Langchain examples for testing

and send with otlp and full chatbot demo

---

So... turns out Langchain uses the v1beta1 prediction service client
under the hood directly.. So we should probably instrument that after
all instead of the main wrapper API.

It also has a streaming option so we should try to support that as well,
and it has ainvoke() for asyncio.

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/.dockerignore

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+.venv
+docker-compose.yaml

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/.python-version

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+3.13

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/README.md

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+This sample contains part of the LangGraph chatbot demo taken from
+https://python.langchain.com/docs/tutorials/chatbot, running with OTel instrumentation. It
+sends traces and logs to the OTel collector which sends them to GCP. Docker compose wraps
+everything to make it easy to run.
+
+## Running the example
+
+I recommend running in Cloud Shell, it's super simple. You will see GenAI spans in trace
+explorer right away. Make sure the Vertex and Trace APIs are enabled in the project.
+
+### Cloud Shell or GCE
+
+```sh
+git clone --branch=vertex-langgraph https://github.com/aabmass/opentelemetry-python-contrib.git
+cd opentelemetry-python-contrib/instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo
+docker compose up --build --abort-on-container-exit
+```
+
+### Locally with Application Default Credentials
+
+```sh
+git clone --branch=vertex-langgraph https://github.com/aabmass/opentelemetry-python-contrib.git
+cd opentelemetry-python-contrib/instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo
+
+# Export the credentials to `GOOGLE_APPLICATION_CREDENTIALS` environment variable so it is
+# available inside the docker containers
+export GOOGLE_APPLICATION_CREDENTIALS=$HOME/.config/gcloud/application_default_credentials.json
+# Lets collector read mounted config
+export USERID="$(id -u)"
+# Specify the project ID
+export GOOGLE_CLOUD_PROJECT=<your project id>
+docker compose up --build --abort-on-container-exit
+```

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/chatbot.py

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+# https://python.langchain.com/docs/tutorials/chatbot
+
+from os import environ
+from typing import Sequence
+
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    HumanMessage,
+    SystemMessage,
+    trim_messages,
+)
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_google_vertexai import ChatVertexAI
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import START, MessagesState, StateGraph
+from langgraph.graph.message import add_messages
+from typing_extensions import Annotated, TypedDict
+
+from opentelemetry import trace
+
+
+def main() -> None:
+    model = ChatVertexAI(
+        model="gemini-1.5-flash",
+        project=environ.get("GOOGLE_CLOUD_PROJECT", None),
+    )
+
+    # # Define a new graph
+    # workflow = StateGraph(state_schema=MessagesState)
+
+    # # Define the function that calls the model
+    # def call_model(state: MessagesState):
+    #     response = model.invoke(state["messages"])
+    #     return {"messages": response}
+
+    # # Define the (single) node in the graph
+    # workflow.add_edge(START, "model")
+    # workflow.add_node("model", call_model)
+
+    # # Add memory
+    # memory = MemorySaver()
+    # app = workflow.compile(checkpointer=memory)
+
+    # config = {"configurable": {"thread_id": "abc123"}}
+
+    # query = "Hi! I'm Bob."
+
+    # input_messages = [HumanMessage(query)]
+    # output = app.invoke({"messages": input_messages}, config)
+    # output["messages"][
+    #     -1
+    # ].pretty_print()  # output contains all messages in state
+
+    # query = "What's my name?"
+
+    # input_messages = [HumanMessage(query)]
+    # output = app.invoke({"messages": input_messages}, config)
+    # output["messages"][-1].pretty_print()
+
+    # config = {"configurable": {"thread_id": "abc234"}}
+
+    # input_messages = [HumanMessage(query)]
+    # output = app.invoke({"messages": input_messages}, config)
+    # output["messages"][-1].pretty_print()
+
+    # config = {"configurable": {"thread_id": "abc123"}}
+
+    # input_messages = [HumanMessage(query)]
+    # output = app.invoke({"messages": input_messages}, config)
+    # output["messages"][-1].pretty_print()
+
+    # prompt_template = ChatPromptTemplate.from_messages(
+    #     [
+    #         (
+    #             "system",
+    #             "You talk like a pirate. Answer all questions to the best of your ability.",
+    #         ),
+    #         MessagesPlaceholder(variable_name="messages"),
+    #     ]
+    # )
+
+    # workflow = StateGraph(state_schema=MessagesState)
+
+    # def call_model(state: MessagesState):
+    #     # highlight-start
+    #     prompt = prompt_template.invoke(state)
+    #     response = model.invoke(prompt)
+    #     # highlight-end
+    #     return {"messages": response}
+
+    # workflow.add_edge(START, "model")
+    # workflow.add_node("model", call_model)
+
+    # memory = MemorySaver()
+    # app = workflow.compile(checkpointer=memory)
+
+    # config = {"configurable": {"thread_id": "abc345"}}
+    # query = "Hi! I'm Jim."
+
+    # input_messages = [HumanMessage(query)]
+    # output = app.invoke({"messages": input_messages}, config)
+    # output["messages"][-1].pretty_print()
+
+    # query = "What is my name?"
+
+    # input_messages = [HumanMessage(query)]
+    # output = app.invoke({"messages": input_messages}, config)
+    # output["messages"][-1].pretty_print()
+
+    prompt_template = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "You are a helpful assistant. Answer all questions to the best of your ability in {language}.",
+            ),
+            MessagesPlaceholder(variable_name="messages"),
+        ]
+    )
+
+    # # highlight-next-line
+    class State(TypedDict):
+        # highlight-next-line
+        messages: Annotated[Sequence[BaseMessage], add_messages]
+        # highlight-next-line
+        language: str
+
+    # workflow = StateGraph(state_schema=State)
+
+    # def call_model(state: State):
+    #     prompt = prompt_template.invoke(state)
+    #     response = model.invoke(prompt)
+    #     return {"messages": [response]}
+
+    # workflow.add_edge(START, "model")
+    # workflow.add_node("model", call_model)
+
+    # memory = MemorySaver()
+    # app = workflow.compile(checkpointer=memory)
+
+    # config = {"configurable": {"thread_id": "abc456"}}
+    # query = "Hi! I'm Bob."
+    # language = "Spanish"
+
+    # input_messages = [HumanMessage(query)]
+    # output = app.invoke(
+    #     # highlight-next-line
+    #     {"messages": input_messages, "language": language},
+    #     config,
+    # )
+    # output["messages"][-1].pretty_print()
+
+    # query = "What is my name?"
+
+    # input_messages = [HumanMessage(query)]
+    # output = app.invoke(
+    #     {"messages": input_messages},
+    #     config,
+    # )
+    # output["messages"][-1].pretty_print()
+
+    trimmer = trim_messages(
+        max_tokens=65,
+        strategy="last",
+        token_counter=model,
+        include_system=True,
+        allow_partial=False,
+        start_on="human",
+    )
+
+    messages = [
+        SystemMessage(content="you're a good assistant"),
+        HumanMessage(content="hi! I'm bob"),
+        AIMessage(content="hi!"),
+        HumanMessage(content="I like vanilla ice cream"),
+        AIMessage(content="nice"),
+        HumanMessage(content="whats 2 + 2"),
+        AIMessage(content="4"),
+        HumanMessage(content="thanks"),
+        AIMessage(content="no problem!"),
+        HumanMessage(content="having fun?"),
+        AIMessage(content="yes!"),
+    ]
+
+    trimmer.invoke(messages)
+
+    workflow = StateGraph(state_schema=State)
+
+    def call_model(state: State):
+        # highlight-start
+        trimmed_messages = trimmer.invoke(state["messages"])
+        prompt = prompt_template.invoke(
+            {"messages": trimmed_messages, "language": state["language"]}
+        )
+        response = model.invoke(prompt)
+        # highlight-end
+        return {"messages": [response]}
+
+    workflow.add_edge(START, "model")
+    workflow.add_node("model", call_model)
+
+    memory = MemorySaver()
+    app = workflow.compile(checkpointer=memory)
+
+    config = {"configurable": {"thread_id": "abc567"}}
+    query = "What is my name?"
+    language = "English"
+
+    # highlight-next-line
+    input_messages = messages + [HumanMessage(query)]
+    output = app.invoke(
+        {"messages": input_messages, "language": language},
+        config,
+    )
+    output["messages"][-1].pretty_print()
+
+    config = {"configurable": {"thread_id": "abc678"}}
+    query = "What math problem did I ask?"
+    language = "English"
+
+    input_messages = messages + [HumanMessage(query)]
+    output = app.invoke(
+        {"messages": input_messages, "language": language},
+        config,
+    )
+    output["messages"][-1].pretty_print()
+
+    config = {"configurable": {"thread_id": "abc789"}}
+    query = "Hi I'm Todd, please tell me a joke."
+    language = "English"
+
+    input_messages = [HumanMessage(query)]
+    # highlight-next-line
+    for chunk, metadata in app.stream(
+        {"messages": input_messages, "language": language},
+        config,
+        # highlight-next-line
+        stream_mode="messages",
+    ):
+        if isinstance(chunk, AIMessage):  # Filter to just model responses
+            print(chunk.content, end="|")
+
+
+with trace.get_tracer(__name__).start_as_current_span("demo-root-span"):
+    main()

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/docker-compose.yaml

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+services:
+  app:
+    build:
+      dockerfile_inline: |
+        FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim
+        RUN apt-get update && apt-get install -y git
+
+        WORKDIR app/
+        COPY pyproject.toml uv.lock /app
+        RUN uv sync --frozen --no-dev
+        ENV PATH="/app/.venv/bin:$PATH"
+        COPY . /app
+        ENTRYPOINT []
+        CMD ["opentelemetry-instrument", "python", "chatbot.py"]
+    volumes:
+      - ${GOOGLE_APPLICATION_CREDENTIALS:-/dev/null}:${GOOGLE_APPLICATION_CREDENTIALS:-/dev/null}:ro
+    environment:
+      - OTEL_EXPORTER_OTLP_ENDPOINT=http://otelcol:4317
+      - OTEL_SERVICE_NAME=langgraph-chatbot-demo
+      - OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true
+      - OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true
+
+      - GOOGLE_CLOUD_PROJECT
+      - GOOGLE_CLOUD_QUOTA_PROJECT
+      - GOOGLE_APPLICATION_CREDENTIALS
+    depends_on:
+      - otelcol
+
+  otelcol:
+    image: otel/opentelemetry-collector-contrib:0.118.0
+    volumes:
+      - ./otel-collector-config.yaml:/etc/otelcol-contrib/config.yaml:ro
+      - ${GOOGLE_APPLICATION_CREDENTIALS:-/dev/null}:${GOOGLE_APPLICATION_CREDENTIALS:-/dev/null}:ro
+    environment:
+      - GOOGLE_CLOUD_PROJECT
+      - GOOGLE_CLOUD_QUOTA_PROJECT
+      - GOOGLE_APPLICATION_CREDENTIALS
+    # If the collector does not have permission to read the mounted volumes, set
+    # USERID=$(id -u) to run the container as the current user
+    user: $USERID
+
+volumes:
+  logs:

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/main.py

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+# pylint: skip-file
+from langchain_google_vertexai import ChatVertexAI
+
+# NOTE: OpenTelemetry Python Logs and Events APIs are in beta
+from opentelemetry import _events, _logs, trace
+
+# from opentelemetry.exporter.otlp.proto.grpc._log_exporter import (
+#     OTLPLogExporter,
+# )
+# from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
+#     OTLPSpanExporter,
+# )
+from opentelemetry.instrumentation.vertexai import VertexAIInstrumentor
+from opentelemetry.sdk._events import EventLoggerProvider
+from opentelemetry.sdk._logs import LoggerProvider
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import (
+    BatchSpanProcessor,
+    ConsoleSpanExporter,
+)
+
+# configure tracing
+trace.set_tracer_provider(TracerProvider())
+trace.get_tracer_provider().add_span_processor(
+    BatchSpanProcessor(ConsoleSpanExporter())
+)
+
+# 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())
+
+# instrument VertexAI
+VertexAIInstrumentor().instrument()
+
+
+def main():
+    model = ChatVertexAI(
+        model="gemini-1.5-flash", temperature=0.2, max_output_tokens=20
+    )
+    res = model.invoke("Hello, world!")
+    print(res)
+
+
+if __name__ == "__main__":
+    main()