diff --git a/cognee/infrastructure/databases/relational/get_relational_engine.py b/cognee/infrastructure/databases/relational/get_relational_engine.py
index c0a66e28e..d035f5baf 100644
--- a/cognee/infrastructure/databases/relational/get_relational_engine.py
+++ b/cognee/infrastructure/databases/relational/get_relational_engine.py
@@ -1,9 +1,9 @@
-from functools import lru_cache
+# from functools import lru_cache
 
 from .config import get_relational_config
 from .create_relational_engine import create_relational_engine
 
-@lru_cache
+# @lru_cache
 def get_relational_engine():
     relational_config = get_relational_config()
 
diff --git a/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py
index edc8eb57f..6526e59c3 100644
--- a/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py
+++ b/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py
@@ -1,4 +1,6 @@
+import asyncio
 import logging
+import math
 from typing import List, Optional
 import litellm
 from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine
@@ -36,11 +38,26 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
                 api_base = self.endpoint,
                 api_version = self.api_version
             )
-        except litellm.exceptions.BadRequestError as error:
+            return [data["embedding"] for data in response.data]
+
+        except litellm.exceptions.ContextWindowExceededError as error:
+            if isinstance(text, list):
+                parts = [text[0:math.ceil(len(text)/2)], text[math.ceil(len(text)/2):]]
+                parts_futures = [self.embed_text(part) for part in parts]
+                embeddings = await asyncio.gather(*parts_futures)
+
+                all_embeddings = []
+                for embeddings_part in embeddings:
+                    all_embeddings.extend(embeddings_part)
+
+                return [data["embedding"] for data in all_embeddings]
+
+            logger.error("Context window exceeded for embedding text: %s", str(error))
+            raise error
+
+        except Exception as error:
             logger.error("Error embedding text: %s", str(error))
             raise error
 
-        return [data["embedding"] for data in response.data]
-
     def get_vector_size(self) -> int:
         return self.dimensions
diff --git a/evals/eval_swe_bench.py b/evals/eval_swe_bench.py
index 9cd679429..ee13c92e4 100644
--- a/evals/eval_swe_bench.py
+++ b/evals/eval_swe_bench.py
@@ -7,20 +7,19 @@ from pathlib import Path
 from swebench.harness.utils import load_swebench_dataset
 from swebench.inference.make_datasets.create_instance import PATCH_EXAMPLE
 
-import cognee
 from cognee.api.v1.search import SearchType
 from cognee.infrastructure.llm.get_llm_client import get_llm_client
 from cognee.infrastructure.llm.prompts import read_query_prompt
 from cognee.modules.pipelines import Task, run_tasks
 from cognee.modules.retrieval.brute_force_triplet_search import \
     brute_force_triplet_search
-from cognee.shared.data_models import SummarizedContent
+# from cognee.shared.data_models import SummarizedContent
 from cognee.shared.utils import render_graph
 from cognee.tasks.repo_processor import (enrich_dependency_graph,
                                          expand_dependency_graph,
                                          get_repo_file_dependencies)
 from cognee.tasks.storage import add_data_points
-from cognee.tasks.summarization import summarize_code
+# from cognee.tasks.summarization import summarize_code
 from evals.eval_utils import download_github_repo, retrieved_edges_to_string
 
 
@@ -43,8 +42,21 @@ def check_install_package(package_name):
 
 
 async def generate_patch_with_cognee(instance, llm_client, search_type=SearchType.CHUNKS):
+    import os
+    import pathlib
+    import cognee
+    from cognee.infrastructure.databases.relational import create_db_and_tables
+
+    file_path = Path(__file__).parent
+    data_directory_path = str(pathlib.Path(os.path.join(file_path, ".data_storage/code_graph")).resolve())
+    cognee.config.data_root_directory(data_directory_path)
+    cognee_directory_path = str(pathlib.Path(os.path.join(file_path, ".cognee_system/code_graph")).resolve())
+    cognee.config.system_root_directory(cognee_directory_path)
+
     await cognee.prune.prune_data()
-    await cognee.prune.prune_system()
+    await cognee.prune.prune_system(metadata = True)
+
+    await create_db_and_tables()
 
     # repo_path = download_github_repo(instance, '../RAW_GIT_REPOS')
     
diff --git a/notebooks/cognee_code_graph_demo.ipynb b/notebooks/cognee_code_graph_demo.ipynb
index 5e21e9dad..f5735dcb8 100644
--- a/notebooks/cognee_code_graph_demo.ipynb
+++ b/notebooks/cognee_code_graph_demo.ipynb
@@ -17,60 +17,63 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from cognee.modules.users.methods import get_default_user\n",
+    "import os\n",
+    "import pathlib\n",
+    "import cognee\n",
+    "from cognee.infrastructure.databases.relational import create_db_and_tables\n",
     "\n",
-    "from cognee.modules.data.methods import get_datasets\n",
-    "from cognee.modules.data.methods.get_dataset_data import get_dataset_data\n",
-    "from cognee.modules.data.models import Data\n",
+    "notebook_path = os.path.abspath(\"\")\n",
+    "data_directory_path = str(pathlib.Path(os.path.join(notebook_path, \".data_storage/code_graph\")).resolve())\n",
+    "cognee.config.data_root_directory(data_directory_path)\n",
+    "cognee_directory_path = str(pathlib.Path(os.path.join(notebook_path, \".cognee_system/code_graph\")).resolve())\n",
+    "cognee.config.system_root_directory(cognee_directory_path)\n",
     "\n",
-    "from cognee.modules.pipelines.tasks.Task import Task\n",
-    "from cognee.tasks.documents import classify_documents, check_permissions_on_documents, extract_chunks_from_documents\n",
-    "from cognee.tasks.graph import extract_graph_from_code\n",
+    "await cognee.prune.prune_data()\n",
+    "await cognee.prune.prune_system(metadata = True)\n",
+    "\n",
+    "await create_db_and_tables()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from os import path\n",
+    "from pathlib import Path\n",
+    "from cognee.infrastructure.files.storage import LocalStorage\n",
+    "import git\n",
+    "\n",
+    "notebook_path = path.abspath(\"\")\n",
+    "repo_clone_location = path.join(notebook_path, \"data/graphrag\")\n",
+    "\n",
+    "LocalStorage.remove_all(repo_clone_location)\n",
+    "\n",
+    "git.Repo.clone_from(\n",
+    "    \"git@github.com:microsoft/graphrag.git\",\n",
+    "    Path(repo_clone_location),\n",
+    "    branch = \"main\",\n",
+    "    single_branch = True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cognee.tasks.repo_processor import enrich_dependency_graph, expand_dependency_graph, get_repo_file_dependencies\n",
     "from cognee.tasks.storage import add_data_points\n",
-    "from cognee.shared.SourceCodeGraph import SourceCodeGraph\n",
+    "from cognee.modules.pipelines.tasks.Task import Task\n",
     "\n",
-    "from cognee.modules.pipelines import run_tasks\n",
-    "\n",
-    "from cognee.shared.utils import render_graph\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "user = await get_default_user()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "existing_datasets = await get_datasets(user.id)\n",
-    "\n",
-    "datasets = {}\n",
-    "for dataset in existing_datasets:\n",
-    "    dataset_name = dataset.name.replace(\".\", \"_\").replace(\" \", \"_\")\n",
-    "    data_documents: list[Data] = await get_dataset_data(dataset_id = dataset.id)\n",
-    "    datasets[dataset_name] = data_documents\n",
-    "print(datasets.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "tasks = [\n",
-    "    Task(classify_documents),\n",
-    "    Task(check_permissions_on_documents, user = user, permissions = [\"write\"]),\n",
-    "    Task(extract_chunks_from_documents), # Extract text chunks based on the document type.\n",
-    "    Task(add_data_points, task_config = { \"batch_size\": 10 }),\n",
-    "    Task(extract_graph_from_code, graph_model = SourceCodeGraph, task_config = { \"batch_size\": 10 }), # Generate knowledge graphs from the document chunks.\n",
+    "    Task(get_repo_file_dependencies),\n",
+    "    Task(add_data_points, task_config = { \"batch_size\": 50 }),\n",
+    "    Task(enrich_dependency_graph, task_config = { \"batch_size\": 50 }),\n",
+    "    Task(expand_dependency_graph, task_config = { \"batch_size\": 50 }),\n",
+    "    Task(add_data_points, task_config = { \"batch_size\": 50 }),\n",
     "]"
    ]
   },
@@ -80,21 +83,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "async def run_codegraph_pipeline(tasks, data_documents):\n",
-    "    pipeline = run_tasks(tasks, data_documents, \"code_graph_pipeline\")\n",
-    "    results = []\n",
-    "    async for result in pipeline:\n",
-    "        results.append(result)\n",
-    "    return(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = await run_codegraph_pipeline(tasks, datasets[\"main_dataset\"])"
+    "from cognee.modules.pipelines import run_tasks\n",
+    "\n",
+    "notebook_path = os.path.abspath(\"\")\n",
+    "repo_clone_location = os.path.join(notebook_path, \"data/graphrag\")\n",
+    "\n",
+    "pipeline = run_tasks(tasks, repo_clone_location, \"code_graph_pipeline\")\n",
+    "\n",
+    "async for result in pipeline:\n",
+    "    print(result)"
    ]
   },
   {
@@ -103,6 +100,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from cognee.shared.utils import render_graph\n",
     "await render_graph(None, include_nodes = True, include_labels = True)"
    ]
   },
@@ -116,7 +114,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cognee",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -130,7 +128,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,
diff --git a/poetry.lock b/poetry.lock
index 9b309de51..0f3d414bd 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -6424,6 +6424,138 @@ botocore = ">=1.33.2,<2.0a.0"
 [package.extras]
 crt = ["botocore[crt] (>=1.33.2,<2.0a.0)"]
 
+[[package]]
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+optional = false
+python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -7193,6 +7422,75 @@ files = [
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
 test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
 
+[[package]]
+name = "transformers"
+version = "4.46.3"
+description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "transformers-4.46.3-py3-none-any.whl", hash = "sha256:a12ef6f52841fd190a3e5602145b542d03507222f2c64ebb7ee92e8788093aef"},
+    {file = "transformers-4.46.3.tar.gz", hash = "sha256:8ee4b3ae943fe33e82afff8e837f4b052058b07ca9be3cb5b729ed31295f72cc"},
+]
+
+[package.dependencies]
+filelock = "*"
+huggingface-hub = ">=0.23.2,<1.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+pyyaml = ">=5.1"
+regex = "!=2019.12.17"
+requests = "*"
+safetensors = ">=0.4.1"
+tokenizers = ">=0.20,<0.21"
+tqdm = ">=4.27"
+
+[package.extras]
+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)", "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 (>=0.3.0)"]
+codecarbon = ["codecarbon (==1.2.0)"]
+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)", "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"]
+integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+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)", "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)"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=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 (>=0.20,<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 (>=0.20,<0.21)", "torch", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
 [[package]]
 name = "tweepy"
 version = "4.14.0"
@@ -7840,4 +8138,4 @@ weaviate = ["weaviate-client"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9.0,<3.12"
-content-hash = "e2360f4be222743bb83b1e7316185c5f62bd73c0baaab3eee984e1c84f1cea65"
+content-hash = "54118c733c3852c7685873a6a75789544475df45117488fa9c7071fd5bde1e8b"
diff --git a/pyproject.toml b/pyproject.toml
index 46d0a89a6..33a9ebf55 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -71,6 +71,7 @@ pgvector = "^0.3.5"
 psycopg2 = {version = "^2.9.10", optional = true}
 llama-index-core = {version = "^0.11.22", optional = true}
 deepeval = {version = "^2.0.1", optional = true}
+transformers = "^4.46.3"
 
 [tool.poetry.extras]
 filesystem = ["s3fs", "botocore"]
@@ -96,6 +97,7 @@ debugpy = "1.8.2"
 pylint = "^3.0.3"
 ruff = "^0.2.2"
 tweepy = "4.14.0"
+gitpython = "^3.1.43"
 
 [tool.poetry.group.docs.dependencies]
 mkdocs-material = "^9.5.42"