notebooks/02_RAG_with_local_llms.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ResByte/llm-notebooks/blob/main/notebooks/02_RAG_with_local_llms.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Local LLMs\n",
        "This notebook runs local LLMs using `llama-cpp-python`"
      ],
      "metadata": {
        "id": "yV5e5uGW5lKx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Installations"
      ],
      "metadata": {
        "id": "UHMxZpga5r3j"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PeMU_fi35fgM",
        "outputId": "37156b86-1514-44b9-e4a2-4dd6564d796f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.8/8.8 MB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for llama-cpp-python (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!CMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" pip install -q llama-cpp-python"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Setup"
      ],
      "metadata": {
        "id": "dCWMIqb56Zt1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q llama-index"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwp8ETdK51Nj",
        "outputId": "8122ef1c-fc59-4285-b05f-86ae6eeaa18b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.7/15.7 MB\u001b[0m \u001b[31m55.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.0/143.0 kB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.4/225.4 kB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m85.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\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",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_url = \"https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf?download=true\""
      ],
      "metadata": {
        "id": "NP8272gr6eis"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.llms import LlamaCPP"
      ],
      "metadata": {
        "id": "hciPt7O36mCJ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.llms.llama_utils import (\n",
        "    messages_to_prompt,\n",
        "    completion_to_prompt,\n",
        ")"
      ],
      "metadata": {
        "id": "MErjw-en6psJ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm  = LlamaCPP(\n",
        "    model_url=model_url,\n",
        "    model_path=None,\n",
        "    temperature=0.1,\n",
        "    max_new_tokens=256,\n",
        "    context_window=3900,\n",
        "    generate_kwargs={},\n",
        "    model_kwargs={\"n_gpu_layers\":1},\n",
        "    messages_to_prompt=messages_to_prompt,\n",
        "    completion_to_prompt=completion_to_prompt,\n",
        "    verbose=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eR1DPXmR6tZu",
        "outputId": "e16eefdc-f278-4196-d554-e83aab32c339"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading url https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf?download=true to path /tmp/llama_index/models/mistral-7b-instruct-v0.1.Q4_K_M.gguf?download=true\n",
            "total size (MB): 4368.44\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "4167it [00:20, 198.81it/s]                          \n",
            "AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = llm.complete('Tell me about Tokyo?')\n",
        "print(response.text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67UHVWXf7P96",
        "outputId": "3d2fc8be-1615-4089-d415-d6bc24603afb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Tokyo is the capital and largest city of Japan, located on the eastern coast of Honshu, the main island of Japan. It is a bustling metropolis with a population of over 13 million people. Tokyo is known for its modern skyscrapers, busy streets, and vibrant culture. The city has a rich history dating back to the Edo period (1603-1868) when it was the center of political power in Japan. Today, Tokyo is a major global financial center and a hub for business, technology, and innovation. Some popular attractions in Tokyo include the Tokyo Tower, the Imperial Palace, and the Tsukiji Fish Market.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Query Engine"
      ],
      "metadata": {
        "id": "enBbBJZK8REe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import set_global_tokenizer\n",
        "from transformers import AutoTokenizer"
      ],
      "metadata": {
        "id": "ERjY3nRa7iap"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "set_global_tokenizer(\n",
        "    AutoTokenizer.from_pretrained(\"NousResearch/Llama-2-7b-chat-hf\").encode\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "1cfcd663eec24ab2a4e8b60f96e80d2a",
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            "1a319334f7ae4a6db6a51dc951aaa09c",
            "d01e7b35b28244ac961ecb38772c182f",
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            "e45318bf30044bfda104ac611631ad1e",
            "f63c776a5dc24cf792018d59edce6317",
            "780408258e3542e18d112979920cd496",
            "4d7f1e4256d448798b3f145bf4489772",
            "d86624de75734d46884773b3f23986eb",
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            "84865a626e1649ad838b6f7bf0a902ff",
            "ff447d05a9c04e7ea12e2fc8fef5686d"
          ]
        },
        "id": "yc3izgGz8YBP",
        "outputId": "299428c4-b6f9-477e-9d1b-7d1bd8e8b82e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/746 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1cfcd663eec24ab2a4e8b60f96e80d2a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d43b9e6c801743919f512d6ab88e3560"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aceca8b6e2cb47338f73dcceaf994744"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/21.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ded86d00b0794f828ac34fff83f48538"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "special_tokens_map.json:   0%|          | 0.00/435 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a319334f7ae4a6db6a51dc951aaa09c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using hf embeddings\n",
        "from llama_index.embeddings import HuggingFaceEmbedding\n",
        "embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
            "c3421640b66048c994e80b2d8c4d0d67",
            "ca25adbfb6cd47c2adb5890623291f65",
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            "bf8d22a2e01d4e308d924b328019150a",
            "d92af9d8c3d843c29d3aab83ea04f5fd",
            "5bceffd0a4574018be00b1b13d4c47c5",
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            "de2ab8bc61d146eb97a6a8f87ac829ca",
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            "dc8a2fa982d843fa83fe302b9c12a7f9",
            "2b151215f2164905ad1faa62ea0c5a1d",
            "ad9b5b6044fd4fbb9d38cc46143e3291",
            "d95f582051d54e8c9dba1a4897ff2ebc",
            "7ac43f23f26043e9a8c88f3f770ad0b1"
          ]
        },
        "id": "DW4mkBvL8i-c",
        "outputId": "2285fa89-a158-4cc3-bb75-d489732cef1f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/743 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c3421640b66048c994e80b2d8c4d0d67"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/133M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9b47e92e2bb446fa9e777319efbaab68"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/366 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6faf25d2eb594195bbe9b7258c338400"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "40dc59431e94459f9282208c5d7c3a52"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "466d46607cc74112bc281452e7708b6d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d92af9d8c3d843c29d3aab83ea04f5fd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import ServiceContext"
      ],
      "metadata": {
        "id": "ynDJwYKG85ur"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "service_context = ServiceContext.from_defaults(\n",
        "    llm=llm,\n",
        "    embed_model=embed_model\n",
        ")"
      ],
      "metadata": {
        "id": "EujPMVwG9JM8"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset Setup"
      ],
      "metadata": {
        "id": "TN7ep604PbXn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p 'data/paul_graham/'\n",
        "!curl 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -o 'data/paul_graham/paul_graham_essay.txt'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dleyvulNbQKH",
        "outputId": "9ed5ef35-22f8-47a4-be63-e46d9a3a5b2e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100 75042  100 75042    0     0   203k      0 --:--:-- --:--:-- --:--:--  204k\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import SimpleDirectoryReader"
      ],
      "metadata": {
        "id": "j-idpSo_9R7R"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "documents = SimpleDirectoryReader(\n",
        "    \"./data/paul_graham\"\n",
        ").load_data()"
      ],
      "metadata": {
        "id": "mlDiR0-KbhSN"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import VectorStoreIndex"
      ],
      "metadata": {
        "id": "7guLX3c-fibo"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index = VectorStoreIndex.from_documents(\n",
        "    documents, service_context=service_context\n",
        ")"
      ],
      "metadata": {
        "id": "HEguSDBZ9gkz"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = index.as_query_engine()"
      ],
      "metadata": {
        "id": "4qZ7_CnmfTtw"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"What did the author do growing up?\")\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayVPeKKWf1Yv",
        "outputId": "e8c63995-d96e-4cc8-a9d6-2fb12e0dbb43"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Llama.generate: prefix-match hit\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The author grew up writing short stories and programming on an IBM 1401 computer in school. They didn't have any data stored on punched cards to input into programs, so they couldn't do much with the computer. With microcomputers, they started programming in earnest and wrote simple games, a program to predict how high their model rockets would fly, and a word processor. They also studied philosophy in college but switched to AI after finding it boring.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response.source_nodes[0].get_text()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "418S07Mwh7N9",
        "outputId": "ddbb57ec-f412-4d76-f783-111bf83f29dd"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'What I Worked On\\n\\nFebruary 2021\\n\\nBefore college the two main things I worked on, outside of school, were writing and programming. I didn\\'t write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.\\n\\nThe first programs I tried writing were on the IBM 1401 that our school district used for what was then called \"data processing.\" This was in 9th grade, so I was 13 or 14. The school district\\'s 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain\\'s lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.\\n\\nThe language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.\\n\\nI was puzzled by the 1401. I couldn\\'t figure out what to do with it. And in retrospect there\\'s not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn\\'t have any data stored on punched cards. The only other option was to do things that didn\\'t rely on any input, like calculate approximations of pi, but I didn\\'t know enough math to do anything interesting of that type. So I\\'m not surprised I can\\'t remember any programs I wrote, because they can\\'t have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn\\'t. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager\\'s expression made clear.\\n\\nWith microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1]\\n\\nThe first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer.\\n\\nComputers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he\\'d write 2 pages at a time and then print them out, but it was a lot better than a typewriter.\\n\\nThough I liked programming, I didn\\'t plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn\\'t much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored.\\n\\nI couldn\\'t have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response.source_nodes[1].get_text()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "BQqH5fZaf8Xp",
        "outputId": "560cdbda-46b3-4f38-900e-d05d0aa6257d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge.\\n\\nThe following spring, lightning struck. I was invited to give a talk at a Lisp conference, so I gave one about how we'd used Lisp at Viaweb. Afterward I put a postscript file of this talk online, on paulgraham.com, which I'd created years before using Viaweb but had never used for anything. In one day it got 30,000 page views. What on earth had happened? The referring urls showed that someone had posted it on Slashdot. [10]\\n\\nWow, I thought, there's an audience. If I write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything.\\n\\nThis had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11]\\n\\nIn the print era, the channel for publishing essays had been vanishingly small. Except for a few officially anointed thinkers who went to the right parties in New York, the only people allowed to publish essays were specialists writing about their specialties. There were so many essays that had never been written, because there had been no way to publish them. Now they could be, and I was going to write them. [12]\\n\\nI've worked on several different things, but to the extent there was a turning point where I figured out what to work on, it was when I started publishing essays online. From then on I knew that whatever else I did, I'd always write essays too.\\n\\nI knew that online essays would be a marginal medium at first. Socially they'd seem more like rants posted by nutjobs on their GeoCities sites than the genteel and beautifully typeset compositions published in The New Yorker. But by this point I knew enough to find that encouraging instead of discouraging.\\n\\nOne of the most conspicuous patterns I've noticed in my life is how well it has worked, for me at least, to work on things that weren't prestigious. Still life has always been the least prestigious form of painting. Viaweb and Y Combinator both seemed lame when we started them. I still get the glassy eye from strangers when they ask what I'm writing, and I explain that it's an essay I'm going to publish on my web site. Even Lisp, though prestigious intellectually in something like the way Latin is, also seems about as hip.\\n\\nIt's not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it's a sign both that there's something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren't prestigious doesn't guarantee you're on the right track, it at least guarantees you're not on the most common type of wrong one.\\n\\nOver the next several years I wrote lots of essays about all kinds of different topics. O'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate"
      ],
      "metadata": {
        "id": "SeQUtIv4iM4a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.evaluation import generate_question_context_pairs\n",
        "from llama_index.node_parser import SimpleNodeParser\n",
        "from llama_index import VectorStoreIndex"
      ],
      "metadata": {
        "id": "4SNf97PEiMZL"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build index with a chunk_size of 512\n",
        "node_parser = SimpleNodeParser.from_defaults(chunk_size=512)\n",
        "nodes = node_parser.get_nodes_from_documents(documents)\n",
        "vector_index = VectorStoreIndex(nodes, service_context=service_context)"
      ],
      "metadata": {
        "id": "KNqSQjtkiCQc"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "qa_dataset = generate_question_context_pairs(\n",
        "    nodes,\n",
        "    llm=llm,\n",
        "    num_questions_per_chunk=2\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlRzMon1i7Eo",
        "outputId": "66098d8f-9942-4e32-df12-3d42c6434fc4"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.evaluation import RetrieverEvaluator"
      ],
      "metadata": {
        "id": "98-nukGtjd4V"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = vector_index.as_retriever(similarity_top_k=2)"
      ],
      "metadata": {
        "id": "MT61hee-uX2a"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "retriever_evaluator = RetrieverEvaluator.from_metric_names(\n",
        "    [\"mrr\", \"hit_rate\"], retriever=retriever\n",
        ")"
      ],
      "metadata": {
        "id": "_Ge1rT1MuQxX"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z596D7wpv_A3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nest_asyncio\n",
        "nest_asyncio.apply()"
      ],
      "metadata": {
        "id": "i2eYTKxUv5se"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_results = await retriever_evaluator.aevaluate_dataset(qa_dataset)"
      ],
      "metadata": {
        "id": "HYQqs36iuZza"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "n_GJegyHueJW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "def display_results(name, eval_results):\n",
        "    \"\"\"Display results from evaluate.\"\"\"\n",
        "\n",
        "    metric_dicts = []\n",
        "    for eval_result in eval_results:\n",
        "        metric_dict = eval_result.metric_vals_dict\n",
        "        metric_dicts.append(metric_dict)\n",
        "\n",
        "    full_df = pd.DataFrame(metric_dicts)\n",
        "\n",
        "    hit_rate = full_df[\"hit_rate\"].mean()\n",
        "    mrr = full_df[\"mrr\"].mean()\n",
        "\n",
        "    metric_df = pd.DataFrame(\n",
        "        {\"Retriever Name\": [name], \"Hit Rate\": [hit_rate], \"MRR\": [mrr]}\n",
        "    )\n",
        "\n",
        "    return metric_df"
      ],
      "metadata": {
        "id": "BrywOtbWug8b"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display_results(\"top-2 eval\", eval_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "tzj6riR1v2Nq",
        "outputId": "7d57e52f-75de-401e-cb32-8d2db3e195da"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Retriever Name  Hit Rate       MRR\n",
              "0     top-2 eval  0.717391  0.634058"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cee5b82f-2d9f-429c-b039-f81392d1c21e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Retriever Name</th>\n",
              "      <th>Hit Rate</th>\n",
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              "      <th>0</th>\n",
              "      <td>top-2 eval</td>\n",
              "      <td>0.717391</td>\n",
              "      <td>0.634058</td>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cee5b82f-2d9f-429c-b039-f81392d1c21e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cee5b82f-2d9f-429c-b039-f81392d1c21e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cee5b82f-2d9f-429c-b039-f81392d1c21e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IjqG-7axv3pQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}