[docs] platform core concept revamp and overview update (#3664)

docs/core-concepts/memory-operations/search.mdx

@@ -5,20 +5,23 @@ icon: "magnifying-glass"
 iconType: "solid"
 ---
 
-## Overview
+# How Mem0 Searches Memory
 
-The `search` operation allows you to retrieve relevant memories based on a natural language query and optional filters like user ID, agent ID, categories, and more. This is the foundation of giving your agents memory-aware behavior.
+Mem0’s search operation lets agents ask natural-language questions and get back the memories that matter most. It’s the bridge between everything you’ve stored and the next response your agent writes.
 
-Mem0 supports:
-- Semantic similarity search
-- Metadata filtering (with advanced logic)
-- Reranking and thresholds
-- Cross-agent, multi-session context resolution
+<Info>
+  **Why it matters**
+  - Retrieves the right facts without rebuilding prompts from scratch.
+  - Supports both managed Platform and OSS so you can test locally and deploy at scale.
+  - Keeps results relevant with filters, rerankers, and thresholds.
+</Info>
 
-This applies to both:
-- **Mem0 Platform** (hosted API with full-scale features)
-- **Mem0 Open Source** (local-first with LLM inference and local vector DB)
+## Key terms
 
+- **Query** – Natural-language question or statement you pass to `search`.
+- **Filters** – JSON logic (AND/OR, comparison operators) that narrows results by user, categories, dates, etc.
+- **top_k / threshold** – Controls how many memories return and the minimum similarity score.
+- **Rerank** – Optional second pass that boosts precision when a reranker is configured.
 
 ## Architecture
 
@@ -26,23 +29,26 @@ This applies to both:
   <img src="../../images/search_architecture.png" />
 </Frame>
 
-When you call `search`, Mem0 performs the following steps:
+<Steps>
+<Step title="Query processing">
+Mem0 cleans and enriches your natural-language query so the downstream embedding search is accurate.
+</Step>
+<Step title="Vector search">
+Embeddings locate the closest memories using cosine similarity across your scoped dataset.
+</Step>
+<Step title="Filtering & reranking">
+Logical filters narrow candidates; rerankers or thresholds fine-tune ordering.
+</Step>
+<Step title="Results delivery">
+Formatted memories (with metadata and timestamps) return to your agent or calling service.
+</Step>
+</Steps>
 
-1. **Query Processing**
-   An LLM refines and optimizes your natural language query.
-
-2. **Vector Search**
-   Semantic embeddings are used to find the most relevant memories using cosine similarity.
-
-3. **Filtering & Ranking**
-   Logical and comparison-based filters are applied. Memories are scored, filtered, and optionally reranked.
-
-4. **Results Delivery**
-   Relevant memories are returned with associated metadata and timestamps.
+This pipeline runs the same way for the hosted Platform API and the OSS SDK.
 
 ---
 
-## Example: Mem0 Platform
+## Search with Mem0 Platform
 
 <CodeGroup>
 ```python Python
@@ -81,7 +87,7 @@ const results = await client.search(query, {
 
 ---
 
-## Example: Mem0 Open Source
+## Search with Mem0 Open Source
 
 <CodeGroup>
 ```python Python
@@ -118,7 +124,11 @@ const memories = memory.search("food preferences", {
 
 ---
 
-## Using Filters
+<Info icon="check">
+  Expect an array of memory documents. Platform responses include vectors, metadata, and timestamps; OSS returns your stored schema.
+</Info>
+
+## Filter patterns
 
 Filters help narrow down search results. Common use cases:
 
@@ -152,7 +162,7 @@ client.search("preferences", filters={
 
 ---
 
-## Tips for Better Search
+## Tips for better search
 
 - **Use natural language**: Mem0 understands intent, so describe what you're looking for naturally
 - **Scope with session IDs**: Always provide at least `user_id` to scope search to relevant memories
@@ -167,7 +177,38 @@ client.search("preferences", filters={
 For the full list of filter logic, comparison operators, and optional search parameters, see the
 [Search Memory API Reference](/api-reference/memory/search-memories).
 
-## Need help?
-If you have any questions, please feel free to reach out to us using one of the following methods:
+## Managed vs OSS differences
 
-<Snippet file="get-help.mdx"/>
+| Capability | Mem0 Platform | Mem0 OSS |
+| --- | --- | --- |
+| Filters | Logical operators (`AND`, `OR`, comparisons) with field-level access | Basic field filters, extend via Python hooks |
+| Reranking | Toggle `rerank=True` with managed reranker catalog | Requires configuring local or third-party rerankers |
+| Thresholds | Request-level configuration (`threshold`, `top_k`) | Controlled via SDK parameters |
+| Response metadata | Includes confidence scores, timestamps, dashboard visibility | Determined by your storage backend |
+
+## Put it into practice
+
+- Revisit the <Link href="/core-concepts/memory-operations/add">Add Memory</Link> guide to ensure you capture the context you expect to retrieve.
+- Configure rerankers and filters in <Link href="/platform/features/advanced-retrieval">Advanced Retrieval</Link> for higher precision.
+
+## See it live
+
+- <Link href="/examples/customer-support-agent">Customer Support Agent</Link> demonstrates scoped search with rerankers.
+- <Link href="/examples/personalized-search-tavily-mem0">Personalized Search with Tavily</Link> shows hybrid search in action.
+
+{/* DEBUG: verify CTA targets */}
+
+<CardGroup cols={2}>
+  <Card
+    title="Tune Update Concepts"
+    description="Learn how to adjust memories after search results come back."
+    icon="pencil"
+    href="/core-concepts/memory-operations/update"
+  />
+  <Card
+    title="Build Hybrid Search"
+    description="Follow the cookbook to combine Mem0 with external search."
+    icon="rocket"
+    href="/examples/personalized-search-tavily-mem0"
+  />
+</CardGroup>