From e5916b7c169da92caf9a1cdfa3d452a8bb6fcc44 Mon Sep 17 00:00:00 2001
From: Zoya Hammad <zoyahammadk@gmail.com>
Date: Sun, 23 Mar 2025 16:58:17 +0500
Subject: [PATCH] Added Knowledge Worker to community-contributions

---
 .../markdown_knowledge_worker.ipynb           | 359 ++++++++++++++++++
 .../ui_markdown_knowledge_worker.ipynb        | 353 +++++++++++++++++
 2 files changed, 712 insertions(+)
 create mode 100644 week5/community-contributions/markdown_knowledge_worker.ipynb
 create mode 100644 week5/community-contributions/ui_markdown_knowledge_worker.ipynb

diff --git a/week5/community-contributions/markdown_knowledge_worker.ipynb b/week5/community-contributions/markdown_knowledge_worker.ipynb
new file mode 100644
index 0000000..51597f5
--- /dev/null
+++ b/week5/community-contributions/markdown_knowledge_worker.ipynb
@@ -0,0 +1,359 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c25c6e94-f3de-4367-b2bf-269ba7160977",
+   "metadata": {},
+   "source": [
+    "## An Expert Knowledge Worker Question-Answering Agent using RAG"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15169580-cf11-4dee-8ec7-3a4ef59b19ee",
+   "metadata": {},
+   "source": [
+    "Aims\n",
+    "- Reads README.md files and loads data using TextLoader\n",
+    "- Splits into chunks using CharacterTextSplitter\n",
+    "- Converts chunks into vector embeddings and creates a datastore\n",
+    "- 2D and 3D visualisations\n",
+    "- Langchain to set up a conversation retrieval chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "051cf881-357d-406b-8eae-1610651e40f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import glob\n",
+    "from dotenv import load_dotenv\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccfd403a-5bdb-4a8c-b3fd-d47ae79e43f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports for langchain, plotly and Chroma\n",
+    "\n",
+    "from langchain.document_loaders import DirectoryLoader, TextLoader\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.schema import Document\n",
+    "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
+    "from langchain.embeddings import HuggingFaceEmbeddings\n",
+    "from langchain_chroma import Chroma\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.chains import ConversationalRetrievalChain\n",
+    "import numpy as np\n",
+    "from sklearn.manifold import TSNE\n",
+    "import plotly.graph_objects as go\n",
+    "import plotly.express as px\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d853868-d2f6-43e1-b27c-b8e91d06b724",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL = \"gpt-4o-mini\"\n",
+    "db_name = \"vector_db\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f152fc3b-0bf4-4d51-948f-95da1ebc030a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load environment variables in a file called .env\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24e621ac-df06-4af6-a60d-a9ed7adb884a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in documents using LangChain's loaders\n",
+    "\n",
+    "folder = \"my-knowledge-base/\"\n",
+    "text_loader_kwargs={'autodetect_encoding': True}\n",
+    "\n",
+    "loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n",
+    "folder_docs = loader.load()\n",
+    "\n",
+    "for doc in folder_docs:\n",
+    "    filename_md = os.path.basename(doc.metadata[\"source\"])  \n",
+    "    filename, _ = os.path.splitext(filename_md)  \n",
+    "    doc.metadata[\"filename\"] = filename\n",
+    "\n",
+    "documents = folder_docs \n",
+    "\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=200)\n",
+    "chunks = text_splitter.split_documents(documents)\n",
+    "\n",
+    "print(f\"Total number of chunks: {len(chunks)}\")\n",
+    "print(f\"Files found: {set(doc.metadata['filename'] for doc in documents)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f02f08ee-5ade-4f79-a500-045a8f1a532f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n",
+    "\n",
+    "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+    "\n",
+    "# Delete if already exists\n",
+    "\n",
+    "if os.path.exists(db_name):\n",
+    "    Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n",
+    "\n",
+    "# Create vectorstore\n",
+    "\n",
+    "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n",
+    "print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f665f4d-ccb1-43fb-b901-040117925732",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's investigate the vectors\n",
+    "\n",
+    "collection = vectorstore._collection\n",
+    "count = collection.count()\n",
+    "\n",
+    "sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n",
+    "dimensions = len(sample_embedding)\n",
+    "print(f\"There are {count:,} vectors with {dimensions:,} dimensions in the vector store\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6208a971-e8b7-48bc-be7a-6dcb82967fd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pre work\n",
+    "\n",
+    "result = collection.get(include=['embeddings','documents','metadatas'])\n",
+    "vectors = np.array(result['embeddings'])  \n",
+    "documents = result['documents']\n",
+    "metadatas = result['metadatas']\n",
+    "filenames = [metadata['filename'] for metadata in metadatas]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb27bc8a-453b-4b19-84b4-dc495bb0e544",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "def random_color():\n",
+    "        return f\"rgb({random.randint(0,255)},{random.randint(0,255)},{random.randint(0,255)})\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78db67e5-ef10-4581-b8ac-3e0281ceba45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_embeddings_2d(result):\n",
+    "    vectors = np.array(result['embeddings'])  \n",
+    "    documents = result['documents']\n",
+    "    metadatas = result['metadatas']\n",
+    "    filenames = [metadata['filename'] for metadata in metadatas]\n",
+    "    filenames_unique = sorted(set(filenames))\n",
+    "\n",
+    "    # color assignment\n",
+    "    color_map = {name: random_color() for name in filenames_unique}\n",
+    "    colors = [color_map[name] for name in filenames]\n",
+    "\n",
+    "    tsne = TSNE(n_components=2, random_state=42,perplexity=4)\n",
+    "    reduced_vectors = tsne.fit_transform(vectors)\n",
+    "\n",
+    "    # Create the 2D scatter plot\n",
+    "    fig = go.Figure(data=[go.Scatter(\n",
+    "        x=reduced_vectors[:, 0],\n",
+    "        y=reduced_vectors[:, 1],\n",
+    "        mode='markers',\n",
+    "        marker=dict(size=5,color=colors, opacity=0.8),\n",
+    "        text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n",
+    "        hoverinfo='text'\n",
+    "    )])\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        title='2D Chroma Vector Store Visualization',\n",
+    "        scene=dict(xaxis_title='x',yaxis_title='y'),\n",
+    "        width=800,\n",
+    "        height=600,\n",
+    "        margin=dict(r=20, b=10, l=10, t=40)\n",
+    "    )\n",
+    "\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c250166-cb5b-4a75-8981-fae2d6dfe509",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "show_embeddings_2d(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b290e38-0800-4453-b664-7a7622ff5ed2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_embeddings_3d(result):\n",
+    "    vectors = np.array(result['embeddings'])  \n",
+    "    documents = result['documents']\n",
+    "    metadatas = result['metadatas']\n",
+    "    filenames = [metadata['filename'] for metadata in metadatas]\n",
+    "    filenames_unique = sorted(set(filenames))\n",
+    "\n",
+    "    # color assignment\n",
+    "    color_map = {name: random_color() for name in filenames_unique}\n",
+    "    colors = [color_map[name] for name in filenames]\n",
+    "\n",
+    "    tsne = TSNE(n_components=3, random_state=42)\n",
+    "    reduced_vectors = tsne.fit_transform(vectors)\n",
+    "\n",
+    "    fig = go.Figure(data=[go.Scatter3d(\n",
+    "        x=reduced_vectors[:, 0],\n",
+    "        y=reduced_vectors[:, 1],\n",
+    "        z=reduced_vectors[:, 2],\n",
+    "        mode='markers',\n",
+    "        marker=dict(size=5, color=colors, opacity=0.8),\n",
+    "        text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n",
+    "        hoverinfo='text'\n",
+    "    )])\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        title='3D Chroma Vector Store Visualization',\n",
+    "        scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n",
+    "        width=900,\n",
+    "        height=700,\n",
+    "        margin=dict(r=20, b=10, l=10, t=40)\n",
+    "    )\n",
+    "\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45d1d034-2503-4176-b1e4-f248e31c4770",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "show_embeddings_3d(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e79946a1-f93a-4b3a-8d19-deef40dec223",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a new Chat with OpenAI\n",
+    "llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n",
+    "\n",
+    "# set up the conversation memory for the chat\n",
+    "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
+    "\n",
+    "# the retriever is an abstraction over the VectorStore that will be used during RAG\n",
+    "retriever = vectorstore.as_retriever(search_kwargs={\"k\": 50})\n",
+    "\n",
+    "# putting it together: set up the conversation chain with the GPT 3.5 LLM, the vector store and memory\n",
+    "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59f90c85-c113-4482-8574-8a728ef25459",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def chat(question, history):\n",
+    "    result = conversation_chain.invoke({\"question\": question})\n",
+    "    return result[\"answer\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0520a8ff-01a4-4fa6-9dc8-57da87272edc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4949b17-cd9c-4bff-bd5b-0f80df72e7dc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/week5/community-contributions/ui_markdown_knowledge_worker.ipynb b/week5/community-contributions/ui_markdown_knowledge_worker.ipynb
new file mode 100644
index 0000000..5bf6f56
--- /dev/null
+++ b/week5/community-contributions/ui_markdown_knowledge_worker.ipynb
@@ -0,0 +1,353 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d13be0fd-db15-4ab1-860a-b00257051339",
+   "metadata": {},
+   "source": [
+    "## Gradio UI for Markdown-Based Q&A with Visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc63fbdb-66a9-4c10-8dbd-11476b5e2d21",
+   "metadata": {},
+   "source": [
+    "This interface enables users to:\n",
+    "- Upload Markdown files for processing\n",
+    "- Visualize similarity between document chunks in 2D and 3D using embeddings\n",
+    "- Ask questions and receive RAG enabled responses\n",
+    "- Mantain conversation context for better question answering\n",
+    "- Clear chat history when required for fresh sessions\n",
+    "- Store and retrieve embeddings using ChromaDB\n",
+    "\n",
+    "Integrates LangChain, ChromaDB, and OpenAI to process, store, and retrieve information efficiently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91da28d8-8e29-44b7-a62a-a3a109753727",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e47f670a-e2cb-4700-95d0-e59e440677a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports for langchain, plotly and Chroma\n",
+    "\n",
+    "from langchain.document_loaders import DirectoryLoader, TextLoader\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.schema import Document\n",
+    "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
+    "from langchain.embeddings import HuggingFaceEmbeddings\n",
+    "from langchain_chroma import Chroma\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.chains import ConversationalRetrievalChain\n",
+    "import numpy as np\n",
+    "from sklearn.manifold import TSNE\n",
+    "import plotly.graph_objects as go\n",
+    "import plotly.express as px\n",
+    "import matplotlib.pyplot as plt\n",
+    "from random import randint\n",
+    "import shutil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "362d4976-2553-4ed8-8fbb-49806145cad1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install --upgrade gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "968b6e96-557e-439f-b2f1-942c05168641",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL = \"gpt-4o-mini\"\n",
+    "db_name = \"vector_db\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "537f66de-6abf-4b34-8e05-6b9a9df8ae82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load environment variables in a file called .env\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "246c1c1b-fcfa-4f4c-b99c-024598751361",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "folder = \"my-knowledge-base/\"\n",
+    "db_name = \"vectorstore_db\"\n",
+    "\n",
+    "def process_files(files):\n",
+    "    os.makedirs(folder, exist_ok=True)\n",
+    "\n",
+    "    processed_files = []\n",
+    "    for file in files:\n",
+    "        file_path = os.path.join(folder, os.path.basename(file))  # Get filename\n",
+    "        shutil.copy(file, file_path)\n",
+    "        processed_files.append(os.path.basename(file))\n",
+    "\n",
+    "    # Load documents using LangChain's DirectoryLoader\n",
+    "    text_loader_kwargs = {'autodetect_encoding': True}\n",
+    "    loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n",
+    "    folder_docs = loader.load()\n",
+    "\n",
+    "    # Assign filenames as metadata\n",
+    "    for doc in folder_docs:\n",
+    "        filename_md = os.path.basename(doc.metadata[\"source\"])\n",
+    "        filename, _ = os.path.splitext(filename_md)\n",
+    "        doc.metadata[\"filename\"] = filename\n",
+    "\n",
+    "    documents = folder_docs \n",
+    "\n",
+    "    # Split documents into chunks\n",
+    "    text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=200)\n",
+    "    chunks = text_splitter.split_documents(documents)\n",
+    "\n",
+    "    # Initialize embeddings\n",
+    "    embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+    "\n",
+    "    # Delete previous vectorstore\n",
+    "    if os.path.exists(db_name):\n",
+    "        Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n",
+    "\n",
+    "    # Store in ChromaDB\n",
+    "    vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n",
+    "\n",
+    "    # Retrieve results\n",
+    "    collection = vectorstore._collection\n",
+    "    result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n",
+    "\n",
+    "    llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n",
+    "    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
+    "    retriever = vectorstore.as_retriever(search_kwargs={\"k\": 35})\n",
+    "    global conversation_chain\n",
+    "    conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)\n",
+    "\n",
+    "    processed_text = \"**Processed Files:**\\n\\n\" + \"\\n\".join(f\"- {file}\" for file in processed_files)\n",
+    "    return result, processed_text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48678d3a-0ab2-4aa4-aa9e-4160c6a9cb24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def random_color():\n",
+    "        return f\"rgb({randint(0,255)},{randint(0,255)},{randint(0,255)})\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6caed889-9bb4-42ad-b1c2-da051aefc802",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_embeddings_2d(result):\n",
+    "    vectors = np.array(result['embeddings'])  \n",
+    "    documents = result['documents']\n",
+    "    metadatas = result['metadatas']\n",
+    "    filenames = [metadata['filename'] for metadata in metadatas]\n",
+    "    filenames_unique = sorted(set(filenames))\n",
+    "\n",
+    "    # color assignment\n",
+    "    color_map = {name: random_color() for name in filenames_unique}\n",
+    "    colors = [color_map[name] for name in filenames]\n",
+    "\n",
+    "    tsne = TSNE(n_components=2, random_state=42,perplexity=4)\n",
+    "    reduced_vectors = tsne.fit_transform(vectors)\n",
+    "\n",
+    "    # Create the 2D scatter plot\n",
+    "    fig = go.Figure(data=[go.Scatter(\n",
+    "        x=reduced_vectors[:, 0],\n",
+    "        y=reduced_vectors[:, 1],\n",
+    "        mode='markers',\n",
+    "        marker=dict(size=5,color=colors, opacity=0.8),\n",
+    "        text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n",
+    "        hoverinfo='text'\n",
+    "    )])\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        title='2D Chroma Vector Store Visualization',\n",
+    "        scene=dict(xaxis_title='x',yaxis_title='y'),\n",
+    "        width=800,\n",
+    "        height=600,\n",
+    "        margin=dict(r=20, b=10, l=10, t=40)\n",
+    "    )\n",
+    "\n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de993495-c8cd-4313-a6bb-7d27494ecc13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_embeddings_3d(result):\n",
+    "    vectors = np.array(result['embeddings'])  \n",
+    "    documents = result['documents']\n",
+    "    metadatas = result['metadatas']\n",
+    "    filenames = [metadata['filename'] for metadata in metadatas]\n",
+    "    filenames_unique = sorted(set(filenames))\n",
+    "\n",
+    "    # color assignment\n",
+    "    color_map = {name: random_color() for name in filenames_unique}\n",
+    "    colors = [color_map[name] for name in filenames]\n",
+    "\n",
+    "    tsne = TSNE(n_components=3, random_state=42)\n",
+    "    reduced_vectors = tsne.fit_transform(vectors)\n",
+    "\n",
+    "    fig = go.Figure(data=[go.Scatter3d(\n",
+    "        x=reduced_vectors[:, 0],\n",
+    "        y=reduced_vectors[:, 1],\n",
+    "        z=reduced_vectors[:, 2],\n",
+    "        mode='markers',\n",
+    "        marker=dict(size=5, color=colors, opacity=0.8),\n",
+    "        text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n",
+    "        hoverinfo='text'\n",
+    "    )])\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        title='3D Chroma Vector Store Visualization',\n",
+    "        scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n",
+    "        width=900,\n",
+    "        height=700,\n",
+    "        margin=dict(r=20, b=10, l=10, t=40)\n",
+    "    )\n",
+    "\n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b7bf62b-c559-4e97-8135-48cd8d97a40e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def chat(question, history):\n",
+    "    result = conversation_chain.invoke({\"question\": question})\n",
+    "    return result[\"answer\"]\n",
+    "\n",
+    "def visualise_data(result):\n",
+    "    fig_2d = show_embeddings_2d(result)\n",
+    "    fig_3d = show_embeddings_3d(result)\n",
+    "    return fig_2d,fig_3d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99217109-fbee-4269-81c7-001e6f768a72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "css = \"\"\"\n",
+    ".btn {background-color: #1d53d1;}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1429ea1-1d9f-4be6-b270-01997864c642",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with gr.Blocks(css=css) as ui:\n",
+    "    gr.Markdown(\"# Markdown-Based Q&A with Visualization\")\n",
+    "    with gr.Row():\n",
+    "        file_input = gr.Files(file_types=[\".md\"], label=\"Upload Markdown Files\")\n",
+    "        with gr.Column(scale=1):\n",
+    "            processed_output = gr.Markdown(\"Progress\")\n",
+    "    with gr.Row():\n",
+    "        process_btn = gr.Button(\"Process Files\",elem_classes=[\"btn\"])\n",
+    "    with gr.Row():\n",
+    "        question = gr.Textbox(label=\"Chat \", lines=10)\n",
+    "        answer = gr.Markdown(label= \"Response\")\n",
+    "    with gr.Row():\n",
+    "        question_btn = gr.Button(\"Ask a Question\",elem_classes=[\"btn\"])\n",
+    "        clear_btn = gr.Button(\"Clear Output\",elem_classes=[\"btn\"])\n",
+    "    with gr.Row():\n",
+    "        plot_2d = gr.Plot(label=\"2D Visualization\")\n",
+    "        plot_3d = gr.Plot(label=\"3D Visualization\")\n",
+    "    with gr.Row():\n",
+    "        visualise_btn = gr.Button(\"Visualise Data\",elem_classes=[\"btn\"])\n",
+    "\n",
+    "    result = gr.State([])\n",
+    "    # Action: When button is clicked, process files and update visualization\n",
+    "    clear_btn.click(fn=lambda:(\"\", \"\"), inputs=[],outputs=[question, answer])\n",
+    "    process_btn.click(process_files, inputs=[file_input], outputs=[result,processed_output])\n",
+    "    question_btn.click(chat, inputs=[question], outputs= [answer])\n",
+    "    visualise_btn.click(visualise_data, inputs=[result], outputs=[plot_2d,plot_3d])\n",
+    "\n",
+    "# Launch Gradio app\n",
+    "ui.launch(inbrowser=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3686048-ac29-4df1-b816-e58996913ef1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}