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Installation

Note: On Windows add the arg -f https://download.pytorch.org/whl/torch_stable.html to install PyTorch correctly

The Building Blocks of Haystack

Here’s a sample of some Haystack code showing a question answering system using a retriever and a reader. For a working code example, check out our starter tutorial.

# DocumentStore: holds all your data
document_store = ElasticsearchDocumentStore()

# Clean & load your documents into the DocumentStore
dicts = convert_files_to_dicts(doc_dir, clean_func=clean_wiki_text)
document_store.write_documents(dicts)

# Retriever: A Fast and simple algo to indentify the most promising candidate documents
retriever = ElasticsearchRetriever(document_store)

# Reader: Powerful but slower neural network trained for QA
model_name = "deepset/roberta-base-squad2"
reader = FARMReader(model_name)

# Pipeline: Combines all the components
pipe = ExtractiveQAPipeline(reader, retriever)

# Voilà! Ask a question!
question = "Who is the father of Sansa Stark?"
prediction = pipe.run(query=question)
print_answers(prediction)

Loading Documents into the DocumentStore

In Haystack, DocumentStores expect Documents in a dictionary format. They are loaded as follows:

document_store = ElasticsearchDocumentStore()
dicts = [
    {
        'text': DOCUMENT_TEXT_HERE,
        'meta': {'name': DOCUMENT_NAME, ...}
    }, ...
]
document_store.write_documents(dicts)

When we talk about Documents in Haystack, we are referring specifically to the individual blocks of text that are being held in the DocumentStore. You might want to use all the text in one file as a Document, or split it into multiple Documents. This splitting can have a big impact on speed and performance.

Running Search Queries

There are many different flavours of search that can be created using Haystack. But to give just one example of what can be achieved, let's look more closely at an Open Domain Question Answering (ODQA) Pipeline.

Querying in an ODQA system involves searching for an answer to a given question within the full document store. This process will:

• make the Retriever filter for a small set of relevant candidate documents

• get the Reader to process this set of candidate documents

• return potential answers to the given question

Usually, there are tight time constraints on querying and so it needs to be a lightweight operation. When documents are loaded, Haystack will precompute any of the results that might be useful at query time.

In Haystack, querying is performed with a Pipeline object which connects the reader to the retriever.

# Pipeline: Combines all the components
pipe = ExtractiveQAPipeline(reader, retriever)

# Voilà! Ask a question!
question = "Who is the father of Sansa Stark?"
prediction = pipe.run(query=question)
print_answers(prediction)

When the query is complete, you can expect to see results that look something like this:

[
    {   'answer': 'Eddard',
        'context': 's Nymeria after a legendary warrior queen. She travels '
                   "with her father, Eddard, to King's Landing when he is made "
                   'Hand of the King. Before she leaves,'
    }, ...
]

Custom Search Pipelines

Haystack providers many different building blocks for you to mix and match. They include:

• Readers
• Retrievers (sparse and dense)
• DocumentStores
• Summarizers
• Generators
• Translators

These can all be combined in the configuration that you want. Have a look at our Pipelines page to see what's possible!