Zilliz Cloud Pipeline
Zilliz Cloud Pipelines 将您的非结构化数据转换为可搜索的向量集合，将数据的嵌入、摄取、搜索和删除链接起来。
Zilliz Cloud Pipelines 可在 Zilliz Cloud 控制台和通过 RestFul API 获取。
此笔记本演示了如何准备 Zilliz Cloud Pipelines 并通过 LangChain Retriever 使用它们。
准备 Zilliz Cloud Pipelines
要使管道准备好用于 LangChain Retriever，您需要在 Zilliz Cloud 中创建和配置服务。
1. 设置数据库
2. 创建管道
使用 LangChain Retriever
%pip install --upgrade --quiet langchain-milvus
from langchain_milvus import ZillizCloudPipelineRetriever
retriever = ZillizCloudPipelineRetriever(
pipeline_ids={
"ingestion": "<YOUR_INGESTION_PIPELINE_ID>", # skip this line if you do NOT need to add documents
"search": "<YOUR_SEARCH_PIPELINE_ID>", # skip this line if you do NOT need to get relevant documents
"deletion": "<YOUR_DELETION_PIPELINE_ID>", # skip this line if you do NOT need to delete documents
},
token="<YOUR_ZILLIZ_CLOUD_API_KEY>",
)
API 参考：ZillizCloudPipelineRetriever
添加文档
要添加文档，您可以使用 add_texts 或 add_doc_url 方法，该方法将来自文本列表或带有相应元数据的预签名/公共 URL 的文档插入存储中。
如果使用文本摄取管道，则可以使用
add_texts方法，该方法将一批文本及其相应的元数据插入 Zilliz Cloud 存储中。参数
texts：文本字符串列表。metadata：元数据的键值字典将作为摄取管道所需的保留字段插入。默认为 None。
# retriever.add_texts(
# texts = ["example text 1e", "example text 2"],
# metadata={"<FIELD_NAME>": "<FIELD_VALUE>"} # skip this line if no preserved field is required by the ingestion pipeline
# )
如果使用文档摄取管道，则可以使用
add_doc_url方法，该方法将来自 URL 的文档及其相应的元数据插入 Zilliz Cloud 存储中。参数
doc_url：文档 URL。metadata：元数据的键值字典将作为摄取管道所需的保留字段插入。默认为 None。
以下示例使用文档摄取管道，该管道需要 milvus 版本作为元数据。我们将使用一个示例文档，描述如何在 Milvus v2.3.x 中删除实体。
retriever.add_doc_url(
doc_url="https://publicdataset.zillizcloud.com/milvus_doc.md",
metadata={"version": "v2.3.x"},
)
{'token_usage': 1247, 'doc_name': 'milvus_doc.md', 'num_chunks': 6}
获取相关文档
要查询检索器，您可以使用 get_relevant_documents 方法，该方法返回一个 LangChain Document 对象列表。
参数
query：查找相关文档的字符串。top_k：结果数。默认为 10。offset：搜索结果中要跳过的记录数。默认为 0。output_fields：输出中要显示的额外字段。filter：用于过滤搜索结果的 Milvus 表达式。默认为 ""。run_manager：要使用的回调处理程序。
retriever.get_relevant_documents(
"Can users delete entities by complex boolean expressions?"
)
[Document(page_content='# Delete Entities\nThis topic describes how to delete entities in Milvus. \nMilvus supports deleting entities by primary key or complex boolean expressions. Deleting entities by primary key is much faster and lighter than deleting them by complex boolean expressions. This is because Milvus executes queries first when deleting data by complex boolean expressions. \nDeleted entities can still be retrieved immediately after the deletion if the consistency level is set lower than Strong.\nEntities deleted beyond the pre-specified span of time for Time Travel cannot be retrieved again.\nFrequent deletion operations will impact the system performance. \nBefore deleting entities by comlpex boolean expressions, make sure the collection has been loaded.\nDeleting entities by complex boolean expressions is not an atomic operation. Therefore, if it fails halfway through, some data may still be deleted.\nDeleting entities by complex boolean expressions is supported only when the consistency is set to Bounded. For details, see Consistency.\\\n\\\n# Delete Entities\n## Prepare boolean expression\nPrepare the boolean expression that filters the entities to delete. \nMilvus supports deleting entities by primary key or complex boolean expressions. For more information on expression rules and supported operators, see Boolean Expression Rules.', metadata={'id': 448986959321277978, 'distance': 0.7871403694152832}),
Document(page_content='# Delete Entities\n## Prepare boolean expression\n### Simple boolean expression\nUse a simple expression to filter data with primary key values of 0 and 1: \n```python\nexpr = "book_id in [0,1]"\n```\\\n\\\n# Delete Entities\n## Prepare boolean expression\n### Complex boolean expression\nTo filter entities that meet specific conditions, define complex boolean expressions. \nFilter entities whose word_count is greater than or equal to 11000: \n```python\nexpr = "word_count >= 11000"\n``` \nFilter entities whose book_name is not Unknown: \n```python\nexpr = "book_name != Unknown"\n``` \nFilter entities whose primary key values are greater than 5 and word_count is smaller than or equal to 9999: \n```python\nexpr = "book_id > 5 && word_count <= 9999"\n```', metadata={'id': 448986959321277979, 'distance': 0.7775762677192688}),
Document(page_content='# Delete Entities\n## Delete entities\nDelete the entities with the boolean expression you created. Milvus returns the ID list of the deleted entities.\n```python\nfrom pymilvus import Collection\ncollection = Collection("book") # Get an existing collection.\ncollection.delete(expr)\n``` \nParameter\tDescription\nexpr\tBoolean expression that specifies the entities to delete.\npartition_name (optional)\tName of the partition to delete entities from.\\\n\\\n# Upsert Entities\nThis topic describes how to upsert entities in Milvus. \nUpserting is a combination of insert and delete operations. In the context of a Milvus vector database, an upsert is a data-level operation that will overwrite an existing entity if a specified field already exists in a collection, and insert a new entity if the specified value doesn’t already exist. \nThe following example upserts 3,000 rows of randomly generated data as the example data. When performing upsert operations, it\'s important to note that the operation may compromise performance. This is because the operation involves deleting data during execution.', metadata={'id': 448986959321277980, 'distance': 0.680284857749939}),
Document(page_content='# Upsert Entities\n## Flush data\nWhen data is upserted into Milvus it is updated and inserted into segments. Segments have to reach a certain size to be sealed and indexed. Unsealed segments will be searched brute force. In order to avoid this with any remainder data, it is best to call flush(). The flush() call will seal any remaining segments and send them for indexing. It is important to only call this method at the end of an upsert session. Calling it too often will cause fragmented data that will need to be cleaned later on.\\\n\\\n# Upsert Entities\n## Limits\nUpdating primary key fields is not supported by upsert().\nupsert() is not applicable and an error can occur if autoID is set to True for primary key fields.', metadata={'id': 448986959321277983, 'distance': 0.5672488212585449}),
Document(page_content='# Upsert Entities\n## Prepare data\nFirst, prepare the data to upsert. The type of data to upsert must match the schema of the collection, otherwise Milvus will raise an exception. \nMilvus supports default values for scalar fields, excluding a primary key field. This indicates that some fields can be left empty during data inserts or upserts. For more information, refer to Create a Collection. \n```python\n# Generate data to upsert\n\nimport random\nnb = 3000\ndim = 8\nvectors = [[random.random() for _ in range(dim)] for _ in range(nb)]\ndata = [\n[i for i in range(nb)],\n[str(i) for i in range(nb)],\n[i for i in range(10000, 10000+nb)],\nvectors,\n[str("dy"*i) for i in range(nb)]\n]\n```', metadata={'id': 448986959321277981, 'distance': 0.5107149481773376}),
Document(page_content='# Upsert Entities\n## Upsert data\nUpsert the data to the collection. \n```python\nfrom pymilvus import Collection\ncollection = Collection("book") # Get an existing collection.\nmr = collection.upsert(data)\n``` \nParameter\tDescription\ndata\tData to upsert into Milvus.\npartition_name (optional)\tName of the partition to upsert data into.\ntimeout (optional)\tAn optional duration of time in seconds to allow for the RPC. If it is set to None, the client keeps waiting until the server responds or error occurs.\nAfter upserting entities into a collection that has previously been indexed, you do not need to re-index the collection, as Milvus will automatically create an index for the newly upserted data. For more information, refer to Can indexes be created after inserting vectors?', metadata={'id': 448986959321277982, 'distance': 0.4341375529766083})]