llamacloud-demo/examples/batch_eval.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# ⚠️ Important Notice\n\nThis notebook (and repository) is deprecated.\n\nFor the latest python examples, please refer to the `llama-cloud-services` repository examples: \nhttps://github.com/run-llama/llama_cloud_services/tree/main/examples\n\n---"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "318d5a8d-7f6f-4fff-8c2d-cd0e290b6f49",
      "metadata": {},
      "source": [
        "# Building and Evaluating a RAG Pipeline with LlamaCloud and our Batch Evaluator\n",
        "\n",
        "In this notebook we show you how to easily construct a RAG pipeline with a LlamaCloud Index, and then run evaluations against that index using our batch evaluator."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1188feb6-bedf-40d5-8812-29b90431f8b5",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "Here we define some basic imports."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "84324692-17f5-4584-8932-6687ab811b66",
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "# attach to the same event-loop\n",
        "import nest_asyncio\n",
        "\n",
        "nest_asyncio.apply()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "dc3f08bd-81ad-4b05-89cc-33409e3d1f71",
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Response\n",
        "from llama_index.llms.openai import OpenAI\n",
        "from llama_index.core.evaluation import (\n",
        "    FaithfulnessEvaluator,\n",
        "    RelevancyEvaluator,\n",
        "    CorrectnessEvaluator,\n",
        ")\n",
        "from llama_index.core.node_parser import SentenceSplitter\n",
        "import pandas as pd\n",
        "\n",
        "pd.set_option(\"display.max_colwidth\", 0)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "95eb1f3a-b0bf-4588-8302-3799c520ffc3",
      "metadata": {},
      "source": [
        "## Build RAG Pipeline from LlamaCloud Index\n",
        "\n",
        "The LlamaCloud index is built over the 2021 Lyft and Uber 10K documents.\n",
        "\n",
        "To create the index, follow the instructions:\n",
        "1. You can download them here ([Uber 10K](https://www.dropbox.com/s/te0a2w227v27iag/uber_2021.pdf?dl=1), [Lyft 10K](https://www.dropbox.com/s/qctkz6nxhm0y5qe/lyft_2021.pdf?dl=1))\n",
        "2. Follow instructions on `https://cloud.llamaindex.ai/` to signup for an account. Create a pipeline by uploading these documents."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e44c21cc-ad10-40b5-89ca-f0d95c2a6714",
      "metadata": {},
      "outputs": [],
      "source": [
        "! pip install llama-index-indices-managed-llama-cloud"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e83560c6",
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "c7bc2ff3-8c95-4de4-965f-1ca5bcf61f9b",
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "from llama_index.indices.managed.llama_cloud import LlamaCloudIndex\n",
        "\n",
        "index = LlamaCloudIndex(\n",
        "  name=\"<index_name>\", \n",
        "  project_name=\"<project_name>\",\n",
        "  api_key=os.environ[\"LLAMA_CLOUD_API_KEY\"]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "8b5eafcb-5529-4fa5-a681-0d5e014ec517",
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "query = \"Tell me about the risk factors for Uber\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "520199ca-4271-4708-9efc-76e244f4cc22",
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "response = index.as_query_engine().query(query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "b30d952c-d502-4888-a7e8-3a72ca0d052f",
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Airbyte is a platform that allows users to sync their data from various sources into a destination database, such as Snowflake. It provides functionalities for data ingestion and transformation, enabling users to easily move and work with data from different platforms.\n"
          ]
        }
      ],
      "source": [
        "print(str(response))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "026a1616-874f-4e59-a7d5-b1be6a5525cd",
      "metadata": {},
      "source": [
        "## Setup Batch Evaluator\n",
        "\n",
        "Here we setup a batch evaluator, which can run evaluations over a batch dataset. We start by defining the set of metrics that we want to measure over this dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "34208172-079b-48d0-8bb2-6e57288ccc57",
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "# gpt-4\n",
        "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
        "gpt35 = OpenAI(model=\"gpt-3.5-turbo\")\n",
        "\n",
        "faithfulness_gpt4 = FaithfulnessEvaluator(llm=gpt4)\n",
        "relevancy_gpt4 = RelevancyEvaluator(llm=gpt4)\n",
        "correctness_gpt4 = CorrectnessEvaluator(llm=gpt4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d59da19e-9c75-48f7-8235-b0a458ca6a04",
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "print(response.source_nodes[2].get_content())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "47ed4750-92cd-49ec-b1a3-03bdbc425d97",
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "from llama_index.core.evaluation import BatchEvalRunner\n",
        "\n",
        "runner = BatchEvalRunner(\n",
        "    {\"faithfulness\": faithfulness_gpt4, \"relevancy\": relevancy_gpt4},\n",
        "    workers=8,\n",
        ")\n",
        "\n",
        "eval_results = await runner.aevaluate_queries(\n",
        "    index.as_query_engine(), queries=[query]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c89268b0-299f-4da5-b729-0e344988a1de",
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "eval_results"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "48ece9e7-5e70-46c4-88c3-0eb7bd733892",
      "metadata": {},
      "source": [
        "## Upload Results\n",
        "\n",
        "Once you obtain a set of eval results, you're able to upload them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5aedc2a7-3eae-49c7-9f1b-242d15917693",
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "runner.upload_eval_results(\n",
        "    project_name=\"default project\",\n",
        "    app_name=\"default app\",\n",
        "    results=eval_results\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ed0a112-93da-4df7-b1c6-3e779ae53211",
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
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