ai-engineer-workshop/notebooks/03_optimize.ipynb

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   "source": [
    "# Part 3: Experiment and optimize the RAG application"
   ]
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    "- GitHub repository: https://github.com/Disiok/ai-engineer-workshop/\n",
    "- Ray documentation: https://docs.ray.io/\n",
    "- LlamaIndex documentation: https://gpt-index.readthedocs.io/en/stable/"
   ]
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    "In this tutorial, we will focus on improving our RAG application by experimenting with different configurations and optimizing individual components as well how they are composed together in the overall pipeline.\n",
    "This builds on top of the evaluation functions that we've setup in the previous part of the notebook (i.e. to evaluate the retrieval and end-to-end performance of the RAG system).\n",
    "\n",
    "Here, we will explore 4 specific strategies:\n",
    "1. searching for optimal configurations for standard components\n",
    "2. using second-stage re-rankers\n",
    "3. using different representations for retrieval vs. generation\n",
    "4. fine-tuning embedding models"
   ]
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    "import os\n",
    "\n",
    "# os.environ[\"OPENAI_API_KEY\"] = ...\n",
    "\n",
    "os.environ[\"DB_CONNECTION_STRING\"] = \"dbname=postgres user=postgres host=localhost password=postgres\""
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      "2023-10-08 16:35:57,171\tINFO worker.py:1642 -- Started a local Ray instance.\n"
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    "import ray\n",
    "\n",
    "# Credentials\n",
    "ray.init(runtime_env={\n",
    "    \"env_vars\": {\n",
    "        \"OPENAI_API_KEY\": os.environ[\"OPENAI_API_KEY\"], \n",
    "        \"DB_CONNECTION_STRING\": os.environ[\"DB_CONNECTION_STRING\"],\n",
    "    },\n",
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   "id": "ab654d85-882a-40bd-bd8e-3c94758cbee9",
   "metadata": {
    "tags": []
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   "source": [
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()"
   ]
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   "id": "7e9d2bfa-b98f-4d84-84a1-91a6494ad816",
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   "source": [
    "## Strategy 1: Search for optimal configuration for standard components"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41320856-89be-44af-a01d-5307fdd1e92d",
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   "source": [
    "With our evaluator set, we're ready to start experimenting with the various components in our LLM application. While we could perform this as a large [tuning experiment](https://docs.ray.io/en/latest/tune/index.html), where we can search across promising combintion of values/decisions, we're going to evaluation one decision at a time and fix the best value for the next experiment.\n",
    "\n",
    "**Note**: this approach is slightly biased because many of our decisions are not indepedent (ex. `chunk_size` and `top_k` should ideally be evaluated across many combinations of values)."
   ]
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"
    }
   },
   "cell_type": "markdown",
   "id": "d9b9580e-28bb-416d-8c39-e578cd117d96",
   "metadata": {},
   "source": [
    "![experiment.png](attachment:7481d3bd-e5b1-4e95-9285-61754b942513.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dbf5817-7a8c-4f64-b9c8-e84fe8e5c190",
   "metadata": {},
   "source": [
    "#### Utilities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c6e46e-788c-434e-9ffb-2937534932e4",
   "metadata": {
    "tags": []
   },
   "source": [
    "Before we get started with our experiments, we're going to define some utility functions that we'll use to easily run and evaluate retrieval and response generation using different experiment configurations. This will leverage some of the code we've written in previous notebooks on building an index (part 1), and evaluating retriever and query engines (part 2)."
   ]
  },
  {
   "attachments": {
    "0d23fcdb-d770-40a7-b247-9bbf3943cbd5.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "e43c7f29-8c3a-4f4f-a642-fe7be5b59427",
   "metadata": {},
   "source": [
    "![eval_functions.png](attachment:0d23fcdb-d770-40a7-b247-9bbf3943cbd5.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3b464eac-e2a9-4d08-ae48-5814c59484b4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "# Paths\n",
    "ROOT_DIR = Path(os.getcwd()).parent\n",
    "EXPERIMENTS_DIR = Path(ROOT_DIR, \"experiments\")\n",
    "\n",
    "DATA_DIR = Path(ROOT_DIR, 'datasets')\n",
    "DOCS_PATH = Path(DATA_DIR, \"docs.ray.io/en/master/\")\n",
    "SQL_DUMP_DIR = Path(DATA_DIR, \"sql_dumps\")\n",
    "SQL_DUMP_DIR.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e982bd1-4810-4b06-b872-ba2a0c50be25",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import subprocess\n",
    "from data import build_index\n",
    "\n",
    "def execute_bash(command):\n",
    "    results = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)\n",
    "    return results\n",
    "\n",
    "def build_or_load_index(docs_path, embedding_model_name, chunk_size, chunk_overlap):\n",
    "    # Drop current Vector DB and prepare for new one\n",
    "    execute_bash(f'docker exec -u postgres ai-engineer-workshop-postgres-1 psql -c \"DROP TABLE data_document;\"')\n",
    "    SQL_DUMP_FP = Path(SQL_DUMP_DIR, f\"{embedding_model_name.split('/')[-1]}_{chunk_size}_{chunk_overlap}.sql\")\n",
    "    \n",
    "    # Vector DB\n",
    "    if SQL_DUMP_FP.exists():  # Load from SQL dump\n",
    "        print('Loading from SQL dump')\n",
    "        execute_bash(f'docker exec -u postgres ai-engineer-workshop-postgres-1 psql < {SQL_DUMP_FP}')\n",
    "    else:  \n",
    "        print('Creating new index')\n",
    "        build_index(docs_path, embedding_model_name, chunk_size, chunk_overlap)\n",
    "        \n",
    "        print('Saving to SQL dump')\n",
    "        execute_bash(f\"docker exec -u postgres ai-engineer-workshop-postgres-1 pg_dump -c > {SQL_DUMP_FP}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f328c952-fc4c-48dc-b877-51e013757045",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json \n",
    "\n",
    "from utils import get_retriever, get_query_engine\n",
    "from eval import evaluate_retrieval, evaluate_e2e, get_hit_rate, get_mean_score\n",
    "from pprint import pprint\n",
    "\n",
    "DEFAULT_EXP_CONFIG = {\n",
    "    'embedding_model_name': \"text-embedding-ada-002\",\n",
    "    'chunk_size': 1024,\n",
    "    'chunk_overlap': 50,\n",
    "    'similarity_top_k': 5,\n",
    "    'llm_model_name': 'gpt-3.5-turbo',\n",
    "    'temperature': 0.1,\n",
    "}\n",
    "\n",
    "def run_retrieval_exp(\n",
    "    docs_path,\n",
    "    exp_config={},\n",
    "    verbose=False,\n",
    "):\n",
    "    run_exp_config = DEFAULT_EXP_CONFIG.copy()\n",
    "    run_exp_config.update(exp_config)\n",
    "    if verbose:\n",
    "        pprint(run_exp_config)\n",
    "    \n",
    "    build_or_load_index( \n",
    "        docs_path=docs_path,\n",
    "        embedding_model_name=run_exp_config['embedding_model_name'],\n",
    "        chunk_size=run_exp_config['chunk_size'],\n",
    "        chunk_overlap=run_exp_config['chunk_overlap'],\n",
    "    )\n",
    "    retriever = get_retriever(\n",
    "        similarity_top_k=run_exp_config['similarity_top_k'],\n",
    "        embedding_model_name=run_exp_config['embedding_model_name'],\n",
    "    )\n",
    "    \n",
    "    golden_dataset_path = Path(\"../datasets/eval-dataset-v1.jsonl\")\n",
    "    with open(golden_dataset_path, \"r\") as f:\n",
    "        golden_dataset = [json.loads(item) for item in list(f)]\n",
    "\n",
    "    queries = [item['question'] for item in golden_dataset]\n",
    "    golden_sources = [item['source'] for item in golden_dataset]\n",
    "    \n",
    "    results = evaluate_retrieval(retriever, queries, golden_sources)\n",
    "    return results\n",
    "\n",
    "def run_e2e_exp(\n",
    "    docs_path,\n",
    "    exp_config={},\n",
    "    verbose=False, \n",
    "):\n",
    "    run_exp_config = DEFAULT_EXP_CONFIG.copy()\n",
    "    run_exp_config.update(exp_config)\n",
    "    if verbose:\n",
    "        pprint(run_exp_config)\n",
    "\n",
    "    build_or_load_index( \n",
    "        docs_path=docs_path,\n",
    "        embedding_model_name=run_exp_config['embedding_model_name'],\n",
    "        chunk_size=run_exp_config['chunk_size'],\n",
    "        chunk_overlap=run_exp_config['chunk_overlap'],\n",
    "    )\n",
    "    \n",
    "    query_engine = get_query_engine(\n",
    "        similarity_top_k=run_exp_config['similarity_top_k'],\n",
    "        embedding_model_name=run_exp_config['embedding_model_name'],\n",
    "        llm_model_name=run_exp_config['llm_model_name'],\n",
    "        temperature=run_exp_config['temperature'],\n",
    "    )\n",
    "    \n",
    "    with open(\"../datasets/golden-responses.json\", \"r\") as file:\n",
    "        golden_dataset = json.load(file)\n",
    "    \n",
    "    queries = [item['question'] for item in golden_dataset]\n",
    "    golden_response = [item['response'] for item in golden_dataset]\n",
    "    golden_sources = [item['source'] for item in golden_dataset]\n",
    "\n",
    "    results = evaluate_e2e(query_engine, queries, golden_response, verbose=True)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5404225-a7bb-4a84-955e-b428212d7698",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Chunk Size "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a80598a9-aec1-422e-bfa6-db85a6a29dca",
   "metadata": {},
   "source": [
    "Next, we'll access various chunk sizes. Smaller chunks (but not too small!) are able to encapsulate atomic concepts which yields more precise retrieval. While larger chunks may be more noisy. Popular strategies include using small chunks but retrieving a bit of the [surrounding chunks](https://gpt-index.readthedocs.io/en/latest/end_to_end_tutorials/dev_practices/production_rag.html#decoupling-chunks-used-for-retrieval-vs-chunks-used-for-synthesis) around it (since it may have relevnat info) or store [mulitple embeddings](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector) per document (ex. summary embedding per document). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3422311c-30c4-4431-9dd4-d31faa70a964",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "CHUNK_SIZES = [\n",
    "    256,\n",
    "    512, \n",
    "    1024,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "12e78197-e32c-4c11-947a-97d5f3522d3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running chunk size experiment:  256\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:23<00:00,  7.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.384180790960452\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:36<00:00,  3.69s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:59<00:00,  5.95s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.25\n",
      "Running chunk size experiment:  512\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:10<00:00, 16.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.4745762711864407\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:34<00:00,  3.48s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:50<00:00,  5.08s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.3\n",
      "Running chunk size experiment:  1024\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:08<00:00, 21.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.519774011299435\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:34<00:00,  3.45s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:58<00:00,  5.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for chunk_size in CHUNK_SIZES:\n",
    "    print('Running chunk size experiment: ', chunk_size)\n",
    "    retrieval_results = run_retrieval_exp(DOCS_PATH, {'chunk_size': chunk_size})\n",
    "    hit_rate = get_hit_rate(retrieval_results)\n",
    "    print('Retrieval hit rate: ', hit_rate)\n",
    "    \n",
    "    e2e_results = run_e2e_exp(DOCS_PATH, {'chunk_size': chunk_size})\n",
    "    mean_score = get_mean_score(e2e_results)\n",
    "    print('End to end mean score: ', mean_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04d6a63e-c589-405a-aa24-c8a7540f7cac",
   "metadata": {},
   "source": [
    "Seem that a larger chunk size improves retrieval, but the end-to-end generation performance plateaus at 512 (too much context might be too noisy)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7220b93-370b-4389-a815-737555025923",
   "metadata": {
    "tags": []
   },
   "source": [
    "**Note**: If we were to use larger chunk sizes, keep in mind that [most](https://huggingface.co/spaces/mteb/leaderboard) open source embedding models have a maximum sequence length of 512 sub-word tokens. This means that if our chunk contains more than 512 sub-word tokens, the embedding wouldn't account for it anyway (unless we finetune our embedding model to have longer sequence lengths)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22dbac8d-6457-4f6b-963a-265b40200055",
   "metadata": {},
   "source": [
    "### Top K"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f56bd100-b447-4611-9b29-7d8e18bcec60",
   "metadata": {},
   "source": [
    "**Note**: Keep in mind that the `chunk_size` you chose multiplied by the `top_k` below fits inside the LLM's context length. We're experimenting with the chunk size and number of chunks as if they were indepdent variables but they area heavily related. Especially since all of our LLMs have a finite maximum context length. So ideally, we would tune for a combination of `chunk_size` * `top_k`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bcf3e18d-5325-4b2a-bcc7-f06c04536959",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "TOP_K = [1, 3, 5, 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3024e655-915a-49da-9b4c-258386fe37e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running top k experiment:  1\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:08<00:00, 21.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.1694915254237288\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:30<00:00,  3.06s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:59<00:00,  5.96s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  3.9\n",
      "Running top k experiment:  3\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:08<00:00, 21.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.3615819209039548\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:31<00:00,  3.20s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:52<00:00,  5.22s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.0\n",
      "Running top k experiment:  5\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:08<00:00, 21.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.519774011299435\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:35<00:00,  3.58s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:49<00:00,  4.93s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.3\n",
      "Running top k experiment:  7\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:08<00:00, 20.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.615819209039548\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:53<00:00,  5.32s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:53<00:00,  5.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for top_k in TOP_K:\n",
    "    print('Running top k experiment: ', top_k)\n",
    "    retrieval_results = run_retrieval_exp(DOCS_PATH, {'similarity_top_k': top_k})\n",
    "    hit_rate = get_hit_rate(retrieval_results)\n",
    "    print('Retrieval hit rate: ', hit_rate)\n",
    "    \n",
    "    e2e_results = run_e2e_exp(DOCS_PATH, {'similarity_top_k': top_k})\n",
    "    mean_score = get_mean_score(e2e_results)\n",
    "    print('End to end mean score: ', mean_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25a77aa8-4f2c-4820-bc83-542a74537811",
   "metadata": {},
   "source": [
    "Increasing our number of chunks improves our retrieval and quality scores. But it also seems that the benefit of increasing the number of chunks is starting to taper off."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70cec2cf-93f3-4749-bae1-000e498ebdba",
   "metadata": {},
   "source": [
    "### Embedding Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "365e9ed1-7056-497c-b392-66a02ec59a8d",
   "metadata": {
    "tags": []
   },
   "source": [
    "So far, we've used [`thenlper/gte-base`](https://huggingface.co/thenlper/gte-base) as our embedding model because it's a relatively small (0.22 GB) and performant option. But now, let's explore other popular options such the current leader on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard), [`BAAI/bge-large-en`](https://huggingface.co/BAAI/bge-large-en) (1.34 GB), and OpenAI's [`text-embedding-ada-002`](https://openai.com/blog/new-and-improved-embedding-model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7e0333d5-2515-43df-bde7-02a5300b4b38",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "EMBED_MODELS = [\n",
    "    \"thenlper/gte-base\", \n",
    "    \"BAAI/bge-large-en\", \n",
    "    \"text-embedding-ada-002\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "61ee9b15-0eab-4638-af68-cf5b5a53363c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running embedding model experiment:  thenlper/gte-base\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:08<00:00, 21.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.519774011299435\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:42<00:00,  4.27s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [01:01<00:00,  6.16s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.25\n",
      "Running embedding model experiment:  BAAI/bge-large-en\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:11<00:00, 15.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.22033898305084745\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 1/10 [00:05<00:50,  5.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2m\u001b[1m\u001b[36m(autoscaler +16m9s)\u001b[0m [autoscaler] Downscaling node i-0c0d2d21fc3d066bf due to node idle termination.\n",
      "\u001b[2m\u001b[1m\u001b[36m(autoscaler +16m10s)\u001b[0m [autoscaler] Cluster resized to {16 CPU, 1 GPU}.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:40<00:00,  4.03s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [01:01<00:00,  6.17s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.15\n",
      "Running embedding model experiment:  text-embedding-ada-002\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:48<00:00,  3.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.384180790960452\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:48<00:00,  4.87s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:59<00:00,  5.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  3.85\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for embed_model in EMBED_MODELS:\n",
    "    print('Running embedding model experiment: ', embed_model)\n",
    "    retrieval_results = run_retrieval_exp(DOCS_PATH, {'embedding_model_name': embed_model})\n",
    "    hit_rate = get_hit_rate(retrieval_results)\n",
    "    print('Retrieval hit rate: ', hit_rate)\n",
    "    \n",
    "    e2e_results = run_e2e_exp(DOCS_PATH, {'embedding_model_name': embed_model})\n",
    "    mean_score = get_mean_score(e2e_results)\n",
    "    print('End to end mean score: ', mean_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54eb2037-6d62-4f27-9c1c-8c3e1c3bcf08",
   "metadata": {},
   "source": [
    "This is an interesting outcome because the #1 (`BAAI/bge-large-en`) on the current leaderboard isn't necessarily the best for our specific task. Using the smaller `thenlper/gte-base` produced the best retrieval and quality scores in our experiments."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b0af3af-23b7-4894-b583-f091d1c9dc4e",
   "metadata": {},
   "source": [
    "### LLMs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "961aa377-625c-445e-aed2-a7e8156b64cb",
   "metadata": {},
   "source": [
    "We're now going to use the best configurations from above to evaluate different choices for the main LLM.\n",
    "\n",
    "**Note**:\n",
    "- We've been using a specific LLM so far to decide on the configuration so that specific LLM's performance here will be a bit biased.\n",
    "- This list is not exhaustive and even for the LLMs we use, there are versions with longer context windows available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2f321c4a-2132-44a4-a293-eeaaa73fcdd6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "LLMS = [\n",
    "    \"gpt-3.5-turbo\",\n",
    "    \"gpt-4\",\n",
    "    \"meta-llama/Llama-2-7b-chat-hf\", \n",
    "    \"meta-llama/Llama-2-13b-chat-hf\", \n",
    "    \"meta-llama/Llama-2-70b-chat-hf\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "737fd4c5-c3f2-4fee-92ce-5aae4e7c0cc0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running LLM experiment:  gpt-3.5-turbo\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:08<00:00, 20.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.519774011299435\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:38<00:00,  3.86s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:58<00:00,  5.85s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.25\n",
      "Running LLM experiment:  gpt-4\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:08<00:00, 21.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.519774011299435\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [01:51<00:00, 11.17s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:59<00:00,  5.96s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.85\n",
      "Running LLM experiment:  meta-llama/Llama-2-7b-chat-hf\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:08<00:00, 21.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.519774011299435\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [01:11<00:00,  7.13s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  3.65\n",
      "Running LLM experiment:  meta-llama/Llama-2-13b-chat-hf\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:08<00:00, 20.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.519774011299435\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:59<00:00,  5.99s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [01:11<00:00,  7.17s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  3.9\n",
      "Running LLM experiment:  meta-llama/Llama-2-70b-chat-hf\n",
      "Loading from SQL dump\n",
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [00:08<00:00, 20.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.519774011299435\n",
      "Loading from SQL dump\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [01:33<00:00,  9.34s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [01:14<00:00,  7.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for llm in LLMS:\n",
    "    print('Running LLM experiment: ', llm)\n",
    "    retrieval_results = run_retrieval_exp(DOCS_PATH, {'llm_model_name': llm})\n",
    "    hit_rate = get_hit_rate(retrieval_results)\n",
    "    print('Retrieval hit rate: ', hit_rate)\n",
    "    \n",
    "    e2e_results = run_e2e_exp(DOCS_PATH, {'llm_model_name': llm})\n",
    "    mean_score = get_mean_score(e2e_results)\n",
    "    print('End to end mean score: ', mean_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a0b6b88-40c7-4886-ac17-bd8892ef1fcd",
   "metadata": {
    "tags": []
   },
   "source": [
    "We see that OpenAI models have the best performance, but `meta-llama/Llama-2-70b-chat-hf` is a strong contender with a score that surpasses `gpt-3.5-turbo`.  \n",
    "Note that since we are using `gpt-4` as the evaluator, there's some known bias where it prefers OpenAI family of models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8cc765-d64a-470e-bc3d-03252041fc76",
   "metadata": {},
   "source": [
    "**Note**: Some of our LLMs have much larger context lengths, ex. `gpt-4` is 8192 and `gpt-3.5-turbo-16k` is 16384. We could increase the number of chunks that we use for these since we saw that increasing `top_k` continued to improve the retrieval and quality scores. However, we will keep this value fixed for now since the performance started to taper off anyway and so we can compare these performances under the exact same configurations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bf29dc4-9cc2-45b8-8e19-a698d5a2ae50",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Strategy 2: Use a second-stage reranker"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b53217-cae1-4ba4-a59e-665ba2584e13",
   "metadata": {},
   "source": [
    "As we've seen above, increasing the number of retrieved documents make it more likely for us to recover the \"golden\" context that we need to answer a user query. However, it also means that we need to feed the LLM more and potentially irrelvant context, this can increase latency and make it more likely to \"distract\" the LLM.\n",
    "\n",
    "One strategy to mitigate this is to introduce a re-ranking stage to the retrieval pipeline. This way, we can first retrieve a high number of relevant nodes quickly (optimizing for recall), and then use an accurate ranking model to filter for the most relevant nodes (optimizing for precision).\n",
    "\n",
    "This can improve retrieval and generation performance, without needing to feed more context into the LLM."
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "dfbc798d-54c9-4418-864c-3ef86a134cb5",
   "metadata": {},
   "source": [
    "![reranker.png](attachment:992f1a13-3213-4845-b6e1-8191e8dd9a9e.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef97f1ac-aa16-4366-b2de-600a360bcf0f",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f0d995b0-94dc-4156-a111-b5031b153cc2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "DATASETS_DIRECTORY = Path(\"/efs/shared_storage/simon/datasets\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "37b7c35f-d0f1-4797-b939-a95fef83eab8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "from llama_index.schema import Document\n",
    "\n",
    "def read_json(filename):\n",
    "    with open(filename, 'r') as f:\n",
    "        data = json.load(f)\n",
    "    return data\n",
    "\n",
    "def to_doc(entry_dict):\n",
    "    return Document(text=entry_dict['text'], metadata={'source': entry_dict['source']}) \n",
    "\n",
    "def load_corpus(filename):\n",
    "    sections = read_json(filename)\n",
    "    docs = [to_doc(dict_) for dict_ in sections]\n",
    "    return docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "2634bf07-e960-4d72-b800-febcf60a2eb8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "docs = load_corpus(DATASETS_DIRECTORY / 'eval_full_corpus.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adcbef44-2d36-42a3-9470-4f6ce41c68d8",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Build index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a1fa1ce7-2849-4f72-904b-b112bdfa124a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d11315ae66847afb35d35d7750b8c34",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Parsing documents into nodes:   0%|          | 0/8944 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a55d74005d14d55aeee712c92f25f1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating embeddings:   0%|          | 0/9537 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "from llama_index import ServiceContext, VectorStoreIndex\n",
    "\n",
    "service_context = ServiceContext.from_defaults(\n",
    "    embed_model=HuggingFaceEmbeddings(\n",
    "        model_name='thenlper/gte-base'\n",
    "    ),\n",
    ")\n",
    "\n",
    "index = VectorStoreIndex.from_documents(\n",
    "    docs, \n",
    "    service_context=service_context, \n",
    "    show_progress=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f2fdcfb-cba5-4ac6-86b2-befffd8854be",
   "metadata": {},
   "source": [
    "#### Configure rerankers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "d1f6b891-e817-4597-bde5-eb041576f182",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from llama_index.indices.postprocessor import SentenceTransformerRerank, LLMRerank\n",
    "\n",
    "st_reranker = SentenceTransformerRerank(\n",
    "    top_n=5, model=\"cross-encoder/ms-marco-MiniLM-L-6-v2\"\n",
    ")\n",
    "\n",
    "llm_reranker = LLMRerank(\n",
    "    choice_batch_size=4, top_n=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ba7a5d-80b2-430e-889f-b728370bfc5b",
   "metadata": {},
   "source": [
    "#### Load evaluation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "bc15c518-7ebe-4de5-80c3-4702d35f1b70",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "golden_dataset_path = Path(\"../datasets/eval-dataset-v1.jsonl\")\n",
    "with open(golden_dataset_path, \"r\") as f:\n",
    "    golden_dataset = [json.loads(item) for item in list(f)]\n",
    "\n",
    "retrieval_queries = [item['question'] for item in golden_dataset]\n",
    "golden_sources = [item['source'] for item in golden_dataset]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "1f061899-e212-415d-9839-f987efa7bc84",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open(\"../datasets/golden-responses.json\", \"r\") as file:\n",
    "    golden_dataset = json.load(file)\n",
    "\n",
    "e2e_queries = [item['question'] for item in golden_dataset]\n",
    "golden_response = [item['response'] for item in golden_dataset]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f271f76-2c07-48e8-99a3-a74a2904e0ea",
   "metadata": {},
   "source": [
    "#### Run evaluation experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "0e3d493f-a502-4880-865f-7c858aedb7d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from llama_index.indices.query.schema import QueryBundle\n",
    "\n",
    "class RetrieverWithRerank:\n",
    "    def __init__(self, retriever, reranker):\n",
    "        self.retriever = retriever\n",
    "        self.reranker = reranker\n",
    "\n",
    "    def retrieve(self, query):\n",
    "        nodes = self.retriever.retrieve(query) \n",
    "        nodes = self.reranker.postprocess_nodes(nodes, QueryBundle(query))\n",
    "        return nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "7d3ec494-b73e-4c19-936c-714d128a81c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running reranker experiment:  SentenceTransformerRerank\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [01:15<00:00,  2.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.5084745762711864\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [01:00<00:00,  6.03s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [01:19<00:00,  7.96s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.4\n",
      "Running reranker experiment:  LLMRerank\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [41:28<00:00, 14.06s/it]   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval hit rate:  0.559322033898305\n",
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [01:59<00:00, 11.94s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [01:13<00:00,  7.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End to end mean score:  4.45\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for reranker in [st_reranker, llm_reranker]:\n",
    "    print('Running reranker experiment: ', reranker.__class__.__name__)\n",
    "            \n",
    "    retriever = index.as_retriever(similarity_top_k=20)\n",
    "    retriever = RetrieverWithRerank(retriever, reranker)\n",
    "    \n",
    "    retrieval_results = evaluate_retrieval(retriever, retrieval_queries, golden_sources)\n",
    "    hit_rate = get_hit_rate(retrieval_results)\n",
    "    print('Retrieval hit rate: ', hit_rate)\n",
    "    \n",
    "    query_engine = index.as_query_engine(similarity_top_k=20, node_postprocessors=[reranker])\n",
    "    e2e_results = evaluate_e2e(query_engine, e2e_queries, golden_response, verbose=True)\n",
    "    mean_score = get_mean_score(e2e_results)\n",
    "    print('End to end mean score: ', mean_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af16dc39-cae6-4c35-9619-2e149436efe7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Strategy 3: Use different data representation for retrieval vs. generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5482a0-bca0-4aed-9cb8-ed274d6c840f",
   "metadata": {},
   "source": [
    "The best data representation for retrieval isn't always the best for generation.\n",
    "\n",
    "Often, it's best to embed the knowledge corpus in very small chunk sizes for the purpose of retrieval. This is because we aren't cramming a lot of textual information into a single vector, so each vector can retain more semantic information, without having to \"average out\" the information.\n",
    "\n",
    "On the other hand, to generate high quality responses, we often need broader context around the specific information that we are querying for. For example, after retrieving a relevant code snippet, we also want to provide the surrounding documentation and discussion as context for a high quality response."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "429d1b0b-0c14-4d82-80df-f84832202ecb",
   "metadata": {},
   "source": [
    "In this section, we show how a strategy where we use small, sentence level chunks for retrieval and a surrounding window as context for generation can help improve both retrieval and generation quality."
   ]
  },
  {
   "attachments": {
    "e8bd336d-c2b7-480f-9b09-5a398d325d36.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "e0e2c8fd-08e3-44bf-a8be-b4b3b85f8d7b",
   "metadata": {},
   "source": [
    "![sentence_window.png](attachment:e8bd336d-c2b7-480f-9b09-5a398d325d36.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42f188dc-6ed7-4a03-ab50-63f79ca66157",
   "metadata": {},
   "source": [
    "### Experiment configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "44794a10-e6e2-4056-895b-aa54e87272ad",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "BASE_MODEL = 'thenlper/gte-base'  \n",
    "WINDOW_SIZE = 3\n",
    "TOP_K = 20\n",
    "\n",
    "TEST_SUBSAMPLE_N = 10\n",
    "\n",
    "EXPERIMENT_NAME = 'sentence_window'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93ce312c-6b94-4cfd-864f-1c88aadba8ac",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d59b0142-7a36-46d4-a718-aea453046d86",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "DATASETS_DIRECTORY = Path(\"/efs/shared_storage/simon/datasets\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c37c1e92-e01a-4d18-8565-ddf58fc92ace",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "from llama_index.schema import Document\n",
    "\n",
    "def read_json(filename):\n",
    "    with open(filename, 'r') as f:\n",
    "        data = json.load(f)\n",
    "    return data\n",
    "\n",
    "def to_doc(entry_dict):\n",
    "    return Document(text=entry_dict['text'], metadata={'source': entry_dict['source']}) \n",
    "\n",
    "def load_corpus(filename):\n",
    "    sections = read_json(filename)\n",
    "    docs = [to_doc(dict_) for dict_ in sections]\n",
    "    return docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dbaa805c-5f2d-4e9a-8a05-361569fa3d57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "docs = load_corpus(DATASETS_DIRECTORY / 'eval_full_corpus.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ce2f26-2405-4afb-b23b-15fd7677ea49",
   "metadata": {},
   "source": [
    "### Build index and configure query engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9084256d-746a-4a48-b86b-a9c229565f5e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c99e06c615d94a7cb63d2678faf51669",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Parsing documents into nodes:   0%|          | 0/8944 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76d7012531a64f428f27233cb12d2a95",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating embeddings:   0%|          | 0/59755 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "from llama_index import ServiceContext, set_global_service_context, VectorStoreIndex\n",
    "from llama_index.llms import OpenAI\n",
    "from llama_index.node_parser import SentenceWindowNodeParser\n",
    "from llama_index.indices.postprocessor import MetadataReplacementPostProcessor\n",
    "\n",
    "# create the sentence window node parser\n",
    "node_parser = SentenceWindowNodeParser.from_defaults(\n",
    "    window_size=WINDOW_SIZE,\n",
    "    window_metadata_key=\"window\",\n",
    "    original_text_metadata_key=\"original_text\",\n",
    ")\n",
    "\n",
    "service_context = ServiceContext.from_defaults(\n",
    "    embed_model=HuggingFaceEmbeddings(\n",
    "        model_name=BASE_MODEL\n",
    "    ),\n",
    "    node_parser=node_parser,\n",
    ")\n",
    "\n",
    "index = VectorStoreIndex.from_documents(\n",
    "    docs, \n",
    "    service_context=service_context, \n",
    "    show_progress=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "19a89f7a-3322-4004-aed3-f11be2f505b8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "retriever = index.as_retriever(similarity_top_k=TOP_K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "5931476f-9771-4309-91bb-45b8a70a3533",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "query_engine = index.as_query_engine(\n",
    "    similarity_top_k=TOP_K,\n",
    "    # the target key defaults to `window` to match the node_parser's default\n",
    "    node_postprocessors=[\n",
    "        MetadataReplacementPostProcessor(target_metadata_key=\"window\")\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e35f1d2e-41c1-4b9b-9028-0bb4f9e3678e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Run evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d4ca3bc2-7669-4851-8089-0538f69ebf37",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "\n",
    "from eval import evaluate_e2e, evaluate_retrieval"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b455d17f-1fdf-4e54-8262-c38936f5c327",
   "metadata": {},
   "source": [
    "#### Retrieval-only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "59ece77f-fd8c-4cea-ab2e-8b1e9b71f030",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "golden_dataset_path = Path(\"../datasets/eval-dataset-v1.jsonl\")\n",
    "with open(golden_dataset_path, \"r\") as f:\n",
    "    golden_dataset = [json.loads(item) for item in list(f)]\n",
    "\n",
    "queries = [item['question'] for item in golden_dataset]\n",
    "golden_sources = [item['source'] for item in golden_dataset]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e0dc6a56-48c2-4f73-9081-0cd8d2ce9ed5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 177/177 [06:18<00:00,  2.14s/it]\n"
     ]
    }
   ],
   "source": [
    "results = evaluate_retrieval(retriever, queries, golden_sources)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "becd0345-c4ad-4323-a5bb-bd240ed5e909",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.632768361581921"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hit_rate = get_hit_rate(results)\n",
    "hit_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e352b295-417d-4b9c-a121-e127c59cd41e",
   "metadata": {},
   "source": [
    "#### End-to-end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0b40255b-c731-4707-9973-163125bbf5ec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open(\"../datasets/golden-responses.json\", \"r\") as file:\n",
    "    golden_dataset = json.load(file)\n",
    "\n",
    "queries = [item['question'] for item in golden_dataset]\n",
    "golden_response = [item['response'] for item in golden_dataset]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "dda72414-4d88-4add-8177-b5411fb478cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running inference\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [02:16<00:00, 13.61s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running eval\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [01:03<00:00,  6.37s/it]\n"
     ]
    }
   ],
   "source": [
    "results = evaluate_e2e(query_engine, queries, golden_response, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "47697436-a9b5-4e15-9d64-fc2a740785b6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.2"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_score = get_mean_score(results)\n",
    "mean_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbe6a76a-d806-4d71-bed7-99e0f5c0295c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Strategy 4: Fine-tune embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "000b6a58-495c-44c8-b760-a2ce1cbed6c5",
   "metadata": {},
   "source": [
    "In this section, we explore fine-tuning embedding model to improve retrieval performance.\n",
    "We consider the \"cold start\" regime, where we haven't deployed the model, and haven't collected any user queries or labeled \"golden\" context. \n",
    "\n",
    "Therefore, we consider a synthetic data approach, where we leverage LLM to generation question, relevant context, answer pairs from our knowledge corpus."
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "id": "4e839cd5-5d05-46ee-9459-606a50d61515",
   "metadata": {},
   "source": [
    "![finetune_embedding.png](attachment:ef01d621-ba08-4948-b09a-019ea79ed5f2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f656b8f3-eae3-4e68-bba5-aac91b04d8c7",
   "metadata": {},
   "source": [
    "### Experiment configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d1fd8ae-469e-4fa6-899c-18e5150ab2e4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Since we are working with a relatively large set of documents, \n",
    "# we sub-sample the data for quicker iterations.\n",
    "SUBSAMPLE_RATIO = 0.05\n",
    "\n",
    "# can be any sentence-transformer compatible model\n",
    "# https://www.sbert.net/docs/pretrained_models.html\n",
    "BASE_MODEL = 'thenlper/gte-base'  \n",
    "\n",
    "# Select a chunk size that is: \n",
    "# 1) under the context window limit of the chosen model, and\n",
    "# 2) close to the desired chunk size at retrieval time\n",
    "FINETUNE_CHUNK_SIZE = 512\n",
    "\n",
    "\n",
    "ROOT_DIR = Path(os.getcwd()).parent\n",
    "print(ROOT_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "131f313b-03af-4596-a698-7c2921463d87",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "402f4d6a-79f8-4f85-bb89-7e119bc753c7",
   "metadata": {},
   "source": [
    "We start by loading our knowledge corpus (i.e. the ray documentation webpages that we've downloaded and parsed previously)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "21078dd5-f719-4445-82d4-21fe48432227",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "DATASETS_DIRECTORY = Path(\"/efs/shared_storage/simon/datasets\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "19e6f02c-8855-4452-878e-ba42cafc5d4f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "from llama_index.schema import Document\n",
    "\n",
    "def read_json(filename):\n",
    "    with open(filename, 'r') as f:\n",
    "        data = json.load(f)\n",
    "    return data\n",
    "\n",
    "def to_doc(entry_dict):\n",
    "    return Document(text=entry_dict['text'], metadata={'source': entry_dict['source']}) \n",
    "\n",
    "def load_corpus(filename):\n",
    "    sections = read_json(filename)\n",
    "    docs = [to_doc(dict_) for dict_ in sections]\n",
    "    return docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e0ed5169-3fb5-4e18-b51b-662c9d3f94ac",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "docs = load_corpus(DATASETS_DIRECTORY / 'eval_full_corpus.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007dc69e-a871-45fa-a005-0cfc481ab916",
   "metadata": {},
   "source": [
    "Now, we split the documents into chunks. The main considerations on the chunk size here are:\n",
    "1. need to fit into the context window of the embedding model that we want to finetune (512 for the sentence transformer model that we selected, similar to most open source models.)\n",
    "2. should be close to the chunk size we want to use at retrieval time (so it's best to run some experiments to determine the best chunk size for your application first, see strategy 1). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0535a133-5ff4-4c91-a255-a068c03de951",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from llama_index.node_parser import SimpleNodeParser\n",
    "\n",
    "parser = SimpleNodeParser.from_defaults(chunk_size=FINETUNE_CHUNK_SIZE)\n",
    "nodes = parser.get_nodes_from_documents(docs, show_progress=True)\n",
    "print('Parsed {} docs into {} nodes'.format(len(docs), len(nodes)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd7a2551-9c0a-4d32-8262-188da2e07fcf",
   "metadata": {},
   "source": [
    "### Subsample and create train/validation split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "35e7da8f-4766-4060-8ce8-5a8abb01c9e9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from utils import train_test_split, subsample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8269e608-370b-4dd2-9ad7-0e83b77abb70",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Split dataset into: 11665 train nodes, 2917 val nodes\n"
     ]
    }
   ],
   "source": [
    "train_nodes, val_nodes = train_test_split(nodes)\n",
    "print('Split dataset into: {} train nodes, {} val nodes'.format(len(train_nodes), len(val_nodes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f409e028-0363-4a19-8378-a2f3ceb3dace",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subsampled dataset into: 583 train nodes, 145 val nodes\n"
     ]
    }
   ],
   "source": [
    "train_nodes = subsample(train_nodes, SUBSAMPLE_RATIO)\n",
    "val_nodes = subsample(val_nodes, SUBSAMPLE_RATIO)\n",
    "print('Subsampled dataset into: {} train nodes, {} val nodes'.format(len(train_nodes), len(val_nodes)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3305f44d-b49d-4dd1-b3bb-c9622927ce55",
   "metadata": {},
   "source": [
    "### Generate synthetic dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95a7e80a-f36b-4622-9696-373ce7a06e72",
   "metadata": {},
   "source": [
    "Now, we will generate a synthetic dataset of question, \"golden\" context, and \"golden\" answer dataset by leveraing an LLM (by default using gpt-3.5-turbo from OpenAI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0cc7562-65f1-4670-b6d3-23129a031700",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from llama_index.finetuning import generate_qa_embedding_pairs\n",
    "\n",
    "train_dataset = generate_qa_embedding_pairs(train_nodes)\n",
    "val_dataset = generate_qa_embedding_pairs(val_nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bdd2bc9-d71c-4461-a9ac-6bd5b348b4bb",
   "metadata": {},
   "source": [
    "Let's save the dataset for future use since it's fairly time consuming to generate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "648de625-9676-49b9-b141-a157c6535f70",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataset.save_json(Path(ROOT_DIR, \"finetune/synthetic_train_dataset.json\"))\n",
    "val_dataset.save_json(Path(ROOT_DIR, \"finetune/synthetic_val_dataset.json\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a373622f-ba1e-468b-946f-82d94082ca50",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Run embedding finetuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d1c718-75d5-4f40-9547-6e76b8b9740e",
   "metadata": {},
   "source": [
    "Now, we are ready to fine-tune our embedding model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e611c7c7-51b8-42d4-9cdb-94fc2b4d1ea8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from llama_index.finetuning import EmbeddingQAFinetuneDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3b869fd8-34f1-4f9a-91c9-47b36024b248",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataset = EmbeddingQAFinetuneDataset.from_json(Path(ROOT_DIR, \"finetune/synthetic_train_dataset.json\"))\n",
    "val_dataset = EmbeddingQAFinetuneDataset.from_json(Path(ROOT_DIR, \"finetune/synthetic_val_dataset.json\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "079210b6-bbc1-4277-bb44-a41409771cec",
   "metadata": {},
   "source": [
    "We can construct a fine-tune engine, which is an easy to use interface for running fine-tuning jobs (either locally or via a API-based service).\n",
    "\n",
    "Here, we use the sentence transformer fine-tuning engine to run fine-tuning locally on the ray cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "405e5e63-61ef-4920-ba1e-b9e7ee052dce",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from llama_index.finetuning import SentenceTransformersFinetuneEngine\n",
    "\n",
    "finetune_engine = SentenceTransformersFinetuneEngine(\n",
    "    train_dataset,\n",
    "    model_id=BASE_MODEL,\n",
    "    model_output_path=\"../finetune/exp_finetune_test\",\n",
    "    val_dataset=val_dataset,\n",
    "    epochs=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdad2de8-7ee7-447a-905c-9e27681e9b8f",
   "metadata": {},
   "source": [
    "For demonstration purpose, we will run the fine-tuning job for 2 epoches over our synthetic dataset.\n",
    "In practice, you should use the validation loss to determine how many epochs to fine-tune the embedding model for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03a4347c-819b-4673-b03d-e950261e9fe9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "finetune_engine.finetune()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4705c2b6-f3d2-4404-8d41-4db76b3e600d",
   "metadata": {},
   "source": [
    "After the fine-tuning job finishes, we can easy get a referene to the fine-tuned model to be used in our LlamaIndex application."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ecfeecc2-e3e2-4313-9c3d-3c0153799948",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "embed_model = finetune_engine.get_finetuned_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc3bf8b-1943-4bbb-9166-5b068d7c179e",
   "metadata": {},
   "source": [
    "### Evaluate our fine-tuned embedding model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36776fa-ba10-4d3b-993c-d0f2842feaf6",
   "metadata": {},
   "source": [
    "Now, we will leverage the retrieval evaluation process we built out in [part 2](www.google.com) to assess the quality of our fine-tuned embedding model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23b36038-21c4-4e87-83ab-f58cc49ee200",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import json\n",
    "from pathlib import Path\n",
    "\n",
    "# Load labeled eval dataset\n",
    "golden_dataset_path = Path(\"../datasets/eval-dataset-v1.jsonl\")\n",
    "\n",
    "with open(golden_dataset_path, \"r\") as f:\n",
    "    test_dataset = [json.loads(item) for item in list(f)]\n",
    "    \n",
    "len(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9f2b3e4-3ed7-4607-a325-e577c7db1b09",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_dataset[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4bca6da6-c5a7-4c75-97ff-7732146de50a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "queries = [item['question'] for item in test_dataset]\n",
    "golden_sources = [item['source'] for item in test_dataset]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53472a36-e93f-4756-8b61-1fd30bc94295",
   "metadata": {
    "tags": []
   },
   "source": [
    "Now, we can build index with our fine-tuned embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9c7221e3-c54b-4661-9daf-8668ec058bbf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from llama_index import VectorStoreIndex, ServiceContext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ca6eeb7-dde7-4036-b38f-c4fb27159c9f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "service_context = ServiceContext.from_defaults(\n",
    "    embed_model=embed_model,\n",
    "    chunk_size=512,\n",
    ")\n",
    "\n",
    "index = VectorStoreIndex.from_documents(\n",
    "    docs, \n",
    "    service_context=service_context, \n",
    "    show_progress=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5cdba926-1fd2-429d-83b4-1375638f9dbe",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "retriever = index.as_retriever(similarity_top_k=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2748abe7-9a68-4116-aaad-f4912c04581e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Run retrieval evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dab2c5f-2e7a-420c-bbec-6d7992242cfb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from eval import evaluate_retrieval\n",
    "\n",
    "results = evaluate_retrieval(retriever, queries, golden_sources)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9fa5c8c-60c2-45fa-9f6b-abc357824afe",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hit_rate = get_hit_rate(results)\n",
    "hit_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ff61817-13a0-44cc-866a-1947f61bd420",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "average_score = sum(result[\"score\"] for result in results) / len(results)\n",
    "average_score"
   ]
  }
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