Livro de receitas - Integração LlamaIndex & Milvus
Este é um livro de receitas simples que demonstra como usar a integração LlamaIndex Langfuse. Utiliza o Milvus Lite para armazenar os documentos e a consulta.
O Milvus Lite é a versão leve do Milvus, uma base de dados vetorial de código aberto que potencia as aplicações de IA com embeddings vectoriais e pesquisa de semelhanças.
Configuração
Certifique-se de que tem os sites llama-index e langfuse instalados.
$ pip install llama-index langfuse llama-index-vector-stores-milvus --upgrade
Inicialize a integração. Obtenha as suas chaves de API nas definições do projeto Langfuse e substitua public_key secret_key pelos seus valores de chave. Este exemplo utiliza o OpenAI para incorporação e conclusão do chat, pelo que também precisa de especificar a sua chave OpenAI na variável de ambiente.
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"] = ""
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])
Índice usando Milvus Lite
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
)
Consultar
# 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)
Explorar traços no Langfuse
# As we want to immediately see result in Langfuse, we need to flush the callback handler
langfuse_callback_handler.flush()
Concluído! Pode ver os vestígios do seu índice e consulta no seu projeto Langfuse.
Exemplos de traços (ligações públicas):
Traço em Langfuse:
Traços de Langfuse
Interessado em recursos mais avançados?
Consulte os documentos completos de integração para saber mais sobre os recursos avançados e como usá-los:
- Interoperabilidade com o Langfuse Python SDK e outras integrações
- Adicionar metadados e atributos personalizados aos traços
