LangSmith

# 1. Install LangSmith
pip install langsmith

# 2. Set environment variables
export LANGCHAIN_TRACING_V2="true"
export LANGCHAIN_API_KEY="your-langsmith-api-key"
export LANGCHAIN_PROJECT="my-project"

# 3. Run your LangChain code - traces are automatic!
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4")
response = llm.invoke("Hello, how are you?")
print(response.content)

# View traces at: https://smith.langchain.com

from langsmith import traceable
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4")

@traceable(name="process_query")
def process_user_query(query: str) -> str:
    """Process a user query with context."""
    
    # This will appear as a child span
    context = fetch_context(query)
    
    # LLM call is automatically traced
    prompt = f"Context: {context}\n\nQuestion: {query}"
    response = llm.invoke(prompt)
    
    return response.content

@traceable(name="fetch_context")
def fetch_context(query: str) -> str:
    # Simulated database lookup
    return "Relevant context from database..."

# Run it
result = process_user_query("What is LangSmith?")
print(result)

from langsmith import Client
from langsmith.evaluation import evaluate

client = Client()

# Create a dataset
dataset = client.create_dataset("qa-examples")
client.create_examples(
    inputs=[
        {"question": "What is Python?"},
        {"question": "What is JavaScript?"},
    ],
    outputs=[
        {"answer": "A programming language"},
        {"answer": "A scripting language"},
    ],
    dataset_id=dataset.id,
)

# Define your target function
def answer_question(inputs: dict) -> dict:
    response = llm.invoke(inputs["question"])
    return {"answer": response.content}

# Run evaluation
results = evaluate(
    answer_question,
    data="qa-examples",
    evaluators=["correctness", "helpfulness"],
)
print(results)