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haystack/tutorials/Tutorial14_Query_Classifier.ipynb
bogdankostic 834f8c4902
Change return types of indexing pipeline nodes (#2342) 
* Change return types of file converters

* Change return types of preprocessor

* Change return types of crawler

* Adapt utils to functions to new return types

* Adapt __init__.py to new method names

* Prevent circular imports

* Update Documentation & Code Style

* Let DocStores' run method accept Documents

* Adapt tests to new return types

* Update Documentation & Code Style

* Put "# type: ignore" to right place

* Remove id_hash_keys property from Document primitive

* Update Documentation & Code Style

* Adapt tests to new return types and missing id_hash_keys property

* Fix mypy

* Fix mypy

* Adapt PDFToTextOCRConverter

* Remove id_hash_keys from RestAPI tests

* Update Documentation & Code Style

* Rename tests

* Remove redundant setting of content_type="text"

* Add DeprecationWarning

* Add id_hash_keys to elasticsearch_index_to_document_store

* Change document type from dict to Docuemnt in PreProcessor test

* Fix file path in Tutorial 5

* Remove added output in Tutorial 5

* Update Documentation & Code Style

* Fix file_paths in Tutorial 9 + fix gz files in fetch_archive_from_http

* Adapt tutorials to new return types

* Adapt tutorial 14 to new return types

* Update Documentation & Code Style

* Change assertions to HaystackErrors

* Import HaystackError correctly

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2022-03-29 13:53:35 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "O-W2ZQ6CN-gZ",
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"# Query Classifier Tutorial\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial14_Query_Classifier.ipynb)\n",
"\n",
"In this tutorial we introduce the query classifier the goal of introducing this feature was to optimize the overall flow of Haystack pipeline by detecting the nature of user queries. Now, the Haystack can detect primarily three types of queries using both light-weight SKLearn Gradient Boosted classifier or Transformer based more robust classifier. The three categories of queries are as follows:\n",
"\n",
"\n",
"### 1. Keyword Queries: \n",
"Such queries don't have semantic meaning and merely consist of keywords. For instance these three are the examples of keyword queries.\n",
"\n",
"* arya stark father\n",
"* jon snow country\n",
"* arya stark younger brothers\n",
"\n",
"### 2. Interrogative Queries: \n",
"In such queries users usually ask a question, regardless of presence of \"?\" in the query the goal here is to detect the intent of the user whether any question is asked or not in the query. For example:\n",
"\n",
"* who is the father of arya stark ?\n",
"* which country was jon snow filmed ?\n",
"* who are the younger brothers of arya stark ?\n",
"\n",
"### 3. Declarative Queries: \n",
"Such queries are variation of keyword queries, however, there is semantic relationship between words. Fo example:\n",
"\n",
"* Arya stark was a daughter of a lord.\n",
"* Jon snow was filmed in a country in UK.\n",
"* Bran was brother of a princess.\n",
"\n",
"In this tutorial, you will learn how the `TransformersQueryClassifier` and `SklearnQueryClassifier` classes can be used to intelligently route your queries, based on the nature of the user query. Also, you can choose between a lightweight Gradients boosted classifier or a transformer based classifier.\n",
"\n",
"Furthermore, there are two types of classifiers you can use out of the box from Haystack.\n",
"1. Keyword vs Statement/Question Query Classifier\n",
"2. Statement vs Question Query Classifier\n",
"\n",
"As evident from the name the first classifier detects the keywords search queries and semantic statements like sentences/questions. The second classifier differentiates between question based queries and declarative sentences."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yaaKv3_ZN-gb",
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"### Prepare environment\n",
"\n",
"#### Colab: Enable the GPU runtime\n",
"Make sure you enable the GPU runtime to experience decent speed in this tutorial. \n",
"**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/_src/img/colab_gpu_runtime.jpg\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TNlqD5HeN-gc",
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"These lines are to install Haystack through pip"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CjA5n5lMN-gd",
"outputId": "da688e25-ad0e-41d3-94cf-581858fc05a4",
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"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 --upgrade pip\n",
"!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]\n",
"\n",
"# Install pygraphviz\n",
"!apt install libgraphviz-dev\n",
"!pip install pygraphviz\n",
"\n",
"# In Colab / No Docker environments: Start Elasticsearch from source\n",
"! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
"! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
"! chown -R daemon:daemon elasticsearch-7.9.2\n",
"\n",
"import os\n",
"from subprocess import Popen, PIPE, STDOUT\n",
"\n",
"es_server = Popen(\n",
" [\"elasticsearch-7.9.2/bin/elasticsearch\"], stdout=PIPE, stderr=STDOUT, preexec_fn=lambda: os.setuid(1) # as daemon\n",
")\n",
"# wait until ES has started\n",
"! sleep 30"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fAfd2cOQN-gd",
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"If running from Colab or a no Docker environment, you will want to start Elasticsearch from source"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z7Tu5OQnN-ge",
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Initialization\n",
"\n",
"Here are some core imports"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Vm9gqTioN-gf",
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"Then let's fetch some data (in this case, pages from the Game of Thrones wiki) and prepare it so that it can\n",
"be used indexed into our `DocumentStore`"
]
},
{
"cell_type": "code",
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"base_uri": "https://localhost:8080/",
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},
"id": "Ig7dgfdHN-gg",
"outputId": "62383a2b-d653-4f9f-e3a9-4b2cb90ee007",
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"from haystack.utils import (\n",
" print_answers,\n",
" print_documents,\n",
" fetch_archive_from_http,\n",
" convert_files_to_docs,\n",
" clean_wiki_text,\n",
" launch_es,\n",
")\n",
"from haystack.pipelines import Pipeline\n",
"from haystack.document_stores import ElasticsearchDocumentStore\n",
"from haystack.nodes import (\n",
" ElasticsearchRetriever,\n",
" EmbeddingRetriever,\n",
" FARMReader,\n",
" TransformersQueryClassifier,\n",
" SklearnQueryClassifier,\n",
")\n",
"\n",
"# Download and prepare data - 517 Wikipedia articles for Game of Thrones\n",
"doc_dir = \"data/tutorial14\"\n",
"s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt14.zip\"\n",
"fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
"\n",
"# convert files to dicts containing documents that can be indexed to our datastore\n",
"got_docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)\n",
"\n",
"# Initialize DocumentStore and index documents\n",
"launch_es()\n",
"document_store = ElasticsearchDocumentStore()\n",
"document_store.delete_documents()\n",
"document_store.write_documents(got_docs)\n",
"\n",
"# Initialize Sparse retriever\n",
"es_retriever = ElasticsearchRetriever(document_store=document_store)\n",
"\n",
"# Initialize dense retriever\n",
"embedding_retriever = EmbeddingRetriever(\n",
" document_store=document_store,\n",
" model_format=\"sentence_transformers\",\n",
" embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\",\n",
")\n",
"document_store.update_embeddings(embedding_retriever, update_existing_embeddings=False)\n",
"\n",
"reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5kPAbP4EN-gk",
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Keyword vs Question/Statement Classifier\n",
"\n",
"The keyword vs question/statement query classifier essentially distinguishes between the keyword queries and statements/questions. So you can intelligently route to different retrieval nodes based on the nature of the query. Using this classifier can potentially yield the following benefits:\n",
"\n",
"* Getting better search results (e.g. by routing only proper questions to DPR / QA branches and not keyword queries)\n",
"* Less GPU costs (e.g. if 50% of your traffic is only keyword queries you could just use elastic here and save the GPU resources for the other 50% of traffic with semantic queries)\n",
"\n",
"![image]()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K4wZ3xkQCHjY"
},
"source": [
"Below, we define a `SklearnQueryClassifier` and show how to use it:\n",
"\n",
"Read more about the trained model and dataset used [here](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/readme.txt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Sz-oZ5eJN-gl",
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# Here we build the pipeline\n",
"sklearn_keyword_classifier = Pipeline()\n",
"sklearn_keyword_classifier.add_node(component=SklearnQueryClassifier(), name=\"QueryClassifier\", inputs=[\"Query\"])\n",
"sklearn_keyword_classifier.add_node(\n",
" component=embedding_retriever, name=\"EmbeddingRetriever\", inputs=[\"QueryClassifier.output_1\"]\n",
")\n",
"sklearn_keyword_classifier.add_node(component=es_retriever, name=\"ESRetriever\", inputs=[\"QueryClassifier.output_2\"])\n",
"sklearn_keyword_classifier.add_node(component=reader, name=\"QAReader\", inputs=[\"ESRetriever\", \"EmbeddingRetriever\"])\n",
"sklearn_keyword_classifier.draw(\"pipeline_classifier.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fP6Cpcb-o0HK"
},
"outputs": [],
"source": [
"# Run only the dense retriever on the full sentence query\n",
"res_1 = sklearn_keyword_classifier.run(query=\"Who is the father of Arya Stark?\")\n",
"print(\"Embedding Retriever Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_1, details=\"minimum\")\n",
"\n",
"# Run only the sparse retriever on a keyword based query\n",
"res_2 = sklearn_keyword_classifier.run(query=\"arya stark father\")\n",
"print(\"ES Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_2, details=\"minimum\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EZru--pao1UG"
},
"outputs": [],
"source": [
"# Run only the dense retriever on the full sentence query\n",
"res_3 = sklearn_keyword_classifier.run(query=\"which country was jon snow filmed ?\")\n",
"print(\"Embedding Retriever Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_3, details=\"minimum\")\n",
"\n",
"# Run only the sparse retriever on a keyword based query\n",
"res_4 = sklearn_keyword_classifier.run(query=\"jon snow country\")\n",
"print(\"ES Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_4, details=\"minimum\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MWCMG8MJo1tB"
},
"outputs": [],
"source": [
"# Run only the dense retriever on the full sentence query\n",
"res_5 = sklearn_keyword_classifier.run(query=\"who are the younger brothers of arya stark ?\")\n",
"print(\"Embedding Retriever Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_5, details=\"minimum\")\n",
"\n",
"# Run only the sparse retriever on a keyword based query\n",
"res_6 = sklearn_keyword_classifier.run(query=\"arya stark younger brothers\")\n",
"print(\"ES Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_6, details=\"minimum\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dQ5YMyd4CQPC"
},
"source": [
"## Transformer Keyword vs Question/Statement Classifier\n",
"\n",
"Firstly, it's essential to understand the trade-offs between SkLearn and Transformer query classifiers. The transformer classifier is more accurate than SkLearn classifier however, it requires more memory and most probably GPU for faster inference however the transformer size is roughly `50 MBs`. Whereas, SkLearn is less accurate however is much more faster and doesn't require GPU for inference.\n",
"\n",
"Below, we define a `TransformersQueryClassifier` and show how to use it:\n",
"\n",
"Read more about the trained model and dataset used [here](https://huggingface.co/shahrukhx01/bert-mini-finetune-question-detection)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yuddZL3FCPeq"
},
"outputs": [],
"source": [
"# Here we build the pipeline\n",
"transformer_keyword_classifier = Pipeline()\n",
"transformer_keyword_classifier.add_node(\n",
" component=TransformersQueryClassifier(), name=\"QueryClassifier\", inputs=[\"Query\"]\n",
")\n",
"transformer_keyword_classifier.add_node(\n",
" component=embedding_retriever, name=\"EmbeddingRetriever\", inputs=[\"QueryClassifier.output_1\"]\n",
")\n",
"transformer_keyword_classifier.add_node(component=es_retriever, name=\"ESRetriever\", inputs=[\"QueryClassifier.output_2\"])\n",
"transformer_keyword_classifier.add_node(component=reader, name=\"QAReader\", inputs=[\"ESRetriever\", \"EmbeddingRetriever\"])\n",
"transformer_keyword_classifier.draw(\"pipeline_classifier.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uFmJJIb_q-X7"
},
"outputs": [],
"source": [
"# Run only the dense retriever on the full sentence query\n",
"res_1 = transformer_keyword_classifier.run(query=\"Who is the father of Arya Stark?\")\n",
"print(\"Embedding Retriever Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_1, details=\"minimum\")\n",
"\n",
"# Run only the sparse retriever on a keyword based query\n",
"res_2 = transformer_keyword_classifier.run(query=\"arya stark father\")\n",
"print(\"ES Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_2, details=\"minimum\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GMPNcTz8rdix"
},
"outputs": [],
"source": [
"# Run only the dense retriever on the full sentence query\n",
"res_3 = transformer_keyword_classifier.run(query=\"which country was jon snow filmed ?\")\n",
"print(\"Embedding Retriever Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_3, details=\"minimum\")\n",
"\n",
"# Run only the sparse retriever on a keyword based query\n",
"res_4 = transformer_keyword_classifier.run(query=\"jon snow country\")\n",
"print(\"ES Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_4, details=\"minimum\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jN5zdLJbrzOh"
},
"outputs": [],
"source": [
"# Run only the dense retriever on the full sentence query\n",
"res_5 = transformer_keyword_classifier.run(query=\"who are the younger brothers of arya stark ?\")\n",
"print(\"Embedding Retriever Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_5, details=\"minimum\")\n",
"\n",
"# Run only the sparse retriever on a keyword based query\n",
"res_6 = transformer_keyword_classifier.run(query=\"arya stark younger brothers\")\n",
"print(\"ES Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_6, details=\"minimum\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zLwdVwMXDcoS"
},
"source": [
"## Question vs Statement Classifier\n",
"\n",
"One possible use case of this classifier could be to route queries after the document retrieval to only send questions to QA reader and in case of declarative sentence, just return the DPR/ES results back to user to enhance user experience and only show answers when user explicitly asks it.\n",
"\n",
"![image]()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SMVFFRtMPVIt"
},
"source": [
"Below, we define a `TransformersQueryClassifier` and show how to use it:\n",
"\n",
"Read more about the trained model and dataset used [here](https://huggingface.co/shahrukhx01/question-vs-statement-classifier)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BIisEJrzDr-9"
},
"outputs": [],
"source": [
"# Here we build the pipeline\n",
"transformer_question_classifier = Pipeline()\n",
"transformer_question_classifier.add_node(component=embedding_retriever, name=\"EmbeddingRetriever\", inputs=[\"Query\"])\n",
"transformer_question_classifier.add_node(\n",
" component=TransformersQueryClassifier(model_name_or_path=\"shahrukhx01/question-vs-statement-classifier\"),\n",
" name=\"QueryClassifier\",\n",
" inputs=[\"EmbeddingRetriever\"],\n",
")\n",
"transformer_question_classifier.add_node(component=reader, name=\"QAReader\", inputs=[\"QueryClassifier.output_1\"])\n",
"transformer_question_classifier.draw(\"question_classifier.png\")\n",
"\n",
"# Run only the QA reader on the question query\n",
"res_1 = transformer_question_classifier.run(query=\"Who is the father of Arya Stark?\")\n",
"print(\"Embedding Retriever Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_1, details=\"minimum\")\n",
"\n",
"res_2 = transformer_question_classifier.run(query=\"Arya Stark was the daughter of a Lord.\")\n",
"print(\"ES Results\" + \"\\n\" + \"=\" * 15)\n",
"print_documents(res_2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sJcWRK4Hwyx2"
},
"source": [
"## Standalone Query Classifier\n",
"Below we run queries classifiers standalone to better understand their outputs on each of the three types of queries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XhPMEqBzxA8V",
"outputId": "be3ba2ac-b557-4cb3-9eed-41928f644b6e"
},
"outputs": [],
"source": [
"# Here we create the keyword vs question/statement query classifier\n",
"from haystack.nodes import TransformersQueryClassifier\n",
"\n",
"queries = [\n",
" \"arya stark father\",\n",
" \"jon snow country\",\n",
" \"who is the father of arya stark\",\n",
" \"which country was jon snow filmed?\",\n",
"]\n",
"\n",
"keyword_classifier = TransformersQueryClassifier()\n",
"\n",
"for query in queries:\n",
" result = keyword_classifier.run(query=query)\n",
" if result[1] == \"output_1\":\n",
" category = \"question/statement\"\n",
" else:\n",
" category = \"keyword\"\n",
"\n",
" print(f\"Query: {query}, raw_output: {result}, class: {category}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "l4eH3SSaxZ0O",
"outputId": "53384108-3d4c-4547-d32a-4a63aa1b74a0"
},
"outputs": [],
"source": [
"# Here we create the question vs statement query classifier\n",
"from haystack.nodes import TransformersQueryClassifier\n",
"\n",
"queries = [\n",
" \"Lord Eddard was the father of Arya Stark.\",\n",
" \"Jon Snow was filmed in United Kingdom.\",\n",
" \"who is the father of arya stark?\",\n",
" \"Which country was jon snow filmed in?\",\n",
"]\n",
"\n",
"question_classifier = TransformersQueryClassifier(model_name_or_path=\"shahrukhx01/question-vs-statement-classifier\")\n",
"\n",
"for query in queries:\n",
" result = question_classifier.run(query=query)\n",
" if result[1] == \"output_1\":\n",
" category = \"question\"\n",
" else:\n",
" category = \"statement\"\n",
"\n",
" print(f\"Query: {query}, raw_output: {result}, class: {category}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9VMUHR-BN-gl",
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Conclusion\n",
"\n",
"The query classifier gives you more possibility to be more creative with the pipelines and use different retrieval nodes in a flexible fashion. Moreover, as in the case of Question vs Statement classifier you can also choose the queries which you want to send to the reader.\n",
"\n",
"Finally, you also have the possible of bringing your own classifier and plugging it into either `TransformersQueryClassifier(model_name_or_path=\"<huggingface_model_name_or_file_path>\")` or using the `SklearnQueryClassifier(model_name_or_path=\"url_to_classifier_or_file_path_as_pickle\", vectorizer_name_or_path=\"url_to_vectorizer_or_file_path_as_pickle\")`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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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