feat: add superlinked retriever integration

[fm1320]

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

[vercel]

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Project	Deployment	Preview	Comments	Updated (UTC)
langchain	✅ Ready	Preview	Comment	Aug 13, 2025 4:01pm

[codspeed-hq]

CodSpeed WallTime Performance Report

Merging #32433 will not alter performance

_{Comparing fm1320:feat/add-superlinked-integration (56129ad) with master (4f71c35)}

⚠️ Unknown Walltime execution environment detected

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For the most accurate results, we recommend using CodSpeed Macro Runners: bare-metal machines fine-tuned for performance measurement consistency.

Summary

✅ 13 untouched benchmarks

[codspeed-hq]

CodSpeed Instrumentation Performance Report

Merging #32433 will not alter performance

_{Comparing fm1320:feat/add-superlinked-integration (56129ad) with master (4f71c35)}

Summary

✅ 14 untouched benchmarks

[mdrxy]

ready for review @fm1320 ?

[fm1320]

Hey @mdrxy still finishing up some of the stuff with the external package, will update this as a docs only PR soon

[fm1320]

@mdrxy maybe you can have a look now, here is the PyPI package it references