haystack_rag.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "source": [
        "Source:\n",
        "- https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode\n",
        "- https://haystack.deepset.ai/tutorials/06_better_retrieval_via_embedding_retrieval\n"
      ],
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    {
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      "source": [
        "!pip install farm-haystack[colab,faiss,inference]\n",
        "!pip install datasets>=2.6.1\n",
        "!pip install openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fqZ7Fjp0vrfD",
        "outputId": "ac607aa0-6027-4ab4-810b-a9e9e8c36293"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: sentence-transformers, docopt\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=125923 sha256=5c965b146f5654c9594d5889ff401ca55024baec5df575d1505a1a51bb1946ae\n",
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            "Successfully built sentence-transformers docopt\n",
            "Installing collected packages: tokenizers, sseclient-py, sentencepiece, monotonic, faiss-cpu, events, docopt, url-normalize, sqlalchemy, safetensors, rank-bm25, psycopg2-binary, pillow, num2words, lazy-imports, h11, cattrs, boilerpy3, backoff, tiktoken, sqlalchemy-utils, scikit-learn, requests-cache, prompthub-py, posthog, huggingface-hub, httpcore, transformers, quantulum3, httpx, accelerate, sentence-transformers, farm-haystack\n",
            "  Attempting uninstall: sqlalchemy\n",
            "    Found existing installation: SQLAlchemy 2.0.22\n",
            "    Uninstalling SQLAlchemy-2.0.22:\n",
            "      Successfully uninstalled SQLAlchemy-2.0.22\n",
            "  Attempting uninstall: pillow\n",
            "    Found existing installation: Pillow 9.4.0\n",
            "    Uninstalling Pillow-9.4.0:\n",
            "      Successfully uninstalled Pillow-9.4.0\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.2.2\n",
            "    Uninstalling scikit-learn-1.2.2:\n",
            "      Successfully uninstalled scikit-learn-1.2.2\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",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\n",
            "ipython-sql 0.5.0 requires sqlalchemy>=2.0, but you have sqlalchemy 1.4.50 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed accelerate-0.24.1 backoff-2.2.1 boilerpy3-1.0.7 cattrs-23.1.2 docopt-0.6.2 events-0.5 faiss-cpu-1.7.2 farm-haystack-1.21.2 h11-0.14.0 httpcore-1.0.1 httpx-0.25.1 huggingface-hub-0.18.0 lazy-imports-0.3.1 monotonic-1.6 num2words-0.5.13 pillow-9.0.0 posthog-3.0.2 prompthub-py-4.0.0 psycopg2-binary-2.9.9 quantulum3-0.9.0 rank-bm25-0.2.2 requests-cache-0.9.8 safetensors-0.4.0 scikit-learn-1.3.2 sentence-transformers-2.2.2 sentencepiece-0.1.99 sqlalchemy-1.4.50 sqlalchemy-utils-0.41.1 sseclient-py-1.8.0 tiktoken-0.5.1 tokenizers-0.13.3 transformers-4.32.1 url-normalize-1.4.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting openai\n",
            "  Downloading openai-0.28.1-py3-none-any.whl (76 kB)\n",
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            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.3.1)\n",
            "Installing collected packages: openai\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",
            "llmx 0.0.15a0 requires cohere, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed openai-0.28.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zu2qi2sdvl-W",
        "outputId": "60fdc827-3b55-4f03-a17b-d6bfda1627c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter OpenAI API key:··········\n"
          ]
        }
      ],
      "source": [
        "from getpass import getpass\n",
        "\n",
        "openai_key = getpass(\"Enter OpenAI API key:\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 1: Load documents"
      ],
      "metadata": {
        "id": "2CrIhdSlEIbc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We'll use the Wikipedia pages of Seven Wonders of the Ancient World as Documents.\n",
        "# We preprocessed the data and uploaded to a Hugging Face Space: Seven Wonders.\n",
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n"
      ],
      "metadata": {
        "id": "BCLhTYkpyX1H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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            "678002179a0f4ed8ae22cd8be759143c",
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            "6ff8b016bb394b0cab4fd55b2739bad9",
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            "77910d4527564dc89a0167880b84e223",
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            "100503e379c14ae1a40f21c1119ed502",
            "2efddf263b6142e2a6f6c31e32bc0b8c",
            "d34036c9723d40e3947fa5021bbeaa22",
            "2b8eae36de8248388d20de31c7ee34e8",
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            "90a08886b1b84406819e9651eefd2b5a",
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            "b4ffb03415c149f792c01c1930defb62",
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          ]
        },
        "outputId": "8561aec4-acf8-4880-983f-94fa04b6ef0b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        },
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          "metadata": {}
        },
        {
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            ],
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              "model_id": "2efddf263b6142e2a6f6c31e32bc0b8c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "09f9b99bcce444528c979ba2c9d9d069"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Work with an external file?\n",
        "\n",
        "### File Converter\n",
        "- extract text from files in different formats and cast it into the unified Document format\n",
        "- Azure, CSV, Docx, Image, JSON, Makrdown, PDF, Parsr, Tika, Text\n",
        "\n",
        "\n",
        "### Pre processor\n",
        "- normalize white spaces, get rid of headers and footers, clean empty lines in your Documents, or split them into smaller pieces"
      ],
      "metadata": {
        "id": "58rmBhxQUSs6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 2: Create document store"
      ],
      "metadata": {
        "id": "P_Ioai27ELqe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.document_stores import FAISSDocumentStore\n",
        "document_store = FAISSDocumentStore()\n",
        "document_store.write_documents(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8n1yjoTxqMm",
        "outputId": "705009a2-cab3-4165-f342-9840ca387664"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Writing Documents: 10000it [00:00, 17844.25it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 3: Create Retriever"
      ],
      "metadata": {
        "id": "yEUzZoV5EP8p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.nodes import EmbeddingRetriever, PromptNode\n",
        "\n",
        "\n",
        "retriever = EmbeddingRetriever(document_store=document_store,\n",
        "                               embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\")"
      ],
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
            "  return self.fget.__get__(instance, owner)()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Important:\n",
        "# Now that we initialized the Retriever, we need to call update_embeddings() to iterate over all\n",
        "# previously indexed documents and update their embedding representation.\n",
        "# While this can be a time consuming operation (depending on the corpus size), it only needs to be done once.\n",
        "# At query time, we only need to embed the query and compare it to the existing document embeddings, which is very fast.\n",
        "\n",
        "document_store.update_embeddings(retriever)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "8ffcb8e6512a4944b0e07aa598f37566",
            "e0ab447e98d1451bad348db130ee922f",
            "a2eb41702f4c4093b2066cefc2f9c183",
            "b796af9d5d0442a893227180fd34facf",
            "a6ac2545d5ef48b4af8db57dc32e6bab",
            "a5b79fe1b68a4cc4b463be80019e06ab",
            "99322d87725f441e8c5cc670b17eb317",
            "1cba76fdeed24df4a8571eed95d98c6a",
            "76ff71272a8a4957b187e4e37852e195",
            "16eebb3e60fd4eeb8309654760879d97",
            "fb4c3e65f67f45429338cacf92fff862"
          ]
        },
        "id": "970w-agHYbmS",
        "outputId": "2cd92105-26b4-490e-cd6e-0aa58582e3f8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rUpdating Embedding:   0%|          | 0/151 [00:00<?, ? docs/s]"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ffcb8e6512a4944b0e07aa598f37566"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Documents Processed: 10000 docs [04:30, 36.96 docs/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 4: Define LLM"
      ],
      "metadata": {
        "id": "4fCJDySlEZ1-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.nodes import PromptNode, PromptTemplate, AnswerParser\n",
        "\n",
        "rag_prompt = PromptTemplate(\n",
        "    prompt=\"\"\"Synthesize a comprehensive answer from the following text for the given question.\n",
        "                             Provide a clear and concise response that summarizes the key points and information presented in the text.\n",
        "                             Your answer should be in your own words and be no longer than 50 words.\n",
        "                             \\n\\n Related text: {join(documents)} \\n\\n Question: {query} \\n\\n Answer:\"\"\",\n",
        "    output_parser=AnswerParser(),\n",
        ")\n",
        "\n",
        "prompt_node = PromptNode(\n",
        "    model_name_or_path=\"gpt-3.5-turbo\", api_key=openai_key, default_prompt_template=rag_prompt\n",
        ")"
      ],
      "metadata": {
        "id": "nBJ1ICSqvwva"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 5: Create a pipeline"
      ],
      "metadata": {
        "id": "8KYvRkQQEfoC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.pipelines import Pipeline\n",
        "\n",
        "pipe = Pipeline()\n",
        "pipe.add_node(component=retriever, name=\"retriever\", inputs=[\"Query\"])\n",
        "pipe.add_node(component=prompt_node, name=\"prompt_node\", inputs=[\"retriever\"])"
      ],
      "metadata": {
        "id": "NBG4o4eDzqs-"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output = pipe.run(query=\"What does Rhodes Statue look like?\")"
      ],
      "metadata": {
        "id": "hm39LASH4Vap",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c0f42706387a45178fb8f21c53b93fb3",
            "cb82648785244d1e8caca44552f5184c",
            "e1852b52d4a747b3bb0f3e18e3346a33",
            "6bcef7ceab8f465ba5ecfce02348a1f3",
            "c626ae81be614c1db641a6cd89ae14c4",
            "86ce42ee27bc423d837c9727919591a2",
            "a6002de916c0455294b041a78a32c1b5",
            "0ad79240be00492b85b6b52996fb15c6",
            "11432fa860194638b40ee9249886d315",
            "1c5672aa08f04740afa89af239344e49",
            "3d485736e3654994970fda9ba75c24bf"
          ]
        },
        "outputId": "41c398dc-135c-455f-9aa2-7b498b1bbc5d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c0f42706387a45178fb8f21c53b93fb3"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output['answers'][0].answer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "CgErlNn84K-2",
        "outputId": "f7407d97-8156-41ee-9976-8da9ea7d0a56"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The appearance of the Rhodes Statue is not known for certain, but it is believed to have had a standard rendering for the time. The head would have had curly hair with spikes of bronze or silver flame radiating, similar to the images found on contemporary Rhodian coins.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    }
  ]
}