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Based on insights from the graphrag team, several bug fixes related to
data and model validators.
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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.
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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.
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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
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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.
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Eduard van Valkenburg