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AwaDB

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 --upgrade --quiet  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.

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

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