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81 lines
3.6 KiB
Python
Executable File
81 lines
3.6 KiB
Python
Executable File
from haystack import Finder
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from haystack.database.elasticsearch import ElasticsearchDocumentStore
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from haystack.retriever.dense import EmbeddingRetriever
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from haystack.utils import print_answers
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import pandas as pd
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import requests
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import logging
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import subprocess
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import time
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## "FAQ-Style QA": Utilizing existing FAQs for Question Answering
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# While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
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#
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# Pros:
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# - Very fast at inference time
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# - Utilize existing FAQ data
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# - Quite good control over answers
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#
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# Cons:
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# - Generalizability: We can only answer questions that are similar to existing ones in FAQ
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#
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# In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
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LAUNCH_ELASTICSEARCH=True
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if LAUNCH_ELASTICSEARCH:
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logging.info("Starting Elasticsearch ...")
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status = subprocess.run(
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['docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2'], shell=True
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)
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if status.returncode:
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raise Exception("Failed to launch Elasticsearch. If you want to connect to an existing Elasticsearch instance"
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"then set LAUNCH_ELASTICSEARCH in the script to False.")
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time.sleep(15)
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### Init the DocumentStore
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# In contrast to Tutorial 1 (extractive QA), we:
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#
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# * specify the name of our `text_field` in Elasticsearch that we want to return as an answer
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# * specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
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# * set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
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document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
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index="document",
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text_field="answer",
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embedding_field="question_emb",
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embedding_dim=768,
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excluded_meta_data=["question_emb"])
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### Create a Retriever using embeddings
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# Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
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# We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
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#
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retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", gpu=False)
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# Download a csv containing some FAQ data
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# Here: Some question-answer pairs related to COVID-19
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temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
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open('small_faq_covid.csv', 'wb').write(temp.content)
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# Get dataframe with columns "question", "answer" and some custom metadata
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df = pd.read_csv("small_faq_covid.csv")
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# Minimal cleaning
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df.fillna(value="", inplace=True)
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df["question"] = df["question"].apply(lambda x: x.strip())
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print(df.head())
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# Get embeddings for our questions from the FAQs
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questions = list(df["question"].values)
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df["question_emb"] = retriever.embed_queries(texts=questions)
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# Convert Dataframe to list of dicts and index them in our DocumentStore
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docs_to_index = df.to_dict(orient="records")
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document_store.write_documents(docs_to_index)
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# Init reader & and use Finder to get answer (same as in Tutorial 1)
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finder = Finder(reader=None, retriever=retriever)
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prediction = finder.get_answers_via_similar_questions(question="How is the virus spreading?", top_k_retriever=10)
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print_answers(prediction, details="all")
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