Metadata-Version: 2.4
Name: langchain-opensolr
Version: 0.7.0
Summary: LangChain integration for Opensolr — managed Apache Solr with server-side embeddings and hybrid (BM25 + kNN) search
Author-email: Opensolr <support@opensolr.com>
License: MIT
Project-URL: Homepage, https://opensolr.com
Project-URL: Documentation, https://opensolr.com/opensolr-platform-user-documentation/ai-vector
Project-URL: Repository, https://github.com/opensolr/langchain-opensolr
Keywords: langchain,opensolr,solr,vector,hybrid-search,rag,embeddings
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: langchain-core>=0.3.0
Requires-Dist: httpx>=0.25.0
Dynamic: license-file

# langchain-opensolr

LangChain integration for [Opensolr](https://opensolr.com) — managed Apache Solr
with **server-side embeddings** and native **hybrid (BM25 + kNN) search**.

**See it live (real news index, hybrid + AI answer):** https://search.opensolr.com/news__dense?q=how+am+I+supposed+to+save+money%3F

**Product page:** [opensolr.com/langchain](https://opensolr.com/langchain) ·
**Platform:** [opensolr.com](https://opensolr.com) (managed Solr hosting since 2011 —
free forever, no card)

No local embedding model. No third-party embedding API key. One set of
credentials, and the vectors are computed on Opensolr's GPU infrastructure
(multilingual E5-large-instruct, 1024 dimensions, cosine).

```bash
pip install langchain-opensolr
```

## The whole tutorial

```python
from langchain_opensolr import OpensolrVectorStore

vs = OpensolrVectorStore(
    index="mysite__dense",          # vector-enabled Opensolr index
    email="you@example.com",
    api_key="YOUR_OPENSOLR_API_KEY",
    create_if_missing=True,          # provisions the index on first use
)

vs.add_texts(
    ["Hybrid search fuses BM25 with vector similarity",
     "Cats sleep sixteen hours a day"],
    metadatas=[{"category": "search"}, {"category": "animals"}],
)

docs = vs.similarity_search("how do lexical and semantic search combine?", k=1)
print(docs[0].page_content)
```

That's it — no embedding model was configured, because embedding happens on
the server at both index and query time.

## Try it without an account

There is a public demo account. Point the package at it and search, ingestion and
answers work immediately, with no signup:

```bash
export OPENSOLR_EMAIL=mcp@opensolr.com
export OPENSOLR_API_KEY=420b8b23e7b12dc8ab838932145a5065
```

`mcp_demo_d1__dense` is already loaded with 300 news articles, so search, filtering
and grounded answers work the moment you connect. You also get the full write path:
create your own index on the account, ingest into it, query it, delete it.
Reading files and pictures (the file loader and file indexing below, search by image)
needs your own key: the shared demo key does not include them.

Know what you are working with:

- **Anything you create there is deleted after 3 days.** Automatically, without warning
  or export. That includes indexes you created and every document in them.
- **The account is shared with everyone reading this.** Your index is visible to them,
  they can change or delete it, and you can do the same to theirs. Never put anything
  real, private or client-owned in it.
- **The limits are per index, and deliberately small.** 200 MB of bandwidth and 50 MB
  of disk per index. Bandwidth is the one you will hit first: it covers a demo, a
  tutorial and a proof of concept, and it will not carry an application.

When you want an index that is private, yours and still there next week, get your own
key — [free forever, no card](https://opensolr.com/register) — and change the two
variables above. Nothing else in your code changes.

## Hybrid search

Pure vector search fails on exact identifiers; pure BM25 fails on meaning.
Opensolr's `{!hybrid}` query parser fuses both scores **per document**:

```python
docs = vs.similarity_search(
    "affordable restaurants",
    k=5,
    hybrid=True,
    mode="union",     # union | keywords_required | meaning_required | intersection
    alpha=0.5,        # 0 = all semantic … 1 = all lexical
)
```

## Search operators

The query string understands the operators people already expect from a search box. They work
in every mode — keyword, hybrid and pure vector.

| Operator | Meaning | Example |
| --- | --- | --- |
| `"word1 word2"` | Phrase — those words together, in that order | `"machine learning"` |
| `+word` | Required — every result must contain it | `+laptop 15 inch gaming` |
| `+"word1 word2"` | Required phrase | `+"13 inch"` |
| `-word` | Excluded — drop any document containing it | `laptop -refurbished` |
| `-"word1 word2"` | Excluded phrase | `-"open box"` |

They compose: `+laptop +"13 inch" -refurbished` returns only 13-inch laptops and never a
refurbished one.

A prefixed term (`+` or `-`, word or phrase) becomes a **filter**, applied to the whole result
set. That matters as soon as a vector is involved: the semantic side of a hybrid search has no
concept of negation, so left as query text `-refurbished` would actually pull refurbished
listings *towards* the top rather than removing them. As a filter it binds every document,
whichever side of the search found it, and the exclusion is absolute.

An unprefixed phrase (`"machine learning"` with no `+` in front) is a keyword-side relevance
signal rather than a filter — use `+"machine learning"` when you need it enforced.

`+` and `-` only count at the start of a word, so `e-mail`, `covid-19` and `1+1` are searched
for literally.

## Metadata filters

```python
vs.similarity_search("search engines", k=5, filter={"category": "search"})
vs.similarity_search("anything", k=5, filter='meta_rank:[2 TO *]')   # raw Solr fq
```

Metadata round-trips losslessly (stored as JSON alongside filterable
`meta_*` fields).

## As a retriever, in any chain

```python
retriever = vs.as_retriever(search_kwargs={"k": 5, "hybrid": True})
```

## Standalone embeddings

Use Opensolr's embedding endpoint with any other LangChain component:

```python
from langchain_opensolr import OpensolrEmbeddings

emb = OpensolrEmbeddings(email="you@example.com", api_key="...", index="mysite__dense")
emb.embed_query("budget-friendly dining")   # -> 1024 floats
```

## Notes

- Vector-enabled indexes run on Opensolr's Solr 9.x environments — currently
  `us` (Chicago), `de` (Germany), `fi` (Finland). Pass `location=` to choose.
  The list is fetched live from the platform, so new regions work without a
  package upgrade — and **additional dedicated regions can be deployed on
  request** (paid add-on): [support@opensolr.com](mailto:support@opensolr.com).
- A free Opensolr account (free forever, no card) includes an AI quota that
  comfortably covers this README end to end:
  [opensolr.com](https://opensolr.com).
- Full platform docs: [AI & Vector Search](https://opensolr.com/opensolr-platform-user-documentation/ai-vector).

## Development

```bash
pip install -e . pytest
pytest tests/unit_tests
OPENSOLR_EMAIL=... OPENSOLR_API_KEY=... OPENSOLR_INDEX=... pytest tests/integration_tests
```

## 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
**Control Panel → Data Ingestion** — a per-job status board (queued /
processing / completed / failed, with processed / success / failed document
counts per job) — 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.

## Search by image

Search your index with a **photo** instead of a text query. Opensolr reads the
picture three ways — visual labels (what it depicts), OCR text (words printed on
it), and any barcode / QR code — and turns that into an ordinary search. Nothing
new is stored in Solr; the picture simply becomes words.

```python
# What the picture reads as (labels, OCR text, barcodes) — no search yet:
read = vs.image_to_words("photo.jpg")     # path, bytes, or base64
# {'text': 'red running shoe', 'mode': 'clip',
#  'labels': ['running shoe', 'sneaker'], 'codes': ['0123456789012']}

# Search the index with the picture:
docs = vs.search_by_image("photo.jpg", k=4)          # engine picks the best reading
docs = vs.search_by_image("photo.jpg", using="meaning")  # visual labels
docs = vs.search_by_image("photo.jpg", using="text")     # only OCR text
docs = vs.search_by_image("photo.jpg", using="code")     # exact barcode / QR match
docs = vs.search_by_image("photo.jpg", using="all")      # labels + OCR + codes
```

`search_by_image` accepts the same tuning as `similarity_search` — `hybrid`,
`mode`, `alpha`, `fresh_bias`, `filter` — so an image query runs through the
exact hybrid pipeline a text query does.

## Local files: PDFs page by page, scans through OCR

Read a file or a whole folder into LangChain `Document`s with `OpensolrLoader` —
one Document per PDF page (`metadata["page"]`, from 1), one per any other file:

```python
from langchain_opensolr import OpensolrLoader

loader = OpensolrLoader("./contracts", index="mysite__dense",
                        email="you@example.com", api_key="YOUR_OPENSOLR_API_KEY",
                        pattern="*.pdf")
docs = loader.load()          # or loader.lazy_load()
# metadata: source, page, is_ocr, language, content_type
```

Or index them straight into the store — no splitting, no loader, PDFs page by page:

```python
result = vs.add_files("./contracts", pattern="*.pdf", metadata={"team": "legal"}, wait=True)
# {"documents": [{path, source, uri, id}], "pending": [], "failed": [], "jobs": [...]}
```

- Each file is read on Opensolr's servers: its text, the text in its pictures (OCR,
  more than 100 languages), a short description of the pictures without text. Word,
  RTF, OpenDocument, text, HTML and picture files (scans) are one document each; a
  **PDF is one document per page**.
- The bytes travel once: a file the account already sent (same content) is not
  uploaded again. Pictures read by OCR count 0.5 of an AI request each; a file read
  before is free.
- Files still being read when `timeout` runs out are listed in `loader.pending` (or
  `result["pending"]`): run again to get them, nothing is sent twice. Files that could
  not be read are in `loader.failed` / `result["failed"]`.
- An indexed file's `uri` is `https://ingest.opensolr.com/<index>/<source>`, where
  `source` is its path from the folder you sent (`metadata["source"]` on every hit);
  sending it again replaces it. Its date is the file's modification time.

## PDF pages in search results

On an index where PDFs are stored page by page (files indexed as above, PDFs sent to
Data Ingestion with `rtf`, PDFs found by the Web Crawler), a search returns each PDF
**once**, as its best page. Its metadata carries `page` (the page number),
`page_url` (opens the PDF at that page, `...pdf#page=14`), `document_id`,
`more_pages` (its other matching pages, each with `page` and `page_url`) and
`matching_pages`; every hit carries its `uri`. `group_pages=False` returns the pages
as separate results.

## Searching a period

`date_from` and `date_to` (`YYYY-MM-DD`, both included, either one can be left out)
keep only the documents dated in that period (`creation_date`):

```python
docs = vs.similarity_search("rate decision", k=5, hybrid=True,
                            date_from="2026-01-01", date_to="2026-03-31")
```

They combine with every other option — filters, `fresh_bias`, hybrid or lexical.

## Your index schema

Documents follow the Opensolr document model (`title`, `description`, `text`,
`meta_*` custom fields). The whole schema, every field and every type suffix,
is explained in the [Index Schema Reference](https://opensolr.com/opensolr-platform-user-documentation/schema-reference).
To see your own copy: **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.

## Grounded RAG answers

One call: hybrid retrieval picks the top hits, whose content becomes the LLM
context, and Opensolr's server-side LLM answers — the same pipeline that powers
the AI answers on our hosted search pages. No LLM key, no chain to assemble:

```python
answer = vs.ai_answer(
    "what does the refund policy say?",
    rag_docs=3,        # how many hybrid hits feed the LLM (default 3)
    rag_words=1500,    # words of text taken from each hit (default 1500)
    # instruction="Answer in German, cite the exact titles you used",  # optional
)
```


### Search tuning

Retrieval (search and RAG grounding) runs through the platform's tuned
pipeline: global defaults → your index's saved **Search Tuning** (Control
Panel → Index Settings → Search Tuning: semantic↔lexical balance, field
weights, minimum match, search mode, vector candidate pool, content quality
boost) → optional per-call overrides via `tuning`:

```
tuning={"search_mode": "keywords_required", "fw_title": 0.2,
        "mm": "strict", "vector_topk": 500, "quality_boost": 0.3}
```

Defaults match the platform's PHP configuration exactly — customize in the
Control Panel once, or per call from code.

#### Fresh Results Bias

Rank newer documents higher without hiding anything older. Every score is
multiplied by a recency curve on `creation_date` — full weight for a document
published today, about half after a year:

```python
store.similarity_search_with_score("solar inverter warranty", fresh_bias=True)
client.hybrid_search(index, query, fresh_bias=True)
client.ai_answer(index, question, tuning={"fresh_bias": 1})
```

It **re-orders and never filters**: the hit count is identical either way,
nothing old becomes unreachable, and a document with no `creation_date` simply
keeps its place instead of being pushed to the bottom. It applies to all three
retrieval shapes — vector-only, keyword-only and the fused hybrid ranking —
because the boost wraps the final score rather than one half of it. Off by
default.

This is the same control visitors get as the **Fresh** toggle beside the sort
options on the hosted Opensolr search page, so a query behaves identically here
and there.

> `fresh_bias` and `freshness_boost` are two different knobs and the names
> invite confusion. `freshness_boost` is a hard window in **days** — anything
> older is filtered out and the hit count drops. `fresh_bias` filters nothing.

## How it's tested

Every release is validated against **live Opensolr infrastructure** — no mocks:

- **Unit tests** (offline): location aliases, filter→fq mapping, query building, escaping.
- **End-to-end suite**: the full write path through the async Data Ingestion
  queue (queued → server-side enrichment → searchable), semantic / hybrid /
  lexical retrieval, metadata round-trip, filters, id round-trip (your ids
  and the Solr `md5(uri)` ids), deletes by id and by query.
- **Real-corpus validation**: searches run against a 340-document replica of
  opensolr.com's own production search index. Verified: pure-semantic hits
  with zero keyword overlap ("how do I get my data back after a disaster" →
  backup &amp; restore docs), cross-lingual queries (Romanian query → English
  content), exact-term surfacing in hybrid mode, all four hybrid modes, and
  the full alpha range 0 → 1.
- **PDF ingestion**: a real PDF ingested via `rtf:true` — server-side text
  extraction (13k+ chars), automatic content-type detection, then retrieved
  with a purely semantic query against its contents.
- **Grounded RAG answers**: `ai_answer` verified end-to-end — a question answerable only from the ingested PDF returns the correct answer, sourced from the PDF's extracted text via hybrid retrieval.

```bash
pytest tests/unit_tests
OPENSOLR_EMAIL=... OPENSOLR_API_KEY=... OPENSOLR_INDEX=... pytest tests/integration_tests
```

MIT license.
