mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-10-30 17:29:29 +00:00
3031 lines
112 KiB
Plaintext
3031 lines
112 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "bEH-CRbeA6NU"
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},
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"source": [
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"# Better Retrieval via \"Dense Passage Retrieval\"\n",
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"\n",
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"[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)\n",
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"\n",
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"### Importance of Retrievers\n",
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"\n",
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"The Retriever has a huge impact on the performance of our overall search pipeline.\n",
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"\n",
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"\n",
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"### Different types of Retrievers\n",
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"#### Sparse\n",
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"Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.\n",
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"\n",
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"**Examples**: BM25, TF-IDF\n",
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"\n",
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"**Pros**: Simple, fast, well explainable\n",
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"\n",
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"**Cons**: Relies on exact keyword matches between query and text\n",
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" \n",
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"\n",
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"#### Dense\n",
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"These retrievers use neural network models to create \"dense\" embedding vectors. Within this family there are two different approaches: \n",
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"\n",
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"a) Single encoder: Use a **single model** to embed both query and passage. \n",
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"b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage\n",
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"\n",
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"Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...). \n",
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"\n",
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"**Examples**: REALM, DPR, Sentence-Transformers\n",
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"\n",
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"**Pros**: Captures semantinc similarity instead of \"word matches\" (e.g. synonyms, related topics ...)\n",
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"\n",
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"**Cons**: Computationally more heavy, initial training of model\n",
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"\n",
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"\n",
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"### \"Dense Passage Retrieval\"\n",
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"\n",
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"In this Tutorial, we want to highlight one \"Dense Dual-Encoder\" called Dense Passage Retriever. \n",
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"It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906. \n",
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"\n",
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"Original Abstract: \n",
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"\n",
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"_\"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.\"_\n",
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"\n",
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"Paper: https://arxiv.org/abs/2004.04906 \n",
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"Original Code: https://fburl.com/qa-dpr \n",
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"\n",
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"\n",
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"*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "3K27Y5FbA6NV"
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},
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"source": [
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"### Prepare environment\n",
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"\n",
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"#### Colab: Enable the GPU runtime\n",
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"Make sure you enable the GPU runtime to experience decent speed in this tutorial. \n",
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"**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
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"\n",
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"<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/_src/img/colab_gpu_runtime.jpg\">"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 357
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},
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"colab_type": "code",
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"id": "JlZgP8q1A6NW",
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"outputId": "c893ac99-b7a0-4d49-a8eb-1a9951d364d9"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mon Aug 24 11:56:45 2020 \r\n",
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"+-----------------------------------------------------------------------------+\r\n",
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"| NVIDIA-SMI 440.100 Driver Version: 440.100 CUDA Version: 10.2 |\r\n",
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"|-------------------------------+----------------------+----------------------+\r\n",
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"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n",
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"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n",
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"|===============================+======================+======================|\r\n",
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"| 0 Tesla V100-SXM2... Off | 00000000:00:1E.0 Off | 0 |\r\n",
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"| N/A 41C P0 39W / 300W | 0MiB / 16160MiB | 0% Default |\r\n",
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"+-------------------------------+----------------------+----------------------+\r\n",
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" \r\n",
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"+-----------------------------------------------------------------------------+\r\n",
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"| Processes: GPU Memory |\r\n",
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"| GPU PID Type Process name Usage |\r\n",
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"|=============================================================================|\r\n",
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"| No running processes found |\r\n",
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"+-----------------------------------------------------------------------------+\r\n"
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]
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}
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],
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"source": [
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"# Make sure you have a GPU running\n",
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"!nvidia-smi"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"colab_type": "code",
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"id": "NM36kbRFA6Nc",
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"outputId": "af1a9d85-9557-4d68-ea87-a01f00c584f9"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting git+https://github.com/deepset-ai/haystack.git\n",
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" Cloning https://github.com/deepset-ai/haystack.git to /tmp/pip-req-build-fqgbr4x7\n",
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" Running command git clone -q https://github.com/deepset-ai/haystack.git /tmp/pip-req-build-fqgbr4x7\n",
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"Requirement already satisfied (use --upgrade to upgrade): farm-haystack==0.3.0 from git+https://github.com/deepset-ai/haystack.git in /home/ubuntu/deepset/haystack\n",
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"Requirement already satisfied: farm==0.4.6 in /home/ubuntu/deepset/FARM (from farm-haystack==0.3.0) (0.4.6)\n",
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"Requirement already satisfied: fastapi in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (0.59.0)\n",
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"Requirement already satisfied: uvicorn in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (0.11.6)\n",
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"Requirement already satisfied: gunicorn in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (20.0.4)\n",
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"Requirement already satisfied: pandas in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (1.0.5)\n",
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"Requirement already satisfied: psycopg2-binary in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (2.8.5)\n",
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"Requirement already satisfied: sklearn in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (0.0)\n",
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"Requirement already satisfied: elasticsearch in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (7.8.0)\n",
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"Requirement already satisfied: elastic-apm in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (5.8.1)\n",
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"Requirement already satisfied: tox in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (3.17.1)\n",
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"Requirement already satisfied: coverage in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (5.2)\n",
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"Requirement already satisfied: langdetect in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (1.0.8)\n",
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"Requirement already satisfied: wget in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (3.2)\n",
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"Requirement already satisfied: python-multipart in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (0.0.5)\n",
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"Requirement already satisfied: python-docx in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (0.8.10)\n",
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"Requirement already satisfied: sqlalchemy_utils in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (0.36.8)\n",
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"Requirement already satisfied: faiss-cpu in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (1.6.3)\n",
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"Requirement already satisfied: tika in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm-haystack==0.3.0) (1.24)\n",
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"Requirement already satisfied: setuptools in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (49.1.0)\n",
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"Requirement already satisfied: wheel in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (0.34.2)\n",
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"Requirement already satisfied: torch==1.5.* in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (1.5.1)\n",
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"Requirement already satisfied: tqdm in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (4.47.0)\n",
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"Requirement already satisfied: boto3 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (1.14.20)\n",
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"Requirement already satisfied: requests in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (2.24.0)\n",
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"Requirement already satisfied: scipy>=1.3.2 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (1.5.1)\n",
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"Requirement already satisfied: seqeval in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (0.0.12)\n",
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"Requirement already satisfied: mlflow==1.0.0 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (1.0.0)\n",
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"Requirement already satisfied: transformers==3.0.2 in /home/ubuntu/transformers_3.0.2/transformers/src (from farm==0.4.6->farm-haystack==0.3.0) (3.0.2)\n",
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"Requirement already satisfied: dotmap==1.3.0 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (1.3.0)\n",
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"Requirement already satisfied: Werkzeug==0.16.1 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (0.16.1)\n",
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"Requirement already satisfied: flask in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (1.1.2)\n",
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"Requirement already satisfied: flask-restplus in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (0.13.0)\n",
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"Requirement already satisfied: flask-cors in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (3.0.8)\n",
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"Requirement already satisfied: dill in /home/ubuntu/py3_6/lib/python3.6/site-packages (from farm==0.4.6->farm-haystack==0.3.0) (0.3.2)\n",
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"Requirement already satisfied: starlette==0.13.4 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from fastapi->farm-haystack==0.3.0) (0.13.4)\n",
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"Requirement already satisfied: pydantic<2.0.0,>=0.32.2 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from fastapi->farm-haystack==0.3.0) (1.6.1)\n",
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"Requirement already satisfied: click==7.* in /home/ubuntu/py3_6/lib/python3.6/site-packages (from uvicorn->farm-haystack==0.3.0) (7.1.2)\n",
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"Requirement already satisfied: websockets==8.* in /home/ubuntu/py3_6/lib/python3.6/site-packages (from uvicorn->farm-haystack==0.3.0) (8.1)\n",
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"Requirement already satisfied: httptools==0.1.*; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\" in /home/ubuntu/py3_6/lib/python3.6/site-packages (from uvicorn->farm-haystack==0.3.0) (0.1.1)\n",
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"Requirement already satisfied: h11<0.10,>=0.8 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from uvicorn->farm-haystack==0.3.0) (0.9.0)\n",
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"Requirement already satisfied: uvloop>=0.14.0; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\" in /home/ubuntu/py3_6/lib/python3.6/site-packages (from uvicorn->farm-haystack==0.3.0) (0.14.0)\n",
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"Requirement already satisfied: numpy>=1.13.3 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from pandas->farm-haystack==0.3.0) (1.19.0)\n",
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"Requirement already satisfied: python-dateutil>=2.6.1 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from pandas->farm-haystack==0.3.0) (2.8.1)\n",
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"Requirement already satisfied: pytz>=2017.2 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from pandas->farm-haystack==0.3.0) (2020.1)\n",
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"Requirement already satisfied: scikit-learn in /home/ubuntu/py3_6/lib/python3.6/site-packages (from sklearn->farm-haystack==0.3.0) (0.23.1)\n",
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"Requirement already satisfied: certifi in /home/ubuntu/py3_6/lib/python3.6/site-packages (from elasticsearch->farm-haystack==0.3.0) (2020.6.20)\n",
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"Requirement already satisfied: urllib3>=1.21.1 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from elasticsearch->farm-haystack==0.3.0) (1.25.9)\n",
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"Requirement already satisfied: virtualenv!=20.0.0,!=20.0.1,!=20.0.2,!=20.0.3,!=20.0.4,!=20.0.5,!=20.0.6,!=20.0.7,>=16.0.0 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from tox->farm-haystack==0.3.0) (20.0.27)\n",
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"Requirement already satisfied: filelock>=3.0.0 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from tox->farm-haystack==0.3.0) (3.0.12)\n",
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"Requirement already satisfied: pluggy>=0.12.0 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from tox->farm-haystack==0.3.0) (0.13.1)\n",
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"Requirement already satisfied: importlib-metadata<2,>=0.12; python_version < \"3.8\" in /home/ubuntu/py3_6/lib/python3.6/site-packages (from tox->farm-haystack==0.3.0) (1.7.0)\n",
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"Requirement already satisfied: SQLAlchemy>=1.0 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from sqlalchemy_utils->farm-haystack==0.3.0) (1.3.18)\n"
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]
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},
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{
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: future in /home/ubuntu/py3_6/lib/python3.6/site-packages (from torch==1.5.*->farm==0.4.6->farm-haystack==0.3.0) (0.18.2)\n",
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"Requirement already satisfied: cloudpickle in /home/ubuntu/py3_6/lib/python3.6/site-packages (from mlflow==1.0.0->farm==0.4.6->farm-haystack==0.3.0) (1.5.0)\n",
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"Requirement already satisfied: protobuf>=3.6.0 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from mlflow==1.0.0->farm==0.4.6->farm-haystack==0.3.0) (3.12.2)\n",
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"Requirement already satisfied: tokenizers==0.8.1.rc2 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from transformers==3.0.2->farm==0.4.6->farm-haystack==0.3.0) (0.8.1rc2)\n",
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"Requirement already satisfied: regex!=2019.12.17 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from transformers==3.0.2->farm==0.4.6->farm-haystack==0.3.0) (2020.6.8)\n",
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"Requirement already satisfied: sentencepiece!=0.1.92 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from transformers==3.0.2->farm==0.4.6->farm-haystack==0.3.0) (0.1.91)\n",
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"Requirement already satisfied: aniso8601>=0.82 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from flask-restplus->farm==0.4.6->farm-haystack==0.3.0) (8.0.0)\n",
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"Requirement already satisfied: appdirs<2,>=1.4.3 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from virtualenv!=20.0.0,!=20.0.1,!=20.0.2,!=20.0.3,!=20.0.4,!=20.0.5,!=20.0.6,!=20.0.7,>=16.0.0->tox->farm-haystack==0.3.0) (1.4.4)\n",
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"Requirement already satisfied: importlib-resources>=1.0; python_version < \"3.7\" in /home/ubuntu/py3_6/lib/python3.6/site-packages (from virtualenv!=20.0.0,!=20.0.1,!=20.0.2,!=20.0.3,!=20.0.4,!=20.0.5,!=20.0.6,!=20.0.7,>=16.0.0->tox->farm-haystack==0.3.0) (3.0.0)\n",
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"Requirement already satisfied: docutils<0.16,>=0.10 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from botocore<1.18.0,>=1.17.20->boto3->farm==0.4.6->farm-haystack==0.3.0) (0.15.2)\n",
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"Requirement already satisfied: h5py in /home/ubuntu/py3_6/lib/python3.6/site-packages (from Keras>=2.2.4->seqeval->farm==0.4.6->farm-haystack==0.3.0) (2.10.0)\n",
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"Requirement already satisfied: websocket-client>=0.32.0 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from docker>=3.6.0->mlflow==1.0.0->farm==0.4.6->farm-haystack==0.3.0) (0.57.0)\n",
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"Requirement already satisfied: Mako in /home/ubuntu/py3_6/lib/python3.6/site-packages (from alembic->mlflow==1.0.0->farm==0.4.6->farm-haystack==0.3.0) (1.1.3)\n",
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"Requirement already satisfied: python-editor>=0.3 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from alembic->mlflow==1.0.0->farm==0.4.6->farm-haystack==0.3.0) (1.0.4)\n",
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"Requirement already satisfied: gitdb<5,>=4.0.1 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from gitpython>=2.1.0->mlflow==1.0.0->farm==0.4.6->farm-haystack==0.3.0) (4.0.5)\n",
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"Requirement already satisfied: tabulate>=0.7.7 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from databricks-cli>=0.8.0->mlflow==1.0.0->farm==0.4.6->farm-haystack==0.3.0) (0.8.7)\n",
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"Requirement already satisfied: MarkupSafe>=0.23 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from Jinja2>=2.10.1->flask->farm==0.4.6->farm-haystack==0.3.0) (1.1.1)\n",
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"Requirement already satisfied: attrs>=17.4.0 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from jsonschema->flask-restplus->farm==0.4.6->farm-haystack==0.3.0) (19.3.0)\n",
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"Requirement already satisfied: pyrsistent>=0.14.0 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from jsonschema->flask-restplus->farm==0.4.6->farm-haystack==0.3.0) (0.16.0)\n",
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"Requirement already satisfied: smmap<4,>=3.0.1 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from gitdb<5,>=4.0.1->gitpython>=2.1.0->mlflow==1.0.0->farm==0.4.6->farm-haystack==0.3.0) (3.0.4)\n",
|
||
"Building wheels for collected packages: farm-haystack\n",
|
||
" Building wheel for farm-haystack (setup.py) ... \u001b[?25ldone\n",
|
||
"\u001b[?25h Created wheel for farm-haystack: filename=farm_haystack-0.3.0-py3-none-any.whl size=99007 sha256=c46bad086db77ddc557d67d6a47b0e8ead6a76c20451e21bd7e56e7b3adf5434\n",
|
||
" Stored in directory: /tmp/pip-ephem-wheel-cache-s2p1ltpe/wheels/5b/d7/60/7a15bd24f2905dfa70aa762413b9570b9d37add064b151aaf0\n",
|
||
"Successfully built farm-haystack\n",
|
||
"\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.2.2 is available.\n",
|
||
"You should consider upgrading via the '/home/ubuntu/py3_6/bin/python3.6 -m pip install --upgrade pip' command.\u001b[0m\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
|
||
"Collecting torch==1.5.1+cu101\n",
|
||
" Downloading https://download.pytorch.org/whl/cu101/torch-1.5.1%2Bcu101-cp36-cp36m-linux_x86_64.whl (704.4 MB)\n",
|
||
"\u001b[K |████████████████████████████████| 704.4 MB 9.3 kB/s eta 0:00:011\n",
|
||
"\u001b[?25hCollecting torchvision==0.6.1+cu101\n",
|
||
" Downloading https://download.pytorch.org/whl/cu101/torchvision-0.6.1%2Bcu101-cp36-cp36m-linux_x86_64.whl (6.6 MB)\n",
|
||
"\u001b[K |████████████████████████████████| 6.6 MB 881 kB/s eta 0:00:01\n",
|
||
"\u001b[?25hRequirement already satisfied: numpy in /home/ubuntu/py3_6/lib/python3.6/site-packages (from torch==1.5.1+cu101) (1.19.0)\n",
|
||
"Requirement already satisfied: future in /home/ubuntu/py3_6/lib/python3.6/site-packages (from torch==1.5.1+cu101) (0.18.2)\n",
|
||
"Requirement already satisfied: pillow>=4.1.1 in /home/ubuntu/py3_6/lib/python3.6/site-packages (from torchvision==0.6.1+cu101) (7.2.0)\n",
|
||
"Installing collected packages: torch, torchvision\n",
|
||
" Attempting uninstall: torch\n",
|
||
" Found existing installation: torch 1.5.1\n",
|
||
" Uninstalling torch-1.5.1:\n",
|
||
" Successfully uninstalled torch-1.5.1\n",
|
||
"Successfully installed torch-1.5.1+cu101 torchvision-0.6.1+cu101\n",
|
||
"\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.2.2 is available.\n",
|
||
"You should consider upgrading via the '/home/ubuntu/py3_6/bin/python3.6 -m pip install --upgrade pip' command.\u001b[0m\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Install the latest release of Haystack in your own environment \n",
|
||
"#! pip install farm-haystack\n",
|
||
"\n",
|
||
"# Install the latest master of Haystack\n",
|
||
"!pip install grpcio-tools==1.34.1\n",
|
||
"!pip install git+https://github.com/deepset-ai/haystack.git\n",
|
||
"\n",
|
||
"# If you run this notebook on Google Colab, you might need to\n",
|
||
"# restart the runtime after installing haystack."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {
|
||
"colab": {},
|
||
"colab_type": "code",
|
||
"id": "xmRuhTQ7A6Nh"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from haystack.utils import clean_wiki_text, convert_files_to_dicts, fetch_archive_from_http, print_answers\n",
|
||
"from haystack.nodes import FARMReader, TransformersReader\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"colab_type": "text",
|
||
"id": "q3dSo7ZtA6Nl"
|
||
},
|
||
"source": [
|
||
"### Document Store\n",
|
||
"\n",
|
||
"#### Option 1: FAISS\n",
|
||
"\n",
|
||
"FAISS is a library for efficient similarity search on a cluster of dense vectors.\n",
|
||
"The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood\n",
|
||
"to store the document text and other meta data. The vector embeddings of the text are\n",
|
||
"indexed on a FAISS Index that later is queried for searching answers.\n",
|
||
"The default flavour of FAISSDocumentStore is \"Flat\" but can also be set to \"HNSW\" for\n",
|
||
"faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.\n",
|
||
"For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 51
|
||
},
|
||
"colab_type": "code",
|
||
"id": "1cYgDJmrA6Nv",
|
||
"outputId": "a8aa6da1-9acf-43b1-fa3c-200123e9bdce",
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"08/25/2020 08:27:51 - INFO - faiss - Loading faiss with AVX2 support.\n",
|
||
"08/25/2020 08:27:51 - INFO - faiss - Loading faiss.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from haystack.document_stores import FAISSDocumentStore\n",
|
||
"\n",
|
||
"document_store = FAISSDocumentStore(faiss_index_factory_str=\"Flat\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%% md\n"
|
||
}
|
||
},
|
||
"source": [
|
||
"#### Option 2: Milvus\n",
|
||
"\n",
|
||
"Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.\n",
|
||
"Like FAISS it has both a \"Flat\" and \"HNSW\" mode but it outperforms FAISS when it comes to dynamic data management.\n",
|
||
"It does require a little more setup, however, as it is run through Docker and requires the setup of some config files.\n",
|
||
"See [their docs](https://milvus.io/docs/v1.0.0/milvus_docker-cpu.md) for more details."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from haystack.utils import launch_milvus\n",
|
||
"from haystack.document_stores import MilvusDocumentStore\n",
|
||
"\n",
|
||
"launch_milvus()\n",
|
||
"document_store = MilvusDocumentStore()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"colab_type": "text",
|
||
"id": "06LatTJBA6N0",
|
||
"pycharm": {
|
||
"name": "#%% md\n"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Cleaning & indexing documents\n",
|
||
"\n",
|
||
"Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 156
|
||
},
|
||
"colab_type": "code",
|
||
"id": "iqKnu6wxA6N1",
|
||
"outputId": "bb5dcc7b-b65f-49ed-db0b-842981af213b",
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"08/25/2020 08:27:53 - INFO - haystack.indexing.utils - Found data stored in `data/article_txt_got`. Delete this first if you really want to fetch new data.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Let's first get some files that we want to use\n",
|
||
"doc_dir = \"data/article_txt_got\"\n",
|
||
"s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip\"\n",
|
||
"fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
|
||
"\n",
|
||
"# Convert files to dicts\n",
|
||
"dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)\n",
|
||
"\n",
|
||
"# Now, let's write the dicts containing documents to our DB.\n",
|
||
"document_store.write_documents(dicts)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"colab_type": "text",
|
||
"id": "wgjedxx_A6N6"
|
||
},
|
||
"source": [
|
||
"### Initalize Retriever, Reader & Pipeline\n",
|
||
"\n",
|
||
"#### Retriever\n",
|
||
"\n",
|
||
"**Here:** We use a `DensePassageRetriever`\n",
|
||
"\n",
|
||
"**Alternatives:**\n",
|
||
"\n",
|
||
"- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters\n",
|
||
"- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)\n",
|
||
"- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000,
|
||
"referenced_widgets": [
|
||
"20affb86c4574e3a9829136fdfe40470",
|
||
"7f8c2c86bbb74a18ac8bd24046d99d34",
|
||
"84311c037c6e44b5b621237f59f027a0",
|
||
"05d793fc179746e9b74cbcbc1a3389eb",
|
||
"ad2ce6a8b4f844ac93b425f1261c131f",
|
||
"bb45d5e4c9944fcd87b408e2fbfea440",
|
||
"248d02e01dea4a63a3296e28e4537eaf",
|
||
"74a9c43eb61a43aa973194b0b70e18f5",
|
||
"58fc3339f13644aea1d4c6d8e1d43a65",
|
||
"460bef2bfa7d4aa480639095555577ac",
|
||
"8553a48fb3144739b99fa04adf8b407c",
|
||
"babe35bb292f4010b64104b2b5bc92af",
|
||
"887412c45ce744efbcc875b563770c29",
|
||
"b4b950d899df4e3fbed9255b281e988a",
|
||
"89535c589aa64648b82a9794a2888e78",
|
||
"f35430501bb14fba8dbd5fb797c2e509",
|
||
"eb5d93a8416a437e9cb039650756ac74",
|
||
"5b8d5975d2674e7e9ada64e77c463c0a",
|
||
"4afa2be1c2c5483f932a42ea4a7897af",
|
||
"0e7186eeb5fa47d89c8c111ebe43c5af",
|
||
"fa946133dfcc4a6ebc6fef2ef9dd92f7",
|
||
"518b6a993e42490297289f2328d0270a",
|
||
"cea074a636d34a75b311569fc3f0b3ab",
|
||
"2630fd2fa91d498796af6d7d8d73aba4"
|
||
]
|
||
},
|
||
"colab_type": "code",
|
||
"id": "kFwiPP60A6N7",
|
||
"outputId": "07249856-3222-4898-9246-68e9ecbf5a1b",
|
||
"pycharm": {
|
||
"is_executing": true
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"08/25/2020 08:28:12 - INFO - haystack.database.faiss - Updating embeddings for 2497 docs ...\n",
|
||
"/pytorch/torch/csrc/utils/python_arg_parser.cpp:756: UserWarning: This overload of nonzero is deprecated:\n",
|
||
"\tnonzero(Tensor input, *, Tensor out)\n",
|
||
"Consider using one of the following signatures instead:\n",
|
||
"\tnonzero(Tensor input, *, bool as_tuple)\n",
|
||
"08/25/2020 08:28:13 - INFO - haystack.retriever.dense - Embedded 80 / 2497 texts\n",
|
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]
|
||
}
|
||
],
|
||
"source": [
|
||
"from haystack.nodes import DensePassageRetriever\n",
|
||
"retriever = DensePassageRetriever(document_store=document_store,\n",
|
||
" query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n",
|
||
" passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n",
|
||
" max_seq_len_query=64,\n",
|
||
" max_seq_len_passage=256,\n",
|
||
" batch_size=16,\n",
|
||
" use_gpu=True,\n",
|
||
" embed_title=True,\n",
|
||
" use_fast_tokenizers=True)\n",
|
||
"# Important: \n",
|
||
"# Now that after we have the DPR initialized, 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 corpus size), it only needs to be done once. \n",
|
||
"# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.\n",
|
||
"document_store.update_embeddings(retriever)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"colab_type": "text",
|
||
"id": "rnVR28OXA6OA"
|
||
},
|
||
"source": [
|
||
"#### Reader\n",
|
||
"\n",
|
||
"Similar to previous Tutorials we now initalize our reader.\n",
|
||
"\n",
|
||
"Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"##### FARMReader"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
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"execution_count": 6,
|
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 739,
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"referenced_widgets": [
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"1b4166bda5ae48aa8539e0fa5521007a",
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"c8f1f7e8462d4d14a507816f67953eae"
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]
|
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},
|
||
"colab_type": "code",
|
||
"id": "fyIuWVwhA6OB",
|
||
"outputId": "33113253-8b95-4604-f9e5-1aa28ee66a91"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"08/25/2020 08:28:54 - INFO - farm.utils - device: cuda n_gpu: 1, distributed training: False, automatic mixed precision training: None\n",
|
||
"08/25/2020 08:28:54 - INFO - farm.infer - Could not find `deepset/roberta-base-squad2` locally. Try to download from model hub ...\n",
|
||
"08/25/2020 08:28:59 - WARNING - farm.modeling.language_model - Could not automatically detect from language model name what language it is. \n",
|
||
"\t We guess it's an *ENGLISH* model ... \n",
|
||
"\t If not: Init the language model by supplying the 'language' param.\n",
|
||
"08/25/2020 08:29:06 - WARNING - farm.modeling.prediction_head - Some unused parameters are passed to the QuestionAnsweringHead. Might not be a problem. Params: {\"loss_ignore_index\": -1}\n",
|
||
"08/25/2020 08:29:09 - INFO - farm.utils - device: cuda n_gpu: 1, distributed training: False, automatic mixed precision training: None\n",
|
||
"08/25/2020 08:29:10 - INFO - farm.infer - Got ya 7 parallel workers to do inference ...\n",
|
||
"08/25/2020 08:29:10 - INFO - farm.infer - 0 0 0 0 0 0 0 \n",
|
||
"08/25/2020 08:29:10 - INFO - farm.infer - /w\\ /w\\ /w\\ /w\\ /w\\ /w\\ /w\\\n",
|
||
"08/25/2020 08:29:10 - INFO - farm.infer - /'\\ / \\ /'\\ /'\\ / \\ / \\ /'\\\n",
|
||
"08/25/2020 08:29:10 - INFO - farm.infer - \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Load a local model or any of the QA models on\n",
|
||
"# Hugging Face's model hub (https://huggingface.co/models)\n",
|
||
"\n",
|
||
"reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"colab_type": "text",
|
||
"id": "unhLD18yA6OF"
|
||
},
|
||
"source": [
|
||
"### Pipeline\n",
|
||
"\n",
|
||
"With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.\n",
|
||
"Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.\n",
|
||
"To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.\n",
|
||
"You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"colab": {},
|
||
"colab_type": "code",
|
||
"id": "TssPQyzWA6OG"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from haystack.pipelines import ExtractiveQAPipeline\n",
|
||
"pipe = ExtractiveQAPipeline(reader, retriever)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"colab_type": "text",
|
||
"id": "bXlBBxKXA6OL"
|
||
},
|
||
"source": [
|
||
"## Voilà! Ask a question!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 275
|
||
},
|
||
"colab_type": "code",
|
||
"id": "Zi97Hif2A6OM",
|
||
"outputId": "5eb9363d-ba92-45d5-c4d0-63ada3073f02"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"08/25/2020 08:30:28 - INFO - haystack.finder - Reader is looking for detailed answer in 9168 chars ...\n",
|
||
"Inferencing Samples: 0%| | 0/1 [00:00<?, ? Batches/s]/home/ubuntu/deepset/FARM/farm/modeling/prediction_head.py:1073: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
|
||
" start_logits_normalized = nn.functional.softmax(start_logits)\n",
|
||
"/home/ubuntu/deepset/FARM/farm/modeling/prediction_head.py:1076: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
|
||
" end_logits_normalized = nn.functional.softmax(end_logits)\n",
|
||
"Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 3.56 Batches/s]\n",
|
||
"Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 38.79 Batches/s]\n",
|
||
"Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 39.61 Batches/s]\n",
|
||
"Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 53.05 Batches/s]\n",
|
||
"Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 37.39 Batches/s]\n",
|
||
"Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 67.21 Batches/s]\n",
|
||
"Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 67.10 Batches/s]\n",
|
||
"Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 66.66 Batches/s]\n",
|
||
"Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 47.91 Batches/s]\n",
|
||
"Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 33.05 Batches/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# You can configure how many candidates the reader and retriever shall return\n",
|
||
"# The higher top_k for retriever, the better (but also the slower) your answers.\n",
|
||
"prediction = pipe.run(\n",
|
||
" query=\"Who created the Dothraki vocabulary?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"print_answers(prediction, details=\"minimum\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"## About us\n",
|
||
"\n",
|
||
"This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
|
||
"\n",
|
||
"We bring NLP to the industry via open source! \n",
|
||
"Our focus: Industry specific language models & large scale QA systems. \n",
|
||
" \n",
|
||
"Some of our other work: \n",
|
||
"- [German BERT](https://deepset.ai/german-bert)\n",
|
||
"- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
|
||
"- [FARM](https://github.com/deepset-ai/FARM)\n",
|
||
"\n",
|
||
"Get in touch:\n",
|
||
"[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
|
||
"\n",
|
||
"By the way: [we're hiring!](https://www.deepset.ai/jobs)"
|
||
]
|
||
}
|
||
],
|
||
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|
||
"accelerator": "GPU",
|
||
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|
||
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|
||
"name": "Tutorial6_Better_Retrieval_via_DPR.ipynb",
|
||
"provenance": []
|
||
},
|
||
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|
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|
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|
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