Cookbook：LlamaIndex 与 Milvus 集成

%pip install llama-index "langfuse<3.0.0" llama-index-vector-stores-milvus --upgrade

import os
 
# Get keys for your project from the project settings page
# https://litefuse.cloud
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["LANGFUSE_HOST"] = "https://litefuse.cloud"
 
# Your openai key
os.environ["OPENAI_API_KEY"] = ""

from llama_index.core import Settings
from llama_index.core.callbacks import CallbackManager
from langfuse.llama_index import LlamaIndexCallbackHandler
 
langfuse_callback_handler = LlamaIndexCallbackHandler()
Settings.callback_manager = CallbackManager([langfuse_callback_handler])

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
from llama_index.core import StorageContext
from llama_index.vector_stores.milvus import MilvusVectorStore
 
 
vector_store = MilvusVectorStore(
    uri="tmp/milvus_demo.db", dim=1536, overwrite=False
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
 
index = VectorStoreIndex.from_documents(
    [doc1,doc2], storage_context=storage_context
)

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

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

# As we want to immediately see result in Litefuse, we need to flush the callback handler
langfuse_callback_handler.flush()