feat: add embedding tracer support for Bedrock, LiteLLM, OpenAI

.gitignore

@@ -16,4 +16,6 @@ codegen.log
 Brewfile.lock.json
 
 .ipynb_checkpoints
-.DS_Store
+.DS_Store
+
+.worktrees/

src/openlayer/lib/integrations/_openai_embedding_common.py

@@ -0,0 +1,88 @@
+"""Shared parsing helpers for OpenAI-shaped embedding tracers (OpenAI, AsyncOpenAI, LiteLLM)."""
+
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+
+def parse_embedding_response(
+    response: Any,
+) -> Tuple[Union[List[float], List[List[float]]], int, int]:
+    """Extract (embeddings, dimensions, count) from an OpenAI-shaped EmbeddingResponse.
+
+    For a single input, returns the vector directly.
+    For a batch, returns a list of vectors.
+    """
+    try:
+        data = getattr(response, "data", None)
+        if data is None and isinstance(response, dict):
+            data = response.get("data", [])
+        if not data:
+            return [], 0, 0
+        embeddings = [
+            item["embedding"] if isinstance(item, dict) else item.embedding
+            for item in data
+        ]
+        if not embeddings:
+            return [], 0, 0
+        if len(embeddings) == 1:
+            return embeddings[0], len(embeddings[0]), 1
+        return embeddings, len(embeddings[0]), len(embeddings)
+    except Exception:
+        return [], 0, 0
+
+
+def get_embedding_model_parameters(kwargs: Dict[str, Any]) -> Dict[str, Any]:
+    """Extract embedding-relevant model parameters from create() kwargs."""
+    return {
+        "dimensions": kwargs.get("dimensions"),
+        "encoding_format": kwargs.get("encoding_format"),
+        "user": kwargs.get("user"),
+    }
+
+
+def build_embedding_step_kwargs(
+    response: Any,
+    call_kwargs: Dict[str, Any],
+    start_time: float,
+    end_time: float,
+    *,
+    name: str,
+    provider: str,
+    inference_id: Optional[str] = None,
+) -> Dict[str, Any]:
+    """Build the kwargs to pass to ``tracer.add_embedding_step_to_trace``.
+
+    Common boilerplate for OpenAI-shaped responses (OpenAI sync/async, LiteLLM).
+    Callers may layer extra fields (cost, extra_metadata, model_parameters) on
+    top of the returned dict before invoking the tracer helper.
+    """
+    model_name = getattr(response, "model", call_kwargs.get("model", "unknown"))
+    embeddings, dim, count = parse_embedding_response(response)
+    usage = getattr(response, "usage", None)
+    prompt_tokens = getattr(usage, "prompt_tokens", 0) if usage else 0
+    total_tokens = getattr(usage, "total_tokens", prompt_tokens) if usage else prompt_tokens
+
+    step_kwargs: Dict[str, Any] = {
+        "name": name,
+        "end_time": end_time,
+        "inputs": {"input": call_kwargs.get("input")},
+        "output": embeddings,
+        "latency": (end_time - start_time) * 1000,
+        "tokens": total_tokens,
+        "prompt_tokens": prompt_tokens,
+        "model": model_name,
+        "model_parameters": get_embedding_model_parameters(call_kwargs),
+        "embedding_dimensions": dim,
+        "embedding_count": count,
+        "raw_output": (
+            response.model_dump()
+            if hasattr(response, "model_dump")
+            else str(response)
+        ),
+        "provider": provider,
+        "metadata": {"provider": provider},
+    }
+    # Only include id when truthy: passing id=None would overwrite the step's
+    # auto-generated UUID via step.log() → setattr().
+    if inference_id:
+        step_kwargs["id"] = inference_id
+    return step_kwargs

src/openlayer/lib/integrations/async_openai_tracer.py

@@ -16,6 +16,8 @@
 if TYPE_CHECKING:
     import openai
 
+from ..tracing import tracer
+from ._openai_embedding_common import build_embedding_step_kwargs
 from .openai_tracer import (
     get_model_parameters,
     create_trace_args,
@@ -155,6 +157,23 @@ async def traced_responses_create_func(*args, **kwargs):
     else:
         logger.debug("Responses API not available in this AsyncOpenAI client version")
 
+    # Patch Embeddings API
+    if hasattr(client, "embeddings"):
+        embeddings_create_func = client.embeddings.create
+
+        @wraps(embeddings_create_func)
+        async def traced_embeddings_create_func(*args, **kwargs):
+            inference_id = kwargs.pop("inference_id", None)
+            return await handle_embedding_async(
+                *args,
+                **kwargs,
+                original_func=embeddings_create_func,
+                inference_id=inference_id,
+                is_azure_openai=is_azure_openai,
+            )
+
+        client.embeddings.create = traced_embeddings_create_func
+
     return client
 
 
@@ -698,3 +717,41 @@ async def handle_async_non_streaming_parse(
         )
 
     return response
+
+
+# ----------------------------- Async Embeddings ----------------------------- #
+async def handle_embedding_async(
+    original_func: callable,
+    *args,
+    inference_id: Optional[str] = None,
+    is_azure_openai: bool = False,
+    **kwargs,
+) -> Any:
+    """Trace an async AsyncOpenAI client.embeddings.create() call."""
+    start_time = time.time()
+    response = await original_func(*args, **kwargs)
+    end_time = time.time()
+
+    try:
+        if is_azure_openai:
+            name, provider = "Azure OpenAI Embedding", "Azure"
+        else:
+            name, provider = "OpenAI Embedding", "OpenAI"
+
+        tracer.add_embedding_step_to_trace(
+            **build_embedding_step_kwargs(
+                response,
+                kwargs,
+                start_time,
+                end_time,
+                name=name,
+                provider=provider,
+                inference_id=inference_id,
+            )
+        )
+    except Exception as e:
+        logger.error(
+            "Failed to trace the OpenAI embedding request with Openlayer. %s", e
+        )
+
+    return response

src/openlayer/lib/integrations/bedrock_tracer.py

@@ -5,7 +5,7 @@
 import logging
 import time
 from functools import wraps
-from typing import TYPE_CHECKING, Any, Dict, Iterator, Optional, Union
+from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional, Tuple, Union
 
 from botocore.response import StreamingBody
 
@@ -25,6 +25,11 @@
 logger = logging.getLogger(__name__)
 
 
+def _is_embedding_model(model_id: str) -> bool:
+    """Return True when modelId refers to a Bedrock embedding model."""
+    return "embed" in (model_id or "").lower()
+
+
 def trace_bedrock(client: "boto3.client") -> "boto3.client":
     """Patch the Bedrock client to trace model invocations.
 
@@ -63,6 +68,14 @@ def trace_bedrock(client: "boto3.client") -> "boto3.client":
     @wraps(invoke_model_func)
     def traced_invoke_model(*args, **kwargs):
         inference_id = kwargs.pop("inference_id", None)
+        model_id = kwargs.get("modelId", "")
+        if _is_embedding_model(model_id):
+            return handle_embedding_invoke(
+                *args,
+                **kwargs,
+                invoke_func=invoke_model_func,
+                inference_id=inference_id,
+            )
         return handle_non_streaming_invoke(
             *args,
             **kwargs,
@@ -153,6 +166,112 @@ def handle_non_streaming_invoke(
     return response
 
 
+def handle_embedding_invoke(
+    invoke_func: callable,
+    *args,
+    inference_id: Optional[str] = None,
+    **kwargs,
+) -> Dict[str, Any]:
+    """Handle invoke_model for embedding models (Titan, Cohere)."""
+    start_time = time.time()
+    response = invoke_func(*args, **kwargs)
+    end_time = time.time()
+
+    try:
+        # Parse the request body
+        body_str = kwargs.get("body", "{}")
+        if isinstance(body_str, bytes):
+            body_str = body_str.decode("utf-8")
+        body_data = json.loads(body_str) if isinstance(body_str, str) else body_str
+
+        # Read and replace the response body so callers can still consume it
+        original_body = response["body"]
+        response_body_bytes = original_body.read()
+        response_data = json.loads(
+            response_body_bytes.decode("utf-8")
+            if isinstance(response_body_bytes, bytes)
+            else response_body_bytes
+        )
+        new_stream = io.BytesIO(response_body_bytes)
+        response["body"] = StreamingBody(new_stream, len(response_body_bytes))
+
+        model_id = kwargs.get("modelId", "")
+        inputs = _parse_embedding_input(body_data, model_id)
+        embeddings, dim, count = _parse_embedding_output(response_data, model_id)
+        prompt_tokens = _parse_embedding_tokens(response_data, model_id)
+        model_parameters = _get_embedding_model_parameters(body_data, model_id)
+
+        tracer.add_embedding_step_to_trace(
+            name="AWS Bedrock Embedding",
+            end_time=end_time,
+            inputs=inputs,
+            output=embeddings,
+            latency=(end_time - start_time) * 1000,
+            tokens=prompt_tokens,
+            prompt_tokens=prompt_tokens,
+            model=model_id,
+            model_parameters=model_parameters,
+            embedding_dimensions=dim,
+            embedding_count=count,
+            raw_output=response_data,
+            provider="Bedrock",
+            id=inference_id,
+            metadata={},
+        )
+
+    except Exception as e:
+        logger.error(
+            "Failed to trace the Bedrock embedding invocation with Openlayer. %s", e
+        )
+
+    return response
+
+
+def _parse_embedding_input(body_data: Dict[str, Any], model_id: str) -> Dict[str, Any]:
+    if model_id.startswith("amazon.titan-embed"):
+        return {"input": body_data.get("inputText", "")}
+    if model_id.startswith("cohere.embed"):
+        return {"input": body_data.get("texts", [])}
+    return {"input": body_data}
+
+
+def _parse_embedding_output(
+    response_data: Dict[str, Any], model_id: str
+) -> Tuple[Union[List[float], List[List[float]]], int, int]:
+    """Returns (embeddings, dimensions, count)."""
+    if model_id.startswith("amazon.titan-embed"):
+        emb = response_data.get("embedding", [])
+        return emb, len(emb), 1
+    if model_id.startswith("cohere.embed"):
+        embs = response_data.get("embeddings", [])
+        dim = len(embs[0]) if embs else 0
+        return embs, dim, len(embs)
+    return [], 0, 0
+
+
+def _parse_embedding_tokens(response_data: Dict[str, Any], model_id: str) -> int:
+    if model_id.startswith("amazon.titan-embed"):
+        return response_data.get("inputTextTokenCount", 0)
+    return 0
+
+
+def _get_embedding_model_parameters(
+    body_data: Dict[str, Any], model_id: str
+) -> Dict[str, Any]:
+    if model_id.startswith("amazon.titan-embed"):
+        return {
+            "dimensions": body_data.get("dimensions"),
+            "normalize": body_data.get("normalize"),
+        }
+    if model_id.startswith("cohere.embed"):
+        return {
+            "input_type": body_data.get("input_type"),
+            "truncate": body_data.get("truncate"),
+            "embedding_types": body_data.get("embedding_types"),
+        }
+    return {}
+
+
 def handle_streaming_invoke(
     invoke_func: callable,
     *args,