Add Docs for Follow-Up Queries in Suggest Queries Mode

docs/agents/_includes/query_agent.mts

@@ -606,6 +606,30 @@ for (const suggestedQuery of suggestResponse.queries) {
 }
 // END SuggestQueries
 
+// START SuggestQueriesWithConversation
+// Build a conversation history
+const suggestConversation: ChatMessage[] = [
+    {
+        role: "user",
+        content: "What are some popular machine learning frameworks?",
+    },
+    {
+        role: "assistant",
+        content: "Some popular ML frameworks include TensorFlow, PyTorch, and JAX.",
+    },
+];
+
+// Suggest follow-up queries based on the conversation context
+const suggestWithConvoResponse = await qa.suggestQueries({
+    conversation: suggestConversation,
+    numQueries: 3,
+});
+
+for (const suggestedQuery of suggestWithConvoResponse.queries) {
+    console.log(suggestedQuery.query);
+}
+// END SuggestQueriesWithConversation
+
 await client.close()
 
 }

docs/agents/_includes/query_agent.py

@@ -689,3 +689,25 @@ async def run_streaming_query():
 for suggested_query in response.queries:
     print(suggested_query.query)
 # END SuggestQueries
+
+# START SuggestQueriesWithConversation
+from weaviate.agents.classes import ChatMessage
+
+# Build a conversation history
+conversation = [
+    ChatMessage(role="user", content="What are some popular machine learning frameworks?"),
+    ChatMessage(
+        role="assistant",
+        content="Some popular ML frameworks include TensorFlow, PyTorch, and JAX.",
+    ),
+]
+
+# Suggest follow-up queries based on the conversation context
+response = qa.suggest_queries(
+    conversation=conversation,
+    num_queries=3,
+)
+
+for suggested_query in response.queries:
+    print(suggested_query.query)
+# END SuggestQueriesWithConversation

docs/agents/query/usage.md

@@ -499,6 +499,31 @@ You can optionally specify:
     </TabItem>
 </Tabs>
 
+#### `Suggest Queries`: Follow-Up Queries
+
+You can pass a `conversation` to `Suggest Queries` to generate follow-up query suggestions based on the conversation history. This is useful for guiding users toward relevant next questions after an initial exchange.
+
+The `conversation` parameter accepts a list of `ChatMessage` objects, using the same format as [conversational queries](#conversational-queries).
+
+<Tabs className="code" groupId="languages">
+    <TabItem value="py_agents" label="Python">
+        <FilteredTextBlock
+            text={PyCode}
+            startMarker="# START SuggestQueriesWithConversation"
+            endMarker="# END SuggestQueriesWithConversation"
+            language="py"
+        />
+    </TabItem>
+    <TabItem value="ts_agents" label="JavaScript/TypeScript">
+        <FilteredTextBlock
+            text={TSCode}
+            startMarker="// START SuggestQueriesWithConversation"
+            endMarker="// END SuggestQueriesWithConversation"
+            language="ts"
+        />
+    </TabItem>
+</Tabs>
+
 ## Stream responses
 
 The Query Agent can also stream responses, allowing you to receive the answer as it is being generated.