rag_llm_easy.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Wu7Fp4WiIjMO"
      },
      "outputs": [],
      "source": [
        "!pip install -q --upgrade google-cloud-aiplatform\n",
        "!pip install -q pypdf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1vhl_UaD_dVA"
      },
      "source": [
        "## Deploying Gemma to Vertex AI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "337B0bt531Dz"
      },
      "outputs": [],
      "source": [
        "GCP_PROJECT_ID = 'gdsc-llm-gcp'\n",
        "PROJECT_NUMBER = '983720878576'\n",
        "ENDPOINT = '1774723917416497152'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "n81CJNW54GkW"
      },
      "outputs": [],
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user(project_id=GCP_PROJECT_ID)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "0StETUAO3p16"
      },
      "outputs": [],
      "source": [
        "from typing import Dict, List, Union\n",
        "from google.cloud import aiplatform\n",
        "from google.protobuf import json_format\n",
        "from google.protobuf.struct_pb2 import Value\n",
        "\n",
        "def predict_custom_trained_model_sample(\n",
        "    project: str,\n",
        "    endpoint_id: str,\n",
        "    instances: Union[Dict, List[Dict]],\n",
        "    location: str = \"us-central1\",\n",
        "    api_endpoint: str = \"us-central1-aiplatform.googleapis.com\",\n",
        "):\n",
        "    \"\"\"\n",
        "    `instances` can be either single instance of type dict or a list\n",
        "    of instances.\n",
        "    \"\"\"\n",
        "    client_options = {\"api_endpoint\": api_endpoint}\n",
        "\n",
        "    client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)\n",
        "\n",
        "    instances = instances if isinstance(instances, list) else [instances]\n",
        "\n",
        "    instances = [\n",
        "        json_format.ParseDict(instance_dict, Value()) for instance_dict in instances\n",
        "    ]\n",
        "\n",
        "    parameters_dict = {}\n",
        "\n",
        "    parameters = json_format.ParseDict(parameters_dict, Value())\n",
        "\n",
        "    endpoint = client.endpoint_path(\n",
        "        project=project, location=location, endpoint=endpoint_id\n",
        "    )\n",
        "\n",
        "\n",
        "    response = client.predict(\n",
        "        endpoint=endpoint, instances=instances, parameters=parameters\n",
        "    )\n",
        "\n",
        "    res_list = list(response.predictions)\n",
        "    prediction = res_list[0].split('\\nOutput:\\n')[-1]\n",
        "\n",
        "    return prediction\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "AmhyXb565yFi"
      },
      "outputs": [],
      "source": [
        "instances = [\n",
        "    {\n",
        "        \"prompt\": \"What is a car?\",\n",
        "        \"max_tokens\": 50,\n",
        "        \"temperature\": 1.0,\n",
        "        \"top_p\": 1.0,\n",
        "        \"top_k\": 10,\n",
        "    },\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "zCaIdBIJ3z_W"
      },
      "outputs": [],
      "source": [
        "res = predict_custom_trained_model_sample(\n",
        "    project=PROJECT_NUMBER,\n",
        "    endpoint_id=ENDPOINT,\n",
        "    location=\"us-central1\",\n",
        "    instances=instances\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zgs08BfSGl2_",
        "outputId": "e0d1cc85-6e05-4912-a723-ea73865025b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A car is a **powered vehicle** that can transport people or goods. It is typically propelled by an engine and can travel on roads, highways, or off-road. \n",
            "\n",
            "**Characteristics of a car:**\n",
            "\n",
            "* Has four wheels\n"
          ]
        }
      ],
      "source": [
        "print(res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e1spS3c0BDBU"
      },
      "source": [
        "## Vector Embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "TYJf16SyBiE-"
      },
      "outputs": [],
      "source": [
        "from vertexai.language_models import TextEmbeddingModel\n",
        "\n",
        "def text_embedding(text_input) -> list:\n",
        "    \"\"\"\n",
        "    Text embedding with a Large Language Model.\n",
        "\n",
        "    Args:\n",
        "      - text_input (str).\n",
        "\n",
        "    Returns\n",
        "      - vector (List[floats]).\n",
        "\n",
        "    \"\"\"\n",
        "    model = TextEmbeddingModel.from_pretrained(\"textembedding-gecko@001\")\n",
        "    embeddings = model.get_embeddings([text_input])\n",
        "    for embedding in embeddings:\n",
        "        vector = embedding.values\n",
        "    return vector\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nzxx-27ZG0PU",
        "outputId": "4a4a03f4-1b42-4b62-e87e-daf0a08a9c9f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:google.cloud.aiplatform.initializer:Failed to convert project number to project ID.\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/google/api_core/grpc_helpers.py\", line 72, in error_remapped_callable\n",
            "    return callable_(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/grpc/_channel.py\", line 1181, in __call__\n",
            "    return _end_unary_response_blocking(state, call, False, None)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/grpc/_channel.py\", line 1006, in _end_unary_response_blocking\n",
            "    raise _InactiveRpcError(state)  # pytype: disable=not-instantiable\n",
            "grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:\n",
            "\tstatus = StatusCode.PERMISSION_DENIED\n",
            "\tdetails = \"Cloud Resource Manager API has not been used in project gdsc-llm-gcp before or it is disabled. Enable it by visiting https://console.developers.google.com/apis/api/cloudresourcemanager.googleapis.com/overview?project=gdsc-llm-gcp then retry. If you enabled this API recently, wait a few minutes for the action to propagate to our systems and retry.\"\n",
            "\tdebug_error_string = \"UNKNOWN:Error received from peer ipv4:172.217.15.234:443 {created_time:\"2024-05-07T18:12:23.199345682+00:00\", grpc_status:7, grpc_message:\"Cloud Resource Manager API has not been used in project gdsc-llm-gcp before or it is disabled. Enable it by visiting https://console.developers.google.com/apis/api/cloudresourcemanager.googleapis.com/overview?project=gdsc-llm-gcp then retry. If you enabled this API recently, wait a few minutes for the action to propagate to our systems and retry.\"}\"\n",
            ">\n",
            "\n",
            "The above exception was the direct cause of the following exception:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/google/cloud/aiplatform/initializer.py\", line 82, in _set_project_as_env_var_or_google_auth_default\n",
            "    project_id = resource_manager_utils.get_project_id(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/google/cloud/aiplatform/utils/resource_manager_utils.py\", line 48, in get_project_id\n",
            "    project = projects_client.get_project(name=f\"projects/{project_number}\")\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/google/cloud/resourcemanager_v3/services/projects/client.py\", line 766, in get_project\n",
            "    response = rpc(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/google/api_core/gapic_v1/method.py\", line 113, in __call__\n",
            "    return wrapped_func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/google/api_core/retry.py\", line 349, in retry_wrapped_func\n",
            "    return retry_target(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/google/api_core/retry.py\", line 191, in retry_target\n",
            "    return target()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/google/api_core/timeout.py\", line 120, in func_with_timeout\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/google/api_core/grpc_helpers.py\", line 74, in error_remapped_callable\n",
            "    raise exceptions.from_grpc_error(exc) from exc\n",
            "google.api_core.exceptions.PermissionDenied: 403 Cloud Resource Manager API has not been used in project gdsc-llm-gcp before or it is disabled. Enable it by visiting https://console.developers.google.com/apis/api/cloudresourcemanager.googleapis.com/overview?project=gdsc-llm-gcp then retry. If you enabled this API recently, wait a few minutes for the action to propagate to our systems and retry. [links {\n",
            "  description: \"Google developers console API activation\"\n",
            "  url: \"https://console.developers.google.com/apis/api/cloudresourcemanager.googleapis.com/overview?project=gdsc-llm-gcp\"\n",
            "}\n",
            ", reason: \"SERVICE_DISABLED\"\n",
            "domain: \"googleapis.com\"\n",
            "metadata {\n",
            "  key: \"consumer\"\n",
            "  value: \"projects/gdsc-llm-gcp\"\n",
            "}\n",
            "metadata {\n",
            "  key: \"service\"\n",
            "  value: \"cloudresourcemanager.googleapis.com\"\n",
            "}\n",
            "]\n"
          ]
        },
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              " 0.01822257600724697,\n",
              " 0.011598577722907066,\n",
              " -0.06363803893327713,\n",
              " 0.00796960387378931,\n",
              " -0.02803410217165947,\n",
              " -0.009026517160236835,\n",
              " -0.0670410692691803,\n",
              " 0.03298084810376167,\n",
              " 0.01013550441712141,\n",
              " 0.0003850006905850023,\n",
              " 0.0030597697477787733,\n",
              " 0.11658581346273422,\n",
              " 0.047074779868125916,\n",
              " 0.0394400954246521,\n",
              " 0.007008957210928202,\n",
              " 0.05449085310101509,\n",
              " -0.00880775973200798,\n",
              " -0.018613414838910103,\n",
              " 0.021603483706712723,\n",
              " -0.012142656370997429,\n",
              " 0.02633216232061386,\n",
              " 0.0410999171435833,\n",
              " -0.003862511133775115,\n",
              " 0.028579017147421837]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "text_embedding('My name is Cesar and Im a masters student at IE')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1SMldSJpCdVy"
      },
      "source": [
        "# Storing embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idDYBvFdS1Z9"
      },
      "source": [
        "### Task 1. Find a data structure and function that can store text chunks and their embeddings. Write a function to easily add a new text chunk to that data structure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "VOvvtkNcCnHM"
      },
      "outputs": [],
      "source": [
        "vector_memory = {}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "PXykBxy3CsVl"
      },
      "outputs": [],
      "source": [
        "def add_chunk(text_string, vector_memory=vector_memory):\n",
        "  \"\"\"\n",
        "  Adds new text to vector memory.\n",
        "\n",
        "  Args:\n",
        "    - text_string (str). The text we want to embed and store.\n",
        "    - vector_memory (Dict) Dictionary mapping from text_strings to embedding vectors.\n",
        "  \"\"\"\n",
        "  if text_string not in vector_memory:\n",
        "    text_vector = text_embedding(_____) # Hint: Fill in with the correct variable that should be embedded\n",
        "    vector_memory[text_string] = text_vector\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iwhqZ_7dTv9n"
      },
      "source": [
        "# Retrieving embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05wGfLrsTHZy"
      },
      "source": [
        "### Task 2. Write a function to retrieve the k most similar embeddings from that data structure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "EfEImTEFDePL"
      },
      "outputs": [],
      "source": [
        "def find_k_nearest_neighbors(query: str, k, vector_memory=vector_memory):\n",
        "  \"\"\"\n",
        "  Given a query string, retrieves the k most similar vectors.\n",
        "\n",
        "  Args:\n",
        "    - query (string).\n",
        "    - k (int)\n",
        "    - vector_memory (Dict). Maps of text and embeddings\n",
        "\n",
        "  Returns:\n",
        "    - k most similar text strings.\n",
        "  \"\"\"\n",
        "\n",
        "  distances = {}\n",
        "  query_vector = text_embedding(query)\n",
        "\n",
        "  for item in vector_memory:\n",
        "    embedding = vector_memory[item]\n",
        "    distance = [sum((a-b)**2 for a, b in zip(embedding, _____))] # Hint: Fill in with the correct variable related to the query\n",
        "    distances[item] = distance\n",
        "\n",
        "  sorted_list = sorted(distances.items(), key=lambda _____: _____[1]) # Hint: Replace the first blank with the variable used to sort and the second with how to access distance\n",
        "\n",
        "  nearest_neighbors = [_____ for item in sorted_list[:k]] # Hint: Replace blank with the proper way to get the text from the sorted items\n",
        "  return nearest_neighbors\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0rP1rTwnTxup"
      },
      "source": [
        "# Loading PDFs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ON3TO3LxTQO1"
      },
      "source": [
        "### Task 3. Write a function that takes a filename, extracts the text of a pdf document, and splits it into chunks. You can use pypdf for that."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Laz567LrN14a",
        "outputId": "6ffe2450-037e-4dd6-f3a6-c82c90fc7394"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pypdf in /usr/local/lib/python3.10/dist-packages (4.2.0)\n",
            "Requirement already satisfied: typing_extensions>=4.0 in /usr/local/lib/python3.10/dist-packages (from pypdf) (4.11.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install pypdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HzjGT_8LNWtt"
      },
      "outputs": [],
      "source": [
        "import pypdf\n",
        "filename = '1706.03762.pdf'\n",
        "with open(filename, 'rb') as pdf_file:\n",
        "    pdf_reader = pypdf.PdfReader(_____)  # Hint: Fill in with the variable that represents the opened file\n",
        "    page = pdf_reader.pages[1]\n",
        "    print(_____.extract_text())  # Hint: Fill in with the variable that represents the page from which you want to extract text\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "NTVNmPf7J1RN"
      },
      "outputs": [],
      "source": [
        "import pypdf\n",
        "\n",
        "def extract_text_from_pdf(filename):\n",
        "  \"\"\"\n",
        "  Loads a PDF file and chunks it.\n",
        "\n",
        "  Args:\n",
        "    - filename (str): path to PDF file.\n",
        "\n",
        "  Returns:\n",
        "    - List of paragraphs from the PDF.\n",
        "  \"\"\"\n",
        "  \n",
        "  with open(filename, 'rb') as pdf_file:\n",
        "    pdf_reader = pypdf.PdfReader(pdf_file)\n",
        "    chunks = []\n",
        "\n",
        "    for page in pdf_reader.pages:\n",
        "      text = page.extract_text()\n",
        "      paragraphs = text.split(_____)  # Hint: Fill in with the correct delimiter for splitting text into paragraphs\n",
        "\n",
        "    return _____  # Hint: Replace blank with the correct variable to return that holds all the paragraphs\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ny_IsntpTINt"
      },
      "source": [
        "### Task 4. Load text chunks, create embeddings and save them in your vector data structure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "bckVPPhkTfXN"
      },
      "outputs": [],
      "source": [
        "# Get the chunks from the PDF\n",
        "chunks = extract_text_from_pdf('1706.03762.pdf')\n",
        "\n",
        "# Add each chunk to the vector memory\n",
        "for chunk in chunks:\n",
        "  add_chunk(chunk)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "egn_lJlAUOMJ"
      },
      "source": [
        "### Task 5. Get a question from the user and find the most relevant context from the PDF you loaded."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zo_VNvl_UUN9",
        "outputId": "6df40b98-cca0-4b52-df62-e1adda4291b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your query: What is transformers\n"
          ]
        }
      ],
      "source": [
        "# Obtain the query from the user\n",
        "query = input('Enter your query: ')\n",
        "k = 3\n",
        "\n",
        "# Query the vector memory\n",
        "results = find_k_nearest_neighbors(query, k)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ldz6EEo-UnC0"
      },
      "source": [
        "### Task 6. Use prompt engineering and the Gemma LLM to answer the question based on the context you retrieved."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "LmAv6KPoUsIm"
      },
      "outputs": [],
      "source": [
        "# Convert the results to a string that can be used in the prompt\n",
        "context = '\\n'.join(results)\n",
        "\n",
        "# Prompt the LLM with the results\n",
        "prompt = f\"\"\"Based on the following context items, please answer the query.\n",
        "Give yourself room to think by extracting relevant passages from the context before answering the query.\n",
        "Don't return the thinking, only return the answer.\n",
        "Make sure your answers are as explanatory as possible.\n",
        "{context}\n",
        "\\nRelevant passages: <extract relevant passages from the context here>\n",
        "User query: {query}\n",
        "Answer:\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "uMLVctiPGNdn"
      },
      "outputs": [],
      "source": [
        "instances = [\n",
        "    {\n",
        "        \"prompt\": prompt,\n",
        "        \"max_tokens\": 50,\n",
        "        \"temperature\": 1.0,\n",
        "        \"top_p\": 1.0,\n",
        "        \"top_k\": 10,\n",
        "    },\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "6XICH5j7GNdn"
      },
      "outputs": [],
      "source": [
        "res = predict_custom_trained_model_sample(\n",
        "    project=PROJECT_NUMBER,\n",
        "    endpoint_id=ENDPOINT,\n",
        "    location=\"us-central1\",\n",
        "    instances=instances\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5pMD0U-GNdo",
        "outputId": "4fbc41eb-fa93-4fc1-9130-a2f5ac932341"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Transformers are electronic devices that alter the voltage or current of an alternating current (AC) electrical signal without changing its frequency. They consist of two or more coils of wire called windings, which are wrapped around a magnetic core. The alternating current flows through the\n"
          ]
        }
      ],
      "source": [
        "print(res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mghju027Lobc"
      },
      "source": [
        "Next steps:\n",
        "- Store vectors in embedding database\n",
        "- deploy trained model\n",
        "- Use chat history as context\n",
        "- Update response into specific format\n",
        "- Deploy function to the cloud"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}