Compression Retriever - Embeddings Filter

This commit is contained in:
vinodkiran
2023-12-22 12:12:16 +05:30
parent 94236c4b5f
commit fb02632f4b
2 changed files with 104 additions and 0 deletions
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import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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'
class EmbeddingsFilterRetriever_Retrievers implements INode {
label: string
name: string
version: number
description: string
type: string
icon: string
category: string
baseClasses: string[]
inputs: INodeParams[]
outputs: INodeOutputsValue[]
badge: string
constructor() {
this.label = 'Embeddings Filter Retriever'
this.name = 'embeddingsFilterRetriever'
this.version = 1.0
this.type = 'EmbeddingsFilterRetriever'
this.icon = 'compressionRetriever.svg'
this.category = 'Retrievers'
this.badge = 'NEW'
this.description = 'A document compressor that uses embeddings to drop documents unrelated to the query'
this.baseClasses = [this.type, 'BaseRetriever']
this.inputs = [
{
label: 'Base Retriever',
name: 'baseRetriever',
type: 'VectorStoreRetriever'
},
{
label: 'Embeddings',
name: 'embeddings',
type: 'Embeddings',
optional: false
},
{
label: 'Similarity Threshold',
name: 'similarityThreshold',
description:
'Threshold for determining when two documents are similar enough to be considered redundant. Must be specified if `k` is not set',
type: 'number',
default: 0.8,
step: 0.1,
optional: true
},
{
label: 'K',
name: 'k',
description:
'The number of relevant documents to return. Can be explicitly set to undefined, in which case similarity_threshold must be specified. Defaults to 20',
type: 'number',
default: 20,
step: 1,
optional: true,
additionalParams: true
}
]
}
async init(nodeData: INodeData): Promise<any> {
const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
const embeddings = nodeData.inputs?.embeddings as Embeddings
const similarityThreshold = nodeData.inputs?.similarityThreshold as string
const k = nodeData.inputs?.k 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 baseCompressor = new EmbeddingsFilter({
embeddings: embeddings,
similarityThreshold: similarityThresholdNumber,
k: kNumber
})
return new ContextualCompressionRetriever({
baseCompressor,
baseRetriever: baseRetriever
})
}
}
module.exports = { nodeClass: EmbeddingsFilterRetriever_Retrievers }
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