DocumentStores

You can think of the DocumentStore as a "database" that:

• stores your texts and meta data
• provides them to the retriever at query time

There are different DocumentStores in Haystack to fit different use cases and tech stacks.

Initialisation

Initialising a new DocumentStore within Haystack is straight forward.

Types

Input Format

DocumentStores expect Documents in dictionary form, like that below. They are loaded using the DocumentStore.write_documents() method. See Preprocessing for more information on the cleaning and splitting steps that will help you maximize Haystack's performance.

from haystack.document_store import ElasticsearchDocumentStore

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

Writing Documents (Sparse Retrievers)

Haystack allows for you to write store documents in an optimised fashion so that query times can be kept low. For sparse, keyword based retrievers such as BM25 and TF-IDF, you simply have to call DocumentStore.write_documents(). The creation of the inverted index which optimises querying speed is handled automatically.

document_store.write_documents(dicts)

Writing Documents (Dense Retrievers)

For dense neural network based retrievers like Dense Passage Retrieval, or Embedding Retrieval, indexing involves computing the Document embeddings which will be compared against the Query embedding.

The storing of the text is handled by DocumentStore.write_documents() and the computation of the embeddings is started by DocumentStore.update_embeddings().

document_store.write_documents(dicts)
document_store.update_embeddings(retriever)

This step is computationally intensive since it will engage the transformer based encoders. Having GPU acceleration will significantly speed this up.

Choosing the Right Document Store

The Document Stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:

Elasticsearch

Pros:

• Fast & accurate sparse retrieval with many tuning options
• Basic support for dense retrieval
• Production-ready
• Support also for Open Distro

Cons:

• Slow for dense retrieval with more than ~ 1 Mio documents

Milvus

Pros:

• Scalable DocumentStore that excels at handling vectors (hence suited to dense retrieval methods like DPR)
• Encapsulates multiple ANN libraries (e.g. FAISS and ANNOY) and provides added reliability
• Runs as a separate service (e.g. a Docker container)
• Allows dynamic data management

Cons:

• No efficient sparse retrieval

FAISS

Pros:

• Fast & accurate dense retrieval
• Highly scalable due to approximate nearest neighbour algorithms (ANN)
• Many options to tune dense retrieval via different index types (more info here)

Cons:

• No efficient sparse retrieval

In Memory

Pros:

• Simple
• Exists already in many environments

Cons:

• Only compatible with minimal TF-IDF Retriever
• Bad retrieval performance
• Not recommended for production

SQL

Pros:

• Simple & fast to test
• No database requirements
• Supports MySQL, PostgreSQL and SQLite

Cons:

• Not scalable
• Not persisting your data on disk

Weaviate

Pros:

• Simple vector search
• Stores everything in one place: documents, meta data and vectors - so less network overhead when scaling this up
• Allows combination of vector search and scalar filtering, i.e. you can filter for a certain tag and do dense retrieval on that subset

Cons:

• Less options for ANN algorithms than FAISS or Milvus
• No BM25 / Tf-idf retrieval