Llama_3_RAG_Query_Routing.ipynb

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            "Building wheels for collected packages: flash-attn, tinysegmenter, feedfinder2, jieba3k, sgmllib3k\n",
            "  Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for flash-attn: filename=flash_attn-2.5.8-cp310-cp310-linux_x86_64.whl size=120853537 sha256=53979129f883680327bf5d13027cd014e2d054f4fb5b8856916686ae315e57d6\n",
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            "Successfully built flash-attn tinysegmenter feedfinder2 jieba3k sgmllib3k\n",
            "Installing collected packages: tinysegmenter, striprtf, sgmllib3k, ninja, jieba3k, wsproto, pypdf, pyee, outcome, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, html2text, feedparser, einops, cssselect, chromedriver-autoinstaller, trio, requests-file, playwright, nvidia-cusparse-cu12, nvidia-cudnn-cu12, feedfinder2, trio-websocket, tldextract, text-generation, nvidia-cusolver-cu12, selenium, newspaper3k, llama-index-legacy, sentence-transformers, llama-parse, llama-index-readers-web, llama-index-readers-file, llama-index-indices-managed-llama-cloud, llama-index-embeddings-openai, flash-attn, bitsandbytes, accelerate, llama-index-readers-llama-parse, llama-index-multi-modal-llms-openai, llama-index-embeddings-huggingface, llama-index-cli, llama-index-agent-openai, llama-index-program-openai, llama-index-llms-huggingface, llama-index-question-gen-openai, llama-index\n",
            "Successfully installed accelerate-0.30.0 bitsandbytes-0.43.1 chromedriver-autoinstaller-0.6.4 cssselect-1.2.0 einops-0.8.0 feedfinder2-0.0.4 feedparser-6.0.11 flash-attn-2.5.8 html2text-2020.1.16 jieba3k-0.35.1 llama-index-0.10.34 llama-index-agent-openai-0.2.3 llama-index-cli-0.1.12 llama-index-embeddings-huggingface-0.2.0 llama-index-embeddings-openai-0.1.9 llama-index-indices-managed-llama-cloud-0.1.6 llama-index-legacy-0.9.48 llama-index-llms-huggingface-0.1.5 llama-index-multi-modal-llms-openai-0.1.5 llama-index-program-openai-0.1.6 llama-index-question-gen-openai-0.1.3 llama-index-readers-file-0.1.20 llama-index-readers-llama-parse-0.1.4 llama-index-readers-web-0.1.12 llama-parse-0.4.2 newspaper3k-0.2.8 ninja-1.11.1.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 outcome-1.3.0.post0 playwright-1.43.0 pyee-11.1.0 pypdf-4.2.0 requests-file-2.0.0 selenium-4.20.0 sentence-transformers-2.7.0 sgmllib3k-1.0.0 striprtf-0.0.26 text-generation-0.7.0 tinysegmenter-0.3 tldextract-5.1.2 trio-0.25.0 trio-websocket-0.11.1 wsproto-1.2.0\n"
          ]
        }
      ],
      "source": [
        "!pip install llama-index-llms-groq\n",
        "!pip install llama-index llama-index-llms-huggingface llama-index-embeddings-huggingface transformers accelerate bitsandbytes llama-index-readers-web matplotlib flash-attn"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load data"
      ],
      "metadata": {
        "id": "ssMCDO6SDYCz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.readers.web import BeautifulSoupWebReader\n",
        "\n",
        "url = \"https://www.theverge.com/2023/9/29/23895675/ai-bot-social-network-openai-meta-chatbots\"\n",
        "docs = BeautifulSoupWebReader().load_data([url])"
      ],
      "metadata": {
        "id": "FGs5S84mDW9V"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### LLM + Embeddings"
      ],
      "metadata": {
        "id": "8lBupz9jD-rU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## LLM\n",
        "\n",
        "from llama_index.llms.groq import Groq\n",
        "from google.colab import userdata\n",
        "\n",
        "llm = Groq(\n",
        "    model = \"llama3-70b-8192\",\n",
        "    api_key = userdata.get('GROQ_API_KEY')\n",
        ")"
      ],
      "metadata": {
        "id": "chyeQi2mEBu7"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Embeddings\n",
        "\n",
        "from llama_index.core import Settings\n",
        "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
        "\n",
        "Settings.llm = llm\n",
        "Settings.embed_model = HuggingFaceEmbedding(\n",
        "    model_name = \"BAAI/bge-small-en-v1.5\"\n",
        ")"
      ],
      "metadata": {
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        "id": "NPp1NH18EnLm",
        "outputId": "c5e8c5fe-4196-4dc5-8191-540c53962b37"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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"
          ]
        },
        {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "24dbfb7dee3245b68a7d69969c8eefab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b29f6c0c59e49bda2b09180b70c59aa"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Index"
      ],
      "metadata": {
        "id": "rdS7STClFIqE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core import VectorStoreIndex\n",
        "from llama_index.core import SummaryIndex\n",
        "\n",
        "vector_index = VectorStoreIndex.from_documents(docs)\n",
        "summary_index = SummaryIndex.from_documents(docs)"
      ],
      "metadata": {
        "id": "Bzb3YVIFFCGZ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core.response.notebook_utils import display_response\n",
        "\n",
        "import logging\n",
        "import sys\n",
        "\n",
        "logging.basicConfig(\n",
        "    stream=sys.stdout,\n",
        "    level=logging.INFO\n",
        ")\n",
        "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
      ],
      "metadata": {
        "id": "FE-zQFkcFgt3"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Basic Query Engine"
      ],
      "metadata": {
        "id": "yU3lF5kaFonj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = vector_index.as_query_engine(\n",
        "    response_mode=\"compact\"\n",
        ")\n",
        "\n",
        "response = query_engine.query(\"How do OpenAI and Meta differ on AI tools?\")\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "id": "jIUdSplLFnYw",
        "outputId": "d32d4d72-96dd-4e31-fd00-c2c9527fe758"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** OpenAI tends to present its products as productivity tools, whereas Meta is in the entertainment business."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = vector_index.as_query_engine(\n",
        "    response_mode=\"compact\"\n",
        ")\n",
        "\n",
        "response = query_engine.query(\"What are the new features added by OpenAI to ChatGPT?\")\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65
        },
        "id": "0q0_CVuwF7D-",
        "outputId": "8d7d915f-bffd-4613-fbdc-f613584a0444"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** According to the context, the new features added by OpenAI to ChatGPT include the ability to interact with its large language model via voice and the ability to upload images and ask questions about them."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Router Query Engine"
      ],
      "metadata": {
        "id": "4vEFhdkfGKVb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core.tools import QueryEngineTool, ToolMetadata\n",
        "\n",
        "vector_tool = QueryEngineTool(\n",
        "    vector_index.as_query_engine(),\n",
        "    metadata = ToolMetadata(\n",
        "        name = 'vector_search',\n",
        "        description = \"Useful for searching for specific facts.\",\n",
        "    ),\n",
        ")\n",
        "\n",
        "summary_tool = QueryEngineTool(\n",
        "    summary_index.as_query_engine(response_mode = \"tree_summarize\"),\n",
        "    metadata = ToolMetadata(\n",
        "        name = \"summary\",\n",
        "        description = \"Useful for summarizing an entire document.\",\n",
        "    ),\n",
        ")"
      ],
      "metadata": {
        "id": "Y51psG-FGGmR"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Settings.debug = True"
      ],
      "metadata": {
        "id": "toA7HamKG81d"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Single Selector"
      ],
      "metadata": {
        "id": "cRM6dukHG-6k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core.query_engine import RouterQueryEngine\n",
        "\n",
        "query_engine = RouterQueryEngine.from_defaults(\n",
        "    [vector_tool, summary_tool],\n",
        "    select_multi=False,\n",
        "    verbose=True,\n",
        ")\n",
        "\n",
        "response = query_engine.query(\"What was mentioned about Meta?\")\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "F54JL8dVG-O5",
        "outputId": "67b6f7b5-3e4e-4271-f760-775a5473864c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;3;38;5;200mSelecting query engine 0: The question asks about a specific fact mentioned about Meta, so it's useful for searching for specific facts..\n",
            "\u001b[0m"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** Meta is building LLMs (Large Language Models) and has found its own uses for generative AI and voices. The company unveiled 28 personality-driven chatbots to be used in Meta's messaging apps, with celebrities like Charli D'Amelio, Dwyane Wade, Kendall Jenner, MrBeast, Snoop Dogg, Tom Brady, and Paris Hilton lending their voices to the effort."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\n",
        "    \"What was mentioned about Meta? What other companies are mentioned and what was said about them?\"\n",
        ")\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "id": "AYtHrdgcHac3",
        "outputId": "25b51839-d947-43ab-a4cb-2e05fb9ecac1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;3;38;5;200mSelecting query engine 0: The question is asking for specific facts about Meta and other companies, so a summary method that is useful for searching for specific facts is most relevant..\n",
            "\u001b[0m"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** According to the context, Meta was mentioned as a company that is building Large Language Models (LLMs) and has found its own uses for generative AI and voices. Specifically, Meta unveiled 28 personality-driven chatbots to be used in its messaging apps, with celebrities like Charli D'Amelio, Dwyane Wade, and Snoop Dogg lending their voices to the effort.\n\nOther companies mentioned in the context are OpenAI and Adobe. OpenAI was mentioned as a company that tends to present its products as productivity tools, and its ChatGPT was discussed as a tool that has become more powerful with the addition of voice features. Adobe was mentioned in the context of Figma's CEO discussing life after the company's failed sale to Adobe."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Multi Selector"
      ],
      "metadata": {
        "id": "M-hLXzL5HstL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core.query_engine import RouterQueryEngine\n",
        "\n",
        "query_engine = RouterQueryEngine.from_defaults(\n",
        "    [vector_tool, summary_tool],\n",
        "    select_multi = True,\n",
        "    verbose = True\n",
        ")\n",
        "response = query_engine.query(\n",
        "    \"What was mentioned about Meta? What other companies are mentioned and what was said about them?\"\n",
        ")\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "id": "TUnNkiIIHqSv",
        "outputId": "4ad7d0f8-d7db-4fae-e45e-14a2786aa057"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;3;38;5;200mSelecting query engine 0: This choice is relevant because the question is asking for specific facts about Meta and other companies, which requires searching for specific information..\n",
            "\u001b[0m"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** According to the context, Meta was mentioned as a company that is building Large Language Models (LLMs) and has found its own uses for generative AI and voices. Specifically, Meta unveiled 28 personality-driven chatbots to be used in its messaging apps, with celebrities like Charli D'Amelio, Dwyane Wade, and Snoop Dogg lending their voices to the effort.\n\nOther companies mentioned in the context are OpenAI and Adobe. OpenAI was mentioned as a company that tends to present its products as productivity tools, and its ChatGPT was discussed as a tool that has become more powerful with the addition of voice interactions. Adobe was mentioned in the context of Figma's CEO discussing life after the company's failed sale to Adobe."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\n",
        "    \"Summarize what was mentioned about OpenAI?\"\n",
        ")\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "mMg5J5OSIF5O",
        "outputId": "b6946e41-b69c-4312-dea5-8cee3b0486a4"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;3;38;5;200mSelecting query engine 1: The question asks to summarize what was mentioned about OpenAI, which implies summarizing an entire document or text..\n",
            "\u001b[0m"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** According to the article, OpenAI announced updates for ChatGPT, including a feature that allows users to interact with its large language model via voice, and another feature that lets users upload images and ask questions about them. This makes ChatGPT feel more powerful and gives it a hint of personality. The voice feature is currently available to ChatGPT Plus subscribers, and free users won't be able to use it for some time. The article suggests that this technology could lead to a synthetic companion that is smarter, more patient, more empathetic, and more available than human companions."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xCZz8bc0IJko"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}