Langfuse SDK Performance Test

%pip install langfuse --upgrade

import os

# Get keys for your project from the project settings page: https://cloud.langfuse.com
os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...")
os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...")
os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com") # 🇪🇺 EU region
# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com

# Your openai key
os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...")

from langfuse import get_client

langfuse = get_client()

import pandas as pd
import timeit

def time_func(func, runs=100):
    durations = []
    for _ in range(runs):
        start = timeit.default_timer()
        func()
        stop = timeit.default_timer()
        durations.append(stop - start)

    desc = pd.Series(durations).describe()
    desc.index = [f'{name} (sec)' if name != 'count' else name for name in desc.index]
    return desc

time_func(lambda: langfuse.start_observation(name="perf-span").end())

root_span = langfuse.start_observation(name="perf-root-span")

time_func(lambda: root_span.start_observation(name="perf-generation", as_type="generation").end())

root_span.end()

def traced_operation():
    with langfuse.start_as_current_observation(as_type="span", name="perf-span"):
        pass

time_func(traced_operation)

from langfuse import observe

@observe()
def observed_function():
    return "ok"

time_func(observed_function)

%pip install langchain langchain-openai --upgrade

from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser

prompt = ChatPromptTemplate.from_template("what is the city {person} is from?")
model = ChatOpenAI(max_tokens=10)
chain = prompt | model | StrOutputParser()

from langfuse.langchain import CallbackHandler
langfuse_handler = CallbackHandler()

langchain_stats_no_langfuse = time_func(lambda: chain.invoke({"person":"Paul Graham"}))
langchain_stats_no_langfuse

count         100.000000
mean (sec)      0.529463
std (sec)       0.685193
min (sec)       0.306092
25% (sec)       0.373373
50% (sec)       0.407278
75% (sec)       0.530427
max (sec)       7.107237
dtype: float64

langchain_stats_with_langfuse = time_func(lambda: chain.invoke({"person":"Paul Graham"}, {"callbacks":[langfuse_handler]}))
langchain_stats_with_langfuse

count         100.000000
mean (sec)      0.618286
std (sec)       0.165149
min (sec)       0.464992
25% (sec)       0.518323
50% (sec)       0.598474
75% (sec)       0.675420
max (sec)       1.838614
dtype: float64

%pip install langfuse openai --upgrade --quiet

import openai

time_func(lambda: openai.chat.completions.create(
  model="gpt-3.5-turbo",
  messages=[
      {"role": "user", "content": "what is the city Paul Graham is from?"}],
  temperature=0,
  max_tokens=10,
))

count         100.000000
mean (sec)      0.524097
std (sec)       0.220446
min (sec)       0.288002
25% (sec)       0.395479
50% (sec)       0.507395
75% (sec)       0.571789
max (sec)       1.789671
dtype: float64

from langfuse.openai import openai

time_func(lambda: openai.chat.completions.create(
  model="gpt-3.5-turbo",
  messages=[
      {"role": "user", "content": "what is the city Paul Graham is from?"}],
  temperature=0,
  max_tokens=10,
))

count         100.000000
mean (sec)      0.515243
std (sec)       0.286902
min (sec)       0.283431
25% (sec)       0.378736
50% (sec)       0.435775
75% (sec)       0.558746
max (sec)       2.613779
dtype: float64

%pip install llama-index openinference-instrumentation-llama-index --upgrade --quiet

from llama_index.core import Document

doc1 = Document(text="""
Maxwell "Max" Silverstein, a lauded movie director, screenwriter, and producer, was born on October 25, 1978, in Boston, Massachusetts. A film enthusiast from a young age, his journey began with home movies shot on a Super 8 camera. His passion led him to the University of Southern California (USC), majoring in Film Production. Eventually, he started his career as an assistant director at Paramount Pictures. Silverstein's directorial debut, “Doors Unseen,” a psychological thriller, earned him recognition at the Sundance Film Festival and marked the beginning of a successful directing career.
""")
doc2 = Document(text="""
Throughout his career, Silverstein has been celebrated for his diverse range of filmography and unique narrative technique. He masterfully blends suspense, human emotion, and subtle humor in his storylines. Among his notable works are "Fleeting Echoes," "Halcyon Dusk," and the Academy Award-winning sci-fi epic, "Event Horizon's Brink." His contribution to cinema revolves around examining human nature, the complexity of relationships, and probing reality and perception. Off-camera, he is a dedicated philanthropist living in Los Angeles with his wife and two children.
""")

# Example index construction + LLM query
from llama_index.core import VectorStoreIndex

time_func(lambda: VectorStoreIndex.from_documents([doc1,doc2]))

count         100.000000
mean (sec)      0.171673
std (sec)       0.058332
min (sec)       0.112696
25% (sec)       0.136361
50% (sec)       0.157330
75% (sec)       0.178455
max (sec)       0.459417
dtype: float64

index = VectorStoreIndex.from_documents([doc1,doc2])
time_func(lambda: index.as_query_engine().query("What did he do growing up?"))

count         100.000000
mean (sec)      0.795817
std (sec)       0.338263
min (sec)       0.445060
25% (sec)       0.614282
50% (sec)       0.756573
75% (sec)       0.908411
max (sec)       3.495263
dtype: float64

from openinference.instrumentation.llama_index import LlamaIndexInstrumentor

# Initialize LlamaIndex instrumentation
LlamaIndexInstrumentor().instrument()

time_func(lambda: VectorStoreIndex.from_documents([doc1,doc2]))

index = VectorStoreIndex.from_documents([doc1,doc2])
time_func(lambda: index.as_query_engine().query("What did he do growing up?"))