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AwaDB
 Contents 
Similarity search with score
Restore the table created and added data before
AwaDB#
AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.
This notebook shows how to use functionality related to the AwaDB.
!pip install awadb
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import AwaDB
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../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.
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’: ‘../../../state_of_the_union.txt’}), 0.561813814013747)
Restore the table created and added data before#
AwaDB automatically persists added document data
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/vectorstores/examples/awadb.html