This PR upgrades langchain-community to pydantic 2.
* Most of this PR was auto-generated using code mods with gritql
(https://github.com/eyurtsev/migrate-pydantic/tree/main)
* Subsequently, some code was fixed manually due to accommodate
differences between pydantic 1 and 2
Breaking Changes:
- Use TEXTEMBED_API_KEY and TEXTEMBEB_API_URL for env variables for text
embed integrations:
cbea780492
Other changes:
- Added pydantic_settings as a required dependency for community. This
may be removed if we have enough time to convert the dependency into an
optional one.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
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>
