> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt
> Use this file to discover all available pages before exploring further.

# NomicEmbeddings 集成

> 使用 LangChain Python 集成 NomicEmbeddings 嵌入模型。

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](https://reference.langchain.com/python/langchain-nomic/embeddings/NomicEmbeddings)。

## 概述

### 集成详情

<ItemTable category="embeddings" item="Nomic" />

## 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/](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：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](/langsmith/home) API 密钥：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](/oss/python/langchain/rag)。

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。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 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
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
'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：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[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：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[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](https://reference.langchain.com/python/langchain-nomic/embeddings/NomicEmbeddings)。

***

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