fix(langchain): use default EmbeddingInvocation operation_name instead of hardcoded value

instrumentation-genai/opentelemetry-instrumentation-langchain/src/opentelemetry/instrumentation/langchain/__init__.py

@@ -204,7 +204,6 @@ def _start_embedding(instance, texts):
 
             # Create embedding invocation
             embedding = UtilEmbeddingInvocation(
-                operation_name="embedding",
                 request_model=request_model,
                 input_texts=texts if isinstance(texts, list) else [texts],
                 provider=provider,

instrumentation-genai/opentelemetry-instrumentation-langchain/tests/cassettes/test_langchain_embedding_call.yaml

@@ -0,0 +1,78 @@
+interactions:
+- request:
+    body: |-
+      {
+        "input": [
+          "What is the capital of France?"
+        ],
+        "model": "text-embedding-ada-002",
+        "encoding_format": "float"
+      }
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      authorization:
+      - Bearer test_openai_api_key
+      connection:
+      - keep-alive
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.108.1
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.108.1
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.10
+    method: POST
+    uri: https://api.openai.com/v1/embeddings
+  response:
+    body:
+      string: |-
+        {
+          "object": "list",
+          "data": [
+            {
+              "object": "embedding",
+              "embedding": [0.0023064255, -0.009327292, 0.015797347, -0.0077586975, -0.013595423],
+              "index": 0
+            }
+          ],
+          "model": "text-embedding-ada-002-v2",
+          "usage": {
+            "prompt_tokens": 7,
+            "total_tokens": 7
+          }
+        }
+    headers:
+      content-type:
+      - application/json
+      date:
+      - Sun, 21 Sep 2025 04:09:41 GMT
+      openai-organization:
+      - test_openai_org_id
+      x-request-id:
+      - emb-50308e7e-2aac-4167-a8fb-03f9f5ed8169
+      content-length:
+      - '256'
+    status:
+      code: 200
+      message: OK
+version: 1

instrumentation-genai/opentelemetry-instrumentation-langchain/tests/cassettes/test_langchain_embedding_call_error.yaml

@@ -0,0 +1,71 @@
+interactions:
+- request:
+    body: |-
+      {
+        "input": [
+          "What is the capital of France?"
+        ],
+        "model": "text-embedding-ada-002",
+        "encoding_format": "float"
+      }
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      authorization:
+      - Bearer test_openai_api_key
+      connection:
+      - keep-alive
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.108.1
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.108.1
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.10
+    method: POST
+    uri: https://api.openai.com/v1/embeddings
+  response:
+    body:
+      string: |-
+        {
+          "error": {
+            "message": "Incorrect API key provided: test-api-****. You can find your API key at https://platform.openai.com/account/api-keys.",
+            "type": "invalid_request_error",
+            "param": null,
+            "code": "invalid_api_key"
+          }
+        }
+    headers:
+      content-type:
+      - application/json
+      date:
+      - Sun, 21 Sep 2025 04:09:41 GMT
+      openai-organization:
+      - test_openai_org_id
+      x-request-id:
+      - emb-err-50308e7e-2aac-4167-a8fb-03f9f5ed8169
+      content-length:
+      - '256'
+    status:
+      code: 401
+      message: Unauthorized
+version: 1

instrumentation-genai/opentelemetry-instrumentation-langchain/tests/test_langchain_embedding.py

@@ -0,0 +1,154 @@
+"""Minimal LangChain embedding instrumentation test.
+
+Follows the same VCR cassette integration pattern as test_langchain_llm.py
+to validate that embedding instrumentation emits correct telemetry:
+
+1. An embedding invocation succeeds using the recorded VCR cassette.
+2. A span is emitted with GenAI semantic convention attributes for an embeddings op.
+3. The default operation_name is 'embeddings' (from EmbeddingInvocation types.py default).
+4. Core request model attribute exists and is plausible.
+5. Metrics (duration at minimum) are produced and contain at least one data point.
+"""
+
+from __future__ import annotations
+
+# mypy: ignore-errors
+# pyright: reportGeneralTypeIssues=false, reportUnknownMemberType=false, reportUnknownVariableType=false, reportUnknownParameterType=false, reportUnknownArgumentType=false, reportAttributeAccessIssue=false, reportCallIssue=false
+
+from typing import Any, List
+import pytest
+from pytest import MonkeyPatch
+from pydantic import SecretStr
+
+from langchain_openai import OpenAIEmbeddings
+
+from opentelemetry.semconv._incubating.attributes import gen_ai_attributes
+from opentelemetry.semconv._incubating.metrics import gen_ai_metrics
+from opentelemetry.sdk.trace import ReadableSpan  # test-only type reference
+from opentelemetry.trace.status import StatusCode
+from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
+from opentelemetry.sdk.metrics.export import InMemoryMetricReader
+
+
+EMBEDDINGS = gen_ai_attributes.GenAiOperationNameValues.EMBEDDINGS.value
+
+
+@pytest.mark.vcr()
+def test_langchain_embedding_call(
+    span_exporter: InMemorySpanExporter,
+    metric_reader: InMemoryMetricReader,
+    instrument_with_content: Any,
+    monkeypatch: MonkeyPatch,
+):
+    # Arrange
+    monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+    model = "text-embedding-ada-002"
+    embeddings = OpenAIEmbeddings(
+        model=model,
+        api_key=SecretStr("test-api-key"),
+        check_embedding_ctx_length=False,  # avoid tiktoken download in test
+    )
+
+    # Act
+    result = embeddings.embed_query("What is the capital of France?")
+
+    # Basic functional assertion – result must be a list of floats
+    assert isinstance(result, list), "Expected a list of floats"
+    assert len(result) > 0, "Expected non-empty embedding vector"
+    assert all(isinstance(v, float) for v in result), "All values must be floats"
+
+    # Spans
+    spans: List[ReadableSpan] = span_exporter.get_finished_spans()  # type: ignore[assignment]
+    assert spans, "Expected at least one span"
+    embedding_span = None
+    for s in spans:
+        attrs_obj = getattr(s, "attributes", None)
+        op_name = None
+        try:
+            if attrs_obj is not None:
+                op_name = attrs_obj.get(gen_ai_attributes.GEN_AI_OPERATION_NAME)
+        except Exception:
+            op_name = None
+        if op_name == EMBEDDINGS:
+            embedding_span = s
+            break
+    assert embedding_span is not None, "No embeddings operation span found"
+
+    # Span attribute sanity
+    attrs = getattr(embedding_span, "attributes", {})
+    assert attrs.get(gen_ai_attributes.GEN_AI_OPERATION_NAME) == EMBEDDINGS
+    assert attrs.get(gen_ai_attributes.GEN_AI_REQUEST_MODEL) == model
+    # If token usage captured ensure it is a non-negative integer
+    tok_val = attrs.get(gen_ai_attributes.GEN_AI_USAGE_INPUT_TOKENS)
+    if tok_val is not None:
+        assert isinstance(tok_val, int) and tok_val >= 0
+
+    # Span name should follow "{operation_name} {request_model}" convention
+    assert embedding_span.name == f"embeddings {model}"
+
+    # Metrics – ensure at least duration histogram present with >=1 point
+    metrics_data = metric_reader.get_metrics_data()
+    found_duration = False
+    if metrics_data:
+        for rm in getattr(metrics_data, "resource_metrics", []) or []:
+            for scope in getattr(rm, "scope_metrics", []) or []:
+                for metric in getattr(scope, "metrics", []) or []:
+                    if metric.name == gen_ai_metrics.GEN_AI_CLIENT_OPERATION_DURATION:
+                        dps = getattr(metric.data, "data_points", [])
+                        if dps:
+                            assert dps[0].sum >= 0
+                            found_duration = True
+    assert found_duration, "Duration metric missing"
+
+
+@pytest.mark.vcr()
+def test_langchain_embedding_call_error(
+    span_exporter: InMemorySpanExporter,
+    instrument_with_content: Any,
+    monkeypatch: MonkeyPatch,
+):
+    """When the embedding API returns an error the wrapper must:
+    1. Still emit a span with operation_name == 'embeddings'.
+    2. Mark the span status as ERROR.
+    3. Re-raise the original exception so the caller sees the failure.
+    """
+    # Arrange
+    monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+    model = "text-embedding-ada-002"
+    embeddings = OpenAIEmbeddings(
+        model=model,
+        api_key=SecretStr("test-api-key"),
+        check_embedding_ctx_length=False,  # avoid tiktoken download in test
+        max_retries=0,  # fail immediately, don't retry on 401
+    )
+
+    # Act – the call should raise because the cassette returns a 401
+    with pytest.raises(Exception):
+        embeddings.embed_query("What is the capital of France?")
+
+    # Spans – an embedding span must still be emitted
+    spans: List[ReadableSpan] = span_exporter.get_finished_spans()  # type: ignore[assignment]
+    assert spans, "Expected at least one span even on error"
+    embedding_span = None
+    for s in spans:
+        attrs_obj = getattr(s, "attributes", None)
+        op_name = None
+        try:
+            if attrs_obj is not None:
+                op_name = attrs_obj.get(gen_ai_attributes.GEN_AI_OPERATION_NAME)
+        except Exception:
+            op_name = None
+        if op_name == EMBEDDINGS:
+            embedding_span = s
+            break
+    assert embedding_span is not None, (
+        "No embeddings operation span found on error path"
+    )
+
+    # Span attribute sanity
+    attrs = getattr(embedding_span, "attributes", {})
+    assert attrs.get(gen_ai_attributes.GEN_AI_OPERATION_NAME) == EMBEDDINGS
+    assert attrs.get(gen_ai_attributes.GEN_AI_REQUEST_MODEL) == model
+
+    # Span must be marked as error
+    assert embedding_span.status.status_code == StatusCode.ERROR