mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-07-21 07:51:40 +00:00
81 lines
3.6 KiB
Python
Executable File
81 lines
3.6 KiB
Python
Executable File
from haystack import Finder
|
|
from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
|
|
|
|
from haystack.retriever.dense import EmbeddingRetriever
|
|
from haystack.utils import print_answers
|
|
import pandas as pd
|
|
import requests
|
|
import logging
|
|
import subprocess
|
|
import time
|
|
## "FAQ-Style QA": Utilizing existing FAQs for Question Answering
|
|
|
|
# While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
|
|
#
|
|
# Pros:
|
|
# - Very fast at inference time
|
|
# - Utilize existing FAQ data
|
|
# - Quite good control over answers
|
|
#
|
|
# Cons:
|
|
# - Generalizability: We can only answer questions that are similar to existing ones in FAQ
|
|
#
|
|
# In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
|
|
LAUNCH_ELASTICSEARCH=True
|
|
|
|
if LAUNCH_ELASTICSEARCH:
|
|
logging.info("Starting Elasticsearch ...")
|
|
status = subprocess.run(
|
|
['docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2'], shell=True
|
|
)
|
|
if status.returncode:
|
|
raise Exception("Failed to launch Elasticsearch. If you want to connect to an existing Elasticsearch instance"
|
|
"then set LAUNCH_ELASTICSEARCH in the script to False.")
|
|
time.sleep(15)
|
|
|
|
### Init the DocumentStore
|
|
# In contrast to Tutorial 1 (extractive QA), we:
|
|
#
|
|
# * specify the name of our `text_field` in Elasticsearch that we want to return as an answer
|
|
# * 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
|
|
# * set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
|
|
|
|
document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
|
|
index="document",
|
|
embedding_field="question_emb",
|
|
embedding_dim=768,
|
|
excluded_meta_data=["question_emb"])
|
|
|
|
### Create a Retriever using embeddings
|
|
# Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
|
|
# We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
|
|
#
|
|
retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)
|
|
|
|
# Download a csv containing some FAQ data
|
|
# Here: Some question-answer pairs related to COVID-19
|
|
temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
|
|
open('small_faq_covid.csv', 'wb').write(temp.content)
|
|
|
|
# Get dataframe with columns "question", "answer" and some custom metadata
|
|
df = pd.read_csv("small_faq_covid.csv")
|
|
# Minimal cleaning
|
|
df.fillna(value="", inplace=True)
|
|
df["question"] = df["question"].apply(lambda x: x.strip())
|
|
print(df.head())
|
|
|
|
# Get embeddings for our questions from the FAQs
|
|
questions = list(df["question"].values)
|
|
df["question_emb"] = retriever.embed_queries(texts=questions)
|
|
df = df.rename(columns={"answer": "text"})
|
|
|
|
# Convert Dataframe to list of dicts and index them in our DocumentStore
|
|
docs_to_index = df.to_dict(orient="records")
|
|
document_store.write_documents(docs_to_index)
|
|
|
|
|
|
# Init reader & and use Finder to get answer (same as in Tutorial 1)
|
|
finder = Finder(reader=None, retriever=retriever)
|
|
prediction = finder.get_answers_via_similar_questions(question="How is the virus spreading?", top_k_retriever=10)
|
|
print_answers(prediction, details="all")
|