Release v3.8.21 - AWS Bedrock ARN parsing fix (#545)

* capture different types of google genai inputs (#534)

Co-authored-by: Obinna Okafor <obinna.okafor01@gmail.com>

* Read stream before saving attributes. Parse modelId in a more robust way.

* Decide based on model name on the set of attributes tracked.

* Added basic test for model ID parsing.

* Added replay tests for AWS Bedrock.

* Added test for streaming requests too.

* Obinna/fix awsbedrock streaming response (#538)

* fix aws bedrock streaming bug

* bump version

* add deprecated dependency

* restrict current pinecone instrumentation to v6.0.2

* hard code boto3 dependency version

* fix duplicate event stream

* set test aws key

* Fix AWS Bedrock ARN parsing issue in converse methods (#544)

* Fix AWS Bedrock ARN parsing issue in converse methods

- Replace modelId.split('.') with parse_vendor_and_model_name_from_model_id
- Fixes crash when using ARN or cross-region model IDs with multiple dots
- Makes patch_converse and patch_converse_stream consistent with other methods

Resolves ValueError: too many values to unpack (expected 2) when using:
- ARN format: arn:aws:bedrock:us-east-1:<account_id>:inference-profile/us.anthropic.claude-3-haiku-20240307-v1:0
- Cross-region format: us.anthropic.claude-sonnet-4-20250514-v1:0

Co-Authored-By: karthik@scale3labs.com <karthik@scale3labs.com>

* Bump version to 3.8.21 for ARN parsing bug fix

Co-Authored-By: karthik@scale3labs.com <karthik@scale3labs.com>

---------

Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: karthik@scale3labs.com <karthik@scale3labs.com>

---------

Co-authored-by: obinnascale3 <109410793+obinnascale3@users.noreply.github.com>
Co-authored-by: Obinna Okafor <obinna.okafor01@gmail.com>
Co-authored-by: m1kl0sh <miklos.erdelyi@moduscreate.com>
Co-authored-by: Obinna Okafor <obinna@scale3labs.com>
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: karthik@scale3labs.com <karthik@scale3labs.com>

Author: 5 people

pyproject.toml

@@ -34,6 +34,7 @@ dependencies = [
   "ujson>=5.10.0",
   "boto3==1.38.0",
   "setuptools",
+  "Deprecated==1.2.18",
 ]
 
 requires-python = ">=3.9"
@@ -47,7 +48,7 @@ dev = [
   "qdrant-client",
   "graphlit-client",
   "python-dotenv",
-  "pinecone",
+  "pinecone>=3.1.0,<=6.0.2",
   "langchain",
   "langchain-community",
   "langchain-openai",

src/langtrace_python_sdk/constants/instrumentation/aws_bedrock.py

@@ -5,6 +5,10 @@
         "METHOD": "aws_bedrock.invoke_model",
         "ENDPOINT": "/invoke-model",
     },
+    "INVOKE_MODEL_WITH_RESPONSE_STREAM": {
+        "METHOD": "aws_bedrock.invoke_model_with_response_stream",
+        "ENDPOINT": "/invoke-model-with-response-stream",
+    },
     "CONVERSE": {
         "METHOD": AWSBedrockMethods.CONVERSE.value,
         "ENDPOINT": "/converse",

src/langtrace_python_sdk/instrumentation/aws_bedrock/patch.py

@@ -15,8 +15,10 @@
 """
 
 import json
+import io
 
 from wrapt import ObjectProxy
+from itertools import tee
 from .stream_body_wrapper import BufferedStreamBody
 from functools import wraps
 from langtrace.trace_attributes import (
@@ -43,6 +45,7 @@
     set_span_attributes,
     set_usage_attributes,
 )
+from langtrace_python_sdk.utils import set_event_prompt
 
 
 def converse_stream(original_method, version, tracer):
@@ -104,7 +107,7 @@ def traced_method(wrapped, instance, args, kwargs):
 def patch_converse_stream(original_method, tracer, version):
     def traced_method(*args, **kwargs):
         modelId = kwargs.get("modelId")
-        (vendor, _) = modelId.split(".")
+        vendor, _ = parse_vendor_and_model_name_from_model_id(modelId)
         input_content = [
             {
                 "role": message.get("role", "user"),
@@ -128,7 +131,9 @@ def traced_method(*args, **kwargs):
             response = original_method(*args, **kwargs)
 
             if span.is_recording():
-                set_span_streaming_response(span, response)
+                stream1, stream2 = tee(response["stream"])
+                set_span_streaming_response(span, stream1)
+                response["stream"] = stream2
             return response
 
     return traced_method
@@ -137,7 +142,7 @@ def traced_method(*args, **kwargs):
 def patch_converse(original_method, tracer, version):
     def traced_method(*args, **kwargs):
         modelId = kwargs.get("modelId")
-        (vendor, _) = modelId.split(".")
+        vendor, _ = parse_vendor_and_model_name_from_model_id(modelId)
         input_content = [
             {
                 "role": message.get("role", "user"),
@@ -167,12 +172,29 @@ def traced_method(*args, **kwargs):
     return traced_method
 
 
+def parse_vendor_and_model_name_from_model_id(model_id):
+    if model_id.startswith("arn:aws:bedrock:"):
+        # This needs to be in one of the following forms:
+        # arn:aws:bedrock:region:account-id:foundation-model/vendor.model-name
+        # arn:aws:bedrock:region:account-id:custom-model/vendor.model-name/model-id
+        parts = model_id.split("/")
+        identifiers = parts[1].split(".")
+        return identifiers[0], identifiers[1]
+    parts = model_id.split(".")
+    if len(parts) == 1:
+        return parts[0], parts[0]
+    else:
+        return parts[-2], parts[-1]
+
+
 def patch_invoke_model(original_method, tracer, version):
     def traced_method(*args, **kwargs):
         modelId = kwargs.get("modelId")
-        (vendor, _) = modelId.split(".")
+        vendor, _ = parse_vendor_and_model_name_from_model_id(modelId)
         span_attributes = {
             **get_langtrace_attributes(version, vendor, vendor_type="framework"),
+            SpanAttributes.LLM_PATH: APIS["INVOKE_MODEL"]["ENDPOINT"],
+            SpanAttributes.LLM_IS_STREAMING: False,
             **get_extra_attributes(),
         }
         with tracer.start_as_current_span(
@@ -193,9 +215,11 @@ def patch_invoke_model_with_response_stream(original_method, tracer, version):
     @wraps(original_method)
     def traced_method(*args, **kwargs):
         modelId = kwargs.get("modelId")
-        (vendor, _) = modelId.split(".")
+        vendor, _ = parse_vendor_and_model_name_from_model_id(modelId)
         span_attributes = {
             **get_langtrace_attributes(version, vendor, vendor_type="framework"),
+            SpanAttributes.LLM_PATH: APIS["INVOKE_MODEL_WITH_RESPONSE_STREAM"]["ENDPOINT"],
+            SpanAttributes.LLM_IS_STREAMING: True,
             **get_extra_attributes(),
         }
         span = tracer.start_span(
@@ -217,7 +241,7 @@ def handle_streaming_call(span, kwargs, response):
     def stream_finished(response_body):
         request_body = json.loads(kwargs.get("body"))
 
-        (vendor, model) = kwargs.get("modelId").split(".")
+        vendor, model = parse_vendor_and_model_name_from_model_id(kwargs.get("modelId"))
 
         set_span_attribute(span, SpanAttributes.LLM_REQUEST_MODEL, model)
         set_span_attribute(span, SpanAttributes.LLM_RESPONSE_MODEL, model)
@@ -241,18 +265,22 @@ def stream_finished(response_body):
 
 def handle_call(span, kwargs, response):
     modelId = kwargs.get("modelId")
-    (vendor, model_name) = modelId.split(".")
+    vendor, model_name = parse_vendor_and_model_name_from_model_id(modelId)
+    read_response_body = response.get("body").read()
+    request_body = json.loads(kwargs.get("body"))
+    response_body = json.loads(read_response_body)
     response["body"] = BufferedStreamBody(
-        response["body"]._raw_stream, response["body"]._content_length
+        io.BytesIO(read_response_body), len(read_response_body)
     )
-    request_body = json.loads(kwargs.get("body"))
-    response_body = json.loads(response.get("body").read())
 
     set_span_attribute(span, SpanAttributes.LLM_RESPONSE_MODEL, modelId)
     set_span_attribute(span, SpanAttributes.LLM_REQUEST_MODEL, modelId)
 
     if vendor == "amazon":
-        set_amazon_attributes(span, request_body, response_body)
+        if model_name.startswith("titan-embed-text"):
+            set_amazon_embedding_attributes(span, request_body, response_body)
+        else:
+            set_amazon_attributes(span, request_body, response_body)
 
     if vendor == "anthropic":
         if "prompt" in request_body:
@@ -356,6 +384,27 @@ def set_amazon_attributes(span, request_body, response_body):
     set_event_completion(span, completions)
 
 
+def set_amazon_embedding_attributes(span, request_body, response_body):
+    input_text = request_body.get("inputText")
+    set_event_prompt(span, input_text)
+
+    embeddings = response_body.get("embedding", [])
+    input_tokens = response_body.get("inputTextTokenCount")
+    set_usage_attributes(
+        span,
+        {
+            "input_tokens": input_tokens,
+            "output": len(embeddings),
+        },
+    )
+    set_span_attribute(
+        span, SpanAttributes.LLM_REQUEST_MODEL, request_body.get("modelId")
+    )
+    set_span_attribute(
+        span, SpanAttributes.LLM_RESPONSE_MODEL, request_body.get("modelId")
+    )
+
+
 def set_anthropic_completions_attributes(span, request_body, response_body):
     set_span_attribute(
         span,
@@ -442,10 +491,10 @@ def _set_response_attributes(span, kwargs, result):
         )
 
 
-def set_span_streaming_response(span, response):
+def set_span_streaming_response(span, response_stream):
     streaming_response = ""
     role = None
-    for event in response["stream"]:
+    for event in response_stream:
         if "messageStart" in event:
             role = event["messageStart"]["role"]
         elif "contentBlockDelta" in event:
@@ -475,13 +524,15 @@ def __init__(
         stream_done_callback=None,
     ):
         super().__init__(response)
-
         self._stream_done_callback = stream_done_callback
         self._accumulating_body = {"generation": ""}
+        self.last_chunk = None
 
     def __iter__(self):
         for event in self.__wrapped__:
+            # Process the event
             self._process_event(event)
+            # Yield the original event immediately
             yield event
 
     def _process_event(self, event):
@@ -496,7 +547,11 @@ def _process_event(self, event):
             self._stream_done_callback(decoded_chunk)
             return
         if "generation" in decoded_chunk:
-            self._accumulating_body["generation"] += decoded_chunk.get("generation")
+            generation = decoded_chunk.get("generation")
+            if self.last_chunk == generation:
+                return
+            self.last_chunk = generation
+            self._accumulating_body["generation"] += generation
 
         if type == "message_start":
             self._accumulating_body = decoded_chunk.get("message")
@@ -505,9 +560,11 @@ def _process_event(self, event):
                 decoded_chunk.get("content_block")
             )
         elif type == "content_block_delta":
-            self._accumulating_body["content"][-1]["text"] += decoded_chunk.get(
-                "delta"
-            ).get("text")
+            text = decoded_chunk.get("delta").get("text")
+            if self.last_chunk == text:
+                return
+            self.last_chunk = text
+            self._accumulating_body["content"][-1]["text"] += text
 
         elif self.has_finished(type, decoded_chunk):
             self._accumulating_body["invocation_metrics"] = decoded_chunk.get(

src/langtrace_python_sdk/instrumentation/pinecone/instrumentation.py

@@ -33,7 +33,7 @@ class PineconeInstrumentation(BaseInstrumentor):
     The PineconeInstrumentation class represents the Pinecone instrumentation"""
 
     def instrumentation_dependencies(self) -> Collection[str]:
-        return ["pinecone >= 3.1.0"]
+        return ["pinecone >= 3.1.0", "pinecone <= 6.0.2"]
 
     def _instrument(self, **kwargs):
         tracer_provider = kwargs.get("tracer_provider")

src/langtrace_python_sdk/version.py

@@ -1 +1 @@
-__version__ = "3.8.18"
+__version__ = "3.8.21"

src/tests/aws_bedrock/cassettes/test_chat_completion.yaml

@@ -0,0 +1,45 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "user", "content": "Say this is a test three times"}],
+      "anthropic_version": "bedrock-2023-05-31", "max_tokens": 100}'
+    headers:
+      Accept:
+      - !!binary |
+        YXBwbGljYXRpb24vanNvbg==
+      Content-Length:
+      - '139'
+      Content-Type:
+      - !!binary |
+        YXBwbGljYXRpb24vanNvbg==
+      User-Agent:
+      - !!binary |
+        Qm90bzMvMS4zOC4xOCBtZC9Cb3RvY29yZSMxLjM4LjE4IHVhLzIuMSBvcy9tYWNvcyMyNC40LjAg
+        bWQvYXJjaCNhcm02NCBsYW5nL3B5dGhvbiMzLjEzLjEgbWQvcHlpbXBsI0NQeXRob24gbS9aLGIg
+        Y2ZnL3JldHJ5LW1vZGUjc3RhbmRhcmQgQm90b2NvcmUvMS4zOC4xOA==
+    method: POST
+    uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/us.anthropic.claude-3-7-sonnet-20250219-v1%3A0/invoke
+  response:
+    body:
+      string: '{"id":"msg_bdrk_01NJB1bDTLkFh6pgfoAD5hkb","type":"message","role":"assistant","model":"claude-3-7-sonnet-20250219","content":[{"type":"text","text":"This
+        is a test.\nThis is a test.\nThis is a test."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":14,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":20}}'
+    headers:
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '355'
+      Content-Type:
+      - application/json
+      Date:
+      - Mon, 19 May 2025 16:42:05 GMT
+      X-Amzn-Bedrock-Input-Token-Count:
+      - '14'
+      X-Amzn-Bedrock-Invocation-Latency:
+      - '926'
+      X-Amzn-Bedrock-Output-Token-Count:
+      - '20'
+      x-amzn-RequestId:
+      - c0a92363-ec28-4a8b-9c09-571131d946b0
+    status:
+      code: 200
+      message: OK
+version: 1

src/tests/aws_bedrock/cassettes/test_generate_embedding.yaml

@@ -0,0 +1,48 @@
+"""Unit tests configuration module."""
+
+import pytest
+import os
+
+from boto3.session import Session
+from botocore.config import Config
+
+from langtrace_python_sdk.instrumentation.aws_bedrock.instrumentation import (
+    AWSBedrockInstrumentation,
+)
+
+
+@pytest.fixture(autouse=True)
+def environment():
+    if not os.getenv("AWS_ACCESS_KEY_ID"):
+        os.environ["AWS_ACCESS_KEY_ID"] = "test_aws_access_key_id"
+    if not os.getenv("AWS_SECRET_ACCESS_KEY"):
+        os.environ["AWS_SECRET_ACCESS_KEY"] = "test_aws_secret_access_key"
+
+
+@pytest.fixture
+def aws_bedrock_client():
+    bedrock_config = Config(
+        region_name="us-east-1",
+        connect_timeout=300,
+        read_timeout=300,
+        retries={"total_max_attempts": 2, "mode": "standard"},
+    )
+    return Session().client("bedrock-runtime", config=bedrock_config)
+
+
+@pytest.fixture(scope="module")
+def vcr_config():
+    return {
+        "filter_headers": [
+            "authorization",
+            "X-Amz-Date",
+            "X-Amz-Security-Token",
+            "amz-sdk-invocation-id",
+            "amz-sdk-request",
+        ]
+    }
+
+
+@pytest.fixture(scope="session", autouse=True)
+def instrument():
+    AWSBedrockInstrumentation().instrument()