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Merge pull request #179 from FlowiseAI/feature/MultiRetrievalChain
Feature/MultiRetrievalQAChain
This commit is contained in:
Henry Heng
@@ -0,0 +1,68 @@
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import { BaseLanguageModel } from 'langchain/base_language'
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import { INode, INodeData, INodeParams, VectorStoreRetriever } from '../../../src/Interface'
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import { getBaseClasses } from '../../../src/utils'
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import { MultiRetrievalQAChain } from 'langchain/chains'
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class MultiRetrievalQAChain_Chains implements INode {
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label: string
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name: string
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type: string
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icon: string
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category: string
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baseClasses: string[]
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description: string
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inputs: INodeParams[]
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constructor() {
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this.label = 'Multi Retrieval QA Chain'
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this.name = 'multiRetrievalQAChain'
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this.type = 'MultiRetrievalQAChain'
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this.icon = 'chain.svg'
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this.category = 'Chains'
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this.description = 'QA Chain that automatically picks an appropriate vector store from multiple retrievers'
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this.baseClasses = [this.type, ...getBaseClasses(MultiRetrievalQAChain)]
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this.inputs = [
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{
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label: 'Language Model',
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name: 'model',
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type: 'BaseLanguageModel'
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},
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{
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label: 'Vector Store Retriever',
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name: 'vectorStoreRetriever',
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type: 'VectorStoreRetriever',
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list: true
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}
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]
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}
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async init(nodeData: INodeData): Promise<any> {
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const model = nodeData.inputs?.model as BaseLanguageModel
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const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as VectorStoreRetriever[]
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const retrieverNames = []
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const retrieverDescriptions = []
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const retrievers = []
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for (const vs of vectorStoreRetriever) {
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retrieverNames.push(vs.name)
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retrieverDescriptions.push(vs.description)
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retrievers.push(vs.vectorStore.asRetriever())
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}
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const chain = MultiRetrievalQAChain.fromRetrievers(model, retrieverNames, retrieverDescriptions, retrievers, undefined, {
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verbose: process.env.DEBUG === 'true' ? true : false
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} as any)
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return chain
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}
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async run(nodeData: INodeData, input: string): Promise<string> {
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const chain = nodeData.instance as MultiRetrievalQAChain
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const res = await chain.call({ input })
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return res?.text
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}
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}
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module.exports = { nodeClass: MultiRetrievalQAChain_Chains }
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@@ -0,0 +1,6 @@
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<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">
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<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
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<path d="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
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<path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
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<path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
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</svg>
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|
After Width: | Height: | Size: 489 B |
@@ -0,0 +1,61 @@
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import { VectorStore } from 'langchain/vectorstores/base'
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import { INode, INodeData, INodeParams, VectorStoreRetriever, VectorStoreRetrieverInput } from '../../../src/Interface'
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class VectorStoreRetriever_Retrievers implements INode {
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label: string
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name: string
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description: string
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type: string
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icon: string
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category: string
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baseClasses: string[]
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inputs: INodeParams[]
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constructor() {
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this.label = 'Vector Store Retriever'
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this.name = 'vectorStoreRetriever'
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this.type = 'VectorStoreRetriever'
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this.icon = 'vectorretriever.svg'
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this.category = 'Retrievers'
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this.description = 'Store vector store as retriever to be later queried by MultiRetrievalQAChain'
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this.baseClasses = [this.type]
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this.inputs = [
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{
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label: 'Vector Store',
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name: 'vectorStore',
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type: 'VectorStore'
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},
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{
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label: 'Retriever Name',
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name: 'name',
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type: 'string',
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placeholder: 'netflix movies'
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},
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{
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label: 'Retriever Description',
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name: 'description',
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type: 'string',
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rows: 3,
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description: 'Description of when to use the vector store retriever',
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placeholder: 'Good for answering questions about netflix movies'
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}
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]
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}
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async init(nodeData: INodeData): Promise<any> {
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const name = nodeData.inputs?.name as string
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const description = nodeData.inputs?.description as string
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const vectorStore = nodeData.inputs?.vectorStore as VectorStore
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const obj = {
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name,
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description,
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vectorStore
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} as VectorStoreRetrieverInput
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const retriever = new VectorStoreRetriever(obj)
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return retriever
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}
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}
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module.exports = { nodeClass: VectorStoreRetriever_Retrievers }
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@@ -0,0 +1,9 @@
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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-database-export" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
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<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
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<path d="M4 6c0 1.657 3.582 3 8 3s8 -1.343 8 -3s-3.582 -3 -8 -3s-8 1.343 -8 3"></path>
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<path d="M4 6v6c0 1.657 3.582 3 8 3c1.118 0 2.183 -.086 3.15 -.241"></path>
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<path d="M20 12v-6"></path>
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<path d="M4 12v6c0 1.657 3.582 3 8 3c.157 0 .312 -.002 .466 -.005"></path>
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<path d="M16 19h6"></path>
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<path d="M19 16l3 3l-3 3"></path>
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</svg>
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|
After Width: | Height: | Size: 647 B |
@@ -95,6 +95,7 @@ export interface IMessage {
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*/
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import { PromptTemplate as LangchainPromptTemplate, PromptTemplateInput } from 'langchain/prompts'
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import { VectorStore } from 'langchain/vectorstores/base'
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export class PromptTemplate extends LangchainPromptTemplate {
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promptValues: ICommonObject
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@@ -124,3 +125,21 @@ export class PromptRetriever {
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this.systemMessage = `${fields.systemMessage}\n${fixedTemplate}`
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}
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}
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export interface VectorStoreRetrieverInput {
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name: string
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description: string
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vectorStore: VectorStore
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}
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export class VectorStoreRetriever {
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name: string
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description: string
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vectorStore: VectorStore
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constructor(fields: VectorStoreRetrieverInput) {
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this.name = fields.name
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this.description = fields.description
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this.vectorStore = fields.vectorStore
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}
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}
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@@ -0,0 +1,859 @@
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{
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"description": "A chain that automatically picks an appropriate retriever from multiple different vector databases",
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"nodes": [
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{
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"width": 300,
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"height": 505,
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"id": "vectorStoreRetriever_0",
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"position": {
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"x": 712.9322670298264,
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"y": 860.5462810572917
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},
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"type": "customNode",
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"data": {
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"id": "vectorStoreRetriever_0",
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"label": "Vector Store Retriever",
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"name": "vectorStoreRetriever",
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"type": "VectorStoreRetriever",
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"baseClasses": ["VectorStoreRetriever"],
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"category": "Retrievers",
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"description": "Store vector store as retriever. Used with MultiRetrievalQAChain",
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"inputParams": [
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{
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"label": "Retriever Name",
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"name": "name",
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"type": "string",
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"placeholder": "netflix movies",
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"id": "vectorStoreRetriever_0-input-name-string"
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},
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{
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"label": "Retriever Description",
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"name": "description",
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"type": "string",
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"rows": 3,
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"description": "Description of when to use the vector store retriever",
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"placeholder": "Good for answering questions about netflix movies",
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"id": "vectorStoreRetriever_0-input-description-string"
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}
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],
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"inputAnchors": [
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{
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"label": "Vector Store",
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"name": "vectorStore",
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"type": "VectorStore",
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"id": "vectorStoreRetriever_0-input-vectorStore-VectorStore"
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}
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],
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"inputs": {
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"vectorStore": "{{supabaseExistingIndex_0.data.instance}}",
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"name": "aqua teen",
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"description": "Good for answering questions about Aqua Teen Hunger Force theme song"
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},
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"outputAnchors": [
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{
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"id": "vectorStoreRetriever_0-output-vectorStoreRetriever-VectorStoreRetriever",
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"name": "vectorStoreRetriever",
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"label": "VectorStoreRetriever",
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"type": "VectorStoreRetriever"
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}
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],
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"outputs": {},
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||||
"selected": false
|
||||
},
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||||
"selected": false,
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||||
"positionAbsolute": {
|
||||
"x": 712.9322670298264,
|
||||
"y": 860.5462810572917
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
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||||
"width": 300,
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"height": 280,
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"id": "multiRetrievalQAChain_0",
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||||
"position": {
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||||
"x": 1563.0150452201099,
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||||
"y": 460.78375893303934
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "multiRetrievalQAChain_0",
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||||
"label": "Multi Retrieval QA Chain",
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"name": "multiRetrievalQAChain",
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"type": "MultiRetrievalQAChain",
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"baseClasses": ["MultiRetrievalQAChain", "MultiRouteChain", "BaseChain", "BaseLangChain"],
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"category": "Chains",
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"description": "QA Chain that automatically picks an appropriate vector store from multiple retrievers",
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"inputParams": [],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Language Model",
|
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"name": "model",
|
||||
"type": "BaseLanguageModel",
|
||||
"id": "multiRetrievalQAChain_0-input-model-BaseLanguageModel"
|
||||
},
|
||||
{
|
||||
"label": "Vector Store Retriever",
|
||||
"name": "vectorStoreRetriever",
|
||||
"type": "VectorStoreRetriever",
|
||||
"list": true,
|
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"id": "multiRetrievalQAChain_0-input-vectorStoreRetriever-VectorStoreRetriever"
|
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}
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],
|
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"inputs": {
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"model": "{{chatOpenAI_0.data.instance}}",
|
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"vectorStoreRetriever": [
|
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"{{vectorStoreRetriever_0.data.instance}}",
|
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"{{vectorStoreRetriever_1.data.instance}}",
|
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"{{vectorStoreRetriever_2.data.instance}}"
|
||||
]
|
||||
},
|
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"outputAnchors": [
|
||||
{
|
||||
"id": "multiRetrievalQAChain_0-output-multiRetrievalQAChain-MultiRetrievalQAChain|MultiRouteChain|BaseChain|BaseLangChain",
|
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"name": "multiRetrievalQAChain",
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||||
"label": "MultiRetrievalQAChain",
|
||||
"type": "MultiRetrievalQAChain | MultiRouteChain | BaseChain | BaseLangChain"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 1563.0150452201099,
|
||||
"y": 460.78375893303934
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 505,
|
||||
"id": "vectorStoreRetriever_1",
|
||||
"position": {
|
||||
"x": 711.4902931206071,
|
||||
"y": 315.2414600651632
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "vectorStoreRetriever_1",
|
||||
"label": "Vector Store Retriever",
|
||||
"name": "vectorStoreRetriever",
|
||||
"type": "VectorStoreRetriever",
|
||||
"baseClasses": ["VectorStoreRetriever"],
|
||||
"category": "Retrievers",
|
||||
"description": "Store vector store as retriever. Used with MultiRetrievalQAChain",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Retriever Name",
|
||||
"name": "name",
|
||||
"type": "string",
|
||||
"placeholder": "netflix movies",
|
||||
"id": "vectorStoreRetriever_1-input-name-string"
|
||||
},
|
||||
{
|
||||
"label": "Retriever Description",
|
||||
"name": "description",
|
||||
"type": "string",
|
||||
"rows": 3,
|
||||
"description": "Description of when to use the vector store retriever",
|
||||
"placeholder": "Good for answering questions about netflix movies",
|
||||
"id": "vectorStoreRetriever_1-input-description-string"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Vector Store",
|
||||
"name": "vectorStore",
|
||||
"type": "VectorStore",
|
||||
"id": "vectorStoreRetriever_1-input-vectorStore-VectorStore"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"vectorStore": "{{chromaExistingIndex_0.data.instance}}",
|
||||
"name": "mst3k",
|
||||
"description": "Good for answering questions about Mystery Science Theater 3000 theme song"
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "vectorStoreRetriever_1-output-vectorStoreRetriever-VectorStoreRetriever",
|
||||
"name": "vectorStoreRetriever",
|
||||
"label": "VectorStoreRetriever",
|
||||
"type": "VectorStoreRetriever"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 711.4902931206071,
|
||||
"y": 315.2414600651632
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 505,
|
||||
"id": "vectorStoreRetriever_2",
|
||||
"position": {
|
||||
"x": 706.0716220151372,
|
||||
"y": -217.51566869136752
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "vectorStoreRetriever_2",
|
||||
"label": "Vector Store Retriever",
|
||||
"name": "vectorStoreRetriever",
|
||||
"type": "VectorStoreRetriever",
|
||||
"baseClasses": ["VectorStoreRetriever"],
|
||||
"category": "Retrievers",
|
||||
"description": "Store vector store as retriever. Used with MultiRetrievalQAChain",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Retriever Name",
|
||||
"name": "name",
|
||||
"type": "string",
|
||||
"placeholder": "netflix movies",
|
||||
"id": "vectorStoreRetriever_2-input-name-string"
|
||||
},
|
||||
{
|
||||
"label": "Retriever Description",
|
||||
"name": "description",
|
||||
"type": "string",
|
||||
"rows": 3,
|
||||
"description": "Description of when to use the vector store retriever",
|
||||
"placeholder": "Good for answering questions about netflix movies",
|
||||
"id": "vectorStoreRetriever_2-input-description-string"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Vector Store",
|
||||
"name": "vectorStore",
|
||||
"type": "VectorStore",
|
||||
"id": "vectorStoreRetriever_2-input-vectorStore-VectorStore"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"vectorStore": "{{pineconeExistingIndex_0.data.instance}}",
|
||||
"name": "animaniacs",
|
||||
"description": "Good for answering questions about Animaniacs theme song"
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "vectorStoreRetriever_2-output-vectorStoreRetriever-VectorStoreRetriever",
|
||||
"name": "vectorStoreRetriever",
|
||||
"label": "VectorStoreRetriever",
|
||||
"type": "VectorStoreRetriever"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 706.0716220151372,
|
||||
"y": -217.51566869136752
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 524,
|
||||
"id": "chatOpenAI_0",
|
||||
"position": {
|
||||
"x": 1206.027762600755,
|
||||
"y": -212.35338654620222
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "chatOpenAI_0",
|
||||
"label": "ChatOpenAI",
|
||||
"name": "chatOpenAI",
|
||||
"type": "ChatOpenAI",
|
||||
"baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "BaseLangChain"],
|
||||
"category": "Chat Models",
|
||||
"description": "Wrapper around OpenAI large language models that use the Chat endpoint",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "OpenAI Api Key",
|
||||
"name": "openAIApiKey",
|
||||
"type": "password",
|
||||
"id": "chatOpenAI_0-input-openAIApiKey-password"
|
||||
},
|
||||
{
|
||||
"label": "Model Name",
|
||||
"name": "modelName",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"label": "gpt-4",
|
||||
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|
||||
"target": "multiRetrievalQAChain_0",
|
||||
"targetHandle": "multiRetrievalQAChain_0-input-model-BaseLanguageModel",
|
||||
"type": "buttonedge",
|
||||
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|BaseLangChain-multiRetrievalQAChain_0-multiRetrievalQAChain_0-input-model-BaseLanguageModel",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "pineconeExistingIndex_0",
|
||||
"sourceHandle": "pineconeExistingIndex_0-output-vectorStore-Pinecone|VectorStore",
|
||||
"target": "vectorStoreRetriever_2",
|
||||
"targetHandle": "vectorStoreRetriever_2-input-vectorStore-VectorStore",
|
||||
"type": "buttonedge",
|
||||
"id": "pineconeExistingIndex_0-pineconeExistingIndex_0-output-vectorStore-Pinecone|VectorStore-vectorStoreRetriever_2-vectorStoreRetriever_2-input-vectorStore-VectorStore",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "openAIEmbeddings_0",
|
||||
"sourceHandle": "openAIEmbeddings_0-output-openAIEmbeddings-OpenAIEmbeddings|Embeddings",
|
||||
"target": "pineconeExistingIndex_0",
|
||||
"targetHandle": "pineconeExistingIndex_0-input-embeddings-Embeddings",
|
||||
"type": "buttonedge",
|
||||
"id": "openAIEmbeddings_0-openAIEmbeddings_0-output-openAIEmbeddings-OpenAIEmbeddings|Embeddings-pineconeExistingIndex_0-pineconeExistingIndex_0-input-embeddings-Embeddings",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "chromaExistingIndex_0",
|
||||
"sourceHandle": "chromaExistingIndex_0-output-vectorStore-Chroma|VectorStore",
|
||||
"target": "vectorStoreRetriever_1",
|
||||
"targetHandle": "vectorStoreRetriever_1-input-vectorStore-VectorStore",
|
||||
"type": "buttonedge",
|
||||
"id": "chromaExistingIndex_0-chromaExistingIndex_0-output-vectorStore-Chroma|VectorStore-vectorStoreRetriever_1-vectorStoreRetriever_1-input-vectorStore-VectorStore",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "openAIEmbeddings_0",
|
||||
"sourceHandle": "openAIEmbeddings_0-output-openAIEmbeddings-OpenAIEmbeddings|Embeddings",
|
||||
"target": "chromaExistingIndex_0",
|
||||
"targetHandle": "chromaExistingIndex_0-input-embeddings-Embeddings",
|
||||
"type": "buttonedge",
|
||||
"id": "openAIEmbeddings_0-openAIEmbeddings_0-output-openAIEmbeddings-OpenAIEmbeddings|Embeddings-chromaExistingIndex_0-chromaExistingIndex_0-input-embeddings-Embeddings",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "openAIEmbeddings_0",
|
||||
"sourceHandle": "openAIEmbeddings_0-output-openAIEmbeddings-OpenAIEmbeddings|Embeddings",
|
||||
"target": "supabaseExistingIndex_0",
|
||||
"targetHandle": "supabaseExistingIndex_0-input-embeddings-Embeddings",
|
||||
"type": "buttonedge",
|
||||
"id": "openAIEmbeddings_0-openAIEmbeddings_0-output-openAIEmbeddings-OpenAIEmbeddings|Embeddings-supabaseExistingIndex_0-supabaseExistingIndex_0-input-embeddings-Embeddings",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "supabaseExistingIndex_0",
|
||||
"sourceHandle": "supabaseExistingIndex_0-output-vectorStore-Supabase|VectorStore",
|
||||
"target": "vectorStoreRetriever_0",
|
||||
"targetHandle": "vectorStoreRetriever_0-input-vectorStore-VectorStore",
|
||||
"type": "buttonedge",
|
||||
"id": "supabaseExistingIndex_0-supabaseExistingIndex_0-output-vectorStore-Supabase|VectorStore-vectorStoreRetriever_0-vectorStoreRetriever_0-input-vectorStore-VectorStore",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,5 +1,5 @@
|
||||
{
|
||||
"description": "Use the agent to choose between multiple different vector databases",
|
||||
"description": "Use the agent to choose between multiple different vector databases, with the ability to use other tools",
|
||||
"nodes": [
|
||||
{
|
||||
"width": 300,
|
||||
|
||||
Reference in New Issue
Block a user