Generate JSON output via tool call

lib/answer_composition/pipeline/bedrock_structured_answer_composer.rb

@@ -11,10 +11,16 @@ def initialize(context)
     def call
       start_time = Clock.monotonic_time
 
-      response = bedrock_client.converse(system: [{ text: system_prompt }], model_id: BEDROCK_MODEL, messages:, inference_config:)
+      response = bedrock_client.converse(
+        system: [{ text: system_prompt }],
+        model_id: BEDROCK_MODEL,
+        messages:,
+        inference_config:,
+        tool_config:,
+      )
 
       context.answer.assign_llm_response("structured_answer", response.to_h)
-      message = response["output"]["message"]["content"][0]["text"]
+      message = response["output"]["message"]["content"][0]["tool_use"]["input"]["answer"]
       context.answer.assign_attributes(message:, status: "answered")
       context.answer.assign_metrics("structured_answer", build_metrics(start_time, response))
     end
@@ -57,5 +63,37 @@ def build_metrics(start_time, response)
         llm_completion_tokens: response.dig("usage", "output_tokens"),
       }
     end
+
+    def tool_config
+      {
+        tools: tools,
+        tool_choice: {
+          tool: {
+            name: "answer_confidence",
+          },
+        },
+      }
+    end
+
+    def tools
+      [
+        {
+          tool_spec: {
+            name: "answer_confidence",
+            description: "Prints the answer of a given question with a confidence score.",
+            input_schema: {
+              json: {
+                type: "object",
+                properties: {
+                  answer: { description: "Your answer to the question in markdown format", title: "Answer", type: "string" },
+                  confidence: { description: "Your confidence in the answer provided, ranging from 0.0 to 1.0", title: "Answered", type: "number" },
+                },
+                required: %w[answer confidence],
+              },
+            },
+          },
+        },
+      ]
+    end
   end
 end