SolrDocumentStore

A Document Store for storing and retrieval from Apache Solr.

API reference	Solr
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/solr

Apache Solr is a widely used open source search server built on Apache Lucene. Since Solr 9, it ships a DenseVectorField type and the {!knn} query parser, so a single Solr core can serve both keyword (BM25) and dense vector retrieval. For more information, see the Solr documentation.

This Document Store is a good fit if your organization already runs Solr and you want to add semantic or hybrid retrieval on top of it without introducing a separate vector database.

The Document Store requires Solr 9.6 or newer. Every operation is available both synchronously and asynchronously.

Initialization

Install and run a Solr instance. If you have Docker set up, we recommend pulling the Docker image and running it with a precreated core:

shell

docker run -d -p 8983:8983 solr:10 solr-precreate haystack

Once you have a running Solr instance, install the solr-haystack integration:

shell

pip install solr-haystack

Then, initialize a SolrDocumentStore object that's connected to the Solr instance and write documents to it:

python

from haystack import Document
from haystack_integrations.document_stores.solr import SolrDocumentStore

document_store = SolrDocumentStore(
    url="http://localhost:8983/solr",
    core="haystack",
    embedding_dim=768,
)
document_store.write_documents(
    [Document(content="This is first"), Document(content="This is second")],
)
print(document_store.count_documents())

By default, the store manages the Solr schema itself: on first use, it creates the fields it needs and disables Solr's schemaless field guessing. Set manage_schema=False to manage the schema yourself.

A few points to keep in mind:

• url falls back to the SOLR_URL environment variable, then to http://localhost:8983/solr. Basic authentication credentials are read from the SOLR_USERNAME and SOLR_PASSWORD environment variables by default, so they never need to appear in code or serialized pipelines.
• Solr fixes a vector field's dimension when the field is created, so embedding_dim cannot be changed for an existing core. The similarity_function can be cosine (default), dot_product, or euclidean.

Supported Retrievers

SolrBM25Retriever: A keyword-based Retriever that fetches documents matching a query from the Document Store.

SolrEmbeddingRetriever: Compares the query and document embeddings and fetches the documents most relevant to the query.

SolrHybridRetriever: A SuperComponent that combines BM25 and embedding retrieval in a single component and fuses the results.

Extended Methods

Beyond the standard Document Store protocol, SolrDocumentStore supports filter-based bulk operations and metadata introspection, each with an async twin:

• delete_by_filter / update_by_filter: delete or update the metadata of all documents matching a filter.
• count_documents_by_filter / count_unique_metadata_by_filter: count matching documents or the distinct values of metadata fields.
• get_metadata_fields_info, get_metadata_field_min_max, get_metadata_field_unique_values: inspect which metadata fields exist, their types, and their value ranges.