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AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.
You’ll need to install langchain-community with pip install -qU langchain-community to use this integration This notebook shows how to use functionality related to the AwaDB. 
pip install -qU  awadb

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import AwaDB
from langchain_text_splitters import CharacterTextSplitter

loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

db = AwaDB.from_documents(docs)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)

print(docs[0].page_content)

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

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Similarity search with score

The returned distance score is between 0-1. 0 is dissimilar, 1 is the most similar 
docs = db.similarity_search_with_score(query)

print(docs[0])

(Document(page_content='And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../how_to/state_of_the_union.txt'}), 0.561813814013747)

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Restore the table created and added data before

AwaDB automatically persists added document data. If you can restore the table you created and added before, you can just do this as below: 
import awadb

awadb_client = awadb.Client()
ret = awadb_client.Load("langchain_awadb")
if ret:
    print("awadb load table success")
else:
    print("awadb load table failed")
awadb load table success
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