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BagelDB (Open Vector Database for AI), is like GitHub for AI data. It is a collaborative platform where users can create, share, and manage vector datasets. It can support private projects for independent developers, internal collaborations for enterprises, and public contributions for data DAOs.

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Installation and Setup

pip install betabageldb langchain-community

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Create VectorStore from texts

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()

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Create VectorStore from docs

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

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Get all text/doc from Cluster

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()

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Create cluster with metadata & filter using metadata

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()
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