mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-09-19 13:13:39 +00:00
70 lines
2.1 KiB
Markdown
70 lines
2.1 KiB
Markdown
|
<!---
|
|||
|
|
title: "Use Cases"
|
|||
|
|
metaTitle: "Use Cases"
|
|||
|
|
metaDescription: ""
|
|||
|
|
slug: "/docs/use_cases"
|
|||
|
|
date: "2020-11-16"
|
|||
|
|
id: "use_casesmd"
|
|||
|
|
--->
|
|||
|
|
|
|||
|
|
|
|||
|
|
# Use cases
|
|||
|
|
|
|||
|
|
## Semantic Search System
|
|||
|
|
|
|||
|
|
Take the leap from using keyword search on your own documents to semantic search with Haystack.
|
|||
|
|
|
|||
|
|
|
|||
|
|
* Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
|
|||
|
|
|
|||
|
|
|
|||
|
|
* Perform question driven queries.
|
|||
|
|
|
|||
|
|
Expect to see results that highlight the very sentence that contains the answer to your question.
|
|||
|
|
Thanks to the power of Transformer based language models, results are chosen based on compatibility in meaning
|
|||
|
|
rather than lexical overlap.
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
## Information Extractor
|
|||
|
|
|
|||
|
|
Automate the extraction of relevant information from a set of documents that pertain to the same topics but for different entities.
|
|||
|
|
|
|||
|
|
Haystack can:
|
|||
|
|
|
|||
|
|
|
|||
|
|
* Apply a set of standard questions to each document in a store
|
|||
|
|
|
|||
|
|
|
|||
|
|
* Return a NO_ANSWER if a given document does not contain the answer to a question
|
|||
|
|
|
|||
|
|
Say you have the financial reports for different companies over different years.
|
|||
|
|
You can gather a set of standard questions which are applicable to each financial report,
|
|||
|
|
like *what is the revenue forecast for 2020?* or *what are the main sources of income?*.
|
|||
|
|
Haystack will try to find an answer for each question within each document!
|
|||
|
|
|
|||
|
|
We’ve seen this style of application be particularly effective in the sphere of finance and patent law
|
|||
|
|
but we see a lot of potential in using this to gain a better overview of academic papers and internal business documents.
|
|||
|
|
|
|||
|
|
<!-- _comment: !!Image!! -->
|
|||
|
|
## FAQ Style Question Answering
|
|||
|
|
|
|||
|
|
Leverage existing FAQ documents and semantic similarity search to answer new incoming questions.
|
|||
|
|
The workflow is as follows:
|
|||
|
|
|
|||
|
|
|
|||
|
|
* Store a set of FAQ documents in Haystack
|
|||
|
|
|
|||
|
|
|
|||
|
|
* The user presents a new question
|
|||
|
|
|
|||
|
|
|
|||
|
|
* Haystack will find the closest match to the new question in the FAQ documents
|
|||
|
|
|
|||
|
|
|
|||
|
|
* The user will be presented with the most similar Question Answer pair
|
|||
|
|
|
|||
|
|
Haystack’s flexibility allows you to give new users more dynamic access to your existing documentation.
|