n8n-nodes-base
Version:
Base nodes of n8n
237 lines • 8.44 kB
JavaScript
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.getActiveCredentialType = getActiveCredentialType;
exports.getHost = getHost;
exports.extractResourceLocatorValue = extractResourceLocatorValue;
exports.detectInputFormat = detectInputFormat;
exports.generateExampleFromSchema = generateExampleFromSchema;
exports.validateRequestBody = validateRequestBody;
const n8n_workflow_1 = require("n8n-workflow");
function getActiveCredentialType(context, itemIndex = 0) {
const authentication = context.getNodeParameter('authentication', itemIndex, 'accessToken');
return authentication === 'oAuth2' ? 'databricksOAuth2Api' : 'databricksApi';
}
async function getHost(context, credentialType) {
const credentials = await context.getCredentials(credentialType);
return credentials.host.replace(/\/$/, '');
}
function extractResourceLocatorValue(param) {
if (typeof param === 'object' && param !== null) {
return param.value || '';
}
return param || '';
}
function detectFormatFromProperties(properties, invocationUrl, isOneOf = false) {
if (properties.messages)
return {
format: 'chat',
schema: properties.messages,
requiredFields: ['messages'],
invocationUrl,
};
if (properties.prompt)
return {
format: 'completions',
schema: properties.prompt,
requiredFields: ['prompt'],
invocationUrl,
};
if (properties.input &&
(!isOneOf || (!properties.dataframe_records && !properties.dataframe_split)))
return {
format: 'embeddings',
schema: properties.input,
requiredFields: ['input'],
invocationUrl,
};
if (properties.dataframe_split)
return {
format: 'dataframe_split',
schema: properties.dataframe_split,
requiredFields: ['dataframe_split'],
invocationUrl,
};
if (properties.dataframe_records)
return {
format: 'dataframe_records',
schema: properties.dataframe_records,
requiredFields: ['dataframe_records'],
invocationUrl,
};
if (properties.inputs)
return {
format: 'inputs',
schema: properties.inputs,
requiredFields: ['inputs'],
invocationUrl,
};
if (properties.instances)
return {
format: 'instances',
schema: properties.instances,
requiredFields: ['instances'],
invocationUrl,
};
return null;
}
function detectInputFormat(openApiSchema) {
const invocationUrl = openApiSchema.servers?.[0]?.url;
if (!invocationUrl) {
throw new n8n_workflow_1.UserError('No server URL found in OpenAPI schema');
}
const pathKeys = Object.keys(openApiSchema.paths);
if (!pathKeys.length) {
throw new n8n_workflow_1.UserError('No paths found in OpenAPI schema');
}
const invocationPath = pathKeys[0];
const postOperation = openApiSchema.paths[invocationPath]?.post;
if (!postOperation?.requestBody?.content?.['application/json']?.schema) {
throw new n8n_workflow_1.UserError('No request schema found');
}
const schema = postOperation.requestBody.content['application/json'].schema;
if (schema.oneOf && schema.oneOf.length > 0) {
for (const option of schema.oneOf) {
const properties = (option.properties || {});
const result = detectFormatFromProperties(properties, invocationUrl, true);
if (result)
return result;
}
}
const properties = (schema.properties || {});
const result = detectFormatFromProperties(properties, invocationUrl);
if (result)
return result;
return { format: 'generic', schema, requiredFields: [], invocationUrl };
}
function generateExampleFromSchema(schema, format) {
const schemaObj = schema;
if (schemaObj?.properties) {
try {
const exampleObj = {};
for (const [key, propValue] of Object.entries(schemaObj.properties)) {
const propType = propValue.type;
if (key === 'messages' && propType === 'array') {
exampleObj.messages = [{ role: 'user', content: 'Hello! How can you help me today?' }];
}
else if (key === 'prompt' && propType === 'string') {
exampleObj.prompt = 'What is Databricks?';
}
else if (key === 'input' && propType === 'array') {
exampleObj.input = ['Text to embed'];
}
else if (key === 'max_tokens' && propType === 'integer') {
exampleObj.max_tokens = 256;
}
else if (key === 'temperature' && propType === 'number') {
exampleObj.temperature = 0.7;
}
else if (key === 'top_p' && propType === 'number') {
exampleObj.top_p = 0.9;
}
else if (key === 'top_k' && propType === 'integer') {
exampleObj.top_k = 40;
}
else if (key === 'stream' && propType === 'boolean') {
exampleObj.stream = false;
}
else if (key === 'n' && propType === 'integer') {
exampleObj.n = 1;
}
else if (key === 'stop' && propValue.oneOf) {
exampleObj.stop = ['\\n'];
}
}
if (Object.keys(exampleObj).length > 0) {
return JSON.stringify(exampleObj, null, 2);
}
}
catch (e) {
// Fall through to default examples
}
}
const examples = {
chat: `{
"messages": [
{
"role": "user",
"content": "Hello! How are you?"
}
],
"max_tokens": 256,
"temperature": 0.7
}`,
completions: `{
"prompt": "What is machine learning?",
"max_tokens": 256,
"temperature": 0.7,
"top_p": 0.9
}`,
embeddings: `{
"input": [
"Example text to embed"
]
}`,
dataframe_split: `{
"dataframe_split": {
"columns": ["feature1", "feature2"],
"data": [[1.0, 2.0], [3.0, 4.0]]
}
}`,
dataframe_records: `{
"dataframe_records": [
{"feature1": 1.0, "feature2": 2.0}
]
}`,
inputs: `{
"inputs": {
"tensor1": [1, 2, 3]
}
}`,
instances: `{
"instances": [
{"tensor1": 1}
]
}`,
};
return examples[format] || '{}';
}
function validateRequestBody(requestBody, detectedFormat) {
switch (detectedFormat) {
case 'chat':
if (!requestBody.messages || !Array.isArray(requestBody.messages)) {
throw new n8n_workflow_1.UserError('Invalid chat format: "messages" array is required');
}
break;
case 'completions':
if (!requestBody.prompt) {
throw new n8n_workflow_1.UserError('Invalid completions format: "prompt" is required');
}
break;
case 'embeddings':
if (!requestBody.input) {
throw new n8n_workflow_1.UserError('Invalid embeddings format: "input" is required');
}
break;
case 'dataframe_split':
if (!requestBody.dataframe_split?.data) {
throw new n8n_workflow_1.UserError('Invalid dataframe_split format: "dataframe_split.data" is required');
}
break;
case 'dataframe_records':
if (!requestBody.dataframe_records || !Array.isArray(requestBody.dataframe_records)) {
throw new n8n_workflow_1.UserError('Invalid dataframe_records format: "dataframe_records" array is required');
}
break;
case 'inputs':
if (!requestBody.inputs) {
throw new n8n_workflow_1.UserError('Invalid inputs format: "inputs" is required');
}
break;
case 'instances':
if (!requestBody.instances || !Array.isArray(requestBody.instances)) {
throw new n8n_workflow_1.UserError('Invalid instances format: "instances" array is required');
}
break;
}
}
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