diff --git a/LLM/09_rag_langchain.ipynb b/LLM/09_rag_langchain.ipynb
index 0a9283f..d1bb182 100644
--- a/LLM/09_rag_langchain.ipynb
+++ b/LLM/09_rag_langchain.ipynb
@@ -264,8 +264,8 @@
     "Key Components:\n",
     "- LangChain: Framework for developing applications powered by language models\n",
     "- HuggingFace: Platform for accessing pre-trained models and embeddings\n",
-    "- Chroma: Vector store for efficient similarity search\n",
-    "- LlamaCpp: Efficient C++ implementation of Llama models"
+    "- FAISS: Vector store for efficient similarity search\n",
+    "- LlamaCpp: Efficient C++ implementation of Llama models\n"
    ]
   },
   {
@@ -284,7 +284,7 @@
     "from langchain_community.document_loaders import DirectoryLoader, TextLoader\n",
     "from langchain_community.document_loaders.pdf import PyPDFLoader\n",
     "from langchain_huggingface import HuggingFaceEmbeddings\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import FAISS\n",
     "from langchain_community.llms import LlamaCpp\n",
     "from langchain import chains, text_splitter, PromptTemplate\n",
     "from huggingface_hub import hf_hub_download\n",
@@ -292,7 +292,7 @@
     "import sys\n",
     "\n",
     "# Setting constants and environment variables\n",
-    "VECTOR_DB_DIR = \"vector_dbs\""
+    "VECTOR_DB_DIR = \"vector_dbs\"\n"
    ]
   },
   {
@@ -508,12 +508,12 @@
     "    embedding generation, and storage.\n",
     "    \n",
     "    Args:\n",
-    "        document_url (str): URL of the document to process\n",
+    "        source (str): URL or path of the document to process\n",
+    "        source_type (str): Type of the source ('url' or 'local')\n",
     "        embedding_fn: Function to generate embeddings\n",
-    "        persist_dir (str): Directory for storing the vector database\n",
     "        \n",
     "    Returns:\n",
-    "        Chroma: Initialized vector store with document embeddings\n",
+    "        FAISS: Initialized vector store with document embeddings\n",
     "    \"\"\"\n",
     "    source_hash = get_source_hash(source, source_type)\n",
     "    persist_dir = os.path.join(VECTOR_DB_DIR, source_hash)\n",
@@ -521,21 +521,21 @@
     "    # Check if embeddings already exist\n",
     "    if os.path.exists(persist_dir):\n",
     "        print(\"Loading existing embeddings...\")\n",
-    "        return Chroma(persist_directory=persist_dir, embedding_function=embedding_fn)\n",
+    "        return FAISS.load_local(persist_dir, embedding_fn, allow_dangerous_deserialization=True)\n",
     "\n",
     "    # Create new embeddings if they don't exist\n",
     "    start_time = time.time()\n",
     "    print(\"Creating new embeddings...\")\n",
     "    document = load_document(source, source_type)\n",
     "    documents = split_document(document)\n",
-    "    vector_store = Chroma.from_documents(\n",
+    "    vector_store = FAISS.from_documents(\n",
     "        documents=documents,\n",
-    "        embedding=embedding_fn,\n",
-    "        persist_directory=persist_dir\n",
+    "        embedding=embedding_fn\n",
     "    )\n",
+    "    vector_store.save_local(persist_dir)\n",
     "    \n",
     "    print(f\"Embedding time: {time.time() - start_time:.2f} seconds\")\n",
-    "    return vector_store"
+    "    return vector_store\n"
    ]
   },
   {
@@ -678,8 +678,6 @@
    },
    "outputs": [],
    "source": [
-    "#from langchain_community.vectorstores.chroma import Chroma\n",
-    "from langchain_chroma import Chroma\n",
     "def main():\n",
     "    \"\"\"Main function to run the RAG system.\n",
     "    \n",
@@ -748,7 +746,7 @@
     "    except KeyboardInterrupt:\n",
     "        print(\"\\nProgram interrupted by user\")\n",
     "    except Exception as e:\n",
-    "        print(f\"An error occurred: {str(e)}\")"
+    "        print(f\"An error occurred: {str(e)}\")\n"
    ]
   },
   {
diff --git a/LLM/10_rag_pytorch.ipynb b/LLM/10_rag_pytorch.ipynb
index f0c23e7..677dad5 100644
--- a/LLM/10_rag_pytorch.ipynb
+++ b/LLM/10_rag_pytorch.ipynb
@@ -131,7 +131,7 @@
     "Key Components:\n",
     "- LangChain: Framework for developing applications powered by language models\n",
     "- HuggingFace: Platform for accessing pre-trained models and embeddings\n",
-    "- Chroma: Vector store for efficient similarity search"
+    "- FAISS: Vector store for efficient similarity search\n"
    ]
   },
   {
@@ -143,6 +143,7 @@
     "from langchain import chains, text_splitter, PromptTemplate\n",
     "from langchain_community.embeddings.fastembed import FastEmbedEmbeddings\n",
     "from langchain_community import document_loaders, embeddings, vectorstores\n",
+    "from langchain_community.vectorstores import FAISS\n",
     "from langchain.llms.base import LLM\n",
     "from typing import Any, List, Optional, Dict\n",
     "from pydantic import Field\n",
@@ -158,7 +159,7 @@
     "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
-    "VECTOR_DB_DIR = \"vector_dbs\""
+    "VECTOR_DB_DIR = \"vector_dbs\"\n"
    ]
   },
   {
@@ -268,25 +269,11 @@
     "        raise ValueError(f\"Unsupported embedding type: {embedding_type}\")\n",
     "\n",
     "def clear_vector_store(persist_dir=VECTOR_DB_DIR):\n",
-    "    import shutil\n",
     "    if os.path.exists(persist_dir):\n",
     "        try:\n",
-    "            # Create a temporary Chroma instance to properly close any open connections\n",
-    "            temp_db = vectorstores.Chroma(\n",
-    "                persist_directory=persist_dir,\n",
-    "                embedding_function=FastEmbedEmbeddings()\n",
-    "            )\n",
-    "            temp_db.persist()\n",
-    "            del temp_db  # Explicitly delete the instance\n",
-    "            \n",
-    "            # Add a small delay to ensure connections are closed\n",
-    "            time.sleep(1)\n",
-    "            \n",
     "            shutil.rmtree(persist_dir)\n",
     "        except Exception as e:\n",
     "            print(f\"Warning: Error while clearing vector store: {e}\")\n",
-    "            # If regular deletion fails, try forcing it\n",
-    "            os.system(f\"rm -rf {persist_dir}\")\n",
     "\n"
    ]
   },
@@ -329,15 +316,13 @@
     "    document = load_document(document_url)\n",
     "    documents = split_document(document)\n",
     "    \n",
-    "    vector_store = vectorstores.Chroma.from_documents(\n",
+    "    vector_store = FAISS.from_documents(\n",
     "        documents=documents,\n",
-    "        embedding=embedding_fn,\n",
-    "        persist_directory=unique_persist_dir,\n",
-    "        collection_metadata={\"hnsw:space\": \"cosine\"}\n",
+    "        embedding=embedding_fn\n",
     "    )\n",
-    "    vector_store.persist()\n",
+    "    vector_store.save_local(unique_persist_dir)\n",
     "    print(f\"Embedding time: {time.time() - start_time:.2f} seconds\")\n",
-    "    return vector_store, unique_persist_dir"
+    "    return vector_store, unique_persist_dir\n"
    ]
   },
   {
@@ -422,37 +407,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#from langchain_community.vectorstores.chroma import Chroma\n",
     "def main(document_url, question, embedding_type=\"huggingface\", model_path=\"meta-llama/Llama-2-7b-chat-hf\"):\n",
     "    try:\n",
     "        llm = LoadLLM(model_path=model_path)\n",
     "        embedding_fn = initialize_embedding_fn(embedding_type)\n",
     "        vector_store, persist_dir = get_or_create_embeddings(document_url, embedding_fn)\n",
     "        answer = handle_user_interaction(vector_store, llm, question)\n",
-    "        vector_store.persist()\n",
     "        del vector_store\n",
     "        # Cleanup old directories if needed\n",
     "        cleanup_old_directories(VECTOR_DB_DIR, keep_last=5)\n",
     "        return answer\n",
     "    except Exception as e:\n",
     "        print(f\"An error occurred: {e}\")\n",
-    "        return None\n",
-    "\n",
-    "def cleanup_old_directories(base_dir, keep_last=5):\n",
-    "    \"\"\"Clean up old vector store directories, keeping only the most recent ones.\"\"\"\n",
-    "    try:\n",
-    "        dirs = [os.path.join(base_dir, d) for d in os.listdir(base_dir)]\n",
-    "        dirs = [d for d in dirs if os.path.isdir(d)]\n",
-    "        dirs.sort(key=lambda x: os.path.getctime(x), reverse=True)\n",
-    "        \n",
-    "        # Remove all but the last keep_last directories\n",
-    "        for dir_path in dirs[keep_last:]:\n",
-    "            try:\n",
-    "                shutil.rmtree(dir_path)\n",
-    "            except Exception as e:\n",
-    "                print(f\"Warning: Could not remove directory {dir_path}: {e}\")\n",
-    "    except Exception as e:\n",
-    "        print(f\"Warning: Error during cleanup: {e}\")"
+    "        return None\n"
    ]
   },
   {
diff --git a/LLM/rag/requirements.txt b/LLM/rag/requirements.txt
index 71315a4..997ab82 100644
--- a/LLM/rag/requirements.txt
+++ b/LLM/rag/requirements.txt
@@ -4,9 +4,8 @@ langchain-core==1.3.3
 langchain-huggingface==0.1.2
 huggingface-hub>=0.30.0
 sentence-transformers==3.4.1
-chromadb==1.5.9
+faiss-cpu>=1.7.4
 transformers>=5.5.0
 pypdf==6.14.2
 torch>=2.13.0
-langchain-chroma==0.2.2
 beautifulsoup4==4.13.3
\ No newline at end of file
diff --git a/Video-Description-Generation-Query-Retrieval/Video_RAG_Ollama.ipynb b/Video-Description-Generation-Query-Retrieval/Video_RAG_Ollama.ipynb
index 128785f..fdafec2 100644
--- a/Video-Description-Generation-Query-Retrieval/Video_RAG_Ollama.ipynb
+++ b/Video-Description-Generation-Query-Retrieval/Video_RAG_Ollama.ipynb
@@ -21,9 +21,9 @@
    "id": "3a593030",
    "metadata": {},
    "source": [
-    "This notebook demonstrates how to generate video descriptions using the **Qwen2.5-VL (Qwen 2.5 Vision-Language model)** via **Ollama** and store their embeddings in **ChromaDB** for efficient semantic search on **Intel® Core™ Ultra Processors**. \n",
+    "This notebook demonstrates how to generate video descriptions using the **Qwen2.5-VL (Qwen 2.5 Vision-Language model)** via **Ollama** and store their embeddings in a **lightweight vector database** for efficient semantic search on **Intel® Core™ Ultra Processors**. \n",
     "\n",
-    "For each video, a description is generated using Ollama's vision model and stored as an embedding in ChromaDB. When a user submits a query, cosine similarity search is performed in ChromaDB to retrieve the most relevant video description. The matching video is then displayed inline.\n",
+    "For each video, a description is generated using Ollama's vision model and stored as an embedding in a numpy-based vector store. When a user submits a query, cosine similarity search is performed to retrieve the most relevant video description. The matching video is then displayed inline.\n",
     "\n",
     "This sample uses the videos from the [**stepfun-ai/Step-Video-T2V-Eval**](https://huggingface.co/datasets/stepfun-ai/Step-Video-T2V-Eval) Hugging Face dataset.\n",
     "\n",
@@ -31,7 +31,7 @@
     "- Uses Ollama as the GPU backend\n",
     "- Simpler setup - no complex model loading required\n",
     "- Uses Qwen2.5-VL vision model through Ollama\n",
-    "- ChromaDB and semantic search functionality"
+    "- Lightweight numpy vector store and semantic search functionality\n"
    ]
   },
   {
@@ -50,8 +50,8 @@
     "- During the initial data load, videos from the dataset are processed using **Ollama's Qwen2.5-VL vision model**\n",
     "- The model generates descriptions for each video\n",
     "- Generated video descriptions are converted into embeddings using **Sentence Transformers** (all-MiniLM-L6-v2 model)\n",
-    "- These embeddings, along with descriptions and video metadata, are stored in a persistent local **ChromaDB** collection\n",
-    "- When a user submits a query, the text is encoded into an embedding and used to perform semantic search (via cosine similarity) over the ChromaDB collection\n",
+    "- These embeddings, along with descriptions and video metadata, are stored in a persistent local **vector database** (numpy + JSON)\n",
+    "- When a user submits a query, the text is encoded into an embedding and used to perform semantic search (via cosine similarity) over the vector database\n",
     "- The most relevant video description and associated video file are returned and displayed"
    ]
   },
@@ -91,7 +91,6 @@
     "# Install dependencies if not already installed\n",
     "required_packages = [\n",
     "    \"ollama>=0.4.0\",\n",
-    "    \"chromadb>=1.0.12\",\n",
     "    \"sentence-transformers>=4.1.0\",\n",
     "    \"opencv-python>=4.8.0\",\n",
     "    \"numpy>=1.24.0\",\n",
@@ -109,7 +108,7 @@
     "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n",
     "        print(f\"✓ {package} installed successfully\")\n",
     "\n",
-    "print(\"\\n✅ All dependencies are ready!\")"
+    "print(\"\\n✅ All dependencies are ready!\")\n"
    ]
   },
   {
@@ -172,11 +171,12 @@
    "source": [
     "import os\n",
     "import base64\n",
+    "import json\n",
     "import random\n",
     "import shutil\n",
     "import logging\n",
-    "import chromadb\n",
     "import warnings\n",
+    "import numpy as np\n",
     "import ollama\n",
     "from tqdm import tqdm\n",
     "from IPython.display import Video, display\n",
@@ -186,6 +186,82 @@
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbf2f6b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SimpleVectorDB:\n",
+    "    \"\"\"Lightweight persistent vector database using numpy cosine similarity and JSON metadata.\"\"\"\n",
+    "\n",
+    "    def __init__(self, path):\n",
+    "        self._path = path\n",
+    "        self._data_file = os.path.join(path, \"data.json\")\n",
+    "        self._emb_file = os.path.join(path, \"embeddings.npy\")\n",
+    "        self._embeddings = None\n",
+    "        self._documents = []\n",
+    "        self._metadatas = []\n",
+    "        self._ids = []\n",
+    "        os.makedirs(path, exist_ok=True)\n",
+    "        self._load()\n",
+    "\n",
+    "    def _load(self):\n",
+    "        if os.path.exists(self._data_file) and os.path.exists(self._emb_file):\n",
+    "            with open(self._data_file, \"r\", encoding=\"utf-8\") as f:\n",
+    "                data = json.load(f)\n",
+    "            self._documents = data[\"documents\"]\n",
+    "            self._metadatas = data[\"metadatas\"]\n",
+    "            self._ids = data[\"ids\"]\n",
+    "            self._embeddings = np.load(self._emb_file)\n",
+    "\n",
+    "    def _save(self):\n",
+    "        with open(self._data_file, \"w\", encoding=\"utf-8\") as f:\n",
+    "            json.dump({\"documents\": self._documents, \"metadatas\": self._metadatas, \"ids\": self._ids}, f)\n",
+    "        if self._embeddings is not None:\n",
+    "            np.save(self._emb_file, self._embeddings)\n",
+    "\n",
+    "    def add(self, embeddings, documents, metadatas, ids):\n",
+    "        new_emb = np.array(embeddings, dtype=np.float32)\n",
+    "        self._embeddings = new_emb if self._embeddings is None else np.vstack([self._embeddings, new_emb])\n",
+    "        self._documents.extend(documents)\n",
+    "        self._metadatas.extend(metadatas)\n",
+    "        self._ids.extend(ids)\n",
+    "        self._save()\n",
+    "\n",
+    "    def count(self):\n",
+    "        return len(self._ids)\n",
+    "\n",
+    "    def get(self, include=None):\n",
+    "        result = {\"ids\": self._ids}\n",
+    "        if include and \"documents\" in include:\n",
+    "            result[\"documents\"] = self._documents\n",
+    "        if include and \"metadatas\" in include:\n",
+    "            result[\"metadatas\"] = self._metadatas\n",
+    "        return result\n",
+    "\n",
+    "    def query(self, query_embeddings, n_results=3, include=None):\n",
+    "        if self._embeddings is None or len(self._ids) == 0:\n",
+    "            return {\"ids\": [[]], \"documents\": [[]], \"metadatas\": [[]], \"distances\": [[]]}\n",
+    "        q = np.array(query_embeddings[0], dtype=np.float32)\n",
+    "        q_norm = np.linalg.norm(q)\n",
+    "        emb_norms = np.linalg.norm(self._embeddings, axis=1)\n",
+    "        denom = np.where(emb_norms * q_norm == 0, 1e-10, emb_norms * q_norm)\n",
+    "        cosine_sim = np.dot(self._embeddings, q) / denom\n",
+    "        distances = 1.0 - cosine_sim\n",
+    "        n = min(n_results, len(self._ids))\n",
+    "        top_idx = np.argsort(distances)[:n].tolist()\n",
+    "        result = {\"ids\": [[self._ids[i] for i in top_idx]]}\n",
+    "        if not include or \"documents\" in include:\n",
+    "            result[\"documents\"] = [[self._documents[i] for i in top_idx]]\n",
+    "        if not include or \"metadatas\" in include:\n",
+    "            result[\"metadatas\"] = [[self._metadatas[i] for i in top_idx]]\n",
+    "        if not include or \"distances\" in include:\n",
+    "            result[\"distances\"] = [[float(distances[i]) for i in top_idx]]\n",
+    "        return result"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "699f3cf3",
@@ -424,7 +500,7 @@
    "id": "d381f556",
    "metadata": {},
    "source": [
-    "## Get or create ChromaDB collection"
+    "## Initialize vector database"
    ]
   },
   {
@@ -436,28 +512,24 @@
    "source": [
     "def get_or_create_database():\n",
     "    \"\"\"\n",
-    "    Connects to or creates a persistent ChromaDB collection.\n",
+    "    Connects to or creates a persistent vector database collection.\n",
     "\n",
     "    Returns:\n",
     "        tuple: (collection, existing_descriptions)\n",
     "    \"\"\"\n",
     "    try:\n",
-    "        client = chromadb.PersistentClient(path=DATABASE_PATH)\n",
-    "        collection = client.get_or_create_collection(\n",
-    "            name=COLLECTION_NAME,\n",
-    "            metadata={\"hnsw:space\": \"cosine\"}\n",
-    "        )\n",
-    "        \n",
+    "        collection = SimpleVectorDB(path=DATABASE_PATH)\n",
+    "\n",
     "        logging.info(\" Checking existing descriptions in database...\")\n",
     "        all_items = collection.get(include=[\"metadatas\", \"documents\"])\n",
-    "        \n",
+    "\n",
     "        existing_descriptions = {}\n",
-    "        for metadata, doc in zip(all_items['metadatas'], all_items['documents']):\n",
+    "        for metadata, doc in zip(all_items.get('metadatas', []), all_items.get('documents', [])):\n",
     "            existing_descriptions[metadata['video_filename']] = doc\n",
-    "        \n",
+    "\n",
     "        logging.info(f\" Found {len(existing_descriptions)} existing descriptions\")\n",
     "        return collection, existing_descriptions\n",
-    "        \n",
+    "\n",
     "    except Exception as e:\n",
     "        logging.exception(f\" Error while checking database: {str(e)}\")\n",
     "        return None, {}"
@@ -483,7 +555,7 @@
     "Each video will be processed:\n",
     "1. Check if description already exists in database\n",
     "2. If not, generate description using Ollama\n",
-    "3. Create embedding and store in ChromaDB"
+    "3. Create embedding and store in vector database\n"
    ]
   },
   {
@@ -499,7 +571,7 @@
     "    \n",
     "    Args:\n",
     "        selected_video_files (list): List of video file paths.\n",
-    "        collection: ChromaDB collection object.\n",
+    "        collection: Vector database collection object.\n",
     "        existing_descriptions (dict): Already processed videos.\n",
     "        embedding_model: Sentence Transformer model.\n",
     "    \"\"\"\n",
@@ -526,7 +598,7 @@
     "            # Create embedding\n",
     "            embedding = embedding_model.encode(description_text).tolist()\n",
     "            \n",
-    "            # Store in ChromaDB\n",
+    "            # Store in vector database\n",
     "            collection.add(\n",
     "                embeddings=[embedding],\n",
     "                documents=[description_text],\n",
@@ -580,27 +652,27 @@
    "source": [
     "def query_videos_descriptions(query, collection, embedding_model):\n",
     "    \"\"\"\n",
-    "    Query ChromaDB collection to find similar videos.\n",
-    "    \n",
+    "    Query vector database to find similar videos.\n",
+    "\n",
     "    Args:\n",
     "        query (str): User query.\n",
-    "        collection: ChromaDB collection object.\n",
+    "        collection: Vector database collection object.\n",
     "        embedding_model: Sentence Transformer model.\n",
-    "    \n",
+    "\n",
     "    Returns:\n",
     "        dict: Query results.\n",
     "    \"\"\"\n",
     "    try:\n",
     "        query_embedding = embedding_model.encode(query).tolist()\n",
-    "        \n",
+    "\n",
     "        results = collection.query(\n",
     "            query_embeddings=[query_embedding],\n",
     "            n_results=1,\n",
     "            include=[\"documents\", \"metadatas\", \"distances\"]\n",
     "        )\n",
-    "        \n",
+    "\n",
     "        logging.info(f\" Search results for: '{query}'\\n\")\n",
-    "        \n",
+    "\n",
     "        for doc, metadata, distance in zip(\n",
     "            results['documents'][0],\n",
     "            results['metadatas'][0],\n",
@@ -611,12 +683,12 @@
     "            logging.info(f\" Similarity score: {similarity_score:.3f}\")\n",
     "            logging.info(f\" Distance: {distance:.3f}\")\n",
     "            logging.info(f\" Video description: {doc}\\n\")\n",
-    "        \n",
+    "\n",
     "        return results\n",
-    "        \n",
+    "\n",
     "    except Exception as e:\n",
     "        logging.exception(f\" Error while querying video descriptions: {str(e)}\")\n",
-    "        return None"
+    "        return None\n"
    ]
   },
   {
diff --git a/Video-Description-Generation-Query-Retrieval/pyproject.toml b/Video-Description-Generation-Query-Retrieval/pyproject.toml
index 1c7be22..3b687c9 100644
--- a/Video-Description-Generation-Query-Retrieval/pyproject.toml
+++ b/Video-Description-Generation-Query-Retrieval/pyproject.toml
@@ -7,7 +7,6 @@ requires-python = ">=3.12.0"
 dependencies = [
     "ollama>=0.4.0",
     "streamlit>=1.54.0",
-    "chromadb>=1.5.9",
     "sentence-transformers>=4.1.0",
     "opencv-python>=4.8.0",
     "numpy>=1.24.0",
@@ -29,6 +28,5 @@ dependencies = [
     "filelock>=3.20.1",
     "pygments>=2.20.0",
     "soupsieve>=2.8.4",
-    "pydantic-settings>=2.14.2",
     "jupyterlab>=4.5.9",
 ]
diff --git a/Video-Description-Generation-Query-Retrieval/st_video_rag_demo.py b/Video-Description-Generation-Query-Retrieval/st_video_rag_demo.py
index 293bb0e..444e922 100644
--- a/Video-Description-Generation-Query-Retrieval/st_video_rag_demo.py
+++ b/Video-Description-Generation-Query-Retrieval/st_video_rag_demo.py
@@ -1,9 +1,10 @@
 import os
 import base64
+import json
 import shutil
 import logging
-import chromadb
 import warnings
+import numpy as np
 import ollama
 import cv2
 import streamlit as st
@@ -12,6 +13,76 @@
 warnings.filterwarnings("ignore")
 logging.basicConfig(level=logging.INFO)
 
+
+class SimpleVectorDB:
+    """Lightweight persistent vector database using numpy cosine similarity and JSON metadata."""
+
+    def __init__(self, path):
+        self._path = path
+        self._data_file = os.path.join(path, "data.json")
+        self._emb_file = os.path.join(path, "embeddings.npy")
+        self._embeddings = None
+        self._documents = []
+        self._metadatas = []
+        self._ids = []
+        os.makedirs(path, exist_ok=True)
+        self._load()
+
+    def _load(self):
+        if os.path.exists(self._data_file) and os.path.exists(self._emb_file):
+            with open(self._data_file, "r", encoding="utf-8") as f:
+                data = json.load(f)
+            self._documents = data["documents"]
+            self._metadatas = data["metadatas"]
+            self._ids = data["ids"]
+            self._embeddings = np.load(self._emb_file)
+
+    def _save(self):
+        with open(self._data_file, "w", encoding="utf-8") as f:
+            json.dump({"documents": self._documents, "metadatas": self._metadatas, "ids": self._ids}, f)
+        if self._embeddings is not None:
+            np.save(self._emb_file, self._embeddings)
+
+    def add(self, embeddings, documents, metadatas, ids):
+        new_emb = np.array(embeddings, dtype=np.float32)
+        self._embeddings = new_emb if self._embeddings is None else np.vstack([self._embeddings, new_emb])
+        self._documents.extend(documents)
+        self._metadatas.extend(metadatas)
+        self._ids.extend(ids)
+        self._save()
+
+    def count(self):
+        return len(self._ids)
+
+    def get(self, include=None):
+        result = {"ids": self._ids}
+        if include and "documents" in include:
+            result["documents"] = self._documents
+        if include and "metadatas" in include:
+            result["metadatas"] = self._metadatas
+        return result
+
+    def query(self, query_embeddings, n_results=3, include=None):
+        if self._embeddings is None or len(self._ids) == 0:
+            return {"ids": [[]], "documents": [[]], "metadatas": [[]], "distances": [[]]}
+        q = np.array(query_embeddings[0], dtype=np.float32)
+        q_norm = np.linalg.norm(q)
+        emb_norms = np.linalg.norm(self._embeddings, axis=1)
+        denom = np.where(emb_norms * q_norm == 0, 1e-10, emb_norms * q_norm)
+        cosine_sim = np.dot(self._embeddings, q) / denom
+        distances = 1.0 - cosine_sim
+        n = min(n_results, len(self._ids))
+        top_idx = np.argsort(distances)[:n].tolist()
+        result = {"ids": [[self._ids[i] for i in top_idx]]}
+        if not include or "documents" in include:
+            result["documents"] = [[self._documents[i] for i in top_idx]]
+        if not include or "metadatas" in include:
+            result["metadatas"] = [[self._metadatas[i] for i in top_idx]]
+        if not include or "distances" in include:
+            result["distances"] = [[float(distances[i]) for i in top_idx]]
+        return result
+
+
 # Configuration
 OLLAMA_BASE_URL = "http://localhost:11434"
 DATABASE_PATH = "./Video_descriptions_database_ollama"
@@ -253,38 +324,14 @@ def get_video_paths(folder, max_count):
 
 
 def initialize_database():
-    """Initialize ChromaDB and embedding model."""
+    """Initialize vector database and embedding model."""
     try:
-        client = chromadb.PersistentClient(path=DATABASE_PATH)
-        collection = client.get_or_create_collection(
-            name=COLLECTION_NAME,
-            metadata={"hnsw:space": "cosine"}
-        )
-        
+        collection = SimpleVectorDB(path=DATABASE_PATH)
         embedding_model = SentenceTransformer(EMBEDDING_MODEL)
-        
         return collection, embedding_model
     except Exception as e:
-        error_msg = str(e)
-        
-        if "Could not connect to tenant" in error_msg or "default_tenant" in error_msg:
-            st.error(f"Database initialization error: {e}")
-            st.warning("⚠️ ChromaDB database is corrupted or incompatible.")
-            
-            if st.button("🔄 Reset Database", type="primary"):
-                try:
-                    if os.path.exists(DATABASE_PATH):
-                        shutil.rmtree(DATABASE_PATH)
-                        st.success("✅ Database removed. Click 'Start Processing Videos' again.")
-                        st.rerun()
-                except Exception as reset_error:
-                    st.error(f"Failed to reset database: {reset_error}")
-            
-            st.info("💡 **To fix manually:**\n\n1. Close this app\n2. Delete folder: `Video_descriptions_database_ollama`\n3. Restart the app")
-            return None, None
-        else:
-            st.error(f"Database initialization error: {e}")
-            return None, None
+        st.error(f"Database initialization error: {e}")
+        return None, None
 
 
 def get_existing_descriptions(collection):
@@ -307,7 +354,7 @@ def get_existing_descriptions(collection):
     with col1:
         st.markdown('<div class="metric-card"><h3>🤖 AI Model</h3><p>Vision-Language Models</p></div>', unsafe_allow_html=True)
     with col2:
-        st.markdown('<div class="metric-card"><h3>💾 Storage</h3><p>ChromaDB Vector Store</p></div>', unsafe_allow_html=True)
+        st.markdown('<div class="metric-card"><h3>💾 Storage</h3><p>NumPy Vector Store</p></div>', unsafe_allow_html=True)
     with col3:
         st.markdown('<div class="metric-card"><h3>🔍 Search</h3><p>Semantic Similarity</p></div>', unsafe_allow_html=True)
     
@@ -530,7 +577,7 @@ def get_existing_descriptions(collection):
     with cols2[3]:
         st.markdown("### ➡️")
     with cols2[4]:
-        st.markdown("### 💾\n**ChromaDB**\nVector store")
+        st.markdown("### 💾\n**Vector Store**\nnumpy + JSON")
     
     st.markdown("---")
     
@@ -570,7 +617,7 @@ def get_existing_descriptions(collection):
         - Semantic representation
         
         **3. Vector Storage**
-        - ChromaDB persistent storage
+        - NumPy persistent storage
         - Cosine similarity metric
         - Efficient retrieval
         """)
@@ -612,7 +659,7 @@ def get_existing_descriptions(collection):
         st.markdown("""
         **Infrastructure**
         - Ollama Runtime
-        - ChromaDB
+        - NumPy Vector Store
         - Streamlit UI
         """)
     
@@ -660,7 +707,7 @@ def get_existing_descriptions(collection):
     # Core Technologies
     - Ollama: Local LLM runtime
     - Vision-Language Models: Qwen, Llama, etc.
-    - ChromaDB: Efficient vector database
+    - NumPy Vector Store: Lightweight vector database
     - Sentence Transformers: Text embedding generation
     - Streamlit: Interactive web interface
     - OpenCV: Video frame extraction
diff --git a/Video-Description-Generation-Query-Retrieval/uv.lock b/Video-Description-Generation-Query-Retrieval/uv.lock
index fa7e29c..33dd27f 100644
--- a/Video-Description-Generation-Query-Retrieval/uv.lock
+++ b/Video-Description-Generation-Query-Retrieval/uv.lock
@@ -136,65 +136,6 @@ wheels = [
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 ]
 
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