Orama Cloud Client

Install

npm i @oramacloud/client

Integrating with Orama Cloud

import { OramaClient } from "@oramacloud/client";

const client = new OramaClient({
  endpoint: "<Your Orama Cloud Endpoint>",
  api_key: "<Your Orama Cloud API Key>",
});

const results = await client.search({
  term: "red leather shoes",
});

Advanced search

const results = await client.search({
  term: "red leather shoes",
  where: {
    price: {
      lte: 9.99,
    },
    gender: "unisex",
  },
  limit: 5,
  offset: 1,
});

Generating embeddings via the Secure Proxy

import { OramaProxy } from "@oramacloud/client";

const proxy = new OramaClient({
  api_key: "<Your Orama Secure Proxy API Key>",
});

const embeddings = await proxy.generateEmbeddings(
  "red leather shoes",
  "openai/text-embedding-ada-002"
);

console.log(embeddings);
// [-0.019633075, -0.00820422, -0.013555876, -0.011825735, 0.006641511, -0.012948156, ...]

Available models:

• orama/gte-small. 384 dimensions, operated by Orama Cloud (preferred)
• orama/gte-medium. 768 dimensions, operated by Orama Cloud
• orama/gte-large. 1024 dimensions, operated by Orama Cloud
• openai/text-embedding-ada-002. 1536 dimensions, proxied to OpenAI
• openai/text-embedding-3-small. 1536 dimensions, proxied to OpenAI
• openai/text-embedding-3-large. 3072 dimensions, proxied to OpenAI

Generating chat completions via the Secure Proxy

You can generate chat completions via the Secure Proxy in two different ways:

Returning a single string

import { OramaProxy } from "@oramacloud/client";

const proxy = new OramaClient({
  api_key: "<Your Orama Secure Proxy API Key>",
});

const chatParams = {
  model: "openai/gpt-4",
  messages: [{ role: "user", content: "Who is Michael Scott?" }],
};

const response = await proxy.chat(chatParams);
console.log(response);

// "Michael Scott is a fictional character from the television show "The Office" (US version) ..."

Available models:

• openai/gpt-4-1106-preview
• openai/gpt-4
• openai/gpt-3.5-turbo
• openai/gpt-3.5-turbo-16k

Returning a stream (via async iterators)

import { OramaProxy } from "@oramacloud/client";

const proxy = new OramaClient({
  api_key: "<Your Orama Secure Proxy API Key>",
});

const chatParams = {
  model: "openai/gpt-4",
  messages: [{ role: "user", content: "Who is Michael Scott?" }],
};

for await (const message of proxy.chatStream(chatParams)) {
  console.log(message);
}

// Michael
// Scott is
// a fictional
//  character from the
//  television show
// "The
// Office" (US
// version)
// ...

Available models:

• openai/gpt-4-1106-preview
• openai/gpt-4
• openai/gpt-3.5-turbo
• openai/gpt-3.5-turbo-16k

With React

import { OramaCloud, useSearch } from "@oramacloud/client/react";

export function App() {
  return (
    <OramaCloud
      endpoint="<Your Orama Cloud Endpoint>"
      apiKey="<Your Orama Cloud API Key>"
    >
      <Search />
    </OramaCloud>
  );
}

function Search() {
  const { results, error } = useSearch({
    term: "red leather shoes",
    limit: 10,
    offset: 5,
  });

  return (
    <>
      {results.hits.map((hit) => {
        <div key={hit.id}>
          <p> {hit.document.myCustomProperty} </p>
        </div>;
      })}
    </>
  );
}

With Vue

Import the composable into your component and it's ready to use.

<template>
  <li v-for="hit in results?.hits" :key="hit.id">
    {{ hit.id }}
  </li>
</template>

<script setup>
import { useSearch } from "@oramacloud/client/vue";
import { orama } from './orama'

const { results } = useSearch({
  cloudConfig: {
    apiKey: "<Your Orama Cloud API Key>",
    endpoint: "<Your Orama Cloud Endpoint>",
  },
  term: "guitar",
  limit: 5
});
</script>