Add Postgres vector store using pgvector

packages/components/credentials/PostgresApi.credential.ts

@@ -0,0 +1,31 @@
+import { INodeParams, INodeCredential } from '../src/Interface'
+
+class PostgresApi implements INodeCredential {
+    label: string
+    name: string
+    version: number
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Postgres API'
+        this.name = 'PostgresApi'
+        this.version = 1.0
+        this.inputs = [
+            {
+                label: 'User',
+                name: 'user',
+                type: 'string',
+                placeholder: '<POSTGRES_USERNAME>'
+            },
+            {
+                label: 'Password',
+                name: 'password',
+                type: 'password',
+                placeholder: '<POSTGRES_PASSWORD>'
+            }
+        ]
+    }
+}
+
+module.exports = { credClass: PostgresApi }

packages/components/nodes/vectorstores/Postgres_Existing/Postgres_Exisiting.ts

@@ -0,0 +1,172 @@
+import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { Embeddings } from 'langchain/embeddings/base'
+import { Document } from 'langchain/document'
+import { DataSourceOptions } from 'typeorm'
+import { TypeORMVectorStore, TypeORMVectorStoreDocument } from 'langchain/vectorstores/typeorm'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { Pool } from 'pg'
+
+class Postgres_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 = 'Postgres Load Existing Index'
+        this.name = 'postgresExistingIndex'
+        this.version = 1.0
+        this.type = 'Postgres'
+        this.icon = 'postgres.svg'
+        this.category = 'Vector Stores'
+        this.description = 'Load existing index from Postgres using pgvector (i.e: Document has been upserted)'
+        this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['PostgresApi']
+        }
+        this.inputs = [
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'Embeddings'
+            },
+            {
+                label: 'Host',
+                name: 'host',
+                type: 'string'
+            },
+            {
+                label: 'Database',
+                name: 'database',
+                type: 'string'
+            },
+            {
+                label: 'Port',
+                name: 'port',
+                type: 'number',
+                placeholder: '6432',
+                optional: true
+            },
+            {
+                label: 'Table Name',
+                name: 'tableName',
+                type: 'string',
+                placeholder: 'embeddings',
+                additionalParams: true,
+                optional: 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: 'Postgres Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Postgres Vector Store',
+                name: 'vectorStore',
+                baseClasses: [this.type, ...getBaseClasses(TypeORMVectorStore)]
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const user = getCredentialParam('user', credentialData, nodeData)
+        const password = getCredentialParam('password', credentialData, nodeData)
+        const tableName = nodeData.inputs?.tableName as string
+        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 postgresConnectionOptions = {
+            type: 'postgres',
+            host: nodeData.inputs?.host as string,
+            port: nodeData.inputs?.port as number,
+            username: user,
+            password: password,
+            database: nodeData.inputs?.database as string
+        }
+
+        const args = {
+            postgresConnectionOptions: postgresConnectionOptions as DataSourceOptions,
+            tableName: tableName
+        }
+
+        const vectorStore = await TypeORMVectorStore.fromDataSource(embeddings, args)
+
+        // Rewrite the method to use pg pool connection instead of the default connection
+        /* Otherwise a connection error is displayed when the chain tries to execute the function
+            [chain/start] [1:chain:ConversationalRetrievalQAChain] Entering Chain run with input: { "question": "what the document is about", "chat_history": [] }
+            [retriever/start] [1:chain:ConversationalRetrievalQAChain > 2:retriever:VectorStoreRetriever] Entering Retriever run with input: { "query": "what the document is about" }
+            [ERROR]: uncaughtException:  Illegal invocation TypeError: Illegal invocation at Socket.ref (node:net:1524:18) at Connection.ref (.../node_modules/pg/lib/connection.js:183:17) at Client.ref (.../node_modules/pg/lib/client.js:591:21) at BoundPool._pulseQueue (/node_modules/pg-pool/index.js:148:28) at .../node_modules/pg-pool/index.js:184:37 at process.processTicksAndRejections (node:internal/process/task_queues:77:11)
+        */
+        vectorStore.similaritySearchVectorWithScore = async (query: number[], k: number, filter?: any) => {
+            const embeddingString = `[${query.join(',')}]`
+            const _filter = filter ?? '{}'
+
+            const queryString = `
+                    SELECT *, embedding <=> $1 as "_distance"
+                    FROM ${tableName}
+                    WHERE metadata @> $2
+                    ORDER BY "_distance" ASC
+                    LIMIT $3;`
+
+            const poolOptions = {
+                host: postgresConnectionOptions.host,
+                port: postgresConnectionOptions.port,
+                user: postgresConnectionOptions.username,
+                password: postgresConnectionOptions.password,
+                database: postgresConnectionOptions.database
+            }
+            const pool = new Pool(poolOptions)
+            const conn = await pool.connect()
+
+            const documents = await conn.query(queryString, [embeddingString, _filter, k])
+
+            conn.release()
+
+            const results = [] as [TypeORMVectorStoreDocument, number][]
+            for (const doc of documents.rows) {
+                if (doc._distance != null && doc.pageContent != null) {
+                    const document = new Document(doc) as TypeORMVectorStoreDocument
+                    document.id = doc.id
+                    results.push([document, doc._distance])
+                }
+            }
+
+            return results
+        }
+
+        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: Postgres_Existing_VectorStores }

packages/components/nodes/vectorstores/Postgres_Upsert/Postgres_Upsert.ts

@@ -0,0 +1,211 @@
+import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { Embeddings } from 'langchain/embeddings/base'
+import { Document } from 'langchain/document'
+import { DataSourceOptions } from 'typeorm'
+import { TypeORMVectorStore, TypeORMVectorStoreDocument } from 'langchain/vectorstores/typeorm'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { flatten } from 'lodash'
+import { Pool } from 'pg'
+
+class PostgresUpsert_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 = 'Postgres Upsert Document'
+        this.name = 'postgresUpsert'
+        this.version = 1.0
+        this.type = 'Postgres'
+        this.icon = 'postgres.svg'
+        this.category = 'Vector Stores'
+        this.description = 'Upsert documents to Postgres using pgvector'
+        this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            description: 'Needed when using Postgres cloud hosted',
+            optional: true,
+            credentialNames: ['PostgresApi']
+        }
+        this.inputs = [
+            {
+                label: 'Document',
+                name: 'document',
+                type: 'Document',
+                list: true
+            },
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'Embeddings'
+            },
+            {
+                label: 'Host',
+                name: 'host',
+                type: 'string'
+            },
+            {
+                label: 'Database',
+                name: 'database',
+                type: 'string'
+            },
+            {
+                label: 'Port',
+                name: 'port',
+                type: 'number',
+                placeholder: '6432',
+                optional: true
+            },
+            {
+                label: 'Table Name',
+                name: 'tableName',
+                type: 'string',
+                placeholder: 'embeddings',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Content Column Name',
+                name: 'contentColumnName',
+                type: 'string',
+                placeholder: 'content',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Vector Column Name',
+                name: 'vectorColumnName',
+                type: 'string',
+                placeholder: 'vector',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Metadata Column Name',
+                name: 'metadataColumnName',
+                type: 'string',
+                placeholder: 'metadata',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                placeholder: '4',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'Postgres Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Postgres Vector Store',
+                name: 'vectorStore',
+                baseClasses: [this.type, ...getBaseClasses(TypeORMVectorStore)]
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const user = getCredentialParam('user', credentialData, nodeData)
+        const password = getCredentialParam('password', credentialData, nodeData)
+        const tableName = nodeData.inputs?.tableName as string
+        const docs = nodeData.inputs?.document as Document[]
+        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 postgresConnectionOptions = {
+            type: 'postgres',
+            host: nodeData.inputs?.host as string,
+            port: nodeData.inputs?.port as number,
+            username: user,
+            password: password,
+            database: nodeData.inputs?.database as string
+        }
+
+        const args = {
+            postgresConnectionOptions: postgresConnectionOptions as DataSourceOptions,
+            tableName: tableName
+        }
+
+        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 vectorStore = await TypeORMVectorStore.fromDocuments(finalDocs, embeddings, args)
+
+        // Rewrite the method to use pg pool connection instead of the default connection
+        /* Otherwise a connection error is displayed when the chain tries to execute the function
+            [chain/start] [1:chain:ConversationalRetrievalQAChain] Entering Chain run with input: { "question": "what the document is about", "chat_history": [] }
+            [retriever/start] [1:chain:ConversationalRetrievalQAChain > 2:retriever:VectorStoreRetriever] Entering Retriever run with input: { "query": "what the document is about" }
+            [ERROR]: uncaughtException:  Illegal invocation TypeError: Illegal invocation at Socket.ref (node:net:1524:18) at Connection.ref (.../node_modules/pg/lib/connection.js:183:17) at Client.ref (.../node_modules/pg/lib/client.js:591:21) at BoundPool._pulseQueue (/node_modules/pg-pool/index.js:148:28) at .../node_modules/pg-pool/index.js:184:37 at process.processTicksAndRejections (node:internal/process/task_queues:77:11)
+        */
+        vectorStore.similaritySearchVectorWithScore = async (query: number[], k: number, filter?: any) => {
+            const embeddingString = `[${query.join(',')}]`
+            const _filter = filter ?? '{}'
+
+            const queryString = `
+                SELECT *, embedding <=> $1 as "_distance"
+                FROM ${tableName}
+                WHERE metadata @> $2
+                ORDER BY "_distance" ASC
+                LIMIT $3;`
+
+            const poolOptions = {
+                host: postgresConnectionOptions.host,
+                port: postgresConnectionOptions.port,
+                user: postgresConnectionOptions.username,
+                password: postgresConnectionOptions.password,
+                database: postgresConnectionOptions.database
+            }
+            const pool = new Pool(poolOptions)
+            const conn = await pool.connect()
+
+            const documents = await conn.query(queryString, [embeddingString, _filter, k])
+
+            conn.release()
+
+            const results = [] as [TypeORMVectorStoreDocument, number][]
+            for (const doc of documents.rows) {
+                if (doc._distance != null && doc.pageContent != null) {
+                    const document = new Document(doc) as TypeORMVectorStoreDocument
+                    document.id = doc.id
+                    results.push([document, doc._distance])
+                }
+            }
+
+            return results
+        }
+
+        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: PostgresUpsert_VectorStores }

packages/components/package.json

@@ -50,6 +50,7 @@
         "notion-to-md": "^3.1.1",
         "pdf-parse": "^1.1.1",
         "pdfjs-dist": "^3.7.107",
+        "pg": "^8.11.2",
         "playwright": "^1.35.0",
         "puppeteer": "^20.7.1",
         "pyodide": ">=0.21.0-alpha.2",
@@ -63,6 +64,7 @@
     "devDependencies": {
         "@types/gulp": "4.0.9",
         "@types/node-fetch": "2.6.2",
+        "@types/pg": "^8.10.2",
         "@types/ws": "^8.5.3",
         "gulp": "^4.0.2",
         "typescript": "^4.8.4"

packages/server/.env.example

@@ -1,26 +0,0 @@
-PORT=3000
-PASSPHRASE=MYPASSPHRASE # Passphrase used to create encryption key
-# DATABASE_PATH=/your_database_path/.flowise
-# APIKEY_PATH=/your_api_key_path/.flowise
-# SECRETKEY_PATH=/your_api_key_path/.flowise
-# LOG_PATH=/your_log_path/.flowise/logs
-
-# DATABASE_TYPE=postgres
-# DATABASE_PORT=""
-# DATABASE_HOST=""
-# DATABASE_NAME="flowise"
-# DATABASE_USER=""
-# DATABASE_PASSWORD=""
-# OVERRIDE_DATABASE=true
-
-# FLOWISE_USERNAME=user
-# FLOWISE_PASSWORD=1234
-# DEBUG=true
-# LOG_LEVEL=debug (error | warn | info | verbose | debug)
-# TOOL_FUNCTION_BUILTIN_DEP=crypto,fs
-# TOOL_FUNCTION_EXTERNAL_DEP=moment,lodash
-
-# LANGCHAIN_TRACING_V2=true
-# LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
-# LANGCHAIN_API_KEY=your_api_key
-# LANGCHAIN_PROJECT=your_project

packages/ui/.env.example

@@ -1 +0,0 @@
-PORT=8080