Langfuse

from langfuse import Langfuse
from langfuse.decorators import observe, langfuse_context
from openai import OpenAI

# Initialize Langfuse (auto-reads LANGFUSE_* env vars)
langfuse = Langfuse()
client = OpenAI()

@observe()
def generate_story(topic: str) -> str:
    """Generate a story with automatic tracing."""
    
    # Add custom metadata to the trace
    langfuse_context.update_current_observation(
        metadata={"topic": topic}
    )
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a creative storyteller."},
            {"role": "user", "content": f"Write a short story about {topic}"}
        ]
    )
    
    return response.choices[0].message.content

# Every call is automatically traced
story = generate_story("a robot learning to paint")
print(story)

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langfuse.callback import CallbackHandler

# Initialize Langfuse callback handler
langfuse_handler = CallbackHandler(
    public_key="pk-...",
    secret_key="sk-...",
    host="https://cloud.langfuse.com"  # or your self-hosted URL
)

# Create LangChain components
llm = ChatOpenAI(model="gpt-4")
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{input}")
])

chain = prompt | llm

# Run with Langfuse tracing enabled
result = chain.invoke(
    {"input": "Explain quantum computing simply"},
    config={"callbacks": [langfuse_handler]}
)

print(result.content)

from langfuse import Langfuse
from openai import OpenAI

langfuse = Langfuse()
client = OpenAI()

# Fetch a prompt from Langfuse (version controlled in UI)
prompt = langfuse.get_prompt("customer-support-v2")

# Compile the prompt with variables
compiled_prompt = prompt.compile(
    customer_name="John",
    issue="billing inquiry",
    product="Enterprise Plan"
)

# Use compiled prompt with OpenAI
response = client.chat.completions.create(
    model=prompt.config["model"],  # Model defined in prompt config
    messages=[{"role": "user", "content": compiled_prompt}],
    temperature=prompt.config.get("temperature", 0.7)
)

# Link generation to the prompt for analytics
langfuse.generation(
    name="customer-support-response",
    prompt=prompt,
    output=response.choices[0].message.content
)