langsmith-observability

from langsmith import traceable
from openai import OpenAI
 
client = OpenAI()
 
@traceable
def generate_response(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content
 
# Automatically traced to LangSmith
result = generate_response("What is machine learning?")

from langsmith.wrappers import wrap_openai
from openai import OpenAI
 
# Wrap client for automatic tracing
client = wrap_openai(OpenAI())
 
# All calls automatically traced
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

from langsmith import traceable
 
@traceable(run_type="chain")
def process_query(query: str) -> str:
    # Parent run
    context = retrieve_context(query)  # Child run
    response = generate_answer(query, context)  # Child run
    return response
 
@traceable(run_type="retriever")
def retrieve_context(query: str) -> list:
    return vector_store.search(query)
 
@traceable(run_type="llm")
def generate_answer(query: str, context: list) -> str:
    return llm.invoke(f"Context: {context}\n\nQuestion: {query}")

import os
os.environ["LANGSMITH_PROJECT"] = "my-project"
 
# Or per-function
@traceable(project_name="my-project")
def my_function():
    pass

from langsmith import Client
 
client = Client()
 
# List runs
runs = list(client.list_runs(
    project_name="my-project",
    filter='eq(status, "success")',
    limit=100
))
 
# Get run details
run = client.read_run(run_id="...")
 
# Create feedback
client.create_feedback(
    run_id="...",
    key="correctness",
    score=0.9,
    comment="Good answer"
)

from langsmith import Client
 
client = Client()
 
# Create dataset
dataset = client.create_dataset("qa-test-set", description="QA evaluation")
 
# Add examples
client.create_examples(
    inputs=[
        {"question": "What is Python?"},
        {"question": "What is ML?"}
    ],
    outputs=[
        {"answer": "A programming language"},
        {"answer": "Machine learning"}
    ],
    dataset_id=dataset.id
)

from langsmith import evaluate
 
def my_model(inputs: dict) -> dict:
    # Your model logic
    return {"answer": generate_answer(inputs["question"])}
 
def correctness_evaluator(run, example):
    prediction = run.outputs["answer"]
    reference = example.outputs["answer"]
    score = 1.0 if reference.lower() in prediction.lower() else 0.0
    return {"key": "correctness", "score": score}
 
results = evaluate(
    my_model,
    data="qa-test-set",
    evaluators=[correctness_evaluator],
    experiment_prefix="v1"
)
 
print(f"Average score: {results.aggregate_metrics['correctness']}")

from langsmith.evaluation import LangChainStringEvaluator
 
# Use LangChain evaluators
results = evaluate(
    my_model,
    data="qa-test-set",
    evaluators=[
        LangChainStringEvaluator("qa"),
        LangChainStringEvaluator("cot_qa")
    ]
)

from langsmith import tracing_context
 
with tracing_context(
    project_name="experiment-1",
    tags=["production", "v2"],
    metadata={"version": "2.0"}
):
    # All traceable calls inherit context
    result = my_function()

from langsmith import trace
 
with trace(
    name="custom_operation",
    run_type="tool",
    inputs={"query": "test"}
) as run:
    result = do_something()
    run.end(outputs={"result": result})

def sanitize_inputs(inputs: dict) -> dict:
    if "password" in inputs:
        inputs["password"] = "***"
    return inputs
 
@traceable(process_inputs=sanitize_inputs)
def login(username: str, password: str):
    return authenticate(username, password)

import os
os.environ["LANGSMITH_TRACING_SAMPLING_RATE"] = "0.1"  # 10% sampling

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
 
# Tracing enabled automatically with LANGSMITH_TRACING=true
llm = ChatOpenAI(model="gpt-4o")
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{input}")
])
 
chain = prompt | llm
 
# All chain runs traced automatically
response = chain.invoke({"input": "Hello!"})

from langsmith import Client
 
client = Client()
 
# Pull prompt from hub
prompt = client.pull_prompt("my-org/qa-prompt")
 
# Use in application
result = prompt.invoke({"question": "What is AI?"})

from langsmith import AsyncClient
 
async def main():
    client = AsyncClient()
 
    runs = []
    async for run in client.list_runs(project_name="my-project"):
        runs.append(run)
 
    return runs

from langsmith import Client
 
client = Client()
 
# Collect user feedback
def record_feedback(run_id: str, user_rating: int, comment: str = None):
    client.create_feedback(
        run_id=run_id,
        key="user_rating",
        score=user_rating / 5.0,  # Normalize to 0-1
        comment=comment
    )
 
# In your application
record_feedback(run_id="...", user_rating=4, comment="Helpful response")

from langsmith import test
 
@test
def test_qa_accuracy():
    result = my_qa_function("What is Python?")
    assert "programming" in result.lower()

from langsmith import evaluate
 
def run_evaluation():
    results = evaluate(
        my_model,
        data="regression-test-set",
        evaluators=[accuracy_evaluator]
    )
 
    # Fail CI if accuracy drops
    assert results.aggregate_metrics["accuracy"] >= 0.9, \
        f"Accuracy {results.aggregate_metrics['accuracy']} below threshold"

import os
# Ensure tracing is enabled
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = "your-key"
 
# Verify connection
from langsmith import Client
client = Client()
print(client.list_projects())  # Should work

# Enable background batching (default)
from langsmith import Client
client = Client(auto_batch_tracing=True)
 
# Or use sampling
os.environ["LANGSMITH_TRACING_SAMPLING_RATE"] = "0.1"

# Hide sensitive/large fields
@traceable(
    process_inputs=lambda x: {k: v for k, v in x.items() if k != "large_field"}
)
def my_function(data):
    pass