[docs] graph memory docs fix (#3728)

docs/open-source/features/graph-memory.mdx

@@ -15,7 +15,7 @@ Graph Memory extends Mem0 by persisting nodes and edges alongside embeddings, so
 
 ## How Graph Memory Maps Context
 
-Mem0 extracts entities and relationships from every memory write, stores embeddings in your vector database, and mirrors relationships in a graph backend. On retrieval, vector search narrows candidates, then the graph supplies context and re-ranks results.
+Mem0 extracts entities and relationships from every memory write, stores embeddings in your vector database, and mirrors relationships in a graph backend. On retrieval, vector search narrows candidates while the graph returns related context alongside the results.
 
 ```mermaid
 graph LR
@@ -37,8 +37,8 @@ Mem0’s extraction LLM identifies entities, relationships, and timestamps from
 <Step title="Store vectors and edges together">
 Embeddings land in your configured vector database while nodes and edges flow into a Bolt-compatible graph backend (Neo4j, Memgraph, Neptune, or Kuzu).
 </Step>
-<Step title="Blend graph context at search time">
-`memory.search` first performs vector similarity, then follows connected nodes to boost (or filter) answers before optionally handing results to a reranker.
+<Step title="Expose graph context at search time">
+`memory.search` performs vector similarity (optionally reranked by your configured reranker) and returns the results list. Graph Memory runs in parallel and adds related entities in the `relations` array—it does not reorder the vector hits automatically.
 </Step>
 </Steps>
 
@@ -162,6 +162,10 @@ results.results.forEach((hit) => {
 Expect to see **Alice met Bob at GraphConf 2025** in the output. In Neo4j Browser run `MATCH (p:Person)-[r]->(q:Person) RETURN p,r,q LIMIT 5;` to confirm the edge exists.
 </Info>
 
+<Note>
+Graph Memory enriches responses by adding related entities in the `relations` key. The ordering of `results` always comes from vector search (plus any reranker you configure); graph edges do not reorder those hits automatically.
+</Note>
+
 ## Operate Graph Memory Day-to-Day
 
 <AccordionGroup>

docs/platform/features/graph-memory.mdx

@@ -14,8 +14,8 @@ This feature allows your AI applications to understand connections between entit
 The Graph Memory feature analyzes how each entity connects and relates to each other. When enabled:
 
 1. Mem0 automatically builds a graph representation of entities
-2. Retrieval considers graph relationships between entities
-3. Results include entities that may be contextually important even if they're not direct semantic matches
+2. Vector search returns the top semantic matches (with any reranker you configure)
+3. Graph relations are returned alongside those results to provide additional context—they do not reorder the vector hits
 
 ## Using Graph Memory
 
@@ -179,6 +179,10 @@ console.log(results);
 
 </CodeGroup>
 
+<Note>
+`results` always reflects the vector search order (optionally reranked). Graph Memory augments that response by adding related entities in the `relations` array; it does not re-rank the vector results automatically.
+</Note>
+
 ### Retrieving All Memories with Graph Memory
 
 When retrieving all memories, Graph Memory provides additional relationship context:
@@ -339,4 +343,3 @@ Graph Memory requires additional processing and may increase response times slig
 If you have any questions, please feel free to reach out to us using one of the following methods:
 
 <Snippet file="get-help.mdx" />
-