BagelDB

pip install betabageldb langchain-community 

from langchain_community.vectorstores import Bagel  texts = ["hello bagel", "hello langchain", "I love salad", "my car", "a dog"] # create cluster and add texts cluster = Bagel.from_texts(cluster_name="testing", texts=texts) 

# similarity search cluster.similarity_search("bagel", k=3) 

[Document(page_content='hello bagel', metadata={}),  Document(page_content='my car', metadata={}),  Document(page_content='I love salad', metadata={})] 

# the score is a distance metric, so lower is better cluster.similarity_search_with_score("bagel", k=3) 

[(Document(page_content='hello bagel', metadata={}), 0.27392977476119995),  (Document(page_content='my car', metadata={}), 1.4783176183700562),  (Document(page_content='I love salad', metadata={}), 1.5342965126037598)] 

# delete the cluster cluster.delete_cluster() 

from langchain_community.document_loaders import TextLoader from langchain_text_splitters import CharacterTextSplitter  loader = TextLoader("../../how_to/state_of_the_union.txt") documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) docs = text_splitter.split_documents(documents)[:10] 

# create cluster with docs cluster = Bagel.from_documents(cluster_name="testing_with_docs", documents=docs) 

# similarity search query = "What did the president say about Ketanji Brown Jackson" docs = cluster.similarity_search(query) print(docs[0].page_content[:102]) 

Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the 

texts = ["hello bagel", "this is langchain"] cluster = Bagel.from_texts(cluster_name="testing", texts=texts) cluster_data = cluster.get() 

# all keys cluster_data.keys() 

dict_keys(['ids', 'embeddings', 'metadatas', 'documents']) 

# all values and keys cluster_data 

{'ids': ['578c6d24-3763-11ee-a8ab-b7b7b34f99ba',  '578c6d25-3763-11ee-a8ab-b7b7b34f99ba',  'fb2fc7d8-3762-11ee-a8ab-b7b7b34f99ba',  'fb2fc7d9-3762-11ee-a8ab-b7b7b34f99ba',  '6b40881a-3762-11ee-a8ab-b7b7b34f99ba',  '6b40881b-3762-11ee-a8ab-b7b7b34f99ba',  '581e691e-3762-11ee-a8ab-b7b7b34f99ba',  '581e691f-3762-11ee-a8ab-b7b7b34f99ba'],  'embeddings': None,  'metadatas': [{}, {}, {}, {}, {}, {}, {}, {}],  'documents': ['hello bagel',  'this is langchain',  'hello bagel',  'this is langchain',  'hello bagel',  'this is langchain',  'hello bagel',  'this is langchain']} 

cluster.delete_cluster() 

texts = ["hello bagel", "this is langchain"] metadatas = [{"source": "notion"}, {"source": "google"}]  cluster = Bagel.from_texts(cluster_name="testing", texts=texts, metadatas=metadatas) cluster.similarity_search_with_score("hello bagel", where={"source": "notion"}) 

[(Document(page_content='hello bagel', metadata={'source': 'notion'}), 0.0)] 

# delete the cluster cluster.delete_cluster()