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使用 Langfuse 跟踪 RAG 中的查询

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这是一个简单的实用手册，演示如何使用 Langfuse 跟踪 RAG 中的查询。RAG 管道是使用 LlamaIndex 实现的，并使用 Milvus Lite 来存储和检索文档。

在本快速入门中，我们将向您展示如何设置使用 Milvus Lite 作为向量存储的 LlamaIndex 应用程序。我们还将向您展示如何使用 Langfuse LlamaIndex 集成来跟踪您的应用程序。

Langfuse 是一个开源的 LLM 工程平台，帮助团队协作调试、分析和迭代他们的 LLM 应用程序。所有平台功能都原生集成，以加速开发工作流程。

Milvus Lite 是 Milvus 的轻量级版本，Milvus 是一个开源向量数据库，通过向量嵌入和相似性搜索为 AI 应用程序提供支持。

设置​

确保您已安装 llama-index 和 langfuse。

$ pip install llama-index langfuse llama-index-vector-stores-milvus --upgrade

初始化集成。从 Langfuse 项目设置 获取您的 API 密钥，并将 public_key secret_key 替换为您的密钥值。此示例使用 OpenAI 进行嵌入和聊天完成，因此您还需要在环境变量中指定您的 OpenAI 密钥。

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])

使用 Milvus Lite 建立索引​

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
)

查询​

# 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)

在 Langfuse 中探索跟踪​

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

完成！✨ 您可以在 Langfuse 项目中看到索引和查询的跟踪。

示例跟踪（公共链接）：

  1. Query
  2. Query (chat)

Langfuse 中的跟踪：

Langfuse Traces

对更高级的功能感兴趣？​

查看完整的集成文档了解更多高级功能及其使用方法：

  • 与 Langfuse Python SDK 和其他集成的互操作性
  • 向跟踪添加自定义元数据和属性