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TogetherEmbeddings

这将帮助您开始使用LangChain的Together嵌入模型。有关TogetherEmbeddings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
Togetherlangchain-together

设置

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

凭证

前往https://api.together.xyz/注册Together并生成API密钥。完成后设置TOGETHER_API_KEY环境变量:

import getpass
import os

if not os.getenv("TOGETHER_API_KEY"):
os.environ["TOGETHER_API_KEY"] = getpass.getpass("Enter your Together 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 Together 集成位于 langchain-together 包中:

%pip install -qU langchain-together

[notice] A new release of pip is available: 24.0 -> 24.2
[notice] To update, run: python -m pip install --upgrade pip
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:

from langchain_together import TogetherEmbeddings

embeddings = TogetherEmbeddings(
model="togethercomputer/m2-bert-80M-8k-retrieval",
)

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 与外部知识合作的教程 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。在此示例中,我们将在 InMemoryVectorStore 中索引和检索一个示例文档。

<!--IMPORTS:[{"imported": "InMemoryVectorStore", "source": "langchain_core.vectorstores", "docs": "https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.in_memory.InMemoryVectorStore.html", "title": "TogetherEmbeddings"}]-->
# 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 中使用的文本和检索 invoke 操作创建嵌入。

您可以直接调用这些方法以获取适合您用例的嵌入。

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
[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011,

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
[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011, 
[0.066308185, -0.032866564, 0.115751594, 0.19082588, 0.14017, -0.26976448, -0.056340694, -0.26923394

API 参考

有关 TogetherEmbeddings 功能和配置选项的详细文档,请参阅 API 参考

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