farcasclaudiu/Flowise
1
0
Fork 0
mirror of https://github.com/farcasclaudiu/Flowise.git synced 2026-06-28 09:00:52 +03:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'main' into feature/BabyAGI

Browse Source
This commit is contained in:
Henry
2023-04-20 22:14:10 +01:00
parent d9cbaf8a28 33cc2afdd3
commit 180fe8a49a
60 changed files with 3647 additions and 447 deletions
Download Patch File Download Diff File Expand all files Collapse all files
packages/components/nodes/agents/ConversationalAgent/ConversationalAgent.ts
+5 -2
View File
@@ -1,5 +1,5 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { initializeAgentExecutor, AgentExecutor } from 'langchain/agents'
import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/agents'
import { Tool } from 'langchain/tools'
import { BaseChatModel } from 'langchain/chat_models/base'
import { BaseChatMemory } from 'langchain/memory'
@@ -48,7 +48,10 @@ class ConversationalAgent_Agents implements INode {
const tools = nodeData.inputs?.tools as Tool[]
const memory = nodeData.inputs?.memory as BaseChatMemory
const executor = await initializeAgentExecutor(tools, model, 'chat-conversational-react-description', true)
const executor = await initializeAgentExecutorWithOptions(tools, model, {
agentType: 'chat-conversational-react-description',
verbose: true
})
executor.memory = memory
return executor
}
packages/components/nodes/agents/MRKLAgentChat/MRKLAgentChat.ts
+6 -4
View File
@@ -1,8 +1,8 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { initializeAgentExecutor, AgentExecutor } from 'langchain/agents'
import { Tool } from 'langchain/tools'
import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/agents'
import { BaseChatModel } from 'langchain/chat_models/base'
import { getBaseClasses } from '../../../src/utils'
import { Tool } from 'langchain/tools'
class MRKLAgentChat_Agents implements INode {
label: string
@@ -40,8 +40,10 @@ class MRKLAgentChat_Agents implements INode {
async init(nodeData: INodeData): Promise<any> {
const model = nodeData.inputs?.model as BaseChatModel
const tools = nodeData.inputs?.tools as Tool[]
const executor = await initializeAgentExecutor(tools, model, 'chat-zero-shot-react-description', true)
const executor = await initializeAgentExecutorWithOptions(tools, model, {
agentType: 'chat-zero-shot-react-description',
verbose: true
})
return executor
}
packages/components/nodes/agents/MRKLAgentLLM/MRKLAgentLLM.ts
+5 -2
View File
@@ -1,5 +1,5 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { initializeAgentExecutor, AgentExecutor } from 'langchain/agents'
import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/agents'
import { Tool } from 'langchain/tools'
import { BaseLLM } from 'langchain/llms/base'
import { getBaseClasses } from '../../../src/utils'
@@ -41,7 +41,10 @@ class MRKLAgentLLM_Agents implements INode {
const model = nodeData.inputs?.model as BaseLLM
const tools = nodeData.inputs?.tools as Tool[]
const executor = await initializeAgentExecutor(tools, model, 'zero-shot-react-description', true)
const executor = await initializeAgentExecutorWithOptions(tools, model, {
agentType: 'zero-shot-react-description',
verbose: true
})
return executor
}
packages/components/nodes/chains/LLMChain/LLMChain.ts
+77 -43
View File
@@ -1,8 +1,7 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { LLMChain } from 'langchain/chains'
import { BaseLanguageModel } from 'langchain/base_language'
import { BasePromptTemplate } from 'langchain/prompts'
class LLMChain_Chains implements INode {
label: string
@@ -13,6 +12,7 @@ class LLMChain_Chains implements INode {
baseClasses: string[]
description: string
inputs: INodeParams[]
outputs: INodeOutputsValue[]
constructor() {
this.label = 'LLM Chain'
@@ -34,65 +34,99 @@ class LLMChain_Chains implements INode {
type: 'BasePromptTemplate'
},
{
label: 'Format Prompt Values',
name: 'promptValues',
label: 'Chain Name',
name: 'chainName',
type: 'string',
rows: 5,
placeholder: `{
"input_language": "English",
"output_language": "French"
}`,
placeholder: 'Name Your Chain',
optional: true
}
]
this.outputs = [
{
label: 'LLM Chain',
name: 'llmChain',
baseClasses: [this.type, ...getBaseClasses(LLMChain)]
},
{
label: 'Output Prediction',
name: 'outputPrediction',
baseClasses: ['string']
}
]
}
async init(nodeData: INodeData): Promise<any> {
async init(nodeData: INodeData, input: string): Promise<any> {
const model = nodeData.inputs?.model as BaseLanguageModel
const prompt = nodeData.inputs?.prompt as BasePromptTemplate
const prompt = nodeData.inputs?.prompt
const output = nodeData.outputs?.output as string
const promptValues = prompt.promptValues as ICommonObject
const chain = new LLMChain({ llm: model, prompt })
return chain
if (output === this.name) {
const chain = new LLMChain({ llm: model, prompt })
return chain
} else if (output === 'outputPrediction') {
const chain = new LLMChain({ llm: model, prompt })
const inputVariables = chain.prompt.inputVariables as string[] // ["product"]
const res = await runPrediction(inputVariables, chain, input, promptValues)
// eslint-disable-next-line no-console
console.log('\x1b[92m\x1b[1m\n*****OUTPUT PREDICTION*****\n\x1b[0m\x1b[0m')
// eslint-disable-next-line no-console
console.log(res)
return res
}
}
async run(nodeData: INodeData, input: string): Promise<string> {
const inputVariables = nodeData.instance.prompt.inputVariables as string[] // ["product"]
const chain = nodeData.instance as LLMChain
const promptValues = nodeData.inputs?.prompt.promptValues as ICommonObject
if (inputVariables.length === 1) {
const res = await chain.run(input)
return res
} else if (inputVariables.length > 1) {
const promptValuesStr = nodeData.inputs?.promptValues as string
if (!promptValuesStr) throw new Error('Please provide Prompt Values')
const res = await runPrediction(inputVariables, chain, input, promptValues)
// eslint-disable-next-line no-console
console.log('\x1b[93m\x1b[1m\n*****FINAL RESULT*****\n\x1b[0m\x1b[0m')
// eslint-disable-next-line no-console
console.log(res)
return res
}
}
const promptValues = JSON.parse(promptValuesStr.replace(/\s/g, ''))
const runPrediction = async (inputVariables: string[], chain: LLMChain, input: string, promptValues: ICommonObject) => {
if (inputVariables.length === 1) {
const res = await chain.run(input)
return res
} else if (inputVariables.length > 1) {
let seen: string[] = []
let seen: string[] = []
for (const variable of inputVariables) {
seen.push(variable)
if (promptValues[variable]) {
seen.pop()
}
for (const variable of inputVariables) {
seen.push(variable)
if (promptValues[variable]) {
seen.pop()
}
if (seen.length === 1) {
const lastValue = seen.pop()
if (!lastValue) throw new Error('Please provide Prompt Values')
const options = {
...promptValues,
[lastValue]: input
}
const res = await chain.call(options)
return res?.text
} else {
throw new Error('Please provide Prompt Values')
}
} else {
const res = await chain.run(input)
return res
}
if (seen.length === 0) {
// All inputVariables have fixed values specified
const options = {
...promptValues
}
const res = await chain.call(options)
return res?.text
} else if (seen.length === 1) {
// If one inputVariable is not specify, use input (user's question) as value
const lastValue = seen.pop()
if (!lastValue) throw new Error('Please provide Prompt Values')
const options = {
...promptValues,
[lastValue]: input
}
const res = await chain.call(options)
return res?.text
} else {
throw new Error(`Please provide Prompt Values for: ${seen.join(', ')}`)
}
} else {
const res = await chain.run(input)
return res
}
}
packages/components/nodes/chains/VectorDBQAChain/VectorDBQAChain.ts
+57
View File
@@ -0,0 +1,57 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { VectorDBQAChain } from 'langchain/chains'
import { BaseLanguageModel } from 'langchain/base_language'
import { VectorStore } from 'langchain/vectorstores'
class VectorDBQAChain_Chains implements INode {
label: string
name: string
type: string
icon: string
category: string
baseClasses: string[]
description: string
inputs: INodeParams[]
constructor() {
this.label = 'VectorDB QA Chain'
this.name = 'vectorDBQAChain'
this.type = 'VectorDBQAChain'
this.icon = 'chain.svg'
this.category = 'Chains'
this.description = 'QA chain for vector databases'
this.baseClasses = [this.type, ...getBaseClasses(VectorDBQAChain)]
this.inputs = [
{
label: 'Language Model',
name: 'model',
type: 'BaseLanguageModel'
},
{
label: 'Vector Store',
name: 'vectorStore',
type: 'VectorStore'
}
]
}
async init(nodeData: INodeData): Promise<any> {
const model = nodeData.inputs?.model as BaseLanguageModel
const vectorStore = nodeData.inputs?.vectorStore as VectorStore
const chain = VectorDBQAChain.fromLLM(model, vectorStore)
return chain
}
async run(nodeData: INodeData, input: string): Promise<string> {
const chain = nodeData.instance as VectorDBQAChain
const obj = {
query: input
}
const res = await chain.call(obj)
return res?.text
}
}
module.exports = { nodeClass: VectorDBQAChain_Chains }
packages/components/nodes/chains/VectorDBQAChain/chain.svg
+6
View File
@@ -0,0 +1,6 @@
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-dna" 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="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
<path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
<path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
</svg>
Side by Side

After

Width:  |  Height:  |  Size: 489 B

packages/components/nodes/prompts/ChatPromptTemplate/ChatPromptTemplate.ts
+25 -3
View File
@@ -1,4 +1,4 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate } from 'langchain/prompts'
@@ -25,15 +25,28 @@ class ChatPromptTemplate_Prompts implements INode {
label: 'System Message',
name: 'systemMessagePrompt',
type: 'string',
rows: 3,
rows: 4,
placeholder: `You are a helpful assistant that translates {input_language} to {output_language}.`
},
{
label: 'Human Message',
name: 'humanMessagePrompt',
type: 'string',
rows: 3,
rows: 4,
placeholder: `{text}`
},
{
label: 'Format Prompt Values',
name: 'promptValues',
type: 'string',
rows: 4,
placeholder: `{
"input_language": "English",
"output_language": "French"
}`,
optional: true,
acceptVariable: true,
list: true
}
]
}
@@ -41,11 +54,20 @@ class ChatPromptTemplate_Prompts implements INode {
async init(nodeData: INodeData): Promise<any> {
const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
const humanMessagePrompt = nodeData.inputs?.humanMessagePrompt as string
const promptValuesStr = nodeData.inputs?.promptValues as string
const prompt = ChatPromptTemplate.fromPromptMessages([
SystemMessagePromptTemplate.fromTemplate(systemMessagePrompt),
HumanMessagePromptTemplate.fromTemplate(humanMessagePrompt)
])
let promptValues: ICommonObject = {}
if (promptValuesStr) {
promptValues = JSON.parse(promptValuesStr.replace(/\s/g, ''))
}
// @ts-ignore
prompt.promptValues = promptValues
return prompt
}
}
packages/components/nodes/prompts/FewShotPromptTemplate/FewShotPromptTemplate.ts
+3 -3
View File
@@ -27,7 +27,7 @@ class FewShotPromptTemplate_Prompts implements INode {
label: 'Examples',
name: 'examples',
type: 'string',
rows: 5,
rows: 4,
placeholder: `[
{ "word": "happy", "antonym": "sad" },
{ "word": "tall", "antonym": "short" },
@@ -42,14 +42,14 @@ class FewShotPromptTemplate_Prompts implements INode {
label: 'Prefix',
name: 'prefix',
type: 'string',
rows: 3,
rows: 4,
placeholder: `Give the antonym of every input`
},
{
label: 'Suffix',
name: 'suffix',
type: 'string',
rows: 3,
rows: 4,
placeholder: `Word: {input}\nAntonym:`
},
{
packages/components/nodes/prompts/PromptTemplate/PromptTemplate.ts
+25 -4
View File
@@ -1,6 +1,6 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { ICommonObject, INode, INodeData, INodeParams, PromptTemplate } from '../../../src/Interface'
import { getBaseClasses, getInputVariables } from '../../../src/utils'
import { PromptTemplate, PromptTemplateInput } from 'langchain/prompts'
import { PromptTemplateInput } from 'langchain/prompts'
class PromptTemplate_Prompts implements INode {
label: string
@@ -19,20 +19,40 @@ class PromptTemplate_Prompts implements INode {
this.icon = 'prompt.svg'
this.category = 'Prompts'
this.description = 'Schema to represent a basic prompt for an LLM'
this.baseClasses = [this.type, ...getBaseClasses(PromptTemplate)]
this.baseClasses = [...getBaseClasses(PromptTemplate)]
this.inputs = [
{
label: 'Template',
name: 'template',
type: 'string',
rows: 5,
rows: 4,
placeholder: `What is a good name for a company that makes {product}?`
},
{
label: 'Format Prompt Values',
name: 'promptValues',
type: 'string',
rows: 4,
placeholder: `{
"input_language": "English",
"output_language": "French"
}`,
optional: true,
acceptVariable: true,
list: true
}
]
}
async init(nodeData: INodeData): Promise<any> {
const template = nodeData.inputs?.template as string
const promptValuesStr = nodeData.inputs?.promptValues as string
let promptValues: ICommonObject = {}
if (promptValuesStr) {
promptValues = JSON.parse(promptValuesStr.replace(/\s/g, ''))
}
const inputVariables = getInputVariables(template)
try {
@@ -41,6 +61,7 @@ class PromptTemplate_Prompts implements INode {
inputVariables
}
const prompt = new PromptTemplate(options)
prompt.promptValues = promptValues
return prompt
} catch (e) {
throw new Error(e)
packages/components/nodes/tools/AIPlugin/AIPlugin.ts
+41
View File
@@ -0,0 +1,41 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { AIPluginTool } from 'langchain/tools'
import { getBaseClasses } from '../../../src/utils'
class AIPlugin implements INode {
label: string
name: string
description: string
type: string
icon: string
category: string
baseClasses: string[]
inputs?: INodeParams[]
constructor() {
this.label = 'AI Plugin'
this.name = 'aiPlugin'
this.type = 'AIPlugin'
this.icon = 'aiplugin.svg'
this.category = 'Tools'
this.description = 'Execute actions using ChatGPT Plugin Url'
this.baseClasses = [this.type, ...getBaseClasses(AIPluginTool)]
this.inputs = [
{
label: 'Plugin Url',
name: 'pluginUrl',
type: 'string',
placeholder: 'https://www.klarna.com/.well-known/ai-plugin.json'
}
]
}
async init(nodeData: INodeData): Promise<any> {
const pluginUrl = nodeData.inputs?.pluginUrl as string
const aiplugin = await AIPluginTool.fromPluginUrl(pluginUrl)
return aiplugin
}
}
module.exports = { nodeClass: AIPlugin }
packages/components/nodes/tools/AIPlugin/aiplugin.svg
+7
View File
@@ -0,0 +1,7 @@
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-plug" 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="M9.785 6l8.215 8.215l-2.054 2.054a5.81 5.81 0 1 1 -8.215 -8.215l2.054 -2.054z"></path>
<path d="M4 20l3.5 -3.5"></path>
<path d="M15 4l-3.5 3.5"></path>
<path d="M20 9l-3.5 3.5"></path>
</svg>
Side by Side

After

Width:  |  Height:  |  Size: 498 B

packages/components/nodes/tools/ChainTool/ChainTool.ts
+73
View File
@@ -0,0 +1,73 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { ChainTool } from 'langchain/tools'
import { BaseChain } from 'langchain/chains'
class ChainTool_Tools implements INode {
label: string
name: string
description: string
type: string
icon: string
category: string
baseClasses: string[]
inputs: INodeParams[]
constructor() {
this.label = 'Chain Tool'
this.name = 'chainTool'
this.type = 'ChainTool'
this.icon = 'chaintool.svg'
this.category = 'Tools'
this.description = 'Use a chain as allowed tool for agent'
this.baseClasses = [this.type, ...getBaseClasses(ChainTool)]
this.inputs = [
{
label: 'Chain Name',
name: 'name',
type: 'string',
placeholder: 'state-of-union-qa'
},
{
label: 'Chain Description',
name: 'description',
type: 'string',
rows: 3,
placeholder:
'State of the Union QA - useful for when you need to ask questions about the most recent state of the union address.'
},
{
label: 'Return Direct',
name: 'returnDirect',
type: 'boolean',
optional: true
},
{
label: 'Base Chain',
name: 'baseChain',
type: 'BaseChain'
}
]
}
async init(nodeData: INodeData): Promise<any> {
const name = nodeData.inputs?.name as string
const description = nodeData.inputs?.description as string
const baseChain = nodeData.inputs?.baseChain as BaseChain
const returnDirect = nodeData.inputs?.returnDirect as boolean
const obj = {
name,
description,
chain: baseChain
} as any
if (returnDirect) obj.returnDirect = returnDirect
const tool = new ChainTool(obj)
return tool
}
}
module.exports = { nodeClass: ChainTool_Tools }
packages/components/nodes/tools/ChainTool/chaintool.svg
+4
View File
@@ -0,0 +1,4 @@
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-tool" 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="M7 10h3v-3l-3.5 -3.5a6 6 0 0 1 8 8l6 6a2 2 0 0 1 -3 3l-6 -6a6 6 0 0 1 -8 -8l3.5 3.5"></path>
</svg>
Side by Side

After

Width:  |  Height:  |  Size: 396 B

packages/components/nodes/vectorstores/Chroma_Existing/Chroma_Existing.ts
+24 -3
View File
@@ -1,6 +1,7 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { Chroma } from 'langchain/vectorstores/chroma'
import { Embeddings } from 'langchain/embeddings/base'
import { getBaseClasses } from '../../../src/utils'
class Chroma_Existing_VectorStores implements INode {
label: string
@@ -11,6 +12,7 @@ class Chroma_Existing_VectorStores implements INode {
category: string
baseClasses: string[]
inputs: INodeParams[]
outputs: INodeOutputsValue[]
constructor() {
this.label = 'Chroma Load Existing Index'
@@ -32,17 +34,36 @@ class Chroma_Existing_VectorStores implements INode {
type: 'string'
}
]
this.outputs = [
{
label: 'Chroma Retriever',
name: 'retriever',
baseClasses: [this.type, 'BaseRetriever']
},
{
label: 'Chroma Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...getBaseClasses(Chroma)]
}
]
}
async init(nodeData: INodeData): Promise<any> {
const collectionName = nodeData.inputs?.collectionName as string
const embeddings = nodeData.inputs?.embeddings as Embeddings
const output = nodeData.outputs?.output as string
const vectorStore = await Chroma.fromExistingCollection(embeddings, {
collectionName
})
const retriever = vectorStore.asRetriever()
return retriever
if (output === 'retriever') {
const retriever = vectorStore.asRetriever()
return retriever
} else if (output === 'vectorStore') {
return vectorStore
}
return vectorStore
}
}
packages/components/nodes/vectorstores/Chroma_Upsert/Chroma_Upsert.ts
+24 -3
View File
@@ -1,7 +1,8 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { Chroma } from 'langchain/vectorstores/chroma'
import { Embeddings } from 'langchain/embeddings/base'
import { Document } from 'langchain/document'
import { getBaseClasses } from '../../../src/utils'
class ChromaUpsert_VectorStores implements INode {
label: string
@@ -12,6 +13,7 @@ class ChromaUpsert_VectorStores implements INode {
category: string
baseClasses: string[]
inputs: INodeParams[]
outputs: INodeOutputsValue[]
constructor() {
this.label = 'Chroma Upsert Document'
@@ -38,12 +40,25 @@ class ChromaUpsert_VectorStores implements INode {
type: 'string'
}
]
this.outputs = [
{
label: 'Chroma Retriever',
name: 'retriever',
baseClasses: [this.type, 'BaseRetriever']
},
{
label: 'Chroma Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...getBaseClasses(Chroma)]
}
]
}
async init(nodeData: INodeData): Promise<any> {
const collectionName = nodeData.inputs?.collectionName as string
const docs = nodeData.inputs?.document as Document[]
const embeddings = nodeData.inputs?.embeddings as Embeddings
const output = nodeData.outputs?.output as string
const finalDocs = []
for (let i = 0; i < docs.length; i += 1) {
@@ -53,8 +68,14 @@ class ChromaUpsert_VectorStores implements INode {
const vectorStore = await Chroma.fromDocuments(finalDocs, embeddings, {
collectionName
})
const retriever = vectorStore.asRetriever()
return retriever
if (output === 'retriever') {
const retriever = vectorStore.asRetriever()
return retriever
} else if (output === 'vectorStore') {
return vectorStore
}
return vectorStore
}
}
packages/components/nodes/vectorstores/Pinecone_Existing/Pinecone_Existing.ts
+24 -3
View File
@@ -1,7 +1,8 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { PineconeClient } from '@pinecone-database/pinecone'
import { PineconeStore } from 'langchain/vectorstores/pinecone'
import { Embeddings } from 'langchain/embeddings/base'
import { getBaseClasses } from '../../../src/utils'
class Pinecone_Existing_VectorStores implements INode {
label: string
@@ -12,6 +13,7 @@ class Pinecone_Existing_VectorStores implements INode {
category: string
baseClasses: string[]
inputs: INodeParams[]
outputs: INodeOutputsValue[]
constructor() {
this.label = 'Pinecone Load Existing Index'
@@ -43,6 +45,18 @@ class Pinecone_Existing_VectorStores implements INode {
type: 'string'
}
]
this.outputs = [
{
label: 'Pinecone Retriever',
name: 'retriever',
baseClasses: [this.type, 'BaseRetriever']
},
{
label: 'Pinecone Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...getBaseClasses(PineconeStore)]
}
]
}
async init(nodeData: INodeData): Promise<any> {
@@ -50,6 +64,7 @@ class Pinecone_Existing_VectorStores implements INode {
const pineconeEnv = nodeData.inputs?.pineconeEnv as string
const index = nodeData.inputs?.pineconeIndex as string
const embeddings = nodeData.inputs?.embeddings as Embeddings
const output = nodeData.outputs?.output as string
const client = new PineconeClient()
await client.init({
@@ -62,8 +77,14 @@ class Pinecone_Existing_VectorStores implements INode {
const vectorStore = await PineconeStore.fromExistingIndex(embeddings, {
pineconeIndex
})
const retriever = vectorStore.asRetriever()
return retriever
if (output === 'retriever') {
const retriever = vectorStore.asRetriever()
return retriever
} else if (output === 'vectorStore') {
return vectorStore
}
return vectorStore
}
}
packages/components/nodes/vectorstores/Pinecone_Upsert/Pinecone_Upsert.ts
+24 -3
View File
@@ -1,8 +1,9 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { PineconeClient } from '@pinecone-database/pinecone'
import { PineconeStore } from 'langchain/vectorstores/pinecone'
import { Embeddings } from 'langchain/embeddings/base'
import { Document } from 'langchain/document'
import { getBaseClasses } from '../../../src/utils'
class PineconeUpsert_VectorStores implements INode {
label: string
@@ -13,6 +14,7 @@ class PineconeUpsert_VectorStores implements INode {
category: string
baseClasses: string[]
inputs: INodeParams[]
outputs: INodeOutputsValue[]
constructor() {
this.label = 'Pinecone Upsert Document'
@@ -49,6 +51,18 @@ class PineconeUpsert_VectorStores implements INode {
type: 'string'
}
]
this.outputs = [
{
label: 'Pinecone Retriever',
name: 'retriever',
baseClasses: [this.type, 'BaseRetriever']
},
{
label: 'Pinecone Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...getBaseClasses(PineconeStore)]
}
]
}
async init(nodeData: INodeData): Promise<any> {
@@ -57,6 +71,7 @@ class PineconeUpsert_VectorStores implements INode {
const index = nodeData.inputs?.pineconeIndex as string
const docs = nodeData.inputs?.document as Document[]
const embeddings = nodeData.inputs?.embeddings as Embeddings
const output = nodeData.outputs?.output as string
const client = new PineconeClient()
await client.init({
@@ -74,8 +89,14 @@ class PineconeUpsert_VectorStores implements INode {
const vectorStore = await PineconeStore.fromDocuments(finalDocs, embeddings, {
pineconeIndex
})
const retriever = vectorStore.asRetriever()
return retriever
if (output === 'retriever') {
const retriever = vectorStore.asRetriever()
return retriever
} else if (output === 'vectorStore') {
return vectorStore
}
return vectorStore
}
}
packages/components/package.json
+1 -1
View File
@@ -27,7 +27,7 @@
"dotenv": "^16.0.0",
"express": "^4.17.3",
"form-data": "^4.0.0",
"langchain": "^0.0.53",
"langchain": "^0.0.59",
"moment": "^2.29.3",
"node-fetch": "2",
"pdf-parse": "^1.1.1",
packages/components/src/Interface.ts
+27 -13
View File
@@ -2,18 +2,7 @@
* Types
*/
export type NodeParamsType =
| 'asyncOptions'
| 'options'
| 'string'
| 'number'
| 'boolean'
| 'password'
| 'json'
| 'code'
| 'date'
| 'file'
| 'folder'
export type NodeParamsType = 'options' | 'string' | 'number' | 'boolean' | 'password' | 'json' | 'code' | 'date' | 'file' | 'folder'
export type CommonType = string | number | boolean | undefined | null
@@ -40,6 +29,13 @@ export interface INodeOptionsValue {
description?: string
}
export interface INodeOutputsValue {
label: string
name: string
baseClasses: string[]
description?: string
}
export interface INodeParams {
label: string
name: string
@@ -50,6 +46,7 @@ export interface INodeParams {
optional?: boolean | INodeDisplay
rows?: number
list?: boolean
acceptVariable?: boolean
placeholder?: string
fileType?: string
}
@@ -75,12 +72,15 @@ export interface INodeProperties {
export interface INode extends INodeProperties {
inputs?: INodeParams[]
getInstance?(nodeData: INodeData): Promise<string>
output?: INodeOutputsValue[]
init?(nodeData: INodeData, input: string, options?: ICommonObject): Promise<any>
run?(nodeData: INodeData, input: string, options?: ICommonObject): Promise<string>
}
export interface INodeData extends INodeProperties {
id: string
inputs?: ICommonObject
outputs?: ICommonObject
instance?: any
}
@@ -88,3 +88,17 @@ export interface IMessage {
message: string
type: MessageType
}
/**
* Classes
*/
import { PromptTemplate as LangchainPromptTemplate, PromptTemplateInput } from 'langchain/prompts'
export class PromptTemplate extends LangchainPromptTemplate {
promptValues: ICommonObject
constructor(input: PromptTemplateInput) {
super(input)
}
}
packages/server/.env.example
+1
View File
@@ -0,0 +1 @@
PORT=3000
packages/server/bin/dev
Regular → Executable
View File
packages/server/bin/dev.cmd
Regular → Executable
View File
packages/server/bin/run
Regular → Executable
View File
packages/server/bin/run.cmd
Regular → Executable
View File
packages/server/marketplaces/Antonym.json
+98 -57
View File
@@ -3,11 +3,11 @@
"nodes": [
{
"width": 300,
"height": 360,
"height": 533,
"id": "promptTemplate_0",
"position": {
"x": 294.38456937448433,
"y": 66.5400435451831
"x": 567,
"y": 85
},
"type": "customNode",
"data": {
@@ -23,13 +23,26 @@
"label": "Template",
"name": "template",
"type": "string",
"rows": 5,
"placeholder": "What is a good name for a company that makes {product}?"
"rows": 4,
"placeholder": "What is a good name for a company that makes {product}?",
"id": "promptTemplate_0-input-template-string"
},
{
"label": "Format Prompt Values",
"name": "promptValues",
"type": "string",
"rows": 4,
"placeholder": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}",
"optional": true,
"acceptVariable": true,
"list": true,
"id": "promptTemplate_0-input-promptValues-string"
}
],
"inputAnchors": [],
"inputs": {
"template": "Word: {word}\\nAntonym: {antonym}\\n"
"template": "Word: {word}\\nAntonym: {antonym}\\n",
"promptValues": ""
},
"outputAnchors": [
{
@@ -39,22 +52,23 @@
"type": "PromptTemplate | BaseStringPromptTemplate | BasePromptTemplate"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"dragging": false,
"positionAbsolute": {
"x": 294.38456937448433,
"y": 66.5400435451831
},
"dragging": false
"x": 567,
"y": 85
}
},
{
"width": 300,
"height": 886,
"height": 955,
"id": "fewShotPromptTemplate_0",
"position": {
"x": 719.2200337843097,
"y": 67.20405755860693
"x": 942.9569947740308,
"y": 82.93222833361332
},
"type": "customNode",
"data": {
@@ -70,28 +84,32 @@
"label": "Examples",
"name": "examples",
"type": "string",
"rows": 5,
"placeholder": "[\n { \"word\": \"happy\", \"antonym\": \"sad\" },\n { \"word\": \"tall\", \"antonym\": \"short\" },\n]"
"rows": 4,
"placeholder": "[\n { \"word\": \"happy\", \"antonym\": \"sad\" },\n { \"word\": \"tall\", \"antonym\": \"short\" },\n]",
"id": "fewShotPromptTemplate_0-input-examples-string"
},
{
"label": "Prefix",
"name": "prefix",
"type": "string",
"rows": 3,
"placeholder": "Give the antonym of every input"
"rows": 4,
"placeholder": "Give the antonym of every input",
"id": "fewShotPromptTemplate_0-input-prefix-string"
},
{
"label": "Suffix",
"name": "suffix",
"type": "string",
"rows": 3,
"placeholder": "Word: {input}\nAntonym:"
"rows": 4,
"placeholder": "Word: {input}\nAntonym:",
"id": "fewShotPromptTemplate_0-input-suffix-string"
},
{
"label": "Example Seperator",
"name": "exampleSeparator",
"type": "string",
"placeholder": "\n\n"
"placeholder": "\n\n",
"id": "fewShotPromptTemplate_0-input-exampleSeparator-string"
},
{
"label": "Template Format",
@@ -107,7 +125,8 @@
"name": "jinja-2"
}
],
"default": "f-string"
"default": "f-string",
"id": "fewShotPromptTemplate_0-input-templateFormat-options"
}
],
"inputAnchors": [
@@ -134,12 +153,13 @@
"type": "FewShotPromptTemplate | BaseStringPromptTemplate | BasePromptTemplate"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 719.2200337843097,
"y": 67.20405755860693
"x": 942.9569947740308,
"y": 82.93222833361332
},
"dragging": false
},
@@ -148,8 +168,8 @@
"height": 472,
"id": "openAI_0",
"position": {
"x": 1089.6434062122398,
"y": 27.515288538129425
"x": 1304.9299247555505,
"y": 8.707397857674266
},
"type": "customNode",
"data": {
@@ -164,7 +184,8 @@
{
"label": "OpenAI Api Key",
"name": "openAIApiKey",
"type": "password"
"type": "password",
"id": "openAI_0-input-openAIApiKey-password"
},
{
"label": "Model Name",
@@ -189,20 +210,22 @@
}
],
"default": "text-davinci-003",
"optional": true
"optional": true,
"id": "openAI_0-input-modelName-options"
},
{
"label": "Temperature",
"name": "temperature",
"type": "number",
"default": 0.7,
"optional": true
"optional": true,
"id": "openAI_0-input-temperature-number"
}
],
"inputAnchors": [],
"inputs": {
"modelName": "text-davinci-003",
"temperature": 0.7
"temperature": "0"
},
"outputAnchors": [
{
@@ -212,22 +235,23 @@
"type": "OpenAI | BaseLLM | BaseLanguageModel"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 1089.6434062122398,
"y": 27.515288538129425
"x": 1304.9299247555505,
"y": 8.707397857674266
},
"dragging": false
},
{
"width": 300,
"height": 461,
"height": 405,
"id": "llmChain_0",
"position": {
"x": 1499.2654451385026,
"y": 356.3275374721362
"x": 1669.2177402155296,
"y": 338.65158088371567
},
"type": "customNode",
"data": {
@@ -240,12 +264,12 @@
"description": "Chain to run queries against LLMs",
"inputParams": [
{
"label": "Format Prompt Values",
"name": "promptValues",
"label": "Chain Name",
"name": "chainName",
"type": "string",
"rows": 5,
"placeholder": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}",
"optional": true
"placeholder": "Name Your Chain",
"optional": true,
"id": "llmChain_0-input-chainName-string"
}
],
"inputAnchors": [
@@ -265,38 +289,44 @@
"inputs": {
"model": "{{openAI_0.data.instance}}",
"prompt": "{{fewShotPromptTemplate_0.data.instance}}",
"promptValues": ""
"chainName": ""
},
"outputAnchors": [
{
"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
"name": "llmChain",
"label": "LLMChain",
"type": "LLMChain | BaseChain"
"name": "output",
"label": "Output",
"type": "options",
"options": [
{
"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
"name": "llmChain",
"label": "LLM Chain",
"type": "LLMChain | BaseChain"
},
{
"id": "llmChain_0-output-outputPrediction-string",
"name": "outputPrediction",
"label": "Output Prediction",
"type": "string"
}
],
"default": "llmChain"
}
],
"outputs": {
"output": "llmChain"
},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 1499.2654451385026,
"y": 356.3275374721362
"x": 1669.2177402155296,
"y": 338.65158088371567
},
"dragging": false
}
],
"edges": [
{
"source": "promptTemplate_0",
"sourceHandle": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
"target": "fewShotPromptTemplate_0",
"targetHandle": "fewShotPromptTemplate_0-input-examplePrompt-PromptTemplate",
"type": "buttonedge",
"id": "promptTemplate_0-promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate-fewShotPromptTemplate_0-fewShotPromptTemplate_0-input-examplePrompt-PromptTemplate",
"data": {
"label": ""
}
},
{
"source": "openAI_0",
"sourceHandle": "openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
@@ -318,6 +348,17 @@
"data": {
"label": ""
}
},
{
"source": "promptTemplate_0",
"sourceHandle": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
"target": "fewShotPromptTemplate_0",
"targetHandle": "fewShotPromptTemplate_0-input-examplePrompt-PromptTemplate",
"type": "buttonedge",
"id": "promptTemplate_0-promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate-fewShotPromptTemplate_0-fewShotPromptTemplate_0-input-examplePrompt-PromptTemplate",
"data": {
"label": ""
}
}
]
}
packages/server/marketplaces/ChatGPTPlugin.json
+307
View File
@@ -0,0 +1,307 @@
{
"description": "Use ChatGPT Plugins within LangChain abstractions with GET and POST Tools",
"nodes": [
{
"width": 300,
"height": 278,
"id": "aiPlugin_0",
"position": {
"x": 992.9213747553727,
"y": 115.80946637479596
},
"type": "customNode",
"data": {
"id": "aiPlugin_0",
"label": "AI Plugin",
"name": "aiPlugin",
"type": "AIPlugin",
"baseClasses": ["AIPlugin", "Tool"],
"category": "Tools",
"description": "Execute actions using ChatGPT Plugin Url",
"inputParams": [
{
"label": "Plugin Url",
"name": "pluginUrl",
"type": "string",
"placeholder": "https://www.klarna.com/.well-known/ai-plugin.json"
}
],
"inputAnchors": [],
"inputs": {
"pluginUrl": "https://www.klarna.com/.well-known/ai-plugin.json"
},
"outputAnchors": [
{
"id": "aiPlugin_0-output-aiPlugin-AIPlugin|Tool",
"name": "aiPlugin",
"label": "AIPlugin",
"type": "AIPlugin | Tool"
}
],
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 992.9213747553727,
"y": 115.80946637479596
},
"dragging": false
},
{
"width": 300,
"height": 143,
"id": "requestsPost_0",
"position": {
"x": 638.2831241951309,
"y": 294.0784991300699
},
"type": "customNode",
"data": {
"id": "requestsPost_0",
"label": "Requests Post",
"name": "requestsPost",
"type": "RequestsPost",
"baseClasses": ["RequestsPost", "Tool"],
"category": "Tools",
"description": "Execute HTTP POST requests",
"inputParams": [],
"inputAnchors": [],
"inputs": {},
"outputAnchors": [
{
"id": "requestsPost_0-output-requestsPost-RequestsPost|Tool",
"name": "requestsPost",
"label": "RequestsPost",
"type": "RequestsPost | Tool"
}
],
"selected": false
},
"positionAbsolute": {
"x": 638.2831241951309,
"y": 294.0784991300699
},
"selected": false,
"dragging": false
},
{
"width": 300,
"height": 143,
"id": "requestsGet_0",
"position": {
"x": 703.0477667387721,
"y": 476.8955204497346
},
"type": "customNode",
"data": {
"id": "requestsGet_0",
"label": "Requests Get",
"name": "requestsGet",
"type": "RequestsGet",
"baseClasses": ["RequestsGet", "Tool"],
"category": "Tools",
"description": "Execute HTTP GET requests",
"inputParams": [],
"inputAnchors": [],
"inputs": {},
"outputAnchors": [
{
"id": "requestsGet_0-output-requestsGet-RequestsGet|Tool",
"name": "requestsGet",
"label": "RequestsGet",
"type": "RequestsGet | Tool"
}
],
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 703.0477667387721,
"y": 476.8955204497346
},
"dragging": false
},
{
"width": 300,
"height": 280,
"id": "mrklAgentChat_0",
"position": {
"x": 1363.057715565282,
"y": 479.27393467974
},
"type": "customNode",
"data": {
"id": "mrklAgentChat_0",
"label": "MRKL Agent for Chat Models",
"name": "mrklAgentChat",
"type": "AgentExecutor",
"baseClasses": ["AgentExecutor", "BaseChain"],
"category": "Agents",
"description": "Agent that uses the ReAct Framework to decide what action to take, optimized to be used with Chat Models",
"inputParams": [],
"inputAnchors": [
{
"label": "Allowed Tools",
"name": "tools",
"type": "Tool",
"list": true,
"id": "mrklAgentChat_0-input-tools-Tool"
},
{
"label": "Chat Model",
"name": "model",
"type": "BaseChatModel",
"id": "mrklAgentChat_0-input-model-BaseChatModel"
}
],
"inputs": {
"tools": ["{{requestsGet_0.data.instance}}", "{{requestsPost_0.data.instance}}", "{{aiPlugin_0.data.instance}}"],
"model": "{{chatOpenAI_0.data.instance}}"
},
"outputAnchors": [
{
"id": "mrklAgentChat_0-output-mrklAgentChat-AgentExecutor|BaseChain",
"name": "mrklAgentChat",
"label": "AgentExecutor",
"type": "AgentExecutor | BaseChain"
}
],
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 1363.057715565282,
"y": 479.27393467974
},
"dragging": false
},
{
"width": 300,
"height": 472,
"id": "chatOpenAI_0",
"position": {
"x": 724.4534948088211,
"y": 668.3578659651726
},
"type": "customNode",
"data": {
"id": "chatOpenAI_0",
"label": "ChatOpenAI",
"name": "chatOpenAI",
"type": "ChatOpenAI",
"baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
"category": "Chat Models",
"description": "Wrapper around OpenAI large language models that use the Chat endpoint",
"inputParams": [
{
"label": "OpenAI Api Key",
"name": "openAIApiKey",
"type": "password"
},
{
"label": "Model Name",
"name": "modelName",
"type": "options",
"options": [
{
"label": "gpt-4",
"name": "gpt-4"
},
{
"label": "gpt-4-0314",
"name": "gpt-4-0314"
},
{
"label": "gpt-4-32k-0314",
"name": "gpt-4-32k-0314"
},
{
"label": "gpt-3.5-turbo",
"name": "gpt-3.5-turbo"
},
{
"label": "gpt-3.5-turbo-0301",
"name": "gpt-3.5-turbo-0301"
}
],
"default": "gpt-3.5-turbo",
"optional": true
},
{
"label": "Temperature",
"name": "temperature",
"type": "number",
"default": 0.9,
"optional": true
}
],
"inputAnchors": [],
"inputs": {
"modelName": "gpt-3.5-turbo",
"temperature": "0"
},
"outputAnchors": [
{
"id": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
"name": "chatOpenAI",
"label": "ChatOpenAI",
"type": "ChatOpenAI | BaseChatModel | BaseLanguageModel"
}
],
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 724.4534948088211,
"y": 668.3578659651726
},
"dragging": false
}
],
"edges": [
{
"source": "aiPlugin_0",
"sourceHandle": "aiPlugin_0-output-aiPlugin-AIPlugin|Tool",
"target": "mrklAgentChat_0",
"targetHandle": "mrklAgentChat_0-input-tools-Tool",
"type": "buttonedge",
"id": "aiPlugin_0-aiPlugin_0-output-aiPlugin-AIPlugin|Tool-mrklAgentChat_0-mrklAgentChat_0-input-tools-Tool",
"data": {
"label": ""
}
},
{
"source": "requestsGet_0",
"sourceHandle": "requestsGet_0-output-requestsGet-RequestsGet|Tool",
"target": "mrklAgentChat_0",
"targetHandle": "mrklAgentChat_0-input-tools-Tool",
"type": "buttonedge",
"id": "requestsGet_0-requestsGet_0-output-requestsGet-RequestsGet|Tool-mrklAgentChat_0-mrklAgentChat_0-input-tools-Tool",
"data": {
"label": ""
}
},
{
"source": "requestsPost_0",
"sourceHandle": "requestsPost_0-output-requestsPost-RequestsPost|Tool",
"target": "mrklAgentChat_0",
"targetHandle": "mrklAgentChat_0-input-tools-Tool",
"type": "buttonedge",
"id": "requestsPost_0-requestsPost_0-output-requestsPost-RequestsPost|Tool-mrklAgentChat_0-mrklAgentChat_0-input-tools-Tool",
"data": {
"label": ""
}
},
{
"source": "chatOpenAI_0",
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
"target": "mrklAgentChat_0",
"targetHandle": "mrklAgentChat_0-input-model-BaseChatModel",
"type": "buttonedge",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel-mrklAgentChat_0-mrklAgentChat_0-input-model-BaseChatModel",
"data": {
"label": ""
}
}
]
}
packages/server/marketplaces/Multiple VectorDB.json
+1020
View File
File diff suppressed because it is too large Load Diff
packages/server/marketplaces/Prompt Chaining.json
+508
View File
@@ -0,0 +1,508 @@
{
"description": "Use output from a chain as prompt for another chain",
"nodes": [
{
"width": 300,
"height": 533,
"id": "promptTemplate_0",
"position": {
"x": 796.6293062501211,
"y": 523.6130142453178
},
"type": "customNode",
"data": {
"id": "promptTemplate_0",
"label": "Prompt Template",
"name": "promptTemplate",
"type": "PromptTemplate",
"baseClasses": ["PromptTemplate", "BaseStringPromptTemplate", "BasePromptTemplate"],
"category": "Prompts",
"description": "Schema to represent a basic prompt for an LLM",
"inputParams": [
{
"label": "Template",
"name": "template",
"type": "string",
"rows": 4,
"placeholder": "What is a good name for a company that makes {product}?",
"id": "promptTemplate_0-input-template-string"
},
{
"label": "Format Prompt Values",
"name": "promptValues",
"type": "string",
"rows": 4,
"placeholder": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}",
"optional": true,
"acceptVariable": true,
"list": true,
"id": "promptTemplate_0-input-promptValues-string"
}
],
"inputAnchors": [],
"inputs": {
"template": "You are an AI who performs one task based on the following objective: {objective}.\nRespond with how you would complete this task:",
"promptValues": "{\n \"objective\": \"{{question}}\"\n}"
},
"outputAnchors": [
{
"id": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
"name": "promptTemplate",
"label": "PromptTemplate",
"type": "PromptTemplate | BaseStringPromptTemplate | BasePromptTemplate"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 796.6293062501211,
"y": 523.6130142453178
},
"dragging": false
},
{
"width": 300,
"height": 405,
"id": "llmChain_0",
"position": {
"x": 1239.1590462985343,
"y": 477.999065568104
},
"type": "customNode",
"data": {
"id": "llmChain_0",
"label": "LLM Chain",
"name": "llmChain",
"type": "LLMChain",
"baseClasses": ["LLMChain", "BaseChain"],
"category": "Chains",
"description": "Chain to run queries against LLMs",
"inputParams": [
{
"label": "Chain Name",
"name": "chainName",
"type": "string",
"placeholder": "Name Your Chain",
"optional": true,
"id": "llmChain_0-input-chainName-string"
}
],
"inputAnchors": [
{
"label": "Language Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "llmChain_0-input-model-BaseLanguageModel"
},
{
"label": "Prompt",
"name": "prompt",
"type": "BasePromptTemplate",
"id": "llmChain_0-input-prompt-BasePromptTemplate"
}
],
"inputs": {
"model": "{{openAI_0.data.instance}}",
"prompt": "{{promptTemplate_0.data.instance}}",
"chainName": "FirstChain"
},
"outputAnchors": [
{
"name": "output",
"label": "Output",
"type": "options",
"options": [
{
"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
"name": "llmChain",
"label": "LLM Chain",
"type": "LLMChain | BaseChain"
},
{
"id": "llmChain_0-output-outputPrediction-string",
"name": "outputPrediction",
"label": "Output Prediction",
"type": "string"
}
],
"default": "llmChain"
}
],
"outputs": {
"output": "outputPrediction"
},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 1239.1590462985343,
"y": 477.999065568104
},
"dragging": false
},
{
"width": 300,
"height": 472,
"id": "openAI_0",
"position": {
"x": 801.1835381596817,
"y": 21.196316952440355
},
"type": "customNode",
"data": {
"id": "openAI_0",
"label": "OpenAI",
"name": "openAI",
"type": "OpenAI",
"baseClasses": ["OpenAI", "BaseLLM", "BaseLanguageModel"],
"category": "LLMs",
"description": "Wrapper around OpenAI large language models",
"inputParams": [
{
"label": "OpenAI Api Key",
"name": "openAIApiKey",
"type": "password",
"id": "openAI_0-input-openAIApiKey-password"
},
{
"label": "Model Name",
"name": "modelName",
"type": "options",
"options": [
{
"label": "text-davinci-003",
"name": "text-davinci-003"
},
{
"label": "text-davinci-002",
"name": "text-davinci-002"
},
{
"label": "text-curie-001",
"name": "text-curie-001"
},
{
"label": "text-babbage-001",
"name": "text-babbage-001"
}
],
"default": "text-davinci-003",
"optional": true,
"id": "openAI_0-input-modelName-options"
},
{
"label": "Temperature",
"name": "temperature",
"type": "number",
"default": 0.7,
"optional": true,
"id": "openAI_0-input-temperature-number"
}
],
"inputAnchors": [],
"inputs": {
"modelName": "text-davinci-003",
"temperature": "0"
},
"outputAnchors": [
{
"id": "openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
"name": "openAI",
"label": "OpenAI",
"type": "OpenAI | BaseLLM | BaseLanguageModel"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 801.1835381596817,
"y": 21.196316952440355
},
"dragging": false
},
{
"width": 300,
"height": 405,
"id": "llmChain_1",
"position": {
"x": 2078.2072357874076,
"y": 476.5404337093371
},
"type": "customNode",
"data": {
"id": "llmChain_1",
"label": "LLM Chain",
"name": "llmChain",
"type": "LLMChain",
"baseClasses": ["LLMChain", "BaseChain"],
"category": "Chains",
"description": "Chain to run queries against LLMs",
"inputParams": [
{
"label": "Chain Name",
"name": "chainName",
"type": "string",
"placeholder": "Name Your Chain",
"optional": true,
"id": "llmChain_1-input-chainName-string"
}
],
"inputAnchors": [
{
"label": "Language Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "llmChain_1-input-model-BaseLanguageModel"
},
{
"label": "Prompt",
"name": "prompt",
"type": "BasePromptTemplate",
"id": "llmChain_1-input-prompt-BasePromptTemplate"
}
],
"inputs": {
"model": "{{openAI_1.data.instance}}",
"prompt": "{{promptTemplate_1.data.instance}}",
"chainName": "LastChain"
},
"outputAnchors": [
{
"name": "output",
"label": "Output",
"type": "options",
"options": [
{
"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
"name": "llmChain",
"label": "LLM Chain",
"type": "LLMChain | BaseChain"
},
{
"id": "llmChain_0-output-outputPrediction-string",
"name": "outputPrediction",
"label": "Output Prediction",
"type": "string"
}
],
"default": "llmChain"
}
],
"outputs": {
"output": "llmChain"
},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 2078.2072357874076,
"y": 476.5404337093371
},
"dragging": false
},
{
"width": 300,
"height": 533,
"id": "promptTemplate_1",
"position": {
"x": 1686.7296107958396,
"y": 520.6957505277837
},
"type": "customNode",
"data": {
"id": "promptTemplate_1",
"label": "Prompt Template",
"name": "promptTemplate",
"type": "PromptTemplate",
"baseClasses": ["PromptTemplate", "BaseStringPromptTemplate", "BasePromptTemplate"],
"category": "Prompts",
"description": "Schema to represent a basic prompt for an LLM",
"inputParams": [
{
"label": "Template",
"name": "template",
"type": "string",
"rows": 4,
"placeholder": "What is a good name for a company that makes {product}?",
"id": "promptTemplate_1-input-template-string"
},
{
"label": "Format Prompt Values",
"name": "promptValues",
"type": "string",
"rows": 4,
"placeholder": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}",
"optional": true,
"acceptVariable": true,
"list": true,
"id": "promptTemplate_1-input-promptValues-string"
}
],
"inputAnchors": [],
"inputs": {
"template": "You are a task creation AI that uses the result of an execution agent to create new tasks with the following objective: {objective}.\nThe last completed task has the result: {result}.\nBased on the result, create new tasks to be completed by the AI system that do not overlap with result.\nReturn the tasks as an array.",
"promptValues": "{\n \"objective\": \"{{question}}\",\n \"result\": \"{{llmChain_0.data.instance}}\"\n}"
},
"outputAnchors": [
{
"id": "promptTemplate_1-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
"name": "promptTemplate",
"label": "PromptTemplate",
"type": "PromptTemplate | BaseStringPromptTemplate | BasePromptTemplate"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 1686.7296107958396,
"y": 520.6957505277837
},
"dragging": false
},
{
"width": 300,
"height": 472,
"id": "openAI_1",
"position": {
"x": 1688.3665789878662,
"y": 16.528695004385895
},
"type": "customNode",
"data": {
"id": "openAI_1",
"label": "OpenAI",
"name": "openAI",
"type": "OpenAI",
"baseClasses": ["OpenAI", "BaseLLM", "BaseLanguageModel"],
"category": "LLMs",
"description": "Wrapper around OpenAI large language models",
"inputParams": [
{
"label": "OpenAI Api Key",
"name": "openAIApiKey",
"type": "password",
"id": "openAI_1-input-openAIApiKey-password"
},
{
"label": "Model Name",
"name": "modelName",
"type": "options",
"options": [
{
"label": "text-davinci-003",
"name": "text-davinci-003"
},
{
"label": "text-davinci-002",
"name": "text-davinci-002"
},
{
"label": "text-curie-001",
"name": "text-curie-001"
},
{
"label": "text-babbage-001",
"name": "text-babbage-001"
}
],
"default": "text-davinci-003",
"optional": true,
"id": "openAI_1-input-modelName-options"
},
{
"label": "Temperature",
"name": "temperature",
"type": "number",
"default": 0.7,
"optional": true,
"id": "openAI_1-input-temperature-number"
}
],
"inputAnchors": [],
"inputs": {
"modelName": "text-davinci-003",
"temperature": "0"
},
"outputAnchors": [
{
"id": "openAI_1-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
"name": "openAI",
"label": "OpenAI",
"type": "OpenAI | BaseLLM | BaseLanguageModel"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 1688.3665789878662,
"y": 16.528695004385895
},
"dragging": false
}
],
"edges": [
{
"source": "promptTemplate_0",
"sourceHandle": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
"target": "llmChain_0",
"targetHandle": "llmChain_0-input-prompt-BasePromptTemplate",
"type": "buttonedge",
"id": "promptTemplate_0-promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate-llmChain_0-llmChain_0-input-prompt-BasePromptTemplate",
"data": {
"label": ""
}
},
{
"source": "openAI_0",
"sourceHandle": "openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
"target": "llmChain_0",
"targetHandle": "llmChain_0-input-model-BaseLanguageModel",
"type": "buttonedge",
"id": "openAI_0-openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel-llmChain_0-llmChain_0-input-model-BaseLanguageModel",
"data": {
"label": ""
}
},
{
"source": "promptTemplate_1",
"sourceHandle": "promptTemplate_1-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
"target": "llmChain_1",
"targetHandle": "llmChain_1-input-prompt-BasePromptTemplate",
"type": "buttonedge",
"id": "promptTemplate_1-promptTemplate_1-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate-llmChain_1-llmChain_1-input-prompt-BasePromptTemplate",
"data": {
"label": ""
}
},
{
"source": "openAI_1",
"sourceHandle": "openAI_1-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
"target": "llmChain_1",
"targetHandle": "llmChain_1-input-model-BaseLanguageModel",
"type": "buttonedge",
"id": "openAI_1-openAI_1-output-openAI-OpenAI|BaseLLM|BaseLanguageModel-llmChain_1-llmChain_1-input-model-BaseLanguageModel",
"data": {
"label": ""
}
},
{
"source": "llmChain_0",
"sourceHandle": "llmChain_0-output-outputPrediction-string",
"target": "promptTemplate_1",
"targetHandle": "promptTemplate_1-input-promptValues-string",
"type": "buttonedge",
"id": "llmChain_0-llmChain_0-output-outputPrediction-string-promptTemplate_1-promptTemplate_1-input-promptValues-string",
"data": {
"label": ""
}
}
]
}
packages/server/marketplaces/Simple LLM Chain.json
+117 -82
View File
@@ -6,8 +6,8 @@
"height": 472,
"id": "openAI_0",
"position": {
"x": 968.1753795547951,
"y": -8.62176310944858
"x": 618,
"y": 97
},
"type": "customNode",
"data": {
@@ -22,7 +22,8 @@
{
"label": "OpenAI Api Key",
"name": "openAIApiKey",
"type": "password"
"type": "password",
"id": "openAI_0-input-openAIApiKey-password"
},
{
"label": "Model Name",
@@ -47,14 +48,16 @@
}
],
"default": "text-davinci-003",
"optional": true
"optional": true,
"id": "openAI_0-input-modelName-options"
},
{
"label": "Temperature",
"name": "temperature",
"type": "number",
"default": 0.7,
"optional": true
"optional": true,
"id": "openAI_0-input-temperature-number"
}
],
"inputAnchors": [],
@@ -70,69 +73,23 @@
"type": "OpenAI | BaseLLM | BaseLanguageModel"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"dragging": false,
"positionAbsolute": {
"x": 968.1753795547951,
"y": -8.62176310944858
},
"dragging": false
"x": 618,
"y": 97
}
},
{
"width": 300,
"height": 360,
"id": "promptTemplate_0",
"position": {
"x": 970.576876549135,
"y": 502.493937944275
},
"type": "customNode",
"data": {
"id": "promptTemplate_0",
"label": "Prompt Template",
"name": "promptTemplate",
"type": "PromptTemplate",
"baseClasses": ["PromptTemplate", "BaseStringPromptTemplate", "BasePromptTemplate"],
"category": "Prompts",
"description": "Schema to represent a basic prompt for an LLM",
"inputParams": [
{
"label": "Template",
"name": "template",
"type": "string",
"rows": 5,
"placeholder": "What is a good name for a company that makes {product}?"
}
],
"inputAnchors": [],
"inputs": {
"template": "What is a good name for a company that makes {product}?"
},
"outputAnchors": [
{
"id": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
"name": "promptTemplate",
"label": "PromptTemplate",
"type": "PromptTemplate | BaseStringPromptTemplate | BasePromptTemplate"
}
],
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 970.576876549135,
"y": 502.493937944275
},
"dragging": false
},
{
"width": 300,
"height": 461,
"height": 405,
"id": "llmChain_0",
"position": {
"x": 1414.1175742139496,
"y": 340.4040954840462
"x": 998.3768292410252,
"y": 426.849642225371
},
"type": "customNode",
"data": {
@@ -145,12 +102,12 @@
"description": "Chain to run queries against LLMs",
"inputParams": [
{
"label": "Format Prompt Values",
"name": "promptValues",
"label": "Chain Name",
"name": "chainName",
"type": "string",
"rows": 5,
"placeholder": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}",
"optional": true
"placeholder": "Name Your Chain",
"optional": true,
"id": "llmChain_0-input-chainName-string"
}
],
"inputAnchors": [
@@ -170,38 +127,105 @@
"inputs": {
"model": "{{openAI_0.data.instance}}",
"prompt": "{{promptTemplate_0.data.instance}}",
"promptValues": ""
"chainName": ""
},
"outputAnchors": [
{
"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
"name": "llmChain",
"label": "LLMChain",
"type": "LLMChain | BaseChain"
"name": "output",
"label": "Output",
"type": "options",
"options": [
{
"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
"name": "llmChain",
"label": "LLM Chain",
"type": "LLMChain | BaseChain"
},
{
"id": "llmChain_0-output-outputPrediction-string",
"name": "outputPrediction",
"label": "Output Prediction",
"type": "string"
}
],
"default": "llmChain"
}
],
"outputs": {
"output": "llmChain"
},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 1414.1175742139496,
"y": 340.4040954840462
"x": 998.3768292410252,
"y": 426.849642225371
},
"dragging": false
},
{
"width": 300,
"height": 533,
"id": "promptTemplate_0",
"position": {
"x": 618.658978699234,
"y": 589.2586352262571
},
"type": "customNode",
"data": {
"id": "promptTemplate_0",
"label": "Prompt Template",
"name": "promptTemplate",
"type": "PromptTemplate",
"baseClasses": ["PromptTemplate", "BaseStringPromptTemplate", "BasePromptTemplate"],
"category": "Prompts",
"description": "Schema to represent a basic prompt for an LLM",
"inputParams": [
{
"label": "Template",
"name": "template",
"type": "string",
"rows": 4,
"placeholder": "What is a good name for a company that makes {product}?",
"id": "promptTemplate_0-input-template-string"
},
{
"label": "Format Prompt Values",
"name": "promptValues",
"type": "string",
"rows": 4,
"placeholder": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}",
"optional": true,
"acceptVariable": true,
"list": true,
"id": "promptTemplate_0-input-promptValues-string"
}
],
"inputAnchors": [],
"inputs": {
"template": "What is a good name for a company that makes {product}?",
"promptValues": ""
},
"outputAnchors": [
{
"id": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
"name": "promptTemplate",
"label": "PromptTemplate",
"type": "PromptTemplate | BaseStringPromptTemplate | BasePromptTemplate"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 618.658978699234,
"y": 589.2586352262571
},
"dragging": false
}
],
"edges": [
{
"source": "promptTemplate_0",
"sourceHandle": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
"target": "llmChain_0",
"targetHandle": "llmChain_0-input-prompt-BasePromptTemplate",
"type": "buttonedge",
"id": "promptTemplate_0-promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate-llmChain_0-llmChain_0-input-prompt-BasePromptTemplate",
"data": {
"label": ""
}
},
{
"source": "openAI_0",
"sourceHandle": "openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
@@ -212,6 +236,17 @@
"data": {
"label": ""
}
},
{
"source": "promptTemplate_0",
"sourceHandle": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
"target": "llmChain_0",
"targetHandle": "llmChain_0-input-prompt-BasePromptTemplate",
"type": "buttonedge",
"id": "promptTemplate_0-promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate-llmChain_0-llmChain_0-input-prompt-BasePromptTemplate",
"data": {
"label": ""
}
}
]
}
packages/server/marketplaces/Translator.json
+112 -76
View File
@@ -3,66 +3,91 @@
"nodes": [
{
"width": 300,
"height": 460,
"id": "chatPromptTemplate_0",
"height": 405,
"id": "llmChain_0",
"position": {
"x": 524,
"y": 237
"x": 1136.5578350285277,
"y": 619.2492937692573
},
"type": "customNode",
"data": {
"id": "chatPromptTemplate_0",
"label": "Chat Prompt Template",
"name": "chatPromptTemplate",
"type": "ChatPromptTemplate",
"baseClasses": ["ChatPromptTemplate", "BaseChatPromptTemplate", "BasePromptTemplate"],
"category": "Prompts",
"description": "Schema to represent a chat prompt",
"id": "llmChain_0",
"label": "LLM Chain",
"name": "llmChain",
"type": "LLMChain",
"baseClasses": ["LLMChain", "BaseChain"],
"category": "Chains",
"description": "Chain to run queries against LLMs",
"inputParams": [
{
"label": "System Message",
"name": "systemMessagePrompt",
"label": "Chain Name",
"name": "chainName",
"type": "string",
"rows": 3,
"placeholder": "You are a helpful assistant that translates {input_language} to {output_language}."
},
{
"label": "Human Message",
"name": "humanMessagePrompt",
"type": "string",
"rows": 3,
"placeholder": "{text}"
"placeholder": "Name Your Chain",
"optional": true,
"id": "llmChain_0-input-chainName-string"
}
],
"inputAnchors": [
{
"label": "Language Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "llmChain_0-input-model-BaseLanguageModel"
},
{
"label": "Prompt",
"name": "prompt",
"type": "BasePromptTemplate",
"id": "llmChain_0-input-prompt-BasePromptTemplate"
}
],
"inputAnchors": [],
"inputs": {
"systemMessagePrompt": "You are a helpful assistant that translates {input_language} to {output_language}.",
"humanMessagePrompt": "{input}"
"model": "{{chatOpenAI_0.data.instance}}",
"prompt": "{{chatPromptTemplate_0.data.instance}}",
"chainName": "Language Translation"
},
"outputAnchors": [
{
"id": "chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate",
"name": "chatPromptTemplate",
"label": "ChatPromptTemplate",
"type": "ChatPromptTemplate | BaseChatPromptTemplate | BasePromptTemplate"
"name": "output",
"label": "Output",
"type": "options",
"options": [
{
"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
"name": "llmChain",
"label": "LLM Chain",
"type": "LLMChain | BaseChain"
},
{
"id": "llmChain_0-output-outputPrediction-string",
"name": "outputPrediction",
"label": "Output Prediction",
"type": "string"
}
],
"default": "llmChain"
}
],
"outputs": {
"output": "llmChain"
},
"selected": false
},
"selected": false,
"dragging": false,
"positionAbsolute": {
"x": 524,
"y": 237
}
"x": 1136.5578350285277,
"y": 619.2492937692573
},
"dragging": false
},
{
"width": 300,
"height": 472,
"id": "chatOpenAI_0",
"position": {
"x": 855.1997276913991,
"y": 24.090553068402556
"x": 776.3729862229602,
"y": 290.4580650723551
},
"type": "customNode",
"data": {
@@ -77,7 +102,8 @@
{
"label": "OpenAI Api Key",
"name": "openAIApiKey",
"type": "password"
"type": "password",
"id": "chatOpenAI_0-input-openAIApiKey-password"
},
{
"label": "Model Name",
@@ -106,20 +132,22 @@
}
],
"default": "gpt-3.5-turbo",
"optional": true
"optional": true,
"id": "chatOpenAI_0-input-modelName-options"
},
{
"label": "Temperature",
"name": "temperature",
"type": "number",
"default": 0.9,
"optional": true
"optional": true,
"id": "chatOpenAI_0-input-temperature-number"
}
],
"inputAnchors": [],
"inputs": {
"modelName": "gpt-3.5-turbo",
"temperature": 0.9
"temperature": "0"
},
"outputAnchors": [
{
@@ -129,75 +157,83 @@
"type": "ChatOpenAI | BaseChatModel | BaseLanguageModel"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 855.1997276913991,
"y": 24.090553068402556
"x": 776.3729862229602,
"y": 290.4580650723551
},
"dragging": false
},
{
"width": 300,
"height": 461,
"id": "llmChain_0",
"height": 710,
"id": "chatPromptTemplate_0",
"position": {
"x": 1192.2235692202612,
"y": 361.71736677076257
"x": 428.40848918154023,
"y": 291.77611240963313
},
"type": "customNode",
"data": {
"id": "llmChain_0",
"label": "LLM Chain",
"name": "llmChain",
"type": "LLMChain",
"baseClasses": ["LLMChain", "BaseChain"],
"category": "Chains",
"description": "Chain to run queries against LLMs",
"id": "chatPromptTemplate_0",
"label": "Chat Prompt Template",
"name": "chatPromptTemplate",
"type": "ChatPromptTemplate",
"baseClasses": ["ChatPromptTemplate", "BaseChatPromptTemplate", "BasePromptTemplate"],
"category": "Prompts",
"description": "Schema to represent a chat prompt",
"inputParams": [
{
"label": "System Message",
"name": "systemMessagePrompt",
"type": "string",
"rows": 4,
"placeholder": "You are a helpful assistant that translates {input_language} to {output_language}.",
"id": "chatPromptTemplate_0-input-systemMessagePrompt-string"
},
{
"label": "Human Message",
"name": "humanMessagePrompt",
"type": "string",
"rows": 4,
"placeholder": "{text}",
"id": "chatPromptTemplate_0-input-humanMessagePrompt-string"
},
{
"label": "Format Prompt Values",
"name": "promptValues",
"type": "string",
"rows": 5,
"rows": 4,
"placeholder": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}",
"optional": true
}
],
"inputAnchors": [
{
"label": "Language Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "llmChain_0-input-model-BaseLanguageModel"
},
{
"label": "Prompt",
"name": "prompt",
"type": "BasePromptTemplate",
"id": "llmChain_0-input-prompt-BasePromptTemplate"
"optional": true,
"acceptVariable": true,
"list": true,
"id": "chatPromptTemplate_0-input-promptValues-string"
}
],
"inputAnchors": [],
"inputs": {
"model": "{{chatOpenAI_0.data.instance}}",
"prompt": "{{chatPromptTemplate_0.data.instance}}",
"systemMessagePrompt": "You are a helpful assistant that translates {input_language} to {output_language}.",
"humanMessagePrompt": "{input}",
"promptValues": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}"
},
"outputAnchors": [
{
"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
"name": "llmChain",
"label": "LLMChain",
"type": "LLMChain | BaseChain"
"id": "chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate",
"name": "chatPromptTemplate",
"label": "ChatPromptTemplate",
"type": "ChatPromptTemplate | BaseChatPromptTemplate | BasePromptTemplate"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 1192.2235692202612,
"y": 361.71736677076257
"x": 428.40848918154023,
"y": 291.77611240963313
},
"dragging": false
}
packages/server/src/ChatflowPool.ts
+6 -5
View File
@@ -1,9 +1,8 @@
import { INodeData } from 'flowise-components'
import { IActiveChatflows } from './Interface'
import { IActiveChatflows, INodeData, IReactFlowNode } from './Interface'
/**
* This pool is to keep track of active test triggers (event listeners),
* so we can clear the event listeners whenever user refresh or exit page
* This pool is to keep track of active chatflow pools
* so we can prevent building langchain flow all over again
*/
export class ChatflowPool {
activeChatflows: IActiveChatflows = {}
@@ -12,9 +11,11 @@ export class ChatflowPool {
* Add to the pool
* @param {string} chatflowid
* @param {INodeData} endingNodeData
* @param {IReactFlowNode[]} startingNodes
*/
add(chatflowid: string, endingNodeData: INodeData) {
add(chatflowid: string, endingNodeData: INodeData, startingNodes: IReactFlowNode[]) {
this.activeChatflows[chatflowid] = {
startingNodes,
endingNodeData,
inSync: true
}
packages/server/src/Interface.ts
+8 -1
View File
@@ -1,4 +1,4 @@
import { INode, INodeData } from 'flowise-components'
import { INode, INodeData as INodeDataFromComponent, INodeParams } from 'flowise-components'
export type MessageType = 'apiMessage' | 'userMessage'
@@ -38,6 +38,12 @@ export interface INodeDirectedGraph {
[key: string]: string[]
}
export interface INodeData extends INodeDataFromComponent {
inputAnchors: INodeParams[]
inputParams: INodeParams[]
outputAnchors: INodeParams[]
}
export interface IReactFlowNode {
id: string
position: {
@@ -111,6 +117,7 @@ export interface IncomingInput {
export interface IActiveChatflows {
[key: string]: {
startingNodes: IReactFlowNode[]
endingNodeData: INodeData
inSync: boolean
}
packages/server/src/index.ts
+44 -10
View File
@@ -4,15 +4,22 @@ import cors from 'cors'
import http from 'http'
import * as fs from 'fs'
import { IChatFlow, IncomingInput, IReactFlowNode, IReactFlowObject } from './Interface'
import { getNodeModulesPackagePath, getStartingNodes, buildLangchain, getEndingNode, constructGraphs } from './utils'
import { IChatFlow, IncomingInput, IReactFlowNode, IReactFlowObject, INodeData } from './Interface'
import {
getNodeModulesPackagePath,
getStartingNodes,
buildLangchain,
getEndingNode,
constructGraphs,
resolveVariables,
isStartNodeDependOnInput
} from './utils'
import { cloneDeep } from 'lodash'
import { getDataSource } from './DataSource'
import { NodesPool } from './NodesPool'
import { ChatFlow } from './entity/ChatFlow'
import { ChatMessage } from './entity/ChatMessage'
import { ChatflowPool } from './ChatflowPool'
import { INodeData } from 'flowise-components'
export class App {
app: express.Application
@@ -196,12 +203,19 @@ export class App {
let nodeToExecuteData: INodeData
/* Check if:
* - Node Data already exists in pool
* - Still in sync (i.e the flow has not been modified since)
* - Flow doesn't start with nodes that depend on incomingInput.question
***/
if (
Object.prototype.hasOwnProperty.call(this.chatflowPool.activeChatflows, chatflowid) &&
this.chatflowPool.activeChatflows[chatflowid].inSync
this.chatflowPool.activeChatflows[chatflowid].inSync &&
!isStartNodeDependOnInput(this.chatflowPool.activeChatflows[chatflowid].startingNodes)
) {
nodeToExecuteData = this.chatflowPool.activeChatflows[chatflowid].endingNodeData
} else {
/*** Get chatflows and prepare data ***/
const chatflow = await this.AppDataSource.getRepository(ChatFlow).findOneBy({
id: chatflowid
})
@@ -209,33 +223,53 @@ export class App {
const flowData = chatflow.flowData
const parsedFlowData: IReactFlowObject = JSON.parse(flowData)
const nodes = parsedFlowData.nodes
const edges = parsedFlowData.edges
/*** Get Ending Node with Directed Graph ***/
const { graph, nodeDependencies } = constructGraphs(parsedFlowData.nodes, parsedFlowData.edges)
const { graph, nodeDependencies } = constructGraphs(nodes, edges)
const directedGraph = graph
const endingNodeId = getEndingNode(nodeDependencies, directedGraph)
if (!endingNodeId) return res.status(500).send(`Ending node must be either a Chain or Agent`)
const endingNodeData = nodes.find((nd) => nd.id === endingNodeId)?.data
if (!endingNodeData) return res.status(500).send(`Ending node must be either a Chain or Agent`)
if (
endingNodeData.outputs &&
Object.keys(endingNodeData.outputs).length &&
!Object.values(endingNodeData.outputs).includes(endingNodeData.name)
) {
return res
.status(500)
.send(
`Output of ${endingNodeData.label} (${endingNodeData.id}) must be ${endingNodeData.label}, can't be an Output Prediction`
)
}
/*** Get Starting Nodes with Non-Directed Graph ***/
const constructedObj = constructGraphs(parsedFlowData.nodes, parsedFlowData.edges, true)
const constructedObj = constructGraphs(nodes, edges, true)
const nonDirectedGraph = constructedObj.graph
const { startingNodeIds, depthQueue } = getStartingNodes(nonDirectedGraph, endingNodeId)
/*** BFS to traverse from Starting Nodes to Ending Node ***/
const reactFlowNodes = await buildLangchain(
startingNodeIds,
parsedFlowData.nodes,
nodes,
graph,
depthQueue,
this.nodesPool.componentNodes
this.nodesPool.componentNodes,
incomingInput.question
)
const nodeToExecute = reactFlowNodes.find((node: IReactFlowNode) => node.id === endingNodeId)
if (!nodeToExecute) return res.status(404).send(`Node ${endingNodeId} not found`)
nodeToExecuteData = nodeToExecute.data
const reactFlowNodeData: INodeData = resolveVariables(nodeToExecute.data, reactFlowNodes, incomingInput.question)
nodeToExecuteData = reactFlowNodeData
this.chatflowPool.add(chatflowid, nodeToExecuteData)
const startingNodes = nodes.filter((nd) => startingNodeIds.includes(nd.id))
this.chatflowPool.add(chatflowid, nodeToExecuteData, startingNodes)
}
const nodeInstanceFilePath = this.nodesPool.componentNodes[nodeToExecuteData.name].filePath as string
packages/server/src/utils/index.ts
+57 -12
View File
@@ -8,10 +8,14 @@ import {
INodeDirectedGraph,
INodeQueue,
IReactFlowEdge,
IReactFlowNode
IReactFlowNode,
IVariableDict,
INodeData
} from '../Interface'
import { cloneDeep, get } from 'lodash'
import { ICommonObject, INodeData } from 'flowise-components'
import { ICommonObject, getInputVariables } from 'flowise-components'
const QUESTION_VAR_PREFIX = 'question'
/**
* Returns the home folder path of the user if
@@ -166,13 +170,15 @@ export const getEndingNode = (nodeDependencies: INodeDependencies, graph: INodeD
* @param {INodeDirectedGraph} graph
* @param {IDepthQueue} depthQueue
* @param {IComponentNodes} componentNodes
* @param {string} question
*/
export const buildLangchain = async (
startingNodeIds: string[],
reactFlowNodes: IReactFlowNode[],
graph: INodeDirectedGraph,
depthQueue: IDepthQueue,
componentNodes: IComponentNodes
componentNodes: IComponentNodes,
question: string
) => {
const flowNodes = cloneDeep(reactFlowNodes)
@@ -200,9 +206,9 @@ export const buildLangchain = async (
const nodeModule = await import(nodeInstanceFilePath)
const newNodeInstance = new nodeModule.nodeClass()
const reactFlowNodeData: INodeData = resolveVariables(reactFlowNode.data, flowNodes)
const reactFlowNodeData: INodeData = resolveVariables(reactFlowNode.data, flowNodes, question)
flowNodes[nodeIndex].data.instance = await newNodeInstance.init(reactFlowNodeData)
flowNodes[nodeIndex].data.instance = await newNodeInstance.init(reactFlowNodeData, question)
} catch (e: any) {
console.error(e)
throw new Error(e)
@@ -247,11 +253,14 @@ export const buildLangchain = async (
* Get variable value from outputResponses.output
* @param {string} paramValue
* @param {IReactFlowNode[]} reactFlowNodes
* @param {string} question
* @param {boolean} isAcceptVariable
* @returns {string}
*/
export const getVariableValue = (paramValue: string, reactFlowNodes: IReactFlowNode[]) => {
export const getVariableValue = (paramValue: string, reactFlowNodes: IReactFlowNode[], question: string, isAcceptVariable = false) => {
let returnVal = paramValue
const variableStack = []
const variableDict = {} as IVariableDict
let startIdx = 0
const endIdx = returnVal.length - 1
@@ -269,17 +278,36 @@ export const getVariableValue = (paramValue: string, reactFlowNodes: IReactFlowN
const variableEndIdx = startIdx
const variableFullPath = returnVal.substring(variableStartIdx, variableEndIdx)
if (isAcceptVariable && variableFullPath === QUESTION_VAR_PREFIX) {
variableDict[`{{${variableFullPath}}}`] = question
}
// Split by first occurence of '.' to get just nodeId
const [variableNodeId, _] = variableFullPath.split('.')
const executedNode = reactFlowNodes.find((nd) => nd.id === variableNodeId)
if (executedNode) {
const variableInstance = get(executedNode.data, 'instance')
returnVal = variableInstance
const variableValue = get(executedNode.data, 'instance')
if (isAcceptVariable) {
variableDict[`{{${variableFullPath}}}`] = variableValue
} else {
returnVal = variableValue
}
}
variableStack.pop()
}
startIdx += 1
}
if (isAcceptVariable) {
const variablePaths = Object.keys(variableDict)
variablePaths.sort() // Sort by length of variable path because longer path could possibly contains nested variable
variablePaths.forEach((path) => {
const variableValue = variableDict[path]
// Replace all occurence
returnVal = returnVal.split(path).join(variableValue)
})
return returnVal
}
return returnVal
}
@@ -287,25 +315,26 @@ export const getVariableValue = (paramValue: string, reactFlowNodes: IReactFlowN
* Loop through each inputs and resolve variable if neccessary
* @param {INodeData} reactFlowNodeData
* @param {IReactFlowNode[]} reactFlowNodes
* @param {string} question
* @returns {INodeData}
*/
export const resolveVariables = (reactFlowNodeData: INodeData, reactFlowNodes: IReactFlowNode[]): INodeData => {
export const resolveVariables = (reactFlowNodeData: INodeData, reactFlowNodes: IReactFlowNode[], question: string): INodeData => {
const flowNodeData = cloneDeep(reactFlowNodeData)
const types = 'inputs'
const getParamValues = (paramsObj: ICommonObject) => {
for (const key in paramsObj) {
const paramValue: string = paramsObj[key]
if (Array.isArray(paramValue)) {
const resolvedInstances = []
for (const param of paramValue) {
const resolvedInstance = getVariableValue(param, reactFlowNodes)
const resolvedInstance = getVariableValue(param, reactFlowNodes, question)
resolvedInstances.push(resolvedInstance)
}
paramsObj[key] = resolvedInstances
} else {
const resolvedInstance = getVariableValue(paramValue, reactFlowNodes)
const isAcceptVariable = reactFlowNodeData.inputParams.find((param) => param.name === key)?.acceptVariable ?? false
const resolvedInstance = getVariableValue(paramValue, reactFlowNodes, question, isAcceptVariable)
paramsObj[key] = resolvedInstance
}
}
@@ -317,3 +346,19 @@ export const resolveVariables = (reactFlowNodeData: INodeData, reactFlowNodes: I
return flowNodeData
}
/**
* Rebuild flow if LLMChain has dependency on other chains
* User Question => Prompt_0 => LLMChain_0 => Prompt-1 => LLMChain_1
* @param {IReactFlowNode[]} startingNodes
* @returns {boolean}
*/
export const isStartNodeDependOnInput = (startingNodes: IReactFlowNode[]): boolean => {
for (const node of startingNodes) {
for (const inputName in node.data.inputs) {
const inputVariables = getInputVariables(node.data.inputs[inputName])
if (inputVariables.length > 0) return true
}
}
return false
}
packages/ui/.env.example
+1
View File
@@ -0,0 +1 @@
PORT=8080
packages/ui/package.json
+1
View File
@@ -25,6 +25,7 @@
"prismjs": "^1.28.0",
"prop-types": "^15.7.2",
"react": "^18.2.0",
"react-code-blocks": "^0.0.9-0",
"react-datepicker": "^4.8.0",
"react-device-detect": "^1.17.0",
"react-dom": "^18.2.0",
packages/ui/src/assets/images/cURL.svg
+1
View File
@@ -0,0 +1 @@
<svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><rect width="32" height="32" rx="4" fill="#1683a5"></rect><path d="M6.71,14A5,5,0,0,1,8.82,9.29l2.64-2.2c1.67-1.37,2.52-1.41,4.6-1.41H21.7c1.19,0,2.45.27,2.45,1.79s-1.4,1.78-2.45,1.78H15.44a3.31,3.31,0,0,0-2,.89L11.24,12c-.55.44-1,.81-1,1.52v4.41c0,.7.41,1.07,1,1.52l2.16,1.82a3.34,3.34,0,0,0,2,.89H21.7c1.05,0,2.45.23,2.45,1.78s-1.26,1.78-2.45,1.78H16.06c-2.08,0-2.94,0-4.6-1.4L8.82,22.09A5.05,5.05,0,0,1,6.71,17.4Z" fill="#fff"></path></svg>
Side by Side

After

Width:  |  Height:  |  Size: 670 B

packages/ui/src/assets/images/javascript.svg
+1
View File
@@ -0,0 +1 @@
<svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><rect width="32" height="32" fill="#f7df1e"></rect><path d="M21.5,25a3.27,3.27,0,0,0,3,1.83c1.25,0,2-.63,2-1.49,0-1-.81-1.39-2.19-2L23.56,23C21.39,22.1,20,20.94,20,18.49c0-2.25,1.72-4,4.41-4a4.44,4.44,0,0,1,4.27,2.41l-2.34,1.5a2,2,0,0,0-1.93-1.29,1.31,1.31,0,0,0-1.44,1.29c0,.9.56,1.27,1.85,1.83l.75.32c2.55,1.1,4,2.21,4,4.72,0,2.71-2.12,4.19-5,4.19a5.78,5.78,0,0,1-5.48-3.07Zm-10.63.26c.48.84.91,1.55,1.94,1.55s1.61-.39,1.61-1.89V14.69h3V25c0,3.11-1.83,4.53-4.49,4.53a4.66,4.66,0,0,1-4.51-2.75Z"></path></svg>
Side by Side

After

Width:  |  Height:  |  Size: 737 B

packages/ui/src/assets/images/python.svg
+1
View File
@@ -0,0 +1 @@
<svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M15.84.5a16.4,16.4,0,0,0-3.57.32C9.1,1.39,8.53,2.53,8.53,4.64V7.48H16v1H5.77a4.73,4.73,0,0,0-4.7,3.74,14.82,14.82,0,0,0,0,7.54c.57,2.28,1.86,3.82,4,3.82h2.6V20.14a4.73,4.73,0,0,1,4.63-4.63h7.38a3.72,3.72,0,0,0,3.73-3.73V4.64A4.16,4.16,0,0,0,19.65.82,20.49,20.49,0,0,0,15.84.5ZM11.78,2.77a1.39,1.39,0,0,1,1.38,1.46,1.37,1.37,0,0,1-1.38,1.38A1.42,1.42,0,0,1,10.4,4.23,1.44,1.44,0,0,1,11.78,2.77Z" fill="#5a9fd4"></path><path d="M16.16,31.5a16.4,16.4,0,0,0,3.57-.32c3.17-.57,3.74-1.71,3.74-3.82V24.52H16v-1H26.23a4.73,4.73,0,0,0,4.7-3.74,14.82,14.82,0,0,0,0-7.54c-.57-2.28-1.86-3.82-4-3.82h-2.6v3.41a4.73,4.73,0,0,1-4.63,4.63H12.35a3.72,3.72,0,0,0-3.73,3.73v7.14a4.16,4.16,0,0,0,3.73,3.82A20.49,20.49,0,0,0,16.16,31.5Zm4.06-2.27a1.39,1.39,0,0,1-1.38-1.46,1.37,1.37,0,0,1,1.38-1.38,1.42,1.42,0,0,1,1.38,1.38A1.44,1.44,0,0,1,20.22,29.23Z" fill="#ffd43b"></path></svg>
Side by Side

After

Width:  |  Height:  |  Size: 1.1 KiB

packages/ui/src/store/constant.js
+1
View File
@@ -2,4 +2,5 @@
export const gridSpacing = 3
export const drawerWidth = 260
export const appDrawerWidth = 320
export const maxScroll = 100000
export const baseURL = process.env.NODE_ENV === 'production' ? window.location.origin : window.location.origin.replace(':8080', ':3000')
packages/ui/src/store/context/ReactFlowContext.js
+48 -1
View File
@@ -1,9 +1,12 @@
import { createContext, useState } from 'react'
import PropTypes from 'prop-types'
import { getUniqueNodeId } from 'utils/genericHelper'
import { cloneDeep } from 'lodash'
const initialValue = {
reactFlowInstance: null,
setReactFlowInstance: () => {},
duplicateNode: () => {},
deleteNode: () => {},
deleteEdge: () => {}
}
@@ -40,9 +43,13 @@ export const ReactFlowContext = ({ children }) => {
if (node.id === targetNodeId) {
let value
const inputAnchor = node.data.inputAnchors.find((ancr) => ancr.name === targetInput)
const inputParam = node.data.inputParams.find((param) => param.name === targetInput)
if (inputAnchor && inputAnchor.list) {
const values = node.data.inputs[targetInput] || []
value = values.filter((item) => !item.includes(sourceNodeId))
} else if (inputParam && inputParam.acceptVariable) {
value = node.data.inputs[targetInput].replace(`{{${sourceNodeId}.data.instance}}`, '') || ''
} else {
value = ''
}
@@ -60,13 +67,53 @@ export const ReactFlowContext = ({ children }) => {
}
}
const duplicateNode = (id) => {
const nodes = reactFlowInstance.getNodes()
const originalNode = nodes.find((n) => n.id === id)
if (originalNode) {
const newNodeId = getUniqueNodeId(originalNode.data, nodes)
const clonedNode = cloneDeep(originalNode)
const duplicatedNode = {
...clonedNode,
id: newNodeId,
position: {
x: clonedNode.position.x + 400,
y: clonedNode.position.y
},
positionAbsolute: {
x: clonedNode.positionAbsolute.x + 400,
y: clonedNode.positionAbsolute.y
},
data: {
...clonedNode.data,
id: newNodeId
},
selected: false
}
const dataKeys = ['inputParams', 'inputAnchors', 'outputAnchors']
for (const key of dataKeys) {
for (const item of duplicatedNode.data[key]) {
if (item.id) {
item.id = item.id.replace(id, newNodeId)
}
}
}
reactFlowInstance.setNodes([...nodes, duplicatedNode])
}
}
return (
<flowContext.Provider
value={{
reactFlowInstance,
setReactFlowInstance,
deleteNode,
deleteEdge
deleteEdge,
duplicateNode
}}
>
{children}
packages/ui/src/themes/palette.js
-2
View File
@@ -80,8 +80,6 @@ export default function themePalette(theme) {
main: theme.customization.isDarkMode ? theme.colors?.darkPrimary800 : theme.colors?.grey50
},
canvasHeader: {
executionLight: theme.colors?.successLight,
executionDark: theme.colors?.successDark,
deployLight: theme.colors?.primaryLight,
deployDark: theme.colors?.primaryDark,
saveLight: theme.colors?.secondaryLight,
packages/ui/src/ui-component/dialog/APICodeDialog.js
+163
View File
@@ -0,0 +1,163 @@
import { createPortal } from 'react-dom'
import { useState } from 'react'
import PropTypes from 'prop-types'
import { Tabs, Tab, Dialog, DialogContent, DialogTitle, Box } from '@mui/material'
import { CopyBlock, atomOneDark } from 'react-code-blocks'
import { baseURL } from 'store/constant'
import pythonSVG from 'assets/images/python.svg'
import javascriptSVG from 'assets/images/javascript.svg'
import cURLSVG from 'assets/images/cURL.svg'
function TabPanel(props) {
const { children, value, index, ...other } = props
return (
<div
role='tabpanel'
hidden={value !== index}
id={`attachment-tabpanel-${index}`}
aria-labelledby={`attachment-tab-${index}`}
{...other}
>
{value === index && <Box sx={{ p: 1 }}>{children}</Box>}
</div>
)
}
TabPanel.propTypes = {
children: PropTypes.node,
index: PropTypes.number.isRequired,
value: PropTypes.number.isRequired
}
function a11yProps(index) {
return {
id: `attachment-tab-${index}`,
'aria-controls': `attachment-tabpanel-${index}`
}
}
const APICodeDialog = ({ show, dialogProps, onCancel }) => {
const portalElement = document.getElementById('portal')
const codes = ['Python', 'JavaScript', 'cURL']
const [value, setValue] = useState(0)
const handleChange = (event, newValue) => {
setValue(newValue)
}
const getCode = (codeLang) => {
if (codeLang === 'Python') {
return `import requests
API_URL = "${baseURL}/api/v1/prediction/${dialogProps.chatflowid}"
def query(payload):
response = requests.post(API_URL, json=payload)
return response.json()
output = query({
"question": "Hey, how are you?",
})
`
} else if (codeLang === 'JavaScript') {
return `async function query(data) {
const response = await fetch(
"${baseURL}/api/v1/prediction/${dialogProps.chatflowid}",
{
method: "POST",
body: {
"question": "Hey, how are you?"
},
}
);
const result = await response.json();
return result;
}
`
} else if (codeLang === 'cURL') {
return `curl ${baseURL}/api/v1/prediction/${dialogProps.chatflowid} \\
-X POST \\
-d '{"question": "Hey, how are you?"}'`
}
return ''
}
const getLang = (codeLang) => {
if (codeLang === 'Python') {
return 'python'
} else if (codeLang === 'JavaScript') {
return 'javascript'
} else if (codeLang === 'cURL') {
return 'bash'
}
return 'python'
}
const getSVG = (codeLang) => {
if (codeLang === 'Python') {
return pythonSVG
} else if (codeLang === 'JavaScript') {
return javascriptSVG
} else if (codeLang === 'cURL') {
return cURLSVG
}
return pythonSVG
}
const component = show ? (
<Dialog
open={show}
fullWidth
maxWidth='md'
onClose={onCancel}
aria-labelledby='alert-dialog-title'
aria-describedby='alert-dialog-description'
>
<DialogTitle sx={{ fontSize: '1rem' }} id='alert-dialog-title'>
{dialogProps.title}
</DialogTitle>
<DialogContent>
<Tabs value={value} onChange={handleChange} aria-label='tabs'>
{codes.map((codeLang, index) => (
<Tab
icon={
<img
style={{ objectFit: 'cover', height: 'auto', width: 'auto', marginLeft: 10 }}
src={getSVG(codeLang)}
alt='code'
/>
}
iconPosition='left'
key={index}
label={codeLang}
{...a11yProps(index)}
></Tab>
))}
</Tabs>
<div style={{ marginTop: 10 }}></div>
{codes.map((codeLang, index) => (
<TabPanel key={index} value={value} index={index}>
<CopyBlock
theme={atomOneDark}
text={getCode(codeLang)}
language={getLang(codeLang)}
showLineNumbers={false}
wrapLines
/>
</TabPanel>
))}
</DialogContent>
</Dialog>
) : null
return createPortal(component, portalElement)
}
APICodeDialog.propTypes = {
show: PropTypes.bool,
dialogProps: PropTypes.object,
onCancel: PropTypes.func
}
export default APICodeDialog
packages/ui/src/ui-component/dialog/EditPromptValuesDialog.css
+6
View File
@@ -0,0 +1,6 @@
.editor__textarea {
outline: 0;
}
.editor__textarea::placeholder {
color: rgba(120, 120, 120, 0.5);
}
packages/ui/src/ui-component/dialog/EditPromptValuesDialog.js
+256
View File
@@ -0,0 +1,256 @@
import { createPortal } from 'react-dom'
import { useState, useEffect } from 'react'
import { useSelector } from 'react-redux'
import PropTypes from 'prop-types'
import {
Button,
Dialog,
DialogActions,
DialogContent,
Box,
List,
ListItemButton,
ListItem,
ListItemAvatar,
ListItemText,
Typography,
Stack
} from '@mui/material'
import { useTheme } from '@mui/material/styles'
import PerfectScrollbar from 'react-perfect-scrollbar'
import { StyledButton } from 'ui-component/button/StyledButton'
import { DarkCodeEditor } from 'ui-component/editor/DarkCodeEditor'
import { LightCodeEditor } from 'ui-component/editor/LightCodeEditor'
import './EditPromptValuesDialog.css'
import { baseURL } from 'store/constant'
const EditPromptValuesDialog = ({ show, dialogProps, onCancel, onConfirm }) => {
const portalElement = document.getElementById('portal')
const theme = useTheme()
const customization = useSelector((state) => state.customization)
const languageType = 'json'
const [inputValue, setInputValue] = useState('')
const [inputParam, setInputParam] = useState(null)
const [textCursorPosition, setTextCursorPosition] = useState({})
useEffect(() => {
if (dialogProps.value) setInputValue(dialogProps.value)
if (dialogProps.inputParam) setInputParam(dialogProps.inputParam)
return () => {
setInputValue('')
setInputParam(null)
setTextCursorPosition({})
}
}, [dialogProps])
const onMouseUp = (e) => {
if (e.target && e.target.selectionEnd && e.target.value) {
const cursorPosition = e.target.selectionEnd
const textBeforeCursorPosition = e.target.value.substring(0, cursorPosition)
const textAfterCursorPosition = e.target.value.substring(cursorPosition, e.target.value.length)
const body = {
textBeforeCursorPosition,
textAfterCursorPosition
}
setTextCursorPosition(body)
} else {
setTextCursorPosition({})
}
}
const onSelectOutputResponseClick = (node, isUserQuestion = false) => {
let variablePath = isUserQuestion ? `question` : `${node.id}.data.instance`
if (textCursorPosition) {
let newInput = ''
if (textCursorPosition.textBeforeCursorPosition === undefined && textCursorPosition.textAfterCursorPosition === undefined)
newInput = `${inputValue}${`{{${variablePath}}}`}`
else newInput = `${textCursorPosition.textBeforeCursorPosition}{{${variablePath}}}${textCursorPosition.textAfterCursorPosition}`
setInputValue(newInput)
}
}
const component = show ? (
<Dialog open={show} fullWidth maxWidth='md' aria-labelledby='alert-dialog-title' aria-describedby='alert-dialog-description'>
<DialogContent>
<div style={{ display: 'flex', flexDirection: 'row' }}>
{inputParam && inputParam.type === 'string' && (
<div style={{ flex: 70 }}>
<Typography sx={{ mb: 2, ml: 1 }} variant='h4'>
{inputParam.label}
</Typography>
<PerfectScrollbar
style={{
border: '1px solid',
borderColor: theme.palette.grey['500'],
borderRadius: '12px',
height: '100%',
maxHeight: 'calc(100vh - 220px)',
overflowX: 'hidden',
backgroundColor: 'white'
}}
>
{customization.isDarkMode ? (
<DarkCodeEditor
disabled={dialogProps.disabled}
value={inputValue}
onValueChange={(code) => setInputValue(code)}
placeholder={inputParam.placeholder}
type={languageType}
onMouseUp={(e) => onMouseUp(e)}
onBlur={(e) => onMouseUp(e)}
style={{
fontSize: '0.875rem',
minHeight: 'calc(100vh - 220px)',
width: '100%'
}}
/>
) : (
<LightCodeEditor
disabled={dialogProps.disabled}
value={inputValue}
onValueChange={(code) => setInputValue(code)}
placeholder={inputParam.placeholder}
type={languageType}
onMouseUp={(e) => onMouseUp(e)}
onBlur={(e) => onMouseUp(e)}
style={{
fontSize: '0.875rem',
minHeight: 'calc(100vh - 220px)',
width: '100%'
}}
/>
)}
</PerfectScrollbar>
</div>
)}
{!dialogProps.disabled && inputParam && inputParam.acceptVariable && (
<div style={{ flex: 30 }}>
<Stack flexDirection='row' sx={{ mb: 1, ml: 2 }}>
<Typography variant='h4'>Select Variable</Typography>
</Stack>
<PerfectScrollbar style={{ height: '100%', maxHeight: 'calc(100vh - 220px)', overflowX: 'hidden' }}>
<Box sx={{ pl: 2, pr: 2 }}>
<List>
<ListItemButton
sx={{
p: 0,
borderRadius: `${customization.borderRadius}px`,
boxShadow: '0 2px 14px 0 rgb(32 40 45 / 8%)',
mb: 1
}}
disabled={dialogProps.disabled}
onClick={() => onSelectOutputResponseClick(null, true)}
>
<ListItem alignItems='center'>
<ListItemAvatar>
<div
style={{
width: 50,
height: 50,
borderRadius: '50%',
backgroundColor: 'white'
}}
>
<img
style={{
width: '100%',
height: '100%',
padding: 10,
objectFit: 'contain'
}}
alt='AI'
src='https://raw.githubusercontent.com/zahidkhawaja/langchain-chat-nextjs/main/public/parroticon.png'
/>
</div>
</ListItemAvatar>
<ListItemText
sx={{ ml: 1 }}
primary='question'
secondary={`User's question from chatbox`}
/>
</ListItem>
</ListItemButton>
{dialogProps.availableNodesForVariable &&
dialogProps.availableNodesForVariable.length > 0 &&
dialogProps.availableNodesForVariable.map((node, index) => {
const selectedOutputAnchor = node.data.outputAnchors[0].options.find(
(ancr) => ancr.name === node.data.outputs['output']
)
return (
<ListItemButton
key={index}
sx={{
p: 0,
borderRadius: `${customization.borderRadius}px`,
boxShadow: '0 2px 14px 0 rgb(32 40 45 / 8%)',
mb: 1
}}
disabled={dialogProps.disabled}
onClick={() => onSelectOutputResponseClick(node)}
>
<ListItem alignItems='center'>
<ListItemAvatar>
<div
style={{
width: 50,
height: 50,
borderRadius: '50%',
backgroundColor: 'white'
}}
>
<img
style={{
width: '100%',
height: '100%',
padding: 10,
objectFit: 'contain'
}}
alt={node.data.name}
src={`${baseURL}/api/v1/node-icon/${node.data.name}`}
/>
</div>
</ListItemAvatar>
<ListItemText
sx={{ ml: 1 }}
primary={
node.data.inputs.chainName ? node.data.inputs.chainName : node.data.id
}
secondary={`${selectedOutputAnchor?.label ?? 'output'} from ${
node.data.label
}`}
/>
</ListItem>
</ListItemButton>
)
})}
</List>
</Box>
</PerfectScrollbar>
</div>
)}
</div>
</DialogContent>
<DialogActions>
<Button onClick={onCancel}>{dialogProps.cancelButtonName}</Button>
<StyledButton disabled={dialogProps.disabled} variant='contained' onClick={() => onConfirm(inputValue, inputParam.name)}>
{dialogProps.confirmButtonName}
</StyledButton>
</DialogActions>
</Dialog>
) : null
return createPortal(component, portalElement)
}
EditPromptValuesDialog.propTypes = {
show: PropTypes.bool,
dialogProps: PropTypes.object,
onCancel: PropTypes.func,
onConfirm: PropTypes.func
}
export default EditPromptValuesDialog
packages/ui/src/ui-component/dropdown/Dropdown.js
+4 -2
View File
@@ -18,7 +18,7 @@ const StyledPopper = styled(Popper)({
}
})
export const Dropdown = ({ name, value, options, onSelect, disabled = false }) => {
export const Dropdown = ({ name, value, options, onSelect, disabled = false, disableClearable = false }) => {
const customization = useSelector((state) => state.customization)
const findMatchingOptions = (options = [], value) => options.find((option) => option.name === value)
const getDefaultOptionValue = () => ''
@@ -29,6 +29,7 @@ export const Dropdown = ({ name, value, options, onSelect, disabled = false }) =
<Autocomplete
id={name}
disabled={disabled}
disableClearable={disableClearable}
size='small'
options={options || []}
value={findMatchingOptions(options, internalValue) || getDefaultOptionValue()}
@@ -59,5 +60,6 @@ Dropdown.propTypes = {
value: PropTypes.string,
options: PropTypes.array,
onSelect: PropTypes.func,
disabled: PropTypes.bool
disabled: PropTypes.bool,
disableClearable: PropTypes.bool
}
packages/ui/src/ui-component/editor/DarkCodeEditor.js
+3 -1
View File
@@ -8,11 +8,12 @@ import './prism-dark.css'
import PropTypes from 'prop-types'
import { useTheme } from '@mui/material/styles'
export const DarkCodeEditor = ({ value, placeholder, type, style, onValueChange, onMouseUp, onBlur }) => {
export const DarkCodeEditor = ({ value, placeholder, disabled = false, type, style, onValueChange, onMouseUp, onBlur }) => {
const theme = useTheme()
return (
<Editor
disabled={disabled}
value={value}
placeholder={placeholder}
highlight={(code) => highlight(code, type === 'json' ? languages.json : languages.js)}
@@ -32,6 +33,7 @@ export const DarkCodeEditor = ({ value, placeholder, type, style, onValueChange,
DarkCodeEditor.propTypes = {
value: PropTypes.string,
placeholder: PropTypes.string,
disabled: PropTypes.bool,
type: PropTypes.string,
style: PropTypes.object,
onValueChange: PropTypes.func,
packages/ui/src/ui-component/editor/LightCodeEditor.js
+3 -1
View File
@@ -8,11 +8,12 @@ import './prism-light.css'
import PropTypes from 'prop-types'
import { useTheme } from '@mui/material/styles'
export const LightCodeEditor = ({ value, placeholder, type, style, onValueChange, onMouseUp, onBlur }) => {
export const LightCodeEditor = ({ value, placeholder, disabled = false, type, style, onValueChange, onMouseUp, onBlur }) => {
const theme = useTheme()
return (
<Editor
disabled={disabled}
value={value}
placeholder={placeholder}
highlight={(code) => highlight(code, type === 'json' ? languages.json : languages.js)}
@@ -32,6 +33,7 @@ export const LightCodeEditor = ({ value, placeholder, type, style, onValueChange
LightCodeEditor.propTypes = {
value: PropTypes.string,
placeholder: PropTypes.string,
disabled: PropTypes.bool,
type: PropTypes.string,
style: PropTypes.object,
onValueChange: PropTypes.func,
packages/ui/src/ui-component/input/Input.js
+53 -19
View File
@@ -1,28 +1,58 @@
import { useState } from 'react'
import PropTypes from 'prop-types'
import { FormControl, OutlinedInput } from '@mui/material'
import EditPromptValuesDialog from 'ui-component/dialog/EditPromptValuesDialog'
export const Input = ({ inputParam, value, onChange, disabled = false }) => {
export const Input = ({ inputParam, value, onChange, disabled = false, showDialog, dialogProps, onDialogCancel, onDialogConfirm }) => {
const [myValue, setMyValue] = useState(value ?? '')
const getInputType = (type) => {
switch (type) {
case 'string':
return 'text'
case 'password':
return 'password'
case 'number':
return 'number'
default:
return 'text'
}
}
return (
<FormControl sx={{ mt: 1, width: '100%' }} size='small'>
<OutlinedInput
id={inputParam.name}
size='small'
disabled={disabled}
type={inputParam.type === 'string' ? 'text' : inputParam.type}
placeholder={inputParam.placeholder}
multiline={!!inputParam.rows}
maxRows={inputParam.rows || 0}
minRows={inputParam.rows || 0}
value={myValue}
name={inputParam.name}
onChange={(e) => {
setMyValue(e.target.value)
onChange(e.target.value)
<>
<FormControl sx={{ mt: 1, width: '100%' }} size='small'>
<OutlinedInput
id={inputParam.name}
size='small'
disabled={disabled}
type={getInputType(inputParam.type)}
placeholder={inputParam.placeholder}
multiline={!!inputParam.rows}
rows={inputParam.rows ?? 1}
value={myValue}
name={inputParam.name}
onChange={(e) => {
setMyValue(e.target.value)
onChange(e.target.value)
}}
inputProps={{
style: {
height: inputParam.rows ? '90px' : 'inherit'
}
}}
/>
</FormControl>
<EditPromptValuesDialog
show={showDialog}
dialogProps={dialogProps}
onCancel={onDialogCancel}
onConfirm={(newValue, inputParamName) => {
setMyValue(newValue)
onDialogConfirm(newValue, inputParamName)
}}
/>
</FormControl>
></EditPromptValuesDialog>
</>
)
}
@@ -30,5 +60,9 @@ Input.propTypes = {
inputParam: PropTypes.object,
value: PropTypes.string,
onChange: PropTypes.func,
disabled: PropTypes.bool
disabled: PropTypes.bool,
showDialog: PropTypes.bool,
dialogProps: PropTypes.object,
onDialogCancel: PropTypes.func,
onDialogConfirm: PropTypes.func
}
packages/ui/src/ui-component/switch/Switch.js
+28
View File
@@ -0,0 +1,28 @@
import { useState } from 'react'
import PropTypes from 'prop-types'
import { FormControl, Switch } from '@mui/material'
export const SwitchInput = ({ value, onChange, disabled = false }) => {
const [myValue, setMyValue] = useState(!!value ?? false)
return (
<>
<FormControl sx={{ mt: 1, width: '100%' }} size='small'>
<Switch
disabled={disabled}
checked={myValue}
onChange={(event) => {
setMyValue(event.target.checked)
onChange(event.target.checked)
}}
/>
</FormControl>
</>
)
}
SwitchInput.propTypes = {
value: PropTypes.string,
onChange: PropTypes.func,
disabled: PropTypes.bool
}
packages/ui/src/ui-component/tooltip/TooltipWithParser.js
+3 -7
View File
@@ -9,13 +9,9 @@ export const TooltipWithParser = ({ title }) => {
return (
<Tooltip title={parser(title)} placement='right'>
<div style={{ display: 'flex', alignItems: 'center' }}>
<IconButton sx={{ height: 25, width: 25 }}>
<Info
style={{ background: 'transparent', color: customization.isDarkMode ? 'white' : 'inherit', height: 18, width: 18 }}
/>
</IconButton>
</div>
<IconButton sx={{ height: 25, width: 25 }}>
<Info style={{ background: 'transparent', color: customization.isDarkMode ? 'white' : 'inherit', height: 18, width: 18 }} />
</IconButton>
</Tooltip>
)
}
packages/ui/src/utils/genericHelper.js
+118 -48
View File
@@ -22,23 +22,12 @@ export const getUniqueNodeId = (nodeData, nodes) => {
return nodeId
}
export const initializeNodeData = (nodeParams) => {
export const initializeDefaultNodeData = (nodeParams) => {
const initialValues = {}
for (let i = 0; i < nodeParams.length; i += 1) {
const input = nodeParams[i]
// Load from nodeParams default values
initialValues[input.name] = input.default || ''
// Special case for array, always initialize the item if default is not set
if (input.type === 'array' && !input.default) {
const newObj = {}
for (let j = 0; j < input.array.length; j += 1) {
newObj[input.array[j].name] = input.array[j].default || ''
}
initialValues[input.name] = [newObj]
}
}
return initialValues
@@ -46,62 +35,119 @@ export const initializeNodeData = (nodeParams) => {
export const initNode = (nodeData, newNodeId) => {
const inputAnchors = []
const inputParams = []
const incoming = nodeData.inputs ? nodeData.inputs.length : 0
const outgoing = 1
const whitelistTypes = ['asyncOptions', 'options', 'string', 'number', 'boolean', 'password', 'json', 'code', 'date', 'file', 'folder']
const whitelistTypes = ['options', 'string', 'number', 'boolean', 'password', 'json', 'code', 'date', 'file', 'folder']
for (let i = 0; i < incoming; i += 1) {
if (whitelistTypes.includes(nodeData.inputs[i].type)) continue
const newInput = {
...nodeData.inputs[i],
id: `${newNodeId}-input-${nodeData.inputs[i].name}-${nodeData.inputs[i].type}`
}
inputAnchors.push(newInput)
if (whitelistTypes.includes(nodeData.inputs[i].type)) {
inputParams.push(newInput)
} else {
inputAnchors.push(newInput)
}
}
const outputAnchors = []
for (let i = 0; i < outgoing; i += 1) {
const newOutput = {
id: `${newNodeId}-output-${nodeData.name}-${nodeData.baseClasses.join('|')}`,
name: nodeData.name,
label: nodeData.type,
type: nodeData.baseClasses.join(' | ')
if (nodeData.outputs && nodeData.outputs.length) {
const options = []
for (let j = 0; j < nodeData.outputs.length; j += 1) {
let baseClasses = ''
let type = ''
const outputBaseClasses = nodeData.outputs[j].baseClasses ?? []
if (outputBaseClasses.length > 1) {
baseClasses = outputBaseClasses.join('|')
type = outputBaseClasses.join(' | ')
} else if (outputBaseClasses.length === 1) {
baseClasses = outputBaseClasses[0]
type = outputBaseClasses[0]
}
const newOutputOption = {
id: `${newNodeId}-output-${nodeData.outputs[j].name}-${baseClasses}`,
name: nodeData.outputs[j].name,
label: nodeData.outputs[j].label,
type
}
options.push(newOutputOption)
}
const newOutput = {
name: 'output',
label: 'Output',
type: 'options',
options,
default: nodeData.outputs[0].name
}
outputAnchors.push(newOutput)
} else {
const newOutput = {
id: `${newNodeId}-output-${nodeData.name}-${nodeData.baseClasses.join('|')}`,
name: nodeData.name,
label: nodeData.type,
type: nodeData.baseClasses.join(' | ')
}
outputAnchors.push(newOutput)
}
outputAnchors.push(newOutput)
}
nodeData.id = newNodeId
nodeData.inputAnchors = inputAnchors
nodeData.outputAnchors = outputAnchors
/*
Initial inputs = [
/* Initial
inputs = [
{
label: 'field_label',
name: 'field'
label: 'field_label_1',
name: 'string'
},
{
label: 'field_label_2',
name: 'CustomType'
}
]
// Turn into inputs object with default values
Converted inputs = { 'field': 'defaultvalue' }
=> Convert to inputs, inputParams, inputAnchors
=> inputs = { 'field': 'defaultvalue' } // Turn into inputs object with default values
// Move remaining inputs that are not part of inputAnchors to inputParams
inputParams = [
{
label: 'field_label',
name: 'field'
}
]
=> // For inputs that are part of whitelistTypes
inputParams = [
{
label: 'field_label_1',
name: 'string'
}
]
=> // For inputs that are not part of whitelistTypes
inputAnchors = [
{
label: 'field_label_2',
name: 'CustomType'
}
]
*/
if (nodeData.inputs) {
nodeData.inputParams = nodeData.inputs.filter(({ name }) => !nodeData.inputAnchors.some((exclude) => exclude.name === name))
nodeData.inputs = initializeNodeData(nodeData.inputs)
nodeData.inputAnchors = inputAnchors
nodeData.inputParams = inputParams
nodeData.inputs = initializeDefaultNodeData(nodeData.inputs)
} else {
nodeData.inputAnchors = []
nodeData.inputParams = []
nodeData.inputs = {}
}
if (nodeData.outputs) {
nodeData.outputs = initializeDefaultNodeData(outputAnchors)
} else {
nodeData.outputs = {}
}
nodeData.outputAnchors = outputAnchors
nodeData.id = newNodeId
return nodeData
}
@@ -133,7 +179,9 @@ export const isValidConnection = (connection, reactFlowInstance) => {
return true
}
} else {
const targetNodeInputAnchor = targetNode.data.inputAnchors.find((ancr) => ancr.id === targetHandle)
const targetNodeInputAnchor =
targetNode.data.inputAnchors.find((ancr) => ancr.id === targetHandle) ||
targetNode.data.inputParams.find((ancr) => ancr.id === targetHandle)
if (
(targetNodeInputAnchor &&
!targetNodeInputAnchor?.list &&
@@ -144,7 +192,6 @@ export const isValidConnection = (connection, reactFlowInstance) => {
}
}
}
return false
}
@@ -200,6 +247,7 @@ export const generateExportFlowData = (flowData) => {
inputAnchors: node.data.inputAnchors,
inputs: {},
outputAnchors: node.data.outputAnchors,
outputs: node.data.outputs,
selected: false
}
@@ -225,11 +273,33 @@ export const generateExportFlowData = (flowData) => {
return exportJson
}
export const copyToClipboard = (e) => {
const src = e.src
if (Array.isArray(src) || typeof src === 'object') {
navigator.clipboard.writeText(JSON.stringify(src, null, ' '))
} else {
navigator.clipboard.writeText(src)
export const getAvailableNodesForVariable = (nodes, edges, target, targetHandle) => {
// example edge id = "llmChain_0-llmChain_0-output-outputPrediction-string-llmChain_1-llmChain_1-input-promptValues-string"
// {source} -{sourceHandle} -{target} -{targetHandle}
const parentNodes = []
const inputEdges = edges.filter((edg) => edg.target === target && edg.targetHandle === targetHandle)
if (inputEdges && inputEdges.length) {
for (let j = 0; j < inputEdges.length; j += 1) {
const node = nodes.find((nd) => nd.id === inputEdges[j].source)
parentNodes.push(node)
}
}
return parentNodes
}
export const rearrangeToolsOrdering = (newValues, sourceNodeId) => {
// RequestsGet and RequestsPost have to be in order before other tools
newValues.push(`{{${sourceNodeId}.data.instance}}`)
const sortKey = (item) => {
if (item.includes('requestsGet')) {
return 0
} else if (item.includes('requestsPost')) {
return 1
} else {
return 2
}
}
newValues.sort((a, b) => sortKey(a) - sortKey(b))
}
packages/ui/src/views/canvas/CanvasHeader.js
+33 -1
View File
@@ -8,11 +8,12 @@ import { useTheme } from '@mui/material/styles'
import { Avatar, Box, ButtonBase, Typography, Stack, TextField } from '@mui/material'
// icons
import { IconSettings, IconChevronLeft, IconDeviceFloppy, IconPencil, IconCheck, IconX } from '@tabler/icons'
import { IconSettings, IconChevronLeft, IconDeviceFloppy, IconPencil, IconCheck, IconX, IconWorldWww } from '@tabler/icons'
// project imports
import Settings from 'views/settings'
import SaveChatflowDialog from 'ui-component/dialog/SaveChatflowDialog'
import APICodeDialog from 'ui-component/dialog/APICodeDialog'
// API
import chatflowsApi from 'api/chatflows'
@@ -35,6 +36,8 @@ const CanvasHeader = ({ chatflow, handleSaveFlow, handleDeleteFlow, handleLoadFl
const [flowName, setFlowName] = useState('')
const [isSettingsOpen, setSettingsOpen] = useState(false)
const [flowDialogOpen, setFlowDialogOpen] = useState(false)
const [apiDialogOpen, setAPIDialogOpen] = useState(false)
const [apiDialogProps, setAPIDialogProps] = useState({})
const updateChatflowApi = useApi(chatflowsApi.updateChatflow)
const canvas = useSelector((state) => state.canvas)
@@ -76,6 +79,14 @@ const CanvasHeader = ({ chatflow, handleSaveFlow, handleDeleteFlow, handleLoadFl
}
}
const onAPIDialogClick = () => {
setAPIDialogProps({
title: 'Use this chatflow with API',
chatflowid: chatflow.id
})
setAPIDialogOpen(true)
}
const onSaveChatflowClick = () => {
if (chatflow.id) handleSaveFlow(chatflow.name)
else setFlowDialogOpen(true)
@@ -219,6 +230,26 @@ const CanvasHeader = ({ chatflow, handleSaveFlow, handleDeleteFlow, handleLoadFl
)}
</Box>
<Box>
<ButtonBase title='API Endpoint' sx={{ borderRadius: '50%', mr: 2 }}>
<Avatar
variant='rounded'
sx={{
...theme.typography.commonAvatar,
...theme.typography.mediumAvatar,
transition: 'all .2s ease-in-out',
background: theme.palette.canvasHeader.deployLight,
color: theme.palette.canvasHeader.deployDark,
'&:hover': {
background: theme.palette.canvasHeader.deployDark,
color: theme.palette.canvasHeader.deployLight
}
}}
color='inherit'
onClick={onAPIDialogClick}
>
<IconWorldWww stroke={1.5} size='1.3rem' />
</Avatar>
</ButtonBase>
<ButtonBase title='Save Chatflow' sx={{ borderRadius: '50%', mr: 2 }}>
<Avatar
variant='rounded'
@@ -277,6 +308,7 @@ const CanvasHeader = ({ chatflow, handleSaveFlow, handleDeleteFlow, handleLoadFl
onCancel={() => setFlowDialogOpen(false)}
onConfirm={onConfirmSaveName}
/>
<APICodeDialog show={apiDialogOpen} dialogProps={apiDialogProps} onCancel={() => setAPIDialogOpen(false)} />
</>
)
}
packages/ui/src/views/canvas/CanvasNode.js
+15 -3
View File
@@ -12,7 +12,7 @@ import NodeOutputHandler from './NodeOutputHandler'
// const
import { baseURL } from 'store/constant'
import { IconTrash } from '@tabler/icons'
import { IconTrash, IconCopy } from '@tabler/icons'
import { flowContext } from 'store/context/ReactFlowContext'
const CardWrapper = styled(MainCard)(({ theme }) => ({
@@ -33,7 +33,7 @@ const CardWrapper = styled(MainCard)(({ theme }) => ({
const CanvasNode = ({ data }) => {
const theme = useTheme()
const { deleteNode } = useContext(flowContext)
const { deleteNode, duplicateNode } = useContext(flowContext)
return (
<>
@@ -76,10 +76,22 @@ const CanvasNode = ({ data }) => {
</Box>
<div style={{ flexGrow: 1 }}></div>
<IconButton
title='Duplicate'
onClick={() => {
duplicateNode(data.id)
}}
sx={{ height: 35, width: 35, '&:hover': { color: theme?.palette.primary.main } }}
color={theme?.customization?.isDarkMode ? theme.colors?.paper : 'inherit'}
>
<IconCopy />
</IconButton>
<IconButton
title='Delete'
onClick={() => {
deleteNode(data.id)
}}
sx={{ height: 35, width: 35, mr: 1 }}
sx={{ height: 35, width: 35, mr: 1, '&:hover': { color: 'red' } }}
color={theme?.customization?.isDarkMode ? theme.colors?.paper : 'inherit'}
>
<IconTrash />
</IconButton>
packages/ui/src/views/canvas/NodeInputHandler.js
+84 -7
View File
@@ -4,13 +4,17 @@ import { useEffect, useRef, useState, useContext } from 'react'
// material-ui
import { useTheme, styled } from '@mui/material/styles'
import { Box, Typography, Tooltip } from '@mui/material'
import { Box, Typography, Tooltip, IconButton } from '@mui/material'
import { tooltipClasses } from '@mui/material/Tooltip'
import { IconArrowsMaximize } from '@tabler/icons'
// project import
import { Dropdown } from 'ui-component/dropdown/Dropdown'
import { Input } from 'ui-component/input/Input'
import { File } from 'ui-component/file/File'
import { SwitchInput } from 'ui-component/switch/Switch'
import { flowContext } from 'store/context/ReactFlowContext'
import { isValidConnection } from 'utils/genericHelper'
import { isValidConnection, getAvailableNodesForVariable } from 'utils/genericHelper'
const CustomWidthTooltip = styled(({ className, ...props }) => <Tooltip {...props} classes={{ popper: className }} />)({
[`& .${tooltipClasses.tooltip}`]: {
@@ -23,9 +27,35 @@ const CustomWidthTooltip = styled(({ className, ...props }) => <Tooltip {...prop
const NodeInputHandler = ({ inputAnchor, inputParam, data, disabled = false }) => {
const theme = useTheme()
const ref = useRef(null)
const { reactFlowInstance } = useContext(flowContext)
const updateNodeInternals = useUpdateNodeInternals()
const [position, setPosition] = useState(0)
const { reactFlowInstance } = useContext(flowContext)
const [showExpandDialog, setShowExpandDialog] = useState(false)
const [expandDialogProps, setExpandDialogProps] = useState({})
const onExpandDialogClicked = (value, inputParam) => {
const dialogProp = {
value,
inputParam,
disabled,
confirmButtonName: 'Save',
cancelButtonName: 'Cancel'
}
if (!disabled) {
const nodes = reactFlowInstance.getNodes()
const edges = reactFlowInstance.getEdges()
const nodesForVariable = inputParam.acceptVariable ? getAvailableNodesForVariable(nodes, edges, data.id, inputParam.id) : []
dialogProp.availableNodesForVariable = nodesForVariable
}
setExpandDialogProps(dialogProp)
setShowExpandDialog(true)
}
const onExpandDialogSave = (newValue, inputParamName) => {
setShowExpandDialog(false)
data.inputs[inputParamName] = newValue
}
useEffect(() => {
if (ref.current && ref.current.offsetTop && ref.current.clientHeight) {
@@ -68,11 +98,47 @@ const NodeInputHandler = ({ inputAnchor, inputParam, data, disabled = false }) =
{inputParam && (
<>
{inputParam.acceptVariable && (
<CustomWidthTooltip placement='left' title={inputParam.type}>
<Handle
type='target'
position={Position.Left}
key={inputParam.id}
id={inputParam.id}
isValidConnection={(connection) => isValidConnection(connection, reactFlowInstance)}
style={{
height: 10,
width: 10,
backgroundColor: data.selected ? theme.palette.primary.main : theme.palette.text.secondary,
top: position
}}
/>
</CustomWidthTooltip>
)}
<Box sx={{ p: 2 }}>
<Typography>
{inputParam.label}
{!inputParam.optional && <span style={{ color: 'red' }}>&nbsp;*</span>}
</Typography>
<div style={{ display: 'flex', flexDirection: 'row' }}>
<Typography>
{inputParam.label}
{!inputParam.optional && <span style={{ color: 'red' }}>&nbsp;*</span>}
</Typography>
<div style={{ flexGrow: 1 }}></div>
{inputParam.type === 'string' && inputParam.rows && (
<IconButton
size='small'
sx={{
height: 25,
width: 25
}}
title='Expand'
color='primary'
onClick={() =>
onExpandDialogClicked(data.inputs[inputParam.name] ?? inputParam.default ?? '', inputParam)
}
>
<IconArrowsMaximize />
</IconButton>
)}
</div>
{inputParam.type === 'file' && (
<File
disabled={disabled}
@@ -81,12 +147,23 @@ const NodeInputHandler = ({ inputAnchor, inputParam, data, disabled = false }) =
value={data.inputs[inputParam.name] ?? inputParam.default ?? 'Choose a file to upload'}
/>
)}
{inputParam.type === 'boolean' && (
<SwitchInput
disabled={disabled}
onChange={(newValue) => (data.inputs[inputParam.name] = newValue)}
value={data.inputs[inputParam.name] ?? inputParam.default ?? false}
/>
)}
{(inputParam.type === 'string' || inputParam.type === 'password' || inputParam.type === 'number') && (
<Input
disabled={disabled}
inputParam={inputParam}
onChange={(newValue) => (data.inputs[inputParam.name] = newValue)}
value={data.inputs[inputParam.name] ?? inputParam.default ?? ''}
showDialog={showExpandDialog}
dialogProps={expandDialogProps}
onDialogCancel={() => setShowExpandDialog(false)}
onDialogConfirm={(newValue, inputParamName) => onExpandDialogSave(newValue, inputParamName)}
/>
)}
{inputParam.type === 'options' && (
packages/ui/src/views/canvas/NodeOutputHandler.js
+71 -20
View File
@@ -8,6 +8,7 @@ import { Box, Typography, Tooltip } from '@mui/material'
import { tooltipClasses } from '@mui/material/Tooltip'
import { flowContext } from 'store/context/ReactFlowContext'
import { isValidConnection } from 'utils/genericHelper'
import { Dropdown } from 'ui-component/dropdown/Dropdown'
const CustomWidthTooltip = styled(({ className, ...props }) => <Tooltip {...props} classes={{ popper: className }} />)({
[`& .${tooltipClasses.tooltip}`]: {
@@ -17,11 +18,12 @@ const CustomWidthTooltip = styled(({ className, ...props }) => <Tooltip {...prop
// ===========================|| NodeOutputHandler ||=========================== //
const NodeOutputHandler = ({ outputAnchor, data }) => {
const NodeOutputHandler = ({ outputAnchor, data, disabled = false }) => {
const theme = useTheme()
const ref = useRef(null)
const updateNodeInternals = useUpdateNodeInternals()
const [position, setPosition] = useState(0)
const [dropdownValue, setDropdownValue] = useState(null)
const { reactFlowInstance } = useContext(flowContext)
useEffect(() => {
@@ -39,33 +41,82 @@ const NodeOutputHandler = ({ outputAnchor, data }) => {
}, 0)
}, [data.id, position, updateNodeInternals])
useEffect(() => {
if (dropdownValue) {
setTimeout(() => {
updateNodeInternals(data.id)
}, 0)
}
}, [data.id, dropdownValue, updateNodeInternals])
return (
<div ref={ref}>
<CustomWidthTooltip placement='right' title={outputAnchor.type}>
<Handle
type='source'
position={Position.Right}
key={outputAnchor.id}
id={outputAnchor.id}
isValidConnection={(connection) => isValidConnection(connection, reactFlowInstance)}
style={{
height: 10,
width: 10,
backgroundColor: data.selected ? theme.palette.primary.main : theme.palette.text.secondary,
top: position
}}
/>
</CustomWidthTooltip>
<Box sx={{ p: 2, textAlign: 'end' }}>
<Typography>{outputAnchor.label}</Typography>
</Box>
{outputAnchor.type !== 'options' && !outputAnchor.options && (
<>
<CustomWidthTooltip placement='right' title={outputAnchor.type}>
<Handle
type='source'
position={Position.Right}
key={outputAnchor.id}
id={outputAnchor.id}
isValidConnection={(connection) => isValidConnection(connection, reactFlowInstance)}
style={{
height: 10,
width: 10,
backgroundColor: data.selected ? theme.palette.primary.main : theme.palette.text.secondary,
top: position
}}
/>
</CustomWidthTooltip>
<Box sx={{ p: 2, textAlign: 'end' }}>
<Typography>{outputAnchor.label}</Typography>
</Box>
</>
)}
{outputAnchor.type === 'options' && outputAnchor.options && outputAnchor.options.length > 0 && (
<>
<CustomWidthTooltip
placement='right'
title={
outputAnchor.options.find((opt) => opt.name === data.outputs?.[outputAnchor.name])?.type ?? outputAnchor.type
}
>
<Handle
type='source'
position={Position.Right}
id={outputAnchor.options.find((opt) => opt.name === data.outputs?.[outputAnchor.name])?.id ?? ''}
isValidConnection={(connection) => isValidConnection(connection, reactFlowInstance)}
style={{
height: 10,
width: 10,
backgroundColor: data.selected ? theme.palette.primary.main : theme.palette.text.secondary,
top: position
}}
/>
</CustomWidthTooltip>
<Box sx={{ p: 2, textAlign: 'end' }}>
<Dropdown
disabled={disabled}
disableClearable={true}
name={outputAnchor.name}
options={outputAnchor.options}
onSelect={(newValue) => {
setDropdownValue(newValue)
data.outputs[outputAnchor.name] = newValue
}}
value={data.outputs[outputAnchor.name] ?? outputAnchor.default ?? 'choose an option'}
/>
</Box>
</>
)}
</div>
)
}
NodeOutputHandler.propTypes = {
outputAnchor: PropTypes.object,
data: PropTypes.object
data: PropTypes.object,
disabled: PropTypes.bool
}
export default NodeOutputHandler
packages/ui/src/views/canvas/index.js
+11 -2
View File
@@ -38,7 +38,7 @@ import useConfirm from 'hooks/useConfirm'
import { IconX } from '@tabler/icons'
// utils
import { getUniqueNodeId, initNode, getEdgeLabelName } from 'utils/genericHelper'
import { getUniqueNodeId, initNode, getEdgeLabelName, rearrangeToolsOrdering } from 'utils/genericHelper'
import useNotifier from 'utils/useNotifier'
const nodeTypes = { customNode: CanvasNode }
@@ -108,10 +108,18 @@ const Canvas = () => {
setTimeout(() => setDirty(), 0)
let value
const inputAnchor = node.data.inputAnchors.find((ancr) => ancr.name === targetInput)
const inputParam = node.data.inputParams.find((param) => param.name === targetInput)
if (inputAnchor && inputAnchor.list) {
const newValues = node.data.inputs[targetInput] || []
newValues.push(`{{${sourceNodeId}.data.instance}}`)
if (targetInput === 'tools') {
rearrangeToolsOrdering(newValues, sourceNodeId)
} else {
newValues.push(`{{${sourceNodeId}.data.instance}}`)
}
value = newValues
} else if (inputParam && inputParam.acceptVariable) {
value = node.data.inputs[targetInput] || ''
} else {
value = `{{${sourceNodeId}.data.instance}}`
}
@@ -489,6 +497,7 @@ const Canvas = () => {
onConnect={onConnect}
onInit={setReactFlowInstance}
fitView
minZoom={0.1}
>
<Controls
style={{
packages/ui/src/views/chatmessage/ChatMessage.js
+3 -1
View File
@@ -34,6 +34,8 @@ import useApi from 'hooks/useApi'
import useConfirm from 'hooks/useConfirm'
import useNotifier from 'utils/useNotifier'
import { maxScroll } from 'store/constant'
export const ChatMessage = ({ chatflowid }) => {
const theme = useTheme()
const customization = useSelector((state) => state.customization)
@@ -116,7 +118,7 @@ export const ChatMessage = ({ chatflowid }) => {
const scrollToBottom = () => {
if (ps.current) {
ps.current.scrollTo({ top: Number.MAX_SAFE_INTEGER, behavior: 'smooth' })
ps.current.scrollTo({ top: maxScroll, behavior: 'smooth' })
}
}
packages/ui/src/views/marketplaces/MarketplaceCanvas.js
+1
View File
@@ -83,6 +83,7 @@ const MarketplaceCanvas = () => {
nodeTypes={nodeTypes}
edgeTypes={edgeTypes}
fitView
minZoom={0.1}
>
<Controls
style={{
packages/ui/src/views/marketplaces/MarketplaceCanvasNode.js
+2 -2
View File
@@ -88,7 +88,7 @@ const MarketplaceCanvasNode = ({ data }) => {
</>
)}
{data.inputAnchors.map((inputAnchor, index) => (
<NodeInputHandler key={index} inputAnchor={inputAnchor} data={data} />
<NodeInputHandler disabled={true} key={index} inputAnchor={inputAnchor} data={data} />
))}
{data.inputParams.map((inputParam, index) => (
<NodeInputHandler disabled={true} key={index} inputParam={inputParam} data={data} />
@@ -108,7 +108,7 @@ const MarketplaceCanvasNode = ({ data }) => {
<Divider />
{data.outputAnchors.map((outputAnchor, index) => (
<NodeOutputHandler key={index} outputAnchor={outputAnchor} data={data} />
<NodeOutputHandler disabled={true} key={index} outputAnchor={outputAnchor} data={data} />
))}
</Box>
</CardWrapper>