Feature/Extract Metadata Retriever (#3579)

add extract metadata retriever

packages/components/nodes/retrievers/ExtractMetadataRetriever/ExtractMetadataRetriever.ts

@@ -0,0 +1,216 @@
+import { Document } from '@langchain/core/documents'
+import { VectorStore, VectorStoreRetriever, VectorStoreRetrieverInput } from '@langchain/core/vectorstores'
+import { INode, INodeData, INodeParams, INodeOutputsValue } from '../../../src/Interface'
+import { handleEscapeCharacters } from '../../../src'
+import { z } from 'zod'
+import { convertStructuredSchemaToZod, ExtractTool } from '../../sequentialagents/commonUtils'
+import { ChatGoogleGenerativeAI } from '@langchain/google-genai'
+
+const queryPrefix = 'query'
+const defaultPrompt = `Extract keywords from the query: {{${queryPrefix}}}`
+
+class ExtractMetadataRetriever_Retrievers implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    badge?: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'Extract Metadata Retriever'
+        this.name = 'extractMetadataRetriever'
+        this.version = 1.0
+        this.type = 'ExtractMetadataRetriever'
+        this.icon = 'dynamicMetadataRetriever.svg'
+        this.category = 'Retrievers'
+        this.description = 'Extract keywords/metadata from the query and use it to filter documents'
+        this.baseClasses = [this.type, 'BaseRetriever']
+        this.badge = 'BETA'
+        this.inputs = [
+            {
+                label: 'Vector Store',
+                name: 'vectorStore',
+                type: 'VectorStore'
+            },
+            {
+                label: 'Chat Model',
+                name: 'model',
+                type: 'BaseChatModel'
+            },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
+            },
+            {
+                label: 'Prompt',
+                name: 'dynamicMetadataFilterRetrieverPrompt',
+                type: 'string',
+                description: 'Prompt to extract metadata from query',
+                rows: 4,
+                additionalParams: true,
+                default: defaultPrompt
+            },
+            {
+                label: 'JSON Structured Output',
+                name: 'dynamicMetadataFilterRetrieverStructuredOutput',
+                type: 'datagrid',
+                description:
+                    'Instruct the model to give output in a JSON structured schema. This output will be used as the metadata filter for connected vector store',
+                datagrid: [
+                    { field: 'key', headerName: 'Key', editable: true },
+                    {
+                        field: 'type',
+                        headerName: 'Type',
+                        type: 'singleSelect',
+                        valueOptions: ['String', 'String Array', 'Number', 'Boolean', 'Enum'],
+                        editable: true
+                    },
+                    { field: 'enumValues', headerName: 'Enum Values', editable: true },
+                    { field: 'description', headerName: 'Description', flex: 1, editable: true }
+                ],
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to vector store topK',
+                placeholder: '4',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'Extract Metadata Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Document',
+                name: 'document',
+                description: 'Array of document objects containing metadata and pageContent',
+                baseClasses: ['Document', 'json']
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                description: 'Concatenated string from pageContent of documents',
+                baseClasses: ['string', 'json']
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, input: string): Promise<any> {
+        const vectorStore = nodeData.inputs?.vectorStore as VectorStore
+        let llm = nodeData.inputs?.model
+        const llmStructuredOutput = nodeData.inputs?.dynamicMetadataFilterRetrieverStructuredOutput
+        const topK = nodeData.inputs?.topK as string
+        const dynamicMetadataFilterRetrieverPrompt = nodeData.inputs?.dynamicMetadataFilterRetrieverPrompt as string
+        const query = nodeData.inputs?.query as string
+        const finalInputQuery = query ? query : input
+
+        const output = nodeData.outputs?.output as string
+
+        if (llmStructuredOutput && llmStructuredOutput !== '[]') {
+            try {
+                const structuredOutput = z.object(convertStructuredSchemaToZod(llmStructuredOutput))
+
+                if (llm instanceof ChatGoogleGenerativeAI) {
+                    const tool = new ExtractTool({
+                        schema: structuredOutput
+                    })
+                    // @ts-ignore
+                    const modelWithTool = llm.bind({
+                        tools: [tool]
+                    }) as any
+                    llm = modelWithTool
+                } else {
+                    // @ts-ignore
+                    llm = llm.withStructuredOutput(structuredOutput)
+                }
+            } catch (exception) {
+                console.error(exception)
+            }
+        }
+
+        const retriever = DynamicMetadataRetriever.fromVectorStore(vectorStore, {
+            structuredLLM: llm,
+            prompt: dynamicMetadataFilterRetrieverPrompt,
+            topK: topK ? parseInt(topK, 10) : (vectorStore as any)?.k ?? 4
+        })
+
+        if (output === 'retriever') return retriever
+        else if (output === 'document') return await retriever.getRelevantDocuments(finalInputQuery)
+        else if (output === 'text') {
+            let finaltext = ''
+
+            const docs = await retriever.getRelevantDocuments(finalInputQuery)
+
+            for (const doc of docs) finaltext += `${doc.pageContent}\n`
+
+            return handleEscapeCharacters(finaltext, false)
+        }
+
+        return retriever
+    }
+}
+
+type RetrieverInput<V extends VectorStore> = Omit<VectorStoreRetrieverInput<V>, 'k'> & {
+    topK?: number
+    structuredLLM: any
+    prompt: string
+}
+
+class DynamicMetadataRetriever<V extends VectorStore> extends VectorStoreRetriever<V> {
+    topK = 4
+    structuredLLM: any
+    prompt = ''
+
+    constructor(input: RetrieverInput<V>) {
+        super(input)
+        this.topK = input.topK ?? this.topK
+        this.structuredLLM = input.structuredLLM ?? this.structuredLLM
+        this.prompt = input.prompt ?? this.prompt
+    }
+
+    async getFilter(query: string): Promise<any> {
+        const structuredResponse = await this.structuredLLM.invoke(this.prompt.replace(`{{${queryPrefix}}}`, query))
+        return structuredResponse
+    }
+
+    async getRelevantDocuments(query: string): Promise<Document[]> {
+        const newFilter = await this.getFilter(query)
+        // @ts-ignore
+        this.filter = { ...this.filter, ...newFilter }
+        const results = await this.vectorStore.similaritySearchWithScore(query, this.topK, this.filter)
+
+        const finalDocs: Document[] = []
+        for (const result of results) {
+            finalDocs.push(
+                new Document({
+                    pageContent: result[0].pageContent,
+                    metadata: result[0].metadata
+                })
+            )
+        }
+        return finalDocs
+    }
+
+    static fromVectorStore<V extends VectorStore>(vectorStore: V, options: Omit<RetrieverInput<V>, 'vectorStore'>) {
+        return new this<V>({ ...options, vectorStore })
+    }
+}
+
+module.exports = { nodeClass: ExtractMetadataRetriever_Retrievers }