From 6c6d36a3bdb890fc15bda594c13ae3b0cbffbee5 Mon Sep 17 00:00:00 2001
From: Ranjan Shrestha <37866947+ranjan-stha@users.noreply.github.com>
Date: Wed, 16 Oct 2024 10:21:31 +0545
Subject: [PATCH] Fix Model download and FastAPI response (#4)

* Fixed the message and status code;
* fixed the issue with model scan and storage;
* updated the python version; packages updated
---
 .python-version     |    1 +
 Dockerfile          |    2 +-
 app.py              |    5 +-
 embedding_models.py |    6 +-
 poetry.lock         | 1888 +++++++++++++++++++++++--------------------
 pyproject.toml      |    4 +-
 utils.py            |   38 +-
 7 files changed, 1043 insertions(+), 901 deletions(-)
 create mode 100644 .python-version

diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..e4fba21
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/Dockerfile b/Dockerfile
index f43b15b..0f38e9c 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,4 +1,4 @@
-FROM python:3.10-slim-buster
+FROM python:3.12-slim-bullseye
 
 LABEL maintainer="TC Developers"
 
diff --git a/app.py b/app.py
index 0ec5e0e..477c141 100644
--- a/app.py
+++ b/app.py
@@ -2,7 +2,7 @@
 from typing import List, Optional, Union
 
 from dotenv import load_dotenv
-from fastapi import FastAPI
+from fastapi import FastAPI, Response, status
 from pydantic import BaseModel
 
 from embedding_models import (
@@ -37,7 +37,8 @@ class RequestSchemaForEmbeddings(BaseModel):
 
 @app.get("/")
 async def home():
-    return "Embedding handler using models for texts", 200
+    """Returns a message"""
+    return Response(content="Embedding handler using models for texts", status_code=status.HTTP_200_OK)
 
 
 @app.post("/get_embeddings")
diff --git a/embedding_models.py b/embedding_models.py
index fe25ecc..7fc28f7 100644
--- a/embedding_models.py
+++ b/embedding_models.py
@@ -8,7 +8,7 @@
 from sentence_transformers import SentenceTransformer
 from torch import Tensor
 
-from utils import download_models
+from utils import check_models
 
 
 @dataclass
@@ -24,8 +24,8 @@ def __post_init__(self):
         """
         Post initialization
         """
-        models_info = download_models(sent_embedding_model=self.model)
-        self.st_embedding_model = SentenceTransformer(model_name_or_path=models_info["model_path"])
+        model_path = check_models(sent_embedding_model=self.model)
+        self.st_embedding_model = SentenceTransformer(model_name_or_path=model_path)
 
     def embed_documents(self, texts: list) -> np.ndarray:
         """
diff --git a/poetry.lock b/poetry.lock
index b731d59..6749ac6 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,125 +2,124 @@
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.0"
+version = "2.4.3"
 description = "Happy Eyeballs for asyncio"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = "sha256:7ce92076e249169a13c2f49320d1967425eaf1f407522d707d59cac7628d62bd"},
-    {file = "aiohappyeyeballs-2.4.0.tar.gz", hash = "sha256:55a1714f084e63d49639800f95716da97a1f173d46a16dfcfda0016abb93b6b2"},
+    {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
+    {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.10.5"
+version = "3.10.10"
 description = "Async http client/server framework (asyncio)"
 category = "main"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
 aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
-async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
-yarl = ">=1.0,<2.0"
+yarl = ">=1.12.0,<2.0"
 
 [package.extras]
 speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
@@ -154,38 +153,24 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.4.0"
+version = "4.6.2.post1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 category = "main"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 [package.dependencies]
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 idna = ">=2.8"
 sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
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-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)"]
-
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-name = "async-timeout"
-version = "4.0.3"
-description = "Timeout context manager for asyncio programs"
-category = "main"
-optional = false
-python-versions = ">=3.7"
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-]
+doc = ["Sphinx (>=7.4,<8.0)", "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", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
+trio = ["trio (>=0.26.1)"]
 
 [[package]]
 name = "attrs"
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+version = "3.4.0"
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-
 [[package]]
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 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 category = "main"
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -412,19 +397,19 @@ standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "htt
 
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 [[package]]
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 [package.extras]
@@ -648,14 +640,14 @@ files = [
 
 [[package]]
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-version = "1.0.5"
+version = "1.0.6"
 description = "A minimal low-level HTTP client."
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -666,7 +658,7 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (>=1.0.0,<2.0.0)"]
-trio = ["trio (>=0.22.0,<0.26.0)"]
+trio = ["trio (>=0.22.0,<1.0)"]
 
 [[package]]
 name = "httpx"
@@ -731,16 +723,19 @@ typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "t
 
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+version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 category = "main"
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 python-versions = ">=3.6"
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 ]
 
+[package.extras]
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+
 [[package]]
 name = "jinja2"
 version = "3.1.4"
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 [[package]]
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 [package.dependencies]
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 langchain-core = ">=0.2.38,<0.3.0"
 langchain-text-splitters = ">=0.2.0,<0.3.0"
 langsmith = ">=0.1.17,<0.2.0"
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-    {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
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 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
 requests = ">=2,<3"
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 [[package]]
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-version = "0.2.16"
+version = "0.2.17"
 description = "Community contributed LangChain integrations."
 category = "main"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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-    {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
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 PyYAML = ">=5.3"
 requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
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 [[package]]
 name = "langchain-core"
-version = "0.2.39"
+version = "0.2.41"
 description = "Building applications with LLMs through composability"
 category = "main"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 [package.dependencies]
@@ -950,18 +950,18 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langchain-openai"
-version = "0.1.24"
+version = "0.1.25"
 description = "An integration package connecting OpenAI and LangChain"
 category = "main"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 tiktoken = ">=0.7,<1"
 
@@ -982,14 +982,14 @@ langchain-core = ">=0.2.38,<0.3.0"
 
 [[package]]
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-version = "0.1.120"
+version = "0.1.135"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
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 ]
 requests = ">=2,<3"
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-
 [[package]]
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 version = "1.0.0"
@@ -1228,21 +1227,22 @@ files = [
 
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 test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
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@@ -1493,21 +1493,129 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
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 [package.dependencies]
@@ -1823,6 +1931,21 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "requests-toolbelt"
+version = "1.0.0"
+description = "A utility belt for advanced users of python-requests"
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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+
+[package.dependencies]
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+
 [[package]]
 name = "safetensors"
 version = "0.4.5"
@@ -2055,28 +2178,30 @@ test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.3
 
 [[package]]
 name = "sentence-transformers"
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 ]
 
 [package.dependencies]
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 Pillow = "*"
 scikit-learn = "*"
 scipy = "*"
 torch = ">=1.11.0"
 tqdm = "*"
-transformers = ">=4.38.0,<5.0.0"
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 [package.extras]
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+openvino = ["optimum-intel[openvino] (>=1.20.0)"]
 train = ["accelerate (>=0.20.3)", "datasets"]
 
 [[package]]
@@ -2141,61 +2266,61 @@ files = [
 
 [[package]]
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+version = "2.0.35"
 description = "Database Abstraction Library"
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 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -2229,14 +2354,14 @@ sqlcipher = ["sqlcipher3_binary"]
 
 [[package]]
 name = "starlette"
-version = "0.38.5"
+version = "0.38.6"
 description = "The little ASGI library that shines."
 category = "main"
 optional = false
 python-versions = ">=3.8"
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 [[package]]
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 description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
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 [package.dependencies]
@@ -2346,112 +2466,112 @@ blobfile = ["blobfile (>=2)"]
 
 [[package]]
 name = "tokenizers"
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 description = ""
 category = "main"
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 python-versions = ">=3.7"
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+    {file = "tokenizers-0.20.1-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:e7edb8ec12c100d5458d15b1e47c0eb30ad606a05641f19af7563bc3d1608c14"},
+    {file = "tokenizers-0.20.1-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:de291633fb9303555793cc544d4a86e858da529b7d0b752bcaf721ae1d74b2c9"},
+    {file = "tokenizers-0.20.1.tar.gz", hash = "sha256:84edcc7cdeeee45ceedb65d518fffb77aec69311c9c8e30f77ad84da3025f002"},
 ]
 
 [package.dependencies]
@@ -2464,28 +2584,30 @@ testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
 
 [[package]]
 name = "torch"
-version = "2.0.0+cpu"
+version = "2.2.0+cpu"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 category = "main"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "torch-2.0.0%2Bcpu-cp310-cp310-linux_x86_64.whl", hash = "sha256:7c9e668462cd0813fc4baaeaa137efce2516e73cf381120846edf45d4ec21174"},
+    {file = "torch-2.2.0%2Bcpu-cp312-cp312-linux_x86_64.whl", hash = "sha256:8258824bec0181e01a086aef5809016116a97626af2dcbf932d4e0b192d9c1b8"},
 ]
 
 [package.dependencies]
 filelock = "*"
+fsspec = "*"
 jinja2 = "*"
 networkx = "*"
 sympy = "*"
-typing-extensions = "*"
+typing-extensions = ">=4.8.0"
 
 [package.extras]
 opt-einsum = ["opt-einsum (>=3.3)"]
+optree = ["optree (>=0.9.1)"]
 
 [package.source]
 type = "url"
-url = "https://download.pytorch.org/whl/cpu/torch-2.0.0%2Bcpu-cp310-cp310-linux_x86_64.whl"
+url = "https://download.pytorch.org/whl/cpu/torch-2.2.0%2Bcpu-cp312-cp312-linux_x86_64.whl#sha256=8258824bec0181e01a086aef5809016116a97626af2dcbf932d4e0b192d9c1b8"
 [[package]]
 name = "tqdm"
 version = "4.66.5"
@@ -2509,14 +2631,14 @@ telegram = ["requests"]
 
 [[package]]
 name = "transformers"
-version = "4.44.2"
+version = "4.45.2"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 category = "main"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "transformers-4.44.2-py3-none-any.whl", hash = "sha256:1c02c65e7bfa5e52a634aff3da52138b583fc6f263c1f28d547dc144ba3d412d"},
-    {file = "transformers-4.44.2.tar.gz", hash = "sha256:36aa17cc92ee154058e426d951684a2dab48751b35b49437896f898931270826"},
+    {file = "transformers-4.45.2-py3-none-any.whl", hash = "sha256:c551b33660cfc815bae1f9f097ecfd1e65be623f13c6ee0dda372bd881460210"},
+    {file = "transformers-4.45.2.tar.gz", hash = "sha256:72bc390f6b203892561f05f86bbfaa0e234aab8e927a83e62b9d92ea7e3ae101"},
 ]
 
 [package.dependencies]
@@ -2528,21 +2650,21 @@ pyyaml = ">=5.1"
 regex = "!=2019.12.17"
 requests = "*"
 safetensors = ">=0.4.1"
-tokenizers = ">=0.19,<0.20"
+tokenizers = ">=0.20,<0.21"
 tqdm = ">=4.27"
 
 [package.extras]
-accelerate = ["accelerate (>=0.21.0)"]
-agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
-all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
+accelerate = ["accelerate (>=0.26.0)"]
+agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
+all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision"]
 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-benchmark = ["optimum-benchmark (>=0.2.0)"]
+benchmark = ["optimum-benchmark (>=0.3.0)"]
 codecarbon = ["codecarbon (==1.2.0)"]
-deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
-deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
-dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+deepspeed = ["accelerate (>=0.26.0)", "deepspeed (>=0.9.3)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.20,<0.21)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
@@ -2553,7 +2675,7 @@ natten = ["natten (>=0.14.6,<0.15.0)"]
 onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
 onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
 optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.5.1)", "urllib3 (<2.0.0)"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "libcst", "rich", "ruff (==0.5.1)", "urllib3 (<2.0.0)"]
 ray = ["ray[tune] (>=2.7.0)"]
 retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
 ruff = ["ruff (==0.5.1)"]
@@ -2563,16 +2685,17 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
 tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+tiktoken = ["blobfile", "tiktoken"]
 timm = ["timm (<=0.9.16)"]
-tokenizers = ["tokenizers (>=0.19,<0.20)"]
-torch = ["accelerate (>=0.21.0)", "torch"]
+tokenizers = ["tokenizers (>=0.20,<0.21)"]
+torch = ["accelerate (>=0.26.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.20,<0.21)", "torch", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
@@ -2643,111 +2766,118 @@ standard = ["colorama (>=0.4)", "httptools (>=0.5.0)", "python-dotenv (>=0.13)",
 
 [[package]]
 name = "yarl"
-version = "1.11.1"
+version = "1.15.2"
 description = "Yet another URL library"
 category = "main"
 optional = false
 python-versions = ">=3.8"
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+    {file = "yarl-1.15.2-cp39-cp39-win_amd64.whl", hash = "sha256:81dadafb3aa124f86dc267a2168f71bbd2bfb163663661ab0038f6e4b8edb810"},
+    {file = "yarl-1.15.2-py3-none-any.whl", hash = "sha256:0d3105efab7c5c091609abacad33afff33bdff0035bece164c98bcf5a85ef90a"},
+    {file = "yarl-1.15.2.tar.gz", hash = "sha256:a39c36f4218a5bb668b4f06874d676d35a035ee668e6e7e3538835c703634b84"},
 ]
 
 [package.dependencies]
 idna = ">=2.0"
 multidict = ">=4.0"
+propcache = ">=0.2.0"
 
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.10"
-content-hash = "cd0c8de8af6b030d7130d6e18a814ee4ee3f7ba45a7db0715c01b2574deac10a"
+python-versions = "^3.12"
+content-hash = "5c951f9da3888e68cad55670307b8fa5a8c6d564d4a8da43d131696fc3d68fc1"
diff --git a/pyproject.toml b/pyproject.toml
index 9e75def..ca1529d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,12 +7,12 @@ license = "GNU Affero General Public License v3.0"
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = "^3.10"
+python = "^3.12"
 langchain = "^0.2.15"
 langchain-community = "^0.2.14"
 langchain-openai = "^0.1.23"
 sentence-transformers = "^3.1.0"
-torch = {url = "https://download.pytorch.org/whl/cpu/torch-2.0.0%2Bcpu-cp310-cp310-linux_x86_64.whl"}
+torch = {url = "https://download.pytorch.org/whl/cpu/torch-2.2.0%2Bcpu-cp312-cp312-linux_x86_64.whl#sha256=8258824bec0181e01a086aef5809016116a97626af2dcbf932d4e0b192d9c1b8"}
 fastapi = "^0.114.0"
 uvicorn = "^0.22.0"
 sentry-sdk = "^1.5.8"
diff --git a/utils.py b/utils.py
index e854911..5d32be6 100644
--- a/utils.py
+++ b/utils.py
@@ -9,7 +9,15 @@
 logger.setLevel(logging.INFO)
 
 
-def download_models(sent_embedding_model: str):
+def download_model(embedding_model: str, models_path: str):
+    """Downloads the model"""
+    logger.info("Downloading the model")
+    embedding_model_local_path = snapshot_download(repo_id=embedding_model, cache_dir=models_path)
+    return embedding_model_local_path
+
+
+def check_models(sent_embedding_model: str):
+    """Check if the model already exists"""
     models_path = Path("/opt/models")
     models_info_path = models_path / "model_info.json"
 
@@ -17,21 +25,23 @@ def download_models(sent_embedding_model: str):
         os.makedirs(models_path)
 
     if not any(os.listdir(models_path)):
-        logger.info("Downloading the model")
-        embedding_model_local_path = snapshot_download(repo_id=sent_embedding_model, cache_dir=models_path)
+        embedding_model_local_path = download_model(embedding_model=sent_embedding_model, models_path=models_path)
         models_info = {
-            "model": sent_embedding_model,
-            "model_path": embedding_model_local_path,
+            sent_embedding_model: embedding_model_local_path,
         }
 
         with open(models_info_path, "w", encoding="utf-8") as m_info_f:
             json.dump(models_info, m_info_f)
-
-    else:
-        if os.path.exists(models_info_path):
-            logger.info("Models already exists.")
-            logger.info(models_info_path)
-            with open(models_info_path, "r", encoding="utf-8") as m_info_f:
-                models_info = json.load(m_info_f)
-
-    return models_info
+        return embedding_model_local_path
+    if os.path.exists(models_info_path):
+        with open(models_info_path, "r", encoding="utf-8") as m_info_f:
+            models_info_dict = json.load(m_info_f)
+        if sent_embedding_model not in models_info_dict.keys():
+            embedding_model_local_path = download_model(embedding_model=sent_embedding_model, models_path=models_path)
+            models_info_dict[sent_embedding_model] = embedding_model_local_path
+            with open(models_info_path, "w", encoding="utf-8") as m_info_f:
+                json.dump(models_info_dict, m_info_f)
+            return embedding_model_local_path
+
+        logger.info("Model is already available.")
+        return models_info_dict[sent_embedding_model]