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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.
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‎instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langchain/.dockerignore‎

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.venv
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docker-compose.yaml

‎instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langchain/.python-version‎

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3.13

‎instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langchain/README.md‎

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‎instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langchain/chatbot.py‎

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

‎instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langchain/docker-compose.yaml‎

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

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

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

‎instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langchain/otel-collector-config.yaml‎

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receivers:
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otlp:
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protocols:
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grpc:
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endpoint: "0.0.0.0:4317"
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extensions:
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googleclientauth:
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project: ${GOOGLE_CLOUD_PROJECT}
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quota_project: ${GOOGLE_CLOUD_QUOTA_PROJECT}
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scopes:
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- "https://www.googleapis.com/auth/trace.append"
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- "https://www.googleapis.com/auth/cloud-platform"
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processors:
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resource:
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attributes:
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- key: gcp.project_id
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value: ${GOOGLE_CLOUD_PROJECT}
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action: insert
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exporters:
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googlecloud:
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project: ${GOOGLE_CLOUD_PROJECT}
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log:
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default_log_name: "collector-otlp-logs"
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otlp:
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endpoint: https://telemetry.us-central1.rep.googleapis.com:443
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auth:
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authenticator: googleclientauth
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service:
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extensions: [googleclientauth]
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pipelines:
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traces:
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receivers: [otlp]
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processors: [resource]
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exporters: [otlp]
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logs:
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receivers: [otlp]
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processors: [resource]
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exporters: [googlecloud]

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

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[project]
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name = "langgraph-chatbot-demo"
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version = "0.1.0"
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description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.9"
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dependencies = [
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"langchain-core>=0.3.31",
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"langchain-google-vertexai>=2.0.7",
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"langgraph>0.2.27",
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"opentelemetry-distro>=0.50b0",
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"opentelemetry-exporter-otlp-proto-grpc>=1.29.0",
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"opentelemetry-instrumentation-vertexai",
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]
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[tool.uv.sources]
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opentelemetry-instrumentation-vertexai = { git = "https://github.com/aabmass/opentelemetry-python-contrib.git", subdirectory = "instrumentation-genai/opentelemetry-instrumentation-vertexai", branch = "vertex-langgraph" }

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