mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-07-22 16:31:16 +00:00

* 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
|
|
|
|
from haystack.nodes import EmbeddingRetriever
|
|
from haystack.utils import launch_es, print_answers
|
|
import pandas as pd
|
|
import requests
|
|
import logging
|
|
import subprocess
|
|
import time
|
|
|
|
|
|
def tutorial4_faq_style_qa():
|
|
## "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_es()
|
|
|
|
### 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=384,
|
|
excluded_meta_data=["question_emb"],
|
|
similarity="cosine",
|
|
)
|
|
|
|
### 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="sentence-transformers/all-MiniLM-L6-v2", 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={"question": "content"})
|
|
|
|
# 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)
|
|
|
|
# Initialize a Pipeline (this time without a reader) and ask questions
|
|
|
|
from haystack.pipelines import FAQPipeline
|
|
|
|
pipe = FAQPipeline(retriever=retriever)
|
|
|
|
prediction = pipe.run(query="How is the virus spreading?", params={"Retriever": {"top_k": 10}})
|
|
print_answers(prediction, details="medium")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tutorial4_faq_style_qa()
|
|
|
|
# This Haystack script was made with love by deepset in Berlin, Germany
|
|
# Haystack: https://github.com/deepset-ai/haystack
|
|
# deepset: https://deepset.ai/
|