EdgeDB AI's JavaScript package
@edgedb/ai offers a convenient wrapper around ext::ai. Install it with
npm or via your package manager of choice:
$
npm install @edgedb/ai # or$
yarn add @edgedb/ai # or$
pnpm add @edgedb/ai # or$
bun add @edgedb/aiUsage
Start by importing createClient from edgedb and createAI from
@edgedb/ai:
import { createClient } from "edgedb";
import { createAI } from "@edgedb/ai";Create an EdgeDB client. Create an instance of the AI client by passing in the EdgeDB client and any options for the AI provider (like the text generation model):
const client = createClient();
const gpt4Ai = createAI(client, {
model: "gpt-4-turbo-preview",
});You may use any of the supported text generation models. Add your query as context:
const astronomyAi = gpt4Ai.withContext({
query: "Astronomy"
});This “query” property doesn’t have to be a proper query at all. It can be any
expression that produces a set of objects, like Astronomy in the example
above which will return all objects of that type. On the other hand, if you
want to narrow the field more, you can give it a query like select Astronomy
filter .topic = "Mars".
The default text generation prompt will ask your selected provider to limit answer to information provided in the context and will pass the queried objects’ AI index as context along with that prompt.
Call your AI client’s queryRag method, passing in a text query.
console.log(
await astronomyAi.queryRag("What color is the sky on Mars?")
);You can chain additional calls of withContext or withConfig to create
additional AI clients, identical except for the newly specified values.
const fastAstronomyAi = astronomyAi.withConfig({
model: "gpt-3.5-turbo",
});
console.log(
await fastAstronomyAi.queryRag("What color is the sky on Mars?")
);
const fastChemistryAi = fastAstronomyAi.withContext({
query: "Chemistry"
});
console.log(
await fastChemistryAi.queryRag("What is the atomic number of gold?")
);API Reference
Creates an instance of EdgeDBAI with the specified client and options.
-
client– An EdgeDB client instance. -
options.model(string) – Required. Specifies the AI model to use. This could be a version of GPT or any other model supported by EdgeDB AI. -
options.prompt– Optional. Defines the input prompt for the AI model. The prompt can be a simple string, an ID referencing a stored prompt, or a custom prompt structure that includes roles and content for more complex interactions. The default is the built-in system prompt.
EdgeDBAI
Instances of EdgeDBAI offer methods for client configuration and utilizing
RAG.
Public methods
Returns a new EdgeDBAI instance with updated configuration options.
-
options.model(string) – Required. Specifies the AI model to use. This could be a version of GPT or any other model supported by EdgeDB AI. -
options.prompt– Optional. Defines the input prompt for the AI model. The prompt can be a simple string, an ID referencing a stored prompt, or a custom prompt structure that includes roles and content for more complex interactions. The default is the built-in system prompt.
Returns a new EdgeDBAI instance with an updated query context.
-
context.query(string) – Required. Specifies an expression to determine the relevant objects and index to serve as context for text generation. You may set this to any expression that produces a set of objects, even if it is not a standalone query. -
context.variables(string) – Optional. Variable settings required for the context query. -
context.globals(string) – Optional. Variable settings required for the context query. -
context.max_object_count(number) – Optional. A maximum number of objects to return from the context query.
Sends a query with context to the configured AI model and returns the response as a string.
-
message(string) – Required. The message to be sent to the text generation provider’s API. -
context.query(string) – Required. Specifies an expression to determine the relevant objects and index to serve as context for text generation. You may set this to any expression that produces a set of objects, even if it is not a standalone query. -
context.variables(string) – Optional. Variable settings required for the context query. -
context.globals(string) – Optional. Variable settings required for the context query. -
context.max_object_count(number) – Optional. A maximum number of objects to return from the context query.
Can be used in two ways:
-
- as an async iterator - if you want to process streaming data in
-
real-time as it arrives, ideal for handling long-running streams.
-
- as a Promise that resolves to a full Response object - you have
-
complete control over how you want to handle the stream, this might be useful when you want to manipulate the raw stream or parse it in a custom way.
-
message(string) – Required. The message to be sent to the text generation provider’s API. -
context.query(string) – Required. Specifies an expression to determine the relevant objects and index to serve as context for text generation. You may set this to any expression that produces a set of objects, even if it is not a standalone query. -
context.variables(string) – Optional. Variable settings required for the context query. -
context.globals(string) – Optional. Variable settings required for the context query. -
context.max_object_count(number) – Optional. A maximum number of objects to return from the context query.