DocumentStore

Install Elasticsearch and then start an instance.

If you have Docker set up, we recommend pulling the Docker image and running it.

docker pull docker.elastic.co/elasticsearch/elasticsearch:7.9.2
docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2

Next, you can initialize the Haystack object connecting to this instance:

document_store = ElasticsearchDocumentStore()

from haystack.document_stores import InMemoryDocumentStore
document_store = InMemoryDocumentStore()

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The InMemoryDocumentStore works with the BM25Retriever.

MilvusDocumentStore is an external integration for Haystack. You can find the source code of MilvusDocumentStore in the milvus-haystack GitHub repo.

To use the MilvusDocumentStore, follow the official Milvus documentation and start a Milvus instance using Docker.

Install the milvus-haystack package:

pip install -e milvus-haystack

Then, import and initialize the MilvusDocumentStore:

from haystack import Document
from milvus_documentstore import MilvusDocumentStore

ds = MilvusDocumentStore()
ds.write_documents([Document("Some Content")])
ds.get_all_documents()  # prints [<Document: {'content': 'foo', 'content_type': 'text', ...>]

MongoDB Atlas is a multi-cloud database service built by people behind MongoDB. Using MongoDBAtlasDocumentStore, you can connect to databases deployed on MongoDB Atlas. To start with MongoDB, check out their documentation.

First, install MongoDBAtlasDocumentStore using Haystack mongodb optional dependency:

pip install -e "farm-haystack[mongodb]"

Then, import and initialize the MongoDBAtlasDocumentStore:

from haystack.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
from haystack import Document

ds= MongoDBAtlasDocumentStore(
    mongo_connection_string="mongodb+srv://{mongo_atlas_username}:{mongo_atlas_password}@{mongo_atlas_host}/?{mongo_atlas_params_string}",
    database_name="database_name",
    collection_name="collection_name",
)
ds.write_documents([Document("Some Content")])
ds.get_all_documents()

Learn how to get started here. If you have Docker set up, we recommend pulling the Docker image and running it.

docker pull opensearchproject/opensearch:1.0.1
docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" opensearchproject/opensearch:1.0.1

Next, you can initialize the Haystack object connecting to this instance:

from haystack.document_stores import OpenSearchDocumentStore
document_store = OpenSearchDocumentStore()

PineconeDocumentStore is a fast and scalable vector database that supports filtered search. It's a managed document store, which means the vectors are stored in the cloud. To learn more, see Pinecone documentation.

To initialize PineconeDocumentStore, you need to have an API key from an active Pinecone account.

from haystack.document_stores import PineconeDocumentStore

document_store = PineconeDocumentStore(
    api_key='YOUR_API_KEY',
    similarity="cosine",
    index='your_index_name',
    embedding_dim=768
)

QdrantDocumentStore is an external integration the Qdrant team maintains. It's optimized for high-dimensional vector search, and it supports various similarity metrics. You can find the QdrantDocumentStore in the qdrant-haystack GitHub repo.

QdrantDocumentStore supports all the configuration properties available in the Qdrant Python client. For more information, see Qdrant documentation.

To use this document store, install it first:

pip install qdrant-haystack

And then, initialize it:

from qdrant_haystack.document_stores import QdrantDocumentStore

document_store = QdrantDocumentStore(
    ":memory:",
    index="Document",
    embedding_dim=512,
    recreate_index=True,
    hnsw_config={"m": 16, "ef_construct": 64}  # Optional
)

You can then write Documents to it using the write_documents() method.

To learn more about the document store, see our Blog post.

docker run -d -p 8080:8080 --env AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED='true' --env PERSISTENCE_DATA_PATH='/var/lib/weaviate' semitechnologies/weaviate:1.17.2

See the Weaviate docs for more details.

Afterwards, you can use it in Haystack:

from haystack.document_stores import WeaviateDocumentStore
document_store = WeaviateDocumentStore()

Each DocumentStore constructor allows for arguments specifying how to connect to existing databases and the names of indexes. See the API documentation for more info.

The FAISSDocumentStore requires no external setup. Use this code to start it:

from haystack.document_stores import FAISSDocumentStore

document_store = FAISSDocumentStore()

Initiating the FAISSDocumentStore creates the faiss_document_store.db database file on your disk. If later on, you want to remove the DocumentStore, that's the file you need to delete.

Save Your DocumentStore

You can save FAISS document stores to disk and then reload them.

Under the hood, the FAISSDocumentStore contains a SQL database and a FAISS index. The database is saved to your disk when you initialize the FAISSDocumentStore. The FAISS index is not saved anywhere and stays in memory. To save it, call the save() method. Note that you must always initialize the DocumentStore before saving it.

from haystack.document_stores import FAISSDocumentStore

# First, initialize the document store:
document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")

# Save the document store:
document_store.save(index_path="my_faiss_index.faiss")
# Saving the document store creates two files: my_faiss_index.faiss and my_faiss_index.json

Saving a FAISSDocumentStore creates two files on your disk:

• my_faiss_index.faiss - This file contains the index.
• my_faiss_index.json - This file contains the parameters used to initialize the DocumentStore. For example faiss_index_factory_str="Flat".

You can change the name and the location of the files. Just pass the new names or paths in the index_path and config_path parameters:

from haystack.document_stores import FAISSDocumentStore

document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
document_store.save(index_path="data/my_index.faiss", config_path="data/my_config.json")

Load a Saved DocumentStore

You can load a saved DocumentStore using the faiss_index_path parameter of FAISSDocumentStore or using the index_path parameter of the load() method. As a value, pass the index file path you indicated when saving the DocumentStore.

Here's how you load a saved DocumentStore:

# Load the saved index into a new DocumentStore instance:
new_document_store = FAISSDocumentStore(faiss_index_path="my_faiss_index.faiss")

# or use the `load()` method to create a new DocumentStore instance: 
new_document_store = FAISSDocumentStore.load(index_path="my_faiss_index.faiss")

# Also, provide `config_path` parameter if you set it when calling the `save()` method: 
new_document_store = FAISSDocumentStore.load(index_path="data/my_index.faiss", config_path="data/my_config.json")

# Check if the DocumentStore is loaded correctly
assert new_document_store.faiss_index_factory_str == "Flat"

Remember that load() is a class method, so don't call it on an instance but on a class.