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.
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 upThis 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.
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 |
You can install the official JavaScript SDK with npm:
npm i @orama/coreThen, 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.