oramacore

OramaCore is the complete runtime you need for your projects, answer engines, copilots, and search. It includes a fully-fledged full-text search engine, vector database, LLM interface, and many more utilities.

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OramaCore is a database designed for AI projects, answer engines, copilots, and search functionalities. It offers features such as a full-text search engine, vector database, LLM interface, and various utilities. The tool is currently under active development and not recommended for production use due to potential API changes. OramaCore aims to provide a comprehensive solution for managing data and enabling advanced search capabilities in AI applications.

README:

OramaCore is the AI runtime you need for your projects, answer engines, copilots, and search.

It includes a fully-fledged full-text search engine, vector database, LLM interface with action planning and reasoning, a JavaScript runtime to write and run your own custom agents on your data, and many more utilities.

Getting Started

The absolute easier way to get started is by following the docker-compose.yml file that you can find in this repository.

You can either clone the entire repo or setup oramasearch/oramacore:latest as image in your docker-compose.yml file under the oramacore service.

Then compile your configuration file and run it:

docker compose up

This will create the following architecture, allowing you to perform high-performance RAG with little to zero configuration.

An NVIDIA GPU is highly recommended for running the application. For production usage, we recommend using minimum one NVIDIA A100. Optimal configuration would include four NVIDIA H100.

Available Dockerfiles

Depending on your machine, you may want to use different Docker images.

Application	CPU/GPU	Docker image
OramaCore	X86_64	`oramasearch/oramacore`
OramaCore	ARM64 (Mac M series for example)	`oramasearch/oramacore-arm64`
AI Server	Any CPU architecture, no CUDA access	`oramasearch/oramacore-ai-server`
AI Server	Any CPU architecture, CUDA available	`oramasearch/oramacore-ai-server-cuda`

Using the JavaScript SDK

You can install the official JavaScript SDK with npm:

npm i @orama/core

Then, you can start by creating a collection (a database index) with all of the data you want to perform AI search & experiences on:

import { OramaCoreManager } from "@orama/core";

const orama = new OramaCoreManager({
    url: "http://localhost:8080",
    masterAPIKey: "<master-api-key>", // The master API key set in your config file
});

const newCollection = await orama.createCollection({
    id: "products",
    writeAPIKey: "my-write-api-key", // A custom API key to perform write operations on your collection
    readAPIKey: "my-read-api-key", // A custom API key to perform read operations on your collection
});

Then, insert some data:

import { CollectionManager } from "@orama/core";

const collection = new CollectionManager({
    url: "http://localhost:8080",
    collectionID: "<COLLECTION_ID>",
    writeAPIKey: "<write_api_key>",
});

// You can insert a single document
await collection.insert({
    title: "My first document",
    content: "This is the content of my first document.",
});

// Or you can insert multiple documents by passing an array of objects
await collection.insert([
    {
        title: "My first document",
        content: "This is the content of my first document.",
    },
    {
        title: "My second document",
        content: "This is the content of my second document.",
    },
]);

OramaCore will automatically generate highly optimized embeddings for you and will store them inside its built-in vector database.

Now you can perform vector, hybrid, full-text search, or let OramaCore decide which one is best for your specific query:

import { CollectionManager } from "@orama/core";

const collection = new CollectionManager({
    url: "http://localhost:8080",
    collectionID: "<COLLECTION_ID>",
    readAPIKey: "<read_api_key>",
});

const results = await collection.search({
    term: "The quick brown fox",
    mode: "auto", // can be "fulltext", "vector", "hybrid", or "auto"
});

You can also perform Answer Sessions as you'd do on Perplexity or SearchGPT, but on your own data!

import { CollectionManager } from "@orama/core";

const collection = new CollectionManager({
    url: "http://localhost:8080",
    collectionID: "<COLLECTION_ID>",
    readAPIKey: "<read_api_key>",
});

const answerSession = collection.createAnswerSession({
    initialMessages: [
        {
            role: "user",
            content: "How do I install OramaCore?",
        },
        {
            role: "assistant",
            content:
                "You can install OramaCore by pulling the oramasearch/oramacore:latest Docker image",
        },
    ],
    events: {
        onStateChange(state) {
            console.log("State changed:", state);
        },
    },
});

Read more on the official documentation.

License

AGPLv3

For Tasks:

Click tags to check more tools for each tasks

index data search documents generate embeddings setup llm inference perform load testing

For Jobs:

data scientist machine learning engineer ai researcher search engineer software developer

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