---
title: AwaDB
---

>[AwaDB](https://github.com/awa-ai/awadb)는 LLM Application에서 사용하는 embedding vector의 검색 및 저장을 위한 AI Native 데이터베이스입니다.

이 integration을 사용하려면 `pip install -qU langchain-community`로 `langchain-community`를 설치해야 합니다

이 notebook은 `AwaDB`와 관련된 기능을 사용하는 방법을 보여줍니다.

```python
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.

​
score를 포함한 유사도 검색

반환되는 distance score는 0-1 사이입니다. 0은 유사하지 않음, 1은 가장 유사함을 의미합니다 
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)

​
이전에 생성하고 데이터를 추가한 table 복원하기

AwaDB는 추가된 document 데이터를 자동으로 유지합니다. 이전에 생성하고 추가한 table을 복원하려면, 아래와 같이 하면 됩니다: 
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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