added response structure

Author: aninibread

src/content/docs/autorag/use-autorag/rest-api.mdx

@@ -61,6 +61,12 @@ You can get your `ACCOUNT_ID` by navigating to Workers & Pages on the Dashboard,
 
 <Render file="ai-search-api-params" product="autorag" />
 
+### Response
+
+This is the response structure without `stream` enabled.
+
+<Render file="ai-search-response" product="autorag" />
+
 ## Search
 
 This REST API searches for results from your data source and returns the relevant results:
@@ -90,3 +96,7 @@ You can get your `ACCOUNT_ID` by navigating to Workers & Pages on the Dashboard,
 ### Parameters
 
 <Render file="search-api-params" product="autorag" />
+
+### Response
+
+<Render file="search-response" product="autorag" />

src/content/docs/autorag/use-autorag/workers-binding.mdx

@@ -42,14 +42,20 @@ const answer = await env.AI.AutoRAG("my-autorag").aiSearch({
 	ranking_options: {
 		score_threshold: 0.7,
 	},
-	stream: true,
+	stream: false,
 });
 ```
 
 ### Parameters
 
 <Render file="ai-search-api-params" product="autorag" />
 
+### Response
+
+This is the response structure without `stream` enabled.
+
+<Render file="ai-search-response" product="autorag" />
+
 ## `search()`
 
 This method searches for results from your corpus and returns the relevant results, for the AutoRAG instance named `my-autorag`:
@@ -69,6 +75,10 @@ const answer = await env.AI.AutoRAG("my-autorag").search({
 
 <Render file="search-api-params" product="autorag" />
 
+### Response
+
+<Render file="search-response" product="autorag" />
+
 ## Local development
 
 Local development is supported by proxying requests to your deployed AutoRAG instance. When running in local mode, your application forwards queries to the configured remote AutoRAG instance and returns the generated responses as if they were served locally.

src/content/partials/autorag/ai-search-response.mdx

@@ -0,0 +1,40 @@
+---
+{}
+---
+
+```json
+{
+	"success": true,
+	"result": {
+		"object": "vector_store.search_results.page",
+		"search_query": "How do I train a llama to deliver coffee?",
+		"response": "To train a llama to deliver coffee:\n\n1. **Build trust** — Llamas appreciate patience (and decaf).\n2. **Know limits** — Max 3 cups per llama, per `llama-logistics.md`.\n3. **Use voice commands** — Start with \"Espresso Express!\"\n4. **Set a route** — Carrot GPS markers help.\n5. **Track metrics** — Monitor spills, detours, and snack breaks via telemetry.\n\nNote: This is a demo. Please don’t caffeinate your llama.",
+		"data": [
+			{
+				"file_id": "llama001",
+				"filename": "docs/llama-logistics.md",
+				"score": 0.98,
+				"content": [
+					{
+						"type": "text",
+						"text": "Llamas can carry 3 drinks max. Avoid iced coffee — they hate condensation."
+					}
+				]
+			},
+			{
+				"file_id": "llama042",
+				"filename": "docs/llama-commands.md",
+				"score": 0.95,
+				"content": [
+					{
+						"type": "text",
+						"text": "Start with basic commands like 'Espresso Express!' Llamas love alliteration."
+					}
+				]
+			}
+		],
+		"has_more": false,
+		"next_page": null
+	}
+}
+```

src/content/partials/autorag/search-response.mdx

@@ -0,0 +1,39 @@
+---
+{}
+---
+
+```json
+{
+	"success": true,
+	"result": {
+		"object": "vector_store.search_results.page",
+		"search_query": "How do I train a llama to deliver coffee?",
+		"data": [
+			{
+				"file_id": "llama001",
+				"filename": "docs/llama-logistics.md",
+				"score": 0.98,
+				"content": [
+					{
+						"type": "text",
+						"text": "Llamas can carry 3 drinks max. Avoid iced coffee — they hate condensation."
+					}
+				]
+			},
+			{
+				"file_id": "llama042",
+				"filename": "docs/llama-commands.md",
+				"score": 0.95,
+				"content": [
+					{
+						"type": "text",
+						"text": "Start with basic commands like 'Espresso Express!' Llamas love alliteration."
+					}
+				]
+			}
+		],
+		"has_more": false,
+		"next_page": null
+	}
+}
+```