dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
346 lines • 15 kB
JavaScript
;
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
const node_fetch_1 = __importDefault(require("node-fetch"));
const documents_1 = require("@langchain/core/documents");
const chains_1 = require("langchain/chains");
const utils_1 = require("../../../src/utils");
const Moderation_1 = require("../../moderation/Moderation");
const OutputParserHelpers_1 = require("../../outputparsers/OutputParserHelpers");
// functionality based on https://github.com/vectara/vectara-answer
const reorderCitations = (unorderedSummary) => {
const allCitations = unorderedSummary.match(/\[\d+\]/g) || [];
const uniqueCitations = [...new Set(allCitations)];
const citationToReplacement = {};
uniqueCitations.forEach((citation, index) => {
citationToReplacement[citation] = `[${index + 1}]`;
});
return unorderedSummary.replace(/\[\d+\]/g, (match) => citationToReplacement[match]);
};
const applyCitationOrder = (searchResults, unorderedSummary) => {
const orderedSearchResults = [];
const allCitations = unorderedSummary.match(/\[\d+\]/g) || [];
const addedIndices = new Set();
for (let i = 0; i < allCitations.length; i++) {
const citation = allCitations[i];
const index = Number(citation.slice(1, citation.length - 1)) - 1;
if (addedIndices.has(index))
continue;
orderedSearchResults.push(searchResults[index]);
addedIndices.add(index);
}
return orderedSearchResults;
};
class VectaraChain_Chains {
constructor() {
this.label = 'Vectara QA Chain';
this.name = 'vectaraQAChain';
this.version = 2.0;
this.type = 'VectaraQAChain';
this.icon = 'vectara.png';
this.category = 'Chains';
this.description = 'QA chain for Vectara';
this.baseClasses = [this.type, ...(0, utils_1.getBaseClasses)(chains_1.VectorDBQAChain)];
this.inputs = [
{
label: 'Vectara Store',
name: 'vectaraStore',
type: 'VectorStore'
},
{
label: 'Summarizer Prompt Name',
name: 'summarizerPromptName',
description: 'Summarize the results fetched from Vectara. Read <a target="_blank" href="https://docs.vectara.com/docs/learn/grounded-generation/select-a-summarizer">more</a>',
type: 'options',
options: [
{
label: 'vectara-summary-ext-v1.2.0 (gpt-3.5-turbo)',
name: 'vectara-summary-ext-v1.2.0',
description: 'base summarizer, available to all Vectara users'
},
{
label: 'vectara-experimental-summary-ext-2023-10-23-small (gpt-3.5-turbo)',
name: 'vectara-experimental-summary-ext-2023-10-23-small',
description: `In beta, available to both Growth and <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users`
},
{
label: 'vectara-summary-ext-v1.3.0 (gpt-4.0)',
name: 'vectara-summary-ext-v1.3.0',
description: 'Only available to <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users'
},
{
label: 'vectara-experimental-summary-ext-2023-10-23-med (gpt-4.0)',
name: 'vectara-experimental-summary-ext-2023-10-23-med',
description: `In beta, only available to <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users`
}
],
default: 'vectara-summary-ext-v1.2.0'
},
{
label: 'Response Language',
name: 'responseLang',
description: 'Return the response in specific language. If not selected, Vectara will automatically detects the language. Read <a target="_blank" href="https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-response-languages">more</a>',
type: 'options',
options: [
{
label: 'English',
name: 'eng'
},
{
label: 'German',
name: 'deu'
},
{
label: 'French',
name: 'fra'
},
{
label: 'Chinese',
name: 'zho'
},
{
label: 'Korean',
name: 'kor'
},
{
label: 'Arabic',
name: 'ara'
},
{
label: 'Russian',
name: 'rus'
},
{
label: 'Thai',
name: 'tha'
},
{
label: 'Dutch',
name: 'nld'
},
{
label: 'Italian',
name: 'ita'
},
{
label: 'Portuguese',
name: 'por'
},
{
label: 'Spanish',
name: 'spa'
},
{
label: 'Japanese',
name: 'jpn'
},
{
label: 'Polish',
name: 'pol'
},
{
label: 'Turkish',
name: 'tur'
},
{
label: 'Vietnamese',
name: 'vie'
},
{
label: 'Indonesian',
name: 'ind'
},
{
label: 'Czech',
name: 'ces'
},
{
label: 'Ukrainian',
name: 'ukr'
},
{
label: 'Greek',
name: 'ell'
},
{
label: 'Hebrew',
name: 'heb'
},
{
label: 'Farsi/Persian',
name: 'fas'
},
{
label: 'Hindi',
name: 'hin'
},
{
label: 'Urdu',
name: 'urd'
},
{
label: 'Swedish',
name: 'swe'
},
{
label: 'Bengali',
name: 'ben'
},
{
label: 'Malay',
name: 'msa'
},
{
label: 'Romanian',
name: 'ron'
}
],
optional: true,
default: 'eng'
},
{
label: 'Max Summarized Results',
name: 'maxSummarizedResults',
description: 'Maximum results used to build the summarized response',
type: 'number',
default: 7
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
];
}
async init() {
return null;
}
async run(nodeData, input) {
const vectorStore = nodeData.inputs?.vectaraStore;
const responseLang = nodeData.inputs?.responseLang ?? 'eng';
const summarizerPromptName = nodeData.inputs?.summarizerPromptName;
const maxSummarizedResultsStr = nodeData.inputs?.maxSummarizedResults;
const maxSummarizedResults = maxSummarizedResultsStr ? parseInt(maxSummarizedResultsStr, 10) : 7;
const topK = vectorStore?.k ?? 10;
const headers = await vectorStore.getJsonHeader();
const vectaraFilter = vectorStore.vectaraFilter ?? {};
const corpusId = vectorStore.corpusId ?? [];
const customerId = vectorStore.customerId ?? '';
const corpusKeys = corpusId.map((corpusId) => ({
customerId,
corpusId,
metadataFilter: vectaraFilter?.filter ?? '',
lexicalInterpolationConfig: { lambda: vectaraFilter?.lambda ?? 0.025 }
}));
// Vectara reranker ID for MMR (https://docs.vectara.com/docs/api-reference/search-apis/reranking#maximal-marginal-relevance-mmr-reranker)
const mmrRerankerId = 272725718;
const mmrEnabled = vectaraFilter?.mmrConfig?.enabled;
const moderations = nodeData.inputs?.inputModeration;
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the Vectara chain
input = await (0, Moderation_1.checkInputs)(moderations, input);
}
catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500));
// if (options.shouldStreamResponse) {
// streamResponse(options.sseStreamer, options.chatId, e.message)
// }
return (0, OutputParserHelpers_1.formatResponse)(e.message);
}
}
const data = {
query: [
{
query: input,
start: 0,
numResults: mmrEnabled ? vectaraFilter?.mmrTopK : topK,
corpusKey: corpusKeys,
contextConfig: {
sentencesAfter: vectaraFilter?.contextConfig?.sentencesAfter ?? 2,
sentencesBefore: vectaraFilter?.contextConfig?.sentencesBefore ?? 2
},
...(mmrEnabled
? {
rerankingConfig: {
rerankerId: mmrRerankerId,
mmrConfig: {
diversityBias: vectaraFilter?.mmrConfig.diversityBias
}
}
}
: {}),
summary: [
{
summarizerPromptName,
responseLang,
maxSummarizedResults
}
]
}
]
};
try {
const response = await (0, node_fetch_1.default)(`https://api.vectara.io/v1/query`, {
method: 'POST',
headers: headers?.headers,
body: JSON.stringify(data)
});
if (response.status !== 200) {
throw new Error(`Vectara API returned status code ${response.status}`);
}
const result = await response.json();
const responses = result.responseSet[0].response;
const documents = result.responseSet[0].document;
let rawSummarizedText = '';
// remove responses that are not in the topK (in case of MMR)
// Note that this does not really matter functionally due to the reorder citations, but it is more efficient
const maxResponses = mmrEnabled ? Math.min(responses.length, topK) : responses.length;
if (responses.length > maxResponses) {
responses.splice(0, maxResponses);
}
// Add metadata to each text response given its corresponding document metadata
for (let i = 0; i < responses.length; i += 1) {
const responseMetadata = responses[i].metadata;
const documentMetadata = documents[responses[i].documentIndex].metadata;
const combinedMetadata = {};
responseMetadata.forEach((item) => {
combinedMetadata[item.name] = item.value;
});
documentMetadata.forEach((item) => {
combinedMetadata[item.name] = item.value;
});
responses[i].metadata = combinedMetadata;
}
// Create the summarization response
const summaryStatus = result.responseSet[0].summary[0].status;
if (summaryStatus.length > 0 && summaryStatus[0].code === 'BAD_REQUEST') {
throw new Error(`BAD REQUEST: Too much text for the summarizer to summarize. Please try reducing the number of search results to summarize, or the context of each result by adjusting the 'summary_num_sentences', and 'summary_num_results' parameters respectively.`);
}
if (summaryStatus.length > 0 &&
summaryStatus[0].code === 'NOT_FOUND' &&
summaryStatus[0].statusDetail === 'Failed to retrieve summarizer.') {
throw new Error(`BAD REQUEST: summarizer ${summarizerPromptName} is invalid for this account.`);
}
// Reorder citations in summary and create the list of returned source documents
rawSummarizedText = result.responseSet[0].summary[0]?.text;
let summarizedText = reorderCitations(rawSummarizedText);
let summaryResponses = applyCitationOrder(responses, rawSummarizedText);
const sourceDocuments = summaryResponses.map((response) => new documents_1.Document({
pageContent: response.text,
metadata: response.metadata
}));
return { text: summarizedText, sourceDocuments: sourceDocuments };
}
catch (error) {
throw new Error(error);
}
}
}
module.exports = { nodeClass: VectaraChain_Chains };
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