diff --git a/.github/workflows/python-integration-tests.yml b/.github/workflows/python-integration-tests.yml
index 20516a4164e3..076c66b3368a 100644
--- a/.github/workflows/python-integration-tests.yml
+++ b/.github/workflows/python-integration-tests.yml
@@ -96,6 +96,8 @@ jobs:
           AZURE_KEY_VAULT_CLIENT_ID: ${{secrets.AZURE_KEY_VAULT_CLIENT_ID}}
           AZURE_KEY_VAULT_CLIENT_SECRET: ${{secrets.AZURE_KEY_VAULT_CLIENT_SECRET}}
           ACA_POOL_MANAGEMENT_ENDPOINT: ${{secrets.ACA_POOL_MANAGEMENT_ENDPOINT}}
+          MISTRALAI_API_KEY: ${{secrets.MISTRALAI_API_KEY}}
+          MISTRALAI_CHAT_MODEL_ID: ${{ vars.MISTRALAI_CHAT_MODEL_ID }}
         run: |
           if ${{ matrix.os == 'ubuntu-latest' }}; then
             docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest
@@ -163,6 +165,8 @@ jobs:
           AZURE_KEY_VAULT_CLIENT_ID: ${{secrets.AZURE_KEY_VAULT_CLIENT_ID}}
           AZURE_KEY_VAULT_CLIENT_SECRET: ${{secrets.AZURE_KEY_VAULT_CLIENT_SECRET}}
           ACA_POOL_MANAGEMENT_ENDPOINT: ${{secrets.ACA_POOL_MANAGEMENT_ENDPOINT}}
+          MISTRALAI_API_KEY: ${{secrets.MISTRALAI_API_KEY}}
+          MISTRALAI_CHAT_MODEL_ID: ${{ vars.MISTRALAI_CHAT_MODEL_ID }}
         run: |
           if ${{ matrix.os == 'ubuntu-latest' }}; then
             docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest
diff --git a/python/poetry.lock b/python/poetry.lock
index cf7c61be4a13..54e60a60748e 100644
--- a/python/poetry.lock
+++ b/python/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "accelerate"
@@ -266,13 +266,13 @@ cryptography = "*"
 
 [[package]]
 name = "azure-ai-inference"
-version = "1.0.0b1"
+version = "1.0.0b2"
 description = "Microsoft Azure Ai Inference Client Library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "azure-ai-inference-1.0.0b1.tar.gz", hash = "sha256:e44c58ccc38a6ae335193d4ba99d9424b1d9cdfc7f18d5285dc17ba988404dfd"},
-    {file = "azure_ai_inference-1.0.0b1-py3-none-any.whl", hash = "sha256:c62279a36c232a98cb69bcbbf6b8f1974316922350e20f990bc27c041881c854"},
+    {file = "azure-ai-inference-1.0.0b2.tar.gz", hash = "sha256:efe2ad0c79ab80d3323edb919cb769a5e21d8ed2c56fe17f9b737893432e0a99"},
+    {file = "azure_ai_inference-1.0.0b2-py3-none-any.whl", hash = "sha256:fb35a30867419590d2c7a52e287c456c9bd264690bdfcdec54930c3e73897a8f"},
 ]
 
 [package.dependencies]
@@ -327,13 +327,13 @@ typing-extensions = ">=4.6.0"
 
 [[package]]
 name = "azure-identity"
-version = "1.16.1"
+version = "1.17.1"
 description = "Microsoft Azure Identity Library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "azure-identity-1.16.1.tar.gz", hash = "sha256:6d93f04468f240d59246d8afde3091494a5040d4f141cad0f49fc0c399d0d91e"},
-    {file = "azure_identity-1.16.1-py3-none-any.whl", hash = "sha256:8fb07c25642cd4ac422559a8b50d3e77f73dcc2bbfaba419d06d6c9d7cff6726"},
+    {file = "azure-identity-1.17.1.tar.gz", hash = "sha256:32ecc67cc73f4bd0595e4f64b1ca65cd05186f4fe6f98ed2ae9f1aa32646efea"},
+    {file = "azure_identity-1.17.1-py3-none-any.whl", hash = "sha256:db8d59c183b680e763722bfe8ebc45930e6c57df510620985939f7f3191e0382"},
 ]
 
 [package.dependencies]
@@ -341,6 +341,7 @@ azure-core = ">=1.23.0"
 cryptography = ">=2.5"
 msal = ">=1.24.0"
 msal-extensions = ">=0.3.0"
+typing-extensions = ">=4.0.0"
 
 [[package]]
 name = "azure-search-documents"
@@ -486,13 +487,13 @@ files = [
 
 [[package]]
 name = "certifi"
-version = "2024.2.2"
+version = "2024.6.2"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
-    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
+    {file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
+    {file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
 ]
 
 [[package]]
@@ -733,13 +734,13 @@ numpy = "*"
 
 [[package]]
 name = "chromadb"
-version = "0.5.0"
+version = "0.5.3"
 description = "Chroma."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "chromadb-0.5.0-py3-none-any.whl", hash = "sha256:8193dc65c143b61d8faf87f02c44ecfa778d471febd70de517f51c5d88a06009"},
-    {file = "chromadb-0.5.0.tar.gz", hash = "sha256:7954af614a9ff7b2902ddbd0a162f33f7ec0669e2429903905c4f7876d1f766f"},
+    {file = "chromadb-0.5.3-py3-none-any.whl", hash = "sha256:b3874f08356e291c68c6d2e177db472cd51f22f3af7b9746215b748fd1e29982"},
+    {file = "chromadb-0.5.3.tar.gz", hash = "sha256:05d887f56a46b2e0fc6ac5ab979503a27b9ee50d5ca9e455f83b2fb9840cd026"},
 ]
 
 [package.dependencies]
@@ -748,10 +749,11 @@ build = ">=1.0.3"
 chroma-hnswlib = "0.7.3"
 fastapi = ">=0.95.2"
 grpcio = ">=1.58.0"
+httpx = ">=0.27.0"
 importlib-resources = "*"
 kubernetes = ">=28.1.0"
 mmh3 = ">=4.0.1"
-numpy = ">=1.22.5"
+numpy = ">=1.22.5,<2.0.0"
 onnxruntime = ">=1.14.1"
 opentelemetry-api = ">=1.2.0"
 opentelemetry-exporter-otlp-proto-grpc = ">=1.2.0"
@@ -832,63 +834,63 @@ test = ["pytest"]
 
 [[package]]
 name = "coverage"
-version = "7.5.1"
+version = "7.5.4"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "coverage-7.5.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:39afcd3d4339329c5f58de48a52f6e4e50f6578dd6099961cf22228feb25f38f"},
-    {file = "coverage-7.5.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4a7b0ceee8147444347da6a66be737c9d78f3353b0681715b668b72e79203e4a"},
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-    {file = "coverage-7.5.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5fd215c0c7d7aab005221608a3c2b46f58c0285a819565887ee0b718c052aa4e"},
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 [[package]]
@@ -1065,13 +1067,13 @@ wmi = ["wmi (>=1.5.1)"]
 
 [[package]]
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 [package.dependencies]
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 [[package]]
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@@ -1187,18 +1189,18 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.14.0"
+version = "3.15.4"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
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 typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
@@ -1300,13 +1302,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2024.5.0"
+version = "2024.6.1"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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@@ -1315,6 +1317,7 @@ adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
 dev = ["pre-commit", "ruff"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
 dropbox = ["dropbox", "dropboxdrivefs", "requests"]
 full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
 fuse = ["fusepy"]
@@ -1354,13 +1357,13 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4
 
 [[package]]
 name = "google-api-core"
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+version = "2.19.1"
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 [package.dependencies]
@@ -1375,7 +1378,7 @@ grpcio-status = [
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 [package.extras]
@@ -1385,13 +1388,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
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@@ -1429,17 +1432,17 @@ dev = ["Pillow", "absl-py", "black", "ipython", "nose2", "pandas", "pytype", "py
 
 [[package]]
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 description = "Common protobufs used in Google APIs"
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 [package.extras]
 grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
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 [[package]]
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 [package.dependencies]
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 [[package]]
@@ -1535,69 +1538,69 @@ protobuf = ">=4.21.6"
 
 [[package]]
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+
+[package.dependencies]
+grpcio = ">=1.62.2"
 protobuf = ">=4.21.6,<5.0dev"
 setuptools = "*"
 
@@ -1734,13 +1737,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.23.1"
+version = "0.23.4"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1818,22 +1821,22 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "7.0.0"
+version = "7.1.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
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 perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
 name = "importlib-resources"
@@ -1910,13 +1913,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.24.0"
+version = "8.26.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
@@ -1935,7 +1938,7 @@ typing-extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
 [package.extras]
 all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
 black = ["black"]
-doc = ["docrepr", "exceptiongroup", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "stack-data", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "intersphinx-registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli", "typing-extensions"]
 kernel = ["ipykernel"]
 matplotlib = ["matplotlib"]
 nbconvert = ["nbconvert"]
@@ -1943,7 +1946,7 @@ nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
+test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
 test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
 [[package]]
@@ -2030,19 +2033,19 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jsonschema-path"
-version = "0.3.2"
+version = "0.3.3"
 description = "JSONSchema Spec with object-oriented paths"
 optional = false
-python-versions = ">=3.8.0,<4.0.0"
+python-versions = "<4.0.0,>=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
 pathable = ">=0.4.1,<0.5.0"
 PyYAML = ">=5.1"
-referencing = ">=0.28.0,<0.32.0"
+referencing = ">=0.28.0,<0.36.0"
 requests = ">=2.31.0,<3.0.0"
 
 [[package]]
@@ -2114,13 +2117,13 @@ files = [
 
 [[package]]
 name = "kubernetes"
-version = "29.0.0"
+version = "30.1.0"
 description = "Kubernetes python client"
 optional = false
 python-versions = ">=3.6"
 files = [
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-    {file = "kubernetes-29.0.0.tar.gz", hash = "sha256:c4812e227ae74d07d53c88293e564e54b850452715a59a927e7e1bc6b9a60459"},
+    {file = "kubernetes-30.1.0-py2.py3-none-any.whl", hash = "sha256:e212e8b7579031dd2e512168b617373bc1e03888d41ac4e04039240a292d478d"},
+    {file = "kubernetes-30.1.0.tar.gz", hash = "sha256:41e4c77af9f28e7a6c314e3bd06a8c6229ddd787cad684e0ab9f69b498e98ebc"},
 ]
 
 [package.dependencies]
@@ -2279,13 +2282,13 @@ files = [
 
 [[package]]
 name = "marshmallow"
-version = "3.21.2"
+version = "3.21.3"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "marshmallow-3.21.2.tar.gz", hash = "sha256:82408deadd8b33d56338d2182d455db632c6313aa2af61916672146bb32edc56"},
+    {file = "marshmallow-3.21.3-py3-none-any.whl", hash = "sha256:86ce7fb914aa865001a4b2092c4c2872d13bc347f3d42673272cabfdbad386f1"},
+    {file = "marshmallow-3.21.3.tar.gz", hash = "sha256:4f57c5e050a54d66361e826f94fba213eb10b67b2fdb02c3e0343ce207ba1662"},
 ]
 
 [package.dependencies]
@@ -2459,6 +2462,22 @@ files = [
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 ]
 
+[[package]]
+name = "mistralai"
+version = "0.4.1"
+description = ""
+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
+    {file = "mistralai-0.4.1-py3-none-any.whl", hash = "sha256:c11d636093c9eec923f00ac9dff13e4619eb751d44d7a3fea5b665a0e8f99f93"},
+    {file = "mistralai-0.4.1.tar.gz", hash = "sha256:22a88c24b9e3176021b466c1d78e6582eef700688803460fd449254fb7647979"},
+]
+
+[package.dependencies]
+httpx = ">=0.25,<1"
+orjson = ">=3.9.10,<3.11"
+pydantic = ">=2.5.2,<3"
+
 [[package]]
 name = "mistune"
 version = "3.0.2"
@@ -2593,24 +2612,24 @@ files = [
 
 [[package]]
 name = "more-itertools"
-version = "10.2.0"
+version = "10.3.0"
 description = "More routines for operating on iterables, beyond itertools"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "more-itertools-10.3.0.tar.gz", hash = "sha256:e5d93ef411224fbcef366a6e8ddc4c5781bc6359d43412a65dd5964e46111463"},
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 ]
 
 [[package]]
 name = "motor"
-version = "3.4.0"
+version = "3.5.0"
 description = "Non-blocking MongoDB driver for Tornado or asyncio"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2618,12 +2637,12 @@ pymongo = ">=4.5,<5"
 
 [package.extras]
 aws = ["pymongo[aws] (>=4.5,<5)"]
+docs = ["aiohttp", "readthedocs-sphinx-search (>=0.3,<1.0)", "sphinx (>=5.3,<8)", "sphinx-rtd-theme (>=2,<3)", "tornado"]
 encryption = ["pymongo[encryption] (>=4.5,<5)"]
 gssapi = ["pymongo[gssapi] (>=4.5,<5)"]
 ocsp = ["pymongo[ocsp] (>=4.5,<5)"]
 snappy = ["pymongo[snappy] (>=4.5,<5)"]
-srv = ["pymongo[srv] (>=4.5,<5)"]
-test = ["aiohttp (!=3.8.6)", "mockupdb", "motor[encryption]", "pytest (>=7)", "tornado (>=5)"]
+test = ["aiohttp (!=3.8.6)", "mockupdb", "pymongo[encryption] (>=4.5,<5)", "pytest (>=7)", "tornado (>=5)"]
 zstd = ["pymongo[zstd] (>=4.5,<5)"]
 
 [[package]]
@@ -2645,51 +2664,47 @@ tests = ["pytest (>=4.6)"]
 
 [[package]]
 name = "msal"
-version = "1.28.0"
+version = "1.29.0"
 description = "The Microsoft Authentication Library (MSAL) for Python library enables your app to access the Microsoft Cloud by supporting authentication of users with Microsoft Azure Active Directory accounts (AAD) and Microsoft Accounts (MSA) using industry standard OAuth2 and OpenID Connect."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
-cryptography = ">=0.6,<45"
+cryptography = ">=2.5,<45"
 PyJWT = {version = ">=1.0.0,<3", extras = ["crypto"]}
 requests = ">=2.0.0,<3"
 
 [package.extras]
-broker = ["pymsalruntime (>=0.13.2,<0.15)"]
+broker = ["pymsalruntime (>=0.13.2,<0.17)"]
 
 [[package]]
 name = "msal-extensions"
-version = "1.1.0"
+version = "1.2.0"
 description = "Microsoft Authentication Library extensions (MSAL EX) provides a persistence API that can save your data on disk, encrypted on Windows, macOS and Linux. Concurrent data access will be coordinated by a file lock mechanism."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
-msal = ">=0.4.1,<2.0.0"
-packaging = "*"
-portalocker = [
-    {version = ">=1.0,<3", markers = "platform_system != \"Windows\""},
-    {version = ">=1.6,<3", markers = "platform_system == \"Windows\""},
-]
+msal = ">=1.29,<2"
+portalocker = ">=1.4,<3"
 
 [[package]]
 name = "msgraph-core"
-version = "1.0.0"
+version = "1.1.0"
 description = "Core component of the Microsoft Graph Python SDK"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2827,38 +2842,38 @@ files = [
 
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 name = "mypy"
-version = "1.10.0"
+version = "1.10.1"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2994,18 +3009,15 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "nodeenv"
-version = "1.8.0"
+version = "1.9.1"
 description = "Node.js virtual environment builder"
 optional = false
-python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 files = [
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 ]
 
-[package.dependencies]
-setuptools = "*"
-
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -3178,6 +3190,7 @@ description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
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 ]
@@ -3211,55 +3224,55 @@ signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
 
 [[package]]
 name = "onnxruntime"
-version = "1.18.0"
+version = "1.18.1"
 description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
 coloredlogs = "*"
 flatbuffers = "*"
-numpy = ">=1.21.6"
+numpy = ">=1.21.6,<2.0"
 packaging = "*"
 protobuf = "*"
 sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.31.0"
+version = "1.35.7"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
-    {file = "openai-1.31.0-py3-none-any.whl", hash = "sha256:82044ee3122113f2a468a1f308a8882324d09556ba5348687c535d3655ee331c"},
-    {file = "openai-1.31.0.tar.gz", hash = "sha256:54ae0625b005d6a3b895db2b8438dae1059cffff0cd262a26e9015c13a29ab06"},
+    {file = "openai-1.35.7-py3-none-any.whl", hash = "sha256:3d1e0b0aac9b0db69a972d36dc7efa7563f8e8d65550b27a48f2a0c2ec207e80"},
+    {file = "openai-1.35.7.tar.gz", hash = "sha256:009bfa1504c9c7ef64d87be55936d142325656bbc6d98c68b669d6472e4beb09"},
 ]
 
 [package.dependencies]
@@ -3339,42 +3352,42 @@ openapi-schema-validator = ">=0.6.0,<0.7.0"
 
 [[package]]
 name = "opentelemetry-api"
-version = "1.24.0"
+version = "1.25.0"
 description = "OpenTelemetry Python API"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_api-1.24.0-py3-none-any.whl", hash = "sha256:0f2c363d98d10d1ce93330015ca7fd3a65f60be64e05e30f557c61de52c80ca2"},
-    {file = "opentelemetry_api-1.24.0.tar.gz", hash = "sha256:42719f10ce7b5a9a73b10a4baf620574fb8ad495a9cbe5c18d76b75d8689c67e"},
+    {file = "opentelemetry_api-1.25.0-py3-none-any.whl", hash = "sha256:757fa1aa020a0f8fa139f8959e53dec2051cc26b832e76fa839a6d76ecefd737"},
+    {file = "opentelemetry_api-1.25.0.tar.gz", hash = "sha256:77c4985f62f2614e42ce77ee4c9da5fa5f0bc1e1821085e9a47533a9323ae869"},
 ]
 
 [package.dependencies]
 deprecated = ">=1.2.6"
-importlib-metadata = ">=6.0,<=7.0"
+importlib-metadata = ">=6.0,<=7.1"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-common"
-version = "1.24.0"
+version = "1.25.0"
 description = "OpenTelemetry Protobuf encoding"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_exporter_otlp_proto_common-1.24.0-py3-none-any.whl", hash = "sha256:e51f2c9735054d598ad2df5d3eca830fecfb5b0bda0a2fa742c9c7718e12f641"},
-    {file = "opentelemetry_exporter_otlp_proto_common-1.24.0.tar.gz", hash = "sha256:5d31fa1ff976cacc38be1ec4e3279a3f88435c75b38b1f7a099a1faffc302461"},
+    {file = "opentelemetry_exporter_otlp_proto_common-1.25.0-py3-none-any.whl", hash = "sha256:15637b7d580c2675f70246563363775b4e6de947871e01d0f4e3881d1848d693"},
+    {file = "opentelemetry_exporter_otlp_proto_common-1.25.0.tar.gz", hash = "sha256:c93f4e30da4eee02bacd1e004eb82ce4da143a2f8e15b987a9f603e0a85407d3"},
 ]
 
 [package.dependencies]
-opentelemetry-proto = "1.24.0"
+opentelemetry-proto = "1.25.0"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-grpc"
-version = "1.24.0"
+version = "1.25.0"
 description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.24.0-py3-none-any.whl", hash = "sha256:f40d62aa30a0a43cc1657428e59fcf82ad5f7ea8fff75de0f9d9cb6f739e0a3b"},
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.24.0.tar.gz", hash = "sha256:217c6e30634f2c9797999ea9da29f7300479a94a610139b9df17433f915e7baa"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.25.0-py3-none-any.whl", hash = "sha256:3131028f0c0a155a64c430ca600fd658e8e37043cb13209f0109db5c1a3e4eb4"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.25.0.tar.gz", hash = "sha256:c0b1661415acec5af87625587efa1ccab68b873745ca0ee96b69bb1042087eac"},
 ]
 
 [package.dependencies]
@@ -3382,22 +3395,19 @@ deprecated = ">=1.2.6"
 googleapis-common-protos = ">=1.52,<2.0"
 grpcio = ">=1.0.0,<2.0.0"
 opentelemetry-api = ">=1.15,<2.0"
-opentelemetry-exporter-otlp-proto-common = "1.24.0"
-opentelemetry-proto = "1.24.0"
-opentelemetry-sdk = ">=1.24.0,<1.25.0"
-
-[package.extras]
-test = ["pytest-grpc"]
+opentelemetry-exporter-otlp-proto-common = "1.25.0"
+opentelemetry-proto = "1.25.0"
+opentelemetry-sdk = ">=1.25.0,<1.26.0"
 
 [[package]]
 name = "opentelemetry-instrumentation"
-version = "0.45b0"
+version = "0.46b0"
 description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation-0.45b0-py3-none-any.whl", hash = "sha256:06c02e2c952c1b076e8eaedf1b82f715e2937ba7eeacab55913dd434fbcec258"},
-    {file = "opentelemetry_instrumentation-0.45b0.tar.gz", hash = "sha256:6c47120a7970bbeb458e6a73686ee9ba84b106329a79e4a4a66761f933709c7e"},
+    {file = "opentelemetry_instrumentation-0.46b0-py3-none-any.whl", hash = "sha256:89cd721b9c18c014ca848ccd11181e6b3fd3f6c7669e35d59c48dc527408c18b"},
+    {file = "opentelemetry_instrumentation-0.46b0.tar.gz", hash = "sha256:974e0888fb2a1e01c38fbacc9483d024bb1132aad92d6d24e2e5543887a7adda"},
 ]
 
 [package.dependencies]
@@ -3407,55 +3417,55 @@ wrapt = ">=1.0.0,<2.0.0"
 
 [[package]]
 name = "opentelemetry-instrumentation-asgi"
-version = "0.45b0"
+version = "0.46b0"
 description = "ASGI instrumentation for OpenTelemetry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation_asgi-0.45b0-py3-none-any.whl", hash = "sha256:8be1157ed62f0db24e45fdf7933c530c4338bd025c5d4af7830e903c0756021b"},
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+    {file = "opentelemetry_instrumentation_asgi-0.46b0-py3-none-any.whl", hash = "sha256:f13c55c852689573057837a9500aeeffc010c4ba59933c322e8f866573374759"},
+    {file = "opentelemetry_instrumentation_asgi-0.46b0.tar.gz", hash = "sha256:02559f30cf4b7e2a737ab17eb52aa0779bcf4cc06573064f3e2cb4dcc7d3040a"},
 ]
 
 [package.dependencies]
 asgiref = ">=3.0,<4.0"
 opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.45b0"
-opentelemetry-semantic-conventions = "0.45b0"
-opentelemetry-util-http = "0.45b0"
+opentelemetry-instrumentation = "0.46b0"
+opentelemetry-semantic-conventions = "0.46b0"
+opentelemetry-util-http = "0.46b0"
 
 [package.extras]
 instruments = ["asgiref (>=3.0,<4.0)"]
 
 [[package]]
 name = "opentelemetry-instrumentation-fastapi"
-version = "0.45b0"
+version = "0.46b0"
 description = "OpenTelemetry FastAPI Instrumentation"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation_fastapi-0.45b0-py3-none-any.whl", hash = "sha256:77d9c123a363129148f5f66d44094f3d67aaaa2b201396d94782b4a7f9ce4314"},
-    {file = "opentelemetry_instrumentation_fastapi-0.45b0.tar.gz", hash = "sha256:5a6b91e1c08a01601845fcfcfdefd0a2aecdb3c356d4a436a3210cb58c21487e"},
+    {file = "opentelemetry_instrumentation_fastapi-0.46b0-py3-none-any.whl", hash = "sha256:e0f5d150c6c36833dd011f0e6ef5ede6d7406c1aed0c7c98b2d3b38a018d1b33"},
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 ]
 
 [package.dependencies]
 opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.45b0"
-opentelemetry-instrumentation-asgi = "0.45b0"
-opentelemetry-semantic-conventions = "0.45b0"
-opentelemetry-util-http = "0.45b0"
+opentelemetry-instrumentation = "0.46b0"
+opentelemetry-instrumentation-asgi = "0.46b0"
+opentelemetry-semantic-conventions = "0.46b0"
+opentelemetry-util-http = "0.46b0"
 
 [package.extras]
 instruments = ["fastapi (>=0.58,<1.0)"]
 
 [[package]]
 name = "opentelemetry-proto"
-version = "1.24.0"
+version = "1.25.0"
 description = "OpenTelemetry Python Proto"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_proto-1.24.0-py3-none-any.whl", hash = "sha256:bcb80e1e78a003040db71ccf83f2ad2019273d1e0828089d183b18a1476527ce"},
-    {file = "opentelemetry_proto-1.24.0.tar.gz", hash = "sha256:ff551b8ad63c6cabb1845ce217a6709358dfaba0f75ea1fa21a61ceddc78cab8"},
+    {file = "opentelemetry_proto-1.25.0-py3-none-any.whl", hash = "sha256:f07e3341c78d835d9b86665903b199893befa5e98866f63d22b00d0b7ca4972f"},
+    {file = "opentelemetry_proto-1.25.0.tar.gz", hash = "sha256:35b6ef9dc4a9f7853ecc5006738ad40443701e52c26099e197895cbda8b815a3"},
 ]
 
 [package.dependencies]
@@ -3463,95 +3473,98 @@ protobuf = ">=3.19,<5.0"
 
 [[package]]
 name = "opentelemetry-sdk"
-version = "1.24.0"
+version = "1.25.0"
 description = "OpenTelemetry Python SDK"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "opentelemetry_sdk-1.25.0-py3-none-any.whl", hash = "sha256:d97ff7ec4b351692e9d5a15af570c693b8715ad78b8aafbec5c7100fe966b4c9"},
+    {file = "opentelemetry_sdk-1.25.0.tar.gz", hash = "sha256:ce7fc319c57707ef5bf8b74fb9f8ebdb8bfafbe11898410e0d2a761d08a98ec7"},
 ]
 
 [package.dependencies]
-opentelemetry-api = "1.24.0"
-opentelemetry-semantic-conventions = "0.45b0"
+opentelemetry-api = "1.25.0"
+opentelemetry-semantic-conventions = "0.46b0"
 typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "opentelemetry-semantic-conventions"
-version = "0.45b0"
+version = "0.46b0"
 description = "OpenTelemetry Semantic Conventions"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "opentelemetry_semantic_conventions-0.46b0.tar.gz", hash = "sha256:fbc982ecbb6a6e90869b15c1673be90bd18c8a56ff1cffc0864e38e2edffaefa"},
 ]
 
+[package.dependencies]
+opentelemetry-api = "1.25.0"
+
 [[package]]
 name = "opentelemetry-util-http"
-version = "0.45b0"
+version = "0.46b0"
 description = "Web util for OpenTelemetry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_util_http-0.45b0-py3-none-any.whl", hash = "sha256:6628868b501b3004e1860f976f410eeb3d3499e009719d818000f24ce17b6e33"},
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 ]
 
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 name = "orjson"
-version = "3.10.3"
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 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
-docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+docs = ["furo", "olefile", "sphinx (>=7.3)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
 fpx = ["olefile"]
 mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
@@ -3964,13 +3988,13 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "portalocker"
-version = "2.8.2"
+version = "2.10.0"
 description = "Wraps the portalocker recipe for easy usage"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4050,13 +4074,13 @@ virtualenv = ">=20.10.0"
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.43"
+version = "3.0.47"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -4064,20 +4088,20 @@ wcwidth = "*"
 
 [[package]]
 name = "proto-plus"
-version = "1.23.0"
+version = "1.24.0"
 description = "Beautiful, Pythonic protocol buffers."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
-protobuf = ">=3.19.0,<5.0.0dev"
+protobuf = ">=3.19.0,<6.0.0dev"
 
 [package.extras]
-testing = ["google-api-core[grpc] (>=1.31.5)"]
+testing = ["google-api-core (>=1.31.5)"]
 
 [[package]]
 name = "protobuf"
@@ -4101,27 +4125,28 @@ files = [
 
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+version = "6.0.0"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
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 ]
 
 [package.extras]
@@ -4129,99 +4154,89 @@ test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
 
 [[package]]
 name = "psycopg"
-version = "3.1.19"
+version = "3.2.1"
 description = "PostgreSQL database adapter for Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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 psycopg-pool = {version = "*", optional = true, markers = "extra == \"pool\""}
-typing-extensions = ">=4.1"
+typing-extensions = ">=4.4"
 tzdata = {version = "*", markers = "sys_platform == \"win32\""}
 
 [package.extras]
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-c = ["psycopg-c (==3.1.19)"]
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 [[package]]
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 optional = false
-python-versions = ">=3.7"
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 [package.dependencies]
@@ -4632,6 +4637,7 @@ dnspython = ">=1.16.0,<3.0.0"
 
 [package.extras]
 aws = ["pymongo-auth-aws (>=1.1.0,<2.0.0)"]
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 ocsp = ["certifi", "cryptography (>=2.5)", "pyopenssl (>=17.2.0)", "requests (<3.0.0)", "service-identity (>=18.1.0)"]
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 [[package]]
 name = "referencing"
-version = "0.31.1"
+version = "0.35.1"
 description = "JSON Referencing + Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -5114,13 +5121,13 @@ files = [
 
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 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -5643,18 +5650,18 @@ dev = ["pre-commit", "pytest", "ruff (>=0.3.0)"]
 
 [[package]]
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-version = "70.0.0"
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 python-versions = ">=3.8"
 files = [
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 [[package]]
 name = "shellingham"
@@ -5759,51 +5766,51 @@ full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7
 
 [[package]]
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+version = "1.0.2"
 description = "std-uritemplate implementation for Python"
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 python-versions = "<4.0,>=3.8"
 files = [
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 ]
 
 [[package]]
 name = "sympy"
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 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
-mpmath = ">=0.19"
+mpmath = ">=1.1.0,<1.4.0"
 
 [[package]]
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+version = "2021.13.0"
 description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
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 ]
 
 [[package]]
 name = "tenacity"
-version = "8.3.0"
+version = "8.4.2"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tenacity-8.3.0-py3-none-any.whl", hash = "sha256:3649f6443dbc0d9b01b9d8020a9c4ec7a1ff5f6f3c6c8a036ef371f573fe9185"},
-    {file = "tenacity-8.3.0.tar.gz", hash = "sha256:953d4e6ad24357bceffbc9707bc74349aca9d245f68eb65419cf0c249a1949a2"},
+    {file = "tenacity-8.4.2-py3-none-any.whl", hash = "sha256:9e6f7cf7da729125c7437222f8a522279751cdfbe6b67bfe64f75d3a348661b2"},
+    {file = "tenacity-8.4.2.tar.gz", hash = "sha256:cd80a53a79336edba8489e767f729e4f391c896956b57140b5d7511a64bbd3ef"},
 ]
 
 [package.extras]
@@ -6078,20 +6085,20 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "transformers"
-version = "4.41.2"
+version = "4.42.3"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "transformers-4.41.2-py3-none-any.whl", hash = "sha256:05555d20e43f808de1ef211ab64803cdb513170cef70d29a888b589caebefc67"},
-    {file = "transformers-4.41.2.tar.gz", hash = "sha256:80a4db216533d573e9cc7388646c31ed9480918feb7c55eb211249cb23567f87"},
+    {file = "transformers-4.42.3-py3-none-any.whl", hash = "sha256:a61a0df9609b7d69229d941b2fd857c841ba3043d6da503d0da1a4b133f65b92"},
+    {file = "transformers-4.42.3.tar.gz", hash = "sha256:7539873ff45809145265cbc94ea4619d2713c41ceaa277b692d8b0be3430f7eb"},
 ]
 
 [package.dependencies]
 accelerate = {version = ">=0.21.0", optional = true, markers = "extra == \"torch\""}
 filelock = "*"
-huggingface-hub = ">=0.23.0,<1.0"
-numpy = ">=1.17"
+huggingface-hub = ">=0.23.2,<1.0"
+numpy = ">=1.17,<2.0"
 packaging = ">=20.0"
 pyyaml = ">=5.1"
 regex = "!=2019.12.17"
@@ -6104,14 +6111,15 @@ tqdm = ">=4.27"
 [package.extras]
 accelerate = ["accelerate (>=0.21.0)"]
 agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
-all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
+all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+benchmark = ["optimum-benchmark (>=0.2.0)"]
 codecarbon = ["codecarbon (==1.2.0)"]
 deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
-deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "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 (==0.1.5)", "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 (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "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)", "librosa", "nltk", "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-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "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", "tokenizers (>=0.19,<0.20)", "torch", "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)", "librosa", "nltk", "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-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "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 (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
-dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "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", "librosa", "nltk", "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)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "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", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "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 (==0.4.4)", "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 (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "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)", "librosa", "nltk", "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-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "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 (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "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)", "librosa", "nltk", "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-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "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 (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "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", "librosa", "nltk", "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)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "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 (>=0.19,<0.20)", "torch", "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"]
@@ -6122,25 +6130,26 @@ natten = ["natten (>=0.14.6,<0.15.0)"]
 onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
 onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
 optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.4.4)", "urllib3 (<2.0.0)"]
 ray = ["ray[tune] (>=2.7.0)"]
 retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+ruff = ["ruff (==0.4.4)"]
 sagemaker = ["sagemaker (>=2.31.0)"]
 sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
 serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "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 (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "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 (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
 tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-timm = ["timm"]
+timm = ["timm (<=0.9.16)"]
 tokenizers = ["tokenizers (>=0.19,<0.20)"]
 torch = ["accelerate (>=0.21.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17,<2.0)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
@@ -6197,13 +6206,13 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.12.0"
+version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.12.0-py3-none-any.whl", hash = "sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594"},
-    {file = "typing_extensions-4.12.0.tar.gz", hash = "sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8"},
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
 
 [[package]]
@@ -6386,13 +6395,13 @@ tqdm = "*"
 
 [[package]]
 name = "uvicorn"
-version = "0.29.0"
+version = "0.30.1"
 description = "The lightning-fast ASGI server."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "uvicorn-0.29.0-py3-none-any.whl", hash = "sha256:2c2aac7ff4f4365c206fd773a39bf4ebd1047c238f8b8268ad996829323473de"},
-    {file = "uvicorn-0.29.0.tar.gz", hash = "sha256:6a69214c0b6a087462412670b3ef21224fa48cae0e452b5883e8e8bdfdd11dd0"},
+    {file = "uvicorn-0.30.1-py3-none-any.whl", hash = "sha256:cd17daa7f3b9d7a24de3617820e634d0933b69eed8e33a516071174427238c81"},
+    {file = "uvicorn-0.30.1.tar.gz", hash = "sha256:d46cd8e0fd80240baffbcd9ec1012a712938754afcf81bce56c024c1656aece8"},
 ]
 
 [package.dependencies]
@@ -6467,13 +6476,13 @@ files = [
 
 [[package]]
 name = "virtualenv"
-version = "20.26.2"
+version = "20.26.3"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "virtualenv-20.26.2-py3-none-any.whl", hash = "sha256:a624db5e94f01ad993d476b9ee5346fdf7b9de43ccaee0e0197012dc838a0e9b"},
-    {file = "virtualenv-20.26.2.tar.gz", hash = "sha256:82bf0f4eebbb78d36ddaee0283d43fe5736b53880b8a8cdcd37390a07ac3741c"},
+    {file = "virtualenv-20.26.3-py3-none-any.whl", hash = "sha256:8cc4a31139e796e9a7de2cd5cf2489de1217193116a8fd42328f1bd65f434589"},
+    {file = "virtualenv-20.26.3.tar.gz", hash = "sha256:4c43a2a236279d9ea36a0d76f98d84bd6ca94ac4e0f4a3b9d46d05e10fea542a"},
 ]
 
 [package.dependencies]
@@ -6487,86 +6496,86 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 
 [[package]]
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 optional = false
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+    {file = "watchfiles-0.22.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:72a44e9481afc7a5ee3291b09c419abab93b7e9c306c9ef9108cb76728ca58d2"},
+    {file = "watchfiles-0.22.0.tar.gz", hash = "sha256:988e981aaab4f3955209e7e28c7794acdb690be1efa7f16f8ea5aba7ffdadacb"},
 ]
 
 [package.dependencies]
@@ -6585,13 +6594,13 @@ files = [
 
 [[package]]
 name = "weaviate-client"
-version = "4.6.4"
+version = "4.6.5"
 description = "A python native Weaviate client"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "weaviate_client-4.6.4-py3-none-any.whl", hash = "sha256:19b76fb923a5f0b6fcb7471ef3cd990d2791ede71731e53429e1066a9dbf2af2"},
-    {file = "weaviate_client-4.6.4.tar.gz", hash = "sha256:5378db8a33bf1d48adff3f9efa572d9fb04eaeb36444817cab56f1ba3c595500"},
+    {file = "weaviate_client-4.6.5-py3-none-any.whl", hash = "sha256:ed5b1c26c86081b5286e7b292de80e0380c964d34b4bffc842c1eb9dfadf7e15"},
+    {file = "weaviate_client-4.6.5.tar.gz", hash = "sha256:3926fd0c350c54b668b824f9085959904562821ebb6fc237b7e253daf4645904"},
 ]
 
 [package.dependencies]
@@ -6913,26 +6922,27 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zipp"
-version = "3.18.2"
+version = "3.19.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
-    {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
+    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [extras]
-all = ["azure-ai-inference", "azure-core", "azure-cosmos", "azure-identity", "azure-search-documents", "chromadb", "google-generativeai", "ipykernel", "milvus", "motor", "pinecone-client", "psycopg", "pyarrow", "pymilvus", "qdrant-client", "redis", "sentence-transformers", "transformers", "usearch", "weaviate-client"]
+all = ["azure-ai-inference", "azure-core", "azure-cosmos", "azure-identity", "azure-search-documents", "chromadb", "google-generativeai", "ipykernel", "milvus", "mistralai", "motor", "pinecone-client", "psycopg", "pyarrow", "pymilvus", "qdrant-client", "redis", "sentence-transformers", "transformers", "usearch", "weaviate-client"]
 azure = ["azure-ai-inference", "azure-core", "azure-cosmos", "azure-identity", "azure-search-documents"]
 chromadb = ["chromadb"]
 google = ["google-generativeai"]
 hugging-face = ["sentence-transformers", "transformers"]
 milvus = ["milvus", "pymilvus"]
+mistralai = ["mistralai"]
 mongo = ["motor"]
 notebooks = ["ipykernel"]
 pinecone = ["pinecone-client"]
@@ -6945,4 +6955,4 @@ weaviate = ["weaviate-client"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10,<3.13"
-content-hash = "8e4bbafac15b7796bad6d303daf134022273a2cd188e026e1c3340308d6e252d"
+content-hash = "1e7fd185338e4f1ca9f06c3d0c3b8ee14d0bf73484999e92fdc1d328c5185df3"
diff --git a/python/pyproject.toml b/python/pyproject.toml
index 38e6a226f3d7..062e731ecd12 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -66,8 +66,9 @@ redis = { version = "^4.6.0", optional = true}
 usearch = { version = "^2.9", optional = true}
 pyarrow = { version = ">=12.0.1,<17.0.0", optional = true}
 weaviate-client = { version = ">=3.18,<5.0", optional = true}
+# mistralai
+mistralai = { version = "^0.4.1"}
 
-# Groups are for development only (installed through Poetry)
 [tool.poetry.group.dev.dependencies]
 pre-commit = ">=3.7.1"
 ruff = ">=0.4.5"
@@ -128,16 +129,19 @@ usearch = "^2.9"
 pyarrow = ">=12.0.1,<17.0.0"
 # weaviate
 weaviate-client = ">=3.18,<5.0"
+# mistralai
+mistralai = "^0.4.1"
 
 # Extras are exposed to pip, this allows a user to easily add the right dependencies to their environment
 [tool.poetry.extras]
-all = ["google-generativeai", "transformers", "sentence-transformers", "qdrant-client", "chromadb", "pymilvus", "milvus", "weaviate-client", "pinecone-client", "psycopg", "redis", "azure-ai-inference", "azure-search-documents", "azure-core", "azure-identity", "azure-cosmos", "usearch", "pyarrow", "ipykernel", "motor"]
+all = ["google-generativeai", "transformers", "sentence-transformers", "qdrant-client", "chromadb", "pymilvus", "milvus", "weaviate-client", "pinecone-client", "psycopg", "redis", "azure-ai-inference", "azure-search-documents", "azure-core", "azure-identity", "azure-cosmos", "usearch", "pyarrow", "ipykernel", "motor", "mistralai"]
 
 azure = ["azure-ai-inference", "azure-search-documents", "azure-core", "azure-identity", "azure-cosmos", "msgraph-sdk"]
 chromadb = ["chromadb"]
 google = ["google-generativeai"]
 hugging_face = ["transformers", "sentence-transformers"]
 milvus = ["pymilvus", "milvus"]
+mistralai = ["mistralai"]
 mongo = ["motor"]
 notebooks = ["ipykernel"]
 pinecone = ["pinecone-client"]
diff --git a/python/samples/concepts/auto_function_calling/chat_mistral_function_calling.py b/python/samples/concepts/auto_function_calling/chat_mistral_function_calling.py
new file mode 100644
index 000000000000..fbaa389a1108
--- /dev/null
+++ b/python/samples/concepts/auto_function_calling/chat_mistral_function_calling.py
@@ -0,0 +1,174 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+from functools import reduce
+from typing import TYPE_CHECKING
+
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai.mistral_ai.services.mistral_ai_chat_completion import (
+    MistralAIChatCompletion,
+    MistralAIChatPromptExecutionSettings,
+)
+from semantic_kernel.contents import ChatHistory
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.core_plugins import MathPlugin, TimePlugin
+from semantic_kernel.functions import KernelArguments
+
+if TYPE_CHECKING:
+    from semantic_kernel.functions import KernelFunction
+
+
+system_message = """
+You are a chat bot. Your name is Mosscap and
+you have one goal: figure out what people need.
+Your full name, should you need to know it, is
+Splendid Speckled Mosscap. You communicate
+effectively, but you tend to answer with long
+flowery prose. You are also a math wizard,
+especially for adding and subtracting.
+You also excel at joke telling, where your tone is often sarcastic.
+Once you have the answer I am looking for,
+you will return a full answer to me as soon as possible.
+"""
+
+kernel = Kernel()
+
+kernel.add_service(MistralAIChatCompletion(service_id="chat", ai_model_id="mistral-large-latest"))
+
+plugins_directory = os.path.join(__file__, "../../../../../prompt_template_samples/")
+# adding plugins to the kernel
+# the joke plugin in the FunPlugins is a semantic plugin and has the function calling disabled.
+# kernel.import_plugin_from_prompt_directory("chat", plugins_directory, "FunPlugin")
+# the math plugin is a core plugin and has the function calling enabled.
+kernel.add_plugin(MathPlugin(), plugin_name="math")
+kernel.add_plugin(TimePlugin(), plugin_name="time")
+
+chat_function = kernel.add_function(
+    prompt="{{$chat_history}}{{$user_input}}",
+    plugin_name="ChatBot",
+    function_name="Chat",
+)
+# enabling or disabling function calling is done by setting the function_call parameter for the completion.
+# when the function_call parameter is set to "auto" the model will decide which function to use, if any.
+# if you only want to use a specific function, set the name of that function in this parameter,
+# the format for that is 'PluginName-FunctionName', (i.e. 'math-Add').
+# if the model or api version does not support this you will get an error.
+
+# Note: the number of responses for auto invoking tool calls is limited to 1.
+# If configured to be greater than one, this value will be overridden to 1.
+execution_settings = MistralAIChatPromptExecutionSettings(
+    service_id="chat",
+    max_tokens=2000,
+    temperature=0.7,
+    top_p=0.8,
+    function_choice_behavior=FunctionChoiceBehavior.Auto()
+)
+
+history = ChatHistory()
+
+history.add_system_message(system_message)
+history.add_user_message("Hi there, who are you?")
+history.add_assistant_message("I am Mosscap, a chat bot. I'm trying to figure out what people need.")
+
+arguments = KernelArguments(settings=execution_settings)
+
+
+def print_tool_calls(message: ChatMessageContent) -> None:
+    # A helper method to pretty print the tool calls from the message.
+    # This is only triggered if auto invoke tool calls is disabled.
+    items = message.items
+    formatted_tool_calls = []
+    for i, item in enumerate(items, start=1):
+        if isinstance(item, FunctionCallContent):
+            tool_call_id = item.id
+            function_name = item.name
+            function_arguments = item.arguments
+            formatted_str = (
+                f"tool_call {i} id: {tool_call_id}\n"
+                f"tool_call {i} function name: {function_name}\n"
+                f"tool_call {i} arguments: {function_arguments}"
+            )
+            formatted_tool_calls.append(formatted_str)
+    print("Tool calls:\n" + "\n\n".join(formatted_tool_calls))
+
+
+async def handle_streaming(
+    kernel: Kernel,
+    chat_function: "KernelFunction",
+    arguments: KernelArguments,
+) -> None:
+    response = kernel.invoke_stream(
+        chat_function,
+        return_function_results=False,
+        arguments=arguments,
+    )
+
+    print("Mosscap:> ", end="")
+    streamed_chunks: list[StreamingChatMessageContent] = []
+    async for message in response:
+        if not execution_settings.function_choice_behavior.auto_invoke_kernel_functions and isinstance(
+            message[0], StreamingChatMessageContent
+        ):
+            streamed_chunks.append(message[0])
+        else:
+            print(str(message[0]), end="")
+
+    if streamed_chunks:
+        streaming_chat_message = reduce(lambda first, second: first + second, streamed_chunks)
+        print("Auto tool calls is disabled, printing returned tool calls...")
+        print_tool_calls(streaming_chat_message)
+
+    print("\n")
+
+
+async def chat() -> bool:
+    try:
+        user_input = input("User:> ")
+    except KeyboardInterrupt:
+        print("\n\nExiting chat...")
+        return False
+    except EOFError:
+        print("\n\nExiting chat...")
+        return False
+
+    if user_input == "exit":
+        print("\n\nExiting chat...")
+        return False
+    arguments["user_input"] = user_input
+    arguments["chat_history"] = history
+
+    stream = True
+    if stream:
+        await handle_streaming(kernel, chat_function, arguments=arguments)
+    else:
+        result = await kernel.invoke(chat_function, arguments=arguments)
+
+        # If tools are used, and auto invoke tool calls is False, the response will be of type
+        # ChatMessageContent with information about the tool calls, which need to be sent
+        # back to the model to get the final response.
+        function_calls = [item for item in result.value[-1].items if isinstance(item, FunctionCallContent)]
+        if not execution_settings.function_choice_behavior.auto_invoke_kernel_functions and len(function_calls) > 0:
+            print_tool_calls(result.value[0])
+            return True
+
+        print(f"Mosscap:> {result}")
+    return True
+
+
+async def main() -> None:
+    chatting = True
+    print(
+        "Welcome to the chat bot!\
+        \n  Type 'exit' to exit.\
+        \n  Try a math question to see the function calling in action (i.e. what is 3+3?)."
+    )
+    while chatting:
+        chatting = await chat()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/python/samples/concepts/chat_completion/chat_mistral_api.py b/python/samples/concepts/chat_completion/chat_mistral_api.py
new file mode 100644
index 000000000000..9e1ebc20cddc
--- /dev/null
+++ b/python/samples/concepts/chat_completion/chat_mistral_api.py
@@ -0,0 +1,71 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai.mistral_ai import MistralAIChatCompletion
+from semantic_kernel.contents import ChatHistory
+from semantic_kernel.functions import KernelArguments
+
+system_message = """
+You are a chat bot. Your name is Mosscap and
+you have one goal: figure out what people need.
+Your full name, should you need to know it, is
+Splendid Speckled Mosscap. You communicate
+effectively, but you tend to answer with long
+flowery prose.
+"""
+
+kernel = Kernel()
+
+service_id = "mistral-ai-chat"
+kernel.add_service(MistralAIChatCompletion(service_id=service_id, ai_model_id="mistral-small-latest"))
+
+settings = kernel.get_prompt_execution_settings_from_service_id(service_id)
+settings.max_tokens = 2000
+settings.temperature = 0.7
+settings.top_p = 0.8
+
+chat_function = kernel.add_function(
+    plugin_name="ChatBot",
+    function_name="Chat",
+    prompt="{{$chat_history}}{{$user_input}}",
+    template_format="semantic-kernel",
+    prompt_execution_settings=settings,
+)
+
+chat_history = ChatHistory(system_message=system_message)
+chat_history.add_user_message("Hi there, who are you?")
+chat_history.add_assistant_message("I am Mosscap, a chat bot. I'm trying to figure out what people need")
+chat_history.add_user_message("I want to find a hotel in Seattle with free wifi and a pool.")
+
+
+async def chat() -> bool:
+    try:
+        user_input = input("User:> ")
+    except KeyboardInterrupt:
+        print("\n\nExiting chat...")
+        return False
+    except EOFError:
+        print("\n\nExiting chat...")
+        return False
+
+    if user_input == "exit":
+        print("\n\nExiting chat...")
+        return False
+
+    answer = await kernel.invoke(chat_function, KernelArguments(user_input=user_input, chat_history=chat_history))
+    chat_history.add_user_message(user_input)
+    chat_history.add_assistant_message(str(answer))
+    print(f"Mosscap:> {answer}")
+    return True
+
+
+async def main() -> None:
+    chatting = True
+    while chatting:
+        chatting = await chat()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/python/semantic_kernel/connectors/ai/chat_completion_client_base.py b/python/semantic_kernel/connectors/ai/chat_completion_client_base.py
index ab92d29fd65f..8203f3c6729e 100644
--- a/python/semantic_kernel/connectors/ai/chat_completion_client_base.py
+++ b/python/semantic_kernel/connectors/ai/chat_completion_client_base.py
@@ -1,9 +1,19 @@
 # Copyright (c) Microsoft. All rights reserved.
-
+import asyncio
+import logging
 from abc import ABC, abstractmethod
 from collections.abc import AsyncGenerator
+from functools import reduce
 from typing import TYPE_CHECKING, Any
 
+from semantic_kernel.connectors.ai.function_call_behavior import (
+    FunctionCallConfiguration,
+)
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.exceptions import (
+    ServiceInvalidExecutionSettingsError,
+)
 from semantic_kernel.services.ai_service_client_base import AIServiceClientBase
 
 if TYPE_CHECKING:
@@ -11,9 +21,15 @@
     from semantic_kernel.contents.chat_history import ChatHistory
     from semantic_kernel.contents.chat_message_content import ChatMessageContent
     from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+    from semantic_kernel.kernel import Kernel
+
+logger: logging.Logger = logging.getLogger(__name__)
 
 
 class ChatCompletionClientBase(AIServiceClientBase, ABC):
+
+    # region abstract methods
+
     @abstractmethod
     async def get_chat_message_contents(
         self,
@@ -21,7 +37,11 @@ async def get_chat_message_contents(
         settings: "PromptExecutionSettings",
         **kwargs: Any,
     ) -> list["ChatMessageContent"]:
-        """This is the method that is called from the kernel to get a response from a chat-optimized LLM.
+        """Method to convert between connector and semantic kernel objects.
+        
+        This method is called by the invoke method this method needs to be implemented by the corresponding connector.
+        The Goal of the method is to return a list of ChatMessageContent that represent the response from connector
+        to automate function calling.
 
         Args:
             chat_history (ChatHistory): A list of chats in a chat_history object, that can be
@@ -41,7 +61,11 @@ def get_streaming_chat_message_contents(
         settings: "PromptExecutionSettings",
         **kwargs: Any,
     ) -> AsyncGenerator[list["StreamingChatMessageContent"], Any]:
-        """This is the method that is called from the kernel to get a stream response from a chat-optimized LLM.
+        """Method to convert between connector and semantic kernel objects.
+        
+        This method is called by the invoke method this method needs to be implemented by the corresponding connector.
+        The Goal of the method is to return a list of ChatMessageContent that represent the response from connector
+        to automate function calling.
 
         Args:
             chat_history (ChatHistory): A list of chat chat_history, that can be rendered into a
@@ -54,6 +78,31 @@ def get_streaming_chat_message_contents(
         """
         ...
 
+    # endregion
+    # region function calling config
+
+    def update_settings_from_function_call_configuration(
+        self,
+        function_call_configuration: "FunctionCallConfiguration",
+        settings: "PromptExecutionSettings",
+        type: str,
+    ) -> None:
+        """This Method is used to convert the function call configuration to the desired format for the connector.
+        
+        Configure the conversion from kernel function to the desired format for the connector.
+        To implement function calling in the connector, this method should be overridden.
+
+        The settings object needs to be updated with the function call configuration.
+
+        Args:
+            function_call_configuration (FunctionCallConfiguration): The function call configuration.
+            settings (PromptExecutionSettings): The settings to update.
+            type (str): The type of the function choice configuration.
+        """
+        raise NotImplementedError("Function Calling is not implemented for this Connector.")
+    
+    # endregion
+
     def _prepare_chat_history_for_request(
         self,
         chat_history: "ChatHistory",
@@ -77,3 +126,236 @@ def _prepare_chat_history_for_request(
             List[Dict[str, Optional[str]]]: The prepared chat history.
         """
         return [message.to_dict(role_key=role_key, content_key=content_key) for message in chat_history.messages]
+    
+    # region invoke 
+    async def invoke(
+        self,
+        chat_history: "ChatHistory",
+        settings: "PromptExecutionSettings",
+        **kwargs: Any
+    ) -> list["ChatMessageContent"]:
+        """This method is called by the kernel to perform the chat completion and potentially execute function calls.
+
+        The Method calls the get_chat_message_contents method to get the response from the connector.
+        Then it checks the function calling behavior and executes the function calls if needed.
+
+        Args:
+            chat_history (ChatHistory): A list of chat chat_history, that can be rendered into a
+                set of chat_history, from system, user, assistant and function.
+            settings (PromptExecutionSettings): Settings for the request.
+            kwargs (Dict[str, Any]): The optional arguments.
+        
+        Returns:
+            List[Dict[str, Optional[str]]]: The prepared chat history.
+        """
+        function_call_behavior = self._check_for_function_calling_behavior(settings, **kwargs)
+        if not function_call_behavior:
+            return await self.get_chat_message_contents(chat_history, settings, **kwargs)
+        
+        kernel = kwargs.get("kernel", None)
+
+        settings.function_choice_behavior.configure(
+            kernel=kernel,
+            update_settings_callback=self.update_settings_from_function_call_configuration,
+            settings=settings,
+        )
+
+        return await self._execute_function_call_behavior(chat_history, settings, **kwargs)
+
+    async def _execute_function_call_behavior(
+            self,
+            chat_history: "ChatHistory",
+            settings: "PromptExecutionSettings",
+            **kwargs: Any
+    ) -> list["ChatMessageContent"]:
+        """Takes Semantic Kernel Objects and performs function calling with the selected behavior.
+
+        Args:
+            chat_history (ChatHistory): A list of chat chat_history, that can be rendered into a
+                set of chat_history, from system, user, assistant and function.
+            settings (PromptExecutionSettings): Settings for the request.
+            kwargs (Dict[str, Any]): The optional arguments.
+        
+        Returns:
+            List[Dict[str, Optional[str]]]: The prepared chat history.
+        """
+        kernel: Kernel = kwargs.get("kernel", None)
+        arguments = kwargs.get("arguments", None)
+        # loop for auto-invoke function calls
+        for request_index in range(settings.function_choice_behavior.maximum_auto_invoke_attempts):
+            completions = await self.get_chat_message_contents(chat_history=chat_history, settings=settings, **kwargs)
+            # there is only one chat message, this was checked earlier
+            chat_history.add_message(message=completions[0])
+            # get the function call contents from the chat message
+            function_calls = [item for item in chat_history.messages[-1].items if isinstance(item, FunctionCallContent)]
+            if (fc_count := len(function_calls)) == 0:
+                return completions
+
+            logger.info(f"processing {fc_count} tool calls in parallel.")
+
+            # this function either updates the chat history with the function call results
+            # or returns the context, with terminate set to True
+            # in which case the loop will break and the function calls are returned.
+            results = await asyncio.gather(
+                *[
+                    kernel.invoke_function_call(
+                        function_call=function_call,
+                        chat_history=chat_history,
+                        arguments=arguments,
+                        function_call_count=fc_count,
+                        request_index=request_index,
+                        function_behavior=settings.function_choice_behavior,
+                    )
+                    for function_call in function_calls
+                ],
+            )
+
+            if any(result.terminate for result in results if result is not None):
+                return completions
+        else:
+            # do a final call, without function calling when the max has been reached.
+            return await self.get_chat_message_contents(
+                    chat_history=chat_history,
+                    settings=settings,
+                    **kwargs
+                )
+        
+    # endregion
+    # region invoke_streaming
+
+    async def invoke_streaming(
+        self,
+        chat_history: "ChatHistory",
+        settings: "PromptExecutionSettings",
+        **kwargs: Any
+    ) -> AsyncGenerator[list[StreamingChatMessageContent | None], Any]:
+        """This method is called by the kernel to perform the chat completion and potentially execute function calls.
+
+        The Method calls the get_chat_message_contents method to get the response from the connector.
+        Then it checks the function calling behavior and executes the function calls if needed.
+
+        Args:
+            chat_history (ChatHistory): A list of chat chat_history, that can be rendered into a
+                set of chat_history, from system, user, assistant and function.
+            settings (PromptExecutionSettings): Settings for the request.
+            kwargs (Dict[str, Any]): The optional arguments.
+
+        Yields:
+            A stream representing the response(s) from the LLM.
+        """
+        function_call_behavior = self._check_for_function_calling_behavior(settings, **kwargs)
+        if not function_call_behavior:
+            async for messages in self.get_streaming_chat_message_contents(chat_history, settings, **kwargs):
+                yield messages
+            return
+        
+        kernel = kwargs.get("kernel", None)
+        settings.function_choice_behavior.configure(
+            kernel=kernel,
+            update_settings_callback=self.update_settings_from_function_call_configuration,
+            settings=settings,
+        )
+
+        async for chunk in self._execute_function_call_behavior_streaming(chat_history, settings, **kwargs):
+            yield chunk
+        
+    async def _execute_function_call_behavior_streaming(
+            self,
+            chat_history: "ChatHistory",
+            settings: "PromptExecutionSettings",
+            **kwargs: Any
+    ) -> AsyncGenerator[list[StreamingChatMessageContent | None], Any]:
+        """Takes Semantic Kernel Objects and performs function calling with the selected behavior.
+
+        Args:
+            chat_history (ChatHistory): A list of chat chat_history, that can be rendered into a
+                set of chat_history, from system, user, assistant and function.
+            settings (PromptExecutionSettings): Settings for the request.
+            kwargs (Dict[str, Any]): The optional arguments.
+
+        Yields:
+            A stream representing the response(s) from the LLM.
+        """
+        kernel: Kernel = kwargs.get("kernel", None)
+        arguments = kwargs.get("arguments", None)
+
+        request_attempts = (
+            settings.function_choice_behavior.maximum_auto_invoke_attempts
+            if (settings.function_choice_behavior and settings.function_choice_behavior.auto_invoke_kernel_functions)
+            else 1
+        )
+        # hold the messages, if there are more than one response, it will not be used, so we flatten
+        for request_index in range(request_attempts):
+            all_messages: list[StreamingChatMessageContent] = []
+            function_call_returned = False
+            async for messages in self.get_streaming_chat_message_contents(chat_history, settings, **kwargs):
+                for msg in messages:
+                    if msg is not None:
+                        all_messages.append(msg)
+                        if any(isinstance(item, FunctionCallContent) for item in msg.items):
+                            function_call_returned = True
+                yield messages
+
+            if (
+                settings.function_choice_behavior is None
+                or (
+                    settings.function_choice_behavior
+                    and not settings.function_choice_behavior.auto_invoke_kernel_functions
+                )
+                or not function_call_returned
+            ):
+                # no need to process function calls
+                # note that we don't check the FinishReason and instead check whether there are any tool calls,
+                # as the service may return a FinishReason of "stop" even if there are tool calls to be made,
+                # in particular if a required tool is specified.
+                return
+
+            # there is one response stream in the messages, combining now to create the full completion
+            # depending on the prompt, the message may contain both function call content and others
+            full_completion: StreamingChatMessageContent = reduce(lambda x, y: x + y, all_messages)
+            function_calls = [item for item in full_completion.items if isinstance(item, FunctionCallContent)]
+            chat_history.add_message(message=full_completion)
+
+            fc_count = len(function_calls)
+            logger.info(f"processing {fc_count} tool calls in parallel.")
+
+            # this function either updates the chat history with the function call results
+            # or returns the context, with terminate set to True
+            # in which case the loop will break and the function calls are returned.
+            # Exceptions are not caught, that is up to the developer, can be done with a filter
+            results = await asyncio.gather(
+                *[
+                    kernel.invoke_function_call(
+                        function_call=function_call,
+                        chat_history=chat_history,
+                        arguments=arguments,
+                        function_call_count=fc_count,
+                        request_index=request_index,
+                        function_behavior=settings.function_choice_behavior,
+                    )
+                    for function_call in function_calls
+                ],
+            )
+            if any(result.terminate for result in results if result is not None):
+                return
+
+    # endregion
+        
+    def _check_for_function_calling_behavior(self, settings: "PromptExecutionSettings", **kwargs: Any) -> bool:
+        """Check if function calling is enabled."""
+        if settings.function_choice_behavior is None:
+            return False
+        
+        kernel = kwargs.get("kernel", None)
+        arguments = kwargs.get("arguments", None)
+        if settings.function_choice_behavior is not None:
+            if kernel is None:
+                raise ServiceInvalidExecutionSettingsError(
+                    "The kernel is required for tool calls."
+                )
+            if arguments is None and settings.function_choice_behavior.auto_invoke_kernel_functions:
+                raise ServiceInvalidExecutionSettingsError(
+                    "The kernel arguments are required for auto invoking tool calls."
+                )
+        return True
+        
diff --git a/python/semantic_kernel/connectors/ai/mistral_ai/__init__.py b/python/semantic_kernel/connectors/ai/mistral_ai/__init__.py
new file mode 100644
index 000000000000..db7942b20f82
--- /dev/null
+++ b/python/semantic_kernel/connectors/ai/mistral_ai/__init__.py
@@ -0,0 +1,15 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from semantic_kernel.connectors.ai.mistral_ai.prompt_execution_settings.mistral_ai_prompt_execution_settings import (
+    MistralAIChatPromptExecutionSettings,
+    MistralAIEmbeddingPromptExecutionSettings,
+)
+from semantic_kernel.connectors.ai.mistral_ai.services.mistral_ai_chat_completion import MistralAIChatCompletion
+from semantic_kernel.connectors.ai.mistral_ai.services.mistral_ai_text_embedding import MistralAITextEmbedding
+
+__all__ = [
+    "MistralAIChatCompletion",
+    "MistralAIChatPromptExecutionSettings",
+    "MistralAIEmbeddingPromptExecutionSettings",
+    "MistralAITextEmbedding"
+]
diff --git a/python/semantic_kernel/connectors/ai/mistral_ai/prompt_execution_settings/__init__.py b/python/semantic_kernel/connectors/ai/mistral_ai/prompt_execution_settings/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/python/semantic_kernel/connectors/ai/mistral_ai/prompt_execution_settings/mistral_ai_prompt_execution_settings.py b/python/semantic_kernel/connectors/ai/mistral_ai/prompt_execution_settings/mistral_ai_prompt_execution_settings.py
new file mode 100644
index 000000000000..8fa3a1e3386a
--- /dev/null
+++ b/python/semantic_kernel/connectors/ai/mistral_ai/prompt_execution_settings/mistral_ai_prompt_execution_settings.py
@@ -0,0 +1,39 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+from typing import Any, Literal
+
+from pydantic import Field
+
+from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
+
+logger = logging.getLogger(__name__)
+
+
+class MistralAIPromptExecutionSettings(PromptExecutionSettings):
+    """Common request settings for MistralAI services."""
+
+    ai_model_id: str | None = Field(None, serialization_alias="model")
+
+
+class MistralAIChatPromptExecutionSettings(MistralAIPromptExecutionSettings):
+    """Specific settings for the Chat Completion endpoint."""
+
+    response_format: dict[Literal["type"], Literal["text", "json_object"]] | None = None
+    tools: list[dict[str, Any]] | None = Field(None, max_length=64)
+    tool_choice: str | None = None
+    messages: list[dict[str, Any]] | None = None
+    safe_mode: bool = False
+    safe_prompt: bool = False
+    max_tokens: int | None = Field(None, gt=0)
+    seed: int | None = None
+    temperature: float | None = Field(None, ge=0.0, le=2.0)
+    top_p: float | None = Field(None, ge=0.0, le=1.0)
+    random_seed: int | None = None
+
+
+class MistralAIEmbeddingPromptExecutionSettings(MistralAIPromptExecutionSettings):
+    """Specific settings for the Text Embedding endpoint."""
+    
+    input: list[str] | None = None
+    
diff --git a/python/semantic_kernel/connectors/ai/mistral_ai/services/__init__.py b/python/semantic_kernel/connectors/ai/mistral_ai/services/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/python/semantic_kernel/connectors/ai/mistral_ai/services/mistral_ai_chat_completion.py b/python/semantic_kernel/connectors/ai/mistral_ai/services/mistral_ai_chat_completion.py
new file mode 100644
index 000000000000..40acdce2fdb2
--- /dev/null
+++ b/python/semantic_kernel/connectors/ai/mistral_ai/services/mistral_ai_chat_completion.py
@@ -0,0 +1,314 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+from collections.abc import AsyncGenerator
+from typing import Any
+
+from mistralai.async_client import MistralAsyncClient
+from mistralai.models.chat_completion import (
+    ChatCompletionResponse,
+    ChatCompletionResponseChoice,
+    ChatCompletionResponseStreamChoice,
+    ChatCompletionStreamResponse,
+)
+from pydantic import ValidationError
+
+from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
+from semantic_kernel.connectors.ai.function_choice_behavior import FunctionCallChoiceConfiguration, FunctionChoiceType
+from semantic_kernel.connectors.ai.mistral_ai.prompt_execution_settings.mistral_ai_prompt_execution_settings import (
+    MistralAIChatPromptExecutionSettings,
+)
+from semantic_kernel.connectors.ai.mistral_ai.settings.mistral_ai_settings import MistralAISettings
+from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.contents.streaming_text_content import StreamingTextContent
+from semantic_kernel.contents.text_content import TextContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.contents.utils.finish_reason import FinishReason
+from semantic_kernel.exceptions.service_exceptions import (
+    ServiceInitializationError,
+    ServiceResponseException,
+)
+from semantic_kernel.functions.kernel_function_metadata import KernelFunctionMetadata
+from semantic_kernel.utils.experimental_decorator import experimental_class
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@experimental_class
+class MistralAIChatCompletion(ChatCompletionClientBase):
+    """Mistral Chat completion class."""
+
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+    total_tokens: int = 0
+    async_client: MistralAsyncClient | None = None
+    requests_per_second: int = 1
+
+    def __init__(
+        self,
+        ai_model_id: str | None = None,
+        service_id: str | None = None,
+        api_key: str | None = None,
+        requests_per_second: int | None = None,
+        async_client: MistralAsyncClient | None = None,
+        env_file_path: str | None = None,
+        env_file_encoding: str | None = None,
+    ) -> None:
+        """Initialize an MistralAIChatCompletion service.
+
+        Args:
+            ai_model_id (str): MistralAI model name, see
+                https://docs.mistral.ai/getting-started/models/
+            service_id (str | None): Service ID tied to the execution settings.
+            api_key (str | None): The optional API key to use. If provided will override,
+                the env vars or .env file value.
+            requests_per_second (int | None): The number of requests per second to make,
+                Free Tier is limited to 1 Request per second.
+            async_client (Optional[MistralAsyncClient]): An existing client to use. (Optional)
+            env_file_path (str | None): Use the environment settings file as a fallback
+                to environment variables. (Optional)
+            env_file_encoding (str | None): The encoding of the environment settings file. (Optional)
+        """
+        try:
+            mistralai_settings = MistralAISettings.create(
+                api_key=api_key,
+                chat_model_id=ai_model_id,
+                requests_per_second=requests_per_second,
+                env_file_path=env_file_path,
+                env_file_encoding=env_file_encoding,
+            )
+        except ValidationError as ex:
+            raise ServiceInitializationError("Failed to create MistralAI settings.", ex) from ex
+        
+        if not async_client:
+            async_client = MistralAsyncClient(
+                api_key=mistralai_settings.api_key.get_secret_value(),
+            )
+        if not mistralai_settings.chat_model_id:
+            raise ServiceInitializationError("The MistralAI chat model ID is required.")
+
+        super().__init__(
+            async_client=async_client,
+            requests_per_second=requests_per_second or mistralai_settings.requests_per_second,
+            service_id=service_id or mistralai_settings.chat_model_id,
+            ai_model_id=ai_model_id or mistralai_settings.chat_model_id,
+        )
+
+    async def get_chat_message_contents(
+        self,
+        chat_history: ChatHistory,
+        settings: MistralAIChatPromptExecutionSettings,
+        **kwargs: Any,
+    ) -> list["ChatMessageContent"]:
+        """Executes a chat completion request and returns the result.
+
+        Args:
+            chat_history (ChatHistory): The chat history to use for the chat completion.
+            settings (MistralAIChatPromptExecutionSettings): The settings to use
+                for the chat completion request.
+            kwargs (Dict[str, Any]): The optional arguments.
+
+        Returns:
+            List[ChatMessageContent]: The completion result(s).
+        """
+        if not settings.ai_model_id:
+            settings.ai_model_id = self.ai_model_id
+
+        settings.messages = self._prepare_chat_history_for_request(chat_history)
+        try:
+            response = await self.async_client.chat(**settings.prepare_settings_dict())
+        except Exception as ex:
+            raise ServiceResponseException(
+                f"{type(self)} service failed to complete the prompt",
+                ex,
+        ) from ex
+        
+        self.store_usage(response)
+        response_metadata = self._get_metadata_from_response(response)
+        return [self._create_chat_message_content(response, choice, response_metadata) for choice in response.choices]
+        
+    async def get_streaming_chat_message_contents(
+        self,
+        chat_history: ChatHistory,
+        settings: MistralAIChatPromptExecutionSettings,
+        **kwargs: Any,
+    ) -> AsyncGenerator[list[StreamingChatMessageContent | None], Any]:
+        """Executes a streaming chat completion request and returns the result.
+
+        Args:
+            chat_history (ChatHistory): The chat history to use for the chat completion.
+            settings (MistralAIChatPromptExecutionSettings): The settings to use
+                for the chat completion request.
+            kwargs (Dict[str, Any]): The optional arguments.
+
+        Yields:
+            List[StreamingChatMessageContent]: A stream of
+                StreamingChatMessageContent when using Azure.
+        """
+        if not settings.ai_model_id:
+            settings.ai_model_id = self.ai_model_id
+
+        settings.messages = self._prepare_chat_history_for_request(chat_history)
+        try:
+            response = self.async_client.chat_stream(**settings.prepare_settings_dict())
+        except Exception as ex:
+            raise ServiceResponseException(
+                f"{type(self)} service failed to complete the prompt",
+                ex,
+            ) from ex
+        async for chunk in response:
+            if len(chunk.choices) == 0:
+                continue
+            chunk_metadata = self._get_metadata_from_response(chunk)
+            yield [
+                self._create_streaming_chat_message_content(chunk, choice, chunk_metadata) for choice in chunk.choices
+            ]
+        await asyncio.sleep(1 / self.requests_per_second)
+
+    # endregion
+    # region content conversion to SK
+
+    def _create_chat_message_content(
+        self, response: ChatCompletionResponse, choice: ChatCompletionResponseChoice, response_metadata: dict[str, Any]
+    ) -> "ChatMessageContent":
+        """Create a chat message content object from a choice."""
+        metadata = self._get_metadata_from_chat_choice(choice)
+        metadata.update(response_metadata)
+
+        items: list[Any] = self._get_tool_calls_from_chat_choice(choice)
+        
+        if choice.message.content:
+            items.append(TextContent(text=choice.message.content))
+
+        return ChatMessageContent(
+            inner_content=response,
+            ai_model_id=self.ai_model_id,
+            metadata=metadata,
+            role=AuthorRole(choice.message.role),
+            items=items,
+            finish_reason=FinishReason(choice.finish_reason) if choice.finish_reason else None,
+        )
+
+    def _create_streaming_chat_message_content(
+        self,
+        chunk: ChatCompletionStreamResponse,
+        choice: ChatCompletionResponseStreamChoice,
+        chunk_metadata: dict[str, Any],
+    ) -> StreamingChatMessageContent | None:
+        """Create a streaming chat message content object from a choice."""
+        metadata = self._get_metadata_from_chat_choice(choice)
+        metadata.update(chunk_metadata)
+
+        items: list[Any] = self._get_tool_calls_from_chat_choice(choice)
+
+        if choice.delta.content is not None:
+            items.append(StreamingTextContent(choice_index=choice.index, text=choice.delta.content))
+        return StreamingChatMessageContent(
+            choice_index=choice.index,
+            inner_content=chunk,
+            ai_model_id=self.ai_model_id,
+            metadata=metadata,
+            role=AuthorRole(choice.delta.role) if choice.delta.role else AuthorRole.ASSISTANT,
+            finish_reason=FinishReason(choice.finish_reason) if choice.finish_reason else None,
+            items=items,
+        )
+
+    def _get_metadata_from_response(
+            self, 
+            response: ChatCompletionResponse | ChatCompletionStreamResponse
+    ) -> dict[str, Any]:
+        """Get metadata from a chat response."""
+        metadata = {
+            "id": response.id,
+            "created": response.created,
+        }
+        # Check if usage exists and has a value, then add it to the metadata
+        if hasattr(response, "usage") and response.usage is not None:
+            metadata["usage"] = response.usage
+        return metadata
+
+    def _get_metadata_from_chat_choice(
+        self,
+        choice: ChatCompletionResponseChoice | ChatCompletionResponseStreamChoice
+    ) -> dict[str, Any]:
+        """Get metadata from a chat choice."""
+        return {
+            "logprobs": getattr(choice, "logprobs", None),
+        }
+    
+    def _get_tool_calls_from_chat_choice(self,
+        choice: ChatCompletionResponseChoice | ChatCompletionResponseStreamChoice
+    ) -> list[FunctionCallContent]:
+        """Get tool calls from a chat choice."""
+        content = choice.message if isinstance(choice, ChatCompletionResponseChoice) else choice.delta
+        if content.tool_calls is None:
+            return []
+        return [
+            FunctionCallContent(
+                id=tool.id,
+                index=getattr(tool, "index", None),
+                name=tool.function.name,
+                arguments=tool.function.arguments,
+            )
+            for tool in content.tool_calls
+        ]
+
+    # endregion
+
+    # region function calling config
+
+    def update_settings_from_function_call_configuration(
+        self,
+        function_choice_configuration: "FunctionCallChoiceConfiguration",
+        settings: "MistralAIChatPromptExecutionSettings",
+        type: str,
+    ) -> None:
+        """Update the settings from a FunctionChoiceConfiguration."""
+        if function_choice_configuration.available_functions:
+            if type == FunctionChoiceType.REQUIRED:
+                settings.tool_choice = "any"
+            else:
+                settings.tool_choice = type
+            settings.tools = [
+                self.kernel_function_metadata_to_mistral_tool_format(f)
+                for f in function_choice_configuration.available_functions
+            ]
+
+    def kernel_function_metadata_to_mistral_tool_format(
+        self,
+        metadata: KernelFunctionMetadata,
+    ) -> dict[str, Any]:
+        """Convert the kernel function metadata to MistralAI format."""
+        return {
+            "type": "function",
+            "function": {
+                "name": metadata.fully_qualified_name,
+                "description": metadata.description or "",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        param.name: param.schema_data for param in metadata.parameters
+                    },
+                    "required": [p.name for p in metadata.parameters if p.is_required],
+                },
+            },
+        }
+    # endregion
+
+    def get_prompt_execution_settings_class(self) -> "PromptExecutionSettings":
+        """Create a request settings object."""
+        return MistralAIChatPromptExecutionSettings
+    
+    def store_usage(self, response):
+        """Store the usage information from the response."""
+        if not isinstance(response, AsyncGenerator):
+            logger.info(f"MistralAI usage: {response.usage}")
+            self.prompt_tokens += response.usage.prompt_tokens
+            self.total_tokens += response.usage.total_tokens
+            if hasattr(response.usage, "completion_tokens"):
+                self.completion_tokens += response.usage.completion_tokens
diff --git a/python/semantic_kernel/connectors/ai/mistral_ai/services/mistral_ai_text_embedding.py b/python/semantic_kernel/connectors/ai/mistral_ai/services/mistral_ai_text_embedding.py
new file mode 100644
index 000000000000..17c9c049b5b9
--- /dev/null
+++ b/python/semantic_kernel/connectors/ai/mistral_ai/services/mistral_ai_text_embedding.py
@@ -0,0 +1,77 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from typing import Any
+
+from mistralai.async_client import MistralAsyncClient
+from mistralai.models.embeddings import EmbeddingResponse
+from numpy import array, ndarray
+from pydantic import ValidationError
+
+from semantic_kernel.connectors.ai.embeddings.embedding_generator_base import EmbeddingGeneratorBase
+from semantic_kernel.connectors.ai.mistral_ai.settings.mistral_ai_settings import MistralAISettings
+from semantic_kernel.exceptions.service_exceptions import ServiceInitializationError
+from semantic_kernel.utils.experimental_decorator import experimental_class
+
+
+@experimental_class
+class MistralAITextEmbedding(EmbeddingGeneratorBase):
+    """Mistral AI Inference Text Embedding Service."""
+
+    client: MistralAsyncClient | None = None,
+
+    def __init__(
+        self,
+        ai_model_id: str | None = None,
+        api_key: str | None = None,
+        service_id: str | None = None,
+        env_file_path: str | None = None,
+        env_file_encoding: str | None = None,
+        client: MistralAsyncClient | None = None,
+    ) -> None:
+        """Initialize the Mistral AI Text Embedding service.
+
+        If no arguments are provided, the service will attempt to load the settings from the environment.
+        The following environment variables are used:
+        - MISTRALAI_API_KEY
+        - MISTRALAI_EMBEDDING_MODEL_ID
+
+        Args:
+            ai_model_id: (str): A string that is used to identify the model such as the model name. (Required)
+            api_key (str | None): The API key for the Mistral AI service deployment. (Optional)
+            service_id (str | None): Service ID for the embedding completion service. (Optional)
+            env_file_path (str | None): The path to the environment file. (Optional)
+            env_file_encoding (str | None): The encoding of the environment file. (Optional)
+            client (MistralAsyncClient | None): The Mistral AI client to use. (Optional)
+
+        Raises:
+            ServiceInitializationError: If an error occurs during initialization.
+        """
+        try:
+            mistralai_settings = MistralAISettings.create(
+                api_key=api_key,
+                embedding_model_id=ai_model_id,
+                env_file_path=env_file_path,
+                env_file_encoding=env_file_encoding,
+            )
+        except ValidationError as e:
+            raise ServiceInitializationError(f"Failed to validate Mistral AI settings: {e}") from e
+
+        if not client:
+            client = MistralAsyncClient(
+                api_key=mistralai_settings.api_key.get_secret_value()
+            )
+
+        super().__init__(
+            service_id=service_id or mistralai_settings.embedding_model_id,
+            ai_model_id=ai_model_id or mistralai_settings.embedding_model_id,
+            client=client,
+        )
+
+    async def generate_embeddings(self, texts: list[str], **kwargs: Any) -> ndarray:
+        """Generate embeddings from the Mistral AI service."""
+        response: EmbeddingResponse = await self.client.embeddings(
+            model=self.ai_model_id,
+            input=texts
+        )
+
+        return array([array(item.embedding) for item in response.data])
diff --git a/python/semantic_kernel/connectors/ai/mistral_ai/settings/__init__.py b/python/semantic_kernel/connectors/ai/mistral_ai/settings/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/python/semantic_kernel/connectors/ai/mistral_ai/settings/mistral_ai_settings.py b/python/semantic_kernel/connectors/ai/mistral_ai/settings/mistral_ai_settings.py
new file mode 100644
index 000000000000..4e861d13e0f8
--- /dev/null
+++ b/python/semantic_kernel/connectors/ai/mistral_ai/settings/mistral_ai_settings.py
@@ -0,0 +1,36 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from typing import ClassVar
+
+from pydantic import SecretStr
+
+from semantic_kernel.kernel_pydantic import KernelBaseSettings
+
+
+class MistralAISettings(KernelBaseSettings):
+    """MistralAI model settings.
+
+    The settings are first loaded from environment variables with the prefix 'MISTRALAI_'. If the
+    environment variables are not found, the settings can be loaded from a .env file with the
+    encoding 'utf-8'. If the settings are not found in the .env file, the settings are ignored;
+    however, validation will fail alerting that the settings are missing.
+
+    Optional settings for prefix 'MISTRALAI_' are:
+    - api_key: SecretStr - MISTRAL API key, see https://console.mistral.ai/api-keys
+        (Env var MISTRALAI_API_KEY)
+    - chat_model_id: str | None - The The Mistral AI chat model ID to use see https://docs.mistral.ai/getting-started/models/.
+        (Env var MISTRALAI_CHAT_MODEL_ID)
+    - emmbedding_model_id: str | None - The The Mistral AI embedding model ID to use see https://docs.mistral.ai/getting-started/models/.
+        (Env var MISTRALAI_EMBEDDING_MODEL_ID)
+    - requests_per_second: str | None - Mistral has a Requests per second limit,
+        for function calling we have to wait this time for the second request.
+        (Env var MISTRALAI_REQUESTS_PER_SECOND)
+    - env_file_path: str | None - if provided, the .env settings are read from this file path location
+    """
+
+    env_prefix: ClassVar[str] = "MISTRALAI_"
+
+    api_key: SecretStr
+    chat_model_id: str | None = None
+    embedding_model_id: str | None = None
+    requests_per_second: int = 1
diff --git a/python/semantic_kernel/connectors/ai/open_ai/services/open_ai_chat_completion_base.py b/python/semantic_kernel/connectors/ai/open_ai/services/open_ai_chat_completion_base.py
index 5047b1c0901b..c8f4d73f808d 100644
--- a/python/semantic_kernel/connectors/ai/open_ai/services/open_ai_chat_completion_base.py
+++ b/python/semantic_kernel/connectors/ai/open_ai/services/open_ai_chat_completion_base.py
@@ -64,6 +64,29 @@ class OpenAIChatCompletionBase(OpenAIHandler, ChatCompletionClientBase):
     def get_prompt_execution_settings_class(self) -> "PromptExecutionSettings":
         """Create a request settings object."""
         return OpenAIChatPromptExecutionSettings
+    
+    async def invoke(
+        self,
+        chat_history: "ChatHistory",
+        settings: "PromptExecutionSettings",
+        **kwargs: Any
+    ) -> list["ChatMessageContent"]:
+        """This method is called by the kernel to perform the chat completion and potentially execute function calls.
+
+        The Method calls the get_chat_message_contents method to get the response from the connector.
+        Then it checks the function calling behavior and executes the function calls if needed.
+
+        Args:
+            chat_history (ChatHistory): A list of chat chat_history, that can be rendered into a
+                set of chat_history, from system, user, assistant and function.
+            settings (PromptExecutionSettings): Settings for the request.
+            kwargs (Dict[str, Any]): The optional arguments.
+        
+        Returns:
+            List[Dict[str, Optional[str]]]: The prepared chat history.
+        """
+        # Override parent method to ensure not breaking something
+        return await self.get_chat_message_contents(chat_history, settings, **kwargs)
 
     async def get_chat_message_contents(
         self,
@@ -152,6 +175,35 @@ async def get_chat_message_contents(
             settings.function_choice_behavior.auto_invoke_kernel_functions = False
             return await self._send_chat_request(settings)
 
+    async def invoke_streaming(
+        self,
+        chat_history: ChatHistory,
+        settings: OpenAIChatPromptExecutionSettings,
+        **kwargs: Any,
+    ) -> AsyncGenerator[list[StreamingChatMessageContent | None], Any]:
+        """This method is called by the kernel to perform the chat completion and potentially execute function calls.
+
+        The Method calls the get_chat_message_contents method to get the response from the connector.
+        Then it checks the function calling behavior and executes the function calls if needed.
+
+        Args:
+            chat_history (ChatHistory): A list of chat chat_history, that can be rendered into a
+                set of chat_history, from system, user, assistant and function.
+            settings (PromptExecutionSettings): Settings for the request.
+            kwargs (Dict[str, Any]): The optional arguments.
+
+        Yields:
+            A stream representing the response(s) from the LLM.
+        """
+        # Override parent method to ensure not breaking something
+        async for message in self.get_streaming_chat_message_contents(
+            chat_history=chat_history,
+            settings=settings,
+            **kwargs
+        ):
+            yield message
+        return
+
     async def get_streaming_chat_message_contents(
         self,
         chat_history: ChatHistory,
diff --git a/python/semantic_kernel/functions/kernel_function_from_prompt.py b/python/semantic_kernel/functions/kernel_function_from_prompt.py
index c83fba398eae..791f4ad1ed34 100644
--- a/python/semantic_kernel/functions/kernel_function_from_prompt.py
+++ b/python/semantic_kernel/functions/kernel_function_from_prompt.py
@@ -168,7 +168,7 @@ async def _invoke_internal(self, context: FunctionInvocationContext) -> None:
         if isinstance(prompt_render_result.ai_service, ChatCompletionClientBase):
             chat_history = ChatHistory.from_rendered_prompt(prompt_render_result.rendered_prompt)
             try:
-                chat_message_contents = await prompt_render_result.ai_service.get_chat_message_contents(
+                chat_message_contents = await prompt_render_result.ai_service.invoke(
                     chat_history=chat_history,
                     settings=prompt_render_result.execution_settings,
                     **{"kernel": context.kernel, "arguments": context.arguments},
@@ -205,11 +205,12 @@ async def _invoke_internal_stream(self, context: FunctionInvocationContext) -> N
 
         if isinstance(prompt_render_result.ai_service, ChatCompletionClientBase):
             chat_history = ChatHistory.from_rendered_prompt(prompt_render_result.rendered_prompt)
-            value: AsyncGenerator = prompt_render_result.ai_service.get_streaming_chat_message_contents(
+            value: AsyncGenerator = prompt_render_result.ai_service.invoke_streaming(
                 chat_history=chat_history,
                 settings=prompt_render_result.execution_settings,
                 **{"kernel": context.kernel, "arguments": context.arguments},
             )
+
         elif isinstance(prompt_render_result.ai_service, TextCompletionClientBase):
             value = prompt_render_result.ai_service.get_streaming_text_contents(
                 prompt=prompt_render_result.rendered_prompt, settings=prompt_render_result.execution_settings
diff --git a/python/tests/conftest.py b/python/tests/conftest.py
index 6e28e5129485..b85b086b2b41 100644
--- a/python/tests/conftest.py
+++ b/python/tests/conftest.py
@@ -248,6 +248,30 @@ def openai_unit_test_env(monkeypatch, exclude_list, override_env_param_dict):
 
     return env_vars
 
+@pytest.fixture()
+def mistralai_unit_test_env(monkeypatch, exclude_list, override_env_param_dict):
+    """Fixture to set environment variables for AzureOpenAISettings."""
+    if exclude_list is None:
+        exclude_list = []
+
+    if override_env_param_dict is None:
+        override_env_param_dict = {}
+
+    env_vars = {
+        "MISTRALAI_CHAT_MODEL_ID": "test_chat_model_id",
+        "MISTRALAI_API_KEY": "test_api_key"
+    }
+
+    env_vars.update(override_env_param_dict)
+
+    for key, value in env_vars.items():
+        if key not in exclude_list:
+            monkeypatch.setenv(key, value)
+        else:
+            monkeypatch.delenv(key, raising=False)
+
+    return env_vars
+
 
 @pytest.fixture()
 def google_palm_unit_test_env(monkeypatch, exclude_list, override_env_param_dict):
diff --git a/python/tests/integration/completions/test_chat_completions.py b/python/tests/integration/completions/test_chat_completions.py
index c70e548910bf..9149a1026147 100644
--- a/python/tests/integration/completions/test_chat_completions.py
+++ b/python/tests/integration/completions/test_chat_completions.py
@@ -19,6 +19,10 @@
 from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
 from semantic_kernel.connectors.ai.function_call_behavior import FunctionCallBehavior
 from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai.mistral_ai.prompt_execution_settings.mistral_ai_prompt_execution_settings import (
+    MistralAIChatPromptExecutionSettings,
+)
+from semantic_kernel.connectors.ai.mistral_ai.services.mistral_ai_chat_completion import MistralAIChatCompletion
 from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.azure_chat_prompt_execution_settings import (
     AzureChatPromptExecutionSettings,
 )
@@ -35,6 +39,7 @@
 from semantic_kernel.contents.image_content import ImageContent
 from semantic_kernel.contents.utils.author_role import AuthorRole
 from semantic_kernel.core_plugins.math_plugin import MathPlugin
+from semantic_kernel.core_plugins.time_plugin import TimePlugin
 from tests.integration.completions.test_utils import retry
 
 
@@ -46,6 +51,7 @@ def setup(
 ):
     kernel.add_service(services[service][0])
     kernel.add_plugin(MathPlugin(), plugin_name="math")
+    kernel.add_plugin(TimePlugin(), plugin_name="time")
     kernel.add_function(
         function_name="chat",
         plugin_name="chat",
@@ -88,6 +94,7 @@ def services() -> dict[str, tuple[ChatCompletionClientBase, type[PromptExecution
     return {
         "openai": (OpenAIChatCompletion(), OpenAIChatPromptExecutionSettings),
         "azure": (AzureChatCompletion(), AzureChatPromptExecutionSettings),
+        "mistralai": (MistralAIChatCompletion(), MistralAIChatPromptExecutionSettings),
         "azure_custom_client": (azure_custom_client, AzureChatPromptExecutionSettings),
         "azure_ai_inference": (azure_ai_inference_client, AzureAIInferenceChatPromptExecutionSettings),
     }
@@ -383,6 +390,73 @@ def services() -> dict[str, tuple[ChatCompletionClientBase, type[PromptExecution
             ["house", "germany"],
             id="azure_ai_inference_image_input_file",
         ),
+        pytest.param(
+            "mistralai",
+            {},
+            [
+                ChatMessageContent(role=AuthorRole.USER, items=[TextContent(text="Hello")]),
+                ChatMessageContent(role=AuthorRole.USER, items=[TextContent(text="How are you today?")]),
+            ],
+            ["Hello", "well"],
+            id="mistral_text_input",
+        ),
+        pytest.param(
+            "mistralai",
+            {
+                "function_call_behavior": FunctionChoiceBehavior.Auto(
+                    auto_invoke=False, filters={"excluded_plugins": ["chat"]}
+                )
+            },
+            [
+                ChatMessageContent(role=AuthorRole.USER, items=[TextContent(text="What is the current time?")]),
+            ],
+            [""],
+            id="mistral_tool_call_false",
+        ),
+        pytest.param(
+            "mistralai",
+            {
+                "function_call_behavior": FunctionChoiceBehavior.Auto(
+                    auto_invoke=True, filters={"excluded_plugins": ["chat"]}
+                )
+            },
+            [
+                ChatMessageContent(role=AuthorRole.USER, items=[
+                    TextContent(
+                        text="What is the current time? Please use function calling!"
+                        )
+                    ]
+                ),
+            ],
+            [""],
+            id="mistral_tool_call_true_start_chat",
+        ),
+        pytest.param(
+            "mistralai",
+            {},
+            [
+                [
+                    ChatMessageContent(
+                        role=AuthorRole.USER,
+                        items=[TextContent(text="What was our 2024 revenue?")],
+                    ),
+                    ChatMessageContent(
+                        role=AuthorRole.ASSISTANT,
+                        items=[
+                            FunctionCallContent(
+                                id="123456789", name="finance-search", arguments='{"company": "contoso", "year": 2024}'
+                            )
+                        ],
+                    ),
+                    ChatMessageContent(
+                        role=AuthorRole.TOOL,
+                        items=[FunctionResultContent(id="123456789", name="finance-search", result="1.2B")],
+                    ),
+                ],
+            ],
+            ["1.2"],
+            id="mistral_tool_call_flow",
+        ),
     ],
 )
 
diff --git a/python/tests/integration/embeddings/test_embedding_service.py b/python/tests/integration/embeddings/test_embedding_service.py
new file mode 100644
index 000000000000..6509aaffea56
--- /dev/null
+++ b/python/tests/integration/embeddings/test_embedding_service.py
@@ -0,0 +1,81 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import pytest
+
+import semantic_kernel as sk
+from semantic_kernel.connectors.ai.embeddings.embedding_generator_base import EmbeddingGeneratorBase
+from semantic_kernel.connectors.ai.hugging_face import HuggingFaceTextEmbedding
+from semantic_kernel.connectors.ai.mistral_ai import MistralAITextEmbedding
+from semantic_kernel.connectors.ai.open_ai import OpenAITextEmbedding
+from semantic_kernel.core_plugins.text_memory_plugin import TextMemoryPlugin
+from semantic_kernel.kernel import Kernel
+from semantic_kernel.memory.semantic_text_memory import SemanticTextMemory
+
+pytestmark = pytest.mark.parametrize("embeddings_generator",
+    [
+        pytest.param(
+            MistralAITextEmbedding(),
+            id="MistralEmbeddings"
+        ),
+        pytest.param(
+            OpenAITextEmbedding(ai_model_id="text-embedding-ada-002"),
+            id="OpenAIEmbeddings"
+        ),
+        pytest.param(
+            HuggingFaceTextEmbedding(
+                service_id="sentence-transformers/all-MiniLM-L6-v2", 
+                ai_model_id="sentence-transformers/all-MiniLM-L6-v2"
+            ),
+            id="HuggingFaceEmbeddings"
+        )
+    ]
+)
+
+
+@pytest.mark.asyncio(scope="module")
+async def test_embedding_service(kernel: Kernel, embeddings_generator: EmbeddingGeneratorBase):
+    kernel.add_service(embeddings_generator)
+
+    memory = SemanticTextMemory(storage=sk.memory.VolatileMemoryStore(), embeddings_generator=embeddings_generator)
+    kernel.add_plugin(TextMemoryPlugin(memory), "TextMemoryPlugin")
+
+    await memory.save_reference(
+        "test",
+        external_id="info1",
+        text="this is a test",
+        external_source_name="external source",
+    )
+
+    # Add some documents to the semantic memory
+    await memory.save_information("test", id="info1", text="Sharks are fish.")
+    await memory.save_information("test", id="info2", text="Whales are mammals.")
+    await memory.save_information("test", id="info3", text="Penguins are birds.")
+    await memory.save_information("test", id="info4", text="Dolphins are mammals.")
+    await memory.save_information("test", id="info5", text="Flies are insects.")
+
+    # Search for documents
+    query = "What are mammals?"
+    result = await memory.search("test", query, limit=2, min_relevance_score=0.0)
+    print(f"Query: {query}")
+    print(f"\tAnswer 1: {result[0].text}")
+    print(f"\tAnswer 2: {result[1].text}\n")
+    assert "mammals." in result[0].text
+    assert "mammals." in result[1].text
+
+    query = "What are fish?"
+    result = await memory.search("test", query, limit=1, min_relevance_score=0.0)
+    print(f"Query: {query}")
+    print(f"\tAnswer: {result[0].text}\n")
+    assert result[0].text == "Sharks are fish."
+
+    query = "What are insects?"
+    result = await memory.search("test", query, limit=1, min_relevance_score=0.0)
+    print(f"Query: {query}")
+    print(f"\tAnswer: {result[0].text}\n")
+    assert result[0].text == "Flies are insects."
+
+    query = "What are birds?"
+    result = await memory.search("test", query, limit=1, min_relevance_score=0.0)
+    print(f"Query: {query}")
+    print(f"\tAnswer: {result[0].text}\n")
+    assert result[0].text == "Penguins are birds."
diff --git a/python/tests/unit/connectors/mistral_ai/services/test_mistralai_chat_completion.py b/python/tests/unit/connectors/mistral_ai/services/test_mistralai_chat_completion.py
new file mode 100644
index 000000000000..fa44928d27ae
--- /dev/null
+++ b/python/tests/unit/connectors/mistral_ai/services/test_mistralai_chat_completion.py
@@ -0,0 +1,75 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+# Copyright (c) Microsoft. All rights reserved.
+
+from unittest.mock import MagicMock
+
+import pytest
+from mistralai.async_client import MistralAsyncClient
+
+from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
+from semantic_kernel.connectors.ai.mistral_ai.services.mistral_ai_chat_completion import MistralAIChatCompletion
+from semantic_kernel.exceptions import ServiceInitializationError
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+from semantic_kernel.kernel import Kernel
+
+
+@pytest.mark.asyncio
+async def test_complete_chat_stream_contents(kernel: Kernel):
+    chat_history = MagicMock()
+    settings = MagicMock()
+    settings.number_of_responses = 1
+    arguments = KernelArguments()
+
+    chat_completion_base = MistralAIChatCompletion(
+        ai_model_id="test_model_id", service_id="test", api_key="", async_client=MagicMock(spec=MistralAsyncClient)
+    )
+
+    async for content in chat_completion_base.get_streaming_chat_message_contents(
+        chat_history, settings, kernel=kernel, arguments=arguments
+    ):
+        assert content is not None
+
+
+@pytest.mark.asyncio
+async def test_complete_chat_contents(kernel: Kernel):
+    chat_history = MagicMock()
+    settings = MagicMock()
+    settings.number_of_responses = 1
+    arguments = KernelArguments()
+
+    chat_completion_base = MistralAIChatCompletion(
+        ai_model_id="test_model_id", service_id="test", api_key="", async_client=MagicMock(spec=MistralAsyncClient)
+    )
+
+    content = await chat_completion_base.get_chat_message_contents(
+        chat_history, settings, kernel=kernel, arguments=arguments
+    )
+    assert content is not None
+
+
+def test_mistral_ai_chat_completion_init(mistralai_unit_test_env) -> None:
+    # Test successful initialization
+    mistral_ai_chat_completion = MistralAIChatCompletion()
+
+    assert mistral_ai_chat_completion.ai_model_id == mistralai_unit_test_env["MISTRALAI_CHAT_MODEL_ID"]
+    assert isinstance(mistral_ai_chat_completion, ChatCompletionClientBase)
+
+
+@pytest.mark.parametrize("exclude_list", [["MISTRALAI_API_KEY"]], indirect=True)
+def test_mistral_ai_chat_completion_init_with_empty_api_key(mistralai_unit_test_env) -> None:
+    ai_model_id = "test_model_id"
+
+    with pytest.raises(ServiceInitializationError):
+        MistralAIChatCompletion(
+            ai_model_id=ai_model_id,
+            env_file_path="test.env",
+        )
+
+
+@pytest.mark.parametrize("exclude_list", [["MISTRALAI_CHAT_MODEL_ID"]], indirect=True)
+def test_mistral_ai_chat_completion_init_with_empty_model_id(mistralai_unit_test_env) -> None:
+    with pytest.raises(ServiceInitializationError):
+        MistralAIChatCompletion(
+            env_file_path="test.env",
+        )
diff --git a/python/tests/unit/connectors/mistral_ai/test_mistralai_request_settings.py b/python/tests/unit/connectors/mistral_ai/test_mistralai_request_settings.py
new file mode 100644
index 000000000000..287970ddb433
--- /dev/null
+++ b/python/tests/unit/connectors/mistral_ai/test_mistralai_request_settings.py
@@ -0,0 +1,113 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from semantic_kernel.connectors.ai.mistral_ai.prompt_execution_settings.mistral_ai_prompt_execution_settings import (
+    MistralAIChatPromptExecutionSettings,
+)
+from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
+
+
+def test_default_mistralai_chat_prompt_execution_settings():
+    settings = MistralAIChatPromptExecutionSettings()
+    assert settings.temperature is None
+    assert settings.top_p is None
+    assert settings.max_tokens is None
+    assert settings.messages is None
+
+
+def test_custom_mistralai_chat_prompt_execution_settings():
+    settings = MistralAIChatPromptExecutionSettings(
+        temperature=0.5,
+        top_p=0.5,
+        max_tokens=128,
+        messages=[{"role": "system", "content": "Hello"}],
+    )
+    assert settings.temperature == 0.5
+    assert settings.top_p == 0.5
+    assert settings.max_tokens == 128
+    assert settings.messages == [{"role": "system", "content": "Hello"}]
+
+
+def test_mistralai_chat_prompt_execution_settings_from_default_completion_config():
+    settings = PromptExecutionSettings(service_id="test_service")
+    chat_settings = MistralAIChatPromptExecutionSettings.from_prompt_execution_settings(settings)
+    assert chat_settings.service_id == "test_service"
+    assert chat_settings.temperature is None
+    assert chat_settings.top_p is None
+    assert chat_settings.max_tokens is None
+
+
+def test_mistral_chat_prompt_execution_settings_from_openai_prompt_execution_settings():
+    chat_settings = MistralAIChatPromptExecutionSettings(service_id="test_service", temperature=1.0)
+    new_settings = MistralAIChatPromptExecutionSettings(service_id="test_2", temperature=0.0)
+    chat_settings.update_from_prompt_execution_settings(new_settings)
+    assert chat_settings.service_id == "test_2"
+    assert chat_settings.temperature == 0.0
+
+
+def test_mistral_chat_prompt_execution_settings_from_custom_completion_config():
+    settings = PromptExecutionSettings(
+        service_id="test_service",
+        extension_data={
+            "temperature": 0.5,
+            "top_p": 0.5,
+            "max_tokens": 128,
+            "messages": [{"role": "system", "content": "Hello"}],
+        },
+    )
+    chat_settings = MistralAIChatPromptExecutionSettings.from_prompt_execution_settings(settings)
+    assert chat_settings.temperature == 0.5
+    assert chat_settings.top_p == 0.5
+    assert chat_settings.max_tokens == 128
+
+
+def test_openai_chat_prompt_execution_settings_from_custom_completion_config_with_none():
+    settings = PromptExecutionSettings(
+        service_id="test_service",
+        extension_data={
+            "temperature": 0.5,
+            "top_p": 0.5,
+            "max_tokens": 128,
+            "messages": [{"role": "system", "content": "Hello"}],
+        },
+    )
+    chat_settings = MistralAIChatPromptExecutionSettings.from_prompt_execution_settings(settings)
+    assert chat_settings.temperature == 0.5
+    assert chat_settings.top_p == 0.5
+    assert chat_settings.max_tokens == 128
+    assert chat_settings.tools is None
+    assert chat_settings.tool_choice is None
+
+
+def test_openai_chat_prompt_execution_settings_from_custom_completion_config_with_functions():
+    settings = PromptExecutionSettings(
+        service_id="test_service",
+        extension_data={
+            "temperature": 0.5,
+            "top_p": 0.5,
+            "max_tokens": 128,
+            "tools": [{}],
+            "messages": [{"role": "system", "content": "Hello"}],
+        },
+    )
+    chat_settings = MistralAIChatPromptExecutionSettings.from_prompt_execution_settings(settings)
+    assert chat_settings.temperature == 0.5
+    assert chat_settings.top_p == 0.5
+    assert chat_settings.max_tokens == 128
+    assert chat_settings.tools == [{}]
+
+
+def test_create_options():
+    settings = MistralAIChatPromptExecutionSettings(
+        service_id="test_service",
+        extension_data={
+            "temperature": 0.5,
+            "top_p": 0.5,
+            "max_tokens": 128,
+            "tools": [{}],
+            "messages": [{"role": "system", "content": "Hello"}],
+        },
+    )
+    options = settings.prepare_settings_dict()
+    assert options["temperature"] == 0.5
+    assert options["top_p"] == 0.5
+    assert options["max_tokens"] == 128
diff --git a/python/tests/unit/connectors/test_chat_completion_client_base.py b/python/tests/unit/connectors/test_chat_completion_client_base.py
new file mode 100644
index 000000000000..7f42fb9ba9a3
--- /dev/null
+++ b/python/tests/unit/connectors/test_chat_completion_client_base.py
@@ -0,0 +1,214 @@
+# Copyright (c) Microsoft. All rights reserved.
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai.azure_ai_inference import (
+    AzureAIInferenceChatCompletion,
+    AzureAIInferenceChatPromptExecutionSettings,
+)
+from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
+from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior, FunctionChoiceType
+from semantic_kernel.connectors.ai.mistral_ai import MistralAIChatCompletion, MistralAIChatPromptExecutionSettings
+from semantic_kernel.connectors.ai.ollama import OllamaChatCompletion, OllamaPromptExecutionSettings
+from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents.chat_message_content import (
+    ChatMessageContent,
+    FunctionCallContent,
+    FunctionResultContent,
+    TextContent,
+)
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+
+pytestmark = pytest.mark.parametrize('completion_object , prompt_execution_settings , function_calling_implemented', [
+    pytest.param(
+        MistralAIChatCompletion(),
+        MistralAIChatPromptExecutionSettings(),
+        True,
+        id="mistral_none_settings"
+    ),
+    pytest.param(
+        MistralAIChatCompletion(),
+        MistralAIChatPromptExecutionSettings(
+            function_choice_behavior=FunctionChoiceBehavior.Auto()
+        ),
+        True,
+        id="mistral_tool_auto"
+    ),
+    pytest.param(
+        MistralAIChatCompletion(),
+        MistralAIChatPromptExecutionSettings(
+            function_choice_behavior=FunctionChoiceBehavior.Required()
+        ),
+        True,
+        id="mistral_tool_required"
+    ),
+    pytest.param(
+        MistralAIChatCompletion(),
+        MistralAIChatPromptExecutionSettings(
+            function_choice_behavior=FunctionChoiceBehavior.NoneInvoke()
+        ),
+        True,
+        id="mistral_tool_none_invoke"
+    ),
+    pytest.param(
+        AzureAIInferenceChatCompletion(ai_model_id="test"),
+        AzureAIInferenceChatPromptExecutionSettings(),
+        False,
+        id="azure_ai_inference_none_settings"
+    ),
+    pytest.param(
+        AzureAIInferenceChatCompletion(ai_model_id="test"),
+        AzureAIInferenceChatPromptExecutionSettings(
+            function_choice_behavior=FunctionChoiceBehavior.NoneInvoke()
+        ),
+        False,
+        id="azure_ai_inference_not_implemented"
+    ),
+    pytest.param(
+        OllamaChatCompletion(ai_model_id="test"),
+        OllamaPromptExecutionSettings(), 
+        False,
+        id="ollama_none_settings"
+    ),
+    pytest.param(
+        OllamaChatCompletion(ai_model_id="test"),
+        OllamaPromptExecutionSettings(
+            function_choice_behavior=FunctionChoiceBehavior.NoneInvoke()
+        ),
+        False,
+        id="ollama_not_implemented"
+    ),
+]
+)
+
+
+@pytest.mark.asyncio
+async def test_invoke_function_call_processing_with_function_call_content_result(
+    completion_object: ChatCompletionClientBase,
+    prompt_execution_settings: PromptExecutionSettings,
+    function_calling_implemented: bool,
+    kernel: Kernel
+):
+    # Prepare Connector Mocks
+    mock_text = MagicMock(spec=TextContent)
+    mock_message_text_content = ChatMessageContent(
+        role=AuthorRole.ASSISTANT, items=[mock_text]
+    )
+    mock_function_call = MagicMock(spec=FunctionCallContent)
+    mock_message_function_call = ChatMessageContent(
+        role=AuthorRole.ASSISTANT, items=[mock_function_call]
+    )
+
+    if (
+        prompt_execution_settings.function_choice_behavior is not None
+        and prompt_execution_settings.function_choice_behavior.type is not FunctionChoiceType.NONE
+    ):
+        # Mock Tool Flow FunctionCall --> FunctionResult --> TextContent
+        mock_messages = [[mock_message_function_call], [mock_message_text_content]]
+    else:
+        # Mock None Flow TextContent
+        mock_messages = [[mock_message_text_content]]
+
+    def fake_function_result(
+            function_call,
+            chat_history: ChatHistory,
+            arguments,
+            function_call_count,
+            request_index,
+            function_behavior,
+    ):
+        mock_function_result = MagicMock(spec=FunctionResultContent)
+        mock_message_function_result = ChatMessageContent(
+            role=AuthorRole.TOOL, items=[mock_function_result]
+        )
+        chat_history.add_message(message=mock_message_function_result)
+        return
+
+    with (
+        patch.object(
+            completion_object.__class__, 
+            'get_chat_message_contents',
+              side_effect=mock_messages),
+        patch(
+            "semantic_kernel.kernel.Kernel.invoke_function_call",
+            side_effect=fake_function_result,
+            new_callable=AsyncMock,
+            
+        ) as mock_process_function_call,
+    ):
+        # Check for Proper Error when function Calling is not implemented
+        if not function_calling_implemented and prompt_execution_settings.function_choice_behavior is not None:
+            with pytest.raises(NotImplementedError):
+                await completion_object.invoke(
+                    chat_history=ChatHistory(system_message="Test"),
+                    settings=prompt_execution_settings,
+                    kernel=kernel,
+                    arguments=KernelArguments()
+                )
+            return
+
+        result = await completion_object.invoke(
+            chat_history=ChatHistory(system_message="Test"),
+            settings=prompt_execution_settings,
+            kernel=kernel,
+            arguments=KernelArguments()
+        )
+
+        # Flow without Function Choice Settings should return TextContent
+        if prompt_execution_settings.function_choice_behavior is None:
+            assert result == [mock_message_text_content]
+            return
+
+        # Full Automation Flow should return TextContent
+        if prompt_execution_settings.function_choice_behavior.type is not FunctionChoiceType.NONE:
+            mock_process_function_call.assert_awaited()
+        else:
+            mock_process_function_call.assert_not_awaited()
+        assert result == [mock_message_text_content]
+
+
+@pytest.mark.asyncio
+async def test_invoke_function_call_processing_with_text_content_content_result(
+    completion_object: ChatCompletionClientBase,
+    prompt_execution_settings: PromptExecutionSettings,
+    function_calling_implemented: bool,
+    kernel: Kernel,
+):
+    
+    arguments = KernelArguments()
+    chat_history = ChatHistory(system_message="Test")
+
+    mock_text = MagicMock(spec=TextContent)
+    mock_message_text_content = ChatMessageContent(
+        role=AuthorRole.ASSISTANT, items=[mock_text]
+    )
+    mock_messages = [[mock_message_text_content]]
+
+    with (
+        patch.object(
+            completion_object.__class__, 
+            'get_chat_message_contents',
+              side_effect=mock_messages),
+    ):
+        # Check for Proper Error when function Calling is not implemented
+        if not function_calling_implemented and prompt_execution_settings.function_choice_behavior is not None:
+            with pytest.raises(NotImplementedError):
+                await completion_object.invoke(
+                    chat_history=ChatHistory(system_message="Test"),
+                    settings=prompt_execution_settings,
+                    kernel=kernel,
+                    arguments=KernelArguments()
+                )
+            return
+
+        result = await completion_object.invoke(
+            chat_history=chat_history,
+            settings=prompt_execution_settings,
+            kernel=kernel,
+            arguments=arguments
+        )
+        assert result == [mock_message_text_content]