Improve SEO metadata across documentation pages (#4447)

2026-03-20 02:52:39 -07:00
parent 401754ca65
commit f05e50d940
134 changed files with 197 additions and 14 deletions
@@ -1,6 +1,6 @@
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 title: Config
-description: "Configuration options for rerankers in Mem0"
+description: "Reference for shared and provider-specific reranker configuration options in Mem0, including top_k and API key settings."
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 ## Common Configuration Parameters
@@ -1,5 +1,6 @@
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 title: Custom Prompts
+description: "Customize the LLM reranker prompt template in Mem0 to control how search results are ranked and scored."
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 When using LLM rerankers, you can customize the prompts used for ranking to better suit your specific use case and domain.
@@ -1,6 +1,6 @@
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 title: Cohere
-description: "Reranking with Cohere"
+description: "Configure Cohere as a reranker in Mem0 with support for English and multilingual reranking models."
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 Cohere provides enterprise-grade reranking models with excellent multilingual support and production-ready performance.
@@ -1,6 +1,6 @@
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 title: LLM as Reranker
-description: 'Flexible reranking using LLMs'
+description: "Use any LLM as a flexible reranker in Mem0 with custom prompts and domain-specific scoring logic."
 ---

 <Warning>
@@ -1,6 +1,6 @@
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 title: Zero Entropy
-description: 'Neural reranking with Zero Entropy'
+description: "Configure Zero Entropy neural reranking models in Mem0 with zerank-1 and zerank-1-small support."
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 [Zero Entropy](https://www.zeroentropy.dev) provides neural reranking models that significantly improve search relevance with fast performance.
@@ -1,5 +1,6 @@
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 title: Performance Optimization
+description: "Best practices for optimizing reranker performance in Mem0, covering candidate sizing, batching, and tuning."
 ---

 Optimizing reranker performance is crucial for maintaining fast search response times while improving result quality. This guide covers best practices for different reranker types.