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Addition of Pinecone MMR search

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This commit is contained in:
vinodkiran
2023-12-22 09:08:53 +05:30
parent 250465497b
commit 1ffa568974
1 changed files with 59 additions and 4 deletions
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packages/components/nodes/vectorstores/Pinecone/Pinecone.ts
+59 -4
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@@ -23,11 +23,11 @@ class Pinecone_VectorStores implements INode {
constructor() {
this.label = 'Pinecone'
this.name = 'pinecone'
this.version = 1.0
this.version = 2.0
this.type = 'Pinecone'
this.icon = 'pinecone.svg'
this.category = 'Vector Stores'
this.description = `Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database`
this.description = `Upsert embedded data and perform search upon query using Pinecone, a leading fully managed hosted vector database`
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
this.badge = 'NEW'
this.credential = {
@@ -77,6 +77,43 @@ 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
}
]
this.outputs = [
@@ -141,6 +178,7 @@ class Pinecone_VectorStores implements INode {
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
@@ -176,8 +214,25 @@ class Pinecone_VectorStores implements INode {
const vectorStore = await PineconeStore.fromExistingIndex(embeddings, obj)
if (output === 'retriever') {
const retriever = vectorStore.asRetriever(k)
return 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