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光圈数据库

ApertureDB 是一个数据库，用于存储、索引和管理多模态数据，如文本、图像、视频、边界框和嵌入内容，以及它们的相关元数据。

此笔记本介绍如何使用 ApertureDB 的嵌入功能。

安装 ApertureDB Python SDK

这将安装用于为 ApertureDB 编写客户端代码的 Python SDK。

%pip install --upgrade --quiet aperturedb
Note: you may need to restart the kernel to use updated packages.

运行 ApertureDB 实例

要继续，您应该启动并运行一个 ApertureDB 实例，并配置您的环境以使用它。
有多种方法可以做到这一点，例如：

docker run --publish 55555:55555 aperturedata/aperturedb-standalone
adb config create local --active --no-interactive

下载一些 Web 文档

我们将在这里对一个网页进行一次小型爬取。

# For loading documents from web
from langchain_community.document_loaders import WebBaseLoader

loader = WebBaseLoader("https://docs.aperturedata.io")
docs = loader.load()
API 参考：WebBaseLoader
USER_AGENT environment variable not set, consider setting it to identify your requests.

选择嵌入模型

我们想要使用 OllamaEmbeddings，所以我们必须导入必要的模块。

Ollama 可以设置为 docker 容器，如文档中所述，例如：

# Run server
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
# Tell server to load a specific model
docker exec ollama ollama run llama2
from langchain_community.embeddings import OllamaEmbeddings

embeddings = OllamaEmbeddings()
API 参考：OllamaEmbeddings

将文档拆分为段

我们希望将单个文档转换为多个段。

from langchain_text_splitters import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter()
documents = text_splitter.split_documents(docs)

从文档和嵌入创建 vectorstore

此代码在 ApertureDB 实例中创建一个 vectorstore。 在实例中，此 vectorstore 表示为 “描述符集”。 默认情况下，描述符集名为langchain.以下代码将为每个文档生成嵌入，并将其作为描述符存储在 ApertureDB 中。这将需要几秒钟，因为会生成嵌入。

from langchain_community.vectorstores import ApertureDB

vector_db = ApertureDB.from_documents(documents, embeddings)
API 参考：ApertureDB

选择大型语言模型

同样，我们使用我们为本地处理设置的 Ollama 服务器。

from langchain_community.llms import Ollama

llm = Ollama(model="llama2")
API 参考：Ollama

构建 RAG 链

现在我们有了创建 RAG（检索-增强生成）链所需的所有组件。此链执行以下作：

  1. 为用户查询生成嵌入描述符
  2. 使用向量存储查找与用户查询类似的文本段
  3. 使用提示模板将用户查询和上下文文档传递给 LLM
  4. 返回 LLM 的答案
# Create prompt
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_template("""Answer the following question based only on the provided context:

<context>
{context}
</context>

Question: {input}""")


# Create a chain that passes documents to an LLM
from langchain.chains.combine_documents import create_stuff_documents_chain

document_chain = create_stuff_documents_chain(llm, prompt)


# Treat the vectorstore as a document retriever
retriever = vector_db.as_retriever()


# Create a RAG chain that connects the retriever to the LLM
from langchain.chains import create_retrieval_chain

retrieval_chain = create_retrieval_chain(retriever, document_chain)
Based on the provided context, ApertureDB can store images. In fact, it is specifically designed to manage multimodal data such as images, videos, documents, embeddings, and associated metadata including annotations. So, ApertureDB has the capability to store and manage images.

运行 RAG 链

最后，我们将问题传递给链并得到我们的答案。这将需要几秒钟来运行，因为 LLM 会从查询和上下文文档生成答案。

user_query = "How can ApertureDB store images?"
response = retrieval_chain.invoke({"input": user_query})
print(response["answer"])
Based on the provided context, ApertureDB can store images in several ways:

1. Multimodal data management: ApertureDB offers a unified interface to manage multimodal data such as images, videos, documents, embeddings, and associated metadata including annotations. This means that images can be stored along with other types of data in a single database instance.
2. Image storage: ApertureDB provides image storage capabilities through its integration with the public cloud providers or on-premise installations. This allows customers to host their own ApertureDB instances and store images on their preferred cloud provider or on-premise infrastructure.
3. Vector database: ApertureDB also offers a vector database that enables efficient similarity search and classification of images based on their semantic meaning. This can be useful for applications where image search and classification are important, such as in computer vision or machine learning workflows.

Overall, ApertureDB provides flexible and scalable storage options for images, allowing customers to choose the deployment model that best suits their needs.