Cookbook LlamaIndex Integration (Instrumentation Module)

%pip install langfuse llama_index --upgrade

import os
 
# Get keys for your project from the project settings page
# https://cloud.langfuse.com
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" # 🇪🇺 EU region
# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # 🇺🇸 US region
 
# Your openai key
os.environ["OPENAI_API_KEY"] = ""

import llama_index.core.instrumentation as instrument
from langfuse.llama_index import LlamaIndexSpanHandler
 
langfuse_span_handler = LlamaIndexSpanHandler()
instrument.get_dispatcher().add_span_handler(langfuse_span_handler)

# Example context, thx ChatGPT
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
 
index = VectorStoreIndex.from_documents([doc1,doc2])

# Query
response = index.as_query_engine().query("What did he do growing up?")
print(response)

He made home movies using a Super 8 camera.

# Chat
response = index.as_chat_engine().chat("What did he do growing up?")
print(response)

He made home movies using a Super 8 camera growing up.