diff --git a/copilot/app/agent/agent.py b/copilot/app/agent/agent.py
index d7e8b279..25cabec4 100644
--- a/copilot/app/agent/agent.py
+++ b/copilot/app/agent/agent.py
@@ -141,7 +141,7 @@ def question_for_agent(
             for output in self.agent.stream({"question": input_data["input"], "conversation": input_data["conversation"]}):
 
                 for key, value in output.items():
-                    logger.info(f"testing steps {key}: {value}")
+                    # logger.info(f"testing steps {key}: {value}")
                     LogWriter.info(f"request_id={req_id_cv.get()} executed node {key}")
 
             LogWriter.info(f"request_id={req_id_cv.get()} EXIT question_for_agent")
diff --git a/copilot/docs/notebooks/FeedbackAnalysis.ipynb b/copilot/docs/notebooks/FeedbackAnalysis.ipynb
new file mode 100644
index 00000000..447882c8
--- /dev/null
+++ b/copilot/docs/notebooks/FeedbackAnalysis.ipynb
@@ -0,0 +1,493 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import base64\n",
+    "\n",
+    "def create_headers(username, password):\n",
+    "    \"\"\"Create headers with Base64 encoded credentials.\"\"\"\n",
+    "    credentials = f\"{username}:{password}\"\n",
+    "    encoded_credentials = base64.b64encode(credentials.encode(\"utf-8\")).decode(\"utf-8\")\n",
+    "    return {\n",
+    "        'accept': 'application/json',\n",
+    "        'Authorization': f'Basic {encoded_credentials}'\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_user_conversation_ids(username, password):\n",
+    "    \"\"\"Fetch conversation IDs for a given user.\"\"\"\n",
+    "    headers = create_headers(username, password)\n",
+    "    user_url = f'http://COPILOT_ADDRESS/ui/user/{username}'\n",
+    "    \n",
+    "    response = requests.get(user_url, headers=headers)\n",
+    "    \n",
+    "    if response.status_code == 200:\n",
+    "        data = response.json()\n",
+    "        return [item['conversation_id'] for item in data]\n",
+    "    else:\n",
+    "        print(f\"Request failed with status code {response.status_code}\")\n",
+    "        print(response.text)\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_conversation_data(username, password, conversation_id):\n",
+    "    \"\"\"Fetch conversation data for a given conversation ID.\"\"\"\n",
+    "    headers = create_headers(username, password)\n",
+    "    conversation_url = f'http://COPILOT_ADDRESS/ui/conversation/{conversation_id}'\n",
+    "    \n",
+    "    response = requests.get(conversation_url, headers=headers)\n",
+    "    \n",
+    "    if response.status_code == 200:\n",
+    "        data = response.json()\n",
+    "\n",
+    "        # Create dictionaries to hold user questions and system answers\n",
+    "        questions = {message[\"message_id\"]: message for message in data if message[\"role\"] == \"user\"}\n",
+    "        answers = {message[\"parent_id\"]: message for message in data if message[\"role\"] == \"system\"}\n",
+    "        \n",
+    "        # Return questions, answers\n",
+    "        # Organize into Q&A pairs\n",
+    "        qa_pairs = []\n",
+    "        for q_id, question in questions.items():\n",
+    "            if q_id in answers:\n",
+    "                qa_pairs.append({\n",
+    "                    \"question\": question[\"content\"],\n",
+    "                    \"answer\": answers[q_id][\"content\"],\n",
+    "                    \"feedback\": answers[q_id][\"feedback\"]\n",
+    "                })\n",
+    "        \n",
+    "        return qa_pairs\n",
+    "    else:\n",
+    "        print(f\"Request failed with status code {response.status_code}\")\n",
+    "        print(response.text)\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fetch_user_conversations(username, password, conversation_id=None):\n",
+    "    if conversation_id:\n",
+    "        conversations = {}\n",
+    "        # Fetch a specific conversation\n",
+    "        data = get_conversation_data(username, password, conversation_id)\n",
+    "        \n",
+    "        if data:\n",
+    "            conversations[conversation_id] = data\n",
+    "            return conversations\n",
+    "        else:\n",
+    "            return \"Conversation not found or could not be retrieved.\"\n",
+    "            \n",
+    "    else:\n",
+    "        # Fetch all conversations\n",
+    "        conversation_ids = get_user_conversation_ids(username, password)\n",
+    "        conversations = {}\n",
+    "        \n",
+    "        for conv_id in conversation_ids:\n",
+    "            data = get_conversation_data(username, password, conv_id)\n",
+    "            if data:\n",
+    "                conversations[conv_id] = data\n",
+    "    \n",
+    "        return conversations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conversation_data = fetch_user_conversations(\"YOUR_DB_USERNAME\", \"YOUR_DB_PASSWORD\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openai\n",
+    "from sklearn.cluster import KMeans\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "openai.api_key = 'OPENAI_API_KEY'\n",
+    "\n",
+    "questions = []\n",
+    "feedbacks = []\n",
+    "for i in conversation_data.values():\n",
+    "    for j in i:\n",
+    "        questions.append(j.get('question'))\n",
+    "        feedbacks.append(j.get('feedback'))\n",
+    "\n",
+    "def get_embeddings(questions):\n",
+    "    response = openai.embeddings.create(\n",
+    "        input=questions,\n",
+    "        model=\"text-embedding-ada-002\"\n",
+    "    )\n",
+    "    return [embedding.embedding for embedding in response.data]\n",
+    "\n",
+    "embeddings = get_embeddings(questions)\n",
+    "\n",
+    "# Determine the optimal number of clusters using the elbow method\n",
+    "def plot_elbow_method(embeddings, max_clusters=10):\n",
+    "    inertias = []\n",
+    "    for n in range(1, max_clusters + 1):\n",
+    "        kmeans = KMeans(n_clusters=n, random_state=0)\n",
+    "        kmeans.fit(embeddings)\n",
+    "        inertias.append(kmeans.inertia_)\n",
+    "    \n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    plt.plot(range(1, max_clusters + 1), inertias, marker='o')\n",
+    "    plt.xlabel('Number of clusters')\n",
+    "    plt.ylabel('Inertia/WCSS')\n",
+    "    plt.title('Elbow Method For Optimal Number of Clusters')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the elbow method graph to determine the best number of clusters\n",
+    "plot_elbow_method(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimal_cluster = 6\n",
+    "\n",
+    "kmeans = KMeans(n_clusters=optimal_cluster, random_state=0)\n",
+    "kmeans.fit(embeddings)\n",
+    "labels = kmeans.labels_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Group questions by their cluster labels\n",
+    "clustered_questions = {i: [] for i in range(optimal_cluster)}\n",
+    "for i, label in enumerate(labels):\n",
+    "    clustered_questions[label].append(questions[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_summary(questions):\n",
+    "\n",
+    "    # Call OpenAI API to generate a summary\n",
+    "    response = openai.chat.completions.create(\n",
+    "        model=\"gpt-4o-mini\",\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": \"you are an expert to summary texts into a short and concise theme\"},\n",
+    "            {\"role\": \"user\", \"content\":f\"summarize the following questions into a main theme, and only return the label of theme, questions: {questions}\"}\n",
+    "        ],\n",
+    "        max_tokens=20,\n",
+    "        temperature=0.1\n",
+    "    )\n",
+    "    \n",
+    "    # Extract and return the generated summary\n",
+    "    summary = response.choices[0].message.content.strip()\n",
+    "    return summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cluster 0:\n",
+      "  - Provide more details about this transaction 871054, like the card used and merchant involved in this transaction.\n",
+      "  - Are there any activities of the user associated with transaction 871054 that might be interesting to look into for further investigation? \n",
+      "  - What are more details of the transaction 565964?\n",
+      "Main theme: Transaction Details and Investigation\n",
+      "\n",
+      "Cluster 1:\n",
+      "  - what is the merchant involved in this transaction?\n",
+      "  - What do we know about this merchant?\n",
+      "Main theme: Merchant Information\n",
+      "\n",
+      "Cluster 2:\n",
+      "  - Find all transactions, from 2/1/21 to 5/1/21 above average amount for that card. Sort the results.\n",
+      "  - List all merchants that have more than 2500 transactions from 2021-02-01 to 2021-10-01.\n",
+      "Main theme: Transaction Analysis\n",
+      "\n",
+      "Cluster 3:\n",
+      "  - what is the card used in this transaction?\n",
+      "  - What is the ID of the card used in transaction 565964?\n",
+      "Main theme: Card Transaction Identification\n",
+      "\n",
+      "Cluster 4:\n",
+      "  - List the largest 10 transactions made by the merchant fraud_Kuhn LLC?\n",
+      "Main theme: Merchant Fraud Transactions\n",
+      "\n",
+      "Cluster 5:\n",
+      "  - How many transactions are there?\n",
+      "Main theme: Transaction Count Inquiry\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "main_themes = {}\n",
+    "\n",
+    "# Print questions for each cluster and use ChatGPT to summarize the main theme\n",
+    "for cluster, qs in clustered_questions.items():\n",
+    "    print(f\"Cluster {cluster}:\")\n",
+    "    for q in qs:\n",
+    "        print(f\"  - {q}\")\n",
+    "    # Summarize main theme using ChatGPT or another method\n",
+    "    theme_summary = generate_summary(qs)  # Define this function or use an LLM\n",
+    "    print(f\"Main theme: {theme_summary}\\n\")\n",
+    "    main_themes[cluster] = theme_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "def plot_kmeans_clusters(embeddings, feedbacks, optimal_clusters):\n",
+    "    \"\"\"\n",
+    "    Perform KMeans clustering, reduce dimensions using PCA, and plot the clusters in 2D.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - embeddings: Array-like, shape (n_samples, n_features)\n",
+    "        The input data to be clustered.\n",
+    "    - feedbacks: Array-like, shape (n_samples,)\n",
+    "        An array indicating the type of feedback for each sample (1 or 2).\n",
+    "    - optimal_clusters: int\n",
+    "        The number of clusters to form.\n",
+    "\n",
+    "    Returns:\n",
+    "    - None: Displays a 2D plot of the clustered data.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Perform KMeans clustering with the optimal number of clusters\n",
+    "    kmeans = KMeans(n_clusters=optimal_clusters, random_state=0)\n",
+    "    clusters = kmeans.fit_predict(embeddings)\n",
+    "\n",
+    "    # Perform PCA to reduce dimensions to 2 for 2D plotting\n",
+    "    pca = PCA(n_components=2)\n",
+    "    reduced_embeddings = pca.fit_transform(embeddings)\n",
+    "\n",
+    "    # Plotting the clusters in 2D\n",
+    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "\n",
+    "    # Define colors for clusters\n",
+    "    colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k']\n",
+    "\n",
+    "    # Plot each cluster with different color and annotate\n",
+    "    for cluster in range(optimal_clusters):\n",
+    "        cluster_points = reduced_embeddings[clusters == cluster]\n",
+    "        ax.scatter(cluster_points[:, 0], cluster_points[:, 1], c=colors[cluster % len(colors)], label=f'Cluster {cluster}')\n",
+    "\n",
+    "    for cluster in main_themes:\n",
+    "        cluster_center = kmeans.cluster_centers_[cluster]\n",
+    "        reduced_center = pca.transform([cluster_center])[0]\n",
+    "        ax.text(reduced_center[0], reduced_center[1], main_themes[cluster], fontsize=12, weight='bold')\n",
+    "\n",
+    "    # Highlight feedback with different markers\n",
+    "    for i, (x, y) in enumerate(reduced_embeddings):\n",
+    "        if feedbacks[i] == 1:\n",
+    "            ax.scatter(x, y, c='black', marker='^')\n",
+    "        elif feedbacks[i] == 2:\n",
+    "            ax.scatter(x, y, c='black', marker='x')\n",
+    "\n",
+    "    ax.set_xlabel('PCA Component 1')\n",
+    "    ax.set_ylabel('PCA Component 2')\n",
+    "    ax.legend()\n",
+    "    plt.show()\n",
+    "\n",
+    "    return clusters, main_themes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "clusters, main_themes = plot_kmeans_clusters(embeddings, feedbacks, optimal_cluster)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0vDE1p02bpokTJ2rWrFkKDQ3VM888o7Vr19oeKfGHP/xBq1at0po1a1S3bl0tXLhQM2fOdDhWiRIlNH78ePXq1Uvh4eHy9/fXypUrbdvffPNNBQUFqXnz5urYsaMiIyNVv359uz7eeecdde3aVS+//LJq1qypl156ye6ndm711ltvqVu3bmrdurUOHz58T9cHrisyMlITJ07UuHHj1KhRI/3888/q27dvgfUfFxen6OhoNWjQQOnp6fr8889t74XKzs7WsGHDbP9eatSoobfffjvPvpo3b67169dr/fr1Cg8PV0REhLZt26aEhATVrl3brm3fvn31yy+/qHHjxho2bJiio6M1ePDgAjsvFE2uNJ8lafHixSpfvryqVq2qLl266NChQ1q5cqXdfiVKlNDGjRt14cIFNWrUSF27dlWbNm20YMECu75atWql7OxsW8j09PRUy5Yt5eHh4fB+HgB5I1eSK3HvXOk+TK7EnbjSfJbIlUBRQ6YkU+LeudI9mEyJ/HClOS2RK4sSD/P7B+8DAPA7ERERqlevnubOnevsoQAAAMCFkSsBAABwv8iUAO4FvykOAAAAAAAAAAAAALAsiuIAAAAAAAAAAAAAAMvi8ekAAAAAAAAAAAAAAMviN8UBAAAAAAAAAAAAAJZFURwAAAAAAAAAAAAAYFkUxQEAAAAAAAAAAAAAlkVRHAAAAAAAAAAAAABgWRTFAQAAAAAAAAAAAACWRVEcAArY5MmTVa9ePdvnqKgode7c+YEeEwAAAK6PXAkAAID7RaYEgBsoigOwtKioKHl4eDgsR48edfbQnCYxMTHXa3LrkpiY6OxhAgAAFCnkSkfkSgAAgLtDpnREpgTwoBRz9gAAoLA988wzWrJkid26cuXKOWk0zte8eXOdOXPG9jk6OlqXL1+2u0alS5d2xtAAAACKNHKlPXIlAADA3SNT2iNTAnhQ+E1xAJbn7e2tkJAQu8XLy0uS9O9//1v169eXj4+PqlSpoilTpui3336z7ZuRkaFBgwapXLlyCggIUOvWrbVv3z67/uPi4hQcHKySJUtq4MCBunr1aq7jmDJliq2fIUOG6Pr167ZtGzZsUIsWLRQYGKgyZcroj3/8o44dO2a3/+nTp9WzZ0+VLl1afn5+atiwoZKTk3M91rFjx1SlShUNHz5cxhi7bcWLF7e7Fr6+vrZrdPjwYVWsWFEXLlyw2ycmJkZPPfWUJGnp0qUKDAzUmjVrVL16dfn4+CgyMlKnTp2y2+dO1xYAAMDVkCvJlQAAAPeLTEmmBOAcFMUBuK2tW7eqb9++io6O1qFDh7Ro0SItXbpUM2bMsLV58cUX9eOPP2r9+vXavXu36tevrzZt2tiC2Mcff6zJkydr5syZSklJUfny5fX22287HCshIUGpqalKTEzU8uXLtXr1ak2ZMsW2PSsrS6NGjVJKSooSEhLk6emp559/Xjk5OZKkzMxMtWrVSv/73//02Wefad++fRo3bpxt+62+/fZbtWjRQr169dKCBQvk4eGR72vSsmVLValSRR988IFt3a+//qoPP/xQAwYMsK27cuWKZsyYoWXLlikpKUkZGRnq0aPHXV1bAAAAqyBXOiJXAgAA3B0ypSMyJYACZQDAwvr162e8vLyMn5+fbenatasxxpg2bdqYmTNn2rX/4IMPTPny5Y0xxmzdutUEBASYq1ev2rWpWrWqWbRokTHGmGbNmpmXX37ZbnuTJk1M3bp17cZQunRpk5WVZVv3zjvvGH9/f5OdnZ3ruM+dO2ckmf379xtjjFm0aJEpWbKkOX/+fK7tY2NjTd26dU1SUpIJCgoyb7zxxp0ujd34OnXqZPs8e/ZsExoaavu8atUq4+/vbzIzM40xxixZssRIMjt27LC1SU1NNZJMcnKyMebO1xYAAMDVkCvvjFwJAABwe2TKOyNTAigs/KY4AMt7+umntXfvXtsyb948SdK+ffs0depU+fv725aXXnpJZ86c0ZUrV7Rv3z5lZmaqTJkydm2OHz9ue1xQamqqmjRpYne8Zs2aOYyhbt26KlGihF2bzMxM22N8jhw5op49e6pKlSoKCAhQpUqVJEknT56UJO3du1dPPvnkbd+fc/LkSbVr106TJk3S6NGj7/l6RUVF6ejRo9qxY4ekG48g6tatm/z8/GxtihUrpkaNGtk+16xZU4GBgUpNTZV052sLAADgisiVd4dcCQAA4IhMeXfIlAAKSjFnDwAACpufn5+qVavmsD4zM1NTpkxRly5dHLb5+PgoMzNT5cuXV2JiosP2wMDAAh1jx44d9dhjj2nx4sWqUKGCcnJyVLt2bdu7fHx9fe/YR7ly5VShQgUtX75cAwYMUEBAwD2N5eGHH1bHjh21ZMkSVa5cWevXr8/1GtzOna4tAACAKyJX3h1yJQAAgCMy5d0hUwIoKBTFAbit+vXrKy0tLdcQenN7enq6ihUrZvtpyN8LDQ1VcnKy+vbta1t386cWb7Vv3z798ssvtsC4Y8cO+fv7q2LFijp//rzS0tK0ePFiPfXUU5Kk//73v3b716lTR++9954ulsa5LwAAA3RJREFUXLiQ509g+vr66osvvtCzzz6ryMhIbdq0SSVLlrzjdcjNoEGD1LNnTz366KOqWrWqwsPD7bb/9ttvSklJUePGjSVJaWlpysjIUGhoqKQ7X1sAAAArIVfmjVwJAACQP2TKvJEpARQEHp8OwG1NmjRJy5Yt05QpU3Tw4EGlpqZqxYoVeu211yRJbdu2VbNmzdS5c2dt2rRJJ06c0LZt2/Tqq68qJSVFkhQdHa33339fS5Ys0eHDhxUbG6uDBw86HOv69esaOHCgDh06pHXr1ik2NlbDhw+Xp6engoKCVKZMGb377rs6evSo/vOf/2jUqFF2+/fs2VMhISHq3LmzkpKS9P3332vVqlXavn27XTs/Pz+tXbtWxYoVU4cOHZSZmXlP1yYyMlIBAQGaPn26+vfv77D9oYce0ogRI5ScnKzdu3crKipKTZs2tQXPO11bAAAAKyFX5o1cCQAAkD9kyryRKQEUBIriANxWZGSkvvjiC23atEmNGjVS06ZN9dZbb+mxxx6TJHl4eGjdunVq2bKl+vfvrxo1aqhHjx764YcfFBwcLEnq3r27Jk6cqHHjxqlBgwb64YcfNHToUIdjtWnTRtWrV1fLli3VvXt3/elPf9LkyZMlSZ6enlqxYoV2796t2rVra+TIkfrb3/5mt3/x4sW1adMmPfzww3r22WcVFhamuLg4eXl5ORzL399f69evlzFGzz33nLKysu762nh6eioqKkrZ2dl2P1l6U4kSJTR+/Hj16tVL4eHh8vf318qVK/N9bQEAAKyEXJk3ciUAAED+kCnzRqYEUBA8jDHG2YMAABQ9AwcO1Llz5/TZZ5/ZrV+6dKliYmKUkZHhnIEBAADApZArAQAAcL/IlADuF+8UBwDYuXTpkvbv36+PPvrIIWQCAAAA+UWuBAAAwP0iUwIoKBTFAQB2OnXqpJ07d2rIkCFq166ds4cDAAAAF0WuBAAAwP0iUwIoKDw+HQAAAAAAAAAAAABgWZ7OHgAAAAAAAAAAAAAAAIWFojgAAAAAAAAAAAAAwLIoigMAAAAAAAAAAAAALIuiOAAAAAAAAAAAAADAsiiKAwAAAAAAAAAAAAAsi6I4AAAAAAAAAAAAAMCyKIoDAAAAAAAAAAAAACyLojgAAAAAAAAAAAAAwLIoigMAAAAAAAAAAAAALOv/AE+8T2obgjZeAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 2000x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "\n",
+    "# Group questions by cluster\n",
+    "clustered_questions = defaultdict(list)\n",
+    "for i, cluster in enumerate(clusters):\n",
+    "    clustered_questions[cluster].append({'question': questions[i], 'feedback': feedbacks[i]})\n",
+    "\n",
+    "# Create a conversation_data variable with clustered questions and feedback\n",
+    "conversation_data = []\n",
+    "for cluster, items in clustered_questions.items():\n",
+    "    for item in items:\n",
+    "        conversation_data.append({\"question\": item['question'], \"feedback\": item['feedback']})\n",
+    "\n",
+    "# Plot feedback distribution in each cluster\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(20, 10)) # Adjust the nrow and ncols based on the number of clusters\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "for cluster, items in clustered_questions.items():\n",
+    "    feedback_counts = defaultdict(int)\n",
+    "    for item in items:\n",
+    "        feedback_counts[item['feedback']] += 1\n",
+    "    \n",
+    "    feedback_types = ['No Feedback', 'Thumbs Up', 'Thumbs Down']\n",
+    "    counts = [feedback_counts[0], feedback_counts[1], feedback_counts[2]]\n",
+    "    \n",
+    "    axes[cluster].bar(feedback_types, counts, color=['gray', 'green', 'red'])\n",
+    "    axes[cluster].set_title(f'Cluster {cluster}: {main_themes.get(cluster, \"Unknown\")}')\n",
+    "    axes[cluster].set_xlabel('Feedback Type')\n",
+    "    axes[cluster].set_ylabel('Count')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Questions in Cluster 3 grouped by feedback type:\n",
+      "Total number of questions: 2\n",
+      "\n",
+      "Feedback Type: Thumbs Down (Total: 2)\n",
+      "  - what is the card used in this transaction?\n",
+      "  - What is the ID of the card used in transaction 565964?\n"
+     ]
+    }
+   ],
+   "source": [
+    "# After plotting feedback distribution, group and print all questions by feedback type in cluster 1\n",
+    "cluster_to_view = 3  # Change this to the cluster number you want to view\n",
+    "\n",
+    "# Initialize a dictionary to store questions grouped by feedback type\n",
+    "grouped_by_feedback = defaultdict(list)\n",
+    "\n",
+    "# Group questions by feedback type\n",
+    "for item in clustered_questions[cluster_to_view]:\n",
+    "    question = item['question']\n",
+    "    feedback = item['feedback']\n",
+    "    feedback_str = \"No Feedback\" if feedback == 0 else \"Thumbs Up\" if feedback == 1 else \"Thumbs Down\"\n",
+    "    grouped_by_feedback[feedback_str].append(question)\n",
+    "\n",
+    "# Print questions grouped by feedback type\n",
+    "print(f\"Questions in Cluster {cluster_to_view} grouped by feedback type:\")\n",
+    "\n",
+    "# Total number of questions in the cluster\n",
+    "total_questions = sum(len(questions) for questions in grouped_by_feedback.values())\n",
+    "print(f\"Total number of questions: {total_questions}\")\n",
+    "\n",
+    "for feedback_type, questions in grouped_by_feedback.items():\n",
+    "    print(f\"\\nFeedback Type: {feedback_type} (Total: {len(questions)})\")\n",
+    "    for question in questions:\n",
+    "        print(f\"  - {question}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/copilot/docs/notebooks/TransactionFraud_demo.ipynb b/copilot/docs/notebooks/TransactionFraud_demo.ipynb
index 39153aed..a05db1aa 100644
--- a/copilot/docs/notebooks/TransactionFraud_demo.ipynb
+++ b/copilot/docs/notebooks/TransactionFraud_demo.ipynb
@@ -16,15 +16,15 @@
     "from pyTigerGraph import TigerGraphConnection\n",
     "\n",
     "conn = TigerGraphConnection(\n",
-    "    host=\"https://tg-26bfd0cd-6582-414e-937e-e2c83ecb5a79.us-east-1.i.tgcloud.io\", \n",
+    "    host=\"https://YOUR_DB_ADDRESS\", \n",
     "    graphname=\"Transaction_Fraud\", \n",
-    "    username=\"supportai\", \n",
-    "    password=\"supportai\"\n",
+    "    username=\"YOUR_DB_USERNAME\", \n",
+    "    password=\"YOUR_DB_PASSWORD\"\n",
     ")\n",
     "\n",
     "conn.getToken()\n",
     "\n",
-    "conn.ai.configureCoPilotHost(hostname=\"http://0.0.0.0:8000\")"
+    "conn.ai.configureCoPilotHost(hostname=\"http://COPILOT_ADDRESS\")"
    ]
   },
   {
@@ -34,18 +34,18 @@
     "List of questions:\n",
     "1. Find all transactions, from 2/1/21 to 5/1/21, above average amount for that card. Sort the results.\n",
     "\n",
-    "2. Provide more details about the transaction 871054, like the card used and merchant involved in this transaction.\n",
+    "2. Provide more details about the transaction 871054.\n",
     "\n",
-    "3. What is the average transaction amount for the card used in this transaction over the past 6 months?\n",
+    "3. What is the average transaction amount for the card 4039101933538921 used in this transaction over the past 6 months?\n",
     "\n",
-    "4. What do we know about the merchant involved in the transaction 871054?\n",
+    "4. What do we know about the merchant fraud_Kuhn LLC involved in the transaction 871054?\n",
     "\n",
     "5. Are there any activities of the user associated with the transaction 871054 that might be interesting to look into for further investigation? "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -53,31 +53,45 @@
      "output_type": "stream",
      "text": [
       "Run 1:\n",
-      "I'm sorry, I don't know the answer to that question. Please try rephrasing your question.\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[3], line 27\u001b[0m\n\u001b[1;32m     22\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m     23\u001b[0m payload \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     24\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquery\u001b[39m\u001b[38;5;124m\"\u001b[39m: question\n\u001b[1;32m     25\u001b[0m }\n\u001b[0;32m---> 27\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mrequests\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpost\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhttp://0.0.0.0:8000/Transaction_Fraud/query_with_history\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     28\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpayload\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     29\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mHTTPBasicAuth\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43musername\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpassword\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     30\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28mprint\u001b[39m (json\u001b[38;5;241m.\u001b[39mloads(res\u001b[38;5;241m.\u001b[39mtext)\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnatural_language_response\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno answer\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m     32\u001b[0m end_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n",
-      "File \u001b[0;32m~/Documents/github/CoPilot/venv/lib/python3.11/site-packages/requests/api.py:115\u001b[0m, in \u001b[0;36mpost\u001b[0;34m(url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(url, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, json\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    104\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a POST request.\u001b[39;00m\n\u001b[1;32m    105\u001b[0m \n\u001b[1;32m    106\u001b[0m \u001b[38;5;124;03m    :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;124;03m    :rtype: requests.Response\u001b[39;00m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 115\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpost\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/github/CoPilot/venv/lib/python3.11/site-packages/requests/api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[0;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/github/CoPilot/venv/lib/python3.11/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
-      "File \u001b[0;32m~/Documents/github/CoPilot/venv/lib/python3.11/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
-      "File \u001b[0;32m~/Documents/github/CoPilot/venv/lib/python3.11/site-packages/requests/adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    483\u001b[0m     timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m    485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 486\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    492\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    495\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    501\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n",
-      "File \u001b[0;32m~/Documents/github/CoPilot/venv/lib/python3.11/site-packages/urllib3/connectionpool.py:715\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    712\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_proxy(conn)\n\u001b[1;32m    714\u001b[0m \u001b[38;5;66;03m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[0;32m--> 715\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    716\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    717\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    718\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    719\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    720\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    721\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    722\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    723\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    725\u001b[0m \u001b[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[1;32m    726\u001b[0m \u001b[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[1;32m    727\u001b[0m \u001b[38;5;66;03m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[1;32m    728\u001b[0m \u001b[38;5;66;03m# mess.\u001b[39;00m\n\u001b[1;32m    729\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/Documents/github/CoPilot/venv/lib/python3.11/site-packages/urllib3/connectionpool.py:467\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    462\u001b[0m             httplib_response \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[1;32m    463\u001b[0m         \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    464\u001b[0m             \u001b[38;5;66;03m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[1;32m    465\u001b[0m             \u001b[38;5;66;03m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[1;32m    466\u001b[0m             \u001b[38;5;66;03m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[0;32m--> 467\u001b[0m             \u001b[43msix\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_from\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    468\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError, SocketError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    469\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n",
-      "File \u001b[0;32m<string>:3\u001b[0m, in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
-      "File \u001b[0;32m~/Documents/github/CoPilot/venv/lib/python3.11/site-packages/urllib3/connectionpool.py:462\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    459\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m    460\u001b[0m     \u001b[38;5;66;03m# Python 3\u001b[39;00m\n\u001b[1;32m    461\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 462\u001b[0m         httplib_response \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    463\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    464\u001b[0m         \u001b[38;5;66;03m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[1;32m    465\u001b[0m         \u001b[38;5;66;03m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[1;32m    466\u001b[0m         \u001b[38;5;66;03m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[1;32m    467\u001b[0m         six\u001b[38;5;241m.\u001b[39mraise_from(e, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
-      "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.9/Frameworks/Python.framework/Versions/3.11/lib/python3.11/http/client.py:1395\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1393\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1394\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1395\u001b[0m         \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1396\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[1;32m   1397\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
-      "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.9/Frameworks/Python.framework/Versions/3.11/lib/python3.11/http/client.py:325\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    323\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m    324\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 325\u001b[0m     version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    326\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[1;32m    327\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.9/Frameworks/Python.framework/Versions/3.11/lib/python3.11/http/client.py:286\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    285\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 286\u001b[0m     line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadline\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_MAXLINE\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    287\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[1;32m    288\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.9/Frameworks/Python.framework/Versions/3.11/lib/python3.11/socket.py:706\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    704\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    705\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 706\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    707\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m    708\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+      "The transactions from 2/1/21 to 5/1/21 above average amount for that card are as follows: Transaction with id 39843 on 2021-04-17 17:36:29 with amount 7855.36, Transaction with id 871054 on 2021-02-02 00:00:00 with amount 5321.52, Transaction with id 40831 on 2021-04-24 23:45:10 with amount 2934.13, Transaction with id 34336 on 2021-04-24 12:24:53 with amount 4935.42, Transaction with id 473824 on 2021-04-18 14:29:51 with amount 3713.09, Transaction with id 7815 on 2021-04-17 16:05:56 with amount 6044.24, Transaction with id 769 on 2021-04-17 23:31:10 with amount 2802.55.\n",
+      "Transaction 871054 is a payment transaction that occurred at 2021-02-02 00:00:00. The amount of the transaction was 5321.52. It is marked as a fraudulent transaction. There are no repeated cards, merchant transactions, or common code transactions associated with this transaction. The transaction does not have any merchant category, occupation, or gender associated with it. The age and city population related to this transaction are also not available.\n",
+      "The average transaction amount for the card number 4039101933538921 over the past 6 months is 82.47301270417434.\n",
+      "The merchant involved in the transaction 871054 is named fraud_Kuhn LLC. This merchant falls under the 'home' category. The merchant's Pagerank Score is 1.235146 and it belongs to the community with the ID 13631497. However, the size of this community is 0.\n",
+      "Yes, there are two activities associated with the transaction 871054 that might be interesting for further investigation. The first activity is a 'Buyer address update', indicating that the buyer has recently moved to another state. The second activity is a 'Card average amount anomaly', suggesting that the transaction amount is significantly larger than the average amount typically spent using the card.\n",
+      "\n",
+      "Run 2:\n",
+      "The transactions from 2/1/21 to 5/1/21 that were above the average amount for that card are as follows: Transaction ID 39843 on 2021-04-17 with an amount of 7855.36, Transaction ID 40831 on 2021-04-24 with an amount of 2934.13, Transaction ID 871054 on 2021-02-02 with an amount of 5321.52, Transaction ID 473824 on 2021-04-18 with an amount of 3713.09, Transaction ID 34336 on 2021-04-24 with an amount of 4935.42, Transaction ID 7815 on 2021-04-17 with an amount of 6044.24, and Transaction ID 769 on 2021-04-17 with an amount of 2802.55.\n",
+      "Transaction 871054 is a payment transaction that occurred at the time 2021-02-02 00:00:00. The amount involved in this transaction is 5321.52. This transaction has been flagged as fraudulent. There are no repeated cards, common merchant transactions, or common code transactions associated with this transaction. The merchant category, occupation, and gender associated with this transaction are not specified. The age and city population are also not provided.\n",
+      "The average transaction amount for the card 4039101933538921 over the past 6 months is 82.47301270417434.\n",
+      "The merchant involved in the transaction 871054 is named fraud_Kuhn LLC. This merchant falls under the 'home' category. It has a Pagerank Score of 1.235146 and is part of the community with the ID 13631497. However, the size of this community is 0.\n",
+      "Yes, there are two activities associated with the transaction 871054 that might be interesting for further investigation. The first activity is a 'Buyer address update' indicating that the buyer has recently moved to another state. The second activity is a 'Card average amount anomaly' which suggests that the transaction amount is significantly larger than the average amount typically spent using the card.\n",
+      "\n",
+      "Run 3:\n",
+      "The transactions from 2/1/21 to 5/1/21 above the average amount for that card are as follows: Transaction ID 769 on 2021-04-17 with an amount of 2802.55, Transaction ID 473824 on 2021-04-18 with an amount of 3713.09, Transaction ID 871054 on 2021-02-02 with an amount of 5321.52, Transaction ID 39843 on 2021-04-17 with an amount of 7855.36, Transaction ID 7815 on 2021-04-17 with an amount of 6044.24, Transaction ID 40831 on 2021-04-24 with an amount of 2934.13, and Transaction ID 34336 on 2021-04-24 with an amount of 4935.42.\n",
+      "Transaction 871054 is a payment transaction that occurred at 2021-02-02 00:00:00. The amount involved in the transaction is 5321.52. The transaction is marked as fraudulent. There are no repeated cards, common merchant transactions, common card transactions, or merchant categories involved in this transaction. The transaction does not have any in-degree or out-degree. The age, city population, occupation, and gender related to this transaction are not specified.\n",
+      "The average transaction amount for the card 4039101933538921 over the past 6 months is 82.47301270417434.\n",
+      "The merchant involved in the transaction 871054 is named fraud_Kuhn LLC. This merchant falls under the 'home' category. The merchant has a Pagerank Score of 1.235146. The Community ID associated with this merchant is 13631497, however, the community size is 0.\n",
+      "Yes, there are two activities associated with the transaction 871054 that might be interesting for further investigation. The first activity is a 'Buyer address update' indicating that the buyer has recently moved to another state. The second activity is a 'Card average amount anomaly' which suggests that the transaction amount is significantly larger than the average amount typically spent using the card.\n",
+      "\n",
+      "Run 4:\n",
+      "The transactions from 2/1/21 to 5/1/21 that were above the average amount for that card are as follows: Transaction ID 40831 on 2021-04-24 with an amount of 2934.13, Transaction ID 871054 on 2021-02-02 with an amount of 5321.52, Transaction ID 39843 on 2021-04-17 with an amount of 7855.36, Transaction ID 769 on 2021-04-17 with an amount of 2802.55, Transaction ID 473824 on 2021-04-18 with an amount of 3713.09, Transaction ID 34336 on 2021-04-24 with an amount of 4935.42, and Transaction ID 7815 on 2021-04-17 with an amount of 6044.24.\n",
+      "Transaction 871054 is a payment transaction that occurred at 2021-02-02 00:00:00. The amount involved in the transaction is 5321.52. The transaction is marked as fraudulent. There are no repeated cards, common merchant transactions, common card transactions, or merchant categories involved in this transaction. The transaction does not have any in-degree or out-degree. The age, city population, occupation, and gender related to this transaction are not specified.\n",
+      "The average transaction amount for the card 4039101933538921 over the past 6 months is 82.47301270417434.\n",
+      "The merchant involved in the transaction 871054 is named fraud_Kuhn LLC. This merchant falls under the 'home' category. It has a Pagerank Score of 1.235146 and is part of the community with the ID 13631497. However, the size of this community is 0.\n",
+      "Yes, there are two activities associated with the transaction 871054 that might be interesting for further investigation. The first activity is a 'Buyer address update', indicating that the buyer has recently moved to another state. The second activity is a 'Card average amount anomaly', suggesting that the transaction amount is significantly larger than the average amount typically spent using the card.\n",
+      "\n",
+      "Run 5:\n",
+      "The transactions from 2/1/21 to 5/1/21 above average amount for that card are as follows: Transaction with id 39843 on 2021-04-17 17:36:29 with amount 7855.36, Transaction with id 40831 on 2021-04-24 23:45:10 with amount 2934.13, Transaction with id 473824 on 2021-04-18 14:29:51 with amount 3713.09, Transaction with id 871054 on 2021-02-02 00:00:00 with amount 5321.52, Transaction with id 769 on 2021-04-17 23:31:10 with amount 2802.55, Transaction with id 7815 on 2021-04-17 16:05:56 with amount 6044.24, Transaction with id 34336 on 2021-04-24 12:24:53 with amount 4935.42.\n",
+      "Transaction 871054 is a payment transaction that occurred at 00:00:00 on 2021-02-02. The amount of the transaction was 5321.52. This transaction has been flagged as fraudulent. There are no repeated cards, common merchant transactions, or common credit card transactions associated with this transaction. The transaction does not have any merchant category information, age, city population, occupation, or gender associated with it.\n",
+      "The average transaction amount for the card 4039101933538921 over the past 6 months is 82.47301270417434.\n",
+      "The merchant involved in the transaction 871054 is named fraud_Kuhn LLC. This merchant falls under the 'home' category. The merchant has a Pagerank Score of 1.235146 and is part of a community with the ID 13631497. However, the size of this community is 0.\n",
+      "Yes, there are two activities associated with the transaction 871054 that might be interesting for further investigation. The first activity is a 'Buyer address update' indicating that the buyer has recently moved to another state. The second activity is a 'Card average amount anomaly' which suggests that the transaction amount is significantly larger than the average amount typically spent using the card.\n",
+      "\n",
+      "Average execution time for question 'Find all transactions, from 2/1/21 to 5/1/21 above average amount for that card. Sort the results.' over 5 runs: 37.3324 seconds\n",
+      "Average execution time for question 'Provide more details about the transaction 871054.' over 5 runs: 43.4484 seconds\n",
+      "Average execution time for question 'What is the average transaction amount for the card used 4039101933538921 in this transaction over the past 6 months?' over 5 runs: 20.6316 seconds\n",
+      "Average execution time for question 'Provide more details about the merchant fraud_Kuhn LLC involved in the transaction 871054' over 5 runs: 24.8842 seconds\n",
+      "Average execution time for question 'Are there any activities of the user associated with the transaction 871054 that might be interesting to look into for further investigation?' over 5 runs: 35.2934 seconds\n"
      ]
     }
    ],
@@ -91,8 +105,8 @@
     "\n",
     "questions = [\n",
     "    \"Find all transactions, from 2/1/21 to 5/1/21 above average amount for that card. Sort the results.\",\n",
-    "    \"Provide more details about the transaction 871054, like the card used and merchant involved in this transaction.\",\n",
-    "    \"What is the average transaction amount for the card used in this transaction over the past 6 months?\",\n",
+    "    \"Provide more details about the transaction 871054.\",\n",
+    "    \"What is the average transaction amount for the card used 4039101933538921 in this transaction over the past 6 months?\",\n",
     "    \"Provide more details about the merchant fraud_Kuhn LLC involved in the transaction 871054\",\n",
     "    \"Are there any activities of the user associated with the transaction 871054 that might be interesting to look into for further investigation?\"\n",
     "]\n",
@@ -108,7 +122,7 @@
     "            \"query\": question\n",
     "        }\n",
     "\n",
-    "        res = requests.post(\"http://0.0.0.0:8000/Transaction_Fraud/query_with_history\", \n",
+    "        res = requests.post(\"http://COPILOT_ADDRESS/Transaction_Fraud/query_with_history\", \n",
     "                            json=payload, \n",
     "                            auth=HTTPBasicAuth(conn.username, conn.password)\n",
     "        )\n",
diff --git a/copilot/requirements.txt b/copilot/requirements.txt
index 1c333a4d..7689b978 100644
--- a/copilot/requirements.txt
+++ b/copilot/requirements.txt
@@ -71,6 +71,7 @@ langgraph==0.0.40
 langsmith==0.1.24
 lxml==4.9.3
 marshmallow==3.20.1
+matplotlib==3.9.1
 minio==7.2.5
 multidict==6.0.4
 mypy-extensions==1.0.0
@@ -108,6 +109,7 @@ regex==2023.10.3
 requests==2.31.0
 rsa==4.9
 s3transfer==0.7.0
+scikit-learn==1.5.1
 sentry-sdk==1.32.0
 setproctitle==1.3.3
 shapely==2.0.2