Added span support for llm invocation

Author: wrisa

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/manual/.env

@@ -0,0 +1,8 @@
+# Update this with your real OpenAI API key
+OPENAI_API_KEY=sk-YOUR_API_KEY
+
+# Uncomment and change to your OTLP endpoint
+# OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
+# OTEL_EXPORTER_OTLP_PROTOCOL=grpc
+
+OTEL_SERVICE_NAME=opentelemetry-python-langchain-manual

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/manual/README.rst

@@ -0,0 +1,39 @@
+OpenTelemetry Langcahin Instrumentation Example
+============================================
+
+This is an example of how to instrument Langchain when configuring OpenTelemetry SDK and instrumentations manually.
+
+When :code:`main.py <main.py>`_ is run, it exports traces to an OTLP-compatible endpoint.
+Traces include details such as the span name and other attributes.
+
+Note: :code:`.env <.env>`_ file configures additional environment variables:
+- :code:`OTEL_LOGS_EXPORTER=otlp` to specify exporter type.
+- :code:`OPENAI_API_KEY` open AI key for accessing the OpenAI API.
+- :code:`OTEL_EXPORTER_OTLP_ENDPOINT` to specify the endpoint for exporting traces (default is http://localhost:4317).
+
+Setup
+-----
+
+Minimally, update the :code:`.env <.env>`_ file with your :code:`OPENAI_API_KEY`.
+An OTLP compatible endpoint should be listening for traces http://localhost:4317.
+If not, update :code:`OTEL_EXPORTER_OTLP_ENDPOINT` as well.
+
+Next, set up a virtual environment like this:
+
+::
+
+    python3 -m venv .venv
+    source .venv/bin/activate
+    pip install "python-dotenv[cli]"
+    pip install -r requirements.txt
+
+Run
+---
+
+Run the example like this:
+
+::
+
+    dotenv run -- python main.py
+
+You should see the capital of France generated by Langchain ChatOpenAI while traces export to your configured observability tool.

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/manual/main.py

@@ -0,0 +1,45 @@
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_openai import ChatOpenAI
+
+from opentelemetry.instrumentation.langchain import LangChainInstrumentor
+from opentelemetry import trace
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+
+# Configure tracing
+trace.set_tracer_provider(TracerProvider())
+span_processor = BatchSpanProcessor(OTLPSpanExporter())
+trace.get_tracer_provider().add_span_processor(span_processor)
+
+def main():
+
+    # Set up instrumentation
+    LangChainInstrumentor().instrument()
+
+    # ChatOpenAI
+    llm = ChatOpenAI(
+        model="gpt-3.5-turbo",
+        temperature=0.1,
+        max_tokens=100,
+        top_p=0.9,
+        frequency_penalty=0.5,
+        presence_penalty=0.5,
+        stop_sequences=["\n", "Human:", "AI:"],
+        seed=100,
+    )
+
+    messages = [
+        SystemMessage(content="You are a helpful assistant!"),
+        HumanMessage(content="What is the capital of France?"),
+    ]
+
+    result = llm.invoke(messages)
+
+    print("LLM output:\n", result)
+
+    # Un-instrument after use
+    LangChainInstrumentor().uninstrument()
+
+if __name__ == "__main__":
+    main()

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/manual/requirements.txt

@@ -0,0 +1,7 @@
+langchain==0.3.21
+langchain_openai
+opentelemetry-sdk~=1.30.0
+opentelemetry-exporter-otlp-proto-grpc~=1.30.0
+
+# Uncomment after lanchain instrumetation is released
+# opentelemetry-instrumentation-langchain~=2.0b0.dev

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/zero-code/.env

@@ -0,0 +1,8 @@
+# Update this with your real OpenAI API key
+OPENAI_API_KEY=sk-YOUR_API_KEY
+
+# Uncomment and change to your OTLP endpoint
+# OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
+# OTEL_EXPORTER_OTLP_PROTOCOL=grpc
+
+OTEL_SERVICE_NAME=opentelemetry-python-langchain-zero-code

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/zero-code/README.rst

@@ -0,0 +1,40 @@
+OpenTelemetry Langchain Zero-Code Instrumentation Example
+======================================================
+
+This is an example of how to instrument Langchain with zero code changes,
+using `opentelemetry-instrument`.
+
+When :code:`main.py <main.py>`_ is run, it exports traces to an OTLP-compatible endpoint.
+Traces include details such as the span name and other attributes.
+
+Note: :code:`.env <.env>`_ file configures additional environment variables:
+- :code:`OTEL_LOGS_EXPORTER=otlp` to specify exporter type.
+- :code:`OPENAI_API_KEY` open AI key for accessing the OpenAI API.
+- :code:`OTEL_EXPORTER_OTLP_ENDPOINT` to specify the endpoint for exporting traces (default is http://localhost:4317).
+
+Setup
+-----
+
+Minimally, update the :code:`.env <.env>`_ file with your :code:`OPENAI_API_KEY`.
+An OTLP compatible endpoint should be listening for traces http://localhost:4317.
+If not, update :code:`OTEL_EXPORTER_OTLP_ENDPOINT` as well.
+
+Next, set up a virtual environment like this:
+
+::
+
+    python3 -m venv .venv
+    source .venv/bin/activate
+    pip install "python-dotenv[cli]"
+    pip install -r requirements.txt
+
+Run
+---
+
+Run the example like this:
+
+::
+
+    dotenv run -- opentelemetry-instrument python main.py
+
+You should see the capital of France generated by Langchain ChatOpenAI while traces export to your configured observability tool.

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/zero-code/main.py

@@ -0,0 +1,58 @@
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_openai import ChatOpenAI
+
+# from opentelemetry import trace
+# from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
+# from opentelemetry.sdk.trace import TracerProvider
+# from opentelemetry.sdk.trace.export import BatchSpanProcessor
+# # Configure tracing
+# trace.set_tracer_provider(TracerProvider())
+# span_processor = BatchSpanProcessor(OTLPSpanExporter())
+# trace.get_tracer_provider().add_span_processor(span_processor)
+
+from flask import Flask, request, jsonify
+
+# Set up logging
+import logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = Flask(__name__)
+
+@app.post("/chat")
+def chat():
+    try:
+        print("LLM output1:\n")
+        payload = request.get_json(silent=True) or request.form
+        llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.1, max_tokens=100)
+        print("LLM output2:\n")
+        messages = [
+            SystemMessage(content="You are a helpful assistant!"),
+            HumanMessage(content="What is the capital of France?"),
+        ]
+        print("LLM output3:\n")
+        result = llm.invoke(messages).content
+        print("LLM output:\n", result)
+        # return result.content
+    except Exception as e:
+        logger.error(f"Error processing chat request: {e}")
+        str = f"Error processing chat request: {e}"
+        return jsonify({str}), 500
+    # from opentelemetry import  _logs
+    # from opentelemetry.exporter.otlp.proto.grpc._log_exporter import (
+    #     OTLPLogExporter,
+    # )
+    # from opentelemetry.sdk._logs import LoggerProvider
+    # from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
+    # _logs.set_logger_provider(LoggerProvider())
+    # _logs.get_logger_provider().add_log_record_processor(
+    #     BatchLogRecordProcessor(OTLPLogExporter())
+    # )
+    # import logging
+    # logger = logging.getLogger(__name__)
+    # logging.basicConfig(level=logging.DEBUG)
+    # logger.debug("OpenTelemetry instrumentation for LangChain encountered an error in $$$$$$$$$$$$$$$$")
+
+if __name__ == "__main__":
+    # When run directly: python app.py
+    app.run(host="0.0.0.0", port=5003)

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/zero-code/requirements.txt

@@ -0,0 +1,7 @@
+langchain==0.3.21
+langchain_openai
+opentelemetry-sdk~=1.30.0
+opentelemetry-exporter-otlp-proto-grpc~=1.30.0
+
+# Uncomment after lanchain instrumetation is released
+# opentelemetry-instrumentation-langchain~=2.0b0.dev

instrumentation-genai/opentelemetry-instrumentation-langchain/pyproject.toml

@@ -25,9 +25,9 @@ classifiers = [
   "Programming Language :: Python :: 3.13",
 ]
 dependencies = [
-  "opentelemetry-api ~= 1.30",
-  "opentelemetry-instrumentation ~= 0.51b0",
-  "opentelemetry-semantic-conventions ~= 0.51b0"
+  "opentelemetry-api == 1.30",
+  "opentelemetry-instrumentation == 0.51b0",
+  "opentelemetry-semantic-conventions == 0.51b0"
 ]
 
 [project.optional-dependencies]

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

@@ -0,0 +1,113 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Langchain instrumentation supporting `ChatOpenAI`, it can be enabled by
+using ``LangChainInstrumentor``.
+
+Usage
+-----
+.. code:: python
+    from opentelemetry.instrumentation.langchain import LangChainInstrumentor
+    from langchain_core.messages import HumanMessage, SystemMessage
+    from langchain_openai import ChatOpenAI
+
+    LangChainInstrumentor().instrument()
+    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0, max_tokens=1000)
+    messages = [
+        SystemMessage(content="You are a helpful assistant!"),
+        HumanMessage(content="What is the capital of France?"),
+    ]
+    result = llm.invoke(messages)
+    LangChainInstrumentor().uninstrument()
+
+API
+---
+"""
+
+from typing import Collection
+
+from wrapt import wrap_function_wrapper
+
+from opentelemetry.instrumentation.langchain.config import Config
+from opentelemetry.instrumentation.langchain.version import __version__
+from opentelemetry.instrumentation.langchain.package import _instruments
+from opentelemetry.instrumentation.langchain.callback_handler import (
+    OpenTelemetryLangChainCallbackHandler,
+)
+from opentelemetry.instrumentation.instrumentor import BaseInstrumentor
+from opentelemetry.instrumentation.utils import unwrap
+from opentelemetry.trace import get_tracer
+from opentelemetry.semconv.schemas import Schemas
+
+
+class LangChainInstrumentor(BaseInstrumentor):
+    """
+    OpenTelemetry instrumentor for LangChain.
+    This adds a custom callback handler to the LangChain callback manager
+    to capture LLM telemetry.
+    """
+
+    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):
+        """
+        Enable Langchain instrumentation.
+        """
+        tracer_provider = kwargs.get("tracer_provider")
+        tracer = get_tracer(
+            __name__,
+            __version__,
+            tracer_provider,
+            schema_url=Schemas.V1_28_0.value,
+        )
+
+        otel_callback_handler = OpenTelemetryLangChainCallbackHandler(
+            tracer=tracer,
+        )
+
+        wrap_function_wrapper(
+            module="langchain_core.callbacks",
+            name="BaseCallbackManager.__init__",
+            wrapper=_BaseCallbackManagerInitWrapper(otel_callback_handler),
+        )
+
+    def _uninstrument(self, **kwargs):
+        """
+        Cleanup instrumentation (unwrap).
+        """
+        unwrap("langchain_core.callbacks.base", "BaseCallbackManager.__init__")
+
+
+class _BaseCallbackManagerInitWrapper:
+    """
+    Wrap the BaseCallbackManager __init__ to insert custom callback handler in the manager's handlers list.
+    """
+
+    def __init__(self, callback_handler):
+        self._otel_handler = callback_handler
+
+    def __call__(self, wrapped, instance, args, kwargs):
+        wrapped(*args, **kwargs)
+        # Ensure our OTel callback is present if not already.
+        for handler in instance.inheritable_handlers:
+            if isinstance(handler, type(self._otel_handler)):
+                break
+        else:
+            instance.add_handler(self._otel_handler, inherit=True)