Nebius 集成

使用 LangChain Python 与 Nebius 集成。

All functionality related to Nebius Token Factory

Nebius Token Factory provides API access to a wide range of state-of-the-art large language models and embedding models for various use cases.

安装和设置

The Nebius integration can be installed via pip:

pip

pip install langchain-nebius

To use Nebius Token Factory, you’ll need an API key which you can obtain from Nebius Token Factory. The API key can be passed as an initialization parameter api_key or set as the environment variable NEBIUS_API_KEY.

import os
os.environ["NEBIUS_API_KEY"] = "YOUR-NEBIUS-API-KEY"

Available models

The full list of supported models can be found in the Nebius Token Factory Models Page.

聊天模型

ChatNebius

The ChatNebius class allows you to interact with Nebius Token Factory’s chat models.

查看使用示例.

from langchain_nebius import ChatNebius

# Initialize the chat model
chat = ChatNebius(
    model="moonshotai/Kimi-K2.5",  # Choose from available models
    temperature=0.6,
    top_p=0.95
)

向量嵌入模型

NebiusEmbeddings

The NebiusEmbeddings class allows you to generate vector embeddings using Nebius Token Factory’s embedding models.

查看使用示例.

from langchain_nebius import NebiusEmbeddings

# Initialize embeddings
embeddings = NebiusEmbeddings(
    model="Qwen/Qwen3-Embedding-8B"  # Default embedding model
)

检索器

NebiusRetriever

The NebiusRetriever enables efficient similarity search using embeddings from Nebius Token Factory. It leverages high-quality embedding models to enable semantic search over documents.

查看使用示例.

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"),
    Document(page_content="Berlin is the capital of Germany"),
]

# Initialize embeddings
embeddings = NebiusEmbeddings()

# Create retriever
retriever = NebiusRetriever(
    embeddings=embeddings,
    docs=docs,
    k=2  # Number of documents to return
)

工具

NebiusRetrievalTool

The NebiusRetrievalTool allows you to create a tool for agents based on the NebiusRetriever.

from langchain_nebius import NebiusEmbeddings, NebiusRetriever, NebiusRetrievalTool
from langchain_core.documents import Document

# Create sample documents
docs = [
    Document(page_content="Paris is the capital of France and has the Eiffel Tower"),
    Document(page_content="Berlin is the capital of Germany and has the Brandenburg Gate"),
]

# Create embeddings and retriever
embeddings = NebiusEmbeddings()
retriever = NebiusRetriever(embeddings=embeddings, docs=docs)

# Create retrieval tool
tool = NebiusRetrievalTool(
    retriever=retriever,
    name="nebius_search",
    description="Search for information about European capitals"
)

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