Skip to main content

NomicEmbeddings

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

集成细节

ProviderPackage
Nomiclangchain-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。

凭证

前往https://atlas.nomic.ai/注册Nomic并生成API密钥。完成后设置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: ")

如果您想要自动跟踪模型调用,您还可以通过取消注释下面的内容来设置您的 LangSmith API 密钥:

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_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
Note: you may need to restart the kernel to use updated packages.

实例化

现在我们可以实例化我们的模型对象并生成聊天完成:

<!--IMPORTS:[{"imported": "NomicEmbeddings", "source": "langchain_nomic", "docs": "https://python.langchain.com/api_reference/nomic/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html", "title": "NomicEmbeddings"}]-->
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.
)

索引和检索

嵌入模型通常用于检索增强生成 (RAG) 流程,既作为索引数据的一部分,也用于后续的检索。有关更详细的说明,请参见我们在 使用外部知识的教程 下的 RAG 教程。

下面,查看如何使用我们上面初始化的 embeddings 对象来索引和检索数据。在这个例子中,我们将索引并检索 InMemoryVectorStore 中的一个示例文档。

<!--IMPORTS:[{"imported": "InMemoryVectorStore", "source": "langchain_core.vectorstores", "docs": "https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.in_memory.InMemoryVectorStore.html", "title": "NomicEmbeddings"}]-->
# 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调用 embeddings.embed_documents(...)embeddings.embed_query(...) 来为 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适合您自己用例的嵌入。

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

相关


Was this page helpful?


You can also leave detailed feedback on GitHub.

扫我,入群扫我,找书