botocore: don't crash when sending content as string with InvokeModel (…

…#3342)

As apparently langchain does with Claude.

instrumentation/opentelemetry-instrumentation-botocore/src/opentelemetry/instrumentation/botocore/extensions/bedrock_utils.py

@@ -386,6 +386,10 @@ def extract_tool_results(
     if not content:
         return
 
+    # langchain sends content as string with InvokeModel and Anthropic Claude
+    if isinstance(content, str):
+        return
+
     # Converse format
     tool_results = [
         item["toolResult"] for item in content if "toolResult" in item

instrumentation/opentelemetry-instrumentation-botocore/tests/cassettes/test_invoke_model_with_content_user_content_as_string.yaml

@@ -0,0 +1,74 @@
+interactions:
+- request:
+    body: |-
+      {
+        "messages": [
+          {
+            "role": "user",
+            "content": "say this is a test"
+          }
+        ],
+        "max_tokens": 10,
+        "anthropic_version": "bedrock-2023-05-31"
+      }
+    headers:
+      Content-Length:
+      - '126'
+      User-Agent:
+      - Boto3/1.35.56 md/Botocore#1.35.56 ua/2.0 os/linux#6.1.0-1034-oem md/arch#x86_64
+        lang/python#3.10.12 md/pyimpl#CPython cfg/retry-mode#legacy Botocore/1.35.56
+      X-Amz-Date:
+      - 20250306T091535Z
+      X-Amz-Security-Token:
+      - test_aws_security_token
+      X-Amzn-Trace-Id:
+      - Root=1-fcd0825a-03f328cca8cde3e741cd83b4;Parent=5a8eccf8d7b031e7;Sampled=1
+      amz-sdk-invocation-id:
+      - e8a6c9c5-8a45-4ad7-881b-0761d121abc7
+      amz-sdk-request:
+      - attempt=1
+      authorization:
+      - Bearer test_aws_authorization
+    method: POST
+    uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/us.anthropic.claude-3-5-sonnet-20240620-v1%3A0/invoke
+  response:
+    body:
+      string: |-
+        {
+          "id": "msg_bdrk_01SjMAZgb8kNMweUzf3moSbU",
+          "type": "message",
+          "role": "assistant",
+          "model": "claude-3-5-sonnet-20240620",
+          "content": [
+            {
+              "type": "text",
+              "text": "This is a test."
+            }
+          ],
+          "stop_reason": "end_turn",
+          "stop_sequence": null,
+          "usage": {
+            "input_tokens": 12,
+            "output_tokens": 8
+          }
+        }
+    headers:
+      Connection:
+      - keep-alive
+      Content-Type:
+      - application/json
+      Date:
+      - Thu, 06 Mar 2025 09:15:36 GMT
+      Set-Cookie: test_set_cookie
+      X-Amzn-Bedrock-Input-Token-Count:
+      - '12'
+      X-Amzn-Bedrock-Invocation-Latency:
+      - '544'
+      X-Amzn-Bedrock-Output-Token-Count:
+      - '8'
+      x-amzn-RequestId:
+      - e97cb4f9-61fe-4a62-a29a-a582ddd17414
+    status:
+      code: 200
+      message: OK
+version: 1

instrumentation/opentelemetry-instrumentation-botocore/tests/test_botocore_bedrock.py

@@ -1256,6 +1256,53 @@ def test_invoke_model_with_content(
     assert_message_in_logs(logs[1], "gen_ai.choice", choice_body, span)
 
 
+@pytest.mark.vcr()
+def test_invoke_model_with_content_user_content_as_string(
+    span_exporter,
+    log_exporter,
+    bedrock_runtime_client,
+    instrument_with_content,
+):
+    llm_model_value = "us.anthropic.claude-3-5-sonnet-20240620-v1:0"
+    max_tokens = 10
+    body = json.dumps(
+        {
+            "messages": [{"role": "user", "content": "say this is a test"}],
+            "max_tokens": max_tokens,
+            "anthropic_version": "bedrock-2023-05-31",
+        }
+    )
+    response = bedrock_runtime_client.invoke_model(
+        body=body,
+        modelId=llm_model_value,
+    )
+
+    (span,) = span_exporter.get_finished_spans()
+    assert_completion_attributes_from_streaming_body(
+        span,
+        llm_model_value,
+        response,
+        "chat",
+        request_max_tokens=max_tokens,
+    )
+
+    logs = log_exporter.get_finished_logs()
+    assert len(logs) == 2
+    user_content = {"content": "say this is a test"}
+    assert_message_in_logs(logs[0], "gen_ai.user.message", user_content, span)
+
+    message = {
+        "role": "assistant",
+        "content": [{"type": "text", "text": "This is a test."}],
+    }
+    choice_body = {
+        "index": 0,
+        "finish_reason": "end_turn",
+        "message": message,
+    }
+    assert_message_in_logs(logs[1], "gen_ai.choice", choice_body, span)
+
+
 @pytest.mark.parametrize(
     "model_family",
     ["amazon.nova", "anthropic.claude"],