diff --git "a/task3_model_building/Research/model_Research_docsummaryindex.ipynb" "b/task3_model_building/Research/model_Research_docsummaryindex.ipynb"
new file mode 100644--- /dev/null
+++ "b/task3_model_building/Research/model_Research_docsummaryindex.ipynb"
@@ -0,0 +1,731 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "dfbc8159-1f08-4664-8b3f-e37b030dbcfc.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "bad29816-c1ce-4c8c-845a-5f6abcb2df3f",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:dfbc8159-1f08-4664-8b3f-e37b030dbcfc.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86c50a6e-8fb7-466c-b5ab-1337caf066f5",
+   "metadata": {},
+   "source": [
+    "## Limitations of Existing Approaches\n",
+    "There are a few limitations of embedding retrieval using text chunks.\n",
+    "\n",
+    "Text chunks lack global context. Oftentimes the question requires context beyond what is indexed in a specific chunk.\n",
+    "Careful tuning of top-k / similarity score thresholds. Make the value too small and you’ll miss context. Make the value too big and cost/latency might increase with more irrelevant context.\n",
+    "Embeddings don’t always select the most relevant context for a question. Embeddings are inherently determined separately between text and the context.\n",
+    "Adding keyword filters are one way to enhance the retrieval results. But that comes with its own set of challenges. We would need to adequately determine the proper keywords for each document, either manually or through an NLP keyword extraction/topic tagging model. Also we would need to adequately infer the proper keywords from the query."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a7bca69-325a-4dad-bb07-66bbeddc8ed5",
+   "metadata": {},
+   "source": [
+    "## How It Works(Document Summary Index)\n",
+    "During build-time, we ingest each document, and use a LLM to extract a summary from each document. We also split the document up into text chunks (nodes). Both the summary and the nodes are stored within our Document Store abstraction. We maintain a mapping from the summary to the source document/nodes.\n",
+    "\n",
+    "During query-time, we retrieve relevant documents to the query based on their summaries, using the following approaches:\n",
+    "\n",
+    "LLM-based Retrieval: We present sets of document summaries to the LLM, and ask the LLM to determine which documents are relevant + their relevance score.\n",
+    "Embedding-based Retrieval: We retrieve relevant documents based on summary embedding similarity (with a top-k cutoff).\n",
+    "Note that this approach of retrieval for document summaries (even with the embedding-based approach) is different than embedding-based retrieval over text chunks. The retrieval classes for the document summary index retrieve all nodes for any selected document, instead of returning relevant chunks at the node-level.\n",
+    "\n",
+    "Storing summaries for a document also enables LLM-based retrieval. Instead of feeding the entire document to the LLM in the beginning, we can first have the LLM inspect the concise document summary to see if it’s relevant to the query at all. This leverages the reasoning capabilities of LLM’s which are more advanced than embedding-based lookup, but avoids the cost/latency of feeding the entire document to the LLM\n",
+    "\n",
+    "Additional Insights\n",
+    "Document retrieval with summaries can be thought of as a “middle ground” between semantic search and brute-force summarization across all docs. We look up documents based on summary relevance with the given query, and then return all *nodes* corresponding to the retrieved docs.\n",
+    "\n",
+    "Why should we do this? This retrieval method gives user more context than top-k over a text-chunk, by retrieving context at a document-level. But, it’s also a more flexible/automatic approach than topic modeling; no more worrying about whether your text has the right keyword tags!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13662caa-7ed4-498b-9820-e9719524aae1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4275600d-586e-4001-a458-d7f3cba3226e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install llama-index-llms-huggingface-api\n",
+    "!pip install llama-index-embeddings-huggingface\n",
+    "!pip install llama-index-llms-llama-cpp\n",
+    "!pip install llama-index\n",
+    "!pip install huggingface_hub\n",
+    "!pip install transformers\n",
+    "!pip install torch\n",
+    "!pip install gradio\n",
+    "!pip install llama-index-llms-huggingface\n",
+    "! pip install llama-index-llms-groq\n",
+    "!pip install llama-index-llms-gemini"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e489b70d-a252-481b-884b-318e8fc38186",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/pydantic/_internal/_fields.py:161: UserWarning: Field \"model_id\" has conflict with protected namespace \"model_\".\n",
+      "\n",
+      "You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n",
+    "from llama_index.core.tools import QueryEngineTool, ToolMetadata\n",
+    "from llama_index.core.callbacks import CallbackManager, LlamaDebugHandler\n",
+    "from llama_index.core import ServiceContext, StorageContext\n",
+    "from llama_index.llms.huggingface import HuggingFaceLLM\n",
+    "from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n",
+    "from llama_index.core import Settings\n",
+    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
+    "from llama_index.core.retrievers import VectorIndexRetriever\n",
+    "from llama_index.core.query_engine import RetrieverQueryEngine\n",
+    "from llama_index.core.postprocessor import SimilarityPostprocessor\n",
+    "from llama_index.core import SimpleDirectoryReader, load_index_from_storage\n",
+    "import os\n",
+    "import nest_asyncio\n",
+    "import os\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "from llama_index.core import SimpleDirectoryReader, get_response_synthesizer\n",
+    "from llama_index.core import DocumentSummaryIndex\n",
+    "from llama_index.core.node_parser import SentenceSplitter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b5dc3276-7f37-4284-9c83-3f699fd10b29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nest_asyncio\n",
+    "\n",
+    "nest_asyncio.apply()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "77b089e0-1418-40aa-a060-a123f9c1439b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import sys\n",
+    "\n",
+    "logging.basicConfig(stream=sys.stdout, level=logging.WARNING)\n",
+    "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+    "\n",
+    "# # Uncomment if you want to temporarily disable logger\n",
+    "#logger = logging.getLogger()\n",
+    "#logger.disabled = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d65a45d9-cd4e-44e5-b5c8-26aa42bdd54f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#if you wann run on your local compute \n",
+    "\n",
+    "from llama_index.llms.llama_cpp import LlamaCPP\n",
+    "\n",
+    "def messages_to_prompt(messages):\n",
+    "    prompt = \"\"\n",
+    "    for message in messages:\n",
+    "        if message.role == 'system':\n",
+    "            prompt += f\"<|system|>\\n{message.content}</s>\\n\"\n",
+    "        elif message.role == 'user':\n",
+    "            prompt += f\"<|user|>\\n{message.content}</s>\\n\"\n",
+    "        elif message.role == 'assistant':\n",
+    "            prompt += f\"<|assistant|>\\n{message.content}</s>\\n\"\n",
+    "\n",
+    "    # ensure we start with a system prompt, insert blank if needed\n",
+    "    if not prompt.startswith(\"<|system|>\\n\"):\n",
+    "        prompt = \"<|system|>\\n</s>\\n\" + prompt\n",
+    "\n",
+    "    # add final assistant prompt\n",
+    "    prompt = prompt + \"<|assistant|>\\n\"\n",
+    "\n",
+    "    return prompt\n",
+    "\n",
+    "def completion_to_prompt(completion):\n",
+    "    return f\"<|system|>\\n</s>\\n<|user|>\\n{completion}</s>\\n<|assistant|>\\n\"\n",
+    "\n",
+    "model_url = \"https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF/resolve/main/zephyr-7b-beta.Q4_0.gguf\"\n",
+    "\n",
+    "llm = LlamaCPP(\n",
+    "    model_url=model_url,\n",
+    "    model_path=None,\n",
+    "    temperature=0.1,\n",
+    "    max_new_tokens=2000,\n",
+    "    context_window= 32769,\n",
+    "    generate_kwargs={},\n",
+    "    messages_to_prompt=messages_to_prompt,\n",
+    "    completion_to_prompt=completion_to_prompt,\n",
+    "    verbose=True,\n",
+    ")\n",
+    "\n",
+    "response = llm.complete(\"Hello, how are you?\")\n",
+    "print(str(response))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cfb1f727-f3fd-4142-ba8a-5790a69f9214",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##If you wish for groq =API\n",
+    "#from llama_index.llms.groq import Groq\n",
+    "\n",
+    "#Settings.llm = Groq(model=\"llama-3.1-70b-versatile\", api_key=\"gsk_..........\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21caa939-247a-4272-af3b-221b16acc33f",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (2752512220.py, line 5)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[5], line 5\u001b[0;36m\u001b[0m\n\u001b[0;31m    --------------------------------------------------------------------------\u001b[0m\n\u001b[0m                                                                              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "#--------------------------------------------------------------------------\n",
+    "#If you wish for huggingfaceAPI\n",
+    "#HF_TOKEN = \"hf_..............\" \n",
+    "#login(token=HF_TOKEN)\n",
+    "#os.environ['HuggingFace_API_TOKEN'] = HF_TOKEN\n",
+    "\n",
+    "# Settings.llm = HuggingFaceInferenceAPI(model_name = \"meta-llama/Meta-Llama-3-8B-Instruct\", token=HF_TOKEN)\n",
+    "\n",
+    "#-----------------------------------\n",
+    "#from llama_index.llms.gemini import Gemini\n",
+    "\n",
+    "#Settings.llm = Gemini(model=\"models/gemini-1.5-flash\", api_key=\"AI..............\")\n",
+    "#resp = llm.complete(\"Write a poem about a magic backpack\")\n",
+    "#print(resp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a5019381-df68-400b-b8bc-c3f37f725d0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#from llama_index.llms.gemini import Gemini\n",
+    "\n",
+    "#Settings.llm = Gemini(model=\"models/gemini-1.5-flash\", api_key=\"AI....................\")\n",
+    "#resp = llm.complete(\"Write a poem about a magic backpack\")\n",
+    "#print(resp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2dfd52cd-efed-437a-8ea0-a0b59f4f1cad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embed_model = HuggingFaceEmbedding(model_name=\"thenlper/gte-large\")\n",
+    "Settings.embed_model = embed_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d01a4b9-c203-498f-9c4e-cc79edc42ac3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# LLM (gpt-3.5-turbo)\n",
+    "llm = Settings.llm\n",
+    "splitter = SentenceSplitter(chunk_size=4000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3feb274f-bdd5-4bad-9960-3a8679e3366f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DO NOT RUN to use index\n",
+    "#Define the directory containing the articles\n",
+    "reader = SimpleDirectoryReader(input_dir=\"./bill24\")\n",
+    "# Load documents with parallel processing\n",
+    "documents = reader.load_data(num_workers=4)\n",
+    "print(f\"Loaded {len(documents)} documents.\")\n",
+    "\n",
+    "# default mode of building the index\n",
+    "response_synthesizer = get_response_synthesizer(\n",
+    "    response_mode=\"tree_summarize\",use_async=True\n",
+    ")\n",
+    "doc_summary_index = DocumentSummaryIndex.from_documents(\n",
+    "    documents,\n",
+    "    llm=llm,\n",
+    "    transformations=[splitter],\n",
+    "    response_synthesizer=response_synthesizer,\n",
+    "    show_progress=True,\n",
+    ")\n",
+    "\n",
+    "doc_summary_index.storage_context.persist(\"index_bill24\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d8cd673f-784c-4720-9ad4-1c968d7407b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#from llama_index.core import load_index_from_storage\n",
+    "#from llama_index.core import StorageContext\n",
+    "# rebuild storage context\n",
+    "storage_context1 = StorageContext.from_defaults(persist_dir=\"./index_bill1\")\n",
+    "index_bill1 = load_index_from_storage(storage_context1)\n",
+    "\n",
+    "storage_context2 = StorageContext.from_defaults(persist_dir=\"./index_bill2\")\n",
+    "index_bill2 = load_index_from_storage(storage_context2)\n",
+    "\n",
+    "storage_context3 = StorageContext.from_defaults(persist_dir=\"./index_bill3\")\n",
+    "index_bill3 = load_index_from_storage(storage_context3)\n",
+    "\n",
+    "storage_context4 = StorageContext.from_defaults(persist_dir=\"./index_bill4\")\n",
+    "index_bill4 = load_index_from_storage(storage_context4)\n",
+    "\n",
+    "storage_context5 = StorageContext.from_defaults(persist_dir=\"./index_bill5\")\n",
+    "index_bill5 = load_index_from_storage(storage_context5)\n",
+    "\n",
+    "storage_context6 = StorageContext.from_defaults(persist_dir=\"./index_bill6\")\n",
+    "index_bill6 = load_index_from_storage(storage_context6)\n",
+    "\n",
+    "storage_context7 = StorageContext.from_defaults(persist_dir=\"./index_bill7\")\n",
+    "index_bill7 = load_index_from_storage(storage_context7)\n",
+    "\n",
+    "storage_context8 = StorageContext.from_defaults(persist_dir=\"./index_bill8\")\n",
+    "index_bill8 = load_index_from_storage(storage_context8)\n",
+    "\n",
+    "storage_context9 = StorageContext.from_defaults(persist_dir=\"./index_bill9\")\n",
+    "index_bill9 = load_index_from_storage(storage_context9)\n",
+    "\n",
+    "storage_context10 = StorageContext.from_defaults(persist_dir=\"./index_bill10\")\n",
+    "index_bill10 = load_index_from_storage(storage_context10)\n",
+    "\n",
+    "storage_context11 = StorageContext.from_defaults(persist_dir=\"./index_bill11\")\n",
+    "index_bill11 = load_index_from_storage(storage_context11)\n",
+    "\n",
+    "storage_context12 = StorageContext.from_defaults(persist_dir=\"./index_bill12\")\n",
+    "index_bill12 = load_index_from_storage(storage_context12)\n",
+    "\n",
+    "storage_context13 = StorageContext.from_defaults(persist_dir=\"./index_bill13\")\n",
+    "index_bill13 = load_index_from_storage(storage_context13)\n",
+    "\n",
+    "storage_context14 = StorageContext.from_defaults(persist_dir=\"./index_bill14\")\n",
+    "index_bill14 = load_index_from_storage(storage_context14)\n",
+    "\n",
+    "storage_context15 = StorageContext.from_defaults(persist_dir=\"./index_bill15\")\n",
+    "index_bill15 = load_index_from_storage(storage_context15)\n",
+    "\n",
+    "storage_context16 = StorageContext.from_defaults(persist_dir=\"./index_bill16\")\n",
+    "index_bill16 = load_index_from_storage(storage_context16)\n",
+    "\n",
+    "storage_context17 = StorageContext.from_defaults(persist_dir=\"./index_bill17\")\n",
+    "index_bill17 = load_index_from_storage(storage_context17)\n",
+    "\n",
+    "storage_context18 = StorageContext.from_defaults(persist_dir=\"./index_bill18\")\n",
+    "index_bill18 = load_index_from_storage(storage_context18)\n",
+    "\n",
+    "storage_context19 = StorageContext.from_defaults(persist_dir=\"./index_bill19\")\n",
+    "index_bill19 = load_index_from_storage(storage_context19)\n",
+    "\n",
+    "storage_context20 = StorageContext.from_defaults(persist_dir=\"./index_bill20\")\n",
+    "index_bill20 = load_index_from_storage(storage_context20)\n",
+    "\n",
+    "storage_context21 = StorageContext.from_defaults(persist_dir=\"./index_bill21\")\n",
+    "index_bill21 = load_index_from_storage(storage_context21)\n",
+    "\n",
+    "storage_context22 = StorageContext.from_defaults(persist_dir=\"./index_bill22\")\n",
+    "index_bill22 = load_index_from_storage(storage_context22)\n",
+    "\n",
+    "storage_context23 = StorageContext.from_defaults(persist_dir=\"./index_bill23\")\n",
+    "index_bill23 = load_index_from_storage(storage_context23)\n",
+    "\n",
+    "storage_context24 = StorageContext.from_defaults(persist_dir=\"./index_bill24\")\n",
+    "index_bill24 = load_index_from_storage(storage_context24)\n",
+    "\n",
+    "storage_context25 = StorageContext.from_defaults(persist_dir=\"./index_bill25\")\n",
+    "index_bill25 = load_index_from_storage(storage_context25)\n",
+    "\n",
+    "storage_context26 = StorageContext.from_defaults(persist_dir=\"./index_bill26\")\n",
+    "index_bill26 = load_index_from_storage(storage_context26)\n",
+    "\n",
+    "storage_context27 = StorageContext.from_defaults(persist_dir=\"./index_bill27\")\n",
+    "index_bill27 = load_index_from_storage(storage_context27)\n",
+    "\n",
+    "storage_context28 = StorageContext.from_defaults(persist_dir=\"./index_bill28\")\n",
+    "index_bill28 = load_index_from_storage(storage_context28)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d58e9c31-013f-46c2-b14b-24c4af3087bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.core.retrievers import QueryFusionRetriever\n",
+    "\n",
+    "retriever = QueryFusionRetriever(\n",
+    "    [index_bill1.as_retriever(),\n",
+    "    index_bill2.as_retriever(),\n",
+    "    index_bill3.as_retriever(),\n",
+    "    index_bill4.as_retriever(),\n",
+    "    index_bill5.as_retriever(),\n",
+    "    index_bill6.as_retriever(),\n",
+    "    index_bill7.as_retriever(),\n",
+    "    index_bill8.as_retriever(),\n",
+    "    index_bill9.as_retriever(),\n",
+    "    index_bill10.as_retriever(),\n",
+    "    index_bill11.as_retriever(),\n",
+    "    index_bill12.as_retriever(),\n",
+    "    index_bill13.as_retriever(),\n",
+    "    index_bill14.as_retriever(),\n",
+    "    index_bill15.as_retriever(),\n",
+    "    index_bill16.as_retriever(),\n",
+    "    index_bill17.as_retriever(),\n",
+    "    index_bill18.as_retriever(),\n",
+    "    index_bill19.as_retriever(),\n",
+    "    index_bill20.as_retriever(),\n",
+    "    index_bill21.as_retriever(),\n",
+    "    index_bill22.as_retriever(),\n",
+    "    index_bill23.as_retriever(),\n",
+    "    index_bill24.as_retriever(),\n",
+    "    index_bill25.as_retriever(),\n",
+    "    index_bill26.as_retriever(),\n",
+    "    index_bill27.as_retriever(),\n",
+    "    index_bill28.as_retriever()],\n",
+    "    similarity_top_k=4,\n",
+    "    num_queries=1,  # set this to 1 to disable query generation\n",
+    "    use_async=True,\n",
+    "    verbose=True,\n",
+    "    query_gen_prompt= (\"\"\"\\\n",
+    "You are a helpful QA assistant. Using the context information provided below, \\\n",
+    "respond to the query accurately and comprehensively, relying solely on the context and not on any prior knowledge. \\\n",
+    "Ensure your response is clear and concise.\\\n",
+    "\"\"\")\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c663cff3-c79e-45d6-92d4-ba095594e3f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#model default prompt\n",
+    "\n",
+    "#QUERY_GEN_PROMPT = (\n",
+    "#    \"You are a helpful assistant that generates multiple search queries based on a \"\n",
+    " #   \"single input query. Generate {num_queries} search queries, one on each line, \"\n",
+    "  #  \"related to the following input query:\\n\"\n",
+    "   # \"Query: {query}\\n\"\n",
+    "    # \"Queries:\\n\"\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8ca6ff3f-5782-4a84-9b7d-22fc2aa503e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use retriever as part of a query engine\n",
+    "from llama_index.core.query_engine import RetrieverQueryEngine\n",
+    "\n",
+    "# configure response synthesizer\n",
+    "response_synthesizer = get_response_synthesizer(response_mode=\"tree_summarize\")\n",
+    "\n",
+    "# assemble query engine\n",
+    "query_engine = RetrieverQueryEngine(\n",
+    "    retriever=retriever,\n",
+    "    response_synthesizer=response_synthesizer,\n",
+    ")\n",
+    "\n",
+    "# query\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "aa7ff231-b4cc-4891-b98b-3d708641136d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This document does not contain information about sports teams in Toronto. \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = query_engine.query(\"What are the sports teams in Toronto?\")\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "27f397db-6fda-4296-a7c0-e3f0d569b8fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This act authorizes the Minister of Health to make payments of up to $2.5 billion from the Consolidated Revenue Fund for expenses related to COVID-19 tests. It also allows the Minister to transfer COVID-19 tests and instruments used in relation to those tests to provinces, territories, and other bodies and persons in Canada. \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = query_engine.query(\"explain An Act respecting certain measures related\"\n",
+    "\"to COVID-19\")\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "cdb26489-5ff7-4d52-aece-112f13519d09",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Online Streaming Act amends the Broadcasting Act to include online undertakings as a distinct class of broadcasting undertakings. It also specifies that the Act does not apply to programs uploaded to an online undertaking that provides a social media service by a user of the service, unless the programs are prescribed by regulation. The Act updates the broadcasting policy for Canada, enhancing the vitality of official language minority communities in Canada and fostering the full recognition and use of both English and French in Canadian society. It also provides the Commission with the power to require that persons carrying on broadcasting undertakings make expenditures to support the Canadian broadcasting system. \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = query_engine.query(\"list all context\")\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "97c71c98-7ccb-4491-a84a-17f4776ccf3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "**`Final Response:`** The provided text discusses amendments to the Criminal Code, Firearms Act, Nuclear Safety and Control Act, Immigration and Refugee Protection Act, and An Act to amend certain Acts and Regulations in relation to firearms."
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "response = query_engine.query(\"list all titles that you can answer\")\n",
+    "\n",
+    "from llama_index.core.response.notebook_utils import display_response\n",
+    "\n",
+    "display_response(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "cc177e35-9444-4294-b853-3cac0e6d5a04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#explore query_engines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8d4bb416-55bb-4c1c-8663-a739d8e7fccc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "None\n",
+      "Page 1 \n",
+      "First Session, Forty-fourth Parliament,\n",
+      "70-71 Elizabeth II, 2021-2022\n",
+      "STATUTES OF CANADA 2022\n",
+      "CHAPTER 2\n",
+      "An Act respecting certain measures related\n",
+      "to COVID-19\n",
+      "ASSENTED TO\n",
+      "MARCH 4, 2022\n",
+      "BILL C-10\n",
+      "\n",
+      "Page 2 \n",
+      "RECOMMENDATION\n",
+      "Her Excellency the Governor General recommends to the House\n",
+      "of Commons the appropriation of public revenue under the cir-\n",
+      "cumstances, in the manner and for the purposes set out in a\n",
+      "measure entitled “An Act respecting certain measures related to\n",
+      "COVID-19”.\n",
+      "SUMMARY\n",
+      "This enactment authorizes the Minister of Health to make pay-\n",
+      "ments of up to $2.5 billion out of the Consolidated Revenue Fund\n",
+      "in relation to coronavirus disease 2019 (COVID-19) tests.\n",
+      "It also authorizes that Minister to transfer COVID-19 tests and in-\n",
+      "struments used in relation to those tests to the provinces and\n",
+      "territories and to bodies and persons in Canada.\n",
+      "Available on the House of Commons website at the following address:\n",
+      "www.ourcommons.ca\n",
+      "2021-2022\n",
+      "\n",
+      "Page 3 \n",
+      "70-71 ELIZABETH II\n",
+      "CHAPTER 2\n",
+      "An Act respecting certain measures related to\n",
+      "COVID-19\n",
+      "[Assented to 4th March, 2022]\n",
+      "Her Majesty, by and with the advice and consent of\n",
+      "the Senate and House of Commons of Canada,\n",
+      "enacts as follows:\n",
+      "Payments out of C.R.F.\n",
+      "1 The Minister of Health may make payments, the total\n",
+      "of which may not exceed $2.5 billion, out of the Consoli-\n",
+      "dated Revenue Fund for any expenses incurred on or af-\n",
+      "ter January 1, 2022 in relation to coronavirus disease\n",
+      "2019 (COVID-19) tests.\n",
+      "Transfers\n",
+      "2 The Minister of Health may transfer to any province or\n",
+      "territory, or to any body or person in Canada, any coro-\n",
+      "navirus disease 2019 (COVID-19) tests or instruments\n",
+      "used in relation to those tests acquired by Her Majesty in\n",
+      "right of Canada on or after April 1, 2021.\n",
+      "Published under authority of the Speaker of the House of Commons\n",
+      "2021-2022\n",
+      "\n",
+      "Page 4 \n",
+      "Available on the House of Commons website\n",
+      "Disponible sur le site Web de la Chambre des com\n"
+     ]
+    }
+   ],
+   "source": [
+    "retrieved_nodes = retriever.retrieve(\"explain covid\")\n",
+    "\n",
+    "print(len(retrieved_nodes))\n",
+    "\n",
+    "print(retrieved_nodes[0].score)\n",
+    "print(retrieved_nodes[0].node.get_text())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4efb2a7-1a80-4935-960f-2bc86a8d7629",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retrieved_nodes = retriever.retrieve(\"An Act to provide for the establishment of a national council for reconciliation\"\n",
+    "\"ASSENTED TO APRIL 30, 2024\"\n",
+    "\"BILL C-29\")\n",
+    "\n",
+    "print(len(retrieved_nodes))\n",
+    "\n",
+    "print(retrieved_nodes[0].score)\n",
+    "print(retrieved_nodes[0].node.get_text())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63a04ae1-a0ae-4df4-89d0-8a42a147aaf5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "beb2308d-c418-403d-8826-0bbf3495a91f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}