Using vector databases for context in AI

In the realm of Artificial Intelligence (AI), understanding and retaining context stands as a pivotal factor for decision-making and enhanced comprehension. Vector databases, are the foundational pillars in encapsulating your own data to be used in conjunction with AI and LLMs. Vector databases are empowering these systems to absorb and retain intricate contextual information.

Understanding Vector Databases

Vector databases are specialised data storage systems engineered to efficiently manage and retrieve vectorised data - also known as embeddings. These databases store information in a vector format, where each data entity is represented as a multidimensional numerical vector, encapsulating various attributes and relationships, thus fostering the preservation of rich context. That is text, video or audio is translated into numbers with many attributes in the multidimensional space. Then mathematics are used to calculate the proximity between these numbers. Loosely speaking that is what a neural network in an LLM does, it computes proximity (similarity) between the vectors. A bit like how our brains do pattern recognition. The vector database is the database where the vectors are stored. Without a vector databases under architectures like RAG, it is impossible to bring own data or context into an LLM app in an AI model as all it will know will be only what it is trained on from the public internet. Vector databases enable you to bring your own data to AI.

Examples of Vector Databases

Several platforms offer vector databases, such as Pinecone, Faiss by Facebook, Annoy, Milvus, and Elasticsearch with dense vector support. These databases cater to diverse use cases, offering functionalities tailored to handle vast amounts of vectorised information, be it images, text, audio, or other complex data types.

Importance in AI Context

Within the AI landscape, vector databases play a pivotal role in serving specific data and context for AI models. Particularly, in the Retrieval-Augmented Generation (RAG) architecture, where retrieval of relevant information is an essential part of content generation, vector databases act as repositories, storing precomputed embeddings from your own private data. These embeddings encode the semantic and contextual essence of your data, facilitating efficient retrieval in your AI apps and Bots. Bringing vector databases to your AI apps or chatbots will bring your own data to your AI apps, agents and chatbots will speak your data!

Advantages for Organisations and AI Applications

Organisations can harness the prowess of vector databases within Retrieval-Augmented Generation (RAG) architectures to elevate their AI applications and enable them to use organisational specific data:

1. Enhanced Contextual Understanding: By leveraging vector databases, AI models grasp nuanced contextual information, enabling more informed decision-making and more precise content generation based on specific and private organisational context.

2. Improved Efficiency in Information Retrieval: Vector databases expedite the retrieval of pertinent information by enabling similarity searches based on vector representations, augmenting the speed and accuracy of AI applications.

3. Scalability and Flexibility: These databases offer scalability and flexibility, accommodating diverse data types and expanding corpora, essential for the evolving needs of AI-driven applications.

4. Optimised Resource Utilisation: Vector databases streamline resource utilisation by efficiently storing and retrieving vectorised data, thus optimising computational resources and infrastructure.

Closing Thoughts

In the AI landscape, where the comprehension of context is paramount, vector databases emerge as linchpins, fortifying AI systems with the capability to retain and comprehend context-rich information. Their integration within Retrieval-Augmented Generation (RAG)  architectures not only elevates AI applications but also empowers organisations to glean profound insights, fostering a new era of context-driven AI innovation from data.

In essence, the power vested in vector databases will reshape the trajectory of AI, propelling it toward unparalleled contextualisation and intelligent decision-making based on in house and organisations own data.

But the enigma persists: What precisely will be the data fuelling the AI model?

Vscode container development

If you're a software developer, you know how important it is to have a development environment that is flexible, efficient, and easy to use. PyCharm is a popular IDE (Integrated Development Environment) for Python developers, but there are other options out there that may suit your needs better. One such option is Visual Studio Code, or VS Code for short.

After using PyCharm for a while, I decided to give VS Code a try, and I was pleasantly surprised by one of its features: the remote container development extension. This extension allows you to develop your code in containers, with no footprint on your local machine at all. This means that you can have a truly ephemeral solution, enabling abstraction to the maximum.

So, how does it work? First, you need to create two files: a Dockerfile and a devcontainer.json file. These files should be located in a hidden .devcontainer folder at the root location of any of your GitHub projects.

The Dockerfile is used to build the container image that will be used for development. Here's a sample Dockerfile that installs Python3, sudo, and SQLite3:

FROM ubuntu:20.04

ARG DEBIAN_FRONTEND=noninteractive

RUN apt-get update -y

RUN apt-get install -y python3

RUN apt-get install -y sudo

RUN apt-get install -y sqlite3

The devcontainer.json file is used to configure the development environment in the container. Here's a sample devcontainer.json file that sets the workspace folder to "/workspaces/alpha", installs the "ms-python.python" extension, and forwards port 8000:

{

    "name": "hammer",

    "build": {

        "context": ".",

        "dockerfile": "./Dockerfile"

    },

    "workspaceFolder": "/workspaces/alpha",

    "extensions": [

        "ms-python.python"

    ],

    "forwardPorts": [

        8000

    ]

}

Once you have these files ready, you can clone your GitHub code down to a Visual Studio Code container volume. Here's how to do it:

1. Start Visual Studio Code
2. Make sure you have the "Remote Development" extension installed and enabled
3. Go to the "Remote Explorer" extension from the button menu
4. Click "Clone Repository in Container Volume" at the bottom left
5. In the Command Palette, choose "Clone a repository from GitHub in a Container Volume" and pick your GitHub repo.

That's it! You are now tracking your code inside a container volume, built by a Dockerfile which is also being tracked on GitHub together with all your environment-specific extensions you require for development.

The VS Code remote container development extension is a powerful tool for developers who need a flexible, efficient, and easy-to-use development environment. By using containers, you can create an ephemeral solution that allows you to abstract away the complexities of development environments and focus on your code. If you're looking for a new IDE or just want to try something different, give VS Code a try with the remote container development extension.