diff --git a/.gitignore b/.gitignore
index 653f675..4dfa69e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -65,4 +65,5 @@ __pycache__/
 
 # Project
 keys
-openai_key
\ No newline at end of file
+openai_key
+*mlruns/
\ No newline at end of file
diff --git a/README.md b/README.md
index e6545c3..7e37db6 100644
--- a/README.md
+++ b/README.md
@@ -15,10 +15,9 @@ Evaluates LLM-based applications.
 - [x] Streamlit page to view the evaluation results.
 - [x] Combine the evaluation results into a single metric.
 - [x] Evaluate and compare different LLM apps (GPT-3.5, GPT-4, with RAG)
-- [ ] Streamlit page to visualize the comparison.
+- [x] Integrate with MLflow for experiment tracking.
 - [ ] Streamlit page to view, edit and add test cases.
-- [ ] Integrate with MLflow for experiment tracking (?)
-
+- [ ] Refactor Evaluator.
 
 ## Using
 
diff --git a/poetry.lock b/poetry.lock
index 108bce6..5ac0769 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -122,6 +122,25 @@ files = [
 [package.dependencies]
 frozenlist = ">=1.1.0"
 
+[[package]]
+name = "alembic"
+version = "1.12.0"
+description = "A database migration tool for SQLAlchemy."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "alembic-1.12.0-py3-none-any.whl", hash = "sha256:03226222f1cf943deee6c85d9464261a6c710cd19b4fe867a3ad1f25afda610f"},
+    {file = "alembic-1.12.0.tar.gz", hash = "sha256:8e7645c32e4f200675e69f0745415335eb59a3663f5feb487abfa0b30c45888b"},
+]
+
+[package.dependencies]
+Mako = "*"
+SQLAlchemy = ">=1.3.0"
+typing-extensions = ">=4"
+
+[package.extras]
+tz = ["python-dateutil"]
+
 [[package]]
 name = "altair"
 version = "5.1.1"
@@ -481,6 +500,17 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
+[[package]]
+name = "cloudpickle"
+version = "2.2.1"
+description = "Extended pickling support for Python objects"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "cloudpickle-2.2.1-py3-none-any.whl", hash = "sha256:61f594d1f4c295fa5cd9014ceb3a1fc4a70b0de1164b94fbc2d854ccba056f9f"},
+    {file = "cloudpickle-2.2.1.tar.gz", hash = "sha256:d89684b8de9e34a2a43b3460fbca07d09d6e25ce858df4d5a44240403b6178f5"},
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -516,6 +546,139 @@ questionary = ">=2.0,<3.0"
 termcolor = ">=1.1,<3"
 tomlkit = ">=0.5.3,<1.0.0"
 
+[[package]]
+name = "contourpy"
+version = "1.1.0"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.8"
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+
+[package.extras]
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+test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
+
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 version = "2.4.0"
@@ -624,6 +787,41 @@ typer = ">=0.4.0"
 [package.extras]
 pyproject = ["toml (>=0.10)"]
 
+[[package]]
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+
+[[package]]
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+urllib3 = ">=1.26.7,<3"
+
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 version = "0.6.1"
@@ -661,6 +859,27 @@ files = [
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+
+[package.extras]
+ssh = ["paramiko (>=2.4.3)"]
+
 [[package]]
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 version = "0.15"
@@ -672,6 +891,17 @@ files = [
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+[[package]]
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@@ -716,6 +946,94 @@ docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)", "sphinx-autodoc-typehints (>=1
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+Werkzeug = ">=2.3.7"
+
+[package.extras]
+async = ["asgiref (>=3.2)"]
+dotenv = ["python-dotenv"]
+
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 [[package]]
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 version = "1.4.0"
@@ -929,6 +1247,26 @@ files = [
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 test = ["objgraph", "psutil"]
 
+[[package]]
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 name = "identify"
 version = "2.5.29"
@@ -973,6 +1311,24 @@ docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker
 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"]
 
+[[package]]
+name = "importlib-resources"
+version = "6.1.0"
+description = "Read resources from Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
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+
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -1001,6 +1357,17 @@ openai = ">=0.28.0,<0.29.0"
 pydantic = ">=2.0.2,<3.0.0"
 typer = ">=0.9.0,<0.10.0"
 
+[[package]]
+name = "itsdangerous"
+version = "2.1.2"
+description = "Safely pass data to untrusted environments and back."
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "jinja2"
 version = "3.1.2"
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 referencing = ">=0.28.0"
 
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 [[package]]
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 version = "0.0.305"
@@ -1172,6 +1652,43 @@ typing-extensions = ">=4.5.0"
 typing-inspect = ">=0.8.0"
 urllib3 = "<2"
 
+[[package]]
+name = "mako"
+version = "1.2.4"
+description = "A super-fast templating language that borrows the best ideas from the existing templating languages."
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
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+[package.dependencies]
+MarkupSafe = ">=0.9.2"
+
+[package.extras]
+babel = ["Babel"]
+lingua = ["lingua"]
+testing = ["pytest"]
+
+[[package]]
+name = "markdown"
+version = "3.5"
+description = "Python implementation of John Gruber's Markdown."
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.extras]
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+testing = ["coverage", "pyyaml"]
+
 [[package]]
 name = "markdown-it-py"
 version = "3.0.0"
@@ -1285,6 +1802,56 @@ docs = ["alabaster (==0.7.13)", "autodocsumm (==0.2.11)", "sphinx (==7.0.1)", "s
 lint = ["flake8 (==6.0.0)", "flake8-bugbear (==23.7.10)", "mypy (==1.4.1)", "pre-commit (>=2.4,<4.0)"]
 tests = ["pytest", "pytz", "simplejson"]
 
+[[package]]
+name = "matplotlib"
+version = "3.8.0"
+description = "Python plotting package"
+optional = false
+python-versions = ">=3.9"
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+    {file = "matplotlib-3.8.0.tar.gz", hash = "sha256:df8505e1c19d5c2c26aff3497a7cbd3ccfc2e97043d1e4db3e76afa399164b69"},
+]
+
+[package.dependencies]
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+cycler = ">=0.10"
+fonttools = ">=4.22.0"
+importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
+kiwisolver = ">=1.0.1"
+numpy = ">=1.21,<2"
+packaging = ">=20.0"
+pillow = ">=6.2.0"
+pyparsing = ">=2.3.1"
+python-dateutil = ">=2.7"
+setuptools_scm = ">=7"
+
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -1296,6 +1863,56 @@ files = [
     {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
 ]
 
+[[package]]
+name = "mlflow"
+version = "2.7.1"
+description = "MLflow: A Platform for ML Development and Productionization"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "mlflow-2.7.1.tar.gz", hash = "sha256:853b2038496fb8f85cad205943dede877f65ab86e856f296587885dafcef643d"},
+]
+
+[package.dependencies]
+alembic = "<1.10.0 || >1.10.0,<2"
+click = ">=7.0,<9"
+cloudpickle = "<3"
+databricks-cli = ">=0.8.7,<1"
+docker = ">=4.0.0,<7"
+entrypoints = "<1"
+Flask = "<3"
+gitpython = ">=2.1.0,<4"
+gunicorn = {version = "<22", markers = "platform_system != \"Windows\""}
+importlib-metadata = ">=3.7.0,<4.7.0 || >4.7.0,<7"
+Jinja2 = [
+    {version = ">=2.11,<4", markers = "platform_system != \"Windows\""},
+    {version = ">=3.0,<4", markers = "platform_system == \"Windows\""},
+]
+markdown = ">=3.3,<4"
+matplotlib = "<4"
+numpy = "<2"
+packaging = "<24"
+pandas = "<3"
+protobuf = ">=3.12.0,<5"
+pyarrow = ">=4.0.0,<14"
+pytz = "<2024"
+pyyaml = ">=5.1,<7"
+querystring-parser = "<2"
+requests = ">=2.17.3,<3"
+scikit-learn = "<2"
+scipy = "<2"
+sqlalchemy = ">=1.4.0,<3"
+sqlparse = ">=0.4.0,<1"
+waitress = {version = "<3", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+aliyun-oss = ["aliyunstoreplugin"]
+databricks = ["azure-storage-file-datalake (>12)", "boto3 (>1)", "google-cloud-storage (>=1.30.0)"]
+extras = ["azureml-core (>=1.2.0)", "boto3", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=1.2.0,!=1.3.1)", "mlserver-mlflow (>=1.2.0,!=1.3.1)", "prometheus-flask-exporter", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "virtualenv"]
+gateway = ["aiohttp (<4)", "fastapi (<1)", "psutil (<6)", "pydantic (>=1.0,<3)", "uvicorn[standard] (<1)", "watchfiles (<1)"]
+sqlserver = ["mlflow-dbstore"]
+
 [[package]]
 name = "multidict"
 version = "6.0.4"
@@ -1561,6 +2178,22 @@ files = [
     {file = "numpy-1.25.2.tar.gz", hash = "sha256:fd608e19c8d7c55021dffd43bfe5492fab8cc105cc8986f813f8c3c048b38760"},
 ]
 
+[[package]]
+name = "oauthlib"
+version = "3.2.2"
+description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "oauthlib-3.2.2-py3-none-any.whl", hash = "sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca"},
+    {file = "oauthlib-3.2.2.tar.gz", hash = "sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918"},
+]
+
+[package.extras]
+rsa = ["cryptography (>=3.0.0)"]
+signals = ["blinker (>=1.4.0)"]
+signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
+
 [[package]]
 name = "openai"
 version = "0.28.1"
@@ -2151,6 +2784,37 @@ files = [
 [package.extras]
 plugins = ["importlib-metadata"]
 
+[[package]]
+name = "pyjwt"
+version = "2.8.0"
+description = "JSON Web Token implementation in Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "PyJWT-2.8.0-py3-none-any.whl", hash = "sha256:59127c392cc44c2da5bb3192169a91f429924e17aff6534d70fdc02ab3e04320"},
+    {file = "PyJWT-2.8.0.tar.gz", hash = "sha256:57e28d156e3d5c10088e0c68abb90bfac3df82b40a71bd0daa20c65ccd5c23de"},
+]
+
+[package.extras]
+crypto = ["cryptography (>=3.4.0)"]
+dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
+docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
+tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
+
+[[package]]
+name = "pyparsing"
+version = "3.1.1"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.6.8"
+files = [
+    {file = "pyparsing-3.1.1-py3-none-any.whl", hash = "sha256:32c7c0b711493c72ff18a981d24f28aaf9c1fb7ed5e9667c9e84e3db623bdbfb"},
+    {file = "pyparsing-3.1.1.tar.gz", hash = "sha256:ede28a1a32462f5a9705e07aea48001a08f7cf81a021585011deba701581a0db"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
 [[package]]
 name = "pytest"
 version = "7.4.2"
@@ -2267,6 +2931,29 @@ files = [
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 version = "6.0.1"
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+[package.dependencies]
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+
+[package.extras]
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+doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"]
+test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
 [[package]]
 name = "setuptools"
 version = "68.2.2"
@@ -2639,6 +3425,28 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments
 testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
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+[[package]]
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+optional = false
+python-versions = ">=3.8"
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+typing-extensions = "*"
+
+[package.extras]
+docs = ["entangled-cli[rich]", "mkdocs", "mkdocs-entangled-plugin", "mkdocs-material", "mkdocstrings[python]", "pygments"]
+rich = ["rich"]
+test = ["build", "pytest", "rich", "wheel"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -2761,6 +3569,22 @@ postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
 sqlcipher = ["sqlcipher3-binary"]
 
+[[package]]
+name = "sqlparse"
+version = "0.4.4"
+description = "A non-validating SQL parser."
+optional = false
+python-versions = ">=3.5"
+files = [
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+[package.extras]
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+doc = ["sphinx"]
+test = ["pytest", "pytest-cov"]
+
 [[package]]
 name = "streamlit"
 version = "1.27.1"
@@ -2800,6 +3624,20 @@ watchdog = {version = ">=2.1.5", markers = "platform_system != \"Darwin\""}
 [package.extras]
 snowflake = ["snowflake-snowpark-python"]
 
+[[package]]
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+version = "0.9.0"
+description = "Pretty-print tabular data"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.extras]
+widechars = ["wcwidth"]
+
 [[package]]
 name = "tenacity"
 version = "8.2.3"
@@ -2839,6 +3677,17 @@ files = [
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 ]
 
+[[package]]
+name = "threadpoolctl"
+version = "3.2.0"
+description = "threadpoolctl"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "threadpoolctl-3.2.0-py3-none-any.whl", hash = "sha256:2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032"},
+    {file = "threadpoolctl-3.2.0.tar.gz", hash = "sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355"},
+]
+
 [[package]]
 name = "tiktoken"
 version = "0.5.1"
@@ -3101,6 +3950,21 @@ platformdirs = ">=3.9.1,<4"
 docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
 test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
 
+[[package]]
+name = "waitress"
+version = "2.1.2"
+description = "Waitress WSGI server"
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "waitress-2.1.2-py3-none-any.whl", hash = "sha256:7500c9625927c8ec60f54377d590f67b30c8e70ef4b8894214ac6e4cad233d2a"},
+    {file = "waitress-2.1.2.tar.gz", hash = "sha256:780a4082c5fbc0fde6a2fcfe5e26e6efc1e8f425730863c04085769781f51eba"},
+]
+
+[package.extras]
+docs = ["Sphinx (>=1.8.1)", "docutils", "pylons-sphinx-themes (>=1.0.9)"]
+testing = ["coverage (>=5.0)", "pytest", "pytest-cover"]
+
 [[package]]
 name = "watchdog"
 version = "3.0.0"
@@ -3151,6 +4015,39 @@ files = [
     {file = "wcwidth-0.2.8.tar.gz", hash = "sha256:8705c569999ffbb4f6a87c6d1b80f324bd6db952f5eb0b95bc07517f4c1813d4"},
 ]
 
+[[package]]
+name = "websocket-client"
+version = "1.6.4"
+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "websocket-client-1.6.4.tar.gz", hash = "sha256:b3324019b3c28572086c4a319f91d1dcd44e6e11cd340232978c684a7650d0df"},
+    {file = "websocket_client-1.6.4-py3-none-any.whl", hash = "sha256:084072e0a7f5f347ef2ac3d8698a5e0b4ffbfcab607628cadabc650fc9a83a24"},
+]
+
+[package.extras]
+docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
+[[package]]
+name = "werkzeug"
+version = "3.0.0"
+description = "The comprehensive WSGI web application library."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "werkzeug-3.0.0-py3-none-any.whl", hash = "sha256:cbb2600f7eabe51dbc0502f58be0b3e1b96b893b05695ea2b35b43d4de2d9962"},
+    {file = "werkzeug-3.0.0.tar.gz", hash = "sha256:3ffff4dcc32db52ef3cc94dff3000a3c2846890f3a5a51800a27b909c5e770f0"},
+]
+
+[package.dependencies]
+MarkupSafe = ">=2.1.1"
+
+[package.extras]
+watchdog = ["watchdog (>=2.3)"]
+
 [[package]]
 name = "yarl"
 version = "1.9.2"
@@ -3256,4 +4153,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.9.7 || >3.9.7,<4.0"
-content-hash = "f8ebed467c0e2246c1050ecee4f9df702405a4267f4bfec310c25e902b3c206a"
+content-hash = "88a34038805ac8fb160b56a8f259eecccae996027c8e8ffb391b0b38744c155b"
diff --git a/pyproject.toml b/pyproject.toml
index 5a7316c..360d9d8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,6 +24,7 @@ streamlit = ">=1.19.0"
 instructor = "^0.2.8"
 langchain = "^0.0.305"
 llama-index = "^0.8.36"
+mlflow = "^2.7.1"
 
 [tool.poetry.group.test.dependencies]  # https://python-poetry.org/docs/master/managing-dependencies/
 black = ">=23.3.0"
diff --git a/src/llm_app_eval/evaluator.py b/src/llm_app_eval/evaluator.py
index c692eaa..abe5894 100644
--- a/src/llm_app_eval/evaluator.py
+++ b/src/llm_app_eval/evaluator.py
@@ -2,8 +2,9 @@
 import os
 import time
 from datetime import datetime
-from typing import Callable, Optional
+from typing import Callable, Optional, Union
 
+import mlflow
 import pandas as pd
 from llm_app import BaseApp, InputFormat, OutputFormat
 from pydantic import BaseModel
@@ -49,11 +50,10 @@ def __init__(
         self.properties = properties
         self.results_dir = results_dir
 
-    def evaluate(
+    def evaluate_app(
         self,
         llm_app: BaseApp,
         exp_name: Optional[str] = None,
-        exp_descr: str = "",
     ):
         # If no experiment name is provided, use the current timestamp
         if not exp_name:
@@ -63,64 +63,109 @@ def evaluate(
         os.makedirs(exp_dir, exist_ok=True)
 
         # Loop over test cases
-        test_case_results = []
-        for test_case in tqdm(
-            self.test_set, desc="Evaluating test cases", unit="test case", total=len(self.test_set)
-        ):
-            # Pass the test case to the LLM app
-            # Measure the time it takes to run the LLM app
-            start_time = time.time()
-            app_output = llm_app(app_input=test_case.test_input)
-            latency = time.time() - start_time
-            # Evaluate properties
-            property_results = {}
-            for prop in self.properties:
-                # print(f"Evaluating property {prop.property_name}")
-                r = prop.eval_func(test_case=test_case, llm_app_result=app_output)
-                # If the property is None, then it is not applicable to this test case, so skip it
-                if r:
-                    # Store the property results per test case in a list
-                    property_results[prop.property_name] = PropertyResult(
-                        feedback=r.feedback,
-                        score=r.score if "score" in r.model_fields else float(r.pass_fail),
-                    )
-            # Store results as JSON
-            tcr = TestCaseResult(
-                test_case_id=test_case.test_id,
-                output=app_output,
-                property_results=property_results,
-                latency=latency,
+        mlflow.set_experiment(exp_name)
+        with mlflow.start_run(nested=True):
+            mlflow.log_params(llm_app.cfg)
+            test_case_results = []
+            for test_case in tqdm(
+                self.test_set,
+                desc="Evaluating test cases",
+                unit="test case",
+                total=len(self.test_set),
+            ):
+                # Pass the test case to the LLM app
+                # Measure the time it takes to run the LLM app
+                start_time = time.time()
+                app_output = llm_app(app_input=test_case.test_input)
+                latency = time.time() - start_time
+                # Evaluate properties
+                property_results = {}
+                for prop in self.properties:
+                    # print(f"Evaluating property {prop.property_name}")
+                    r = prop.eval_func(test_case=test_case, llm_app_result=app_output)
+                    # If the property is None, then it is not applicable to this test case, so skip it
+                    if r:
+                        # Store the property results per test case in a list
+                        property_results[prop.property_name] = PropertyResult(
+                            feedback=r.feedback,
+                            score=r.score if "score" in r.model_fields else float(r.pass_fail),
+                        )
+                # Store results as JSON
+                tcr = TestCaseResult(
+                    test_case_id=test_case.test_id,
+                    output=app_output,
+                    property_results=property_results,
+                    latency=latency,
+                )
+                test_case_results.append(tcr)
+                tcr_json = tcr.model_dump_json()
+                with open(os.path.join(exp_dir, f"{tcr.test_case_id}.json"), "w") as f:
+                    f.write(tcr_json)
+
+            # Save the Llm app config dict as JSON
+            with open(os.path.join(exp_dir, "llm_app.json"), "w") as f:
+                f.write(json.dumps(llm_app.cfg))
+
+            # Convert all test case results into a dataframe
+            df = pd.DataFrame([tcr.dict() for tcr in test_case_results])
+            # Split the `property_results` into separate columns. The `property_results` column is a dict of dicts.
+            # Each top level key is a property name. Each second level key is a property result (feedback and pass_fail).
+            # The `property_results` column is split into separate columns for each combination of property name and property result.
+            # The values of these columns are the values of the `feedback` and `pass_fail` respectively.
+            df = df.join(pd.json_normalize(df["property_results"]))
+            # Split the `output` column into separate columns.
+            df = df.join(pd.json_normalize(df["output"]))
+            # Drop the `property_results` and `output` columns.
+            df = df.drop(columns=["property_results", "output"])
+            # Drop the empty columns.
+            df = df.dropna(axis=1, how="all")
+            # Add the input and reference output to the dataframe, based on the test case id.
+            df = df.merge(
+                pd.DataFrame(
+                    {
+                        "test_case_id": [test_case.test_id for test_case in self.test_set],
+                        "test_input": [
+                            test_case.test_input.question for test_case in self.test_set
+                        ],
+                        "reference_output": [
+                            test_case.reference_output.answer
+                            for test_case in self.test_set
+                            if test_case.reference_output
+                        ],
+                    }
+                ),
+                on="test_case_id",
             )
-            test_case_results.append(tcr)
-            tcr_json = tcr.model_dump_json()
-            with open(os.path.join(exp_dir, f"{tcr.test_case_id}.json"), "w") as f:
-                f.write(tcr_json)
-        # Save the Llm app config dict as JSON
-        with open(os.path.join(exp_dir, "llm_app.json"), "w") as f:
-            f.write(json.dumps(llm_app.cfg))
-
-        # Convert all test case results into a dataframe
-        df = pd.DataFrame([tcr.dict() for tcr in test_case_results])
-        # Split the `property_results` into separate columns. The `property_results` column is a dict of dicts.
-        # Each top level key is a property name. Each second level key is a property result (feedback and pass_fail).
-        # The `property_results` column is split into separate columns for each combination of property name and property result.
-        # The values of these columns are the values of the `feedback` and `pass_fail` respectively.
-        df = df.join(pd.json_normalize(df["property_results"]))
-        # Drop the `property_results` column.
-        df = df.drop(columns=["property_results"])
-        # Save the dataframe as CSV
-        df.to_csv(os.path.join(exp_dir, "results.csv"), index=False)
-        # Aggregate the results by taking the mean over the test cases for the `latency` and all `pass_fail` columns.
-        agg_columns = ["latency"] + [col for col in df.columns if "score" in col]
-        df_agg = df[agg_columns].mean().reset_index()
-        df_agg.columns = ["metric", "value"]
-        # Pivot the metric column to get a column for each metric and a row for each LLM app.
-        df_agg = df_agg.pivot_table(index=None, columns="metric", values="value")
-        # Drop the `metric` index.
-        df_agg = df_agg.reset_index(drop=True)
-        # Add the llm app config dict as columns in front of the aggregated results.
-        df_agg = pd.concat([pd.json_normalize(llm_app.cfg), df_agg], axis=1)
-        # Save the aggregated results as CSV.
-        df_agg.to_csv(os.path.join(exp_dir, "results_agg.csv"), index=False)
+            # Save the dataframe as CSV
+            df.to_csv(os.path.join(exp_dir, "results.csv"), index=False)
+
+            # Aggregate the results by taking the mean over the test cases for the `latency` and all `pass_fail` columns.
+            agg_columns = ["latency"] + [col for col in df.columns if "score" in col]
+            df_agg = df[agg_columns].mean().reset_index()
+            df_agg.columns = ["metric", "value"]
+            # Pivot the metric column to get a column for each metric and a row for each LLM app.
+            df_agg = df_agg.pivot_table(index=None, columns="metric", values="value")
+            # Drop the `metric` index.
+            df_agg = df_agg.reset_index(drop=True)
+            # Add the llm app config dict as columns in front of the aggregated results.
+            df_agg = pd.concat([pd.json_normalize(llm_app.cfg), df_agg], axis=1)
+            # Save the aggregated results as CSV.
+            df_agg.to_csv(os.path.join(exp_dir, "results_agg.csv"), index=False)
+
+            # Log results to MLflow
+            mlflow.log_table(df, artifact_file="eval_results.json")
+            # Loop over the columns of df_agg and log each column as a metric.
+            for col in df_agg.columns:
+                if col in agg_columns:
+                    mlflow.log_metric(key=col, value=df_agg[col].values[0])
 
         return df_agg
+
+    def evaluate(self, llm_apps: Union[list[BaseApp], BaseApp], exp_name: Optional[str] = ""):
+        if isinstance(llm_apps, BaseApp):
+            llm_apps = [llm_apps]
+        # Evaluate the LLM app(s)
+        results = [self.evaluate_app(llm_app=llm_app, exp_name=exp_name) for llm_app in llm_apps]
+        # Combine the resulting dataframes into one dataframe.
+        results_df = pd.concat(results, axis=0)
+        return results_df
diff --git a/src/llm_app_eval/example.ipynb b/src/llm_app_eval/example.ipynb
index 7fb0924..90c66b2 100644
--- a/src/llm_app_eval/example.ipynb
+++ b/src/llm_app_eval/example.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,32 +125,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Evaluating test cases: 100%|██████████| 3/3 [00:17<00:00,  5.87s/test case]\n",
-      "Evaluating test cases: 100%|██████████| 3/3 [00:09<00:00,  3.08s/test case]\n",
-      "Evaluating test cases: 100%|██████████| 3/3 [00:31<00:00, 10.55s/test case]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Evaluate the LLM apps on the test set by using the properties.\n",
     "ev = Evaluator(test_set=test_cases, properties=properties, results_dir=\"data/eval_results\")\n",
-    "results = []\n",
-    "for llm_app in llm_apps:\n",
-    "    results.append(ev.evaluate(llm_app))\n",
-    "# Combine the resulting dataframes into one dataframe.\n",
-    "results_df = pd.concat(results, axis=0)"
+    "exp_name = input(\"Experiment name: \")\n",
+    "results_df = ev.evaluate(llm_apps, exp_name)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -187,28 +174,28 @@
        "      <th>0</th>\n",
        "      <td>gpt-3.5-turbo-0613</td>\n",
        "      <td>Answer the question.</td>\n",
-       "      <td>0.896354</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>11.281277</td>\n",
-       "      <td>3.301628</td>\n",
+       "      <td>0.893214</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>8.539927</td>\n",
+       "      <td>46.845016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>gpt-3.5-turbo-0613</td>\n",
        "      <td>You are a first-aid expert. Answer the questio...</td>\n",
-       "      <td>0.900779</td>\n",
+       "      <td>0.899895</td>\n",
        "      <td>0.666667</td>\n",
-       "      <td>2.259376</td>\n",
-       "      <td>1.603137</td>\n",
+       "      <td>2.762177</td>\n",
+       "      <td>1.940822</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>gpt-4</td>\n",
        "      <td>You are a first-aid expert. Answer the questio...</td>\n",
-       "      <td>0.903506</td>\n",
+       "      <td>0.908613</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>6.988899</td>\n",
-       "      <td>9.029494</td>\n",
+       "      <td>5.177815</td>\n",
+       "      <td>6.374512</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -221,17 +208,17 @@
        "0               gpt-4  You are a first-aid expert. Answer the questio...   \n",
        "\n",
        "   CosineSimilarity.score  FactuallyConsistent.score  Verbosity.score  \\\n",
-       "0                0.896354                   0.666667        11.281277   \n",
-       "0                0.900779                   0.666667         2.259376   \n",
-       "0                0.903506                   1.000000         6.988899   \n",
+       "0                0.893214                   1.000000         8.539927   \n",
+       "0                0.899895                   0.666667         2.762177   \n",
+       "0                0.908613                   1.000000         5.177815   \n",
        "\n",
-       "    latency  \n",
-       "0  3.301628  \n",
-       "0  1.603137  \n",
-       "0  9.029494  "
+       "     latency  \n",
+       "0  46.845016  \n",
+       "0   1.940822  \n",
+       "0   6.374512  "
       ]
      },
-     "execution_count": 33,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }