Replace this entire comment with:
- **Description:** @kurtisvg has raised a point that it's a good idea to
have a fixed version for embeddings (since otherwise a user might run a
query with one version vs a vectorstore where another version was used).
In order to avoid breaking changes, I'd suggest to give users a warning,
and make a `model_name` a required argument in 1.5 months.
**Description**
For the Momento Vector Index (MVI) vector store implementation, pass
through `filter_expression` kwarg to the MVI client, if specified. This
change will enable the MVI self query implementation in a future PR.
Also fixes some integration tests.
Description: Adding Summarization to Vectara, to reflect it provides not
only vector-store type functionality but also can return a summary.
Also added:
MMR capability (in the Vectara platform side)
Updated templates
Updated documentation and IPYNB examples
Tag maintainer: @baskaryan
Twitter handle: @ofermend
---------
Co-authored-by: Ofer Mendelevitch <ofermend@gmail.com>
## Description
This PR intends to add support for Qdrant's new [sparse vector
retrieval](https://qdrant.tech/articles/sparse-vectors/) by introducing
a new retriever class, `QdrantSparseVectorRetriever`.
Necessary usage docs and integration tests have been added for the
retriever.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: A new vector store Jaguar is being added. Class, test
scripts, and documentation is added.
Issue: None -- This is the first PR contributing to LangChain
Dependencies: This depends on "pip install -U jaguardb-http-client"
client http package
Tag maintainer: @baskaryan, @eyurtsev, @hwchase1
Twitter handle: @workbot
---------
Co-authored-by: JY <jyjy@jaguardb>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This is technically a breaking change because it'll switch out default
models from `text-davinci-003` to `gpt-3.5-turbo-instruct`, but OpenAI
is shutting off those endpoints on 1/4 anyways.
Feels less disruptive to switch out the default instead.
- **Description:** VertexAIEmbeddings performance improvements
- **Twitter handle:** @vladkol
## Improvements
- Dynamic batch size, starting from 250, lowering down to 5. Batch size
varies across regions.
Some regions support larger batches, and it significantly improves
performance.
When running large batches of texts in `us-central1`, performance gain
can be up to 3.5x.
The dynamic batching also makes sure every batch is below 20K token
limit.
- New model parameter `embeddings_type` that translates to `task_type`
parameter of the API. Newer model versions support [different embeddings
task
types](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings#api_changes_to_models_released_on_or_after_august_2023).
Replace this entire comment with:
- **Description:** added support for new Google GenerativeAI models
- **Twitter handle:** lkuligin
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
h/t to @lkuligin
- **Description:** added new models on VertexAI
- **Twitter handle:** @lkuligin
---------
Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
