diff --git a/README.md b/README.md
index 904a5fe..95f6d9c 100644
--- a/README.md
+++ b/README.md
@@ -63,6 +63,14 @@ WANDBOT_API_URL="http://localhost:8000"
 WANDB_TRACING_ENABLED="true"
 WANDB_PROJECT="wandbot-dev"
 WANDB_ENTITY="wandbot"
+WANDB_REPORT_API_ENABLE_V2="true"
+WANDB_REPORT_API_DISABLE_MESSAGE="true"
+```
+
+The best practive would be to create a `.env` file in the root of this repo and execute this command in your terminal:
+
+```
+set -o allexport; source .env; set +o allexport
 ```
 
 Once these environment variables are set, you can start the Q&A bot application using the following commands:
diff --git a/poetry.lock b/poetry.lock
index fbf0b42..e4e11ac 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 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 = "aiofiles"
@@ -164,13 +164,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.2.0"
+version = "4.3.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
-    {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
+    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
+    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
 ]
 
 [package.dependencies]
@@ -284,13 +284,13 @@ test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock
 
 [[package]]
 name = "asgiref"
-version = "3.7.2"
+version = "3.8.1"
 description = "ASGI specs, helper code, and adapters"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "asgiref-3.7.2-py3-none-any.whl", hash = "sha256:89b2ef2247e3b562a16eef663bc0e2e703ec6468e2fa8a5cd61cd449786d4f6e"},
-    {file = "asgiref-3.7.2.tar.gz", hash = "sha256:9e0ce3aa93a819ba5b45120216b23878cf6e8525eb3848653452b4192b92afed"},
+    {file = "asgiref-3.8.1-py3-none-any.whl", hash = "sha256:3e1e3ecc849832fe52ccf2cb6686b7a55f82bb1d6aee72a58826471390335e47"},
+    {file = "asgiref-3.8.1.tar.gz", hash = "sha256:c343bd80a0bec947a9860adb4c432ffa7db769836c64238fc34bdc3fec84d590"},
 ]
 
 [package.dependencies]
@@ -449,33 +449,33 @@ lxml = ["lxml"]
 
 [[package]]
 name = "black"
-version = "24.1.1"
+version = "24.3.0"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "black-24.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d7de8d330763c66663661a1ffd432274a2f92f07feeddd89ffd085b5744f85e7"},
+    {file = "black-24.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:7bb041dca0d784697af4646d3b62ba4a6b028276ae878e53f6b4f74ddd6db99f"},
+    {file = "black-24.3.0-py3-none-any.whl", hash = "sha256:41622020d7120e01d377f74249e677039d20e6344ff5851de8a10f11f513bf93"},
+    {file = "black-24.3.0.tar.gz", hash = "sha256:a0c9c4a0771afc6919578cec71ce82a3e31e054904e7197deacbc9382671c41f"},
 ]
 
 [package.dependencies]
@@ -522,19 +522,34 @@ files = [
     {file = "blinker-1.7.0.tar.gz", hash = "sha256:e6820ff6fa4e4d1d8e2747c2283749c3f547e4fee112b98555cdcdae32996182"},
 ]
 
+[[package]]
+name = "bs4"
+version = "0.0.2"
+description = "Dummy package for Beautiful Soup (beautifulsoup4)"
+optional = false
+python-versions = "*"
+files = [
+    {file = "bs4-0.0.2-py2.py3-none-any.whl", hash = "sha256:abf8742c0805ef7f662dce4b51cca104cffe52b835238afc169142ab9b3fbccc"},
+    {file = "bs4-0.0.2.tar.gz", hash = "sha256:a48685c58f50fe127722417bae83fe6badf500d54b55f7e39ffe43b798653925"},
+]
+
+[package.dependencies]
+beautifulsoup4 = "*"
+
 [[package]]
 name = "build"
-version = "1.0.3"
+version = "1.1.1"
 description = "A simple, correct Python build frontend"
 optional = false
 python-versions = ">= 3.7"
 files = [
-    {file = "build-1.0.3-py3-none-any.whl", hash = "sha256:589bf99a67df7c9cf07ec0ac0e5e2ea5d4b37ac63301c4986d1acb126aa83f8f"},
-    {file = "build-1.0.3.tar.gz", hash = "sha256:538aab1b64f9828977f84bc63ae570b060a8ed1be419e7870b8b4fc5e6ea553b"},
+    {file = "build-1.1.1-py3-none-any.whl", hash = "sha256:8ed0851ee76e6e38adce47e4bee3b51c771d86c64cf578d0c2245567ee200e73"},
+    {file = "build-1.1.1.tar.gz", hash = "sha256:8eea65bb45b1aac2e734ba2cc8dad3a6d97d97901a395bd0ed3e7b46953d2a31"},
 ]
 
 [package.dependencies]
 colorama = {version = "*", markers = "os_name == \"nt\""}
+importlib-metadata = {version = ">=4.6", markers = "python_full_version < \"3.10.2\""}
 packaging = ">=19.0"
 pyproject_hooks = "*"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
@@ -547,13 +562,13 @@ virtualenv = ["virtualenv (>=20.0.35)"]
 
 [[package]]
 name = "cachetools"
-version = "5.3.2"
+version = "5.3.3"
 description = "Extensible memoizing collections and decorators"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"},
-    {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"},
+    {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"},
+    {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"},
 ]
 
 [[package]]
@@ -780,13 +795,13 @@ numpy = "*"
 
 [[package]]
 name = "chromadb"
-version = "0.4.22"
+version = "0.4.24"
 description = "Chroma."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "chromadb-0.4.22-py3-none-any.whl", hash = "sha256:ad210b27b4cda2f09d15adc9c83c81bfa66b69f39648a27b637306e40de0680d"},
-    {file = "chromadb-0.4.22.tar.gz", hash = "sha256:c793149e1c2bbbb52d77602c6c0594c5752f04cd9be12619250ddad2082af27a"},
+    {file = "chromadb-0.4.24-py3-none-any.whl", hash = "sha256:3a08e237a4ad28b5d176685bd22429a03717fe09d35022fb230d516108da01da"},
+    {file = "chromadb-0.4.24.tar.gz", hash = "sha256:a5c80b4e4ad9b236ed2d4899a5b9e8002b489293f2881cb2cadab5b199ee1c72"},
 ]
 
 [package.dependencies]
@@ -804,6 +819,7 @@ opentelemetry-api = ">=1.2.0"
 opentelemetry-exporter-otlp-proto-grpc = ">=1.2.0"
 opentelemetry-instrumentation-fastapi = ">=0.41b0"
 opentelemetry-sdk = ">=1.2.0"
+orjson = ">=3.9.12"
 overrides = ">=7.3.1"
 posthog = ">=2.4.0"
 pulsar-client = ">=3.1.0"
@@ -834,13 +850,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cohere"
-version = "4.45"
+version = "4.57"
 description = "Python SDK for the Cohere API"
 optional = false
 python-versions = ">=3.8,<4.0"
 files = [
-    {file = "cohere-4.45-py3-none-any.whl", hash = "sha256:bdaa2e5e1c64cf3b1d55caf9d483a33fa8eafed731a999fb0934ae12c0638b75"},
-    {file = "cohere-4.45.tar.gz", hash = "sha256:63b21b2dc3abd718b18cae726a69d1b096a34eb59f3331c20469fd0df1672816"},
+    {file = "cohere-4.57-py3-none-any.whl", hash = "sha256:479bdea81ae119e53f671f1ae808fcff9df88211780525d7ef2f7b99dfb32e59"},
+    {file = "cohere-4.57.tar.gz", hash = "sha256:71ace0204a92d1a2a8d4b949b88b353b4f22fc645486851924284cc5a0eb700d"},
 ]
 
 [package.dependencies]
@@ -898,13 +914,13 @@ development = ["black", "flake8", "mypy", "pytest", "types-colorama"]
 
 [[package]]
 name = "comm"
-version = "0.2.1"
+version = "0.2.2"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "comm-0.2.1-py3-none-any.whl", hash = "sha256:87928485c0dfc0e7976fd89fc1e187023cf587e7c353e4a9b417555b44adf021"},
-    {file = "comm-0.2.1.tar.gz", hash = "sha256:0bc91edae1344d39d3661dcbc36937181fdaddb304790458f8b044dbc064b89a"},
+    {file = "comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3"},
+    {file = "comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e"},
 ]
 
 [package.dependencies]
@@ -929,19 +945,46 @@ marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"
 
 [[package]]
-name = "dataclasses-json-speakeasy"
-version = "0.5.11"
-description = "Easily serialize dataclasses to and from JSON."
+name = "datasets"
+version = "2.14.4"
+description = "HuggingFace community-driven open-source library of datasets"
 optional = false
-python-versions = ">=3.7,<4.0"
+python-versions = ">=3.8.0"
 files = [
-    {file = "dataclasses_json_speakeasy-0.5.11-py3-none-any.whl", hash = "sha256:ac52a069a01e8521015d682f37849bfdf056c36fa3f81497055e201fec684104"},
-    {file = "dataclasses_json_speakeasy-0.5.11.tar.gz", hash = "sha256:418a987cea2ccf4e4be662f39faa5cc79b47b147c9d1a69d6928d6a27e0c17e8"},
+    {file = "datasets-2.14.4-py3-none-any.whl", hash = "sha256:29336bd316a7d827ccd4da2236596279b20ca2ac78f64c04c9483da7cbc2459b"},
+    {file = "datasets-2.14.4.tar.gz", hash = "sha256:ef29c2b5841de488cd343cfc26ab979bff77efa4d2285af51f1ad7db5c46a83b"},
 ]
 
 [package.dependencies]
-marshmallow = ">=3.18.0,<4.0.0"
-typing-inspect = ">=0.4.0,<1"
+aiohttp = "*"
+dill = ">=0.3.0,<0.3.8"
+fsspec = {version = ">=2021.11.1", extras = ["http"]}
+huggingface-hub = ">=0.14.0,<1.0.0"
+multiprocess = "*"
+numpy = ">=1.17"
+packaging = "*"
+pandas = "*"
+pyarrow = ">=8.0.0"
+pyyaml = ">=5.1"
+requests = ">=2.19.0"
+tqdm = ">=4.62.1"
+xxhash = "*"
+
+[package.extras]
+apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"]
+audio = ["librosa", "soundfile (>=0.12.1)"]
+benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
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+vision = ["Pillow (>=6.2.1)"]
 
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 name = "db-dtypes"
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 [[package]]
@@ -998,6 +1045,24 @@ files = [
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+[[package]]
+name = "deepdiff"
+version = "6.7.1"
+description = "Deep Difference and Search of any Python object/data. Recreate objects by adding adding deltas to each other."
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[package.extras]
+cli = ["click (==8.1.3)", "pyyaml (==6.0.1)"]
+optimize = ["orjson"]
+
 [[package]]
 name = "defusedxml"
 version = "0.7.1"
@@ -1026,6 +1091,31 @@ wrapt = ">=1.10,<2"
 [package.extras]
 dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
 
+[[package]]
+name = "dill"
+version = "0.3.7"
+description = "serialize all of Python"
+optional = false
+python-versions = ">=3.7"
+files = [
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+[package.extras]
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+
+[[package]]
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+version = "1.0.8"
+description = "JSON decoder for Python that can extract data from the muck"
+optional = false
+python-versions = "*"
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+
 [[package]]
 name = "discord"
 version = "2.3.2"
@@ -1087,13 +1177,13 @@ six = ">=1.4.0"
 
 [[package]]
 name = "emoji"
-version = "2.10.1"
+version = "2.11.0"
 description = "Emoji for Python"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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 [package.extras]
@@ -1129,38 +1219,42 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
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 ]
 
+[package.dependencies]
+numpy = "*"
+
 [[package]]
 name = "fastapi"
 version = "0.109.2"
@@ -1182,42 +1276,42 @@ all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)"
 
 [[package]]
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 description = "Fast read/write of AVRO files"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.extras]
@@ -1242,18 +1336,18 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.13.1"
+version = "3.13.3"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "filelock-3.13.3-py3-none-any.whl", hash = "sha256:5ffa845303983e7a0b7ae17636509bc97997d58afeafa72fb141a17b152284cb"},
+    {file = "filelock-3.13.3.tar.gz", hash = "sha256:a79895a25bbefdf55d1a2a0a80968f7dbb28edcd6d4234a0afb3f37ecde4b546"},
 ]
 
 [package.extras]
-docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
+docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
 typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
@@ -1305,13 +1399,13 @@ Flask = ">=0.9"
 
 [[package]]
 name = "flatbuffers"
-version = "23.5.26"
+version = "24.3.25"
 description = "The FlatBuffers serialization format for Python"
 optional = false
 python-versions = "*"
 files = [
-    {file = "flatbuffers-23.5.26-py2.py3-none-any.whl", hash = "sha256:c0ff356da363087b915fde4b8b45bdda73432fc17cddb3c8157472eab1422ad1"},
-    {file = "flatbuffers-23.5.26.tar.gz", hash = "sha256:9ea1144cac05ce5d86e2859f431c6cd5e66cd9c78c558317c7955fb8d4c78d89"},
+    {file = "flatbuffers-24.3.25-py2.py3-none-any.whl", hash = "sha256:8dbdec58f935f3765e4f7f3cf635ac3a77f83568138d6a2311f524ec96364812"},
+    {file = "flatbuffers-24.3.25.tar.gz", hash = "sha256:de2ec5b203f21441716617f38443e0a8ebf3d25bf0d9c0bb0ce68fa00ad546a4"},
 ]
 
 [[package]]
@@ -1413,15 +1507,18 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2024.2.0"
+version = "2024.3.1"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2024.2.0-py3-none-any.whl", hash = "sha256:817f969556fa5916bc682e02ca2045f96ff7f586d45110fcb76022063ad2c7d8"},
-    {file = "fsspec-2024.2.0.tar.gz", hash = "sha256:b6ad1a679f760dda52b1168c859d01b7b80648ea6f7f7c7f5a8a91dc3f3ecb84"},
+    {file = "fsspec-2024.3.1-py3-none-any.whl", hash = "sha256:918d18d41bf73f0e2b261824baeb1b124bcf771767e3a26425cd7dec3332f512"},
+    {file = "fsspec-2024.3.1.tar.gz", hash = "sha256:f39780e282d7d117ffb42bb96992f8a90795e4d0fb0f661a70ca39fe9c43ded9"},
 ]
 
+[package.dependencies]
+aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
+
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
@@ -1462,20 +1559,20 @@ smmap = ">=3.0.1,<6"
 
 [[package]]
 name = "gitpython"
-version = "3.1.41"
+version = "3.1.42"
 description = "GitPython is a Python library used to interact with Git repositories"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "GitPython-3.1.42-py3-none-any.whl", hash = "sha256:1bf9cd7c9e7255f77778ea54359e54ac22a72a5b51288c457c881057b7bb9ecd"},
+    {file = "GitPython-3.1.42.tar.gz", hash = "sha256:2d99869e0fef71a73cbd242528105af1d6c1b108c60dfabd994bf292f76c3ceb"},
 ]
 
 [package.dependencies]
 gitdb = ">=4.0.1,<5"
 
 [package.extras]
-test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "sumtypes"]
+test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar"]
 
 [[package]]
 name = "giturlparse"
@@ -1490,18 +1587,27 @@ files = [
 
 [[package]]
 name = "google-api-core"
-version = "2.16.2"
+version = "2.18.0"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "google-api-core-2.18.0.tar.gz", hash = "sha256:62d97417bfc674d6cef251e5c4d639a9655e00c45528c4364fbfebb478ce72a9"},
+    {file = "google_api_core-2.18.0-py3-none-any.whl", hash = "sha256:5a63aa102e0049abe85b5b88cb9409234c1f70afcda21ce1e40b285b9629c1d6"},
 ]
 
 [package.dependencies]
 google-auth = ">=2.14.1,<3.0.dev0"
 googleapis-common-protos = ">=1.56.2,<2.0.dev0"
+grpcio = [
+    {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
+    {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
+]
+grpcio-status = [
+    {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
+    {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
+]
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0"
 requests = ">=2.18.0,<3.0.0.dev0"
 
@@ -1512,13 +1618,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-auth"
-version = "2.27.0"
+version = "2.29.0"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "google-auth-2.29.0.tar.gz", hash = "sha256:672dff332d073227550ffc7457868ac4218d6c500b155fe6cc17d2b13602c360"},
+    {file = "google_auth-2.29.0-py2.py3-none-any.whl", hash = "sha256:d452ad095688cd52bae0ad6fafe027f6a6d6f560e810fec20914e17a09526415"},
 ]
 
 [package.dependencies]
@@ -1535,17 +1641,18 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
 name = "google-cloud-bigquery"
-version = "3.17.2"
+version = "3.20.0"
 description = "Google BigQuery API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "google-cloud-bigquery-3.20.0.tar.gz", hash = "sha256:b18c597cbccd522a2894292f393d1d9712a3bd208d9cd36b65f69c46b4c8519f"},
+    {file = "google_cloud_bigquery-3.20.0-py2.py3-none-any.whl", hash = "sha256:ce6f5385bd62f0b6c62e6c4b87241d9dae4eafddf034b4dee1f909f1e9368f57"},
 ]
 
 [package.dependencies]
-google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 google-cloud-core = ">=1.6.0,<3.0.0dev"
 google-resumable-media = ">=0.6.0,<3.0dev"
 packaging = ">=20.0.0"
@@ -1681,13 +1788,13 @@ requests = ["requests (>=2.18.0,<3.0.0dev)"]
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.62.0"
+version = "1.63.0"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "googleapis-common-protos-1.63.0.tar.gz", hash = "sha256:17ad01b11d5f1d0171c06d3ba5c04c54474e883b66b949722b4938ee2694ef4e"},
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 ]
 
 [package.dependencies]
@@ -1810,69 +1917,85 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "grpcio"
-version = "1.60.1"
+version = "1.62.1"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.7"
 files = [
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+description = "Status proto mapping for gRPC"
+optional = false
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+protobuf = ">=4.21.6"
 
 [[package]]
 name = "h11"
@@ -1887,13 +2010,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.2"
+version = "1.0.5"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1904,7 +2027,7 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.23.0)"]
+trio = ["trio (>=0.22.0,<0.26.0)"]
 
 [[package]]
 name = "httptools"
@@ -1956,13 +2079,13 @@ test = ["Cython (>=0.29.24,<0.30.0)"]
 
 [[package]]
 name = "httpx"
-version = "0.26.0"
+version = "0.27.0"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1980,13 +2103,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.20.3"
+version = "0.22.1"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1999,15 +2122,17 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
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 cli = ["InquirerPy (==0.3.4)"]
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 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
-inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
-quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+inference = ["aiohttp", "minijinja (>=1.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
-torch = ["torch"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
 [[package]]
@@ -2056,18 +2181,18 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.1"
+version = "6.4.0"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_resources-6.1.1-py3-none-any.whl", hash = "sha256:e8bf90d8213b486f428c9c39714b920041cb02c184686a3dee24905aaa8105d6"},
-    {file = "importlib_resources-6.1.1.tar.gz", hash = "sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a"},
+    {file = "importlib_resources-6.4.0-py3-none-any.whl", hash = "sha256:50d10f043df931902d4194ea07ec57960f66a80449ff867bfe782b4c486ba78c"},
+    {file = "importlib_resources-6.4.0.tar.gz", hash = "sha256:cdb2b453b8046ca4e3798eb1d84f3cce1446a0e8e7b5ef4efb600f19fc398145"},
 ]
 
 [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"]
-testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"]
+testing = ["jaraco.test (>=5.4)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
 
 [[package]]
 name = "iniconfig"
@@ -2082,13 +2207,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.29.1"
+version = "6.29.4"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.29.1-py3-none-any.whl", hash = "sha256:e5dfba210fc9da74a5dae8fa6c41f816e11bd18d10381b2517d9a0d57cc987c4"},
-    {file = "ipykernel-6.29.1.tar.gz", hash = "sha256:1547352b32da95a2761011a8dac2af930c26a0703dfa07690d16b7d74dac0ba1"},
+    {file = "ipykernel-6.29.4-py3-none-any.whl", hash = "sha256:1181e653d95c6808039c509ef8e67c4126b3b3af7781496c7cbfb5ed938a27da"},
+    {file = "ipykernel-6.29.4.tar.gz", hash = "sha256:3d44070060f9475ac2092b760123fadf105d2e2493c24848b6691a7c4f42af5c"},
 ]
 
 [package.dependencies]
@@ -2111,7 +2236,7 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.4)", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipynbname"
@@ -2129,13 +2254,13 @@ ipykernel = "*"
 
 [[package]]
 name = "ipython"
-version = "8.21.0"
+version = "8.22.2"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
-    {file = "ipython-8.21.0-py3-none-any.whl", hash = "sha256:1050a3ab8473488d7eee163796b02e511d0735cf43a04ba2a8348bd0f2eaf8a5"},
-    {file = "ipython-8.21.0.tar.gz", hash = "sha256:48fbc236fbe0e138b88773fa0437751f14c3645fb483f1d4c5dee58b37e5ce73"},
+    {file = "ipython-8.22.2-py3-none-any.whl", hash = "sha256:3c86f284c8f3d8f2b6c662f885c4889a91df7cd52056fd02b7d8d6195d7f56e9"},
+    {file = "ipython-8.22.2.tar.gz", hash = "sha256:2dcaad9049f9056f1fef63514f176c7d41f930daa78d05b82a176202818f2c14"},
 ]
 
 [package.dependencies]
@@ -2144,16 +2269,16 @@ decorator = "*"
 exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
-pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
 prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
-traitlets = ">=5"
+traitlets = ">=5.13.0"
 
 [package.extras]
-all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.23)", "pandas", "pickleshare", "pytest (<8)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["ipython[black,doc,kernel,nbconvert,nbformat,notebook,parallel,qtconsole,terminal]", "ipython[test,test-extra]"]
 black = ["black"]
-doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<8)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "stack-data", "typing-extensions"]
 kernel = ["ipykernel"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
@@ -2161,7 +2286,7 @@ notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
 test = ["pickleshare", "pytest (<8)", "pytest-asyncio (<0.22)", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "pickleshare", "pytest (<8)", "pytest-asyncio (<0.22)", "testpath", "trio"]
+test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
 [[package]]
 name = "isoduration"
@@ -2251,18 +2376,15 @@ files = [
 
 [[package]]
 name = "json5"
-version = "0.9.14"
+version = "0.9.24"
 description = "A Python implementation of the JSON5 data format."
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "json5-0.9.14-py2.py3-none-any.whl", hash = "sha256:740c7f1b9e584a468dbb2939d8d458db3427f2c93ae2139d05f47e453eae964f"},
-    {file = "json5-0.9.14.tar.gz", hash = "sha256:9ed66c3a6ca3510a976a9ef9b8c0787de24802724ab1860bc0153c7fdd589b02"},
+    {file = "json5-0.9.24-py3-none-any.whl", hash = "sha256:4ca101fd5c7cb47960c055ef8f4d0e31e15a7c6c48c3b6f1473fc83b6c462a13"},
+    {file = "json5-0.9.24.tar.gz", hash = "sha256:0c638399421da959a20952782800e5c1a78c14e08e1dc9738fa10d8ec14d58c8"},
 ]
 
-[package.extras]
-dev = ["hypothesis"]
-
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -2344,13 +2466,13 @@ referencing = ">=0.31.0"
 
 [[package]]
 name = "jupyter-client"
-version = "8.6.0"
+version = "8.6.1"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"},
-    {file = "jupyter_client-8.6.0.tar.gz", hash = "sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7"},
+    {file = "jupyter_client-8.6.1-py3-none-any.whl", hash = "sha256:3b7bd22f058434e3b9a7ea4b1500ed47de2713872288c0d511d19926f99b459f"},
+    {file = "jupyter_client-8.6.1.tar.gz", hash = "sha256:e842515e2bab8e19186d89fdfea7abd15e39dd581f94e399f00e2af5a1652d3f"},
 ]
 
 [package.dependencies]
@@ -2366,13 +2488,13 @@ test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pyt
 
 [[package]]
 name = "jupyter-core"
-version = "5.7.1"
+version = "5.7.2"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.7.1-py3-none-any.whl", hash = "sha256:c65c82126453a723a2804aa52409930434598fd9d35091d63dfb919d2b765bb7"},
-    {file = "jupyter_core-5.7.1.tar.gz", hash = "sha256:de61a9d7fc71240f688b2fb5ab659fbb56979458dc66a71decd098e03c79e218"},
+    {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
+    {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
 ]
 
 [package.dependencies]
@@ -2382,17 +2504,17 @@ traitlets = ">=5.3"
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
-test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
+test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-events"
-version = "0.9.0"
+version = "0.10.0"
 description = "Jupyter Event System library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_events-0.9.0-py3-none-any.whl", hash = "sha256:d853b3c10273ff9bc8bb8b30076d65e2c9685579db736873de6c2232dde148bf"},
-    {file = "jupyter_events-0.9.0.tar.gz", hash = "sha256:81ad2e4bc710881ec274d31c6c50669d71bbaa5dd9d01e600b56faa85700d399"},
+    {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
+    {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
 ]
 
 [package.dependencies]
@@ -2411,13 +2533,13 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p
 
 [[package]]
 name = "jupyter-lsp"
-version = "2.2.2"
+version = "2.2.4"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter-lsp-2.2.2.tar.gz", hash = "sha256:256d24620542ae4bba04a50fc1f6ffe208093a07d8e697fea0a8d1b8ca1b7e5b"},
-    {file = "jupyter_lsp-2.2.2-py3-none-any.whl", hash = "sha256:3b95229e4168355a8c91928057c1621ac3510ba98b2a925e82ebd77f078b1aa5"},
+    {file = "jupyter-lsp-2.2.4.tar.gz", hash = "sha256:5e50033149344065348e688608f3c6d654ef06d9856b67655bd7b6bac9ee2d59"},
+    {file = "jupyter_lsp-2.2.4-py3-none-any.whl", hash = "sha256:da61cb63a16b6dff5eac55c2699cc36eac975645adee02c41bdfc03bf4802e77"},
 ]
 
 [package.dependencies]
@@ -2425,13 +2547,13 @@ jupyter-server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
-version = "2.12.5"
+version = "2.13.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server-2.12.5-py3-none-any.whl", hash = "sha256:184a0f82809a8522777cfb6b760ab6f4b1bb398664c5860a27cec696cb884923"},
-    {file = "jupyter_server-2.12.5.tar.gz", hash = "sha256:0edb626c94baa22809be1323f9770cf1c00a952b17097592e40d03e6a3951689"},
+    {file = "jupyter_server-2.13.0-py3-none-any.whl", hash = "sha256:77b2b49c3831fbbfbdb5048cef4350d12946191f833a24e5f83e5f8f4803e97b"},
+    {file = "jupyter_server-2.13.0.tar.gz", hash = "sha256:c80bfb049ea20053c3d9641c2add4848b38073bf79f1729cea1faed32fc1c78e"},
 ]
 
 [package.dependencies]
@@ -2457,17 +2579,17 @@ websocket-client = "*"
 
 [package.extras]
 docs = ["ipykernel", "jinja2", "jupyter-client", "jupyter-server", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
-test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.4)", "pytest-timeout", "requests"]
+test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"]
 
 [[package]]
 name = "jupyter-server-terminals"
-version = "0.5.2"
+version = "0.5.3"
 description = "A Jupyter Server Extension Providing Terminals."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server_terminals-0.5.2-py3-none-any.whl", hash = "sha256:1b80c12765da979513c42c90215481bbc39bd8ae7c0350b4f85bc3eb58d0fa80"},
-    {file = "jupyter_server_terminals-0.5.2.tar.gz", hash = "sha256:396b5ccc0881e550bf0ee7012c6ef1b53edbde69e67cab1d56e89711b46052e8"},
+    {file = "jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa"},
+    {file = "jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269"},
 ]
 
 [package.dependencies]
@@ -2480,13 +2602,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.1.0"
+version = "4.1.5"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.1.0-py3-none-any.whl", hash = "sha256:5380e85fb4f11a227ed2db13103e513cfea274d1011f6210e62d611e92e0369d"},
-    {file = "jupyterlab-4.1.0.tar.gz", hash = "sha256:92cdfd86c53e163fb9e91e14497901153536c5a889c9225dade270f6107a077f"},
+    {file = "jupyterlab-4.1.5-py3-none-any.whl", hash = "sha256:3bc843382a25e1ab7bc31d9e39295a9f0463626692b7995597709c0ab236ab2c"},
+    {file = "jupyterlab-4.1.5.tar.gz", hash = "sha256:c9ad75290cb10bfaff3624bf3fbb852319b4cce4c456613f8ebbaa98d03524db"},
 ]
 
 [package.dependencies]
@@ -2505,7 +2627,7 @@ tornado = ">=6.2.0"
 traitlets = "*"
 
 [package.extras]
-dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.1.15)"]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.2.0)"]
 docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"]
 docs-screenshots = ["altair (==5.2.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.1)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post6)", "matplotlib (==3.8.2)", "nbconvert (>=7.0.0)", "pandas (==2.2.0)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
 test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
@@ -2523,13 +2645,13 @@ files = [
 
 [[package]]
 name = "jupyterlab-server"
-version = "2.25.2"
+version = "2.25.4"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab_server-2.25.2-py3-none-any.whl", hash = "sha256:5b1798c9cc6a44f65c757de9f97fc06fc3d42535afbf47d2ace5e964ab447aaf"},
-    {file = "jupyterlab_server-2.25.2.tar.gz", hash = "sha256:bd0ec7a99ebcedc8bcff939ef86e52c378e44c2707e053fcd81d046ce979ee63"},
+    {file = "jupyterlab_server-2.25.4-py3-none-any.whl", hash = "sha256:eb645ecc8f9b24bac5decc7803b6d5363250e16ec5af814e516bc2c54dd88081"},
+    {file = "jupyterlab_server-2.25.4.tar.gz", hash = "sha256:2098198e1e82e0db982440f9b5136175d73bea2cd42a6480aa6fd502cb23c4f9"},
 ]
 
 [package.dependencies]
@@ -2544,7 +2666,7 @@ requests = ">=2.31"
 [package.extras]
 docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
 openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
-test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0,<8)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
 
 [[package]]
 name = "kubernetes"
@@ -2574,13 +2696,13 @@ adal = ["adal (>=1.0.2)"]
 
 [[package]]
 name = "langchain"
-version = "0.1.5"
+version = "0.1.13"
 description = "Building applications with LLMs through composability"
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain-0.1.5-py3-none-any.whl", hash = "sha256:4614118d4a95b2e7ba3611a0b6b21707a259a21652a04fbe3c31205bcf3fcd50"},
-    {file = "langchain-0.1.5.tar.gz", hash = "sha256:69603a5bb21b044ddea69d38131dbbf47475afdf79728644faa67d1ad325d652"},
+    {file = "langchain-0.1.13-py3-none-any.whl", hash = "sha256:c87657021b777d6b07e55be379a28660a1cd148c31593569869dd6b0b4cab945"},
+    {file = "langchain-0.1.13.tar.gz", hash = "sha256:db330aa79c33501cb1ed97ff465f7645813eaa6cfd742c61e19c2d48e4aaba18"},
 ]
 
 [package.dependencies]
@@ -2588,9 +2710,10 @@ aiohttp = ">=3.8.3,<4.0.0"
 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
 dataclasses-json = ">=0.5.7,<0.7"
 jsonpatch = ">=1.33,<2.0"
-langchain-community = ">=0.0.17,<0.1"
-langchain-core = ">=0.1.16,<0.2"
-langsmith = ">=0.0.83,<0.1"
+langchain-community = ">=0.0.29,<0.1"
+langchain-core = ">=0.1.33,<0.2.0"
+langchain-text-splitters = ">=0.0.1,<0.1"
+langsmith = ">=0.1.17,<0.2.0"
 numpy = ">=1,<2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -2599,7 +2722,7 @@ SQLAlchemy = ">=1.4,<3"
 tenacity = ">=8.1.0,<9.0.0"
 
 [package.extras]
-azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b8)", "openai (<2)"]
+azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b8)", "openai (<2)"]
 clarifai = ["clarifai (>=9.1.0)"]
 cli = ["typer (>=0.9.0,<0.10.0)"]
 cohere = ["cohere (>=4,<5)"]
@@ -2614,20 +2737,20 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langchain-community"
-version = "0.0.18"
+version = "0.0.29"
 description = "Community contributed LangChain integrations."
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_community-0.0.18-py3-none-any.whl", hash = "sha256:b87e20c1fa3f37e9608d7ccc08b4d8ed86f875b8c1e735d0464ae986e41c5a71"},
-    {file = "langchain_community-0.0.18.tar.gz", hash = "sha256:f044f331b418f16148b76929f27cc2107fce2d190ea3fae0cdaf155ceda9892f"},
+    {file = "langchain_community-0.0.29-py3-none-any.whl", hash = "sha256:1652dddf257089b7b5066974b636262b4a5b680339f4539be133b14ae351e67d"},
+    {file = "langchain_community-0.0.29.tar.gz", hash = "sha256:d88107fafa9fe2c5733da9630c68d9ee51cd33b1c88a4950e7a2d9a38f7e7aa3"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1.19,<0.2"
-langsmith = ">=0.0.83,<0.1"
+langchain-core = ">=0.1.33,<0.2.0"
+langsmith = ">=0.1.0,<0.2.0"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
 requests = ">=2,<3"
@@ -2636,23 +2759,22 @@ tenacity = ">=8.1.0,<9.0.0"
 
 [package.extras]
 cli = ["typer (>=0.9.0,<0.10.0)"]
-extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)", "zhipuai (>=1.0.7,<2.0.0)"]
+extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)", "zhipuai (>=1.0.7,<2.0.0)"]
 
 [[package]]
 name = "langchain-core"
-version = "0.1.19"
+version = "0.1.35"
 description = "Building applications with LLMs through composability"
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.1.19-py3-none-any.whl", hash = "sha256:46b5fd54181df5aa6d3041d61beb2b91e5437b6742274e7924a97734ed62cf43"},
-    {file = "langchain_core-0.1.19.tar.gz", hash = "sha256:30539190a63dff53e995f10aefb943b4f7e01aba4bf28fd1e13016b040c0e9da"},
+    {file = "langchain_core-0.1.35-py3-none-any.whl", hash = "sha256:9d790446ea211f4cb620886081cc5a5723bc9a2dc90af1f6205aded2ee61bb71"},
+    {file = "langchain_core-0.1.35.tar.gz", hash = "sha256:862b8415d4deaf4e06833ef826bcef3614d75c3e7fd82b09b1349cc223f02e9a"},
 ]
 
 [package.dependencies]
-anyio = ">=3,<5"
 jsonpatch = ">=1.33,<2.0"
-langsmith = ">=0.0.83,<0.1"
+langsmith = ">=0.1.0,<0.2.0"
 packaging = ">=23.2,<24.0"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -2682,20 +2804,36 @@ extended-testing = ["faker (>=19.3.1,<20.0.0)", "jinja2 (>=3,<4)", "presidio-ana
 
 [[package]]
 name = "langchain-openai"
-version = "0.0.5"
+version = "0.0.8"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain_openai-0.0.5-py3-none-any.whl", hash = "sha256:93b37dfac274adad65e46d5d6e71411e00c6984bcc5e10f1d6bb58e7944dc01b"},
-    {file = "langchain_openai-0.0.5.tar.gz", hash = "sha256:f317fee5b652949ad96ad7edf8ef7a044a6a3f0cc71d1e12f9d5261789fd68c4"},
+    {file = "langchain_openai-0.0.8-py3-none-any.whl", hash = "sha256:4862fc72cecbee0240aaa6df0234d5893dd30cd33ca23ac5cfdd86c11d2c44df"},
+    {file = "langchain_openai-0.0.8.tar.gz", hash = "sha256:b7aba7fcc52305e78b08197ebc54fc45cc06dbc40ba5b913bc48a22b30a4f5c9"},
 ]
 
 [package.dependencies]
-langchain-core = ">=0.1.16,<0.2"
-numpy = ">=1,<2"
+langchain-core = ">=0.1.27,<0.2.0"
 openai = ">=1.10.0,<2.0.0"
-tiktoken = ">=0.5.2,<0.6.0"
+tiktoken = ">=0.5.2,<1"
+
+[[package]]
+name = "langchain-text-splitters"
+version = "0.0.1"
+description = "LangChain text splitting utilities"
+optional = false
+python-versions = ">=3.8.1,<4.0"
+files = [
+    {file = "langchain_text_splitters-0.0.1-py3-none-any.whl", hash = "sha256:f5b802f873f5ff6a8b9259ff34d53ed989666ef4e1582e6d1adb3b5520e3839a"},
+    {file = "langchain_text_splitters-0.0.1.tar.gz", hash = "sha256:ac459fa98799f5117ad5425a9330b21961321e30bc19a2a2f9f761ddadd62aa1"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.1.28,<0.2.0"
+
+[package.extras]
+extended-testing = ["lxml (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langdetect"
@@ -2713,28 +2851,29 @@ six = "*"
 
 [[package]]
 name = "langsmith"
-version = "0.0.87"
+version = "0.1.36"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.0.87-py3-none-any.whl", hash = "sha256:8903d3811b9fc89eb18f5961c8e6935fbd2d0f119884fbf30dc70b8f8f4121fc"},
-    {file = "langsmith-0.0.87.tar.gz", hash = "sha256:36c4cc47e5b54be57d038036a30fb19ce6e4c73048cd7a464b8f25b459694d34"},
+    {file = "langsmith-0.1.36-py3-none-any.whl", hash = "sha256:8022a7460fc1369eed40d32f298e90f02887733494ac2e0a29185aaa64c94352"},
+    {file = "langsmith-0.1.36.tar.gz", hash = "sha256:1cf504013933bb3231ac52ac52946414ab768c1b4dced45cf0891e939047a0f7"},
 ]
 
 [package.dependencies]
+orjson = ">=3.9.14,<4.0.0"
 pydantic = ">=1,<3"
 requests = ">=2,<3"
 
 [[package]]
 name = "litellm"
-version = "1.23.0"
+version = "1.34.6"
 description = "Library to easily interface with LLM API providers"
 optional = false
-python-versions = ">=3.8, !=2.7.*, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*, !=3.7.*"
+python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
 files = [
-    {file = "litellm-1.23.0-py3-none-any.whl", hash = "sha256:5d8718c72521c75e6efaf521f5ca0734989d7c9b2ab451d1aa0812fa2e34885e"},
-    {file = "litellm-1.23.0.tar.gz", hash = "sha256:3ca4f88876c0e043879054750779a47128fc5ac1afcdd700be9a6dd934b1dca2"},
+    {file = "litellm-1.34.6-py3-none-any.whl", hash = "sha256:9e86dc8039192939d2c32f38465c87887d79ca67816dc0a3e3292e4a49b2256c"},
+    {file = "litellm-1.34.6.tar.gz", hash = "sha256:289fc9a7756933bed2034c032856628dd6517ae3420a3e120b8255c285880606"},
 ]
 
 [package.dependencies]
@@ -2749,8 +2888,299 @@ tiktoken = ">=0.4.0"
 tokenizers = "*"
 
 [package.extras]
-extra-proxy = ["streamlit (>=1.29.0,<2.0.0)"]
-proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "fastapi (>=0.104.1,<0.105.0)", "fastapi-sso (>=0.10.0,<0.11.0)", "gunicorn (>=21.2.0,<22.0.0)", "orjson (>=3.9.7,<4.0.0)", "python-multipart (>=0.0.6,<0.0.7)", "pyyaml (>=6.0.1,<7.0.0)", "rq", "uvicorn (>=0.22.0,<0.23.0)"]
+extra-proxy = ["azure-identity (>=1.15.0,<2.0.0)", "azure-keyvault-secrets (>=4.8.0,<5.0.0)", "google-cloud-kms (>=2.21.3,<3.0.0)", "prisma (==0.11.0)", "resend (>=0.8.0,<0.9.0)"]
+proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "cryptography (>=42.0.5,<43.0.0)", "fastapi (>=0.109.1,<0.110.0)", "fastapi-sso (>=0.10.0,<0.11.0)", "gunicorn (>=21.2.0,<22.0.0)", "orjson (>=3.9.7,<4.0.0)", "python-multipart (>=0.0.9,<0.0.10)", "pyyaml (>=6.0.1,<7.0.0)", "rq", "uvicorn (>=0.22.0,<0.23.0)"]
+
+[[package]]
+name = "llama-index"
+version = "0.10.25"
+description = "Interface between LLMs and your data"
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
+    {file = "llama_index-0.10.25-py3-none-any.whl", hash = "sha256:a8806e4285321c4b1e0008bb626deaccfe95d7fde421103b9d4a393dd2951ce7"},
+    {file = "llama_index-0.10.25.tar.gz", hash = "sha256:1266dc66b3a9823416f8ad516af21297434fce1344f75de318ddb63aed952abd"},
+]
+
+[package.dependencies]
+llama-index-agent-openai = ">=0.1.4,<0.3.0"
+llama-index-cli = ">=0.1.2,<0.2.0"
+llama-index-core = ">=0.10.25,<0.11.0"
+llama-index-embeddings-openai = ">=0.1.5,<0.2.0"
+llama-index-indices-managed-llama-cloud = ">=0.1.2,<0.2.0"
+llama-index-legacy = ">=0.9.48,<0.10.0"
+llama-index-llms-openai = ">=0.1.13,<0.2.0"
+llama-index-multi-modal-llms-openai = ">=0.1.3,<0.2.0"
+llama-index-program-openai = ">=0.1.3,<0.2.0"
+llama-index-question-gen-openai = ">=0.1.2,<0.2.0"
+llama-index-readers-file = ">=0.1.4,<0.2.0"
+llama-index-readers-llama-parse = ">=0.1.2,<0.2.0"
+
+[[package]]
+name = "llama-index-agent-openai"
+version = "0.2.1"
+description = "llama-index agent openai integration"
+optional = false
+python-versions = "<4.0,>=3.8.1"
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-    {file = "nbclient-0.9.0-py3-none-any.whl", hash = "sha256:a3a1ddfb34d4a9d17fc744d655962714a866639acd30130e9be84191cd97cd15"},
-    {file = "nbclient-0.9.0.tar.gz", hash = "sha256:4b28c207877cf33ef3a9838cdc7a54c5ceff981194a82eac59d558f05487295e"},
+    {file = "nbclient-0.10.0-py3-none-any.whl", hash = "sha256:f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f"},
+    {file = "nbclient-0.10.0.tar.gz", hash = "sha256:4b3f1b7dba531e498449c4db4f53da339c91d449dc11e9af3a43b4eb5c5abb09"},
 ]
 
 [package.dependencies]
@@ -3269,17 +3726,17 @@ traitlets = ">=5.4"
 [package.extras]
 dev = ["pre-commit"]
 docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
-test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
 
 [[package]]
 name = "nbconvert"
-version = "7.15.0"
-description = "Converting Jupyter Notebooks"
+version = "7.16.3"
+description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "nbconvert-7.15.0-py3-none-any.whl", hash = "sha256:0efd3ca74fd1525560e0312cec235e57dfbf3c5c775c7e61e04c532b28f8da6f"},
-    {file = "nbconvert-7.15.0.tar.gz", hash = "sha256:ff3f54a1a5e1e024beb9fde8946d05b6d0bf68cd14b5f2f9dc5b545c8bc71055"},
+    {file = "nbconvert-7.16.3-py3-none-any.whl", hash = "sha256:ddeff14beeeedf3dd0bc506623e41e4507e551736de59df69a91f86700292b3b"},
+    {file = "nbconvert-7.16.3.tar.gz", hash = "sha256:a6733b78ce3d47c3f85e504998495b07e6ea9cf9bf6ec1c98dda63ec6ad19142"},
 ]
 
 [package.dependencies]
@@ -3305,18 +3762,18 @@ docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sp
 qtpdf = ["nbconvert[qtpng]"]
 qtpng = ["pyqtwebengine (>=5.15)"]
 serve = ["tornado (>=6.1)"]
-test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
 webpdf = ["playwright"]
 
 [[package]]
 name = "nbformat"
-version = "5.9.2"
+version = "5.10.3"
 description = "The Jupyter Notebook format"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "nbformat-5.9.2-py3-none-any.whl", hash = "sha256:1c5172d786a41b82bcfd0c23f9e6b6f072e8fb49c39250219e4acfff1efe89e9"},
-    {file = "nbformat-5.9.2.tar.gz", hash = "sha256:5f98b5ba1997dff175e77e0c17d5c10a96eaed2cbd1de3533d1fc35d5e111192"},
+    {file = "nbformat-5.10.3-py3-none-any.whl", hash = "sha256:d9476ca28676799af85385f409b49d95e199951477a159a576ef2a675151e5e8"},
+    {file = "nbformat-5.10.3.tar.gz", hash = "sha256:60ed5e910ef7c6264b87d644f276b1b49e24011930deef54605188ddeb211685"},
 ]
 
 [package.dependencies]
@@ -3340,6 +3797,24 @@ files = [
     {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
 ]
 
+[[package]]
+name = "networkx"
+version = "3.2.1"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "networkx-3.2.1-py3-none-any.whl", hash = "sha256:f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2"},
+    {file = "networkx-3.2.1.tar.gz", hash = "sha256:9f1bb5cf3409bf324e0a722c20bdb4c20ee39bf1c30ce8ae499c8502b0b5e0c6"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.5)", "numpy (>=1.22)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
+developer = ["changelist (==0.4)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
+doc = ["nb2plots (>=0.7)", "nbconvert (<7.9)", "numpydoc (>=1.6)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.14)", "sphinx (>=7)", "sphinx-gallery (>=0.14)", "texext (>=0.6.7)"]
+extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.11)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
 [[package]]
 name = "nltk"
 version = "3.8.1"
@@ -3367,18 +3842,18 @@ twitter = ["twython"]
 
 [[package]]
 name = "notebook"
-version = "7.0.7"
+version = "7.1.2"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "notebook-7.1.2-py3-none-any.whl", hash = "sha256:fc6c24b9aef18d0cd57157c9c47e95833b9b0bdc599652639acf0bdb61dc7d5f"},
+    {file = "notebook-7.1.2.tar.gz", hash = "sha256:efc2c80043909e0faa17fce9e9b37c059c03af0ec99a4d4db84cb21d9d2e936a"},
 ]
 
 [package.dependencies]
 jupyter-server = ">=2.4.0,<3"
-jupyterlab = ">=4.0.2,<5"
+jupyterlab = ">=4.1.1,<4.2"
 jupyterlab-server = ">=2.22.1,<3"
 notebook-shim = ">=0.2,<0.3"
 tornado = ">=6.2.0"
@@ -3390,13 +3865,13 @@ test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4
 
 [[package]]
 name = "notebook-shim"
-version = "0.2.3"
+version = "0.2.4"
 description = "A shim layer for notebook traits and config"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "notebook_shim-0.2.3-py3-none-any.whl", hash = "sha256:a83496a43341c1674b093bfcebf0fe8e74cbe7eda5fd2bbc56f8e39e1486c0c7"},
-    {file = "notebook_shim-0.2.3.tar.gz", hash = "sha256:f69388ac283ae008cd506dda10d0288b09a017d822d5e8c7129a152cbd3ce7e9"},
+    {file = "notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef"},
+    {file = "notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb"},
 ]
 
 [package.dependencies]
@@ -3407,32 +3882,32 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
 
 [[package]]
 name = "numba"
-version = "0.59.0"
+version = "0.59.1"
 description = "compiling Python code using LLVM"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -3502,13 +3977,13 @@ signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
 
 [[package]]
 name = "objgraph"
-version = "3.6.0"
+version = "3.6.1"
 description = "Draws Python object reference graphs with graphviz"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "objgraph-3.6.1.tar.gz", hash = "sha256:fe96c74147bbcaae8665b396e5388bdcc3197deebba4e6381f05202ee5b453a7"},
 ]
 
 [package.extras]
@@ -3516,36 +3991,36 @@ ipython = ["graphviz"]
 
 [[package]]
 name = "onnxruntime"
-version = "1.17.0"
+version = "1.17.1"
 description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -3558,13 +4033,13 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.11.1"
+version = "1.14.3"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
-    {file = "openai-1.11.1-py3-none-any.whl", hash = "sha256:e0f388ce499f53f58079d0c1f571f356f2b168b84d0d24a412506b6abc714980"},
-    {file = "openai-1.11.1.tar.gz", hash = "sha256:f66b8fe431af43e09594147ef3cdcb79758285de72ebafd52be9700a2af41e99"},
+    {file = "openai-1.14.3-py3-none-any.whl", hash = "sha256:7a465994a7ccf677a110c6cc2ef9d86229bad42c060b585b67049aa749f3b774"},
+    {file = "openai-1.14.3.tar.gz", hash = "sha256:37b514e9c0ff45383ec9b242abd0f7859b1080d4b54b61393ed341ecad1b8eb9"},
 ]
 
 [package.dependencies]
@@ -3581,13 +4056,13 @@ datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
 [[package]]
 name = "opentelemetry-api"
-version = "1.22.0"
+version = "1.23.0"
 description = "OpenTelemetry Python API"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3596,52 +4071,50 @@ importlib-metadata = ">=6.0,<7.0"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-common"
-version = "1.22.0"
+version = "1.23.0"
 description = "OpenTelemetry Protobuf encoding"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "opentelemetry_exporter_otlp_proto_common-1.23.0.tar.gz", hash = "sha256:35e4ea909e7a0b24235bd0aaf17fba49676527feb1823b46565ff246d5a1ab18"},
 ]
 
 [package.dependencies]
-backoff = {version = ">=1.10.0,<3.0.0", markers = "python_version >= \"3.7\""}
-opentelemetry-proto = "1.22.0"
+opentelemetry-proto = "1.23.0"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-grpc"
-version = "1.22.0"
+version = "1.23.0"
 description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.23.0-py3-none-any.whl", hash = "sha256:40f9e3e7761eb34f2a1001f4543028783ac26e2db27e420d5374f2cca0182dad"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.23.0.tar.gz", hash = "sha256:aa1a012eea5342bfef51fcf3f7f22601dcb0f0984a07ffe6025b2fbb6d91a2a9"},
 ]
 
 [package.dependencies]
-backoff = {version = ">=1.10.0,<3.0.0", markers = "python_version >= \"3.7\""}
 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 = "1.22.0"
-opentelemetry-proto = "1.22.0"
-opentelemetry-sdk = ">=1.22.0,<1.23.0"
+opentelemetry-exporter-otlp-proto-common = "1.23.0"
+opentelemetry-proto = "1.23.0"
+opentelemetry-sdk = ">=1.23.0,<1.24.0"
 
 [package.extras]
 test = ["pytest-grpc"]
 
 [[package]]
 name = "opentelemetry-instrumentation"
-version = "0.43b0"
+version = "0.44b0"
 description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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-    {file = "opentelemetry_instrumentation-0.43b0.tar.gz", hash = "sha256:c3755da6c4be8033be0216d0501e11f4832690f4e2eca5a3576fbf113498f0f6"},
+    {file = "opentelemetry_instrumentation-0.44b0-py3-none-any.whl", hash = "sha256:79560f386425176bcc60c59190064597096114c4a8e5154f1cb281bb4e47d2fc"},
+    {file = "opentelemetry_instrumentation-0.44b0.tar.gz", hash = "sha256:8213d02d8c0987b9b26386ae3e091e0477d6331673123df736479322e1a50b48"},
 ]
 
 [package.dependencies]
@@ -3651,57 +4124,57 @@ wrapt = ">=1.0.0,<2.0.0"
 
 [[package]]
 name = "opentelemetry-instrumentation-asgi"
-version = "0.43b0"
+version = "0.44b0"
 description = "ASGI instrumentation for OpenTelemetry"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation_asgi-0.43b0-py3-none-any.whl", hash = "sha256:1f593829fa039e9367820736fb063e92acd15c25b53d7bcb5d319971b8e93fd7"},
-    {file = "opentelemetry_instrumentation_asgi-0.43b0.tar.gz", hash = "sha256:3f6f19333dca31ef696672e4e36cb1c2613c71dc7e847c11ff36a37e1130dadc"},
+    {file = "opentelemetry_instrumentation_asgi-0.44b0-py3-none-any.whl", hash = "sha256:0d95c84a8991008c8a8ac35e15d43cc7768a5bb46f95f129e802ad2990d7c366"},
+    {file = "opentelemetry_instrumentation_asgi-0.44b0.tar.gz", hash = "sha256:72d4d28ec7ccd551eac11edc5ae8cac3586c0a228467d6a95fad7b6d4edd597a"},
 ]
 
 [package.dependencies]
 asgiref = ">=3.0,<4.0"
 opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.43b0"
-opentelemetry-semantic-conventions = "0.43b0"
-opentelemetry-util-http = "0.43b0"
+opentelemetry-instrumentation = "0.44b0"
+opentelemetry-semantic-conventions = "0.44b0"
+opentelemetry-util-http = "0.44b0"
 
 [package.extras]
 instruments = ["asgiref (>=3.0,<4.0)"]
-test = ["opentelemetry-instrumentation-asgi[instruments]", "opentelemetry-test-utils (==0.43b0)"]
+test = ["opentelemetry-instrumentation-asgi[instruments]", "opentelemetry-test-utils (==0.44b0)"]
 
 [[package]]
 name = "opentelemetry-instrumentation-fastapi"
-version = "0.43b0"
+version = "0.44b0"
 description = "OpenTelemetry FastAPI Instrumentation"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation_fastapi-0.43b0-py3-none-any.whl", hash = "sha256:b79c044df68a52e07b35fa12a424e7cc0dd27ff0a171c5fdcc41dea9de8fc938"},
-    {file = "opentelemetry_instrumentation_fastapi-0.43b0.tar.gz", hash = "sha256:2afaaf470622e1a2732182c68f6d2431ffe5e026a7edacd0f83605632b66347f"},
+    {file = "opentelemetry_instrumentation_fastapi-0.44b0-py3-none-any.whl", hash = "sha256:4441482944bea6676816668d56deb94af990e8c6e9582c581047e5d84c91d3c9"},
+    {file = "opentelemetry_instrumentation_fastapi-0.44b0.tar.gz", hash = "sha256:67ed10b93ad9d35238ae0be73cf8acbbb65a4a61fb7444d0aee5b0c492e294db"},
 ]
 
 [package.dependencies]
 opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.43b0"
-opentelemetry-instrumentation-asgi = "0.43b0"
-opentelemetry-semantic-conventions = "0.43b0"
-opentelemetry-util-http = "0.43b0"
+opentelemetry-instrumentation = "0.44b0"
+opentelemetry-instrumentation-asgi = "0.44b0"
+opentelemetry-semantic-conventions = "0.44b0"
+opentelemetry-util-http = "0.44b0"
 
 [package.extras]
 instruments = ["fastapi (>=0.58,<1.0)"]
-test = ["httpx (>=0.22,<1.0)", "opentelemetry-instrumentation-fastapi[instruments]", "opentelemetry-test-utils (==0.43b0)", "requests (>=2.23,<3.0)"]
+test = ["httpx (>=0.22,<1.0)", "opentelemetry-instrumentation-fastapi[instruments]", "opentelemetry-test-utils (==0.44b0)", "requests (>=2.23,<3.0)"]
 
 [[package]]
 name = "opentelemetry-proto"
-version = "1.22.0"
+version = "1.23.0"
 description = "OpenTelemetry Python Proto"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_proto-1.22.0-py3-none-any.whl", hash = "sha256:ce7188d22c75b6d0fe53e7fb58501613d0feade5139538e79dedd9420610fa0c"},
-    {file = "opentelemetry_proto-1.22.0.tar.gz", hash = "sha256:9ec29169286029f17ca34ec1f3455802ffb90131642d2f545ece9a63e8f69003"},
+    {file = "opentelemetry_proto-1.23.0-py3-none-any.whl", hash = "sha256:4c017deca052cb287a6003b7c989ed8b47af65baeb5d57ebf93dde0793f78509"},
+    {file = "opentelemetry_proto-1.23.0.tar.gz", hash = "sha256:e6aaf8b7ace8d021942d546161401b83eed90f9f2cc6f13275008cea730e4651"},
 ]
 
 [package.dependencies]
@@ -3709,40 +4182,109 @@ protobuf = ">=3.19,<5.0"
 
 [[package]]
 name = "opentelemetry-sdk"
-version = "1.22.0"
+version = "1.23.0"
 description = "OpenTelemetry Python SDK"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 pytz = ">=2020.1"
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 postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"]
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 [[package]]
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 description = "Integrate PostHog into any python application."
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 [[package]]
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 [package.dependencies]
 wcwidth = "*"
 
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 [[package]]
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 name = "pycparser"
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 [[package]]
 name = "pydantic-settings"
-version = "2.1.0"
+version = "2.2.1"
 description = "Settings management using Pydantic"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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 python-dotenv = ">=0.21.0"
 
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+yaml = ["pyyaml (>=6.0.1)"]
+
 [[package]]
 name = "pygments"
 version = "2.17.2"
@@ -4401,13 +4965,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pymdown-extensions"
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 description = "Extension pack for Python Markdown."
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -4417,6 +4981,64 @@ pyyaml = "*"
 [package.extras]
 extra = ["pygments (>=2.12)"]
 
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+[package.dependencies]
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+
+[[package]]
+name = "pymupdfb"
+version = "1.24.0"
+description = "MuPDF shared libraries for PyMuPDF."
+optional = false
+python-versions = ">=3.8"
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+]
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 [[package]]
 name = "pynndescent"
 version = "0.5.11"
@@ -4435,6 +5057,24 @@ numba = ">=0.51.2"
 scikit-learn = ">=0.18"
 scipy = ">=1.0"
 
+[[package]]
+name = "pypdf"
+version = "4.1.0"
+description = "A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files"
+optional = false
+python-versions = ">=3.6"
+files = [
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+[package.extras]
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+dev = ["black", "flit", "pip-tools", "pre-commit (<2.18.0)", "pytest-cov", "pytest-socket", "pytest-timeout", "pytest-xdist", "wheel"]
+docs = ["myst_parser", "sphinx", "sphinx_rtd_theme"]
+full = ["Pillow (>=8.0.0)", "PyCryptodome", "cryptography"]
+image = ["Pillow (>=8.0.0)"]
+
 [[package]]
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 version = "0.48.9"
@@ -4470,15 +5110,25 @@ files = [
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+[[package]]
+name = "pysbd"
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+description = "pysbd (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box across many languages."
+optional = false
+python-versions = ">=3"
+files = [
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+]
+
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-version = "8.0.0"
+version = "8.1.1"
 description = "pytest: simple powerful testing with Python"
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 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -4486,11 +5136,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
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 iniconfig = "*"
 packaging = "*"
-pluggy = ">=1.3.0,<2.0"
-tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+pluggy = ">=1.4,<2.0"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+testing = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-asyncio"
@@ -4512,13 +5162,13 @@ testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy
 
 [[package]]
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-version = "2.8.2"
+version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 files = [
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 [package.dependencies]
@@ -4558,17 +5208,17 @@ test = ["mypy", "pyaml", "pytest", "toml", "types-PyYAML", "types-toml"]
 
 [[package]]
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-version = "2024.1.2"
+version = "2024.2.7"
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+dev = ["black (==24.1.1)", "build (==1.0.3)", "flake8 (==7.0.0)", "pytest (==8.0.0)", "twine (==4.0.2)"]
 
 [[package]]
 name = "python-json-logger"
@@ -4628,17 +5278,17 @@ files = [
 
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+version = "2.0.13"
 description = "Pseudo terminal support for Windows from Python."
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 [[package]]
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 [package.dependencies]
 cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
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+langchain = "*"
+langchain-community = "*"
+langchain-core = "*"
+langchain-openai = "*"
+nest-asyncio = "*"
+numpy = "*"
+openai = ">1"
+pysbd = ">=0.3.4"
+tiktoken = "*"
+
+[package.extras]
+all = ["sentence-transformers"]
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 scipy = ">=1.6.0"
 threadpoolctl = ">=2.0.0"
 
@@ -5488,54 +6145,54 @@ test = ["pytest"]
 
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+
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 typing-extensions = ">=4.6.0"
 
 [package.extras]
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 [package.extras]
 full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
 
+[[package]]
+name = "striprtf"
+version = "0.0.26"
+description = "A simple library to convert rtf to text"
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "sympy"
 version = "1.12"
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 [[package]]
 name = "terminado"
-version = "0.18.0"
+version = "0.18.1"
 description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
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 [[package]]
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-version = "3.2.0"
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 python-versions = ">=3.8"
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 ]
 
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 [package.dependencies]
@@ -5883,121 +6554,121 @@ test = ["flake8", "isort", "pytest"]
 
 [[package]]
 name = "tokenizers"
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 description = ""
 optional = false
 python-versions = ">=3.7"
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 ]
 
 [[package]]
@@ -6232,17 +6863,17 @@ tree-sitter = "*"
 
 [[package]]
 name = "typeguard"
-version = "4.1.5"
+version = "4.2.1"
 description = "Run-time type checker for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typeguard-4.1.5-py3-none-any.whl", hash = "sha256:8923e55f8873caec136c892c3bed1f676eae7be57cdb94819281b3d3bc9c0953"},
-    {file = "typeguard-4.1.5.tar.gz", hash = "sha256:ea0a113bbc111bcffc90789ebb215625c963411f7096a7e9062d4e4630c155fd"},
+    {file = "typeguard-4.2.1-py3-none-any.whl", hash = "sha256:7da3bd46e61f03e0852f8d251dcbdc2a336aa495d7daff01e092b55327796eb8"},
+    {file = "typeguard-4.2.1.tar.gz", hash = "sha256:c556a1b95948230510070ca53fa0341fb0964611bd05d598d87fb52115d65fee"},
 ]
 
 [package.dependencies]
-typing-extensions = {version = ">=4.7.0", markers = "python_version < \"3.12\""}
+typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""}
 
 [package.extras]
 doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"]
@@ -6250,45 +6881,42 @@ test = ["coverage[toml] (>=7)", "mypy (>=1.2.0)", "pytest (>=7)"]
 
 [[package]]
 name = "typer"
-version = "0.9.0"
+version = "0.11.0"
 description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "typer-0.9.0-py3-none-any.whl", hash = "sha256:5d96d986a21493606a358cae4461bd8cdf83cbf33a5aa950ae629ca3b51467ee"},
-    {file = "typer-0.9.0.tar.gz", hash = "sha256:50922fd79aea2f4751a8e0408ff10d2662bd0c8bbfa84755a699f3bada2978b2"},
+    {file = "typer-0.11.0-py3-none-any.whl", hash = "sha256:049cc47bef39f46b043eddd9165492209fdd9bc7d79afa7ba9cc5cd017caa817"},
+    {file = "typer-0.11.0.tar.gz", hash = "sha256:a6ce173c0f03d3a41b49c0a945874cc489e91f88faabf76517b2b91c670fcde7"},
 ]
 
 [package.dependencies]
-click = ">=7.1.1,<9.0.0"
+click = ">=8.0.0"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
 all = ["colorama (>=0.4.3,<0.5.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
-dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
-doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"]
-test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.8.19.20240106"
+version = "2.9.0.20240316"
 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-python-dateutil-2.8.19.20240106.tar.gz", hash = "sha256:1f8db221c3b98e6ca02ea83a58371b22c374f42ae5bbdf186db9c9a76581459f"},
-    {file = "types_python_dateutil-2.8.19.20240106-py3-none-any.whl", hash = "sha256:efbbdc54590d0f16152fa103c9879c7d4a00e82078f6e2cf01769042165acaa2"},
+    {file = "types-python-dateutil-2.9.0.20240316.tar.gz", hash = "sha256:5d2f2e240b86905e40944dd787db6da9263f0deabef1076ddaed797351ec0202"},
+    {file = "types_python_dateutil-2.9.0.20240316-py3-none-any.whl", hash = "sha256:6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b"},
 ]
 
 [[package]]
 name = "typing-extensions"
-version = "4.9.0"
+version = "4.10.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
-    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
+    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
+    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
 ]
 
 [[package]]
@@ -6308,13 +6936,13 @@ typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "tzdata"
-version = "2023.4"
+version = "2024.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
-    {file = "tzdata-2023.4-py2.py3-none-any.whl", hash = "sha256:aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3"},
-    {file = "tzdata-2023.4.tar.gz", hash = "sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9"},
+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
 [[package]]
@@ -6342,13 +6970,13 @@ tbb = ["tbb (>=2019.0)"]
 
 [[package]]
 name = "unstructured"
-version = "0.12.3"
+version = "0.12.5"
 description = "A library that prepares raw documents for downstream ML tasks."
 optional = false
 python-versions = ">=3.9.0,<3.12"
 files = [
-    {file = "unstructured-0.12.3-py3-none-any.whl", hash = "sha256:14d606b84eaea2283a755d559ec507a7abbe10cf5c26a18d78bccac17a816a94"},
-    {file = "unstructured-0.12.3.tar.gz", hash = "sha256:2b6c4e38f147170211df59d3d39585e834109832b9d8cd7a5e8db94b221a5a0b"},
+    {file = "unstructured-0.12.5-py3-none-any.whl", hash = "sha256:cce7de36964f556810adb8728d0639d8e9b3ef4567443877609f3c66a54e24d1"},
+    {file = "unstructured-0.12.5.tar.gz", hash = "sha256:5ea6c881049e7d98a88c07192bcb6ab750de41b4e3b594972ed1034bda99dbae"},
 ]
 
 [package.dependencies]
@@ -6373,10 +7001,11 @@ wrapt = "*"
 
 [package.extras]
 airtable = ["pyairtable"]
-all-docs = ["markdown", "msg-parser", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pikepdf", "pypandoc", "pypdf", "python-docx", "python-pptx (<=0.6.23)", "unstructured-inference (==0.7.23)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
+all-docs = ["markdown", "msg-parser", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pikepdf", "pillow-heif", "pypandoc", "pypdf", "python-docx", "python-pptx (<=0.6.23)", "unstructured-inference (==0.7.23)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
+astra = ["astrapy"]
 azure = ["adlfs", "fsspec"]
 azure-cognitive-search = ["azure-search-documents"]
-bedrock = ["boto3", "langchain"]
+bedrock = ["boto3", "langchain-community"]
 biomed = ["bs4"]
 box = ["boxfs", "fsspec"]
 chroma = ["chromadb"]
@@ -6389,7 +7018,7 @@ doc = ["python-docx"]
 docx = ["python-docx"]
 dropbox = ["dropboxdrivefs", "fsspec"]
 elasticsearch = ["elasticsearch"]
-embed-huggingface = ["huggingface", "langchain", "sentence-transformers"]
+embed-huggingface = ["huggingface", "langchain-community", "sentence-transformers"]
 epub = ["pypandoc"]
 gcs = ["bs4", "fsspec", "gcsfs"]
 github = ["pygithub (>1.58.0)"]
@@ -6397,21 +7026,21 @@ gitlab = ["python-gitlab"]
 google-drive = ["google-api-python-client"]
 hubspot = ["hubspot-api-client", "urllib3"]
 huggingface = ["langdetect", "sacremoses", "sentencepiece", "torch", "transformers"]
-image = ["onnx", "pdf2image", "pdfminer.six", "pikepdf", "pypdf", "unstructured-inference (==0.7.23)", "unstructured.pytesseract (>=0.3.12)"]
+image = ["onnx", "pdf2image", "pdfminer.six", "pikepdf", "pillow-heif", "pypdf", "unstructured-inference (==0.7.23)", "unstructured.pytesseract (>=0.3.12)"]
 jira = ["atlassian-python-api"]
-local-inference = ["markdown", "msg-parser", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pikepdf", "pypandoc", "pypdf", "python-docx", "python-pptx (<=0.6.23)", "unstructured-inference (==0.7.23)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
+local-inference = ["markdown", "msg-parser", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pikepdf", "pillow-heif", "pypandoc", "pypdf", "python-docx", "python-pptx (<=0.6.23)", "unstructured-inference (==0.7.23)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
 md = ["markdown"]
 mongodb = ["pymongo"]
 msg = ["msg-parser"]
 notion = ["htmlBuilder", "notion-client"]
 odt = ["pypandoc", "python-docx"]
 onedrive = ["Office365-REST-Python-Client", "bs4", "msal"]
-openai = ["langchain", "openai", "tiktoken"]
+openai = ["langchain-community", "openai", "tiktoken"]
 opensearch = ["opensearch-py"]
 org = ["pypandoc"]
 outlook = ["Office365-REST-Python-Client", "msal"]
 paddleocr = ["unstructured.paddleocr (==2.6.1.3)"]
-pdf = ["onnx", "pdf2image", "pdfminer.six", "pikepdf", "pypdf", "unstructured-inference (==0.7.23)", "unstructured.pytesseract (>=0.3.12)"]
+pdf = ["onnx", "pdf2image", "pdfminer.six", "pikepdf", "pillow-heif", "pypdf", "unstructured-inference (==0.7.23)", "unstructured.pytesseract (>=0.3.12)"]
 pinecone = ["pinecone-client (==2.2.4)"]
 postgres = ["psycopg2-binary"]
 ppt = ["python-pptx (<=0.6.23)"]
@@ -6432,24 +7061,26 @@ xlsx = ["networkx", "openpyxl", "pandas", "xlrd"]
 
 [[package]]
 name = "unstructured-client"
-version = "0.17.0"
+version = "0.22.0"
 description = "Python Client SDK for Unstructured API"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "unstructured-client-0.17.0.tar.gz", hash = "sha256:3b98affa4a47e370b7b0a1b8f0d889f20aa6d8925b2b108b60aa07cf54a04fb5"},
-    {file = "unstructured_client-0.17.0-py3-none-any.whl", hash = "sha256:2b311f0f7304ac64986b0312431fe99945f95a6fa0c88ab91094570c932a9e19"},
+    {file = "unstructured-client-0.22.0.tar.gz", hash = "sha256:d76351c456fcf4067f59135c09a35008a120103621a1ac94cf9448db28ef2e1e"},
+    {file = "unstructured_client-0.22.0-py3-none-any.whl", hash = "sha256:140f7dd8515cc9be3276f66d6fb08d235ea35a50d4eb38a6ef82b60fc3d2fea0"},
 ]
 
 [package.dependencies]
 certifi = ">=2023.7.22"
 charset-normalizer = ">=3.2.0"
-dataclasses-json-speakeasy = ">=0.5.11"
+dataclasses-json = ">=0.6.4"
+deepdiff = ">=6.0"
 idna = ">=3.4"
 jsonpath-python = ">=1.0.6"
 marshmallow = ">=3.19.0"
 mypy-extensions = ">=1.0.0"
 packaging = ">=23.1"
+pypdf = ">=4.0"
 python-dateutil = ">=2.8.2"
 requests = ">=2.31.0"
 six = ">=1.16.0"
@@ -6476,13 +7107,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
 name = "urllib3"
-version = "2.2.0"
+version = "2.2.1"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
-    {file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
+    {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
+    {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
 ]
 
 [package.extras]
@@ -6563,13 +7194,13 @@ test = ["Cython (>=0.29.36,<0.30.0)", "aiohttp (==3.9.0b0)", "aiohttp (>=3.8.1)"
 
 [[package]]
 name = "wandb"
-version = "0.16.1"
+version = "0.16.3"
 description = "A CLI and library for interacting with the Weights & Biases API."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "wandb-0.16.1-py3-none-any.whl", hash = "sha256:1d7423f92520984585bae9693bb637ae08d3e0c1d75ad4b34215bc44431f114c"},
-    {file = "wandb-0.16.1.tar.gz", hash = "sha256:ffe6e8dd8cc8fcd72010c1246fb3d6d226b37c4f111f3f94308a1c0ae28a2fec"},
+    {file = "wandb-0.16.3-py3-none-any.whl", hash = "sha256:b8907ddd775c27dc6c12687386a86b5d6acf291060f9ae680bbc61cc8fc03237"},
+    {file = "wandb-0.16.3.tar.gz", hash = "sha256:d789acda32053b18b7a160d0595837e45a3c8a79d25e1fe1f051875303f480ec"},
 ]
 
 [package.dependencies]
@@ -6589,13 +7220,13 @@ setuptools = "*"
 async = ["httpx (>=0.23.0)"]
 aws = ["boto3"]
 azure = ["azure-identity", "azure-storage-blob"]
-core = ["wandb-core (>=0.17.0b2)"]
 gcp = ["google-cloud-storage"]
 kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
-launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "typing-extensions"]
-media = ["bokeh", "moviepy", "numpy", "pillow", "plotly", "rdkit-pypi", "soundfile"]
+launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "typing-extensions"]
+media = ["bokeh", "moviepy", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit-pypi", "soundfile"]
 models = ["cloudpickle"]
 perf = ["orjson"]
+reports = ["pydantic (>=2.0.0)"]
 sweeps = ["sweeps (>=0.2.0)"]
 
 [[package]]
@@ -6964,6 +7595,123 @@ files = [
     {file = "wrapt-1.16.0.tar.gz", hash = "sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d"},
 ]
 
+[[package]]
+name = "xxhash"
+version = "3.4.1"
+description = "Python binding for xxHash"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "xxhash-3.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:91dbfa55346ad3e18e738742236554531a621042e419b70ad8f3c1d9c7a16e7f"},
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+    {file = "xxhash-3.4.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d0ae4c2e7698adef58710d6e7a32ff518b66b98854b1c68e70eee504ad061d8"},
+    {file = "xxhash-3.4.1-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d6322c4291c3ff174dcd104fae41500e75dad12be6f3085d119c2c8a80956c51"},
+    {file = "xxhash-3.4.1-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:dd59ed668801c3fae282f8f4edadf6dc7784db6d18139b584b6d9677ddde1b6b"},
+    {file = "xxhash-3.4.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:92693c487e39523a80474b0394645b393f0ae781d8db3474ccdcead0559ccf45"},
+    {file = "xxhash-3.4.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4603a0f642a1e8d7f3ba5c4c25509aca6a9c1cc16f85091004a7028607ead663"},
+    {file = "xxhash-3.4.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6fa45e8cbfbadb40a920fe9ca40c34b393e0b067082d94006f7f64e70c7490a6"},
+    {file = "xxhash-3.4.1-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:595b252943b3552de491ff51e5bb79660f84f033977f88f6ca1605846637b7c6"},
+    {file = "xxhash-3.4.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:562d8b8f783c6af969806aaacf95b6c7b776929ae26c0cd941d54644ea7ef51e"},
+    {file = "xxhash-3.4.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:41ddeae47cf2828335d8d991f2d2b03b0bdc89289dc64349d712ff8ce59d0647"},
+    {file = "xxhash-3.4.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c44d584afdf3c4dbb3277e32321d1a7b01d6071c1992524b6543025fb8f4206f"},
+    {file = "xxhash-3.4.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd7bddb3a5b86213cc3f2c61500c16945a1b80ecd572f3078ddbbe68f9dabdfb"},
+    {file = "xxhash-3.4.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9ecb6c987b62437c2f99c01e97caf8d25660bf541fe79a481d05732e5236719c"},
+    {file = "xxhash-3.4.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:696b4e18b7023527d5c50ed0626ac0520edac45a50ec7cf3fc265cd08b1f4c03"},
+    {file = "xxhash-3.4.1.tar.gz", hash = "sha256:0379d6cf1ff987cd421609a264ce025e74f346e3e145dd106c0cc2e3ec3f99a9"},
+]
+
 [[package]]
 name = "yarl"
 version = "1.9.4"
@@ -7069,12 +7817,12 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zenpy"
-version = "2.0.46"
+version = "2.0.47"
 description = "Python wrapper for the Zendesk API"
 optional = false
 python-versions = "*"
 files = [
-    {file = "zenpy-2.0.46.tar.gz", hash = "sha256:8a4ebd34f5fb288e35dd735c66c863c64434d9aebcb5dd89581808d2bc14fa58"},
+    {file = "zenpy-2.0.47.tar.gz", hash = "sha256:1aeb66690ce8fa1af1fde4d9b496ff226cd1371086631304a6a3f6a10273ff4b"},
 ]
 
 [package.dependencies]
@@ -7086,20 +7834,20 @@ six = ">=1.14.0"
 
 [[package]]
 name = "zipp"
-version = "3.17.0"
+version = "3.18.1"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
-    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
+    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
+    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
 ]
 
 [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"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+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", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10.0,<3.12"
-content-hash = "67c6d32b2dbeffc9eb40fbb40f1ffe5d5c472fbc7006b86a72c30fbe7037eaf9"
+content-hash = "4bc3a8b10c7b22196023162fee78a76996a0b38910b4a10a7232a23f4115ac19"
diff --git a/pyproject.toml b/pyproject.toml
index 32ade95..e48ec31 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,8 +12,8 @@ include = ["src/**/*", "LICENSE", "README.md"]
 [tool.poetry.dependencies]
 python = ">=3.10.0,<3.12"
 numpy = "^1.26.1"
-wandb = "<=0.16.1"
-tiktoken = "^0.5.1"
+wandb = "<=0.16.3"
+tiktoken = "^0.6.0"
 pandas = "^2.1.2"
 unstructured = "^0.12.3"
 pydantic-settings = "^2.0.3"
@@ -34,19 +34,21 @@ zenpy = "^2.0.46"
 openai = "^1.3.2"
 weave = "^0.31.0"
 colorlog = "^6.8.0"
-litellm = "^1.15.1"
+litellm = "^1.31.6"
 google-cloud-bigquery = "^3.14.1"
 db-dtypes = "^1.2.0"
 python-frontmatter = "^1.1.0"
 pymdown-extensions = "^10.5"
 langchain = "^0.1.5"
-langchain-openai = "^0.0.5"
+langchain-openai = "^0.0.8"
 chromadb = "^0.4.22"
 langchain-experimental = "^0.0.50"
-simsimd = "^3.7.4"
+simsimd = "3.7.7"
 
 [tool.poetry.dev-dependencies]
 #fasttext = {git = "https://github.com/cfculhane/fastText"} # FastText doesn't come with pybind11 and we need to use this workaround.
+llama_index = "^0.10.20"
+ragas = "^0.1.4"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
diff --git a/src/wandbot/api/app.py b/src/wandbot/api/app.py
index a8b0ffd..8c42738 100644
--- a/src/wandbot/api/app.py
+++ b/src/wandbot/api/app.py
@@ -28,6 +28,9 @@
 It uses logger from the utils module for logging purposes.
 """
 
+import os
+os.environ["CONFIG_PATH"] = "config.yaml"
+
 import asyncio
 from contextlib import asynccontextmanager
 from datetime import datetime, timezone
@@ -38,13 +41,21 @@
 from wandbot.api.routers import chat as chat_router
 from wandbot.api.routers import database as database_router
 from wandbot.api.routers import retrieve as retrieve_router
-from wandbot.ingestion.config import VectorStoreConfig
+from wandbot.ingestion.utils import get_embedding_config
 from wandbot.retriever import VectorStore
-from wandbot.utils import get_logger
+from wandbot.utils import get_logger, load_config
 
 logger = get_logger(__name__)
 last_backup = datetime.now().astimezone(timezone.utc)
 
+config = load_config(os.environ["CONFIG_PATH"])
+logger.info(
+    f"Loaded configuration from {os.environ['CONFIG_PATH']}: {config}"
+)
+vectorstore_config = get_embedding_config(
+    config.embeddings.type, config.embeddings.config
+)
+
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
@@ -56,12 +67,18 @@ async def lifespan(app: FastAPI):
     Returns:
         None
     """
-    vector_store = VectorStore.from_config(VectorStoreConfig())
-    chat_router.chat = chat_router.Chat(vector_store=vector_store)
+    vector_store = VectorStore.from_config(vectorstore_config)
+    chat_router.chat = chat_router.Chat(vector_store=vector_store, config=config)
     database_router.db_client = database_router.DatabaseClient()
-    retrieve_router.retriever = retrieve_router.SimpleRetrievalEngine(
-        vector_store=vector_store
-    )
+    if not config.retrieval_re_ranker.enabled:
+        retrieve_router.retriever = retrieve_router.SimpleRetrievalEngine(
+            vector_store=vector_store
+        )
+    else:
+        retrieve_router.retriever = retrieve_router.SimpleRetrievalEngineWithRerank(
+            vector_store=vector_store
+        )
+
 
     async def backup_db():
         """Periodically backs up the database to a table.
diff --git a/src/wandbot/api/routers/retrieve.py b/src/wandbot/api/routers/retrieve.py
index fe264ec..3c27d65 100644
--- a/src/wandbot/api/routers/retrieve.py
+++ b/src/wandbot/api/routers/retrieve.py
@@ -4,14 +4,17 @@
 from pydantic import BaseModel
 from starlette import status
 
-from wandbot.retriever.base import SimpleRetrievalEngine
+from wandbot.retriever.base import (
+    SimpleRetrievalEngine,
+    SimpleRetrievalEngineWithRerank,
+)
 
 router = APIRouter(
     prefix="/retrieve",
     tags=["retrievers"],
 )
 
-retriever: SimpleRetrievalEngine | None = None
+retriever: SimpleRetrievalEngineWithRerank | None = None
 
 
 class APIRetrievalResult(BaseModel):
@@ -53,6 +56,7 @@ async def retrieve(request: APIRetrievalRequest) -> APIRetrievalResponse:
         sources=request.sources,
     )
 
+    # TODO: Fix this
     return APIRetrievalResponse(
         query=request.query,
         top_k=[
diff --git a/src/wandbot/chat/chat.py b/src/wandbot/chat/chat.py
index ff0f0d7..ccd9b75 100644
--- a/src/wandbot/chat/chat.py
+++ b/src/wandbot/chat/chat.py
@@ -32,8 +32,7 @@
 from wandbot.chat.schemas import ChatRequest, ChatResponse
 from wandbot.database.schemas import QuestionAnswer
 from wandbot.retriever import VectorStore
-from wandbot.utils import Timer, get_logger
-from weave.monitoring import StreamTable
+from wandbot.utils import Timer, get_logger, RAGPipelineConfig
 
 logger = get_logger(__name__)
 
@@ -49,7 +48,10 @@ class Chat:
     config: ChatConfig = ChatConfig()
 
     def __init__(
-        self, vector_store: VectorStore, config: ChatConfig | None = None
+        self, 
+        vector_store: VectorStore,
+        config: RAGPipelineConfig,
+        chat_config: ChatConfig | None = None
     ):
         """Initializes the Chat instance.
 
@@ -57,21 +59,17 @@ def __init__(
             config: An instance of ChatConfig containing configuration settings.
         """
         self.vector_store = vector_store
-        if config is not None:
-            self.config = config
+        self.config = config
+        if chat_config is not None:
+            self.chat_config = chat_config
         self.run = wandb.init(
-            project=self.config.wandb_project,
-            entity=self.config.wandb_entity,
+            project=self.config.project,
+            entity=self.config.entity,
             job_type="chat",
         )
         self.run._label(repo="wandbot")
-        self.chat_table = StreamTable(
-            table_name="chat_logs",
-            project_name=self.config.wandb_project,
-            entity_name=self.config.wandb_entity,
-        )
 
-        self.rag_pipeline = RAGPipeline(vector_store=vector_store)
+        self.rag_pipeline = RAGPipeline(vector_store=vector_store, config=config)
 
     def _get_answer(
         self, question: str, chat_history: List[QuestionAnswer]
@@ -105,10 +103,10 @@ def __call__(self, chat_request: ChatRequest) -> ChatResponse:
                 "total_tokens": result.total_tokens,
                 "prompt_tokens": result.prompt_tokens,
                 "completion_tokens": result.completion_tokens,
+                "completion_cost": result.completion_cost,
             }
             result_dict.update({"application": chat_request.application})
             self.run.log(usage_stats)
-            self.chat_table.log(result_dict)
             return ChatResponse(**result_dict)
         except Exception as e:
             with Timer() as timer:
diff --git a/src/wandbot/chat/config.py b/src/wandbot/chat/config.py
index 7bb851f..7616900 100644
--- a/src/wandbot/chat/config.py
+++ b/src/wandbot/chat/config.py
@@ -27,5 +27,3 @@ class ChatConfig(BaseSettings):
         env="WANDB_INDEX_ARTIFACT",
         validation_alias="wandb_index_artifact",
     )
-    wandb_project: str | None = Field("wandbot_public", env="WANDB_PROJECT")
-    wandb_entity: str | None = Field("wandbot", env="WANDB_ENTITY")
diff --git a/src/wandbot/chat/rag.py b/src/wandbot/chat/rag.py
index 10590ba..4217d01 100644
--- a/src/wandbot/chat/rag.py
+++ b/src/wandbot/chat/rag.py
@@ -3,30 +3,22 @@
 
 from langchain_community.callbacks import get_openai_callback
 from pydantic import BaseModel
-from wandbot.rag.query_handler import QueryEnhancer
-from wandbot.rag.response_synthesis import ResponseSynthesizer
-from wandbot.rag.retrieval import FusionRetrieval
-from wandbot.retriever import VectorStore
-from wandbot.utils import Timer, get_logger
 
-logger = get_logger(__name__)
+import litellm
 
+from wandbot.retriever import VectorStore
+from wandbot.utils import Timer, get_logger, RAGPipelineConfig
+from wandbot.rag.query_handler import QueryEnhancer
+from wandbot.rag.retrieval import FusionRetrieval, SimpleRetrievalWithoutFusion
+from wandbot.rag.response_synthesis import ResponseSynthesizer, SimpleResponseSynthesizer
+from wandbot.retriever.base import SimpleRetrievalEngine, SimpleRetrievalEngineWithRerank
+from wandbot.rag.utils import LiteLLMTokenCostLogger
 
-def get_stats_dict_from_token_callback(token_callback):
-    return {
-        "total_tokens": token_callback.total_tokens,
-        "prompt_tokens": token_callback.prompt_tokens,
-        "completion_tokens": token_callback.completion_tokens,
-        "successful_requests": token_callback.successful_requests,
-    }
 
+logger = get_logger(__name__)
 
-def get_stats_dict_from_timer(timer):
-    return {
-        "start_time": timer.start,
-        "end_time": timer.stop,
-        "time_taken": timer.elapsed,
-    }
+litellm_token_and_cost_counter = LiteLLMTokenCostLogger()
+litellm.callbacks = [litellm_token_and_cost_counter]
 
 
 class RAGPipelineOutput(BaseModel):
@@ -39,6 +31,7 @@ class RAGPipelineOutput(BaseModel):
     total_tokens: int
     prompt_tokens: int
     completion_tokens: int
+    completion_cost: float
     time_taken: float
     start_time: datetime.datetime
     end_time: datetime.datetime
@@ -48,56 +41,98 @@ class RAGPipeline:
     def __init__(
         self,
         vector_store: VectorStore,
+        config: RAGPipelineConfig,
         top_k: int = 5,
         search_type: str = "mmr",
     ):
         self.vector_store = vector_store
-        self.query_enhancer = QueryEnhancer()
-        self.retrieval = FusionRetrieval(
-            vector_store=vector_store, top_k=top_k, search_type=search_type
-        )
-        self.response_synthesizer = ResponseSynthesizer()
+        self.config = config
+
+        if config.query_enhancer.enabled:
+            self.query_enhancer = QueryEnhancer(config=config)
+            if config.rerank_fusion.enabled:
+                self.retrieval = FusionRetrieval(
+                    vector_store=vector_store, top_k=top_k, search_type=search_type
+                )
+            else:
+                self.retrieval = SimpleRetrievalWithoutFusion(
+                    vector_store=vector_store, top_k=top_k, search_type=search_type
+                )
+            self.response_synthesizer = ResponseSynthesizer(config=config)
+        else:
+            if config.retrieval_re_ranker.enabled:
+                self.retrieval = SimpleRetrievalEngineWithRerank(
+                    vector_store=vector_store, top_k=top_k, search_type=search_type
+                )
+            else:
+                self.retrieval = SimpleRetrievalEngine(
+                    vector_store=vector_store, top_k=top_k, search_type=search_type
+                )
+            self.response_synthesizer = SimpleResponseSynthesizer(config=config)
 
     def __call__(
         self, question: str, chat_history: List[Tuple[str, str]] | None = None
-    ):
+    ) -> RAGPipelineOutput: 
         if chat_history is None:
             chat_history = []
 
-        with get_openai_callback() as query_enhancer_cb, Timer() as query_enhancer_tb:
-            enhanced_query = self.query_enhancer.chain.invoke(
-                {"query": question, "chat_history": chat_history}
-            )
-
-        with Timer() as retrieval_tb:
-            retrieval_results = self.retrieval.chain.invoke(enhanced_query)
-
-        with get_openai_callback() as response_cb, Timer() as response_tb:
+        if self.config.query_enhancer.enabled:
+            with Timer() as query_enhancer_tb:
+                enhanced_query = self.query_enhancer.chain.invoke(
+                    {"query": question, "chat_history": chat_history}
+                )
+            with Timer() as retrieval_tb:
+                retrieval_results = self.retrieval.chain.invoke(enhanced_query)
+        else:
+            with Timer() as retrieval_tb:
+                retrieval_results = self.retrieval(
+                    question=question, language="en", top_k=self.retrieval.top_k, search_type=self.retrieval.search_type
+                )
+
+        with Timer() as response_tb:
             response = self.response_synthesizer.chain.invoke(retrieval_results)
 
+        question = question if not self.config.query_enhancer.enabled else enhanced_query["standalone_query"]
+        usage_stats = litellm_token_and_cost_counter.get_totals()
+        time_taken = (
+            query_enhancer_tb.elapsed
+            + retrieval_tb.elapsed
+            + response_tb.elapsed
+            if self.config.query_enhancer.enabled
+            else retrieval_tb.elapsed + response_tb.elapsed
+        )
+        start_time = (
+            query_enhancer_tb.start
+            if self.config.query_enhancer.enabled
+            else retrieval_tb.start
+        )
+        end_time = response_tb.stop
+
+        if isinstance(retrieval_results, dict):
+            context = retrieval_results["context"]
+        else:
+            context = retrieval_results.context
+        sources = "\n".join(
+            [
+                item.metadata.get("source")
+                for item in context
+            ]
+        )
+
         output = RAGPipelineOutput(
-            question=enhanced_query["standalone_query"],
+            question=question,
             answer=response["response"],
-            sources="\n".join(
-                [
-                    item.metadata["source"]
-                    for item in retrieval_results["context"]
-                ]
-            ),
+            sources=sources,
             source_documents=response["context_str"],
             system_prompt=response["response_prompt"],
             model=response["response_model"],
-            total_tokens=query_enhancer_cb.total_tokens
-            + response_cb.total_tokens,
-            prompt_tokens=query_enhancer_cb.prompt_tokens
-            + response_cb.prompt_tokens,
-            completion_tokens=query_enhancer_cb.completion_tokens
-            + response_cb.completion_tokens,
-            time_taken=query_enhancer_tb.elapsed
-            + retrieval_tb.elapsed
-            + response_tb.elapsed,
-            start_time=query_enhancer_tb.start,
-            end_time=response_tb.stop,
+            total_tokens=usage_stats.total_tokens,
+            prompt_tokens=usage_stats.prompt_tokens,
+            completion_tokens=usage_stats.completion_tokens,
+            completion_cost=usage_stats.total_cost,
+            time_taken=time_taken,
+            start_time=start_time,
+            end_time=end_time,
         )
 
         return output
diff --git a/src/wandbot/config.yaml b/src/wandbot/config.yaml
new file mode 100644
index 0000000..5322cc4
--- /dev/null
+++ b/src/wandbot/config.yaml
@@ -0,0 +1,22 @@
+llm:
+  model: "gpt-4-1106-preview" # 1106 1.1
+  temperature: 0.1
+  max_retries: 2
+fallback_llm:
+  model: "gpt-4-1106-preview"
+  temperature: 0.1
+  max_retries: 6
+embeddings:
+  type: "OpenAIEmbedding"
+  config:
+    embeddings_model: "text-embedding-ada-002" # text-embedding-ada-002
+retrieval_re_ranker:
+  enabled: True
+use_you_search_api:
+  enabled: True
+query_enhancer:
+  enabled: True
+rerank_fusion:
+  enabled: False
+chunk_size: 512
+top_k: 5
diff --git a/src/wandbot/evaluation/configs/anthropic_config.yaml b/src/wandbot/evaluation/configs/anthropic_config.yaml
new file mode 100644
index 0000000..184b5fb
--- /dev/null
+++ b/src/wandbot/evaluation/configs/anthropic_config.yaml
@@ -0,0 +1,20 @@
+llm:
+  model: "claude-3-opus-20240229"
+  temperature: 0.1
+  max_retries: 2
+fallback_llm:
+  model: "gpt-3.5-turbo-1106"
+  temperature: 0.1
+  max_retries: 6
+embeddings:
+  type: "VoyageEmbedding"
+  config:
+    embeddings_model: "voyage/voyage-lite-01-instruct"
+retrieval_re_ranker:
+  enabled: False
+use_you_search_api:
+  enabled: False
+query_enhancer_followed_by_rerank_fusion:
+  enabled: True
+chunk_size: 512
+top_k: 5
diff --git a/src/wandbot/evaluation/configs/cohere_config.yaml b/src/wandbot/evaluation/configs/cohere_config.yaml
new file mode 100644
index 0000000..7305fec
--- /dev/null
+++ b/src/wandbot/evaluation/configs/cohere_config.yaml
@@ -0,0 +1,21 @@
+llm:
+  model: "gpt-4-1106-preview"
+  temperature: 0.1
+  max_retries: 2
+fallback_llm:
+  model: "gpt-3.5-turbo-1106"
+  temperature: 0.1
+  max_retries: 6
+embeddings:
+  type: "CohereEmbedding"
+  config:
+    embeddings_model: "embed-english-v3.0"
+    input_type: "search_document"
+retrieval_re_ranker:
+  enabled: False
+use_you_search_api:
+  enabled: False
+query_enhancer_followed_by_rerank_fusion:
+  enabled: True
+chunk_size: 512
+top_k: 5
diff --git a/src/wandbot/evaluation/configs/hf_config.yaml b/src/wandbot/evaluation/configs/hf_config.yaml
new file mode 100644
index 0000000..7b0dc75
--- /dev/null
+++ b/src/wandbot/evaluation/configs/hf_config.yaml
@@ -0,0 +1,20 @@
+llm:
+  model: "gpt-4-1106-preview"
+  temperature: 0.1
+  max_retries: 2
+fallback_llm:
+  model: "gpt-3.5-turbo-1106"
+  temperature: 0.1
+  max_retries: 6
+embeddings:
+  type: "HuggingFaceEmbedding"
+  config:
+    embeddings_model: "huggingface/BAAI/bge-large-zh"
+retrieval_re_ranker:
+  enabled: False
+use_you_search_api:
+  enabled: False
+query_enhancer_followed_by_rerank_fusion:
+  enabled: True
+chunk_size: 512
+top_k: 5
diff --git a/src/wandbot/evaluation/configs/mistral_config.yaml b/src/wandbot/evaluation/configs/mistral_config.yaml
new file mode 100644
index 0000000..d8b6178
--- /dev/null
+++ b/src/wandbot/evaluation/configs/mistral_config.yaml
@@ -0,0 +1,20 @@
+llm:
+  model: "gpt-4-1106-preview"
+  temperature: 0.1
+  max_retries: 2
+fallback_llm:
+  model: "gpt-3.5-turbo-1106"
+  temperature: 0.1
+  max_retries: 6
+embeddings:
+  type: "MistralEmbedding"
+  config:
+    embeddings_model: "mistral/mistral-embed"
+retrieval_re_ranker:
+  enabled: False
+use_you_search_api:
+  enabled: False
+query_enhancer_followed_by_rerank_fusion:
+  enabled: True
+chunk_size: 1024
+top_k: 5
diff --git a/src/wandbot/evaluation/configs/openai_config.yaml b/src/wandbot/evaluation/configs/openai_config.yaml
new file mode 100644
index 0000000..0d24476
--- /dev/null
+++ b/src/wandbot/evaluation/configs/openai_config.yaml
@@ -0,0 +1,21 @@
+llm:
+  model: "gpt-4-0125-preview"
+  temperature: 0.1
+  max_retries: 2
+fallback_llm:
+  model: "gpt-3.5-turbo-1106"
+  temperature: 0.1
+  max_retries: 6
+embeddings:
+  type: "OpenAIEmbedding"
+  config:
+    embeddings_model: "text-embedding-3-small"
+    embedding_dim: 512
+retrieval_re_ranker:
+  enabled: False
+use_you_search_api:
+  enabled: False
+query_enhancer_followed_by_rerank_fusion:
+  enabled: True
+chunk_size: 512
+top_k: 5
diff --git a/src/wandbot/evaluation/configs/voyage_config.yaml b/src/wandbot/evaluation/configs/voyage_config.yaml
new file mode 100644
index 0000000..b9ed036
--- /dev/null
+++ b/src/wandbot/evaluation/configs/voyage_config.yaml
@@ -0,0 +1,20 @@
+llm:
+  model: "gpt-4-1106-preview"
+  temperature: 0.1
+  max_retries: 2
+fallback_llm:
+  model: "gpt-3.5-turbo-1106"
+  temperature: 0.1
+  max_retries: 6
+embeddings:
+  type: "VoyageEmbedding"
+  config:
+    embeddings_model: "voyage/voyage-lite-01-instruct"
+retrieval_re_ranker:
+  enabled: False
+use_you_search_api:
+  enabled: False
+query_enhancer_followed_by_rerank_fusion:
+  enabled: True
+chunk_size: 512
+top_k: 5
diff --git a/src/wandbot/evaluation/eval/__main__.py b/src/wandbot/evaluation/eval/__main__.py
deleted file mode 100644
index 3302f59..0000000
--- a/src/wandbot/evaluation/eval/__main__.py
+++ /dev/null
@@ -1,224 +0,0 @@
-import json
-from typing import Any, Hashable
-
-import nest_asyncio
-import pandas as pd
-import requests
-from llama_index import ServiceContext
-from llama_index.llms import OpenAI
-from ragas import metrics
-from tenacity import retry, stop_after_attempt, wait_random_exponential
-from tqdm import tqdm
-
-from wandbot.evaluation.eval.correctness import (
-    CORRECTNESS_EVAL_TEMPLATE,
-    WandbCorrectnessEvaluator,
-)
-from wandbot.evaluation.eval.factfulness import (
-    FACTFULNESS_EVAL_TEMPLATE,
-    WandbFactfulnessEvaluator,
-)
-from wandbot.evaluation.eval.relevancy import (
-    RELEVANCY_EVAL_TEMPLATE,
-    WandbRelevancyEvaluator,
-)
-from wandbot.utils import cachew, get_logger
-
-logger = get_logger(__name__)
-
-nest_asyncio.apply()
-
-
-EVAL_CACHE = "data/cache/eval_cache/cache.db"
-service_context = ServiceContext.from_defaults(llm=OpenAI("gpt-4-1106-preview"))
-correctness_evaluator = WandbCorrectnessEvaluator(
-    service_context=service_context,
-    eval_template=CORRECTNESS_EVAL_TEMPLATE,
-)
-faithfulness_evaluator = WandbFactfulnessEvaluator(
-    service_context=service_context,
-    eval_template=FACTFULNESS_EVAL_TEMPLATE,
-)
-relevancy_evaluator = WandbRelevancyEvaluator(
-    service_context=service_context,
-    eval_template=RELEVANCY_EVAL_TEMPLATE,
-)
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-@cachew(cache_path=EVAL_CACHE, logger=logger)
-def get_answer(question: str, application: str = "api-eval-bharat") -> str:
-    url = "http://0.0.0.0:8000/query"
-    payload = {
-        "question": question,
-        "language": "en",
-        "application": application,
-    }
-    response = requests.post(url, data=json.dumps(payload))
-    response = response.json()
-    return json.dumps(response)
-
-
-@cachew(cache_path=EVAL_CACHE, logger=logger)
-def get_eval_record(row_str: str, application: str = "api-eval-bharat") -> str:
-    row = json.loads(row_str)
-    response = get_answer(row["question"], application=application)
-    response = json.loads(response)
-    response["ground_truths"] = row["answer"]
-    response["reference_notes"] = row["notes"]
-    response["contexts"] = [
-        "\nSource: " + source["source"] + " \n " + source["text"]
-        for source in json.loads(response["source_documents"])
-    ]
-    response = json.dumps(response)
-    return response
-
-
-def parse_answer_eval(metric: str, row: dict[str, Any]) -> dict[str, Any]:
-    result = {
-        f"{metric}_score": row.get("score"),
-        f"{metric}_result": row.get("passing"),
-        f"{metric}_reason": row.get("feedback"),
-    }
-    return result
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-@cachew(cache_path=EVAL_CACHE, logger=logger)
-def get_answer_correctness(row_str: str) -> str:
-    row = json.loads(row_str)
-    result = correctness_evaluator.evaluate(
-        query=row["question"],
-        response=row["answer"],
-        reference=row["ground_truths"],
-        contexts=row["contexts"],
-        reference_notes=row["reference_notes"],
-    )
-    result = parse_answer_eval("answer_correctness", result.dict())
-    result[
-        "answer_correctness_score_(ragas)"
-    ] = metrics.answer_correctness.score_single(row)
-    result = json.dumps(result)
-    return result
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-@cachew(cache_path=EVAL_CACHE, logger=logger)
-def get_answer_relevancy(row_str: str) -> str:
-    row = json.loads(row_str)
-    result = relevancy_evaluator.evaluate(
-        query=row["question"],
-        response=row["answer"],
-        contexts=row["contexts"],
-        reference=row["ground_truths"],
-    )
-    result = parse_answer_eval("answer_relevancy", result.dict())
-    result[
-        "answer_relevancy_score_(ragas)"
-    ] = metrics.answer_relevancy.score_single(row)
-    result = json.dumps(result)
-    return result
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-@cachew(cache_path=EVAL_CACHE, logger=logger)
-def get_answer_faithfulness(row_str: str) -> str:
-    row = json.loads(row_str)
-    result = faithfulness_evaluator.evaluate(
-        query=row["question"],
-        response=row["answer"],
-        contexts=row["contexts"],
-        reference=row["ground_truths"],
-    )
-
-    result = parse_answer_eval("answer_faithfulness", result.dict())
-    result[
-        "answer_faithfulness_score_(ragas)"
-    ] = metrics.faithfulness.score_single(row)
-    result = json.dumps(result)
-    return result
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-@cachew(cache_path=EVAL_CACHE, logger=logger)
-def get_answer_similarity(row_str: str) -> str:
-    row = json.loads(row_str)
-    result = metrics.answer_similarity.score_single(row)
-    result = json.dumps({"answer_similarity_score_(ragas)": result})
-    return result
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-@cachew(cache_path=EVAL_CACHE, logger=logger)
-def get_context_precision(row_str: str) -> str:
-    row = json.loads(row_str)
-    result = metrics.context_precision.score_single(row)
-    result = json.dumps({"context_precision_score": result})
-    return result
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-@cachew(cache_path=EVAL_CACHE, logger=logger)
-def get_context_recall(row_str: str) -> str:
-    row = json.loads(row_str)
-    result = metrics.context_recall.score_single(row)
-    result = json.dumps({"context_recall_score": result})
-    return result
-
-
-@cachew(cache_path=EVAL_CACHE, logger=logger)
-def evaluate_row(idx: Hashable, row_str: str) -> str:
-    eval_result = {"idx": idx}
-    row = json.loads(row_str)
-    eval_result.update(row)
-    eval_result.update(json.loads(get_answer_correctness(row_str)))
-    eval_result.update(json.loads(get_answer_relevancy(row_str)))
-    eval_result.update(json.loads(get_answer_faithfulness(row_str)))
-    eval_result.update(json.loads(get_answer_similarity(row_str)))
-    eval_result.update(json.loads(get_context_precision(row_str)))
-    eval_result.update(json.loads(get_context_recall(row_str)))
-    eval_result = json.dumps(eval_result)
-    return eval_result
-
-
-@cachew(cache_path=EVAL_CACHE, logger=logger)
-def process_row(
-    idx: Hashable, row_str: str, application: str = "api-eval-bharat"
-) -> str:
-    eval_record = get_eval_record(row_str, application=application)
-    eval_row = evaluate_row(idx, eval_record)
-    return eval_row
-
-
-def main():
-    eval_results = []
-
-    df = pd.read_json(
-        "data/eval/wandbot_cleaned_annotated_dataset_11-12-2023.jsonl",
-        lines=True,
-        orient="records",
-    )
-    correct_df = df[
-        (df["is_wandb_query"] == "YES") & (df["correctness"] == "correct")
-    ]
-
-    with open(
-        "data/eval/wandbot-gpt-4-1106-preview-eval-v1-1.jsonl", "w+"
-    ) as outfile:
-        for idx, row in tqdm(correct_df.iterrows(), total=len(correct_df)):
-            try:
-                row_str = row.to_json()
-                eval_row = process_row(
-                    idx,
-                    row_str,
-                    application="wandbot-gpt-4-1106-preview-eval-v1.1-bharat",
-                )
-                outfile.write(eval_row + "\n")
-                eval_results.append(eval_row)
-            except Exception as e:
-                print(e)
-                print(idx)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/wandbot/evaluation/eval/async_main.py b/src/wandbot/evaluation/eval/async_main.py
new file mode 100644
index 0000000..cf69357
--- /dev/null
+++ b/src/wandbot/evaluation/eval/async_main.py
@@ -0,0 +1,241 @@
+import re
+import json
+import time
+from typing import Any, Hashable
+
+import asyncio
+import httpx
+import wandb
+import pandas as pd
+import aiofiles
+from llama_index.core import ServiceContext
+from llama_index.llms.openai import OpenAI
+from tenacity import retry, stop_after_attempt, wait_random_exponential
+from tqdm import tqdm
+
+from wandbot.evaluation.eval.correctness import (
+    CORRECTNESS_EVAL_TEMPLATE,
+    WandbCorrectnessEvaluator,
+)
+from wandbot.evaluation.eval.factfulness import (
+    FACTFULNESS_EVAL_TEMPLATE,
+    WandbFactfulnessEvaluator,
+)
+from wandbot.evaluation.eval.relevancy import (
+    RELEVANCY_EVAL_TEMPLATE,
+    WandbRelevancyEvaluator,
+)
+from wandbot.utils import cachew, get_logger
+
+logger = get_logger(__name__)
+
+
+service_context = ServiceContext.from_defaults(llm=OpenAI("gpt-4-1106-preview"))
+correctness_evaluator = WandbCorrectnessEvaluator(
+    service_context=service_context,
+    eval_template=CORRECTNESS_EVAL_TEMPLATE,
+)
+faithfulness_evaluator = WandbFactfulnessEvaluator(
+    service_context=service_context,
+    eval_template=FACTFULNESS_EVAL_TEMPLATE,
+)
+relevancy_evaluator = WandbRelevancyEvaluator(
+    service_context=service_context,
+    eval_template=RELEVANCY_EVAL_TEMPLATE,
+)
+
+
+@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
+async def get_answer(question: str, application: str = "api-eval-bharat") -> str:
+    url = "http://0.0.0.0:8000/chat/query"
+    payload = {
+        "question": question,
+        "chat_history": [],
+        "application": application,
+        "language": "en",
+    }
+    async with httpx.AsyncClient(timeout=200.0) as client:
+        response = await client.post(url, data=json.dumps(payload))
+        response_json = response.json()
+    return json.dumps(response_json)
+
+context_metadata_pattern = r"\n?source: .+?\nsource_type: .+?\nhas_code: .+?\n"
+
+
+def get_individual_contexts(source_documents: str) -> list[str]:
+    source_documents = source_documents.split("---")
+    source_documents = [
+        re.sub(context_metadata_pattern, "", source) for source in source_documents
+    ]
+    return source_documents
+
+
+@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
+async def get_eval_record(row_str: str, application: str = "api-eval-bharat") -> str:
+    row = json.loads(row_str)
+    response = await get_answer(row["question"], application=application)
+    response = json.loads(response)
+    response["ground_truths"] = row["answer"]
+    response["reference_notes"] = row["notes"]
+    response["contexts"] = [
+        "Source: " + source + "\n"  + context
+        for source, context in zip(response["sources"].split("\n"), get_individual_contexts(response["source_documents"]))
+    ]
+    response = json.dumps(response)
+    return response
+
+
+def parse_answer_eval(metric: str, row: dict[str, Any]) -> dict[str, Any]:
+    result = {
+        f"{metric}_score": row.get("score"),
+        f"{metric}_result": row.get("passing"),
+        f"{metric}_reason": row.get("feedback"),
+    }
+    return result
+
+
+@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
+async def get_answer_correctness(row_str: str) -> str:
+    row = json.loads(row_str)
+    result = await correctness_evaluator.aevaluate(
+        query=row["question"],
+        response=row["answer"],
+        reference=row["ground_truths"],
+        contexts=row["contexts"],
+        reference_notes=row["reference_notes"],
+    )
+    result = parse_answer_eval("answer_correctness", result.dict())
+    result = json.dumps(result)
+    return result
+
+
+@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
+async def get_answer_relevancy(row_str: str) -> str:
+    row = json.loads(row_str)
+    result = await relevancy_evaluator.aevaluate(
+        query=row["question"],
+        response=row["answer"],
+        contexts=row["contexts"],
+        reference=row["ground_truths"],
+    )
+    result = parse_answer_eval("answer_relevancy", result.dict())
+    result = json.dumps(result)
+    return result
+
+
+@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
+async def get_answer_faithfulness(row_str: str) -> str:
+    row = json.loads(row_str)
+    result = await faithfulness_evaluator.aevaluate(
+        query=row["question"],
+        response=row["answer"],
+        contexts=row["contexts"],
+        reference=row["ground_truths"],
+    )
+
+    result = parse_answer_eval("answer_faithfulness", result.dict())
+    result = json.dumps(result)
+    return result
+
+
+@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
+async def evaluate_row(idx: Hashable, row_str: str) -> str:
+    eval_result = {"idx": idx}
+    row = json.loads(row_str)
+    eval_result.update(row)
+
+    eval_result.update(json.loads(await get_answer_correctness(row_str)))
+    eval_result.update(json.loads(await get_answer_relevancy(row_str)))
+    eval_result.update(json.loads(await get_answer_faithfulness(row_str)))
+
+    eval_result = json.dumps(eval_result)
+    return eval_result
+
+
+@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
+async def process_row(idx, row, outfile):
+    """
+    Process a chunk of the dataframe asynchronously and write results to the file.
+    """
+    row_str = row.to_json()
+    response = await get_eval_record(row_str, application="test-baseline-ayush")
+    logger.info(f"Generated response for idx: {idx}")
+    eval_row = await evaluate_row(idx, response)
+    logger.info(f"Evaluated response for idx: {idx}")
+    try:
+        json.loads(eval_row)
+        await outfile.write(eval_row + "\n")
+    except json.JSONDecodeError:
+        logger.error(f"Failed to parse response for idx: {idx}")
+
+
+def log_eval_result(eval_result_path: str, duration: float) -> None:
+    project = "wandbot-eval"
+    entity = "wandbot"
+
+    run = wandb.init(project=project, entity=entity)
+    
+    eval_df = pd.read_json(eval_result_path, lines=True)
+    eval_df = eval_df.sort_values(by='idx').reset_index(drop=True)
+
+    logger.info(f"Number of eval samples: {len(eval_df)}")
+    run.log({"Evaluation Results": eval_df})
+
+    score_columns = [col for col in eval_df.columns if col.endswith('_score')]
+    mean_scores = eval_df[score_columns].mean()
+    mode_scores = eval_df[score_columns].mode()
+    percent_grade3 = (eval_df[score_columns] == 3).mean()
+    percent_grade2 = (eval_df[score_columns] == 2).mean()
+    percent_grade1 = (eval_df[score_columns] == 1).mean()
+
+    # Select columns ending with "_result" and calculate the percentage of True values
+    result_columns = [col for col in eval_df.columns if col.endswith('_result')]
+    percentage_true_results = (eval_df[result_columns].sum() / eval_df[result_columns].count())
+
+    final_eval_results = {}
+    final_eval_results.update(mean_scores.to_dict())
+    final_eval_results.update(mode_scores.iloc[0].to_dict())
+    final_eval_results.update(percent_grade3.to_dict())
+    final_eval_results.update(percent_grade2.to_dict())
+    final_eval_results.update(percent_grade1.to_dict())
+    final_eval_results.update(percentage_true_results.to_dict())
+
+
+    logger.info(f"Final Eval Results: {json.dumps(final_eval_results, indent=4)}")
+    run.log(final_eval_results)
+
+    run.summary["duration(s)"] = duration
+
+
+async def main():
+    eval_artifact = wandb.Api().artifact("wandbot/wandbot-eval/autoeval_dataset:v3")
+    eval_artifact_dir = eval_artifact.download(root="data/eval")
+
+    df = pd.read_json(
+        "data/eval/wandbot_cleaned_annotated_dataset_11-12-2023.jsonl",
+        lines=True,
+        orient="records",
+    )
+    correct_df = df[
+        (df["is_wandb_query"] == "YES") & (df["correctness"] == "correct")
+    ]
+    logger.info("Number of evaluation samples: %s", len(correct_df))
+
+    start_time = time.time()
+
+    async with aiofiles.open(
+        "data/eval/eval.jsonl", "w+"
+    ) as outfile:
+        tasks = [process_row(idx, row, outfile) for idx, row in tqdm(correct_df.iterrows())]
+        await asyncio.gather(*tasks)
+
+    end_time = time.time()
+    duration = end_time - start_time
+    logger.info(f"Total runtime: {duration:.2f} seconds")
+
+    log_eval_result(
+        "data/eval/eval.jsonl", duration
+    )
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/src/wandbot/evaluation/eval/correctness.py b/src/wandbot/evaluation/eval/correctness.py
index c68e034..a3e0fb9 100644
--- a/src/wandbot/evaluation/eval/correctness.py
+++ b/src/wandbot/evaluation/eval/correctness.py
@@ -2,7 +2,7 @@
 from typing import Any, Optional, Sequence
 
 import regex as re
-from llama_index.evaluation import CorrectnessEvaluator, EvaluationResult
+from llama_index.legacy.evaluation import CorrectnessEvaluator, EvaluationResult
 
 from wandbot.evaluation.eval.utils import (
     make_eval_template,
@@ -98,19 +98,20 @@ async def aevaluate(
         if query is None or response is None or reference is None:
             print(query, response, reference, flush=True)
             raise ValueError("query, response, and reference must be provided")
-
+        
+        context_str = re.sub(
+            "\n+", "\n", "\n---\n".join(contexts) if contexts else ""
+        )
         eval_response = await self._service_context.llm.apredict(
             prompt=self._eval_template,
             query=query,
             generated_answer=response,
             reference_answer=reference,
-            context_str=re.sub(
-                "\n+", "\n", "\n---\n".join(contexts) if contexts else ""
-            ),
+            context_str=context_str,
             reference_notes=kwargs.get("reference_notes", ""),
         )
 
-        passing, reasoning, score = safe_parse_eval_response(
+        passing, reasoning, score = await safe_parse_eval_response(
             eval_response, "correct"
         )
 
diff --git a/src/wandbot/evaluation/eval/factfulness.py b/src/wandbot/evaluation/eval/factfulness.py
index d160d55..65648bc 100644
--- a/src/wandbot/evaluation/eval/factfulness.py
+++ b/src/wandbot/evaluation/eval/factfulness.py
@@ -2,7 +2,7 @@
 import re
 from typing import Any, Optional, Sequence
 
-from llama_index.evaluation import CorrectnessEvaluator, EvaluationResult
+from llama_index.legacy.evaluation import CorrectnessEvaluator, EvaluationResult
 
 from wandbot.evaluation.eval.utils import (
     make_eval_template,
@@ -99,7 +99,7 @@ async def aevaluate(
             ),
         )
 
-        passing, reasoning, score = safe_parse_eval_response(
+        passing, reasoning, score = await safe_parse_eval_response(
             eval_response, "consistent"
         )
 
diff --git a/src/wandbot/evaluation/eval/log_eval.py b/src/wandbot/evaluation/eval/log_eval.py
new file mode 100644
index 0000000..4afb7a1
--- /dev/null
+++ b/src/wandbot/evaluation/eval/log_eval.py
@@ -0,0 +1,64 @@
+import os
+import wandb
+import json
+import argparse
+import pandas as pd
+from pathlib import Path
+
+from wandbot.utils import load_config
+
+
+def main(config_path: str, eval_result_path: str) -> None:
+    config = load_config(config_path)
+
+    project = "wandbot-eval"
+    entity = "wandbot"
+
+    run = wandb.init(project=project, entity=entity, config=config.model_dump())
+    
+    eval_df = pd.read_json(eval_result_path, lines=True)
+    print("Number of eval samples: ", len(eval_df))
+    run.log({"Evaluation Results": eval_df})
+
+    score_columns = [col for col in eval_df.columns if col.endswith('_score')]
+    mean_scores = eval_df[score_columns].mean()
+    mode_scores = eval_df[score_columns].mode()
+    percent_grade3 = (eval_df[score_columns] == 3).mean()
+    percent_grade2 = (eval_df[score_columns] == 2).mean()
+    percent_grade1 = (eval_df[score_columns] == 1).mean()
+
+    # Select columns ending with "_result" and calculate the percentage of True values
+    result_columns = [col for col in eval_df.columns if col.endswith('_result')]
+    percentage_true_results = (eval_df[result_columns].sum() / eval_df[result_columns].count())
+
+    final_eval_results = {}
+    final_eval_results.update(mean_scores.to_dict())
+    final_eval_results.update(mode_scores.iloc[0].to_dict())
+    final_eval_results.update(percent_grade3.to_dict())
+    final_eval_results.update(percent_grade2.to_dict())
+    final_eval_results.update(percent_grade1.to_dict())
+    final_eval_results.update(percentage_true_results.to_dict())
+
+    print("Final Eval Results")
+    print(final_eval_results)
+
+    run.log(final_eval_results)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description='Run ingestion process with a specified configuration.'
+    )
+    parser.add_argument(
+        '--config', 
+        type=str,
+        required=True,
+        help='Path to the configuration YAML file.'
+    )
+    parser.add_argument(
+        '--eval_result',
+        type=str,
+        required=True,
+        help='Path to the evaluation result JSONL file.'
+    )
+    args = parser.parse_args()
+    main(args.config, args.eval_result)
diff --git a/src/wandbot/evaluation/eval/relevancy.py b/src/wandbot/evaluation/eval/relevancy.py
index 361ec69..7e5aff0 100644
--- a/src/wandbot/evaluation/eval/relevancy.py
+++ b/src/wandbot/evaluation/eval/relevancy.py
@@ -1,7 +1,7 @@
 import asyncio
 from typing import Any, Optional, Sequence
 
-from llama_index.evaluation import CorrectnessEvaluator, EvaluationResult
+from llama_index.legacy.evaluation import CorrectnessEvaluator, EvaluationResult
 
 from wandbot.evaluation.eval.utils import (
     make_eval_template,
@@ -96,7 +96,7 @@ async def aevaluate(
             reference_answer=reference,
         )
 
-        passing, reasoning, score = safe_parse_eval_response(
+        passing, reasoning, score = await safe_parse_eval_response(
             eval_response, "relevant"
         )
 
diff --git a/src/wandbot/evaluation/eval/utils.py b/src/wandbot/evaluation/eval/utils.py
index c39e2e4..efafe7c 100644
--- a/src/wandbot/evaluation/eval/utils.py
+++ b/src/wandbot/evaluation/eval/utils.py
@@ -1,7 +1,7 @@
-from llama_index import ChatPromptTemplate
-from llama_index.llms import ChatMessage, MessageRole
-from ragas.llms import OpenAI
-from ragas.utils import json_loader
+from llama_index.core.prompts import ChatPromptTemplate
+from llama_index.core.llms import ChatMessage, MessageRole
+from langchain_openai import ChatOpenAI
+from ragas.llms.json_load import json_loader
 
 
 def make_eval_template(system_template, user_template):
@@ -13,13 +13,12 @@ def make_eval_template(system_template, user_template):
     )
 
 
-def safe_parse_eval_response(eval_response, passing_decision):
+async def safe_parse_eval_response(eval_response, passing_decision):
     try:
-        eval_response_dict = json_loader.safe_load(eval_response, llm=OpenAI())
+        eval_response_dict = await json_loader.safe_load(eval_response, llm=ChatOpenAI())
         score = eval_response_dict.get("score")
         reasoning = eval_response_dict.get("reason")
         decision = eval_response_dict.get("decision") == passing_decision
-
     except Exception as e:
         print(e)
         print(eval_response)
diff --git a/src/wandbot/ingestion/__main__.py b/src/wandbot/ingestion/__main__.py
index 359e8f2..fef52d2 100644
--- a/src/wandbot/ingestion/__main__.py
+++ b/src/wandbot/ingestion/__main__.py
@@ -1,26 +1,52 @@
+import argparse
 import os
 
 from wandbot.ingestion import prepare_data, preprocess_data, vectorstores
 from wandbot.ingestion.report import create_ingestion_report
-from wandbot.utils import get_logger
+from wandbot.utils import get_logger, load_config
 
 logger = get_logger(__name__)
 
 
-def main():
+def main(config_path: str) -> None:
+    config = load_config(config_path)
+    logger.info(config)
+
     project = os.environ.get("WANDB_PROJECT", "wandbot-dev")
     entity = os.environ.get("WANDB_ENTITY", "wandbot")
 
-    raw_artifact = prepare_data.load(project, entity)
+    # raw_artifact = prepare_data.load(project, entity)
+    raw_artifact = "wandbot/wandbot-dev/raw_dataset:v55"
+    logger.info(f"Loaded all the data sources at {raw_artifact}")
+
+    preprocessed_artifact = preprocess_data.load(
+        project, entity, raw_artifact, config
+    )
+    # preprocessed_artifact = "wandbot/wandbot-dev/transformed_data:latest"
+    logger.info(
+        f"Data sources preprocessed and stored at {preprocessed_artifact}"
+    )
 
-    preprocessed_artifact = preprocess_data.load(project, entity, raw_artifact)
     vectorstore_artifact = vectorstores.load(
-        project, entity, preprocessed_artifact
+        project, entity, preprocessed_artifact, config
+    )
+    logger.info(
+        f"Preprocessed chunks embedded and stored at {vectorstore_artifact}"
     )
 
     create_ingestion_report(project, entity, raw_artifact, vectorstore_artifact)
-    print(vectorstore_artifact)
 
 
 if __name__ == "__main__":
-    main()
+    parser = argparse.ArgumentParser(
+        description="Run ingestion process with a specified configuration."
+    )
+    parser.add_argument(
+        "--config",
+        type=str,
+        required=True,
+        help="Path to the configuration YAML file.",
+    )
+
+    args = parser.parse_args()
+    main(args.config)
diff --git a/src/wandbot/ingestion/config.py b/src/wandbot/ingestion/config.py
index 7f3f01d..ca263e5 100644
--- a/src/wandbot/ingestion/config.py
+++ b/src/wandbot/ingestion/config.py
@@ -11,12 +11,18 @@
   docodile_english_store_config = DocodileEnglishStoreConfig()
 """
 
+import os
 import datetime
 import pathlib
-from typing import List, Optional
+from typing import List, Optional, Dict, Union
 from urllib.parse import urlparse
 
-from pydantic import BaseModel, Field, model_validator
+from pydantic import (
+    BaseModel,
+    Field,
+    model_validator,
+    field_validator
+)
 from pydantic_settings import BaseSettings
 from wandbot.utils import get_logger
 
@@ -240,8 +246,55 @@ def _set_cache_paths(cls, values: "DataStoreConfig") -> "DataStoreConfig":
 
 class VectorStoreConfig(BaseSettings):
     name: str = "vectorstore"
-    embeddings_model: str = "text-embedding-3-large"
-    embedding_dim: int = 512
     persist_dir: pathlib.Path = pathlib.Path("data/cache/vectorstore")
     batch_size: int = 256
     artifact_url: str = "wandbot/wandbot-dev/chroma_index:latest"
+    # Optional fields for flexibility
+    embedding_dim: int | None = None
+    input_type: str | None = None
+
+    @field_validator('artifact_url')
+    def set_artifact_url(cls, v: str) -> str:
+        wandb_project = os.environ.get("WANDB_PROJECT", "wandbot-dev")
+        wandb_entity = os.environ.get("WANDB_ENTITY", "wandbot")
+        return f"{wandb_entity}/{wandb_project}/chroma_index:latest"
+    
+    def get_lite_llm_embeddings_params(self) -> Dict[str, Union[str, Optional[int]]]:
+        """
+        Converts the configuration into a dictionary of parameters
+        suitable for initializing LiteLLMEmbeddings.
+        """
+        return {
+            "model": self.embeddings_model,
+            "dimensions": self.embedding_dim,
+            "input_type": self.input_type,
+        }
+    
+
+class OpenAIEmbeddingConfig(VectorStoreConfig):
+    embeddings_model: str = "text-embedding-3-small"
+    persist_dir: pathlib.Path = pathlib.Path(f"data/cache/vectorstore/openai/{embeddings_model}")
+
+
+class CohereEmbeddingConfig(VectorStoreConfig):
+    embeddings_model: str = "embed-english-v3.0"
+    input_type: str = "search_document"
+    persist_dir: pathlib.Path = pathlib.Path(f"data/cache/vectorstore/cohere/{embeddings_model}")
+    batch_size: int = 96
+
+
+class VoyageEmbeddingConfig(VectorStoreConfig):
+    embeddings_model: str = "voyage/voyage-lite-01-instruct"
+    persist_dir: pathlib.Path = pathlib.Path(f"data/cache/vectorstore/{embeddings_model}")
+    batch_size: int = 96
+
+
+class HuggingFaceEmbeddingConfig(VectorStoreConfig):
+    embeddings_model: str = "huggingface/microsoft/codebert-base"
+    persist_dir: pathlib.Path = pathlib.Path(f"data/cache/vectorstore/hf/{embeddings_model.split('/')[-1]}")
+
+
+class MistralEmbeddingConfig(VectorStoreConfig):
+    embeddings_model: str = "mistral/mistral-embed"
+    persist_dir: pathlib.Path = pathlib.Path(f"data/cache/vectorstore/{embeddings_model}")
+    batch_size: int = 32
diff --git a/src/wandbot/ingestion/preprocess_data.py b/src/wandbot/ingestion/preprocess_data.py
index af883f9..d40f6ea 100644
--- a/src/wandbot/ingestion/preprocess_data.py
+++ b/src/wandbot/ingestion/preprocess_data.py
@@ -34,6 +34,7 @@
     filter_smaller_documents,
     get_logger,
     make_document_tokenization_safe,
+    RAGPipelineConfig,
 )
 
 logger = get_logger(__name__)
@@ -162,10 +163,11 @@ def load(
     project: str,
     entity: str,
     source_artifact_path: str,
+    config: RAGPipelineConfig,
     result_artifact_name: str = "transformed_data",
 ) -> str:
     run: wandb.wandb_sdk.wandb_run.Run = wandb.init(
-        project=project, entity=entity, job_type="preprocess_data"
+        project=project, entity=entity, job_type="preprocess_data", config=config.model_dump()
     )
     artifact: wandb.Artifact = run.use_artifact(
         source_artifact_path, type="dataset"
@@ -179,7 +181,7 @@ def load(
     lang_detect = FastTextLangDetect()
     transformer = DocumentTransformer(
         lang_detect=lang_detect,
-        max_size=512,
+        max_size=config.chunk_size,
         min_size=5,
         length_function=length_function,
     )
@@ -192,12 +194,12 @@ def load(
             transformed_documents = process_document_file(
                 documents, transformer
             )
-            config = json.load((document_file.parent / "config.json").open())
+            data_source_config = json.load((document_file.parent / "config.json").open())
             metadata = json.load(
                 (document_file.parent / "metadata.json").open()
             )
             cache_dir = (
-                pathlib.Path(config["data_source"]["cache_dir"]).parent
+                pathlib.Path(data_source_config["data_source"]["cache_dir"]).parent
                 / "transformed_data"
             )
 
@@ -210,9 +212,9 @@ def load(
                 for document in transformed_documents:
                     of.write(json.dumps(document.dict()) + "\n")
 
-            config["chunk_size"] = 512
+            data_source_config["chunk_size"] = config.chunk_size
             with open(transformed_file.parent / "config.json", "w") as of:
-                json.dump(config, of)
+                json.dump(data_source_config, of)
 
             metadata["num_transformed_documents"] = len(transformed_documents)
             with open(transformed_file.parent / "metadata.json", "w") as of:
diff --git a/src/wandbot/ingestion/preprocessors/source_code.py b/src/wandbot/ingestion/preprocessors/source_code.py
index 546b0a9..efd7c7d 100644
--- a/src/wandbot/ingestion/preprocessors/source_code.py
+++ b/src/wandbot/ingestion/preprocessors/source_code.py
@@ -10,7 +10,7 @@
 from tree_sitter import Node
 from tree_sitter_languages import get_language, get_parser
 
-from wandbot.ingestion.preprocess.markdown import (
+from wandbot.ingestion.preprocessors.markdown import (
     CustomMarkdownTextSplitter,
     create_id_from_document,
 )
diff --git a/src/wandbot/ingestion/report.py b/src/wandbot/ingestion/report.py
index 5f68fe6..760fa6c 100644
--- a/src/wandbot/ingestion/report.py
+++ b/src/wandbot/ingestion/report.py
@@ -19,9 +19,8 @@
 import pathlib
 from datetime import datetime
 
-import wandb.apis.reports as wr
-
 import wandb
+import wandb.apis.reports as wr
 
 
 def log_raw_counts(metadata: dict[str, dict[str, int]]) -> list[str]:
@@ -55,7 +54,7 @@ def get_metadata_from_artifacts(
     raw_artifact = wandb.run.use_artifact(raw_artifact, type="dataset")
     raw_artifact_dir = raw_artifact.download()
     vectorstore_artifact = wandb.run.use_artifact(
-        vectorstore_artifact, type="storage_context"
+        vectorstore_artifact, type="vectorstore"
     )
     vectorstore_artifact_dir = vectorstore_artifact.download()
 
diff --git a/src/wandbot/ingestion/utils.py b/src/wandbot/ingestion/utils.py
index 8548efe..e8420ab 100644
--- a/src/wandbot/ingestion/utils.py
+++ b/src/wandbot/ingestion/utils.py
@@ -30,7 +30,7 @@
 import re
 import subprocess
 from pathlib import Path
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, List
 
 import frontmatter
 import giturlparse
@@ -38,8 +38,19 @@
 import markdownify
 from bs4 import BeautifulSoup, Comment
 from git import Repo
+from pydantic_settings import BaseSettings
+
+from litellm import embedding, aembedding
+from langchain_core.embeddings import Embeddings
 
 from wandbot.utils import get_logger
+from wandbot.ingestion.config import (
+    CohereEmbeddingConfig,
+    HuggingFaceEmbeddingConfig,
+    MistralEmbeddingConfig,
+    OpenAIEmbeddingConfig,
+    VoyageEmbeddingConfig,
+)
 
 logger = get_logger(__name__)
 
@@ -288,3 +299,67 @@ def extract_frontmatter(file_path: pathlib.Path) -> Dict[str, Any]:
     with open(file_path, "r") as f:
         contents = frontmatter.load(f)
         return {k: contents[k] for k in contents.keys()}
+
+
+def get_embedding_config(embedding_type: str, config_values: dict) -> BaseSettings:
+    """
+    Get the embedding configuration based on the embedding type and config values.
+
+    Args:
+        embedding_type: The type of the embedding defined in the yaml file.
+        config_values: The configuration values specific to the embedding type.
+    """
+    if embedding_type == "OpenAIEmbedding":
+        return OpenAIEmbeddingConfig(**config_values)
+    elif embedding_type == "CohereEmbedding":
+        return CohereEmbeddingConfig(**config_values)
+    elif embedding_type == "HuggingFaceEmbedding":
+        return HuggingFaceEmbeddingConfig(**config_values)
+    elif embedding_type == "VoyageEmbedding":
+        return VoyageEmbeddingConfig(**config_values)
+    elif embedding_type == "MistralEmbedding":
+        return MistralEmbeddingConfig(**config_values)
+    else:
+        raise ValueError(f"Unsupported embedding type: {embedding_type}")
+
+
+class LiteLLMEmbeddings(Embeddings):
+    def __init__(
+        self,
+        model: str,
+        dimensions: int | None = None,
+        input_type: int | None = None,
+    ):
+        self.args = dict()
+        if dimensions is not None and input_type is None: # OpenAI
+            self.args["model"] = model
+            self.args["dimensions"] = dimensions
+        elif input_type is not None and dimensions is None: # Cohere
+            assert isinstance(input_type, str)
+            self.args["model"] = model
+            self.args["input_type"] = input_type
+        else: # Rest
+            self.args["model"] = model
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed search docs."""
+        self.args["input"] = texts
+        response = embedding(
+            **self.args,
+        )
+
+        embeddings = []
+        for embedding_dict in response.data:
+            embeddings.append(embedding_dict["embedding"])
+
+        return embeddings
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed query text."""
+        self.args["input"] = [text]
+        response = embedding(
+            **self.args,
+        )
+
+        assert len(response.data) == 1
+        return response.data[0]["embedding"]
diff --git a/src/wandbot/ingestion/vectorstores.py b/src/wandbot/ingestion/vectorstores.py
index b447304..b9c4083 100644
--- a/src/wandbot/ingestion/vectorstores.py
+++ b/src/wandbot/ingestion/vectorstores.py
@@ -16,14 +16,12 @@
 import pathlib
 from typing import List
 
+import wandb
 from langchain_community.vectorstores.chroma import Chroma
 from langchain_core.documents import Document
-from langchain_openai import OpenAIEmbeddings
 from tqdm import trange
-
-import wandb
-from wandbot.ingestion.config import VectorStoreConfig
-from wandbot.utils import get_logger
+from wandbot.ingestion.utils import LiteLLMEmbeddings, get_embedding_config
+from wandbot.utils import RAGPipelineConfig, get_logger
 
 logger = get_logger(__name__)
 
@@ -32,7 +30,8 @@ def load(
     project: str,
     entity: str,
     source_artifact_path: str,
-    result_artifact_name: str = "wandbot_index",
+    config: RAGPipelineConfig,
+    result_artifact_name: str = "chroma_index",
 ) -> str:
     """Load the vector store.
 
@@ -42,7 +41,7 @@ def load(
         project: The name of the project.
         entity: The name of the entity.
         source_artifact_path: The path to the source artifact.
-        result_artifact_name: The name of the resulting artifact. Defaults to "wandbot_index".
+        result_artifact_name: The name of the resulting artifact. Defaults to "chroma_index".
 
     Returns:
         The name of the resulting artifact.
@@ -50,20 +49,26 @@ def load(
     Raises:
         wandb.Error: An error occurred during the loading process.
     """
-    config: VectorStoreConfig = VectorStoreConfig()
+    vectorstore_config = get_embedding_config(
+        config.embeddings.type, config.embeddings.config
+    )
+    logger.info(f"Using the following vectorstore config {vectorstore_config}")
+
     run: wandb.wandb_sdk.wandb_run.Run = wandb.init(
-        project=project, entity=entity, job_type="create_vectorstore"
+        project=project,
+        entity=entity,
+        job_type="create_vectorstore",
+        config=config.model_dump(),
     )
     artifact: wandb.Artifact = run.use_artifact(
         source_artifact_path, type="dataset"
     )
     artifact_dir: str = artifact.download()
 
-    # Todo: Change to LiteLLM Embeddings
-    embedding_fn = OpenAIEmbeddings(
-        model=config.embeddings_model, dimensions=config.embedding_dim
+    embedding_fn = LiteLLMEmbeddings(
+        **vectorstore_config.get_lite_llm_embeddings_params()
     )
-    vectorstore_dir = config.persist_dir
+    vectorstore_dir = vectorstore_config.persist_dir
     vectorstore_dir.mkdir(parents=True, exist_ok=True)
 
     document_files: List[pathlib.Path] = list(
@@ -77,19 +82,25 @@ def load(
                 transformed_documents.append(Document(**json.loads(line)))
 
     chroma = Chroma(
-        collection_name=config.name,
+        collection_name=vectorstore_config.name,
         embedding_function=embedding_fn,
-        persist_directory=str(config.persist_dir),
+        persist_directory=str(vectorstore_config.persist_dir.resolve()),
     )
-    for batch_idx in trange(0, len(transformed_documents), config.batch_size):
-        batch = transformed_documents[batch_idx : batch_idx + config.batch_size]
+    for batch_idx in trange(
+        0, len(transformed_documents), vectorstore_config.batch_size
+    ):
+        batch = transformed_documents[
+            batch_idx : batch_idx + vectorstore_config.batch_size
+        ]
         chroma.add_documents(batch)
     chroma.persist()
 
-    result_artifact = wandb.Artifact(name="chroma_index", type="vectorstore")
+    result_artifact = wandb.Artifact(
+        name=result_artifact_name, type="vectorstore"
+    )
 
     result_artifact.add_dir(
-        local_path=str(config.persist_dir),
+        local_path=str(vectorstore_config.persist_dir),
     )
     run.log_artifact(result_artifact)
 
diff --git a/src/wandbot/rag/query_handler.py b/src/wandbot/rag/query_handler.py
index 6e4f6dd..b94966e 100644
--- a/src/wandbot/rag/query_handler.py
+++ b/src/wandbot/rag/query_handler.py
@@ -5,6 +5,7 @@
 
 import regex as re
 from langchain.chains.openai_functions import create_structured_output_runnable
+from langchain.output_parsers import OutputFixingParser, PydanticOutputParser
 from langchain_core.messages import convert_to_messages, get_buffer_string
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.runnables import (
@@ -16,7 +17,7 @@
 from langchain_openai import ChatOpenAI
 from pydantic.v1 import BaseModel, Field
 from wandbot.rag.utils import ChatModel
-from wandbot.utils import get_logger
+from wandbot.utils import RAGPipelineConfig, get_logger, load_config
 
 logger = get_logger(__name__)
 
@@ -108,18 +109,19 @@ class Keyword(BaseModel):
 
     keyword: str = Field(
         ...,
-        description="A search phrase to get the most relevant information related to Weights & Biases from the web "
-        " This will be used to gather information required to answer the query",
+        description="A Keyword that can be used to retrieve for relevant articles required to answer the query",
     )
 
 
 class SubQuery(BaseModel):
-    """A sub query that will help gather information required to answer the query"""
+    """A list of sub-questions that break the complex user query into smaller parts"""
 
     query: str = Field(
         ...,
-        description="The sub query that needs to be answered to answer the query. This will be used to define the "
-        "steps required to answer the query",
+        description="An Informative sub-query from the given user query. "
+        "The sub question should be specific and relevant to the user question. "
+        "The sub question should help in answering the user question. "
+        "The sub question will be used to gather information required to answer the query.",
     )
 
 
@@ -143,25 +145,25 @@ class EnhancedQuery(BaseModel):
     intents: List[Intent] = Field(
         ...,
         description=f"A list of one or more intents associated with the query. Here are the possible intents that "
-        f"can be associated with a query:\n{json.dumps(INTENT_DESCRIPTIONS)}",
+        f"can be associated with a query:\n{json.dumps(INTENT_DESCRIPTIONS, indent=2)}",
         min_items=1,
         max_items=5,
     )
     keywords: List[Keyword] = Field(
         ...,
-        description="A list of diverse search terms associated with the query.",
+        description="A list of keywords associated with the query.",
         min_items=1,
         max_items=5,
     )
     sub_queries: List[SubQuery] = Field(
         ...,
-        description="A list of sub queries that break the query into smaller parts",
+        description="A list of informative sub-queries that resolve the user query",
         min_items=1,
         max_items=5,
     )
     vector_search_queries: List[VectorSearchQuery] = Field(
         ...,
-        description="A list of diverse queries to search for similar documents in the vector space",
+        description="A list of diverse queries to search for similar documents in a vector space",
         min_items=1,
         max_items=5,
     )
@@ -191,7 +193,10 @@ def avoid_query(self) -> bool:
         )
 
     def parse_output(
-        self, query: str, chat_history: Optional[List[Tuple[str, str]]] = None
+        self,
+        query: str,
+        chat_history: Optional[List[Tuple[str, str]]] = None,
+        config: RAGPipelineConfig | None = None,
     ) -> Dict[str, Any]:
         """Parse the output of the model"""
         question = clean_question(query)
@@ -201,22 +206,32 @@ def parse_output(
         else:
             standalone_query = self.standalone_query
 
-        if self.avoid_query:
+        use_search_api = (
+            True if config is None else config.use_you_search_api.enabled
+        )
+
+        if use_search_api:
+            if self.avoid_query:
+                keywords = []
+                sub_queries = []
+                vector_search_queries = []
+            else:
+                keywords = [keyword.keyword for keyword in self.keywords]
+                sub_queries = [
+                    sub_query.query for sub_query in self.sub_queries
+                ]
+                vector_search_queries = [
+                    vector_search_query.query
+                    for vector_search_query in self.vector_search_queries
+                ]
+        else:
             keywords = []
             sub_queries = []
             vector_search_queries = []
-        else:
-            keywords = [keyword.keyword for keyword in self.keywords]
-            sub_queries = [sub_query.query for sub_query in self.sub_queries]
-            vector_search_queries = [
-                vector_search_query.query
-                for vector_search_query in self.vector_search_queries
-            ]
         intents_descriptions = ""
         for intent in self.intents:
             intents_descriptions += (
                 f"{intent.label.value.replace('_', ' ').title()}:"
-                f"\n\t{intent.reasoning}"
                 f"\n\t{QUERY_INTENTS[intent.label.value]}\n\n"
             )
 
@@ -233,7 +248,7 @@ def parse_output(
             "sub_queries": sub_queries,
             "vector_search_queries": vector_search_queries,
             "language": self.language,
-            "avoid_query": self.avoid_query,
+            "avoid_query": self.avoid_query if use_search_api else True,
             "chat_history": chat_history,
             "all_queries": all_queries,
         }
@@ -260,11 +275,20 @@ class QueryEnhancer:
 
     def __init__(
         self,
-        model: str = "gpt-4-0125-preview",
+        config: RAGPipelineConfig | None = None,
+        model: str = "gpt-4-1106-preview",
         fallback_model: str = "gpt-3.5-turbo-1106",
     ):
-        self.model = model  # type: ignore
-        self.fallback_model = fallback_model  # type: ignore
+        self.model = model if not config else config.llm.model  # type: ignore
+        self.fallback_model = fallback_model if not config else config.fallback_llm.model  # type: ignore
+
+        if config:
+            self.model.temperature = 0.0
+            self.model.max_retries = config.llm.max_retries
+            self.fallback_model.temperature = 0.0
+            self.fallback_model.max_retries = config.fallback_llm.max_retries
+
+        self.config = config
         self.prompt = ChatPromptTemplate.from_messages(ENHANCER_PROMPT_MESSAGES)
         self._chain = None
 
@@ -278,8 +302,11 @@ def chain(self) -> Runnable:
         return self._chain
 
     def _load_chain(self, model: ChatOpenAI) -> Runnable:
+        parser = PydanticOutputParser(pydantic_object=EnhancedQuery)
+        fixing_parser = OutputFixingParser.from_llm(parser=parser, llm=model)
+
         query_enhancer_chain = create_structured_output_runnable(
-            EnhancedQuery, model, self.prompt
+            EnhancedQuery, model, self.prompt, output_parser=fixing_parser
         )
 
         input_chain = RunnableParallel(
@@ -301,7 +328,7 @@ def _load_chain(self, model: ChatOpenAI) -> Runnable:
         )
         chain = intermediate_chain | RunnableLambda(
             lambda x: x["enhanced_query"].parse_output(
-                x["query"], convert_to_messages(x["chat_history"])
+                x["query"], convert_to_messages(x["chat_history"]), self.config
             )
         )
 
diff --git a/src/wandbot/rag/response_synthesis.py b/src/wandbot/rag/response_synthesis.py
index e6f4580..1a20077 100644
--- a/src/wandbot/rag/response_synthesis.py
+++ b/src/wandbot/rag/response_synthesis.py
@@ -1,10 +1,19 @@
+import json
 from operator import itemgetter
 
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.runnables import Runnable, RunnableLambda, RunnableParallel
 from langchain_openai import ChatOpenAI
-from wandbot.rag.utils import ChatModel, combine_documents, create_query_str
+
+from wandbot.rag.utils import (
+    ChatModel,
+    combine_documents,
+    create_query_str,
+    combine_simple_documents,
+    create_simple_query_str
+)
+from wandbot.utils import RAGPipelineConfig
 
 RESPONSE_SYNTHESIS_SYSTEM_PROMPT = """You are Wandbot - a support expert in Weights & Biases, wandb and weave. 
 Your goal to help users with questions related to Weight & Biases, `wandb`, and the visualization library `weave`
@@ -82,32 +91,120 @@
  - [^2]: [source](source_url)
  - [^3]: [source](source_url)
  ...
+"""
 
- <!--start-context-information-->
- 
- {context_str}
- 
- <!--end-context-information-->
+RESPONSE_SYNTHESIS_HUMAN_PROMPT = """
+<!--start-context-information-->
+
+{context_str}
+
+<!--end-context-information-->
+<!--start-question-->
+
+**Question**: {query_str}
+
+<!--end-question-->
+<!--final-answer-in-markdown-->
 """
 
+template = json.load(open("data/prompts/chat_prompt_v1_1.json"))
+
+example_1_human = template["messages"][1]["human"]
+example_1_assistant = template["messages"][2]["assistant"]
+example_2_human = template["messages"][3]["human"]
+example_2_assistant = template["messages"][4]["assistant"]
+
 
 RESPONSE_SYNTHESIS_PROMPT_MESSAGES = [
     ("system", RESPONSE_SYNTHESIS_SYSTEM_PROMPT),
-    ("human", "{query_str}"),
+    ("human", example_1_human),
+    ("assistant", example_1_assistant),
+    ("human", example_2_human),
+    ("assistant", example_2_assistant),
+    ("human", RESPONSE_SYNTHESIS_HUMAN_PROMPT),
 ]
 
 
+class SimpleResponseSynthesizer:
+    model: ChatModel = ChatModel()
+    fallback_model: ChatModel = ChatModel(max_retries=6)
+
+    def __init__(
+        self,
+        config: RAGPipelineConfig | None = None,
+        model: str = "gpt-4-0125-preview",
+        fallback_model: str = "gpt-3.5-turbo-1106",
+    ):
+        self.model = model if not config else config.llm.model   # type: ignore
+        self.fallback_model = fallback_model if not config else config.fallback_llm.model # type: ignore
+
+        if config:
+            self.model.temperature = config.llm.temperature
+            self.model.max_retries = config.llm.max_retries
+            self.fallback_model.temperature = config.fallback_llm.temperature
+            self.fallback_model.max_retries = config.fallback_llm.max_retries
+
+        self.prompt = ChatPromptTemplate.from_messages(
+            RESPONSE_SYNTHESIS_PROMPT_MESSAGES
+        )
+        self._chain = None
+
+    @property
+    def chain(self) -> Runnable:
+        if self._chain is None:
+            base_chain = self._load_chain(self.model)
+            fallback_chain = self._load_chain(self.fallback_model)
+            self._chain = base_chain.with_fallbacks([fallback_chain])
+        return self._chain
+
+    def _load_chain(self, model: ChatOpenAI) -> Runnable:
+        response_synthesis_chain = (
+            RunnableLambda(
+                lambda x: {
+                    "query_str": create_simple_query_str(x),
+                    "context_str": combine_simple_documents(x.context),
+                }
+            )
+            | RunnableParallel(
+                query_str=itemgetter("query_str"),
+                context_str=itemgetter("context_str"),
+                response_prompt=self.prompt,
+            )
+            | RunnableParallel(
+                query_str=itemgetter("query_str"),
+                context_str=itemgetter("context_str"),
+                response_prompt=RunnableLambda(
+                    lambda x: x["response_prompt"].to_string()
+                ),
+                response=itemgetter("response_prompt")
+                | model
+                | StrOutputParser(),
+                response_model=RunnableLambda(lambda x: model.model_name),
+            )
+        )
+
+        return response_synthesis_chain
+
+
 class ResponseSynthesizer:
     model: ChatModel = ChatModel()
     fallback_model: ChatModel = ChatModel(max_retries=6)
 
     def __init__(
         self,
+        config: RAGPipelineConfig | None = None,
         model: str = "gpt-4-0125-preview",
         fallback_model: str = "gpt-3.5-turbo-1106",
     ):
-        self.model = model  # type: ignore
-        self.fallback_model = fallback_model  # type: ignore
+        self.model = model if not config else config.llm.model   # type: ignore
+        self.fallback_model = fallback_model if not config else config.fallback_llm.model # type: ignore
+
+        if config:
+            self.model.temperature = config.llm.temperature
+            self.model.max_retries = config.llm.max_retries
+            self.fallback_model.temperature = config.fallback_llm.temperature
+            self.fallback_model.max_retries = config.fallback_llm.max_retries
+
         self.prompt = ChatPromptTemplate.from_messages(
             RESPONSE_SYNTHESIS_PROMPT_MESSAGES
         )
diff --git a/src/wandbot/rag/retrieval.py b/src/wandbot/rag/retrieval.py
index 065c791..134dc62 100644
--- a/src/wandbot/rag/retrieval.py
+++ b/src/wandbot/rag/retrieval.py
@@ -1,4 +1,5 @@
 from typing import List
+from itertools import chain
 
 from langchain.retrievers.document_compressors import CohereRerank
 from langchain_core.documents import Document
@@ -52,6 +53,51 @@ def rerank_results(
     return ranked_results
 
 
+class SimpleRetrievalWithoutFusion:
+    def __init__(
+        self,
+        vector_store: VectorStore,
+        top_k: int = 5,
+        search_type: str = "mmr",
+    ):
+        self.vectorstore = vector_store
+        self.top_k = top_k
+        self.search_type = search_type
+
+        self.retriever = self.vectorstore.as_parent_retriever(
+            search_type=self.search_type, search_kwargs={"k": self.top_k}
+        )
+
+        self._chain = None
+
+    @property
+    def chain(self) -> Runnable:
+        if self._chain is None:
+            self._chain = (
+                RunnablePassthrough().assign(
+                    docs_context=lambda x: self.retriever.batch(
+                        x["all_queries"]
+                    ),
+                    web_context=lambda x: run_web_search(
+                        x["standalone_query"], x["avoid_query"]
+                    ),
+                )
+                | RunnablePassthrough().assign(
+                    full_context=lambda x: x["docs_context"]
+                    + [x["web_context"]]
+                )
+                | RunnablePassthrough().assign(
+                    context=lambda x: rerank_results(
+                        [x["standalone_query"]],
+                        list(chain.from_iterable(x["full_context"])),
+                        self.top_k,
+                        x["language"],
+                    )
+                )
+            )
+        return self._chain
+
+
 class FusionRetrieval:
     def __init__(
         self,
diff --git a/src/wandbot/rag/utils.py b/src/wandbot/rag/utils.py
index f2a11bd..0c1569d 100644
--- a/src/wandbot/rag/utils.py
+++ b/src/wandbot/rag/utils.py
@@ -1,10 +1,17 @@
 import json
+from pydantic import BaseModel
 
 from langchain_core.documents import Document
 from langchain_core.prompts import PromptTemplate, format_document
 from langchain_openai import ChatOpenAI
-from wandbot.utils import clean_document_content
+from langchain_community.chat_models import ChatLiteLLM
 
+from litellm import completion_cost
+from litellm.integrations.custom_logger import CustomLogger
+
+from wandbot.utils import clean_document_content, get_logger
+
+logger = get_logger(__name__)
 
 class ChatModel:
     def __init__(self, temperature: float = 0.1, max_retries: int = 2):
@@ -20,10 +27,11 @@ def __get__(self, obj, obj_type=None):
         return value
 
     def __set__(self, obj, value):
-        model = ChatOpenAI(
+        model = ChatLiteLLM(
             model_name=value,
             temperature=self.temperature,
             max_retries=self.max_retries,
+            max_tokens=4096,
         )
         setattr(obj, self.private_name, model)
 
@@ -42,21 +50,46 @@ def __set__(self, obj, value):
 Intents: 
 
 {intents}
-
-Sub-queries to consider answering: 
-
-{sub_queries}
 """
 )
 
+# Sub-queries to consider answering: 
+
+# {sub_queries}
+
 
 def create_query_str(enhanced_query, document_prompt=DEFAULT_QUESTION_PROMPT):
     page_content = enhanced_query["standalone_query"]
     metadata = {
         "language": enhanced_query["language"],
         "intents": enhanced_query["intents"],
-        "sub_queries": "\t"
-        + "\n\t".join(enhanced_query["sub_queries"]).strip(),
+        # "sub_queries": "\t"
+        # + "\n\t".join(enhanced_query["sub_queries"]).strip(),
+    }
+    doc = Document(page_content=page_content, metadata=metadata)
+    doc = clean_document_content(doc)
+    doc_string = format_document(doc, document_prompt)
+    return doc_string
+
+
+SIMPLE_DEFAULT_QUESTION_PROMPT = PromptTemplate.from_template(
+    template="""# Query
+
+{page_content}
+
+---
+
+# Query Metadata
+
+Language: {language}
+"""
+)
+
+
+def create_simple_query_str(data, document_prompt=SIMPLE_DEFAULT_QUESTION_PROMPT):
+    page_content = data.question
+    metadata = {
+        "language": data.language,
     }
     doc = Document(page_content=page_content, metadata=metadata)
     doc = clean_document_content(doc)
@@ -81,6 +114,23 @@ def combine_documents(
     return document_separator.join(doc_strings)
 
 
+SIMPLE_DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(
+    template="source: {source}\nsource_type: {source_type}\nhas_code: {has_code}\n\n{page_content}"
+)
+
+
+def combine_simple_documents(
+    docs,
+    document_prompt=SIMPLE_DEFAULT_DOCUMENT_PROMPT,
+    document_separator="\n\n---\n\n",
+):
+    cleaned_docs = [clean_document_content(doc) for doc in docs]
+    doc_strings = [
+        format_document(doc, document_prompt) for doc in cleaned_docs
+    ]
+    return document_separator.join(doc_strings)
+
+
 def process_input_for_retrieval(retrieval_input):
     if isinstance(retrieval_input, list):
         retrieval_input = "\n".join(retrieval_input)
@@ -106,3 +156,47 @@ def get_web_contexts(web_results):
         if web_results.get("web_context")
         else []
     )
+
+
+class TotalsModel(BaseModel):
+    completion_tokens: int = 0
+    prompt_tokens: int = 0
+    total_tokens: int = 0
+    total_cost: float = 0.0
+
+
+class LiteLLMTokenCostLogger(CustomLogger):
+    def __init__(self):
+        # Use the Pydantic model for managing totals
+        self.totals = TotalsModel()
+
+    def log_success_event(self, kwargs, response_obj, start_time, end_time):
+        # Update totals using the response object
+        self.totals.completion_tokens += response_obj.usage.completion_tokens
+        self.totals.prompt_tokens += response_obj.usage.prompt_tokens
+        self.totals.total_tokens += response_obj.usage.total_tokens
+        self.totals.total_cost += completion_cost(response_obj)
+        logger.info(
+            f"On Success: Completion Tokens: {response_obj.usage.completion_tokens}, "
+            f"Prompt Tokens: {response_obj.usage.prompt_tokens}, "
+            f"Total Tokens: {response_obj.usage.total_tokens}, "
+            f"Cost: {completion_cost(response_obj)}"
+        )
+
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+        # Similar to the synchronous method, update totals for async events
+        self.totals.completion_tokens += response_obj.usage.completion_tokens
+        self.totals.prompt_tokens += response_obj.usage.prompt_tokens
+        self.totals.total_tokens += response_obj.usage.total_tokens
+        self.totals.total_cost += completion_cost(response_obj)
+        logger.info(
+            f"On Async Success: Completion Tokens: {response_obj.usage.completion_tokens}, "
+            f"Prompt Tokens: {response_obj.usage.prompt_tokens}, "
+            f"Total Tokens: {response_obj.usage.total_tokens}, "
+            f"Cost: {completion_cost(response_obj)}"
+        )
+
+    def get_totals(self):
+        current_totals = self.totals
+        self.totals = TotalsModel()
+        return current_totals
diff --git a/src/wandbot/retriever/base.py b/src/wandbot/retriever/base.py
index b33ffe1..deefce8 100644
--- a/src/wandbot/retriever/base.py
+++ b/src/wandbot/retriever/base.py
@@ -5,41 +5,41 @@
 from langchain_community.document_transformers import EmbeddingsRedundantFilter
 from langchain_community.vectorstores.chroma import Chroma
 from langchain_core.documents import Document
-from langchain_core.runnables import RunnableLambda, RunnableParallel
+from langchain_core.runnables import RunnableLambda, RunnableParallel, RunnablePassthrough
 from wandbot.ingestion.config import VectorStoreConfig
 from wandbot.retriever.reranking import CohereRerankChain
-from wandbot.retriever.utils import OpenAIEmbeddingsModel
+from wandbot.retriever.utils import CustomDocument, SimpleRetrievalEngineOutput
+
+from wandbot.ingestion.utils import LiteLLMEmbeddings
+from wandbot.ingestion.config import OpenAIEmbeddingConfig
+
+from wandbot.utils import get_logger
+
+logger = get_logger(__name__)
 
 
 class VectorStore:
-    embeddings_model: OpenAIEmbeddingsModel = OpenAIEmbeddingsModel(
-        dimensions=512
-    )
-    config: VectorStoreConfig = VectorStoreConfig()
+    config: VectorStoreConfig = OpenAIEmbeddingConfig()
 
     def __init__(
         self,
-        embeddings_model: str,
-        collection_name: str,
-        persist_dir: str,
-        config: VectorStoreConfig = None,
+        config: VectorStoreConfig | None = None,
     ):
         if config is not None:
             self.config = config
-        self.embeddings_model = embeddings_model  # type: ignore
+        self.embeddings_model = LiteLLMEmbeddings(
+            **config.get_lite_llm_embeddings_params()
+        )  # type: ignore
         self.vectorstore = Chroma(
-            collection_name=collection_name,
+            collection_name=config.name,
             embedding_function=self.embeddings_model,  # type: ignore
-            persist_directory=persist_dir,
+            persist_directory=str(config.persist_dir),
         )
 
     @classmethod
     def from_config(cls, config: VectorStoreConfig):
         if config.persist_dir.exists():
             return cls(
-                embeddings_model=config.embeddings_model,
-                collection_name=config.name,
-                persist_dir=str(config.persist_dir),
                 config=config,
             )
         if wandb.run is None:
@@ -50,9 +50,6 @@ def from_config(cls, config: VectorStoreConfig):
         _ = artifact.download(root=str(config.persist_dir))
 
         return cls(
-            embeddings_model=config.embeddings_model,
-            collection_name=config.name,
-            persist_dir=str(config.persist_dir),
             config=config,
         )
 
@@ -84,18 +81,73 @@ def as_parent_retriever(self, search_type="mmr", search_kwargs=None):
 
 
 class SimpleRetrievalEngine:
+    def __init__(self, vector_store: VectorStore, top_k: int=5, search_type: str="mmr"):
+        self.vector_store = vector_store
+        self.embeddings_model = vector_store.embeddings_model  # type: ignore
+        self.top_k = top_k
+        self.search_type = search_type
+
+    def __call__(
+        self,
+        question: str,
+        language: str | None = None,
+        top_k: int = 5,
+        search_type="mmr",
+    ) -> SimpleRetrievalEngineOutput:
+        if language is not None:
+            language_filter = {"language": language}
+        if language_filter:
+            search_kwargs = {"k": top_k * 4, "filter": language_filter}
+        else:
+            search_kwargs = {"k": top_k * 4}
+
+        self.top_k = top_k
+
+        retriever = self.vector_store.as_parent_retriever(
+            search_type=search_type, search_kwargs=search_kwargs
+        )
+
+        retrieval_chain = (
+            RunnableParallel(
+                question=itemgetter("question"),
+                language=itemgetter("language"),
+                context=(
+                    itemgetter("question") | retriever
+                ),
+            )
+        )
+        results = retrieval_chain.invoke(
+            {"question": question, "language": language, "top_k": top_k}
+        )
+
+        outputs = []
+        for result in results["context"]:
+            metadata_dict = {
+                k: v
+                for k, v in result.metadata.items()
+                if k
+                not in ["source_content", "id", "parent_id"]
+            }
+            outputs.append(
+                CustomDocument(page_content=result.page_content, metadata=metadata_dict)
+            )
+
+        return SimpleRetrievalEngineOutput(
+            question=results.get("question"), language=results.get("language"), context=outputs
+        )
+
+
+class SimpleRetrievalEngineWithRerank:
     cohere_rerank_chain = CohereRerankChain()
-    embeddings_model: OpenAIEmbeddingsModel = OpenAIEmbeddingsModel(
-        dimensions=768
-    )
 
-    def __init__(self, vector_store: VectorStore, top_k=5):
+    def __init__(self, vector_store: VectorStore, top_k: int=5, search_type: str="mmr"):
         self.vector_store = vector_store
-        self.embeddings_model = vector_store.config.embeddings_model  # type: ignore
+        self.embeddings_model = vector_store.embeddings_model  # type: ignore
         self.redundant_filter = EmbeddingsRedundantFilter(
             embeddings=self.embeddings_model
         ).transform_documents
         self.top_k = top_k
+        self.search_type = search_type
 
     def __call__(
         self,
@@ -104,7 +156,7 @@ def __call__(
         top_k: int = 5,
         search_type="mmr",
         sources: List[str] = None,
-    ):
+    ) -> SimpleRetrievalEngineOutput:
         filters = {}
         source_filter = None
         language_filter = None
@@ -142,10 +194,10 @@ def __call__(
         results = retrieval_chain.invoke(
             {"question": question, "language": language, "top_k": top_k}
         )
+
         outputs = []
         for result in results:
-            result_dict = {
-                "text": result.page_content,
+            metadata_dict = {
                 "score": result.metadata["relevance_score"],
             }
             metadata_dict = {
@@ -154,7 +206,11 @@ def __call__(
                 if k
                 not in ["relevance_score", "source_content", "id", "parent_id"]
             }
-            result_dict["metadata"] = metadata_dict
-            outputs.append(result_dict)
 
-        return outputs
+            outputs.append(CustomDocument(page_content=result.page_content, metadata=metadata_dict))
+
+        return SimpleRetrievalEngineOutput(
+            question=question, 
+            language=language,
+            context=outputs
+        )
diff --git a/src/wandbot/retriever/utils.py b/src/wandbot/retriever/utils.py
index 371a750..6138630 100644
--- a/src/wandbot/retriever/utils.py
+++ b/src/wandbot/retriever/utils.py
@@ -1,3 +1,5 @@
+from typing import List
+from pydantic import BaseModel
 from langchain_openai import OpenAIEmbeddings
 
 
@@ -20,3 +22,14 @@ def __set__(self, obj, value):
             dimensions=self.dimensions,
         )
         setattr(obj, self.private_name, model)
+
+
+class CustomDocument(BaseModel):
+    page_content: str
+    metadata: dict
+
+
+class SimpleRetrievalEngineOutput(BaseModel):
+    question: str
+    language: str
+    context: List[CustomDocument]
\ No newline at end of file
diff --git a/src/wandbot/utils.py b/src/wandbot/utils.py
index 3a7ccd3..987b425 100644
--- a/src/wandbot/utils.py
+++ b/src/wandbot/utils.py
@@ -27,18 +27,19 @@
 import json
 import logging
 import os
+import yaml
 import pathlib
 import re
 import sqlite3
 import string
-from typing import Any, Coroutine, List, Tuple
 
 import fasttext
 import nest_asyncio
 import tiktoken
 from langchain_core.documents import Document
-from pydantic import Field
+from pydantic import Field, BaseModel
 from pydantic_settings import BaseSettings, SettingsConfigDict
+from typing import Any, Coroutine, List, Tuple, Optional
 
 import wandb
 
@@ -54,7 +55,7 @@ def get_logger(name: str) -> logging.Logger:
     """
     logging.basicConfig(
         format="%(asctime)s : %(levelname)s : %(message)s",
-        level=logging.getLevelName(os.environ.get("LOG_LEVEL", "INFO")),
+        level=logging.getLevelName(os.environ.get("LOG_LEVEL", "CRITICAL")),
     )
     logger = logging.getLogger(name)
     return logger
@@ -299,3 +300,46 @@ def filter_small_document(document: Document) -> bool:
     return [
         document for document in documents if filter_small_document(document)
     ]
+
+
+class LLMConfig(BaseModel):
+    model: str
+    temperature: float
+    max_retries: int
+
+
+class FeatureToggle(BaseModel):
+    enabled: bool
+
+
+class EmbeddingsConfig(BaseModel):
+    type: str
+    config: dict
+
+
+class RAGPipelineConfig(BaseModel):
+    llm: LLMConfig
+    fallback_llm: LLMConfig
+    embeddings: EmbeddingsConfig
+    retrieval_re_ranker: FeatureToggle
+    use_you_search_api: FeatureToggle
+    query_enhancer: FeatureToggle
+    rerank_fusion: FeatureToggle
+    chunk_size: int
+    top_k: int
+    project: str | None = Field("wandbot_public", env="WANDB_PROJECT")
+    entity: str | None = Field("wandbot", env="WANDB_ENTITY")
+ 
+
+def load_config(config_path: str) -> RAGPipelineConfig:
+    """Load and return the YAML configuration as a Pydantic model."""
+    config_path = pathlib.Path(__file__).parent / config_path
+    logger.info(f"Loading configuration from {config_path}")
+
+    with open(config_path, 'r') as file:
+        raw_config = yaml.safe_load(file)
+    
+    embeddings_config = raw_config["embeddings"]["config"]
+    unique_identifier = '-'.join(f"{key}_{value}" for key, value in embeddings_config.items())
+    raw_config["embeddings"]["config"]["persist_dir"] = pathlib.Path(f"data/cache/vectorstore/openai/{unique_identifier}")
+    return RAGPipelineConfig(**raw_config)