feat: add superlinked retriever integration

docs/docs/integrations/providers/superlinked.mdx

@@ -0,0 +1,140 @@
+---
+title: Superlinked
+description: LangChain integration package for the Superlinked retrieval stack
+---
+
+import Link from '@docusaurus/Link';
+
+### Overview
+
+Superlinked enables context‑aware retrieval using multiple space types (text similarity, categorical, numerical, recency, and more). The `langchain-superlinked` package provides a LangChain‑native `SuperlinkedRetriever` that plugs directly into your RAG chains.
+
+### Links
+
+- <Link to="https://github.com/superlinked/langchain-superlinked">Integration repository</Link>
+- <Link to="https://links.superlinked.com/langchain_repo_sl">Superlinked core repository</Link>
+- <Link to="https://links.superlinked.com/langchain_article">Article: Build RAG using LangChain & Superlinked</Link>
+
+### Install
+
+```bash
+pip install -U langchain-superlinked superlinked
+```
+
+### Quickstart
+
+```python
+import superlinked.framework as sl
+from langchain_superlinked import SuperlinkedRetriever
+
+# 1) Define schema
+class DocumentSchema(sl.Schema):
+    id: sl.IdField
+    content: sl.String
+
+doc_schema = DocumentSchema()
+
+# 2) Define space and index
+text_space = sl.TextSimilaritySpace(
+    text=doc_schema.content, model="sentence-transformers/all-MiniLM-L6-v2"
+)
+doc_index = sl.Index([text_space])
+
+# 3) Define query
+query = (
+    sl.Query(doc_index)
+    .find(doc_schema)
+    .similar(text_space.text, sl.Param("query_text"))
+    .select([doc_schema.content])
+    .limit(sl.Param("limit"))
+)
+
+# 4) Minimal app setup
+source = sl.InMemorySource(schema=doc_schema)
+executor = sl.InMemoryExecutor(sources=[source], indices=[doc_index])
+app = executor.run()
+source.put([
+    {"id": "1", "content": "Machine learning algorithms process data efficiently."},
+    {"id": "2", "content": "Natural language processing understands human language."},
+])
+
+# 5) LangChain retriever
+retriever = SuperlinkedRetriever(
+    sl_client=app, sl_query=query, page_content_field="content"
+)
+
+# Search
+docs = retriever.invoke("artificial intelligence", limit=2)
+for d in docs:
+    print(d.page_content)
+```
+
+### What the retriever expects (App and Query)
+
+The retriever takes two core inputs:
+
+- `sl_client`: a Superlinked App created by running an executor (e.g., `InMemoryExecutor(...).run()`)
+- `sl_query`: a `QueryDescriptor` returned by chaining `sl.Query(...).find(...).similar(...).select(...).limit(...)`
+
+Minimal setup:
+
+```python
+import superlinked.framework as sl
+from langchain_superlinked import SuperlinkedRetriever
+
+class Doc(sl.Schema):
+    id: sl.IdField
+    content: sl.String
+
+doc = Doc()
+space = sl.TextSimilaritySpace(text=doc.content, model="sentence-transformers/all-MiniLM-L6-v2")
+index = sl.Index([space])
+
+query = (
+    sl.Query(index)
+    .find(doc)
+    .similar(space.text, sl.Param("query_text"))
+    .select([doc.content])
+    .limit(sl.Param("limit"))
+)
+
+source = sl.InMemorySource(schema=doc)
+app = sl.InMemoryExecutor(sources=[source], indices=[index]).run()
+
+retriever = SuperlinkedRetriever(sl_client=app, sl_query=query, page_content_field="content")
+```
+
+Note: For a persistent vector DB, pass `vector_database=...` to the executor (e.g., Qdrant) before `.run()`.
+
+### Use within a chain
+
+```python
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+prompt = ChatPromptTemplate.from_template(
+    """
+    Answer based on context:\n\nContext: {context}\nQuestion: {question}
+    """
+)
+
+chain = ({"context": retriever | format_docs, "question": RunnablePassthrough()}
+         | prompt
+         | ChatOpenAI())
+
+answer = chain.invoke("How does machine learning work?")
+```
+
+### Resources
+
+- <Link to="https://pypi.org/project/langchain-superlinked/">PyPI: langchain-superlinked</Link>
+- <Link to="https://pypi.org/project/superlinked/">PyPI: superlinked</Link>
+- <Link to="https://github.com/superlinked/langchain-superlinked">Source repository</Link>
+- <Link to="https://links.superlinked.com/langchain_repo_sl">Superlinked core repository</Link>
+- <Link to="https://links.superlinked.com/langchain_article">Build RAG using LangChain & Superlinked (article)</Link>
+
+