botocore: handle system messages events (#3266)
* botocore: handle system event messages * Add CHANGELOG * Please pylint
This commit is contained in:
Riccardo Magliocchetti
GitHub
parent
1623dc0e71
commit
789bf866e3
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@ -15,6 +15,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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- `opentelemetry-instrumentation-botocore` Add support for GenAI user events and lazy initialize tracer
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- `opentelemetry-instrumentation-botocore` Add support for GenAI user events and lazy initialize tracer
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([#3258](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3258))
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([#3258](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3258))
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- `opentelemetry-instrumentation-botocore` Add support for GenAI system events
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([#3266](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3266))
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### Fixed
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### Fixed
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@ -209,10 +209,23 @@ class _BedrockRuntimeExtension(_AwsSdkExtension):
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attributes[key] = value
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attributes[key] = value
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def _get_request_messages(self):
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def _get_request_messages(self):
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"""Extracts and normalize system and user / assistant messages"""
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input_text = None
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input_text = None
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if system := self._call_context.params.get("system", []):
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system_messages = [{"role": "system", "content": system}]
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else:
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system_messages = []
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if not (messages := self._call_context.params.get("messages", [])):
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if not (messages := self._call_context.params.get("messages", [])):
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if body := self._call_context.params.get("body"):
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if body := self._call_context.params.get("body"):
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decoded_body = json.loads(body)
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decoded_body = json.loads(body)
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if system := decoded_body.get("system"):
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if isinstance(system, str):
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content = [{"text": system}]
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else:
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content = system
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system_messages = [{"role": "system", "content": content}]
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messages = decoded_body.get("messages", [])
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messages = decoded_body.get("messages", [])
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if not messages:
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if not messages:
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# transform old school amazon titan invokeModel api to messages
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# transform old school amazon titan invokeModel api to messages
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@ -221,7 +234,7 @@ class _BedrockRuntimeExtension(_AwsSdkExtension):
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{"role": "user", "content": [{"text": input_text}]}
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{"role": "user", "content": [{"text": input_text}]}
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]
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]
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return messages
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return system_messages + messages
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def before_service_call(
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def before_service_call(
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self, span: Span, instrumentor_context: _BotocoreInstrumentorContext
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self, span: Span, instrumentor_context: _BotocoreInstrumentorContext
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@ -161,7 +161,9 @@ def assert_stream_completion_attributes(
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def assert_equal_or_not_present(value, attribute_name, span):
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def assert_equal_or_not_present(value, attribute_name, span):
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if value is not None:
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if value is not None:
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assert value == span.attributes[attribute_name]
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assert value == span.attributes[attribute_name], span.attributes[
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attribute_name
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]
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else:
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else:
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assert attribute_name not in span.attributes, attribute_name
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assert attribute_name not in span.attributes, attribute_name
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@ -0,0 +1,96 @@
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interactions:
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- request:
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body: |-
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{
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"system": [
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{
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"text": "You are a friendly app"
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}
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],
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"text": "Say this is a test"
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}
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]
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},
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{
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"role": "assistant",
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"content": [
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{
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"text": "This is a test"
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}
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]
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},
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{
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"role": "user",
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"content": [
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{
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"text": "Say again this is a test"
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}
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]
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}
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]
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}
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headers:
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Content-Length:
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- '258'
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Content-Type:
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- application/json
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User-Agent:
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- Boto3/1.35.56 md/Botocore#1.35.56 ua/2.0 os/linux#6.1.0-1034-oem md/arch#x86_64
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lang/python#3.10.12 md/pyimpl#CPython cfg/retry-mode#legacy Botocore/1.35.56
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X-Amz-Date:
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- 20250213T113027Z
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X-Amz-Security-Token:
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- test_aws_security_token
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X-Amzn-Trace-Id:
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- Root=1-d9112d64-56325c43023ba04895b18fc1;Parent=d56bf41aee9dd9b2;Sampled=1
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amz-sdk-invocation-id:
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- 4e1fd77b-1b21-4b3a-9b96-d74fb74ede7c
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amz-sdk-request:
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- attempt=1
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authorization:
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- Bearer test_aws_authorization
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method: POST
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uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-v2/converse
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response:
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body:
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string: |-
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{
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"metrics": {
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"latencyMs": 489
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},
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"output": {
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"message": {
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"content": [
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{
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"text": "This is a test"
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}
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],
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"role": "assistant"
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}
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},
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"stopReason": "end_turn",
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"usage": {
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"inputTokens": 37,
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"outputTokens": 8,
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"totalTokens": 45
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}
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}
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headers:
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Connection:
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- keep-alive
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Content-Type:
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- application/json
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Date:
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- Thu, 13 Feb 2025 11:30:28 GMT
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Set-Cookie: test_set_cookie
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x-amzn-RequestId:
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- 67f0b4ef-e481-4c12-853b-d8e992395129
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status:
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code: 200
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message: OK
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version: 1
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@ -0,0 +1,102 @@
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interactions:
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- request:
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body: |-
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{
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"system": [
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{
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"text": "You are a friendly app"
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}
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],
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"text": "Say this is a test"
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}
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]
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},
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{
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"role": "assistant",
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"content": [
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{
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"text": "This is a test"
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}
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]
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},
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{
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"role": "user",
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"content": [
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{
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"text": "Say again this is a test"
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}
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]
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}
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]
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}
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headers:
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Content-Length:
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- '258'
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Content-Type:
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- application/json
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User-Agent:
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- Boto3/1.35.56 md/Botocore#1.35.56 ua/2.0 os/linux#6.1.0-1034-oem md/arch#x86_64
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lang/python#3.10.12 md/pyimpl#CPython cfg/retry-mode#legacy Botocore/1.35.56
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|
X-Amz-Date:
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|
- 20250213T134717Z
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|
X-Amz-Security-Token:
|
||||||
|
- test_aws_security_token
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|
X-Amzn-Trace-Id:
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|
- Root=1-014325aa-ed2dea786b6b5b895661a1fd;Parent=540790d110580421;Sampled=1
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|
amz-sdk-invocation-id:
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|
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|
amz-sdk-request:
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- attempt=1
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authorization:
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- Bearer test_aws_authorization
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method: POST
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uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-v2/converse-stream
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response:
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body:
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headers:
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||||||
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Connection:
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|
- keep-alive
|
||||||
|
Content-Type:
|
||||||
|
- application/vnd.amazon.eventstream
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|
Date:
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|
- Thu, 13 Feb 2025 13:47:17 GMT
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||||||
|
Set-Cookie: test_set_cookie
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||||||
|
Transfer-Encoding:
|
||||||
|
- chunked
|
||||||
|
x-amzn-RequestId:
|
||||||
|
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||||||
|
status:
|
||||||
|
code: 200
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||||||
|
message: OK
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||||||
|
version: 1
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||||||
|
|
@ -0,0 +1,105 @@
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interactions:
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- request:
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body: |-
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{
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"system": [
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{
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"text": "You are a friendly app"
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}
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],
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"text": "Say this is a test"
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}
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]
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},
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{
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"role": "assistant",
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"content": [
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{
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"text": "This is a test"
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}
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]
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},
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{
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"role": "user",
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"content": [
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{
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"text": "Say again this is a test"
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}
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]
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}
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||||||
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]
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||||||
|
}
|
||||||
|
headers:
|
||||||
|
Content-Length:
|
||||||
|
- '258'
|
||||||
|
Content-Type:
|
||||||
|
- application/json
|
||||||
|
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
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||||||
|
lang/python#3.10.12 md/pyimpl#CPython cfg/retry-mode#legacy Botocore/1.35.56
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||||||
|
X-Amz-Date:
|
||||||
|
- 20250213T134611Z
|
||||||
|
X-Amz-Security-Token:
|
||||||
|
- test_aws_security_token
|
||||||
|
X-Amzn-Trace-Id:
|
||||||
|
- Root=1-deb34c78-8ab87e14665141b9e132396b;Parent=8e997adc8051f5a6;Sampled=1
|
||||||
|
amz-sdk-invocation-id:
|
||||||
|
- 81e436d8-7c5d-4025-a9e5-de505ccb9b51
|
||||||
|
amz-sdk-request:
|
||||||
|
- attempt=1
|
||||||
|
authorization:
|
||||||
|
- Bearer test_aws_authorization
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||||||
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method: POST
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||||||
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uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-v2/converse-stream
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||||||
|
response:
|
||||||
|
body:
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|
string: !!binary |
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headers:
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||||||
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||||||
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- keep-alive
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||||||
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Content-Type:
|
||||||
|
- application/vnd.amazon.eventstream
|
||||||
|
Date:
|
||||||
|
- Thu, 13 Feb 2025 14:09:43 GMT
|
||||||
|
Set-Cookie: test_set_cookie
|
||||||
|
Transfer-Encoding:
|
||||||
|
- chunked
|
||||||
|
X-Amzn-Bedrock-Content-Type:
|
||||||
|
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|
||||||
|
x-amzn-RequestId:
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||||||
|
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||||||
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status:
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||||||
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code: 200
|
||||||
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message: OK
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||||||
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||||||
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|
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|
||||||
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interactions:
|
||||||
|
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|
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body: |-
|
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"inputText": "Say this is a test",
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"textGenerationConfig": {
|
||||||
|
"maxTokenCount": 10,
|
||||||
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"temperature": 0.8,
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"topP": 1,
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"stopSequences": [
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||||||
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"|"
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}
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headers:
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|
- '137'
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||||||
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|
||||||
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||||||
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||||||
|
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|
||||||
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X-Amz-Security-Token:
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||||||
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authorization:
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||||||
|
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|
||||||
|
method: POST
|
||||||
|
uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/amazon.titan-text-lite-v1/invoke-with-response-stream
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response:
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string: !!binary |
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Date:
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||||||
|
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- chunked
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"messages": [
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"content": [
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{
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"type": "text"
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}
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||||||
|
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|
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|
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|
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headers:
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|
- keep-alive
|
||||||
|
Content-Type:
|
||||||
|
- application/vnd.amazon.eventstream
|
||||||
|
Date:
|
||||||
|
- Thu, 13 Feb 2025 14:11:43 GMT
|
||||||
|
Set-Cookie: test_set_cookie
|
||||||
|
Transfer-Encoding:
|
||||||
|
- chunked
|
||||||
|
X-Amzn-Bedrock-Content-Type:
|
||||||
|
- application/json
|
||||||
|
x-amzn-RequestId:
|
||||||
|
- 406cb91b-9ad8-4309-baf1-769a289b7df1
|
||||||
|
status:
|
||||||
|
code: 200
|
||||||
|
message: OK
|
||||||
|
version: 1
|
||||||
|
|
@ -0,0 +1,124 @@
|
||||||
|
interactions:
|
||||||
|
- request:
|
||||||
|
body: |-
|
||||||
|
{
|
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"messages": [
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|
{
|
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|
"role": "user",
|
||||||
|
"content": [
|
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|
{
|
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|
"text": "Say this is a test",
|
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|
"type": "text"
|
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|
}
|
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|
]
|
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|
},
|
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|
{
|
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|
"role": "assistant",
|
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|
"content": [
|
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|
{
|
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|
"text": "This is a test",
|
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|
"type": "text"
|
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|
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|
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|
},
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|
{
|
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|
"role": "user",
|
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|
"content": [
|
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|
{
|
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|
"text": "Say again this is a test",
|
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|
"type": "text"
|
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|
}
|
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|
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|
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|
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|
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|
"system": "You are a friendly model",
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|
"max_tokens": 10
|
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|
}
|
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|
headers:
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|
||||||
|
lang/python#3.10.12 md/pyimpl#CPython cfg/retry-mode#legacy Botocore/1.35.56
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||||||
|
X-Amz-Date:
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|
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|
||||||
|
X-Amz-Security-Token:
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||||||
|
- test_aws_security_token
|
||||||
|
X-Amzn-Trace-Id:
|
||||||
|
- Root=1-a7421915-90c1cf280ff1d9482f772c88;Parent=9b34e4bcd8b69db4;Sampled=1
|
||||||
|
amz-sdk-invocation-id:
|
||||||
|
- f8aca0ab-34a1-4185-9551-b5d28c4e0be9
|
||||||
|
amz-sdk-request:
|
||||||
|
- attempt=1
|
||||||
|
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 |
|
||||||
|
AAABtwAAAEvtl0DQCzpldmVudC10eXBlBwAFY2h1bmsNOmNvbnRlbnQtdHlwZQcAEGFwcGxpY2F0
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||||||
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aW9uL2pzb24NOm1lc3NhZ2UtdHlwZQcABWV2ZW50eyJieXRlcyI6ImV5SjBlWEJsSWpvaWJXVnpj
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||||||
|
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||||||
|
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||||||
|
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||||||
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||||||
|
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||||||
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||||||
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||||||
|
a2xtbm9wcXJzdHV2d3h5ekFCQ0RFRkdISUpLTE0iffHQpi0AAAEaAAAAS2UgG/cLOmV2ZW50LXR5
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||||||
|
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|
||||||
|
BwAFZXZlbnR7ImJ5dGVzIjoiZXlKMGVYQmxJam9pWTI5dWRHVnVkRjlpYkc5amExOWtaV3gwWVNJ
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||||||
|
c0ltbHVaR1Y0SWpvd0xDSmtaV3gwWVNJNmV5SjBlWEJsSWpvaWRHVjRkRjlrWld4MFlTSXNJblJs
|
||||||
|
ZUhRaU9pSWdhWE1pZlgwPSIsInAiOiJhYmNkZWZnaGlqa2xtbm9wcXJzdHV2d3h5ekFCQ0RFRkdI
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||||||
|
SUpLTE1OT1BRUlNUVVZXWFlaMDEyMzQ1NjcifYA9TRcAAAEGAAAAS8AwYXQLOmV2ZW50LXR5cGUH
|
||||||
|
AAVjaHVuaw06Y29udGVudC10eXBlBwAQYXBwbGljYXRpb24vanNvbg06bWVzc2FnZS10eXBlBwAF
|
||||||
|
ZXZlbnR7ImJ5dGVzIjoiZXlKMGVYQmxJam9pWTI5dWRHVnVkRjlpYkc5amExOWtaV3gwWVNJc0lt
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||||||
|
bHVaR1Y0SWpvd0xDSmtaV3gwWVNJNmV5SjBlWEJsSWpvaWRHVjRkRjlrWld4MFlTSXNJblJsZUhR
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||||||
|
aU9pSWdZU0o5ZlE9PSIsInAiOiJhYmNkZWZnaGlqa2xtbm9wcXJzdHV2d3h5ekFCQ0RFRkdISUpL
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||||||
|
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||||||
|
EGFwcGxpY2F0aW9uL2pzb24NOm1lc3NhZ2UtdHlwZQcABWV2ZW50eyJieXRlcyI6ImV5SjBlWEJs
|
||||||
|
SWpvaVkyOXVkR1Z1ZEY5aWJHOWphMTlrWld4MFlTSXNJbWx1WkdWNElqb3dMQ0prWld4MFlTSTZl
|
||||||
|
eUowZVhCbElqb2lkR1Y0ZEY5a1pXeDBZU0lzSW5SbGVIUWlPaUlnZEdWemRDSjlmUT09IiwicCI6
|
||||||
|
ImFiY2RlZmdoaWprbG1ub3BxcnN0dXZ3eHl6QUJDREVGR0hJSktMTU5PUFEifUsoqQQAAADHAAAA
|
||||||
|
S98Zc7oLOmV2ZW50LXR5cGUHAAVjaHVuaw06Y29udGVudC10eXBlBwAQYXBwbGljYXRpb24vanNv
|
||||||
|
bg06bWVzc2FnZS10eXBlBwAFZXZlbnR7ImJ5dGVzIjoiZXlKMGVYQmxJam9pWTI5dWRHVnVkRjlp
|
||||||
|
Ykc5amExOXpkRzl3SWl3aWFXNWtaWGdpT2pCOSIsInAiOiJhYmNkZWZnaGlqa2xtbm9wcXJzdHV2
|
||||||
|
d3h5ekFCQ0RFRkdISUpLIn289g6LAAABLgAAAEsxgQWxCzpldmVudC10eXBlBwAFY2h1bmsNOmNv
|
||||||
|
bnRlbnQtdHlwZQcAEGFwcGxpY2F0aW9uL2pzb24NOm1lc3NhZ2UtdHlwZQcABWV2ZW50eyJieXRl
|
||||||
|
cyI6ImV5SjBlWEJsSWpvaWJXVnpjMkZuWlY5a1pXeDBZU0lzSW1SbGJIUmhJanA3SW5OMGIzQmZj
|
||||||
|
bVZoYzI5dUlqb2laVzVrWDNSMWNtNGlMQ0p6ZEc5d1gzTmxjWFZsYm1ObElqcHVkV3hzZlN3aWRY
|
||||||
|
TmhaMlVpT25zaWIzVjBjSFYwWDNSdmEyVnVjeUk2T0gxOSIsInAiOiJhYmNkZWZnaGlqa2xtbm9w
|
||||||
|
cXJzdHV2d3h5ekFCQ0RFRkdISUpLTE1OT1BRUlNUVVYifezNQVkAAAFWAAAAS/gjrr8LOmV2ZW50
|
||||||
|
LXR5cGUHAAVjaHVuaw06Y29udGVudC10eXBlBwAQYXBwbGljYXRpb24vanNvbg06bWVzc2FnZS10
|
||||||
|
eXBlBwAFZXZlbnR7ImJ5dGVzIjoiZXlKMGVYQmxJam9pYldWemMyRm5aVjl6ZEc5d0lpd2lZVzFo
|
||||||
|
ZW05dUxXSmxaSEp2WTJzdGFXNTJiMk5oZEdsdmJrMWxkSEpwWTNNaU9uc2lhVzV3ZFhSVWIydGxi
|
||||||
|
a052ZFc1MElqb3pOeXdpYjNWMGNIVjBWRzlyWlc1RGIzVnVkQ0k2T0N3aWFXNTJiMk5oZEdsdmJr
|
||||||
|
eGhkR1Z1WTNraU9qUXpNaXdpWm1seWMzUkNlWFJsVEdGMFpXNWplU0k2TXpVNGZYMD0iLCJwIjoi
|
||||||
|
YWJjZGVmZ2hpamtsbW5vcHFyc3R1dnd4eXpBQkNERUYifYwQjVc=
|
||||||
|
headers:
|
||||||
|
Connection:
|
||||||
|
- keep-alive
|
||||||
|
Content-Type:
|
||||||
|
- application/vnd.amazon.eventstream
|
||||||
|
Date:
|
||||||
|
- Thu, 13 Feb 2025 14:11:44 GMT
|
||||||
|
Set-Cookie: test_set_cookie
|
||||||
|
Transfer-Encoding:
|
||||||
|
- chunked
|
||||||
|
X-Amzn-Bedrock-Content-Type:
|
||||||
|
- application/json
|
||||||
|
x-amzn-RequestId:
|
||||||
|
- 2423a140-3984-4d26-8741-1484ad05d541
|
||||||
|
status:
|
||||||
|
code: 200
|
||||||
|
message: OK
|
||||||
|
version: 1
|
||||||
|
|
@ -12,6 +12,8 @@
|
||||||
# See the License for the specific language governing permissions and
|
# See the License for the specific language governing permissions and
|
||||||
# limitations under the License.
|
# limitations under the License.
|
||||||
|
|
||||||
|
# pylint:disable=too-many-lines
|
||||||
|
|
||||||
from __future__ import annotations
|
from __future__ import annotations
|
||||||
|
|
||||||
import json
|
import json
|
||||||
|
|
@ -83,6 +85,87 @@ def test_converse_with_content(
|
||||||
assert_message_in_logs(logs[0], "gen_ai.user.message", user_content, span)
|
assert_message_in_logs(logs[0], "gen_ai.user.message", user_content, span)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.skipif(
|
||||||
|
BOTO3_VERSION < (1, 35, 56), reason="Converse API not available"
|
||||||
|
)
|
||||||
|
@pytest.mark.vcr()
|
||||||
|
def test_converse_with_content_different_events(
|
||||||
|
span_exporter,
|
||||||
|
log_exporter,
|
||||||
|
bedrock_runtime_client,
|
||||||
|
instrument_with_content,
|
||||||
|
):
|
||||||
|
# pylint:disable=too-many-locals
|
||||||
|
messages = anthropic_claude_converse_messages()
|
||||||
|
llm_model_value = "anthropic.claude-v2"
|
||||||
|
system_content = anthropic_claude_converse_system()
|
||||||
|
response = bedrock_runtime_client.converse(
|
||||||
|
system=system_content,
|
||||||
|
messages=messages,
|
||||||
|
modelId=llm_model_value,
|
||||||
|
)
|
||||||
|
|
||||||
|
(span,) = span_exporter.get_finished_spans()
|
||||||
|
assert_converse_completion_attributes(
|
||||||
|
span,
|
||||||
|
llm_model_value,
|
||||||
|
response,
|
||||||
|
"chat",
|
||||||
|
)
|
||||||
|
|
||||||
|
logs = log_exporter.get_finished_logs()
|
||||||
|
assert len(logs) == 4
|
||||||
|
assert_message_in_logs(
|
||||||
|
logs[0], "gen_ai.system.message", {"content": system_content}, span
|
||||||
|
)
|
||||||
|
user_message, assistant_message, last_user_message = messages
|
||||||
|
user_content = filter_message_keys(user_message, ["content"])
|
||||||
|
assert_message_in_logs(logs[1], "gen_ai.user.message", user_content, span)
|
||||||
|
assistant_content = filter_message_keys(assistant_message, ["content"])
|
||||||
|
assert_message_in_logs(
|
||||||
|
logs[2], "gen_ai.assistant.message", assistant_content, span
|
||||||
|
)
|
||||||
|
last_user_content = filter_message_keys(last_user_message, ["content"])
|
||||||
|
assert_message_in_logs(
|
||||||
|
logs[3], "gen_ai.user.message", last_user_content, span
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.skipif(
|
||||||
|
BOTO3_VERSION < (1, 35, 56), reason="Converse API not available"
|
||||||
|
)
|
||||||
|
@pytest.mark.vcr()
|
||||||
|
def test_converse_no_content_different_events(
|
||||||
|
span_exporter,
|
||||||
|
log_exporter,
|
||||||
|
bedrock_runtime_client,
|
||||||
|
instrument_no_content,
|
||||||
|
):
|
||||||
|
messages = anthropic_claude_converse_messages()
|
||||||
|
llm_model_value = "anthropic.claude-v2"
|
||||||
|
system_content = anthropic_claude_converse_system()
|
||||||
|
response = bedrock_runtime_client.converse(
|
||||||
|
system=system_content,
|
||||||
|
messages=messages,
|
||||||
|
modelId=llm_model_value,
|
||||||
|
)
|
||||||
|
|
||||||
|
(span,) = span_exporter.get_finished_spans()
|
||||||
|
assert_converse_completion_attributes(
|
||||||
|
span,
|
||||||
|
llm_model_value,
|
||||||
|
response,
|
||||||
|
"chat",
|
||||||
|
)
|
||||||
|
|
||||||
|
logs = log_exporter.get_finished_logs()
|
||||||
|
assert len(logs) == 4
|
||||||
|
assert_message_in_logs(logs[0], "gen_ai.system.message", None, span)
|
||||||
|
assert_message_in_logs(logs[1], "gen_ai.user.message", None, span)
|
||||||
|
assert_message_in_logs(logs[2], "gen_ai.assistant.message", None, span)
|
||||||
|
assert_message_in_logs(logs[3], "gen_ai.user.message", None, span)
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.skipif(
|
@pytest.mark.skipif(
|
||||||
BOTO3_VERSION < (1, 35, 56), reason="Converse API not available"
|
BOTO3_VERSION < (1, 35, 56), reason="Converse API not available"
|
||||||
)
|
)
|
||||||
|
|
@ -225,6 +308,58 @@ def test_converse_stream_with_content(
|
||||||
assert_message_in_logs(logs[0], "gen_ai.user.message", user_content, span)
|
assert_message_in_logs(logs[0], "gen_ai.user.message", user_content, span)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.skipif(
|
||||||
|
BOTO3_VERSION < (1, 35, 56), reason="ConverseStream API not available"
|
||||||
|
)
|
||||||
|
@pytest.mark.vcr()
|
||||||
|
def test_converse_stream_with_content_different_events(
|
||||||
|
span_exporter,
|
||||||
|
log_exporter,
|
||||||
|
bedrock_runtime_client,
|
||||||
|
instrument_with_content,
|
||||||
|
):
|
||||||
|
# pylint:disable=too-many-locals
|
||||||
|
messages = anthropic_claude_converse_messages()
|
||||||
|
llm_model_value = "anthropic.claude-v2"
|
||||||
|
system_content = anthropic_claude_converse_system()
|
||||||
|
response = bedrock_runtime_client.converse_stream(
|
||||||
|
system=system_content,
|
||||||
|
messages=messages,
|
||||||
|
modelId=llm_model_value,
|
||||||
|
)
|
||||||
|
|
||||||
|
# consume the stream in order to have it traced
|
||||||
|
for _ in response["stream"]:
|
||||||
|
pass
|
||||||
|
|
||||||
|
(span,) = span_exporter.get_finished_spans()
|
||||||
|
assert_stream_completion_attributes(
|
||||||
|
span,
|
||||||
|
llm_model_value,
|
||||||
|
input_tokens=mock.ANY,
|
||||||
|
output_tokens=mock.ANY,
|
||||||
|
finish_reason=("end_turn",),
|
||||||
|
operation_name="chat",
|
||||||
|
)
|
||||||
|
|
||||||
|
logs = log_exporter.get_finished_logs()
|
||||||
|
assert len(logs) == 4
|
||||||
|
assert_message_in_logs(
|
||||||
|
logs[0], "gen_ai.system.message", {"content": system_content}, span
|
||||||
|
)
|
||||||
|
user_message, assistant_message, last_user_message = messages
|
||||||
|
user_content = filter_message_keys(user_message, ["content"])
|
||||||
|
assert_message_in_logs(logs[1], "gen_ai.user.message", user_content, span)
|
||||||
|
assistant_content = filter_message_keys(assistant_message, ["content"])
|
||||||
|
assert_message_in_logs(
|
||||||
|
logs[2], "gen_ai.assistant.message", assistant_content, span
|
||||||
|
)
|
||||||
|
last_user_content = filter_message_keys(last_user_message, ["content"])
|
||||||
|
assert_message_in_logs(
|
||||||
|
logs[3], "gen_ai.user.message", last_user_content, span
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.skipif(
|
@pytest.mark.skipif(
|
||||||
BOTO3_VERSION < (1, 35, 56), reason="ConverseStream API not available"
|
BOTO3_VERSION < (1, 35, 56), reason="ConverseStream API not available"
|
||||||
)
|
)
|
||||||
|
|
@ -289,6 +424,47 @@ def test_converse_stream_no_content(
|
||||||
assert_message_in_logs(logs[0], "gen_ai.user.message", None, span)
|
assert_message_in_logs(logs[0], "gen_ai.user.message", None, span)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.skipif(
|
||||||
|
BOTO3_VERSION < (1, 35, 56), reason="ConverseStream API not available"
|
||||||
|
)
|
||||||
|
@pytest.mark.vcr()
|
||||||
|
def test_converse_stream_no_content_different_events(
|
||||||
|
span_exporter,
|
||||||
|
log_exporter,
|
||||||
|
bedrock_runtime_client,
|
||||||
|
instrument_no_content,
|
||||||
|
):
|
||||||
|
messages = anthropic_claude_converse_messages()
|
||||||
|
llm_model_value = "anthropic.claude-v2"
|
||||||
|
system_content = anthropic_claude_converse_system()
|
||||||
|
response = bedrock_runtime_client.converse_stream(
|
||||||
|
system=system_content,
|
||||||
|
messages=messages,
|
||||||
|
modelId=llm_model_value,
|
||||||
|
)
|
||||||
|
|
||||||
|
# consume the stream in order to have it traced
|
||||||
|
for _ in response["stream"]:
|
||||||
|
pass
|
||||||
|
|
||||||
|
(span,) = span_exporter.get_finished_spans()
|
||||||
|
assert_stream_completion_attributes(
|
||||||
|
span,
|
||||||
|
llm_model_value,
|
||||||
|
input_tokens=mock.ANY,
|
||||||
|
output_tokens=mock.ANY,
|
||||||
|
finish_reason=("end_turn",),
|
||||||
|
operation_name="chat",
|
||||||
|
)
|
||||||
|
|
||||||
|
logs = log_exporter.get_finished_logs()
|
||||||
|
assert len(logs) == 4
|
||||||
|
assert_message_in_logs(logs[0], "gen_ai.system.message", None, span)
|
||||||
|
assert_message_in_logs(logs[1], "gen_ai.user.message", None, span)
|
||||||
|
assert_message_in_logs(logs[2], "gen_ai.assistant.message", None, span)
|
||||||
|
assert_message_in_logs(logs[3], "gen_ai.user.message", None, span)
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.skipif(
|
@pytest.mark.skipif(
|
||||||
BOTO3_VERSION < (1, 35, 56), reason="ConverseStream API not available"
|
BOTO3_VERSION < (1, 35, 56), reason="ConverseStream API not available"
|
||||||
)
|
)
|
||||||
|
|
@ -391,6 +567,8 @@ def get_invoke_model_body(
|
||||||
temperature=None,
|
temperature=None,
|
||||||
top_p=None,
|
top_p=None,
|
||||||
stop_sequences=None,
|
stop_sequences=None,
|
||||||
|
system=None,
|
||||||
|
messages=None,
|
||||||
):
|
):
|
||||||
def set_if_not_none(config, key, value):
|
def set_if_not_none(config, key, value):
|
||||||
if value is not None:
|
if value is not None:
|
||||||
|
|
@ -404,10 +582,14 @@ def get_invoke_model_body(
|
||||||
set_if_not_none(config, "topP", top_p)
|
set_if_not_none(config, "topP", top_p)
|
||||||
set_if_not_none(config, "stopSequences", stop_sequences)
|
set_if_not_none(config, "stopSequences", stop_sequences)
|
||||||
body = {
|
body = {
|
||||||
"messages": [{"role": "user", "content": [{"text": prompt}]}],
|
"messages": messages
|
||||||
|
if messages
|
||||||
|
else [{"role": "user", "content": [{"text": prompt}]}],
|
||||||
"inferenceConfig": config,
|
"inferenceConfig": config,
|
||||||
"schemaVersion": "messages-v1",
|
"schemaVersion": "messages-v1",
|
||||||
}
|
}
|
||||||
|
if system:
|
||||||
|
body["system"] = system
|
||||||
elif llm_model == "amazon.titan-text-lite-v1":
|
elif llm_model == "amazon.titan-text-lite-v1":
|
||||||
config = {}
|
config = {}
|
||||||
set_if_not_none(config, "maxTokenCount", max_tokens)
|
set_if_not_none(config, "maxTokenCount", max_tokens)
|
||||||
|
|
@ -417,11 +599,15 @@ def get_invoke_model_body(
|
||||||
body = {"inputText": prompt, "textGenerationConfig": config}
|
body = {"inputText": prompt, "textGenerationConfig": config}
|
||||||
elif llm_model == "anthropic.claude-v2":
|
elif llm_model == "anthropic.claude-v2":
|
||||||
body = {
|
body = {
|
||||||
"messages": [
|
"messages": messages
|
||||||
|
if messages
|
||||||
|
else [
|
||||||
{"role": "user", "content": [{"text": prompt, "type": "text"}]}
|
{"role": "user", "content": [{"text": prompt, "type": "text"}]}
|
||||||
],
|
],
|
||||||
"anthropic_version": "bedrock-2023-05-31",
|
"anthropic_version": "bedrock-2023-05-31",
|
||||||
}
|
}
|
||||||
|
if system:
|
||||||
|
body["system"] = system
|
||||||
set_if_not_none(body, "max_tokens", max_tokens)
|
set_if_not_none(body, "max_tokens", max_tokens)
|
||||||
set_if_not_none(body, "temperature", temperature)
|
set_if_not_none(body, "temperature", temperature)
|
||||||
set_if_not_none(body, "top_p", top_p)
|
set_if_not_none(body, "top_p", top_p)
|
||||||
|
|
@ -486,6 +672,69 @@ def test_invoke_model_with_content(
|
||||||
assert_message_in_logs(logs[0], "gen_ai.user.message", user_content, span)
|
assert_message_in_logs(logs[0], "gen_ai.user.message", user_content, span)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.parametrize(
|
||||||
|
"model_family",
|
||||||
|
["amazon.nova", "anthropic.claude"],
|
||||||
|
)
|
||||||
|
@pytest.mark.vcr()
|
||||||
|
def test_invoke_model_with_content_different_events(
|
||||||
|
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 = 10
|
||||||
|
if llm_model_value == "amazon.nova-micro-v1:0":
|
||||||
|
messages = amazon_nova_messages()
|
||||||
|
system = amazon_nova_system()
|
||||||
|
elif llm_model_value == "anthropic.claude-v2":
|
||||||
|
messages = anthropic_claude_messages()
|
||||||
|
system = anthropic_claude_system()
|
||||||
|
|
||||||
|
body = get_invoke_model_body(
|
||||||
|
llm_model_value,
|
||||||
|
system=system,
|
||||||
|
messages=messages,
|
||||||
|
max_tokens=max_tokens,
|
||||||
|
)
|
||||||
|
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) == 4
|
||||||
|
assert_message_in_logs(
|
||||||
|
logs[0],
|
||||||
|
"gen_ai.system.message",
|
||||||
|
{"content": [{"text": "You are a friendly model"}]},
|
||||||
|
span,
|
||||||
|
)
|
||||||
|
user_message, assistant_message, last_user_message = messages
|
||||||
|
user_content = filter_message_keys(user_message, ["content"])
|
||||||
|
assert_message_in_logs(logs[1], "gen_ai.user.message", user_content, span)
|
||||||
|
assistant_content = filter_message_keys(assistant_message, ["content"])
|
||||||
|
assert_message_in_logs(
|
||||||
|
logs[2], "gen_ai.assistant.message", assistant_content, span
|
||||||
|
)
|
||||||
|
last_user_content = filter_message_keys(last_user_message, ["content"])
|
||||||
|
assert_message_in_logs(
|
||||||
|
logs[3], "gen_ai.user.message", last_user_content, span
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.parametrize(
|
@pytest.mark.parametrize(
|
||||||
"model_family",
|
"model_family",
|
||||||
["amazon.nova", "amazon.titan", "anthropic.claude"],
|
["amazon.nova", "amazon.titan", "anthropic.claude"],
|
||||||
|
|
@ -525,6 +774,55 @@ def test_invoke_model_no_content(
|
||||||
assert_message_in_logs(logs[0], "gen_ai.user.message", None, span)
|
assert_message_in_logs(logs[0], "gen_ai.user.message", None, span)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.parametrize(
|
||||||
|
"model_family",
|
||||||
|
["amazon.nova", "anthropic.claude"],
|
||||||
|
)
|
||||||
|
@pytest.mark.vcr()
|
||||||
|
def test_invoke_model_no_content_different_events(
|
||||||
|
span_exporter,
|
||||||
|
log_exporter,
|
||||||
|
bedrock_runtime_client,
|
||||||
|
instrument_no_content,
|
||||||
|
model_family,
|
||||||
|
):
|
||||||
|
llm_model_value = get_model_name_from_family(model_family)
|
||||||
|
max_tokens = 10
|
||||||
|
if llm_model_value == "amazon.nova-micro-v1:0":
|
||||||
|
messages = amazon_nova_messages()
|
||||||
|
system = amazon_nova_system()
|
||||||
|
elif llm_model_value == "anthropic.claude-v2":
|
||||||
|
messages = anthropic_claude_messages()
|
||||||
|
system = anthropic_claude_system()
|
||||||
|
|
||||||
|
body = get_invoke_model_body(
|
||||||
|
llm_model_value,
|
||||||
|
max_tokens=max_tokens,
|
||||||
|
system=system,
|
||||||
|
messages=messages,
|
||||||
|
)
|
||||||
|
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) == 4
|
||||||
|
assert_message_in_logs(logs[0], "gen_ai.system.message", None, span)
|
||||||
|
assert_message_in_logs(logs[1], "gen_ai.user.message", None, span)
|
||||||
|
assert_message_in_logs(logs[2], "gen_ai.assistant.message", None, span)
|
||||||
|
assert_message_in_logs(logs[3], "gen_ai.user.message", None, span)
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.vcr()
|
@pytest.mark.vcr()
|
||||||
def test_invoke_model_with_invalid_model(
|
def test_invoke_model_with_invalid_model(
|
||||||
span_exporter,
|
span_exporter,
|
||||||
|
|
@ -647,6 +945,221 @@ def test_invoke_model_with_response_stream_with_content(
|
||||||
assert_message_in_logs(logs[0], "gen_ai.user.message", user_content, span)
|
assert_message_in_logs(logs[0], "gen_ai.user.message", user_content, span)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.parametrize(
|
||||||
|
"model_family",
|
||||||
|
["amazon.nova", "anthropic.claude"],
|
||||||
|
)
|
||||||
|
@pytest.mark.vcr()
|
||||||
|
def test_invoke_model_with_response_stream_with_content_different_events(
|
||||||
|
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)
|
||||||
|
if llm_model_value == "amazon.nova-micro-v1:0":
|
||||||
|
messages = amazon_nova_messages()
|
||||||
|
system = amazon_nova_system()
|
||||||
|
finish_reason = "max_tokens"
|
||||||
|
elif llm_model_value == "anthropic.claude-v2":
|
||||||
|
messages = anthropic_claude_messages()
|
||||||
|
system = anthropic_claude_system()
|
||||||
|
finish_reason = "end_turn"
|
||||||
|
|
||||||
|
max_tokens = 10
|
||||||
|
body = get_invoke_model_body(
|
||||||
|
llm_model_value,
|
||||||
|
system=system,
|
||||||
|
messages=messages,
|
||||||
|
max_tokens=max_tokens,
|
||||||
|
)
|
||||||
|
response = bedrock_runtime_client.invoke_model_with_response_stream(
|
||||||
|
body=body,
|
||||||
|
modelId=llm_model_value,
|
||||||
|
)
|
||||||
|
|
||||||
|
# consume the stream in order to have it traced
|
||||||
|
for _ in response["body"]:
|
||||||
|
pass
|
||||||
|
|
||||||
|
(span,) = span_exporter.get_finished_spans()
|
||||||
|
assert_stream_completion_attributes(
|
||||||
|
span,
|
||||||
|
llm_model_value,
|
||||||
|
input_tokens=mock.ANY,
|
||||||
|
output_tokens=mock.ANY,
|
||||||
|
request_max_tokens=max_tokens,
|
||||||
|
finish_reason=(finish_reason,),
|
||||||
|
operation_name="chat",
|
||||||
|
)
|
||||||
|
|
||||||
|
logs = log_exporter.get_finished_logs()
|
||||||
|
assert len(logs) == 4
|
||||||
|
assert_message_in_logs(
|
||||||
|
logs[0],
|
||||||
|
"gen_ai.system.message",
|
||||||
|
{"content": [{"text": "You are a friendly model"}]},
|
||||||
|
span,
|
||||||
|
)
|
||||||
|
user_message, assistant_message, last_user_message = messages
|
||||||
|
user_content = filter_message_keys(user_message, ["content"])
|
||||||
|
assert_message_in_logs(logs[1], "gen_ai.user.message", user_content, span)
|
||||||
|
assistant_content = filter_message_keys(assistant_message, ["content"])
|
||||||
|
assert_message_in_logs(
|
||||||
|
logs[2], "gen_ai.assistant.message", assistant_content, span
|
||||||
|
)
|
||||||
|
last_user_content = filter_message_keys(last_user_message, ["content"])
|
||||||
|
assert_message_in_logs(
|
||||||
|
logs[3], "gen_ai.user.message", last_user_content, span
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.parametrize(
|
||||||
|
"model_family",
|
||||||
|
["amazon.nova", "amazon.titan", "anthropic.claude"],
|
||||||
|
)
|
||||||
|
@pytest.mark.vcr()
|
||||||
|
def test_invoke_model_with_response_stream_no_content(
|
||||||
|
span_exporter,
|
||||||
|
log_exporter,
|
||||||
|
bedrock_runtime_client,
|
||||||
|
instrument_no_content,
|
||||||
|
model_family,
|
||||||
|
):
|
||||||
|
# pylint:disable=too-many-locals,too-many-branches
|
||||||
|
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) == 1
|
||||||
|
assert_message_in_logs(logs[0], "gen_ai.user.message", None, span)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.parametrize(
|
||||||
|
"model_family",
|
||||||
|
["amazon.nova", "anthropic.claude"],
|
||||||
|
)
|
||||||
|
@pytest.mark.vcr()
|
||||||
|
def test_invoke_model_with_response_stream_no_content_different_events(
|
||||||
|
span_exporter,
|
||||||
|
log_exporter,
|
||||||
|
bedrock_runtime_client,
|
||||||
|
instrument_no_content,
|
||||||
|
model_family,
|
||||||
|
):
|
||||||
|
llm_model_value = get_model_name_from_family(model_family)
|
||||||
|
if llm_model_value == "amazon.nova-micro-v1:0":
|
||||||
|
messages = amazon_nova_messages()
|
||||||
|
system = amazon_nova_system()
|
||||||
|
finish_reason = "max_tokens"
|
||||||
|
elif llm_model_value == "anthropic.claude-v2":
|
||||||
|
messages = anthropic_claude_messages()
|
||||||
|
system = anthropic_claude_system()
|
||||||
|
finish_reason = "end_turn"
|
||||||
|
|
||||||
|
max_tokens = 10
|
||||||
|
body = get_invoke_model_body(
|
||||||
|
llm_model_value,
|
||||||
|
system=system,
|
||||||
|
messages=messages,
|
||||||
|
max_tokens=max_tokens,
|
||||||
|
)
|
||||||
|
response = bedrock_runtime_client.invoke_model_with_response_stream(
|
||||||
|
body=body,
|
||||||
|
modelId=llm_model_value,
|
||||||
|
)
|
||||||
|
|
||||||
|
# consume the stream in order to have it traced
|
||||||
|
for _ in response["body"]:
|
||||||
|
pass
|
||||||
|
|
||||||
|
(span,) = span_exporter.get_finished_spans()
|
||||||
|
assert_stream_completion_attributes(
|
||||||
|
span,
|
||||||
|
llm_model_value,
|
||||||
|
input_tokens=mock.ANY,
|
||||||
|
output_tokens=mock.ANY,
|
||||||
|
request_max_tokens=max_tokens,
|
||||||
|
finish_reason=(finish_reason,),
|
||||||
|
operation_name="chat",
|
||||||
|
)
|
||||||
|
|
||||||
|
logs = log_exporter.get_finished_logs()
|
||||||
|
assert len(logs) == 4
|
||||||
|
assert_message_in_logs(logs[0], "gen_ai.system.message", None, span)
|
||||||
|
assert_message_in_logs(logs[1], "gen_ai.user.message", None, span)
|
||||||
|
assert_message_in_logs(logs[2], "gen_ai.assistant.message", None, span)
|
||||||
|
assert_message_in_logs(logs[3], "gen_ai.user.message", None, span)
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.vcr()
|
@pytest.mark.vcr()
|
||||||
def test_invoke_model_with_response_stream_handles_stream_error(
|
def test_invoke_model_with_response_stream_handles_stream_error(
|
||||||
span_exporter,
|
span_exporter,
|
||||||
|
|
@ -672,7 +1185,7 @@ def test_invoke_model_with_response_stream_handles_stream_error(
|
||||||
EventStream,
|
EventStream,
|
||||||
"_parse_event",
|
"_parse_event",
|
||||||
side_effect=EventStreamError(
|
side_effect=EventStreamError(
|
||||||
{"modelStreamErrorException": {}}, "InvokeModelWithRespnseStream"
|
{"modelStreamErrorException": {}}, "InvokeModelWithResponseStream"
|
||||||
),
|
),
|
||||||
):
|
):
|
||||||
with pytest.raises(EventStreamError):
|
with pytest.raises(EventStreamError):
|
||||||
|
|
@ -726,3 +1239,49 @@ def test_invoke_model_with_response_stream_invalid_model(
|
||||||
|
|
||||||
logs = log_exporter.get_finished_logs()
|
logs = log_exporter.get_finished_logs()
|
||||||
assert len(logs) == 0
|
assert len(logs) == 0
|
||||||
|
|
||||||
|
|
||||||
|
def amazon_nova_messages():
|
||||||
|
return [
|
||||||
|
{"role": "user", "content": [{"text": "Say this is a test"}]},
|
||||||
|
{"role": "assistant", "content": [{"text": "This is a test"}]},
|
||||||
|
{
|
||||||
|
"role": "user",
|
||||||
|
"content": [{"text": "Say again this is a test"}],
|
||||||
|
},
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def amazon_nova_system():
|
||||||
|
return [
|
||||||
|
{"text": "You are a friendly model"},
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def anthropic_claude_converse_messages():
|
||||||
|
return amazon_nova_messages()
|
||||||
|
|
||||||
|
|
||||||
|
def anthropic_claude_converse_system():
|
||||||
|
return amazon_nova_system()
|
||||||
|
|
||||||
|
|
||||||
|
def anthropic_claude_messages():
|
||||||
|
return [
|
||||||
|
{
|
||||||
|
"role": "user",
|
||||||
|
"content": [{"text": "Say this is a test", "type": "text"}],
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"role": "assistant",
|
||||||
|
"content": [{"text": "This is a test", "type": "text"}],
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"role": "user",
|
||||||
|
"content": [{"text": "Say again this is a test", "type": "text"}],
|
||||||
|
},
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def anthropic_claude_system():
|
||||||
|
return "You are a friendly model"
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue