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MMR Search for Pinecone, Weaviate, Zep and Supabase

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This commit is contained in:
vinodkiran
2023-12-22 12:08:48 +05:30
parent 579f66e57e
commit 56b043264a
5 changed files with 98 additions and 109 deletions
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packages/components/nodes/vectorstores/Pinecone/Pinecone.ts
+5 -68
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@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
import { Document } from 'langchain/document'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
class Pinecone_VectorStores implements INode {
label: string
@@ -23,11 +24,11 @@ class Pinecone_VectorStores implements INode {
constructor() {
this.label = 'Pinecone'
this.name = 'pinecone'
this.version = 2.0
this.version = 3.0
this.type = 'Pinecone'
this.icon = 'pinecone.svg'
this.category = 'Vector Stores'
this.description = `Upsert embedded data and perform search upon query using Pinecone, a leading fully managed hosted vector database`
this.description = `Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database`
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
this.badge = 'NEW'
this.credential = {
@@ -77,45 +78,9 @@ class Pinecone_VectorStores implements INode {
type: 'number',
additionalParams: true,
optional: true
},
{
label: 'Search Type',
name: 'searchType',
type: 'options',
default: 'similarity',
options: [
{
label: 'Similarity',
name: 'similarity'
},
{
label: 'Max Marginal Relevance',
name: 'mmr'
}
],
additionalParams: true,
optional: true
},
{
label: 'Fetch K (for MMR Search)',
name: 'fetchK',
description: 'Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR',
placeholder: '20',
type: 'number',
additionalParams: true,
optional: true
},
{
label: 'Lambda (for MMR Search)',
name: 'lambda',
description:
'Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR',
placeholder: '0.5',
type: 'number',
additionalParams: true,
optional: true
}
]
addMMRInputParams(this.inputs)
this.outputs = [
{
label: 'Pinecone Retriever',
@@ -177,10 +142,6 @@ class Pinecone_VectorStores implements INode {
const pineconeMetadataFilter = nodeData.inputs?.pineconeMetadataFilter
const docs = nodeData.inputs?.document as Document[]
const embeddings = nodeData.inputs?.embeddings as Embeddings
const output = nodeData.outputs?.output as string
const searchType = nodeData.outputs?.searchType as string
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
@@ -213,31 +174,7 @@ class Pinecone_VectorStores implements INode {
const vectorStore = await PineconeStore.fromExistingIndex(embeddings, obj)
if (output === 'retriever') {
if ('mmr' === searchType) {
const fetchK = nodeData.inputs?.fetchK as string
const lambda = nodeData.inputs?.lambda as string
const f = fetchK ? parseInt(fetchK) : 20
const l = lambda ? parseFloat(lambda) : 0.5
const retriever = vectorStore.asRetriever({
searchType: 'mmr',
k: 5,
searchKwargs: {
fetchK: f,
lambda: l
}
})
return retriever
} else {
// "searchType" is "similarity"
const retriever = vectorStore.asRetriever(k)
return retriever
}
} else if (output === 'vectorStore') {
;(vectorStore as any).k = k
return vectorStore
}
return vectorStore
return resolveVectorStoreOrRetriever(nodeData, vectorStore)
}
}