ApertureDB

pip install -qU aperturedb

Note: you may need to restart the kernel to use updated packages.

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

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

loader = WebBaseLoader("https://docs.aperturedata.io")
docs = loader.load()

USER_AGENT environment variable not set, consider setting it to identify your requests.

# 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()

from langchain_text_splitters import RecursiveCharacterTextSplitter

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

from langchain_community.vectorstores import ApertureDB

vector_db = ApertureDB.from_documents(documents, embeddings)

from langchain_community.llms import Ollama

llm = Ollama(model="llama2")

# 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.

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.