Merge branch 'main' into feature/ChatHistory2

packages/components/nodes/documentloaders/PlainText/PlainText.ts

@@ -0,0 +1,88 @@
+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { TextSplitter } from 'langchain/text_splitter'
+import { Document } from 'langchain/document'
+
+class PlainText_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Plain Text'
+        this.name = 'plainText'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'plaintext.svg'
+        this.category = 'Document Loaders'
+        this.description = `Load data from plain text`
+        this.baseClasses = [this.type]
+        this.inputs = [
+            {
+                label: 'Text',
+                name: 'text',
+                type: 'string',
+                rows: 4,
+                placeholder:
+                    'Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua...'
+            },
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            },
+            {
+                label: 'Metadata',
+                name: 'metadata',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+        const text = nodeData.inputs?.text as string
+        const metadata = nodeData.inputs?.metadata
+
+        let alldocs: Document<Record<string, any>>[] = []
+
+        if (textSplitter) {
+            const docs = await textSplitter.createDocuments([text])
+            alldocs.push(...docs)
+        } else {
+            alldocs.push(
+                new Document({
+                    pageContent: text
+                })
+            )
+        }
+
+        if (metadata) {
+            const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
+            let finaldocs: Document<Record<string, any>>[] = []
+            for (const doc of alldocs) {
+                const newdoc = {
+                    ...doc,
+                    metadata: {
+                        ...doc.metadata,
+                        ...parsedMetadata
+                    }
+                }
+                finaldocs.push(newdoc)
+            }
+            return finaldocs
+        }
+
+        return alldocs
+    }
+}
+
+module.exports = { nodeClass: PlainText_DocumentLoaders }

packages/components/nodes/documentloaders/PlainText/plaintext.svg

@@ -0,0 +1,7 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-highlight" 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="M3 19h4l10.5 -10.5a2.828 2.828 0 1 0 -4 -4l-10.5 10.5v4"></path>
+   <path d="M12.5 5.5l4 4"></path>
+   <path d="M4.5 13.5l4 4"></path>
+   <path d="M21 15v4h-8l4 -4z"></path>
+</svg>

packages/components/nodes/retrievers/SimilarityThresholdRetriever/SimilarityThresholdRetriever.ts

@@ -0,0 +1,107 @@
+import { VectorStore } from 'langchain/vectorstores/base'
+import { INode, INodeData, INodeParams, INodeOutputsValue } from '../../../src/Interface'
+import { handleEscapeCharacters } from '../../../src'
+import { ScoreThresholdRetriever } from 'langchain/retrievers/score_threshold'
+
+class SimilarityThresholdRetriever_Retrievers 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 = 'Similarity Score Threshold Retriever'
+        this.name = 'similarityThresholdRetriever'
+        this.version = 1.0
+        this.type = 'SimilarityThresholdRetriever'
+        this.icon = 'similaritythreshold.svg'
+        this.category = 'Retrievers'
+        this.description = 'Return results based on the minimum similarity percentage'
+        this.baseClasses = [this.type, 'BaseRetriever']
+        this.inputs = [
+            {
+                label: 'Vector Store',
+                name: 'vectorStore',
+                type: 'VectorStore'
+            },
+            {
+                label: 'Minimum Similarity Score (%)',
+                name: 'minSimilarityScore',
+                description: 'Finds results with at least this similarity score',
+                type: 'number',
+                default: 80,
+                step: 1
+            },
+            {
+                label: 'Max K',
+                name: 'maxK',
+                description: `The maximum number of results to fetch`,
+                type: 'number',
+                default: 20,
+                step: 1
+            },
+            {
+                label: 'K Increment',
+                name: 'kIncrement',
+                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
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'Similarity Threshold Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: ['Document']
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, input: string): Promise<any> {
+        const vectorStore = nodeData.inputs?.vectorStore as VectorStore
+        const minSimilarityScore = nodeData.inputs?.minSimilarityScore as number
+        const maxK = nodeData.inputs?.maxK as string
+        const kIncrement = nodeData.inputs?.kIncrement as string
+
+        const output = nodeData.outputs?.output as string
+
+        const retriever = ScoreThresholdRetriever.fromVectorStore(vectorStore, {
+            minSimilarityScore: minSimilarityScore ? minSimilarityScore / 100 : 0.9,
+            maxK: maxK ? parseInt(maxK, 10) : 100,
+            kIncrement: kIncrement ? parseInt(kIncrement, 10) : 2
+        })
+
+        if (output === 'retriever') return retriever
+        else if (output === 'document') return await retriever.getRelevantDocuments(input)
+        else if (output === 'text') {
+            let finaltext = ''
+
+            const docs = await retriever.getRelevantDocuments(input)
+
+            for (const doc of docs) finaltext += `${doc.pageContent}\n`
+
+            return handleEscapeCharacters(finaltext, false)
+        }
+
+        return retriever
+    }
+}
+
+module.exports = { nodeClass: SimilarityThresholdRetriever_Retrievers }

packages/components/nodes/retrievers/SimilarityThresholdRetriever/similaritythreshold.svg

@@ -0,0 +1,5 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-chart-line" 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="M4 19l16 0"></path>
+   <path d="M4 15l4 -6l4 2l4 -5l4 4"></path>
+</svg>

packages/ui/src/views/chatmessage/ChatMessage.js

@@ -65,27 +65,10 @@ export const ChatMessage = ({ open, chatflowid, isDialog }) => {
         window.open(data, '_blank')
     }
 
-    const handleVectaraMetadata = (message) => {
-        if (message.sourceDocuments && message.sourceDocuments[0].metadata.length)
-            message.sourceDocuments = message.sourceDocuments.map((docs) => {
-                const newMetadata = docs.metadata.reduce((newMetadata, metadata) => {
-                    newMetadata[metadata.name] = metadata.value
-                    return newMetadata
-                }, {})
-                return {
-                    pageContent: docs.pageContent,
-                    metadata: newMetadata
-                }
-            })
-        return message
-    }
-
     const removeDuplicateURL = (message) => {
         const visitedURLs = []
         const newSourceDocuments = []
 
-        message = handleVectaraMetadata(message)
-
         message.sourceDocuments.forEach((source) => {
             if (isValidURL(source.metadata.source) && !visitedURLs.includes(source.metadata.source)) {
                 visitedURLs.push(source.metadata.source)
@@ -159,8 +142,6 @@ export const ChatMessage = ({ open, chatflowid, isDialog }) => {
             if (response.data) {
                 let data = response.data
 
-                data = handleVectaraMetadata(data)
-
                 if (!chatId) {
                     setChatId(data.chatId)
                     localStorage.setItem(`${chatflowid}_INTERNAL`, data.chatId)