Metadata-Version: 2.4
Name: opensolr-haystack
Version: 0.2.0
Summary: Haystack integration for Opensolr — managed Apache Solr DocumentStore with server-side embeddings and hybrid BM25+kNN retrieval
Author-email: Opensolr <support@opensolr.com>
License: MIT
Project-URL: Homepage, https://opensolr.com/langchain
Project-URL: Repository, https://github.com/phpcip/opensolr-haystack
Keywords: haystack,opensolr,solr,vector,hybrid-search,rag,document-store
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: haystack-ai>=2.0.0
Requires-Dist: httpx>=0.25.0
Dynamic: license-file

# opensolr-haystack

[Haystack](https://haystack.deepset.ai) integration for
[Opensolr](https://opensolr.com) — managed Apache Solr as a DocumentStore,
with **server-side embeddings** and native **hybrid (BM25 + kNN) retrieval**.

No embedder components needed in your pipeline — texts and queries are
embedded on Opensolr's GPU infrastructure (multilingual E5-large-instruct,
1024 dimensions, cosine).

**Product page:** [opensolr.com/langchain](https://opensolr.com/langchain) ·
free 15-day trial, no card, at [opensolr.com](https://opensolr.com)

```bash
pip install opensolr-haystack
```

## Quickstart

```python
from haystack import Document, Pipeline
from haystack_integrations.document_stores.opensolr import OpensolrDocumentStore
from haystack_integrations.components.retrievers.opensolr import OpensolrHybridRetriever

# credentials default to OPENSOLR_EMAIL / OPENSOLR_API_KEY env vars
store = OpensolrDocumentStore(index="mysite__dense", create_if_missing=True)

store.write_documents([
    Document(content="Hybrid search fuses BM25 with vector similarity"),
    Document(content="Cats sleep sixteen hours a day"),
])

pipe = Pipeline()
pipe.add_component("retriever", OpensolrHybridRetriever(document_store=store))
result = pipe.run({"retriever": {"query": "how do keyword and semantic search combine?"}})
print(result["retriever"]["documents"])
```

Note there is **no embedder** in the pipeline — not for documents, not for
the query. The store embeds server-side at both index and query time.

## Hybrid retrieval

`OpensolrHybridRetriever` fuses BM25 and kNN scores per document via
Opensolr's native `{!hybrid}` Solr query parser:

```python
OpensolrHybridRetriever(
    document_store=store,
    top_k=10,
    hybrid=True,     # False = pure semantic kNN
    alpha=0.5,       # 0 = all semantic … 1 = all lexical
)
```

Standard Haystack filters are supported and map to Solr `fq`:

```python
pipe.run({"retriever": {
    "query": "search engines",
    "filters": {"field": "meta.category", "operator": "==", "value": "docs"},
}})
```

## Notes

- Vector-enabled indexes run on Opensolr's Solr 9.x environments — currently
  `us` (Chicago), `de` (Germany), `fi` (Finland). **Additional dedicated
  regions can be deployed on request** (paid add-on):
  [support@opensolr.com](mailto:support@opensolr.com).
- Every index is also plain Apache Solr with the native `/select` API —
  facets, highlighting, spellcheck included.
- Siblings: [`langchain-opensolr`](https://pypi.org/project/langchain-opensolr/) ·
  [`llama-index-opensolr`](https://pypi.org/project/llama-index-opensolr/) ·
  [`opensolr-mcp`](https://pypi.org/project/opensolr-mcp/)

## How writing works (Data Ingestion API)

Writes go through Opensolr's [Data Ingestion API](https://opensolr.com/learn/api-data-ingestion/204/data-ingestion-api-push-documents-to-your-opensolr-index-programmatically)
— the same pipeline the Drupal and WordPress connectors use. It is
**asynchronous**: documents are queued, then embeddings, sentiment, language
and all crawler-identical derived fields are computed **server-side**, and
documents become searchable within about a minute. Progress is visible in the
Opensolr Control Panel and via the `ingest_status` API. Each document's
identity is its `uri` (the Solr id is `md5(uri)`): pass a real URL in
metadata (`{"uri": "https://..."}`), or a deterministic one is synthesized
from your id. Re-submitting the same `uri` updates the document. Pass
`{"rtf": True, "uri": "https://.../file.pdf"}` and the server extracts the
text from PDF/DOCX/XLSX for you.

## Lexical-only mode

Don't need vectors? Pure keyword search skips the embedding call entirely —
zero AI quota, and it works on **any** Opensolr index, including non-vector
ones and older Solr versions.

## Your index schema

Documents follow the Opensolr document model (`title`, `description`, `text`,
`meta_*` custom fields). To see the full schema: **Control Panel → click your
index → Configuration → Edit File → schema.xml**. Prefer zero-effort data
entry? Configure the **Web Crawler** in the Control Panel (Index Tools →
WebCrawler): add your site URL, validate it, and Opensolr indexes the whole
site for you.

MIT license.
