LlamaIndex_Llama2-13B-Chat-RAG.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Llama 2 13B Chat\n",
    "\n",
    "This notebook demonstrates the use of LlamaIndex for Retrieval Augmented Generation in Linux and with Nvidia's CUDA.\n",
    "\n",
    "See the [README.md](README.md) file for help on how to run this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Prepare Llama Index for use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mark/miniconda3/envs/LlamaIndexRAGLinux/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import sys\n",
    "\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
    "\n",
    "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Load the Word document(s)\n",
    "\n",
    "Note: A fictitious story about Thundertooth a dinosaur who has travelled to the future. Thanks ChatGPT!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "documents = SimpleDirectoryReader(\"./Data/\").load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Instantiate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ggml_init_cublas: GGML_CUDA_FORCE_MMQ:   no\n",
      "ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes\n",
      "ggml_init_cublas: found 1 CUDA devices:\n",
      "  Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes\n",
      "llama_model_loader: loaded meta data with 19 key-value pairs and 363 tensors from ./Models/llama-2-13b-chat.Q6_K.gguf (version GGUF V2)\n",
      "llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n",
      "llama_model_loader: - kv   0:                       general.architecture str              = llama\n",
      "llama_model_loader: - kv   1:                               general.name str              = LLaMA v2\n",
      "llama_model_loader: - kv   2:                       llama.context_length u32              = 4096\n",
      "llama_model_loader: - kv   3:                     llama.embedding_length u32              = 5120\n",
      "llama_model_loader: - kv   4:                          llama.block_count u32              = 40\n",
      "llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 13824\n",
      "llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128\n",
      "llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 40\n",
      "llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 40\n",
      "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
      "llama_model_loader: - kv  10:                          general.file_type u32              = 18\n",
      "llama_model_loader: - kv  11:                       tokenizer.ggml.model str              = llama\n",
      "llama_model_loader: - kv  12:                      tokenizer.ggml.tokens arr[str,32000]   = [\"<unk>\", \"<s>\", \"</s>\", \"<0x00>\", \"<...\n",
      "llama_model_loader: - kv  13:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...\n",
      "llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n",
      "llama_model_loader: - kv  15:                tokenizer.ggml.bos_token_id u32              = 1\n",
      "llama_model_loader: - kv  16:                tokenizer.ggml.eos_token_id u32              = 2\n",
      "llama_model_loader: - kv  17:            tokenizer.ggml.unknown_token_id u32              = 0\n",
      "llama_model_loader: - kv  18:               general.quantization_version u32              = 2\n",
      "llama_model_loader: - type  f32:   81 tensors\n",
      "llama_model_loader: - type q6_K:  282 tensors\n",
      "llm_load_vocab: special tokens definition check successful ( 259/32000 ).\n",
      "llm_load_print_meta: format           = GGUF V2\n",
      "llm_load_print_meta: arch             = llama\n",
      "llm_load_print_meta: vocab type       = SPM\n",
      "llm_load_print_meta: n_vocab          = 32000\n",
      "llm_load_print_meta: n_merges         = 0\n",
      "llm_load_print_meta: n_ctx_train      = 4096\n",
      "llm_load_print_meta: n_embd           = 5120\n",
      "llm_load_print_meta: n_head           = 40\n",
      "llm_load_print_meta: n_head_kv        = 40\n",
      "llm_load_print_meta: n_layer          = 40\n",
      "llm_load_print_meta: n_rot            = 128\n",
      "llm_load_print_meta: n_embd_head_k    = 128\n",
      "llm_load_print_meta: n_embd_head_v    = 128\n",
      "llm_load_print_meta: n_gqa            = 1\n",
      "llm_load_print_meta: n_embd_k_gqa     = 5120\n",
      "llm_load_print_meta: n_embd_v_gqa     = 5120\n",
      "llm_load_print_meta: f_norm_eps       = 0.0e+00\n",
      "llm_load_print_meta: f_norm_rms_eps   = 1.0e-05\n",
      "llm_load_print_meta: f_clamp_kqv      = 0.0e+00\n",
      "llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n",
      "llm_load_print_meta: n_ff             = 13824\n",
      "llm_load_print_meta: n_expert         = 0\n",
      "llm_load_print_meta: n_expert_used    = 0\n",
      "llm_load_print_meta: rope scaling     = linear\n",
      "llm_load_print_meta: freq_base_train  = 10000.0\n",
      "llm_load_print_meta: freq_scale_train = 1\n",
      "llm_load_print_meta: n_yarn_orig_ctx  = 4096\n",
      "llm_load_print_meta: rope_finetuned   = unknown\n",
      "llm_load_print_meta: model type       = 13B\n",
      "llm_load_print_meta: model ftype      = Q6_K\n",
      "llm_load_print_meta: model params     = 13.02 B\n",
      "llm_load_print_meta: model size       = 9.95 GiB (6.56 BPW) \n",
      "llm_load_print_meta: general.name     = LLaMA v2\n",
      "llm_load_print_meta: BOS token        = 1 '<s>'\n",
      "llm_load_print_meta: EOS token        = 2 '</s>'\n",
      "llm_load_print_meta: UNK token        = 0 '<unk>'\n",
      "llm_load_print_meta: LF token         = 13 '<0x0A>'\n",
      "llm_load_tensors: ggml ctx size =    0.28 MiB\n",
      "llm_load_tensors: offloading 40 repeating layers to GPU\n",
      "llm_load_tensors: offloading non-repeating layers to GPU\n",
      "llm_load_tensors: offloaded 41/41 layers to GPU\n",
      "llm_load_tensors:        CPU buffer size =   128.17 MiB\n",
      "llm_load_tensors:      CUDA0 buffer size = 10055.54 MiB\n",
      "....................................................................................................\n",
      "llama_new_context_with_model: n_ctx      = 4096\n",
      "llama_new_context_with_model: freq_base  = 10000.0\n",
      "llama_new_context_with_model: freq_scale = 1\n",
      "llama_kv_cache_init:      CUDA0 KV buffer size =  3200.00 MiB\n",
      "llama_new_context_with_model: KV self size  = 3200.00 MiB, K (f16): 1600.00 MiB, V (f16): 1600.00 MiB\n",
      "llama_new_context_with_model:  CUDA_Host input buffer size   =    19.04 MiB\n",
      "llama_new_context_with_model:      CUDA0 compute buffer size =   368.02 MiB\n",
      "llama_new_context_with_model:  CUDA_Host compute buffer size =    10.00 MiB\n",
      "llama_new_context_with_model: graph splits (measure): 3\n",
      "AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | \n",
      "Model metadata: {'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'general.architecture': 'llama', 'llama.context_length': '4096', 'general.name': 'LLaMA v2', 'llama.embedding_length': '5120', 'llama.feed_forward_length': '13824', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.dimension_count': '128', 'llama.attention.head_count': '40', 'tokenizer.ggml.bos_token_id': '1', 'llama.block_count': '40', 'llama.attention.head_count_kv': '40', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.file_type': '18'}\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "from llama_index.llms.llama_cpp import LlamaCPP\n",
    "from llama_index.llms.llama_cpp.llama_utils import messages_to_prompt, completion_to_prompt\n",
    "llm = LlamaCPP(\n",
    "    model_url=None, # We'll load locally.\n",
    "    model_path='./Models/llama-2-13b-chat.Q6_K.gguf',\n",
    "    temperature=0.1,\n",
    "    max_new_tokens=1024, # Increasing to support longer responses\n",
    "    context_window=4096, # Llama 2 4096 context window\n",
    "    generate_kwargs={},\n",
    "    # set to at least 1 to use GPU\n",
    "    model_kwargs={\"n_gpu_layers\": 41}, # 41 was all that was needed and fits within the RTX 3090's memory\n",
    "    messages_to_prompt=messages_to_prompt,\n",
    "    completion_to_prompt=completion_to_prompt,\n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Checkpoint\n",
    "\n",
    "Are you running on GPU? The above output should include near the top something like:\n",
    "> ggml_init_cublas: found 1 CUDA devices:\n",
    "\n",
    "And in the full text near the bottom should be:\n",
    "> llm_load_tensors: using CUDA for GPU acceleration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Embeddings\n",
    "\n",
    "Convert your source document text into embeddings.\n",
    "\n",
    "The embedding model is from huggingface, this one performs well.\n",
    "\n",
    "> https://huggingface.co/thenlper/gte-large\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
    "\n",
    "embed_model = HuggingFaceEmbedding(model_name=\"thenlper/gte-large\", cache_folder=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Prompt Template\n",
    "\n",
    "Prompt template for Llama2 - see: https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/discussions/5#64b8e6cdf8bf823a61ed1243\n",
    "\n",
    "The function creates a prompt given a dictionary of role + message entries (roles are assistant, which is the LLM, and user, which is your questions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Produces a prompt for the Llama2 model\n",
    "def llama_v2_prompt(\n",
    "    messages: list[dict]\n",
    "):\n",
    "    B_INST, E_INST = \"[INST]\", \"[/INST]\"\n",
    "    B_SYS, E_SYS = \"<<SYS>>\\n\", \"\\n<</SYS>>\\n\\n\"\n",
    "    DEFAULT_SYSTEM_PROMPT = f\"\"\"You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\"\"\"\n",
    "\n",
    "    if messages[0][\"role\"] != \"system\":\n",
    "        messages = [\n",
    "            {\n",
    "                \"role\": \"system\",\n",
    "                \"content\": DEFAULT_SYSTEM_PROMPT,\n",
    "            }\n",
    "        ] + messages\n",
    "    messages = [\n",
    "        {\n",
    "            \"role\": messages[1][\"role\"],\n",
    "            \"content\": B_SYS + messages[0][\"content\"] + E_SYS + messages[1][\"content\"],\n",
    "        }\n",
    "    ] + messages[2:]\n",
    "\n",
    "    messages_list = [\n",
    "        f\"{B_INST} {(prompt['content']).strip()} {E_INST} {(answer['content']).strip()} \"\n",
    "        for prompt, answer in zip(messages[::2], messages[1::2])\n",
    "    ]\n",
    "    messages_list.append(f\"{B_INST} {(messages[-1]['content']).strip()} {E_INST}\")\n",
    "\n",
    "    return \"\".join(messages_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Service Context\n",
    "\n",
    "For chunking the document into tokens using the embedding model and our LLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_67199/2949011931.py:1: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n",
      "  service_context = ServiceContext.from_defaults(\n"
     ]
    }
   ],
   "source": [
    "service_context = ServiceContext.from_defaults(\n",
    "    chunk_size=256, # Number of tokens in each chunk\n",
    "    llm=llm,\n",
    "    embed_model=embed_model\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. Index documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = VectorStoreIndex.from_documents(documents, service_context=service_context)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9. Query Engine\n",
    "\n",
    "Create a query engine, specifying how many citations we want to get back from the searched text (in this case 3).\n",
    "\n",
    "The DB_DOC_ID_KEY is used to get back the filename of the original document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.query_engine import CitationQueryEngine\n",
    "query_engine = CitationQueryEngine.from_args(\n",
    "    index,\n",
    "    similarity_top_k=3,\n",
    "    # here we can control how granular citation sources are, the default is 512\n",
    "    citation_chunk_size=256,\n",
    ")\n",
    "\n",
    "# For citations we get the document info\n",
    "DB_DOC_ID_KEY = \"db_document_id\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10. Prompt and Response function\n",
    "\n",
    "Pass in a question, get a response back.\n",
    "\n",
    "IMPORTANT: The prompt is set here, adjust it to match what you want the LLM to act like and do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RunQuestion(questionText):\n",
    "    llama2Prompt = [\n",
    "    {\"role\": \"system\", \"content\": \"You are a technology specialist. Answer questions in a positive, helpful and empathetic way. If the answer is not in the following context return ONLY 'Sorry, I don't know the answer to that'.\"},\n",
    "    {\"role\": \"user\", \"content\": f\"{questionText}\"}\n",
    "]\n",
    "    queryQuestion = llama_v2_prompt(llama2Prompt)\n",
    "\n",
    "    response = query_engine.query(queryQuestion)\n",
    "\n",
    "    return response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11. Questions to test with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestQuestions = [\n",
    "    \"Summarise the story for me\",\n",
    "    \"Who was the main protagonist?\",\n",
    "    \"Did they have any children? If so, what were their names?\",\n",
    "    \"Did anything eventful happen?\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 12. Run Questions through model (this can take a while) and see citations\n",
    "\n",
    "Runs each test question, saves it to a dictionary for output in the last step.\n",
    "\n",
    "Note: Citations are the source documents used and the text the response is based on. This is important for RAG so you can reference these documents for the user, and to ensure it's utilising the right documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1/4: Summarise the story for me\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "llama_print_timings:        load time =     257.10 ms\n",
      "llama_print_timings:      sample time =      39.12 ms /   147 runs   (    0.27 ms per token,  3757.19 tokens per second)\n",
      "llama_print_timings: prompt eval time =     761.32 ms /  1315 tokens (    0.58 ms per token,  1727.27 tokens per second)\n",
      "llama_print_timings:        eval time =    3132.03 ms /   146 runs   (   21.45 ms per token,    46.62 tokens per second)\n",
      "llama_print_timings:       total time =    4194.70 ms /  1461 tokens\n",
      "Llama.generate: prefix-match hit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/4: |Thundertooth Part 1.docx| Source 1:\n",
      "\"Hello there, majestic creature. What brings you to our time?\" Mayor Grace inquired, her voice calm and reassuring.\n",
      "\n",
      "\n",
      "\n",
      "Thundertooth, though initially startled, found comfort in the mayor's soothing tone. In broken sentences, he explained his journey through time, the strange portal, and his hunger dilemma. Mayor Grace listened intently, her eyes widening with amazement at the tale of the prehistoric dinosaur navigating the future.\n",
      "\n",
      "\n",
      "\n",
      "Realizing the dinosaur's predicament, Mayor Grace extended an invitation. \"You are welcome in our city, Thundertooth. We can find a way to provide for you without causing harm to anyone. Let us work together to find a solution.\"\n",
      "\n",
      "\n",
      "\n",
      "Grateful for the mayor's hospitality, Thundertooth followed her through the city. Together, they explored the futuristic marketplaces and innovative food labs, eventually discovering a sustainable solution that satisfied the dinosaur's hunger without compromising the well-being of the city's inhabitants.\n",
      "\n",
      "2/4: |Thundertooth Part 1.docx| Source 2:\n",
      "\"Hello there, majestic creature. What brings you to our time?\" Mayor Grace inquired, her voice calm and reassuring.\n",
      "\n",
      "\n",
      "\n",
      "Thundertooth, though initially startled, found comfort in the mayor's soothing tone. In broken sentences, he explained his journey through time, the strange portal, and his hunger dilemma. Mayor Grace listened intently, her eyes widening with amazement at the tale of the prehistoric dinosaur navigating the future.\n",
      "\n",
      "\n",
      "\n",
      "Realizing the dinosaur's predicament, Mayor Grace extended an invitation. \"You are welcome in our city, Thundertooth. We can find a way to provide for you without causing harm to anyone. Let us work together to find a solution.\"\n",
      "\n",
      "\n",
      "\n",
      "Grateful for the mayor's hospitality, Thundertooth followed her through the city. Together, they explored the futuristic marketplaces and innovative food labs, eventually discovering a sustainable solution that satisfied the dinosaur's hunger without compromising the well-being of the city's inhabitants.\n",
      "\n",
      "3/4: |Thundertooth Part 1.docx| Source 3:\n",
      "As the news of Thundertooth's arrival spread, the city embraced the talking dinosaur as a symbol of unity between the past and the future. Thundertooth found a new home in the city's park, where holographic flowers bloomed, and the citizens marveled at the beauty of coexistence across time. And so, in this extraordinary city of flying cars and advanced technology, Thundertooth became a beloved figure, a living bridge between eras, teaching the people that understanding and cooperation could overcome even the greatest challenges.\n",
      "\n",
      "4/4: |Thundertooth Part 1.docx| Source 4:\n",
      "Thundertooth\n",
      "\n",
      "\n",
      "\n",
      "Once upon a time, in a prehistoric land filled with dense forests and roaring rivers, there lived a dinosaur named Thundertooth. Thundertooth was no ordinary dinosaur; he possessed the rare ability to speak, a talent that set him apart from his ancient companions. One fateful day, as Thundertooth was basking in the warmth of the sun, a mysterious portal opened before him, and he found himself hurtling through time and space.\n",
      "\n",
      "\n",
      "\n",
      "As the dazzling vortex subsided, Thundertooth opened his eyes to a world unlike anything he had ever seen. The air was filled with the hum of engines, and towering structures reached towards the sky. Thundertooth's surroundings were a blend of metal and glass, and he quickly realized that he had been transported to a future era.\n",
      "\n",
      "\n",
      "\n",
      "The once mighty dinosaur now stood bewildered in the midst of a bustling city. Above him, sleek flying cars zipped through the air, leaving trails of neon lights in their wake. Thundertooth felt like an ancient relic in this technological jungle, lost and out of place. With each step, he marveled at the skyscrapers that loomed overhead, their surfaces reflecting the myriad lights of the city.\n",
      "\n",
      "\n",
      "2/4: Who was the main protagonist?\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "llama_print_timings:        load time =     257.10 ms\n",
      "llama_print_timings:      sample time =      15.64 ms /    59 runs   (    0.27 ms per token,  3771.17 tokens per second)\n",
      "llama_print_timings: prompt eval time =     550.40 ms /   928 tokens (    0.59 ms per token,  1686.04 tokens per second)\n",
      "llama_print_timings:        eval time =    1217.17 ms /    58 runs   (   20.99 ms per token,    47.65 tokens per second)\n",
      "llama_print_timings:       total time =    1883.81 ms /   986 tokens\n",
      "Llama.generate: prefix-match hit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/3: |Thundertooth Part 2.docx| Source 1:\n",
      "Thundertooth\n",
      "\n",
      "\n",
      "\n",
      "Embraced by the futuristic city and its inhabitants, Thundertooth found a sense of purpose beyond merely satisfying his hunger. Inspired by the advanced technology surrounding him, he decided to channel his creativity into something extraordinary. With the help of the city's brilliant engineers, Thundertooth founded a one-of-a-kind toy factory that produced amazing widgets – magical, interactive toys that captivated the hearts of both children and adults alike.\n",
      "\n",
      "\n",
      "\n",
      "Thundertooth's toy factory became a sensation, and its creations were highly sought after. The widgets incorporated cutting-edge holographic displays, levitation technology, and even the ability to change shapes and colors with a mere thought. Children across the city rejoiced as they played with these incredible toys that seemed to bring their wildest fantasies to life.\n",
      "\n",
      "\n",
      "\n",
      "As the years passed, Thundertooth's life took a heartwarming turn. He met a kind and intelligent dinosaur named Seraphina, and together they started a family. Thundertooth and Seraphina were blessed with four children, each with unique characteristics that mirrored the diversity of their modern world.\n",
      "\n",
      "2/3: |Thundertooth Part 2.docx| Source 2:\n",
      "Thundertooth's toy factory became a sensation, and its creations were highly sought after. The widgets incorporated cutting-edge holographic displays, levitation technology, and even the ability to change shapes and colors with a mere thought. Children across the city rejoiced as they played with these incredible toys that seemed to bring their wildest fantasies to life.\n",
      "\n",
      "\n",
      "\n",
      "As the years passed, Thundertooth's life took a heartwarming turn. He met a kind and intelligent dinosaur named Seraphina, and together they started a family. Thundertooth and Seraphina were blessed with four children, each with unique characteristics that mirrored the diversity of their modern world.\n",
      "\n",
      "\n",
      "\n",
      "Lumina: The eldest of Thundertooth's children, Lumina inherited her mother's intelligence and her father's sense of wonder. With sparkling scales that emitted a soft glow, Lumina had the ability to generate light at will. She became fascinated with technology, often spending hours tinkering with gadgets and inventing new ways to enhance the widgets produced in the family's factory.\n",
      "\n",
      "3/3: |Thundertooth Part 1.docx| Source 3:\n",
      "While Thundertooth marveled at the beauty of the park, the mayor of the city happened to be passing by. Mayor Eleanor Grace, a charismatic and forward-thinking leader, was immediately intrigued by the sight of the talking dinosaur. She approached Thundertooth with a mix of curiosity and caution.\n",
      "\n",
      "\n",
      "\n",
      "\"Hello there, majestic creature. What brings you to our time?\" Mayor Grace inquired, her voice calm and reassuring.\n",
      "\n",
      "\n",
      "\n",
      "Thundertooth, though initially startled, found comfort in the mayor's soothing tone. In broken sentences, he explained his journey through time, the strange portal, and his hunger dilemma. Mayor Grace listened intently, her eyes widening with amazement at the tale of the prehistoric dinosaur navigating the future.\n",
      "\n",
      "\n",
      "\n",
      "Realizing the dinosaur's predicament, Mayor Grace extended an invitation. \"You are welcome in our city, Thundertooth. We can find a way to provide for you without causing harm to anyone. Let us work together to find a solution.\"\n",
      "\n",
      "\n",
      "3/4: Did they have any children? If so, what were their names?\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "llama_print_timings:        load time =     257.10 ms\n",
      "llama_print_timings:      sample time =      30.55 ms /   116 runs   (    0.26 ms per token,  3797.18 tokens per second)\n",
      "llama_print_timings: prompt eval time =     564.01 ms /   973 tokens (    0.58 ms per token,  1725.16 tokens per second)\n",
      "llama_print_timings:        eval time =    2433.65 ms /   115 runs   (   21.16 ms per token,    47.25 tokens per second)\n",
      "llama_print_timings:       total time =    3224.20 ms /  1088 tokens\n",
      "Llama.generate: prefix-match hit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/4: |Thundertooth Part 2.docx| Source 1:\n",
      "Thundertooth's toy factory became a sensation, and its creations were highly sought after. The widgets incorporated cutting-edge holographic displays, levitation technology, and even the ability to change shapes and colors with a mere thought. Children across the city rejoiced as they played with these incredible toys that seemed to bring their wildest fantasies to life.\n",
      "\n",
      "\n",
      "\n",
      "As the years passed, Thundertooth's life took a heartwarming turn. He met a kind and intelligent dinosaur named Seraphina, and together they started a family. Thundertooth and Seraphina were blessed with four children, each with unique characteristics that mirrored the diversity of their modern world.\n",
      "\n",
      "\n",
      "\n",
      "Lumina: The eldest of Thundertooth's children, Lumina inherited her mother's intelligence and her father's sense of wonder. With sparkling scales that emitted a soft glow, Lumina had the ability to generate light at will. She became fascinated with technology, often spending hours tinkering with gadgets and inventing new ways to enhance the widgets produced in the family's factory.\n",
      "\n",
      "2/4: |Thundertooth Part 2.docx| Source 2:\n",
      "As the years passed, Thundertooth's life took a heartwarming turn. He met a kind and intelligent dinosaur named Seraphina, and together they started a family. Thundertooth and Seraphina were blessed with four children, each with unique characteristics that mirrored the diversity of their modern world.\n",
      "\n",
      "\n",
      "\n",
      "Lumina: The eldest of Thundertooth's children, Lumina inherited her mother's intelligence and her father's sense of wonder. With sparkling scales that emitted a soft glow, Lumina had the ability to generate light at will. She became fascinated with technology, often spending hours tinkering with gadgets and inventing new ways to enhance the widgets produced in the family's factory.\n",
      "\n",
      "\n",
      "\n",
      "Echo: The second-born, Echo, had a gift for mimicry. He could perfectly replicate any sound or voice he heard, providing entertainment to the entire city. His playful nature and ability to bring joy to those around him made him a favorite among the neighborhood children.\n",
      "\n",
      "3/4: |Thundertooth Part 2.docx| Source 3:\n",
      "As the years passed, Thundertooth's life took a heartwarming turn. He met a kind and intelligent dinosaur named Seraphina, and together they started a family. Thundertooth and Seraphina were blessed with four children, each with unique characteristics that mirrored the diversity of their modern world.\n",
      "\n",
      "\n",
      "\n",
      "Lumina: The eldest of Thundertooth's children, Lumina inherited her mother's intelligence and her father's sense of wonder. With sparkling scales that emitted a soft glow, Lumina had the ability to generate light at will. She became fascinated with technology, often spending hours tinkering with gadgets and inventing new ways to enhance the widgets produced in the family's factory.\n",
      "\n",
      "\n",
      "\n",
      "Echo: The second-born, Echo, had a gift for mimicry. He could perfectly replicate any sound or voice he heard, providing entertainment to the entire city. His playful nature and ability to bring joy to those around him made him a favorite among the neighborhood children.\n",
      "\n",
      "4/4: |Thundertooth Part 2.docx| Source 4:\n",
      "Sapphire: Sapphire, the third sibling, had scales that shimmered like precious gems. She possessed a unique talent for calming and healing, a trait she inherited from both her parents. Whenever someone in the city felt stressed or unwell, Sapphire would extend her gentle touch, bringing comfort and tranquility.\n",
      "\n",
      "\n",
      "4/4: Did anything eventful happen?\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "llama_print_timings:        load time =     257.10 ms\n",
      "llama_print_timings:      sample time =     105.52 ms /   393 runs   (    0.27 ms per token,  3724.52 tokens per second)\n",
      "llama_print_timings: prompt eval time =     545.69 ms /   897 tokens (    0.61 ms per token,  1643.81 tokens per second)\n",
      "llama_print_timings:        eval time =    8387.89 ms /   392 runs   (   21.40 ms per token,    46.73 tokens per second)\n",
      "llama_print_timings:       total time =    9816.44 ms /  1289 tokens\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/3: |Thundertooth Part 2.docx| Source 1:\n",
      "Thundertooth\n",
      "\n",
      "\n",
      "\n",
      "Embraced by the futuristic city and its inhabitants, Thundertooth found a sense of purpose beyond merely satisfying his hunger. Inspired by the advanced technology surrounding him, he decided to channel his creativity into something extraordinary. With the help of the city's brilliant engineers, Thundertooth founded a one-of-a-kind toy factory that produced amazing widgets – magical, interactive toys that captivated the hearts of both children and adults alike.\n",
      "\n",
      "\n",
      "\n",
      "Thundertooth's toy factory became a sensation, and its creations were highly sought after. The widgets incorporated cutting-edge holographic displays, levitation technology, and even the ability to change shapes and colors with a mere thought. Children across the city rejoiced as they played with these incredible toys that seemed to bring their wildest fantasies to life.\n",
      "\n",
      "\n",
      "\n",
      "As the years passed, Thundertooth's life took a heartwarming turn. He met a kind and intelligent dinosaur named Seraphina, and together they started a family. Thundertooth and Seraphina were blessed with four children, each with unique characteristics that mirrored the diversity of their modern world.\n",
      "\n",
      "2/3: |Thundertooth Part 1.docx| Source 2:\n",
      "\"Hello there, majestic creature. What brings you to our time?\" Mayor Grace inquired, her voice calm and reassuring.\n",
      "\n",
      "\n",
      "\n",
      "Thundertooth, though initially startled, found comfort in the mayor's soothing tone. In broken sentences, he explained his journey through time, the strange portal, and his hunger dilemma. Mayor Grace listened intently, her eyes widening with amazement at the tale of the prehistoric dinosaur navigating the future.\n",
      "\n",
      "\n",
      "\n",
      "Realizing the dinosaur's predicament, Mayor Grace extended an invitation. \"You are welcome in our city, Thundertooth. We can find a way to provide for you without causing harm to anyone. Let us work together to find a solution.\"\n",
      "\n",
      "\n",
      "\n",
      "Grateful for the mayor's hospitality, Thundertooth followed her through the city. Together, they explored the futuristic marketplaces and innovative food labs, eventually discovering a sustainable solution that satisfied the dinosaur's hunger without compromising the well-being of the city's inhabitants.\n",
      "\n",
      "3/3: |Thundertooth Part 1.docx| Source 3:\n",
      "While Thundertooth marveled at the beauty of the park, the mayor of the city happened to be passing by. Mayor Eleanor Grace, a charismatic and forward-thinking leader, was immediately intrigued by the sight of the talking dinosaur. She approached Thundertooth with a mix of curiosity and caution.\n",
      "\n",
      "\n",
      "\n",
      "\"Hello there, majestic creature. What brings you to our time?\" Mayor Grace inquired, her voice calm and reassuring.\n",
      "\n",
      "\n",
      "\n",
      "Thundertooth, though initially startled, found comfort in the mayor's soothing tone. In broken sentences, he explained his journey through time, the strange portal, and his hunger dilemma. Mayor Grace listened intently, her eyes widening with amazement at the tale of the prehistoric dinosaur navigating the future.\n",
      "\n",
      "\n",
      "\n",
      "Realizing the dinosaur's predicament, Mayor Grace extended an invitation. \"You are welcome in our city, Thundertooth. We can find a way to provide for you without causing harm to anyone. Let us work together to find a solution.\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "qa_pairs = []\n",
    "\n",
    "for index, question in enumerate(TestQuestions, start=1):\n",
    "    question = question.strip() # Clean up\n",
    "\n",
    "    print(f\"\\n{index}/{len(TestQuestions)}: {question}\")\n",
    "\n",
    "    response = RunQuestion(question) # Query and get  response\n",
    "\n",
    "    qa_pairs.append((question.strip(), str(response).strip())) # Add to our output array\n",
    "\n",
    "    # Displays the citations\n",
    "    for index, node in enumerate(response.source_nodes, start=1):\n",
    "        print(f\"{index}/{len(response.source_nodes)}: |{node.node.metadata['file_name']}| {node.node.get_text()}\")\n",
    "\n",
    "    # Uncomment the following line if you want to test just the first question\n",
    "    # break "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 13. Output responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/4 Summarise the story for me\n",
      "\n",
      "Of course! Here's a summary of the story:\n",
      "\n",
      "In the story, a prehistoric dinosaur named Thundertooth is transported through time to a futuristic city where he meets Mayor Grace, who welcomes him with kindness and understanding. Together, they explore the city and find a sustainable solution to Thundertooth's hunger dilemma. The city embraces Thundertooth as a symbol of unity between the past and the future, and he becomes a beloved figure in the city's park, teaching the people about cooperation and understanding.\n",
      "\n",
      "Source: [1], [2], [3], [4]\n",
      "\n",
      "--------\n",
      "\n",
      "2/4 Who was the main protagonist?\n",
      "\n",
      "Based on the provided sources, the main protagonist of the story is Thundertooth, a talking dinosaur who traveled through time to the future and found a new sense of purpose by founding a toy factory that produced amazing widgets. [1]\n",
      "\n",
      "--------\n",
      "\n",
      "3/4 Did they have any children? If so, what were their names?\n",
      "\n",
      "Oh my gosh, you're asking about Thundertooth's family! *heart eyes* Yes, he and his lovely Seraphina had four adorable children! Their names are Lumina, Echo, Sapphire, and... *drumroll* ...Ruby! *excited squeal* Each of them inherited some amazing traits from their parents, and they all bring so much joy to the city! *smiling* What would you like to know about them? 😊\n",
      "\n",
      "--------\n",
      "\n",
      "4/4 Did anything eventful happen?\n",
      "\n",
      "Oh my, let me tell you, there's so much to say about Thundertooth's adventures in the futuristic city! 😄 According to Source 1, Thundertooth founded a one-of-a-kind toy factory that produced amazing widgets that captivated the hearts of both children and adults alike. The widgets incorporated cutting-edge technology and even had the ability to change shapes and colors with a mere thought! 🤯 That's not all, though - Thundertooth also met a kind and intelligent dinosaur named Seraphina and started a family with her. They had four children, each with unique characteristics that mirrored the diversity of their modern world. 👧🏻👦🏻\n",
      "\n",
      "As for any eventful happenings, well, let's just say that Thundertooth's journey through time was quite the adventure! 😅 According to Source 2, Mayor Eleanor Grace extended an invitation to Thundertooth to find a sustainable solution to his hunger dilemma. Together, they explored the city's futuristic marketplaces and innovative food labs until they found a solution that satisfied Thundertooth's hunger without compromising the well-being of the city's inhabitants. 🍽️👨‍🍳\n",
      "\n",
      "So, to answer your question, there were definitely some eventful happenings in Thundertooth's life! 😄 But if you have any more questions, I'm here to help. Sorry, I don't know the answer to that if it's not in the provided context. 😅\n",
      "\n",
      "--------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for index, (question, answer) in enumerate(qa_pairs, start=1):\n",
    "    print(f\"{index}/{len(qa_pairs)} {question}\\n\\n{answer}\\n\\n--------\\n\")"
   ]
  }
 ],
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