dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
111 lines (101 loc) • 4.37 kB
text/typescript
import { AzureOpenAIInput, ClientOptions, AzureOpenAIEmbeddings, OpenAIEmbeddingsParams } from '@langchain/openai'
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
const serverCredentialsExists =
!!process.env.AZURE_OPENAI_API_KEY &&
!!process.env.AZURE_OPENAI_API_INSTANCE_NAME &&
(!!process.env.AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME || !!process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME) &&
!!process.env.AZURE_OPENAI_API_VERSION
class AzureOpenAIEmbedding_Embeddings implements INode {
label: string
name: string
version: number
type: string
icon: string
category: string
description: string
baseClasses: string[]
credential: INodeParams
inputs: INodeParams[]
constructor() {
this.label = 'Azure OpenAI Embeddings'
this.name = 'azureOpenAIEmbeddings'
this.version = 2.0
this.type = 'AzureOpenAIEmbeddings'
this.icon = 'Azure.svg'
this.category = 'Embeddings'
this.description = 'Azure OpenAI API to generate embeddings for a given text'
this.baseClasses = [this.type, ...getBaseClasses(AzureOpenAIEmbeddings)]
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['azureOpenAIApi'],
optional: serverCredentialsExists
}
this.inputs = [
{
label: 'Batch Size',
name: 'batchSize',
type: 'number',
default: '100',
optional: true,
additionalParams: true
},
{
label: 'Timeout',
name: 'timeout',
type: 'number',
optional: true,
additionalParams: true
},
{
label: 'BasePath',
name: 'basepath',
type: 'string',
optional: true,
additionalParams: true
},
{
label: 'BaseOptions',
name: 'baseOptions',
type: 'json',
optional: true,
additionalParams: true
}
]
}
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
const batchSize = nodeData.inputs?.batchSize as string
const timeout = nodeData.inputs?.timeout as string
const basePath = nodeData.inputs?.basepath as string
const baseOptions = nodeData.inputs?.baseOptions
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const azureOpenAIApiKey = getCredentialParam('azureOpenAIApiKey', credentialData, nodeData)
const azureOpenAIApiInstanceName = getCredentialParam('azureOpenAIApiInstanceName', credentialData, nodeData)
const azureOpenAIApiDeploymentName = getCredentialParam('azureOpenAIApiDeploymentName', credentialData, nodeData)
const azureOpenAIApiVersion = getCredentialParam('azureOpenAIApiVersion', credentialData, nodeData)
const obj: Partial<OpenAIEmbeddingsParams> & Partial<AzureOpenAIInput> & { configuration?: ClientOptions } = {
azureOpenAIApiKey,
azureOpenAIApiInstanceName,
azureOpenAIApiDeploymentName,
azureOpenAIApiVersion,
azureOpenAIBasePath: basePath || undefined
}
if (batchSize) obj.batchSize = parseInt(batchSize, 10)
if (timeout) obj.timeout = parseInt(timeout, 10)
if (baseOptions) {
try {
const parsedBaseOptions = typeof baseOptions === 'object' ? baseOptions : JSON.parse(baseOptions)
obj.configuration = {
defaultHeaders: parsedBaseOptions
}
} catch (exception) {
console.error('Error parsing base options', exception)
}
}
const model = new AzureOpenAIEmbeddings(obj)
return model
}
}
module.exports = { nodeClass: AzureOpenAIEmbedding_Embeddings }