@@ -0,0 +1,109 @@
+---
+layout: integration
+name: Apache Solr
+description: Use an Apache Solr search server as a Document Store with Haystack
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: https://www.linkedin.com/company/deepset-ai/
+pypi: https://pypi.org/project/solr-haystack
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/solr
+type: Document Store
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/solr.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+- [License](#license)
+
+## Overview
+
+[![PyPI - Version](https://img.shields.io/pypi/v/solr-haystack.svg)](https://pypi.org/project/solr-haystack)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/solr-haystack.svg)](https://pypi.org/project/solr-haystack)
+[![test](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/solr.yml/badge.svg)](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/solr.yml)
+
+[Apache Solr](https://solr.apache.org/) is a widely used open source search server built on Apache Lucene. Since Solr 9, it ships a `DenseVectorField` and the `{!knn}` query parser, so it can serve both keyword (BM25) and dense vector retrieval — everything a RAG pipeline needs from a Document Store.
+
+The integration provides:
+
+- `SolrDocumentStore`, with sync and async support for all operations
+- `SolrBM25Retriever`, `SolrEmbeddingRetriever`, and `SolrHybridRetriever`
+- Full metadata filtering support, including exact round-trips of Python metadata types
+- Extended methods such as `delete_by_filter`, `update_by_filter`, `count_documents_by_filter`, and metadata field introspection
+
+The Document Store requires Solr 9.6 or newer and talks to Solr over its JSON HTTP API via `httpx`, so no Solr client library is needed.
+
+## Installation
+
+```console
+pip install solr-haystack
+```
+
+## Usage
+
+Start a Solr instance, for example with Docker:
+
+```console
+docker run -d -p 8983:8983 solr:10 solr-precreate haystack
+```
+
+Then initialize a `SolrDocumentStore` connected to the instance and write documents to it. By default, the store manages the schema itself: it creates the fields it needs and disables Solr's schemaless field guessing.
+
+```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())
+```
+
+The store reads the `SOLR_URL`, `SOLR_USERNAME`, and `SOLR_PASSWORD` environment variables by default, so credentials never need to appear in code or serialized pipelines.
+
+### Retrieval
+
+The integration supports different retrieval types through different retriever components:
+
+- [`SolrBM25Retriever`](https://docs.haystack.deepset.ai/docs/solrbm25retriever): A keyword-based retriever that fetches documents matching a query using Solr's BM25 similarity.
+- [`SolrEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/solrembeddingretriever): Compares the query and document embeddings using Solr's `{!knn}` dense vector search and fetches the documents most relevant to the query.
+- [`SolrHybridRetriever`](https://docs.haystack.deepset.ai/docs/solrhybridretriever): A SuperComponent that embeds the query, runs BM25 and embedding retrieval over the same core, and fuses the results with reciprocal rank fusion.
+
+Here is a hybrid retrieval example — one component embeds the query, retrieves with both methods, and merges the result lists:
+
+```python
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack_integrations.components.retrievers.solr import SolrHybridRetriever
+from haystack_integrations.document_stores.solr import SolrDocumentStore
+
+document_store = SolrDocumentStore(core="haystack", embedding_dim=384)
+retriever = SolrHybridRetriever(
+    document_store=document_store,
+    embedder=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"),
+)
+retriever.warm_up()
+result = retriever.run(query="Apache Solr")
+print(result["documents"])
+```
+
+### Metadata
+
+Metadata values keep their Python types across the round-trip: the integer `1`, the string `"1"`, the float `1.0`, and `True` stay four distinct values. Filters work on all metadata fields, and extended methods let you delete or update documents by filter, count documents and unique metadata values, and inspect metadata fields — each with an async twin.
+
+## License
+
+`solr-haystack` is distributed under the terms of the [Apache-2.0 license](https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/solr/LICENSE.txt).