llama_basic_rag.ipynb

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   "source": [
    "# Open-Source only RAG\n",
    "\n",
    "- Sentence Transformers as embedding model\n",
    "- Postgres as vector store\n",
    "- Llama 2 as LLM"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {
    "id": "63935557-a11c-4a22-9248-9c746cc89c4c"
   },
   "source": [
    "#### Sentence Transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7108d3f",
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    "colab": {
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    },
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   },
   "outputs": [],
   "source": [
    "#%pip install llama-index-readers-file pymupdf\n",
    "#%pip install llama-index-vector-stores-postgres\n",
    "#%pip install llama-index-embeddings-huggingface\n",
    "#%pip install llama-index-llms-llama-cpp"
   ]
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     "text": [
      "/Users/busraoguzoglu/Desktop/Okul/586Project/.venv/lib/python3.11/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": [
    "# sentence transformers\n",
    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
    "embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df10089c-917e-4191-a718-0ef7149a6a1e",
   "metadata": {
    "id": "df10089c-917e-4191-a718-0ef7149a6a1e"
   },
   "source": [
    "#### Llama CPP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "85f8a556-9f37-42a3-a88a-f688ad355ee5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "85f8a556-9f37-42a3-a88a-f688ad355ee5",
    "outputId": "78e48d53-104f-40c2-b237-2b05f2158f3d"
   },
   "outputs": [],
   "source": [
    "#!pip install llama-cpp-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3cb975f6-c192-4a26-ae50-e9a319d2a66b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3cb975f6-c192-4a26-ae50-e9a319d2a66b",
    "outputId": "a6d22056-2611-4376-ebe5-068bfa550ce3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/busraoguzoglu/Desktop/Okul/586Project/.venv/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field \"model_url\" in LlamaCPP has conflict with protected namespace \"model_\".\n",
      "\n",
      "You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.\n",
      "  warnings.warn(\n",
      "/Users/busraoguzoglu/Desktop/Okul/586Project/.venv/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field \"model_path\" in LlamaCPP has conflict with protected namespace \"model_\".\n",
      "\n",
      "You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.\n",
      "  warnings.warn(\n",
      "/Users/busraoguzoglu/Desktop/Okul/586Project/.venv/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field \"model_kwargs\" in LlamaCPP has conflict with protected namespace \"model_\".\n",
      "\n",
      "You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.\n",
      "  warnings.warn(\n",
      "llama_model_loader: loaded meta data with 19 key-value pairs and 363 tensors from /Users/busraoguzoglu/Library/Caches/llama_index/models/llama-2-13b-chat.Q4_0.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              = 2\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 q4_0:  281 tensors\n",
      "llama_model_loader: - type q6_K:    1 tensors\n",
      "llm_load_vocab: special tokens cache size = 3\n",
      "llm_load_vocab: token to piece cache size = 0.1684 MB\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: vocab_only       = 0\n",
      "llm_load_print_meta: n_ctx_train      = 4096\n",
      "llm_load_print_meta: n_embd           = 5120\n",
      "llm_load_print_meta: n_layer          = 40\n",
      "llm_load_print_meta: n_head           = 40\n",
      "llm_load_print_meta: n_head_kv        = 40\n",
      "llm_load_print_meta: n_rot            = 128\n",
      "llm_load_print_meta: n_swa            = 0\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: f_logit_scale    = 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: causal attn      = 1\n",
      "llm_load_print_meta: pooling type     = 0\n",
      "llm_load_print_meta: rope type        = 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_ctx_orig_yarn  = 4096\n",
      "llm_load_print_meta: rope_finetuned   = unknown\n",
      "llm_load_print_meta: ssm_d_conv       = 0\n",
      "llm_load_print_meta: ssm_d_inner      = 0\n",
      "llm_load_print_meta: ssm_d_state      = 0\n",
      "llm_load_print_meta: ssm_dt_rank      = 0\n",
      "llm_load_print_meta: ssm_dt_b_c_rms   = 0\n",
      "llm_load_print_meta: model type       = 13B\n",
      "llm_load_print_meta: model ftype      = Q4_0\n",
      "llm_load_print_meta: model params     = 13.02 B\n",
      "llm_load_print_meta: model size       = 6.86 GiB (4.53 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_print_meta: max token length = 48\n",
      "llm_load_tensors: ggml ctx size =    0.34 MiB\n",
      "ggml_backend_metal_log_allocated_size: allocated buffer, size =   170.20 MiB, ( 1226.59 / 21845.34)\n",
      "llm_load_tensors: offloading 1 repeating layers to GPU\n",
      "llm_load_tensors: offloaded 1/41 layers to GPU\n",
      "llm_load_tensors:        CPU buffer size =  7023.90 MiB\n",
      "llm_load_tensors:      Metal buffer size =   170.20 MiB\n",
      "...................................................................................................\n",
      "llama_new_context_with_model: n_ctx      = 3904\n",
      "llama_new_context_with_model: n_batch    = 512\n",
      "llama_new_context_with_model: n_ubatch   = 512\n",
      "llama_new_context_with_model: flash_attn = 0\n",
      "llama_new_context_with_model: freq_base  = 10000.0\n",
      "llama_new_context_with_model: freq_scale = 1\n",
      "ggml_metal_init: allocating\n",
      "ggml_metal_init: found device: Apple M2 Pro\n",
      "ggml_metal_init: picking default device: Apple M2 Pro\n",
      "ggml_metal_init: using embedded metal library\n",
      "ggml_metal_init: GPU name:   Apple M2 Pro\n",
      "ggml_metal_init: GPU family: MTLGPUFamilyApple8  (1008)\n",
      "ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)\n",
      "ggml_metal_init: GPU family: MTLGPUFamilyMetal3  (5001)\n",
      "ggml_metal_init: simdgroup reduction support   = true\n",
      "ggml_metal_init: simdgroup matrix mul. support = true\n",
      "ggml_metal_init: hasUnifiedMemory              = true\n",
      "ggml_metal_init: recommendedMaxWorkingSetSize  = 22906.50 MB\n",
      "llama_kv_cache_init:        CPU KV buffer size =  2973.75 MiB\n",
      "llama_kv_cache_init:      Metal KV buffer size =    76.25 MiB\n",
      "llama_new_context_with_model: KV self size  = 3050.00 MiB, K (f16): 1525.00 MiB, V (f16): 1525.00 MiB\n",
      "llama_new_context_with_model:        CPU  output buffer size =     0.12 MiB\n",
      "llama_new_context_with_model:      Metal compute buffer size =   352.63 MiB\n",
      "llama_new_context_with_model:        CPU compute buffer size =   352.63 MiB\n",
      "llama_new_context_with_model: graph nodes  = 1286\n",
      "llama_new_context_with_model: graph splits = 627\n",
      "AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 1 | SVE = 0 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | \n",
      "Model metadata: {'general.quantization_version': '2', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'tokenizer.ggml.bos_token_id': '1', 'tokenizer.ggml.model': 'llama', 'llama.attention.head_count_kv': '40', 'llama.context_length': '4096', 'llama.attention.head_count': '40', 'llama.rope.dimension_count': '128', 'general.file_type': '2', 'llama.feed_forward_length': '13824', 'llama.embedding_length': '5120', 'llama.block_count': '40', 'general.architecture': 'llama', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'general.name': 'LLaMA v2'}\n",
      "Using fallback chat format: llama-2\n"
     ]
    }
   ],
   "source": [
    "from llama_index.llms.llama_cpp import LlamaCPP\n",
    "\n",
    "# model_url = \"https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/resolve/main/llama-2-13b-chat.ggmlv3.q4_0.bin\"\n",
    "model_url = \"https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q4_0.gguf\"\n",
    "\n",
    "llm = LlamaCPP(\n",
    "    # You can pass in the URL to a GGML model to download it automatically\n",
    "    model_url=model_url,\n",
    "    # optionally, you can set the path to a pre-downloaded model instead of model_url\n",
    "    model_path=None,\n",
    "    temperature=0.1,\n",
    "    max_new_tokens=256,\n",
    "    # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room\n",
    "    context_window=3900,\n",
    "    # kwargs to pass to __call__()\n",
    "    generate_kwargs={},\n",
    "    # kwargs to pass to __init__()\n",
    "    # set to at least 1 to use GPU\n",
    "    model_kwargs={\"n_gpu_layers\": 1},\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba02cfe2-8b51-4e01-a840-d6508c76ade3",
   "metadata": {
    "id": "ba02cfe2-8b51-4e01-a840-d6508c76ade3"
   },
   "source": [
    "#### Postgres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "347fbc8c-776a-43df-add5-8ead2b1a6e44",
   "metadata": {
    "id": "347fbc8c-776a-43df-add5-8ead2b1a6e44"
   },
   "outputs": [],
   "source": [
    "#!pip install psycopg2-binary pgvector asyncpg \"sqlalchemy[asyncio]\" greenlet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e730f40-5aa4-4e89-b4ff-26d84183dfed",
   "metadata": {
    "id": "8e730f40-5aa4-4e89-b4ff-26d84183dfed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to rag_db successfully.\n"
     ]
    }
   ],
   "source": [
    "import psycopg2\n",
    "\n",
    "# Define your database connection parameters\n",
    "db_name = \"rag_db\"  # Use the existing database name here\n",
    "host = \"localhost\"\n",
    "password = \"password\"  # Replace with your actual PostgreSQL password\n",
    "port = \"5432\"  # Default PostgreSQL port\n",
    "user = \"myuser\"  # Replace with your actual PostgreSQL username\n",
    "\n",
    "# Connect directly to the 'rag_db' database\n",
    "conn = psycopg2.connect(\n",
    "    dbname=db_name,  # Connect directly to 'rag_db'\n",
    "    host=host,\n",
    "    password=password,\n",
    "    port=port,\n",
    "    user=user,\n",
    ")\n",
    "conn.autocommit = True\n",
    "\n",
    "# Optionally, perform any operations on 'rag_db' using a cursor\n",
    "with conn.cursor() as c:\n",
    "    # Drop and create operations are not needed if the database already exists\n",
    "    # Here you can perform other database setup actions if necessary\n",
    "    print(f\"Connected to {db_name} successfully.\")\n",
    "\n",
    "# Close the connection when done\n",
    "#conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504c0237",
   "metadata": {},
   "source": [
    "Create table (checks if exist, truncates if already exist and generates again)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ba2d25a-a0aa-4d11-93b0-6288dc008148",
   "metadata": {
    "id": "6ba2d25a-a0aa-4d11-93b0-6288dc008148"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table 'data_pmc_table' exists. Cleaning it...\n",
      "Table 'data_pmc_table' has been created.\n"
     ]
    }
   ],
   "source": [
    "from sqlalchemy import create_engine, text\n",
    "from sqlalchemy.exc import OperationalError\n",
    "from llama_index.vector_stores.postgres import PGVectorStore\n",
    "\n",
    "# Connection parameters\n",
    "db_params = {\n",
    "    \"user\": \"myuser\",      # Replace with your actual username\n",
    "    \"password\": \"password\",  # Replace with your actual password\n",
    "    \"host\": \"localhost\",   # Adjust if your database is hosted elsewhere\n",
    "    \"port\": \"5432\",        # Default PostgreSQL port\n",
    "    \"database\": \"rag_db\"   # Replace with your actual database name\n",
    "}\n",
    "\n",
    "# Create SQLAlchemy engine\n",
    "engine = create_engine(f\"postgresql://{db_params['user']}:{db_params['password']}@{db_params['host']}:{db_params['port']}/{db_params['database']}\")\n",
    "\n",
    "# Update table name to include 'data_' prefix\n",
    "table_name = \"data_pmc_table\"  # Postgre adds 'data' in front of table_name\n",
    "embed_dim = 384  # Embedding dimension\n",
    "\n",
    "try:\n",
    "    with engine.connect() as conn:\n",
    "        # Check if the table exists\n",
    "        result = conn.execute(text(f\"\"\"\n",
    "            SELECT EXISTS (\n",
    "                SELECT FROM information_schema.tables \n",
    "                WHERE table_schema = 'public' AND table_name = :table_name\n",
    "            );\n",
    "        \"\"\"), {\"table_name\": table_name}).scalar()\n",
    "\n",
    "        if result:\n",
    "            print(f\"Table '{table_name}' exists. Cleaning it...\")\n",
    "            conn.execute(text(f\"TRUNCATE TABLE {table_name};\"))\n",
    "        else:\n",
    "            print(f\"Table '{table_name}' does not exist. Creating it...\")\n",
    "\n",
    "        # Create a new table for vector store\n",
    "        vector_store = PGVectorStore.from_params(\n",
    "            database=db_params[\"database\"],\n",
    "            host=db_params[\"host\"],\n",
    "            password=db_params[\"password\"],\n",
    "            port=db_params[\"port\"],\n",
    "            user=db_params[\"user\"],\n",
    "            table_name=\"pmc_table\",  # Use unprefixed name; PGVectorStore adds 'data_' automatically\n",
    "            embed_dim=embed_dim,\n",
    "        )\n",
    "        print(f\"Table '{table_name}' has been created.\")\n",
    "except OperationalError as e:\n",
    "    print(f\"Error: {e}\")\n",
    "    print(\"Make sure the database exists and connection parameters are correct.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e65e0a69-0668-4df4-a809-71f38695cfea",
   "metadata": {
    "id": "e65e0a69-0668-4df4-a809-71f38695cfea"
   },
   "source": [
    "## Ingestion Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a35d89",
   "metadata": {},
   "source": [
    "### Data Loading and Preprocessing:\n",
    "\n",
    "Preprocessing of the document does this:\n",
    "\n",
    "1- Remove all text after the stopwords, like 'References', because they do not have any information for us.\n",
    "\n",
    "2- Remove section numbering, letters, extra spaces, new lines.\n",
    "\n",
    "3- Remove 'tables' from the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "640fcc02-0d50-4443-b8c7-f3953e006461",
   "metadata": {
    "id": "640fcc02-0d50-4443-b8c7-f3953e006461"
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from llama_index.readers.file import PyMuPDFReader\n",
    "from llama_index.core.node_parser import SentenceSplitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e329aa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "# Define stop keywords\n",
    "stop_keywords = [\"References\", \"REFERENCES\", \"references\", \"Conflict of interest\", \"Conflicts of Interest\", \"Conflicts\", \n",
    "                 \"ACKNOWLEDGMENTS\", \"AUTHOR CONTRIBUTIONS\"]\n",
    "\n",
    "def preprocess_text(text, stop_keywords):\n",
    "    \"\"\"Preprocess text by stopping at the earliest occurrence of any keyword, ignoring case.\"\"\"\n",
    "    earliest_position = len(text)  # Default to the end of the text\n",
    "    \n",
    "    for keyword in stop_keywords:\n",
    "        pattern = rf\"\\b{re.escape(keyword)}\\b\"  # Match exact word boundaries\n",
    "        match = re.search(pattern, text, re.IGNORECASE)\n",
    "        if match:\n",
    "            print(f\"Keyword '{keyword}' found at position {match.start()}\")  # Debugging\n",
    "            earliest_position = min(earliest_position, match.start())\n",
    "    \n",
    "    # Truncate the text at the earliest keyword match\n",
    "    return text[:earliest_position]\n",
    "\n",
    "def clean_text(block_text):\n",
    "    \"\"\"Clean and process individual blocks of text.\"\"\"\n",
    "    block_text = \" \".join(block_text.split())  # Remove extra spaces and newlines\n",
    "    block_text = re.sub(r'^\\d+\\.\\s+', '', block_text)  # Remove section numbering\n",
    "    block_text = re.sub(r'[^A-Za-z0-9\\s,.]', '', block_text)  # Keep only basic punctuation and letters\n",
    "\n",
    "    # Heuristic: Skip blocks that appear to be tables (e.g., many numbers or special formats)\n",
    "    if len(block_text) < 10 or re.match(r'Page \\d+', block_text):\n",
    "        return None  # Skip short blocks or page numbers\n",
    "\n",
    "    # Remove blocks that have a high density of numbers or specific patterns (tables)\n",
    "    if re.search(r'\\d{2,}', block_text) and re.search(r'(\\d+\\.\\d+|\\d+%)', block_text):\n",
    "        return None  # Skip blocks with many numeric values\n",
    "\n",
    "    # Remove blocks with common table headers like 'Table', 'Mediation Effect', etc.\n",
    "    if re.search(r'\\b(Table|Effect|Mediation|Summary|IV|Mediator|Cont)\\b', block_text, re.IGNORECASE):\n",
    "        return None\n",
    "\n",
    "    return block_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1bd9d0ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.pdf: 10 documents loaded\n",
      "Keyword 'References' found at position 2699\n",
      "Keyword 'REFERENCES' found at position 2699\n",
      "Keyword 'references' found at position 2699\n",
      "4.pdf: 12 documents loaded\n",
      "Keyword 'References' found at position 1735\n",
      "Keyword 'REFERENCES' found at position 1735\n",
      "Keyword 'references' found at position 1735\n",
      "Keyword 'ACKNOWLEDGMENTS' found at position 1191\n",
      "Keyword 'AUTHOR CONTRIBUTIONS' found at position 34\n",
      "1.pdf: 18 documents loaded\n",
      "Keyword 'References' found at position 2673\n",
      "Keyword 'REFERENCES' found at position 2673\n",
      "Keyword 'references' found at position 2673\n",
      "Keyword 'Conflicts of Interest' found at position 2604\n",
      "Keyword 'Conflicts' found at position 2604\n",
      "Keyword 'ACKNOWLEDGMENTS' found at position 2413\n",
      "Keyword 'AUTHOR CONTRIBUTIONS' found at position 1263\n",
      "3.pdf: 16 documents loaded\n",
      "Keyword 'References' found at position 1400\n",
      "Keyword 'REFERENCES' found at position 1400\n",
      "Keyword 'references' found at position 1400\n",
      "2.pdf: 10 documents loaded\n",
      "Keyword 'ACKNOWLEDGMENTS' found at position 5396\n",
      "Keyword 'AUTHOR CONTRIBUTIONS' found at position 4939\n",
      "Keyword 'References' found at position 206\n",
      "Keyword 'REFERENCES' found at position 206\n",
      "Keyword 'references' found at position 206\n",
      "Processed documents written to processed_documents.txt\n"
     ]
    }
   ],
   "source": [
    "# Load and preprocess documents\n",
    "folder_path = Path(\"./data\")\n",
    "documents = []\n",
    "\n",
    "output_path = Path(\"./processed_documents.txt\")  # File to save processed documents\n",
    "with output_path.open(\"w\", encoding=\"utf-8\") as output_file:\n",
    "    for pdf_file in folder_path.glob(\"*.pdf\"):\n",
    "        loader = PyMuPDFReader()\n",
    "        loaded_docs = loader.load(file_path=str(pdf_file))\n",
    "        print(f\"{pdf_file.name}: {len(loaded_docs)} documents loaded\")\n",
    "\n",
    "        for doc_idx, doc in enumerate(loaded_docs):\n",
    "            # Preprocess text\n",
    "            processed_text = preprocess_text(doc.text, stop_keywords)\n",
    "            \n",
    "            # Clean and process each block of text\n",
    "            cleaned_blocks = []\n",
    "            for block in processed_text.split(\"\\n\"):\n",
    "                cleaned_block = clean_text(block)\n",
    "                if cleaned_block:\n",
    "                    cleaned_blocks.append(cleaned_block)\n",
    "            \n",
    "            final_text = \"\\n\".join(cleaned_blocks)\n",
    "            doc.text = final_text  # Replace with cleaned and processed text\n",
    "            doc.metadata = {\"source\": pdf_file.name}  # Add metadata\n",
    "            documents.append(doc)\n",
    "            \n",
    "            # Write the processed text into the output file\n",
    "            output_file.write(f\"Document {doc_idx} from {pdf_file.name}:\\n\")\n",
    "            output_file.write(final_text + \"\\n\")\n",
    "            output_file.write(\"=\"*80 + \"\\n\")  # Separator between documents\n",
    "\n",
    "print(f\"Processed documents written to {output_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b581ed9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doc ID: 3a625f1e-3131-4683-afab-09e572918015\n",
      "Text: MENGI ELIK et al. Roininen, K., Tuorila, H., Zandstra, E., de\n",
      "Graaf, C., Vehkalahti, K., Stubenitsky, K.,  Mela, D. J. 2001.\n",
      "Differences in health and taste attitudes and reported behaviour among\n",
      "Finnish, Dutch and British consumers A crossnational validation of the\n",
      "health and taste atti tude scales HTAS. Appetite, 371, 3345. Steptoe,\n",
      "A., Wardle...\n",
      "66\n"
     ]
    }
   ],
   "source": [
    "print(documents[65])\n",
    "print(len(documents))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c001b7c-3e79-4d11-bd0e-dc774da25de1",
   "metadata": {
    "id": "9c001b7c-3e79-4d11-bd0e-dc774da25de1"
   },
   "source": [
    "### Splitting Documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c8125e1e-097a-4588-a65a-102dba5b8eff",
   "metadata": {
    "id": "c8125e1e-097a-4588-a65a-102dba5b8eff"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "\n",
    "text_parser = SentenceSplitter(\n",
    "    chunk_size=1024,\n",
    "    # separator=\" \",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "972b4008-eb9f-48e6-95c8-1328e231f98b",
   "metadata": {
    "id": "972b4008-eb9f-48e6-95c8-1328e231f98b"
   },
   "outputs": [],
   "source": [
    "text_chunks = []\n",
    "# maintain relationship with source doc index, to help inject doc metadata in (3)\n",
    "doc_idxs = []\n",
    "for doc_idx, doc in enumerate(documents):\n",
    "    cur_text_chunks = text_parser.split_text(doc.text)\n",
    "    #print(f\"Document {doc_idx} has {len(cur_text_chunks)} chunks\")\n",
    "    text_chunks.extend(cur_text_chunks)\n",
    "    doc_idxs.extend([doc_idx] * len(cur_text_chunks))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "100a3730",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87\n"
     ]
    }
   ],
   "source": [
    "print(len(text_chunks))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aaae403-d46e-450b-b19f-27ca66e28f1c",
   "metadata": {
    "id": "5aaae403-d46e-450b-b19f-27ca66e28f1c"
   },
   "source": [
    "### Construct Nodes from Text Chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c3afadfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.schema import TextNode\n",
    "\n",
    "nodes = []\n",
    "for idx, text_chunk in enumerate(text_chunks):\n",
    "    node = TextNode(\n",
    "        text=text_chunk,\n",
    "        metadata={\n",
    "            **documents[doc_idxs[idx]].metadata,  # This now includes 'source'\n",
    "            \"doc_index\": doc_idxs[idx]  # Optional: document index\n",
    "        }\n",
    "    )\n",
    "    nodes.append(node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e3c24558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextNode(id_='a3c0669e-4d30-47ae-b8c4-4568d52ecd46', embedding=None, metadata={'source': '5.pdf', 'doc_index': 1}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='This change in the lifestyle of the population is partly, due to\\nthe need for measures to contain the spread of COVID19, such\\nas social isolation. This has proven to be effective8 and has led\\nmany people to perform their work remotely.9 This mobility\\nrestriction has had direct effects on psychological factors, such\\nas an increase in cases of anxiety and depression and a reduction\\nin the practice of physical activities.1012 In addition, eating\\nhabits were also inuenced both by economic factors, due to the\\nreduction in the populations income, as well as by the\\nconsumption of foods with higher energy density.13,14\\nIn the period before the pandemic, the consumption of fresh\\nand minimally processed foods represented approximately 70\\nof the total caloric intake by the Brazilian population, and ultra\\nprocessed foods contributed to onefth of the calories\\nconsumed.15 Such studies enhance the need for attention to\\ndietary practices, as the consumption of ultraprocessed foods\\ntends to have a greater energy density, greater amounts of\\nsaturated and trans fat, sodium, sugar, and less bres, which\\nsuggests an increased risk of developing obesity and other\\nchronic diseases related to food intake.16\\nChanges in eating habits were also observed during the\\nCOVID19 pandemic. Staying at home has also led to the\\ndevelopment of unbalanced eating habits The COVID19\\npandemic has resulted in a sedentary life for people under\\nlockdowns and excessive food consumption due to spending\\nmuch time at home.17 Due to connement, some individuals\\nincreased the number of meals eaten throughout the day and\\nconsumed more snacks between meals.18 Additionally, an\\nincrease in the consumption of ultraprocessed products was\\nalso observed among people with lower education.19 On the\\nother hand, other studies did not show changes in the eating\\npatterns of individuals during the pandemic.10,13\\nIn addition, some individuals improved their diet during this\\nperiod,13,20 with a greater consumption of vegetables, fruits,\\nbeans, and other legumes.19 One of the factors that may be\\nassociated with this diet improvement is the fear of health\\nis also worth noting that due to the pandemic and social isolation\\nmeasures,1 food consumption at home increased. This fact, as\\nwell as having meals with the family, may have resulted in a\\npositive association to improve the nutritional quality of these\\nSocial isolation measures also cause conditions that favour\\nphysical inactivity and sedentary behaviour.18 Yet, the\\nmaintenance or improvement of physical activity levels became\\nessential during the pandemic, as it maintains the physiological\\nfunctions that help to reduce the physical and mental\\nconsequences of COVID19,11,21 besides contributing to the\\nreduction of anxiety symptoms, improving sleep and the\\nimmune system stimulation.12 For example, Taheri et al.3\\nfound that increased physical activity during lockdowns was\\nlinked to reduced rates of depression, likely because of the\\nbenecial effects of vigorous exercise. The research under\\nscored a link between emotional eating and mood disorders like\\ndepression, anxiety, and stress, particularly among elite athletes\\nwith decreased physical activity. Emotional eating, prompted by\\nnegative emotions such as stress and loneliness, can impact\\ndietary decisions, and vice versa.\\nAlthough some studies have assessed the diet10,12,13,18,19 and\\nsome isolated factors such as physical activity11,12,20 and the\\nindividuals psychological factors10,21 during the pandemic,\\nlittle is known about the association between eating habits and\\nhealthpromoting actions adopted by people during this period,\\nespecially by those who work remotely. Therefore, this study\\naimed to evaluate the workers healthy eating practices and\\nverify their relationship with the quality of life and physical\\nactivity of these individuals during the COVID19 pandemic.\\nMaterials and methods\\nType of study, sample design and ethics committee\\nExploratory, quantitative, descriptive, and crosssectional study.\\nThis study was conducted according to the guidelines laid down\\nin the Declaration of Helsinki and all procedures involving\\nhuman subjects were approved by the Research Ethics\\nCommittee of the Federal University of Paran CAAE N.\\nobtained from all subjects.\\nThe study was conducted online with adult workers 18\\nyears old who were allowed to have meals at the Food and\\nNutrition Units FNU of two companies accredited to the\\nWorkers Food Program WFP, located in the city of Curitiba,\\nBrazil, which were selected for convenience.', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65eac30b-27ec-4206-947c-81104dc8babe",
   "metadata": {
    "id": "65eac30b-27ec-4206-947c-81104dc8babe"
   },
   "source": [
    "### Generate Embeddings for each Node\n",
    "\n",
    "Using sentence_transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6363aa6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for node in nodes:\n",
    "    node_embedding = embed_model.get_text_embedding(\n",
    "        node.get_content(metadata_mode=\"text\")\n",
    "    )\n",
    "    node.embedding = node_embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e5b4d10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "[0.002242590067908168, 0.025284932926297188, 0.03787685185670853, 0.017316807061433792, 0.035318728536367416, 0.045880142599344254, 0.02748068794608116, -0.004848837852478027, 0.00520316930487752, -0.02245437726378441, -0.012843466363847256, -0.07361365854740143, 0.0024977654684334993, 0.016995027661323547, 0.0012620296329259872, -0.028180761262774467, -0.0007740127039141953, -0.02978023886680603, 0.00105327891651541, 0.019632909446954727, -0.015793396160006523, -0.012700777500867844, -0.015133718959987164, -0.005372608546167612, 0.041983943432569504, 0.017965346574783325, -0.01487655844539404, -0.03357880935072899, -0.07126981765031815, -0.20361019670963287, -0.021399321034550667, -0.03841941058635712, 0.03099704347550869, -0.01860302872955799, -0.057519495487213135, -0.005936900153756142, 0.03111499361693859, 0.016658559441566467, -0.00020628668426070362, -0.011791287921369076, 0.0005544592277146876, 0.042607758194208145, 0.04287157580256462, -0.04061812534928322, 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-0.00683241244405508, 0.049907438457012177, 0.005639525130391121, 0.002802118193358183, 0.010164890438318253, -0.012285984121263027, 0.004141231533139944, -0.0024354292545467615, 0.003955172840505838, 0.04352770000696182, -0.004585852846503258, -0.024837587028741837, 3.974588616983965e-05, -0.06868946552276611, 0.008239241316914558, -0.038643211126327515, -0.019851498305797577, -0.04066988080739975, -0.015195436775684357, -0.016213329508900642, 0.01533939503133297, -0.0015234042657539248, -0.02014482393860817, 0.018614765256643295, -0.013393723405897617, 0.00035620536073111, -0.03432205691933632, 0.030388325452804565, -0.03320643678307533, 0.026439234614372253, 0.010864447802305222, -0.031249770894646645, 0.001946020289324224, 0.036778565496206284, 0.03069119341671467, -0.00039876578375697136, 0.03173777088522911, 0.046246498823165894, 0.01835496909916401, -0.017597755417227745, 0.0259685181081295, 0.030262762680649757, -0.0026028924621641636, -0.03433782607316971, -0.1921631097793579, -0.004565014969557524, -0.017545638605952263, -0.01064576767385006, 0.01645556092262268, 0.0267029982060194, -0.004670586436986923, -0.018074383959174156, 0.033894386142492294, 0.043526507914066315, 0.025777962058782578, 0.030044812709093094, -0.07079204171895981, 0.009976845234632492, -0.007210322190076113, 0.006094967480748892, 0.03448737412691116, -0.016350209712982178, -0.05191349983215332, 0.011105784215033054, 0.02397254668176174, -0.025739358738064766, -0.01711621694266796, 0.009901708923280239, 0.08650289475917816, -0.018478430807590485, 0.13852138817310333, 0.03409045562148094, 0.005977158434689045, -0.009918534196913242, -0.016942966729402542, 0.019497854635119438, 0.006882022134959698, -0.13172666728496552, 0.02631423808634281, -0.00010380897583672777, 0.029436111450195312, -0.010009640827775002, -0.0699744001030922, -0.01978972740471363, -0.02264607697725296, 0.0032593978103250265, -0.012121242471039295, -0.03574235364794731, -0.049526844173669815, -0.0072351437993347645, 0.010021239519119263, 0.015042934566736221, -0.007613723631948233, -0.00261043943464756, -0.0005215888377279043, 0.020002994686365128, 0.005736755672842264, 0.0038807711098343134, -0.020388778299093246, -0.0022140974178910255, -0.0663839653134346, 0.017325887456536293, -0.054709676653146744, -0.003797190962359309, -0.043754685670137405, -0.015855085104703903, 0.033008720725774765, -0.027634024620056152, 0.023319920524954796, -0.007978891022503376, -0.000751338608097285, 0.002906056120991707, 0.014440339058637619, -0.0033666747622191906, 0.0010008501121774316, 0.09670896083116531, -0.02343018911778927, -0.03786907717585564, 0.03707652539014816, -0.005151569377630949, -0.016016876325011253, -0.04119575768709183, -0.03698486462235451, -0.050295595079660416, 0.03974775969982147, -0.047675568610429764, 0.009599569253623486, 0.026942402124404907, 0.04771116003394127, -0.018892407417297363, 0.03843342512845993, 0.0020755724981427193, 0.023275291547179222, -0.04549434036016464, 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-0.011039351113140583, -0.04051142558455467, 0.01004552748054266, 0.02850707620382309, -0.04607021436095238, 0.015405644662678242, -0.0426204651594162, -0.031587760895490646, -0.06538783758878708, 0.008935515768826008, 0.02798296883702278, -0.0073683843947947025, -0.039758145809173584, 0.048603467643260956, 0.009606742300093174, 0.04061078280210495, -0.015339182689785957, 0.025729844346642494, -0.018629731610417366, 0.031830914318561554, 0.014351311139762402, -0.0011711473343893886, 0.0006340436520986259, -0.07966912537813187, 0.0490456186234951, 0.030710244551301003]\n",
      "87\n"
     ]
    }
   ],
   "source": [
    "print(type(nodes[0].embedding))\n",
    "print(nodes[0].embedding)\n",
    "print(len(nodes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7fe354a-4f87-4b13-aae4-f7b7b1fe118e",
   "metadata": {
    "id": "e7fe354a-4f87-4b13-aae4-f7b7b1fe118e"
   },
   "source": [
    "### Load Nodes into a Vector Store\n",
    "\n",
    "We now insert these nodes into our `PostgresVectorStore`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "44add008-616b-4e47-8f61-553befeb7ca4",
   "metadata": {
    "id": "44add008-616b-4e47-8f61-553befeb7ca4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['9335822d-da95-4cd1-96b0-26f37630c81f',\n",
       " 'a3c0669e-4d30-47ae-b8c4-4568d52ecd46',\n",
       " '5bb6a178-5c8c-411f-99a6-587b88bb594c',\n",
       " 'a7840a08-a6f2-49f9-81b0-aa21124950e9',\n",
       " '1436a6d7-5e34-40cf-84f2-d62670bd8305',\n",
       " '58db4ed5-b2be-429f-92b3-741cc8754dc1',\n",
       " '15bda1ff-90bb-47a6-9393-d7c42a535db9',\n",
       " 'ffa85912-c596-4048-aa35-cca55b75173a',\n",
       " 'f6e6da0f-076c-4eb6-a118-3bf2b65620de',\n",
       " 'c8797635-28db-4f9d-9851-87bb5ae3df1b',\n",
       " 'dcf5d20e-7f74-4394-988c-7a2ebe0a19df',\n",
       " '2f7d1c22-8e50-4404-8771-9ef32f2ee8c5',\n",
       " 'e6cf3c08-6c1d-478e-8fbb-f393dabb73db',\n",
       " '32bae8d1-237a-4e00-a455-c4448dc336c1',\n",
       " '83fa0ae2-b33b-4d81-bd36-cc8bc91fddba',\n",
       " '69714e59-74f8-4797-8fec-8520917fe2aa',\n",
       " 'f9409bd8-bf9f-4236-8592-d49a36a99e63',\n",
       " 'a752f5d2-84f2-4ac2-b5e5-aa1877a3dda1',\n",
       " '255d8b17-4442-4a58-92e3-e9f791abf2d2',\n",
       " '321cec65-2444-4d89-a8b6-c25c4b7cbb6e',\n",
       " 'e1e6f2b8-300e-435f-908f-fdb4260ca4bf',\n",
       " '43829464-cd11-4b0f-a481-565548c5c5a2',\n",
       " '8f6ebc90-f1ac-4d54-8d20-ac7a82c3ba82',\n",
       " '76e4559c-3c20-4ec9-ac2d-237ad8cbb989',\n",
       " '1c222097-9e77-49fa-948b-fc09a3f436e9',\n",
       " 'a9c86046-7207-428e-bc84-fdd624a39d82',\n",
       " '87a5b63c-b358-41f8-aca7-c270f3e5aa17',\n",
       " '99adcb58-621f-4485-a7be-aa4af06a4919',\n",
       " '9d74ef88-8215-4e71-b380-4d49f595da28',\n",
       " 'db47b90d-2093-418b-9f9c-5526581dc4d8',\n",
       " 'c83528eb-0413-4c88-bed7-c0f71cef7ea7',\n",
       " '45603d31-964a-4d2c-9d26-8160e428e20d',\n",
       " '4f5c076d-d77c-405d-be4e-7aaca2f3ab8e',\n",
       " 'd095a684-3cf0-462e-a3a3-c8e25120a52b',\n",
       " '43847e24-bfb7-4b9c-aec6-2af32a0ca45c',\n",
       " '32795996-c730-4f78-8406-73d6e114c0a8',\n",
       " '8491047c-7c21-4172-854a-c109a38f4ac8',\n",
       " '662219b1-ba10-41cc-8e3c-e39b3f5a9c06',\n",
       " 'dc179f95-7476-47d9-bb16-c27066200908',\n",
       " '815b954e-220e-4e7b-ade1-f00006ef1bfa',\n",
       " 'f9a820de-8dcd-403a-8a1c-5fde1658f8b9',\n",
       " '88b5edca-1042-414d-9c41-d54925180234',\n",
       " '2d5ee759-ef7a-4f4b-a034-a209f0c0f80e',\n",
       " '6de5781c-6678-4dac-8246-689a1a52fc6f',\n",
       " 'ddffa535-d17c-4da9-bc36-25fb633e4369',\n",
       " 'defd20cc-b522-40a1-a6fc-2f5af25d2a6f',\n",
       " '03c80ca0-b7db-4e32-b04e-096fc90f9f82',\n",
       " '9eedfc0e-a493-48aa-ad10-b8734da8b041',\n",
       " '5819342c-5f46-4ee8-8115-d9df4bdc73c7',\n",
       " '6f7bcf86-fb54-453e-bbba-8afa679602f3',\n",
       " '1bf1d2f0-0a85-412a-aece-086f549b69bb',\n",
       " '54279a02-71e5-4ea8-a5e8-a4ed3117cf8a',\n",
       " '05551bf1-6868-4d4c-96ef-a5eb5d14728b',\n",
       " '1dc5f3cd-c924-4db8-a86f-809c937aed6d',\n",
       " 'b85572e4-8dba-4eff-9a07-5b190ce0bddd',\n",
       " '5c537518-cdde-4486-89fb-51f30feeeba9',\n",
       " '9017a0f7-0cff-47e5-8180-8e5613dc1af9',\n",
       " 'bfe1ef50-510f-4be7-946b-86d1ade5041b',\n",
       " '6c1bf227-48ea-4a3d-bd81-7bf0c59ee990',\n",
       " '03b3b224-a7e0-4f36-af1d-e378c404e3a8',\n",
       " '190ffb3b-463c-4a14-b05b-d8abcc8db8e2',\n",
       " 'e73222eb-a4eb-43e8-b3c4-6654ad90589f',\n",
       " 'a77f7128-5cbe-40d7-9222-22d372f9e439',\n",
       " 'c46c9d0b-68d6-4fff-8e49-ecf9111901c0',\n",
       " '697ca64b-795f-401d-8c31-2c25bc52fe19',\n",
       " '11506bf8-431a-47f8-84bf-b27342bf649a',\n",
       " 'd6de0dcf-4a7a-4070-8fd4-bf9deb590476',\n",
       " 'da9af0de-486e-4e87-9391-d4eea38dd166',\n",
       " '9c3b0fc8-cb22-4f12-9573-eb0a5f1a8a2a',\n",
       " 'f892289a-6daf-487f-8b38-543d31898c94',\n",
       " '25a0fe91-5042-428b-8a68-b9288eb7a4c9',\n",
       " 'bf513efd-6ec1-4a7b-8efe-9fad54067e83',\n",
       " 'd76453e5-d82c-4a30-ade0-82dce4e769ec',\n",
       " '5be75f43-10d8-4a50-b9b9-83b23a6fa971',\n",
       " '3e0bfa50-eabf-407f-9b35-c2ff657e850a',\n",
       " '4be00768-d54d-4287-bf30-8a7608ddab0d',\n",
       " '2a2cb969-1891-4067-9094-bf4b486c0535',\n",
       " '0c37caf7-c9f0-4784-8dc5-3c3bc9760bfe',\n",
       " '5c95e41d-eb8d-41e3-9c09-a8e37421bc1c',\n",
       " '7f851f24-64d8-492a-8352-bfed764eb645',\n",
       " 'c16e2174-30bd-4bcc-bb6d-0fefaf38282e',\n",
       " '54d4b6e9-f69c-4234-b5e1-40e4682b6e60',\n",
       " 'fe7abb7f-f118-4df5-8216-67ec7f726680',\n",
       " '45bb0c40-c61c-4f01-af32-a874fe382714',\n",
       " 'f8f6ea5d-5948-47c9-9f62-d5fb9a8fcf6b',\n",
       " '6baf097a-b7dd-4a7d-a43c-5f8515cbd72a',\n",
       " 'd7cbbf17-087d-43e7-a47f-7d4891897065']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector_store.add(nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d574a062-1900-4b74-be9a-6248ffb8bbbe",
   "metadata": {
    "id": "d574a062-1900-4b74-be9a-6248ffb8bbbe"
   },
   "source": [
    "## Retrieval Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "344347cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import QueryBundle\n",
    "from llama_index.core.retrievers import BaseRetriever\n",
    "from typing import Any, List\n",
    "from llama_index.core.schema import NodeWithScore\n",
    "from typing import Optional\n",
    "from llama_index.core.vector_stores import VectorStoreQuery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f817dbf4-926c-4aa2-a3b6-946c45df0893",
   "metadata": {
    "id": "f817dbf4-926c-4aa2-a3b6-946c45df0893"
   },
   "outputs": [],
   "source": [
    "class VectorDBRetriever(BaseRetriever):\n",
    "    \"\"\"Retriever over a postgres vector store.\"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        vector_store: PGVectorStore,\n",
    "        embed_model: Any,\n",
    "        query_mode: str = \"default\",\n",
    "        similarity_top_k: int = 2,\n",
    "    ) -> None:\n",
    "        \"\"\"Init params.\"\"\"\n",
    "        self._vector_store = vector_store\n",
    "        self._embed_model = embed_model\n",
    "        self._query_mode = query_mode\n",
    "        self._similarity_top_k = similarity_top_k\n",
    "        super().__init__()\n",
    "\n",
    "    def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:\n",
    "        \"\"\"Retrieve.\"\"\"\n",
    "        query_embedding = embed_model.get_query_embedding(\n",
    "            query_bundle.query_str\n",
    "        )\n",
    "        vector_store_query = VectorStoreQuery(\n",
    "            query_embedding=query_embedding,\n",
    "            similarity_top_k=self._similarity_top_k,\n",
    "            mode=self._query_mode,\n",
    "        )\n",
    "        query_result = vector_store.query(vector_store_query)\n",
    "\n",
    "        nodes_with_scores = []\n",
    "        for index, node in enumerate(query_result.nodes):\n",
    "            score: Optional[float] = None\n",
    "            if query_result.similarities is not None:\n",
    "                score = query_result.similarities[index]\n",
    "            nodes_with_scores.append(NodeWithScore(node=node, score=score))\n",
    "\n",
    "        return nodes_with_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6dbaf309-a3fb-4d01-bc7b-4efab92e6e3d",
   "metadata": {
    "id": "6dbaf309-a3fb-4d01-bc7b-4efab92e6e3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.VectorDBRetriever at 0x3611fe510>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever = VectorDBRetriever(\n",
    "    vector_store, embed_model, query_mode=\"default\", similarity_top_k=2\n",
    ")\n",
    "\n",
    "retriever"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "977c28a6-065a-408a-b007-e611d2d99153",
   "metadata": {
    "id": "977c28a6-065a-408a-b007-e611d2d99153"
   },
   "source": [
    "## Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ae459e87-daff-433d-a2bd-fd4a934357ef",
   "metadata": {
    "id": "ae459e87-daff-433d-a2bd-fd4a934357ef"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.query_engine import RetrieverQueryEngine\n",
    "\n",
    "query_engine = RetrieverQueryEngine.from_args(retriever, llm=llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "312bdaba-6a91-4601-96f9-c64d1ae09007",
   "metadata": {
    "id": "312bdaba-6a91-4601-96f9-c64d1ae09007"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Llama.generate: 23 prefix-match hit, remaining 453 prompt tokens to eval\n",
      "\n",
      "llama_print_timings:        load time =   10227.48 ms\n",
      "llama_print_timings:      sample time =       0.38 ms /    14 runs   (    0.03 ms per token, 36939.31 tokens per second)\n",
      "llama_print_timings: prompt eval time =    9614.98 ms /   453 tokens (   21.23 ms per token,    47.11 tokens per second)\n",
      "llama_print_timings:        eval time =    1339.13 ms /    13 runs   (  103.01 ms per token,     9.71 tokens per second)\n",
      "llama_print_timings:       total time =   10959.66 ms /   466 tokens\n"
     ]
    }
   ],
   "source": [
    "query_str = \"Which individuals play a central role in promoting healthy eating?\"\n",
    "\n",
    "response = query_engine.query(query_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "72f1096f-96b6-4a90-8524-b9ebe4532661",
   "metadata": {
    "id": "72f1096f-96b6-4a90-8524-b9ebe4532661",
    "outputId": "49ef7e7d-51d9-427c-8b91-317e08795690"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Please provide the answer based on the given context information.\n"
     ]
    }
   ],
   "source": [
    "print(str(response))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f7838289-5a59-4042-b4b7-037f66d99be4",
   "metadata": {
    "id": "f7838289-5a59-4042-b4b7-037f66d99be4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An exploratory and descriptive crosssectional study\n",
      "was conducted using a qualitative and quantitative methodology and convenience sampling. Two\n",
      "focus groups and a questionnaire were utilized 300 participants from all academic years completed\n",
      "the survey. Differences in definitions of healthy eating and perceived barriers were found between\n",
      "genders and students at different stages of training p  0.05. In their understanding of healthy eating,\n",
      "the students placed importance on balance, variety, moderation, and individual factors. Although\n",
      "students considered it easy to follow a healthy diet, familys eating habits, time availability, and\n",
      "emotional states were found to be the main barriers to the implementation of healthy practices. The\n",
      "obtained data supports the need to critically address perceptions of healthy eating throughout the\n",
      "training of nutrition and food science professionals. The insights obtained on the perceived barriers\n",
      "highlight the importance of considering both individual and environmental factors.\n",
      "Keywords healthy eating perception college students dietetics nutrition food technology\n",
      "Introduction\n",
      "In industrialized societies, the issue of healthy eating has become omnipresent in dis\n",
      "courses of the media, public policy, and expert opinion, and is a subject of increased public\n",
      "awareness 1. The growing concern about the effects of diet on health is due to changes in\n",
      "the epidemiological, demographic, and nutritional profiles of the population 2,3. Since 1990,\n",
      "the prevalence of obesity among the adult population has more than doubled worldwide,\n",
      "Nutrients 2024, 16, 1365. httpsdoi.org10.3390nu16091365\n",
      "httpswww.mdpi.comjournalnutrients\n"
     ]
    }
   ],
   "source": [
    "print(response.source_nodes[0].get_content())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "474a9a11",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Llama.generate: 13 prefix-match hit, remaining 1253 prompt tokens to eval\n",
      "\n",
      "llama_print_timings:        load time =   10227.48 ms\n",
      "llama_print_timings:      sample time =       2.22 ms /    87 runs   (    0.03 ms per token, 39118.71 tokens per second)\n",
      "llama_print_timings: prompt eval time =   26279.94 ms /  1253 tokens (   20.97 ms per token,    47.68 tokens per second)\n",
      "llama_print_timings:        eval time =    9835.99 ms /    86 runs   (  114.37 ms per token,     8.74 tokens per second)\n",
      "llama_print_timings:       total time =   36148.13 ms /  1339 tokens\n"
     ]
    }
   ],
   "source": [
    "query_str = \"What does nutrition literacy significantly predicted in the social-ecological framework?\"\n",
    "\n",
    "response = query_engine.query(query_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d652a56d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Based on the provided context information, nutrition literacy significantly predicted adherence to healthy-unhealthy diet patterns in adults with a nutrition-related chronic condition, as well as health-promoting behaviors among college students in Taiwan. Additionally, nutrition literacy was found to be associated with eating habits and BMI in adolescents in Lebanon.\n"
     ]
    }
   ],
   "source": [
    "print(str(response))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6605d444",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeWithScore(node=TextNode(id_='bccff988-f457-4cad-946b-037732a0d87c', embedding=None, metadata={'source': '4.pdf', 'doc_index': 20}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='Lai, I.J. Chang, L.C. Nutrition literacy is associated with healthyeating behaviour among college students in Taiwan.\\nHealth Educ. J. 2019, 78, 756769. CrossRef\\nYuen, E. Thomson, M. Gardiner, H. Measuring Nutrition and Food Literacy in Adults A Systematic Review and Appraisal of\\nExisting Measurement Tools. HLRP Health Lit. Res. Pract. 2018, 2, e134e160. CrossRef PubMed\\nSilk, K.J. Sherry, J. Winn, B. Keesecker, N. Horodynski, M.A. Sayir, A. Increasing Nutrition Literacy Testing the Effectiveness\\nof Print, Web site, and Game Modalities. J. Nutr. Educ. Behav. 2008, 40, 310. CrossRef\\nAihara, Y. Minai, J. Barriers and catalysts of nutrition literacy among elderly Japanese people. Health Promot. Int. 2011, 26,\\n421431. CrossRef\\nZoellner, J. Connell, C. Bounds, W. Crook, L. Yadrick, K. Nutrition Literacy Status and Preferred Nutrition Communication\\nChannels among Adults in the Lower Mississippi Delta. Prev. Chronic Dis. 2009, 6, A128.\\nNoroozi, A. Khademolhosseini, F. Lari, H. Tahmasebi, R. The Mediator Role of Mental Health Literacy in the Relationship\\nbetween Demographic Variables and HealthPromoting Behaviours. Iran. J. Psychiatry Behav. Sci. 2018, 12. CrossRef\\nKim, S. Oh, J. The Relationship between EHealth Literacy and HealthPromoting Behaviors in Nursing Students A Multiple\\nMediation Model. Int. J. Environ. Res. Public Health 2021, 18, 5804. CrossRef PubMed\\nLastrucci, V. Lorini, C. Caini, S. Bonaccorsi, G. Florence Health Literacy Research Group. Health literacy as a mediator of the\\nrelationship between socioeconomic status and health A crosssectional study in a populationbased sample in Florence. PLoS\\nONE 2019, 14, e0227007. CrossRef PubMed\\nTaylor, M.K. Sullivan, D.K. Ellerbeck, E.F. Gajewski, B.J. Gibbs, H.D. Nutrition literacy predicts adherence to healthyunhealthy\\ndiet patterns in adults with a nutritionrelated chronic condition. Public Health Nutr. 2019, 22, 21572169. CrossRef PubMed\\nTaleb, S. Itani, L. Nutrition Literacy among Adolescents and Its Association with Eating Habits and BMI in Tripoli, Lebanon.\\nDiseases 2021, 9, 25. CrossRef PubMed\\nKoca, B. Arkan, G. The relationship between adolescents nutrition literacy and food habits, and affecting factors. Public Health\\nNutr. 2021, 24, 717728. CrossRef\\nDoustmohammadian, A. Mohammadi, N.K. Omidvar, N. Amini, M. Abdollahi, M. EiniZinab, H. Amirhamidi, Z.\\nEsfandiari, S. Nutbeam, D. Food and nutrition literacy FNLIT and its predictors in primary schoolchildren in Iran. Health\\nPromot. Int. 2019, 34, 10021013. CrossRef\\nLynn, M.R. Determination and quantication of content validity. Nurs. Res. 1986, 35, 382385. CrossRef\\nNielsenBohlman, L. Panzer, A. Kindig, D. Health Literacy A Prescription to End Confusion National Academies Press Washington,\\nDC, USA, 2004.\\nLiao, L.L. Lai, I.J. Shih, S.F. Chang, L.C. Development and validation of the nutrition literacy measure for Taiwanese college\\nstudents. Taiwan J. Public Health 2018, 37, 582597.\\nStory, M. NeumarkSztainer, D. French, S. Individual and Environmental Inuences on Adolescent Eating Behaviors. J. Am. Diet.\\nAssoc. 2002, 102, S40S51.', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.892857564347087)"
      ]
     },
     "execution_count": 40,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.source_nodes[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "63c8af54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lai, I.J. Chang, L.C. Nutrition literacy is associated with healthyeating behaviour among college students in Taiwan.\n",
      "Health Educ. J. 2019, 78, 756769. CrossRef\n",
      "Yuen, E. Thomson, M. Gardiner, H. Measuring Nutrition and Food Literacy in Adults A Systematic Review and Appraisal of\n",
      "Existing Measurement Tools. HLRP Health Lit. Res. Pract. 2018, 2, e134e160. CrossRef PubMed\n",
      "Silk, K.J. Sherry, J. Winn, B. Keesecker, N. Horodynski, M.A. Sayir, A. Increasing Nutrition Literacy Testing the Effectiveness\n",
      "of Print, Web site, and Game Modalities. J. Nutr. Educ. Behav. 2008, 40, 310. CrossRef\n",
      "Aihara, Y. Minai, J. Barriers and catalysts of nutrition literacy among elderly Japanese people. Health Promot. Int. 2011, 26,\n",
      "421431. CrossRef\n",
      "Zoellner, J. Connell, C. Bounds, W. Crook, L. Yadrick, K. Nutrition Literacy Status and Preferred Nutrition Communication\n",
      "Channels among Adults in the Lower Mississippi Delta. Prev. Chronic Dis. 2009, 6, A128.\n",
      "Noroozi, A. Khademolhosseini, F. Lari, H. Tahmasebi, R. The Mediator Role of Mental Health Literacy in the Relationship\n",
      "between Demographic Variables and HealthPromoting Behaviours. Iran. J. Psychiatry Behav. Sci. 2018, 12. CrossRef\n",
      "Kim, S. Oh, J. The Relationship between EHealth Literacy and HealthPromoting Behaviors in Nursing Students A Multiple\n",
      "Mediation Model. Int. J. Environ. Res. Public Health 2021, 18, 5804. CrossRef PubMed\n",
      "Lastrucci, V. Lorini, C. Caini, S. Bonaccorsi, G. Florence Health Literacy Research Group. Health literacy as a mediator of the\n",
      "relationship between socioeconomic status and health A crosssectional study in a populationbased sample in Florence. PLoS\n",
      "ONE 2019, 14, e0227007. CrossRef PubMed\n",
      "Taylor, M.K. Sullivan, D.K. Ellerbeck, E.F. Gajewski, B.J. Gibbs, H.D. Nutrition literacy predicts adherence to healthyunhealthy\n",
      "diet patterns in adults with a nutritionrelated chronic condition. Public Health Nutr. 2019, 22, 21572169. CrossRef PubMed\n",
      "Taleb, S. Itani, L. Nutrition Literacy among Adolescents and Its Association with Eating Habits and BMI in Tripoli, Lebanon.\n",
      "Diseases 2021, 9, 25. CrossRef PubMed\n",
      "Koca, B. Arkan, G. The relationship between adolescents nutrition literacy and food habits, and affecting factors. Public Health\n",
      "Nutr. 2021, 24, 717728. CrossRef\n",
      "Doustmohammadian, A. Mohammadi, N.K. Omidvar, N. Amini, M. Abdollahi, M. EiniZinab, H. Amirhamidi, Z.\n",
      "Esfandiari, S. Nutbeam, D. Food and nutrition literacy FNLIT and its predictors in primary schoolchildren in Iran. Health\n",
      "Promot. Int. 2019, 34, 10021013. CrossRef\n",
      "Lynn, M.R. Determination and quantication of content validity. Nurs. Res. 1986, 35, 382385. CrossRef\n",
      "NielsenBohlman, L. Panzer, A. Kindig, D. Health Literacy A Prescription to End Confusion National Academies Press Washington,\n",
      "DC, USA, 2004.\n",
      "Liao, L.L. Lai, I.J. Shih, S.F. Chang, L.C. Development and validation of the nutrition literacy measure for Taiwanese college\n",
      "students. Taiwan J. Public Health 2018, 37, 582597.\n",
      "Story, M. NeumarkSztainer, D. French, S. Individual and Environmental Inuences on Adolescent Eating Behaviors. J. Am. Diet.\n",
      "Assoc. 2002, 102, S40S51.\n"
     ]
    }
   ],
   "source": [
    "print(response.source_nodes[0].get_content())"
   ]
  }
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