Merge pull request #786 from FlowiseAI/feature/ZepVS

Feature/ZepVS

packages/components/nodes/memory/ZepMemory/ZepMemory.ts

@@ -120,7 +120,8 @@ class ZepMemory_Memory implements INode {
         zep.loadMemoryVariables = async (values) => {
             let data = await tmpFunc.bind(zep, values)()
             if (autoSummary && zep.returnMessages && data[zep.memoryKey] && data[zep.memoryKey].length) {
-                const memory = await zep.zepClient.getMemory(zep.sessionId, parseInt(k, 10) ?? 10)
+                const zepClient = await zep.zepClientPromise
+                const memory = await zepClient.memory.getMemory(zep.sessionId, parseInt(k, 10) ?? 10)
                 if (memory?.summary) {
                     let summary = autoSummaryTemplate.replace(/{summary}/g, memory.summary.content)
                     // eslint-disable-next-line no-console
@@ -190,23 +191,6 @@ class ZepMemoryExtended extends ZepMemory {
         super(fields)
         this.isSessionIdUsingChatMessageId = fields.isSessionIdUsingChatMessageId
     }
-
-    async clear(): Promise<void> {
-        // Only clear when sessionId is using chatId
-        // If sessionId is specified, clearing and inserting again will error because the sessionId has been soft deleted
-        // If using chatId, it will not be a problem because the sessionId will always be the new chatId
-        if (this.isSessionIdUsingChatMessageId) {
-            try {
-                await this.zepClient.deleteMemory(this.sessionId)
-            } catch (error) {
-                console.error('Error deleting session: ', error)
-            }
-
-            // Clear the superclass's chat history
-            await super.clear()
-        }
-        await this.chatHistory.clear()
-    }
 }
 
 module.exports = { nodeClass: ZepMemory_Memory }

packages/components/nodes/vectorstores/Zep/Zep_Existing.ts

@@ -0,0 +1,235 @@
+import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { ZepVectorStore, IZepConfig } from 'langchain/vectorstores/zep'
+import { Embeddings } from 'langchain/embeddings/base'
+import { Document } from 'langchain/document'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { IDocument, ZepClient } from '@getzep/zep-js'
+
+class Zep_Existing_VectorStores implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    credential: INodeParams
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'Zep Load Existing Index'
+        this.name = 'zepExistingIndex'
+        this.version = 1.0
+        this.type = 'Zep'
+        this.icon = 'zep.png'
+        this.category = 'Vector Stores'
+        this.description = 'Load existing index from Zep (i.e: Document has been upserted)'
+        this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            optional: true,
+            description: 'Configure JWT authentication on your Zep instance (Optional)',
+            credentialNames: ['zepMemoryApi']
+        }
+        this.inputs = [
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'Embeddings'
+            },
+            {
+                label: 'Base URL',
+                name: 'baseURL',
+                type: 'string',
+                default: 'http://127.0.0.1:8000'
+            },
+            {
+                label: 'Zep Collection',
+                name: 'zepCollection',
+                type: 'string',
+                placeholder: 'my-first-collection'
+            },
+            {
+                label: 'Zep Metadata Filter',
+                name: 'zepMetadataFilter',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Embedding Dimension',
+                name: 'dimension',
+                type: 'number',
+                default: 1536,
+                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: 'Pinecone Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Pinecone Vector Store',
+                name: 'vectorStore',
+                baseClasses: [this.type, ...getBaseClasses(ZepVectorStore)]
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const baseURL = nodeData.inputs?.baseURL as string
+        const zepCollection = nodeData.inputs?.zepCollection as string
+        const zepMetadataFilter = nodeData.inputs?.zepMetadataFilter
+        const dimension = nodeData.inputs?.dimension as number
+        const embeddings = nodeData.inputs?.embeddings as Embeddings
+        const output = nodeData.outputs?.output as string
+        const topK = nodeData.inputs?.topK as string
+        const k = topK ? parseFloat(topK) : 4
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const apiKey = getCredentialParam('apiKey', credentialData, nodeData)
+
+        const zepConfig: IZepConfig & Partial<ZepFilter> = {
+            apiUrl: baseURL,
+            collectionName: zepCollection,
+            embeddingDimensions: dimension,
+            isAutoEmbedded: false
+        }
+        if (apiKey) zepConfig.apiKey = apiKey
+        if (zepMetadataFilter) {
+            const metadatafilter = typeof zepMetadataFilter === 'object' ? zepMetadataFilter : JSON.parse(zepMetadataFilter)
+            zepConfig.filter = metadatafilter
+        }
+
+        const vectorStore = await ZepExistingVS.fromExistingIndex(embeddings, zepConfig)
+
+        if (output === 'retriever') {
+            const retriever = vectorStore.asRetriever(k)
+            return retriever
+        } else if (output === 'vectorStore') {
+            ;(vectorStore as any).k = k
+            return vectorStore
+        }
+        return vectorStore
+    }
+}
+
+interface ZepFilter {
+    filter: Record<string, any>
+}
+
+function zepDocsToDocumentsAndScore(results: IDocument[]): [Document, number][] {
+    return results.map((d) => [
+        new Document({
+            pageContent: d.content,
+            metadata: d.metadata
+        }),
+        d.score ? d.score : 0
+    ])
+}
+
+function assignMetadata(value: string | Record<string, unknown> | object | undefined): Record<string, unknown> | undefined {
+    if (typeof value === 'object' && value !== null) {
+        return value as Record<string, unknown>
+    }
+    if (value !== undefined) {
+        console.warn('Metadata filters must be an object, Record, or undefined.')
+    }
+    return undefined
+}
+
+class ZepExistingVS extends ZepVectorStore {
+    filter?: Record<string, any>
+    args?: IZepConfig & Partial<ZepFilter>
+
+    constructor(embeddings: Embeddings, args: IZepConfig & Partial<ZepFilter>) {
+        super(embeddings, args)
+        this.filter = args.filter
+        this.args = args
+    }
+
+    async initalizeCollection(args: IZepConfig & Partial<ZepFilter>) {
+        this.client = await ZepClient.init(args.apiUrl, args.apiKey)
+        try {
+            this.collection = await this.client.document.getCollection(args.collectionName)
+        } catch (err) {
+            if (err instanceof Error) {
+                if (err.name === 'NotFoundError') {
+                    await this.createNewCollection(args)
+                } else {
+                    throw err
+                }
+            }
+        }
+    }
+
+    async createNewCollection(args: IZepConfig & Partial<ZepFilter>) {
+        if (!args.embeddingDimensions) {
+            throw new Error(
+                `Collection ${args.collectionName} not found. You can create a new Collection by providing embeddingDimensions.`
+            )
+        }
+
+        this.collection = await this.client.document.addCollection({
+            name: args.collectionName,
+            description: args.description,
+            metadata: args.metadata,
+            embeddingDimensions: args.embeddingDimensions,
+            isAutoEmbedded: false
+        })
+    }
+
+    async similaritySearchVectorWithScore(
+        query: number[],
+        k: number,
+        filter?: Record<string, unknown> | undefined
+    ): Promise<[Document, number][]> {
+        if (filter && this.filter) {
+            throw new Error('cannot provide both `filter` and `this.filter`')
+        }
+        const _filters = filter ?? this.filter
+        const ANDFilters = []
+        for (const filterKey in _filters) {
+            let filterVal = _filters[filterKey]
+            if (typeof filterVal === 'string') filterVal = `"${filterVal}"`
+            ANDFilters.push({ jsonpath: `$[*] ? (@.${filterKey} == ${filterVal})` })
+        }
+        const newfilter = {
+            where: { and: ANDFilters }
+        }
+        await this.initalizeCollection(this.args!).catch((err) => {
+            console.error('Error initializing collection:', err)
+            throw err
+        })
+        const results = await this.collection.search(
+            {
+                embedding: new Float32Array(query),
+                metadata: assignMetadata(newfilter)
+            },
+            k
+        )
+        return zepDocsToDocumentsAndScore(results)
+    }
+
+    static async fromExistingIndex(embeddings: Embeddings, dbConfig: IZepConfig & Partial<ZepFilter>): Promise<ZepVectorStore> {
+        const instance = new this(embeddings, dbConfig)
+        return instance
+    }
+}
+
+module.exports = { nodeClass: Zep_Existing_VectorStores }

packages/components/nodes/vectorstores/Zep/Zep_Upsert.ts

@@ -0,0 +1,133 @@
+import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { ZepVectorStore, IZepConfig } from 'langchain/vectorstores/zep'
+import { Embeddings } from 'langchain/embeddings/base'
+import { Document } from 'langchain/document'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { flatten } from 'lodash'
+
+class Zep_Upsert_VectorStores implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    credential: INodeParams
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'Zep Upsert Document'
+        this.name = 'zepUpsert'
+        this.version = 1.0
+        this.type = 'Zep'
+        this.icon = 'zep.png'
+        this.category = 'Vector Stores'
+        this.description = 'Upsert documents to Zep'
+        this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            optional: true,
+            description: 'Configure JWT authentication on your Zep instance (Optional)',
+            credentialNames: ['zepMemoryApi']
+        }
+        this.inputs = [
+            {
+                label: 'Document',
+                name: 'document',
+                type: 'Document',
+                list: true
+            },
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'Embeddings'
+            },
+            {
+                label: 'Base URL',
+                name: 'baseURL',
+                type: 'string',
+                default: 'http://127.0.0.1:8000'
+            },
+            {
+                label: 'Zep Collection',
+                name: 'zepCollection',
+                type: 'string',
+                placeholder: 'my-first-collection'
+            },
+            {
+                label: 'Embedding Dimension',
+                name: 'dimension',
+                type: 'number',
+                default: 1536,
+                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: 'Zep Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Zep Vector Store',
+                name: 'vectorStore',
+                baseClasses: [this.type, ...getBaseClasses(ZepVectorStore)]
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const baseURL = nodeData.inputs?.baseURL as string
+        const zepCollection = nodeData.inputs?.zepCollection as string
+        const dimension = (nodeData.inputs?.dimension as number) ?? 1536
+        const docs = nodeData.inputs?.document as Document[]
+        const embeddings = nodeData.inputs?.embeddings as Embeddings
+        const topK = nodeData.inputs?.topK as string
+        const k = topK ? parseFloat(topK) : 4
+        const output = nodeData.outputs?.output as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const apiKey = getCredentialParam('apiKey', credentialData, nodeData)
+
+        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 zepConfig: IZepConfig = {
+            apiUrl: baseURL,
+            collectionName: zepCollection,
+            embeddingDimensions: dimension,
+            isAutoEmbedded: false
+        }
+        if (apiKey) zepConfig.apiKey = apiKey
+
+        const vectorStore = await ZepVectorStore.fromDocuments(finalDocs, embeddings, zepConfig)
+
+        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: Zep_Upsert_VectorStores }

packages/components/package.json

@@ -18,7 +18,7 @@
     "dependencies": {
         "@aws-sdk/client-dynamodb": "^3.360.0",
         "@dqbd/tiktoken": "^1.0.7",
-        "@getzep/zep-js": "^0.4.1",
+        "@getzep/zep-js": "^0.6.3",
         "@huggingface/inference": "^2.6.1",
         "@notionhq/client": "^2.2.8",
         "@opensearch-project/opensearch": "^1.2.0",
@@ -40,7 +40,7 @@
         "google-auth-library": "^9.0.0",
         "graphql": "^16.6.0",
         "html-to-text": "^9.0.5",
-        "langchain": "^0.0.126",
+        "langchain": "^0.0.128",
         "linkifyjs": "^4.1.1",
         "mammoth": "^1.5.1",
         "moment": "^2.29.3",