贝果
贝果 (
Open Inference platform for AI)，类似于 AI 数据的 GitHub。
这是一个协作平台，用户可以创建、共享和管理推理数据集。它可以支持独立开发者的私有项目、企业内部合作以及数据 DAO 的公共贡献。
安装与设置
pip install bagelML langchain-community
从文本创建 VectorStore
from langchain_community.vectorstores import Bagel
texts = ["hello bagel", "hello langchain", "I love salad", "my car", "a dog"]
# 创建集群并添加文本
cluster = Bagel.from_texts(cluster_name="testing", texts=texts)
# 相似性搜索
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={})]
# 分数是距离度量，因此越低越好
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)]
# 删除集群
cluster.delete_cluster()
从文档创建 VectorStore
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]
# 使用文档创建集群
cluster = Bagel.from_documents(cluster_name="testing_with_docs", documents=docs)
# 相似性搜索
query = "总统对 Ketanji Brown Jackson 说了什么"
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
从集群中获取所有文本/文档
texts = ["hello bagel", "this is langchain"]
cluster = Bagel.from_texts(cluster_name="testing", texts=texts)
cluster_data = cluster.get()
# 所有键
cluster_data.keys()
dict_keys(['ids', 'embeddings', 'metadatas', 'documents'])
# 所有值和键
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()
使用元数据创建集群并通过元数据过滤
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)]
# 删除集群
cluster.delete_cluster()