diff --git "a/notebooks/indexing_and_pipeline_creation.ipynb" "b/notebooks/indexing_and_pipeline_creation.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebooks/indexing_and_pipeline_creation.ipynb"
@@ -0,0 +1,10079 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "3922a573",
+      "metadata": {
+        "id": "3922a573"
+      },
+      "source": [
+        "# Indexing and pipeline creation\n",
+        "This notebook is inspired by [\"Build Your First QA System\" tutorial](https://haystack.deepset.ai/tutorials/first-qa-system), from Haystack documentation.\n",
+        "\n",
+        "Here we use a collection of articles about Twin Peaks to answer a variety of questions about that awesome TV series!\n",
+        "\n",
+        "The following steps are performed:\n",
+        "* load and preprocess data\n",
+        "* create document store and write documents\n",
+        "* initialize retriever and generate document embeddings\n",
+        "* initialize reader\n",
+        "* compose and try Question Answering pipeline\n",
+        "* save and export index"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "viixGIJcKPSQ",
+      "metadata": {
+        "id": "viixGIJcKPSQ"
+      },
+      "source": [
+        "## Preliminary operations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "MevE4jEZ5QBT",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MevE4jEZ5QBT",
+        "outputId": "727734c9-8366-4686-cbe1-bf3d149db1c5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "VYWRJ-Lf55nV",
+      "metadata": {
+        "id": "VYWRJ-Lf55nV"
+      },
+      "outputs": [],
+      "source": [
+        "# install dependencies\n",
+        "! pip install farm-haystack[faiss-gpu]==1.4.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "QVDuHAMIK4bg",
+      "metadata": {
+        "id": "QVDuHAMIK4bg"
+      },
+      "source": [
+        "## Load and preprocess data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "72139774",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:40:46.176031Z",
+          "iopub.status.busy": "2022-01-09T08:40:46.175755Z",
+          "iopub.status.idle": "2022-01-09T08:40:46.179554Z",
+          "shell.execute_reply": "2022-01-09T08:40:46.178704Z",
+          "shell.execute_reply.started": "2022-01-09T08:40:46.175959Z"
+        },
+        "id": "72139774"
+      },
+      "outputs": [],
+      "source": [
+        "import glob, json"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "4421e328",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:40:47.846999Z",
+          "iopub.status.busy": "2022-01-09T08:40:47.846757Z",
+          "iopub.status.idle": "2022-01-09T08:40:48.327632Z",
+          "shell.execute_reply": "2022-01-09T08:40:48.326829Z",
+          "shell.execute_reply.started": "2022-01-09T08:40:47.846975Z"
+        },
+        "id": "4421e328"
+      },
+      "outputs": [],
+      "source": [
+        "DATA_DIRECTORY = '/content/drive/MyDrive/Colab Notebooks/wklp/data'\n",
+        "\n",
+        "docs=[]\n",
+        "\n",
+        "for json_file in glob.glob(f'{DATA_DIRECTORY}/*.json'):\n",
+        "    with open(json_file, 'r') as fin:\n",
+        "        json_content=json.load(fin)\n",
+        "        \n",
+        "    doc={'content': json_content['text'],\n",
+        "        'meta': {'name': json_content['name'],\n",
+        "                 'url': json_content['url']}}\n",
+        "    docs.append(doc)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "GR6qWQAn72WG",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GR6qWQAn72WG",
+        "outputId": "6295735e-93ca-4047-8c64-727f2cdf5458"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "1087"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(docs)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "aa231b94",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:40:48.796741Z",
+          "iopub.status.busy": "2022-01-09T08:40:48.796550Z",
+          "iopub.status.idle": "2022-01-09T08:40:48.805224Z",
+          "shell.execute_reply": "2022-01-09T08:40:48.804705Z",
+          "shell.execute_reply.started": "2022-01-09T08:40:48.796722Z"
+        },
+        "id": "aa231b94",
+        "outputId": "af3c814e-3325-4c1b-e0a3-5314e1cf217a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'content': \"Pete Lindstrom\\nPete Lindstrom was a citizen of Twin Peaks, Washington who was killed in the Blizzard of 1889.\\nHis death was witnessed by Knut Zimmerman, who reported that wind had plunged a candle from the Annual Candlelighting and Christmas Tree Ceremony into the back of Lindstrom's head, killing him.\",\n",
+              " 'meta': {'name': 'Pete_Lindstrom',\n",
+              "  'url': 'https://twinpeaks.fandom.com/wiki/Pete_Lindstrom'}}"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs[5]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "bc5adb1c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:41:04.538529Z",
+          "iopub.status.busy": "2022-01-09T08:41:04.538227Z",
+          "iopub.status.idle": "2022-01-09T08:41:05.147190Z",
+          "shell.execute_reply": "2022-01-09T08:41:05.146513Z",
+          "shell.execute_reply.started": "2022-01-09T08:41:04.538503Z"
+        },
+        "id": "bc5adb1c",
+        "outputId": "2fc943a9-b55c-4f03-f577-343cb675e115"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "  0%|          | 0/1087 [00:00<?, ?docs/s]WARNING - haystack.nodes.preprocessor.preprocessor -  One or more sentence found with word count higher than the split length.\n",
+            "100%|██████████| 1087/1087 [00:01<00:00, 717.14docs/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# preprocess documents, splitting by chunks of 200 words\n",
+        "\n",
+        "from haystack.nodes import PreProcessor\n",
+        "\n",
+        "processor = PreProcessor(\n",
+        "    clean_empty_lines=True,\n",
+        "    clean_whitespace=True,\n",
+        "    clean_header_footer=True,\n",
+        "    split_by=\"word\",\n",
+        "    split_length=200,\n",
+        "   split_respect_sentence_boundary=True,\n",
+        "    split_overlap=0,\n",
+        "    language ='en'\n",
+        ")\n",
+        "preprocessed_docs = processor.process(docs)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "41986306",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:41:07.414905Z",
+          "iopub.status.busy": "2022-01-09T08:41:07.414681Z",
+          "iopub.status.idle": "2022-01-09T08:41:07.418856Z",
+          "shell.execute_reply": "2022-01-09T08:41:07.418094Z",
+          "shell.execute_reply.started": "2022-01-09T08:41:07.414884Z"
+        },
+        "id": "41986306",
+        "outputId": "453ee893-7bfc-4be4-e54b-b2da973913e6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<Document: id=3f6b71a59e1226326e53871d05393810, content='Pete Lindstrom\n",
+            "Pete Lindstrom was a citizen of Twin Peaks, Washington who was killed in the Blizzard ...'>\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(preprocessed_docs[5])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "b9PS0PkM_1EF",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "b9PS0PkM_1EF",
+        "outputId": "659eef80-dfde-42cf-f944-08bf0bba73a5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "2825"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(preprocessed_docs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "Yu3bAUPoLrPI",
+      "metadata": {
+        "id": "Yu3bAUPoLrPI"
+      },
+      "source": [
+        "## Create document store ([FAISS](https://github.com/facebookresearch/faiss)) and write documents\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "bfe846df",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:40:59.678181Z",
+          "iopub.status.busy": "2022-01-09T08:40:59.678003Z",
+          "iopub.status.idle": "2022-01-09T08:40:59.753228Z",
+          "shell.execute_reply": "2022-01-09T08:40:59.752500Z",
+          "shell.execute_reply.started": "2022-01-09T08:40:59.678161Z"
+        },
+        "id": "bfe846df"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.document_stores import FAISSDocumentStore\n",
+        "\n",
+        "# the document store settings are those compatible with Embedding Retriever\n",
+        "document_store = FAISSDocumentStore(\n",
+        "    similarity=\"dot_product\",\n",
+        "    embedding_dim=768)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "191144b4",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "ab82841df4bd4d4e89da2f5b48af7d90",
+            "7d18cadf709e49ceaa6f4b48dc950e1b",
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+            "465495a0c471437ea28fa01d97c80cd8"
+          ]
+        },
+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:41:10.695292Z",
+          "iopub.status.busy": "2022-01-09T08:41:10.695064Z",
+          "iopub.status.idle": "2022-01-09T08:41:22.144864Z",
+          "shell.execute_reply": "2022-01-09T08:41:22.144203Z",
+          "shell.execute_reply.started": "2022-01-09T08:41:10.695271Z"
+        },
+        "id": "191144b4",
+        "outputId": "2ce7622e-773e-4155-f7f4-f2b23c18d3c7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ab82841df4bd4d4e89da2f5b48af7d90",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Writing Documents:   0%|          | 0/2825 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# write documents\n",
+        "document_store.write_documents(preprocessed_docs)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "O7Eo9F7yf1N_",
+      "metadata": {
+        "id": "O7Eo9F7yf1N_"
+      },
+      "source": [
+        "## Initialize retriever (Embedding Retriever) and generate document embeddings\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "7993e609",
+      "metadata": {
+        "colab": {
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+        },
+        "id": "7993e609",
+        "outputId": "b86e1d63-a3db-4d1c-aeb4-ca840afcb3aa"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "INFO - haystack.modeling.utils -  Using devices: CUDA:0\n",
+            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n",
+            "INFO - haystack.nodes.retriever.dense -  Init retriever using embeddings of model sentence-transformers/multi-qa-mpnet-base-dot-v1\n"
+          ]
+        },
+        {
+          "data": {
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+        },
+        {
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+            },
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b106a182a7104492924accb9657143a4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "WARNING - haystack.nodes.retriever._embedding_encoder -  You are using a Sentence Transformer with the dot_product function. We recommend using cosine instead. This can be set when initializing the DocumentStore\n",
+            "INFO - haystack.document_stores.faiss -  Updating embeddings for 2811 docs...\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "bb823c0999954c32b3e1533db7f49c98",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Updating Embedding:   0%|          | 0/2811 [00:00<?, ? docs/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8dfb4906a1ba43e8a851db3dae32df1c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/88 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from haystack.nodes import EmbeddingRetriever\n",
+        "\n",
+        "retriever = EmbeddingRetriever(\n",
+        "    document_store=document_store,\n",
+        "   embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\",\n",
+        "   model_format=\"sentence_transformers\"\n",
+        ")\n",
+        "\n",
+        "# generate embeddings\n",
+        "document_store.update_embeddings(retriever)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9x7Bo95fgTkm",
+      "metadata": {
+        "id": "9x7Bo95fgTkm"
+      },
+      "source": [
+        "## Initialize reader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "9oJ3b3ukcT10",
+      "metadata": {
+        "id": "9oJ3b3ukcT10"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.nodes import FARMReader\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "f5299f38",
+      "metadata": {
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+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:42:49.473881Z",
+          "iopub.status.busy": "2022-01-09T08:42:49.473628Z",
+          "iopub.status.idle": "2022-01-09T08:42:58.250644Z",
+          "shell.execute_reply": "2022-01-09T08:42:58.249808Z",
+          "shell.execute_reply.started": "2022-01-09T08:42:49.473851Z"
+        },
+        "id": "f5299f38",
+        "outputId": "7e552326-a2af-4e16-d382-2a56dcb6d9fc"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "INFO - haystack.modeling.utils -  Using devices: CUDA:0\n",
+            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n",
+            "INFO - haystack.modeling.model.language_model -  LOADING MODEL\n",
+            "INFO - haystack.modeling.model.language_model -  =============\n",
+            "INFO - haystack.modeling.model.language_model -  Could not find deepset/roberta-base-squad2 locally.\n",
+            "INFO - haystack.modeling.model.language_model -  Looking on Transformers Model Hub (in local cache and online)...\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "64573f262bb249ad8dad207da25e2c98",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/571 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/473M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "INFO - haystack.modeling.model.language_model -  Loaded deepset/roberta-base-squad2\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0ed4a621743e44079dea899f4e0bd313",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/878k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5917611fefc34b4ab553ef863653ecec",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/446k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "536b410b566145a7948931f8b3bbbb74",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/772 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "493b9ae5e9f34501aba49462f3421a28",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/79.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "INFO - haystack.modeling.utils -  Using devices: CUDA\n",
+            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n",
+            "INFO - haystack.modeling.infer -  Got ya 2 parallel workers to do inference ...\n",
+            "INFO - haystack.modeling.infer -   0     0  \n",
+            "INFO - haystack.modeling.infer -  /w\\   /w\\ \n",
+            "INFO - haystack.modeling.infer -  /'\\   / \\ \n"
+          ]
+        }
+      ],
+      "source": [
+        "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\",\n",
+        "                    use_gpu=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "tRgVAepagXo1",
+      "metadata": {
+        "id": "tRgVAepagXo1"
+      },
+      "source": [
+        "## Compose and try Question Answering pipeline (retriever + reader)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "a2226345",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:42:58.252390Z",
+          "iopub.status.busy": "2022-01-09T08:42:58.252188Z",
+          "iopub.status.idle": "2022-01-09T08:42:58.256054Z",
+          "shell.execute_reply": "2022-01-09T08:42:58.255544Z",
+          "shell.execute_reply.started": "2022-01-09T08:42:58.252363Z"
+        },
+        "id": "a2226345"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.pipelines import ExtractiveQAPipeline\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "0fc15887",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:42:58.257027Z",
+          "iopub.status.busy": "2022-01-09T08:42:58.256867Z",
+          "iopub.status.idle": "2022-01-09T08:42:58.261446Z",
+          "shell.execute_reply": "2022-01-09T08:42:58.260756Z",
+          "shell.execute_reply.started": "2022-01-09T08:42:58.257009Z"
+        },
+        "id": "0fc15887"
+      },
+      "outputs": [],
+      "source": [
+        "pipe = ExtractiveQAPipeline(reader, retriever)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "id": "24fd8084",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:42:58.262798Z",
+          "iopub.status.busy": "2022-01-09T08:42:58.262569Z",
+          "iopub.status.idle": "2022-01-09T08:42:58.267587Z",
+          "shell.execute_reply": "2022-01-09T08:42:58.267054Z",
+          "shell.execute_reply.started": "2022-01-09T08:42:58.262772Z"
+        },
+        "id": "24fd8084"
+      },
+      "outputs": [],
+      "source": [
+        "import time\n",
+        "from haystack.utils import print_answers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "e8bae423",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 885,
+          "referenced_widgets": [
+            "0a9cd640e34b412a845d2049327f76e9",
+            "406420d71785423798bfc2f12997f2b5",
+            "85cc872e79bd4bc6be5659fb29db6be7",
+            "19d0592fbf6745aabd3a32f6b164e5b6",
+            "2d015e220e704e53b2589a5768fdfe78",
+            "8c65b786c90647d8888cd303bfcbba32",
+            "b7f90482885e4564b7fded010a0eaba8",
+            "d65370713aa642d1a4511914c0f892e7",
+            "63d9b21eb5f0427e8090db6138f639b7",
+            "8c53a755a77e4855b6eb6feab4e56259",
+            "7bcaf8ebe1ed4065ba263c00974ef272"
+          ]
+        },
+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:56:33.759683Z",
+          "iopub.status.busy": "2022-01-09T08:56:33.759457Z",
+          "iopub.status.idle": "2022-01-09T08:56:34.894724Z",
+          "shell.execute_reply": "2022-01-09T08:56:34.894183Z",
+          "shell.execute_reply.started": "2022-01-09T08:56:33.759662Z"
+        },
+        "id": "e8bae423",
+        "outputId": "6b8c8306-36cf-42e9-f2ab-0b20ff7561b8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0a9cd640e34b412a845d2049327f76e9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Inferencing Samples:   0%|          | 0/1 [00:00<?, ? Batches/s]/usr/local/lib/python3.7/dist-packages/haystack/modeling/model/prediction_head.py:483: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+            "  start_indices = flat_sorted_indices // max_seq_len\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00,  6.74 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 28.05 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 16.98 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 25.82 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 24.78 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 10.44 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 19.66 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 38.78 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 47.32 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 31.77 Batches/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "1.4476244449615479\n",
+            "\n",
+            "Query: Where is Twin Peaks\n",
+            "Answers:\n",
+            "[   {   'answer': 'Washington',\n",
+            "        'context': 'Highway J\\n'\n",
+            "                   'Highway J was a highway that ran through Twin Peaks, '\n",
+            "                   'Washington. Notable buildings\\n'\n",
+            "                   \"Gentleman Jim's\\n\"\n",
+            "                   \"Horne's Department Store\\n\"\n",
+            "                   'Pine View Motel ',\n",
+            "        'score': 0.942712664604187},\n",
+            "    {   'answer': 'Washington',\n",
+            "        'context': 'Chapel-in-the-Woods\\n'\n",
+            "                   'Chapel-in-the-Woods was a chapel in Twin Peaks, '\n",
+            "                   'Washington. Hank Jennings and Norma Jennings as well as Ed '\n",
+            "                   'Hurley and Nadine Hurle',\n",
+            "        'score': 0.7930099964141846},\n",
+            "    {   'answer': 'northeastern Washington State',\n",
+            "        'context': 'eriff Harry S. Truman\\n'\n",
+            "                   'Twin Peaks was a small logging town in northeastern '\n",
+            "                   'Washington State, five miles south of the Canadian border '\n",
+            "                   'and twelve miles w',\n",
+            "        'score': 0.7441704869270325},\n",
+            "    {   'answer': 'Washington State',\n",
+            "        'context': 'as the composer and that the new series would shoot on '\n",
+            "                   'location in Washington State. Filming began in September '\n",
+            "                   '2015 and wrapped in April 2016. The se',\n",
+            "        'score': 0.5651967525482178},\n",
+            "    {   'answer': 'along the shores of Black Lake',\n",
+            "        'context': 'od National Forest. By 1888, the town of Twin Peaks '\n",
+            "                   'existed along the shores of Black Lake and was settled by '\n",
+            "                   'refugees, trappers, and thieves. The chi',\n",
+            "        'score': 0.47422048449516296}]\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "start_time=time.time()\n",
+        "\n",
+        "prediction = pipe.run(\n",
+        "    query=\"Where is Twin Peaks\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
+        ")\n",
+        "\n",
+        "end_time=time.time()\n",
+        "\n",
+        "print()\n",
+        "print(end_time - start_time)\n",
+        "print_answers(prediction, details=\"medium\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "be150456",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 989,
+          "referenced_widgets": [
+            "d36ff2c8c9ab426591490c78a414627f",
+            "89540d29c9a94ba28562973bc1c1ea23",
+            "158ccaa5232a48c39e93c957e90c3daa",
+            "91afc316136742208fce45d759f4d4bc",
+            "b391ae6b199644a7be34e0348eead92f",
+            "f233ed5628af41b1b7dd22e0f2c43f24",
+            "dcf900f173304c6dbabb6752678fcfc6",
+            "bdf5f9adb0d44ed3b8c9b955a974ca61",
+            "3dabef3a25fc44e48c76da70e1ad96dd",
+            "a50beef16802477299f5ed6e8f997619",
+            "d0f73dbb0bca41e0b535142f34a7f951"
+          ]
+        },
+        "execution": {
+          "iopub.execute_input": "2022-01-09T08:56:36.820622Z",
+          "iopub.status.busy": "2022-01-09T08:56:36.820402Z",
+          "iopub.status.idle": "2022-01-09T08:56:36.830219Z",
+          "shell.execute_reply": "2022-01-09T08:56:36.826251Z",
+          "shell.execute_reply.started": "2022-01-09T08:56:36.820601Z"
+        },
+        "id": "be150456",
+        "outputId": "59615441-0f0c-492c-a959-e1c9bc7a13fb"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d36ff2c8c9ab426591490c78a414627f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Inferencing Samples:   0%|          | 0/1 [00:00<?, ? Batches/s]/usr/local/lib/python3.7/dist-packages/haystack/modeling/model/prediction_head.py:483: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+            "  start_indices = flat_sorted_indices // max_seq_len\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 15.02 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 11.34 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 28.71 Batches/s]\n",
+            "Inferencing Samples: 100%|█████��████| 1/1 [00:00<00:00, 14.61 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 22.03 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 29.09 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 27.55 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 27.18 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 28.01 Batches/s]\n",
+            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 34.82 Batches/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "1.180633544921875\n",
+            "\n",
+            "Query: Who is Mike\n",
+            "Answers:\n",
+            "[   {   'answer': 'inhabiting spirit',\n",
+            "        'context': 's. Cooper refused to give him his medicine and he changed '\n",
+            "                   'into the inhabiting spirit, Mike. He explained Gerard as '\n",
+            "                   'being his host and described BOB as',\n",
+            "        'score': 0.6887995302677155},\n",
+            "    {   'answer': 'his name is Mike and that he lived above a convenience '\n",
+            "                  'store with a man named BOB',\n",
+            "        'context': 'walk with me,\" and tells them that his name is Mike and '\n",
+            "                   'that he lived above a convenience store with a man named '\n",
+            "                   'BOB. He says that he was in the eleva',\n",
+            "        'score': 0.3988475129008293},\n",
+            "    {   'answer': 'one-armed man',\n",
+            "        'context': 'duos in the series named \"Mike\" and \"Bob,\" the other being '\n",
+            "                   'Mike (the one-armed man) and BOB. Co-author Mark Frost '\n",
+            "                   'stated that Mike and Bobby remained ',\n",
+            "        'score': 0.3507264107465744},\n",
+            "    {   'answer': \"Cooper, who in a catatonic state had taken Jones' identity, \"\n",
+            "                  'and told him he had been tricked. He warned Cooper that now '\n",
+            "                  'either he or his doppelgänger must die. Mike again appeared '\n",
+            "                  'to Cooper, urging him to wake up and to not die. He '\n",
+            "                  'appeared to him again later, but did not say anything. He '\n",
+            "                  'appeared to Cooper again when he finally awoke and informed '\n",
+            "                  'him that his doppelganger',\n",
+            "        'context': \" Cooper, who in a catatonic state had taken Jones' \"\n",
+            "                   'identity, and told him he had been tricked. He warned '\n",
+            "                   'Cooper that now either he or his doppelgänger must die. '\n",
+            "                   'Mike again appeared to Cooper, urging him to wake up and '\n",
+            "                   'to not die. He appeared to him again later, but did not '\n",
+            "                   'say anything. He appeared to Cooper again when he finally '\n",
+            "                   'awoke and informed him that his doppelganger',\n",
+            "        'score': 0.22238559275865555},\n",
+            "    {   'answer': \"BOB's host\",\n",
+            "        'context': 'tting out the candles. Soon thereafter, Mike harassed '\n",
+            "                   \"Leland Palmer - BOB's host - in a road rage incident, \"\n",
+            "                   'where he accused Leland of \"stealing the c',\n",
+            "        'score': 0.1489737629890442}]\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "start_time=time.time()\n",
+        "\n",
+        "prediction = pipe.run(\n",
+        "    query=\"Who is Mike\", \n",
+        "    params={\"Retriever\": {\"top_k\": 10}, \n",
+        "            \"Reader\": {\"top_k\": 5}}\n",
+        ")\n",
+        "\n",
+        "end_time=time.time()\n",
+        "\n",
+        "print()\n",
+        "print(end_time - start_time)\n",
+        "print_answers(prediction, details=\"medium\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9QhguDpYf_5u",
+      "metadata": {
+        "id": "9QhguDpYf_5u"
+      },
+      "source": [
+        "## Save and export index\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "jLKDYZ1tnNZo",
+      "metadata": {
+        "id": "jLKDYZ1tnNZo"
+      },
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "import glob"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "7DVPCyzAhPEA",
+      "metadata": {
+        "id": "7DVPCyzAhPEA"
+      },
+      "outputs": [],
+      "source": [
+        "document_store.save(\"my_faiss_index.faiss\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "8fYMVd_ggJnw",
+      "metadata": {
+        "id": "8fYMVd_ggJnw"
+      },
+      "outputs": [],
+      "source": [
+        "OUT_DIR = '/content/drive/MyDrive/Colab Notebooks/wklp/'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "F1GnHXZ4nPJI",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F1GnHXZ4nPJI",
+        "outputId": "b6a92307-2cd2-4aad-d6eb-084c3ed81dcd"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "my_faiss_index.faiss\n",
+            "my_faiss_index.json\n",
+            "faiss_document_store.db\n",
+            "faiss_document_store.db\n"
+          ]
+        }
+      ],
+      "source": [
+        "for f in glob.glob('*faiss*.*')+glob.glob('faiss*.*'):\n",
+        "  print(f)\n",
+        "  shutil.copy(f, OUT_DIR)"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "name": "indexing_and_pipeline_creation.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "saturn (Python 3)",
+      "language": "python",
+      "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": "3.9.5"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "00476982a9a0444ca524a25256c22b1c": {
+          "model_module": "@jupyter-widgets/base",
+          "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
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