Python: Introducing the Faiss Connector - #10810
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PR Overview
This PR introduces a new Faiss connector for semantic-kernel, including implementations for FaissCollection and FaissStore along with comprehensive unit tests and sample usage updates.
- Adds FaissCollection and FaissStore classes to integrate Faiss as an in-memory similarity search engine.
- Updates tests, configuration (pyproject.toml), and sample usage to support the new Faiss connector.
Reviewed Changes
| File | Description |
|---|---|
| python/tests/unit/connectors/memory/faiss/test_faiss.py | Adds unit tests for Faiss functionality. |
| python/semantic_kernel/connectors/memory/faiss.py | Implements the FaissCollection and FaissStore classes. |
| python/tests/conftest.py | Updates import for Kernel initialization. |
| python/pyproject.toml | Adds dependency for faiss-cpu. |
| python/samples/concepts/memory/complex_memory.py | Integrates FaissCollection into sample usage. |
| python/semantic_kernel/connectors/memory/in_memory/in_memory_collection.py | Refines generic types for in-memory collections. |
| python/semantic_kernel/data/vector_storage/vector_store.py | Minor type annotations update. |
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TaoChenOSU
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### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> This PR adds a FaissVectorStore and FaissCollection Faiss is a really fast similarity search engine, which runs in memory. You can also use GPU's with it, but you will have to build the package yourself. ### Description Closes microsoft#4130 TODO: - [x] other index kinds - [x] deal with distance functions - [x] validate GPU setup - [x] maybe subclass InMemoryCollection - [x] tests <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [x] I didn't break anyone 😄
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Motivation and Context
This PR adds a FaissVectorStore and FaissCollection
Faiss is a really fast similarity search engine, which runs in memory.
You can also use GPU's with it, but you will have to build the package yourself.
Description
Closes #4130
TODO:
Contribution Checklist