@n8n/n8n-nodes-langchain
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171 lines • 6 kB
JavaScript
;
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
var __hasOwnProp = Object.prototype.hasOwnProperty;
var __export = (target, all) => {
for (var name in all)
__defProp(target, name, { get: all[name], enumerable: true });
};
var __copyProps = (to, from, except, desc) => {
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__defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
}
return to;
};
var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod);
var EmbeddingsAzureOpenAi_node_exports = {};
__export(EmbeddingsAzureOpenAi_node_exports, {
EmbeddingsAzureOpenAi: () => EmbeddingsAzureOpenAi
});
module.exports = __toCommonJS(EmbeddingsAzureOpenAi_node_exports);
var import_openai = require("@langchain/openai");
var import_n8n_workflow = require("n8n-workflow");
var import_httpProxyAgent = require("../../../utils/httpProxyAgent");
var import_logWrapper = require("../../../utils/logWrapper");
var import_sharedFields = require("../../../utils/sharedFields");
class EmbeddingsAzureOpenAi {
constructor() {
this.description = {
displayName: "Embeddings Azure OpenAI",
name: "embeddingsAzureOpenAi",
icon: "file:azure.svg",
credentials: [
{
name: "azureOpenAiApi",
required: true
}
],
group: ["transform"],
version: 1,
description: "Use Embeddings Azure OpenAI",
defaults: {
name: "Embeddings Azure OpenAI"
},
codex: {
categories: ["AI"],
subcategories: {
AI: ["Embeddings"]
},
resources: {
primaryDocumentation: [
{
url: "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.embeddingsazureopenai/"
}
]
}
},
inputs: [],
outputs: [import_n8n_workflow.NodeConnectionTypes.AiEmbedding],
outputNames: ["Embeddings"],
properties: [
(0, import_sharedFields.getConnectionHintNoticeField)([import_n8n_workflow.NodeConnectionTypes.AiVectorStore]),
{
displayName: "Model (Deployment) Name",
name: "model",
type: "string",
description: "The name of the model(deployment) to use",
default: ""
},
{
displayName: "Options",
name: "options",
placeholder: "Add Option",
description: "Additional options to add",
type: "collection",
default: {},
options: [
{
displayName: "Batch Size",
name: "batchSize",
default: 512,
typeOptions: { maxValue: 2048 },
description: "Maximum number of documents to send in each request",
type: "number"
},
{
displayName: "Strip New Lines",
name: "stripNewLines",
default: true,
description: "Whether to strip new lines from the input text",
type: "boolean"
},
{
displayName: "Timeout",
name: "timeout",
default: -1,
description: "Maximum amount of time a request is allowed to take in seconds. Set to -1 for no timeout.",
type: "number"
},
{
displayName: "Dimensions",
name: "dimensions",
default: void 0,
description: "The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.",
type: "options",
options: [
{
name: "256",
value: 256
},
{
name: "512",
value: 512
},
{
name: "1024",
value: 1024
},
{
name: "1536",
value: 1536
},
{
name: "3072",
value: 3072
}
]
}
]
}
]
};
}
async supplyData(itemIndex) {
this.logger.debug("Supply data for embeddings");
const credentials = await this.getCredentials("azureOpenAiApi");
const modelName = this.getNodeParameter("model", itemIndex);
const options = this.getNodeParameter("options", itemIndex, {});
if (options.timeout === -1) {
options.timeout = void 0;
}
const embeddings = new import_openai.AzureOpenAIEmbeddings({
azureOpenAIApiDeploymentName: modelName,
// instance name only needed to set base url
azureOpenAIApiInstanceName: !credentials.endpoint ? credentials.resourceName : void 0,
azureOpenAIApiKey: credentials.apiKey,
azureOpenAIApiVersion: credentials.apiVersion,
// azureOpenAIEndpoint and configuration.baseURL are both ignored here
// only setting azureOpenAIBasePath worked
azureOpenAIBasePath: credentials.endpoint ? `${credentials.endpoint}/openai/deployments` : void 0,
configuration: {
fetchOptions: {
dispatcher: (0, import_httpProxyAgent.getProxyAgent)(
credentials.endpoint ?? `https://${credentials.resourceName}.openai.azure.com`
)
}
},
...options
});
return {
response: (0, import_logWrapper.logWrapper)(embeddings, this)
};
}
}
// Annotate the CommonJS export names for ESM import in node:
0 && (module.exports = {
EmbeddingsAzureOpenAi
});
//# sourceMappingURL=EmbeddingsAzureOpenAi.node.js.map