This page contains reference documentation for Milvus. See the docs for conceptual guides, tutorials, and examples on using Milvus modules.
Base class for Milvus built-in functions.
See: https://milvus.io/docs/manage-collections.md#Function
Milvus BM25 built-in function.
Supports both single-language and multi-language analyzers.
See:
Milvus Text Embedding built-in function (Data In Data Out).
This function allows Milvus to automatically generate embeddings from text by calling external embedding service providers (OpenAI, Bedrock
Interface for Sparse embedding models.
You can inherit from it and implement your custom sparse embedding model.
By default, the asynchronous methods are implemented using the synchronous methods; h
Sparse embedding model based on BM25.
**Note: We recommend using the Milvus built-in BM25 function to implement sparse embedding in your application. This class is more of a reference because it requ
Zilliz Cloud Pipeline retriever.
Hybrid search retriever that uses Milvus Collection to retrieve documents based on multiple fields.
For more information, please refer to: https://milvus.io/docs/release_notes.md#Multi-Embedding---Hy
Zilliz vector store.
You need to have pymilvus installed and a
running Zilliz database.
See the following documentation for how to run a Zilliz instance: https://docs.zilliz.com/docs/create-clus
Milvus vector store integration.
