langchain/docs/extras/integrations/text_embedding/awadb.ipynb

AwaDB

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.

In [1]:

# pip install awadb

import the library

In [2]:

from langchain.embeddings import AwaEmbeddings

In [3]:

Embedding = AwaEmbeddings()

Set embedding model

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.

In [4]:

text = "our embedding test"

Embedding.set_model("all-mpnet-base-v2")

In [5]:

res_query = Embedding.embed_query("The test information")
res_document = Embedding.embed_documents(["test1", "another test"])