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Merge branch 'main' into chore/Upgrade-LC-version 

# Conflicts:
#	packages/components/package.json
This commit is contained in:
Henry 2024-02-07 02:01:32 +08:00
parent 02fe500f21 188e2c748c
commit 73112ad122
54 changed files with 5331 additions and 139 deletions
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60
.github/workflows/autoSyncMergedPullRequest.yml vendored
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@ -1,33 +1,33 @@
name: autoSyncMergedPullRequest
on:
pull_request_target:
types:
- closed
branches: [ "main" ]
pull_request_target:
types:
- closed
branches: ['main']
jobs:
autoSyncMergedPullRequest:
if: github.event.pull_request.merged == true
runs-on: ubuntu-latest
permissions:
contents: write
steps:
- uses: actions/checkout@v3
- name: Show PR info
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
echo The PR #${{ github.event.pull_request.number }} was merged on main branch!
- name: Repository Dispatch
uses: peter-evans/repository-dispatch@v2
with:
token: ${{ secrets.AUTOSYNC_TOKEN }}
repository: ${{ secrets.AUTOSYNC_CH_URL }}
event-type: ${{ secrets.AUTOSYNC_PR_EVENT_TYPE }}
client-payload: >-
{
"ref": "${{ github.ref }}",
"prNumber": "${{ github.event.pull_request.number }}",
"prTitle": "${{ github.event.pull_request.title }}",
"prDescription": "${{ github.event.pull_request.description }}",
"sha": "${{ github.sha }}"
}
autoSyncMergedPullRequest:
if: github.event.pull_request.merged == true
runs-on: ubuntu-latest
permissions:
contents: write
steps:
- uses: actions/checkout@v3
- name: Show PR info
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
echo The PR #${{ github.event.pull_request.number }} was merged on main branch!
- name: Repository Dispatch
uses: peter-evans/repository-dispatch@v2
with:
token: ${{ secrets.AUTOSYNC_TOKEN }}
repository: ${{ secrets.AUTOSYNC_CH_URL }}
event-type: ${{ secrets.AUTOSYNC_PR_EVENT_TYPE }}
client-payload: >-
{
"ref": "${{ github.ref }}",
"prNumber": "${{ github.event.pull_request.number }}",
"prTitle": "${{ github.event.pull_request.title }}",
"prDescription": "${{ github.event.pull_request.description }}",
"sha": "${{ github.sha }}"
}

66
.github/workflows/autoSyncSingleCommit.yml vendored
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@ -1,36 +1,36 @@
name: autoSyncSingleCommit
on:
push:
branches:
- main
push:
branches:
- main
jobs:
doNotAutoSyncSingleCommit:
if: github.event.commits[1] != null
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: IGNORE autoSyncSingleCommit
run: |
echo This single commit has came from a merged commit. We will ignore it. This case is handled in autoSyncMergedPullRequest workflow for merge commits comming from merged pull requests only! Beware, the regular merge commits are not handled by any workflow for the moment.
autoSyncSingleCommit:
if: github.event.commits[1] == null
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: autoSyncSingleCommit
env:
GITHUB_CONTEXT: ${{ toJSON(github) }}
run: |
echo Autosync a single commit with id: ${{ github.sha }} from openSource main branch towards cloud hosted version.
- name: Repository Dispatch
uses: peter-evans/repository-dispatch@v2
with:
token: ${{ secrets.AUTOSYNC_TOKEN }}
repository: ${{ secrets.AUTOSYNC_CH_URL }}
event-type: ${{ secrets.AUTOSYNC_SC_EVENT_TYPE }}
client-payload: >-
{
"ref": "${{ github.ref }}",
"sha": "${{ github.sha }}",
"commitMessage": "${{ github.event.commits[0].message }}"
}
doNotAutoSyncSingleCommit:
if: github.event.commits[1] != null
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: IGNORE autoSyncSingleCommit
run: |
echo This single commit has came from a merged commit. We will ignore it. This case is handled in autoSyncMergedPullRequest workflow for merge commits comming from merged pull requests only! Beware, the regular merge commits are not handled by any workflow for the moment.
autoSyncSingleCommit:
if: github.event.commits[1] == null
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: autoSyncSingleCommit
env:
GITHUB_CONTEXT: ${{ toJSON(github) }}
run: |
echo Autosync a single commit with id: ${{ github.sha }} from openSource main branch towards cloud hosted version.
- name: Repository Dispatch
uses: peter-evans/repository-dispatch@v2
with:
token: ${{ secrets.AUTOSYNC_TOKEN }}
repository: ${{ secrets.AUTOSYNC_CH_URL }}
event-type: ${{ secrets.AUTOSYNC_SC_EVENT_TYPE }}
client-payload: >-
{
"ref": "${{ github.ref }}",
"sha": "${{ github.sha }}",
"commitMessage": "${{ github.event.commits[0].message }}"
}

2
package.json
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@ -1,6 +1,6 @@
{
"name": "flowise",
"version": "1.4.11",
"version": "1.5.0",
"private": true,
"homepage": "https://flowiseai.com",
"workspaces": [

135
packages/components/nodes/chatmodels/AzureChatOpenAI/AzureChatOpenAI_LlamaIndex.ts Normal file
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@ -0,0 +1,135 @@
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { OpenAI, ALL_AVAILABLE_OPENAI_MODELS } from 'llamaindex'
interface AzureOpenAIConfig {
apiKey?: string
endpoint?: string
apiVersion?: string
deploymentName?: string
}
class AzureChatOpenAI_LlamaIndex_ChatModels implements INode {
label: string
name: string
version: number
type: string
icon: string
category: string
description: string
baseClasses: string[]
tags: string[]
credential: INodeParams
inputs: INodeParams[]
constructor() {
this.label = 'AzureChatOpenAI'
this.name = 'azureChatOpenAI_LlamaIndex'
this.version = 1.0
this.type = 'AzureChatOpenAI'
this.icon = 'Azure.svg'
this.category = 'Chat Models'
this.description = 'Wrapper around Azure OpenAI Chat LLM specific for LlamaIndex'
this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(OpenAI)]
this.tags = ['LlamaIndex']
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['azureOpenAIApi']
}
this.inputs = [
{
label: 'Model Name',
name: 'modelName',
type: 'options',
options: [
{
label: 'gpt-4',
name: 'gpt-4'
},
{
label: 'gpt-4-32k',
name: 'gpt-4-32k'
},
{
label: 'gpt-3.5-turbo',
name: 'gpt-3.5-turbo'
},
{
label: 'gpt-3.5-turbo-16k',
name: 'gpt-3.5-turbo-16k'
}
],
default: 'gpt-3.5-turbo-16k',
optional: true
},
{
label: 'Temperature',
name: 'temperature',
type: 'number',
step: 0.1,
default: 0.9,
optional: true
},
{
label: 'Max Tokens',
name: 'maxTokens',
type: 'number',
step: 1,
optional: true,
additionalParams: true
},
{
label: 'Top Probability',
name: 'topP',
type: 'number',
step: 0.1,
optional: true,
additionalParams: true
},
{
label: 'Timeout',
name: 'timeout',
type: 'number',
step: 1,
optional: true,
additionalParams: true
}
]
}
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
const modelName = nodeData.inputs?.modelName as keyof typeof ALL_AVAILABLE_OPENAI_MODELS
const temperature = nodeData.inputs?.temperature as string
const maxTokens = nodeData.inputs?.maxTokens as string
const topP = nodeData.inputs?.topP as string
const timeout = nodeData.inputs?.timeout as string
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const azureOpenAIApiKey = getCredentialParam('azureOpenAIApiKey', credentialData, nodeData)
const azureOpenAIApiInstanceName = getCredentialParam('azureOpenAIApiInstanceName', credentialData, nodeData)
const azureOpenAIApiDeploymentName = getCredentialParam('azureOpenAIApiDeploymentName', credentialData, nodeData)
const azureOpenAIApiVersion = getCredentialParam('azureOpenAIApiVersion', credentialData, nodeData)
const obj: Partial<OpenAI> & { azure?: AzureOpenAIConfig } = {
temperature: parseFloat(temperature),
model: modelName,
azure: {
apiKey: azureOpenAIApiKey,
endpoint: `https://${azureOpenAIApiInstanceName}.openai.azure.com`,
apiVersion: azureOpenAIApiVersion,
deploymentName: azureOpenAIApiDeploymentName
}
}
if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
if (topP) obj.topP = parseFloat(topP)
if (timeout) obj.timeout = parseInt(timeout, 10)
const model = new OpenAI(obj)
return model
}
}
module.exports = { nodeClass: AzureChatOpenAI_LlamaIndex_ChatModels }

104
packages/components/nodes/chatmodels/ChatAnthropic/ChatAnthropic_LlamaIndex.ts Normal file
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@ -0,0 +1,104 @@
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { Anthropic } from 'llamaindex'
class ChatAnthropic_LlamaIndex_ChatModels implements INode {
label: string
name: string
version: number
type: string
icon: string
category: string
description: string
tags: string[]
baseClasses: string[]
credential: INodeParams
inputs: INodeParams[]
constructor() {
this.label = 'ChatAnthropic'
this.name = 'chatAnthropic_LlamaIndex'
this.version = 1.0
this.type = 'ChatAnthropic'
this.icon = 'Anthropic.svg'
this.category = 'Chat Models'
this.description = 'Wrapper around ChatAnthropic LLM specific for LlamaIndex'
this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(Anthropic)]
this.tags = ['LlamaIndex']
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['anthropicApi']
}
this.inputs = [
{
label: 'Model Name',
name: 'modelName',
type: 'options',
options: [
{
label: 'claude-2',
name: 'claude-2',
description: 'Claude 2 latest major version, automatically get updates to the model as they are released'
},
{
label: 'claude-instant-1',
name: 'claude-instant-1',
description: 'Claude Instant latest major version, automatically get updates to the model as they are released'
}
],
default: 'claude-2',
optional: true
},
{
label: 'Temperature',
name: 'temperature',
type: 'number',
step: 0.1,
default: 0.9,
optional: true
},
{
label: 'Max Tokens',
name: 'maxTokensToSample',
type: 'number',
step: 1,
optional: true,
additionalParams: true
},
{
label: 'Top P',
name: 'topP',
type: 'number',
step: 0.1,
optional: true,
additionalParams: true
}
]
}
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
const temperature = nodeData.inputs?.temperature as string
const modelName = nodeData.inputs?.modelName as 'claude-2' | 'claude-instant-1' | undefined
const maxTokensToSample = nodeData.inputs?.maxTokensToSample as string
const topP = nodeData.inputs?.topP as string
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const anthropicApiKey = getCredentialParam('anthropicApiKey', credentialData, nodeData)
const obj: Partial<Anthropic> = {
temperature: parseFloat(temperature),
model: modelName,
apiKey: anthropicApiKey
}
if (maxTokensToSample) obj.maxTokens = parseInt(maxTokensToSample, 10)
if (topP) obj.topP = parseFloat(topP)
const model = new Anthropic(obj)
return model
}
}
module.exports = { nodeClass: ChatAnthropic_LlamaIndex_ChatModels }

4
packages/components/nodes/chatmodels/ChatOpenAI/ChatOpenAI.ts
View File

@ -79,6 +79,10 @@ class ChatOpenAI_ChatModels implements INode {
label: 'gpt-3.5-turbo',
name: 'gpt-3.5-turbo'
},
{
label: 'gpt-3.5-turbo-0125',
name: 'gpt-3.5-turbo-0125'
},
{
label: 'gpt-3.5-turbo-1106',
name: 'gpt-3.5-turbo-1106'

156
packages/components/nodes/chatmodels/ChatOpenAI/ChatOpenAI_LlamaIndex.ts Normal file
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@ -0,0 +1,156 @@
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { OpenAI, ALL_AVAILABLE_OPENAI_MODELS } from 'llamaindex'
class ChatOpenAI_LlamaIndex_LLMs implements INode {
label: string
name: string
version: number
type: string
icon: string
category: string
description: string
baseClasses: string[]
tags: string[]
credential: INodeParams
inputs: INodeParams[]
constructor() {
this.label = 'ChatOpenAI'
this.name = 'chatOpenAI_LlamaIndex'
this.version = 1.0
this.type = 'ChatOpenAI'
this.icon = 'openai.svg'
this.category = 'Chat Models'
this.description = 'Wrapper around OpenAI Chat LLM specific for LlamaIndex'
this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(OpenAI)]
this.tags = ['LlamaIndex']
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['openAIApi']
}
this.inputs = [
{
label: 'Model Name',
name: 'modelName',
type: 'options',
options: [
{
label: 'gpt-4',
name: 'gpt-4'
},
{
label: 'gpt-4-turbo-preview',
name: 'gpt-4-turbo-preview'
},
{
label: 'gpt-4-0125-preview',
name: 'gpt-4-0125-preview'
},
{
label: 'gpt-4-1106-preview',
name: 'gpt-4-1106-preview'
},
{
label: 'gpt-4-vision-preview',
name: 'gpt-4-vision-preview'
},
{
label: 'gpt-4-0613',
name: 'gpt-4-0613'
},
{
label: 'gpt-4-32k',
name: 'gpt-4-32k'
},
{
label: 'gpt-4-32k-0613',
name: 'gpt-4-32k-0613'
},
{
label: 'gpt-3.5-turbo',
name: 'gpt-3.5-turbo'
},
{
label: 'gpt-3.5-turbo-1106',
name: 'gpt-3.5-turbo-1106'
},
{
label: 'gpt-3.5-turbo-0613',
name: 'gpt-3.5-turbo-0613'
},
{
label: 'gpt-3.5-turbo-16k',
name: 'gpt-3.5-turbo-16k'
},
{
label: 'gpt-3.5-turbo-16k-0613',
name: 'gpt-3.5-turbo-16k-0613'
}
],
default: 'gpt-3.5-turbo',
optional: true
},
{
label: 'Temperature',
name: 'temperature',
type: 'number',
step: 0.1,
default: 0.9,
optional: true
},
{
label: 'Max Tokens',
name: 'maxTokens',
type: 'number',
step: 1,
optional: true,
additionalParams: true
},
{
label: 'Top Probability',
name: 'topP',
type: 'number',
step: 0.1,
optional: true,
additionalParams: true
},
{
label: 'Timeout',
name: 'timeout',
type: 'number',
step: 1,
optional: true,
additionalParams: true
}
]
}
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
const temperature = nodeData.inputs?.temperature as string
const modelName = nodeData.inputs?.modelName as keyof typeof ALL_AVAILABLE_OPENAI_MODELS
const maxTokens = nodeData.inputs?.maxTokens as string
const topP = nodeData.inputs?.topP as string
const timeout = nodeData.inputs?.timeout as string
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
const obj: Partial<OpenAI> = {
temperature: parseFloat(temperature),
model: modelName,
apiKey: openAIApiKey
}
if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
if (topP) obj.topP = parseFloat(topP)
if (timeout) obj.timeout = parseInt(timeout, 10)
const model = new OpenAI(obj)
return model
}
}
module.exports = { nodeClass: ChatOpenAI_LlamaIndex_LLMs }

94
packages/components/nodes/documentloaders/Folder/Folder.ts
View File

@ -34,6 +34,12 @@ class Folder_DocumentLoaders implements INode {
type: 'string',
placeholder: ''
},
{
label: 'Recursive',
name: 'recursive',
type: 'boolean',
additionalParams: false
},
{
label: 'Text Splitter',
name: 'textSplitter',
@ -54,48 +60,54 @@ class Folder_DocumentLoaders implements INode {
const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
const folderPath = nodeData.inputs?.folderPath as string
const metadata = nodeData.inputs?.metadata
const recursive = nodeData.inputs?.recursive as boolean
const loader = new DirectoryLoader(folderPath, {
'.json': (path) => new JSONLoader(path),
'.txt': (path) => new TextLoader(path),
'.csv': (path) => new CSVLoader(path),
'.docx': (path) => new DocxLoader(path),
// @ts-ignore
'.pdf': (path) => new PDFLoader(path, { pdfjs: () => import('pdf-parse/lib/pdf.js/v1.10.100/build/pdf.js') }),
'.aspx': (path) => new TextLoader(path),
'.asp': (path) => new TextLoader(path),
'.cpp': (path) => new TextLoader(path), // C++
'.c': (path) => new TextLoader(path),
'.cs': (path) => new TextLoader(path),
'.css': (path) => new TextLoader(path),
'.go': (path) => new TextLoader(path), // Go
'.h': (path) => new TextLoader(path), // C++ Header files
'.java': (path) => new TextLoader(path), // Java
'.js': (path) => new TextLoader(path), // JavaScript
'.less': (path) => new TextLoader(path), // Less files
'.ts': (path) => new TextLoader(path), // TypeScript
'.php': (path) => new TextLoader(path), // PHP
'.proto': (path) => new TextLoader(path), // Protocol Buffers
'.python': (path) => new TextLoader(path), // Python
'.py': (path) => new TextLoader(path), // Python
'.rst': (path) => new TextLoader(path), // reStructuredText
'.ruby': (path) => new TextLoader(path), // Ruby
'.rb': (path) => new TextLoader(path), // Ruby
'.rs': (path) => new TextLoader(path), // Rust
'.scala': (path) => new TextLoader(path), // Scala
'.sc': (path) => new TextLoader(path), // Scala
'.scss': (path) => new TextLoader(path), // Sass
'.sol': (path) => new TextLoader(path), // Solidity
'.sql': (path) => new TextLoader(path), //SQL
'.swift': (path) => new TextLoader(path), // Swift
'.markdown': (path) => new TextLoader(path), // Markdown
'.md': (path) => new TextLoader(path), // Markdown
'.tex': (path) => new TextLoader(path), // LaTeX
'.ltx': (path) => new TextLoader(path), // LaTeX
'.html': (path) => new TextLoader(path), // HTML
'.vb': (path) => new TextLoader(path), // Visual Basic
'.xml': (path) => new TextLoader(path) // XML
})
const loader = new DirectoryLoader(
folderPath,
{
'.json': (path) => new JSONLoader(path),
'.txt': (path) => new TextLoader(path),
'.csv': (path) => new CSVLoader(path),
'.docx': (path) => new DocxLoader(path),
// @ts-ignore
'.pdf': (path) => new PDFLoader(path, { pdfjs: () => import('pdf-parse/lib/pdf.js/v1.10.100/build/pdf.js') }),
'.aspx': (path) => new TextLoader(path),
'.asp': (path) => new TextLoader(path),
'.cpp': (path) => new TextLoader(path), // C++
'.c': (path) => new TextLoader(path),
'.cs': (path) => new TextLoader(path),
'.css': (path) => new TextLoader(path),
'.go': (path) => new TextLoader(path), // Go
'.h': (path) => new TextLoader(path), // C++ Header files
'.kt': (path) => new TextLoader(path), // Kotlin
'.java': (path) => new TextLoader(path), // Java
'.js': (path) => new TextLoader(path), // JavaScript
'.less': (path) => new TextLoader(path), // Less files
'.ts': (path) => new TextLoader(path), // TypeScript
'.php': (path) => new TextLoader(path), // PHP
'.proto': (path) => new TextLoader(path), // Protocol Buffers
'.python': (path) => new TextLoader(path), // Python
'.py': (path) => new TextLoader(path), // Python
'.rst': (path) => new TextLoader(path), // reStructuredText
'.ruby': (path) => new TextLoader(path), // Ruby
'.rb': (path) => new TextLoader(path), // Ruby
'.rs': (path) => new TextLoader(path), // Rust
'.scala': (path) => new TextLoader(path), // Scala
'.sc': (path) => new TextLoader(path), // Scala
'.scss': (path) => new TextLoader(path), // Sass
'.sol': (path) => new TextLoader(path), // Solidity
'.sql': (path) => new TextLoader(path), //SQL
'.swift': (path) => new TextLoader(path), // Swift
'.markdown': (path) => new TextLoader(path), // Markdown
'.md': (path) => new TextLoader(path), // Markdown
'.tex': (path) => new TextLoader(path), // LaTeX
'.ltx': (path) => new TextLoader(path), // LaTeX
'.html': (path) => new TextLoader(path), // HTML
'.vb': (path) => new TextLoader(path), // Visual Basic
'.xml': (path) => new TextLoader(path) // XML
},
recursive
)
let docs = []
if (textSplitter) {

2
packages/components/nodes/documentloaders/VectorStoreToDocument/VectorStoreToDocument.ts
View File

@ -51,7 +51,7 @@ class VectorStoreToDocument_DocumentLoaders implements INode {
{
label: 'Document',
name: 'document',
baseClasses: this.baseClasses
baseClasses: [...this.baseClasses, 'json']
},
{
label: 'Text',

77
packages/components/nodes/embeddings/AzureOpenAIEmbedding/AzureOpenAIEmbedding_LlamaIndex.ts Normal file
View File

@ -0,0 +1,77 @@
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { OpenAIEmbedding } from 'llamaindex'
interface AzureOpenAIConfig {
apiKey?: string
endpoint?: string
apiVersion?: string
deploymentName?: string
}
class AzureOpenAIEmbedding_LlamaIndex_Embeddings implements INode {
label: string
name: string
version: number
type: string
icon: string
category: string
description: string
baseClasses: string[]
credential: INodeParams
tags: string[]
inputs: INodeParams[]
constructor() {
this.label = 'Azure OpenAI Embeddings'
this.name = 'azureOpenAIEmbeddingsLlamaIndex'
this.version = 1.0
this.type = 'AzureOpenAIEmbeddings'
this.icon = 'Azure.svg'
this.category = 'Embeddings'
this.description = 'Azure OpenAI API embeddings specific for LlamaIndex'
this.baseClasses = [this.type, 'BaseEmbedding_LlamaIndex', ...getBaseClasses(OpenAIEmbedding)]
this.tags = ['LlamaIndex']
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['azureOpenAIApi']
}
this.inputs = [
{
label: 'Timeout',
name: 'timeout',
type: 'number',
optional: true,
additionalParams: true
}
]
}
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
const timeout = nodeData.inputs?.timeout as string
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const azureOpenAIApiKey = getCredentialParam('azureOpenAIApiKey', credentialData, nodeData)
const azureOpenAIApiInstanceName = getCredentialParam('azureOpenAIApiInstanceName', credentialData, nodeData)
const azureOpenAIApiDeploymentName = getCredentialParam('azureOpenAIApiDeploymentName', credentialData, nodeData)
const azureOpenAIApiVersion = getCredentialParam('azureOpenAIApiVersion', credentialData, nodeData)
const obj: Partial<OpenAIEmbedding> & { azure?: AzureOpenAIConfig } = {
azure: {
apiKey: azureOpenAIApiKey,
endpoint: `https://${azureOpenAIApiInstanceName}.openai.azure.com`,
apiVersion: azureOpenAIApiVersion,
deploymentName: azureOpenAIApiDeploymentName
}
}
if (timeout) obj.timeout = parseInt(timeout, 10)
const model = new OpenAIEmbedding(obj)
return model
}
}
module.exports = { nodeClass: AzureOpenAIEmbedding_LlamaIndex_Embeddings }

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import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { OpenAIEmbedding } from 'llamaindex'
class OpenAIEmbedding_LlamaIndex_Embeddings implements INode {
label: string
name: string
version: number
type: string
icon: string
category: string
description: string
baseClasses: string[]
tags: string[]
credential: INodeParams
inputs: INodeParams[]
constructor() {
this.label = 'OpenAI Embedding'
this.name = 'openAIEmbedding_LlamaIndex'
this.version = 1.0
this.type = 'OpenAIEmbedding'
this.icon = 'openai.svg'
this.category = 'Embeddings'
this.description = 'OpenAI Embedding specific for LlamaIndex'
this.baseClasses = [this.type, 'BaseEmbedding_LlamaIndex', ...getBaseClasses(OpenAIEmbedding)]
this.tags = ['LlamaIndex']
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['openAIApi']
}
this.inputs = [
{
label: 'Model Name',
name: 'modelName',
type: 'options',
options: [
{
label: 'text-embedding-3-large',
name: 'text-embedding-3-large'
},
{
label: 'text-embedding-3-small',
name: 'text-embedding-3-small'
},
{
label: 'text-embedding-ada-002',
name: 'text-embedding-ada-002'
}
],
default: 'text-embedding-ada-002',
optional: true
},
{
label: 'Timeout',
name: 'timeout',
type: 'number',
optional: true,
additionalParams: true
},
{
label: 'BasePath',
name: 'basepath',
type: 'string',
optional: true,
additionalParams: true
}
]
}
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
const timeout = nodeData.inputs?.timeout as string
const modelName = nodeData.inputs?.modelName as string
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
const obj: Partial<OpenAIEmbedding> = {
apiKey: openAIApiKey,
model: modelName
}
if (timeout) obj.timeout = parseInt(timeout, 10)
const model = new OpenAIEmbedding(obj)
return model
}
}
module.exports = { nodeClass: OpenAIEmbedding_LlamaIndex_Embeddings }

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import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { BaseNode, Metadata, BaseRetriever, LLM, ContextChatEngine, ChatMessage } from 'llamaindex'
import { reformatSourceDocuments } from '../EngineUtils'
class ContextChatEngine_LlamaIndex implements INode {
label: string
name: string
version: number
description: string
type: string
icon: string
category: string
baseClasses: string[]
tags: string[]
inputs: INodeParams[]
outputs: INodeOutputsValue[]
sessionId?: string
constructor(fields?: { sessionId?: string }) {
this.label = 'Context Chat Engine'
this.name = 'contextChatEngine'
this.version = 1.0
this.type = 'ContextChatEngine'
this.icon = 'context-chat-engine.png'
this.category = 'Engine'
this.description = 'Answer question based on retrieved documents (context) with built-in memory to remember conversation'
this.baseClasses = [this.type]
this.tags = ['LlamaIndex']
this.inputs = [
{
label: 'Chat Model',
name: 'model',
type: 'BaseChatModel_LlamaIndex'
},
{
label: 'Vector Store Retriever',
name: 'vectorStoreRetriever',
type: 'VectorIndexRetriever'
},
{
label: 'Memory',
name: 'memory',
type: 'BaseChatMemory'
},
{
label: 'Return Source Documents',
name: 'returnSourceDocuments',
type: 'boolean',
optional: true
},
{
label: 'System Message',
name: 'systemMessagePrompt',
type: 'string',
rows: 4,
optional: true,
placeholder:
'I want you to act as a document that I am having a conversation with. Your name is "AI Assistant". You will provide me with answers from the given info. If the answer is not included, say exactly "Hmm, I am not sure." and stop after that. Refuse to answer any question not about the info. Never break character.'
}
]
this.sessionId = fields?.sessionId
}
async init(): Promise<any> {
return null
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
const model = nodeData.inputs?.model as LLM
const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
const memory = nodeData.inputs?.memory as FlowiseMemory
const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
const chatHistory = [] as ChatMessage[]
if (systemMessagePrompt) {
chatHistory.push({
content: systemMessagePrompt,
role: 'user'
})
}
const chatEngine = new ContextChatEngine({ chatModel: model, retriever: vectorStoreRetriever })
const msgs = (await memory.getChatMessages(this.sessionId, false, options.chatHistory)) as IMessage[]
for (const message of msgs) {
if (message.type === 'apiMessage') {
chatHistory.push({
content: message.message,
role: 'assistant'
})
} else if (message.type === 'userMessage') {
chatHistory.push({
content: message.message,
role: 'user'
})
}
}
let text = ''
let isStreamingStarted = false
let sourceDocuments: ICommonObject[] = []
let sourceNodes: BaseNode<Metadata>[] = []
const isStreamingEnabled = options.socketIO && options.socketIOClientId
if (isStreamingEnabled) {
const stream = await chatEngine.chat({ message: input, chatHistory, stream: true })
for await (const chunk of stream) {
text += chunk.response
if (chunk.sourceNodes) sourceNodes = chunk.sourceNodes
if (!isStreamingStarted) {
isStreamingStarted = true
options.socketIO.to(options.socketIOClientId).emit('start', chunk.response)
}
options.socketIO.to(options.socketIOClientId).emit('token', chunk.response)
}
if (returnSourceDocuments) {
sourceDocuments = reformatSourceDocuments(sourceNodes)
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', sourceDocuments)
}
} else {
const response = await chatEngine.chat({ message: input, chatHistory })
text = response?.response
sourceDocuments = reformatSourceDocuments(response?.sourceNodes ?? [])
}
await memory.addChatMessages(
[
{
text: input,
type: 'userMessage'
},
{
text: text,
type: 'apiMessage'
}
],
this.sessionId
)
if (returnSourceDocuments) return { text, sourceDocuments }
else return { text }
}
}
module.exports = { nodeClass: ContextChatEngine_LlamaIndex }

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import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { LLM, ChatMessage, SimpleChatEngine } from 'llamaindex'
class SimpleChatEngine_LlamaIndex implements INode {
label: string
name: string
version: number
description: string
type: string
icon: string
category: string
baseClasses: string[]
tags: string[]
inputs: INodeParams[]
outputs: INodeOutputsValue[]
sessionId?: string
constructor(fields?: { sessionId?: string }) {
this.label = 'Simple Chat Engine'
this.name = 'simpleChatEngine'
this.version = 1.0
this.type = 'SimpleChatEngine'
this.icon = 'chat-engine.png'
this.category = 'Engine'
this.description = 'Simple engine to handle back and forth conversations'
this.baseClasses = [this.type]
this.tags = ['LlamaIndex']
this.inputs = [
{
label: 'Chat Model',
name: 'model',
type: 'BaseChatModel_LlamaIndex'
},
{
label: 'Memory',
name: 'memory',
type: 'BaseChatMemory'
},
{
label: 'System Message',
name: 'systemMessagePrompt',
type: 'string',
rows: 4,
optional: true,
placeholder: 'You are a helpful assistant'
}
]
this.sessionId = fields?.sessionId
}
async init(): Promise<any> {
return null
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
const model = nodeData.inputs?.model as LLM
const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
const memory = nodeData.inputs?.memory as FlowiseMemory
const chatHistory = [] as ChatMessage[]
if (systemMessagePrompt) {
chatHistory.push({
content: systemMessagePrompt,
role: 'user'
})
}
const chatEngine = new SimpleChatEngine({ llm: model })
const msgs = (await memory.getChatMessages(this.sessionId, false, options.chatHistory)) as IMessage[]
for (const message of msgs) {
if (message.type === 'apiMessage') {
chatHistory.push({
content: message.message,
role: 'assistant'
})
} else if (message.type === 'userMessage') {
chatHistory.push({
content: message.message,
role: 'user'
})
}
}
let text = ''
let isStreamingStarted = false
const isStreamingEnabled = options.socketIO && options.socketIOClientId
if (isStreamingEnabled) {
const stream = await chatEngine.chat({ message: input, chatHistory, stream: true })
for await (const chunk of stream) {
text += chunk.response
if (!isStreamingStarted) {
isStreamingStarted = true
options.socketIO.to(options.socketIOClientId).emit('start', chunk.response)
}
options.socketIO.to(options.socketIOClientId).emit('token', chunk.response)
}
} else {
const response = await chatEngine.chat({ message: input, chatHistory })
text = response?.response
}
await memory.addChatMessages(
[
{
text: input,
type: 'userMessage'
},
{
text: text,
type: 'apiMessage'
}
],
this.sessionId
)
return text
}
}
module.exports = { nodeClass: SimpleChatEngine_LlamaIndex }

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import { BaseNode, Metadata } from 'llamaindex'
export const reformatSourceDocuments = (sourceNodes: BaseNode<Metadata>[]) => {
const sourceDocuments = []
for (const node of sourceNodes) {
sourceDocuments.push({
pageContent: (node as any).text,
metadata: node.metadata
})
}
return sourceDocuments
}

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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import {
RetrieverQueryEngine,
ResponseSynthesizer,
CompactAndRefine,
TreeSummarize,
Refine,
SimpleResponseBuilder,
BaseNode,
Metadata
} from 'llamaindex'
import { reformatSourceDocuments } from '../EngineUtils'
class QueryEngine_LlamaIndex implements INode {
label: string
name: string
version: number
description: string
type: string
icon: string
category: string
baseClasses: string[]
tags: string[]
inputs: INodeParams[]
outputs: INodeOutputsValue[]
sessionId?: string
constructor(fields?: { sessionId?: string }) {
this.label = 'Query Engine'
this.name = 'queryEngine'
this.version = 1.0
this.type = 'QueryEngine'
this.icon = 'query-engine.png'
this.category = 'Engine'
this.description = 'Simple query engine built to answer question over your data, without memory'
this.baseClasses = [this.type]
this.tags = ['LlamaIndex']
this.inputs = [
{
label: 'Vector Store Retriever',
name: 'vectorStoreRetriever',
type: 'VectorIndexRetriever'
},
{
label: 'Response Synthesizer',
name: 'responseSynthesizer',
type: 'ResponseSynthesizer',
description:
'ResponseSynthesizer is responsible for sending the query, nodes, and prompt templates to the LLM to generate a response. See <a target="_blank" href="https://ts.llamaindex.ai/modules/low_level/response_synthesizer">more</a>',
optional: true
},
{
label: 'Return Source Documents',
name: 'returnSourceDocuments',
type: 'boolean',
optional: true
}
]
this.sessionId = fields?.sessionId
}
async init(): Promise<any> {
return null
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever
const responseSynthesizerObj = nodeData.inputs?.responseSynthesizer
let queryEngine = new RetrieverQueryEngine(vectorStoreRetriever)
if (responseSynthesizerObj) {
if (responseSynthesizerObj.type === 'TreeSummarize') {
const responseSynthesizer = new ResponseSynthesizer({
responseBuilder: new TreeSummarize(vectorStoreRetriever.serviceContext, responseSynthesizerObj.textQAPromptTemplate),
serviceContext: vectorStoreRetriever.serviceContext
})
queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
} else if (responseSynthesizerObj.type === 'CompactAndRefine') {
const responseSynthesizer = new ResponseSynthesizer({
responseBuilder: new CompactAndRefine(
vectorStoreRetriever.serviceContext,
responseSynthesizerObj.textQAPromptTemplate,
responseSynthesizerObj.refinePromptTemplate
),
serviceContext: vectorStoreRetriever.serviceContext
})
queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
} else if (responseSynthesizerObj.type === 'Refine') {
const responseSynthesizer = new ResponseSynthesizer({
responseBuilder: new Refine(
vectorStoreRetriever.serviceContext,
responseSynthesizerObj.textQAPromptTemplate,
responseSynthesizerObj.refinePromptTemplate
),
serviceContext: vectorStoreRetriever.serviceContext
})
queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
} else if (responseSynthesizerObj.type === 'SimpleResponseBuilder') {
const responseSynthesizer = new ResponseSynthesizer({
responseBuilder: new SimpleResponseBuilder(vectorStoreRetriever.serviceContext),
serviceContext: vectorStoreRetriever.serviceContext
})
queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
}
}
let text = ''
let sourceDocuments: ICommonObject[] = []
let sourceNodes: BaseNode<Metadata>[] = []
let isStreamingStarted = false
const isStreamingEnabled = options.socketIO && options.socketIOClientId
if (isStreamingEnabled) {
const stream = await queryEngine.query({ query: input, stream: true })
for await (const chunk of stream) {
text += chunk.response
if (chunk.sourceNodes) sourceNodes = chunk.sourceNodes
if (!isStreamingStarted) {
isStreamingStarted = true
options.socketIO.to(options.socketIOClientId).emit('start', chunk.response)
}
options.socketIO.to(options.socketIOClientId).emit('token', chunk.response)
}
if (returnSourceDocuments) {
sourceDocuments = reformatSourceDocuments(sourceNodes)
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', sourceDocuments)
}
} else {
const response = await queryEngine.query({ query: input })
text = response?.response
sourceDocuments = reformatSourceDocuments(response?.sourceNodes ?? [])
}
if (returnSourceDocuments) return { text, sourceDocuments }
else return { text }
}
}
module.exports = { nodeClass: QueryEngine_LlamaIndex }

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import { flatten } from 'lodash'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import {
TreeSummarize,
SimpleResponseBuilder,
Refine,
BaseEmbedding,
ResponseSynthesizer,
CompactAndRefine,
QueryEngineTool,
LLMQuestionGenerator,
SubQuestionQueryEngine,
BaseNode,
Metadata,
serviceContextFromDefaults
} from 'llamaindex'
import { reformatSourceDocuments } from '../EngineUtils'
class SubQuestionQueryEngine_LlamaIndex implements INode {
label: string
name: string
version: number
description: string
type: string
icon: string
category: string
baseClasses: string[]
tags: string[]
inputs: INodeParams[]
outputs: INodeOutputsValue[]
sessionId?: string
constructor(fields?: { sessionId?: string }) {
this.label = 'Sub Question Query Engine'
this.name = 'subQuestionQueryEngine'
this.version = 1.0
this.type = 'SubQuestionQueryEngine'
this.icon = 'subQueryEngine.svg'
this.category = 'Engine'
this.description =
'Breaks complex query into sub questions for each relevant data source, then gather all the intermediate reponses and synthesizes a final response'
this.baseClasses = [this.type]
this.tags = ['LlamaIndex']
this.inputs = [
{
label: 'QueryEngine Tools',
name: 'queryEngineTools',
type: 'QueryEngineTool',
list: true
},
{
label: 'Chat Model',
name: 'model',
type: 'BaseChatModel_LlamaIndex'
},
{
label: 'Embeddings',
name: 'embeddings',
type: 'BaseEmbedding_LlamaIndex'
},
{
label: 'Response Synthesizer',
name: 'responseSynthesizer',
type: 'ResponseSynthesizer',
description:
'ResponseSynthesizer is responsible for sending the query, nodes, and prompt templates to the LLM to generate a response. See <a target="_blank" href="https://ts.llamaindex.ai/modules/low_level/response_synthesizer">more</a>',
optional: true
},
{
label: 'Return Source Documents',
name: 'returnSourceDocuments',
type: 'boolean',
optional: true
}
]
this.sessionId = fields?.sessionId
}
async init(): Promise<any> {
return null
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
const embeddings = nodeData.inputs?.embeddings as BaseEmbedding
const model = nodeData.inputs?.model
const serviceContext = serviceContextFromDefaults({
llm: model,
embedModel: embeddings
})
let queryEngineTools = nodeData.inputs?.queryEngineTools as QueryEngineTool[]
queryEngineTools = flatten(queryEngineTools)
let queryEngine = SubQuestionQueryEngine.fromDefaults({
serviceContext,
queryEngineTools,
questionGen: new LLMQuestionGenerator({ llm: model })
})
const responseSynthesizerObj = nodeData.inputs?.responseSynthesizer
if (responseSynthesizerObj) {
if (responseSynthesizerObj.type === 'TreeSummarize') {
const responseSynthesizer = new ResponseSynthesizer({
responseBuilder: new TreeSummarize(serviceContext, responseSynthesizerObj.textQAPromptTemplate),
serviceContext
})
queryEngine = SubQuestionQueryEngine.fromDefaults({
responseSynthesizer,
serviceContext,
queryEngineTools,
questionGen: new LLMQuestionGenerator({ llm: model })
})
} else if (responseSynthesizerObj.type === 'CompactAndRefine') {
const responseSynthesizer = new ResponseSynthesizer({
responseBuilder: new CompactAndRefine(
serviceContext,
responseSynthesizerObj.textQAPromptTemplate,
responseSynthesizerObj.refinePromptTemplate
),
serviceContext
})
queryEngine = SubQuestionQueryEngine.fromDefaults({
responseSynthesizer,
serviceContext,
queryEngineTools,
questionGen: new LLMQuestionGenerator({ llm: model })
})
} else if (responseSynthesizerObj.type === 'Refine') {
const responseSynthesizer = new ResponseSynthesizer({
responseBuilder: new Refine(
serviceContext,
responseSynthesizerObj.textQAPromptTemplate,
responseSynthesizerObj.refinePromptTemplate
),
serviceContext
})
queryEngine = SubQuestionQueryEngine.fromDefaults({
responseSynthesizer,
serviceContext,
queryEngineTools,
questionGen: new LLMQuestionGenerator({ llm: model })
})
} else if (responseSynthesizerObj.type === 'SimpleResponseBuilder') {
const responseSynthesizer = new ResponseSynthesizer({
responseBuilder: new SimpleResponseBuilder(serviceContext),
serviceContext
})
queryEngine = SubQuestionQueryEngine.fromDefaults({
responseSynthesizer,
serviceContext,
queryEngineTools,
questionGen: new LLMQuestionGenerator({ llm: model })
})
}
}
let text = ''
let sourceDocuments: ICommonObject[] = []
let sourceNodes: BaseNode<Metadata>[] = []
let isStreamingStarted = false
const isStreamingEnabled = options.socketIO && options.socketIOClientId
if (isStreamingEnabled) {
const stream = await queryEngine.query({ query: input, stream: true })
for await (const chunk of stream) {
text += chunk.response
if (chunk.sourceNodes) sourceNodes = chunk.sourceNodes
if (!isStreamingStarted) {
isStreamingStarted = true
options.socketIO.to(options.socketIOClientId).emit('start', chunk.response)
}
options.socketIO.to(options.socketIOClientId).emit('token', chunk.response)
}
if (returnSourceDocuments) {
sourceDocuments = reformatSourceDocuments(sourceNodes)
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', sourceDocuments)
}
} else {
const response = await queryEngine.query({ query: input })
text = response?.response
sourceDocuments = reformatSourceDocuments(response?.sourceNodes ?? [])
}
if (returnSourceDocuments) return { text, sourceDocuments }
else return { text }
}
}
module.exports = { nodeClass: SubQuestionQueryEngine_LlamaIndex }

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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-filter-question" 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 d="M15 19l-6 2v-8.5l-4.48 -4.928a2 2 0 0 1 -.52 -1.345v-2.227h16v2.172a2 2 0 0 1 -.586 1.414l-4.414 4.414" /><path d="M19 22v.01" /><path d="M19 19a2.003 2.003 0 0 0 .914 -3.782a1.98 1.98 0 0 0 -2.414 .483" /></svg>
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import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { ResponseSynthesizerClass } from '../base'
class CompactRefine_LlamaIndex 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 = 'Compact and Refine'
this.name = 'compactrefineLlamaIndex'
this.version = 1.0
this.type = 'CompactRefine'
this.icon = 'compactrefine.svg'
this.category = 'Response Synthesizer'
this.description =
'CompactRefine is a slight variation of Refine that first compacts the text chunks into the smallest possible number of chunks.'
this.baseClasses = [this.type, 'ResponseSynthesizer']
this.tags = ['LlamaIndex']
this.inputs = [
{
label: 'Refine Prompt',
name: 'refinePrompt',
type: 'string',
rows: 4,
default: `The original query is as follows: {query}
We have provided an existing answer: {existingAnswer}
We have the opportunity to refine the existing answer (only if needed) with some more context below.
------------
{context}
------------
Given the new context, refine the original answer to better answer the query. If the context isn't useful, return the original answer.
Refined Answer:`,
warning: `Prompt can contains no variables, or up to 3 variables. Variables must be {existingAnswer}, {context} and {query}`,
optional: true
},
{
label: 'Text QA Prompt',
name: 'textQAPrompt',
type: 'string',
rows: 4,
default: `Context information is below.
---------------------
{context}
---------------------
Given the context information and not prior knowledge, answer the query.
Query: {query}
Answer:`,
warning: `Prompt can contains no variables, or up to 2 variables. Variables must be {context} and {query}`,
optional: true
}
]
}
async init(nodeData: INodeData): Promise<any> {
const refinePrompt = nodeData.inputs?.refinePrompt as string
const textQAPrompt = nodeData.inputs?.textQAPrompt as string
const refinePromptTemplate = ({ context = '', existingAnswer = '', query = '' }) =>
refinePrompt.replace('{existingAnswer}', existingAnswer).replace('{context}', context).replace('{query}', query)
const textQAPromptTemplate = ({ context = '', query = '' }) => textQAPrompt.replace('{context}', context).replace('{query}', query)
return new ResponseSynthesizerClass({ textQAPromptTemplate, refinePromptTemplate, type: 'CompactAndRefine' })
}
}
module.exports = { nodeClass: CompactRefine_LlamaIndex }

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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-layers-difference" 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 d="M16 16v2a2 2 0 0 1 -2 2h-8a2 2 0 0 1 -2 -2v-8a2 2 0 0 1 2 -2h2v-2a2 2 0 0 1 2 -2h8a2 2 0 0 1 2 2v8a2 2 0 0 1 -2 2h-2" /><path d="M10 8l-2 0l0 2" /><path d="M8 14l0 2l2 0" /><path d="M14 8l2 0l0 2" /><path d="M16 14l0 2l-2 0" /></svg>
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packages/components/nodes/responsesynthesizer/Refine/Refine.ts Normal file
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import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { ResponseSynthesizerClass } from '../base'
class Refine_LlamaIndex 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 = 'Refine'
this.name = 'refineLlamaIndex'
this.version = 1.0
this.type = 'Refine'
this.icon = 'refine.svg'
this.category = 'Response Synthesizer'
this.description =
'Create and refine an answer by sequentially going through each retrieved text chunk. This makes a separate LLM call per Node. Good for more detailed answers.'
this.baseClasses = [this.type, 'ResponseSynthesizer']
this.tags = ['LlamaIndex']
this.inputs = [
{
label: 'Refine Prompt',
name: 'refinePrompt',
type: 'string',
rows: 4,
default: `The original query is as follows: {query}
We have provided an existing answer: {existingAnswer}
We have the opportunity to refine the existing answer (only if needed) with some more context below.
------------
{context}
------------
Given the new context, refine the original answer to better answer the query. If the context isn't useful, return the original answer.
Refined Answer:`,
warning: `Prompt can contains no variables, or up to 3 variables. Variables must be {existingAnswer}, {context} and {query}`,
optional: true
},
{
label: 'Text QA Prompt',
name: 'textQAPrompt',
type: 'string',
rows: 4,
default: `Context information is below.
---------------------
{context}
---------------------
Given the context information and not prior knowledge, answer the query.
Query: {query}
Answer:`,
warning: `Prompt can contains no variables, or up to 2 variables. Variables must be {context} and {query}`,
optional: true
}
]
}
async init(nodeData: INodeData): Promise<any> {
const refinePrompt = nodeData.inputs?.refinePrompt as string
const textQAPrompt = nodeData.inputs?.textQAPrompt as string
const refinePromptTemplate = ({ context = '', existingAnswer = '', query = '' }) =>
refinePrompt.replace('{existingAnswer}', existingAnswer).replace('{context}', context).replace('{query}', query)
const textQAPromptTemplate = ({ context = '', query = '' }) => textQAPrompt.replace('{context}', context).replace('{query}', query)
return new ResponseSynthesizerClass({ textQAPromptTemplate, refinePromptTemplate, type: 'Refine' })
}
}
module.exports = { nodeClass: Refine_LlamaIndex }

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packages/components/nodes/responsesynthesizer/Refine/refine.svg Normal file
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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-filter-search" 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 d="M11.36 20.213l-2.36 .787v-8.5l-4.48 -4.928a2 2 0 0 1 -.52 -1.345v-2.227h16v2.172a2 2 0 0 1 -.586 1.414l-4.414 4.414" /><path d="M18 18m-3 0a3 3 0 1 0 6 0a3 3 0 1 0 -6 0" /><path d="M20.2 20.2l1.8 1.8" /></svg>
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packages/components/nodes/responsesynthesizer/SimpleResponseBuilder/SimpleResponseBuilder.ts Normal file
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import { INode, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { ResponseSynthesizerClass } from '../base'
class SimpleResponseBuilder_LlamaIndex 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 = 'Simple Response Builder'
this.name = 'simpleResponseBuilderLlamaIndex'
this.version = 1.0
this.type = 'SimpleResponseBuilder'
this.icon = 'simplerb.svg'
this.category = 'Response Synthesizer'
this.description = `Apply a query to a collection of text chunks, gathering the responses in an array, and return a combined string of all responses. Useful for individual queries on each text chunk.`
this.baseClasses = [this.type, 'ResponseSynthesizer']
this.tags = ['LlamaIndex']
this.inputs = []
}
async init(): Promise<any> {
return new ResponseSynthesizerClass({ type: 'SimpleResponseBuilder' })
}
}
module.exports = { nodeClass: SimpleResponseBuilder_LlamaIndex }

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packages/components/nodes/responsesynthesizer/SimpleResponseBuilder/simplerb.svg Normal file
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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-quote" 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 d="M10 11h-4a1 1 0 0 1 -1 -1v-3a1 1 0 0 1 1 -1h3a1 1 0 0 1 1 1v6c0 2.667 -1.333 4.333 -4 5" /><path d="M19 11h-4a1 1 0 0 1 -1 -1v-3a1 1 0 0 1 1 -1h3a1 1 0 0 1 1 1v6c0 2.667 -1.333 4.333 -4 5" /></svg>
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packages/components/nodes/responsesynthesizer/TreeSummarize/TreeSummarize.ts Normal file
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import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { ResponseSynthesizerClass } from '../base'
class TreeSummarize_LlamaIndex 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 = 'TreeSummarize'
this.name = 'treeSummarizeLlamaIndex'
this.version = 1.0
this.type = 'TreeSummarize'
this.icon = 'treesummarize.svg'
this.category = 'Response Synthesizer'
this.description =
'Given a set of text chunks and the query, recursively construct a tree and return the root node as the response. Good for summarization purposes.'
this.baseClasses = [this.type, 'ResponseSynthesizer']
this.tags = ['LlamaIndex']
this.inputs = [
{
label: 'Prompt',
name: 'prompt',
type: 'string',
rows: 4,
default: `Context information from multiple sources is below.
---------------------
{context}
---------------------
Given the information from multiple sources and not prior knowledge, answer the query.
Query: {query}
Answer:`,
warning: `Prompt can contains no variables, or up to 2 variables. Variables must be {context} and {query}`,
optional: true
}
]
}
async init(nodeData: INodeData): Promise<any> {
const prompt = nodeData.inputs?.prompt as string
const textQAPromptTemplate = ({ context = '', query = '' }) => prompt.replace('{context}', context).replace('{query}', query)
return new ResponseSynthesizerClass({ textQAPromptTemplate, type: 'TreeSummarize' })
}
}
module.exports = { nodeClass: TreeSummarize_LlamaIndex }

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packages/components/nodes/responsesynthesizer/TreeSummarize/treesummarize.svg Normal file
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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-tree" 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 d="M12 13l-2 -2" /><path d="M12 12l2 -2" /><path d="M12 21v-13" /><path d="M9.824 16a3 3 0 0 1 -2.743 -3.69a3 3 0 0 1 .304 -4.833a3 3 0 0 1 4.615 -3.707a3 3 0 0 1 4.614 3.707a3 3 0 0 1 .305 4.833a3 3 0 0 1 -2.919 3.695h-4z" /></svg>
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packages/components/nodes/responsesynthesizer/base.ts Normal file
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export class ResponseSynthesizerClass {
type: string
textQAPromptTemplate?: any
refinePromptTemplate?: any
constructor(params: { type: string; textQAPromptTemplate?: any; refinePromptTemplate?: any }) {
this.type = params.type
this.textQAPromptTemplate = params.textQAPromptTemplate
this.refinePromptTemplate = params.refinePromptTemplate
}
}

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packages/components/nodes/retrievers/CohereRerankRetriever/CohereRerank.ts
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@ -48,7 +48,7 @@ export class CohereRerank extends BaseDocumentCompressor {
doc.metadata.relevance_score = result.relevance_score
finalResults.push(doc)
})
return finalResults
return finalResults.splice(0, this.k)
} catch (error) {
return documents
}

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packages/components/nodes/tools/QueryEngineTool/QueryEngineTool.ts Normal file
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import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { VectorStoreIndex } from 'llamaindex'
class QueryEngine_Tools implements INode {
label: string
name: string
version: number
description: string
type: string
icon: string
category: string
tags: string[]
baseClasses: string[]
inputs?: INodeParams[]
constructor() {
this.label = 'QueryEngine Tool'
this.name = 'queryEngineToolLlamaIndex'
this.version = 1.0
this.type = 'QueryEngineTool'
this.icon = 'queryEngineTool.svg'
this.category = 'Tools'
this.tags = ['LlamaIndex']
this.description = 'Tool used to invoke query engine'
this.baseClasses = [this.type]
this.inputs = [
{
label: 'Vector Store Index',
name: 'vectorStoreIndex',
type: 'VectorStoreIndex'
},
{
label: 'Tool Name',
name: 'toolName',
type: 'string',
description: 'Tool name must be small capital letter with underscore. Ex: my_tool'
},
{
label: 'Tool Description',
name: 'toolDesc',
type: 'string',
rows: 4
}
]
}
async init(nodeData: INodeData): Promise<any> {
const vectorStoreIndex = nodeData.inputs?.vectorStoreIndex as VectorStoreIndex
const toolName = nodeData.inputs?.toolName as string
const toolDesc = nodeData.inputs?.toolDesc as string
const queryEngineTool = {
queryEngine: vectorStoreIndex.asQueryEngine({
preFilters: {
...(vectorStoreIndex as any).metadatafilter
}
}),
metadata: {
name: toolName,
description: toolDesc
},
vectorStoreIndex
}
return queryEngineTool
}
}
module.exports = { nodeClass: QueryEngine_Tools }

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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-brand-google-big-query" 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 d="M17.73 19.875a2.225 2.225 0 0 1 -1.948 1.125h-7.283a2.222 2.222 0 0 1 -1.947 -1.158l-4.272 -6.75a2.269 2.269 0 0 1 0 -2.184l4.272 -6.75a2.225 2.225 0 0 1 1.946 -1.158h7.285c.809 0 1.554 .443 1.947 1.158l3.98 6.75a2.33 2.33 0 0 1 0 2.25l-3.98 6.75v-.033z" /><path d="M11.5 11.5m-3.5 0a3.5 3.5 0 1 0 7 0a3.5 3.5 0 1 0 -7 0" /><path d="M14 14l2 2" /></svg>
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packages/components/nodes/vectorstores/Pinecone/Pinecone_LlamaIndex.ts Normal file
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import {
BaseNode,
Document,
Metadata,
VectorStore,
VectorStoreQuery,
VectorStoreQueryResult,
serviceContextFromDefaults,
storageContextFromDefaults,
VectorStoreIndex,
BaseEmbedding
} from 'llamaindex'
import { FetchResponse, Index, Pinecone, ScoredPineconeRecord } from '@pinecone-database/pinecone'
import { flatten } from 'lodash'
import { Document as LCDocument } from 'langchain/document'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { flattenObject, getCredentialData, getCredentialParam } from '../../../src/utils'
class PineconeLlamaIndex_VectorStores implements INode {
label: string
name: string
version: number
description: string
type: string
icon: string
category: string
tags: string[]
baseClasses: string[]
inputs: INodeParams[]
credential: INodeParams
outputs: INodeOutputsValue[]
constructor() {
this.label = 'Pinecone'
this.name = 'pineconeLlamaIndex'
this.version = 1.0
this.type = 'Pinecone'
this.icon = 'pinecone.svg'
this.category = 'Vector Stores'
this.description = `Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database`
this.baseClasses = [this.type, 'VectorIndexRetriever']
this.tags = ['LlamaIndex']
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['pineconeApi']
}
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: 'Pinecone Index',
name: 'pineconeIndex',
type: 'string'
},
{
label: 'Pinecone Namespace',
name: 'pineconeNamespace',
type: 'string',
placeholder: 'my-first-namespace',
additionalParams: true,
optional: true
},
{
label: 'Pinecone Metadata Filter',
name: 'pineconeMetadataFilter',
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: 'Pinecone Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Pinecone Vector Store Index',
name: 'vectorStore',
baseClasses: [this.type, 'VectorStoreIndex']
}
]
}
//@ts-ignore
vectorStoreMethods = {
async upsert(nodeData: INodeData, options: ICommonObject): Promise<void> {
const indexName = nodeData.inputs?.pineconeIndex as string
const pineconeNamespace = nodeData.inputs?.pineconeNamespace as string
const docs = nodeData.inputs?.document as LCDocument[]
const embeddings = nodeData.inputs?.embeddings as BaseEmbedding
const model = nodeData.inputs?.model
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
const pcvs = new PineconeVectorStore({
indexName,
apiKey: pineconeApiKey,
namespace: pineconeNamespace
})
const flattenDocs = docs && docs.length ? flatten(docs) : []
const finalDocs = []
for (let i = 0; i < flattenDocs.length; i += 1) {
if (flattenDocs[i] && flattenDocs[i].pageContent) {
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({ vectorStore: pcvs })
try {
await VectorStoreIndex.fromDocuments(llamadocs, { serviceContext, storageContext })
} catch (e) {
throw new Error(e)
}
}
}
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
const indexName = nodeData.inputs?.pineconeIndex as string
const pineconeNamespace = nodeData.inputs?.pineconeNamespace as string
const pineconeMetadataFilter = nodeData.inputs?.pineconeMetadataFilter
const embeddings = nodeData.inputs?.embeddings as BaseEmbedding
const model = nodeData.inputs?.model
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
const obj: PineconeParams = {
indexName,
apiKey: pineconeApiKey
}
if (pineconeNamespace) obj.namespace = pineconeNamespace
let metadatafilter = {}
if (pineconeMetadataFilter) {
metadatafilter = typeof pineconeMetadataFilter === 'object' ? pineconeMetadataFilter : JSON.parse(pineconeMetadataFilter)
obj.queryFilter = metadatafilter
}
const pcvs = new PineconeVectorStore(obj)
const serviceContext = serviceContextFromDefaults({ llm: model, embedModel: embeddings })
const storageContext = await storageContextFromDefaults({ vectorStore: pcvs })
const index = await VectorStoreIndex.init({
nodes: [],
storageContext,
serviceContext
})
const output = nodeData.outputs?.output as string
if (output === 'retriever') {
const retriever = index.asRetriever()
retriever.similarityTopK = k
;(retriever as any).serviceContext = serviceContext
return retriever
} else if (output === 'vectorStore') {
;(index as any).k = k
if (metadatafilter) {
;(index as any).metadatafilter = metadatafilter
}
return index
}
return index
}
}
type PineconeParams = {
indexName: string
apiKey: string
namespace?: string
chunkSize?: number
queryFilter?: object
}
class PineconeVectorStore implements VectorStore {
storesText: boolean = true
db?: Pinecone
indexName: string
apiKey: string
chunkSize: number
namespace?: string
queryFilter?: object
constructor(params: PineconeParams) {
this.indexName = params?.indexName
this.apiKey = params?.apiKey
this.namespace = params?.namespace ?? ''
this.chunkSize = params?.chunkSize ?? Number.parseInt(process.env.PINECONE_CHUNK_SIZE ?? '100')
this.queryFilter = params?.queryFilter ?? {}
}
private async getDb(): Promise<Pinecone> {
if (!this.db) {
this.db = new Pinecone({
apiKey: this.apiKey
})
}
return Promise.resolve(this.db)
}
client() {
return this.getDb()
}
async index() {
const db: Pinecone = await this.getDb()
return db.Index(this.indexName)
}
async clearIndex() {
const db: Pinecone = await this.getDb()
return await db.index(this.indexName).deleteAll()
}
async add(embeddingResults: BaseNode<Metadata>[]): Promise<string[]> {
if (embeddingResults.length == 0) {
return Promise.resolve([])
}
const idx: Index = await this.index()
const nodes = embeddingResults.map(this.nodeToRecord)
for (let i = 0; i < nodes.length; i += this.chunkSize) {
const chunk = nodes.slice(i, i + this.chunkSize)
const result = await this.saveChunk(idx, chunk)
if (!result) {
return Promise.reject()
}
}
return Promise.resolve([])
}
protected async saveChunk(idx: Index, chunk: any) {
try {
const namespace = idx.namespace(this.namespace ?? '')
await namespace.upsert(chunk)
return true
} catch (err) {
return false
}
}
async delete(refDocId: string): Promise<void> {
const idx = await this.index()
const namespace = idx.namespace(this.namespace ?? '')
return namespace.deleteOne(refDocId)
}
async query(query: VectorStoreQuery): Promise<VectorStoreQueryResult> {
const queryOptions: any = {
vector: query.queryEmbedding,
topK: query.similarityTopK,
filter: this.queryFilter
}
const idx = await this.index()
const namespace = idx.namespace(this.namespace ?? '')
const results = await namespace.query(queryOptions)
const idList = results.matches.map((row) => row.id)
const records: FetchResponse<any> = await namespace.fetch(idList)
const rows = Object.values(records.records)
const nodes = rows.map((row) => {
return new Document({
id_: row.id,
text: this.textFromResultRow(row),
metadata: this.metaWithoutText(row.metadata),
embedding: row.values
})
})
const result = {
nodes: nodes,
similarities: results.matches.map((row) => row.score || 999),
ids: results.matches.map((row) => row.id)
}
return Promise.resolve(result)
}
/**
* Required by VectorStore interface. Currently ignored.
*/
persist(): Promise<void> {
return Promise.resolve()
}
textFromResultRow(row: ScoredPineconeRecord<Metadata>): string {
return row.metadata?.text ?? ''
}
metaWithoutText(meta: Metadata): any {
return Object.keys(meta)
.filter((key) => key != 'text')
.reduce((acc: any, key: string) => {
acc[key] = meta[key]
return acc
}, {})
}
nodeToRecord(node: BaseNode<Metadata>) {
let id: any = node.id_.length ? node.id_ : null
return {
id: id,
values: node.getEmbedding(),
metadata: {
...cleanupMetadata(node.metadata),
text: (node as any).text
}
}
}
}
const cleanupMetadata = (nodeMetadata: ICommonObject) => {
// Pinecone doesn't support nested objects, so we flatten them
const documentMetadata: any = { ...nodeMetadata }
// preserve string arrays which are allowed
const stringArrays: Record<string, string[]> = {}
for (const key of Object.keys(documentMetadata)) {
if (Array.isArray(documentMetadata[key]) && documentMetadata[key].every((el: any) => typeof el === 'string')) {
stringArrays[key] = documentMetadata[key]
delete documentMetadata[key]
}
}
const metadata: {
[key: string]: string | number | boolean | string[] | null
} = {
...flattenObject(documentMetadata),
...stringArrays
}
// Pinecone doesn't support null values, so we remove them
for (const key of Object.keys(metadata)) {
if (metadata[key] == null) {
delete metadata[key]
} else if (typeof metadata[key] === 'object' && Object.keys(metadata[key] as unknown as object).length === 0) {
delete metadata[key]
}
}
return metadata
}
module.exports = { nodeClass: PineconeLlamaIndex_VectorStores }

145
packages/components/nodes/vectorstores/SimpleStore/SimpleStore.ts Normal file
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@ -0,0 +1,145 @@
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
}
]
this.outputs = [
{
label: 'SimpleStore Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'SimpleStore Vector Store Index',
name: 'vectorStore',
baseClasses: [this.type, 'VectorStoreIndex']
}
]
}
//@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 output = nodeData.outputs?.output as string
if (output === 'retriever') {
const retriever = index.asRetriever()
retriever.similarityTopK = k
;(retriever as any).serviceContext = serviceContext
return retriever
} else if (output === 'vectorStore') {
;(index as any).k = k
return index
}
return index
}
}
module.exports = { nodeClass: SimpleStoreUpsert_LlamaIndex_VectorStores }

6
packages/components/nodes/vectorstores/SimpleStore/simplevs.svg Normal file
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@ -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>
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5
packages/components/package.json
View File

@ -1,6 +1,6 @@
{
"name": "flowise-components",
"version": "1.5.2",
"version": "1.6.0",
"description": "Flowiseai Components",
"main": "dist/src/index",
"types": "dist/src/index.d.ts",
@ -63,9 +63,10 @@
"jsonpointer": "^5.0.1",
"langchain": "^0.1.7",
"langfuse": "2.0.2",
"langfuse-langchain": "2.3.3",
"langfuse-langchain": "2.6.0",
"langsmith": "0.0.63",
"linkifyjs": "^4.1.1",
"llamaindex": "^0.0.48",
"lunary": "^0.6.16",
"mammoth": "^1.5.1",
"moment": "^2.29.3",

1
packages/components/src/Interface.ts
View File

@ -98,6 +98,7 @@ export interface INodeProperties {
version: number
category: string // TODO: use enum instead of string
baseClasses: string[]
tags?: string[]
description?: string
filePath?: string
badge?: string

22
packages/components/src/utils.ts
View File

@ -662,6 +662,28 @@ export const convertSchemaToZod = (schema: string | object): ICommonObject => {
}
}
/**
* Flatten nested object
* @param {ICommonObject} obj
* @param {string} parentKey
* @returns {ICommonObject}
*/
export const flattenObject = (obj: ICommonObject, parentKey?: string) => {
let result: any = {}
Object.keys(obj).forEach((key) => {
const value = obj[key]
const _key = parentKey ? parentKey + '.' + key : key
if (typeof value === 'object') {
result = { ...result, ...flattenObject(value, _key) }
} else {
result[_key] = value
}
})
return result
}
/**
* Convert BaseMessage to IMessage
* @param {BaseMessage[]} messages

917
packages/server/marketplaces/chatflows/Context Chat Engine.json Normal file
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@ -0,0 +1,917 @@
{
"description": "Answer question based on retrieved documents (context) with built-in memory to remember conversation using LlamaIndex",
"badge": "NEW",
"nodes": [
{
"width": 300,
"height": 438,
"id": "textFile_0",
"position": {
"x": 221.215421786192,
"y": 94.91489477412404
},
"type": "customNode",
"data": {
"id": "textFile_0",
"label": "Text File",
"version": 3,
"name": "textFile",
"type": "Document",
"baseClasses": ["Document"],
"category": "Document Loaders",
"description": "Load data from text files",
"inputParams": [
{
"label": "Txt File",
"name": "txtFile",
"type": "file",
"fileType": ".txt, .html, .aspx, .asp, .cpp, .c, .cs, .css, .go, .h, .java, .js, .less, .ts, .php, .proto, .python, .py, .rst, .ruby, .rb, .rs, .scala, .sc, .scss, .sol, .sql, .swift, .markdown, .md, .tex, .ltx, .vb, .xml",
"id": "textFile_0-input-txtFile-file"
},
{
"label": "Metadata",
"name": "metadata",
"type": "json",
"optional": true,
"additionalParams": true,
"id": "textFile_0-input-metadata-json"
}
],
"inputAnchors": [
{
"label": "Text Splitter",
"name": "textSplitter",
"type": "TextSplitter",
"optional": true,
"id": "textFile_0-input-textSplitter-TextSplitter"
}
],
"inputs": {
"textSplitter": "{{recursiveCharacterTextSplitter_0.data.instance}}",
"metadata": ""
},
"outputAnchors": [
{
"name": "output",
"label": "Output",
"type": "options",
"options": [
{
"id": "textFile_0-output-document-Document",
"name": "document",
"label": "Document",
"type": "Document"
},
{
"id": "textFile_0-output-text-string|json",
"name": "text",
"label": "Text",
"type": "string | json"
}
],
"default": "document"
}
],
"outputs": {
"output": "document"
},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 221.215421786192,
"y": 94.91489477412404
},
"dragging": false
},
{
"width": 300,
"height": 429,
"id": "recursiveCharacterTextSplitter_0",
"position": {
"x": -203.4868320229876,
"y": 101.32475976329766
},
"type": "customNode",
"data": {
"id": "recursiveCharacterTextSplitter_0",
"label": "Recursive Character Text Splitter",
"version": 2,
"name": "recursiveCharacterTextSplitter",
"type": "RecursiveCharacterTextSplitter",
"baseClasses": ["RecursiveCharacterTextSplitter", "TextSplitter", "BaseDocumentTransformer", "Runnable"],
"category": "Text Splitters",
"description": "Split documents recursively by different characters - starting with \"\\n\\n\", then \"\\n\", then \" \"",
"inputParams": [
{
"label": "Chunk Size",
"name": "chunkSize",
"type": "number",
"default": 1000,
"optional": true,
"id": "recursiveCharacterTextSplitter_0-input-chunkSize-number"
},
{
"label": "Chunk Overlap",
"name": "chunkOverlap",
"type": "number",
"optional": true,
"id": "recursiveCharacterTextSplitter_0-input-chunkOverlap-number"
},
{
"label": "Custom Separators",
"name": "separators",
"type": "string",
"rows": 4,
"description": "Array of custom separators to determine when to split the text, will override the default separators",
"placeholder": "[\"|\", \"##\", \">\", \"-\"]",
"additionalParams": true,
"optional": true,
"id": "recursiveCharacterTextSplitter_0-input-separators-string"
}
],
"inputAnchors": [],
"inputs": {
"chunkSize": 1000,
"chunkOverlap": "",
"separators": ""
},
"outputAnchors": [
{
"id": "recursiveCharacterTextSplitter_0-output-recursiveCharacterTextSplitter-RecursiveCharacterTextSplitter|TextSplitter|BaseDocumentTransformer|Runnable",
"name": "recursiveCharacterTextSplitter",
"label": "RecursiveCharacterTextSplitter",
"type": "RecursiveCharacterTextSplitter | TextSplitter | BaseDocumentTransformer | Runnable"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": -203.4868320229876,
"y": 101.32475976329766
},
"dragging": false
},
{
"width": 300,
"height": 334,
"id": "openAIEmbedding_LlamaIndex_0",
"position": {
"x": 176.27434578083106,
"y": 953.3664298122493
},
"type": "customNode",
"data": {
"id": "openAIEmbedding_LlamaIndex_0",
"label": "OpenAI Embedding",
"version": 1,
"name": "openAIEmbedding_LlamaIndex",
"type": "OpenAIEmbedding",
"baseClasses": ["OpenAIEmbedding", "BaseEmbedding_LlamaIndex", "BaseEmbedding"],
"tags": ["LlamaIndex"],
"category": "Embeddings",
"description": "OpenAI Embedding specific for LlamaIndex",
"inputParams": [
{
"label": "Connect Credential",
"name": "credential",
"type": "credential",
"credentialNames": ["openAIApi"],
"id": "openAIEmbedding_LlamaIndex_0-input-credential-credential"
},
{
"label": "Model Name",
"name": "modelName",
"type": "options",
"options": [
{
"label": "text-embedding-3-large",
"name": "text-embedding-3-large"
},
{
"label": "text-embedding-3-small",
"name": "text-embedding-3-small"
},
{
"label": "text-embedding-ada-002",
"name": "text-embedding-ada-002"
}
],
"default": "text-embedding-ada-002",
"optional": true,
"id": "openAIEmbedding_LlamaIndex_0-input-modelName-options"
},
{
"label": "Timeout",
"name": "timeout",
"type": "number",
"optional": true,
"additionalParams": true,
"id": "openAIEmbedding_LlamaIndex_0-input-timeout-number"
},
{
"label": "BasePath",
"name": "basepath",
"type": "string",
"optional": true,
"additionalParams": true,
"id": "openAIEmbedding_LlamaIndex_0-input-basepath-string"
}
],
"inputAnchors": [],
"inputs": {
"timeout": "",
"basepath": ""
},
"outputAnchors": [
{
"id": "openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding",
"name": "openAIEmbedding_LlamaIndex",
"label": "OpenAIEmbedding",
"type": "OpenAIEmbedding | BaseEmbedding_LlamaIndex | BaseEmbedding"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 176.27434578083106,
"y": 953.3664298122493
},
"dragging": false
},
{
"width": 300,
"height": 585,
"id": "pineconeLlamaIndex_0",
"position": {
"x": 609.3087433345761,
"y": 488.2141798951578
},
"type": "customNode",
"data": {
"id": "pineconeLlamaIndex_0",
"label": "Pinecone",
"version": 1,
"name": "pineconeLlamaIndex",
"type": "Pinecone",
"baseClasses": ["Pinecone", "VectorIndexRetriever"],
"tags": ["LlamaIndex"],
"category": "Vector Stores",
"description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
"inputParams": [
{
"label": "Connect Credential",
"name": "credential",
"type": "credential",
"credentialNames": ["pineconeApi"],
"id": "pineconeLlamaIndex_0-input-credential-credential"
},
{
"label": "Pinecone Index",
"name": "pineconeIndex",
"type": "string",
"id": "pineconeLlamaIndex_0-input-pineconeIndex-string"
},
{
"label": "Pinecone Namespace",
"name": "pineconeNamespace",
"type": "string",
"placeholder": "my-first-namespace",
"additionalParams": true,
"optional": true,
"id": "pineconeLlamaIndex_0-input-pineconeNamespace-string"
},
{
"label": "Pinecone Metadata Filter",
"name": "pineconeMetadataFilter",
"type": "json",
"optional": true,
"additionalParams": true,
"id": "pineconeLlamaIndex_0-input-pineconeMetadataFilter-json"
},
{
"label": "Top K",
"name": "topK",
"description": "Number of top results to fetch. Default to 4",
"placeholder": "4",
"type": "number",
"additionalParams": true,
"optional": true,
"id": "pineconeLlamaIndex_0-input-topK-number"
}
],
"inputAnchors": [
{
"label": "Document",
"name": "document",
"type": "Document",
"list": true,
"optional": true,
"id": "pineconeLlamaIndex_0-input-document-Document"
},
{
"label": "Chat Model",
"name": "model",
"type": "BaseChatModel_LlamaIndex",
"id": "pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex"
},
{
"label": "Embeddings",
"name": "embeddings",
"type": "BaseEmbedding_LlamaIndex",
"id": "pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex"
}
],
"inputs": {
"document": ["{{textFile_0.data.instance}}"],
"model": "{{chatOpenAI_LlamaIndex_1.data.instance}}",
"embeddings": "{{openAIEmbedding_LlamaIndex_0.data.instance}}",
"pineconeIndex": "",
"pineconeNamespace": "",
"pineconeMetadataFilter": "",
"topK": ""
},
"outputAnchors": [
{
"name": "output",
"label": "Output",
"type": "options",
"options": [
{
"id": "pineconeLlamaIndex_0-output-retriever-Pinecone|VectorIndexRetriever",
"name": "retriever",
"label": "Pinecone Retriever",
"type": "Pinecone | VectorIndexRetriever"
},
{
"id": "pineconeLlamaIndex_0-output-retriever-Pinecone|VectorStoreIndex",
"name": "vectorStore",
"label": "Pinecone Vector Store Index",
"type": "Pinecone | VectorStoreIndex"
}
],
"default": "retriever"
}
],
"outputs": {
"output": "retriever"
},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 609.3087433345761,
"y": 488.2141798951578
},
"dragging": false
},
{
"width": 300,
"height": 529,
"id": "chatOpenAI_LlamaIndex_1",
"position": {
"x": -195.15244974578656,
"y": 584.9467028201428
},
"type": "customNode",
"data": {
"id": "chatOpenAI_LlamaIndex_1",
"label": "ChatOpenAI",
"version": 1,
"name": "chatOpenAI_LlamaIndex",
"type": "ChatOpenAI",
"baseClasses": ["ChatOpenAI", "BaseChatModel_LlamaIndex"],
"tags": ["LlamaIndex"],
"category": "Chat Models",
"description": "Wrapper around OpenAI Chat LLM specific for LlamaIndex",
"inputParams": [
{
"label": "Connect Credential",
"name": "credential",
"type": "credential",
"credentialNames": ["openAIApi"],
"id": "chatOpenAI_LlamaIndex_1-input-credential-credential"
},
{
"label": "Model Name",
"name": "modelName",
"type": "options",
"options": [
{
"label": "gpt-4",
"name": "gpt-4"
},
{
"label": "gpt-4-turbo-preview",
"name": "gpt-4-turbo-preview"
},
{
"label": "gpt-4-0125-preview",
"name": "gpt-4-0125-preview"
},
{
"label": "gpt-4-1106-preview",
"name": "gpt-4-1106-preview"
},
{
"label": "gpt-4-vision-preview",
"name": "gpt-4-vision-preview"
},
{
"label": "gpt-4-0613",
"name": "gpt-4-0613"
},
{
"label": "gpt-4-32k",
"name": "gpt-4-32k"
},
{
"label": "gpt-4-32k-0613",
"name": "gpt-4-32k-0613"
},
{
"label": "gpt-3.5-turbo",
"name": "gpt-3.5-turbo"
},
{
"label": "gpt-3.5-turbo-1106",
"name": "gpt-3.5-turbo-1106"
},
{
"label": "gpt-3.5-turbo-0613",
"name": "gpt-3.5-turbo-0613"
},
{
"label": "gpt-3.5-turbo-16k",
"name": "gpt-3.5-turbo-16k"
},
{
"label": "gpt-3.5-turbo-16k-0613",
"name": "gpt-3.5-turbo-16k-0613"
}
],
"default": "gpt-3.5-turbo",
"optional": true,
"id": "chatOpenAI_LlamaIndex_1-input-modelName-options"
},
{
"label": "Temperature",
"name": "temperature",
"type": "number",
"step": 0.1,
"default": 0.9,
"optional": true,
"id": "chatOpenAI_LlamaIndex_1-input-temperature-number"
},
{
"label": "Max Tokens",
"name": "maxTokens",
"type": "number",
"step": 1,
"optional": true,
"additionalParams": true,
"id": "chatOpenAI_LlamaIndex_1-input-maxTokens-number"
},
{
"label": "Top Probability",
"name": "topP",
"type": "number",
"step": 0.1,
"optional": true,
"additionalParams": true,
"id": "chatOpenAI_LlamaIndex_1-input-topP-number"
},
{
"label": "Timeout",
"name": "timeout",
"type": "number",
"step": 1,
"optional": true,
"additionalParams": true,
"id": "chatOpenAI_LlamaIndex_1-input-timeout-number"
}
],
"inputAnchors": [],
"inputs": {
"modelName": "gpt-3.5-turbo-16k",
"temperature": 0.9,
"maxTokens": "",
"topP": "",
"timeout": ""
},
"outputAnchors": [
{
"id": "chatOpenAI_LlamaIndex_1-output-chatOpenAI_LlamaIndex-ChatOpenAI|BaseChatModel_LlamaIndex",
"name": "chatOpenAI_LlamaIndex",
"label": "ChatOpenAI",
"type": "ChatOpenAI | BaseChatModel_LlamaIndex"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": -195.15244974578656,
"y": 584.9467028201428
},
"dragging": false
},
{
"width": 300,
"height": 513,
"id": "contextChatEngine_0",
"position": {
"x": 1550.2553933740128,
"y": 270.7914631777829
},
"type": "customNode",
"data": {
"id": "contextChatEngine_0",
"label": "Context Chat Engine",
"version": 1,
"name": "contextChatEngine",
"type": "ContextChatEngine",
"baseClasses": ["ContextChatEngine"],
"tags": ["LlamaIndex"],
"category": "Engine",
"description": "Answer question based on retrieved documents (context) with built-in memory to remember conversation",
"inputParams": [
{
"label": "Return Source Documents",
"name": "returnSourceDocuments",
"type": "boolean",
"optional": true,
"id": "contextChatEngine_0-input-returnSourceDocuments-boolean"
},
{
"label": "System Message",
"name": "systemMessagePrompt",
"type": "string",
"rows": 4,
"optional": true,
"placeholder": "I want you to act as a document that I am having a conversation with. Your name is \"AI Assistant\". You will provide me with answers from the given info. If the answer is not included, say exactly \"Hmm, I am not sure.\" and stop after that. Refuse to answer any question not about the info. Never break character.",
"id": "contextChatEngine_0-input-systemMessagePrompt-string"
}
],
"inputAnchors": [
{
"label": "Chat Model",
"name": "model",
"type": "BaseChatModel_LlamaIndex",
"id": "contextChatEngine_0-input-model-BaseChatModel_LlamaIndex"
},
{
"label": "Vector Store Retriever",
"name": "vectorStoreRetriever",
"type": "VectorIndexRetriever",
"id": "contextChatEngine_0-input-vectorStoreRetriever-VectorIndexRetriever"
},
{
"label": "Memory",
"name": "memory",
"type": "BaseChatMemory",
"id": "contextChatEngine_0-input-memory-BaseChatMemory"
}
],
"inputs": {
"model": "{{chatOpenAI_LlamaIndex_2.data.instance}}",
"vectorStoreRetriever": "{{pineconeLlamaIndex_0.data.instance}}",
"memory": "{{RedisBackedChatMemory_0.data.instance}}",
"systemMessagePrompt": "",
"returnSourceDocuments": true
},
"outputAnchors": [
{
"id": "contextChatEngine_0-output-contextChatEngine-ContextChatEngine",
"name": "contextChatEngine",
"label": "ContextChatEngine",
"type": "ContextChatEngine"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 1550.2553933740128,
"y": 270.7914631777829
},
"dragging": false
},
{
"width": 300,
"height": 329,
"id": "RedisBackedChatMemory_0",
"position": {
"x": 1081.252815805786,
"y": 990.1701092562037
},
"type": "customNode",
"data": {
"id": "RedisBackedChatMemory_0",
"label": "Redis-Backed Chat Memory",
"version": 2,
"name": "RedisBackedChatMemory",
"type": "RedisBackedChatMemory",
"baseClasses": ["RedisBackedChatMemory", "BaseChatMemory", "BaseMemory"],
"category": "Memory",
"description": "Summarizes the conversation and stores the memory in Redis server",
"inputParams": [
{
"label": "Connect Credential",
"name": "credential",
"type": "credential",
"optional": true,
"credentialNames": ["redisCacheApi", "redisCacheUrlApi"],
"id": "RedisBackedChatMemory_0-input-credential-credential"
},
{
"label": "Session Id",
"name": "sessionId",
"type": "string",
"description": "If not specified, a random id will be used. Learn <a target=\"_blank\" href=\"https://docs.flowiseai.com/memory/long-term-memory#ui-and-embedded-chat\">more</a>",
"default": "",
"additionalParams": true,
"optional": true,
"id": "RedisBackedChatMemory_0-input-sessionId-string"
},
{
"label": "Session Timeouts",
"name": "sessionTTL",
"type": "number",
"description": "Omit this parameter to make sessions never expire",
"additionalParams": true,
"optional": true,
"id": "RedisBackedChatMemory_0-input-sessionTTL-number"
},
{
"label": "Memory Key",
"name": "memoryKey",
"type": "string",
"default": "chat_history",
"additionalParams": true,
"id": "RedisBackedChatMemory_0-input-memoryKey-string"
}
],
"inputAnchors": [],
"inputs": {
"sessionId": "",
"sessionTTL": "",
"memoryKey": "chat_history"
},
"outputAnchors": [
{
"id": "RedisBackedChatMemory_0-output-RedisBackedChatMemory-RedisBackedChatMemory|BaseChatMemory|BaseMemory",
"name": "RedisBackedChatMemory",
"label": "RedisBackedChatMemory",
"type": "RedisBackedChatMemory | BaseChatMemory | BaseMemory"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"dragging": false,
"positionAbsolute": {
"x": 1081.252815805786,
"y": 990.1701092562037
}
},
{
"width": 300,
"height": 529,
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4
packages/server/marketplaces/chatflows/Prompt Chaining with VectorStore.json
View File

@ -190,10 +190,10 @@
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{
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547
packages/server/marketplaces/chatflows/Query Engine.json Normal file
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@ -0,0 +1,547 @@
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"targetHandle": "queryEngine_0-input-vectorStoreRetriever-VectorIndexRetriever",
"type": "buttonedge",
"id": "pineconeLlamaIndex_0-pineconeLlamaIndex_0-output-pineconeLlamaIndex-Pinecone|VectorIndexRetriever-queryEngine_0-queryEngine_0-input-vectorStoreRetriever-VectorIndexRetriever",
"data": {
"label": ""
}
},
{
"source": "openAIEmbedding_LlamaIndex_0",
"sourceHandle": "openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding",
"target": "pineconeLlamaIndex_0",
"targetHandle": "pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex",
"type": "buttonedge",
"id": "openAIEmbedding_LlamaIndex_0-openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding-pineconeLlamaIndex_0-pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex",
"data": {
"label": ""
}
},
{
"source": "compactrefineLlamaIndex_0",
"sourceHandle": "compactrefineLlamaIndex_0-output-compactrefineLlamaIndex-CompactRefine|ResponseSynthesizer",
"target": "queryEngine_0",
"targetHandle": "queryEngine_0-input-responseSynthesizer-ResponseSynthesizer",
"type": "buttonedge",
"id": "compactrefineLlamaIndex_0-compactrefineLlamaIndex_0-output-compactrefineLlamaIndex-CompactRefine|ResponseSynthesizer-queryEngine_0-queryEngine_0-input-responseSynthesizer-ResponseSynthesizer",
"data": {
"label": ""
}
},
{
"source": "chatAnthropic_LlamaIndex_0",
"sourceHandle": "chatAnthropic_LlamaIndex_0-output-chatAnthropic_LlamaIndex-ChatAnthropic|BaseChatModel_LlamaIndex",
"target": "pineconeLlamaIndex_0",
"targetHandle": "pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex",
"type": "buttonedge",
"id": "chatAnthropic_LlamaIndex_0-chatAnthropic_LlamaIndex_0-output-chatAnthropic_LlamaIndex-ChatAnthropic|BaseChatModel_LlamaIndex-pineconeLlamaIndex_0-pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex",
"data": {
"label": ""
}
}
]
}

270
packages/server/marketplaces/chatflows/Simple Chat Engine.json Normal file
View File

@ -0,0 +1,270 @@
{
"description": "Simple chat engine to handle back and forth conversations using LlamaIndex",
"badge": "NEW",
"nodes": [
{
"width": 300,
"height": 462,
"id": "simpleChatEngine_0",
"position": {
"x": 1210.127368000538,
"y": 324.98110560103896
},
"type": "customNode",
"data": {
"id": "simpleChatEngine_0",
"label": "Simple Chat Engine",
"version": 1,
"name": "simpleChatEngine",
"type": "SimpleChatEngine",
"baseClasses": ["SimpleChatEngine"],
"tags": ["LlamaIndex"],
"category": "Engine",
"description": "Simple engine to handle back and forth conversations",
"inputParams": [
{
"label": "System Message",
"name": "systemMessagePrompt",
"type": "string",
"rows": 4,
"optional": true,
"placeholder": "You are a helpful assistant",
"id": "simpleChatEngine_0-input-systemMessagePrompt-string"
}
],
"inputAnchors": [
{
"label": "Chat Model",
"name": "model",
"type": "BaseChatModel_LlamaIndex",
"id": "simpleChatEngine_0-input-model-BaseChatModel_LlamaIndex"
},
{
"label": "Memory",
"name": "memory",
"type": "BaseChatMemory",
"id": "simpleChatEngine_0-input-memory-BaseChatMemory"
}
],
"inputs": {
"model": "{{azureChatOpenAI_LlamaIndex_0.data.instance}}",
"memory": "{{bufferMemory_0.data.instance}}",
"systemMessagePrompt": "You are a helpful assistant."
},
"outputAnchors": [
{
"id": "simpleChatEngine_0-output-simpleChatEngine-SimpleChatEngine",
"name": "simpleChatEngine",
"label": "SimpleChatEngine",
"type": "SimpleChatEngine"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"dragging": false,
"positionAbsolute": {
"x": 1210.127368000538,
"y": 324.98110560103896
}
},
{
"width": 300,
"height": 376,
"id": "bufferMemory_0",
"position": {
"x": 393.9823478014782,
"y": 415.7414943210391
},
"type": "customNode",
"data": {
"id": "bufferMemory_0",
"label": "Buffer Memory",
"version": 1,
"name": "bufferMemory",
"type": "BufferMemory",
"baseClasses": ["BufferMemory", "BaseChatMemory", "BaseMemory"],
"category": "Memory",
"description": "Remembers previous conversational back and forths directly",
"inputParams": [
{
"label": "Memory Key",
"name": "memoryKey",
"type": "string",
"default": "chat_history",
"id": "bufferMemory_0-input-memoryKey-string"
},
{
"label": "Input Key",
"name": "inputKey",
"type": "string",
"default": "input",
"id": "bufferMemory_0-input-inputKey-string"
}
],
"inputAnchors": [],
"inputs": {
"memoryKey": "chat_history",
"inputKey": "input"
},
"outputAnchors": [
{
"id": "bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory",
"name": "bufferMemory",
"label": "BufferMemory",
"type": "BufferMemory | BaseChatMemory | BaseMemory"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 393.9823478014782,
"y": 415.7414943210391
},
"dragging": false
},
{
"width": 300,
"height": 529,
"id": "azureChatOpenAI_LlamaIndex_0",
"position": {
"x": 746.5530862509605,
"y": -54.107978373323306
},
"type": "customNode",
"data": {
"id": "azureChatOpenAI_LlamaIndex_0",
"label": "AzureChatOpenAI",
"version": 1,
"name": "azureChatOpenAI_LlamaIndex",
"type": "AzureChatOpenAI",
"baseClasses": ["AzureChatOpenAI", "BaseChatModel_LlamaIndex"],
"tags": ["LlamaIndex"],
"category": "Chat Models",
"description": "Wrapper around Azure OpenAI Chat LLM specific for LlamaIndex",
"inputParams": [
{
"label": "Connect Credential",
"name": "credential",
"type": "credential",
"credentialNames": ["azureOpenAIApi"],
"id": "azureChatOpenAI_LlamaIndex_0-input-credential-credential"
},
{
"label": "Model Name",
"name": "modelName",
"type": "options",
"options": [
{
"label": "gpt-4",
"name": "gpt-4"
},
{
"label": "gpt-4-32k",
"name": "gpt-4-32k"
},
{
"label": "gpt-3.5-turbo",
"name": "gpt-3.5-turbo"
},
{
"label": "gpt-3.5-turbo-16k",
"name": "gpt-3.5-turbo-16k"
}
],
"default": "gpt-3.5-turbo-16k",
"optional": true,
"id": "azureChatOpenAI_LlamaIndex_0-input-modelName-options"
},
{
"label": "Temperature",
"name": "temperature",
"type": "number",
"step": 0.1,
"default": 0.9,
"optional": true,
"id": "azureChatOpenAI_LlamaIndex_0-input-temperature-number"
},
{
"label": "Max Tokens",
"name": "maxTokens",
"type": "number",
"step": 1,
"optional": true,
"additionalParams": true,
"id": "azureChatOpenAI_LlamaIndex_0-input-maxTokens-number"
},
{
"label": "Top Probability",
"name": "topP",
"type": "number",
"step": 0.1,
"optional": true,
"additionalParams": true,
"id": "azureChatOpenAI_LlamaIndex_0-input-topP-number"
},
{
"label": "Timeout",
"name": "timeout",
"type": "number",
"step": 1,
"optional": true,
"additionalParams": true,
"id": "azureChatOpenAI_LlamaIndex_0-input-timeout-number"
}
],
"inputAnchors": [],
"inputs": {
"modelName": "gpt-3.5-turbo-16k",
"temperature": 0.9,
"maxTokens": "",
"topP": "",
"timeout": ""
},
"outputAnchors": [
{
"id": "azureChatOpenAI_LlamaIndex_0-output-azureChatOpenAI_LlamaIndex-AzureChatOpenAI|BaseChatModel_LlamaIndex",
"name": "azureChatOpenAI_LlamaIndex",
"label": "AzureChatOpenAI",
"type": "AzureChatOpenAI | BaseChatModel_LlamaIndex"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 746.5530862509605,
"y": -54.107978373323306
},
"dragging": false
}
],
"edges": [
{
"source": "bufferMemory_0",
"sourceHandle": "bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory",
"target": "simpleChatEngine_0",
"targetHandle": "simpleChatEngine_0-input-memory-BaseChatMemory",
"type": "buttonedge",
"id": "bufferMemory_0-bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory-simpleChatEngine_0-simpleChatEngine_0-input-memory-BaseChatMemory",
"data": {
"label": ""
}
},
{
"source": "azureChatOpenAI_LlamaIndex_0",
"sourceHandle": "azureChatOpenAI_LlamaIndex_0-output-azureChatOpenAI_LlamaIndex-AzureChatOpenAI|BaseChatModel_LlamaIndex",
"target": "simpleChatEngine_0",
"targetHandle": "simpleChatEngine_0-input-model-BaseChatModel_LlamaIndex",
"type": "buttonedge",
"id": "azureChatOpenAI_LlamaIndex_0-azureChatOpenAI_LlamaIndex_0-output-azureChatOpenAI_LlamaIndex-AzureChatOpenAI|BaseChatModel_LlamaIndex-simpleChatEngine_0-simpleChatEngine_0-input-model-BaseChatModel_LlamaIndex",
"data": {
"label": ""
}
}
]
}

1201
packages/server/marketplaces/chatflows/SubQuestion Query Engine.json Normal file
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2
packages/server/package.json
View File

@ -1,6 +1,6 @@
{
"name": "flowise",
"version": "1.4.11",
"version": "1.5.0",
"description": "Flowiseai Server",
"main": "dist/index",
"types": "dist/index.d.ts",

28
packages/server/src/DataSource.ts
View File

@ -40,7 +40,8 @@ export const init = async (): Promise<void> => {
synchronize: false,
migrationsRun: false,
entities: Object.values(entities),
migrations: mysqlMigrations
migrations: mysqlMigrations,
ssl: getDatabaseSSLFromEnv()
})
break
case 'postgres':
@ -51,18 +52,7 @@ export const init = async (): Promise<void> => {
username: process.env.DATABASE_USER,
password: process.env.DATABASE_PASSWORD,
database: process.env.DATABASE_NAME,
...(process.env.DATABASE_SSL_KEY_BASE64
? {
ssl: {
rejectUnauthorized: false,
cert: Buffer.from(process.env.DATABASE_SSL_KEY_BASE64, 'base64')
}
}
: process.env.DATABASE_SSL === 'true'
? {
ssl: true
}
: {}),
ssl: getDatabaseSSLFromEnv(),
synchronize: false,
migrationsRun: false,
entities: Object.values(entities),
@ -89,3 +79,15 @@ export function getDataSource(): DataSource {
}
return appDataSource
}
const getDatabaseSSLFromEnv = () => {
if (process.env.DATABASE_SSL_KEY_BASE64) {
return {
rejectUnauthorized: false,
ca: Buffer.from(process.env.DATABASE_SSL_KEY_BASE64, 'base64')
}
} else if (process.env.DATABASE_SSL === 'true') {
return true
}
return undefined
}

16
packages/server/src/index.ts
View File

@ -482,7 +482,12 @@ export class App {
const isEndingNode = endingNodeData?.outputs?.output === 'EndingNode'
if (!isEndingNode) {
if (endingNodeData && endingNodeData.category !== 'Chains' && endingNodeData.category !== 'Agents') {
if (
endingNodeData &&
endingNodeData.category !== 'Chains' &&
endingNodeData.category !== 'Agents' &&
endingNodeData.category !== 'Engine'
) {
return res.status(500).send(`Ending node must be either a Chain or Agent`)
}
}
@ -1440,7 +1445,7 @@ export class App {
chatType,
chatId,
memoryType: memoryType ?? (chatId ? IsNull() : undefined),
sessionId: sessionId ?? (chatId ? IsNull() : undefined),
sessionId: sessionId ?? undefined,
createdDate: toDate && fromDate ? Between(fromDate, toDate) : undefined
},
order: {
@ -1690,7 +1695,12 @@ export class App {
const isEndingNode = endingNodeData?.outputs?.output === 'EndingNode'
if (!isEndingNode) {
if (endingNodeData && endingNodeData.category !== 'Chains' && endingNodeData.category !== 'Agents') {
if (
endingNodeData &&
endingNodeData.category !== 'Chains' &&
endingNodeData.category !== 'Agents' &&
endingNodeData.category !== 'Engine'
) {
return res.status(500).send(`Ending node must be either a Chain or Agent`)
}

14
packages/server/src/utils/index.ts
View File

@ -818,7 +818,16 @@ export const findAvailableConfigs = (reactFlowNodes: IReactFlowNode[], component
*/
export const isFlowValidForStream = (reactFlowNodes: IReactFlowNode[], endingNodeData: INodeData) => {
const streamAvailableLLMs = {
'Chat Models': ['azureChatOpenAI', 'chatOpenAI', 'chatAnthropic', 'chatOllama', 'awsChatBedrock', 'chatMistralAI'],
'Chat Models': [
'azureChatOpenAI',
'chatOpenAI',
'chatOpenAI_LlamaIndex',
'chatAnthropic',
'chatAnthropic_LlamaIndex',
'chatOllama',
'awsChatBedrock',
'chatMistralAI'
],
LLMs: ['azureOpenAI', 'openAI', 'ollama']
}
@ -841,6 +850,9 @@ export const isFlowValidForStream = (reactFlowNodes: IReactFlowNode[], endingNod
// Agent that are available to stream
const whitelistAgents = ['openAIFunctionAgent', 'csvAgent', 'airtableAgent', 'conversationalRetrievalAgent']
isValidChainOrAgent = whitelistAgents.includes(endingNodeData.name)
} else if (endingNodeData.category === 'Engine') {
const whitelistEngine = ['contextChatEngine', 'simpleChatEngine', 'queryEngine', 'subQuestionQueryEngine']
isValidChainOrAgent = whitelistEngine.includes(endingNodeData.name)
}
// If no output parser, flow is available to stream

2
packages/ui/package.json
View File

@ -1,6 +1,6 @@
{
"name": "flowise-ui",
"version": "1.4.8",
"version": "1.5.0",
"license": "SEE LICENSE IN LICENSE.md",
"homepage": "https://flowiseai.com",
"author": {

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29
packages/ui/src/ui-component/dialog/NodeInfoDialog.js
View File

@ -132,6 +132,35 @@ const NodeInfoDialog = ({ show, dialogProps, onCancel }) => {
</span>
</div>
)}
{dialogProps.data.tags &&
dialogProps.data.tags.length &&
dialogProps.data.tags.map((tag, index) => (
<div
style={{
display: 'flex',
flexDirection: 'row',
width: 'max-content',
borderRadius: 15,
background: '#cae9ff',
padding: 5,
paddingLeft: 10,
paddingRight: 10,
marginTop: 5,
marginLeft: 10,
marginBottom: 5
}}
key={index}
>
<span
style={{
color: '#023e7d',
fontSize: '0.825rem'
}}
>
{tag.toLowerCase()}
</span>
</div>
))}
</div>
</div>
</div>

1
packages/ui/src/utils/genericHelper.js
View File

@ -277,6 +277,7 @@ export const generateExportFlowData = (flowData) => {
name: node.data.name,
type: node.data.type,
baseClasses: node.data.baseClasses,
tags: node.data.tags,
category: node.data.category,
description: node.data.description,
inputParams: node.data.inputParams,

100
packages/ui/src/views/canvas/AddNodes.js
View File

@ -22,7 +22,9 @@ import {
Popper,
Stack,
Typography,
Chip
Chip,
Tab,
Tabs
} from '@mui/material'
import ExpandMoreIcon from '@mui/icons-material/ExpandMore'
@ -36,12 +38,20 @@ import { StyledFab } from 'ui-component/button/StyledFab'
// icons
import { IconPlus, IconSearch, IconMinus, IconX } from '@tabler/icons'
import LlamaindexPNG from 'assets/images/llamaindex.png'
import LangChainPNG from 'assets/images/langchain.png'
// const
import { baseURL } from 'store/constant'
import { SET_COMPONENT_NODES } from 'store/actions'
// ==============================|| ADD NODES||============================== //
function a11yProps(index) {
return {
id: `attachment-tab-${index}`,
'aria-controls': `attachment-tabpanel-${index}`
}
}
const AddNodes = ({ nodesData, node }) => {
const theme = useTheme()
@ -52,6 +62,7 @@ const AddNodes = ({ nodesData, node }) => {
const [nodes, setNodes] = useState({})
const [open, setOpen] = useState(false)
const [categoryExpanded, setCategoryExpanded] = useState({})
const [tabValue, setTabValue] = useState(0)
const anchorRef = useRef(null)
const prevOpen = useRef(open)
@ -86,6 +97,11 @@ const AddNodes = ({ nodesData, node }) => {
}
}
const handleTabChange = (event, newValue) => {
setTabValue(newValue)
filterSearch(searchValue, newValue)
}
const getSearchedNodes = (value) => {
const passed = nodesData.filter((nd) => {
const passesQuery = nd.name.toLowerCase().includes(value.toLowerCase())
@ -95,23 +111,34 @@ const AddNodes = ({ nodesData, node }) => {
return passed
}
const filterSearch = (value) => {
const filterSearch = (value, newTabValue) => {
setSearchValue(value)
setTimeout(() => {
if (value) {
const returnData = getSearchedNodes(value)
groupByCategory(returnData, true)
groupByCategory(returnData, newTabValue ?? tabValue, true)
scrollTop()
} else if (value === '') {
groupByCategory(nodesData)
groupByCategory(nodesData, newTabValue ?? tabValue)
scrollTop()
}
}, 500)
}
const groupByCategory = (nodes, isFilter) => {
const groupByTags = (nodes, newTabValue = 0) => {
const langchainNodes = nodes.filter((nd) => !nd.tags)
const llmaindexNodes = nodes.filter((nd) => nd.tags && nd.tags.includes('LlamaIndex'))
if (newTabValue === 0) {
return langchainNodes
} else {
return llmaindexNodes
}
}
const groupByCategory = (nodes, newTabValue, isFilter) => {
const taggedNodes = groupByTags(nodes, newTabValue)
const accordianCategories = {}
const result = nodes.reduce(function (r, a) {
const result = taggedNodes.reduce(function (r, a) {
r[a.category] = r[a.category] || []
r[a.category].push(a)
accordianCategories[a.category] = isFilter ? true : false
@ -244,15 +271,72 @@ const AddNodes = ({ nodesData, node }) => {
'aria-label': 'weight'
}}
/>
<Tabs
sx={{ position: 'relative', minHeight: '50px', height: '50px' }}
variant='fullWidth'
value={tabValue}
onChange={handleTabChange}
aria-label='tabs'
>
{['LangChain', 'LlamaIndex'].map((item, index) => (
<Tab
icon={
<div
style={{
borderRadius: '50%'
}}
>
<img
style={{
width: '25px',
height: '25px',
borderRadius: '50%',
objectFit: 'contain'
}}
src={index === 0 ? LangChainPNG : LlamaindexPNG}
alt={item}
/>
</div>
}
iconPosition='start'
sx={{ minHeight: '50px', height: '50px' }}
key={index}
label={item}
{...a11yProps(index)}
></Tab>
))}
<div
style={{
display: 'flex',
flexDirection: 'row',
alignItems: 'center',
borderRadius: 10,
background: 'rgb(254,252,191)',
paddingLeft: 6,
paddingRight: 6,
paddingTop: 1,
paddingBottom: 1,
width: 'max-content',
position: 'absolute',
top: 0,
right: 0,
fontSize: '0.65rem',
fontWeight: 700
}}
>
<span style={{ color: 'rgb(116,66,16)' }}>BETA</span>
</div>
</Tabs>
<Divider />
</Box>
<PerfectScrollbar
containerRef={(el) => {
ps.current = el
}}
style={{ height: '100%', maxHeight: 'calc(100vh - 320px)', overflowX: 'hidden' }}
style={{ height: '100%', maxHeight: 'calc(100vh - 380px)', overflowX: 'hidden' }}
>
<Box sx={{ p: 2 }}>
<Box sx={{ p: 2, pt: 0 }}>
<List
sx={{
width: '100%',

19
packages/ui/src/views/canvas/CanvasNode.js
View File

@ -19,6 +19,7 @@ import NodeInfoDialog from 'ui-component/dialog/NodeInfoDialog'
import { baseURL } from 'store/constant'
import { IconTrash, IconCopy, IconInfoCircle, IconAlertTriangle } from '@tabler/icons'
import { flowContext } from 'store/context/ReactFlowContext'
import LlamaindexPNG from 'assets/images/llamaindex.png'
// ===========================|| CANVAS NODE ||=========================== //
@ -158,9 +159,25 @@ const CanvasNode = ({ data }) => {
{data.label}
</Typography>
</Box>
<div style={{ flexGrow: 1 }}></div>
{data.tags && data.tags.includes('LlamaIndex') && (
<>
<div
style={{
borderRadius: '50%',
padding: 15
}}
>
<img
style={{ width: '25px', height: '25px', borderRadius: '50%', objectFit: 'contain' }}
src={LlamaindexPNG}
alt='LlamaIndex'
/>
</div>
</>
)}
{warningMessage && (
<>
<div style={{ flexGrow: 1 }}></div>
<Tooltip title={<span style={{ whiteSpace: 'pre-line' }}>{warningMessage}</span>} placement='top'>
<IconButton sx={{ height: 35, width: 35 }}>
<IconAlertTriangle size={35} color='orange' />

18
packages/ui/src/views/marketplaces/MarketplaceCanvasNode.js
View File

@ -13,6 +13,7 @@ import AdditionalParamsDialog from 'ui-component/dialog/AdditionalParamsDialog'
// const
import { baseURL } from 'store/constant'
import LlamaindexPNG from 'assets/images/llamaindex.png'
const CardWrapper = styled(MainCard)(({ theme }) => ({
background: theme.palette.card.main,
@ -87,6 +88,23 @@ const MarketplaceCanvasNode = ({ data }) => {
{data.label}
</Typography>
</Box>
<div style={{ flexGrow: 1 }}></div>
{data.tags && data.tags.includes('LlamaIndex') && (
<>
<div
style={{
borderRadius: '50%',
padding: 15
}}
>
<img
style={{ width: '25px', height: '25px', borderRadius: '50%', objectFit: 'contain' }}
src={LlamaindexPNG}
alt='LlamaIndex'
/>
</div>
</>
)}
</div>
{(data.inputAnchors.length > 0 || data.inputParams.length > 0) && (
<>