AwaDB
AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.
You'll need to installlangchain-community withpip install -qU langchain-community to use this integration
This notebook shows how to use functionality related to theAwaDB.
%pip install--upgrade--quiet awadb
from langchain_community.document_loadersimport TextLoader
from langchain_community.vectorstoresimport AwaDB
from langchain_text_splittersimport 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.
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)
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
Related
- Vector storeconceptual guide
- Vector storehow-to guides