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Filip Makraduli 0be7515abc docs: add superlinked retriever integration (#32433 )

# feat(superlinked): add superlinked retriever integration

**Description:** 
Add Superlinked as a custom retriever with full LangChain compatibility.
This integration enables users to leverage Superlinked's multi-modal
vector search capabilities including text similarity, categorical
similarity, recency, and numerical spaces with flexible weighting
strategies. The implementation provides a `SuperlinkedRetriever` class
that extends LangChain's `BaseRetriever` with comprehensive error
handling, parameter validation, and support for various vector databases
(in-memory, Qdrant, Redis, MongoDB).

**Key Features:**
- Full LangChain `BaseRetriever` compatibility with `k` parameter
support
- Multi-modal search spaces (text, categorical, numerical, recency)
- Flexible weighting strategies for complex search scenarios
- Vector database agnostic implementation
- Comprehensive validation and error handling
- Complete test coverage (unit tests, integration tests)
- Detailed documentation with 6 practical usage examples

**Issue:** N/A (new integration)

**Dependencies:** 
- `superlinked==33.5.1` (peer dependency, imported within functions)
- `pandas^2.2.0` (required by superlinked)

**Linkedin handle:** https://www.linkedin.com/in/filipmakraduli/

## Implementation Details

### Files Added/Modified:
- `libs/partners/superlinked/` - Complete package structure
- `libs/partners/superlinked/langchain_superlinked/retrievers.py` - Main
retriever implementation
- `libs/partners/superlinked/tests/unit_tests/test_retrievers.py` - unit
tests
- `libs/partners/superlinked/tests/integration_tests/test_retrievers.py`
- Integration tests with mocking
- `docs/docs/integrations/retrievers/superlinked.ipynb` - Documentation
a few usage examples

### Testing:
- `make format` - passing
- `make lint` - passing 
- `make test` - passing (16 unit tests, integration tests)
- Comprehensive test coverage including error handling, validation, and
edge cases

### Documentation:
- Example notebook with 6 practical scenarios:
  1. Simple text search
  2. Multi-space blog search (content + category + recency)
  3. E-commerce product search (price + brand + ratings)
  4. News article search (sentiment + topics + recency)
  5. LangChain RAG integration example
  6. Qdrant vector database integration

### Code Quality:
- Follows LangChain contribution guidelines
- Backwards compatible
- Optional dependencies imported within functions
- Comprehensive error handling and validation
- Type hints and docstrings throughout

---------

Co-authored-by: Mason Daugherty <mason@langchain.dev>

2025-09-15 13:54:04 +00:00