@singlestore/elegance-sdk
Advanced tools
The SingleStore Elegance SDK is a library for building full-stack AI-powered applications with SingleStoreDB and OpenAI API.
The SingleStore Elegance SDK is a library for building full-stack AI-powered applications with SingleStoreDB and OpenAI API.
npm install @singlestore/elegance-sdk
This guide will show you how to use the SDK in Express.js and React.js.
eleganceServerClient.ts filecreateEleganceServerClient function from @singlestore/elegance-sdk/server// ./server/services/eleganceServerClient.ts
import { createEleganceServerClient } from "@singlestore/elegance-sdk/server";
export const eleganceServerClient = createEleganceServerClient("mysql", {
connection: {
host: process.env.DB_HOST,
user: process.env.DB_USER,
password: process.env.DB_PASSWORD,
database: process.env.DB_NAME
},
openai: {
apiKey: process.env.OPENAI_API_KEY
}
});
elegance/:route (using Express.js as an example).// ./server/routes/elegance.ts
import express from "express";
import type { Routes } from "@singlestore/elegance-sdk/server";
import { eleganceServerClient } from "@/services/eleganceServerClient";
export const eleganceRouter = express.Router();
eleganceRouter.post("/elegance/:route", async (req, res) => {
try {
const route = req.params.route as Routes;
const result = await eleganceServerClient.handleRoute(route, req.body);
return res.status(200).json(result);
} catch (error: any) {
return res.status(error.status).json(error);
}
});
./server/index.ts// ./server/index.ts
import express from "express";
import { eleganceRouter } from "./routes/elegance";
const app = express();
app.use(eleganceRouter);
app.listen(4000, () => {
console.log(`Server started on port: ${4000}`);
});
eleganceClient.ts filecreateEleganceClient function from @singlestore/elegance-sdk// ./client/services/eleganceClient.ts
import { createEleganceClient } from "@singlestore/elegance-sdk";
export const eleganceClient = createEleganceClient("mysql", {
baseURL: "http://localhost:4000"
});
eleganceClient to your component// ./client/components/ExampleComponent.tsx
import { useEffect } from "react";
import { eleganceClient } from "@/services/eleganceClient";
export function ExampleComponent() {
const query = eleganceClient.hooks.useQuery<{ name: string }[]>({ initialValue: [], initialIsLoading: true });
const { execute: executeQuery } = query;
useEffect(() => {
executeQuery({ query: "SELECT name FROM table LIMIT 3" });
}, [executeQuery]);
if (query.isLoading) return "Loading...";
return (
<div>
{query.value.map(item => (
<h4 key={item.name}>{item.name}</h4>
))}
</div>
);
}
Creates an EleganceServerClient instance for a server.
Parameters:
connectionType: "kai" | "mysql"config: {
connection: KaiConnectionConfig | MySQLConnectionConfig;
openai: OpenAIConfig;
}
Returns: eleganceServerClient
Server client that includes a database connection, controllers and OpenAI client. It's used on the server to handle requests from the client and execute logic.
MySQL or MongoDB connection to interact with a database
Accepts a route and executes the controller for that route.
Parameters:
route: string - controller route namebody: object - controller bodyInserts one record.
Parameters:
body: {
collection: string; // Collection name
generateId?: boolean; // Generates the `id` string field
value: MongoOptionalUnlessRequiredId<T>; // Value to insert
options?: MongoInsertOneOptions;
} | {
table: string; // Table name
generateId?: boolean;
value: T;
}
Returns: T
Inserts many records.
Parameters:
body: {
collection: string; // Collection name
values: Array<MongoOptionalUnlessRequiredId<T[number]>>; // Values to insert
generateId?: boolean; // Generates the `id` string field
options?: MongoBulkWriteOptions;
} | {
table: string; // Table name
generateId?: boolean;
values: Array<T>;
}
Returns: Array<T>
Updates many records.
Parameters:
body: {
collection: string; // Collection name
filter: MongoFilter<T[number]>; // Filter to find records to update
update: MongoUpdateFilter<T[number]>;
options?: MongoUpdateOptions;
updatedFilter?: MongoFilter<T[number]>; // Filter to find updated records
} | {
table: string; // Table name
where: MySQLWhere; // MySQL WHERE string value to find records to update
set: MySQLSet; // MySQL SET string value
updatedWhere: MySQLWhere; // MySQL WHERE string value to find updated records
}
Returns: Array<T>
Deletes many records.
Parameters:
body: {
collection: string; // Collection name
filter: MongoFilter<T>; // Filter to find records to delete
options?: MongoDeleteOptions;
} | {
table: string; // Table name
where: MySQLWhere; // MySQL WHERE string value to find records to delete
}
Returns: { message: string }
Gets one record.
Parameters:
body: {
collection: string; // Collection name
filter?: MongoFilter<T>; // Filter to find a record
options?: MongoFindOptions;
} | {
table: string; // Table name
columns?: string[]; // Columns to get by default *
where?: MySQLWhere; // MySQL WHERE string value to find a record
}
Returns: T
Gets many records.
Parameters:
body: {
collection: string; // Collection name
filter?: MongoFilter<T[number]>; // Filter to find records
options?: MongoFindOptions;
} | {
table: string; // Table name
columns?: string[]; // Columns to get by default *
where?: MySQLWhere; // MySQL WHERE string value to find records
skip?: number;
limit?: number;
}
Returns: Array<object>
Executes MySQL or aggregate query.
Parameters:
body: {
collection: string; // Collection name
pipeline: object[]; // Aggregate pipeline
options?: MongoAggregateOptions;
} | {
query: string; // MySQL query
}
Returns: Array<T>
Creates embedding.
Parameters:
body: {
input: string | string[] | object | object[];
}
Returns: Array<number>
Performs vector search in the collection based on the query.
Parameters:
body: {
collection: string; // Collection name
embeddingField: string; // Field name with the embedding by which to perform the search
query: string; // Search query
limit?: number; // Number of records to get
} | {
table: string; // Table name
embeddingField: string;
query: string;
limit?: number;
}
Returns: Array<object>
Accepts a prompt, performs vector search, and creates chat completion for the found records.
Parameters:
body: {
collection: string; // Collection name
prompt: string; // Prompt text
model?: string;
textField?: string; // Field name by which to create a chat completion
embeddingField?: string; // Field name with the embedding by which to perform the search
minSimilarity?: number; // Minimum similarity number to filter search results, used to create a chat completion.
systemRole?: string; // Initial system role
messages?: CreateChatCompletionBody["messages"]; // Additional messages after the prompt message
maxTokens?: CreateChatCompletionBody["max_tokens"];
maxContextLength?: number;
temperature?: CreateChatCompletionBody["temperature"];
} | {
table: string; // Table name
prompt: string;
model?: string;
textField?: string;
embeddingField?: string;
minSimilarity?: number;
systemRole?: string;
messages?: CreateChatCompletionBody["messages"];
maxTokens?: CreateChatCompletionBody["max_tokens"];
maxContextLength?: number;
temperature?: CreateChatCompletionBody["temperature"];
}
Returns:
{
content: string;
context: string;
}
Accepts a CSV or PDF file as a DataURL, splits it into chunks and creates embeddings.
Parameters:
body: {
dataURL: string; // CSV or PDF file DataURL
textField?: string; // Field name in which to save a chunk text
embeddingField?: string; // Field name in which to save an embedding
chunkSize?: number;
}
Returns: Array<{ text: string; embedding: number[]; }>
Accepts a CSV or PDF file as a DataURL, splits it into chunks, creates embeddings, and inserts them into a database.
Parameters:
body: {
collection: string; // Collection name
dataURL: string; // CSV or PDF file DataURL
textField?: string; // Field name in which to save a chunk text
embeddingField?: string; // Field name in which to save an embedding
chunkSize?: number;
} | {
table: string; // Table name
dataURL: string;
textField?: string;
embeddingField?: string;
chunkSize?: number;
}
Returns: Array<{ text: string; embedding: number[]; }>
Default OpenAI client + helpers
Creates embedding using the OpenAI Embeddings API
Parameters:
input: string | Array<string> | object | Array<object> - input valueReturns: Array<number>
Converts an embedding into a buffer that is then inserted into the database.
Parameters:
embedding: Array<number>Returns: Buffer
Converts a DataURL (csv, pdf) into an embedding by splitting the text into chunks.
Parameters:
dataURL: stringoptions?: {
chunkSize?: number;
textField?: string; // Field name in which to save a chunk text
embeddingField?: string; // Field name in which to save an embedding
}
Returns: Array<{ text: string; embedding: number[] }>
Generates a chat completion using the OpenAI ChatCompletion API.
Parameters:
options: ChatCompletionCreateParamsBaseReturns: string
Creates an EleganceClient instance for a client.
Parameters:
connectionType: "kai" | "mysql"config: {
baseURL: string; // Server URL with the '/elegance/:route' route handler.
}
Returns: eleganceServerClient
Client that includes requests and hooks. It is used to make requests to the server.
Clean functions to make requests to the server. They can be used anywhere within a project and are handled like typical async functions. The parameters for each request correspond to the controller parameters by name. To familiarize with the parameters, refer to the eleganceServerClient.controllers.<requestName>.execute section.
Ready-to-use React.js hooks with state handlers that use requests. Each hook has the following type:
<R = any,>(options?: { initialValue?: R; initialIsLoading?: boolean; onError?: (error: DefaultError) => void }) => {
value: R | undefined;
error: DefaultError | undefined;
isLoading: boolean;
setValue: react.Dispatch<react.SetStateAction<Awaited<R> | undefined>>;
setError: react.Dispatch<react.SetStateAction<DefaultError | undefined>>;
setIsLoading: react.Dispatch<react.SetStateAction<boolean>>;
execute: (body: object) => Promise<Awaited<R> | undefined>; // Function that executes a request from the eleganceClient.requests by name
};
© 2023 SingleStore
FAQs
The Elegance SDK is an SDK for quickly building real-time AI full-stack JavaScript applications using SingleStoreDB with support for MySQL and Kai (support for Mongo APIs) connection types and the OpenAI API. It provides a set of ready-made tools for impl
The npm package @singlestore/elegance-sdk receives a total of 7 weekly downloads. As such, @singlestore/elegance-sdk popularity was classified as not popular.
We found that @singlestore/elegance-sdk demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 4 open source maintainers collaborating on the project.
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