add fix for illegal invocation

packages/components/nodes/vectorstores/OpenSearch/OpenSearch_Upsert.ts

@@ -0,0 +1,148 @@
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { OpenSearchVectorStore } from 'langchain/vectorstores/opensearch'
+import { Embeddings } from 'langchain/embeddings/base'
+import { Document } from 'langchain/document'
+import { Client, RequestParams } from '@opensearch-project/opensearch'
+import { flatten } from 'lodash'
+import { getBaseClasses } from '../../../src/utils'
+import { buildMetadataTerms } from './core'
+
+class OpenSearchUpsert_VectorStores 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 = 'OpenSearch Upsert Document'
+        this.name = 'openSearchUpsertDocument'
+        this.version = 1.0
+        this.type = 'OpenSearch'
+        this.icon = 'opensearch.png'
+        this.category = 'Vector Stores'
+        this.description = 'Upsert documents to OpenSearch'
+        this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
+        this.inputs = [
+            {
+                label: 'Document',
+                name: 'document',
+                type: 'Document',
+                list: true
+            },
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'Embeddings'
+            },
+            {
+                label: 'OpenSearch URL',
+                name: 'opensearchURL',
+                type: 'string',
+                placeholder: 'http://127.0.0.1:9200'
+            },
+            {
+                label: 'Index Name',
+                name: 'indexName',
+                type: 'string'
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to 4',
+                placeholder: '4',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'OpenSearch Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'OpenSearch Vector Store',
+                name: 'vectorStore',
+                baseClasses: [this.type, ...getBaseClasses(OpenSearchVectorStore)]
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const docs = nodeData.inputs?.document as Document[]
+        const embeddings = nodeData.inputs?.embeddings as Embeddings
+        const opensearchURL = nodeData.inputs?.opensearchURL as string
+        const indexName = nodeData.inputs?.indexName as string
+        const output = nodeData.outputs?.output as string
+        const topK = nodeData.inputs?.topK as string
+        const k = topK ? parseFloat(topK) : 4
+
+        const flattenDocs = docs && docs.length ? flatten(docs) : []
+        const finalDocs = []
+        for (let i = 0; i < flattenDocs.length; i += 1) {
+            finalDocs.push(new Document(flattenDocs[i]))
+        }
+
+        const client = new Client({
+            nodes: [opensearchURL]
+        })
+
+        const vectorStore = await OpenSearchVectorStore.fromDocuments(finalDocs, embeddings, {
+            client,
+            indexName
+        })
+
+        vectorStore.similaritySearchVectorWithScore = async (
+            query: number[],
+            k: number,
+            filter?: object | undefined
+        ): Promise<[Document, number][]> => {
+            const search: RequestParams.Search = {
+                index: indexName,
+                body: {
+                    query: {
+                        bool: {
+                            filter: { bool: { must: buildMetadataTerms(filter) } },
+                            must: [
+                                {
+                                    knn: {
+                                        embedding: { vector: query, k }
+                                    }
+                                }
+                            ]
+                        }
+                    },
+                    size: k
+                }
+            }
+
+            const { body } = await client.search(search)
+
+            return body.hits.hits.map((hit: any) => [
+                new Document({
+                    pageContent: hit._source.text,
+                    metadata: hit._source.metadata
+                }),
+                hit._score
+            ])
+        }
+
+        if (output === 'retriever') {
+            const retriever = vectorStore.asRetriever(k)
+            return retriever
+        } else if (output === 'vectorStore') {
+            ;(vectorStore as any).k = k
+            return vectorStore
+        }
+        return vectorStore
+    }
+}
+
+module.exports = { nodeClass: OpenSearchUpsert_VectorStores }

packages/components/nodes/vectorstores/OpenSearch/OpenSearch_existing.ts

@@ -0,0 +1,147 @@
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { OpenSearchVectorStore } from 'langchain/vectorstores/opensearch'
+import { Embeddings } from 'langchain/embeddings/base'
+import { Document } from 'langchain/document'
+import { Client, RequestParams } from '@opensearch-project/opensearch'
+import { getBaseClasses } from '../../../src/utils'
+import { buildMetadataTerms } from './core'
+
+class OpenSearch_Existing_VectorStores 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 = 'OpenSearch Load Existing Index'
+        this.name = 'openSearchExistingIndex'
+        this.version = 1.0
+        this.type = 'OpenSearch'
+        this.icon = 'opensearch.png'
+        this.category = 'Vector Stores'
+        this.description = 'Load existing index from OpenSearch (i.e: Document has been upserted)'
+        this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
+        this.inputs = [
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'Embeddings'
+            },
+            {
+                label: 'OpenSearch URL',
+                name: 'opensearchURL',
+                type: 'string',
+                placeholder: 'http://127.0.0.1:9200'
+            },
+            {
+                label: 'Index Name',
+                name: 'indexName',
+                type: 'string'
+            },
+            {
+                label: 'OpenSearch Metadata Filter',
+                name: 'openSearchMetadataFilter',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to 4',
+                placeholder: '4',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'OpenSearch Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'OpenSearch Vector Store',
+                name: 'vectorStore',
+                baseClasses: [this.type, ...getBaseClasses(OpenSearchVectorStore)]
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const embeddings = nodeData.inputs?.embeddings as Embeddings
+        const opensearchURL = nodeData.inputs?.opensearchURL as string
+        const indexName = nodeData.inputs?.indexName as string
+        const output = nodeData.outputs?.output as string
+        const topK = nodeData.inputs?.topK as string
+        const k = topK ? parseFloat(topK) : 4
+        const openSearchMetadataFilter = nodeData.inputs?.openSearchMetadataFilter
+
+        const client = new Client({
+            nodes: [opensearchURL]
+        })
+
+        const vectorStore = new OpenSearchVectorStore(embeddings, {
+            client,
+            indexName
+        })
+
+        vectorStore.similaritySearchVectorWithScore = async (
+            query: number[],
+            k: number,
+            filter?: object | undefined
+        ): Promise<[Document, number][]> => {
+            if (openSearchMetadataFilter) {
+                const metadatafilter =
+                    typeof openSearchMetadataFilter === 'object' ? openSearchMetadataFilter : JSON.parse(openSearchMetadataFilter)
+                filter = metadatafilter
+            }
+            const search: RequestParams.Search = {
+                index: indexName,
+                body: {
+                    query: {
+                        bool: {
+                            filter: { bool: { must: buildMetadataTerms(filter) } },
+                            must: [
+                                {
+                                    knn: {
+                                        embedding: { vector: query, k }
+                                    }
+                                }
+                            ]
+                        }
+                    },
+                    size: k
+                }
+            }
+
+            const { body } = await client.search(search)
+
+            return body.hits.hits.map((hit: any) => [
+                new Document({
+                    pageContent: hit._source.text,
+                    metadata: hit._source.metadata
+                }),
+                hit._score
+            ])
+        }
+
+        if (output === 'retriever') {
+            const retriever = vectorStore.asRetriever(k)
+            return retriever
+        } else if (output === 'vectorStore') {
+            ;(vectorStore as any).k = k
+            return vectorStore
+        }
+        return vectorStore
+    }
+}
+
+module.exports = { nodeClass: OpenSearch_Existing_VectorStores }

packages/components/nodes/vectorstores/OpenSearch/core.ts

@@ -0,0 +1,8 @@
+export const buildMetadataTerms = (filter?: object): { term: Record<string, unknown> }[] => {
+    if (filter == null) return []
+    const result = []
+    for (const [key, value] of Object.entries(filter)) {
+        result.push({ term: { [`metadata.${key}`]: value } })
+    }
+    return result
+}