internal collaborations for enterprises, and public contributions for data DAOs.Installation and Setup‚Äãpip install betabageldbCreate VectorStore from texts‚Äãfrom langchain.vectorstores import Bageltexts = ["hello bagel", "hello langchain", "I love salad", "my car", "a dog"]# create cluster and add textscluster = Bagel.from_texts(cluster_name="testing", texts=texts)# similarity searchcluster.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 bettercluster.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 clustercluster.delete_cluster()Create VectorStore from docs‚Äãfrom langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../modules/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 docscluster = Bagel.from_documents(cluster_name="testing_with_docs", documents=docs)# similarity searchquery = "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 Get all text/doc from Cluster‚Äãtexts = ["hello bagel", "this is langchain"]cluster = Bagel.from_texts(cluster_name="testing", texts=texts)cluster_data = cluster.get()# all keyscluster_data.keys()    dict_keys(['ids', 'embeddings', 'metadatas', 'documents'])# all values and keyscluster_data    {'ids':
BagelDB (Open Vector Database for AI), is like GitHub for AI data.
BagelDB (Open Vector Database for AI), is like GitHub for AI data. ->: internal collaborations for enterprises, and public contributions for data DAOs.Installation and Setup‚Äãpip install betabageldbCreate VectorStore from texts‚Äãfrom langchain.vectorstores import Bageltexts = ["hello bagel", "hello langchain", "I love salad", "my car", "a dog"]# create cluster and add textscluster = Bagel.from_texts(cluster_name="testing", texts=texts)# similarity searchcluster.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 bettercluster.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 clustercluster.delete_cluster()Create VectorStore from docs‚Äãfrom langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../modules/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 docscluster = Bagel.from_documents(cluster_name="testing_with_docs", documents=docs)# similarity searchquery = "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 Get all text/doc from Cluster‚Äãtexts = ["hello bagel", "this is langchain"]cluster = Bagel.from_texts(cluster_name="testing", texts=texts)cluster_data = cluster.get()# all keyscluster_data.keys()    dict_keys(['ids', 'embeddings', 'metadatas', 'documents'])# all values and keyscluster_data    {'ids':