60 lines
1.9 KiB
JavaScript
60 lines
1.9 KiB
JavaScript
import {
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AzureAISearchVectorStore,
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AzureAISearchQueryType,
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} from "@langchain/community/vectorstores/azure_aisearch";
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import { OpenAIEmbeddings, AzureOpenAIEmbeddings } from "@langchain/openai";
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const query = process.argv[2] || "What is CX Automation?";
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// the RAG widget uses the OpenAIEmbeddings class but the config will not work because you cannot pass in the api-version param. DO NOT USE
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// const embedding = new OpenAIEmbeddings({
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// openAIApiKey: openAIApiKey,
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// configuration: {
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// baseURL: `https://${azureOpenAIApiInstanceName}.openai.azure.com/openai/deployments/${azureOpenAIApiDeploymentName}?api-version=${azureOpenAIApiVersion}`,
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// },
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// })
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const embedding = new AzureOpenAIEmbeddings({
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azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME,
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azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME,
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azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION,
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azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,
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});
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const store = new AzureAISearchVectorStore(embedding, {
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endpoint: process.env.AZURE_AISEARCH_ENDPOINT,
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key: process.env.AZURE_AISEARCH_KEY,
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indexName: process.env.AZURE_AISEARCH_INDEX_NAME,
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search: {
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type: AzureAISearchQueryType.SimilarityHybrid,
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},
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});
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function getSourceId(document) {
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if (document.metadata) {
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const mergedMetadata = Object.values(document.metadata).join("");
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const metatDataObj = JSON.parse(mergedMetadata);
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return metatDataObj.source;
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} else return undefined;
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}
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const resultDocuments = await store.similaritySearch(query);
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const sources = resultDocuments.map((doc) => ({
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source_id: getSourceId(doc),
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text: doc.pageContent,
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}));
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const cqaSources = {
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instances: [
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{
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sources: sources,
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question: query,
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knowledgebase_description: process.env.AZURE_AISEARCH_INDEX_NAME,
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extra_guidance: "",
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language_code: "en-GB",
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},
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],
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};
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console.log(JSON.stringify(cqaSources, null, 2));
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