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update retrievers and add mmr to other vector stores

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This commit is contained in:
Henry
2024-01-17 15:55:56 +00:00
parent f151a77d3f
commit 1bf7944776
23 changed files with 642 additions and 121 deletions
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packages/components/nodes/retrievers/CohereRerankRetriever/Cohere.svg
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@@ -0,0 +1 @@
<svg width="32" height="32" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M11.776 18.304c.64 0 1.92-.032 3.712-.768 2.08-.864 6.176-2.4 9.152-4 2.08-1.12 2.976-2.592 2.976-4.576 0-2.72-2.208-4.96-4.96-4.96h-11.52A7.143 7.143 0 0 0 4 11.136c0 3.936 3.008 7.168 7.776 7.168Z" fill="#39594D"/><path fill-rule="evenodd" clip-rule="evenodd" d="M13.728 23.2c0-1.92 1.152-3.68 2.944-4.416l3.616-1.504C23.968 15.776 28 18.464 28 22.432A5.572 5.572 0 0 1 22.432 28h-3.936c-2.624 0-4.768-2.144-4.768-4.8Z" fill="#D18EE2"/><path d="M8.128 19.232A4.138 4.138 0 0 0 4 23.36v.544C4 26.144 5.856 28 8.128 28a4.138 4.138 0 0 0 4.128-4.128v-.544c-.032-2.24-1.856-4.096-4.128-4.096Z" fill="#FF7759"/></svg>
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packages/components/nodes/retrievers/CohereRerankRetriever/CohereRerank.ts
+5 -2
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@@ -7,11 +7,14 @@ export class CohereRerank extends BaseDocumentCompressor {
private COHERE_API_URL = 'https://api.cohere.ai/v1/rerank'
private readonly model: string
private readonly k: number
constructor(cohereAPIKey: string, model: string, k: number) {
private readonly max_chunks_per_doc: number
constructor(cohereAPIKey: string, model: string, k: number, max_chunks_per_doc: number) {
super()
this.cohereAPIKey = cohereAPIKey
this.model = model
this.k = k
this.max_chunks_per_doc = max_chunks_per_doc
}
async compressDocuments(
documents: Document<Record<string, any>>[],
@@ -32,8 +35,8 @@ export class CohereRerank extends BaseDocumentCompressor {
const data = {
model: this.model,
topN: this.k,
max_chunks_per_doc: 10,
query: query,
max_chunks_per_doc: this.max_chunks_per_doc,
return_documents: false,
documents: documents.map((doc) => doc.pageContent)
}
packages/components/nodes/retrievers/CohereRerankRetriever/CohereRerankRetriever.ts
+61 -13
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@@ -1,7 +1,7 @@
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { BaseRetriever } from 'langchain/schema/retriever'
import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
import { getCredentialData, getCredentialParam } from '../../../src'
import { getCredentialData, getCredentialParam, handleEscapeCharacters } from '../../../src'
import { CohereRerank } from './CohereRerank'
import { VectorStoreRetriever } from 'langchain/vectorstores/base'
@@ -15,16 +15,16 @@ class CohereRerankRetriever_Retrievers implements INode {
category: string
baseClasses: string[]
inputs: INodeParams[]
outputs: INodeOutputsValue[]
credential: INodeParams
badge: string
outputs: INodeOutputsValue[]
constructor() {
this.label = 'Cohere Rerank Retriever'
this.name = 'cohereRerankRetriever'
this.version = 1.0
this.type = 'Cohere Rerank Retriever'
this.icon = 'compressionRetriever.svg'
this.icon = 'Cohere.svg'
this.category = 'Retrievers'
this.badge = 'NEW'
this.description = 'Cohere Rerank indexes the documents from most to least semantically relevant to the query.'
@@ -37,7 +37,7 @@ class CohereRerankRetriever_Retrievers implements INode {
}
this.inputs = [
{
label: 'Base Retriever',
label: 'Vector Store Retriever',
name: 'baseRetriever',
type: 'VectorStoreRetriever'
},
@@ -58,36 +58,84 @@ class CohereRerankRetriever_Retrievers implements INode {
default: 'rerank-english-v2.0',
optional: true
},
{
label: 'Query',
name: 'query',
type: 'string',
description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
optional: true,
acceptVariable: true
},
{
label: 'Top K',
name: 'topK',
description: 'Number of top results to fetch. Default to the TopK of the Base Retriever',
placeholder: '0',
placeholder: '4',
type: 'number',
additionalParams: true,
optional: true
},
{
label: 'Max Chunks Per Doc',
name: 'maxChunksPerDoc',
description: 'The maximum number of chunks to produce internally from a document. Default to 10',
placeholder: '10',
type: 'number',
default: 0,
additionalParams: true,
optional: true
}
]
this.outputs = [
{
label: 'Cohere Rerank Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Document',
name: 'document',
baseClasses: ['Document']
},
{
label: 'Text',
name: 'text',
baseClasses: ['string', 'json']
}
]
}
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
const model = nodeData.inputs?.model as string
const query = nodeData.inputs?.query as string
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const cohereApiKey = getCredentialParam('cohereApiKey', credentialData, nodeData)
const topK = nodeData.inputs?.topK as string
let k = topK ? parseFloat(topK) : 4
const k = topK ? parseFloat(topK) : (baseRetriever as VectorStoreRetriever).k ?? 4
const maxChunksPerDoc = nodeData.inputs?.maxChunksPerDoc as string
const max_chunks_per_doc = maxChunksPerDoc ? parseFloat(maxChunksPerDoc) : 10
const output = nodeData.outputs?.output as string
if (k <= 0) {
k = (baseRetriever as VectorStoreRetriever).k
}
const cohereCompressor = new CohereRerank(cohereApiKey, model, k, max_chunks_per_doc)
const cohereCompressor = new CohereRerank(cohereApiKey, model, k)
return new ContextualCompressionRetriever({
const retriever = new ContextualCompressionRetriever({
baseCompressor: cohereCompressor,
baseRetriever: baseRetriever
})
if (output === 'retriever') return retriever
else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
else if (output === 'text') {
let finaltext = ''
const docs = await retriever.getRelevantDocuments(query ? query : input)
for (const doc of docs) finaltext += `${doc.pageContent}\n`
return handleEscapeCharacters(finaltext, false)
}
return retriever
}
}
packages/components/nodes/retrievers/CohereRerankRetriever/compressionRetriever.svg
-7
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@@ -1,7 +0,0 @@
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-chart-bar" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
<path stroke="none" d="M0 0h24v24H0z" fill="none"/>
<path d="M3 12m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v6a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
<path d="M9 8m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v10a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
<path d="M15 4m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v14a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
<path d="M4 20l14 0" />
</svg>
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packages/components/nodes/retrievers/EmbeddingsFilterRetriever/EmbeddingsFilterRetriever.ts
+49 -13
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@@ -3,6 +3,7 @@ import { BaseRetriever } from 'langchain/schema/retriever'
import { Embeddings } from 'langchain/embeddings/base'
import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
import { EmbeddingsFilter } from 'langchain/retrievers/document_compressors/embeddings_filter'
import { handleEscapeCharacters } from '../../../src/utils'
class EmbeddingsFilterRetriever_Retrievers implements INode {
label: string
@@ -29,15 +30,22 @@ class EmbeddingsFilterRetriever_Retrievers implements INode {
this.baseClasses = [this.type, 'BaseRetriever']
this.inputs = [
{
label: 'Base Retriever',
label: 'Vector Store Retriever',
name: 'baseRetriever',
type: 'VectorStoreRetriever'
},
{
label: 'Embeddings',
name: 'embeddings',
type: 'Embeddings',
optional: false
type: 'Embeddings'
},
{
label: 'Query',
name: 'query',
type: 'string',
description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
optional: true,
acceptVariable: true
},
{
label: 'Similarity Threshold',
@@ -61,36 +69,64 @@ class EmbeddingsFilterRetriever_Retrievers implements INode {
additionalParams: true
}
]
this.outputs = [
{
label: 'Embeddings Filter Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Document',
name: 'document',
baseClasses: ['Document']
},
{
label: 'Text',
name: 'text',
baseClasses: ['string', 'json']
}
]
}
async init(nodeData: INodeData): Promise<any> {
async init(nodeData: INodeData, input: string): Promise<any> {
const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
const embeddings = nodeData.inputs?.embeddings as Embeddings
const query = nodeData.inputs?.query as string
const similarityThreshold = nodeData.inputs?.similarityThreshold as string
const k = nodeData.inputs?.k as string
const output = nodeData.outputs?.output as string
if (k === undefined && similarityThreshold === undefined) {
throw new Error(`Must specify one of "k" or "similarity_threshold".`)
}
let similarityThresholdNumber = 0.8
if (similarityThreshold) {
similarityThresholdNumber = parseFloat(similarityThreshold)
}
let kNumber = 0.8
if (k) {
kNumber = parseFloat(k)
}
const similarityThresholdNumber = similarityThreshold ? parseFloat(similarityThreshold) : 0.8
const kNumber = k ? parseFloat(k) : undefined
const baseCompressor = new EmbeddingsFilter({
embeddings: embeddings,
similarityThreshold: similarityThresholdNumber,
k: kNumber
})
return new ContextualCompressionRetriever({
const retriever = new ContextualCompressionRetriever({
baseCompressor,
baseRetriever: baseRetriever
})
if (output === 'retriever') return retriever
else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
else if (output === 'text') {
let finaltext = ''
const docs = await retriever.getRelevantDocuments(query ? query : input)
for (const doc of docs) finaltext += `${doc.pageContent}\n`
return handleEscapeCharacters(finaltext, false)
}
return retriever
}
}
packages/components/nodes/retrievers/HydeRetriever/HydeRetriever.ts
+46 -4
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@@ -1,8 +1,9 @@
import { VectorStore } from 'langchain/vectorstores/base'
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { HydeRetriever, HydeRetrieverOptions, PromptKey } from 'langchain/retrievers/hyde'
import { BaseLanguageModel } from 'langchain/base_language'
import { PromptTemplate } from 'langchain/prompts'
import { handleEscapeCharacters } from '../../../src/utils'
class HydeRetriever_Retrievers implements INode {
label: string
@@ -14,11 +15,12 @@ class HydeRetriever_Retrievers implements INode {
category: string
baseClasses: string[]
inputs: INodeParams[]
outputs: INodeOutputsValue[]
constructor() {
this.label = 'Hyde Retriever'
this.label = 'HyDE Retriever'
this.name = 'HydeRetriever'
this.version = 2.0
this.version = 3.0
this.type = 'HydeRetriever'
this.icon = 'hyderetriever.svg'
this.category = 'Retrievers'
@@ -35,6 +37,14 @@ class HydeRetriever_Retrievers implements INode {
name: 'vectorStore',
type: 'VectorStore'
},
{
label: 'Query',
name: 'query',
type: 'string',
description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
optional: true,
acceptVariable: true
},
{
label: 'Select Defined Prompt',
name: 'promptKey',
@@ -121,15 +131,34 @@ Passage:`
optional: true
}
]
this.outputs = [
{
label: 'HyDE Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Document',
name: 'document',
baseClasses: ['Document']
},
{
label: 'Text',
name: 'text',
baseClasses: ['string', 'json']
}
]
}
async init(nodeData: INodeData): Promise<any> {
async init(nodeData: INodeData, input: string): Promise<any> {
const llm = nodeData.inputs?.model as BaseLanguageModel
const vectorStore = nodeData.inputs?.vectorStore as VectorStore
const promptKey = nodeData.inputs?.promptKey as PromptKey
const customPrompt = nodeData.inputs?.customPrompt as string
const query = nodeData.inputs?.query as string
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const output = nodeData.outputs?.output as string
const obj: HydeRetrieverOptions<any> = {
llm,
@@ -141,6 +170,19 @@ Passage:`
else if (promptKey) obj.promptTemplate = promptKey
const retriever = new HydeRetriever(obj)
if (output === 'retriever') return retriever
else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
else if (output === 'text') {
let finaltext = ''
const docs = await retriever.getRelevantDocuments(query ? query : input)
for (const doc of docs) finaltext += `${doc.pageContent}\n`
return handleEscapeCharacters(finaltext, false)
}
return retriever
}
}
packages/components/nodes/retrievers/LLMFilterRetriever/LLMFilterCompressionRetriever.ts
+51 -11
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@@ -3,6 +3,7 @@ import { BaseRetriever } from 'langchain/schema/retriever'
import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
import { BaseLanguageModel } from 'langchain/base_language'
import { LLMChainExtractor } from 'langchain/retrievers/document_compressors/chain_extract'
import { handleEscapeCharacters } from '../../../src/utils'
class LLMFilterCompressionRetriever_Retrievers implements INode {
label: string
@@ -22,7 +23,7 @@ class LLMFilterCompressionRetriever_Retrievers implements INode {
this.name = 'llmFilterRetriever'
this.version = 1.0
this.type = 'LLMFilterRetriever'
this.icon = 'compressionRetriever.svg'
this.icon = 'llmFilterRetriever.svg'
this.category = 'Retrievers'
this.badge = 'NEW'
this.description =
@@ -30,30 +31,69 @@ class LLMFilterCompressionRetriever_Retrievers implements INode {
this.baseClasses = [this.type, 'BaseRetriever']
this.inputs = [
{
label: 'Base Retriever',
label: 'Vector Store Retriever',
name: 'baseRetriever',
type: 'VectorStoreRetriever'
},
{
label: 'Language Model',
name: 'model',
type: 'BaseLanguageModel',
optional: true
type: 'BaseLanguageModel'
},
{
label: 'Query',
name: 'query',
type: 'string',
description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
optional: true,
acceptVariable: true
}
]
this.outputs = [
{
label: 'LLM Filter Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Document',
name: 'document',
baseClasses: ['Document']
},
{
label: 'Text',
name: 'text',
baseClasses: ['string', 'json']
}
]
}
async init(nodeData: INodeData): Promise<any> {
async init(nodeData: INodeData, input: string): Promise<any> {
const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
const model = nodeData.inputs?.model as BaseLanguageModel
const query = nodeData.inputs?.query as string
const output = nodeData.outputs?.output as string
if (model) {
return new ContextualCompressionRetriever({
baseCompressor: LLMChainExtractor.fromLLM(model),
baseRetriever: baseRetriever
})
if (!model) throw new Error('There must be a LLM model connected to LLM Filter Retriever')
const retriever = new ContextualCompressionRetriever({
baseCompressor: LLMChainExtractor.fromLLM(model),
baseRetriever: baseRetriever
})
if (output === 'retriever') return retriever
else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
else if (output === 'text') {
let finaltext = ''
const docs = await retriever.getRelevantDocuments(query ? query : input)
for (const doc of docs) finaltext += `${doc.pageContent}\n`
return handleEscapeCharacters(finaltext, false)
}
return {}
return retriever
}
}
packages/components/nodes/retrievers/LLMFilterRetriever/compressionRetriever.svg
-7
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@@ -1,7 +0,0 @@
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-chart-bar" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
<path stroke="none" d="M0 0h24v24H0z" fill="none"/>
<path d="M3 12m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v6a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
<path d="M9 8m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v10a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
<path d="M15 4m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v14a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
<path d="M4 20l14 0" />
</svg>
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packages/components/nodes/retrievers/LLMFilterRetriever/llmFilterRetriever.svg
+1
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@@ -0,0 +1 @@
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-filter-check" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M11.18 20.274l-2.18 .726v-8.5l-4.48 -4.928a2 2 0 0 1 -.52 -1.345v-2.227h16v2.172a2 2 0 0 1 -.586 1.414l-4.414 4.414v3" /><path d="M15 19l2 2l4 -4" /></svg>
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packages/components/nodes/retrievers/RRFRetriever/RRFRetriever.ts
+51 -13
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@@ -1,9 +1,10 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { BaseLanguageModel } from 'langchain/base_language'
import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
import { BaseRetriever } from 'langchain/schema/retriever'
import { ReciprocalRankFusion } from './ReciprocalRankFusion'
import { VectorStoreRetriever } from 'langchain/vectorstores/base'
import { handleEscapeCharacters } from '../../../src/utils'
class RRFRetriever_Retrievers implements INode {
label: string
@@ -16,20 +17,21 @@ class RRFRetriever_Retrievers implements INode {
baseClasses: string[]
inputs: INodeParams[]
badge: string
outputs: INodeOutputsValue[]
constructor() {
this.label = 'Reciprocal Rank Fusion Retriever'
this.name = 'RRFRetriever'
this.version = 2.0
this.version = 1.0
this.type = 'RRFRetriever'
this.badge = 'NEW'
this.icon = 'compressionRetriever.svg'
this.icon = 'rrfRetriever.svg'
this.category = 'Retrievers'
this.description = 'Reciprocal Rank Fusion to re-rank search results by multiple query generation.'
this.baseClasses = [this.type, 'BaseRetriever']
this.inputs = [
{
label: 'Base Retriever',
label: 'Vector Store Retriever',
name: 'baseRetriever',
type: 'VectorStoreRetriever'
},
@@ -38,6 +40,14 @@ class RRFRetriever_Retrievers implements INode {
name: 'model',
type: 'BaseLanguageModel'
},
{
label: 'Query',
name: 'query',
type: 'string',
description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
optional: true,
acceptVariable: true
},
{
label: 'Query Count',
name: 'queryCount',
@@ -54,7 +64,6 @@ class RRFRetriever_Retrievers implements INode {
description: 'Number of top results to fetch. Default to the TopK of the Base Retriever',
placeholder: '0',
type: 'number',
default: 0,
additionalParams: true,
optional: true
},
@@ -71,27 +80,56 @@ class RRFRetriever_Retrievers implements INode {
optional: true
}
]
this.outputs = [
{
label: 'Reciprocal Rank Fusion Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Document',
name: 'document',
baseClasses: ['Document']
},
{
label: 'Text',
name: 'text',
baseClasses: ['string', 'json']
}
]
}
async init(nodeData: INodeData): Promise<any> {
async init(nodeData: INodeData, input: string): Promise<any> {
const llm = nodeData.inputs?.model as BaseLanguageModel
const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
const query = nodeData.inputs?.query as string
const queryCount = nodeData.inputs?.queryCount as string
const q = queryCount ? parseFloat(queryCount) : 4
const topK = nodeData.inputs?.topK as string
let k = topK ? parseFloat(topK) : 4
const k = topK ? parseFloat(topK) : (baseRetriever as VectorStoreRetriever).k ?? 4
const constantC = nodeData.inputs?.c as string
let c = topK ? parseFloat(constantC) : 60
if (k <= 0) {
k = (baseRetriever as VectorStoreRetriever).k
}
const c = topK ? parseFloat(constantC) : 60
const output = nodeData.outputs?.output as string
const ragFusion = new ReciprocalRankFusion(llm, baseRetriever as VectorStoreRetriever, q, k, c)
return new ContextualCompressionRetriever({
const retriever = new ContextualCompressionRetriever({
baseCompressor: ragFusion,
baseRetriever: baseRetriever
})
if (output === 'retriever') return retriever
else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
else if (output === 'text') {
let finaltext = ''
const docs = await retriever.getRelevantDocuments(query ? query : input)
for (const doc of docs) finaltext += `${doc.pageContent}\n`
return handleEscapeCharacters(finaltext, false)
}
return retriever
}
}
packages/components/nodes/retrievers/RRFRetriever/compressionRetriever.svg
-7
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@@ -1,7 +0,0 @@
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-chart-bar" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
<path stroke="none" d="M0 0h24v24H0z" fill="none"/>
<path d="M3 12m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v6a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
<path d="M9 8m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v10a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
<path d="M15 4m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v14a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
<path d="M4 20l14 0" />
</svg>
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packages/components/nodes/retrievers/RRFRetriever/rrfRetriever.svg
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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-math-x-divide-y-2" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M3 21l18 -18" /><path d="M15 14l3 4.5" /><path d="M21 14l-4.5 7" /><path d="M3 4l6 6" /><path d="M3 10l6 -6" /></svg>
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packages/components/nodes/retrievers/SimilarityThresholdRetriever/SimilarityThresholdRetriever.ts
+16 -5
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@@ -18,7 +18,7 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
constructor() {
this.label = 'Similarity Score Threshold Retriever'
this.name = 'similarityThresholdRetriever'
this.version = 1.0
this.version = 2.0
this.type = 'SimilarityThresholdRetriever'
this.icon = 'similaritythreshold.svg'
this.category = 'Retrievers'
@@ -30,6 +30,14 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
name: 'vectorStore',
type: 'VectorStore'
},
{
label: 'Query',
name: 'query',
type: 'string',
description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
optional: true,
acceptVariable: true
},
{
label: 'Minimum Similarity Score (%)',
name: 'minSimilarityScore',
@@ -44,7 +52,8 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
description: `The maximum number of results to fetch`,
type: 'number',
default: 20,
step: 1
step: 1,
additionalParams: true
},
{
label: 'K Increment',
@@ -52,7 +61,8 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
description: `How much to increase K by each time. It'll fetch N results, then N + kIncrement, then N + kIncrement * 2, etc.`,
type: 'number',
default: 2,
step: 1
step: 1,
additionalParams: true
}
]
this.outputs = [
@@ -77,6 +87,7 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
async init(nodeData: INodeData, input: string): Promise<any> {
const vectorStore = nodeData.inputs?.vectorStore as VectorStore
const minSimilarityScore = nodeData.inputs?.minSimilarityScore as number
const query = nodeData.inputs?.query as string
const maxK = nodeData.inputs?.maxK as string
const kIncrement = nodeData.inputs?.kIncrement as string
@@ -89,11 +100,11 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
})
if (output === 'retriever') return retriever
else if (output === 'document') return await retriever.getRelevantDocuments(input)
else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
else if (output === 'text') {
let finaltext = ''
const docs = await retriever.getRelevantDocuments(input)
const docs = await retriever.getRelevantDocuments(query ? query : input)
for (const doc of docs) finaltext += `${doc.pageContent}\n`