First commit for langchain instrumentation

Author: wrisa

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

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+# 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
+
+# Change to 'false' to hide prompt and completion content
+OTEL_INSTRUMENTATION_LANGCHAIN_CAPTURE_MESSAGE_CONTENT=true
+
+OTEL_SERVICE_NAME=opentelemetry-python-langchain

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

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+OpenTelemetry LangChain Instrumentation Example
+==============================================
+
+This is an example of how to instrument LangChain calls when configuring
+OpenTelemetry SDK and Instrumentations manually.
+
+When :code:`main.py <main.py>` is run, it exports traces, metrics (and optionally logs)
+to an OTLP-compatible endpoint. Traces include details such as the span name and other attributes.
+Exports metrics like input and output token usage and durations for each operation.
+
+Environment variables:
+
+- ``OTEL_INSTRUMENTATION_LANGCHAIN_CAPTURE_MESSAGE_CONTENT=true`` can be used
+  to capture full prompt/response content.
+
+Setup
+-----
+
+1. **Update** the :code:`.env <.env>` file with any environment variables you
+   need (e.g., your OpenAI key, or :code:`OTEL_EXPORTER_OTLP_ENDPOINT` if not
+   using the default http://localhost:4317).
+2. Set up a virtual environment:
+
+   .. code-block:: console
+
+       python3 -m venv .venv
+       source .venv/bin/activate
+       pip install "python-dotenv[cli]"
+       pip install -r requirements.txt
+
+3. **(Optional)** Install a development version of the new instrumentation:
+
+   .. code-block:: console
+
+       # E.g., from a local path or a git repo
+       pip install -e /path/to/opentelemetry-python-contrib/instrumentation-genai/opentelemetry-instrumentation-langchain
+Run
+---
+
+Run the example like this:
+
+.. code-block:: console
+
+    dotenv run -- python main.py
+
+You should see an example span output while traces are exported to your
+configured observability tool.

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

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+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_openai import ChatOpenAI
+
+from opentelemetry.instrumentation.langchain import LangChainInstrumentor
+
+from opentelemetry import _events, _logs, trace, metrics
+from opentelemetry.exporter.otlp.proto.grpc._log_exporter import (
+    OTLPLogExporter,
+)
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
+    OTLPSpanExporter,
+)
+from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter
+
+from opentelemetry.sdk._events import EventLoggerProvider
+from opentelemetry.sdk._logs import LoggerProvider
+from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+from opentelemetry.sdk.metrics import MeterProvider
+from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
+
+# configure tracing
+trace.set_tracer_provider(TracerProvider())
+trace.get_tracer_provider().add_span_processor(
+    BatchSpanProcessor(OTLPSpanExporter())
+)
+
+metric_reader = PeriodicExportingMetricReader(OTLPMetricExporter())
+metrics.set_meter_provider(MeterProvider(metric_readers=[metric_reader]))
+
+# configure logging and events
+_logs.set_logger_provider(LoggerProvider())
+_logs.get_logger_provider().add_log_record_processor(
+    BatchLogRecordProcessor(OTLPLogExporter())
+)
+_events.set_event_logger_provider(EventLoggerProvider())
+
+def main():
+
+    # Set up instrumentation
+    LangChainInstrumentor().instrument()
+
+    # ChatOpenAI
+    llm = ChatOpenAI(model="gpt-3.5-turbo")
+    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

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+langchain==0.3.21 #todo: find the lowest compatible version
+langchain_openai
+
+opentelemetry-sdk~=1.31.1
+opentelemetry-exporter-otlp-proto-grpc~=1.31.1
+
+python-dotenv[cli]
+
+# For local development: `pip install -e /path/to/opentelemetry-instrumentation-langchain`

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

@@ -0,0 +1,11 @@
+# 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
+
+# Change to 'false' to hide prompt and completion content
+OTEL_INSTRUMENTATION_LANGCHAIN_CAPTURE_MESSAGE_CONTENT=true
+
+OTEL_SERVICE_NAME=opentelemetry-python-langchain

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

@@ -0,0 +1,47 @@
+OpenTelemetry LangChain Instrumentation Example
+==============================================
+
+This is an example of how to instrument LangChain calls when configuring
+OpenTelemetry SDK and Instrumentations manually.
+
+When :code:`main.py <main.py>` is run, it exports traces (and optionally logs)
+to an OTLP-compatible endpoint. Traces include details such as the chain name,
+LLM usage, token usage, and durations for each operation.
+
+Environment variables:
+
+- ``OTEL_INSTRUMENTATION_LANGCHAIN_CAPTURE_MESSAGE_CONTENT=true`` can be used
+  to capture full prompt/response content.
+
+Setup
+-----
+
+1. **Update** the :code:`.env <.env>` file with any environment variables you
+   need (e.g., your OpenAI key, or :code:`OTEL_EXPORTER_OTLP_ENDPOINT` if not
+   using the default http://localhost:4317).
+2. Set up a virtual environment:
+
+   .. code-block:: console
+
+       python3 -m venv .venv
+       source .venv/bin/activate
+       pip install "python-dotenv[cli]"
+       pip install -r requirements.txt
+
+3. **(Optional)** Install a development version of the new instrumentation:
+
+   .. code-block:: console
+
+       # E.g., from a local path or a git repo
+       pip install -e /path/to/opentelemetry-python-contrib/instrumentation-genai/opentelemetry-instrumentation-langchain
+Run
+---
+
+Run the example like this:
+
+.. code-block:: console
+
+    dotenv run -- opentelemetry-instrument python main.py
+
+You should see an example chain output while traces are exported to your
+configured observability tool.

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

@@ -0,0 +1,17 @@
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_openai import ChatOpenAI
+
+def main():
+
+    llm = ChatOpenAI(model="gpt-3.5-turbo")
+
+    messages = [
+        SystemMessage(content="You are a helpful assistant!"),
+        HumanMessage(content="What is the capital of France?"),
+    ]
+
+    result = llm.invoke(messages).content
+    print("LLM output:\n", result)
+
+if __name__ == "__main__":
+    main()

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

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+langchain==0.3.21 #todo: find the lowest compatible version
+langchain_openai
+
+opentelemetry-sdk~=1.31.1
+opentelemetry-exporter-otlp-proto-grpc~=1.31.1
+
+python-dotenv[cli]
+
+# For local developmen: `pip install -e /path/to/opentelemetry-instrumentation-langchain`
+