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langsmith_setup.py
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"""
LangSmith Setup and Observability
Production monitoring for LangChain/LangGraph
"""
import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langsmith import traceable
from langsmith.run_trees import RunTree
from dotenv import load_dotenv
load_dotenv()
# Enable tracing
os.environ["LANGSMITH_TRACING"] = "true"
@traceable(name="basic_chaining")
def demo_basic_tracing():
"""Basic LangSmith tracing."""
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
prompt = ChatPromptTemplate.from_template("Explain {topic} in one sentence.")
chain = prompt | llm | StrOutputParser()
print("Basic Tracing Demo:\n")
print("Running chain with LangSmith tracing enabled...")
result = chain.invoke({"topic": "machine learning"})
print(f"Result: {result}")
print("\nCheck LangSmith dashboard for trace details.")
@traceable(name="named_runs_demo", tags=["production", "summarization"])
def demo_named_runs():
"""Name your runs for easier identification."""
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
prompt = ChatPromptTemplate.from_template("Summarize: {text}")
chain = prompt | llm | StrOutputParser()
print("\nNamed Runs Demo:\n")
result = chain.invoke(
{"text": "LangSmith provides observability for LLM applications."}
)
print(f"Result: {result}")
print("Run tagged with 'production', 'summarization'")
@traceable(name="trace_with_metadata_demo", tags=["metadata", "filtering"])
def demo_trace_with_metadata(user_id: str, request_type: str):
"""Add metadata to traces for filtering."""
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
# Metadata is automatically captured
result = llm.invoke(f"Hello from user {user_id}")
return result.content
if __name__ == "__main__":
demo_basic_tracing()
demo_named_runs()
demo_trace_with_metadata(user_id="user_123", request_type="greeting")