Merge pull request #3175 from liyun95/v2.6.x

add 2.6.x docs

Author: liyun95

assets/credential-config-overflow.png

@@ -269,6 +269,86 @@
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assets/diskann-2.png

@@ -79,7 +79,7 @@ Milvus supports various types of search functions to meet the demands of differe
 - [Range Search](single-vector-search.md#Range-search): Finds vectors within a specified radius from your query vector.
 - [Hybrid Search](multi-vector-search.md): Conducts ANN search based on multiple vector fields.
 - [Full Text Search](full-text-search.md): Full text search based on BM25.
-- [Reranking](reranking.md): Adjusts the order of search results based on additional criteria or a secondary algorithm, refining the initial ANN search results.
+- [Reranking](weighted-ranker.md): Adjusts the order of search results based on additional criteria or a secondary algorithm, refining the initial ANN search results.
 - [Fetch](get-and-scalar-query.md#Get-Entities-by-ID): Retrieves data by their primary keys.
 - [Query](get-and-scalar-query.md#Use-Basic-Operators): Retrieves data using specific expressions.
 

assets/diskann.png

@@ -291,7 +291,7 @@ Please keep the order of list of index params consistent with the order of `vect
 </div>
 
 ### Rerank the candidates
-After the first stage of retrieval, we need to rerank the candidates to get a better result. You can choose [WeightedRanker](https://milvus.io/docs/reranking.md#Weighted-Scoring-WeightedRanker) or [RRFRanker](https://milvus.io/docs/reranking.md#Reciprocal-Rank-Fusion-RRFRanker) depending on your requirements. You can refer to the [Reranking](https://milvus.io/docs/reranking.md#Reranking) for more information.
+After the first stage of retrieval, we need to rerank the candidates to get a better result. You can choose [WeightedRanker](https://milvus.io/docs/weighted-ranker.md#Weighted-Scoring-WeightedRanker) or [RRFRanker](https://milvus.io/docs/weighted-ranker.md#Reciprocal-Rank-Fusion-RRFRanker) depending on your requirements. You can refer to the [Reranking](https://milvus.io/docs/weighted-ranker.md#Reranking) for more information.
 
 Here is an example for weighted reranking:
 

assets/embedding-function-overview.png

@@ -233,7 +233,7 @@ This approach stores documents in a Milvus collection with both vector fields:
 - `embedding`: Dense embeddings generated by OpenAI embedding model for semantic search.
 - `sparse_embedding`: Sparse embeddings computed using BM25BuiltInFunction for full-text search.
 
-In addition, we have applied a reranking strategy using "RRFRanker" with its default parameters. To customize reranker, you are able to configure `hybrid_ranker` and `hybrid_ranker_params` following the [Milvus Reranking Guide](https://milvus.io/docs/reranking.md).
+In addition, we have applied a reranking strategy using "RRFRanker" with its default parameters. To customize reranker, you are able to configure `hybrid_ranker` and `hybrid_ranker_params` following the [Milvus Reranking Guide](https://milvus.io/docs/weighted-ranker.md).
 
 Now, let's test the RAG system with a sample query:
 

assets/exp-decay.png

@@ -294,7 +294,7 @@ class ExampleEmbeddingFunction(BaseSparseEmbeddingFunction):
 
 ## Customize hybrid reranker
 
-Milvus supports two types of [reranking strategies](https://milvus.io/docs/reranking.md): Reciprocal Rank Fusion (RRF) and Weighted Scoring. The default ranker in `MilvusVectorStore` hybrid search is RRF with k=60. To customize the hybrid ranker, modify the following parameters:
+Milvus supports two types of [reranking strategies](https://milvus.io/docs/weighted-ranker.md): Reciprocal Rank Fusion (RRF) and Weighted Scoring. The default ranker in `MilvusVectorStore` hybrid search is RRF with k=60. To customize the hybrid ranker, modify the following parameters:
 
 - `hybrid_ranker (str)`: Specifies the type of ranker used in hybrid search queries. Currently only supports ["RRFRanker", "WeightedRanker"]. Defaults to "RRFRanker".
 - `hybrid_ranker_params (dict, optional)`: Configuration parameters for the hybrid ranker. The structure of this dictionary depends on the specific ranker being used: