Add Docs on Search Mode Diversity Ranking

docs/agents/_includes/query_agent.mts

@@ -382,6 +382,17 @@ for (const obj of basicSearchResponse.searchResults.objects) {
 }
 // END BasicSearchQuery
 
+// START DiversityRanking
+const diversitySearchResponse = await qa.search("summer shoes", {
+    limit: 10,
+    diversityWeight: 0.5
+})
+
+// Access the search results
+for (const obj of diversitySearchResponse.searchResults.objects) {
+    console.log(`Product: ${obj.properties['name']} - ${obj.properties['price']}`)
+}
+// END DiversityRanking
 
 // START BasicAskQuery
 // Perform a query

docs/agents/_includes/query_agent.py

@@ -453,6 +453,18 @@ def populate_weaviate(client, overwrite_existing=False):
     print()
 # END SearchPagination
 
+# START DiversityRanking
+search_response = qa.search(
+    "summer shoes",
+    limit=10,
+    diversity_weight=0.5,
+)
+
+# Access the search results
+for obj in search_response.search_results.objects:
+    print(f"Product: {obj.properties['name']} - ${obj.properties['price']}")
+# END DiversityRanking
+
 # START FollowUpQuery
 # Perform a follow-up query and include the answer from the previous query
 from weaviate.agents.classes import ChatMessage

docs/agents/query/usage.md

@@ -327,6 +327,32 @@ Page 3:
 
 </details>
 
+#### `Search` with diversity ranking
+
+`Search` supports adding diversity weighting to search result rankings using Maximal Marginal Relevance (MMR). This is enabled by passing a `diversity_weight` parameter in the range of 0.0 to 1.0. To use diversity ranking with target vectors, set the single target vector that you want to use in the Query Agent's constructor. Diversity ranking is not yet supported with collections using multi-vector embeddings. Diversity ranking will work with multiple collections, unless they are using **different** embedding models.
+
+<Tabs className="code" groupId="languages">
+    <TabItem value="py_agents" label="Python">
+        <FilteredTextBlock
+            text={PyCode}
+            startMarker="# START DiversityRanking"
+            endMarker="# END DiversityRanking"
+            language="py"
+        />
+    </TabItem>
+    <TabItem value="ts_agents" label="JavaScript/TypeScript">
+    <FilteredTextBlock
+            text={TSCode}
+            startMarker="// START DiversityRanking"
+            endMarker="// END DiversityRanking"
+            language="ts"
+        />
+    </TabItem>
+
+</Tabs>
+
+
+
 ### `Ask`
 
 `Ask` the Query Agent a question using natural language. The Query Agent will process the question, perform the necessary searches in Weaviate, and return the answer.