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Merge pull request #1176 from FlowiseAI/feature/Vec2Doc 

Feature/Add query to vec2doc
This commit is contained in:
Henry Heng 2023-11-02 21:45:59 +00:00 committed by GitHub
parent 74260adba5 689612b0d6
commit d552ca17f8
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 330 additions and 229 deletions
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13
packages/components/nodes/documentloaders/VectorStoreToDocument/VectorStoreToDocument.ts
View File

@ -17,7 +17,7 @@ class VectorStoreToDocument_DocumentLoaders implements INode {
constructor() {
this.label = 'VectorStore To Document'
this.name = 'vectorStoreToDocument'
this.version = 1.0
this.version = 2.0
this.type = 'Document'
this.icon = 'vectorretriever.svg'
this.category = 'Document Loaders'
@ -29,6 +29,14 @@ class VectorStoreToDocument_DocumentLoaders implements INode {
name: 'vectorStore',
type: 'VectorStore'
},
{
label: 'Query',
name: 'query',
type: 'string',
description: 'Query to retrieve documents from vector database. If not specified, user question will be used',
optional: true,
acceptVariable: true
},
{
label: 'Minimum Score (%)',
name: 'minScore',
@ -56,11 +64,12 @@ class VectorStoreToDocument_DocumentLoaders implements INode {
async init(nodeData: INodeData, input: string): Promise<any> {
const vectorStore = nodeData.inputs?.vectorStore as VectorStore
const minScore = nodeData.inputs?.minScore as number
const query = nodeData.inputs?.query as string
const output = nodeData.outputs?.output as string
const topK = (vectorStore as any)?.k ?? 4
const docs = await vectorStore.similaritySearchWithScore(input, topK)
const docs = await vectorStore.similaritySearchWithScore(query ?? input, topK)
// eslint-disable-next-line no-console
console.log('\x1b[94m\x1b[1m\n*****VectorStore Documents*****\n\x1b[0m\x1b[0m')
// eslint-disable-next-line no-console

467
packages/server/marketplaces/chatflows/Prompt Chaining with VectorStore.json
View File

@ -3,120 +3,11 @@
"nodes": [
{
"width": 300,
"height": 503,
"id": "pineconeExistingIndex_0",
"position": {
"x": 1062.7418678410986,
"y": -109.27680365777141
},
"type": "customNode",
"data": {
"id": "pineconeExistingIndex_0",
"label": "Pinecone Load Existing Index",
"version": 1,
"name": "pineconeExistingIndex",
"type": "Pinecone",
"baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
"category": "Vector Stores",
"description": "Load existing index from Pinecone (i.e: Document has been upserted)",
"inputParams": [
{
"label": "Connect Credential",
"name": "credential",
"type": "credential",
"credentialNames": ["pineconeApi"],
"id": "pineconeExistingIndex_0-input-credential-credential"
},
{
"label": "Pinecone Index",
"name": "pineconeIndex",
"type": "string",
"id": "pineconeExistingIndex_0-input-pineconeIndex-string"
},
{
"label": "Pinecone Namespace",
"name": "pineconeNamespace",
"type": "string",
"placeholder": "my-first-namespace",
"additionalParams": true,
"optional": true,
"id": "pineconeExistingIndex_0-input-pineconeNamespace-string"
},
{
"label": "Pinecone Metadata Filter",
"name": "pineconeMetadataFilter",
"type": "json",
"optional": true,
"additionalParams": true,
"id": "pineconeExistingIndex_0-input-pineconeMetadataFilter-json"
},
{
"label": "Top K",
"name": "topK",
"description": "Number of top results to fetch. Default to 4",
"placeholder": "4",
"type": "number",
"additionalParams": true,
"optional": true,
"id": "pineconeExistingIndex_0-input-topK-number"
}
],
"inputAnchors": [
{
"label": "Embeddings",
"name": "embeddings",
"type": "Embeddings",
"id": "pineconeExistingIndex_0-input-embeddings-Embeddings"
}
],
"inputs": {
"embeddings": "{{openAIEmbeddings_0.data.instance}}",
"pineconeIndex": "newindex",
"pineconeNamespace": "",
"pineconeMetadataFilter": "{}",
"topK": ""
},
"outputAnchors": [
{
"name": "output",
"label": "Output",
"type": "options",
"options": [
{
"id": "pineconeExistingIndex_0-output-retriever-Pinecone|VectorStoreRetriever|BaseRetriever",
"name": "retriever",
"label": "Pinecone Retriever",
"type": "Pinecone | VectorStoreRetriever | BaseRetriever"
},
{
"id": "pineconeExistingIndex_0-output-vectorStore-Pinecone|VectorStore",
"name": "vectorStore",
"label": "Pinecone Vector Store",
"type": "Pinecone | VectorStore"
}
],
"default": "retriever"
}
],
"outputs": {
"output": "vectorStore"
},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 1062.7418678410986,
"y": -109.27680365777141
},
"dragging": false
},
{
"width": 300,
"height": 327,
"height": 329,
"id": "openAIEmbeddings_0",
"position": {
"x": 711.3971966563331,
"y": 7.7184225021727
"x": 1198.6643452533754,
"y": -584.4233173804803
},
"type": "customNode",
"data": {
@ -189,18 +80,18 @@
},
"selected": false,
"positionAbsolute": {
"x": 711.3971966563331,
"y": 7.7184225021727
"x": 1198.6643452533754,
"y": -584.4233173804803
},
"dragging": false
},
{
"width": 300,
"height": 473,
"height": 475,
"id": "promptTemplate_0",
"position": {
"x": 348.2881107399286,
"y": -97.74510214137423
"x": 354.2706973608643,
"y": -122.34815000085804
},
"type": "customNode",
"data": {
@ -249,18 +140,18 @@
},
"selected": false,
"positionAbsolute": {
"x": 348.2881107399286,
"y": -97.74510214137423
"x": 354.2706973608643,
"y": -122.34815000085804
},
"dragging": false
},
{
"width": 300,
"height": 522,
"height": 574,
"id": "chatOpenAI_0",
"position": {
"x": 335.7621848973805,
"y": -721.7411273245009
"x": 353.5672832154869,
"y": -730.6436764835541
},
"type": "customNode",
"data": {
@ -396,7 +287,7 @@
],
"inputs": {
"modelName": "gpt-3.5-turbo-16k",
"temperature": 0.9,
"temperature": "0",
"maxTokens": "",
"topP": "",
"frequencyPenalty": "",
@ -418,17 +309,17 @@
"selected": false,
"dragging": false,
"positionAbsolute": {
"x": 335.7621848973805,
"y": -721.7411273245009
"x": 353.5672832154869,
"y": -730.6436764835541
}
},
{
"width": 300,
"height": 522,
"height": 574,
"id": "chatOpenAI_1",
"position": {
"x": 1765.2801848172305,
"y": -737.9261054149061
"x": 2281.9246645710673,
"y": -778.8379360672121
},
"type": "customNode",
"data": {
@ -564,7 +455,7 @@
],
"inputs": {
"modelName": "gpt-3.5-turbo-16k",
"temperature": 0.9,
"temperature": "0",
"maxTokens": "",
"topP": "",
"frequencyPenalty": "",
@ -586,36 +477,153 @@
"selected": false,
"dragging": false,
"positionAbsolute": {
"x": 1765.2801848172305,
"y": -737.9261054149061
"x": 2281.9246645710673,
"y": -778.8379360672121
}
},
{
"width": 300,
"height": 473,
"id": "promptTemplate_1",
"height": 505,
"id": "pineconeExistingIndex_0",
"position": {
"x": 1773.720934090435,
"y": -116.71323227575395
"x": 1544.4998097474581,
"y": -628.8477510577202
},
"type": "customNode",
"data": {
"id": "promptTemplate_1",
"label": "Prompt Template",
"id": "pineconeExistingIndex_0",
"label": "Pinecone Load Existing Index",
"version": 1,
"name": "promptTemplate",
"type": "PromptTemplate",
"baseClasses": ["PromptTemplate", "BaseStringPromptTemplate", "BasePromptTemplate", "Runnable"],
"category": "Prompts",
"description": "Schema to represent a basic prompt for an LLM",
"name": "pineconeExistingIndex",
"type": "Pinecone",
"baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
"category": "Vector Stores",
"description": "Load existing index from Pinecone (i.e: Document has been upserted)",
"inputParams": [
{
"label": "Template",
"name": "template",
"label": "Connect Credential",
"name": "credential",
"type": "credential",
"credentialNames": ["pineconeApi"],
"id": "pineconeExistingIndex_0-input-credential-credential"
},
{
"label": "Pinecone Index",
"name": "pineconeIndex",
"type": "string",
"id": "pineconeExistingIndex_0-input-pineconeIndex-string"
},
{
"label": "Pinecone Namespace",
"name": "pineconeNamespace",
"type": "string",
"placeholder": "my-first-namespace",
"additionalParams": true,
"optional": true,
"id": "pineconeExistingIndex_0-input-pineconeNamespace-string"
},
{
"label": "Pinecone Metadata Filter",
"name": "pineconeMetadataFilter",
"type": "json",
"optional": true,
"additionalParams": true,
"id": "pineconeExistingIndex_0-input-pineconeMetadataFilter-json"
},
{
"label": "Top K",
"name": "topK",
"description": "Number of top results to fetch. Default to 4",
"placeholder": "4",
"type": "number",
"additionalParams": true,
"optional": true,
"id": "pineconeExistingIndex_0-input-topK-number"
}
],
"inputAnchors": [
{
"label": "Embeddings",
"name": "embeddings",
"type": "Embeddings",
"id": "pineconeExistingIndex_0-input-embeddings-Embeddings"
}
],
"inputs": {
"embeddings": "{{openAIEmbeddings_0.data.instance}}",
"pineconeIndex": "",
"pineconeNamespace": "",
"pineconeMetadataFilter": "",
"topK": ""
},
"outputAnchors": [
{
"name": "output",
"label": "Output",
"type": "options",
"options": [
{
"id": "pineconeExistingIndex_0-output-retriever-Pinecone|VectorStoreRetriever|BaseRetriever",
"name": "retriever",
"label": "Pinecone Retriever",
"type": "Pinecone | VectorStoreRetriever | BaseRetriever"
},
{
"id": "pineconeExistingIndex_0-output-vectorStore-Pinecone|VectorStore",
"name": "vectorStore",
"label": "Pinecone Vector Store",
"type": "Pinecone | VectorStore"
}
],
"default": "retriever"
}
],
"outputs": {
"output": "vectorStore"
},
"selected": false
},
"selected": false,
"positionAbsolute": {
"x": 1544.4998097474581,
"y": -628.8477510577202
},
"dragging": false
},
{
"width": 300,
"height": 652,
"id": "chatPromptTemplate_0",
"position": {
"x": 2290.8365353040026,
"y": -168.49082887954518
},
"type": "customNode",
"data": {
"id": "chatPromptTemplate_0",
"label": "Chat Prompt Template",
"version": 1,
"name": "chatPromptTemplate",
"type": "ChatPromptTemplate",
"baseClasses": ["ChatPromptTemplate", "BaseChatPromptTemplate", "BasePromptTemplate", "Runnable"],
"category": "Prompts",
"description": "Schema to represent a chat prompt",
"inputParams": [
{
"label": "System Message",
"name": "systemMessagePrompt",
"type": "string",
"rows": 4,
"placeholder": "What is a good name for a company that makes {product}?",
"id": "promptTemplate_1-input-template-string"
"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",
@ -624,20 +632,21 @@
"optional": true,
"acceptVariable": true,
"list": true,
"id": "promptTemplate_1-input-promptValues-json"
"id": "chatPromptTemplate_0-input-promptValues-json"
}
],
"inputAnchors": [],
"inputs": {
"template": "Use the following pieces of context to answer the question at the end.\n\n{context}\n\nQuestion: {question}\nHelpful Answer:",
"promptValues": "{\"context\":\"{{vectorStoreToDocument_0.data.instance}}\",\"question\":\"{{llmChain_0.data.instance}}\"}"
"systemMessagePrompt": "Using the provided context, answer the user's question to the best of your ability using the resources provided. If there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n\nAnything between the following \\`context\\` html blocks is retrieved from a knowledge bank, not part of the conversation with the user.\n\n<context>\n {context}\n<context/>\n\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer. Anything between the preceding 'context' html blocks is retrieved from a knowledge bank, not part of the conversation with the user.",
"humanMessagePrompt": "{text}",
"promptValues": "{\"context\":\"{{vectorStoreToDocument_0.data.instance}}\",\"text\":\"{{question}}\"}"
},
"outputAnchors": [
{
"id": "promptTemplate_1-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate|Runnable",
"name": "promptTemplate",
"label": "PromptTemplate",
"type": "PromptTemplate | BaseStringPromptTemplate | BasePromptTemplate | Runnable"
"id": "chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate|Runnable",
"name": "chatPromptTemplate",
"label": "ChatPromptTemplate",
"type": "ChatPromptTemplate | BaseChatPromptTemplate | BasePromptTemplate | Runnable"
}
],
"outputs": {},
@ -645,18 +654,18 @@
},
"selected": false,
"positionAbsolute": {
"x": 1773.720934090435,
"y": -116.71323227575395
"x": 2290.8365353040026,
"y": -168.49082887954518
},
"dragging": false
},
{
"width": 300,
"height": 404,
"height": 405,
"id": "llmChain_0",
"position": {
"x": 756.1670091985342,
"y": -592.5151355056942
"x": 747.1299875516488,
"y": -267.01184813798244
},
"type": "customNode",
"data": {
@ -695,7 +704,7 @@
"inputs": {
"model": "{{chatOpenAI_0.data.instance}}",
"prompt": "{{promptTemplate_0.data.instance}}",
"chainName": "QuestionChain"
"chainName": "RephraseQuestion"
},
"outputAnchors": [
{
@ -726,18 +735,18 @@
},
"selected": false,
"positionAbsolute": {
"x": 756.1670091985342,
"y": -592.5151355056942
"x": 747.1299875516488,
"y": -267.01184813798244
},
"dragging": false
},
{
"width": 300,
"height": 404,
"height": 405,
"id": "llmChain_1",
"position": {
"x": 2200.1274896215496,
"y": -144.29167974642334
"x": 2694.8707655351186,
"y": -308.59150355411236
},
"type": "customNode",
"data": {
@ -775,8 +784,8 @@
],
"inputs": {
"model": "{{chatOpenAI_1.data.instance}}",
"prompt": "{{promptTemplate_1.data.instance}}",
"chainName": ""
"prompt": "{{chatPromptTemplate_0.data.instance}}",
"chainName": "FinalResponse"
},
"outputAnchors": [
{
@ -807,30 +816,39 @@
},
"selected": false,
"positionAbsolute": {
"x": 2200.1274896215496,
"y": -144.29167974642334
"x": 2694.8707655351186,
"y": -308.59150355411236
},
"dragging": false
},
{
"width": 300,
"height": 353,
"height": 454,
"id": "vectorStoreToDocument_0",
"position": {
"x": 1407.7038120189868,
"y": -26.16468811205081
"x": 1906.6871314089658,
"y": -157.0046189166955
},
"type": "customNode",
"data": {
"id": "vectorStoreToDocument_0",
"label": "VectorStore To Document",
"version": 1,
"version": 2,
"name": "vectorStoreToDocument",
"type": "Document",
"baseClasses": ["Document"],
"category": "Document Loaders",
"description": "Search documents with scores from vector store",
"inputParams": [
{
"label": "Query",
"name": "query",
"type": "string",
"description": "Query to retrieve documents from vector database. If not specified, user question will be used",
"optional": true,
"acceptVariable": true,
"id": "vectorStoreToDocument_0-input-query-string"
},
{
"label": "Minimum Score (%)",
"name": "minScore",
@ -852,6 +870,7 @@
],
"inputs": {
"vectorStore": "{{pineconeExistingIndex_0.data.instance}}",
"query": "{{llmChain_0.data.instance}}",
"minScore": ""
},
"outputAnchors": [
@ -883,8 +902,8 @@
},
"selected": false,
"positionAbsolute": {
"x": 1407.7038120189868,
"y": -26.16468811205081
"x": 1906.6871314089658,
"y": -157.0046189166955
},
"dragging": false
}
@ -901,6 +920,50 @@
"label": ""
}
},
{
"source": "promptTemplate_0",
"sourceHandle": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate|Runnable",
"target": "llmChain_0",
"targetHandle": "llmChain_0-input-prompt-BasePromptTemplate",
"type": "buttonedge",
"id": "promptTemplate_0-promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate|Runnable-llmChain_0-llmChain_0-input-prompt-BasePromptTemplate",
"data": {
"label": ""
}
},
{
"source": "chatOpenAI_0",
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
"target": "llmChain_0",
"targetHandle": "llmChain_0-input-model-BaseLanguageModel",
"type": "buttonedge",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-llmChain_0-llmChain_0-input-model-BaseLanguageModel",
"data": {
"label": ""
}
},
{
"source": "chatPromptTemplate_0",
"sourceHandle": "chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate|Runnable",
"target": "llmChain_1",
"targetHandle": "llmChain_1-input-prompt-BasePromptTemplate",
"type": "buttonedge",
"id": "chatPromptTemplate_0-chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate|Runnable-llmChain_1-llmChain_1-input-prompt-BasePromptTemplate",
"data": {
"label": ""
}
},
{
"source": "chatOpenAI_1",
"sourceHandle": "chatOpenAI_1-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
"target": "llmChain_1",
"targetHandle": "llmChain_1-input-model-BaseLanguageModel",
"type": "buttonedge",
"id": "chatOpenAI_1-chatOpenAI_1-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-llmChain_1-llmChain_1-input-model-BaseLanguageModel",
"data": {
"label": ""
}
},
{
"source": "pineconeExistingIndex_0",
"sourceHandle": "pineconeExistingIndex_0-output-vectorStore-Pinecone|VectorStore",
@ -915,32 +978,10 @@
{
"source": "vectorStoreToDocument_0",
"sourceHandle": "vectorStoreToDocument_0-output-text-string|json",
"target": "promptTemplate_1",
"targetHandle": "promptTemplate_1-input-promptValues-json",
"target": "chatPromptTemplate_0",
"targetHandle": "chatPromptTemplate_0-input-promptValues-json",
"type": "buttonedge",
"id": "vectorStoreToDocument_0-vectorStoreToDocument_0-output-text-string|json-promptTemplate_1-promptTemplate_1-input-promptValues-json",
"data": {
"label": ""
}
},
{
"source": "chatOpenAI_0",
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
"target": "llmChain_0",
"targetHandle": "llmChain_0-input-model-BaseLanguageModel",
"type": "buttonedge",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-llmChain_0-llmChain_0-input-model-BaseLanguageModel",
"data": {
"label": ""
}
},
{
"source": "promptTemplate_0",
"sourceHandle": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate|Runnable",
"target": "llmChain_0",
"targetHandle": "llmChain_0-input-prompt-BasePromptTemplate",
"type": "buttonedge",
"id": "promptTemplate_0-promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate|Runnable-llmChain_0-llmChain_0-input-prompt-BasePromptTemplate",
"id": "vectorStoreToDocument_0-vectorStoreToDocument_0-output-text-string|json-chatPromptTemplate_0-chatPromptTemplate_0-input-promptValues-json",
"data": {
"label": ""
}
@ -948,32 +989,10 @@
{
"source": "llmChain_0",
"sourceHandle": "llmChain_0-output-outputPrediction-string|json",
"target": "promptTemplate_1",
"targetHandle": "promptTemplate_1-input-promptValues-json",
"target": "vectorStoreToDocument_0",
"targetHandle": "vectorStoreToDocument_0-input-query-string",
"type": "buttonedge",
"id": "llmChain_0-llmChain_0-output-outputPrediction-string|json-promptTemplate_1-promptTemplate_1-input-promptValues-json",
"data": {
"label": ""
}
},
{
"source": "chatOpenAI_1",
"sourceHandle": "chatOpenAI_1-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
"target": "llmChain_1",
"targetHandle": "llmChain_1-input-model-BaseLanguageModel",
"type": "buttonedge",
"id": "chatOpenAI_1-chatOpenAI_1-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-llmChain_1-llmChain_1-input-model-BaseLanguageModel",
"data": {
"label": ""
}
},
{
"source": "promptTemplate_1",
"sourceHandle": "promptTemplate_1-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate|Runnable",
"target": "llmChain_1",
"targetHandle": "llmChain_1-input-prompt-BasePromptTemplate",
"type": "buttonedge",
"id": "promptTemplate_1-promptTemplate_1-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate|Runnable-llmChain_1-llmChain_1-input-prompt-BasePromptTemplate",
"id": "llmChain_0-llmChain_0-output-outputPrediction-string|json-vectorStoreToDocument_0-vectorStoreToDocument_0-input-query-string",
"data": {
"label": ""
}

76
packages/ui/src/ui-component/input/Input.js
View File

@ -1,10 +1,39 @@
import { useState } from 'react'
import { useState, useEffect, useRef } from 'react'
import PropTypes from 'prop-types'
import { FormControl, OutlinedInput } from '@mui/material'
import { FormControl, OutlinedInput, Popover } from '@mui/material'
import ExpandTextDialog from 'ui-component/dialog/ExpandTextDialog'
import SelectVariable from 'ui-component/json/SelectVariable'
import { getAvailableNodesForVariable } from 'utils/genericHelper'
export const Input = ({ inputParam, value, onChange, disabled = false, showDialog, dialogProps, onDialogCancel, onDialogConfirm }) => {
export const Input = ({
inputParam,
value,
nodes,
edges,
nodeId,
onChange,
disabled = false,
showDialog,
dialogProps,
onDialogCancel,
onDialogConfirm
}) => {
const [myValue, setMyValue] = useState(value ?? '')
const [anchorEl, setAnchorEl] = useState(null)
const [availableNodesForVariable, setAvailableNodesForVariable] = useState([])
const ref = useRef(null)
const openPopOver = Boolean(anchorEl)
const handleClosePopOver = () => {
setAnchorEl(null)
}
const setNewVal = (val) => {
const newVal = myValue + val.substring(2)
onChange(newVal)
setMyValue(newVal)
}
const getInputType = (type) => {
switch (type) {
@ -19,6 +48,19 @@ export const Input = ({ inputParam, value, onChange, disabled = false, showDialo
}
}
useEffect(() => {
if (!disabled && nodes && edges && nodeId && inputParam) {
const nodesForVariable = inputParam?.acceptVariable ? getAvailableNodesForVariable(nodes, edges, nodeId, inputParam.id) : []
setAvailableNodesForVariable(nodesForVariable)
}
}, [disabled, inputParam, nodes, edges, nodeId])
useEffect(() => {
if (typeof myValue === 'string' && myValue && myValue.endsWith('{{')) {
setAnchorEl(ref.current)
}
}, [myValue])
return (
<>
<FormControl sx={{ mt: 1, width: '100%' }} size='small'>
@ -55,6 +97,31 @@ export const Input = ({ inputParam, value, onChange, disabled = false, showDialo
}}
></ExpandTextDialog>
)}
<div ref={ref}></div>
{inputParam?.acceptVariable && (
<Popover
open={openPopOver}
anchorEl={anchorEl}
onClose={handleClosePopOver}
anchorOrigin={{
vertical: 'bottom',
horizontal: 'left'
}}
transformOrigin={{
vertical: 'top',
horizontal: 'left'
}}
>
<SelectVariable
disabled={disabled}
availableNodesForVariable={availableNodesForVariable}
onSelectAndReturnVal={(val) => {
setNewVal(val)
handleClosePopOver()
}}
/>
</Popover>
)}
</>
)
}
@ -66,6 +133,9 @@ Input.propTypes = {
disabled: PropTypes.bool,
showDialog: PropTypes.bool,
dialogProps: PropTypes.object,
nodes: PropTypes.array,
edges: PropTypes.array,
nodeId: PropTypes.string,
onDialogCancel: PropTypes.func,
onDialogConfirm: PropTypes.func
}

3
packages/ui/src/views/canvas/NodeInputHandler.js
View File

@ -265,6 +265,9 @@ const NodeInputHandler = ({ inputAnchor, inputParam, data, disabled = false, isA
inputParam={inputParam}
onChange={(newValue) => (data.inputs[inputParam.name] = newValue)}
value={data.inputs[inputParam.name] ?? inputParam.default ?? ''}
nodes={inputParam?.acceptVariable && reactFlowInstance ? reactFlowInstance.getNodes() : []}
edges={inputParam?.acceptVariable && reactFlowInstance ? reactFlowInstance.getEdges() : []}
nodeId={data.id}
showDialog={showExpandDialog}
dialogProps={expandDialogProps}
onDialogCancel={() => setShowExpandDialog(false)}