RAG-Enhanced Knowledge Synthesizer.ipynb

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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.4/96.4 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hBuilding wheels for collected packages: sentence_transformers, pypika\n",
            "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence_transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125924 sha256=70f09451d5c79bdc8f15bf4646a9b893d546226e97b52d82f0c9b15714add964\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53724 sha256=1d8377eaef4114d55e99f9b5db56ad471dac3a1411300289d8a7cd91aba9bdc0\n",
            "  Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n",
            "Successfully built sentence_transformers pypika\n",
            "Installing collected packages: tokenizers, pypika, monotonic, lit, bitsandbytes, websockets, uvloop, python-dotenv, pydantic, pulsar-client, overrides, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, mypy-extensions, marshmallow, jsonpointer, humanfriendly, httptools, h11, einops, chroma-hnswlib, bcrypt, backoff, watchfiles, uvicorn, typing-inspect, starlette, posthog, nvidia-cusolver-cu11, nvidia-cudnn-cu11, langsmith, jsonpatch, coloredlogs, transformers, onnxruntime, fastapi, dataclasses-json, langchain, chromadb, triton, torch, torchvision, xformers, sentence_transformers, accelerate\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.19.1\n",
            "    Uninstalling tokenizers-0.19.1:\n",
            "      Successfully uninstalled tokenizers-0.19.1\n",
            "  Attempting uninstall: pydantic\n",
            "    Found existing installation: pydantic 2.8.2\n",
            "    Uninstalling pydantic-2.8.2:\n",
            "      Successfully uninstalled pydantic-2.8.2\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.42.4\n",
            "    Uninstalling transformers-4.42.4:\n",
            "      Successfully uninstalled transformers-4.42.4\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 2.3.1\n",
            "    Uninstalling triton-2.3.1:\n",
            "      Successfully uninstalled triton-2.3.1\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.3.1+cu121\n",
            "    Uninstalling torch-2.3.1+cu121:\n",
            "      Successfully uninstalled torch-2.3.1+cu121\n",
            "      Successfully uninstalled torchvision-0.18.1+cu121\n",
            "  Attempting uninstall: accelerate\n",
            "    Found existing installation: accelerate 0.32.1\n",
            "    Uninstalling accelerate-0.32.1:\n",
            "      Successfully uninstalled accelerate-0.32.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 1.4.11 requires pydantic>=2.7.0, but you have pydantic 1.10.17 which is incompatible.\n",
            "torchaudio 2.3.1+cu121 requires torch==2.3.1, but you have torch 2.0.1 which is incompatible.\n",
            "torchtext 0.18.0 requires torch>=2.3.0, but you have torch 2.0.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed accelerate-0.22.0 backoff-2.2.1 bcrypt-4.2.0 bitsandbytes-0.41.1 chroma-hnswlib-0.7.3 chromadb-0.4.12 coloredlogs-15.0.1 dataclasses-json-0.6.7 einops-0.6.1 fastapi-0.99.1 h11-0.14.0 httptools-0.6.1 humanfriendly-10.0 jsonpatch-1.33 jsonpointer-3.0.0 langchain-0.0.300 langsmith-0.0.92 lit-18.1.8 marshmallow-3.21.3 monotonic-1.6 mypy-extensions-1.0.0 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 onnxruntime-1.18.1 overrides-7.7.0 posthog-3.5.0 pulsar-client-3.5.0 pydantic-1.10.17 pypika-0.48.9 python-dotenv-1.0.1 sentence_transformers-2.2.2 starlette-0.27.0 tokenizers-0.13.3 torch-2.0.1 torchvision-0.15.2 transformers-4.33.0 triton-2.0.0 typing-inspect-0.9.0 uvicorn-0.30.3 uvloop-0.19.0 watchfiles-0.22.0 websockets-12.0 xformers-0.0.21\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers==4.33.0 accelerate==0.22.0 einops==0.6.1 langchain==0.0.300 xformers==0.0.21 \\\n",
        "bitsandbytes==0.41.1 sentence_transformers==2.2.2 chromadb==0.4.12"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import cuda, bfloat16\n",
        "import torch\n",
        "import transformers\n",
        "from transformers import AutoTokenizer\n",
        "from time import time\n",
        "#import chromadb\n",
        "#from chromadb.config import Settings\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "from langchain.document_loaders import TextLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "from langchain.chains import RetrievalQA\n",
        "from langchain.vectorstores import Chroma"
      ],
      "metadata": {
        "id": "evp2MX2aRPjy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Hugging Face CLI Authentication"
      ],
      "metadata": {
        "id": "KxQqKnVt3qH9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cS-hA8IlSGMw",
        "outputId": "ce1faf51-dd2a-409a-a94d-d7fd4c61c5d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: fineGrained).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Configure Model and Quantization Settings"
      ],
      "metadata": {
        "id": "xYcNs8Bf3tBK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_id = 'meta-llama/Llama-2-7b-chat-hf'\n",
        "\n",
        "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'\n",
        "\n",
        "# set quantization configuration to load large model with less GPU memory\n",
        "# this requires the `bitsandbytes` library\n",
        "bnb_config = transformers.BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type='nf4',\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=bfloat16\n",
        ")"
      ],
      "metadata": {
        "id": "aeLQe1fcRT0O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "time_1 = time()\n",
        "model_config = transformers.AutoConfig.from_pretrained(\n",
        "    model_id,\n",
        ")\n",
        "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    trust_remote_code=True,\n",
        "    config=model_config,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map='auto',\n",
        ")\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "time_2 = time()\n",
        "print(f\"Prepare model, tokenizer: {round(time_2-time_1, 3)} sec.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636,
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        },
        "id": "YkKQrWgeRWje",
        "outputId": "8873f0e9-0056-4994-cf42-67014075f444"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1855fccae5b4445c8cc368a45c27e952"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "4b87979f60464cb58adac98519b5412e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fb47adb43a024be19c20785e51afbbc4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "972a1108cf01411187f1e86eef2c9f8f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
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          },
          "metadata": {}
        },
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            ],
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          "metadata": {}
        },
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            ],
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          "metadata": {}
        },
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "84abdc94a0f44a3f984ebb2449c78bea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8547e34630d148ac9f7541f9aa1db951"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prepare model, tokenizer: 188.614 sec.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Initialize and Time Text Generation Pipeline"
      ],
      "metadata": {
        "id": "0YUrJgaA30GI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "time_1 = time()\n",
        "query_pipeline = transformers.pipeline(\n",
        "        \"text-generation\",\n",
        "        model=model,\n",
        "        tokenizer=tokenizer,\n",
        "        torch_dtype=torch.float16,\n",
        "        device_map=\"auto\",)\n",
        "time_2 = time()\n",
        "print(f\"Prepare pipeline: {round(time_2-time_1, 3)} sec.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKoSQudLSATL",
        "outputId": "57ae8d76-2332-4f0c-a32f-0bb3045d5c63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prepare pipeline: 1.536 sec.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**We define a function for testing the pipeline.**"
      ],
      "metadata": {
        "id": "XO4PRMKQ4Fgb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def test_model(tokenizer, pipeline, prompt_to_test):\n",
        "    \"\"\"\n",
        "    Perform a query\n",
        "    print the result\n",
        "    Args:\n",
        "        tokenizer: the tokenizer\n",
        "        pipeline: the pipeline\n",
        "        prompt_to_test: the prompt\n",
        "    Returns\n",
        "        None\n",
        "    \"\"\"\n",
        "    # adapted from https://huggingface.co/blog/llama2#using-transformers\n",
        "    time_1 = time()\n",
        "    sequences = pipeline(\n",
        "        prompt_to_test,\n",
        "        do_sample=True,\n",
        "        top_k=10,\n",
        "        num_return_sequences=1,\n",
        "        eos_token_id=tokenizer.eos_token_id,\n",
        "        max_length=200,)\n",
        "    time_2 = time()\n",
        "    print(f\"Test inference: {round(time_2-time_1, 3)} sec.\")\n",
        "    for seq in sequences:\n",
        "        print(f\"Result: {seq['generated_text']}\")"
      ],
      "metadata": {
        "id": "TImS5lnsSWIm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Test the query pipeline"
      ],
      "metadata": {
        "id": "CeFh3yEA4MWu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_model(tokenizer,\n",
        "           query_pipeline,\n",
        "           \"Write a short note on Bayesian statistics. Keep it under 100 words.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2hxtiOBuSbxG",
        "outputId": "0ef960dc-92ea-4169-801b-b13ae6ca2add"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test inference: 8.092 sec.\n",
            "Result: Write a short note on Bayesian statistics. Keep it under 100 words.\n",
            "Bayesian statistics is a statistical approach that uses Bayes' theorem to update probabilities based on new data or information. It differs from classical statistics in that it incorporates prior knowledge or beliefs about the parameters being estimated, and updates these probabilities based on the observed data. Bayesian statistics provides a framework for making probabilistic predictions and estimating parameters in a wide range of fields, including machine learning, engineering, and social sciences.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Retrieval Augmented Generation"
      ],
      "metadata": {
        "id": "Uv7NYkZZ4SJ8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Checking the model with a HuggingFace pipeline"
      ],
      "metadata": {
        "id": "J4xu1giQ4kV1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "llm = HuggingFacePipeline(pipeline=query_pipeline)\n",
        "# checking again that everything is working fine\n",
        "llm(prompt=\"Write a short note on Bayesian statistics. Keep it under 100 words.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "AVNcdGXzS2te",
        "outputId": "2d481058-48ab-44f8-941b-d54ac9a25073"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\n\\nBayesian statistics is a statistical approach that uses Bayes' theorem to update probabilities based on new data or information. It differs from classical statistics in that it incorporates prior knowledge or beliefs about the parameter of interest, and updates these probabilities based on the observed data. Bayesian statistics is useful for estimating the probability of rare events or making predictions under uncertainty, and has applications in machine learning, engineering, and medical research.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ingestion of data using Text loder"
      ],
      "metadata": {
        "id": "4uyKYzzX4xXk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loader = TextLoader(\"/content/data/chapters1-19.txt\",\n",
        "                    encoding=\"utf8\")\n",
        "documents = loader.load()"
      ],
      "metadata": {
        "id": "e7IHDqFyS86_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Split data in chunks\n",
        "**We split data in chunks using a recursive character text splitter.**"
      ],
      "metadata": {
        "id": "At29W2Xd43I5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)\n",
        "all_splits = text_splitter.split_documents(documents)"
      ],
      "metadata": {
        "id": "320pegyQWacJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Set Up Sentence Transformers for Embeddings"
      ],
      "metadata": {
        "id": "mf2-aVk15B6g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"sentence-transformers/all-mpnet-base-v2\"\n",
        "model_kwargs = {\"device\": \"cuda\"}\n",
        "\n",
        "embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)"
      ],
      "metadata": {
        "id": "J_O9FrSSTHI2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectordb = Chroma.from_documents(documents=all_splits, embedding=embeddings, persist_directory=\"chroma_db\")"
      ],
      "metadata": {
        "id": "2AL21YeoWLwO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Initialize chain"
      ],
      "metadata": {
        "id": "2spEZaZj5Izd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = vectordb.as_retriever()\n",
        "\n",
        "qa = RetrievalQA.from_chain_type(\n",
        "    llm=llm,\n",
        "    chain_type=\"stuff\",\n",
        "    retriever=retriever,\n",
        "    verbose=True\n",
        ")"
      ],
      "metadata": {
        "id": "ex-59Ir_WPz9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Test the Retrieval-Augmented Generation"
      ],
      "metadata": {
        "id": "gxmYFGV25NEd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def test_rag(qa, query):\n",
        "    print(f\"Query: {query}\\n\")\n",
        "    time_1 = time()\n",
        "    result = qa.run(query)\n",
        "    time_2 = time()\n",
        "    print(f\"Inference time: {round(time_2-time_1, 3)} sec.\")\n",
        "    print(\"\\nResult: \", result)"
      ],
      "metadata": {
        "id": "6wD3DUj_WfWR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's check few queries.**"
      ],
      "metadata": {
        "id": "4nJ1CWnX5QP8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"Write a short note on Bayesian statistics. Keep it under 200 words.\"\n",
        "test_rag(qa, query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MgNe3OUxWiyy",
        "outputId": "6040f768-5083-41d2-c603-67d0b7ec7cff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Query: Write a short note on Bayesian statistics. Keep it under 200 words.\n",
            "\n",
            "\n",
            "\n",
            "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n",
            "Inference time: 16.455 sec.\n",
            "\n",
            "Result:   Bayesian statistics is a method of statistical inference that uses Bayes' theorem to update probabilities based on new data or information. It involves making an educated guess about the causes of a phenomenon given the known effects. The goal is to deduce a probability function on the set of possible causes, granted that we have the probabilities of the various effects. Bayesian statistics is useful when there are multiple possible causes for a phenomenon, and it can be used to incorporate prior knowledge or beliefs about the causes into the analysis. However, it is important to note that Bayesian statistics is not always the best approach, and it may not be appropriate in situations where the data is noisy or limited.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What are Common probability functions. Keep it under 200 words.\"\n",
        "test_rag(qa, query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dO3lbva1Wq4s",
        "outputId": "c8977b45-540c-41a8-9b1b-76ef65952371"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Query: What are Common probability functions. Keep it under 200 words.\n",
            "\n",
            "\n",
            "\n",
            "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n",
            "Inference time: 15.834 sec.\n",
            "\n",
            "Result:   Common probability functions are mathematical models used to determine if a given phenomenon is unexpected. These functions include the normal distribution, the Poisson distribution, and the binomial distribution. The normal distribution is used to model continuous random variables, while the Poisson distribution is used for count data and the binomial distribution for discrete random variables. Understanding these probability functions is essential in Bayesian statistics, as they provide a framework for evaluating the likelihood of a particular outcome.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Query Execution and Document Retrieval"
      ],
      "metadata": {
        "id": "xDiWKAcv5jU5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "docs = vectordb.similarity_search(query)\n",
        "print(f\"Query: {query}\")\n",
        "print(f\"Retrieved documents: {len(docs)}\")\n",
        "for doc in docs:\n",
        "    doc_details = doc.to_json()['kwargs']\n",
        "    print(\"Source: \", doc_details['metadata']['source'])\n",
        "    print(\"Text: \", doc_details['page_content'], \"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZhHoBw5W5Pv",
        "outputId": "a9bc1145-3381-4346-b0c6-c8ee148f0ffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Query: What are Common probability functions. Keep it under 200 words.\n",
            "Retrieved documents: 4\n",
            "Source:  /content/data/chapters1-19.txt\n",
            "Text:  13.3. The mean and standard deviation                                                                                     81\n",
            "\f13.5 Examples of probability functions\n",
            "Three examples of continuous probability functions appear regularly in the scientific literature. \n",
            "\n",
            "Source:  /content/data/chapters1-19.txt\n",
            "Text:  58                                                                                       Chapter 10. More about Bayesian statistics\n",
            "\f                                                                                                             CHAPTER\n",
            "\n",
            "                                                                                                           ELEVEN\n",
            "\n",
            "\n",
            "\n",
            "                                    Common probability functions\n",
            "\n",
            "There are certain probability functions that serve as models that are commonly used when trying to decide if a given\n",
            "phenomena is ‘unexpected’ or not. This chapter describes those that arise most often. \n",
            "\n",
            "Source:  /content/data/chapters1-19.txt\n",
            "Text:  11 Common probability functions                                                                                                                                                                         59\n",
            "   11.1 What does ‘random’ really mean? . . . . . . . . .                                   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   59\n",
            "   11.2 A mathematical translation of the term ‘random’ .                                   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   59\n",
            "   11.3 Some standard counting solutions . . . . . . . . .                                  .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   60 \n",
            "\n",
            "Source:  /content/data/chapters1-19.txt\n",
            "Text:  6 Random variables                                                                                                                                                                        31\n",
            "  6.1 The definition of a random variable . . . . . . . . .                       .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   31\n",
            "  6.2 Probability for a random variable . . . . . . . . . .                       .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   32\n",
            "  6.3 A probability function on the possible values of f .                        .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   33\n",
            "  6.4 Mean and standard distribution for a random variable                        .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   33 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ipvOVQviW8Bd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}