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d7c14cb6f9
Create a Class which allows to use the "text2vec" open source embedding
model.
It should install the model by running 'pip install -U text2vec'.
Example to call the model through LangChain:
from langchain_community.embeddings.text2vec import Text2vecEmbeddings
embedding = Text2vecEmbeddings()
bookend.embed_documents([
"This is a CoSENT(Cosine Sentence) model.",
"It maps sentences to a 768 dimensional dense vector space.",
])
bookend.embed_query(
"It can be used for text matching or semantic search."
)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
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| langchain_community | ||
| scripts | ||
| tests | ||
| Makefile | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
🦜️🧑🤝🧑 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.