feat: add instrumentation to embedding functions for various backends (#5120)

### Description

Wrap the backends OpenAIEmbeddings, AzureOpenAIEmbeddings,
VertexAIEmbeddings, BedrockEmbeddings, CohereEmbeddings,
MistralAIEmbeddings, HuggingFaceEmbeddings, OllamaEmbeddings and their
embed_documents, embed_query, aembed_documents, aembed_query methods to
properly instrument embeddings

#### Issues

Closes
https://linear.app/getsentry/issue/TET-1460/add-embedding-support-for-langchain

#### Reminders
- Please add tests to validate your changes, and lint your code using
`tox -e linters`.
- Add GH Issue ID _&_ Linear ID (if applicable)
- PR title should use [conventional
commit](https://develop.sentry.dev/engineering-practices/commit-messages/#type)
style (`feat:`, `fix:`, `ref:`, `meta:`)
- For external contributors:
[CONTRIBUTING.md](https://github.com/getsentry/sentry-python/blob/master/CONTRIBUTING.md),
[Sentry SDK development docs](https://develop.sentry.dev/sdk/), [Discord
community](https://discord.gg/Ww9hbqr)

Author: constantinius

scripts/populate_tox/config.py

@@ -201,7 +201,7 @@
         "package": "langchain",
         "integration_name": "langchain",
         "deps": {
-            "*": ["openai", "tiktoken", "langchain-openai"],
+            "*": ["pytest-asyncio", "openai", "tiktoken", "langchain-openai"],
             "<=0.1": ["httpx<0.28.0"],
             ">=0.3": ["langchain-community"],
             ">=1.0": ["langchain-classic"],
@@ -214,7 +214,7 @@
         "package": "langchain",
         "integration_name": "langchain",
         "deps": {
-            "*": ["openai", "langchain-openai"],
+            "*": ["pytest-asyncio", "openai", "langchain-openai"],
             "<=0.1": ["httpx<0.28.0"],
             ">=0.3": ["langchain-community"],
             ">=1.0": ["langchain-classic"],

sentry_sdk/integrations/langchain.py

@@ -63,6 +63,48 @@
         AgentExecutor = None
 
 
+# Conditional imports for embeddings providers
+try:
+    from langchain_openai import OpenAIEmbeddings  # type: ignore[import-not-found]
+except ImportError:
+    OpenAIEmbeddings = None
+
+try:
+    from langchain_openai import AzureOpenAIEmbeddings
+except ImportError:
+    AzureOpenAIEmbeddings = None
+
+try:
+    from langchain_google_vertexai import VertexAIEmbeddings  # type: ignore[import-not-found]
+except ImportError:
+    VertexAIEmbeddings = None
+
+try:
+    from langchain_aws import BedrockEmbeddings  # type: ignore[import-not-found]
+except ImportError:
+    BedrockEmbeddings = None
+
+try:
+    from langchain_cohere import CohereEmbeddings  # type: ignore[import-not-found]
+except ImportError:
+    CohereEmbeddings = None
+
+try:
+    from langchain_mistralai import MistralAIEmbeddings  # type: ignore[import-not-found]
+except ImportError:
+    MistralAIEmbeddings = None
+
+try:
+    from langchain_huggingface import HuggingFaceEmbeddings  # type: ignore[import-not-found]
+except ImportError:
+    HuggingFaceEmbeddings = None
+
+try:
+    from langchain_ollama import OllamaEmbeddings  # type: ignore[import-not-found]
+except ImportError:
+    OllamaEmbeddings = None
+
+
 DATA_FIELDS = {
     "frequency_penalty": SPANDATA.GEN_AI_REQUEST_FREQUENCY_PENALTY,
     "function_call": SPANDATA.GEN_AI_RESPONSE_TOOL_CALLS,
@@ -140,6 +182,16 @@ def setup_once():
             AgentExecutor.invoke = _wrap_agent_executor_invoke(AgentExecutor.invoke)
             AgentExecutor.stream = _wrap_agent_executor_stream(AgentExecutor.stream)
 
+        # Patch embeddings providers
+        _patch_embeddings_provider(OpenAIEmbeddings)
+        _patch_embeddings_provider(AzureOpenAIEmbeddings)
+        _patch_embeddings_provider(VertexAIEmbeddings)
+        _patch_embeddings_provider(BedrockEmbeddings)
+        _patch_embeddings_provider(CohereEmbeddings)
+        _patch_embeddings_provider(MistralAIEmbeddings)
+        _patch_embeddings_provider(HuggingFaceEmbeddings)
+        _patch_embeddings_provider(OllamaEmbeddings)
+
 
 class WatchedSpan:
     span = None  # type: Span
@@ -976,3 +1028,105 @@ async def new_iterator_async():
         return result
 
     return new_stream
+
+
+def _patch_embeddings_provider(provider_class):
+    # type: (Any) -> None
+    """Patch an embeddings provider class with monitoring wrappers."""
+    if provider_class is None:
+        return
+
+    if hasattr(provider_class, "embed_documents"):
+        provider_class.embed_documents = _wrap_embedding_method(
+            provider_class.embed_documents
+        )
+    if hasattr(provider_class, "embed_query"):
+        provider_class.embed_query = _wrap_embedding_method(provider_class.embed_query)
+    if hasattr(provider_class, "aembed_documents"):
+        provider_class.aembed_documents = _wrap_async_embedding_method(
+            provider_class.aembed_documents
+        )
+    if hasattr(provider_class, "aembed_query"):
+        provider_class.aembed_query = _wrap_async_embedding_method(
+            provider_class.aembed_query
+        )
+
+
+def _wrap_embedding_method(f):
+    # type: (Callable[..., Any]) -> Callable[..., Any]
+    """Wrap sync embedding methods (embed_documents and embed_query)."""
+
+    @wraps(f)
+    def new_embedding_method(self, *args, **kwargs):
+        # type: (Any, Any, Any) -> Any
+        integration = sentry_sdk.get_client().get_integration(LangchainIntegration)
+        if integration is None:
+            return f(self, *args, **kwargs)
+
+        model_name = getattr(self, "model", None) or getattr(self, "model_name", None)
+        with sentry_sdk.start_span(
+            op=OP.GEN_AI_EMBEDDINGS,
+            name=f"embeddings {model_name}" if model_name else "embeddings",
+            origin=LangchainIntegration.origin,
+        ) as span:
+            span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "embeddings")
+            if model_name:
+                span.set_data(SPANDATA.GEN_AI_REQUEST_MODEL, model_name)
+
+            # Capture input if PII is allowed
+            if (
+                should_send_default_pii()
+                and integration.include_prompts
+                and len(args) > 0
+            ):
+                input_data = args[0]
+                # Normalize to list format
+                texts = input_data if isinstance(input_data, list) else [input_data]
+                set_data_normalized(
+                    span, SPANDATA.GEN_AI_EMBEDDINGS_INPUT, texts, unpack=False
+                )
+
+            result = f(self, *args, **kwargs)
+            return result
+
+    return new_embedding_method
+
+
+def _wrap_async_embedding_method(f):
+    # type: (Callable[..., Any]) -> Callable[..., Any]
+    """Wrap async embedding methods (aembed_documents and aembed_query)."""
+
+    @wraps(f)
+    async def new_async_embedding_method(self, *args, **kwargs):
+        # type: (Any, Any, Any) -> Any
+        integration = sentry_sdk.get_client().get_integration(LangchainIntegration)
+        if integration is None:
+            return await f(self, *args, **kwargs)
+
+        model_name = getattr(self, "model", None) or getattr(self, "model_name", None)
+        with sentry_sdk.start_span(
+            op=OP.GEN_AI_EMBEDDINGS,
+            name=f"embeddings {model_name}" if model_name else "embeddings",
+            origin=LangchainIntegration.origin,
+        ) as span:
+            span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "embeddings")
+            if model_name:
+                span.set_data(SPANDATA.GEN_AI_REQUEST_MODEL, model_name)
+
+            # Capture input if PII is allowed
+            if (
+                should_send_default_pii()
+                and integration.include_prompts
+                and len(args) > 0
+            ):
+                input_data = args[0]
+                # Normalize to list format
+                texts = input_data if isinstance(input_data, list) else [input_data]
+                set_data_normalized(
+                    span, SPANDATA.GEN_AI_EMBEDDINGS_INPUT, texts, unpack=False
+                )
+
+            result = await f(self, *args, **kwargs)
+            return result
+
+    return new_async_embedding_method