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NebiusRetriever 允许使用来自 Nebius AI Studio 的嵌入进行高效的相似性搜索。它利用高质量的嵌入模型来对文档进行语义搜索。 此检索器针对需要对文档集合执行相似性搜索但不需要将向量持久化到向量数据库的场景进行了优化。它使用矩阵运算在内存中执行向量相似性搜索，使其对于中等大小的文档集合高效。

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设置

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安装

Nebius 集成可以通过 pip 安装 
pip install -U langchain-nebius

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凭据

Nebius 需要一个 API 密钥，该密钥可以作为初始化参数 `api_key` 传入，或者设置为环境变量 `NEBIUS_API_KEY`。您可以通过在 Nebius AI Studio 上创建账户来获取 API 密钥。 
import getpass
import os

# Make sure you've set your API key as an environment variable
if "NEBIUS_API_KEY" not in os.environ:
    os.environ["NEBIUS_API_KEY"] = getpass.getpass("Enter your Nebius API key: ")

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实例化

NebiusRetriever 需要一个 NebiusEmbeddings 实例和一个文档列表。以下是初始化它的方法 
from langchain_core.documents import Document
from langchain_nebius import NebiusEmbeddings, NebiusRetriever

# Create sample documents
docs = [
    Document(
        page_content="Paris is the capital of France", metadata={"country": "France"}
    ),
    Document(
        page_content="Berlin is the capital of Germany", metadata={"country": "Germany"}
    ),
    Document(
        page_content="Rome is the capital of Italy", metadata={"country": "Italy"}
    ),
    Document(
        page_content="Madrid is the capital of Spain", metadata={"country": "Spain"}
    ),
    Document(
        page_content="London is the capital of the United Kingdom",
        metadata={"country": "UK"},
    ),
    Document(
        page_content="Moscow is the capital of Russia", metadata={"country": "Russia"}
    ),
    Document(
        page_content="Washington DC is the capital of the United States",
        metadata={"country": "USA"},
    ),
    Document(
        page_content="Tokyo is the capital of Japan", metadata={"country": "Japan"}
    ),
    Document(
        page_content="Beijing is the capital of China", metadata={"country": "China"}
    ),
    Document(
        page_content="Canberra is the capital of Australia",
        metadata={"country": "Australia"},
    ),
]

# Initialize embeddings
embeddings = NebiusEmbeddings()

# Create retriever
retriever = NebiusRetriever(
    embeddings=embeddings,
    docs=docs,
    k=3,  # Number of documents to return
)

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用法

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检索相关文档

您可以使用检索器查找与查询相关的文档 
# Query for European capitals
query = "What are some capitals in Europe?"
results = retriever.invoke(query)

print(f"Query: {query}")
print(f"Top {len(results)} results:")
for i, doc in enumerate(results):
    print(f"{i + 1}. {doc.page_content} (Country: {doc.metadata['country']})")

Query: What are some capitals in Europe?
Top 3 results:
1. Paris is the capital of France (Country: France)
2. Berlin is the capital of Germany (Country: Germany)
3. Rome is the capital of Italy (Country: Italy)

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使用 get_relevant_documents

您也可以直接使用 get_relevant_documents 方法（尽管 invoke 是首选接口） 
# Query for Asian countries
query = "What are the capitals in Asia?"
results = retriever.get_relevant_documents(query)

print(f"Query: {query}")
print(f"Top {len(results)} results:")
for i, doc in enumerate(results):
    print(f"{i + 1}. {doc.page_content} (Country: {doc.metadata['country']})")

Query: What are the capitals in Asia?
Top 3 results:
1. Beijing is the capital of China (Country: China)
2. Tokyo is the capital of Japan (Country: Japan)
3. Canberra is the capital of Australia (Country: Australia)

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自定义结果数量

您可以通过将 k 作为参数传递来在查询时调整结果数量 
# Query for a specific country, with custom k
query = "Where is France?"
results = retriever.invoke(query, k=1)  # Override default k

print(f"Query: {query}")
print(f"Top {len(results)} result:")
for i, doc in enumerate(results):
    print(f"{i + 1}. {doc.page_content} (Country: {doc.metadata['country']})")

Query: Where is France?
Top 1 result:
1. Paris is the capital of France (Country: France)

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异步支持

NebiusRetriever 支持异步操作 
import asyncio


async def retrieve_async():
    query = "What are some capital cities?"
    results = await retriever.ainvoke(query)

    print(f"Async query: {query}")
    print(f"Top {len(results)} results:")
    for i, doc in enumerate(results):
        print(f"{i + 1}. {doc.page_content} (Country: {doc.metadata['country']})")


await retrieve_async()

Async query: What are some capital cities?
Top 3 results:
1. Washington DC is the capital of the United States (Country: USA)
2. Canberra is the capital of Australia (Country: Australia)
3. Paris is the capital of France (Country: France)

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处理空文档

# Create a retriever with empty documents
empty_retriever = NebiusRetriever(
    embeddings=embeddings,
    docs=[],
    k=2,  # Empty document list
)

# Test the retriever with empty docs
results = empty_retriever.invoke("What are the capitals of European countries?")
print(f"Number of results: {len(results)}")

Number of results: 0

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在链中使用

NebiusRetriever 在 LangChain RAG 管道中无缝工作。以下是使用 NebiusRetriever 创建简单 RAG 链的示例 
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_nebius import ChatNebius

# Initialize LLM
llm = ChatNebius(model="meta-llama/Llama-3.3-70B-Instruct-fast")

# Create a prompt template
prompt = ChatPromptTemplate.from_template(
    """
Answer the question based only on the following context:

Context:
{context}

Question: {question}
"""
)


# Format documents function
def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


# Create RAG chain
rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

# Run the chain
answer = rag_chain.invoke("What are three European capitals?")
print(answer)

Based on the context provided, three European capitals are:

1. Paris
2. Berlin
3. Rome

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创建搜索工具

您可以使用 NebiusRetrievalTool 为代理创建工具 
from langchain_nebius import NebiusRetrievalTool

# Create a retrieval tool
tool = NebiusRetrievalTool(
    retriever=retriever,
    name="capital_search",
    description="Search for information about capital cities around the world",
)

# Use the tool
result = tool.invoke({"query": "capitals in Europe", "k": 3})
print("Tool results:")
print(result)

Tool results:
Document 1:
Paris is the capital of France

Document 2:
Berlin is the capital of Germany

Document 3:
Rome is the capital of Italy

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工作原理

NebiusRetriever 的工作原理
  1. 初始化期间
    • 它存储提供的文档
    • 它使用提供的 NebiusEmbeddings 计算所有文档的嵌入
    • 这些嵌入存储在内存中以便快速检索
  2. 检索期间（invoke 或 get_relevant_documents）
    • 它使用相同的嵌入模型嵌入查询
    • 它计算查询嵌入与所有文档嵌入之间的相似性分数
    • 它返回按相似性排序的前 k 个文档
这种方法对于中等大小的文档集合是高效的，因为它避免了对单独的向量数据库的需求，同时仍然提供高质量的语义搜索。

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API 参考

有关 Nebius AI Studio API 的更多详细信息，请访问 Nebius AI Studio 文档。
在 GitHub 上编辑此页面源文件。
以编程方式连接这些文档到 Claude、VSCode 等，通过 MCP 获取实时答案。
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