add llamaindex

2026-06-28 15:00:57 +03:00 · 2023-12-04 20:04:09 +00:00
parent 423c23aaf0
commit 40f8371de9
56 changed files with 4509 additions and 536 deletions
@@ -0,0 +1,124 @@
+import path from 'path'
+import { flatten } from 'lodash'
+import { storageContextFromDefaults, serviceContextFromDefaults, VectorStoreIndex, Document } from 'llamaindex'
+import { Document as LCDocument } from 'langchain/document'
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { getUserHome } from '../../../src'
+
+class SimpleStoreUpsert_LlamaIndex_VectorStores implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    tags: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'SimpleStore'
+        this.name = 'simpleStoreLlamaIndex'
+        this.version = 1.0
+        this.type = 'SimpleVectorStore'
+        this.icon = 'simplevs.svg'
+        this.category = 'Vector Stores'
+        this.description = 'Upsert embedded data to local path and perform similarity search'
+        this.baseClasses = [this.type, 'VectorIndexRetriever']
+        this.tags = ['LlamaIndex']
+        this.inputs = [
+            {
+                label: 'Document',
+                name: 'document',
+                type: 'Document',
+                list: true,
+                optional: true
+            },
+            {
+                label: 'Chat Model',
+                name: 'model',
+                type: 'BaseChatModel_LlamaIndex'
+            },
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'BaseEmbedding_LlamaIndex'
+            },
+            {
+                label: 'Base Path to store',
+                name: 'basePath',
+                description:
+                    'Path to store persist embeddings indexes with persistence. If not specified, default to same path where database is stored',
+                type: 'string',
+                optional: true
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to 4',
+                placeholder: '4',
+                type: 'number',
+                optional: true
+            }
+        ]
+    }
+
+    //@ts-ignore
+    vectorStoreMethods = {
+        async upsert(nodeData: INodeData): Promise<void> {
+            const basePath = nodeData.inputs?.basePath as string
+            const docs = nodeData.inputs?.document as LCDocument[]
+            const embeddings = nodeData.inputs?.embeddings
+            const model = nodeData.inputs?.model
+
+            let filePath = ''
+            if (!basePath) filePath = path.join(getUserHome(), '.flowise', 'llamaindex')
+            else filePath = basePath
+
+            const flattenDocs = docs && docs.length ? flatten(docs) : []
+            const finalDocs = []
+            for (let i = 0; i < flattenDocs.length; i += 1) {
+                finalDocs.push(new LCDocument(flattenDocs[i]))
+            }
+
+            const llamadocs: Document[] = []
+            for (const doc of finalDocs) {
+                llamadocs.push(new Document({ text: doc.pageContent, metadata: doc.metadata }))
+            }
+
+            const serviceContext = serviceContextFromDefaults({ llm: model, embedModel: embeddings })
+            const storageContext = await storageContextFromDefaults({ persistDir: filePath })
+
+            try {
+                await VectorStoreIndex.fromDocuments(llamadocs, { serviceContext, storageContext })
+            } catch (e) {
+                throw new Error(e)
+            }
+        }
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const basePath = nodeData.inputs?.basePath as string
+        const embeddings = nodeData.inputs?.embeddings
+        const model = nodeData.inputs?.model
+        const topK = nodeData.inputs?.topK as string
+        const k = topK ? parseFloat(topK) : 4
+
+        let filePath = ''
+        if (!basePath) filePath = path.join(getUserHome(), '.flowise', 'llamaindex')
+        else filePath = basePath
+
+        const serviceContext = serviceContextFromDefaults({ llm: model, embedModel: embeddings })
+        const storageContext = await storageContextFromDefaults({ persistDir: filePath })
+
+        const index = await VectorStoreIndex.init({ storageContext, serviceContext })
+        const retriever = index.asRetriever()
+        retriever.similarityTopK = k
+
+        return retriever
+    }
+}
+
+module.exports = { nodeClass: SimpleStoreUpsert_LlamaIndex_VectorStores }
@@ -0,0 +1,6 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-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 6m-8 0a8 3 0 1 0 16 0a8 3 0 1 0 -16 0"></path>
+   <path d="M4 6v6a8 3 0 0 0 16 0v-6"></path>
+   <path d="M4 12v6a8 3 0 0 0 16 0v-6"></path>
+</svg>