botocore: add basic tracing for Bedrock InvokeModelWithStreamResponse (#3206)

* Add basic tracing for InvokeModelWithResponseStream * Add changelog and please pylint
2025-07-30 21:56:07 +08:00 · 2025-01-28 09:55:28 +01:00
parent 5478a0b77a
commit 93e6fcfa26
11 changed files with 720 additions and 9 deletions
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@ -47,6 +47,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
  ([#3200](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3200))
 - `opentelemetry-opentelemetry-botocore` Add basic support for GenAI attributes for AWS Bedrock ConverseStream API
  ([#3204](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3204))
+- `opentelemetry-opentelemetry-botocore` Add basic support for GenAI attributes for AWS Bedrock InvokeModelWithStreamResponse API
+  ([#3206](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3206))
 - `opentelemetry-instrumentation-pymssql` Add pymssql instrumentation
  ([#394](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/394))

--- a/instrumentation/opentelemetry-instrumentation-botocore/examples/bedrock-runtime/zero-code/README.rst
+++ b/instrumentation/opentelemetry-instrumentation-botocore/examples/bedrock-runtime/zero-code/README.rst
@ -20,6 +20,7 @@ Available examples
 - `converse.py` uses `bedrock-runtime` `Converse API <https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html>_`.
 - `converse_stream.py` uses `bedrock-runtime` `ConverseStream API <https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ConverseStream.html>_`.
 - `invoke_model.py` uses `bedrock-runtime` `InvokeModel API <https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html>_`.
+- `invoke_model_stream.py` uses `bedrock-runtime` `InvokeModelWithResponseStrea API <https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModelWithResponseStream.html>_`.

 Setup
 -----
--- a/instrumentation/opentelemetry-instrumentation-botocore/examples/bedrock-runtime/zero-code/invoke_model_stream.py
+++ b/instrumentation/opentelemetry-instrumentation-botocore/examples/bedrock-runtime/zero-code/invoke_model_stream.py
@ -0,0 +1,51 @@
+import json
+import os
+
+import boto3
+
+
+def main():
+    chat_model = os.getenv("CHAT_MODEL", "amazon.titan-text-lite-v1")
+    prompt = "Write a short poem on OpenTelemetry."
+    if "amazon.titan" in chat_model:
+        body = {
+            "inputText": prompt,
+            "textGenerationConfig": {},
+        }
+    elif "amazon.nova" in chat_model:
+        body = {
+            "messages": [{"role": "user", "content": [{"text": prompt}]}],
+            "schemaVersion": "messages-v1",
+        }
+    elif "anthropic.claude" in chat_model:
+        body = {
+            "messages": [
+                {"role": "user", "content": [{"text": prompt, "type": "text"}]}
+            ],
+            "anthropic_version": "bedrock-2023-05-31",
+            "max_tokens": 200,
+        }
+    else:
+        raise ValueError()
+    client = boto3.client("bedrock-runtime")
+    response = client.invoke_model_with_response_stream(
+        modelId=chat_model,
+        body=json.dumps(body),
+    )
+
+    answer = ""
+    for event in response["body"]:
+        json_bytes = event.get("chunk", {}).get("bytes", b"")
+        decoded = json_bytes.decode("utf-8")
+        chunk = json.loads(decoded)
+        if "outputText" in chunk:
+            answer += chunk["outputText"]
+        elif "completion" in chunk:
+            answer += chunk["completion"]
+        elif "contentBlockDelta" in chunk:
+            answer += chunk["contentBlockDelta"]["delta"]["text"]
+    print(answer)
+
+
+if __name__ == "__main__":
+    main()
--- a/instrumentation/opentelemetry-instrumentation-botocore/src/opentelemetry/instrumentation/botocore/extensions/bedrock.py
+++ b/instrumentation/opentelemetry-instrumentation-botocore/src/opentelemetry/instrumentation/botocore/extensions/bedrock.py
@ -28,6 +28,7 @@ from botocore.response import StreamingBody

 from opentelemetry.instrumentation.botocore.extensions.bedrock_utils import (
    ConverseStreamWrapper,
+    InvokeModelWithResponseStreamWrapper,
 )
 from opentelemetry.instrumentation.botocore.extensions.types import (
    _AttributeMapT,
@ -66,8 +67,16 @@ class _BedrockRuntimeExtension(_AwsSdkExtension):
    Amazon Bedrock Runtime</a>.
    """

-    _HANDLED_OPERATIONS = {"Converse", "ConverseStream", "InvokeModel"}
-    _DONT_CLOSE_SPAN_ON_END_OPERATIONS = {"ConverseStream"}
+    _HANDLED_OPERATIONS = {
+        "Converse",
+        "ConverseStream",
+        "InvokeModel",
+        "InvokeModelWithResponseStream",
+    }
+    _DONT_CLOSE_SPAN_ON_END_OPERATIONS = {
+        "ConverseStream",
+        "InvokeModelWithResponseStream",
+    }

    def should_end_span_on_exit(self):
        return (
@ -288,6 +297,20 @@ class _BedrockRuntimeExtension(_AwsSdkExtension):
        # InvokeModel
        if "body" in result and isinstance(result["body"], StreamingBody):
            self._invoke_model_on_success(span, result, model_id)
+            return
+
+        # InvokeModelWithResponseStream
+        if "body" in result and isinstance(result["body"], EventStream):
+
+            def invoke_model_stream_done_callback(response):
+                # the callback gets data formatted as the simpler converse API
+                self._converse_on_success(span, response)
+                span.end()
+
+            result["body"] = InvokeModelWithResponseStreamWrapper(
+                result["body"], invoke_model_stream_done_callback, model_id
+            )
+            return

    # pylint: disable=no-self-use
    def _handle_amazon_titan_response(
--- a/instrumentation/opentelemetry-instrumentation-botocore/src/opentelemetry/instrumentation/botocore/extensions/bedrock_utils.py
+++ b/instrumentation/opentelemetry-instrumentation-botocore/src/opentelemetry/instrumentation/botocore/extensions/bedrock_utils.py
@ -18,6 +18,8 @@

 from __future__ import annotations

+import json
+
 from botocore.eventstream import EventStream
 from wrapt import ObjectProxy

@ -46,20 +48,21 @@ class ConverseStreamWrapper(ObjectProxy):
    def _process_event(self, event):
        if "messageStart" in event:
            # {'messageStart': {'role': 'assistant'}}
-            pass
+            return

        if "contentBlockDelta" in event:
            # {'contentBlockDelta': {'delta': {'text': "Hello"}, 'contentBlockIndex': 0}}
-            pass
+            return

        if "contentBlockStop" in event:
            # {'contentBlockStop': {'contentBlockIndex': 0}}
-            pass
+            return

        if "messageStop" in event:
            # {'messageStop': {'stopReason': 'end_turn'}}
            if stop_reason := event["messageStop"].get("stopReason"):
                self._response["stopReason"] = stop_reason
+            return

        if "metadata" in event:
            # {'metadata': {'usage': {'inputTokens': 12, 'outputTokens': 15, 'totalTokens': 27}, 'metrics': {'latencyMs': 2980}}}
@ -72,3 +75,136 @@ class ConverseStreamWrapper(ObjectProxy):
                    self._response["usage"]["outputTokens"] = output_tokens

            self._stream_done_callback(self._response)
+            return
+
+
+# pylint: disable=abstract-method
+class InvokeModelWithResponseStreamWrapper(ObjectProxy):
+    """Wrapper for botocore.eventstream.EventStream"""
+
+    def __init__(
+        self,
+        stream: EventStream,
+        stream_done_callback,
+        model_id: str,
+    ):
+        super().__init__(stream)
+
+        self._stream_done_callback = stream_done_callback
+        self._model_id = model_id
+
+        # accumulating things in the same shape of the Converse API
+        # {"usage": {"inputTokens": 0, "outputTokens": 0}, "stopReason": "finish"}
+        self._response = {}
+
+    def __iter__(self):
+        for event in self.__wrapped__:
+            self._process_event(event)
+            yield event
+
+    def _process_event(self, event):
+        if "chunk" not in event:
+            return
+
+        json_bytes = event["chunk"].get("bytes", b"")
+        decoded = json_bytes.decode("utf-8")
+        try:
+            chunk = json.loads(decoded)
+        except json.JSONDecodeError:
+            return
+
+        if "amazon.titan" in self._model_id:
+            self._process_amazon_titan_chunk(chunk)
+        elif "amazon.nova" in self._model_id:
+            self._process_amazon_nova_chunk(chunk)
+        elif "anthropic.claude" in self._model_id:
+            self._process_anthropic_claude_chunk(chunk)
+
+    def _process_invocation_metrics(self, invocation_metrics):
+        self._response["usage"] = {}
+        if input_tokens := invocation_metrics.get("inputTokenCount"):
+            self._response["usage"]["inputTokens"] = input_tokens
+
+        if output_tokens := invocation_metrics.get("outputTokenCount"):
+            self._response["usage"]["outputTokens"] = output_tokens
+
+    def _process_amazon_titan_chunk(self, chunk):
+        if (stop_reason := chunk.get("completionReason")) is not None:
+            self._response["stopReason"] = stop_reason
+
+        if invocation_metrics := chunk.get("amazon-bedrock-invocationMetrics"):
+            # "amazon-bedrock-invocationMetrics":{
+            #     "inputTokenCount":9,"outputTokenCount":128,"invocationLatency":3569,"firstByteLatency":2180
+            # }
+            self._process_invocation_metrics(invocation_metrics)
+            self._stream_done_callback(self._response)
+
+    def _process_amazon_nova_chunk(self, chunk):
+        if "messageStart" in chunk:
+            # {'messageStart': {'role': 'assistant'}}
+            return
+
+        if "contentBlockDelta" in chunk:
+            # {'contentBlockDelta': {'delta': {'text': "Hello"}, 'contentBlockIndex': 0}}
+            return
+
+        if "contentBlockStop" in chunk:
+            # {'contentBlockStop': {'contentBlockIndex': 0}}
+            return
+
+        if "messageStop" in chunk:
+            # {'messageStop': {'stopReason': 'end_turn'}}
+            if stop_reason := chunk["messageStop"].get("stopReason"):
+                self._response["stopReason"] = stop_reason
+            return
+
+        if "metadata" in chunk:
+            # {'metadata': {'usage': {'inputTokens': 8, 'outputTokens': 117}, 'metrics': {}, 'trace': {}}}
+            if usage := chunk["metadata"].get("usage"):
+                self._response["usage"] = {}
+                if input_tokens := usage.get("inputTokens"):
+                    self._response["usage"]["inputTokens"] = input_tokens
+
+                if output_tokens := usage.get("outputTokens"):
+                    self._response["usage"]["outputTokens"] = output_tokens
+
+            self._stream_done_callback(self._response)
+            return
+
+    def _process_anthropic_claude_chunk(self, chunk):
+        # pylint: disable=too-many-return-statements
+        if not (message_type := chunk.get("type")):
+            return
+
+        if message_type == "message_start":
+            # {'type': 'message_start', 'message': {'id': 'id', 'type': 'message', 'role': 'assistant', 'model': 'claude-2.0', 'content': [], 'stop_reason': None, 'stop_sequence': None, 'usage': {'input_tokens': 18, 'output_tokens': 1}}}
+            return
+
+        if message_type == "content_block_start":
+            # {'type': 'content_block_start', 'index': 0, 'content_block': {'type': 'text', 'text': ''}}
+            return
+
+        if message_type == "content_block_delta":
+            # {'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': 'Here'}}
+            return
+
+        if message_type == "content_block_stop":
+            # {'type': 'content_block_stop', 'index': 0}
+            return
+
+        if message_type == "message_delta":
+            # {'type': 'message_delta', 'delta': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'usage': {'output_tokens': 123}}
+            if (
+                stop_reason := chunk.get("delta", {}).get("stop_reason")
+            ) is not None:
+                self._response["stopReason"] = stop_reason
+            return
+
+        if message_type == "message_stop":
+            # {'type': 'message_stop', 'amazon-bedrock-invocationMetrics': {'inputTokenCount': 18, 'outputTokenCount': 123, 'invocationLatency': 5250, 'firstByteLatency': 290}}
+            if invocation_metrics := chunk.get(
+                "amazon-bedrock-invocationMetrics"
+            ):
+                self._process_invocation_metrics(invocation_metrics)
+            self._stream_done_callback(self._response)
+            return
--- a/instrumentation/opentelemetry-instrumentation-botocore/tests/bedrock_utils.py
+++ b/instrumentation/opentelemetry-instrumentation-botocore/tests/bedrock_utils.py
@ -128,7 +128,7 @@ def assert_converse_completion_attributes(
    )


-def assert_converse_stream_completion_attributes(
+def assert_stream_completion_attributes(
    span: ReadableSpan,
    request_model: str,
    input_tokens: int | None = None,
--- a/instrumentation/opentelemetry-instrumentation-botocore/tests/cassettes/test_invoke_model_with_response_stream_invalid_model.yaml
+++ b/instrumentation/opentelemetry-instrumentation-botocore/tests/cassettes/test_invoke_model_with_response_stream_invalid_model.yaml
@ -0,0 +1,51 @@
+interactions:
+- request:
+    body: null
+    headers:
+      Content-Length:
+      - '0'
+      User-Agent:
+      - !!binary |
+        Qm90bzMvMS4zNS41NiBtZC9Cb3RvY29yZSMxLjM1LjU2IHVhLzIuMCBvcy9saW51eCM2LjEuMC0x
+        MDM0LW9lbSBtZC9hcmNoI3g4Nl82NCBsYW5nL3B5dGhvbiMzLjEwLjEyIG1kL3B5aW1wbCNDUHl0
+        aG9uIGNmZy9yZXRyeS1tb2RlI2xlZ2FjeSBCb3RvY29yZS8xLjM1LjU2
+      X-Amz-Date:
+      - !!binary |
+        MjAyNTAxMjRUMTM0NDM5Wg==
+      X-Amz-Security-Token:
+      - test_aws_security_token
+      X-Amzn-Trace-Id:
+      - !!binary |
+        Um9vdD0xLTFlMjljM2Y1LTU2MzZhOWI4MmViYTYxOTFiOTcwOTI2YTtQYXJlbnQ9NzA1NzBlZjUy
+        YzJkZjliYjtTYW1wbGVkPTE=
+      amz-sdk-invocation-id:
+      - !!binary |
+        ZDg2MjFlMzAtNTk3Yi00ZWM3LWJlNGEtMThkMDQwZTRhMzcw
+      amz-sdk-request:
+      - !!binary |
+        YXR0ZW1wdD0x
+      authorization:
+      - Bearer test_aws_authorization
+    method: POST
+    uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/does-not-exist/invoke-with-response-stream
+  response:
+    body:
+      string: '{"message":"The provided model identifier is invalid."}'
+    headers:
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '55'
+      Content-Type:
+      - application/json
+      Date:
+      - Fri, 24 Jan 2025 13:44:40 GMT
+      Set-Cookie: test_set_cookie
+      x-amzn-ErrorType:
+      - ValidationException:http://internal.amazon.com/coral/com.amazon.bedrock/
+      x-amzn-RequestId:
+      - 6460a108-875d-4e26-bcdf-f03c4c815f74
+    status:
+      code: 400
+      message: Bad Request
+version: 1
--- a/instrumentation/opentelemetry-instrumentation-botocore/tests/cassettes/test_invoke_model_with_response_stream_with_content[amazon.nova].yaml
+++ b/instrumentation/opentelemetry-instrumentation-botocore/tests/cassettes/test_invoke_model_with_response_stream_with_content[amazon.nova].yaml
@ -0,0 +1,144 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "user", "content": [{"text": "Say this is a test"}]}],
+      "inferenceConfig": {"max_new_tokens": 10, "temperature": 0.8, "topP": 1, "stopSequences":
+      ["|"]}, "schemaVersion": "messages-v1"}'
+    headers:
+      Content-Length:
+      - '207'
+      User-Agent:
+      - !!binary |
+        Qm90bzMvMS4zNS41NiBtZC9Cb3RvY29yZSMxLjM1LjU2IHVhLzIuMCBvcy9saW51eCM2LjEuMC0x
+        MDM0LW9lbSBtZC9hcmNoI3g4Nl82NCBsYW5nL3B5dGhvbiMzLjEwLjEyIG1kL3B5aW1wbCNDUHl0
+        aG9uIGNmZy9yZXRyeS1tb2RlI2xlZ2FjeSBCb3RvY29yZS8xLjM1LjU2
+      X-Amz-Date:
+      - !!binary |
+        MjAyNTAxMjRUMTM0NDM3Wg==
+      X-Amz-Security-Token:
+      - test_aws_security_token
+      X-Amzn-Trace-Id:
+      - !!binary |
+        Um9vdD0xLWE0YWY3ZjVkLTY5YmE5ZDNiNjg5YjM2OTRkYThmZDk5NDtQYXJlbnQ9OThiYjVhY2U3
+        MDE2YzZiZTtTYW1wbGVkPTE=
+      amz-sdk-invocation-id:
+      - !!binary |
+        MmZkNDA5NjQtYTBiNS00NzAwLTljYjUtNjI5MWQ2OWU3YTFm
+      amz-sdk-request:
+      - !!binary |
+        YXR0ZW1wdD0x
+      authorization:
+      - Bearer test_aws_authorization
+    method: POST
+    uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/amazon.nova-micro-v1%3A0/invoke-with-response-stream
+  response:
+    body:
+      string: !!binary |
+        AAAA0QAAAEswuRGYCzpldmVudC10eXBlBwAFY2h1bmsNOmNvbnRlbnQtdHlwZQcAEGFwcGxpY2F0
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+    headers:
+      Connection:
+      - keep-alive
+      Content-Type:
+      - application/vnd.amazon.eventstream
+      Date:
+      - Fri, 24 Jan 2025 13:44:37 GMT
+      Set-Cookie: test_set_cookie
+      Transfer-Encoding:
+      - chunked
+      X-Amzn-Bedrock-Content-Type:
+      - application/json
+      x-amzn-RequestId:
+      - 632c3a74-f6a4-43e5-b8ff-9c2f84daf7a6
+    status:
+      code: 200
+      message: OK
+version: 1
--- a/instrumentation/opentelemetry-instrumentation-botocore/tests/cassettes/test_invoke_model_with_response_stream_with_content[amazon.titan].yaml
+++ b/instrumentation/opentelemetry-instrumentation-botocore/tests/cassettes/test_invoke_model_with_response_stream_with_content[amazon.titan].yaml
@ -0,0 +1,61 @@
+interactions:
+- request:
+    body: '{"inputText": "Say this is a test", "textGenerationConfig": {"maxTokenCount":
+      10, "temperature": 0.8, "topP": 1, "stopSequences": ["|"]}}'
+    headers:
+      Content-Length:
+      - '137'
+      User-Agent:
+      - !!binary |
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+      X-Amz-Date:
+      - !!binary |
+        MjAyNTAxMjRUMTM0NDM3Wg==
+      X-Amz-Security-Token:
+      - test_aws_security_token
+      X-Amzn-Trace-Id:
+      - !!binary |
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+      amz-sdk-invocation-id:
+      - !!binary |
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+      amz-sdk-request:
+      - !!binary |
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+      authorization:
+      - Bearer test_aws_authorization
+    method: POST
+    uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/amazon.titan-text-lite-v1/invoke-with-response-stream
+  response:
+    body:
+      string: !!binary |
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+    headers:
+      Connection:
+      - keep-alive
+      Content-Type:
+      - application/vnd.amazon.eventstream
+      Date:
+      - Fri, 24 Jan 2025 13:44:38 GMT
+      Set-Cookie: test_set_cookie
+      Transfer-Encoding:
+      - chunked
+      X-Amzn-Bedrock-Content-Type:
+      - application/json
+      x-amzn-RequestId:
+      - 47aaeeb3-fa59-4aae-b480-01632279e2fa
+    status:
+      code: 200
+      message: OK
+version: 1
--- a/instrumentation/opentelemetry-instrumentation-botocore/tests/cassettes/test_invoke_model_with_response_stream_with_content[anthropic.claude].yaml
+++ b/instrumentation/opentelemetry-instrumentation-botocore/tests/cassettes/test_invoke_model_with_response_stream_with_content[anthropic.claude].yaml
@ -0,0 +1,124 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "user", "content": [{"text": "Say this is a test",
+      "type": "text"}]}], "anthropic_version": "bedrock-2023-05-31", "max_tokens":
+      10, "temperature": 0.8, "top_p": 1, "stop_sequences": ["|"]}'
+    headers:
+      Content-Length:
+      - '211'
+      User-Agent:
+      - !!binary |
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+      X-Amz-Date:
+      - !!binary |
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+      X-Amz-Security-Token:
+      - test_aws_security_token
+      X-Amzn-Trace-Id:
+      - !!binary |
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+      amz-sdk-invocation-id:
+      - !!binary |
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+      amz-sdk-request:
+      - !!binary |
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+      authorization:
+      - Bearer test_aws_authorization
+    method: POST
+    uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-v2/invoke-with-response-stream
+  response:
+    body:
+      string: !!binary |
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+        R1Y0SWpvd0xDSmtaV3gwWVNJNmV5SjBlWEJsSWpvaWRHVjRkRjlrWld4MFlTSXNJblJsZUhRaU9p
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+        T1BRUlNUVVZXIn2PKfsQAAAA+wAAAEu7yCY9CzpldmVudC10eXBlBwAFY2h1bmsNOmNvbnRlbnQt
+        dHlwZQcAEGFwcGxpY2F0aW9uL2pzb24NOm1lc3NhZ2UtdHlwZQcABWV2ZW50eyJieXRlcyI6ImV5
+        SjBlWEJsSWpvaVkyOXVkR1Z1ZEY5aWJHOWphMTlrWld4MFlTSXNJbWx1WkdWNElqb3dMQ0prWld4
+        MFlTSTZleUowZVhCbElqb2lkR1Y0ZEY5a1pXeDBZU0lzSW5SbGVIUWlPaUlnWVNKOWZRPT0iLCJw
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+        LXR5cGUHAAVjaHVuaw06Y29udGVudC10eXBlBwAQYXBwbGljYXRpb24vanNvbg06bWVzc2FnZS10
+        eXBlBwAFZXZlbnR7ImJ5dGVzIjoiZXlKMGVYQmxJam9pWTI5dWRHVnVkRjlpYkc5amExOWtaV3gw
+        WVNJc0ltbHVaR1Y0SWpvd0xDSmtaV3gwWVNJNmV5SjBlWEJsSWpvaWRHVjRkRjlrWld4MFlTSXNJ
+        blJsZUhRaU9pSWdkR1Z6ZENKOWZRPT0iLCJwIjoiYWJjZGVmZ2hpamtsbW5vcHFyc3R1dnd4eXpB
+        QkNERUZHSElKS0xNTiJ9xf99iwAAALwAAABLURuiZAs6ZXZlbnQtdHlwZQcABWNodW5rDTpjb250
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+        bnRlbnQtdHlwZQcAEGFwcGxpY2F0aW9uL2pzb24NOm1lc3NhZ2UtdHlwZQcABWV2ZW50eyJieXRl
+        cyI6ImV5SjBlWEJsSWpvaWJXVnpjMkZuWlY5emRHOXdJaXdpWVcxaGVtOXVMV0psWkhKdlkyc3Rh
+        VzUyYjJOaGRHbHZiazFsZEhKcFkzTWlPbnNpYVc1d2RYUlViMnRsYmtOdmRXNTBJam94TkN3aWIz
+        VjBjSFYwVkc5clpXNURiM1Z1ZENJNk1UQXNJbWx1ZG05allYUnBiMjVNWVhSbGJtTjVJam8xT1Rj
+        c0ltWnBjbk4wUW5sMFpVeGhkR1Z1WTNraU9qSTVNMzE5IiwicCI6ImFiY2RlZmdoaWprbG1ub3Bx
+        cnMifV7iQSc=
+    headers:
+      Connection:
+      - keep-alive
+      Content-Type:
+      - application/vnd.amazon.eventstream
+      Date:
+      - Fri, 24 Jan 2025 13:44:39 GMT
+      Set-Cookie: test_set_cookie
+      Transfer-Encoding:
+      - chunked
+      X-Amzn-Bedrock-Content-Type:
+      - application/json
+      x-amzn-RequestId:
+      - e52df188-e57f-43bb-a1bf-cfb42fd11fcd
+    status:
+      code: 200
+      message: OK
+version: 1
--- a/instrumentation/opentelemetry-instrumentation-botocore/tests/test_botocore_bedrock.py
+++ b/instrumentation/opentelemetry-instrumentation-botocore/tests/test_botocore_bedrock.py
@ -27,7 +27,7 @@ from opentelemetry.trace.status import StatusCode
 from .bedrock_utils import (
    assert_completion_attributes_from_streaming_body,
    assert_converse_completion_attributes,
-    assert_converse_stream_completion_attributes,
+    assert_stream_completion_attributes,
 )

 BOTO3_VERSION = tuple(int(x) for x in boto3.__version__.split("."))
@ -149,9 +149,12 @@ def test_converse_stream_with_content(
            output_tokens = usage["outputTokens"]

    assert text
+    assert finish_reason
+    assert input_tokens
+    assert output_tokens

    (span,) = span_exporter.get_finished_spans()
-    assert_converse_stream_completion_attributes(
+    assert_stream_completion_attributes(
        span,
        llm_model_value,
        input_tokens,
@ -188,7 +191,7 @@ def test_converse_stream_with_invalid_model(
        )

    (span,) = span_exporter.get_finished_spans()
-    assert_converse_stream_completion_attributes(
+    assert_stream_completion_attributes(
        span,
        llm_model_value,
        operation_name="chat",
@ -322,3 +325,118 @@ def test_invoke_model_with_invalid_model(

    logs = log_exporter.get_finished_logs()
    assert len(logs) == 0
+
+
+@pytest.mark.parametrize(
+    "model_family",
+    ["amazon.nova", "amazon.titan", "anthropic.claude"],
+)
+@pytest.mark.vcr()
+def test_invoke_model_with_response_stream_with_content(
+    span_exporter,
+    log_exporter,
+    bedrock_runtime_client,
+    instrument_with_content,
+    model_family,
+):
+    # pylint:disable=too-many-locals
+    llm_model_value = get_model_name_from_family(model_family)
+    max_tokens, temperature, top_p, stop_sequences = 10, 0.8, 1, ["|"]
+    body = get_invoke_model_body(
+        llm_model_value, max_tokens, temperature, top_p, stop_sequences
+    )
+    response = bedrock_runtime_client.invoke_model_with_response_stream(
+        body=body,
+        modelId=llm_model_value,
+    )
+
+    # consume the stream in order to have it traced
+    finish_reason = None
+    input_tokens, output_tokens = None, None
+    text = ""
+    for event in response["body"]:
+        json_bytes = event["chunk"].get("bytes", b"")
+        decoded = json_bytes.decode("utf-8")
+        chunk = json.loads(decoded)
+
+        # amazon.titan
+        if (stop_reason := chunk.get("completionReason")) is not None:
+            finish_reason = stop_reason
+
+        if (output_text := chunk.get("outputText")) is not None:
+            text += output_text
+
+        # amazon.titan, anthropic.claude
+        if invocation_metrics := chunk.get("amazon-bedrock-invocationMetrics"):
+            input_tokens = invocation_metrics["inputTokenCount"]
+            output_tokens = invocation_metrics["outputTokenCount"]
+
+        # anthropic.claude
+        if (message_type := chunk.get("type")) is not None:
+            if message_type == "content_block_start":
+                text += chunk["content_block"]["text"]
+            elif message_type == "content_block_delta":
+                text += chunk["delta"]["text"]
+            elif message_type == "message_delta":
+                finish_reason = chunk["delta"]["stop_reason"]
+
+        # amazon nova
+        if "contentBlockDelta" in chunk:
+            text += chunk["contentBlockDelta"]["delta"]["text"]
+        if "messageStop" in chunk:
+            finish_reason = chunk["messageStop"]["stopReason"]
+        if "metadata" in chunk:
+            usage = chunk["metadata"]["usage"]
+            input_tokens = usage["inputTokens"]
+            output_tokens = usage["outputTokens"]
+
+    assert text
+    assert finish_reason
+    assert input_tokens
+    assert output_tokens
+
+    (span,) = span_exporter.get_finished_spans()
+    assert_stream_completion_attributes(
+        span,
+        llm_model_value,
+        input_tokens,
+        output_tokens,
+        (finish_reason,),
+        "text_completion" if model_family == "amazon.titan" else "chat",
+        top_p,
+        temperature,
+        max_tokens,
+        stop_sequences,
+    )
+
+    logs = log_exporter.get_finished_logs()
+    assert len(logs) == 0
+
+
+@pytest.mark.vcr()
+def test_invoke_model_with_response_stream_invalid_model(
+    span_exporter,
+    log_exporter,
+    bedrock_runtime_client,
+    instrument_with_content,
+):
+    llm_model_value = "does-not-exist"
+    with pytest.raises(bedrock_runtime_client.exceptions.ClientError):
+        bedrock_runtime_client.invoke_model_with_response_stream(
+            body=b"",
+            modelId=llm_model_value,
+        )
+
+    (span,) = span_exporter.get_finished_spans()
+    assert_completion_attributes_from_streaming_body(
+        span,
+        llm_model_value,
+        None,
+        "chat",
+    )
+
+    assert span.status.status_code == StatusCode.ERROR
+    assert span.attributes[ERROR_TYPE] == "ValidationException"
+
+    logs = log_exporter.get_finished_logs()
+    assert len(logs) == 0