Zilliz Cloud Pipeline
Zilliz Cloud Pipelines 將您的非結構化資料轉換為可搜尋的向量集合，串聯資料的嵌入、擷取、搜尋和刪除。
Zilliz Cloud Pipelines 可在 Zilliz Cloud Console 中以及透過 RestFul API 使用。
此筆記本示範如何準備 Zilliz Cloud Pipelines 並透過 LangChain Retriever 使用它們。
準備 Zilliz Cloud Pipelines
若要讓 pipelines 準備好用於 LangChain Retriever，您需要在 Zilliz Cloud 中建立和設定服務。
1. 設定資料庫
2. 建立 Pipelines
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 參考：ZillizCloudPipelineRetriever
新增文件
若要新增文件，您可以使用方法 add_texts 或 add_doc_url，這些方法會將來自文字清單或具有對應中繼資料的預先簽署/公開 URL 的文件插入儲存區。
-
如果使用文字擷取 pipeline，您可以使用方法
add_texts，它會將一批文字與對應的中繼資料插入 Zilliz Cloud 儲存空間。引數
texts：文字字串清單。metadata：中繼資料的鍵值字典將作為擷取 pipeline 所需的保留欄位插入。預設為 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
# )
-
如果使用文件擷取 pipeline，您可以使用方法
add_doc_url，它會將來自 URL 的文件與對應的中繼資料插入 Zilliz Cloud 儲存空間。引數
doc_url：文件 URL。metadata：中繼資料的鍵值字典將作為擷取 pipeline 所需的保留欄位插入。預設為 None。
以下範例適用於文件擷取 pipeline，這需要 milvus 版本作為中繼資料。我們將使用範例文件，說明如何在 Milvus v2.3.x 中刪除實體。
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}
取得相關文件
若要查詢檢索器，您可以使用方法 get_relevant_documents，它會傳回 LangChain Document 物件的清單。
引數
query：尋找相關文件的字串。top_k：結果的數量。預設為 10。offset：在搜尋結果中要跳過的記錄數。預設為 0。output_fields：要在輸出中呈現的額外欄位。filter：用於篩選搜尋結果的 Milvus 運算式。預設為 ""。run_manager：要使用的回呼處理常式。
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})]