diff --git a/backend/core/poetry.lock b/backend/core/poetry.lock
index 6994fba464de..577b400a67cf 100644
--- a/backend/core/poetry.lock
+++ b/backend/core/poetry.lock
@@ -1,5 +1,16 @@
 # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
+[[package]]
+name = "aiofiles"
+version = "24.1.0"
+description = "File support for asyncio."
+optional = false
+python-versions = ">=3.8"
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+]
+
 [[package]]
 name = "aiohttp"
 version = "3.9.5"
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+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.8"
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+
+[package.dependencies]
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+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
+
+[[package]]
+name = "async-timeout"
+version = "4.0.3"
+description = "Timeout context manager for asyncio programs"
+optional = false
+python-versions = ">=3.7"
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+[[package]]
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+    {file = "asyncpg-0.29.0.tar.gz", hash = "sha256:d1c49e1f44fffafd9a55e1a9b101590859d881d639ea2922516f5d9c512d354e"},
+]
+
+[package.dependencies]
+async-timeout = {version = ">=4.0.3", markers = "python_version < \"3.12.0\""}
+
+[package.extras]
+docs = ["Sphinx (>=5.3.0,<5.4.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-asyncio (>=0.3.0,<0.4.0)"]
+test = ["flake8 (>=6.1,<7.0)", "uvloop (>=0.15.3)"]
+
 [[package]]
 name = "attrs"
 version = "23.2.0"
@@ -249,6 +348,124 @@ files = [
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+version = "8.1.7"
+description = "Composable command line interface toolkit"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+description = "Cross-platform colored terminal text."
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+files = [
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+
+[[package]]
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+description = "A library to handle automated deprecations"
+optional = true
+python-versions = "*"
+files = [
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+    {file = "deprecation-2.1.0.tar.gz", hash = "sha256:72b3bde64e5d778694b0cf68178aed03d15e15477116add3fb773e581f9518ff"},
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+[package.dependencies]
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+
+[[package]]
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+version = "2.6.1"
+description = "DNS toolkit"
+optional = false
+python-versions = ">=3.8"
+files = [
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+dnssec = ["cryptography (>=41)"]
+doh = ["h2 (>=4.1.0)", "httpcore (>=1.0.0)", "httpx (>=0.26.0)"]
+doq = ["aioquic (>=0.9.25)"]
+idna = ["idna (>=3.6)"]
+trio = ["trio (>=0.23)"]
+wmi = ["wmi (>=1.5.1)"]
+
+[[package]]
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+version = "2.2.0"
+description = "A robust email address syntax and deliverability validation library."
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.dependencies]
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+
+[[package]]
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+version = "0.111.0"
+description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "fastapi-0.111.0.tar.gz", hash = "sha256:b9db9dd147c91cb8b769f7183535773d8741dd46f9dc6676cd82eab510228cd7"},
+]
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+[package.dependencies]
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+jinja2 = ">=2.11.2"
+orjson = ">=3.2.1"
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+python-multipart = ">=0.0.7"
+starlette = ">=0.37.2,<0.38.0"
+typing-extensions = ">=4.8.0"
+ujson = ">=4.0.1,<4.0.2 || >4.0.2,<4.1.0 || >4.1.0,<4.2.0 || >4.2.0,<4.3.0 || >4.3.0,<5.0.0 || >5.0.0,<5.1.0 || >5.1.0"
+uvicorn = {version = ">=0.12.0", extras = ["standard"]}
+
+[package.extras]
+all = ["email_validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
+
+[[package]]
+name = "fastapi-cli"
+version = "0.0.4"
+description = "Run and manage FastAPI apps from the command line with FastAPI CLI. 🚀"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
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+
+[package.extras]
+standard = ["fastapi", "uvicorn[standard] (>=0.15.0)"]
+
 [[package]]
 name = "frozenlist"
 version = "1.4.1"
@@ -335,6 +552,21 @@ files = [
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+[[package]]
+name = "gotrue"
+version = "2.5.4"
+description = "Python Client Library for Supabase Auth"
+optional = true
+python-versions = "<4.0,>=3.8"
+files = [
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+[package.dependencies]
+httpx = ">=0.23,<0.28"
+pydantic = ">=1.10,<3"
+
 [[package]]
 name = "greenlet"
 version = "3.0.3"
@@ -406,6 +638,110 @@ files = [
 docs = ["Sphinx", "furo"]
 test = ["objgraph", "psutil"]
 
+[[package]]
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+version = "0.14.0"
+description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
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+
+[[package]]
+name = "httpcore"
+version = "1.0.5"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.8"
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+]
+
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+description = "Markdown URL utilities"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"},
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+]
+
 [[package]]
 name = "multidict"
 version = "6.0.5"
@@ -733,6 +1190,23 @@ files = [
     {file = "packaging-24.1.tar.gz", hash = "sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002"},
 ]
 
+[[package]]
+name = "postgrest"
+version = "0.16.8"
+description = "PostgREST client for Python. This library provides an ORM interface to PostgREST."
+optional = true
+python-versions = "<4.0,>=3.8"
+files = [
+    {file = "postgrest-0.16.8-py3-none-any.whl", hash = "sha256:c353a24452f51ab9760cf2b884c4b7457a2653ff36444e66b12615bc4cc8e23e"},
+    {file = "postgrest-0.16.8.tar.gz", hash = "sha256:7b3802a514dc1e0fc8b5bbdeb2c99af35a0bd910e4ddb17855ca4e3422350c84"},
+]
+
+[package.dependencies]
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+httpx = ">=0.24,<0.28"
+pydantic = ">=1.9,<3.0"
+strenum = ">=0.4.9,<0.5.0"
+
 [[package]]
 name = "pydantic"
 version = "2.7.4"
@@ -843,6 +1317,62 @@ files = [
 [package.dependencies]
 typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
+[[package]]
+name = "pygments"
+version = "2.18.0"
+description = "Pygments is a syntax highlighting package written in Python."
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "pygments-2.18.0.tar.gz", hash = "sha256:786ff802f32e91311bff3889f6e9a86e81505fe99f2735bb6d60ae0c5004f199"},
+]
+
+[package.extras]
+windows-terminal = ["colorama (>=0.4.6)"]
+
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+description = "Extensions to the standard Python datetime module"
+optional = true
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+files = [
+    {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
+    {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
+]
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+name = "python-dotenv"
+version = "1.0.1"
+description = "Read key-value pairs from a .env file and set them as environment variables"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "python-dotenv-1.0.1.tar.gz", hash = "sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca"},
+    {file = "python_dotenv-1.0.1-py3-none-any.whl", hash = "sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a"},
+]
+
+[package.extras]
+cli = ["click (>=5.0)"]
+
+[[package]]
+name = "python-multipart"
+version = "0.0.9"
+description = "A streaming multipart parser for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "python_multipart-0.0.9-py3-none-any.whl", hash = "sha256:97ca7b8ea7b05f977dc3849c3ba99d51689822fab725c3703af7c866a0c2b215"},
+    {file = "python_multipart-0.0.9.tar.gz", hash = "sha256:03f54688c663f1b7977105f021043b0793151e4cb1c1a9d4a11fc13d622c4026"},
+]
+
+[package.extras]
+dev = ["atomicwrites (==1.4.1)", "attrs (==23.2.0)", "coverage (==7.4.1)", "hatch", "invoke (==2.2.0)", "more-itertools (==10.2.0)", "pbr (==6.0.0)", "pluggy (==1.4.0)", "py (==1.11.0)", "pytest (==8.0.0)", "pytest-cov (==4.1.0)", "pytest-timeout (==2.2.0)", "pyyaml (==6.0.1)", "ruff (==0.2.1)"]
+
 [[package]]
 name = "pyyaml"
 version = "6.0.1"
@@ -903,6 +1433,22 @@ files = [
     {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
 ]
 
+[[package]]
+name = "realtime"
+version = "1.0.6"
+description = ""
+optional = true
+python-versions = "<4.0,>=3.8"
+files = [
+    {file = "realtime-1.0.6-py3-none-any.whl", hash = "sha256:c66918a106d8ef348d1821f2dbf6683d8833825580d95b2fdea9995406b42838"},
+    {file = "realtime-1.0.6.tar.gz", hash = "sha256:2be0d8a6305513d423604ee319216108fc20105cb7438922d5c8958c48f40a47"},
+]
+
+[package.dependencies]
+python-dateutil = ">=2.8.1,<3.0.0"
+typing-extensions = ">=4.12.2,<5.0.0"
+websockets = ">=11,<13"
+
 [[package]]
 name = "requests"
 version = "2.32.3"
@@ -924,6 +1470,57 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "rich"
+version = "13.7.1"
+description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "rich-13.7.1-py3-none-any.whl", hash = "sha256:4edbae314f59eb482f54e9e30bf00d33350aaa94f4bfcd4e9e3110e64d0d7222"},
+    {file = "rich-13.7.1.tar.gz", hash = "sha256:9be308cb1fe2f1f57d67ce99e95af38a1e2bc71ad9813b0e247cf7ffbcc3a432"},
+]
+
+[package.dependencies]
+markdown-it-py = ">=2.2.0"
+pygments = ">=2.13.0,<3.0.0"
+
+[package.extras]
+jupyter = ["ipywidgets (>=7.5.1,<9)"]
+
+[[package]]
+name = "shellingham"
+version = "1.5.4"
+description = "Tool to Detect Surrounding Shell"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686"},
+    {file = "shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de"},
+]
+
+[[package]]
+name = "six"
+version = "1.16.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = true
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
+    {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
+    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+]
+
+[[package]]
+name = "sniffio"
+version = "1.3.1"
+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
+    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
+]
+
 [[package]]
 name = "sqlalchemy"
 version = "2.0.31"
@@ -1011,6 +1608,103 @@ postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
 sqlcipher = ["sqlcipher3_binary"]
 
+[[package]]
+name = "sqlmodel"
+version = "0.0.19"
+description = "SQLModel, SQL databases in Python, designed for simplicity, compatibility, and robustness."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "sqlmodel-0.0.19-py3-none-any.whl", hash = "sha256:6c8125d4101970d031e9aae970b20cbeaf44149989f8366d939f4ab21aab8763"},
+    {file = "sqlmodel-0.0.19.tar.gz", hash = "sha256:95449b0b48a40a3eecf0a629fa5735b9dfc8a5574a91090d24ca17f02246ad96"},
+]
+
+[package.dependencies]
+pydantic = ">=1.10.13,<3.0.0"
+SQLAlchemy = ">=2.0.14,<2.1.0"
+
+[[package]]
+name = "starlette"
+version = "0.37.2"
+description = "The little ASGI library that shines."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "starlette-0.37.2-py3-none-any.whl", hash = "sha256:6fe59f29268538e5d0d182f2791a479a0c64638e6935d1c6989e63fb2699c6ee"},
+    {file = "starlette-0.37.2.tar.gz", hash = "sha256:9af890290133b79fc3db55474ade20f6220a364a0402e0b556e7cd5e1e093823"},
+]
+
+[package.dependencies]
+anyio = ">=3.4.0,<5"
+
+[package.extras]
+full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
+
+[[package]]
+name = "storage3"
+version = "0.7.6"
+description = "Supabase Storage client for Python."
+optional = true
+python-versions = "<4.0,>=3.8"
+files = [
+    {file = "storage3-0.7.6-py3-none-any.whl", hash = "sha256:d8c23bf87b3a88cafb03761b7f936e4e49daca67741d571513edf746e0f8ba72"},
+    {file = "storage3-0.7.6.tar.gz", hash = "sha256:0b7781cea7fe6382e6b9349b84395808c5f4203dfcac31478304eedc2f81acf6"},
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+[package.dependencies]
+httpx = ">=0.24,<0.28"
+python-dateutil = ">=2.8.2,<3.0.0"
+typing-extensions = ">=4.2.0,<5.0.0"
+
+[[package]]
+name = "strenum"
+version = "0.4.15"
+description = "An Enum that inherits from str."
+optional = true
+python-versions = "*"
+files = [
+    {file = "StrEnum-0.4.15-py3-none-any.whl", hash = "sha256:a30cda4af7cc6b5bf52c8055bc4bf4b2b6b14a93b574626da33df53cf7740659"},
+    {file = "StrEnum-0.4.15.tar.gz", hash = "sha256:878fb5ab705442070e4dd1929bb5e2249511c0bcf2b0eeacf3bcd80875c82eff"},
+]
+
+[package.extras]
+docs = ["myst-parser[linkify]", "sphinx", "sphinx-rtd-theme"]
+release = ["twine"]
+test = ["pylint", "pytest", "pytest-black", "pytest-cov", "pytest-pylint"]
+
+[[package]]
+name = "supabase"
+version = "2.5.1"
+description = "Supabase client for Python."
+optional = true
+python-versions = "<4.0,>=3.8"
+files = [
+    {file = "supabase-2.5.1-py3-none-any.whl", hash = "sha256:74a1f24f04fede1967ef084b50dea688228f7b10eb2f9d73350fe2251a865188"},
+    {file = "supabase-2.5.1.tar.gz", hash = "sha256:c50e0eba5b03de3abd5ac0f887957ca43558ba44c4d17bb44e73ec454b41734c"},
+]
+
+[package.dependencies]
+gotrue = ">=1.3,<3.0"
+httpx = ">=0.24,<0.28"
+postgrest = ">=0.14,<0.17.0"
+realtime = ">=1.0.0,<2.0.0"
+storage3 = ">=0.5.3,<0.8.0"
+supafunc = ">=0.3.1,<0.5.0"
+
+[[package]]
+name = "supafunc"
+version = "0.4.6"
+description = "Library for Supabase Functions"
+optional = true
+python-versions = "<4.0,>=3.8"
+files = [
+    {file = "supafunc-0.4.6-py3-none-any.whl", hash = "sha256:f7ca7b244365e171da7055a64edb462c2ec449cdaa210fc418cfccd132f4cf98"},
+    {file = "supafunc-0.4.6.tar.gz", hash = "sha256:92db51f8f8568d1430285219c9c0072e44207409c416622d7387f609e31928a6"},
+]
+
+[package.dependencies]
+httpx = ">=0.24,<0.28"
+
 [[package]]
 name = "tenacity"
 version = "8.4.2"
@@ -1026,6 +1720,23 @@ files = [
 doc = ["reno", "sphinx"]
 test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
+[[package]]
+name = "typer"
+version = "0.12.3"
+description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "typer-0.12.3-py3-none-any.whl", hash = "sha256:070d7ca53f785acbccba8e7d28b08dcd88f79f1fbda035ade0aecec71ca5c914"},
+    {file = "typer-0.12.3.tar.gz", hash = "sha256:49e73131481d804288ef62598d97a1ceef3058905aa536a1134f90891ba35482"},
+]
+
+[package.dependencies]
+click = ">=8.0.0"
+rich = ">=10.11.0"
+shellingham = ">=1.3.0"
+typing-extensions = ">=3.7.4.3"
+
 [[package]]
 name = "typing-extensions"
 version = "4.12.2"
@@ -1037,6 +1748,93 @@ files = [
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
 
+[[package]]
+name = "ujson"
+version = "5.10.0"
+description = "Ultra fast JSON encoder and decoder for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
 [[package]]
 name = "yarl"
 version = "1.9.4"
@@ -1160,4 +2195,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "dc6781ae577a76ccefee8f8286035abf35e9e5b2486c2c671b2d4bbb9c99bdc5"
+content-hash = "982c385f8e526d48c67312f0852b1643703473608cfa4ef54f0a1bdae56c346e"
diff --git a/backend/core/pyproject.toml b/backend/core/pyproject.toml
index 8dfd73d3107a..a8c4823be462 100644
--- a/backend/core/pyproject.toml
+++ b/backend/core/pyproject.toml
@@ -12,6 +12,15 @@ pydantic = "^2.7.4"
 langchain-core = "^0.2.10"
 langchain = "^0.2.6"
 
+[tool.poetry.group.api.dependencies]
+aiofiles = "^24.1.0"
+python-dotenv = "^1.0.1"
+fastapi = "^0.111.0"
+sqlmodel = "^0.0.19"
+asyncpg = "^0.29.0"
+uvicorn = "^0.30.1"
+supabase = { version = "^2.5.1", optional = true }
+
 [tool.ruff]
 line-length = 88
 exclude = [".git", "__pycache__", ".mypy_cache", ".pytest_cache"]
diff --git a/backend/core/quivr_core/api/__init__.py b/backend/core/quivr_core/api/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/celery_config.py b/backend/core/quivr_core/api/celery_config.py
new file mode 100644
index 000000000000..e4752f8dec35
--- /dev/null
+++ b/backend/core/quivr_core/api/celery_config.py
@@ -0,0 +1,41 @@
+# celery_config.py
+import os
+
+import dotenv
+from celery import Celery
+
+dotenv.load_dotenv()
+
+CELERY_BROKER_URL = os.getenv("CELERY_BROKER_URL", "")
+CELERY_BROKER_QUEUE_NAME = os.getenv("CELERY_BROKER_QUEUE_NAME", "quivr")
+
+celery = Celery(__name__)
+
+if CELERY_BROKER_URL.startswith("sqs"):
+    broker_transport_options = {
+        CELERY_BROKER_QUEUE_NAME: {
+            "my-q": {
+                "url": CELERY_BROKER_URL,
+            }
+        }
+    }
+    celery = Celery(
+        __name__,
+        broker=CELERY_BROKER_URL,
+        task_serializer="json",
+        task_concurrency=4,
+        worker_prefetch_multiplier=1,
+        broker_transport_options=broker_transport_options,
+    )
+    celery.conf.task_default_queue = CELERY_BROKER_QUEUE_NAME
+elif CELERY_BROKER_URL.startswith("redis"):
+    celery = Celery(
+        __name__,
+        broker=f"{CELERY_BROKER_URL}",
+        backend=f"{CELERY_BROKER_URL}",
+        task_concurrency=4,
+        worker_prefetch_multiplier=2,
+        task_serializer="json",
+    )
+else:
+    raise ValueError(f"Unsupported broker URL: {CELERY_BROKER_URL}")
diff --git a/backend/core/quivr_core/api/celery_worker.py b/backend/core/quivr_core/api/celery_worker.py
new file mode 100644
index 000000000000..b4f75fdac9d3
--- /dev/null
+++ b/backend/core/quivr_core/api/celery_worker.py
@@ -0,0 +1,174 @@
+import os
+from tempfile import NamedTemporaryFile
+from uuid import UUID
+
+from celery.schedules import crontab
+
+from quivr_core.api.celery_config import celery
+from quivr_core.api.logger import get_logger
+from quivr_core.api.models.files import File
+from quivr_core.api.models.settings import get_supabase_client
+from quivr_core.api.modules.brain.service.brain_service import BrainService
+from quivr_core.api.modules.brain.service.brain_vector_service import BrainVectorService
+from quivr_core.api.modules.notification.dto.inputs import (
+    NotificationUpdatableProperties,
+)
+from quivr_core.api.modules.notification.entity.notification import (
+    NotificationsStatusEnum,
+)
+from quivr_core.api.modules.notification.service.notification_service import (
+    NotificationService,
+)
+from quivr_core.api.packages.files.crawl.crawler import CrawlWebsite, slugify
+from quivr_core.api.packages.files.parsers.github import process_github
+from quivr_core.api.packages.files.processors import filter_file
+from quivr_core.api.packages.utils.telemetry import maybe_send_telemetry
+
+logger = get_logger(__name__)
+
+notification_service = NotificationService()
+brain_service = BrainService()
+
+
+@celery.task(name="process_file_and_notify")
+def process_file_and_notify(
+    file_name: str,
+    file_original_name: str,
+    brain_id,
+    notification_id=None,
+    integration=None,
+    delete_file=False,
+):
+    try:
+        supabase_client = get_supabase_client()
+        tmp_name = file_name.replace("/", "_")
+        base_file_name = os.path.basename(file_name)
+        _, file_extension = os.path.splitext(base_file_name)
+
+        with NamedTemporaryFile(
+            suffix="_" + tmp_name,  # pyright: ignore reportPrivateUsage=none
+        ) as tmp_file:
+            res = supabase_client.storage.from_("quivr").download(file_name)
+            tmp_file.write(res)
+            tmp_file.flush()
+            file_instance = File(
+                file_name=base_file_name,
+                tmp_file_path=tmp_file.name,
+                bytes_content=res,
+                file_size=len(res),
+                file_extension=file_extension,
+            )
+            brain_vector_service = BrainVectorService(brain_id)
+            if delete_file:  # TODO fix bug
+                brain_vector_service.delete_file_from_brain(
+                    file_original_name, only_vectors=True
+                )
+
+            filter_file(
+                file=file_instance,
+                brain_id=brain_id,
+                original_file_name=file_original_name,
+            )
+
+            if notification_id:
+                notification_service.update_notification_by_id(
+                    notification_id,
+                    NotificationUpdatableProperties(
+                        status=NotificationsStatusEnum.SUCCESS,
+                        description="Your file has been properly uploaded!",
+                    ),
+                )
+            brain_service.update_brain_last_update_time(brain_id)
+
+            return True
+
+    except TimeoutError:
+        logger.error("TimeoutError")
+
+    except Exception as e:
+        logger.exception(e)
+        notification_service.update_notification_by_id(
+            notification_id,
+            NotificationUpdatableProperties(
+                status=NotificationsStatusEnum.ERROR,
+                description=f"An error occurred while processing the file: {e}",
+            ),
+        )
+
+
+@celery.task(name="process_crawl_and_notify")
+def process_crawl_and_notify(
+    crawl_website_url: str,
+    brain_id: UUID,
+    notification_id=None,
+):
+    crawl_website = CrawlWebsite(url=crawl_website_url)
+
+    if not crawl_website.checkGithub():
+        # Build file data
+        extracted_content = crawl_website.process()
+        extracted_content_bytes = extracted_content.encode("utf-8")
+        file_name = slugify(crawl_website.url) + ".txt"
+
+        with NamedTemporaryFile(
+            suffix="_" + file_name,  # pyright: ignore reportPrivateUsage=none
+        ) as tmp_file:
+            tmp_file.write(extracted_content_bytes)
+            tmp_file.flush()
+            file_instance = File(
+                file_name=file_name,
+                tmp_file_path=tmp_file.name,
+                bytes_content=extracted_content_bytes,
+                file_size=len(extracted_content),
+                file_extension=".txt",
+            )
+            filter_file(
+                file=file_instance,
+                brain_id=brain_id,
+                original_file_name=crawl_website_url,
+            )
+            notification_service.update_notification_by_id(
+                notification_id,
+                NotificationUpdatableProperties(
+                    status=NotificationsStatusEnum.SUCCESS,
+                    description="Your URL has been properly crawled!",
+                ),
+            )
+    else:
+        process_github(
+            repo=crawl_website.url,
+            brain_id=brain_id,
+        )
+
+    if notification_id:
+        notification_service.update_notification_by_id(
+            notification_id,
+            NotificationUpdatableProperties(
+                status=NotificationsStatusEnum.SUCCESS,
+                description="Your file has been properly uploaded!",
+            ),
+        )
+
+    brain_service.update_brain_last_update_time(brain_id)
+    return True
+
+
+@celery.task
+def ping_telemetry():
+    maybe_send_telemetry("ping", {"ping": "pong"})
+
+
+celery.conf.beat_schedule = {
+    "ping_telemetry": {
+        "task": f"{__name__}.ping_telemetry",
+        "schedule": crontab(minute="*/30", hour="*"),
+    },
+    "process_sync_active": {
+        "task": "process_sync_active",
+        "schedule": crontab(minute="*/1", hour="*"),
+    },
+    "process_premium_users": {
+        "task": "check_if_is_premium_user",
+        "schedule": crontab(minute="*/1", hour="*"),
+    },
+}
diff --git a/backend/core/quivr_core/api/logger.py b/backend/core/quivr_core/api/logger.py
new file mode 100644
index 000000000000..b839e9aef4f1
--- /dev/null
+++ b/backend/core/quivr_core/api/logger.py
@@ -0,0 +1,45 @@
+import logging
+import os
+from logging.handlers import RotatingFileHandler
+
+from colorlog import (
+    ColoredFormatter,
+)
+
+
+def get_logger(logger_name, log_file="application.log"):
+    log_level = os.getenv("LOG_LEVEL", "WARNING").upper()
+    logger = logging.getLogger(logger_name)
+    logger.setLevel(log_level)
+    logger.propagate = False  # Prevent log propagation to avoid double logging
+
+    formatter = logging.Formatter(
+        "[%(levelname)s] %(name)s [%(filename)s:%(lineno)d]: %(message)s"
+    )
+
+    color_formatter = ColoredFormatter(
+        "%(log_color)s[%(levelname)s]%(reset)s %(name)s [%(filename)s:%(lineno)d]: %(message)s",
+        log_colors={
+            "DEBUG": "cyan",
+            "INFO": "green",
+            "WARNING": "yellow",
+            "ERROR": "red",
+            "CRITICAL": "red,bg_white",
+        },
+        reset=True,
+        style="%",
+    )
+
+    console_handler = logging.StreamHandler()
+    console_handler.setFormatter(color_formatter)
+
+    file_handler = RotatingFileHandler(
+        log_file, maxBytes=5000000, backupCount=5
+    )  # 5MB file
+    file_handler.setFormatter(formatter)
+
+    if not logger.handlers:
+        logger.addHandler(console_handler)
+        logger.addHandler(file_handler)
+
+    return logger
diff --git a/backend/core/quivr_core/api/main.py b/backend/core/quivr_core/api/main.py
new file mode 100644
index 000000000000..d19efc3b2835
--- /dev/null
+++ b/backend/core/quivr_core/api/main.py
@@ -0,0 +1,40 @@
+import logging
+
+from fastapi import FastAPI
+from quivr_core.api.modules.brain.controller import brain_router
+from quivr_core.api.modules.chat.controller import chat_router
+from quivr_core.api.modules.knowledge.controller import knowledge_router
+from quivr_core.api.modules.prompt.controller import prompt_router
+from quivr_core.api.modules.upload.controller import upload_router
+from quivr_core.api.modules.user.controller import user_router
+
+logging.getLogger("httpx").setLevel(logging.WARNING)
+logging.getLogger("LiteLLM").setLevel(logging.WARNING)
+logging.getLogger("litellm").setLevel(logging.WARNING)
+
+app = FastAPI()
+
+
+app.include_router(brain_router)
+app.include_router(chat_router)
+app.include_router(upload_router)
+app.include_router(user_router)
+app.include_router(prompt_router)
+app.include_router(knowledge_router)
+
+
+@app.get("/")
+async def root():
+    return {"status": "OK"}
+
+
+@app.get("/healthz", tags=["Health"])
+async def healthz():
+    return {"status": "ok"}
+
+
+if __name__ == "__main__":
+    # run main.py to debug backend
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=5050, log_level="debug", access_log=False)
diff --git a/backend/core/quivr_core/api/models/__init__.py b/backend/core/quivr_core/api/models/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/models/file_repository.py b/backend/core/quivr_core/api/models/file_repository.py
new file mode 100644
index 000000000000..3fc263ae0b17
--- /dev/null
+++ b/backend/core/quivr_core/api/models/file_repository.py
@@ -0,0 +1,32 @@
+from uuid import UUID
+
+from quivr_core.api.models.settings import get_supabase_client
+
+
+class FileRepository:
+    def __init__(self):
+        self.db = get_supabase_client()
+
+    def set_file_vectors_ids(self, file_sha1: bytes):
+        response = (
+            self.db.table("vectors")
+            .select("id")
+            .filter("file_sha1", "eq", str(file_sha1))
+            .execute()
+        )
+        return response.data
+
+    def get_brain_vectors_by_brain_id_and_file_sha1(
+        self, brain_id: UUID, file_sha1: bytes
+    ):
+        self.set_file_vectors_ids(file_sha1)
+        # Check if file exists in that brain
+        response = (
+            self.db.table("brains_vectors")
+            .select("brain_id, vector_id")
+            .filter("brain_id", "eq", str(brain_id))
+            .filter("file_sha1", "eq", str(file_sha1))
+            .execute()
+        )
+
+        return response
diff --git a/backend/core/quivr_core/api/models/files.py b/backend/core/quivr_core/api/models/files.py
new file mode 100644
index 000000000000..8f0e6938989f
--- /dev/null
+++ b/backend/core/quivr_core/api/models/files.py
@@ -0,0 +1,105 @@
+from pathlib import Path
+from typing import List, Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.documents import Document
+from pydantic import BaseModel
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.models.file_repository import FileRepository
+from quivr_core.api.models.vectors import Vector
+from quivr_core.api.modules.brain.service.brain_vector_service import BrainVectorService
+from quivr_core.api.packages.files.file import compute_sha1_from_content
+
+logger = get_logger(__name__)
+
+
+class File(BaseModel):
+    file_name: str
+    tmp_file_path: Path
+    bytes_content: bytes
+    file_size: int
+    file_extension: str
+    chunk_size: int = 400
+    chunk_overlap: int = 100
+    documents: List[Document] = []
+    file_sha1: Optional[str] = None
+    vectors_ids: Optional[list] = []
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self.vectors_repository = Vector()
+        self.file_repository = FileRepository()
+        data["file_sha1"] = compute_sha1_from_content(data["bytes_content"])
+
+    def compute_documents(self, loader_class):
+        """
+        Compute the documents from the file
+
+        Args:
+            loader_class (class): The class of the loader to use to load the file
+        """
+        logger.info(f"Computing documents from file {self.file_name}")
+        loader = loader_class(self.tmp_file_path)
+        documents = loader.load()
+
+        text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+            chunk_size=self.chunk_size, chunk_overlap=self.chunk_overlap
+        )
+
+        self.documents = text_splitter.split_documents(documents)
+
+    def set_file_vectors_ids(self):
+        """
+        Set the vectors_ids property with the ids of the vectors
+        that are associated with the file in the vectors table
+        """
+        self.vectors_ids = self.vectors_repository.get_vectors_by_file_sha1(
+            self.file_sha1
+        ).data
+
+    def file_already_exists(self):
+        """
+        Check if file already exists in vectors table
+        """
+        self.set_file_vectors_ids()
+
+        # if the file does not exist in vectors then no need to go check in brains_vectors
+        if len(self.vectors_ids) == 0:  # pyright: ignore reportPrivateUsage=none
+            return False
+
+        return True
+
+    def file_already_exists_in_brain(self, brain_id):
+        """
+        Check if file already exists in a brain
+
+        Args:
+            brain_id (str): Brain id
+        """
+        response = self.file_repository.get_brain_vectors_by_brain_id_and_file_sha1(
+            brain_id,
+            self.file_sha1,  # type: ignore
+        )
+
+        if len(response.data) == 0:
+            return False
+
+        return True
+
+    def file_is_empty(self):
+        """
+        Check if file is empty by checking if the file pointer is at the beginning of the file
+        """
+        return self.file_size < 1  # pyright: ignore reportPrivateUsage=none
+
+    def link_file_to_brain(self, brain_id):
+        self.set_file_vectors_ids()
+
+        if self.vectors_ids is None:
+            return
+
+        brain_vector_service = BrainVectorService(brain_id)
+
+        for vector_id in self.vectors_ids:  # pyright: ignore reportPrivateUsage=none
+            brain_vector_service.create_brain_vector(vector_id["id"], self.file_sha1)
diff --git a/backend/core/quivr_core/api/models/settings.py b/backend/core/quivr_core/api/models/settings.py
new file mode 100644
index 000000000000..f35385c306b1
--- /dev/null
+++ b/backend/core/quivr_core/api/models/settings.py
@@ -0,0 +1,171 @@
+from typing import Optional
+from uuid import UUID
+
+from dotenv import load_dotenv
+from langchain.embeddings.base import Embeddings
+from langchain_community.embeddings.ollama import OllamaEmbeddings
+from langchain_community.vectorstores.supabase import SupabaseVectorStore
+from langchain_openai import OpenAIEmbeddings
+from posthog import Posthog
+from pydantic_settings import BaseSettings, SettingsConfigDict
+from sqlalchemy import Engine, create_engine
+from supabase.client import Client, create_client
+
+from quivr_core.api.logger import get_logger
+
+load_dotenv()
+logger = get_logger(__name__)
+
+
+# The `PostHogSettings` class is used to initialize and interact with the PostHog analytics service.
+class PostHogSettings(BaseSettings):
+    model_config = SettingsConfigDict(validate_default=False)
+    posthog_api_key: str | None = None
+    posthog_api_url: str | None = None
+    posthog: Posthog | None = None
+
+    def __init__(self, *args, **kwargs):
+        """
+        The function initializes the "posthog" attribute and calls the "initialize_posthog" method.
+        """
+        super().__init__(*args, **kwargs)
+        self.posthog = None
+        self.initialize_posthog()
+
+    def initialize_posthog(self):
+        """
+        The function initializes a PostHog client with an API key and URL.
+        """
+        if self.posthog_api_key and self.posthog_api_url:
+            self.posthog = Posthog(
+                api_key=self.posthog_api_key, host=self.posthog_api_url
+            )
+
+    def log_event(self, user_id: UUID, event_name: str, event_properties: dict):
+        """
+        The function logs an event with a user ID, event name, and event properties using the PostHog
+        analytics tool.
+
+        :param user_id: The user_id parameter is a UUID (Universally Unique Identifier) that uniquely
+        identifies a user. It is typically used to track and identify individual users in an application
+        or system
+        :type user_id: UUID
+        :param event_name: The event_name parameter is a string that represents the name or type of the
+        event that you want to log. It could be something like "user_signed_up", "item_purchased", or
+        "page_viewed"
+        :type event_name: str
+        :param event_properties: The event_properties parameter is a dictionary that contains additional
+        information or properties related to the event being logged. These properties provide more
+        context or details about the event and can be used for analysis or filtering purposes
+        :type event_properties: dict
+        """
+        if self.posthog:
+            self.posthog.capture(user_id, event_name, event_properties)
+
+    def set_user_properties(self, user_id: UUID, event_name, properties: dict):
+        """
+        The function sets user properties for a given user ID and event name using the PostHog analytics
+        tool.
+
+        :param user_id: The user_id parameter is a UUID (Universally Unique Identifier) that uniquely
+        identifies a user. It is used to associate the user with the event and properties being captured
+        :type user_id: UUID
+        :param event_name: The `event_name` parameter is a string that represents the name of the event
+        that you want to capture. It could be something like "user_signed_up" or "item_purchased"
+        :param properties: The `properties` parameter is a dictionary that contains the user properties
+        that you want to set. Each key-value pair in the dictionary represents a user property, where
+        the key is the name of the property and the value is the value you want to set for that property
+        :type properties: dict
+        """
+        if self.posthog:
+            self.posthog.capture(
+                user_id, event=event_name, properties={"$set": properties}
+            )
+
+    def set_once_user_properties(self, user_id: UUID, event_name, properties: dict):
+        """
+        The function sets user properties for a specific event, ensuring that the properties are only
+        set once.
+
+        :param user_id: The user_id parameter is a UUID (Universally Unique Identifier) that uniquely
+        identifies a user
+        :type user_id: UUID
+        :param event_name: The `event_name` parameter is a string that represents the name of the event
+        that you want to capture. It could be something like "user_signed_up" or "item_purchased"
+        :param properties: The `properties` parameter is a dictionary that contains the user properties
+        that you want to set. Each key-value pair in the dictionary represents a user property, where
+        the key is the property name and the value is the property value
+        :type properties: dict
+        """
+        if self.posthog:
+            self.posthog.capture(
+                user_id, event=event_name, properties={"$set_once": properties}
+            )
+
+
+class BrainSettings(BaseSettings):
+    model_config = SettingsConfigDict(validate_default=False)
+    pg_database_url: str
+    pg_database_async_url: str
+    openai_api_key: str = ""
+    supabase_url: str = ""
+    supabase_service_key: str = ""
+    resend_api_key: str = "null"
+    resend_email_address: str = "brain@mail.quivr.app"
+    ollama_api_base_url: str | None = None
+    langfuse_public_key: str | None = None
+    langfuse_secret_key: str | None = None
+
+
+# Global variables to store the Supabase client and database instances
+_supabase_client: Optional[Client] = None
+_db_engine: Optional[Engine] = None
+_embedding_service = None
+
+settings = BrainSettings()  # type: ignore
+
+
+def get_pg_database_engine():
+    global _db_engine
+    if _db_engine is None:
+        logger.info("Creating Postgres DB engine")
+        _db_engine = create_engine(settings.pg_database_url, pool_pre_ping=True)
+    return _db_engine
+
+
+def get_pg_database_async_engine():
+    global _db_engine
+    if _db_engine is None:
+        logger.info("Creating Postgres DB engine")
+        _db_engine = create_engine(settings.pg_database_async_url, pool_pre_ping=True)
+    return _db_engine
+
+
+def get_supabase_client() -> Client:
+    global _supabase_client
+    if _supabase_client is None:
+        logger.info("Creating Supabase client")
+        _supabase_client = create_client(
+            settings.supabase_url, settings.supabase_service_key
+        )
+    return _supabase_client
+
+
+def get_embedding_client() -> Embeddings:
+    global _embedding_service
+    if settings.ollama_api_base_url:
+        embeddings = OllamaEmbeddings(
+            base_url=settings.ollama_api_base_url,
+        )  # pyright: ignore reportPrivateUsage=none
+    else:
+        embeddings = OpenAIEmbeddings()  # pyright: ignore reportPrivateUsage=none
+    return embeddings
+
+
+def get_documents_vector_store() -> SupabaseVectorStore:
+    embeddings = get_embedding_client()
+    supabase_client: Client = get_supabase_client()
+    documents_vector_store = SupabaseVectorStore(
+        supabase_client, embeddings, table_name="vectors"
+    )
+    return documents_vector_store
diff --git a/backend/core/quivr_core/api/models/vectors.py b/backend/core/quivr_core/api/models/vectors.py
new file mode 100644
index 000000000000..e2d207d4d88b
--- /dev/null
+++ b/backend/core/quivr_core/api/models/vectors.py
@@ -0,0 +1,78 @@
+from postgrest.types import CountMethod
+
+from quivr_core.api.models.settings import get_supabase_client
+
+
+class Vector:
+    def __init__(self):
+        self.db = get_supabase_client()
+
+    def get_vectors_by_file_name(self, file_name):
+        response = (
+            self.db.table("vectors")
+            .select(
+                "metadata->>file_name, metadata->>file_size, metadata->>file_extension, metadata->>file_url",
+                "content",
+                "brains_vectors(brain_id,vector_id)",
+            )
+            .match({"metadata->>file_name": file_name})
+            .execute()
+        )
+
+        return response
+
+    def get_vectors_by_file_sha1(self, file_sha1):
+        response = (
+            self.db.table("vectors")
+            .select("id")
+            .filter("file_sha1", "eq", file_sha1)
+            .execute()
+        )
+
+        return response
+
+    # TODO: remove duplicate similarity_search in supabase vector store
+    def similarity_search(self, query_embedding, table, k, threshold):
+        response = self.db.rpc(
+            table,
+            {
+                "query_embedding": query_embedding,
+                "match_count": k,
+                "match_threshold": threshold,
+            },
+        ).execute()
+        return response
+
+    def update_summary(self, document_id, summary_id):
+        return (
+            self.db.table("summaries")
+            .update({"document_id": document_id})
+            .match({"id": summary_id})
+            .execute()
+        )
+
+    def get_vectors_by_batch(self, batch_id):
+        response = (
+            self.db.table("vectors")
+            .select(
+                "name:metadata->>file_name, size:metadata->>file_size",
+                count=CountMethod.exact,
+            )
+            .eq("id", batch_id)
+            .execute()
+        )
+
+        return response
+
+    def get_vectors_in_batch(self, batch_ids):
+        response = (
+            self.db.table("vectors")
+            .select(
+                "name:metadata->>file_name, size:metadata->>file_size",
+                count=CountMethod.exact,
+            )
+            .in_("id", batch_ids)
+            .execute()
+        )
+
+        return response
diff --git a/backend/core/quivr_core/api/modules/__init__.py b/backend/core/quivr_core/api/modules/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/brain/__init__.py b/backend/core/quivr_core/api/modules/brain/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/brain/controller/__init__.py b/backend/core/quivr_core/api/modules/brain/controller/__init__.py
new file mode 100644
index 000000000000..7e54fbb96673
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/controller/__init__.py
@@ -0,0 +1 @@
+from .brain_routes import brain_router
diff --git a/backend/core/quivr_core/api/modules/brain/controller/brain_routes.py b/backend/core/quivr_core/api/modules/brain/controller/brain_routes.py
new file mode 100644
index 000000000000..396b3d7c76f6
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/controller/brain_routes.py
@@ -0,0 +1,143 @@
+from typing import Annotated
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Request
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.brain.dto.inputs import (
+    BrainQuestionRequest,
+    BrainUpdatableProperties,
+    CreateBrainProperties,
+)
+from quivr_core.api.modules.brain.entity.brain_entity import PublicBrain, RoleEnum
+from quivr_core.api.modules.brain.entity.integration_brain import (
+    IntegrationDescriptionEntity,
+)
+from quivr_core.api.modules.brain.service.brain_service import BrainService
+from quivr_core.api.modules.brain.service.brain_user_service import BrainUserService
+from quivr_core.api.modules.brain.service.get_question_context_from_brain import (
+    get_question_context_from_brain,
+)
+from quivr_core.api.modules.brain.service.integration_brain_service import (
+    IntegrationBrainDescriptionService,
+)
+from quivr_core.api.modules.dependencies import get_current_user, get_service
+from quivr_core.api.modules.prompt.service.prompt_service import PromptService
+from quivr_core.api.modules.user.entity.user_identity import UserIdentity
+from quivr_core.api.packages.utils.telemetry import maybe_send_telemetry
+
+logger = get_logger(__name__)
+brain_router = APIRouter()
+
+brain_service = BrainService()
+brain_user_service = BrainUserService()
+integration_brain_description_service = IntegrationBrainDescriptionService()
+
+PromptServiceDep = Annotated[PromptService, Depends(get_service(PromptService))]
+
+
+@brain_router.get(
+    "/brains/integrations/",
+)
+async def get_integration_brain_description() -> list[IntegrationDescriptionEntity]:
+    """Retrieve the integration brain description."""
+    return integration_brain_description_service.get_all_integration_descriptions()
+
+
+@brain_router.get("/brains/", tags=["Brain"])
+async def retrieve_all_brains_for_user(
+    current_user: UserIdentity = Depends(get_current_user),
+):
+    """Retrieve all brains for the current user."""
+    brains = brain_user_service.get_user_brains(current_user.id)
+    return {"brains": brains}
+
+
+@brain_router.get("/brains/public", tags=["Brain"])
+async def retrieve_public_brains() -> list[PublicBrain]:
+    """Retrieve all Quivr public brains."""
+    return brain_service.get_public_brains()
+
+
+@brain_router.get(
+    "/brains/{brain_id}/",
+    tags=["Brain"],
+)
+async def retrieve_brain_by_id(
+    brain_id: UUID,
+    current_user: UserIdentity = Depends(get_current_user),
+):
+    """Retrieve details of a specific brain by its ID."""
+    brain_details = brain_service.get_brain_details(brain_id, current_user.id)
+    if brain_details is None:
+        raise HTTPException(status_code=404, detail="Brain details not found")
+    return brain_details
+
+
+@brain_router.post("/brains/", tags=["Brain"])
+async def create_new_brain(
+    brain: CreateBrainProperties,
+    request: Request,
+    current_user: UserIdentity = Depends(get_current_user),
+):
+    """Create a new brain for the user."""
+
+    maybe_send_telemetry("create_brain", {"brain_name": brain.name}, request)
+    new_brain = brain_service.create_brain(
+        brain=brain,
+        user_id=current_user.id,
+    )
+    brain_user_service.create_brain_user(
+        user_id=current_user.id,
+        brain_id=new_brain.brain_id,
+        rights=RoleEnum.Owner,
+        is_default_brain=True,
+    )
+
+    return {"id": new_brain.brain_id, "name": brain.name, "rights": "Owner"}
+
+
+@brain_router.put(
+    "/brains/{brain_id}/",
+    dependencies=[],
+    tags=["Brain"],
+)
+async def update_existing_brain(
+    brain_id: UUID,
+    brain_update_data: BrainUpdatableProperties,
+    prompt_service: PromptServiceDep,
+    current_user: UserIdentity = Depends(get_current_user),
+):
+    """Update an existing brain's configuration."""
+    existing_brain = brain_service.get_brain_details(brain_id, current_user.id)
+    if existing_brain is None:
+        raise HTTPException(status_code=404, detail="Brain not found")
+
+    if brain_update_data.prompt_id is None and existing_brain.prompt_id:
+        prompt = prompt_service.get_prompt_by_id(existing_brain.prompt_id)
+        if prompt and prompt.status == "private":
+            prompt_service.delete_prompt_by_id(existing_brain.prompt_id)
+
+            return {"message": f"Prompt {brain_id} has been updated."}
+
+    elif brain_update_data.status == "private" and existing_brain.status == "public":
+        brain_user_service.delete_brain_users(brain_id)
+        return {"message": f"Brain {brain_id} has been deleted."}
+
+    else:
+        brain_service.update_brain_by_id(brain_id, brain_update_data)
+
+        return {"message": f"Brain {brain_id} has been updated."}
+
+
+@brain_router.post(
+    "/brains/{brain_id}/documents",
+    tags=["Brain"],
+)
+async def get_question_context_for_brain(
+    brain_id: UUID, question: BrainQuestionRequest
+):
+    # TODO: Move this endpoint to AnswerGenerator service
+    """Retrieve the question context from a specific brain."""
+    context = get_question_context_from_brain(brain_id, question.question)
+    return {"docs": context}
diff --git a/backend/core/quivr_core/api/modules/brain/dto/__init__.py b/backend/core/quivr_core/api/modules/brain/dto/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/brain/dto/inputs.py b/backend/core/quivr_core/api/modules/brain/dto/inputs.py
new file mode 100644
index 000000000000..068751ac510f
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/dto/inputs.py
@@ -0,0 +1,88 @@
+from typing import Optional
+from uuid import UUID
+
+from pydantic import BaseModel
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.brain.entity.api_brain_definition_entity import (
+    ApiBrainAllowedMethods,
+    ApiBrainDefinitionEntity,
+    ApiBrainDefinitionSchema,
+    ApiBrainDefinitionSecret,
+)
+from quivr_core.api.modules.brain.entity.brain_entity import BrainType
+from quivr_core.api.modules.brain.entity.integration_brain import IntegrationType
+
+logger = get_logger(__name__)
+
+
+class CreateApiBrainDefinition(BaseModel, extra="ignore"):
+    method: ApiBrainAllowedMethods
+    url: str
+    params: Optional[ApiBrainDefinitionSchema] = ApiBrainDefinitionSchema()
+    search_params: ApiBrainDefinitionSchema = ApiBrainDefinitionSchema()
+    secrets: Optional[list[ApiBrainDefinitionSecret]] = []
+    raw: Optional[bool] = False
+    jq_instructions: Optional[str] = None
+
+
+class CreateIntegrationBrain(BaseModel, extra="ignore"):
+    integration_name: str
+    integration_logo_url: str
+    connection_settings: dict
+    integration_type: IntegrationType
+    description: str
+    max_files: int
+
+
+class BrainIntegrationSettings(BaseModel, extra="ignore"):
+    integration_id: str
+    settings: dict
+
+
+class BrainIntegrationUpdateSettings(BaseModel, extra="ignore"):
+    settings: dict
+
+
+class CreateBrainProperties(BaseModel, extra="ignore"):
+    name: Optional[str] = "Default brain"
+    description: str = "This is a description"
+    status: Optional[str] = "private"
+    model: Optional[str] = None
+    temperature: Optional[float] = 0.0
+    max_tokens: Optional[int] = 2000
+    prompt_id: Optional[UUID] = None
+    brain_type: Optional[BrainType] = BrainType.doc
+    brain_definition: Optional[CreateApiBrainDefinition] = None
+    brain_secrets_values: Optional[dict] = {}
+    connected_brains_ids: Optional[list[UUID]] = []
+    integration: Optional[BrainIntegrationSettings] = None
+
+    def dict(self, *args, **kwargs):
+        brain_dict = super().dict(*args, **kwargs)
+        if brain_dict.get("prompt_id"):
+            brain_dict["prompt_id"] = str(brain_dict.get("prompt_id"))
+        return brain_dict
+
+
+class BrainUpdatableProperties(BaseModel, extra="ignore"):
+    name: Optional[str] = None
+    description: Optional[str] = None
+    temperature: Optional[float] = None
+    model: Optional[str] = None
+    max_tokens: Optional[int] = None
+    status: Optional[str] = None
+    prompt_id: Optional[UUID] = None
+    brain_definition: Optional[ApiBrainDefinitionEntity] = None
+    connected_brains_ids: Optional[list[UUID]] = []
+    integration: Optional[BrainIntegrationUpdateSettings] = None
+
+    def dict(self, *args, **kwargs):
+        brain_dict = super().dict(*args, **kwargs)
+        if brain_dict.get("prompt_id"):
+            brain_dict["prompt_id"] = str(brain_dict.get("prompt_id"))
+        return brain_dict
+
+
+class BrainQuestionRequest(BaseModel):
+    question: str
diff --git a/backend/core/quivr_core/api/modules/brain/entity/__init__.py b/backend/core/quivr_core/api/modules/brain/entity/__init__.py
new file mode 100644
index 000000000000..ba083445ccf1
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/entity/__init__.py
@@ -0,0 +1 @@
+from .api_brain_definition_entity import ApiBrainDefinitionEntity
diff --git a/backend/core/quivr_core/api/modules/brain/entity/api_brain_definition_entity.py b/backend/core/quivr_core/api/modules/brain/entity/api_brain_definition_entity.py
new file mode 100644
index 000000000000..d327d44ac3ba
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/entity/api_brain_definition_entity.py
@@ -0,0 +1,47 @@
+from enum import Enum
+from typing import Optional
+from uuid import UUID
+
+from pydantic import BaseModel, Extra
+
+
+class ApiBrainDefinitionSchemaProperty(BaseModel, extra=Extra.forbid):
+    type: str
+    description: str
+    enum: Optional[list] = None
+    name: str
+
+    def dict(self, **kwargs):
+        result = super().dict(**kwargs)
+        if "enum" in result and result["enum"] is None:
+            del result["enum"]
+        return result
+
+
+class ApiBrainDefinitionSchema(BaseModel, extra=Extra.forbid):
+    properties: list[ApiBrainDefinitionSchemaProperty] = []
+    required: list[str] = []
+
+
+class ApiBrainDefinitionSecret(BaseModel, extra=Extra.forbid):
+    name: str
+    type: str
+    description: Optional[str] = None
+
+
+class ApiBrainAllowedMethods(str, Enum):
+    GET = "GET"
+    POST = "POST"
+    PUT = "PUT"
+    DELETE = "DELETE"
+
+
+class ApiBrainDefinitionEntity(BaseModel, extra=Extra.forbid):
+    brain_id: UUID
+    method: ApiBrainAllowedMethods
+    url: str
+    params: ApiBrainDefinitionSchema
+    search_params: ApiBrainDefinitionSchema
+    secrets: list[ApiBrainDefinitionSecret]
+    raw: bool = False
+    jq_instructions: Optional[str] = None
diff --git a/backend/core/quivr_core/api/modules/brain/entity/brain_entity.py b/backend/core/quivr_core/api/modules/brain/entity/brain_entity.py
new file mode 100644
index 000000000000..bf963fcccae1
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/entity/brain_entity.py
@@ -0,0 +1,135 @@
+from datetime import datetime
+from enum import Enum
+from typing import List, Optional
+from uuid import UUID
+
+from pydantic import BaseModel
+
+# from sqlmodel import Enum as PGEnum
+from sqlalchemy.dialects.postgresql import ENUM as PGEnum
+from sqlalchemy.ext.asyncio import AsyncAttrs
+from sqlmodel import TIMESTAMP, Column, Field, Relationship, SQLModel, text
+from sqlmodel import UUID as PGUUID
+
+from quivr_core.api.modules.brain.entity.api_brain_definition_entity import (
+    ApiBrainDefinitionEntity,
+)
+from quivr_core.api.modules.brain.entity.integration_brain import (
+    IntegrationDescriptionEntity,
+    IntegrationEntity,
+)
+from quivr_core.api.modules.prompt.entity.prompt import Prompt
+
+
+class BrainType(str, Enum):
+    doc = "doc"
+    api = "api"
+    composite = "composite"
+    integration = "integration"
+
+
+class Brain(AsyncAttrs, SQLModel, table=True):
+    __tablename__ = "brains"  # type: ignore
+
+    brain_id: UUID | None = Field(
+        default=None,
+        sa_column=Column(
+            PGUUID,
+            server_default=text("uuid_generate_v4()"),
+            primary_key=True,
+        ),
+    )
+    name: str
+    description: str
+    status: str | None = None
+    model: str | None = None
+    max_tokens: int | None = None
+    temperature: float | None = None
+    last_update: datetime | None = Field(
+        default=None,
+        sa_column=Column(
+            TIMESTAMP(timezone=False),
+            server_default=text("CURRENT_TIMESTAMP"),
+        ),
+    )
+    brain_type: BrainType | None = Field(
+        sa_column=Column(
+            PGEnum(BrainType, name="brain_type_enum", create_type=False),
+            default=BrainType.integration,
+        ),
+    )
+    brain_chat_history: List["ChatHistory"] = Relationship(  # noqa: F821
+        back_populates="brain", sa_relationship_kwargs={"lazy": "select"}
+    )
+    prompt_id: UUID | None = Field(default=None, foreign_key="prompts.id")
+    prompt: Prompt | None = Relationship(  # noqa: f821
+        back_populates="brain", sa_relationship_kwargs={"lazy": "joined"}
+    )
+
+    # TODO : add
+    # "meaning" "public"."vector",
+    # "tags" "public"."tags"[]
+
+
+class BrainEntity(BaseModel):
+    brain_id: UUID
+    name: str
+    description: Optional[str] = None
+    temperature: Optional[float] = None
+    model: Optional[str] = None
+    max_tokens: Optional[int] = None
+    status: Optional[str] = None
+    prompt_id: Optional[UUID] = None
+    last_update: datetime
+    brain_type: BrainType
+    brain_definition: Optional[ApiBrainDefinitionEntity] = None
+    connected_brains_ids: Optional[List[UUID]] = None
+    raw: Optional[bool] = None
+    jq_instructions: Optional[str] = None
+    integration: Optional[IntegrationEntity] = None
+    integration_description: Optional[IntegrationDescriptionEntity] = None
+
+    @property
+    def id(self) -> UUID:
+        return self.brain_id
+
+    def dict(self, **kwargs):
+        data = super().dict(
+            **kwargs,
+        )
+        data["id"] = self.id
+        return data
+
+
+class PublicBrain(BaseModel):
+    id: UUID
+    name: str
+    description: Optional[str] = None
+    number_of_subscribers: int = 0
+    last_update: str
+    brain_type: BrainType
+    brain_definition: Optional[ApiBrainDefinitionEntity] = None
+
+
+class RoleEnum(str, Enum):
+    Viewer = "Viewer"
+    Editor = "Editor"
+    Owner = "Owner"
+
+
+class BrainUser(BaseModel):
+    id: UUID
+    user_id: UUID
+    rights: RoleEnum
+    default_brain: bool = False
+
+
+class MinimalUserBrainEntity(BaseModel):
+    id: UUID
+    name: str
+    rights: RoleEnum
+    status: str
+    brain_type: BrainType
+    description: str
+    integration_logo_url: str
+    max_files: int
diff --git a/backend/core/quivr_core/api/modules/brain/entity/integration_brain.py b/backend/core/quivr_core/api/modules/brain/entity/integration_brain.py
new file mode 100644
index 000000000000..1b60b3f3af54
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/entity/integration_brain.py
@@ -0,0 +1,46 @@
+from enum import Enum
+from typing import Optional
+from uuid import UUID
+
+from pydantic import BaseModel
+
+
+class IntegrationType(str, Enum):
+    CUSTOM = "custom"
+    SYNC = "sync"
+    DOC = "doc"
+
+
+class IntegrationBrainTag(str, Enum):
+    NEW = "new"
+    RECOMMENDED = "recommended"
+    MOST_POPULAR = "most_popular"
+    PREMIUM = "premium"
+    COMING_SOON = "coming_soon"
+    COMMUNITY = "community"
+    DEPRECATED = "deprecated"
+
+
+class IntegrationDescriptionEntity(BaseModel):
+    id: UUID
+    integration_name: str
+    integration_logo_url: Optional[str] = None
+    connection_settings: Optional[dict] = None
+    integration_type: IntegrationType
+    tags: Optional[list[IntegrationBrainTag]] = []
+    information: Optional[str] = None
+    description: str
+    max_files: int
+    allow_model_change: bool
+    integration_display_name: str
+    onboarding_brain: bool
+
+
+class IntegrationEntity(BaseModel):
+    id: int
+    user_id: str
+    brain_id: str
+    integration_id: UUID
+    settings: Optional[dict] = None
+    credentials: Optional[dict] = None
+    last_synced: str
diff --git a/backend/core/quivr_core/api/modules/brain/repository/__init__.py b/backend/core/quivr_core/api/modules/brain/repository/__init__.py
new file mode 100644
index 000000000000..46e80777797f
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/repository/__init__.py
@@ -0,0 +1,4 @@
+from .brains import Brains
+from .brains_users import BrainsUsers
+from .brains_vectors import BrainsVectors
+from .integration_brains import IntegrationBrain, IntegrationDescription
diff --git a/backend/core/quivr_core/api/modules/brain/repository/brains.py b/backend/core/quivr_core/api/modules/brain/repository/brains.py
new file mode 100644
index 000000000000..e3ca02a122e5
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/repository/brains.py
@@ -0,0 +1,124 @@
+from uuid import UUID
+
+from sqlalchemy import text
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.models.settings import (
+    get_embedding_client,
+    get_pg_database_engine,
+    get_supabase_client,
+)
+from quivr_core.api.modules.brain.dto.inputs import BrainUpdatableProperties
+from quivr_core.api.modules.brain.entity.brain_entity import BrainEntity, PublicBrain
+
+logger = get_logger(__name__)
+
+
+class Brains:
+    def __init__(self):
+        supabase_client = get_supabase_client()
+        self.db = supabase_client
+        pg_engine = get_pg_database_engine()
+        self.pg_engine = pg_engine
+
+    def create_brain(self, brain):
+        embeddings = get_embedding_client()
+        string_to_embed = f"Name: {brain.name} Description: {brain.description}"
+        brain_meaning = embeddings.embed_query(string_to_embed)
+        brain_dict = brain.dict(
+            exclude={
+                "brain_definition",
+                "brain_secrets_values",
+                "connected_brains_ids",
+                "integration",
+            }
+        )
+        brain_dict["meaning"] = brain_meaning
+        response = (self.db.table("brains").insert(brain_dict)).execute()
+
+        return BrainEntity(**response.data[0])
+
+    def get_public_brains(self):
+        response = (
+            self.db.from_("brains")
+            .select(
+                "id:brain_id, name, description, last_update, brain_type, brain_definition: api_brain_definition(*), number_of_subscribers:brains_users(count)"
+            )
+            .filter("status", "eq", "public")
+            .execute()
+        )
+        public_brains: list[PublicBrain] = []
+
+        for item in response.data:
+            item["number_of_subscribers"] = item["number_of_subscribers"][0]["count"]
+            if not item["brain_definition"]:
+                del item["brain_definition"]
+            else:
+                item["brain_definition"]["secrets"] = []
+
+            public_brains.append(PublicBrain(**item))
+        return public_brains
+
+    def update_brain_last_update_time(self, brain_id):
+        try:
+            with self.pg_engine.begin() as connection:
+                query = """
+                UPDATE brains
+                SET last_update = now()
+                WHERE brain_id = '{brain_id}'
+                """
+                connection.execute(text(query.format(brain_id=brain_id)))
+        except Exception as e:
+            logger.error(e)
+
+    def get_brain_details(self, brain_id):
+        with self.pg_engine.begin() as connection:
+            query = """
+            SELECT * FROM brains
+            WHERE brain_id = '{brain_id}'
+            """
+            response = connection.execute(
+                text(query.format(brain_id=brain_id))
+            ).fetchall()
+        if len(response) == 0:
+            return None
+        return BrainEntity(**response[0]._mapping)
+
+    def delete_brain(self, brain_id: str):
+        with self.pg_engine.begin() as connection:
+            results = connection.execute(
+                text(f"DELETE FROM brains WHERE brain_id = '{brain_id}'")
+            )
+
+        return results
+
+    def update_brain_by_id(
+        self, brain_id: UUID, brain: BrainUpdatableProperties
+    ) -> BrainEntity | None:
+        embeddings = get_embedding_client()
+        string_to_embed = f"Name: {brain.name} Description: {brain.description}"
+        brain_meaning = embeddings.embed_query(string_to_embed)
+        brain_dict = brain.dict(exclude_unset=True)
+        brain_dict["meaning"] = brain_meaning
+        update_brain_response = (
+            self.db.table("brains")
+            .update(brain_dict)
+            .match({"brain_id": brain_id})
+            .execute()
+        ).data
+
+        if len(update_brain_response) == 0:
+            return None
+
+        return BrainEntity(**update_brain_response[0])
+
+    def get_brain_by_id(self, brain_id: UUID) -> BrainEntity | None:
+        # TODO: merge this method with get_brain_details
+        with self.pg_engine.begin() as connection:
+            response = connection.execute(
+                text(f"SELECT * FROM brains WHERE brain_id = '{brain_id}'")
+            ).fetchall()
+
+        if len(response) == 0:
+            return None
+        return BrainEntity(**response[0]._mapping)
diff --git a/backend/core/quivr_core/api/modules/brain/repository/brains_users.py b/backend/core/quivr_core/api/modules/brain/repository/brains_users.py
new file mode 100644
index 000000000000..29be9cfeced8
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/repository/brains_users.py
@@ -0,0 +1,204 @@
+from uuid import UUID
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.models.settings import get_embedding_client, get_supabase_client
+from quivr_core.api.modules.brain.entity.brain_entity import (
+    BrainUser,
+    MinimalUserBrainEntity,
+)
+
+logger = get_logger(__name__)
+
+
+class BrainsUsers:
+    def __init__(self):
+        supabase_client = get_supabase_client()
+        self.db = supabase_client
+
+    def update_meaning(self, brain: MinimalUserBrainEntity):
+        embeddings = get_embedding_client()
+        string_to_embed = f"Name: {brain.name} Description: {brain.description}"
+        brain_meaning = embeddings.embed_query(string_to_embed)
+        brain_dict = {"meaning": brain_meaning}
+        response = (
+            self.db.table("brains")
+            .update(brain_dict)
+            .match({"brain_id": brain.id})
+            .execute()
+        ).data
+
+        if len(response) == 0:
+            return False
+
+        return True
+
+    def get_user_brains(self, user_id) -> list[MinimalUserBrainEntity]:
+        response = (
+            self.db.from_("brains_users")
+            .select(
+                "id:brain_id, rights, brains (brain_id, name, status, brain_type, description, meaning, integrations_user (brain_id, integration_id, integrations (id, integration_name, integration_logo_url, max_files)))"
+            )
+            .filter("user_id", "eq", user_id)
+            .execute()
+        )
+        user_brains: list[MinimalUserBrainEntity] = []
+        for item in response.data:
+            integration_logo_url = ""
+            max_files = 5000
+            if item["brains"]["brain_type"] == "integration":
+                if "integrations_user" in item["brains"]:
+                    for integration_user in item["brains"]["integrations_user"]:
+                        if "integrations" in integration_user:
+                            integration_logo_url = integration_user["integrations"][
+                                "integration_logo_url"
+                            ]
+                            max_files = integration_user["integrations"]["max_files"]
+
+            user_brains.append(
+                MinimalUserBrainEntity(
+                    id=item["brains"]["brain_id"],
+                    name=item["brains"]["name"],
+                    rights=item["rights"],
+                    status=item["brains"]["status"],
+                    brain_type=item["brains"]["brain_type"],
+                    description=(
+                        item["brains"]["description"]
+                        if item["brains"]["description"] is not None
+                        else ""
+                    ),
+                    integration_logo_url=str(integration_logo_url),
+                    max_files=max_files,
+                )
+            )
+            user_brains[-1].rights = item["rights"]
+            if item["brains"]["meaning"] is None:
+                self.update_meaning(user_brains[-1])
+
+        return user_brains
+
+    def get_brain_for_user(self, user_id, brain_id):
+        response = (
+            self.db.from_("brains_users")
+            .select(
+                "id:brain_id, rights, brains (id: brain_id, status, name, brain_type, description)"
+            )
+            .filter("user_id", "eq", user_id)
+            .filter("brain_id", "eq", brain_id)
+            .execute()
+        )
+        if len(response.data) == 0:
+            return None
+        brain_data = response.data[0]
+
+        return MinimalUserBrainEntity(
+            id=brain_data["brains"]["id"],
+            name=brain_data["brains"]["name"],
+            rights=brain_data["rights"],
+            status=brain_data["brains"]["status"],
+            brain_type=brain_data["brains"]["brain_type"],
+            description=(
+                brain_data["brains"]["description"]
+                if brain_data["brains"]["description"] is not None
+                else ""
+            ),
+            integration_logo_url="",
+            max_files=100,
+        )
+
+    def delete_brain_user_by_id(
+        self,
+        user_id: UUID,
+        brain_id: UUID,
+    ):
+        results = (
+            self.db.table("brains_users")
+            .delete()
+            .match({"brain_id": str(brain_id), "user_id": str(user_id)})
+            .execute()
+        )
+        return results.data
+
+    def delete_brain_users(self, brain_id: str):
+        results = (
+            self.db.table("brains_users")
+            .delete()
+            .match({"brain_id": brain_id})
+            .execute()
+        )
+
+        return results
+
+    def create_brain_user(self, user_id: UUID, brain_id, rights, default_brain: bool):
+        response = (
+            self.db.table("brains_users")
+            .insert(
+                {
+                    "brain_id": str(brain_id),
+                    "user_id": str(user_id),
+                    "rights": rights,
+                    "default_brain": default_brain,
+                }
+            )
+            .execute()
+        )
+        return response
+
+    def get_user_default_brain_id(self, user_id: UUID) -> UUID | None:
+        response = (
+            self.db.from_("brains_users")
+            .select("brain_id")
+            .filter("user_id", "eq", str(user_id))
+            .filter("default_brain", "eq", "True")
+            .execute()
+        ).data
+        if len(response) == 0:
+            return None
+        return UUID(response[0].get("brain_id"))
+
+    def get_brain_users(self, brain_id: UUID) -> list[BrainUser]:
+        response = (
+            self.db.table("brains_users")
+            .select("id:brain_id, *")
+            .filter("brain_id", "eq", str(brain_id))
+            .execute()
+        )
+
+        return [BrainUser(**item) for item in response.data]
+
+    def delete_brain_subscribers(self, brain_id: UUID):
+        results = (
+            self.db.table("brains_users")
+            .delete()
+            .match({"brain_id": str(brain_id)})
+            .match({"rights": "Viewer"})
+            .execute()
+        ).data
+
+        return results
+
+    def get_brain_subscribers_count(self, brain_id: UUID) -> int:
+        response = (
+            self.db.from_("brains_users")
+            .select(
+                "count",
+            )
+            .filter("brain_id", "eq", str(brain_id))
+            .execute()
+        ).data
+        if len(response) == 0:
+            raise ValueError(f"Brain with id {brain_id} does not exist.")
+        return response[0]["count"]
+
+    def update_brain_user_default_status(
+        self, user_id: UUID, brain_id: UUID, default_brain: bool
+    ):
+        self.db.table("brains_users").update({"default_brain": default_brain}).match(
+            {"brain_id": brain_id, "user_id": user_id}
+        ).execute()
+
+    def update_brain_user_rights(
+        self, user_id: UUID, brain_id: UUID, rights: str
+    ) -> None:
+        self.db.table("brains_users").update({"rights": rights}).match(
+            {"brain_id": brain_id, "user_id": user_id}
+        ).execute()
diff --git a/backend/core/quivr_core/api/modules/brain/repository/brains_vectors.py b/backend/core/quivr_core/api/modules/brain/repository/brains_vectors.py
new file mode 100644
index 000000000000..dfc551337b96
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/repository/brains_vectors.py
@@ -0,0 +1,102 @@
+from quivr_core.api.logger import get_logger
+from quivr_core.api.models.settings import get_supabase_client
+
+logger = get_logger(__name__)
+
+
+class BrainsVectors:
+    def __init__(self):
+        supabase_client = get_supabase_client()
+        self.db = supabase_client
+
+    def create_brain_vector(self, brain_id, vector_id, file_sha1):
+        response = (
+            self.db.table("brains_vectors")
+            .insert(
+                {
+                    "brain_id": str(brain_id),
+                    "vector_id": str(vector_id),
+                    "file_sha1": file_sha1,
+                }
+            )
+            .execute()
+        )
+        return response.data
+
+    def get_vector_ids_from_file_sha1(self, file_sha1: str):
+        # move to vectors class
+        vectorsResponse = (
+            self.db.table("vectors")
+            .select("id")
+            .filter("file_sha1", "eq", file_sha1)
+            .execute()
+        )
+        return vectorsResponse.data
+
+    def get_brain_vector_ids(self, brain_id):
+        """
+        Retrieve unique brain data (i.e. uploaded files and crawled websites).
+        """
+
+        response = (
+            self.db.from_("brains_vectors")
+            .select("vector_id")
+            .filter("brain_id", "eq", brain_id)
+            .execute()
+        )
+
+        vector_ids = [item["vector_id"] for item in response.data]
+
+        if len(vector_ids) == 0:
+            return []
+
+        return vector_ids
+
+    def delete_file_from_brain(self, brain_id, file_name: str):
+        # First, get the vector_ids associated with the file_name
+        # TODO: filter by brain_id
+        file_vectors = (
+            self.db.table("vectors")
+            .select("id")
+            .filter("metadata->>file_name", "eq", file_name)
+            .execute()
+        )
+
+        file_vectors_ids = [item["id"] for item in file_vectors.data]
+
+        # remove current file vectors from brain vectors
+        self.db.table("brains_vectors").delete().filter(
+            "vector_id", "in", f"({','.join(map(str, file_vectors_ids))})"
+        ).filter("brain_id", "eq", brain_id).execute()
+
+        vectors_used_by_another_brain = (
+            self.db.table("brains_vectors")
+            .select("vector_id")
+            .filter("vector_id", "in", f"({','.join(map(str, file_vectors_ids))})")
+            .filter("brain_id", "neq", brain_id)
+            .execute()
+        )
+
+        vectors_used_by_another_brain_ids = [
+            item["vector_id"] for item in vectors_used_by_another_brain.data
+        ]
+
+        vectors_no_longer_used_ids = [
+            id for id in file_vectors_ids if id not in vectors_used_by_another_brain_ids
+        ]
+
+        self.db.table("vectors").delete().filter(
+            "id", "in", f"({','.join(map(str, vectors_no_longer_used_ids))})"
+        ).execute()
+
+        return {"message": f"File {file_name} in brain {brain_id} has been deleted."}
+
+    def delete_brain_vector(self, brain_id: str):
+        results = (
+            self.db.table("brains_vectors")
+            .delete()
+            .match({"brain_id": brain_id})
+            .execute()
+        )
+
+        return results
diff --git a/backend/core/quivr_core/api/modules/brain/repository/integration_brains.py b/backend/core/quivr_core/api/modules/brain/repository/integration_brains.py
new file mode 100644
index 000000000000..9e39dc486b7f
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/repository/integration_brains.py
@@ -0,0 +1,136 @@
+from typing import List
+from uuid import UUID
+
+from quivr_core.api.models.settings import get_supabase_client
+from quivr_core.api.modules.brain.entity.integration_brain import (
+    IntegrationDescriptionEntity,
+    IntegrationEntity,
+)
+
+
+class IntegrationBrain:
+    """This is all the methods to interact with the integration brain.
+
+    Args:
+        IntegrationBrainInterface (_type_): _description_
+    """
+
+    def __init__(self):
+        self.db = get_supabase_client()
+
+    def get_integration_brain(self, brain_id, user_id=None):
+        query = (
+            self.db.table("integrations_user")
+            .select("*")
+            .filter("brain_id", "eq", brain_id)
+        )
+
+        if user_id:
+            query.filter("user_id", "eq", user_id)
+
+        response = query.execute()
+
+        if len(response.data) == 0:
+            return None
+
+        return IntegrationEntity(**response.data[0])
+
+    def update_last_synced(self, brain_id, user_id):
+        response = (
+            self.db.table("integrations_user")
+            .update({"last_synced": "now()"})
+            .filter("brain_id", "eq", str(brain_id))
+            .filter("user_id", "eq", str(user_id))
+            .execute()
+        )
+        if len(response.data) == 0:
+            return None
+        return IntegrationEntity(**response.data[0])
+
+    def add_integration_brain(self, brain_id, user_id, integration_id, settings):
+        response = (
+            self.db.table("integrations_user")
+            .insert(
+                [
+                    {
+                        "brain_id": str(brain_id),
+                        "user_id": str(user_id),
+                        "integration_id": str(integration_id),
+                        "settings": settings,
+                    }
+                ]
+            )
+            .execute()
+        )
+        if len(response.data) == 0:
+            return None
+        return IntegrationEntity(**response.data[0])
+
+    def update_integration_brain(self, brain_id, user_id, integration_brain):
+        response = (
+            self.db.table("integrations_user")
+            .update(integration_brain.dict(exclude={"brain_id", "user_id"}))
+            .filter("brain_id", "eq", str(brain_id))
+            .filter("user_id", "eq", str(user_id))
+            .execute()
+        )
+        if len(response.data) == 0:
+            return None
+        return IntegrationEntity(**response.data[0])
+
+    def delete_integration_brain(self, brain_id, user_id):
+        self.db.table("integrations_user").delete().filter(
+            "brain_id", "eq", str(brain_id)
+        ).filter("user_id", "eq", str(user_id)).execute()
+        return None
+
+    def get_integration_brain_by_type_integration(
+        self, integration_name
+    ) -> List[IntegrationEntity] | None:
+        response = (
+            self.db.table("integrations_user")
+            .select("*, integrations ()")
+            .filter("integrations.integration_name", "eq", integration_name)
+            .execute()
+        )
+        if len(response.data) == 0:
+            return None
+
+        return [IntegrationEntity(**data) for data in response.data]
+
+
+class IntegrationDescription:
+    def __init__(self):
+        self.db = get_supabase_client()
+
+    def get_integration_description(self, integration_id: UUID):
+        response = (
+            self.db.table("integrations")
+            .select("*")
+            .filter("id", "eq", str(integration_id))
+            .execute()
+        )
+        if len(response.data) == 0:
+            return None
+
+        return IntegrationDescriptionEntity(**response.data[0])
+
+    def get_integration_description_by_user_brain_id(
+        self, brain_id: UUID, user_id: UUID
+    ):
+        response = (
+            self.db.table("integrations_user")
+            .select("*")
+            .filter("brain_id", "eq", str(brain_id))
+            .filter("user_id", "eq", str(user_id))
+            .execute()
+        )
+        if len(response.data) == 0:
+            return None
+
+        integration_id = response.data[0]["integration_id"]
+        return self.get_integration_description(integration_id)
+
+    def get_all_integration_descriptions(self):
+        response = self.db.table("integrations").select("*").execute()
+        return [IntegrationDescriptionEntity(**data) for data in response.data]
diff --git a/backend/core/quivr_core/api/modules/brain/service/__init__.py b/backend/core/quivr_core/api/modules/brain/service/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/brain/service/brain_service.py b/backend/core/quivr_core/api/modules/brain/service/brain_service.py
new file mode 100644
index 000000000000..8b5fe10e0486
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/service/brain_service.py
@@ -0,0 +1,220 @@
+from typing import Optional, Tuple
+from uuid import UUID
+
+from fastapi import HTTPException
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.brain.dto.inputs import (
+    BrainUpdatableProperties,
+    CreateBrainProperties,
+)
+from quivr_core.api.modules.brain.entity.brain_entity import (
+    BrainEntity,
+    BrainType,
+    PublicBrain,
+)
+from quivr_core.api.modules.brain.entity.integration_brain import IntegrationEntity
+from quivr_core.api.modules.brain.repository import (
+    Brains,
+    BrainsUsers,
+    BrainsVectors,
+    IntegrationBrain,
+    IntegrationDescription,
+)
+from quivr_core.api.modules.knowledge.service.knowledge_service import KnowledgeService
+from quivr_core.api.packages.vectorstore.supabase import CustomSupabaseVectorStore
+
+logger = get_logger(__name__)
+
+knowledge_service = KnowledgeService()
+
+
+class BrainService:
+    def __init__(self):
+        self.brain_repository: Brains = Brains()
+        self.brain_user_repository = BrainsUsers()
+        self.brain_vector = BrainsVectors()
+        self.integration_brains_repository = IntegrationBrain()
+        self.integration_description_repository = IntegrationDescription()
+
+    def get_brain_by_id(self, brain_id: UUID):
+        return self.brain_repository.get_brain_by_id(brain_id)
+
+    def get_integration_brain(self, brain_id) -> IntegrationEntity | None:
+        return self.integration_brains_repository.get_integration_brain(brain_id)
+
+    def find_brain_from_question(
+        self,
+        brain_id: UUID,
+        question: str,
+        user,
+        chat_id: UUID,
+        history,
+        vector_store: CustomSupabaseVectorStore,
+    ) -> Tuple[Optional[BrainEntity], dict[str, str]]:
+        """Find the brain to use for a question.
+
+        Args:
+            brain_id (UUID): ID of the brain to use if exists
+            question (str): Question for which to find the brain
+            user (UserEntity): User asking the question
+            chat_id (UUID): ID of the chat
+
+        Returns:
+            Optional[BrainEntity]: Returns the brain to use for the question
+        """
+        metadata = {}
+
+        # Init
+
+        brain_id_to_use = brain_id
+        brain_to_use = None
+
+        # Get the first question from the chat_question
+
+        question = question
+
+        list_brains = []  # To return
+
+        if history and not brain_id_to_use:
+            question = history[0].user_message
+            brain_id_to_use = history[0].brain_id
+            brain_to_use = self.get_brain_by_id(brain_id_to_use)
+
+        # If a brain_id is provided, use it
+        if brain_id_to_use and not brain_to_use:
+            brain_to_use = self.get_brain_by_id(brain_id_to_use)
+
+        else:
+            # Calculate the closest brains to the question
+            list_brains = vector_store.find_brain_closest_query(user.id, question)
+
+            unique_list_brains = []
+            seen_brain_ids = set()
+
+            for brain in list_brains:
+                if brain["id"] not in seen_brain_ids:
+                    unique_list_brains.append(brain)
+                    seen_brain_ids.add(brain["id"])
+
+            metadata["close_brains"] = unique_list_brains[:5]
+
+            if list_brains and not brain_to_use:
+                brain_id_to_use = list_brains[0]["id"]
+                brain_to_use = self.get_brain_by_id(brain_id_to_use)
+
+        return brain_to_use, metadata
+
+    def create_brain(
+        self,
+        user_id: UUID,
+        brain: Optional[CreateBrainProperties],
+    ) -> BrainEntity:
+        if brain is None:
+            brain = CreateBrainProperties()  # type: ignore model and brain_definition
+
+        if brain.brain_type == BrainType.integration:
+            return self.create_brain_integration(user_id, brain)
+
+        created_brain = self.brain_repository.create_brain(brain)
+        return created_brain
+
+    def create_brain_integration(
+        self,
+        user_id: UUID,
+        brain: CreateBrainProperties,
+    ) -> BrainEntity:
+        created_brain = self.brain_repository.create_brain(brain)
+        if brain.integration is not None:
+            self.integration_brains_repository.add_integration_brain(
+                user_id=user_id,
+                brain_id=created_brain.brain_id,
+                integration_id=brain.integration.integration_id,
+                settings=brain.integration.settings,
+            )
+        return created_brain
+
+    def delete_brain(self, brain_id: UUID) -> dict[str, str]:
+        brain_to_delete = self.get_brain_by_id(brain_id=brain_id)
+        if brain_to_delete is None:
+            raise HTTPException(status_code=404, detail="Brain not found.")
+
+        knowledge_service.remove_brain_all_knowledge(brain_id)
+
+        self.brain_vector.delete_brain_vector(str(brain_id))
+        self.brain_user_repository.delete_brain_users(str(brain_id))
+        self.brain_repository.delete_brain(str(brain_id))  # type: ignore
+
+        return {"message": "Brain deleted."}
+
+    def get_brain_prompt_id(self, brain_id: UUID) -> UUID | None:
+        brain = self.get_brain_by_id(brain_id)
+        prompt_id = brain.prompt_id if brain else None
+
+        return prompt_id
+
+    def update_brain_by_id(
+        self, brain_id: UUID, brain_new_values: BrainUpdatableProperties
+    ) -> BrainEntity:
+        """Update a prompt by id"""
+
+        existing_brain = self.brain_repository.get_brain_by_id(brain_id)
+
+        if existing_brain is None:
+            raise HTTPException(
+                status_code=404,
+                detail=f"Brain with id {brain_id} not found",
+            )
+        brain_update_answer = self.brain_repository.update_brain_by_id(
+            brain_id,
+            brain=BrainUpdatableProperties(
+                **brain_new_values.dict(
+                    exclude={"brain_definition", "connected_brains_ids", "integration"}
+                )
+            ),
+        )
+
+        if brain_update_answer is None:
+            raise HTTPException(
+                status_code=404,
+                detail=f"Brain with id {brain_id} not found",
+            )
+
+        if brain_update_answer is None:
+            raise HTTPException(
+                status_code=404,
+                detail=f"Brain with id {brain_id} not found",
+            )
+
+        self.brain_repository.update_brain_last_update_time(brain_id)
+        return brain_update_answer
+
+    def update_brain_last_update_time(self, brain_id: UUID):
+        self.brain_repository.update_brain_last_update_time(brain_id)
+
+    def get_brain_details(
+        self, brain_id: UUID, user_id: UUID | None = None
+    ) -> BrainEntity | None:
+        brain = self.brain_repository.get_brain_details(brain_id)
+        if brain is None:
+            return None
+
+        # TODO: N+1 here !!
+        if brain.brain_type == BrainType.integration:
+            brain.integration = (
+                self.integration_brains_repository.get_integration_brain(
+                    brain_id, user_id
+                )
+            )
+
+            if brain.integration:
+                brain.integration_description = (
+                    self.integration_description_repository.get_integration_description(
+                        brain.integration.integration_id
+                    )
+                )
+
+        return brain
+
+    def get_public_brains(self) -> list[PublicBrain]:
+        return self.brain_repository.get_public_brains()
diff --git a/backend/core/quivr_core/api/modules/brain/service/brain_user_service.py b/backend/core/quivr_core/api/modules/brain/service/brain_user_service.py
new file mode 100644
index 000000000000..ac528e262ae0
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/service/brain_user_service.py
@@ -0,0 +1,79 @@
+from typing import List
+from uuid import UUID
+
+from fastapi import HTTPException
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.brain.entity.brain_entity import (
+    BrainEntity,
+    BrainUser,
+    MinimalUserBrainEntity,
+    RoleEnum,
+)
+from quivr_core.api.modules.brain.repository.brains import Brains
+from quivr_core.api.modules.brain.repository.brains_users import BrainsUsers
+from quivr_core.api.modules.brain.service.brain_service import BrainService
+
+logger = get_logger(__name__)
+
+brain_service = BrainService()
+
+
+class BrainUserService:
+
+    def __init__(self):
+        self.brain_repository = Brains()
+        self.brain_user_repository = BrainsUsers()
+
+    def get_user_default_brain(self, user_id: UUID) -> BrainEntity | None:
+        brain_id = self.brain_user_repository.get_user_default_brain_id(user_id)
+
+        if brain_id is None:
+            return None
+
+        return brain_service.get_brain_by_id(brain_id)
+
+    def delete_brain_user(self, user_id: UUID, brain_id: UUID) -> None:
+        brain_to_delete_user_from = brain_service.get_brain_by_id(brain_id=brain_id)
+        if brain_to_delete_user_from is None:
+            raise HTTPException(status_code=404, detail="Brain not found.")
+
+        self.brain_user_repository.delete_brain_user_by_id(
+            user_id=user_id,
+            brain_id=brain_id,
+        )
+
+    def delete_brain_users(self, brain_id: UUID) -> None:
+        self.brain_user_repository.delete_brain_subscribers(
+            brain_id=brain_id,
+        )
+
+    def create_brain_user(
+        self, user_id: UUID, brain_id: UUID, rights: RoleEnum, is_default_brain: bool
+    ):
+        self.brain_user_repository.create_brain_user(
+            user_id=user_id,
+            brain_id=brain_id,
+            rights=rights,
+            default_brain=is_default_brain,
+        )
+
+    def get_brain_for_user(self, user_id: UUID, brain_id: UUID):
+        return self.brain_user_repository.get_brain_for_user(user_id, brain_id)  # type: ignore
+
+    def get_user_brains(self, user_id: UUID) -> list[MinimalUserBrainEntity]:
+        results = self.brain_user_repository.get_user_brains(user_id)  # type: ignore
+
+        return results  # type: ignore
+
+    def get_brain_users(self, brain_id: UUID) -> List[BrainUser]:
+        return self.brain_user_repository.get_brain_users(brain_id)
+
+    def update_brain_user_rights(
+        self, brain_id: UUID, user_id: UUID, rights: str
+    ) -> None:
+        self.brain_user_repository.update_brain_user_rights(
+            brain_id=brain_id,
+            user_id=user_id,
+            rights=rights,
+        )
diff --git a/backend/core/quivr_core/api/modules/brain/service/brain_vector_service.py b/backend/core/quivr_core/api/modules/brain/service/brain_vector_service.py
new file mode 100644
index 000000000000..bed5e3a82de2
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/service/brain_vector_service.py
@@ -0,0 +1,57 @@
+from uuid import UUID
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.brain.repository.brains_vectors import BrainsVectors
+from quivr_core.api.modules.knowledge.repository.storage import Storage
+from quivr_core.api.packages.embeddings.vectors import get_unique_files_from_vector_ids
+
+logger = get_logger(__name__)
+
+
+class BrainVectorService:
+
+    def __init__(self, brain_id: UUID):
+        self.repository = BrainsVectors()
+        self.id = brain_id
+        self.files = []
+
+    def create_brain_vector(self, vector_id, file_sha1):
+        return self.repository.create_brain_vector(self.id, vector_id, file_sha1)  # type: ignore
+
+    def update_brain_with_file(self, file_sha1: str):
+        # not  used
+        vector_ids = self.repository.get_vector_ids_from_file_sha1(file_sha1)
+        if vector_ids is None or len(vector_ids) == 0:
+            logger.info(f"No vector ids found for file {file_sha1}")
+            return
+
+        for vector_id in vector_ids:
+            self.create_brain_vector(vector_id, file_sha1)
+
+    def get_unique_brain_files(self):
+        """
+        Retrieve unique brain data (i.e. uploaded files and crawled websites).
+        """
+
+        vector_ids = self.repository.get_brain_vector_ids(self.id)  # type: ignore
+        self.files = get_unique_files_from_vector_ids(vector_ids)
+
+        return self.files
+
+    def delete_file_from_brain(self, file_name: str, only_vectors: bool = False):
+        file_name_with_brain_id = f"{self.id}/{file_name}"
+        storage = Storage()
+        if not only_vectors:
+            storage.remove_file(file_name_with_brain_id)
+        return self.repository.delete_file_from_brain(self.id, file_name)  # type: ignore
+
+    def delete_file_url_from_brain(self, file_name: str):
+        return self.repository.delete_file_from_brain(self.id, file_name)  # type: ignore
+
+    @property
+    def brain_size(self):
+        # TODO: change the calculation of the brain size, calculate the size stored for the embeddings + what's in the storage
+        self.get_unique_brain_files()
+        current_brain_size = sum(float(doc["size"]) for doc in self.files)
+
+        return current_brain_size
diff --git a/backend/core/quivr_core/api/modules/brain/service/get_question_context_from_brain.py b/backend/core/quivr_core/api/modules/brain/service/get_question_context_from_brain.py
new file mode 100644
index 000000000000..7b88efe0a084
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/service/get_question_context_from_brain.py
@@ -0,0 +1,66 @@
+from uuid import UUID
+
+from attr import dataclass
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.models.settings import get_embedding_client, get_supabase_client
+from quivr_core.api.modules.upload.service.generate_file_signed_url import (
+    generate_file_signed_url,
+)
+from quivr_core.api.packages.vectorstore.supabase import CustomSupabaseVectorStore
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class DocumentAnswer:
+    file_name: str
+    file_sha1: str
+    file_size: int
+    file_url: str = ""
+    file_id: str = ""
+    file_similarity: float = 0.0
+
+
+def get_question_context_from_brain(brain_id: UUID, question: str) -> list[str]:
+    """Finds the best brain to answer the question based on the question's meaning.
+
+    Args:
+        brain_id (UUID): Id of the brain to search in
+        question (str): Question to search for in the vector store
+
+    Returns:
+        str: _descripton_
+    """
+    # TODO: Move to AnswerGenerator service
+    supabase_client = get_supabase_client()
+    embeddings = get_embedding_client()
+
+    vector_store = CustomSupabaseVectorStore(
+        supabase_client,
+        embeddings,
+        table_name="vectors",
+        brain_id=brain_id,
+        number_docs=20,
+    )
+    documents = vector_store.similarity_search(question, k=20, threshold=0.8)
+
+    answers = []
+    file_sha1s = []
+    for document in documents:
+        if document.metadata["file_sha1"] not in file_sha1s:
+            file_sha1s.append(document.metadata["file_sha1"])
+            file_path_in_storage = f"{brain_id}/{document.metadata['file_name']}"
+            signed_url = generate_file_signed_url(file_path_in_storage)
+            answers.append(
+                DocumentAnswer(
+                    file_name=document.metadata["file_name"],
+                    file_sha1=document.metadata["file_sha1"],
+                    file_size=document.metadata["file_size"],
+                    file_id=document.metadata["id"],
+                    file_similarity=document.metadata["similarity"],
+                    file_url=signed_url.get("signedURL", "") if signed_url else "",
+                ),
+            )
+
+    return answers
diff --git a/backend/core/quivr_core/api/modules/brain/service/integration_brain_service.py b/backend/core/quivr_core/api/modules/brain/service/integration_brain_service.py
new file mode 100644
index 000000000000..4ce8569e67f0
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/service/integration_brain_service.py
@@ -0,0 +1,27 @@
+from quivr_core.api.modules.brain.entity.integration_brain import (
+    IntegrationDescriptionEntity,
+)
+from quivr_core.api.modules.brain.repository.integration_brains import (
+    IntegrationDescription,
+)
+
+
+class IntegrationBrainDescriptionService:
+
+    def __init__(self):
+        self.repository = IntegrationDescription()
+
+    def get_all_integration_descriptions(self) -> list[IntegrationDescriptionEntity]:
+        return self.repository.get_all_integration_descriptions()
+
+    def get_integration_description(
+        self, integration_id
+    ) -> IntegrationDescriptionEntity | None:
+        return self.repository.get_integration_description(integration_id)
+
+    def get_integration_description_by_user_brain_id(
+        self, brain_id, user_id
+    ) -> IntegrationDescriptionEntity | None:
+        return self.repository.get_integration_description_by_user_brain_id(
+            brain_id, user_id
+        )
diff --git a/backend/core/quivr_core/api/modules/brain/service/utils/__init__.py b/backend/core/quivr_core/api/modules/brain/service/utils/__init__.py
new file mode 100644
index 000000000000..1b5b3a524cf5
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/service/utils/__init__.py
@@ -0,0 +1 @@
+from .validate_brain import validate_api_brain
diff --git a/backend/core/quivr_core/api/modules/brain/service/utils/format_chat_history.py b/backend/core/quivr_core/api/modules/brain/service/utils/format_chat_history.py
new file mode 100644
index 000000000000..3e3019432bdf
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/service/utils/format_chat_history.py
@@ -0,0 +1,31 @@
+from typing import Dict, List, Tuple
+
+from langchain.schema import AIMessage, BaseMessage, HumanMessage, SystemMessage
+
+from quivr_core.api.modules.chat.dto.outputs import GetChatHistoryOutput
+
+
+def format_chat_history(
+    history: List[GetChatHistoryOutput],
+) -> List[Dict[str, str]]:
+    """Format the chat history into a list of HumanMessage and AIMessage"""
+    formatted_history = []
+    for chat in history:
+        if chat.user_message:
+            formatted_history.append(HumanMessage(content=chat.user_message))
+        if chat.assistant:
+            formatted_history.append(AIMessage(content=chat.assistant))
+    return formatted_history
+
+
+def format_history_to_openai_mesages(
+    tuple_history: List[Tuple[str, str]], system_message: str, question: str
+) -> List[BaseMessage]:
+    """Format the chat history into a list of Base Messages"""
+    messages = []
+    messages.append(SystemMessage(content=system_message))
+    for human, ai in tuple_history:
+        messages.append(HumanMessage(content=human))
+        messages.append(AIMessage(content=ai))
+    messages.append(HumanMessage(content=question))
+    return messages
diff --git a/backend/core/quivr_core/api/modules/brain/service/utils/get_prompt_to_use_id.py b/backend/core/quivr_core/api/modules/brain/service/utils/get_prompt_to_use_id.py
new file mode 100644
index 000000000000..f27f88fd5f17
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/service/utils/get_prompt_to_use_id.py
@@ -0,0 +1,21 @@
+from typing import Optional
+from uuid import UUID
+
+from quivr_core.api.modules.brain.service.brain_service import BrainService
+
+brain_service = BrainService()
+
+
+def get_prompt_to_use_id(
+    brain_id: Optional[UUID], prompt_id: Optional[UUID]
+) -> Optional[UUID]:
+    if brain_id is None and prompt_id is None:
+        return None
+
+    return (
+        prompt_id
+        if prompt_id
+        else brain_service.get_brain_prompt_id(brain_id)
+        if brain_id
+        else None
+    )
diff --git a/backend/core/quivr_core/api/modules/brain/service/utils/validate_brain.py b/backend/core/quivr_core/api/modules/brain/service/utils/validate_brain.py
new file mode 100644
index 000000000000..faad3b819ca8
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/brain/service/utils/validate_brain.py
@@ -0,0 +1,14 @@
+from fastapi import HTTPException
+
+from quivr_core.api.modules.brain.dto.inputs import CreateBrainProperties
+
+
+def validate_api_brain(brain: CreateBrainProperties):
+    if brain.brain_definition is None:
+        raise HTTPException(status_code=404, detail="Brain definition not found")
+
+    if brain.brain_definition.url is None:
+        raise HTTPException(status_code=404, detail="Brain url not found")
+
+    if brain.brain_definition.method is None:
+        raise HTTPException(status_code=404, detail="Brain method not found")
diff --git a/backend/core/quivr_core/api/modules/brain/tests/test_brain_service.py b/backend/core/quivr_core/api/modules/brain/tests/test_brain_service.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/chat/__init__.py b/backend/core/quivr_core/api/modules/chat/__init__.py
new file mode 100644
index 000000000000..21252557e6b7
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/chat/__init__.py
@@ -0,0 +1,6 @@
+# noqa:
+from quivr_core.api.modules.brain.entity.brain_entity import Brain
+from quivr_core.api.modules.prompt.entity.prompt import Prompt
+from quivr_core.api.modules.user.entity.user_identity import User
+
+__all__ = ["Brain", "User", "Prompt"]
diff --git a/backend/core/quivr_core/api/modules/chat/controller/__init__.py b/backend/core/quivr_core/api/modules/chat/controller/__init__.py
new file mode 100644
index 000000000000..0ec06ba92835
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/chat/controller/__init__.py
@@ -0,0 +1 @@
+from .chat_routes import chat_router
diff --git a/backend/core/quivr_core/api/modules/chat/controller/chat_routes.py b/backend/core/quivr_core/api/modules/chat/controller/chat_routes.py
new file mode 100644
index 000000000000..8c0fba1705af
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/chat/controller/chat_routes.py
@@ -0,0 +1,225 @@
+from typing import Annotated, List, Optional
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Query, Request
+from fastapi.responses import StreamingResponse
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.brain.service.brain_service import BrainService
+from quivr_core.api.modules.chat.dto.chats import ChatItem, ChatQuestion
+from quivr_core.api.modules.chat.dto.inputs import (
+    ChatMessageProperties,
+    ChatUpdatableProperties,
+    CreateChatProperties,
+    QuestionAndAnswer,
+)
+from quivr_core.api.modules.chat.entity.chat import Chat
+from quivr_core.api.modules.chat.service.chat_service import ChatService
+from quivr_core.api.modules.dependencies import get_current_user, get_service
+from quivr_core.api.modules.knowledge.repository.knowledges import KnowledgeRepository
+from quivr_core.api.modules.prompt.service.prompt_service import PromptService
+from quivr_core.api.modules.rag.rag_service import RAGService
+from quivr_core.api.modules.user.entity.user_identity import UserIdentity
+from quivr_core.api.packages.utils.telemetry import maybe_send_telemetry
+
+logger = get_logger(__name__)
+
+brain_service = BrainService()
+knowledge_service = KnowledgeRepository()
+
+
+ChatServiceDep = Annotated[ChatService, Depends(get_service(ChatService))]
+UserIdentityDep = Annotated[UserIdentity, Depends(get_current_user)]
+PromptServiceDep = Annotated[PromptService, Depends(get_service(PromptService))]
+
+
+chat_router = APIRouter()
+
+
+@chat_router.get("/chat/healthz", tags=["Health"])
+async def healthz():
+    return {"status": "ok"}
+
+
+# get all chats
+@chat_router.get("/chat", tags=["Chat"])
+async def get_chats(current_user: UserIdentityDep, chat_service: ChatServiceDep):
+    """
+    Retrieve all chats for the current user.
+
+    - `current_user`: The current authenticated user.
+    - Returns a list of all chats for the user.
+
+    This endpoint retrieves all the chats associated with the current authenticated user. It returns a list of chat objects
+    containing the chat ID and chat name for each chat.
+    """
+    chats = await chat_service.get_user_chats(current_user.id)
+    return {"chats": chats}
+
+
+# delete one chat
+@chat_router.delete("/chat/{chat_id}", tags=["Chat"])
+async def delete_chat(chat_id: UUID, chat_service: ChatServiceDep):
+    """
+    Delete a specific chat by chat ID.
+    """
+
+    chat_service.delete_chat_from_db(chat_id)
+    return {"message": f"{chat_id}  has been deleted."}
+
+
+# update existing chat metadata
+@chat_router.put("/chat/{chat_id}/metadata", tags=["Chat"])
+async def update_chat_metadata_handler(
+    chat_data: ChatUpdatableProperties,
+    chat_id: UUID,
+    current_user: UserIdentityDep,
+    chat_service: ChatServiceDep,
+):
+    """
+    Update chat attributes
+    """
+
+    chat = await chat_service.get_chat_by_id(chat_id)
+    if str(current_user.id) != chat.user_id:
+        raise HTTPException(
+            status_code=403,  # pyright: ignore reportPrivateUsage=none
+            detail="You should be the owner of the chat to update it.",  # pyright: ignore reportPrivateUsage=none
+        )
+    return chat_service.update_chat(chat_id=chat_id, chat_data=chat_data)
+
+
+# update existing message
+@chat_router.put("/chat/{chat_id}/{message_id}", tags=["Chat"])
+async def update_chat_message(
+    chat_message_properties: ChatMessageProperties,
+    chat_id: UUID,
+    message_id: UUID,
+    current_user: UserIdentityDep,
+    chat_service: ChatServiceDep,
+):
+    chat = await chat_service.get_chat_by_id(chat_id)
+    if str(current_user.id) != chat.user_id:
+        raise HTTPException(
+            status_code=403, detail="You should be the owner of the chat to update it."
+        )
+    return chat_service.update_chat_message(
+        chat_id=chat_id,
+        message_id=message_id,
+        chat_message_properties=chat_message_properties,
+    )
+
+
+# create new chat
+@chat_router.post("/chat", tags=["Chat"])
+async def create_chat_handler(
+    chat_data: CreateChatProperties,
+    current_user: UserIdentityDep,
+    chat_service: ChatServiceDep,
+):
+    """
+    Create a new chat with initial chat messages.
+    """
+
+    return await chat_service.create_chat(
+        user_id=current_user.id, new_chat_data=chat_data
+    )
+
+
+# add new question to chat
+@chat_router.post(
+    "/chat/{chat_id}/question",
+    tags=["Chat"],
+)
+async def create_question_handler(
+    request: Request,
+    chat_question: ChatQuestion,
+    chat_id: UUID,
+    current_user: UserIdentityDep,
+    chat_service: ChatServiceDep,
+    prompt_service: PromptServiceDep,
+    brain_id: Annotated[UUID | None, Query()] = None,
+):
+    try:
+        rag_service = RAGService(
+            current_user,
+            brain_id,
+            chat_id,
+            brain_service,
+            prompt_service,
+            chat_service,
+            knowledge_service,
+        )
+        chat_answer = await rag_service.generate_answer(chat_question.question)
+
+        maybe_send_telemetry("question_asked", {"streaming": False}, request)
+        return chat_answer
+
+    except AssertionError:
+        raise HTTPException(
+            status_code=422,
+            detail="inprocessable entity",
+        )
+    except HTTPException as e:
+        raise e
+
+
+# stream new question response from chat
+@chat_router.post(
+    "/chat/{chat_id}/question/stream",
+    tags=["Chat"],
+)
+async def create_stream_question_handler(
+    request: Request,
+    chat_question: ChatQuestion,
+    chat_id: UUID,
+    chat_service: ChatServiceDep,
+    current_user: UserIdentityDep,
+    prompt_service: PromptServiceDep,
+    brain_id: Annotated[UUID | None, Query()] = None,
+) -> StreamingResponse:
+    logger.info(
+        f"Creating question for chat {chat_id} with brain {brain_id} of type {type(brain_id)}"
+    )
+
+    rag_service = RAGService(
+        current_user,
+        brain_id,
+        chat_id,
+        brain_service,
+        prompt_service,
+        chat_service,
+        knowledge_service,
+    )
+    maybe_send_telemetry("question_asked", {"streaming": True}, request)
+
+    return StreamingResponse(
+        rag_service.generate_answer_stream(chat_question.question),
+        media_type="text/event-stream",
+    )
+
+
+# get chat history
+@chat_router.get("/chat/{chat_id}/history", tags=["Chat"])
+async def get_chat_history_handler(
+    chat_id: UUID,
+    chat_service: ChatServiceDep,
+) -> List[ChatItem]:
+    return await chat_service.get_chat_history_with_notifications(chat_id)
+
+
+@chat_router.post(
+    "/chat/{chat_id}/question/answer",
+    tags=["Chat"],
+)
+async def add_question_and_answer_handler(
+    chat_id: UUID,
+    question_and_answer: QuestionAndAnswer,
+    chat_service: ChatServiceDep,
+) -> Optional[Chat]:
+    """
+    Add a new question and anwser to the chat.
+    """
+    history = await chat_service.add_question_and_answer(chat_id, question_and_answer)
+    # TODO(@aminediro) : Do we need to return the chat ??
+    return history.chat
diff --git a/backend/core/quivr_core/api/modules/chat/dto/__init__.py b/backend/core/quivr_core/api/modules/chat/dto/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/chat/dto/chats.py b/backend/core/quivr_core/api/modules/chat/dto/chats.py
new file mode 100644
index 000000000000..f499cf006f80
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/chat/dto/chats.py
@@ -0,0 +1,47 @@
+from enum import Enum
+from typing import List, Optional, Tuple, Union
+from uuid import UUID
+
+from pydantic import BaseModel
+
+from quivr_core.api.modules.chat.dto.outputs import GetChatHistoryOutput
+from quivr_core.api.modules.notification.entity.notification import Notification
+
+
+class ChatMessage(BaseModel):
+    model: str
+    question: str
+    # A list of tuples where each tuple is (speaker, text)
+    history: List[Tuple[str, str]]
+    temperature: float = 0.0
+    max_tokens: int = 256
+    use_summarization: bool = False
+    chat_id: Optional[UUID] = None
+    chat_name: Optional[str] = None
+
+
+class ChatQuestion(BaseModel):
+    question: str
+    model: Optional[str] = None
+    temperature: Optional[float] = None
+    max_tokens: Optional[int] = None
+    brain_id: Optional[UUID] = None
+    prompt_id: Optional[UUID] = None
+
+
+class Sources(BaseModel):
+    name: str
+    source_url: str
+    type: str
+    original_file_name: str
+    citation: str
+
+
+class ChatItemType(Enum):
+    MESSAGE = "MESSAGE"
+    NOTIFICATION = "NOTIFICATION"
+
+
+class ChatItem(BaseModel):
+    item_type: ChatItemType
+    body: Union[GetChatHistoryOutput, Notification]
diff --git a/backend/core/quivr_core/api/modules/chat/dto/inputs.py b/backend/core/quivr_core/api/modules/chat/dto/inputs.py
new file mode 100644
index 000000000000..d3acab722a2a
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/chat/dto/inputs.py
@@ -0,0 +1,46 @@
+from dataclasses import dataclass
+from typing import Optional
+from uuid import UUID
+
+from pydantic import BaseModel
+
+
+class CreateChatHistory(BaseModel):
+    chat_id: UUID
+    user_message: str
+    assistant: str
+    prompt_id: Optional[UUID] = None
+    brain_id: Optional[UUID] = None
+    metadata: Optional[dict] = {}
+
+
+class QuestionAndAnswer(BaseModel):
+    question: str
+    answer: str
+
+
+@dataclass
+class CreateChatProperties:
+    name: str
+
+    def __init__(self, name: str):
+        self.name = name
+
+
+@dataclass
+class ChatUpdatableProperties:
+    chat_name: Optional[str] = None
+
+    def __init__(self, chat_name: Optional[str]):
+        self.chat_name = chat_name
+
+
+class ChatMessageProperties(BaseModel, extra="ignore"):
+    thumbs: Optional[bool]
+
+    def dict(self, *args, **kwargs):
+        chat_dict = super().dict(*args, **kwargs)
+        if chat_dict.get("thumbs"):
+            # Set thumbs to boolean value or None if not present
+            chat_dict["thumbs"] = bool(chat_dict["thumbs"])
+        return chat_dict
diff --git a/backend/core/quivr_core/api/modules/chat/dto/outputs.py b/backend/core/quivr_core/api/modules/chat/dto/outputs.py
new file mode 100644
index 000000000000..86ee1fd6adba
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/chat/dto/outputs.py
@@ -0,0 +1,54 @@
+from datetime import datetime
+from typing import List, Optional
+from uuid import UUID
+
+from pydantic import BaseModel
+
+
+class GetChatHistoryOutput(BaseModel):
+    chat_id: UUID
+    message_id: UUID
+    user_message: str
+    message_time: datetime
+    assistant: str | None = None
+    prompt_title: str | None = None
+    brain_name: str | None = None
+    brain_id: UUID | None = None  # string because UUID is not JSON serializable
+    metadata: Optional[dict] | None = None
+    thumbs: Optional[bool] | None = None
+
+    def dict(self, *args, **kwargs):
+        chat_history = super().dict(*args, **kwargs)
+        chat_history["chat_id"] = str(chat_history.get("chat_id"))
+        chat_history["message_id"] = str(chat_history.get("message_id"))
+
+        return chat_history
+
+
+class FunctionCall(BaseModel):
+    arguments: str
+    name: str
+
+
+class ChatCompletionMessageToolCall(BaseModel):
+    id: str
+    function: FunctionCall
+    type: str = "function"
+
+
+class CompletionMessage(BaseModel):
+    # = "assistant" | "user" | "system" | "tool"
+    role: str
+    content: str | None = None
+    tool_calls: Optional[List[ChatCompletionMessageToolCall]] = None
+
+
+class CompletionResponse(BaseModel):
+    finish_reason: str
+    message: CompletionMessage
+
+
+class BrainCompletionOutput(BaseModel):
+    messages: List[CompletionMessage]
+    question: str
+    response: CompletionResponse
diff --git a/backend/core/quivr_core/api/modules/chat/entity/__init__.py b/backend/core/quivr_core/api/modules/chat/entity/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/chat/entity/chat.py b/backend/core/quivr_core/api/modules/chat/entity/chat.py
new file mode 100644
index 000000000000..53720f344325
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/chat/entity/chat.py
@@ -0,0 +1,81 @@
+from datetime import datetime
+from typing import List
+from uuid import UUID
+
+from sqlalchemy.ext.asyncio import AsyncAttrs
+from sqlmodel import JSON, TIMESTAMP, Column, Field, Relationship, SQLModel, text
+from sqlmodel import UUID as PGUUID
+
+from quivr_core.api.modules.brain.entity.brain_entity import Brain
+from quivr_core.api.modules.user.entity.user_identity import User
+
+
+class Chat(SQLModel, table=True):
+    __tablename__ = "chats"  # type: ignore
+    chat_id: UUID | None = Field(
+        default=None,
+        sa_column=Column(
+            PGUUID,
+            server_default=text("uuid_generate_v4()"),
+            primary_key=True,
+        ),
+    )
+    chat_name: str | None
+    creation_time: datetime | None = Field(
+        default=None,
+        sa_column=Column(
+            TIMESTAMP(timezone=False),
+            server_default=text("CURRENT_TIMESTAMP"),
+        ),
+    )
+    user_id: UUID | None = Field(default=None, foreign_key="users.id")
+    user: User | None = Relationship(back_populates="chats")  # type: ignore
+    chat_history: List["ChatHistory"] | None = Relationship(back_populates="chat")  # type: ignore
+
+
+class ChatHistory(AsyncAttrs, SQLModel, table=True):
+    __tablename__ = "chat_history"  # type: ignore # type : ignore
+
+    message_id: UUID | None = Field(
+        default=None,
+        sa_column=Column(
+            PGUUID,
+            server_default=text("uuid_generate_v4()"),
+            primary_key=True,
+        ),
+    )
+    chat_id: UUID | None = Field(
+        default=None,
+        foreign_key="chats.chat_id",
+        primary_key=True,
+        nullable=False,  # Added nullable constraint
+    )
+    chat: Chat | None = Relationship(
+        back_populates="chat_history", sa_relationship_kwargs={"lazy": "select"}
+    )  # type: ignore
+    user_message: str | None = None
+    assistant: str | None = None
+    message_time: datetime | None = Field(
+        default=None,
+        sa_column=Column(
+            TIMESTAMP(timezone=False),
+            server_default=text("CURRENT_TIMESTAMP"),
+        ),
+    )
+    metadata_: dict | None = Field(
+        default=None, sa_column=Column("metadata", JSON, default=None)
+    )
+    prompt_id: UUID | None = Field(default=None, foreign_key="prompts.id")
+    brain_id: UUID | None = Field(
+        default=None,
+        foreign_key="brains.brain_id",
+    )
+
+    thumbs: bool | None = None
+    brain: Brain | None = Relationship(
+        back_populates="brain_chat_history", sa_relationship_kwargs={"lazy": "select"}
+    )  # type: ignore
+
+    class Config:
+        # Note: Pydantic can't generate schema for arbitrary types
+        arbitrary_types_allowed = True
diff --git a/backend/core/quivr_core/api/modules/chat/repository/__init__.py b/backend/core/quivr_core/api/modules/chat/repository/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/chat/repository/chats.py b/backend/core/quivr_core/api/modules/chat/repository/chats.py
new file mode 100644
index 000000000000..4754be796f69
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/chat/repository/chats.py
@@ -0,0 +1,127 @@
+from typing import Sequence
+from uuid import UUID
+
+from sqlalchemy import exc
+from sqlmodel import select
+from sqlmodel.ext.asyncio.session import AsyncSession
+
+from quivr_core.api.models.settings import get_supabase_client
+from quivr_core.api.modules.chat.dto.inputs import (
+    ChatMessageProperties,
+    QuestionAndAnswer,
+)
+from quivr_core.api.modules.chat.entity.chat import Chat, ChatHistory
+from quivr_core.api.modules.dependencies import BaseRepository
+
+
+class ChatRepository(BaseRepository):
+    def __init__(self, session: AsyncSession):
+        super().__init__(session)
+        # TODO: for now use it instead of session
+        self.db = get_supabase_client()
+
+    async def get_user_chats(self, user_id: UUID) -> Sequence[Chat]:
+        query = select(Chat).where(Chat.user_id == user_id)
+        response = await self.session.exec(query)
+        return response.all()
+
+    async def create_chat(self, new_chat: Chat) -> Chat:
+        try:
+            self.session.add(new_chat)
+            await self.session.commit()
+        except exc.IntegrityError:
+            await self.session.rollback()
+            # TODO(@aminediro): Custom exceptions
+            raise Exception()
+
+        await self.session.refresh(new_chat)
+        return new_chat
+
+    async def get_chat_by_id(self, chat_id: UUID):
+        query = select(Chat).where(Chat.chat_id == chat_id)
+        response = await self.session.exec(query)
+        return response.one()
+
+    async def get_chat_history(self, chat_id: UUID) -> Sequence[ChatHistory]:
+        query = (
+            select(ChatHistory)
+            .where(ChatHistory.chat_id == chat_id)
+            # TODO: type hints of sqlmodel arent stable for order_by
+            .order_by(ChatHistory.message_time)  # type: ignore
+        )
+        response = await self.session.exec(query)
+        return response.all()
+
+    async def add_question_and_answer(
+        self, chat_id: UUID, question_and_answer: QuestionAndAnswer
+    ) -> ChatHistory:
+        chat = ChatHistory(
+            chat_id=chat_id,
+            user_message=question_and_answer.question,
+            assistant=question_and_answer.answer,
+        )
+        try:
+            self.session.add(chat)
+            await self.session.commit()
+        except exc.IntegrityError:
+            await self.session.rollback()
+            # TODO(@aminediro) : for now, build an exception system
+            raise Exception("can't create chat_history ")
+        await self.session.refresh(chat)
+        return chat
+
+    def update_chat_history(self, chat_history):
+        response = (
+            self.db.table("chat_history")
+            .insert(
+                {
+                    "chat_id": str(chat_history.chat_id),
+                    "user_message": chat_history.user_message,
+                    "assistant": chat_history.assistant,
+                    "prompt_id": (
+                        str(chat_history.prompt_id) if chat_history.prompt_id else None
+                    ),
+                    "brain_id": (
+                        str(chat_history.brain_id) if chat_history.brain_id else None
+                    ),
+                    "metadata": chat_history.metadata if chat_history.metadata else {},
+                }
+            )
+            .execute()
+        )
+        return response
+
+    def update_chat(self, chat_id, updates):
+        response = (
+            self.db.table("chats").update(updates).match({"chat_id": chat_id}).execute()
+        )
+
+        return response
+
+    def update_message_by_id(self, message_id, updates):
+        response = (
+            self.db.table("chat_history")
+            .update(updates)
+            .match({"message_id": message_id})
+            .execute()
+        )
+
+        return response
+
+    def delete_chat(self, chat_id):
+        self.db.table("chats").delete().match({"chat_id": chat_id}).execute()
+
+    def delete_chat_history(self, chat_id):
+        self.db.table("chat_history").delete().match({"chat_id": chat_id}).execute()
+
+    def update_chat_message(
+        self, chat_id, message_id, chat_message_properties: ChatMessageProperties
+    ):
+        response = (
+            self.db.table("chat_history")
+            .update(chat_message_properties)
+            .match({"message_id": message_id, "chat_id": chat_id})
+            .execute()
+        )
+
+        return response
diff --git a/backend/core/quivr_core/api/modules/chat/service/__init__.py b/backend/core/quivr_core/api/modules/chat/service/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/chat/service/chat_service.py b/backend/core/quivr_core/api/modules/chat/service/chat_service.py
new file mode 100644
index 000000000000..133161430e31
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/chat/service/chat_service.py
@@ -0,0 +1,207 @@
+import random
+from typing import List
+from uuid import UUID
+
+from fastapi import HTTPException
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.brain.entity.brain_entity import Brain
+from quivr_core.api.modules.brain.service.brain_service import BrainService
+from quivr_core.api.modules.chat.dto.chats import ChatItem
+from quivr_core.api.modules.chat.dto.inputs import (
+    ChatMessageProperties,
+    ChatUpdatableProperties,
+    CreateChatHistory,
+    CreateChatProperties,
+    QuestionAndAnswer,
+)
+from quivr_core.api.modules.chat.dto.outputs import GetChatHistoryOutput
+from quivr_core.api.modules.chat.entity.chat import Chat, ChatHistory
+from quivr_core.api.modules.chat.repository.chats import ChatRepository
+from quivr_core.api.modules.chat.service.utils import (
+    merge_chat_history_and_notifications,
+)
+from quivr_core.api.modules.dependencies import BaseService
+from quivr_core.api.modules.notification.service.notification_service import (
+    NotificationService,
+)
+from quivr_core.api.modules.prompt.entity.prompt import Prompt
+
+logger = get_logger(__name__)
+
+brain_service = BrainService()
+notification_service = NotificationService()
+
+
+class ChatService(BaseService[ChatRepository]):
+    repository_cls = ChatRepository
+
+    def __init__(self, repository: ChatRepository):
+        self.repository = repository
+
+    async def create_chat(
+        self, user_id: UUID, new_chat_data: CreateChatProperties
+    ) -> Chat:
+        # Chat is created upon the user's first question asked
+        logger.info(f"New chat entry in chats table for user {user_id}")
+
+        inserted_chat = await self.repository.create_chat(
+            Chat(chat_name=new_chat_data.name, user_id=user_id)
+        )
+        logger.info(f"Insert response {inserted_chat}")
+
+        return inserted_chat
+
+    def get_follow_up_question(
+        self, brain_id: UUID = None, question: str = None
+    ) -> list[str]:
+        follow_up = [
+            "Summarize the conversation",
+            "Explain in more detail",
+            "Explain like I'm 5",
+            "Provide a list",
+            "Give examples",
+            "Use simpler language",
+            "Elaborate on a specific point",
+            "Provide pros and cons",
+            "Break down into steps",
+            "Illustrate with an image or diagram",
+        ]
+        # Return 3 random follow up questions amongs the list
+        random3 = random.sample(follow_up, 3)
+        return random3
+
+    async def add_question_and_answer(
+        self, chat_id: UUID, question_and_answer: QuestionAndAnswer
+    ) -> ChatHistory:
+        return await self.repository.add_question_and_answer(
+            chat_id, question_and_answer
+        )
+
+    async def get_chat_by_id(self, chat_id: UUID) -> Chat:
+        chat = await self.repository.get_chat_by_id(chat_id)
+        return chat
+
+    async def get_chat_history(self, chat_id: UUID) -> List[GetChatHistoryOutput]:
+        history = await self.repository.get_chat_history(chat_id)
+        enriched_history: List[GetChatHistoryOutput] = []
+        if len(history) == 0:
+            return enriched_history
+        brain: Brain = await history[0].awaitable_attrs.brain
+        prompt: Prompt = await brain.awaitable_attrs.prompt
+        for message in history:
+            enriched_history.append(
+                # TODO : WHY bother with having ids here ??
+                GetChatHistoryOutput(
+                    chat_id=(message.chat_id),
+                    message_id=message.message_id,
+                    user_message=message.user_message,
+                    assistant=message.assistant,
+                    message_time=message.message_time,
+                    brain_name=brain.name if brain else None,
+                    brain_id=brain.brain_id if brain else None,
+                    prompt_title=(prompt.title if prompt else None),
+                    metadata=message.metadata_,
+                    thumbs=message.thumbs,
+                )
+            )
+        return enriched_history
+
+    async def get_chat_history_with_notifications(
+        self,
+        chat_id: UUID,
+    ) -> List[ChatItem]:
+        chat_history = await self.get_chat_history(chat_id)
+        chat_notifications = []
+        return merge_chat_history_and_notifications(chat_history, chat_notifications)
+
+    async def get_user_chats(self, user_id: UUID) -> List[Chat]:
+        return list(await self.repository.get_user_chats(user_id))
+
+    def update_chat_history(self, chat_history: CreateChatHistory) -> ChatHistory:
+        response: List[ChatHistory] = (
+            self.repository.update_chat_history(chat_history)
+        ).data
+        if len(response) == 0:
+            raise HTTPException(
+                status_code=500,
+                detail="An exception occurred while updating chat history.",
+            )
+        return ChatHistory(**response[0])  # pyright: ignore reportPrivateUsage=none
+
+    def update_chat(self, chat_id, chat_data: ChatUpdatableProperties) -> Chat:
+        if not chat_id:
+            logger.error("No chat_id provided")
+            return  # pyright: ignore reportPrivateUsage=none
+
+        updates = {}
+
+        if chat_data.chat_name is not None:
+            updates["chat_name"] = chat_data.chat_name
+
+        updated_chat = None
+
+        if updates:
+            updated_chat = (self.repository.update_chat(chat_id, updates)).data[0]
+            logger.info(f"Chat {chat_id} updated")
+        else:
+            logger.info(f"No updates to apply for chat {chat_id}")
+        return updated_chat  # pyright: ignore reportPrivateUsage=none
+
+    def update_message_by_id(
+        self,
+        message_id: str,
+        user_message: str = None,  # pyright: ignore reportPrivateUsage=none
+        assistant: str = None,  # pyright: ignore reportPrivateUsage=none
+        metadata: dict = None,  # pyright: ignore reportPrivateUsage=none
+    ) -> ChatHistory:
+        if not message_id:
+            logger.error("No message_id provided")
+            return  # pyright: ignore reportPrivateUsage=none
+
+        updates = {}
+
+        if user_message is not None:
+            updates["user_message"] = user_message
+
+        if assistant is not None:
+            updates["assistant"] = assistant
+
+        if metadata is not None:
+            updates["metadata"] = metadata
+
+        updated_message = None
+
+        if updates:
+            updated_message = (
+                self.repository.update_message_by_id(message_id, updates)
+            ).data[  # type: ignore
+                0
+            ]
+            logger.info(f"Message {message_id} updated")
+        else:
+            logger.info(f"No updates to apply for message {message_id}")
+        return ChatHistory(**updated_message)  # pyright: ignore reportPrivateUsage=none
+
+    def delete_chat_from_db(self, chat_id):
+        try:
+            self.repository.delete_chat_history(chat_id)
+        except Exception as e:
+            print(e)
+            pass
+        try:
+            self.repository.delete_chat(chat_id)
+        except Exception as e:
+            print(e)
+            pass
+
+    def update_chat_message(
+        self, chat_id, message_id, chat_message_properties: ChatMessageProperties
+    ):
+        try:
+            return self.repository.update_chat_message(
+                chat_id, message_id, chat_message_properties
+            ).data
+        except Exception as e:
+            print(e)
+            pass
diff --git a/backend/core/quivr_core/api/modules/chat/service/utils.py b/backend/core/quivr_core/api/modules/chat/service/utils.py
new file mode 100644
index 000000000000..10a629d096c1
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/chat/service/utils.py
@@ -0,0 +1,35 @@
+from typing import List
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.chat.dto.chats import ChatItem, ChatItemType
+from quivr_core.api.modules.chat.dto.outputs import GetChatHistoryOutput
+from quivr_core.api.modules.notification.entity.notification import Notification
+
+logger = get_logger(__name__)
+
+
+def merge_chat_history_and_notifications(
+    chat_history: List[GetChatHistoryOutput], notifications: List[Notification]
+) -> List[ChatItem]:
+    chat_history_and_notifications = chat_history + notifications
+
+    chat_history_and_notifications.sort(
+        key=lambda x: (
+            x.message_time
+            if isinstance(x, GetChatHistoryOutput) and x.message_time
+            else x.datetime
+        )
+    )
+
+    transformed_data = []
+    for item in chat_history_and_notifications:
+        if isinstance(item, GetChatHistoryOutput):
+            item_type = ChatItemType.MESSAGE
+            body = item
+        else:
+            item_type = ChatItemType.NOTIFICATION
+            body = item
+        transformed_item = ChatItem(item_type=item_type, body=body)
+        transformed_data.append(transformed_item)
+
+    return transformed_data
diff --git a/backend/core/quivr_core/api/modules/chat/tests/conftest.py b/backend/core/quivr_core/api/modules/chat/tests/conftest.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/chat/tests/test_chats.py b/backend/core/quivr_core/api/modules/chat/tests/test_chats.py
new file mode 100644
index 000000000000..26a0039b2d9e
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/chat/tests/test_chats.py
@@ -0,0 +1,177 @@
+import asyncio
+import os
+from typing import List, Tuple
+from uuid import uuid4
+
+import pytest
+import pytest_asyncio
+import sqlalchemy
+from sqlalchemy.ext.asyncio import create_async_engine
+from sqlmodel import create_engine, select
+from sqlmodel.ext.asyncio.session import AsyncSession
+
+from quivr_core.api.modules.brain.entity.brain_entity import Brain, BrainType
+from quivr_core.api.modules.chat.dto.inputs import QuestionAndAnswer
+from quivr_core.api.modules.chat.entity.chat import Chat, ChatHistory
+from quivr_core.api.modules.chat.repository.chats import ChatRepository
+from quivr_core.api.modules.chat.service.chat_service import ChatService
+from quivr_core.api.modules.user.entity.user_identity import User
+
+pg_database_url = "postgres:postgres@localhost:54322/postgres"
+
+
+@pytest.fixture(scope="session", autouse=True)
+def db_setup():
+    # setup
+    sync_engine = create_engine(
+        "postgresql://" + pg_database_url,
+        echo=True if os.getenv("ORM_DEBUG") else False,
+    )
+    #  TODO(@amine) : for now don't drop anything
+    yield sync_engine
+    # teardown
+    # NOTE: For now we rely on Supabase migrations for defining schemas
+    # SQLModel.metadata.create_all(sync_engine, checkfirst=True)
+    # SQLModel.metadata.drop_all(sync_engine)
+
+
+@pytest_asyncio.fixture(scope="session")
+async def async_engine():
+    engine = create_async_engine(
+        "postgresql+asyncpg://" + pg_database_url,
+        echo=True if os.getenv("ORM_DEBUG") else False,
+    )
+    yield engine
+
+
+@pytest.fixture(scope="session")
+def event_loop(request: pytest.FixtureRequest):
+    loop = asyncio.get_event_loop_policy().new_event_loop()
+    yield loop
+    loop.close()
+
+
+@pytest_asyncio.fixture()
+async def session(async_engine):
+    async with async_engine.connect() as conn:
+        await conn.begin()
+        await conn.begin_nested()
+        async_session = AsyncSession(conn, expire_on_commit=False)
+
+        @sqlalchemy.event.listens_for(
+            async_session.sync_session, "after_transaction_end"
+        )
+        def end_savepoint(session, transaction):
+            if conn.closed:
+                return
+            if not conn.in_nested_transaction():
+                conn.sync_connection.begin_nested()
+
+        yield async_session
+
+
+TestData = Tuple[Brain, User, List[Chat], List[ChatHistory]]
+
+
+@pytest_asyncio.fixture()
+async def test_data(
+    session: AsyncSession,
+) -> TestData:
+    # User data
+    user_1 = (
+        await session.exec(select(User).where(User.email == "admin@quivr.app"))
+    ).one()
+    # Brain data
+    brain_1 = Brain(name="test_brain", description="this is a test brain")
+    # Chat data
+    chat_1 = Chat(chat_name="chat1", user=user_1)
+    chat_2 = Chat(chat_name="chat2", user=user_1)
+
+    chat_history_1 = ChatHistory(
+        user_message="Hello",
+        assistant="Hello! How can I assist you today?",
+        chat=chat_1,
+        brain=brain_1,
+    )
+    chat_history_2 = ChatHistory(
+        user_message="Hello",
+        assistant="Hello! How can I assist you today?",
+        chat=chat_1,
+        brain=brain_1,
+    )
+    session.add(brain_1)
+    session.add(chat_1)
+    session.add(chat_2)
+    session.add(chat_history_1)
+    session.add(chat_history_2)
+
+    await session.refresh(user_1)
+    await session.commit()
+    return brain_1, user_1, [chat_1, chat_2], [chat_history_1, chat_history_2]
+
+
+@pytest.mark.asyncio
+async def test_get_user_chats_empty(session):
+    repo = ChatRepository(session)
+    chats = await repo.get_user_chats(user_id=uuid4())
+    assert len(chats) == 0
+
+
+@pytest.mark.asyncio
+async def test_get_user_chats(session: AsyncSession, test_data: TestData):
+    _, local_user, chats, _ = test_data
+    repo = ChatRepository(session)
+    assert local_user.id is not None
+    query_chats = await repo.get_user_chats(local_user.id)
+    assert len(query_chats) == len(chats)
+
+
+@pytest.mark.asyncio
+async def test_get_chat_history(session: AsyncSession, test_data: TestData):
+    brain_1, _, chats, chat_history = test_data
+    assert chats[0].chat_id
+    assert len(chat_history) > 0
+    assert chat_history[-1].message_time
+    assert chat_history[0].message_time
+
+    repo = ChatRepository(session)
+    query_chat_history = await repo.get_chat_history(chats[0].chat_id)
+    assert chat_history == query_chat_history
+    assert query_chat_history[-1].message_time
+    assert query_chat_history[0].message_time
+    assert query_chat_history[-1].message_time >= query_chat_history[0].message_time
+
+    # TODO: Should be tested in test_brain_repository
+    # Checks that brain is correct
+    assert query_chat_history[-1].brain is not None
+    assert query_chat_history[-1].brain.brain_type == BrainType.integration
+
+
+@pytest.mark.asyncio
+async def test_add_qa(session: AsyncSession, test_data: TestData):
+    _, _, [chat, *_], __ = test_data
+    assert chat.chat_id
+    qa = QuestionAndAnswer(question="question", answer="answer")
+    repo = ChatRepository(session)
+    resp_chat = await repo.add_question_and_answer(chat.chat_id, qa)
+
+    assert resp_chat.chat_id == chat.chat_id
+    assert resp_chat.user_message == qa.question
+    assert resp_chat.assistant == qa.answer
+
+
+## CHAT SERVICE
+
+
+@pytest.mark.asyncio
+async def test_service_get_chat_history(session: AsyncSession, test_data: TestData):
+    brain, _, [chat, *_], __ = test_data
+    assert chat.chat_id
+    repo = ChatRepository(session)
+    service = ChatService(repo)
+    history = await service.get_chat_history(chat.chat_id)
+
+    assert len(history) > 0
+    assert all(h.chat_id == chat.chat_id for h in history)
+    assert history[0].brain_name == brain.name
+    assert history[0].brain_id == brain.brain_id
diff --git a/backend/core/quivr_core/api/modules/dependencies.py b/backend/core/quivr_core/api/modules/dependencies.py
new file mode 100644
index 000000000000..fc34b560bfb7
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/dependencies.py
@@ -0,0 +1,70 @@
+import os
+from typing import AsyncGenerator, Callable, Generic, Type, TypeVar
+from uuid import uuid4
+
+from fastapi import Depends
+from sqlalchemy.ext.asyncio import create_async_engine
+from sqlmodel.ext.asyncio.session import AsyncSession
+
+from quivr_core.api.models.settings import settings
+from quivr_core.api.modules.user.entity.user_identity import UserIdentity
+
+
+class BaseRepository:
+    def __init__(self, session: AsyncSession):
+        self.session = session
+
+
+R = TypeVar("R", bound=BaseRepository)
+
+
+class BaseService(Generic[R]):
+    # associated repository type
+    repository_cls: Type[R]
+
+    def __init__(self, repository: R):
+        self.repository = repository
+
+    @classmethod
+    def get_repository_cls(cls) -> Type[R]:
+        return cls.repository_cls  # type: ignore
+
+
+S = TypeVar("S", bound=BaseService)
+
+async_engine = create_async_engine(
+    settings.pg_database_async_url,
+    echo=True if os.getenv("ORM_DEBUG") else False,
+    future=True,
+)
+
+
+async def get_async_session() -> AsyncGenerator[AsyncSession, None]:
+    async with AsyncSession(async_engine) as session:
+        yield session
+
+
+def get_repository(repository_model: Type[R]) -> Callable[..., R]:
+    def _get_repository(session: AsyncSession = Depends(get_async_session)) -> R:
+        return repository_model(session)
+
+    return _get_repository
+
+
+def get_service(service: Type[S]) -> Callable[..., S]:
+    def _get_service(
+        repository: BaseRepository = Depends(
+            get_repository(service.get_repository_cls())
+        ),
+    ) -> S:
+        return service(repository)
+
+    return _get_service
+
+
+def get_current_user() -> UserIdentity:
+    # TODO: get it one time from db
+    return UserIdentity(
+        id=uuid4(),
+        email="admin@quivr.app",
+    )
diff --git a/backend/core/quivr_core/api/modules/knowledge/__init__.py b/backend/core/quivr_core/api/modules/knowledge/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/knowledge/controller/__init__.py b/backend/core/quivr_core/api/modules/knowledge/controller/__init__.py
new file mode 100644
index 000000000000..911883cdc250
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/controller/__init__.py
@@ -0,0 +1 @@
+from .knowledge_routes import knowledge_router
diff --git a/backend/core/quivr_core/api/modules/knowledge/controller/knowledge_routes.py b/backend/core/quivr_core/api/modules/knowledge/controller/knowledge_routes.py
new file mode 100644
index 000000000000..3c3187b17a3e
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/controller/knowledge_routes.py
@@ -0,0 +1,86 @@
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Query
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.brain.service.brain_vector_service import BrainVectorService
+from quivr_core.api.modules.dependencies import get_current_user
+from quivr_core.api.modules.knowledge.service.knowledge_service import KnowledgeService
+from quivr_core.api.modules.upload.service.generate_file_signed_url import (
+    generate_file_signed_url,
+)
+from quivr_core.api.modules.user.entity.user_identity import UserIdentity
+
+knowledge_router = APIRouter()
+logger = get_logger(__name__)
+
+knowledge_service = KnowledgeService()
+
+
+@knowledge_router.get("/knowledge", tags=["Knowledge"])
+async def list_knowledge_in_brain_endpoint(
+    brain_id: UUID = Query(..., description="The ID of the brain"),
+    current_user: UserIdentity = Depends(get_current_user),
+):
+    """
+    Retrieve and list all the knowledge in a brain.
+    """
+
+    knowledges = knowledge_service.get_all_knowledge(brain_id)
+
+    return {"knowledges": knowledges}
+
+
+@knowledge_router.delete(
+    "/knowledge/{knowledge_id}",
+    tags=["Knowledge"],
+)
+async def delete_endpoint(
+    knowledge_id: UUID,
+    current_user: UserIdentity = Depends(get_current_user),
+    brain_id: UUID = Query(..., description="The ID of the brain"),
+):
+    """
+    Delete a specific knowledge from a brain.
+    """
+
+    knowledge = knowledge_service.get_knowledge(knowledge_id)
+    file_name = knowledge.file_name if knowledge.file_name else knowledge.url
+    knowledge_service.remove_knowledge(knowledge_id)
+
+    brain_vector_service = BrainVectorService(brain_id)
+    if knowledge.file_name:
+        brain_vector_service.delete_file_from_brain(knowledge.file_name)
+    elif knowledge.url:
+        brain_vector_service.delete_file_url_from_brain(knowledge.url)
+
+    return {
+        "message": f"{file_name} of brain {brain_id} has been deleted by user {current_user.email}."
+    }
+
+
+@knowledge_router.get(
+    "/knowledge/{knowledge_id}/signed_download_url",
+    tags=["Knowledge"],
+)
+async def generate_signed_url_endpoint(
+    knowledge_id: UUID,
+    current_user: UserIdentity = Depends(get_current_user),
+):
+    """
+    Generate a signed url to download the file from storage.
+    """
+
+    knowledge = knowledge_service.get_knowledge(knowledge_id)
+
+    if knowledge.file_name == None:
+        raise HTTPException(
+            status_code=404,
+            detail=f"Knowledge with id {knowledge_id} is not a file.",
+        )
+
+    file_path_in_storage = f"{knowledge.brain_id}/{knowledge.file_name}"
+
+    file_signed_url = generate_file_signed_url(file_path_in_storage)
+
+    return file_signed_url
diff --git a/backend/core/quivr_core/api/modules/knowledge/dto/__init__.py b/backend/core/quivr_core/api/modules/knowledge/dto/__init__.py
new file mode 100644
index 000000000000..4f3a4b9f7bb5
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/dto/__init__.py
@@ -0,0 +1,2 @@
+from .inputs import CreateKnowledgeProperties
+from .outputs import DeleteKnowledgeResponse
diff --git a/backend/core/quivr_core/api/modules/knowledge/dto/inputs.py b/backend/core/quivr_core/api/modules/knowledge/dto/inputs.py
new file mode 100644
index 000000000000..b6d008c35f24
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/dto/inputs.py
@@ -0,0 +1,18 @@
+from typing import Optional
+from uuid import UUID
+
+from pydantic import BaseModel
+
+
+class CreateKnowledgeProperties(BaseModel):
+    brain_id: UUID
+    file_name: Optional[str] = None
+    url: Optional[str] = None
+    extension: str = "txt"
+    integration: Optional[str] = None
+    integration_link: Optional[str] = None
+
+    def dict(self, *args, **kwargs):
+        knowledge_dict = super().dict(*args, **kwargs)
+        knowledge_dict["brain_id"] = str(knowledge_dict.get("brain_id"))
+        return knowledge_dict
diff --git a/backend/core/quivr_core/api/modules/knowledge/dto/outputs.py b/backend/core/quivr_core/api/modules/knowledge/dto/outputs.py
new file mode 100644
index 000000000000..905da7cf0d7f
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/dto/outputs.py
@@ -0,0 +1,8 @@
+from uuid import UUID
+
+from pydantic import BaseModel
+
+
+class DeleteKnowledgeResponse(BaseModel):
+    status: str = "delete"
+    knowledge_id: UUID
diff --git a/backend/core/quivr_core/api/modules/knowledge/entity/__init__.py b/backend/core/quivr_core/api/modules/knowledge/entity/__init__.py
new file mode 100644
index 000000000000..6f7620c446d2
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/entity/__init__.py
@@ -0,0 +1 @@
+from .knowledge import Knowledge
diff --git a/backend/core/quivr_core/api/modules/knowledge/entity/knowledge.py b/backend/core/quivr_core/api/modules/knowledge/entity/knowledge.py
new file mode 100644
index 000000000000..504162d0c644
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/entity/knowledge.py
@@ -0,0 +1,12 @@
+from typing import Optional
+from uuid import UUID
+
+from pydantic import BaseModel
+
+
+class Knowledge(BaseModel):
+    id: UUID
+    brain_id: UUID
+    file_name: Optional[str] = None
+    url: Optional[str] = None
+    extension: str = "txt"
diff --git a/backend/core/quivr_core/api/modules/knowledge/repository/__init__.py b/backend/core/quivr_core/api/modules/knowledge/repository/__init__.py
new file mode 100644
index 000000000000..80f13aa5f77f
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/repository/__init__.py
@@ -0,0 +1 @@
+from .knowledges import KnowledgeRepository
diff --git a/backend/core/quivr_core/api/modules/knowledge/repository/knowledge_interface.py b/backend/core/quivr_core/api/modules/knowledge/repository/knowledge_interface.py
new file mode 100644
index 000000000000..7d36e3b02bcc
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/repository/knowledge_interface.py
@@ -0,0 +1,58 @@
+from abc import ABC, abstractmethod
+from typing import List
+from uuid import UUID
+
+from quivr_core.api.modules.knowledge.dto.inputs import CreateKnowledgeProperties
+from quivr_core.api.modules.knowledge.dto.outputs import DeleteKnowledgeResponse
+from quivr_core.api.modules.knowledge.entity.knowledge import Knowledge
+
+
+class KnowledgeInterface(ABC):
+    @abstractmethod
+    def insert_knowledge(self, knowledge: CreateKnowledgeProperties) -> Knowledge:
+        """
+        Add a knowledge
+        """
+        pass
+
+    @abstractmethod
+    def remove_knowledge_by_id(
+        # todo: update remove brain endpoints to first delete the knowledge
+        self,
+        knowledge_id: UUID,
+    ) -> DeleteKnowledgeResponse:
+        """
+        Args:
+            knowledge_id (UUID): The id of the knowledge
+
+        Returns:
+            str: Status message
+        """
+        pass
+
+    @abstractmethod
+    def get_knowledge_by_id(self, knowledge_id: UUID) -> Knowledge:
+        """
+        Get a knowledge by its id
+        Args:
+            brain_id (UUID): The id of the brain
+        """
+        pass
+
+    @abstractmethod
+    def get_all_knowledge_in_brain(self, brain_id: UUID) -> List[Knowledge]:
+        """
+        Get all the knowledge in a brain
+        Args:
+            brain_id (UUID): The id of the brain
+        """
+        pass
+
+    @abstractmethod
+    def remove_brain_all_knowledge(self, brain_id: UUID) -> None:
+        """
+        Remove all knowledge in a brain
+        Args:
+            brain_id (UUID): The id of the brain
+        """
+        pass
diff --git a/backend/core/quivr_core/api/modules/knowledge/repository/knowledges.py b/backend/core/quivr_core/api/modules/knowledge/repository/knowledges.py
new file mode 100644
index 000000000000..b6896f1ee315
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/repository/knowledges.py
@@ -0,0 +1,114 @@
+from uuid import UUID
+
+from fastapi import HTTPException
+
+from quivr_core.api.models.settings import get_supabase_client
+from quivr_core.api.modules.knowledge.dto.outputs import DeleteKnowledgeResponse
+from quivr_core.api.modules.knowledge.entity.knowledge import Knowledge
+from quivr_core.api.modules.knowledge.repository.knowledge_interface import (
+    KnowledgeInterface,
+)
+
+
+class KnowledgeRepository(KnowledgeInterface):
+    def __init__(self):
+        supabase_client = get_supabase_client()
+        self.db = supabase_client
+
+    def insert_knowledge(self, knowledge):
+        """
+        Add a knowledge
+        """
+        # Check if the knowledge already exists
+        knowledge_exists = (
+            self.db.from_("knowledge")
+            .select("*")
+            .filter("brain_id", "eq", knowledge.brain_id)
+            .filter("file_name", "eq", knowledge.file_name)
+            .execute()
+        ).data
+
+        if knowledge_exists:
+            return Knowledge(**knowledge_exists[0])  # TODO fix this
+
+        response = (self.db.from_("knowledge").insert(knowledge.dict()).execute()).data
+        return Knowledge(**response[0])
+
+    def remove_knowledge_by_id(
+        # todo: update remove brain endpoints to first delete the knowledge
+        self,
+        knowledge_id,
+    ):
+        """
+        Args:
+            knowledge_id (UUID): The id of the knowledge
+
+        Returns:
+            str: Status message
+        """
+        response = (
+            self.db.from_("knowledge")
+            .delete()
+            .filter("id", "eq", knowledge_id)
+            .execute()
+            .data
+        )
+
+        if response == []:
+            raise HTTPException(404, "Knowledge not found")
+
+        return DeleteKnowledgeResponse(
+            # change to response[0].brain_id and knowledge_id[0].brain_id
+            status="deleted",
+            knowledge_id=knowledge_id,
+        )
+
+    def get_knowledge_by_id(self, knowledge_id):
+        """
+        Get a knowledge by its id
+        Args:
+            brain_id (UUID): The id of the brain
+        """
+        knowledge = (
+            self.db.from_("knowledge")
+            .select("*")
+            .filter("id", "eq", str(knowledge_id))
+            .execute()
+        ).data
+
+        return Knowledge(**knowledge[0])
+
+    def get_all_knowledge_in_brain(self, brain_id: UUID) -> list[Knowledge]:
+        """
+        Get all the knowledge in a brain
+        Args:
+            brain_id (UUID): The id of the brain
+        """
+        all_knowledge = (
+            self.db.from_("knowledge")
+            .select("*")
+            .filter("brain_id", "eq", str(brain_id))
+            .execute()
+        ).data
+
+        return [Knowledge(**knowledge) for knowledge in all_knowledge]
+
+    def remove_brain_all_knowledge(self, brain_id):
+        """
+        Remove all knowledge in a brain
+        Args:
+            brain_id (UUID): The id of the brain
+        """
+        all_knowledge = self.get_all_knowledge_in_brain(brain_id)
+        knowledge_to_delete_list = []
+
+        for knowledge in all_knowledge:
+            if knowledge.file_name:
+                knowledge_to_delete_list.append(f"{brain_id}/{knowledge.file_name}")
+
+        if knowledge_to_delete_list:
+            self.db.storage.from_("quivr").remove(knowledge_to_delete_list)
+
+        self.db.from_("knowledge").delete().filter(
+            "brain_id", "eq", str(brain_id)
+        ).execute()
diff --git a/backend/core/quivr_core/api/modules/knowledge/repository/storage.py b/backend/core/quivr_core/api/modules/knowledge/repository/storage.py
new file mode 100644
index 000000000000..b98b041a1753
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/repository/storage.py
@@ -0,0 +1,30 @@
+from quivr_core.api.logger import get_logger
+from quivr_core.api.models.settings import get_supabase_client
+from quivr_core.api.modules.knowledge.repository.storage_interface import (
+    StorageInterface,
+)
+
+logger = get_logger(__name__)
+
+
+class Storage(StorageInterface):
+    def __init__(self):
+        supabase_client = get_supabase_client()
+        self.db = supabase_client
+
+    def upload_file(self, file_name: str):
+        """
+        Upload file to storage
+        """
+        self.db.storage.from_("quivr").download(file_name)
+
+    def remove_file(self, file_name: str):
+        """
+        Remove file from storage
+        """
+        try:
+            response = self.db.storage.from_("quivr").remove([file_name])
+            return response
+        except Exception as e:
+            logger.error(e)
+            # raise e
diff --git a/backend/core/quivr_core/api/modules/knowledge/repository/storage_interface.py b/backend/core/quivr_core/api/modules/knowledge/repository/storage_interface.py
new file mode 100644
index 000000000000..228c998276e3
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/repository/storage_interface.py
@@ -0,0 +1,10 @@
+from abc import ABC, abstractmethod
+
+
+class StorageInterface(ABC):
+    @abstractmethod
+    def remove_file(self, file_name: str):
+        """
+        Remove file from storage
+        """
+        pass
diff --git a/backend/core/quivr_core/api/modules/knowledge/service/__init__.py b/backend/core/quivr_core/api/modules/knowledge/service/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/knowledge/service/knowledge_service.py b/backend/core/quivr_core/api/modules/knowledge/service/knowledge_service.py
new file mode 100644
index 000000000000..6798f6f24a85
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/knowledge/service/knowledge_service.py
@@ -0,0 +1,45 @@
+from uuid import UUID
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.knowledge.dto.inputs import CreateKnowledgeProperties
+from quivr_core.api.modules.knowledge.entity.knowledge import Knowledge
+from quivr_core.api.modules.knowledge.repository.knowledge_interface import (
+    KnowledgeInterface,
+)
+from quivr_core.api.modules.knowledge.repository.knowledges import KnowledgeRepository
+
+logger = get_logger(__name__)
+
+
+class KnowledgeService:
+    repository: KnowledgeInterface
+
+    def __init__(self):
+        self.repository = KnowledgeRepository()
+
+    def add_knowledge(self, knowledge_to_add: CreateKnowledgeProperties):
+        knowledge = self.repository.insert_knowledge(knowledge_to_add)
+
+        return knowledge
+
+    def get_all_knowledge(self, brain_id: UUID):
+        knowledges = self.repository.get_all_knowledge_in_brain(brain_id)
+
+        return knowledges
+
+    def get_knowledge(self, knowledge_id: UUID) -> Knowledge:
+        knowledge = self.repository.get_knowledge_by_id(knowledge_id)
+
+        return knowledge
+
+    def remove_brain_all_knowledge(self, brain_id: UUID) -> None:
+        self.repository.remove_brain_all_knowledge(brain_id)
+
+        logger.info(
+            f"All knowledge in brain {brain_id} removed successfully from table"
+        )
+
+    def remove_knowledge(self, knowledge_id: UUID):
+        message = self.repository.remove_knowledge_by_id(knowledge_id)
+
+        return message
diff --git a/backend/core/quivr_core/api/modules/notification/__init__.py b/backend/core/quivr_core/api/modules/notification/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/notification/controller/__init__.py b/backend/core/quivr_core/api/modules/notification/controller/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/notification/dto/__init__.py b/backend/core/quivr_core/api/modules/notification/dto/__init__.py
new file mode 100644
index 000000000000..2d81927d4b12
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/notification/dto/__init__.py
@@ -0,0 +1 @@
+from .inputs import NotificationUpdatableProperties
diff --git a/backend/core/quivr_core/api/modules/notification/dto/inputs.py b/backend/core/quivr_core/api/modules/notification/dto/inputs.py
new file mode 100644
index 000000000000..8c4d22ca25f2
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/notification/dto/inputs.py
@@ -0,0 +1,28 @@
+from typing import Optional
+from uuid import UUID
+
+from pydantic import BaseModel
+from quivr_core.api.modules.notification.entity.notification import (
+    NotificationsStatusEnum,
+)
+
+
+class CreateNotification(BaseModel):
+    """Properties that can be received on notification creation"""
+
+    user_id: UUID
+    status: NotificationsStatusEnum
+    title: str
+    description: Optional[str] = None
+
+    def model_dump(self, *args, **kwargs):
+        notification_dict = super().model_dump(*args, **kwargs)
+        notification_dict["user_id"] = str(notification_dict["user_id"])
+        return notification_dict
+
+
+class NotificationUpdatableProperties(BaseModel):
+    """Properties that can be received on notification update"""
+
+    status: Optional[NotificationsStatusEnum]
+    description: Optional[str]
diff --git a/backend/core/quivr_core/api/modules/notification/entity/__init__.py b/backend/core/quivr_core/api/modules/notification/entity/__init__.py
new file mode 100644
index 000000000000..209eacf8425f
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/notification/entity/__init__.py
@@ -0,0 +1 @@
+from .notification import Notification
diff --git a/backend/core/quivr_core/api/modules/notification/entity/notification.py b/backend/core/quivr_core/api/modules/notification/entity/notification.py
new file mode 100644
index 000000000000..4a1378b8d65f
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/notification/entity/notification.py
@@ -0,0 +1,24 @@
+from datetime import datetime
+from enum import Enum
+from typing import Optional
+from uuid import UUID
+
+from pydantic import BaseModel
+
+
+class NotificationsStatusEnum(str, Enum):
+    INFO = "info"
+    SUCCESS = "success"
+    WARNING = "warning"
+    ERROR = "error"
+
+
+class Notification(BaseModel):
+    id: UUID
+    user_id: UUID
+    status: NotificationsStatusEnum
+    title: str
+    description: Optional[str]
+    archived: Optional[bool] = False
+    read: Optional[bool] = False
+    datetime: Optional[datetime]  # timestamp
diff --git a/backend/core/quivr_core/api/modules/notification/repository/__init__.py b/backend/core/quivr_core/api/modules/notification/repository/__init__.py
new file mode 100644
index 000000000000..2d49d9356a1e
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/notification/repository/__init__.py
@@ -0,0 +1 @@
+from .notifications import Notifications
diff --git a/backend/core/quivr_core/api/modules/notification/repository/notifications.py b/backend/core/quivr_core/api/modules/notification/repository/notifications.py
new file mode 100644
index 000000000000..75b36e8db4c6
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/notification/repository/notifications.py
@@ -0,0 +1,68 @@
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.notification.dto.inputs import CreateNotification
+from quivr_core.api.modules.notification.entity.notification import Notification
+from quivr_core.api.modules.notification.repository.notifications_interface import (
+    NotificationInterface,
+)
+
+logger = get_logger(__name__)
+
+
+class Notifications(NotificationInterface):
+    def __init__(self, supabase_client):
+        self.db = supabase_client
+
+    def add_notification(self, notification: CreateNotification):
+        """
+        Add a notification
+        """
+        response = (
+            self.db.from_("notifications").insert(notification.model_dump()).execute()
+        ).data
+        return Notification(**response[0])
+
+    def update_notification_by_id(
+        self,
+        notification_id,
+        notification,
+    ):
+        if notification_id is None:
+            logger.info("Notification id is required")
+            return None
+
+        """Update a notification by id"""
+        response = (
+            self.db.from_("notifications")
+            .update(notification.model_dump(exclude_unset=True))
+            .filter("id", "eq", notification_id)
+            .execute()
+        ).data
+
+        if response == []:
+            logger.info(f"Notification with id {notification_id} not found")
+            return None
+
+        return Notification(**response[0])
+
+    def remove_notification_by_id(self, notification_id):
+        """
+        Remove a notification by id
+        Args:
+            notification_id (UUID): The id of the notification
+
+        Returns:
+            str: Status message
+        """
+        response = (
+            self.db.from_("notifications")
+            .delete()
+            .filter("id", "eq", notification_id)
+            .execute()
+            .data
+        )
+
+        if response == []:
+            logger.info(f"Notification with id {notification_id} not found")
+            return None
+
+        return {"status": "success"}
diff --git a/backend/core/quivr_core/api/modules/notification/repository/notifications_interface.py b/backend/core/quivr_core/api/modules/notification/repository/notifications_interface.py
new file mode 100644
index 000000000000..423e46df1917
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/notification/repository/notifications_interface.py
@@ -0,0 +1,36 @@
+from abc import ABC, abstractmethod
+from uuid import UUID
+
+from quivr_core.api.modules.notification.dto.inputs import (
+    CreateNotification,
+    NotificationUpdatableProperties,
+)
+from quivr_core.api.modules.notification.entity.notification import Notification
+
+
+class NotificationInterface(ABC):
+    @abstractmethod
+    def add_notification(self, notification: CreateNotification) -> Notification:
+        """
+        Add a notification
+        """
+        pass
+
+    @abstractmethod
+    def update_notification_by_id(
+        self, notification_id: UUID, notification: NotificationUpdatableProperties
+    ) -> Notification:
+        """Update a notification by id"""
+        pass
+
+    @abstractmethod
+    def remove_notification_by_id(self, notification_id: UUID):
+        """
+        Remove a notification by id
+        Args:
+            notification_id (UUID): The id of the notification
+
+        Returns:
+            str: Status message
+        """
+        pass
diff --git a/backend/core/quivr_core/api/modules/notification/service/__init__.py b/backend/core/quivr_core/api/modules/notification/service/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/notification/service/notification_service.py b/backend/core/quivr_core/api/modules/notification/service/notification_service.py
new file mode 100644
index 000000000000..7fd86bd5ef8b
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/notification/service/notification_service.py
@@ -0,0 +1,34 @@
+from quivr_core.api.models.settings import get_supabase_client
+from quivr_core.api.modules.notification.dto.inputs import (
+    CreateNotification,
+    NotificationUpdatableProperties,
+)
+from quivr_core.api.modules.notification.repository.notifications import Notifications
+from quivr_core.api.modules.notification.repository.notifications_interface import (
+    NotificationInterface,
+)
+
+
+class NotificationService:
+    repository: NotificationInterface
+
+    def __init__(self):
+        supabase_client = get_supabase_client()
+        self.repository = Notifications(supabase_client)
+
+    def add_notification(self, notification: CreateNotification):
+        """
+        Add a notification
+        """
+        return self.repository.add_notification(notification)
+
+    def update_notification_by_id(
+        self, notification_id, notification: NotificationUpdatableProperties
+    ):
+        """
+        Update a notification
+        """
+        if notification:
+            return self.repository.update_notification_by_id(
+                notification_id, notification
+            )
diff --git a/backend/core/quivr_core/api/modules/notification/tests/test_notification.py b/backend/core/quivr_core/api/modules/notification/tests/test_notification.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/prompt/__init__.py b/backend/core/quivr_core/api/modules/prompt/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/prompt/controller/__init__.py b/backend/core/quivr_core/api/modules/prompt/controller/__init__.py
new file mode 100644
index 000000000000..225300b6f7bc
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/prompt/controller/__init__.py
@@ -0,0 +1 @@
+from .prompt_routes import prompt_router
diff --git a/backend/core/quivr_core/api/modules/prompt/controller/prompt_routes.py b/backend/core/quivr_core/api/modules/prompt/controller/prompt_routes.py
new file mode 100644
index 000000000000..f87757c25c7d
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/prompt/controller/prompt_routes.py
@@ -0,0 +1,63 @@
+from typing import Annotated
+from uuid import UUID
+
+from fastapi import APIRouter, Depends
+from quivr_core.api.modules.dependencies import get_service
+from quivr_core.api.modules.prompt.entity.prompt import (
+    CreatePromptProperties,
+    Prompt,
+    PromptUpdatableProperties,
+)
+from quivr_core.api.modules.prompt.service import PromptService
+
+prompt_router = APIRouter()
+
+
+PromptServiceDep = Annotated[PromptService, Depends(get_service(PromptService))]
+
+
+@prompt_router.get("/prompts", tags=["Prompt"])
+async def get_prompts(
+    prompt_service: PromptServiceDep,
+) -> list[Prompt]:
+    """
+    Retrieve all public prompt
+    """
+    return prompt_service.get_public_prompts()
+
+
+@prompt_router.get("/prompts/{prompt_id}", tags=["Prompt"])
+async def get_prompt(
+    prompt_id: UUID,
+    prompt_service: PromptServiceDep,
+) -> Prompt | None:
+    """
+    Retrieve a prompt by its id
+    """
+
+    return prompt_service.get_prompt_by_id(prompt_id)
+
+
+@prompt_router.put("/prompts/{prompt_id}", tags=["Prompt"])
+async def update_prompt(
+    prompt_id: UUID,
+    prompt: PromptUpdatableProperties,
+    prompt_service: PromptServiceDep,
+) -> Prompt | None:
+    """
+    Update a prompt by its id
+    """
+
+    return prompt_service.update_prompt_by_id(prompt_id, prompt)
+
+
+@prompt_router.post("/prompts", tags=["Prompt"])
+async def create_prompt_route(
+    prompt: CreatePromptProperties,
+    prompt_service: PromptServiceDep,
+) -> Prompt | None:
+    """
+    Create a prompt by its id
+    """
+
+    return prompt_service.create_prompt(prompt)
diff --git a/backend/core/quivr_core/api/modules/prompt/entity/__init__.py b/backend/core/quivr_core/api/modules/prompt/entity/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/prompt/entity/prompt.py b/backend/core/quivr_core/api/modules/prompt/entity/prompt.py
new file mode 100644
index 000000000000..2e91ee7bd4ce
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/prompt/entity/prompt.py
@@ -0,0 +1,53 @@
+from enum import Enum
+from typing import List, Optional
+from uuid import UUID
+
+from pydantic import BaseModel
+from sqlmodel import UUID as PGUUID
+from sqlmodel import Column, Field, Relationship, SQLModel, text
+
+
+class PromptStatusEnum(str, Enum):
+    private = "private"
+    public = "public"
+
+
+class Prompt(SQLModel, table=True):
+    __tablename__ = "prompts"  # type: ignore
+    id: UUID | None = Field(
+        default=None,
+        sa_column=Column(
+            PGUUID,
+            server_default=text("uuid_generate_v4()"),
+            primary_key=True,
+        ),
+    )
+    content: str | None = None
+    title: str | None = Field(default=None, max_length=255)
+    status: str = Field(default="private", max_length=255)
+    brain: List["Brain"] = Relationship(  # noqa: F821
+        back_populates="prompt", sa_relationship_kwargs={"lazy": "joined"}
+    )
+
+
+class CreatePromptProperties(BaseModel):
+    """Properties that can be received on prompt creation"""
+
+    title: str
+    content: str
+    status: PromptStatusEnum = PromptStatusEnum.private
+
+
+class PromptUpdatableProperties(BaseModel):
+    """Properties that can be received on prompt update"""
+
+    title: Optional[str] = None
+    content: Optional[str] = None
+    status: Optional[PromptStatusEnum] = None
+
+
+class DeletePromptResponse(BaseModel):
+    """Response when deleting a prompt"""
+
+    status: str = "delete"
+    prompt_id: UUID
diff --git a/backend/core/quivr_core/api/modules/prompt/repository/__init__.py b/backend/core/quivr_core/api/modules/prompt/repository/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/prompt/repository/prompts.py b/backend/core/quivr_core/api/modules/prompt/repository/prompts.py
new file mode 100644
index 000000000000..bffde769dfb2
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/prompt/repository/prompts.py
@@ -0,0 +1,104 @@
+from uuid import UUID
+
+from fastapi import HTTPException
+from quivr_core.api.models.settings import get_supabase_client
+from quivr_core.api.modules.dependencies import BaseRepository
+from quivr_core.api.modules.prompt.entity.prompt import DeletePromptResponse, Prompt
+
+
+class PromptRepository(BaseRepository):
+    def __init__(self, session):
+        super().__init__(session)
+        self.db = get_supabase_client()
+
+    def create_prompt(self, prompt):
+        """
+        Create a prompt
+        """
+
+        response = (self.db.from_("prompts").insert(prompt.dict()).execute()).data
+
+        return Prompt(**response[0])
+
+    def delete_prompt_by_id(self, prompt_id: UUID):
+        """
+        Delete a prompt by id
+        Args:
+            prompt_id (UUID): The id of the prompt
+
+        Returns:
+        A dictionary containing the status of the delete and prompt_id of the deleted prompt
+        """
+
+        # Update brains where prompt_id is equal to the value to NULL
+        self.db.from_("brains").update({"prompt_id": None}).filter(
+            "prompt_id", "eq", str(prompt_id)
+        ).execute()
+
+        # Update chat_history where prompt_id is equal to the value to NULL
+        self.db.from_("chat_history").update({"prompt_id": None}).filter(
+            "prompt_id", "eq", str(prompt_id)
+        ).execute()
+
+        # Delete the prompt
+        response = (
+            self.db.from_("prompts")
+            .delete()
+            .filter("id", "eq", str(prompt_id))
+            .execute()
+            .data
+        )
+
+        if response == []:
+            raise HTTPException(404, "Prompt not found")
+
+        return DeletePromptResponse(status="deleted", prompt_id=prompt_id)
+
+    def get_prompt_by_id(self, prompt_id: UUID):
+        """
+        Get a prompt by its id
+
+        Args:
+            prompt_id (UUID): The id of the prompt
+
+        Returns:
+            Prompt: The prompt
+        """
+
+        response = (
+            self.db.from_("prompts")
+            .select("*")
+            .filter("id", "eq", str(prompt_id))
+            .execute()
+        ).data
+
+        if response == []:
+            return None
+        return Prompt(**response[0])
+
+    def get_public_prompts(self):
+        """
+        List all public prompts
+        """
+
+        return (
+            self.db.from_("prompts")
+            .select("*")
+            .filter("status", "eq", "public")
+            .execute()
+        ).data
+
+    def update_prompt_by_id(self, prompt_id: UUID, prompt):
+        """Update a prompt by id"""
+
+        response = (
+            self.db.from_("prompts")
+            .update(prompt.dict(exclude_unset=True))
+            .filter("id", "eq", str(prompt_id))
+            .execute()
+        ).data
+
+        if response == []:
+            raise HTTPException(404, "Prompt not found")
+
+        return Prompt(**response[0])
diff --git a/backend/core/quivr_core/api/modules/prompt/service/__init__.py b/backend/core/quivr_core/api/modules/prompt/service/__init__.py
new file mode 100644
index 000000000000..04be1eb3c873
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/prompt/service/__init__.py
@@ -0,0 +1 @@
+from .prompt_service import PromptService
diff --git a/backend/core/quivr_core/api/modules/prompt/service/prompt_service.py b/backend/core/quivr_core/api/modules/prompt/service/prompt_service.py
new file mode 100644
index 000000000000..c6c033823fc1
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/prompt/service/prompt_service.py
@@ -0,0 +1,71 @@
+from typing import List
+from uuid import UUID
+
+from quivr_core.api.modules.brain.service.utils.get_prompt_to_use_id import (
+    get_prompt_to_use_id,
+)
+from quivr_core.api.modules.dependencies import BaseService
+from quivr_core.api.modules.prompt.entity.prompt import (
+    CreatePromptProperties,
+    DeletePromptResponse,
+    Prompt,
+    PromptUpdatableProperties,
+)
+from quivr_core.api.modules.prompt.repository.prompts import PromptRepository
+
+
+class PromptService(BaseService[PromptRepository]):
+    repository_cls = PromptRepository
+
+    def __init__(self, repository: PromptRepository):
+        self.repository = repository
+
+    def create_prompt(self, prompt: CreatePromptProperties) -> Prompt:
+        return self.repository.create_prompt(prompt)
+
+    def delete_prompt_by_id(self, prompt_id: UUID) -> DeletePromptResponse:
+        """
+        Delete a prompt by id
+        Args:
+            prompt_id (UUID): The id of the prompt
+
+        Returns:
+            Prompt: The prompt
+        """
+        return self.repository.delete_prompt_by_id(prompt_id)
+
+    def get_prompt_by_id(self, prompt_id: UUID) -> Prompt | None:
+        """
+        Get a prompt by its id
+
+        Args:
+            prompt_id (UUID): The id of the prompt
+
+        Returns:
+            Prompt: The prompt
+        """
+        return self.repository.get_prompt_by_id(prompt_id)
+
+    def get_public_prompts(self) -> List[Prompt]:
+        """
+        List all public prompts
+        """
+
+        return self.repository.get_public_prompts()
+
+    def update_prompt_by_id(
+        self, prompt_id: UUID, prompt: PromptUpdatableProperties
+    ) -> Prompt:
+        """Update a prompt by id"""
+
+        return self.repository.update_prompt_by_id(prompt_id, prompt)
+
+    def get_prompt_to_use(
+        self, brain_id: UUID | None, prompt_id: UUID | None
+    ) -> Prompt | None:
+        prompt_to_use_id = get_prompt_to_use_id(brain_id, prompt_id)
+
+        if prompt_to_use_id is None:
+            return None
+
+        return self.get_prompt_by_id(prompt_to_use_id)
diff --git a/backend/core/quivr_core/api/modules/prompt/tests/test_prompt.py b/backend/core/quivr_core/api/modules/prompt/tests/test_prompt.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/rag/__init__.py b/backend/core/quivr_core/api/modules/rag/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/rag/rag_service.py b/backend/core/quivr_core/api/modules/rag/rag_service.py
new file mode 100644
index 000000000000..ab0f79cf1c66
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/rag/rag_service.py
@@ -0,0 +1,293 @@
+import datetime
+from uuid import UUID, uuid4
+
+from langchain_community.chat_models import ChatLiteLLM
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.models.settings import (
+    get_embedding_client,
+    get_supabase_client,
+    settings,
+)
+from quivr_core.api.modules.brain.entity.brain_entity import BrainEntity
+from quivr_core.api.modules.brain.service.brain_service import BrainService
+from quivr_core.api.modules.brain.service.utils.format_chat_history import (
+    format_chat_history,
+)
+from quivr_core.api.modules.chat.dto.inputs import CreateChatHistory
+from quivr_core.api.modules.chat.dto.outputs import GetChatHistoryOutput
+from quivr_core.api.modules.chat.service.chat_service import ChatService
+from quivr_core.api.modules.knowledge.repository.knowledges import KnowledgeRepository
+from quivr_core.api.modules.prompt.entity.prompt import Prompt
+from quivr_core.api.modules.prompt.service.prompt_service import PromptService
+from quivr_core.api.modules.user.entity.user_identity import UserIdentity
+from quivr_core.api.packages.vectorstore.supabase import CustomSupabaseVectorStore
+from quivr_core.config import RAGConfig
+from quivr_core.models import ParsedRAGResponse, RAGResponseMetadata
+from quivr_core.quivr_rag import QuivrQARAG
+
+from .utils import generate_source
+
+logger = get_logger(__name__)
+
+
+class RAGService:
+    def __init__(
+        self,
+        current_user: UserIdentity,
+        brain_id: UUID | None,
+        chat_id: UUID,
+        brain_service: BrainService,
+        prompt_service: PromptService,
+        chat_service: ChatService,
+        knowledge_service: KnowledgeRepository,
+    ):
+        # Services
+        self.brain_service = brain_service
+        self.prompt_service = prompt_service
+        self.chat_service = chat_service
+        self.knowledge_service = knowledge_service
+
+        # Base models
+        self.current_user = current_user
+        self.chat_id = chat_id
+        self.brain = self.get_or_create_brain(brain_id, self.current_user.id)
+        self.prompt = self.get_brain_prompt(self.brain)
+
+        # check at init time
+        # TODO: how to get model ?
+        self.model_to_use = ""
+        # self.model_to_use = "" self.check_and_update_user_usage(
+        #     self.current_user, self.brain
+        # )
+
+    def get_brain_prompt(self, brain: BrainEntity) -> Prompt | None:
+        return (
+            self.prompt_service.get_prompt_by_id(brain.prompt_id)
+            if brain.prompt_id
+            else None
+        )
+
+    def get_llm(self, rag_config: RAGConfig):
+        api_base = (
+            settings.ollama_api_base_url
+            if settings.ollama_api_base_url and rag_config.model.startswith("ollama")
+            else None
+        )
+        return ChatLiteLLM(
+            temperature=rag_config.temperature,
+            max_tokens=rag_config.max_tokens,
+            model=rag_config.model,
+            streaming=rag_config.streaming,
+            verbose=False,
+            api_base=api_base,
+        )  # pyright: ignore reportPrivateUsage=none
+
+    def get_or_create_brain(self, brain_id: UUID | None, user_id: UUID) -> BrainEntity:
+        brain = None
+        if brain_id is not None:
+            brain = self.brain_service.get_brain_details(brain_id, user_id)
+
+        # TODO: Create if doesn't exist
+        assert brain
+
+        if brain.integration:
+            assert brain.integration.user_id == user_id
+        return brain
+
+    # def check_and_update_user_usage(self, user: UserIdentity, brain: BrainEntity):
+    #     """Check user limits and raises if user reached his limits:
+    #     1. Raise if one of the conditions :
+    #        - User doesn't have access to brains
+    #        - Model of brain is not is user_settings.models
+    #        - Latest sum_30d(user_daily_user) < user_settings.max_monthly_usage
+    #        - Check sum(user_settings.daily_user_count)+ model_price <  user_settings.monthly_chat_credits
+    #     2. Updates user usage
+    #     """
+    #     # TODO(@aminediro) : THIS is bug prone, should retrieve it from DB here
+    #     user_usage = UserUsage(id=user.id, email=user.email)
+    #     user_settings = user_usage.get_user_settings()
+    #     all_models = user_usage.get_models()
+
+    #     # TODO(@aminediro): refactor this function
+    #     model_to_use = find_model_and_generate_metadata(
+    #         brain.model,
+    #         user_settings,
+    #         all_models,
+    #     )
+    #     cost = compute_cost(model_to_use, all_models)
+    #     # Raises HTTP if user usage exceeds limits
+    #     update_user_usage(user_usage, user_settings, cost)  # noqa: F821
+    #     return model_to_use
+
+    def create_vector_store(
+        self, brain_id: UUID, max_input: int
+    ) -> CustomSupabaseVectorStore:
+        supabase_client = get_supabase_client()
+        embeddings = get_embedding_client()
+        return CustomSupabaseVectorStore(
+            supabase_client,
+            embeddings,
+            table_name="vectors",
+            brain_id=brain_id,
+            max_input=max_input,
+        )
+
+    def save_answer(self, question: str, answer: ParsedRAGResponse):
+        return self.chat_service.update_chat_history(
+            CreateChatHistory(
+                **{
+                    "chat_id": self.chat_id,
+                    "user_message": question,
+                    "assistant": answer.answer,
+                    "brain_id": self.brain.brain_id,
+                    # TODO: prompt_id should always be not None
+                    "prompt_id": self.prompt.id if self.prompt else None,
+                    "metadata": answer.metadata.model_dump() if answer.metadata else {},
+                }
+            )
+        )
+
+    async def generate_answer(
+        self,
+        question: str,
+    ):
+        logger.info(
+            f"Creating question for chat {self.chat_id} with brain {self.brain.brain_id} "
+        )
+        rag_config = RAGConfig(
+            model=self.model_to_use.name,
+            temperature=self.brain.temperature,
+            max_input=self.model_to_use.max_input,
+            max_tokens=self.brain.max_tokens,
+            prompt=self.prompt.content if self.prompt else None,
+            streaming=False,
+        )
+        history = await self.chat_service.get_chat_history(self.chat_id)
+        # Get list of files
+        list_files = self.knowledge_service.get_all_knowledge_in_brain(
+            self.brain.brain_id
+        )
+        # Build RAG dependencies to inject
+        vector_store = self.create_vector_store(
+            self.brain.brain_id, rag_config.max_input
+        )
+        llm = self.get_llm(rag_config)
+        # Initialize the RAG pipline
+        rag_pipeline = QuivrQARAG(
+            rag_config=rag_config, llm=llm, vector_store=vector_store
+        )
+        #  Format the history, sanitize the input
+        transformed_history = format_chat_history(history)
+
+        parsed_response = rag_pipeline.answer(question, transformed_history, list_files)
+
+        # Save the answer to db
+        new_chat_entry = self.save_answer(question, parsed_response)
+
+        # Format output to be correct
+        return GetChatHistoryOutput(
+            **{
+                "chat_id": self.chat_id,
+                "user_message": question,
+                "assistant": parsed_response.answer,
+                "message_time": new_chat_entry.message_time,
+                "prompt_title": (self.prompt.title if self.prompt else None),
+                "brain_name": self.brain.name if self.brain else None,
+                "message_id": new_chat_entry.message_id,
+                "brain_id": str(self.brain.brain_id) if self.brain else None,
+                "metadata": (
+                    parsed_response.metadata.model_dump()
+                    if parsed_response.metadata
+                    else {}
+                ),
+            }
+        )
+
+    async def generate_answer_stream(
+        self,
+        question: str,
+    ):
+        logger.info(
+            f"Creating question for chat {self.chat_id} with brain {self.brain.brain_id} "
+        )
+        # Build the rag config
+        rag_config = RAGConfig(
+            model=self.model_to_use.name,
+            temperature=self.brain.temperature,
+            max_input=self.model_to_use.max_input,
+            max_tokens=self.brain.max_tokens,
+            prompt=self.prompt.content if self.prompt else "",
+            streaming=True,
+        )
+        # Getting chat history
+        history = await self.chat_service.get_chat_history(self.chat_id)
+        #  Format the history, sanitize the input
+        transformed_history = format_chat_history(history)
+
+        # Get list of files urls
+        # TODO: Why do we get ALL the files ?
+        list_files = self.knowledge_service.get_all_knowledge_in_brain(
+            self.brain.brain_id
+        )
+        llm = self.get_llm(rag_config)
+        vector_store = self.create_vector_store(
+            self.brain.brain_id, rag_config.max_input
+        )
+        # Initialize the rag pipline
+        rag_pipeline = QuivrQARAG(
+            rag_config=rag_config, llm=llm, vector_store=vector_store
+        )
+
+        full_answer = ""
+
+        message_metadata = {
+            "chat_id": self.chat_id,
+            "message_id": uuid4(),  # do we need it ?,
+            "user_message": question,  # TODO: define result
+            "message_time": datetime.datetime.now(),  # TODO: define result
+            "prompt_title": (self.prompt.title if self.prompt else ""),
+            "brain_name": self.brain.name if self.brain else None,
+            "brain_id": self.brain.brain_id if self.brain else None,
+        }
+
+        async for response in rag_pipeline.answer_astream(
+            question, transformed_history, list_files
+        ):
+            # Format output to be correct servicedf;j
+            if not response.last_chunk:
+                streamed_chat_history = GetChatHistoryOutput(
+                    assistant=response.answer,
+                    metadata=response.metadata.model_dump(),
+                    **message_metadata,
+                )
+                full_answer += response.answer
+                yield f"data: {streamed_chat_history.model_dump_json()}"
+
+        # For last chunk  parse the sources, and the full answer
+        streamed_chat_history = GetChatHistoryOutput(
+            assistant=response.answer,
+            metadata=response.metadata.model_dump(),
+            **message_metadata,
+        )
+
+        sources_urls = generate_source(
+            response.metadata.sources,
+            self.brain.brain_id,
+            (
+                streamed_chat_history.metadata["citations"]
+                if streamed_chat_history.metadata
+                else None
+            ),
+        )
+        if streamed_chat_history.metadata:
+            streamed_chat_history.metadata["sources"] = sources_urls
+
+        self.save_answer(
+            question,
+            ParsedRAGResponse(
+                answer=full_answer,
+                metadata=RAGResponseMetadata(**streamed_chat_history.metadata),
+            ),
+        )
+        yield f"data: {streamed_chat_history.model_dump_json()}"
diff --git a/backend/core/quivr_core/api/modules/rag/utils.py b/backend/core/quivr_core/api/modules/rag/utils.py
new file mode 100644
index 000000000000..908fa2b69545
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/rag/utils.py
@@ -0,0 +1,91 @@
+from typing import Any, List
+from uuid import UUID
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.chat.dto.chats import Sources
+from quivr_core.api.modules.upload.service.generate_file_signed_url import (
+    generate_file_signed_url,
+)
+
+logger = get_logger(__name__)
+
+
+def generate_source(
+    source_documents: List[Any] | None,
+    brain_id: UUID,
+    citations: List[int] | None = None,
+) -> List[Sources]:
+    """
+    Generate the sources list for the answer
+    It takes in a list of sources documents and citations that points to the docs index that was used in the answer
+    """
+    # Initialize an empty list for sources
+    sources_list: List[Sources] = []
+
+    # Initialize a dictionary for storing generated URLs
+    generated_urls = {}
+
+    # remove duplicate sources with same name and create a list of unique sources
+    sources_url_cache = {}
+
+    # Get source documents from the result, default to an empty list if not found
+    # If source documents exist
+    if source_documents:
+        logger.info(f"Citations {citations}")
+        for index, doc in enumerate(source_documents):
+            logger.info(f"Processing source document {doc.metadata['file_name']}")
+            if citations is not None:
+                if index not in citations:
+                    logger.info(f"Skipping source document {doc.metadata['file_name']}")
+                    continue
+            # Check if 'url' is in the document metadata
+            is_url = (
+                "original_file_name" in doc.metadata
+                and doc.metadata["original_file_name"] is not None
+                and doc.metadata["original_file_name"].startswith("http")
+            )
+
+            # Determine the name based on whether it's a URL or a file
+            name = (
+                doc.metadata["original_file_name"]
+                if is_url
+                else doc.metadata["file_name"]
+            )
+
+            # Determine the type based on whether it's a URL or a file
+            type_ = "url" if is_url else "file"
+
+            # Determine the source URL based on whether it's a URL or a file
+            if is_url:
+                source_url = doc.metadata["original_file_name"]
+            else:
+                file_path = f"{brain_id}/{doc.metadata['file_name']}"
+                # Check if the URL has already been generated
+                if file_path in generated_urls:
+                    source_url = generated_urls[file_path]
+                else:
+                    # Generate the URL
+                    if file_path in sources_url_cache:
+                        source_url = sources_url_cache[file_path]
+                    else:
+                        generated_url = generate_file_signed_url(file_path)
+                        if generated_url is not None:
+                            source_url = generated_url.get("signedURL", "")
+                        else:
+                            source_url = ""
+                    # Store the generated URL
+                    generated_urls[file_path] = source_url
+
+            # Append a new Sources object to the list
+            sources_list.append(
+                Sources(
+                    name=name,
+                    type=type_,
+                    source_url=source_url,
+                    original_file_name=name,
+                    citation=doc.page_content,
+                )
+            )
+    else:
+        logger.info("No source documents found or source_documents is not a list.")
+    return sources_list
diff --git a/backend/core/quivr_core/api/modules/upload/__init__.py b/backend/core/quivr_core/api/modules/upload/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/upload/controller/__init__.py b/backend/core/quivr_core/api/modules/upload/controller/__init__.py
new file mode 100644
index 000000000000..56030aa683eb
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/upload/controller/__init__.py
@@ -0,0 +1 @@
+from .upload_routes import upload_router
diff --git a/backend/core/quivr_core/api/modules/upload/controller/upload_routes.py b/backend/core/quivr_core/api/modules/upload/controller/upload_routes.py
new file mode 100644
index 000000000000..605a22935f62
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/upload/controller/upload_routes.py
@@ -0,0 +1,97 @@
+import os
+from typing import Optional
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Query, UploadFile
+
+from quivr_core.api.celery_worker import process_file_and_notify
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.dependencies import get_current_user
+from quivr_core.api.modules.knowledge.dto.inputs import CreateKnowledgeProperties
+from quivr_core.api.modules.knowledge.service.knowledge_service import KnowledgeService
+from quivr_core.api.modules.notification.dto.inputs import (
+    CreateNotification,
+    NotificationUpdatableProperties,
+)
+from quivr_core.api.modules.notification.entity.notification import (
+    NotificationsStatusEnum,
+)
+from quivr_core.api.modules.notification.service.notification_service import (
+    NotificationService,
+)
+from quivr_core.api.modules.upload.service.upload_file import upload_file_storage
+from quivr_core.api.modules.user.entity.user_identity import UserIdentity
+from quivr_core.api.packages.utils.telemetry import maybe_send_telemetry
+
+logger = get_logger(__name__)
+upload_router = APIRouter()
+
+notification_service = NotificationService()
+knowledge_service = KnowledgeService()
+
+
+@upload_router.get("/upload/healthz", tags=["Health"])
+async def healthz():
+    return {"status": "ok"}
+
+
+@upload_router.post("/upload", tags=["Upload"])
+async def upload_file(
+    uploadFile: UploadFile,
+    brain_id: UUID = Query(..., description="The ID of the brain"),
+    chat_id: Optional[UUID] = Query(None, description="The ID of the chat"),
+    current_user: UserIdentity = Depends(get_current_user),
+):
+    upload_notification = notification_service.add_notification(
+        CreateNotification(
+            user_id=current_user.id,
+            status=NotificationsStatusEnum.INFO,
+            title=f"Processing File {uploadFile.filename}",
+        )
+    )
+
+    maybe_send_telemetry("upload_file", {"file_name": uploadFile.filename})
+
+    file_content = await uploadFile.read()
+    filename_with_brain_id = str(brain_id) + "/" + str(uploadFile.filename)
+
+    try:
+        upload_file_storage(file_content, filename_with_brain_id)
+
+    except Exception as e:
+        print(e)
+
+        notification_service.update_notification_by_id(
+            upload_notification.id if upload_notification else None,
+            NotificationUpdatableProperties(
+                status=NotificationsStatusEnum.ERROR,
+                description=f"There was an error uploading the file: {e}",
+            ),
+        )
+        if "The resource already exists" in str(e):
+            raise HTTPException(
+                status_code=403,
+                detail=f"File {uploadFile.filename} already exists in storage.",
+            )
+        else:
+            raise HTTPException(
+                status_code=500, detail=f"Failed to upload file to storage. {e}"
+            )
+
+    knowledge_to_add = CreateKnowledgeProperties(
+        brain_id=brain_id,
+        file_name=uploadFile.filename,
+        extension=os.path.splitext(
+            uploadFile.filename  # pyright: ignore reportPrivateUsage=none
+        )[-1].lower(),
+    )
+
+    knowledge_service.add_knowledge(knowledge_to_add)
+
+    process_file_and_notify.delay(
+        file_name=filename_with_brain_id,
+        file_original_name=uploadFile.filename,
+        brain_id=brain_id,
+        notification_id=upload_notification.id,
+    )
+    return {"message": "File processing has started."}
diff --git a/backend/core/quivr_core/api/modules/upload/service/__init__.py b/backend/core/quivr_core/api/modules/upload/service/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/upload/service/generate_file_signed_url.py b/backend/core/quivr_core/api/modules/upload/service/generate_file_signed_url.py
new file mode 100644
index 000000000000..aaaf4b727473
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/upload/service/generate_file_signed_url.py
@@ -0,0 +1,27 @@
+from multiprocessing import get_logger
+
+from supabase.client import Client
+
+from quivr_core.api.models.settings import get_supabase_client
+
+logger = get_logger()
+
+SIGNED_URL_EXPIRATION_PERIOD_IN_SECONDS = 3600
+
+
+def generate_file_signed_url(path):
+    supabase_client: Client = get_supabase_client()
+
+    try:
+        response = supabase_client.storage.from_("quivr").create_signed_url(
+            path,
+            SIGNED_URL_EXPIRATION_PERIOD_IN_SECONDS,
+            options={
+                "download": True,
+                "transform": None,
+            },
+        )
+        logger.info("RESPONSE SIGNED URL", response)
+        return response
+    except Exception as e:
+        logger.error(e)
diff --git a/backend/core/quivr_core/api/modules/upload/service/list_files.py b/backend/core/quivr_core/api/modules/upload/service/list_files.py
new file mode 100644
index 000000000000..1a9c3faf38ce
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/upload/service/list_files.py
@@ -0,0 +1,17 @@
+from multiprocessing import get_logger
+
+from supabase.client import Client
+
+from quivr_core.api.models.settings import get_supabase_client
+
+logger = get_logger()
+
+
+def list_files_from_storage(path):
+    supabase_client: Client = get_supabase_client()
+
+    try:
+        response = supabase_client.storage.from_("quivr").list(path)
+        return response
+    except Exception as e:
+        logger.error(e)
diff --git a/backend/core/quivr_core/api/modules/upload/service/upload_file.py b/backend/core/quivr_core/api/modules/upload/service/upload_file.py
new file mode 100644
index 000000000000..d3733fab8a06
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/upload/service/upload_file.py
@@ -0,0 +1,134 @@
+import json
+import os
+
+from langchain.pydantic_v1 import Field
+from langchain.schema import Document
+from supabase.client import Client
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.models.settings import get_supabase_client
+
+logger = get_logger(__name__)
+
+
+# Mapping of file extensions to MIME types
+mime_types = {
+    ".txt": "text/plain",
+    ".csv": "text/csv",
+    ".md": "text/markdown",
+    ".markdown": "text/markdown",
+    ".telegram": "application/x-telegram",
+    ".m4a": "audio/mp4",
+    ".mp3": "audio/mpeg",
+    ".webm": "audio/webm",
+    ".mp4": "video/mp4",
+    ".mpga": "audio/mpeg",
+    ".wav": "audio/wav",
+    ".mpeg": "video/mpeg",
+    ".pdf": "application/pdf",
+    ".html": "text/html",
+    ".pptx": "application/vnd.openxmlformats-officedocument.presentationml.presentation",
+    ".docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+    ".odt": "application/vnd.oasis.opendocument.text",
+    ".xlsx": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+    ".xls": "application/vnd.ms-excel",
+    ".epub": "application/epub+zip",
+    ".ipynb": "application/x-ipynb+json",
+    ".py": "text/x-python",
+}
+
+
+def check_file_exists(brain_id: str, file_identifier: str) -> bool:
+    supabase_client: Client = get_supabase_client()
+    try:
+        # Check if the file exists
+        logger.info(f"Checking if file {file_identifier} exists.")
+        # This needs to be converted into a file_identifier that is safe for a URL
+
+        response = supabase_client.storage.from_("quivr").list(brain_id)
+
+        # Check if the file_identifier is in the response
+        file_exists = any(
+            file["name"].split(".")[0] == file_identifier.split(".")[0]
+            for file in response
+        )
+        logger.info(f"File identifier: {file_identifier}")
+        logger.info(f"File exists: {file_exists}")
+        if file_exists:
+            logger.info(f"File {file_identifier} exists.")
+            return True
+        else:
+            logger.info(f"File {file_identifier} does not exist.")
+            return False
+    except Exception as e:
+        logger.error(f"An error occurred while checking the file: {e}")
+        return True
+
+
+def upload_file_storage(file, file_identifier: str, upsert: str = "false"):
+    supabase_client: Client = get_supabase_client()
+    response = None
+
+    try:
+        # Get the file extension
+        _, file_extension = os.path.splitext(file_identifier)
+
+        # Get the MIME type for the file extension
+        mime_type = mime_types.get(file_extension, "text/html")
+
+        response = supabase_client.storage.from_("quivr").upload(
+            file_identifier,
+            file,
+            file_options={
+                "content-type": mime_type,
+                "upsert": upsert,
+                "cache-control": "3600",
+            },
+        )
+
+        return response
+    except Exception as e:
+        if "The resource already exists" in str(e) and upsert == "true":
+            response = supabase_client.storage.from_("quivr").update(
+                file_identifier,
+                file,
+                file_options={
+                    "content-type": mime_type,
+                    "upsert": upsert,
+                    "cache-control": "3600",
+                },
+            )
+        else:
+            raise e
+
+
+class DocumentSerializable(Document):
+    """Class for storing a piece of text and associated metadata."""
+
+    page_content: str
+    metadata: dict = Field(default_factory=dict)
+
+    @property
+    def lc_serializable(self) -> bool:
+        return True
+
+    def __repr__(self):
+        return f"Document(page_content='{self.page_content[:50]}...', metadata={self.metadata})"
+
+    def __str__(self):
+        return self.__repr__()
+
+    def to_json(self) -> str:
+        """Convert the Document object to a JSON string."""
+        return json.dumps(
+            {
+                "page_content": self.page_content,
+                "metadata": self.metadata,
+            }
+        )
+
+    @classmethod
+    def from_json(cls, json_str: str):
+        """Create a Document object from a JSON string."""
+        data = json.loads(json_str)
+        return cls(page_content=data["page_content"], metadata=data["metadata"])
diff --git a/backend/core/quivr_core/api/modules/upload/tests/test_files/test.bib b/backend/core/quivr_core/api/modules/upload/tests/test_files/test.bib
new file mode 100644
index 000000000000..14b61838bdf3
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/upload/tests/test_files/test.bib
@@ -0,0 +1,6 @@
+@Article{citekey,
+  author  = "",
+  title   = "",
+  journal = "",
+  year    = "",
+}
diff --git a/backend/core/quivr_core/api/modules/upload/tests/test_files/test.csv b/backend/core/quivr_core/api/modules/upload/tests/test_files/test.csv
new file mode 100644
index 000000000000..a025eb598153
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/upload/tests/test_files/test.csv
@@ -0,0 +1,17 @@
+quivrhq/quivr,Sat May 13 2023 02:20:09 GMT+0200 (heure d’été d’Europe centrale),0
+quivrhq/quivr,Tue May 16 2023 18:03:49 GMT+0200 (heure d’été d’Europe centrale),660
+quivrhq/quivr,Thu May 18 2023 03:04:23 GMT+0200 (heure d’été d’Europe centrale),1380
+quivrhq/quivr,Thu May 18 2023 23:04:11 GMT+0200 (heure d’été d’Europe centrale),2070
+quivrhq/quivr,Sat May 20 2023 04:44:40 GMT+0200 (heure d’été d’Europe centrale),2790
+quivrhq/quivr,Sun May 21 2023 03:19:46 GMT+0200 (heure d’été d’Europe centrale),3510
+quivrhq/quivr,Mon May 22 2023 08:03:18 GMT+0200 (heure d’été d’Europe centrale),4230
+quivrhq/quivr,Tue May 23 2023 16:57:58 GMT+0200 (heure d’été d’Europe centrale),4950
+quivrhq/quivr,Sat May 27 2023 02:18:31 GMT+0200 (heure d’été d’Europe centrale),5640
+quivrhq/quivr,Thu Jun 01 2023 18:45:27 GMT+0200 (heure d’été d’Europe centrale),6360
+quivrhq/quivr,Thu Jun 08 2023 16:33:57 GMT+0200 (heure d’été d’Europe centrale),7080
+quivrhq/quivr,Mon Jun 19 2023 12:58:34 GMT+0200 (heure d’été d’Europe centrale),7800
+quivrhq/quivr,Tue Jun 27 2023 14:45:52 GMT+0200 (heure d’été d’Europe centrale),8520
+quivrhq/quivr,Fri Jun 30 2023 11:43:51 GMT+0200 (heure d’été d’Europe centrale),9210
+quivrhq/quivr,Fri Jul 07 2023 23:08:23 GMT+0200 (heure d’été d’Europe centrale),9930
+quivrhq/quivr,Mon Jul 10 2023 08:13:07 GMT+0200 (heure d’été d’Europe centrale),10650
+quivrhq/quivr,Wed Jul 12 2023 09:40:29 GMT+0200 (heure d’été d’Europe centrale),13837
diff --git a/backend/core/quivr_core/api/modules/upload/tests/test_files/test.pdf b/backend/core/quivr_core/api/modules/upload/tests/test_files/test.pdf
new file mode 100644
index 000000000000..e256f71d70b5
Binary files /dev/null and b/backend/core/quivr_core/api/modules/upload/tests/test_files/test.pdf differ
diff --git a/backend/core/quivr_core/api/modules/upload/tests/test_files/test.txt b/backend/core/quivr_core/api/modules/upload/tests/test_files/test.txt
new file mode 100644
index 000000000000..90bfcb510602
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/upload/tests/test_files/test.txt
@@ -0,0 +1 @@
+this is a test
diff --git a/backend/core/quivr_core/api/modules/upload/tests/test_upload.py b/backend/core/quivr_core/api/modules/upload/tests/test_upload.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/user/__init__.py b/backend/core/quivr_core/api/modules/user/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/user/controller/__init__.py b/backend/core/quivr_core/api/modules/user/controller/__init__.py
new file mode 100644
index 000000000000..d02e1c740e5c
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/user/controller/__init__.py
@@ -0,0 +1 @@
+from .user_controller import user_router
diff --git a/backend/core/quivr_core/api/modules/user/controller/user_controller.py b/backend/core/quivr_core/api/modules/user/controller/user_controller.py
new file mode 100644
index 000000000000..413f4c5b08b5
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/user/controller/user_controller.py
@@ -0,0 +1,93 @@
+from typing import Annotated
+
+from fastapi import APIRouter, Depends
+
+from quivr_core.api.modules.brain.service.brain_user_service import BrainUserService
+from quivr_core.api.modules.dependencies import get_current_user, get_service
+from quivr_core.api.modules.user.dto.inputs import UserUpdatableProperties
+from quivr_core.api.modules.user.entity.user_identity import UserIdentity
+from quivr_core.api.modules.user.service.user_service import UserService
+
+user_router = APIRouter()
+brain_user_service = BrainUserService()
+
+UserServiceDep = Annotated[UserService, Depends(get_service(UserService))]
+
+UserIdentityDep = Annotated[UserIdentity, Depends(get_current_user)]
+
+
+@user_router.get("/user", tags=["User"])
+async def get_user_endpoint(
+    current_user: UserIdentityDep, user_service: UserServiceDep
+):
+    """
+    Get user information and statistics.
+
+    - `current_user`: The current authenticated user.
+    - Returns the user's email, maximum brain size, current brain size, maximum requests number, requests statistics, and the current date.
+
+    This endpoint retrieves information and statistics about the authenticated user. It includes the user's email, maximum brain size,
+    current brain size, maximum requests number, requests statistics, and the current date. The brain size is calculated based on the
+    user's uploaded vectors, and the maximum brain size is obtained from the environment variables. The requests statistics provide
+    information about the user's API usage.
+    """
+
+    user_settings = user_service.get_user_settings(current_user.id)
+
+    return {
+        "email": current_user.email,
+        "current_brain_size": 0,
+        "models": user_settings.get("models", []),
+        "id": current_user.id,
+    }
+
+
+@user_router.put(
+    "/user/identity",
+    tags=["User"],
+)
+def update_user_identity_route(
+    user_identity_updatable_properties: UserUpdatableProperties,
+    current_user: UserIdentityDep,
+    user_service: UserServiceDep,
+) -> UserIdentity:
+    """
+    Update user identity.
+    """
+    return user_service.update_user_properties(
+        current_user.id, user_identity_updatable_properties
+    )
+
+
+@user_router.get(
+    "/user/identity",
+    tags=["User"],
+)
+def get_user_identity_route(
+    current_user: UserIdentityDep,
+    user_service: UserServiceDep,
+) -> UserIdentity:
+    """
+    Get user identity.
+    """
+    return user_service.get_user_identity(current_user.id)
+
+
+@user_router.delete(
+    "/user_data",
+    tags=["User"],
+)
+async def delete_user_data_route(
+    current_user: UserIdentityDep,
+    user_service: UserServiceDep,
+):
+    """
+    Delete a user.
+    - `user_id`: The ID of the user to delete.
+
+    This endpoint deletes a user from the system.
+    """
+
+    user_service.delete_user_data(current_user.id)
+
+    return {"message": "User deleted successfully"}
diff --git a/backend/core/quivr_core/api/modules/user/dto/inputs.py b/backend/core/quivr_core/api/modules/user/dto/inputs.py
new file mode 100644
index 000000000000..348e99af68b8
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/user/dto/inputs.py
@@ -0,0 +1,12 @@
+from typing import Optional
+
+from pydantic import BaseModel
+
+
+class UserUpdatableProperties(BaseModel):
+    # Nothing for now
+    username: Optional[str] = None
+    company: Optional[str] = None
+    onboarded: Optional[bool] = None
+    company_size: Optional[str] = None
+    usage_purpose: Optional[str] = None
diff --git a/backend/core/quivr_core/api/modules/user/entity/user_identity.py b/backend/core/quivr_core/api/modules/user/entity/user_identity.py
new file mode 100644
index 000000000000..8feaccfe0c2e
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/user/entity/user_identity.py
@@ -0,0 +1,28 @@
+from typing import List, Optional
+from uuid import UUID, uuid4
+
+from pydantic import BaseModel
+from sqlmodel import Field, Relationship, SQLModel
+
+
+class User(SQLModel, table=True):
+    __tablename__ = "users"  # type: ignore
+
+    id: UUID | None = Field(
+        primary_key=True,
+        nullable=False,
+        default_factory=uuid4,
+    )
+    email: str
+    onboarded: bool | None = None
+    chats: List["Chat"] | None = Relationship(back_populates="user")  # type: ignore
+
+
+class UserIdentity(BaseModel):
+    id: UUID
+    email: Optional[str] = None
+    username: Optional[str] = None
+    company: Optional[str] = None
+    onboarded: Optional[bool] = None
+    company_size: Optional[str] = None
+    usage_purpose: Optional[str] = None
diff --git a/backend/core/quivr_core/api/modules/user/repository/__init__.py b/backend/core/quivr_core/api/modules/user/repository/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/modules/user/repository/user_repository.py b/backend/core/quivr_core/api/modules/user/repository/user_repository.py
new file mode 100644
index 000000000000..099d9831c723
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/user/repository/user_repository.py
@@ -0,0 +1,146 @@
+from uuid import UUID
+
+from sqlmodel.ext.asyncio.session import AsyncSession
+
+from quivr_core.api.models.settings import get_supabase_client
+from quivr_core.api.modules.dependencies import BaseRepository
+from quivr_core.api.modules.user.dto.inputs import UserUpdatableProperties
+from quivr_core.api.modules.user.entity.user_identity import UserIdentity
+
+
+class UserRepository(BaseRepository):
+    def __init__(self, session: AsyncSession):
+        super().__init__(session)
+        self.db = get_supabase_client()
+
+    def create_user_identity(self, id):
+        response = (
+            self.db.from_("user_identity")
+            .insert(
+                {
+                    "user_id": str(id),
+                }
+            )
+            .execute()
+        )
+        user_identity = response.data[0]
+        return UserIdentity(id=user_identity.get("user_id"))
+
+    def update_user_properties(
+        self,
+        user_id: UUID,
+        user_identity_updatable_properties: UserUpdatableProperties,
+    ):
+        response = (
+            self.db.from_("user_identity")
+            .update(user_identity_updatable_properties.__dict__)
+            .filter("user_id", "eq", user_id)  # type: ignore
+            .execute()
+        )
+
+        if len(response.data) == 0:
+            return self.create_user_identity(user_id)
+
+        user_identity = response.data[0]
+
+        print("USER_IDENTITY", user_identity)
+        return UserIdentity(id=user_id)
+
+    def get_user_identity(self, user_id):
+        response = (
+            self.db.from_("user_identity")
+            .select("*, users (email)")
+            .filter("user_id", "eq", str(user_id))
+            .execute()
+        )
+
+        if len(response.data) == 0:
+            return self.create_user_identity(user_id)
+
+        user_identity = response.data[0]
+
+        user_identity["id"] = user_id  # Add 'id' field to the dictionary
+        user_identity["email"] = user_identity["users"]["email"]
+        return UserIdentity(**user_identity)
+
+    def get_user_id_by_user_email(self, email):
+        response = (
+            self.db.rpc("get_user_id_by_user_email", {"user_email": email})
+            .execute()
+            .data
+        )
+        if len(response) > 0:
+            return response[0]["user_id"]
+        return None
+
+    def get_user_email_by_user_id(self, user_id):
+        response = self.db.rpc(
+            "get_user_email_by_user_id", {"user_id": str(user_id)}
+        ).execute()
+        return response.data[0]["email"]
+
+    def delete_user_data(self, user_id: UUID):
+        response = (
+            self.db.from_("brains_users")
+            .select("brain_id")
+            .filter("rights", "eq", "Owner")
+            .filter("user_id", "eq", str(user_id))
+            .execute()
+        )
+        brain_ids = [row["brain_id"] for row in response.data]
+
+        for brain_id in brain_ids:
+            self.db.table("brains").delete().filter(
+                "brain_id", "eq", brain_id
+            ).execute()
+
+        for brain_id in brain_ids:
+            self.db.table("brains_vectors").delete().filter(
+                "brain_id", "eq", brain_id
+            ).execute()
+
+        for brain_id in brain_ids:
+            self.db.table("chat_history").delete().filter(
+                "brain_id", "eq", brain_id
+            ).execute()
+
+        self.db.table("user_settings").delete().filter(
+            "user_id", "eq", str(user_id)
+        ).execute()
+        self.db.table("user_identity").delete().filter(
+            "user_id", "eq", str(user_id)
+        ).execute()
+        self.db.table("users").delete().filter("id", "eq", str(user_id)).execute()
+
+    def get_user_settings(self, user_id: UUID):
+        """
+        Fetch the user settings from the database
+        """
+
+        user_settings_response = (
+            self.db.from_("user_settings")
+            .select("*")
+            .filter("user_id", "eq", str(user_id))
+            .execute()
+        ).data
+
+        if len(user_settings_response) == 0:
+            # Create the user settings
+            user_settings_response = (
+                self.db.table("user_settings")
+                .insert({"user_id": str(user_id)})
+                .execute()
+            ).data
+
+        if len(user_settings_response) == 0:
+            raise ValueError("User settings could not be created")
+
+        user_settings = user_settings_response[0]
+
+        return user_settings
+
+    def get_models(self):
+        model_settings_response = (self.db.from_("models").select("*").execute()).data
+        if len(model_settings_response) == 0:
+            raise ValueError("An issue occured while fetching the model settings")
+        return model_settings_response
diff --git a/backend/core/quivr_core/api/modules/user/service/__init__.py b/backend/core/quivr_core/api/modules/user/service/__init__.py
new file mode 100644
index 000000000000..254962a4866f
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/user/service/__init__.py
@@ -0,0 +1 @@
+from .user_service import UserService
diff --git a/backend/core/quivr_core/api/modules/user/service/user_service.py b/backend/core/quivr_core/api/modules/user/service/user_service.py
new file mode 100644
index 000000000000..676a24d97124
--- /dev/null
+++ b/backend/core/quivr_core/api/modules/user/service/user_service.py
@@ -0,0 +1,36 @@
+from uuid import UUID
+
+from quivr_core.api.modules.dependencies import BaseService
+from quivr_core.api.modules.user.dto.inputs import UserUpdatableProperties
+from quivr_core.api.modules.user.repository.user_repository import UserRepository
+
+
+class UserService(BaseService[UserRepository]):
+    repository_cls = UserRepository
+
+    def __init__(self, repository: UserRepository):
+        self.repository = repository
+
+    def get_user_id_by_email(self, email: str) -> UUID | None:
+        return self.repository.get_user_id_by_user_email(email)
+
+    def get_user_email_by_user_id(self, user_id: UUID) -> str | None:
+        return self.repository.get_user_email_by_user_id(user_id)
+
+    def get_user_settings(self, user_id: UUID):
+        return self.repository.get_user_settings(user_id)
+
+    def get_user_identity(self, user_id: UUID):
+        return self.repository.get_user_identity(user_id)
+
+    def update_user_properties(
+        self,
+        user_id: UUID,
+        user_identity_updatable_properties: UserUpdatableProperties,
+    ):
+        return self.repository.update_user_properties(
+            user_id, user_identity_updatable_properties
+        )
+
+    def delete_user_data(self, user_id: UUID):
+        return self.repository.delete_user_data(user_id)
diff --git a/backend/core/quivr_core/api/modules/user/tests/test_user_controller.py b/backend/core/quivr_core/api/modules/user/tests/test_user_controller.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/packages/__init__.py b/backend/core/quivr_core/api/packages/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/packages/embeddings/__init__.py b/backend/core/quivr_core/api/packages/embeddings/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/packages/embeddings/vectors.py b/backend/core/quivr_core/api/packages/embeddings/vectors.py
new file mode 100644
index 000000000000..8c1ab42dbe69
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/embeddings/vectors.py
@@ -0,0 +1,74 @@
+from concurrent.futures import ThreadPoolExecutor
+from typing import List
+from uuid import UUID
+
+from pydantic import BaseModel
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.models.settings import (
+    get_documents_vector_store,
+    get_embedding_client,
+)
+from quivr_core.api.models.vectors import Vector
+
+logger = get_logger(__name__)
+
+
+vector_repository = Vector()
+
+
+# TODO: Create interface for embeddings and implement it for Supabase and OpenAI (current Quivr)
+class Neurons(BaseModel):
+    def create_vector(self, docs):
+        documents_vector_store = get_documents_vector_store()
+
+        try:
+            sids = documents_vector_store.add_documents(docs)
+            if sids and len(sids) > 0:
+                return sids
+
+        except Exception as e:
+            logger.error(f"Error creating vector for document {e}")
+
+    def create_embedding(self, content):
+        embeddings = get_embedding_client()
+        return embeddings.embed_query(content)
+
+
+def error_callback(exception):
+    print("An exception occurred:", exception)
+
+
+def process_batch(batch_ids: List[str]):
+    try:
+        if len(batch_ids) == 1:
+            return (vector_repository.get_vectors_by_batch(UUID(batch_ids[0]))).data
+        else:
+            return (vector_repository.get_vectors_in_batch(batch_ids)).data
+    except Exception as e:
+        logger.error("Error retrieving batched vectors", e)
+
+
+# TODO: move to Knowledge class
+def get_unique_files_from_vector_ids(vectors_ids):
+    # Move into Vectors class
+    """
+    Retrieve unique user data vectors.
+    """
+
+    # constants
+    BATCH_SIZE = 5
+
+    with ThreadPoolExecutor() as executor:
+        futures = []
+        for i in range(0, len(vectors_ids), BATCH_SIZE):
+            batch_ids = vectors_ids[i : i + BATCH_SIZE]
+            future = executor.submit(process_batch, batch_ids)
+            futures.append(future)
+
+        # Retrieve the results
+        vectors_responses = [future.result() for future in futures]
+
+    documents = [item for sublist in vectors_responses for item in sublist]
+    unique_files = [dict(t) for t in set(tuple(d.items()) for d in documents)]
+    return unique_files
diff --git a/backend/core/quivr_core/api/packages/files/__init__.py b/backend/core/quivr_core/api/packages/files/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/packages/files/crawl/__init__.py b/backend/core/quivr_core/api/packages/files/crawl/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/packages/files/crawl/crawler.py b/backend/core/quivr_core/api/packages/files/crawl/crawler.py
new file mode 100644
index 000000000000..945968b755e9
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/crawl/crawler.py
@@ -0,0 +1,44 @@
+import os
+import re
+import unicodedata
+
+from langchain_community.document_loaders import PlaywrightURLLoader
+from pydantic import BaseModel
+
+from quivr_core.api.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class CrawlWebsite(BaseModel):
+    url: str
+    js: bool = False
+    depth: int = int(os.getenv("CRAWL_DEPTH", "1"))
+    max_pages: int = 100
+    max_time: int = 60
+
+    def process(self) -> str:
+        # Extract and combine content recursively
+        loader = PlaywrightURLLoader(
+            urls=[self.url], remove_selectors=["header", "footer"]
+        )
+
+        data = loader.load()
+        # Now turn the data into a string
+        logger.info(f"Extracted content from {len(data)} pages")
+        logger.debug(f"Extracted data : {data}")
+        extracted_content = ""
+        for page in data:
+            extracted_content += page.page_content
+
+        return extracted_content
+
+    def checkGithub(self):
+        return "github.com" in self.url
+
+
+def slugify(text):
+    text = unicodedata.normalize("NFKD", text).encode("ascii", "ignore").decode("utf-8")
+    text = re.sub(r"[^\w\s-]", "", text).strip().lower()
+    text = re.sub(r"[-\s]+", "-", text)
+    return text
diff --git a/backend/core/quivr_core/api/packages/files/file.py b/backend/core/quivr_core/api/packages/files/file.py
new file mode 100644
index 000000000000..053ce2c76d78
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/file.py
@@ -0,0 +1,45 @@
+import hashlib
+from io import BytesIO
+
+from fastapi import UploadFile
+
+
+def convert_bytes(bytes, precision=2):
+    """Converts bytes into a human-friendly format."""
+    abbreviations = ["B", "KB", "MB"]
+    if bytes <= 0:
+        return "0 B"
+    size = bytes
+    index = 0
+    while size >= 1024 and index < len(abbreviations) - 1:
+        size /= 1024
+        index += 1
+    return f"{size:.{precision}f} {abbreviations[index]}"
+
+
+def get_file_size(file: UploadFile):
+    if isinstance(file.file, BytesIO):
+        # If the file object is a BytesIO object, get the size of the bytes data
+        file_size = len(file.file.getvalue())
+        return file_size
+    # move the cursor to the end of the file
+    file.file._file.seek(0, 2)  # pyright: ignore reportPrivateUsage=none
+    file_size = (
+        file.file._file.tell()  # pyright: ignore reportPrivateUsage=none
+    )  # Getting the size of the file
+    # move the cursor back to the beginning of the file
+    file.file.seek(0)
+
+    return file_size
+
+
+def compute_sha1_from_file(file_path):
+    with open(file_path, "rb") as file:
+        bytes = file.read()
+        readable_hash = compute_sha1_from_content(bytes)
+    return readable_hash
+
+
+def compute_sha1_from_content(content):
+    readable_hash = hashlib.sha1(content).hexdigest()
+    return readable_hash
diff --git a/backend/core/quivr_core/api/packages/files/parsers/__init__.py b/backend/core/quivr_core/api/packages/files/parsers/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/packages/files/parsers/audio.py b/backend/core/quivr_core/api/packages/files/parsers/audio.py
new file mode 100644
index 000000000000..7b7e08039040
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/audio.py
@@ -0,0 +1,52 @@
+import time
+
+import openai
+from langchain.schema import Document
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from quivr_core.api.models.files import File
+from quivr_core.api.models.settings import get_documents_vector_store
+from quivr_core.api.packages.files.file import compute_sha1_from_content
+
+
+def process_audio(file: File, **kwargs):
+    dateshort = time.strftime("%Y%m%d-%H%M%S")
+    file_meta_name = f"audiotranscript_{dateshort}.txt"
+    documents_vector_store = get_documents_vector_store()
+
+    with open(file.tmp_file_path, "rb") as audio_file:
+        transcript = openai.Audio.transcribe("whisper-1", audio_file)
+
+        file_sha = compute_sha1_from_content(
+            transcript.text.encode("utf-8")  # pyright: ignore reportPrivateUsage=none
+        )
+        file_size = len(
+            transcript.text.encode("utf-8")  # pyright: ignore reportPrivateUsage=none
+        )
+
+        chunk_size = 500
+        chunk_overlap = 0
+
+        text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+            chunk_size=chunk_size, chunk_overlap=chunk_overlap
+        )
+        texts = text_splitter.split_text(
+            transcript.text.encode("utf-8")  # pyright: ignore reportPrivateUsage=none
+        )
+
+        docs_with_metadata = [
+            Document(
+                page_content=text,
+                metadata={
+                    "file_sha1": file_sha,
+                    "file_size": file_size,
+                    "file_name": file_meta_name,
+                    "chunk_size": chunk_size,
+                    "chunk_overlap": chunk_overlap,
+                    "date": dateshort,
+                },
+            )
+            for text in texts
+        ]
+
+        documents_vector_store.add_documents(docs_with_metadata)
diff --git a/backend/core/quivr_core/api/packages/files/parsers/bibtex.py b/backend/core/quivr_core/api/packages/files/parsers/bibtex.py
new file mode 100644
index 000000000000..960bc771d905
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/bibtex.py
@@ -0,0 +1,18 @@
+from langchain_community.document_loaders import BibtexLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_bibtex(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=BibtexLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/code_python.py b/backend/core/quivr_core/api/packages/files/parsers/code_python.py
new file mode 100644
index 000000000000..3e7146be8db5
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/code_python.py
@@ -0,0 +1,18 @@
+from langchain_community.document_loaders import PythonLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_python(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=PythonLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/common.py b/backend/core/quivr_core/api/packages/files/parsers/common.py
new file mode 100644
index 000000000000..642b8ecc9d48
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/common.py
@@ -0,0 +1,118 @@
+import asyncio
+import os
+import tempfile
+import time
+
+import nest_asyncio
+import tiktoken
+import uvloop
+from langchain.schema import Document
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from llama_parse import LlamaParse
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.models.files import File
+from quivr_core.api.modules.brain.service.brain_vector_service import BrainVectorService
+from quivr_core.api.modules.upload.service.upload_file import DocumentSerializable
+from quivr_core.api.packages.embeddings.vectors import Neurons
+
+if not isinstance(asyncio.get_event_loop(), uvloop.Loop):
+    nest_asyncio.apply()
+
+logger = get_logger(__name__)
+
+
+def process_file(
+    file: File,
+    loader_class,
+    brain_id,
+    original_file_name,
+    integration=None,
+    integration_link=None,
+):
+    dateshort = time.strftime("%Y%m%d")
+    neurons = Neurons()
+
+    if os.getenv("LLAMA_CLOUD_API_KEY"):
+        doc = file.file
+        document_ext = os.path.splitext(doc.filename)[1]
+        if document_ext in [".pdf", ".docx", ".doc"]:
+            document_tmp = tempfile.NamedTemporaryFile(
+                suffix=document_ext, delete=False
+            )
+            # Seek to the beginning of the file
+            doc.file.seek(0)
+            document_tmp.write(doc.file.read())
+
+            parser = LlamaParse(
+                result_type="markdown",  # "markdown" and "text" are available
+                parsing_instruction="Extract the tables and transform checkboxes into text. Transform tables to key = value. You can duplicates Keys if needed. For example: Productions Fonts = 300 productions Fonts Company Desktop License = Yes for Maximum of 60 Licensed Desktop users For example checkboxes should be: Premium Activated = Yes License Premier = No If a checkbox is present for a table with multiple options.  Say Yes for the one activated and no for the one not activated. Format using headers.",
+                gpt4o_mode=True,
+                gpt4o_api_key=os.getenv("OPENAI_API_KEY"),
+            )
+
+            document_llama_parsed = parser.load_data(document_tmp.name)
+            document_tmp.close()
+            document_to_langchain = document_llama_parsed[0].to_langchain_format()
+            text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+                chunk_size=file.chunk_size, chunk_overlap=file.chunk_overlap
+            )
+            document_to_langchain = Document(
+                page_content=document_to_langchain.page_content
+            )
+            file.documents = text_splitter.split_documents([document_to_langchain])
+    else:
+        file.compute_documents(loader_class)
+
+    metadata = {
+        "file_sha1": file.file_sha1,
+        "file_size": file.file_size,
+        "file_name": file.file_name,
+        "chunk_size": file.chunk_size,
+        "chunk_overlap": file.chunk_overlap,
+        "date": dateshort,
+        "original_file_name": original_file_name or file.file_name,
+        "integration": integration or "",
+        "integration_link": integration_link or "",
+    }
+    docs = []
+
+    enc = tiktoken.get_encoding("cl100k_base")
+
+    if file.documents is not None:
+        logger.info("Coming here?")
+        for index, doc in enumerate(file.documents, start=1):  # pyright: ignore reportPrivateUsage=none
+            new_metadata = metadata.copy()
+            logger.info(f"Processing document {doc}")
+            # Add filename at beginning of page content
+            doc.page_content = f"Filename: {new_metadata['original_file_name']} Content: {doc.page_content}"
+
+            doc.page_content = doc.page_content.replace("\u0000", "")
+
+            len_chunk = len(enc.encode(doc.page_content))
+
+            # Ensure the text is in UTF-8
+            doc.page_content = doc.page_content.encode("utf-8", "replace").decode(
+                "utf-8"
+            )
+
+            new_metadata["chunk_size"] = len_chunk
+            new_metadata["index"] = index
+            doc_with_metadata = DocumentSerializable(
+                page_content=doc.page_content, metadata=new_metadata
+            )
+            docs.append(doc_with_metadata)
+
+    created_vector = neurons.create_vector(docs)
+
+    brain_vector_service = BrainVectorService(brain_id)
+
+    if created_vector is not None:
+        for created_vector_id in created_vector:
+            result = brain_vector_service.create_brain_vector(
+                created_vector_id, metadata["file_sha1"]
+            )
+            logger.debug(f"Brain vector created: {result}")
+        return len(created_vector)
+    else:
+        return 0
diff --git a/backend/core/quivr_core/api/packages/files/parsers/csv.py b/backend/core/quivr_core/api/packages/files/parsers/csv.py
new file mode 100644
index 000000000000..fc440bf268bc
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/csv.py
@@ -0,0 +1,18 @@
+from langchain_community.document_loaders import CSVLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_csv(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=CSVLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/docx.py b/backend/core/quivr_core/api/packages/files/parsers/docx.py
new file mode 100644
index 000000000000..1766d63cc4e0
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/docx.py
@@ -0,0 +1,18 @@
+from langchain_community.document_loaders import Docx2txtLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_docx(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=Docx2txtLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/epub.py b/backend/core/quivr_core/api/packages/files/parsers/epub.py
new file mode 100644
index 000000000000..c57374fac3f4
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/epub.py
@@ -0,0 +1,18 @@
+from langchain_community.document_loaders.epub import UnstructuredEPubLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_epub(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=UnstructuredEPubLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/github.py b/backend/core/quivr_core/api/packages/files/parsers/github.py
new file mode 100644
index 000000000000..89575499ef8f
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/github.py
@@ -0,0 +1,79 @@
+import os
+import time
+
+from langchain.schema import Document
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import GitLoader
+
+from quivr_core.api.models.files import File
+from quivr_core.api.packages.embeddings.vectors import Neurons
+from quivr_core.api.packages.files.file import compute_sha1_from_content
+
+
+def process_github(
+    repo,
+    brain_id,
+):
+    random_dir_name = os.urandom(16).hex()
+    dateshort = time.strftime("%Y%m%d")
+    loader = GitLoader(
+        clone_url=repo,
+        repo_path="/tmp/" + random_dir_name,
+    )
+    documents = loader.load()
+    os.system("rm -rf /tmp/" + random_dir_name)
+
+    chunk_size = 500
+    chunk_overlap = 0
+    text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+        chunk_size=chunk_size, chunk_overlap=chunk_overlap
+    )
+
+    documents = text_splitter.split_documents(documents)
+
+    for doc in documents:
+        if doc.metadata["file_type"] in [
+            ".pyc",
+            ".png",
+            ".svg",
+            ".env",
+            ".lock",
+            ".gitignore",
+            ".gitmodules",
+            ".gitattributes",
+            ".gitkeep",
+            ".git",
+            ".json",
+        ]:
+            continue
+        metadata = {
+            "file_sha1": compute_sha1_from_content(doc.page_content.encode("utf-8")),
+            "file_size": len(doc.page_content) * 8,
+            "file_name": doc.metadata["file_name"],
+            "chunk_size": chunk_size,
+            "chunk_overlap": chunk_overlap,
+            "date": dateshort,
+            "original_file_name": doc.metadata["original_file_name"],
+        }
+        doc_with_metadata = Document(page_content=doc.page_content, metadata=metadata)
+
+        print(doc_with_metadata.metadata["file_name"])
+
+        file = File(
+            file_sha1=compute_sha1_from_content(doc.page_content.encode("utf-8"))
+        )
+
+        file_exists = file.file_already_exists()
+
+        if not file_exists:
+            neurons = Neurons()
+            created_vector = neurons.create_vector(doc_with_metadata)
+
+        file_exists_in_brain = file.file_already_exists_in_brain(brain_id)
+
+        if not file_exists_in_brain:
+            file.link_file_to_brain(brain_id)
+    return {
+        "message": f"✅ Github with {len(documents)} files has been uploaded.",
+        "type": "success",
+    }
diff --git a/backend/core/quivr_core/api/packages/files/parsers/html.py b/backend/core/quivr_core/api/packages/files/parsers/html.py
new file mode 100644
index 000000000000..afe28e0ab104
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/html.py
@@ -0,0 +1,18 @@
+from langchain_community.document_loaders import UnstructuredHTMLLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_html(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=UnstructuredHTMLLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/markdown.py b/backend/core/quivr_core/api/packages/files/parsers/markdown.py
new file mode 100644
index 000000000000..44d75b6de37e
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/markdown.py
@@ -0,0 +1,18 @@
+from langchain_community.document_loaders import UnstructuredMarkdownLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_markdown(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=UnstructuredMarkdownLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/notebook.py b/backend/core/quivr_core/api/packages/files/parsers/notebook.py
new file mode 100644
index 000000000000..16b91ba0a0e2
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/notebook.py
@@ -0,0 +1,18 @@
+from langchain_community.document_loaders import NotebookLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_ipnyb(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=NotebookLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/odt.py b/backend/core/quivr_core/api/packages/files/parsers/odt.py
new file mode 100644
index 000000000000..f6b10695a8b8
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/odt.py
@@ -0,0 +1,18 @@
+from langchain_community.document_loaders import UnstructuredPDFLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_odt(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=UnstructuredPDFLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/pdf.py b/backend/core/quivr_core/api/packages/files/parsers/pdf.py
new file mode 100644
index 000000000000..653f6abf39f0
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/pdf.py
@@ -0,0 +1,22 @@
+from langchain_community.document_loaders import UnstructuredPDFLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_pdf(
+    file: File,
+    brain_id,
+    original_file_name,
+    integration=None,
+    integration_link=None,
+):
+    return process_file(
+        file=file,
+        loader_class=UnstructuredPDFLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/powerpoint.py b/backend/core/quivr_core/api/packages/files/parsers/powerpoint.py
new file mode 100644
index 000000000000..57502cb4eda6
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/powerpoint.py
@@ -0,0 +1,18 @@
+from langchain_community.document_loaders import UnstructuredFileLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_powerpoint(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=UnstructuredFileLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/telegram.py b/backend/core/quivr_core/api/packages/files/parsers/telegram.py
new file mode 100644
index 000000000000..c18322510f37
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/telegram.py
@@ -0,0 +1,17 @@
+from quivr_core.api.models.files import File
+from quivr_core.api.packages.files.loaders.telegram import TelegramChatFileLoader
+
+from .common import process_file
+
+
+def process_telegram(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=TelegramChatFileLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/txt.py b/backend/core/quivr_core/api/packages/files/parsers/txt.py
new file mode 100644
index 000000000000..46734c768503
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/txt.py
@@ -0,0 +1,18 @@
+from langchain_community.document_loaders import TextLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_txt(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=TextLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/parsers/xlsx.py b/backend/core/quivr_core/api/packages/files/parsers/xlsx.py
new file mode 100644
index 000000000000..53005f7b4ad6
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/parsers/xlsx.py
@@ -0,0 +1,18 @@
+from langchain_community.document_loaders import UnstructuredExcelLoader
+
+from quivr_core.api.models.files import File
+
+from .common import process_file
+
+
+def process_xlsx(
+    file: File, brain_id, original_file_name, integration=None, integration_link=None
+):
+    return process_file(
+        file=file,
+        loader_class=UnstructuredExcelLoader,
+        brain_id=brain_id,
+        original_file_name=original_file_name,
+        integration=integration,
+        integration_link=integration_link,
+    )
diff --git a/backend/core/quivr_core/api/packages/files/processors.py b/backend/core/quivr_core/api/packages/files/processors.py
new file mode 100644
index 000000000000..76e47019f444
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/files/processors.py
@@ -0,0 +1,106 @@
+from quivr_core.api.modules.brain.service.brain_service import BrainService
+
+from .parsers.audio import process_audio
+from .parsers.bibtex import process_bibtex
+from .parsers.code_python import process_python
+from .parsers.csv import process_csv
+from .parsers.docx import process_docx
+from .parsers.epub import process_epub
+from .parsers.html import process_html
+from .parsers.markdown import process_markdown
+from .parsers.notebook import process_ipnyb
+from .parsers.odt import process_odt
+from .parsers.pdf import process_pdf
+from .parsers.powerpoint import process_powerpoint
+from .parsers.txt import process_txt
+from .parsers.xlsx import process_xlsx
+
+file_processors = {
+    ".txt": process_txt,
+    ".csv": process_csv,
+    ".md": process_markdown,
+    ".markdown": process_markdown,
+    ".m4a": process_audio,
+    ".mp3": process_audio,
+    ".webm": process_audio,
+    ".mp4": process_audio,
+    ".mpga": process_audio,
+    ".wav": process_audio,
+    ".mpeg": process_audio,
+    ".pdf": process_pdf,
+    ".html": process_html,
+    ".bib": process_bibtex,
+    ".pptx": process_powerpoint,
+    ".docx": process_docx,
+    ".odt": process_odt,
+    ".xlsx": process_xlsx,
+    ".xls": process_xlsx,
+    ".epub": process_epub,
+    ".ipynb": process_ipnyb,
+    ".py": process_python,
+}
+
+
+def create_response(message, type):
+    return {"message": message, "type": type}
+
+
+brain_service = BrainService()
+
+
+# TODO: Move filter_file to a file service to avoid circular imports from quivr_core.api.models/files.py for File class
+def filter_file(
+    file,
+    brain_id,
+    original_file_name=None,
+):
+    file_exists = file.file_already_exists()
+    file_exists_in_brain = file.file_already_exists_in_brain(brain_id)
+    using_file_name = file.file_name
+
+    brain = brain_service.get_brain_by_id(brain_id)
+    if brain is None:
+        raise Exception("It seems like you're uploading knowledge to an unknown brain.")
+
+    if file_exists_in_brain:
+        return create_response(
+            f"🤔 {using_file_name} already exists in brain {brain.name}.",  # pyright: ignore reportPrivateUsage=none
+            "warning",
+        )
+    elif file.file_is_empty():
+        return create_response(
+            f"❌ {original_file_name} is empty.",  # pyright: ignore reportPrivateUsage=none
+            "error",  # pyright: ignore reportPrivateUsage=none
+        )
+    elif file_exists:
+        file.link_file_to_brain(brain_id)
+        return create_response(
+            f"✅ {using_file_name} has been uploaded to brain {brain.name}.",  # pyright: ignore reportPrivateUsage=none
+            "success",
+        )
+
+    if file.file_extension in file_processors:
+        try:
+            result = file_processors[file.file_extension](
+                file=file,
+                brain_id=brain_id,
+                original_file_name=original_file_name,
+            )
+            if result is None or result == 0:
+                return create_response(
+                    f"？ {using_file_name} has been uploaded to brain. There might have been an error while reading it, please make sure the file is not illformed or just an image",  # pyright: ignore reportPrivateUsage=none
+                    "warning",
+                )
+            return create_response(
+                f"✅ {using_file_name} has been uploaded to brain {brain.name} in {result} chunks",  # pyright: ignore reportPrivateUsage=none
+                "success",
+            )
+        except Exception as e:
+            # Add more specific exceptions as needed.
+            print(f"Error processing file: {e}")
+            raise e
+
+    return create_response(
+        f"❌ {using_file_name} is not supported.",  # pyright: ignore reportPrivateUsage=none
+        "error",
+    )
diff --git a/backend/core/quivr_core/api/packages/quivr_core/__init__.py b/backend/core/quivr_core/api/packages/quivr_core/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/packages/quivr_core/config.py b/backend/core/quivr_core/api/packages/quivr_core/config.py
new file mode 100644
index 000000000000..1f6c5619b661
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/quivr_core/config.py
@@ -0,0 +1,23 @@
+from pydantic import BaseModel, field_validator
+
+
+class RAGConfig(BaseModel):
+    model: str = "gpt-3.5-turbo-0125"  # pyright: ignore reportPrivateUsage=none
+    temperature: float | None = 0.1
+    max_tokens: int | None = 2000
+    max_input: int = 2000
+    streaming: bool = False
+    max_files: int = 20
+    prompt: str | None = None
+
+    @field_validator("temperature", mode="before")
+    def set_default_temperature(cls, v):
+        if v is None:
+            return 0.1
+        return v
+
+    @field_validator("max_tokens", mode="before")
+    def set_default_max_tokens(cls, v):
+        if v is None:
+            return 2000
+        return v
diff --git a/backend/core/quivr_core/api/packages/quivr_core/models.py b/backend/core/quivr_core/api/packages/quivr_core/models.py
new file mode 100644
index 000000000000..56ce0185f5cd
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/quivr_core/models.py
@@ -0,0 +1,58 @@
+from typing import Any
+
+from langchain_core.pydantic_v1 import BaseModel as BaseModelV1
+from langchain_core.pydantic_v1 import Field as FieldV1
+from pydantic import BaseModel
+from typing_extensions import TypedDict
+
+
+class cited_answer(BaseModelV1):
+    """Answer the user question based only on the given sources, and cite the sources used."""
+
+    answer: str = FieldV1(
+        ...,
+        description="The answer to the user question, which is based only on the given sources.",
+    )
+    thoughts: str = FieldV1(
+        ...,
+        description="""Description of the thought process, based only on the given sources.
+        Cite the text as much as possible and give the document name it appears in. In the format : 'Doc_name states : cited_text'. Be the most
+        procedural as possible. Write all the steps needed to find the answer until you find it.""",
+    )
+    citations: list[int] = FieldV1(
+        ...,
+        description="The integer IDs of the SPECIFIC sources which justify the answer.",
+    )
+
+    followup_questions: list[str] = FieldV1(
+        ...,
+        description="Generate up to 3 follow-up questions that could be asked based on the answer given or context provided.",
+    )
+
+
+class RawRAGChunkResponse(TypedDict):
+    answer: dict[str, Any]
+    docs: dict[str, Any]
+
+
+class RawRAGResponse(TypedDict):
+    answer: dict[str, Any]
+    docs: dict[str, Any]
+
+
+class RAGResponseMetadata(BaseModel):
+    citations: list[int] | None = None
+    thoughts: str | list[str] | None = None
+    followup_questions: list[str] | None = None
+    sources: list[Any] | None = None
+
+
+class ParsedRAGResponse(BaseModel):
+    answer: str
+    metadata: RAGResponseMetadata | None = None
+
+
+class ParsedRAGChunkResponse(BaseModel):
+    answer: str
+    metadata: RAGResponseMetadata
+    last_chunk: bool = False
diff --git a/backend/core/quivr_core/api/packages/quivr_core/prompts.py b/backend/core/quivr_core/api/packages/quivr_core/prompts.py
new file mode 100644
index 000000000000..d0fb80cca13c
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/quivr_core/prompts.py
@@ -0,0 +1,58 @@
+import datetime
+
+from langchain.prompts import HumanMessagePromptTemplate, SystemMessagePromptTemplate
+from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
+
+# First step is to create the Rephrasing Prompt
+_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. Keep as much details as possible from previous messages. Keep entity names and all.
+
+Chat History:
+{chat_history}
+Follow Up Input: {question}
+Standalone question:"""
+CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+
+# Next is the answering prompt
+
+template_answer = """
+Context:
+{context}
+
+User Question: {question}
+Answer:
+"""
+
+today_date = datetime.datetime.now().strftime("%B %d, %Y")
+
+system_message_template = (
+    f"Your name is Quivr. You're a helpful assistant. Today's date is {today_date}."
+)
+
+system_message_template += """
+When answering use markdown.
+Use markdown code blocks for code snippets.
+Answer in a concise and clear manner.
+Use the following pieces of context from files provided by the user to answer the users.
+Answer in the same language as the user question.
+If you don't know the answer with the context provided from the files, just say that you don't know, don't try to make up an answer.
+Don't cite the source id in the answer objects, but you can use the source to answer the question.
+You have access to the files to answer the user question (limited to first 20 files):
+{files}
+
+If not None, User instruction to follow to answer: {custom_instructions}
+Don't cite the source id in the answer objects, but you can use the source to answer the question.
+"""
+
+
+ANSWER_PROMPT = ChatPromptTemplate.from_messages(
+    [
+        SystemMessagePromptTemplate.from_template(system_message_template),
+        HumanMessagePromptTemplate.from_template(template_answer),
+    ]
+)
+
+
+# How we format documents
+DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(
+    template="Source: {index} \n {page_content}"
+)
diff --git a/backend/core/quivr_core/api/packages/quivr_core/quivr_rag.py b/backend/core/quivr_core/api/packages/quivr_core/quivr_rag.py
new file mode 100644
index 000000000000..c61e1efe5a87
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/quivr_core/quivr_rag.py
@@ -0,0 +1,225 @@
+import logging
+import os
+from operator import itemgetter
+from typing import AsyncGenerator
+
+from flashrank import Ranker
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain.retrievers.document_compressors.flashrank_rerank import FlashrankRerank
+from langchain_cohere import CohereRerank
+from langchain_community.chat_models import ChatLiteLLM
+from langchain_core.messages.ai import AIMessageChunk
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnableLambda, RunnablePassthrough
+from langchain_core.vectorstores import VectorStore
+from langchain_openai import ChatOpenAI
+
+from quivr_core.api.modules.knowledge.entity.knowledge import Knowledge
+from quivr_core.config import RAGConfig
+from quivr_core.models import ParsedRAGChunkResponse, ParsedRAGResponse, cited_answer
+from quivr_core.prompts import ANSWER_PROMPT, CONDENSE_QUESTION_PROMPT
+from quivr_core.utils import (
+    combine_documents,
+    format_file_list,
+    get_chunk_metadata,
+    model_supports_function_calling,
+    parse_chunk_response,
+    parse_response,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class QuivrQARAG:
+    def __init__(
+        self,
+        *,
+        rag_config: RAGConfig,
+        llm: ChatLiteLLM,
+        vector_store: VectorStore,
+    ):
+        self.rag_config = rag_config
+        self.vector_store = vector_store
+        self.llm = llm
+        self.reranker = self._create_reranker()
+        self.supports_func_calling = model_supports_function_calling(
+            self.rag_config.model
+        )
+
+    @property
+    def retriever(self):
+        return self.vector_store.as_retriever()
+
+    def _create_reranker(self):
+        # TODO: reranker config
+        if os.getenv("COHERE_API_KEY"):
+            compressor = CohereRerank(top_n=20)
+        else:
+            ranker_model_name = "ms-marco-TinyBERT-L-2-v2"
+            flashrank_client = Ranker(model_name=ranker_model_name)
+            compressor = FlashrankRerank(
+                client=flashrank_client, model=ranker_model_name, top_n=20
+            )
+        return compressor
+
+    # TODO : refactor and simplify
+    def filter_history(
+        self, chat_history, max_history: int = 10, max_tokens: int = 2000
+    ):
+        """
+        Filter out the chat history to only include the messages that are relevant to the current question
+
+        Takes in a chat_history= [HumanMessage(content='Qui est Chloé ? '), AIMessage(content="Chloé est une salariée travaillant pour l'entreprise Quivr en tant qu'AI Engineer, sous la direction de son supérieur hiérarchique, Stanislas Girard."), HumanMessage(content='Dis moi en plus sur elle'), AIMessage(content=''), HumanMessage(content='Dis moi en plus sur elle'), AIMessage(content="Désolé, je n'ai pas d'autres informations sur Chloé à partir des fichiers fournis.")]
+        Returns a filtered chat_history with in priority: first max_tokens, then max_history where a Human message and an AI message count as one pair
+        a token is 4 characters
+        """
+        chat_history = chat_history[::-1]
+        total_tokens = 0
+        total_pairs = 0
+        filtered_chat_history = []
+        for i in range(0, len(chat_history), 2):
+            if i + 1 < len(chat_history):
+                human_message = chat_history[i]
+                ai_message = chat_history[i + 1]
+                message_tokens = (
+                    len(human_message.content) + len(ai_message.content)
+                ) // 4
+                if (
+                    total_tokens + message_tokens > max_tokens
+                    or total_pairs >= max_history
+                ):
+                    break
+                filtered_chat_history.append(human_message)
+                filtered_chat_history.append(ai_message)
+                total_tokens += message_tokens
+                total_pairs += 1
+        chat_history = filtered_chat_history[::-1]
+
+        return chat_history
+
+    def build_chain(self, files: str):
+        compression_retriever = ContextualCompressionRetriever(
+            base_compressor=self.reranker, base_retriever=self.retriever
+        )
+
+        loaded_memory = RunnablePassthrough.assign(
+            chat_history=RunnableLambda(
+                lambda x: self.filter_history(x["chat_history"]),
+            ),
+            question=lambda x: x["question"],
+        )
+
+        standalone_question = {
+            "standalone_question": {
+                "question": lambda x: x["question"],
+                "chat_history": itemgetter("chat_history"),
+            }
+            | CONDENSE_QUESTION_PROMPT
+            | self.llm
+            | StrOutputParser(),
+        }
+
+        # Now we retrieve the documents
+        retrieved_documents = {
+            "docs": itemgetter("standalone_question") | compression_retriever,
+            "question": lambda x: x["standalone_question"],
+            "custom_instructions": lambda x: self.rag_config.prompt,
+        }
+
+        final_inputs = {
+            "context": lambda x: combine_documents(x["docs"]),
+            "question": itemgetter("question"),
+            "custom_instructions": itemgetter("custom_instructions"),
+            "files": lambda _: files,  # TODO: shouldn't be here
+        }
+
+        # Override llm if we have a OpenAI model
+        llm = self.llm
+        if self.supports_func_calling:
+            if self.rag_config.temperature:
+                llm_function = ChatOpenAI(
+                    max_tokens=self.rag_config.max_tokens,
+                    model=self.rag_config.model,
+                    temperature=self.rag_config.temperature,
+                )
+            else:
+                llm_function = ChatOpenAI(
+                    max_tokens=self.rag_config.max_tokens,
+                    model=self.rag_config.model,
+                )
+
+            llm = llm_function.bind_tools(
+                [cited_answer],
+                tool_choice="any",
+            )
+
+        answer = {
+            "answer": final_inputs | ANSWER_PROMPT | llm,
+            "docs": itemgetter("docs"),
+        }
+
+        return loaded_memory | standalone_question | retrieved_documents | answer
+
+    def answer(
+        self,
+        question: str,
+        history: list[dict[str, str]],
+        list_files: list[Knowledge],
+        metadata: dict[str, str] = {},
+    ) -> ParsedRAGResponse:
+        concat_list_files = format_file_list(list_files, self.rag_config.max_files)
+        conversational_qa_chain = self.build_chain(concat_list_files)
+        raw_llm_response = conversational_qa_chain.invoke(
+            {
+                "question": question,
+                "chat_history": history,
+                "custom_instructions": (self.rag_config.prompt),
+            },
+            config={"metadata": metadata},
+        )
+        response = parse_response(raw_llm_response, self.rag_config.model)
+        return response
+
+    async def answer_astream(
+        self,
+        question: str,
+        history: list[dict[str, str]],
+        list_files: list[Knowledge],
+        metadata: dict[str, str] = {},
+    ) -> AsyncGenerator[ParsedRAGChunkResponse, ParsedRAGChunkResponse]:
+        concat_list_files = format_file_list(list_files, self.rag_config.max_files)
+        conversational_qa_chain = self.build_chain(concat_list_files)
+
+        rolling_message = AIMessageChunk(content="")
+        sources = []
+
+        async for chunk in conversational_qa_chain.astream(
+            {
+                "question": question,
+                "chat_history": history,
+                "custom_personality": (self.rag_config.prompt),
+            },
+            config={"metadata": metadata},
+        ):
+            # Could receive this anywhere so we need to save it for the last chunk
+            if "docs" in chunk:
+                sources = chunk["docs"] if "docs" in chunk else []
+
+            if "answer" in chunk:
+                rolling_message, parsed_chunk = parse_chunk_response(
+                    rolling_message,
+                    chunk,
+                    self.supports_func_calling,
+                )
+
+                if self.supports_func_calling and len(parsed_chunk.answer) > 0:
+                    yield parsed_chunk
+                else:
+                    yield parsed_chunk
+
+        # Last chunk provies
+        yield ParsedRAGChunkResponse(
+            answer="",
+            metadata=get_chunk_metadata(rolling_message, sources),
+            last_chunk=True,
+        )
diff --git a/backend/core/quivr_core/api/packages/quivr_core/rag_factory.py b/backend/core/quivr_core/api/packages/quivr_core/rag_factory.py
new file mode 100644
index 000000000000..7e88652e8322
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/quivr_core/rag_factory.py
@@ -0,0 +1,32 @@
+from typing import Type
+
+from quivr_core.api.logger import get_logger
+from quivr_core.api.modules.brain.entity.integration_brain import IntegrationEntity
+from quivr_core.api.modules.brain.integrations.Big.Brain import BigBrain
+from quivr_core.api.modules.brain.integrations.GPT4.Brain import GPT4Brain
+from quivr_core.api.modules.brain.integrations.Multi_Contract.Brain import (
+    MultiContractBrain,
+)
+from quivr_core.api.modules.brain.integrations.Notion.Brain import NotionBrain
+from quivr_core.api.modules.brain.integrations.Proxy.Brain import ProxyBrain
+from quivr_core.api.modules.brain.integrations.Self.Brain import SelfBrain
+from quivr_core.api.modules.brain.integrations.SQL.Brain import SQLBrain
+from quivr_core.api.modules.brain.knowledge_brain_qa import KnowledgeBrainQA
+
+logger = get_logger(__name__)
+
+
+class RAGServiceFactory:
+    integration_list: dict[str, Type[KnowledgeBrainQA]] = {
+        "notion": NotionBrain,
+        "gpt4": GPT4Brain,
+        "sql": SQLBrain,
+        "big": BigBrain,
+        "doc": KnowledgeBrainQA,
+        "proxy": ProxyBrain,
+        "self": SelfBrain,
+        "multi-contract": MultiContractBrain,
+    }
+
+    def get_brain_cls(self, integration: IntegrationEntity):
+        pass
diff --git a/backend/core/quivr_core/api/packages/quivr_core/utils.py b/backend/core/quivr_core/api/packages/quivr_core/utils.py
new file mode 100644
index 000000000000..29e1f965c7d8
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/quivr_core/utils.py
@@ -0,0 +1,272 @@
+import logging
+from typing import Any, Dict, List, Tuple
+from uuid import UUID
+
+from langchain.schema import (
+    AIMessage,
+    BaseMessage,
+    HumanMessage,
+    SystemMessage,
+    format_document,
+)
+from langchain_core.messages.ai import AIMessageChunk
+
+from quivr_core.api.modules.chat.dto.chats import Sources
+from quivr_core.api.modules.chat.dto.outputs import GetChatHistoryOutput
+from quivr_core.api.modules.knowledge.entity.knowledge import Knowledge
+from quivr_core.api.modules.upload.service.generate_file_signed_url import (
+    generate_file_signed_url,
+)
+from quivr_core.api.packages.quivr_core.models import (
+    ParsedRAGChunkResponse,
+    ParsedRAGResponse,
+    RAGResponseMetadata,
+    RawRAGResponse,
+)
+from quivr_core.api.packages.quivr_core.prompts import DEFAULT_DOCUMENT_PROMPT
+
+# TODO(@aminediro): define a types packages where we clearly define IO types
+# This should be used for serialization/deseriallization later
+
+
+logger = logging.getLogger(__name__)
+
+
+def model_supports_function_calling(model_name: str):
+    models_supporting_function_calls = [
+        "gpt-4",
+        "gpt-4-1106-preview",
+        "gpt-4-0613",
+        "gpt-3.5-turbo-0125",
+        "gpt-3.5-turbo-1106",
+        "gpt-3.5-turbo-0613",
+        "gpt-4-0125-preview",
+        "gpt-3.5-turbo",
+        "gpt-4-turbo",
+        "gpt-4o",
+    ]
+    return model_name in models_supporting_function_calls
+
+
+def format_chat_history(
+    history: List[GetChatHistoryOutput],
+) -> List[Dict[str, str]]:
+    """Format the chat history into a list of HumanMessage and AIMessage"""
+    formatted_history = []
+    for chat in history:
+        if chat.user_message:
+            formatted_history.append(HumanMessage(content=chat.user_message))
+        if chat.assistant:
+            formatted_history.append(AIMessage(content=chat.assistant))
+    return formatted_history
+
+
+def format_history_to_openai_mesages(
+    tuple_history: List[Tuple[str, str]], system_message: str, question: str
+) -> List[BaseMessage]:
+    """Format the chat history into a list of Base Messages"""
+    messages = []
+    messages.append(SystemMessage(content=system_message))
+    for human, ai in tuple_history:
+        messages.append(HumanMessage(content=human))
+        messages.append(AIMessage(content=ai))
+    messages.append(HumanMessage(content=question))
+    return messages
+
+
+def cited_answer_filter(tool):
+    return tool["name"] == "cited_answer"
+
+
+def get_prev_message_str(msg: AIMessageChunk) -> str:
+    if msg.tool_calls:
+        cited_answer = next(x for x in msg.tool_calls if cited_answer_filter(x))
+        if "args" in cited_answer and "answer" in cited_answer["args"]:
+            return cited_answer["args"]["answer"]
+    return ""
+
+
+def get_chunk_metadata(
+    msg: AIMessageChunk, sources: list[Any] = []
+) -> RAGResponseMetadata:
+    # Initiate the source
+    metadata = {"sources": sources}
+    if msg.tool_calls:
+        cited_answer = next(x for x in msg.tool_calls if cited_answer_filter(x))
+
+        if "args" in cited_answer:
+            gathered_args = cited_answer["args"]
+            if "citations" in gathered_args:
+                citations = gathered_args["citations"]
+                metadata["citations"] = citations
+
+            if "followup_questions" in gathered_args:
+                followup_questions = gathered_args["followup_questions"]
+                metadata["followup_questions"] = followup_questions
+
+            if "thoughts" in gathered_args:
+                thoughts = gathered_args["thoughts"]
+                metadata["thoughts"] = thoughts
+
+    return RAGResponseMetadata(**metadata)
+
+
+# TODO: CONVOLUTED LOGIC !
+# TODO(@aminediro): redo this
+def parse_chunk_response(
+    gathered_msg: AIMessageChunk,
+    raw_chunk: dict[str, Any],
+    supports_func_calling: bool,
+) -> Tuple[AIMessageChunk, ParsedRAGChunkResponse]:
+    # Init with sources
+    answer_str = ""
+    # Get the previously parsed answer
+    prev_answer = get_prev_message_str(gathered_msg)
+
+    if supports_func_calling:
+        gathered_msg += raw_chunk["answer"]
+        if gathered_msg.tool_calls:
+            cited_answer = next(
+                x for x in gathered_msg.tool_calls if cited_answer_filter(x)
+            )
+            if "args" in cited_answer:
+                gathered_args = cited_answer["args"]
+                if "answer" in gathered_args:
+                    # Only send the difference between answer and response_tokens which was the previous answer
+                    gathered_answer = gathered_args["answer"]
+                    answer_str: str = gathered_answer[len(prev_answer) :]
+
+        return gathered_msg, ParsedRAGChunkResponse(
+            answer=answer_str, metadata=RAGResponseMetadata()
+        )
+    else:
+        return gathered_msg, ParsedRAGChunkResponse(
+            answer=raw_chunk["answer"].content, metadata=RAGResponseMetadata()
+        )
+
+
+def parse_response(raw_response: RawRAGResponse, model_name: str) -> ParsedRAGResponse:
+    answer = raw_response["answer"].content
+    sources = raw_response["docs"] or []
+
+    metadata = {"sources": sources}
+
+    if model_supports_function_calling(model_name):
+        if raw_response["answer"].tool_calls:
+            citations = raw_response["answer"].tool_calls[-1]["args"]["citations"]
+            metadata["citations"] = citations
+            followup_questions = raw_response["answer"].tool_calls[-1]["args"][
+                "followup_questions"
+            ]
+            thoughts = raw_response["answer"].tool_calls[-1]["args"]["thoughts"]
+            if followup_questions:
+                metadata["followup_questions"] = followup_questions
+            if thoughts:
+                metadata["thoughts"] = thoughts
+            answer = raw_response["answer"].tool_calls[-1]["args"]["answer"]
+
+    parsed_response = ParsedRAGResponse(
+        answer=answer, metadata=RAGResponseMetadata(**metadata)
+    )
+    return parsed_response
+
+
+def combine_documents(
+    docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"
+):
+    # for each docs, add an index in the metadata to be able to cite the sources
+    for doc, index in zip(docs, range(len(docs))):
+        doc.metadata["index"] = index
+    doc_strings = [format_document(doc, document_prompt) for doc in docs]
+    return document_separator.join(doc_strings)
+
+
+def format_file_list(list_files_array: list[Knowledge], max_files: int = 20) -> str:
+    list_files = [file.file_name or file.url for file in list_files_array]
+    files: list[str] = list(filter(lambda n: n is not None, list_files))  # type: ignore
+    files = files[:max_files]
+
+    files_str = "\n".join(files) if list_files_array else "None"
+    return files_str
+
+
+# TODO: REFACTOR THIS, it does call the DB , so maybe in a service
+def generate_source(
+    source_documents: List[Any] | None,
+    brain_id: UUID,
+    citations: List[int] | None = None,
+) -> List[Sources]:
+    """
+    Generate the sources list for the answer
+    It takes in a list of sources documents and citations that points to the docs index that was used in the answer
+    """
+    # Initialize an empty list for sources
+    sources_list: List[Sources] = []
+
+    # Initialize a dictionary for storing generated URLs
+    generated_urls = {}
+
+    # remove duplicate sources with same name and create a list of unique sources
+    sources_url_cache = {}
+
+    # Get source documents from the result, default to an empty list if not found
+    # If source documents exist
+    if source_documents:
+        logger.info(f"Citations {citations}")
+        for index, doc in enumerate(source_documents):
+            logger.info(f"Processing source document {doc.metadata['file_name']}")
+            if citations is not None:
+                if index not in citations:
+                    logger.info(f"Skipping source document {doc.metadata['file_name']}")
+                    continue
+            # Check if 'url' is in the document metadata
+            is_url = (
+                "original_file_name" in doc.metadata
+                and doc.metadata["original_file_name"] is not None
+                and doc.metadata["original_file_name"].startswith("http")
+            )
+
+            # Determine the name based on whether it's a URL or a file
+            name = (
+                doc.metadata["original_file_name"]
+                if is_url
+                else doc.metadata["file_name"]
+            )
+
+            # Determine the type based on whether it's a URL or a file
+            type_ = "url" if is_url else "file"
+
+            # Determine the source URL based on whether it's a URL or a file
+            if is_url:
+                source_url = doc.metadata["original_file_name"]
+            else:
+                file_path = f"{brain_id}/{doc.metadata['file_name']}"
+                # Check if the URL has already been generated
+                if file_path in generated_urls:
+                    source_url = generated_urls[file_path]
+                else:
+                    # Generate the URL
+                    if file_path in sources_url_cache:
+                        source_url = sources_url_cache[file_path]
+                    else:
+                        generated_url = generate_file_signed_url(file_path)
+                        if generated_url is not None:
+                            source_url = generated_url.get("signedURL", "")
+                        else:
+                            source_url = ""
+                    # Store the generated URL
+                    generated_urls[file_path] = source_url
+
+            # Append a new Sources object to the list
+            sources_list.append(
+                Sources(
+                    name=name,
+                    type=type_,
+                    source_url=source_url,
+                    original_file_name=name,
+                    citation=doc.page_content,
+                )
+            )
+    else:
+        logger.info("No source documents found or source_documents is not a list.")
+    return sources_list
diff --git a/backend/core/quivr_core/api/packages/utils/__init__.py b/backend/core/quivr_core/api/packages/utils/__init__.py
new file mode 100644
index 000000000000..c9e648c9004c
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/utils/__init__.py
@@ -0,0 +1,2 @@
+from .handle_request_validation_error import handle_request_validation_error
+from .parse_message_time import parse_message_time
diff --git a/backend/core/quivr_core/api/packages/utils/handle_request_validation_error.py b/backend/core/quivr_core/api/packages/utils/handle_request_validation_error.py
new file mode 100644
index 000000000000..454f9f8ef25b
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/utils/handle_request_validation_error.py
@@ -0,0 +1,24 @@
+from fastapi import FastAPI, Request, status
+from fastapi.exceptions import RequestValidationError
+from fastapi.responses import JSONResponse
+
+from quivr_core.api.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+def handle_request_validation_error(app: FastAPI):
+    @app.exception_handler(RequestValidationError)
+    async def validation_exception_handler(
+        request: Request, exc: RequestValidationError
+    ):
+        exc_str = f"{exc}".replace("\n", " ").replace("   ", " ")
+        logger.error(request, exc_str)
+        content = {
+            "status_code": status.HTTP_422_UNPROCESSABLE_ENTITY,
+            "message": exc_str,
+            "data": None,
+        }
+        return JSONResponse(
+            content=content, status_code=status.HTTP_422_UNPROCESSABLE_ENTITY
+        )
diff --git a/backend/core/quivr_core/api/packages/utils/parse_message_time.py b/backend/core/quivr_core/api/packages/utils/parse_message_time.py
new file mode 100644
index 000000000000..4aa18a3b8759
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/utils/parse_message_time.py
@@ -0,0 +1,5 @@
+from datetime import datetime
+
+
+def parse_message_time(message_time_str: str):
+    return datetime.strptime(message_time_str, "%Y-%m-%dT%H:%M:%S.%f")
diff --git a/backend/core/quivr_core/api/packages/utils/telemetry.py b/backend/core/quivr_core/api/packages/utils/telemetry.py
new file mode 100644
index 000000000000..c8e45baf3ee8
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/utils/telemetry.py
@@ -0,0 +1,60 @@
+import hashlib
+import json
+import os
+import threading
+
+import httpx
+from fastapi import Request
+
+from quivr_core.api.logger import get_logger
+
+logger = get_logger(__name__)
+
+# Assume these are your Supabase Function endpoint and any necessary headers
+TELEMETRY_URL = "https://ovbvcnwemowuuuaebizd.supabase.co/functions/v1/telemetry"
+HEADERS = {
+    "Content-Type": "application/json",
+}
+
+
+def generate_machine_key():
+    # Get the OpenAI API key from the environment variables
+    seed = os.getenv("OPENAI_API_KEY")
+
+    # Use SHA-256 hash to generate a unique key from the seed
+    unique_key = hashlib.sha256(seed.encode()).hexdigest()
+
+    return unique_key
+
+
+def send_telemetry(event_name: str, event_data: dict, request: Request = None):
+    # Generate a unique machine key
+    machine_key = generate_machine_key()
+    domain = None
+    if request:
+        domain = request.url.hostname
+        logger.info(f"Domain: {domain}")
+        event_data = {**event_data, "domain": domain}
+    # Prepare the payload
+    payload = json.dumps(
+        {
+            "anonymous_identifier": machine_key,
+            "event_name": event_name,
+            "event_data": event_data,
+        }
+    )
+
+    # Send the telemetry data
+    with httpx.Client() as client:
+        _ = client.post(TELEMETRY_URL, headers=HEADERS, data=payload)
+
+
+def maybe_send_telemetry(event_name: str, event_data: dict, request: Request = None):
+    enable_telemetry = os.getenv("TELEMETRY_ENABLED", "false")
+
+    if enable_telemetry.lower() != "true":
+        return
+
+    threading.Thread(
+        target=send_telemetry, args=(event_name, event_data, request)
+    ).start()
diff --git a/backend/core/quivr_core/api/packages/vectorstore/__init__.py b/backend/core/quivr_core/api/packages/vectorstore/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/api/packages/vectorstore/supabase.py b/backend/core/quivr_core/api/packages/vectorstore/supabase.py
new file mode 100644
index 000000000000..980c3a90a5f6
--- /dev/null
+++ b/backend/core/quivr_core/api/packages/vectorstore/supabase.py
@@ -0,0 +1,105 @@
+from typing import Any, List
+from uuid import UUID
+
+from langchain.docstore.document import Document
+from langchain.embeddings.base import Embeddings
+from langchain_community.vectorstores import SupabaseVectorStore
+from supabase.client import Client
+
+from quivr_core.api.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class CustomSupabaseVectorStore(SupabaseVectorStore):
+    """A custom vector store that uses the match_vectors table instead of the vectors table."""
+
+    def __init__(
+        self,
+        client: Client,
+        embedding: Embeddings,
+        table_name: str,
+        brain_id: UUID | None = None,
+        user_id: UUID | None = None,
+        number_docs: int = 35,
+        max_input: int = 2000,
+    ):
+        super().__init__(client, embedding, table_name)
+        self.brain_id = brain_id
+        self.user_id = user_id
+        self.number_docs = number_docs
+        self.max_input = max_input
+
+    def find_brain_closest_query(
+        self,
+        user_id: str,
+        query: str,
+        k: int = 6,
+        table: str = "match_brain",
+        threshold: float = 0.5,
+    ) -> list[dict[str, Any]]:
+        vectors = self._embedding.embed_documents([query])
+        query_embedding = vectors[0]
+
+        res = self._client.rpc(
+            table,
+            {
+                "query_embedding": query_embedding,
+                "match_count": self.number_docs,
+                "p_user_id": str(self.user_id),
+            },
+        ).execute()
+
+        # Get the brain_id of the brain that is most similar to the query
+        # Get the brain_id and name of the brains that are most similar to the query
+        brain_details = [
+            {
+                "id": item.get("id", None),
+                "name": item.get("name", None),
+                "similarity": item.get("similarity", 0.0),
+            }
+            for item in res.data
+        ]
+        return brain_details
+
+    def similarity_search(
+        self,
+        query: str,
+        k: int = 40,
+        table: str = "match_vectors",
+        threshold: float = 0.5,
+        **kwargs: Any,
+    ) -> List[Document]:
+        vectors = self._embedding.embed_documents([query])
+        query_embedding = vectors[0]
+        res = self._client.rpc(
+            table,
+            {
+                "query_embedding": query_embedding,
+                "max_chunk_sum": self.max_input,
+                "p_brain_id": str(self.brain_id),
+            },
+        ).execute()
+
+        match_result = [
+            Document(
+                metadata={
+                    **search.get("metadata", {}),
+                    "id": search.get("id", ""),
+                    "similarity": search.get("similarity", 0.0),
+                },
+                page_content=search.get("content", ""),
+            )
+            for search in res.data
+            if search.get("content")
+        ]
+
+        sorted_match_result_by_file_name_metadata = sorted(
+            match_result,
+            key=lambda x: (
+                x.metadata.get("file_name", ""),
+                x.metadata.get("index", float("inf")),
+            ),
+        )
+
+        return sorted_match_result_by_file_name_metadata
diff --git a/backend/core/quivr_core/api/tests/conftest.py b/backend/core/quivr_core/api/tests/conftest.py
new file mode 100644
index 000000000000..47621f5e72d4
--- /dev/null
+++ b/backend/core/quivr_core/api/tests/conftest.py
@@ -0,0 +1,61 @@
+import asyncio
+import os
+
+import pytest
+import pytest_asyncio
+import sqlalchemy
+from sqlalchemy.ext.asyncio import create_async_engine
+from sqlmodel import create_engine
+from sqlmodel.ext.asyncio.session import AsyncSession
+
+pg_database_url = ""
+
+
+@pytest.fixture(scope="session", autouse=True)
+def db_setup():
+    # setup
+    sync_engine = create_engine(
+        "postgresql://" + pg_database_url,
+        echo=True if os.getenv("ORM_DEBUG") else False,
+    )
+    #  TODO(@amine) : for now don't drop anything
+    # SQLModel.metadata.create_all(sync_engine, checkfirst=True)
+    yield sync_engine
+    # teardown
+    # NOTE: For now we rely on Supabase migrations for defining schemas
+    # SQLModel.metadata.drop_all(sync_engine)
+
+
+@pytest_asyncio.fixture(scope="session")
+async def async_engine():
+    engine = create_async_engine(
+        "postgresql+asyncpg://" + pg_database_url,
+        echo=True if os.getenv("ORM_DEBUG") else False,
+    )
+    yield engine
+
+
+@pytest.fixture(scope="session")
+def event_loop(request: pytest.FixtureRequest):
+    loop = asyncio.get_event_loop_policy().new_event_loop()
+    yield loop
+    loop.close()
+
+
+@pytest_asyncio.fixture()
+async def session(async_engine):
+    async with async_engine.connect() as conn:
+        await conn.begin()
+        await conn.begin_nested()
+        async_session = AsyncSession(conn, expire_on_commit=False)
+
+        @sqlalchemy.event.listens_for(
+            async_session.sync_session, "after_transaction_end"
+        )
+        def end_savepoint(session, transaction):
+            if conn.closed:
+                return
+            if not conn.in_nested_transaction():
+                conn.sync_connection.begin_nested()
+
+        yield async_session
diff --git a/backend/core/quivr_core/storage/__init__.py b/backend/core/quivr_core/storage/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/backend/core/quivr_core/storage/local_storage.py b/backend/core/quivr_core/storage/local_storage.py
new file mode 100644
index 000000000000..cf3e38260016
--- /dev/null
+++ b/backend/core/quivr_core/storage/local_storage.py
@@ -0,0 +1,60 @@
+import mmap
+import os
+from io import BytesIO
+from pathlib import Path
+from typing import BinaryIO
+from uuid import UUID, uuid4
+
+import aiofiles
+
+from quivr_core.storage.storage_base import StorageBase
+
+
+class QuivrFile:
+    __slots__ = ["fd", "filename", "brain_id", "file_size"]
+
+    def __init__(
+        self, fd: BinaryIO, filename: str, brain_id: UUID, file_size: int | None = None
+    ) -> None:
+        self.fd = fd
+        self.brain_id = brain_id
+        self.filename = filename
+        self.file_size = file_size
+
+    @classmethod
+    def from_path(cls, path: str):
+        fd = os.open(path, os.O_RDONLY)
+        file_size = os.stat(path).st_size
+        mmapped_file = mmap.mmap(fd, file_size, access=mmap.ACCESS_READ)
+        # TODO: parse file_name and brain_id
+        file_name = ""
+        return cls(
+            fd=BytesIO(mmapped_file),
+            brain_id=uuid4(),
+            filename=file_name,
+            file_size=file_size,
+        )
+
+    def local_filepath(self) -> str:
+        return os.path.join(str(self.brain_id), self.filename)
+
+
+class LocalStorage(StorageBase):
+    def __init__(self, dir_path: Path | None = None):
+        if dir_path is None:
+            self.dir_path = os.getenv("QUIVR_LOCAL_STORAGE", "~/.cache/quivr/files")
+        else:
+            self.dir_path = dir_path
+        os.makedirs(self.dir_path, exist_ok=True)
+
+    async def upload_file(self, file: QuivrFile, exists_ok: bool = False):
+        path = os.path.join(self.dir_path, file.local_filepath())
+        if os.path.exists(path) and not exists_ok:
+            raise FileExistsError("file already exists")
+
+        async with aiofiles.open(path, "wb") as f:
+            await f.write(file.fd.read())
+
+    # TODO:
+    async def remove_file(self, file_name: str):
+        pass
diff --git a/backend/core/quivr_core/storage/storage_base.py b/backend/core/quivr_core/storage/storage_base.py
new file mode 100644
index 000000000000..265980bc4d57
--- /dev/null
+++ b/backend/core/quivr_core/storage/storage_base.py
@@ -0,0 +1,13 @@
+from abc import ABC, abstractmethod
+
+from quivr_core.storage.local_storage import QuivrFile
+
+
+class StorageBase(ABC):
+    @abstractmethod
+    async def upload_file(self, file: QuivrFile, exists_ok: bool = False):
+        raise Exception("Unimplemented  upload_file method")
+
+    @abstractmethod
+    async def remove_file(self, file_name: str):
+        raise Exception("Unimplemented remove_file method")