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* Testing black on ui/ * Applying black on docstores * Add latest docstring and tutorial changes * Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too * Remove comments * Relax constraints on pydoc-markdown * Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade * Fix a couple of bugs * Add a type: ignore that was missing somehow * Give path to black * Apply Black * Apply Black * Relocate a couple of type: ignore * Update documentation * Make Linux CI run after applying Black * Triggering Black * Apply Black * Remove dependency, does not work well * Remove manually double trailing commas * Update documentation Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
91 lines
3.5 KiB
Python
Executable File
91 lines
3.5 KiB
Python
Executable File
from haystack.document_stores import ElasticsearchDocumentStore
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from haystack.nodes import EmbeddingRetriever
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from haystack.utils import launch_es, 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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def tutorial4_faq_style_qa():
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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_es()
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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(
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host="localhost",
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username="",
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password="",
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index="document",
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embedding_field="question_emb",
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embedding_dim=384,
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excluded_meta_data=["question_emb"],
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similarity="cosine",
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)
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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(
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document_store=document_store, embedding_model="sentence-transformers/all-MiniLM-L6-v2", use_gpu=True
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)
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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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df = df.rename(columns={"question": "content"})
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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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# Initialize a Pipeline (this time without a reader) and ask questions
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from haystack.pipelines import FAQPipeline
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pipe = FAQPipeline(retriever=retriever)
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prediction = pipe.run(query="How is the virus spreading?", params={"Retriever": {"top_k": 10}})
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print_answers(prediction, details="medium")
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if __name__ == "__main__":
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tutorial4_faq_style_qa()
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# This Haystack script was made with love by deepset in Berlin, Germany
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# Haystack: https://github.com/deepset-ai/haystack
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# deepset: https://deepset.ai/
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