add vector to prompt

2026-06-28 19:00:59 +03:00 · 2023-08-16 01:43:11 +01:00
parent af5efaa751
commit 94461025dc
11 changed files with 1194 additions and 40 deletions
@@ -0,0 +1,87 @@
+import { VectorStore } from 'langchain/vectorstores/base'
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { handleEscapeCharacters } from '../../../src/utils'
+
+class VectorStoreToDocument_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'VectorStore To Document'
+        this.name = 'vectorStoreToDocument'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'vectorretriever.svg'
+        this.category = 'Document Loaders'
+        this.description = 'Search documents with scores from vector store'
+        this.baseClasses = [this.type]
+        this.inputs = [
+            {
+                label: 'Vector Store',
+                name: 'vectorStore',
+                type: 'VectorStore'
+            },
+            {
+                label: 'Minimum Score (%)',
+                name: 'minScore',
+                type: 'number',
+                optional: true,
+                placeholder: '75',
+                step: 1,
+                description: 'Minumum score for embeddings documents to be included'
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, input: string): Promise<any> {
+        const vectorStore = nodeData.inputs?.vectorStore as VectorStore
+        const minScore = nodeData.inputs?.minScore as number
+        const output = nodeData.outputs?.output as string
+
+        const topK = (vectorStore as any)?.k ?? 4
+
+        const docs = await vectorStore.similaritySearchWithScore(input, topK)
+        // eslint-disable-next-line no-console
+        console.log('\x1b[94m\x1b[1m\n*****VectorStore Documents*****\n\x1b[0m\x1b[0m')
+        // eslint-disable-next-line no-console
+        console.log(docs)
+
+        if (output === 'document') {
+            let finaldocs = []
+            for (const doc of docs) {
+                if (minScore && doc[1] < minScore / 100) continue
+                finaldocs.push(doc[0])
+            }
+            return finaldocs
+        } else {
+            let finaltext = ''
+            for (const doc of docs) {
+                if (minScore && doc[1] < minScore / 100) continue
+                finaltext += `${doc[0].pageContent}\n`
+            }
+            return handleEscapeCharacters(finaltext, false)
+        }
+    }
+}
+
+module.exports = { nodeClass: VectorStoreToDocument_DocumentLoaders }
@@ -0,0 +1,7 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-file-database" 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>
+   <path d="M12 12.75m-4 0a4 1.75 0 1 0 8 0a4 1.75 0 1 0 -8 0"></path>
+   <path d="M8 12.5v3.75c0 .966 1.79 1.75 4 1.75s4 -.784 4 -1.75v-3.75"></path>
+   <path d="M14 3v4a1 1 0 0 0 1 1h4"></path>
+   <path d="M17 21h-10a2 2 0 0 1 -2 -2v-14a2 2 0 0 1 2 -2h7l5 5v11a2 2 0 0 1 -2 2z"></path>
+</svg>