Skip to main content
这将帮助您使用 LangChain 开始使用 Nomic 嵌入模型。有关 NomicEmbeddings 功能和配置选项的详细文档，请参阅 API 参考。

​
概述

​
集成详情

​
设置

要访问 Nomic 嵌入模型，您需要创建一个 Nomic 账户，获取 API 密钥，并安装 langchain-nomic 集成包。

​
凭据

前往 https://atlas.nomic.ai/ 注册 Nomic 并生成 API 密钥。完成后，请设置 NOMIC_API_KEY 环境变量： 
import getpass
import os

if not os.getenv("NOMIC_API_KEY"):
    os.environ["NOMIC_API_KEY"] = getpass.getpass("Enter your Nomic API key: ")
要启用模型调用的自动追踪，请设置您的 LangSmith API 密钥： 
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

​
安装

LangChain Nomic 集成位于 langchain-nomic 包中： 
pip install -qU langchain-nomic

​
实例化

现在我们可以实例化我们的模型对象并生成聊天补全： 
from langchain_nomic import NomicEmbeddings

embeddings = NomicEmbeddings(
    model="nomic-embed-text-v1.5",
    # dimensionality=256,
    # Nomic's `nomic-embed-text-v1.5` model was [trained with Matryoshka learning](https://blog.nomic.ai/posts/nomic-embed-matryoshka)
    # to enable variable-length embeddings with a single model.
    # This means that you can specify the dimensionality of the embeddings at inference time.
    # The model supports dimensionality from 64 to 768.
    # inference_mode="remote",
    # One of `remote`, `local` (Embed4All), or `dynamic` (automatic). Defaults to `remote`.
    # api_key=... , # if using remote inference,
    # device="cpu",
    # The device to use for local embeddings. Choices include
    # `cpu`, `gpu`, `nvidia`, `amd`, or a specific device name. See
    # the docstring for `GPT4All.__init__` for more info. Typically
    # defaults to CPU. Do not use on macOS.
)

​
索引与检索

嵌入模型通常用于检索增强生成 (RAG) 流程中，既作为数据索引的一部分，也用于后续的检索。更详细的说明，请参阅我们的 RAG 教程。 下面，查看如何使用上面初始化的 embeddings 对象来索引和检索数据。在本示例中，我们将在 InMemoryVectorStore 中索引和检索一个示例文档。 
# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content

'LangChain is the framework for building context-aware reasoning applications'

​
直接使用

在底层，向量存储和检索器实现分别调用 embeddings.embed_documents(...) 和 embeddings.embed_query(...) 为 from_texts 和检索 invoke 操作中使用的文本创建嵌入。 您可以直接调用这些方法以获取您自己用例的嵌入。

​
嵌入单个文本

您可以使用 embed_query 嵌入单个文本或文档： 
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector

[0.024642944, 0.029083252, -0.14013672, -0.09082031, 0.058898926, -0.07489014, -0.0138168335, 0.0037

​
嵌入多个文本

您可以使用 embed_documents 嵌入多个文本： 
text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # Show the first 100 characters of the vector

[0.012771606, 0.023727417, -0.12365723, -0.083740234, 0.06530762, -0.07110596, -0.021896362, -0.0068
[-0.019058228, 0.04058838, -0.15222168, -0.06842041, -0.012130737, -0.07128906, -0.04534912, 0.00522

​
API 参考

有关 NomicEmbeddings 功能和配置选项的详细文档，请参阅 API 参考。
Edit this page on GitHub or file an issue.
Connect these docs to Claude, VSCode, and more via MCP for real-time answers.