feat(alephalpha): Add AlephAlpha instrumentation

.cz.toml

@@ -6,6 +6,8 @@ major_version_zero = true
 update_changelog_on_bump = true
 version = "0.21.5"
 version_files = [
+    "packages/opentelemetry-instrumentation-alephalpha/pyproject.toml:^version",
+    "packages/opentelemetry-instrumentation-alephalpha/opentelemetry/instrumentation/alephalpha/version.py",
     "packages/opentelemetry-instrumentation-anthropic/pyproject.toml:^version",
     "packages/opentelemetry-instrumentation-anthropic/opentelemetry/instrumentation/anthropic/version.py",
     "packages/opentelemetry-instrumentation-bedrock/pyproject.toml:^version",

README.md

@@ -117,6 +117,8 @@ OpenLLMetry can instrument everything that [OpenTelemetry already instruments](h
 - ✅ Vertex AI (GCP)
 - ✅ IBM Watsonx AI
 - ✅ Together AI
+- ✅ Aleph Alpha
+
 
 ### Vector DBs
 

packages/opentelemetry-instrumentation-alephalpha/.flake8

@@ -0,0 +1,11 @@
+[flake8]
+exclude =
+	.git,
+	__pycache__,
+	build,
+	dist,
+	.tox,
+	venv,
+	.venv,
+	.pytest_cache
+max-line-length = 120

packages/opentelemetry-instrumentation-alephalpha/.python-version

@@ -0,0 +1 @@
+3.9.5

packages/opentelemetry-instrumentation-alephalpha/README.md

@@ -0,0 +1,33 @@
+# OpenTelemetry Aleph Alpha Instrumentation
+
+<a href="https://pypi.org/project/opentelemetry-instrumentation-alephalpha/">
+    <img src="https://badge.fury.io/py/opentelemetry-instrumentation-alephalpha.svg">
+</a>
+
+This library allows tracing calls to any of Aleph Alpha's endpoints sent with the official [Aleph Alpha Client](https://github.com/Aleph-Alpha/aleph-alpha-client).
+
+## Installation
+
+```bash
+pip install opentelemetry-instrumentation-alephalpha
+```
+
+## Example usage
+
+```python
+from opentelemetry.instrumentation.alephalpha import AlephAlphaInstrumentor
+
+AlephAlphaInstrumentor().instrument()
+```
+
+## Privacy
+
+**By default, this instrumentation logs prompts, completions, and embeddings to span attributes**. This gives you a clear visibility into how your LLM application is working, and can make it easy to debug and evaluate the quality of the outputs.
+
+However, you may want to disable this logging for privacy reasons, as they may contain highly sensitive data from your users. You may also simply want to reduce the size of your traces.
+
+To disable logging, set the `TRACELOOP_TRACE_CONTENT` environment variable to `false`.
+
+```bash
+TRACELOOP_TRACE_CONTENT=false
+```

packages/opentelemetry-instrumentation-alephalpha/opentelemetry/instrumentation/alephalpha/__init__.py

@@ -0,0 +1,172 @@
+"""OpenTelemetry Aleph Alpha instrumentation"""
+
+import logging
+import os
+from typing import Collection
+from opentelemetry.instrumentation.alephalpha.config import Config
+from opentelemetry.instrumentation.alephalpha.utils import dont_throw
+from wrapt import wrap_function_wrapper
+
+from opentelemetry import context as context_api
+from opentelemetry.trace import get_tracer, SpanKind
+from opentelemetry.trace.status import Status, StatusCode
+
+from opentelemetry.instrumentation.instrumentor import BaseInstrumentor
+from opentelemetry.instrumentation.utils import (
+    _SUPPRESS_INSTRUMENTATION_KEY,
+    unwrap,
+)
+
+from opentelemetry.semconv.ai import SpanAttributes, LLMRequestTypeValues
+from opentelemetry.instrumentation.alephalpha.version import __version__
+
+logger = logging.getLogger(__name__)
+
+_instruments = ("aleph_alpha_client >= 7.1.0, <8",)
+
+WRAPPED_METHODS = [
+    {
+        "method": "complete",
+        "span_name": "alephalpha.completion",
+    },
+]
+
+
+def should_send_prompts():
+    return (
+        os.getenv("TRACELOOP_TRACE_CONTENT") or "true"
+    ).lower() == "true" or context_api.get_value("override_enable_content_tracing")
+
+
+def _set_span_attribute(span, name, value):
+    if value is not None:
+        if value != "":
+            span.set_attribute(name, value)
+    return
+
+
+@dont_throw
+def _set_input_attributes(span, llm_request_type, args, kwargs):
+    _set_span_attribute(span, SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model"))
+
+    if should_send_prompts():
+        if llm_request_type == LLMRequestTypeValues.COMPLETION:
+            _set_span_attribute(span, f"{SpanAttributes.LLM_PROMPTS}.0.role", "user")
+            _set_span_attribute(
+                span,
+                f"{SpanAttributes.LLM_PROMPTS}.0.content",
+                args[0].prompt.items[0].text
+            )
+
+
+@dont_throw
+def _set_response_attributes(span, llm_request_type, response):
+    if should_send_prompts():
+        if llm_request_type == LLMRequestTypeValues.COMPLETION:
+            _set_span_attribute(
+                span,
+                f"{SpanAttributes.LLM_COMPLETIONS}.0.content",
+                response.completions[0].completion,
+            )
+            _set_span_attribute(
+                span, f"{SpanAttributes.LLM_COMPLETIONS}.0.role", "assistant"
+            )
+
+    input_tokens = getattr(response, "num_tokens_prompt_total", 0)
+    output_tokens = getattr(response, "num_tokens_generated", 0)
+
+    _set_span_attribute(
+        span,
+        SpanAttributes.LLM_USAGE_TOTAL_TOKENS,
+        input_tokens + output_tokens,
+    )
+    _set_span_attribute(
+        span,
+        SpanAttributes.LLM_USAGE_COMPLETION_TOKENS,
+        output_tokens,
+    )
+    _set_span_attribute(
+        span,
+        SpanAttributes.LLM_USAGE_PROMPT_TOKENS,
+        input_tokens,
+    )
+
+
+def _with_tracer_wrapper(func):
+    """Helper for providing tracer for wrapper functions."""
+
+    def _with_tracer(tracer, to_wrap):
+        def wrapper(wrapped, instance, args, kwargs):
+            return func(tracer, to_wrap, wrapped, instance, args, kwargs)
+
+        return wrapper
+
+    return _with_tracer
+
+
+def _llm_request_type_by_method(method_name):
+    if method_name == "complete":
+        return LLMRequestTypeValues.COMPLETION
+    else:
+        return LLMRequestTypeValues.UNKNOWN
+
+
+@_with_tracer_wrapper
+def _wrap(tracer, to_wrap, wrapped, instance, args, kwargs):
+    """Instruments and calls every function defined in TO_WRAP."""
+    if context_api.get_value(_SUPPRESS_INSTRUMENTATION_KEY):
+        return wrapped(*args, **kwargs)
+
+    name = to_wrap.get("span_name")
+    llm_request_type = _llm_request_type_by_method(to_wrap.get("method"))
+    span = tracer.start_span(
+        name,
+        kind=SpanKind.CLIENT,
+        attributes={
+            SpanAttributes.LLM_SYSTEM: "AlephAlpha",
+            SpanAttributes.LLM_REQUEST_TYPE: llm_request_type.value,
+        },
+    )
+    if span.is_recording():
+        _set_input_attributes(span, llm_request_type, args, kwargs)
+
+    response = wrapped(*args, **kwargs)
+
+    if response:
+        if span.is_recording():
+
+            _set_response_attributes(span, llm_request_type, response)
+            span.set_status(Status(StatusCode.OK))
+
+    span.end()
+    return response
+
+
+class AlephAlphaInstrumentor(BaseInstrumentor):
+    """An instrumentor for Aleph Alpha's client library."""
+
+    def __init__(self, exception_logger=None):
+        super().__init__()
+        Config.exception_logger = exception_logger
+
+    def instrumentation_dependencies(self) -> Collection[str]:
+        return _instruments
+
+    def _instrument(self, **kwargs):
+        tracer_provider = kwargs.get("tracer_provider")
+        tracer = get_tracer(__name__, __version__, tracer_provider)
+        for wrapped_method in WRAPPED_METHODS:
+            wrap_method = wrapped_method.get("method")
+            wrap_function_wrapper(
+                "aleph_alpha_client",
+                f"Client.{wrap_method}",
+                _wrap(tracer, wrapped_method),
+            )
+
+    def _uninstrument(self, **kwargs):
+        for wrapped_method in WRAPPED_METHODS:
+            wrap_object = wrapped_method.get("object")
+            unwrap(
+                f"aleph_alpha_client.Client.{wrap_object}",
+                wrapped_method.get("method"),
+            )

packages/opentelemetry-instrumentation-alephalpha/opentelemetry/instrumentation/alephalpha/config.py

@@ -0,0 +1,2 @@
+class Config:
+    exception_logger = None

packages/opentelemetry-instrumentation-alephalpha/opentelemetry/instrumentation/alephalpha/utils.py

@@ -0,0 +1,28 @@
+import logging
+from opentelemetry.instrumentation.alephalpha.config import Config
+import traceback
+
+
+def dont_throw(func):
+    """
+    A decorator that wraps the passed in function and logs exceptions instead of throwing them.
+
+    @param func: The function to wrap
+    @return: The wrapper function
+    """
+    # Obtain a logger specific to the function's module
+    logger = logging.getLogger(func.__module__)
+
+    def wrapper(*args, **kwargs):
+        try:
+            return func(*args, **kwargs)
+        except Exception as e:
+            logger.debug(
+                "OpenLLMetry failed to trace in %s, error: %s",
+                func.__name__,
+                traceback.format_exc(),
+            )
+            if Config.exception_logger:
+                Config.exception_logger(e)
+
+    return wrapper

packages/opentelemetry-instrumentation-alephalpha/opentelemetry/instrumentation/alephalpha/version.py

@@ -0,0 +1 @@
+__version__ = "0.21.2"