AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.
This notebook explains how to use AwaEmbeddings in LangChain.
from langchain_community.embeddings import AwaEmbeddings
Embedding = AwaEmbeddings()
Users can use Embedding.set_model() to specify the embedding model.
The input of this function is a string which represents the model's name.
The list of currently supported models can be obtained here \ \
The default model is all-mpnet-base-v2, it can be used without setting.
text = "our embedding test"
Embedding.set_model("all-mpnet-base-v2")
res_query = Embedding.embed_query("The test information")
res_document = Embedding.embed_documents(["test1", "another test"])
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