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c8391d4ff1
Fix of YandexGPT embeddings. The current version uses a single `model_name` for queries and documents, essentially making the `embed_documents` and `embed_query` methods the same. Yandex has a different endpoint (`model_uri`) for encoding documents, see [this](https://yandex.cloud/en/docs/yandexgpt/concepts/embeddings). The bug may impact retrievers built with `YandexGPTEmbeddings` (for instance FAISS database as retriever) since they use both `embed_documents` and `embed_query`. A simple snippet to test the behaviour: ```python from langchain_community.embeddings.yandex import YandexGPTEmbeddings embeddings = YandexGPTEmbeddings() q_emb = embeddings.embed_query('hello world') doc_emb = embeddings.embed_documents(['hello world', 'hello world']) q_emb == doc_emb[0] ``` The response is `True` with the current version and `False` with the changes I made. Twitter: @egor_krash --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com> |
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🦜️🧑🤝🧑 LangChain Community
Quick Install
pip install langchain-community
What is it?
LangChain Community contains third-party integrations that implement the base interfaces defined in LangChain Core, making them ready-to-use in any LangChain application.
For full documentation see the API reference.
📕 Releases & Versioning
langchain-community is currently on version 0.0.x
All changes will be accompanied by a patch version increase.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.