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https://github.com/farcasclaudiu/Flowise.git
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Merge pull request #1462 from vectara/add_mmr_vectara
added support for MMR
This commit is contained in:
Henry Heng
@@ -69,22 +69,23 @@ class VectaraChain_Chains implements INode {
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options: [
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{
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label: 'vectara-summary-ext-v1.2.0 (gpt-3.5-turbo)',
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name: 'vectara-summary-ext-v1.2.0'
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name: 'vectara-summary-ext-v1.2.0',
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description: 'base summarizer, available to all Vectara users'
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},
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{
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label: 'vectara-experimental-summary-ext-2023-10-23-small (gpt-3.5-turbo)',
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name: 'vectara-experimental-summary-ext-2023-10-23-small',
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description: 'In beta, available to both Growth and Scale Vectara users'
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description: `In beta, available to both Growth and <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users`
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},
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{
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label: 'vectara-summary-ext-v1.3.0 (gpt-4.0)',
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name: 'vectara-summary-ext-v1.3.0',
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description: 'Only available to paying Scale Vectara users'
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description: 'Only available to <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users'
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},
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{
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label: 'vectara-experimental-summary-ext-2023-10-23-med (gpt-4.0)',
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name: 'vectara-experimental-summary-ext-2023-10-23-med',
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description: 'In beta, only available to paying Scale Vectara users'
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description: `In beta, only available to <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users`
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}
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],
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default: 'vectara-summary-ext-v1.2.0'
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@@ -228,7 +229,7 @@ class VectaraChain_Chains implements INode {
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async run(nodeData: INodeData, input: string): Promise<object> {
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const vectorStore = nodeData.inputs?.vectaraStore as VectaraStore
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const responseLang = (nodeData.inputs?.responseLang as string) ?? 'auto'
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const responseLang = (nodeData.inputs?.responseLang as string) ?? 'eng'
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const summarizerPromptName = nodeData.inputs?.summarizerPromptName as string
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const maxSummarizedResultsStr = nodeData.inputs?.maxSummarizedResults as string
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const maxSummarizedResults = maxSummarizedResultsStr ? parseInt(maxSummarizedResultsStr, 10) : 7
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@@ -247,17 +248,31 @@ class VectaraChain_Chains implements INode {
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lexicalInterpolationConfig: { lambda: vectaraFilter?.lambda ?? 0.025 }
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}))
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// Vectara reranker ID for MMR (https://docs.vectara.com/docs/api-reference/search-apis/reranking#maximal-marginal-relevance-mmr-reranker)
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const mmrRerankerId = 272725718
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const mmrEnabled = vectaraFilter?.mmrConfig?.enabled
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const data = {
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query: [
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{
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query: input,
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start: 0,
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numResults: topK,
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numResults: mmrEnabled ? vectaraFilter?.mmrTopK : topK,
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corpusKey: corpusKeys,
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contextConfig: {
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sentencesAfter: vectaraFilter?.contextConfig?.sentencesAfter ?? 2,
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sentencesBefore: vectaraFilter?.contextConfig?.sentencesBefore ?? 2
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},
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corpusKey: corpusKeys,
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...(mmrEnabled
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? {
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rerankingConfig: {
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rerankerId: mmrRerankerId,
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mmrConfig: {
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diversityBias: vectaraFilter?.mmrConfig.diversityBias
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}
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}
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}
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: {}),
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summary: [
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{
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summarizerPromptName,
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@@ -285,6 +300,14 @@ class VectaraChain_Chains implements INode {
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const documents = result.responseSet[0].document
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let rawSummarizedText = ''
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// remove responses that are not in the topK (in case of MMR)
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// Note that this does not really matter functionally due to the reorder citations, but it is more efficient
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const maxResponses = mmrEnabled ? Math.min(responses.length, topK) : responses.length
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if (responses.length > maxResponses) {
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responses.splice(0, maxResponses)
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}
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// Add metadata to each text response given its corresponding document metadata
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for (let i = 0; i < responses.length; i += 1) {
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const responseMetadata = responses[i].metadata
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const documentMetadata = documents[responses[i].documentIndex].metadata
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@@ -301,13 +324,13 @@ class VectaraChain_Chains implements INode {
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responses[i].metadata = combinedMetadata
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}
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// Create the summarization response
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const summaryStatus = result.responseSet[0].summary[0].status
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if (summaryStatus.length > 0 && summaryStatus[0].code === 'BAD_REQUEST') {
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throw new Error(
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`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.`
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)
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}
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if (
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summaryStatus.length > 0 &&
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summaryStatus[0].code === 'NOT_FOUND' &&
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@@ -316,8 +339,8 @@ class VectaraChain_Chains implements INode {
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throw new Error(`BAD REQUEST: summarizer ${summarizerPromptName} is invalid for this account.`)
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}
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// Reorder citations in summary and create the list of returned source documents
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rawSummarizedText = result.responseSet[0].summary[0]?.text
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let summarizedText = reorderCitations(rawSummarizedText)
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let summaryResponses = applyCitationOrder(responses, rawSummarizedText)
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@@ -1,5 +1,5 @@
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import { flatten } from 'lodash'
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import { VectaraStore, VectaraLibArgs, VectaraFilter, VectaraContextConfig, VectaraFile } from 'langchain/vectorstores/vectara'
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import { VectaraStore, VectaraLibArgs, VectaraFilter, VectaraContextConfig, VectaraFile, MMRConfig } from 'langchain/vectorstores/vectara'
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import { Document } from 'langchain/document'
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import { Embeddings } from 'langchain/embeddings/base'
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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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@@ -22,7 +22,7 @@ class Vectara_VectorStores implements INode {
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constructor() {
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this.label = 'Vectara'
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this.name = 'vectara'
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this.version = 1.0
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this.version = 2.0
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this.type = 'Vectara'
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this.icon = 'vectara.png'
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this.category = 'Vector Stores'
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@@ -82,7 +82,9 @@ class Vectara_VectorStores implements INode {
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label: 'Lambda',
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name: 'lambda',
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description:
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'Improves retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.',
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'Enable hybrid search to improve retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.' +
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'A value of 0.0 means that only neural search is used, while a value of 1.0 means that only keyword-based search is used. Defaults to 0.0 (neural only).',
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default: 0.0,
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type: 'number',
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additionalParams: true,
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optional: true
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@@ -90,8 +92,30 @@ class Vectara_VectorStores implements INode {
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{
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label: 'Top K',
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name: 'topK',
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description: 'Number of top results to fetch. Defaults to 4',
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placeholder: '4',
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description: 'Number of top results to fetch. Defaults to 5',
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placeholder: '5',
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type: 'number',
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additionalParams: true,
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optional: true
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},
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{
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label: 'MMR K',
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name: 'mmrK',
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description: 'Number of top results to fetch for MMR. Defaults to 50',
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placeholder: '50',
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type: 'number',
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additionalParams: true,
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optional: true
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},
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{
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label: 'MMR diversity bias',
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name: 'mmrDiversityBias',
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step: 0.1,
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description:
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'The diversity bias to use for MMR. This is a value between 0.0 and 1.0' +
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'Values closer to 1.0 optimize for the most diverse results.' +
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'Defaults to 0 (MMR disabled)',
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placeholder: '0.0',
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type: 'number',
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additionalParams: true,
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optional: true
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@@ -191,7 +215,9 @@ class Vectara_VectorStores implements INode {
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const lambda = nodeData.inputs?.lambda as number
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const output = nodeData.outputs?.output as string
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const topK = nodeData.inputs?.topK as string
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const k = topK ? parseFloat(topK) : 4
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const k = topK ? parseFloat(topK) : 5
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const mmrK = nodeData.inputs?.mmrK as number
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const mmrDiversityBias = nodeData.inputs?.mmrDiversityBias as number
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const vectaraArgs: VectaraLibArgs = {
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apiKey: apiKey,
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@@ -208,6 +234,11 @@ class Vectara_VectorStores implements INode {
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if (sentencesBefore) vectaraContextConfig.sentencesBefore = sentencesBefore
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if (sentencesAfter) vectaraContextConfig.sentencesAfter = sentencesAfter
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vectaraFilter.contextConfig = vectaraContextConfig
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const mmrConfig: MMRConfig = {}
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mmrConfig.enabled = mmrDiversityBias > 0
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mmrConfig.mmrTopK = mmrK
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mmrConfig.diversityBias = mmrDiversityBias
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vectaraFilter.mmrConfig = mmrConfig
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const vectorStore = new VectaraStore(vectaraArgs)
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