.51b0"
 description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "opentelemetry_instrumentation-0.50b0-py3-none-any.whl", hash = "sha256:b8f9fc8812de36e1c6dffa5bfc6224df258841fb387b6dfe5df15099daa10630"},
-    {file = "opentelemetry_instrumentation-0.50b0.tar.gz", hash = "sha256:7d98af72de8dec5323e5202e46122e5f908592b22c6d24733aad619f07d82979"},
+    {file = "opentelemetry_instrumentation-0.51b0-py3-none-any.whl", hash = "sha256:c6de8bd26b75ec8b0e54dff59e198946e29de6a10ec65488c357d4b34aa5bdcf"},
+    {file = "opentelemetry_instrumentation-0.51b0.tar.gz", hash = "sha256:4ca266875e02f3988536982467f7ef8c32a38b8895490ddce9ad9604649424fa"},
 ]
 
 [package.dependencies]
 opentelemetry-api = ">=1.4,<2.0"
-opentelemetry-semantic-conventions = "0.50b0"
+opentelemetry-semantic-conventions = "0.51b0"
 packaging = ">=18.0"
 wrapt = ">=1.0.0,<2.0.0"
 
 [[package]]
 name = "opentelemetry-instrumentation-asgi"
-version = "0.50b0"
+version = "0.51b0"
 description = "ASGI instrumentation for OpenTelemetry"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "opentelemetry_instrumentation_asgi-0.50b0-py3-none-any.whl", hash = "sha256:2ba1297f746e55dec5a17fe825689da0613662fb25c004c3965a6c54b1d5be22"},
-    {file = "opentelemetry_instrumentation_asgi-0.50b0.tar.gz", hash = "sha256:3ca4cb5616ae6a3e8ce86e7d5c360a8d8cc8ed722cf3dc8a5e44300774e87d49"},
+    {file = "opentelemetry_instrumentation_asgi-0.51b0-py3-none-any.whl", hash = "sha256:e8072993db47303b633c6ec1bc74726ba4d32bd0c46c28dfadf99f79521a324c"},
+    {file = "opentelemetry_instrumentation_asgi-0.51b0.tar.gz", hash = "sha256:b3fe97c00f0bfa934371a69674981d76591c68d937b6422a5716ca21081b4148"},
 ]
 
 [package.dependencies]
 asgiref = ">=3.0,<4.0"
 opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.50b0"
-opentelemetry-semantic-conventions = "0.50b0"
-opentelemetry-util-http = "0.50b0"
+opentelemetry-instrumentation = "0.51b0"
+opentelemetry-semantic-conventions = "0.51b0"
+opentelemetry-util-http = "0.51b0"
 
 [package.extras]
 instruments = ["asgiref (>=3.0,<4.0)"]
 
 [[package]]
 name = "opentelemetry-instrumentation-fastapi"
-version = "0.50b0"
+version = "0.51b0"
 description = "OpenTelemetry FastAPI Instrumentation"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "opentelemetry_instrumentation_fastapi-0.50b0-py3-none-any.whl", hash = "sha256:8f03b738495e4705fbae51a2826389c7369629dace89d0f291c06ffefdff5e52"},
-    {file = "opentelemetry_instrumentation_fastapi-0.50b0.tar.gz", hash = "sha256:16b9181682136da210295def2bb304a32fb9bdee9a935cdc9da43567f7c1149e"},
+    {file = "opentelemetry_instrumentation_fastapi-0.51b0-py3-none-any.whl", hash = "sha256:10513bbc11a1188adb9c1d2c520695f7a8f2b5f4de14e8162098035901cd6493"},
+    {file = "opentelemetry_instrumentation_fastapi-0.51b0.tar.gz", hash = "sha256:1624e70f2f4d12ceb792d8a0c331244cd6723190ccee01336273b4559bc13abc"},
 ]
 
 [package.dependencies]
 opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.50b0"
-opentelemetry-instrumentation-asgi = "0.50b0"
-opentelemetry-semantic-conventions = "0.50b0"
-opentelemetry-util-http = "0.50b0"
+opentelemetry-instrumentation = "0.51b0"
+opentelemetry-instrumentation-asgi = "0.51b0"
+opentelemetry-semantic-conventions = "0.51b0"
+opentelemetry-util-http = "0.51b0"
 
 [package.extras]
 instruments = ["fastapi (>=0.58,<1.0)"]
 
 [[package]]
 name = "opentelemetry-proto"
-version = "1.29.0"
+version = "1.30.0"
 description = "OpenTelemetry Python Proto"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "opentelemetry_proto-1.29.0-py3-none-any.whl", hash = "sha256:495069c6f5495cbf732501cdcd3b7f60fda2b9d3d4255706ca99b7ca8dec53ff"},
-    {file = "opentelemetry_proto-1.29.0.tar.gz", hash = "sha256:3c136aa293782e9b44978c738fff72877a4b78b5d21a64e879898db7b2d93e5d"},
+    {file = "opentelemetry_proto-1.30.0-py3-none-any.whl", hash = "sha256:c6290958ff3ddacc826ca5abbeb377a31c2334387352a259ba0df37c243adc11"},
+    {file = "opentelemetry_proto-1.30.0.tar.gz", hash = "sha256:afe5c9c15e8b68d7c469596e5b32e8fc085eb9febdd6fb4e20924a93a0389179"},
 ]
 
 [package.dependencies]
@@ -3718,47 +3761,47 @@ protobuf = ">=5.0,<6.0"
 
 [[package]]
 name = "opentelemetry-sdk"
-version = "1.29.0"
+version = "1.30.0"
 description = "OpenTelemetry Python SDK"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "opentelemetry_sdk-1.29.0-py3-none-any.whl", hash = "sha256:173be3b5d3f8f7d671f20ea37056710217959e774e2749d984355d1f9391a30a"},
-    {file = "opentelemetry_sdk-1.29.0.tar.gz", hash = "sha256:b0787ce6aade6ab84315302e72bd7a7f2f014b0fb1b7c3295b88afe014ed0643"},
+    {file = "opentelemetry_sdk-1.30.0-py3-none-any.whl", hash = "sha256:14fe7afc090caad881addb6926cec967129bd9260c4d33ae6a217359f6b61091"},
+    {file = "opentelemetry_sdk-1.30.0.tar.gz", hash = "sha256:c9287a9e4a7614b9946e933a67168450b9ab35f08797eb9bc77d998fa480fa18"},
 ]
 
 [package.dependencies]
-opentelemetry-api = "1.29.0"
-opentelemetry-semantic-conventions = "0.50b0"
+opentelemetry-api = "1.30.0"
+opentelemetry-semantic-conventions = "0.51b0"
 typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "opentelemetry-semantic-conventions"
-version = "0.50b0"
+version = "0.51b0"
 description = "OpenTelemetry Semantic Conventions"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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@@ -4137,14 +4180,14 @@ testing = ["pytest", "pytest-benchmark"]
 
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 [package.extras]
@@ -5313,115 +5357,115 @@ jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
 [[package]]
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 [package.dependencies]
@@ -6075,32 +6028,32 @@ testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
 
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+    {file = "torch-2.6.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:b789069020c5588c70d5c2158ac0aa23fd24a028f34a8b4fcb8fcb4d7efcf5fb"},
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+    {file = "torch-2.6.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:a0d5e1b9874c1a6c25556840ab8920569a7a4137afa8a63a32cee0bc7d89bd4b"},
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+    {file = "torch-2.6.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:9ea955317cfcd3852b1402b62af258ce735c2edeee42ca9419b6bc889e5ae053"},
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+    {file = "torch-2.6.0-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:265f70de5fd45b864d924b64be1797f86e76c8e48a02c2a3a6fc7ec247d2226c"},
 ]
 
 [package.dependencies]
@@ -6108,25 +6061,27 @@ filelock = "*"
 fsspec = "*"
 jinja2 = "*"
 networkx = "*"
-nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+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 = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-setuptools = "*"
-sympy = "*"
-triton = {version = "3.0.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""}
-typing-extensions = ">=4.8.0"
+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 (>=0.11.0)"]
+optree = ["optree (>=0.13.0)"]
 
 [[package]]
 name = "tornado"
@@ -6189,40 +6144,40 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "transformers"
-version = "4.46.3"
+version = "4.49.0"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
-python-versions = ">=3.8.0"
+python-versions = ">=3.9.0"
 groups = ["main"]
 files = [
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+    {file = "transformers-4.49.0-py3-none-any.whl", hash = "sha256:6b4fded1c5fee04d384b1014495b4235a2b53c87503d7d592423c06128cbbe03"},
+    {file = "transformers-4.49.0.tar.gz", hash = "sha256:7e40e640b5b8dc3f48743f5f5adbdce3660c82baafbd3afdfc04143cdbd2089e"},
 ]
 
 [package.dependencies]
 filelock = "*"
-huggingface-hub = ">=0.23.2,<1.0"
+huggingface-hub = ">=0.26.0,<1.0"
 numpy = ">=1.17"
 packaging = ">=20.0"
 pyyaml = ">=5.1"
 regex = "!=2019.12.17"
 requests = "*"
 safetensors = ">=0.4.1"
-tokenizers = ">=0.20,<0.21"
+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"]
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+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)"]
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 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 benchmark = ["optimum-benchmark (>=0.3.0)"]
-codecarbon = ["codecarbon (==1.2.0)"]
+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-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"]
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-dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "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-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 (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+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", "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"]
@@ -6243,39 +6198,36 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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 ]
 
 [[package]]
diff --git a/fieldExtraction/src/constants/investment_values.py b/fieldExtraction/src/constants/investment_values.py
index 8f51ad0..5a03dc5 100644
--- a/fieldExtraction/src/constants/investment_values.py
+++ b/fieldExtraction/src/constants/investment_values.py
@@ -139,3 +139,5 @@ VALID_CARVEOUTS = {
     "READMISSIONS" : "Describes payment for subsequent admission with the same diagnostic category of codes as the initial admission.",    
 }
 
+levels = ["cpt", "hcpcs", "cpt_level3", "cpt_level2", "hcpcs_level2", "cpt_level1", "hcpcs_level1"]
+
diff --git a/fieldExtraction/src/investment/code_funcs.py b/fieldExtraction/src/investment/code_funcs.py
index ae251ba..4a9db75 100644
--- a/fieldExtraction/src/investment/code_funcs.py
+++ b/fieldExtraction/src/investment/code_funcs.py
@@ -21,12 +21,34 @@ from sentence_transformers import SentenceTransformer
 # Load model
 model = SentenceTransformer("all-roberta-large-v1")
 def create_faiss_index(choices):
+    """
+    Creates a FAISS index for a list of choices using cosine similarity (inner product).
+
+    Args:
+        choices (list): A list of text items to index.
+
+    Returns:
+        faiss.IndexFlatIP: A FAISS index for the given embeddings.
+    """
     embeddings = model.encode(choices, normalize_embeddings=True).astype("float32")
     index = faiss.IndexFlatIP(embeddings.shape[1])  # Inner product for cosine similarity
     index.add(embeddings)
     return index
 
 def get_matches_above_threshold(target, choices, index, threshold=0.7, top_k=5):
+    """
+    Retrieves the top-k matches from a FAISS index with similarity scores above a given threshold.
+
+    Args:
+        target (str): The target text to compare against the choices.
+        choices (list): A list of choices to match the target with.
+        index (faiss.IndexFlatIP): The FAISS index for fast nearest-neighbor search.
+        threshold (float, optional): The minimum similarity score to consider a match. Defaults to 0.7.
+        top_k (int, optional): The number of top matches to retrieve. Defaults to 5.
+
+    Returns:
+        list: A list of tuples containing matched choice and score for each match above the threshold.
+    """
     target_vec = model.encode([target], normalize_embeddings=True).astype("float32")
     similarity_scores, match_indices = index.search(target_vec, top_k)
     return [
@@ -35,8 +57,39 @@ def get_matches_above_threshold(target, choices, index, threshold=0.7, top_k=5):
         if score >= threshold
     ]
 
+def crosswalk_levels():
+    """
+    Loads and processes crosswalk mappings for different levels (e.g., CPT, HCPCS) from CSV files, 
+    and returns a dictionary with clean-to-description and description-to-code mappings for each level.
+
+    Returns:
+        dict: A dictionary where each key is a level, and the value is a tuple containing:
+              - clean-to-description mapping (str -> str)
+              - description-to-code mapping (str -> str)
+    """
+    # Get necessary mappings
+    crosswalk_dict = {}
+    for level in investment_values.levels:
+        mapping_df = pd.read_csv(f"crosswalk/mapping_csvs/proc_cd/{level}.csv", dtype=str)
+        level_crosswalk = CrosswalkBuilder().from_df(mapping_df, from_col="Code", to_col="Description")
+        clean_to_description = {get_clean_value(x) : x for x in level_crosswalk.mapping.values()}
+        description_to_code = level_crosswalk.create_reverse_mapping()
+        crosswalk_dict[level] = (clean_to_description, description_to_code)
+
+    return crosswalk_dict  
 
 def code_primary(answer_dicts, filename):
+    """
+    Breaks down the direct code information from CONTRACT_SERVICE_CD_OR_DESC and CONTRACT_REIMBURSEMENT_METHOD fields
+    using an LLM prompt and adds the results to the provided answer dictionaries.
+
+    Args:
+        answer_dicts (list of dict): A list of dictionaries containing service and reimbursement data.
+        filename (str): The name of the file being processed, used for LLM processing.
+
+    Returns:
+        list of dict: The input answer dictionaries with additional primary code breakout information. These are only the codes that are explicitly written in the Service/Methodology
+    """
     code_primary_questions = FieldSet(file_path=config.FIELD_JSON_PATH, field_type="code_primary_breakout").get_prompt_dict()
     code_breakout_answers = []
     for answer_dict in answer_dicts:
@@ -55,6 +108,12 @@ def code_primary(answer_dicts, filename):
     return code_breakout_answers
 
 def get_proc_crosswalk():
+    """
+    Loads and concatenates all CSV files in the 'proc_cd' directory, and creates a crosswalk mapping from the 'Code' to 'Description' columns.
+
+    Returns:
+        CrosswalkBuilder: A CrosswalkBuilder object containing the mapping from procedure codes to descriptions.
+    """
     all_mappings = []
     mapping_dir = "crosswalk/mapping_csvs/proc_cd"
     for filename in os.listdir(mapping_dir):
@@ -66,6 +125,15 @@ def get_proc_crosswalk():
 
 
 def get_clean_value(service_description):
+    """
+    Cleans a service description by removing stop words, replacing certain terms, and standardizing acronyms.
+
+    Args:
+        service_description (str): The service description string to clean.
+
+    Returns:
+        str: The cleaned service description with stop words removed, specific terms replaced, and acronyms expanded.
+    """
     stop_words = {"the", "is", "in", "and", "to", "of", "a", "an", "that", "this", "it", "for", "on", "with", "as", "was", "were", "at", "by", "but", "be"}  # Experiment with not removing 'and'
     def remove_stop_words(text):
         words = text.split()
@@ -83,23 +151,27 @@ def get_clean_value(service_description):
     return service_clean
 
 
-def get_best_match(target, model, thresholds={
+def get_best_match(crosswalk_dict, target, model, thresholds={
     "cpt": 0.85, "cpt_level3": 0.8, "cpt_level2": 0.75, "cpt_level1": 0.7,
     "hcpcs": 0.85, "hcpcs_level3": 0.8, "hcpcs_level2": 0.75, "hcpcs_level1": 0.7
 }):
-    levels = ["cpt", "hcpcs", "cpt_level3", "cpt_level2", "hcpcs_level2", "cpt_level1", "hcpcs_level1"]
+    """
+    Finds the best matching code from a crosswalk dictionary by comparing a target string to precomputed embeddings
+    using a model and similarity threshold values for different levels.
 
-    # Get necessary mappings
-    crosswalk_dict = {}
-    for level in levels:
-        mapping_df = pd.read_csv(f"crosswalk/mapping_csvs/proc_cd/{level}.csv", dtype=str)
-        level_crosswalk = CrosswalkBuilder().from_df(mapping_df, from_col="Code", to_col="Description")
-        clean_to_description = {get_clean_value(x) : x for x in level_crosswalk.mapping.values()}
-        description_to_code = level_crosswalk.create_reverse_mapping()
-        crosswalk_dict[level] = (clean_to_description, description_to_code)
+    Args:
+        crosswalk_dict (dict): Dictionary containing the crosswalk data for different levels.
+        target (str): The target string to match.
+        model: The model used to encode the target string into an embedding.
+        thresholds (dict, optional): A dictionary of similarity thresholds for each level (default values provided).
+
+    Returns:
+        tuple: A tuple containing the best matching code, its description, and the similarity score.
+               If no match is found above the threshold, returns (None, None, 0).
+    """
     
     target_vec = model.encode([target], normalize_embeddings=True).astype("float32")
-    for level in levels:
+    for level in investment_values.levels:
         index, _, choices = embedding_utils.load_faiss_index(index_path=os.path.join("embeddings", level, "faiss_index.bin"),
                                                              embedding_path=os.path.join("embeddings", level, "embeddings.npy"),
                                                              choices_path=os.path.join("embeddings", level, "choices.pkl"),)
@@ -113,7 +185,7 @@ def get_best_match(target, model, thresholds={
     
     return None, None, 0 # No valid match found
 
-def code_indirect(answer_dicts):
+def code_indirect(answer_dicts, crosswalk):
     """
     Maps free-form service descriptions to the best-matching code and description. 
     Caches results for efficiency and skips entries with existing codes or irrelevant text.
@@ -135,7 +207,7 @@ def code_indirect(answer_dicts):
         if (not service                                     # Service DNE
             or "covered service" in service.lower()         # Is generic
             or isinstance(proc_code, list)                  # Is a list
-            or string_utils.is_empty(proc_code)             # Is empty
+            or not string_utils.is_empty(proc_code)         # Is not empty
             ):
             pass # pass for now - we may need to add functionality here later
         else:
@@ -143,7 +215,7 @@ def code_indirect(answer_dicts):
                 code = already_seen[service]['code']
                 description = already_seen[service]['description']
             else:               
-                code, description, score = get_best_match(service, model)
+                code, description, score = get_best_match(crosswalk, service, model)
                 # print(service, " | ", description, " | ", code, " | ", score)
 
             answer_dict["CPT4_PROC_CD"] = code
@@ -152,6 +224,17 @@ def code_indirect(answer_dicts):
     return results
 
 def get_code_description(code, code_mapping):
+    """
+    Retrieves descriptions for a given code or list of codes using the provided code mapping.
+    If no description is found, attempts to resolve by modifying the code (e.g., prepending "0" or handling code patterns).
+
+    Args:
+        code (str, list): A single code or a list of codes to look up.
+        code_mapping (Crosswalk): The mapping object containing code-to-description mappings.
+
+    Returns:
+        str: A comma-separated string of descriptions for the provided codes.
+    """
     if isinstance(code, str):
         if string_utils.is_empty(code):
             return "N/A"
@@ -181,28 +264,57 @@ def get_code_description(code, code_mapping):
 
     return ",".join(code_description)
 
-def code_description_mapping(answer_dicts):
-    # Get mappings
+def get_mappings():
+    """
+    Loads and processes multiple CSV and Excel files containing code mappings for various categories
+    (e.g., PROC, DIAG, Grouper, Rev, Admit Type), and returns a dictionary of crosswalks for each code type.
+
+    Returns:
+        dict: A dictionary containing mappings for PROC, DIAG, Grouper, Rev, and Admit Type codes.
+    """
+
+    all_mappings = {}
+
     # PROC codes
     mapping_path = "crosswalk/mapping_csvs/"
     all_proc_mappings = pd.concat([pd.read_csv(os.path.join(mapping_path, "proc_cd", file), dtype=str) for file in os.listdir(os.path.join(mapping_path, "proc_cd")) if file.endswith(".csv")])
     proc_mapping = CrosswalkBuilder().from_df(all_proc_mappings, from_col="Code", to_col="Description")
+    all_mappings['proc_mapping'] = proc_mapping
 
     # Diag codes
     all_diag_mappings = pd.concat([pd.read_csv(os.path.join(mapping_path, "diag_cd", file), dtype=str) for file in os.listdir(os.path.join(mapping_path, "diag_cd")) if file.endswith(".csv")])
     diag_mapping = CrosswalkBuilder().from_df(all_diag_mappings, from_col="Code", to_col="Description")
+    all_mappings['diag_mapping'] = diag_mapping
     
     # Grouper codes
     all_grouper_mappings = pd.concat([pd.read_csv(os.path.join(mapping_path, "grouper_cd", file), dtype=str) for file in os.listdir(os.path.join(mapping_path, "grouper_cd")) if file.endswith(".csv")])
     grouper_mapping = CrosswalkBuilder().from_df(all_grouper_mappings, from_col="Code", to_col="Description")
     grouper_version_mapping = CrosswalkBuilder().from_df(all_grouper_mappings, from_col="Code", to_col="Version")
+    all_mappings['grouper_mapping'] = grouper_mapping
+    all_mappings['grouper_version_mapping'] = grouper_version_mapping
 
     # Rev codes
     rev_mapping = CrosswalkBuilder().from_excel(path=os.path.join(mapping_path, "rev_cd", "rev_mapping.csv"), from_col="Code", to_col="Description")
+    all_mappings['rev_mapping'] = rev_mapping
 
     # Admit Type codes
     admit_mapping = CrosswalkBuilder().from_excel(path=os.path.join(mapping_path, "admit_type_cd", "admit_mapping.csv"), from_col="Code", to_col="Description")
+    all_mappings['admit_mapping'] = admit_mapping
 
+    return all_mappings
+
+
+def code_description_mapping(answer_dicts, all_mappings):
+    """
+    Maps code descriptions to relevant fields in the answer dictionaries using provided mappings.
+
+    Args:
+        answer_dicts (list): List of dictionaries containing codes to be mapped.
+        all_mappings (dict): Dictionary containing the mappings for different code types.
+
+    Returns:
+        list: Updated list of dictionaries with code descriptions added.
+    """
     for answer_dict in answer_dicts:
         proc_code = answer_dict.get("CPT4_PROC_CD")
         proc_mod = answer_dict.get("CPT4_PROC_MOD")
@@ -211,25 +323,36 @@ def code_description_mapping(answer_dicts):
         grouper_code = answer_dict.get("FACILITY_GROUPER_CD")
         admit_type_code = answer_dict.get("CLAIM_ADMIT_TYPE_CD")
         
-        answer_dict["CPT4_PROC_DESC"] = get_code_description(proc_code, proc_mapping) 
-        answer_dict["CPT4_PROC_MOD_DESC"] = get_code_description(proc_mod, proc_mapping)
-        answer_dict["DIAG_CD_DESC"] = get_code_description(diag_code, diag_mapping)
-        answer_dict["REVENUE_CD_DESC"] = get_code_description(rev_code, rev_mapping)
-        answer_dict["FACILITY_GROUPER_DESC"] = get_code_description(grouper_code, grouper_mapping)
-        answer_dict["GROUPER_TYPE"] = get_code_description(grouper_code, grouper_version_mapping)
-        answer_dict["AUTH_ADMIT_TYPE_DESC"] = get_code_description(admit_type_code, admit_mapping)
+        answer_dict["CPT4_PROC_DESC"] = get_code_description(proc_code, all_mappings['proc_mapping'])
+        answer_dict["CPT4_PROC_MOD_DESC"] = get_code_description(proc_mod, all_mappings['proc_mapping'])
+        answer_dict["DIAG_CD_DESC"] = get_code_description(diag_code, all_mappings['diag_mapping'])
+        answer_dict["REVENUE_CD_DESC"] = get_code_description(rev_code, all_mappings['rev_mapping'])
+        answer_dict["FACILITY_GROUPER_DESC"] = get_code_description(grouper_code, all_mappings['grouper_mapping'])
+        answer_dict["GROUPER_TYPE"] = get_code_description(grouper_code, all_mappings['grouper_version_mapping'])
+        answer_dict["AUTH_ADMIT_TYPE_DESC"] = get_code_description(admit_type_code, all_mappings['admit_mapping'])
         
     return answer_dicts
 
-def get_code_breakout(answer_dicts: list[dict], filename: str):
+def get_code_breakout(answer_dicts: list[dict], filename: str, all_dataset: dict):
+    """
+    Extracts and maps codes from contract data, including primary, indirect, and description mappings.
+
+    Args:
+        answer_dicts (list[dict]): List of dictionaries containing contract-related answers.
+        filename (str): The name of the file being processed.
+        all_dataset (dict): Dictionary containing crosswalk and mappings for code translation.
+
+    Returns:
+        dict: A dictionary with primary, indirect/derived, and mapped code descriptions.
+    """
 
     # Code Primary (Pull codes directly from CONTRACT_SERVICE_CD_OR_DESC and CONTRACT_REIMBURSEMENT_METHOD)
     code_primary_answers = code_primary(answer_dicts, filename)
 
     # Only used for Implicit. Code Indirect (Derive codes indirectly from CONTRACT_SERVICE_CD_OR_DESC and CONTRACT_REIMBURSEMENT_METHOD)
-    code_indirect_answers = code_indirect(code_primary_answers)
+    code_indirect_answers = code_indirect(code_primary_answers, all_dataset['codes_crosswalk'])
 
     # Code Descriptions (Pull standardized descriptions from codes)
-    code_description_answers = code_description_mapping(code_indirect_answers)
+    code_description_answers = code_description_mapping(code_indirect_answers, all_dataset['codes_mappings'])
     
     return code_description_answers
\ No newline at end of file
diff --git a/fieldExtraction/src/investment/file_processing.py b/fieldExtraction/src/investment/file_processing.py
index eba6700..6577297 100644
--- a/fieldExtraction/src/investment/file_processing.py
+++ b/fieldExtraction/src/investment/file_processing.py
@@ -20,7 +20,8 @@ from datetime import datetime
 def datetime_str():
     return datetime.now().strftime("[%Y-%m-%d %H:%M:%S]")
 
-def process_file(file_object, run_timestamp):
+
+def process_file(file_object, all_dataset, run_timestamp):
     filename, contract_text = file_object
     print(f"{datetime_str()} Processing {filename}...")
 
@@ -38,7 +39,7 @@ def process_file(file_object, run_timestamp):
 
     ################## ONE TO N ##################
     if string_utils.contains_reimbursement(contract_text):
-        one_to_n_results = run_one_to_n_prompts(filename, text_dict, exhibit_pages, exhibit_chunk_mapping) # Return df
+        one_to_n_results = run_one_to_n_prompts(filename, text_dict, exhibit_pages, exhibit_chunk_mapping, all_dataset) # Return df
         one_to_n_results['CONTRACT_FILE_NAME'] = filename
         contract_results = pd.merge(one_to_one_results, one_to_n_results, how='outer', on='CONTRACT_FILE_NAME')
     else:
@@ -90,7 +91,7 @@ def run_one_to_one_prompts(filename, contract_text, text_dict, top_sheet_dict):
     return final_df
 
 
-def run_one_to_n_prompts(filename, text_dict, exhibit_pages, exhibit_chunk_mapping):
+def run_one_to_n_prompts(filename, text_dict, exhibit_pages, exhibit_chunk_mapping, all_dataset):
 
     ################## RUN PROMPTS ##################
     one_to_n_results = []
@@ -111,7 +112,7 @@ def run_one_to_n_prompts(filename, text_dict, exhibit_pages, exhibit_chunk_mappi
             exhibit_level_answers['EXHIBIT_PAGE'] = exhibit_page
 
             ################## GET REIMBURSEMENT-LEVEL ANSWERS (INCLUDING DYNAMIC) ##################
-            reimbursement_level_answers = reimbursement_level(exhibit_chunk, filename, reimbursement_level_fields) # Return list of dictionaries
+            reimbursement_level_answers = reimbursement_level(exhibit_chunk, filename, reimbursement_level_fields, all_dataset) # Return list of dictionaries
             
             ################## COMBINE ANSWERS ##################
             full_answer_dict = combine_one_to_n_answers(exhibit_level_answers, reimbursement_level_answers, tin_npi_answers)
@@ -121,3 +122,6 @@ def run_one_to_n_prompts(filename, text_dict, exhibit_pages, exhibit_chunk_mappi
     ################## CONVERT TO DF ##################
     one_to_n_df = pd.DataFrame(one_to_n_results)
     return one_to_n_df
+
+
+
diff --git a/fieldExtraction/src/investment/main.py b/fieldExtraction/src/investment/main.py
index 9d6af8d..3a8961f 100644
--- a/fieldExtraction/src/investment/main.py
+++ b/fieldExtraction/src/investment/main.py
@@ -30,11 +30,14 @@ def main(testing=False, test_params = {}):
             input_dict = {k: v for k, v in input_dict.items() if k in test_params['input_files']}
     
     print(f"Total Input Files : {len(input_dict)}")
+
+    # load all crosswalk files and mappings
+    all_dataset = io_utils.load_all_dataset()
     
     # Process files with run_timestamp
     all_results = []
     with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
-        results = executor.map(lambda item: file_processing.process_file(item, run_timestamp), input_dict.items())
+        results = executor.map(lambda item: file_processing.process_file(item, all_dataset, run_timestamp), input_dict.items())
     all_results.extend(results)
     
     FINAL_RESULT_DF = pd.concat(all_results)
diff --git a/fieldExtraction/src/investment/one_to_n_funcs.py b/fieldExtraction/src/investment/one_to_n_funcs.py
index 8460e95..8f2d079 100644
--- a/fieldExtraction/src/investment/one_to_n_funcs.py
+++ b/fieldExtraction/src/investment/one_to_n_funcs.py
@@ -263,9 +263,9 @@ def get_service_breakout(answer_dicts, filename):
                     filename
                 )
             )
-            already_seen[service_methodology] = service_breakout_answer
+            already_seen[service_methodology] = service_breakout_dict
         else:
-            service_breakout_answer = already_seen[service_methodology]
+            service_breakout_dict = already_seen[service_methodology]
         
         service_breakout_answers.append({**answer_dict, **service_breakout_dict})
     
@@ -330,7 +330,7 @@ def combine_one_to_n_answers(exhibit_level_answers: dict, reimbursement_level_an
     return combined_answers
 
 
-def reimbursement_level(exhibit_text, filename, reimbursement_level_fields):
+def reimbursement_level(exhibit_text, filename, reimbursement_level_fields, all_dataset):
     """
     Processes reimbursement-level fields.
     Currently, runs REIMBURSEMENT_LEVEL_PRIMARY prompt only. Future secondary prompts will go in this function
@@ -349,6 +349,6 @@ def reimbursement_level(exhibit_text, filename, reimbursement_level_fields):
     # Later, we will only run methodology breakout for those without certain carveout indicators. We will run carveout-specific breakouts for the others
     methodology_breakout_answers = get_methodology_breakout(carveout_answers, filename)
     service_breakout_answers = get_service_breakout(methodology_breakout_answers, filename)
-    code_breakout_answers = code_funcs.get_code_breakout(service_breakout_answers, filename)
+    code_breakout_answers = code_funcs.get_code_breakout(service_breakout_answers, filename, all_dataset)
 
     return code_breakout_answers
diff --git a/fieldExtraction/src/utils/io_utils.py b/fieldExtraction/src/utils/io_utils.py
index 67f4c77..0918bde 100644
--- a/fieldExtraction/src/utils/io_utils.py
+++ b/fieldExtraction/src/utils/io_utils.py
@@ -7,6 +7,7 @@ from pyxlsb import open_workbook
 
 import src.tracking.tracking_utils as tracking_utils
 from src import config
+from src.investment.code_funcs import crosswalk_levels, get_mappings
 
 
 def read_local(file_path): # io_utils.py
@@ -258,4 +259,15 @@ def write_s3(df, filename, run_timestamp, output_type):
     if output_type == "final":
         print(f"Saved batch output file: {output_path}")
     elif output_type == "individual":
-        print(f"Saved individual output file: {output_path}")
\ No newline at end of file
+        print(f"Saved individual output file: {output_path}")
+
+
+def load_all_dataset():
+    """_summary_: loads all csv files required for processing contracts including crosswalk files
+    and mappings (embeddings, past results may be added later)
+    """
+    all_dataset = {}
+    all_dataset['codes_crosswalk'] = crosswalk_levels()
+    all_dataset['codes_mappings'] = get_mappings()
+
+    return all_dataset
\ No newline at end of file
diff --git a/fieldExtraction/src/utils/string_utils.py b/fieldExtraction/src/utils/string_utils.py
index 830e008..acaa521 100644
--- a/fieldExtraction/src/utils/string_utils.py
+++ b/fieldExtraction/src/utils/string_utils.py
@@ -289,6 +289,9 @@ def is_empty(value):
     Returns:
         bool: True if the value is empty or invalid, False otherwise.
     """
+    if isinstance(value, list):  # Handle list inputs
+        return not value or all(is_empty(v) for v in value)
+        
     if pd.isna(value):
         return True
     else: