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This PR:
- restructures the data folder, flattening the hierarchy
- it adds overloads to the Get, Upsert, Delete methods, to allow batch
as well, marks the batch versions as deprecated, closes#11301
- restructured the inheritance of VectorStoreRecordCollection, which now
has a base that does record handling, which is shared with the
VectorSearchBase for the serialization, and the search method mixins now
subclass the VectorSearchBase so that there is one less parent direct
parent, a full Collection will now inherit from
VectorStoreRecordCollection and zero or more VectorSearch mixins, this
is done to clean things up and in preparation of adding HybridSearch.
- Also adds a convenience method to each search mixin to create a
VectorStoreTextSearch directly, allowing folks to import one less
concept, closes#11111
### Description
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Most of the code has been moved out of many files and folders, into a
few files which make purpose and collaboration between the classes
easier to understand.
This is a breaking change:
If you have implemented your own VectorStoreRecordCollection, make sure
to:
- Add `VectorStoreRecordCollection[TKey, TModel]` as superclass to the
implementation
- Remove `VectorSearchBase` as superclass from implementation
- Set the SearchMixins to `[TKey, TModel]` instead of just `[TModel]`
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Co-authored-by: Evan Mattson <[email protected]>
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Pinecone offers integrated embeddings, expended the way you can set up
for that
Made some fixes in the DeSer process of the vector stores to deal with
non-returned vectors.
Now (finally) uses local_embeddings field to allow the field to be
present but not used.
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Introducing the Pinecone Vector Store and Collection
This also upgrades the dependencies to v6
Adding the ability to run with asyncio or GRPC
TODO:
- [x] Filtering
- [x] Tests
### Description
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Adds PineconeStore and PineconeCollection
Closes: #9928
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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:
- [x] other index kinds
- [x] deal with distance functions
- [x] validate GPU setup
- [x] maybe subclass InMemoryCollection
- [x] tests
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Closing https://github.com/microsoft/semantic-kernel/issues/9963
### Description
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1. Add text embedding and text completion samples.
2. Move the embedding generator base to a
`semantic_kernel.connectors.ai`.
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Improve the serialization sample
Added a more complex sample for using ChatHistory with a VectorStore to
store in a backend.
Also improves the typing of the vectorstoremodel decorator
### Description
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---------
Co-authored-by: Evan Mattson <[email protected]>
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Adds ChromaStore and ChromaConnector
Closes: #9926
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This change is required to improve the flexibility and maintainability
of our feature annotation system. Previously, separate decorators (e.g.,
`experimental_function` and `experimental_class`) were used to mark
experimental features, resulting in code duplication and limiting our
ability to handle additional feature stages. As our SDK evolves, we need
a unified approach that can support multiple stages—such as
experimental, release candidate, and future states—while also allowing
version information for release candidate features to be centrally
managed.
### Description
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This PR refactors our feature decorators by introducing a unified
`stage` decorator that updates the docstring and attaches metadata for
both functions and classes. Two convenience decorators, `experimental`
and `release_candidate`, are built on top of `stage`:
- The `experimental` decorator marks features as experimental and sets
an `is_experimental` attribute.
- The `release_candidate` decorator supports multiple usage patterns
(with or without parentheses and with an optional version parameter) to
mark features as release candidate and sets an `is_release_candidate`
attribute.
This unified approach reduces duplication, simplifies the codebase, and
lays the groundwork for easily extending feature stages in the future.
This decorator supports the following usage patterns:
- `@experimental` (for both classes and functions)
- `@release_candidate` (no parentheses)
- `@release_candidate()` (empty parentheses)
- `@release_candidate("1.21.3-rc1")` (positional version)
- `@release_candidate(version="1.21.3-rc1")` (keyword version)
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Adds Azure Cosmos DB for MongoDB support using the new vector store
model.
Builds on the MongoDB Atlas integration, replaced the index creation and
settings to make it work.
Closes#6836
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Based on insights from the graphrag team, several bug fixes related to
data and model validators.
### Description
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For all cases of model_validator in before mode, there is no guarantee
that the data passed is a dict,
especially when a model is used as a field in another model, then a
string is passed to the before validator.
So in those cases we check before doing logic, otherwise just pass it on
and let Pydantic handle the invalid data.
Some small logic fixes in the vector store model decorator:
- updated docstring with the right exceptions
- additional checks on attributes
- additional checks on VectorFields with tests
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While reviewing the docs, noticed some illogical behavior, this fixes
that.
### Description
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added unit tests to up the test coverage across the data folder
Also redoes exceptions for everything vector.
This does introduce some breaking changes on the exceptions returned by
the different methods for vector stores, they are still marked
experimental and this will go a long way in moving towards release.
Closes#9485
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Adds the vector search pieces to the two Redis collection types
Features vectorized search and vector text search.
Also features a slight change to the storage format for Hashsets since
the existing was wrong because the index was not picking up those
fields, fixed now, but if you have a Redis Hash Collection running, this
will break things.
Closes#6837
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This PR adds vector search to Weaviate.
All three types of search are supported, however
vectorizable_text_search depends on a setup outside SK.
Also adds a parameter to the Weaviate Collection called 'named_vectors',
default is True.
When set to False it uses unnamed vectors instead of named.
Because of this there is a slight difference in how vectors are
represented to Weaviate which might be breaking.
### Description
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The breaking change is that, vector were set to have the name of the
content that it vectorized, for instance:
With a datamodel like this (shortened some of the class names):
```python
class DataModel:
content: Annotated[str, Data Field(embedding_property_name="vector")]
vector: Annotated[list[float], VectorField()]
```
This would be set as `DataObject(vector: {"content": [vector
content]})`, while it will now set this to `DataObject(vector:
{"vector": [vector content]})` Where DataObject is the Weaviate object
used to insert items.
Closes#6839
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Adds the vector search functions to the existing QdrantCollection.
Currently only support VectorizedSearch.
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We are implementing the Azure Cosmos DB NoSQL vector store and vector
collection.
### Description
Azure Cosmos DB NoSQL vector store & collection implementation.
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---------
Co-authored-by: Eduard van Valkenburg <[email protected]>
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Adds vectorized search and text search to the In Memory connector.
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---------
Co-authored-by: Tao Chen <[email protected]>
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Fixes the mypy pre-commit job, and subsequent fixes from there.
Moved all python tests to use the uv setup task instead of manual cache,
python install and uv install.
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This PR does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes#6832#6833
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---------
Co-authored-by: Tao Chen <[email protected]>
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Add Weaviate DB to Semantic Kernel with the new vector store and vector
collection interfaces.
Address: https://github.com/microsoft/semantic-kernel/issues/6553
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Co-authored-by: Evan Mattson <[email protected]>
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Fixed some small things based on docs creation.
Added include_vectors param where appropriate.
Improved import structure
### Description
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These notes will help understanding how your code works. Thanks! -->
### Contribution Checklist
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- [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 😄
### Motivation and Context
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This PR adds new vector store and vector store record collections as
well as implementations for:
- Azure AI Search
- Redis
- Qdrant
- Volatile (in-memory)
It also adds samples, tests, and unit-tests for these.
Next it adds the vector store record fields, definition and decorator,
with tests and samples.
All marked experimental. Existing Redis, Azure AI Search, Qdrant and
Volatile will be marked as deprecated in the future, once the new
collections are feature complete.
### Description
<!-- 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 😄
