mirror of
https://github.com/deepset-ai/haystack.git
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957e78ed9e
* Upgrade pydoc-markdown and fix the YAMLs to work with it * Pin pydoc-markdown to major version * Generalize pydoc-markdown workflow * Make a single Action to perform all tasks that require committing into the local branch * Merge the code updates and the docs in the Linux CI to prevent the bot from always show the pipeline as green * Installing Jupyter deps for Black * Build cache before running generation tasks * Add check not to run the code generation on master * Simplify push action * Add more test deps in setup.cfg and remove from GH Action workflow * Remove forced upgrades on pip install Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
356 lines
10 KiB
Plaintext
356 lines
10 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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"collapsed": false
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},
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"source": [
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"# Generative QA with \"Retrieval-Augmented Generation\"\n",
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"\n",
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"[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial7_RAG_Generator.ipynb)\n",
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"\n",
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"While extractive QA highlights the span of text that answers a query,\n",
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"generative QA can return a novel text answer that it has composed.\n",
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"In this tutorial, you will learn how to set up a generative system using the\n",
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"[RAG model](https://arxiv.org/abs/2005.11401) which conditions the\n",
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"answer generator on a set of retrieved documents."
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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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"collapsed": false
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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": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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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": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"Here are the packages and imports that we'll need:"
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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": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Install the latest release of Haystack in your own environment\n",
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"#! pip install farm-haystack\n",
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"\n",
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"# Install the latest master of Haystack\n",
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"!pip install --upgrade pip\n",
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"!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,faiss]"
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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": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"from typing import List\n",
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"import requests\n",
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"import pandas as pd\n",
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"from haystack import Document\n",
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"from haystack.document_stores import FAISSDocumentStore\n",
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"from haystack.nodes import RAGenerator, DensePassageRetriever"
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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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"collapsed": false
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},
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"source": [
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"Let's download a csv containing some sample text and preprocess the data.\n"
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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": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Download sample\n",
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"temp = requests.get(\n",
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" \"https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv\"\n",
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")\n",
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"open(\"small_generator_dataset.csv\", \"wb\").write(temp.content)\n",
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"\n",
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"# Create dataframe with columns \"title\" and \"text\"\n",
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"df = pd.read_csv(\"small_generator_dataset.csv\", sep=\",\")\n",
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"# Minimal cleaning\n",
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"df.fillna(value=\"\", inplace=True)\n",
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"\n",
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"print(df.head())"
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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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"collapsed": false
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},
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"source": [
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"We can cast our data into Haystack Document objects.\n",
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"Alternatively, we can also just use dictionaries with \"text\" and \"meta\" fields"
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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": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Use data to initialize Document objects\n",
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"titles = list(df[\"title\"].values)\n",
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"texts = list(df[\"text\"].values)\n",
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"documents: List[Document] = []\n",
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"for title, text in zip(titles, texts):\n",
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" documents.append(Document(content=text, meta={\"name\": title or \"\"}))"
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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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"collapsed": false
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},
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"source": [
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"Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.\n",
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"FAISS is chosen here since it is optimized vector storage."
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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": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Initialize FAISS document store.\n",
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"# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding\n",
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"document_store = FAISSDocumentStore(faiss_index_factory_str=\"Flat\", return_embedding=True)\n",
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"\n",
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"# Initialize DPR Retriever to encode documents, encode question and query documents\n",
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"retriever = DensePassageRetriever(\n",
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" document_store=document_store,\n",
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" query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n",
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" passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n",
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" use_gpu=True,\n",
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" embed_title=True,\n",
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")\n",
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"\n",
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"# Initialize RAG Generator\n",
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"generator = RAGenerator(\n",
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" model_name_or_path=\"facebook/rag-token-nq\",\n",
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" use_gpu=True,\n",
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" top_k=1,\n",
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" max_length=200,\n",
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" min_length=2,\n",
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" embed_title=True,\n",
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" num_beams=2,\n",
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")"
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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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"collapsed": false
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},
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"source": [
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"We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.\n",
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"The `update_embeddings()` method uses the retriever to create an embedding for each document.\n"
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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": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Delete existing documents in documents store\n",
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"document_store.delete_documents()\n",
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"\n",
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"# Write documents to document store\n",
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"document_store.write_documents(documents)\n",
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"\n",
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"# Add documents embeddings to index\n",
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"document_store.update_embeddings(retriever=retriever)"
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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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"collapsed": false
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},
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"source": [
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"Here are our questions:"
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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": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"QUESTIONS = [\n",
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" \"who got the first nobel prize in physics\",\n",
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" \"when is the next deadpool movie being released\",\n",
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" \"which mode is used for short wave broadcast service\",\n",
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" \"who is the owner of reading football club\",\n",
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" \"when is the next scandal episode coming out\",\n",
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" \"when is the last time the philadelphia won the superbowl\",\n",
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" \"what is the most current adobe flash player version\",\n",
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" \"how many episodes are there in dragon ball z\",\n",
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" \"what is the first step in the evolution of the eye\",\n",
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" \"where is gall bladder situated in human body\",\n",
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" \"what is the main mineral in lithium batteries\",\n",
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" \"who is the president of usa right now\",\n",
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" \"where do the greasers live in the outsiders\",\n",
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" \"panda is a national animal of which country\",\n",
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" \"what is the name of manchester united stadium\",\n",
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"]"
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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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"collapsed": false
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},
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"source": [
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"Now let's run our system!\n",
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"The retriever will pick out a small subset of documents that it finds relevant.\n",
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"These are used to condition the generator as it generates the answer.\n",
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"What it should return then are novel text spans that form and answer to your question!"
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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": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Or alternatively use the Pipeline class\n",
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"from haystack.pipelines import GenerativeQAPipeline\n",
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"from haystack.utils import print_answers\n",
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"\n",
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"pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)\n",
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"for question in QUESTIONS:\n",
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" res = pipe.run(query=question, params={\"Generator\": {\"top_k\": 1}, \"Retriever\": {\"top_k\": 5}})\n",
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" print_answers(res, details=\"minimum\")"
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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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"collapsed": false
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},
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"source": [
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"## About us\n",
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"\n",
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"This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
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"\n",
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"We bring NLP to the industry via open source! \n",
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"Our focus: Industry specific language models & large scale QA systems. \n",
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" \n",
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"Some of our other work: \n",
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"- [German BERT](https://deepset.ai/german-bert)\n",
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"- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
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"- [FARM](https://github.com/deepset-ai/FARM)\n",
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"\n",
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"Get in touch:\n",
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"[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",
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"\n",
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"By the way: [we're hiring!](https://www.deepset.ai/jobs)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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"nbformat": 4,
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"nbformat_minor": 2
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