update query engine tool
This commit is contained in:
Henry
parent
e2df5e9e01
commit
778e024c02
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@ -28,12 +28,12 @@ class QueryEngine_LlamaIndex implements INode {
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constructor(fields?: { sessionId?: string }) {
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this.label = 'Query Engine'
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this.name = 'queryEngine'
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this.version = 1.0
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this.version = 2.0
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this.type = 'QueryEngine'
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this.icon = 'query-engine.png'
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this.category = 'Engine'
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this.description = 'Simple query engine built to answer question over your data, without memory'
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this.baseClasses = [this.type]
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this.baseClasses = [this.type, 'BaseQueryEngine']
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this.tags = ['LlamaIndex']
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this.inputs = [
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{
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@ -59,52 +59,13 @@ class QueryEngine_LlamaIndex implements INode {
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this.sessionId = fields?.sessionId
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}
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async init(): Promise<any> {
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return null
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async init(nodeData: INodeData): Promise<any> {
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return prepareEngine(nodeData)
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}
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
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const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
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const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever
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const responseSynthesizerObj = nodeData.inputs?.responseSynthesizer
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let queryEngine = new RetrieverQueryEngine(vectorStoreRetriever)
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if (responseSynthesizerObj) {
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if (responseSynthesizerObj.type === 'TreeSummarize') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new TreeSummarize(vectorStoreRetriever.serviceContext, responseSynthesizerObj.textQAPromptTemplate),
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serviceContext: vectorStoreRetriever.serviceContext
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})
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queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
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} else if (responseSynthesizerObj.type === 'CompactAndRefine') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new CompactAndRefine(
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vectorStoreRetriever.serviceContext,
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responseSynthesizerObj.textQAPromptTemplate,
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responseSynthesizerObj.refinePromptTemplate
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),
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serviceContext: vectorStoreRetriever.serviceContext
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})
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queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
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} else if (responseSynthesizerObj.type === 'Refine') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new Refine(
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vectorStoreRetriever.serviceContext,
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responseSynthesizerObj.textQAPromptTemplate,
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responseSynthesizerObj.refinePromptTemplate
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),
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serviceContext: vectorStoreRetriever.serviceContext
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})
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queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
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} else if (responseSynthesizerObj.type === 'SimpleResponseBuilder') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new SimpleResponseBuilder(vectorStoreRetriever.serviceContext),
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serviceContext: vectorStoreRetriever.serviceContext
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})
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queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
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}
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}
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const queryEngine = prepareEngine(nodeData)
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let text = ''
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let sourceDocuments: ICommonObject[] = []
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@ -140,4 +101,49 @@ class QueryEngine_LlamaIndex implements INode {
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}
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}
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const prepareEngine = (nodeData: INodeData) => {
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const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever
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const responseSynthesizerObj = nodeData.inputs?.responseSynthesizer
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let queryEngine = new RetrieverQueryEngine(vectorStoreRetriever)
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if (responseSynthesizerObj) {
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if (responseSynthesizerObj.type === 'TreeSummarize') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new TreeSummarize(vectorStoreRetriever.serviceContext, responseSynthesizerObj.textQAPromptTemplate),
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serviceContext: vectorStoreRetriever.serviceContext
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})
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queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
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} else if (responseSynthesizerObj.type === 'CompactAndRefine') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new CompactAndRefine(
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vectorStoreRetriever.serviceContext,
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responseSynthesizerObj.textQAPromptTemplate,
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responseSynthesizerObj.refinePromptTemplate
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),
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serviceContext: vectorStoreRetriever.serviceContext
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})
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queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
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} else if (responseSynthesizerObj.type === 'Refine') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new Refine(
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vectorStoreRetriever.serviceContext,
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responseSynthesizerObj.textQAPromptTemplate,
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responseSynthesizerObj.refinePromptTemplate
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),
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serviceContext: vectorStoreRetriever.serviceContext
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})
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queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
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} else if (responseSynthesizerObj.type === 'SimpleResponseBuilder') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new SimpleResponseBuilder(vectorStoreRetriever.serviceContext),
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serviceContext: vectorStoreRetriever.serviceContext
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})
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queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
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}
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}
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return queryEngine
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}
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module.exports = { nodeClass: QueryEngine_LlamaIndex }
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@ -33,13 +33,13 @@ class SubQuestionQueryEngine_LlamaIndex implements INode {
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constructor(fields?: { sessionId?: string }) {
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this.label = 'Sub Question Query Engine'
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this.name = 'subQuestionQueryEngine'
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this.version = 1.0
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this.version = 2.0
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this.type = 'SubQuestionQueryEngine'
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this.icon = 'subQueryEngine.svg'
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this.category = 'Engine'
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this.description =
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'Breaks complex query into sub questions for each relevant data source, then gather all the intermediate reponses and synthesizes a final response'
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this.baseClasses = [this.type]
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this.baseClasses = [this.type, 'BaseQueryEngine']
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this.tags = ['LlamaIndex']
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this.inputs = [
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{
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@ -76,85 +76,13 @@ class SubQuestionQueryEngine_LlamaIndex implements INode {
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this.sessionId = fields?.sessionId
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}
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async init(): Promise<any> {
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return null
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async init(nodeData: INodeData): Promise<any> {
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return prepareEngine(nodeData)
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}
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
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const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
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const embeddings = nodeData.inputs?.embeddings as BaseEmbedding
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const model = nodeData.inputs?.model
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const serviceContext = serviceContextFromDefaults({
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llm: model,
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embedModel: embeddings
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})
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let queryEngineTools = nodeData.inputs?.queryEngineTools as QueryEngineTool[]
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queryEngineTools = flatten(queryEngineTools)
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let queryEngine = SubQuestionQueryEngine.fromDefaults({
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serviceContext,
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queryEngineTools,
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questionGen: new LLMQuestionGenerator({ llm: model })
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})
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const responseSynthesizerObj = nodeData.inputs?.responseSynthesizer
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if (responseSynthesizerObj) {
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if (responseSynthesizerObj.type === 'TreeSummarize') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new TreeSummarize(serviceContext, responseSynthesizerObj.textQAPromptTemplate),
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serviceContext
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})
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queryEngine = SubQuestionQueryEngine.fromDefaults({
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responseSynthesizer,
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serviceContext,
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queryEngineTools,
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questionGen: new LLMQuestionGenerator({ llm: model })
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})
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} else if (responseSynthesizerObj.type === 'CompactAndRefine') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new CompactAndRefine(
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serviceContext,
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responseSynthesizerObj.textQAPromptTemplate,
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responseSynthesizerObj.refinePromptTemplate
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),
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serviceContext
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})
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queryEngine = SubQuestionQueryEngine.fromDefaults({
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responseSynthesizer,
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serviceContext,
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queryEngineTools,
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questionGen: new LLMQuestionGenerator({ llm: model })
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})
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} else if (responseSynthesizerObj.type === 'Refine') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new Refine(
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serviceContext,
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responseSynthesizerObj.textQAPromptTemplate,
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responseSynthesizerObj.refinePromptTemplate
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),
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serviceContext
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})
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queryEngine = SubQuestionQueryEngine.fromDefaults({
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responseSynthesizer,
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serviceContext,
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queryEngineTools,
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questionGen: new LLMQuestionGenerator({ llm: model })
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})
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} else if (responseSynthesizerObj.type === 'SimpleResponseBuilder') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new SimpleResponseBuilder(serviceContext),
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serviceContext
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})
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queryEngine = SubQuestionQueryEngine.fromDefaults({
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responseSynthesizer,
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serviceContext,
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queryEngineTools,
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questionGen: new LLMQuestionGenerator({ llm: model })
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})
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}
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}
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const queryEngine = prepareEngine(nodeData)
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let text = ''
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let sourceDocuments: ICommonObject[] = []
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@ -190,4 +118,82 @@ class SubQuestionQueryEngine_LlamaIndex implements INode {
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}
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}
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const prepareEngine = (nodeData: INodeData) => {
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const embeddings = nodeData.inputs?.embeddings as BaseEmbedding
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const model = nodeData.inputs?.model
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const serviceContext = serviceContextFromDefaults({
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llm: model,
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embedModel: embeddings
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})
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let queryEngineTools = nodeData.inputs?.queryEngineTools as QueryEngineTool[]
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queryEngineTools = flatten(queryEngineTools)
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let queryEngine = SubQuestionQueryEngine.fromDefaults({
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serviceContext,
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queryEngineTools,
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questionGen: new LLMQuestionGenerator({ llm: model })
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})
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const responseSynthesizerObj = nodeData.inputs?.responseSynthesizer
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if (responseSynthesizerObj) {
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if (responseSynthesizerObj.type === 'TreeSummarize') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new TreeSummarize(serviceContext, responseSynthesizerObj.textQAPromptTemplate),
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serviceContext
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})
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queryEngine = SubQuestionQueryEngine.fromDefaults({
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responseSynthesizer,
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serviceContext,
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queryEngineTools,
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questionGen: new LLMQuestionGenerator({ llm: model })
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})
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} else if (responseSynthesizerObj.type === 'CompactAndRefine') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new CompactAndRefine(
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serviceContext,
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responseSynthesizerObj.textQAPromptTemplate,
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responseSynthesizerObj.refinePromptTemplate
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),
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serviceContext
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})
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queryEngine = SubQuestionQueryEngine.fromDefaults({
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responseSynthesizer,
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serviceContext,
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queryEngineTools,
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questionGen: new LLMQuestionGenerator({ llm: model })
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})
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} else if (responseSynthesizerObj.type === 'Refine') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new Refine(
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serviceContext,
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responseSynthesizerObj.textQAPromptTemplate,
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responseSynthesizerObj.refinePromptTemplate
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),
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serviceContext
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})
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queryEngine = SubQuestionQueryEngine.fromDefaults({
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responseSynthesizer,
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serviceContext,
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queryEngineTools,
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questionGen: new LLMQuestionGenerator({ llm: model })
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})
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} else if (responseSynthesizerObj.type === 'SimpleResponseBuilder') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new SimpleResponseBuilder(serviceContext),
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serviceContext
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})
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queryEngine = SubQuestionQueryEngine.fromDefaults({
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responseSynthesizer,
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serviceContext,
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queryEngineTools,
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questionGen: new LLMQuestionGenerator({ llm: model })
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})
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}
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}
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return queryEngine
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}
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module.exports = { nodeClass: SubQuestionQueryEngine_LlamaIndex }
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@ -1,5 +1,5 @@
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import { INode, INodeData, INodeParams } from '../../../src/Interface'
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import { VectorStoreIndex } from 'llamaindex'
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import { BaseQueryEngine } from 'llamaindex'
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class QueryEngine_Tools implements INode {
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label: string
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@ -16,7 +16,7 @@ class QueryEngine_Tools implements INode {
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constructor() {
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this.label = 'QueryEngine Tool'
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this.name = 'queryEngineToolLlamaIndex'
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this.version = 1.0
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this.version = 2.0
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this.type = 'QueryEngineTool'
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this.icon = 'queryEngineTool.svg'
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this.category = 'Tools'
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@ -25,9 +25,9 @@ class QueryEngine_Tools implements INode {
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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 Index',
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name: 'vectorStoreIndex',
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type: 'VectorStoreIndex'
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label: 'Base QueryEngine',
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name: 'baseQueryEngine',
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type: 'BaseQueryEngine'
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},
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{
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label: 'Tool Name',
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@ -45,20 +45,15 @@ class QueryEngine_Tools implements INode {
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}
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async init(nodeData: INodeData): Promise<any> {
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const vectorStoreIndex = nodeData.inputs?.vectorStoreIndex as VectorStoreIndex
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const baseQueryEngine = nodeData.inputs?.baseQueryEngine as BaseQueryEngine
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const toolName = nodeData.inputs?.toolName as string
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const toolDesc = nodeData.inputs?.toolDesc as string
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const queryEngineTool = {
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queryEngine: vectorStoreIndex.asQueryEngine({
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preFilters: {
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...(vectorStoreIndex as any).metadatafilter
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}
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}),
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queryEngine: baseQueryEngine,
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metadata: {
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name: toolName,
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description: toolDesc
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},
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vectorStoreIndex
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}
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}
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return queryEngineTool
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@ -16,10 +16,10 @@
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"data": {
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"id": "queryEngine_0",
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"label": "Query Engine",
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"version": 1,
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"version": 2,
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"name": "queryEngine",
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"type": "QueryEngine",
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"baseClasses": ["QueryEngine"],
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"baseClasses": ["QueryEngine", "BaseQueryEngine"],
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"tags": ["LlamaIndex"],
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"category": "Engine",
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"description": "Simple query engine built to answer question over your data, without memory",
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@ -55,10 +55,10 @@
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},
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"outputAnchors": [
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{
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"id": "queryEngine_0-output-queryEngine-QueryEngine",
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"id": "queryEngine_0-output-queryEngine-QueryEngine|BaseQueryEngine",
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"name": "queryEngine",
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"label": "QueryEngine",
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"type": "QueryEngine"
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"type": "QueryEngine | BaseQueryEngine"
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}
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],
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"outputs": {},
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@ -9,8 +9,8 @@
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"height": 749,
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"id": "compactrefineLlamaIndex_0",
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"position": {
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"x": -1214.7329938486841,
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"y": 56.52482754447425
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"x": -443.9012456561584,
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"y": 826.6100190232154
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},
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"type": "customNode",
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"data": {
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@ -63,8 +63,8 @@
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},
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"selected": false,
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"positionAbsolute": {
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"x": -1214.7329938486841,
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"y": 56.52482754447425
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"x": -443.9012456561584,
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"y": 826.6100190232154
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},
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"dragging": false
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},
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@ -73,8 +73,8 @@
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"height": 611,
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"id": "pineconeLlamaIndex_0",
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"position": {
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"x": 37.23548045607484,
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"y": -119.7364648743818
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"x": 35.45798119088212,
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"y": -132.1789597307308
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},
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"type": "customNode",
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"data": {
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@ -183,14 +183,14 @@
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}
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],
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"outputs": {
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"output": "vectorStore"
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"output": "retriever"
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},
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"selected": false
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},
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"selected": false,
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"positionAbsolute": {
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"x": 37.23548045607484,
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"y": -119.7364648743818
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"x": 35.45798119088212,
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"y": -132.1789597307308
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},
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"dragging": false
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},
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@ -405,79 +405,13 @@
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"y": -127.15143353229783
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}
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},
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{
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"width": 300,
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"height": 511,
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"id": "queryEngineToolLlamaIndex_0",
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"position": {
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"x": 460.37559236135905,
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"y": -565.6224030941121
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},
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"type": "customNode",
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"data": {
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"id": "queryEngineToolLlamaIndex_0",
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"label": "QueryEngine Tool",
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"version": 1,
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"name": "queryEngineToolLlamaIndex",
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"type": "QueryEngineTool",
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"baseClasses": ["QueryEngineTool"],
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"tags": ["LlamaIndex"],
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"category": "Tools",
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"description": "Execute actions using ChatGPT Plugin Url",
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"inputParams": [
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{
|
||||
"label": "Tool Name",
|
||||
"name": "toolName",
|
||||
"type": "string",
|
||||
"description": "Tool name must be small capital letter with underscore. Ex: my_tool",
|
||||
"id": "queryEngineToolLlamaIndex_0-input-toolName-string"
|
||||
},
|
||||
{
|
||||
"label": "Tool Description",
|
||||
"name": "toolDesc",
|
||||
"type": "string",
|
||||
"rows": 4,
|
||||
"id": "queryEngineToolLlamaIndex_0-input-toolDesc-string"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Vector Store Index",
|
||||
"name": "vectorStoreIndex",
|
||||
"type": "VectorStoreIndex",
|
||||
"id": "queryEngineToolLlamaIndex_0-input-vectorStoreIndex-VectorStoreIndex"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"vectorStoreIndex": "{{pineconeLlamaIndex_1.data.instance}}",
|
||||
"toolName": "apple_tool",
|
||||
"toolDesc": "A SEC Form 10K filing describing the financials of Apple Inc (APPL) for the 2022 time period."
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "queryEngineToolLlamaIndex_0-output-queryEngineToolLlamaIndex-QueryEngineTool",
|
||||
"name": "queryEngineToolLlamaIndex",
|
||||
"label": "QueryEngineTool",
|
||||
"type": "QueryEngineTool"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 460.37559236135905,
|
||||
"y": -565.6224030941121
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 611,
|
||||
"id": "pineconeLlamaIndex_1",
|
||||
"position": {
|
||||
"x": 42.17855025460784,
|
||||
"y": -839.8824444107056
|
||||
"x": 43.95604951980056,
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"y": -783.0024679245387
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},
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||||
"type": "customNode",
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||||
"data": {
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||||
|
|
@ -586,162 +520,14 @@
|
|||
}
|
||||
],
|
||||
"outputs": {
|
||||
"output": "vectorStore"
|
||||
"output": "retriever"
|
||||
},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
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"x": 42.17855025460784,
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"y": -839.8824444107056
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 511,
|
||||
"id": "queryEngineToolLlamaIndex_1",
|
||||
"position": {
|
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"x": 462.16721384216123,
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"y": -17.750065363429798
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},
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"type": "customNode",
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||||
"data": {
|
||||
"id": "queryEngineToolLlamaIndex_1",
|
||||
"label": "QueryEngine Tool",
|
||||
"version": 1,
|
||||
"name": "queryEngineToolLlamaIndex",
|
||||
"type": "QueryEngineTool",
|
||||
"baseClasses": ["QueryEngineTool"],
|
||||
"tags": ["LlamaIndex"],
|
||||
"category": "Tools",
|
||||
"description": "Execute actions using ChatGPT Plugin Url",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Tool Name",
|
||||
"name": "toolName",
|
||||
"type": "string",
|
||||
"description": "Tool name must be small capital letter with underscore. Ex: my_tool",
|
||||
"id": "queryEngineToolLlamaIndex_1-input-toolName-string"
|
||||
},
|
||||
{
|
||||
"label": "Tool Description",
|
||||
"name": "toolDesc",
|
||||
"type": "string",
|
||||
"rows": 4,
|
||||
"id": "queryEngineToolLlamaIndex_1-input-toolDesc-string"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Vector Store Index",
|
||||
"name": "vectorStoreIndex",
|
||||
"type": "VectorStoreIndex",
|
||||
"id": "queryEngineToolLlamaIndex_1-input-vectorStoreIndex-VectorStoreIndex"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"vectorStoreIndex": "{{pineconeLlamaIndex_0.data.instance}}",
|
||||
"toolName": "tesla_tool",
|
||||
"toolDesc": "A SEC Form 10K filing describing the financials of Tesla Inc (TSLA) for the 2022 time period."
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "queryEngineToolLlamaIndex_1-output-queryEngineToolLlamaIndex-QueryEngineTool",
|
||||
"name": "queryEngineToolLlamaIndex",
|
||||
"label": "QueryEngineTool",
|
||||
"type": "QueryEngineTool"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 462.16721384216123,
|
||||
"y": -17.750065363429798
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||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 484,
|
||||
"id": "subQuestionQueryEngine_0",
|
||||
"position": {
|
||||
"x": 982.7583030231563,
|
||||
"y": 349.50858200305896
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "subQuestionQueryEngine_0",
|
||||
"label": "Sub Question Query Engine",
|
||||
"version": 1,
|
||||
"name": "subQuestionQueryEngine",
|
||||
"type": "SubQuestionQueryEngine",
|
||||
"baseClasses": ["SubQuestionQueryEngine"],
|
||||
"tags": ["LlamaIndex"],
|
||||
"category": "Engine",
|
||||
"description": "Simple query engine built to answer question over your data, without memory",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Return Source Documents",
|
||||
"name": "returnSourceDocuments",
|
||||
"type": "boolean",
|
||||
"optional": true,
|
||||
"id": "subQuestionQueryEngine_0-input-returnSourceDocuments-boolean"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "QueryEngine Tools",
|
||||
"name": "queryEngineTools",
|
||||
"type": "QueryEngineTool",
|
||||
"list": true,
|
||||
"id": "subQuestionQueryEngine_0-input-queryEngineTools-QueryEngineTool"
|
||||
},
|
||||
{
|
||||
"label": "Chat Model",
|
||||
"name": "model",
|
||||
"type": "BaseChatModel_LlamaIndex",
|
||||
"id": "subQuestionQueryEngine_0-input-model-BaseChatModel_LlamaIndex"
|
||||
},
|
||||
{
|
||||
"label": "Embeddings",
|
||||
"name": "embeddings",
|
||||
"type": "BaseEmbedding_LlamaIndex",
|
||||
"id": "subQuestionQueryEngine_0-input-embeddings-BaseEmbedding_LlamaIndex"
|
||||
},
|
||||
{
|
||||
"label": "Response Synthesizer",
|
||||
"name": "responseSynthesizer",
|
||||
"type": "ResponseSynthesizer",
|
||||
"description": "ResponseSynthesizer is responsible for sending the query, nodes, and prompt templates to the LLM to generate a response. See <a target=\"_blank\" href=\"https://ts.llamaindex.ai/modules/low_level/response_synthesizer\">more</a>",
|
||||
"optional": true,
|
||||
"id": "subQuestionQueryEngine_0-input-responseSynthesizer-ResponseSynthesizer"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"queryEngineTools": ["{{queryEngineToolLlamaIndex_1.data.instance}}", "{{queryEngineToolLlamaIndex_0.data.instance}}"],
|
||||
"model": "{{chatOpenAI_LlamaIndex_1.data.instance}}",
|
||||
"embeddings": "{{openAIEmbedding_LlamaIndex_1.data.instance}}",
|
||||
"responseSynthesizer": "{{compactrefineLlamaIndex_0.data.instance}}",
|
||||
"returnSourceDocuments": true
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "subQuestionQueryEngine_0-output-subQuestionQueryEngine-SubQuestionQueryEngine",
|
||||
"name": "subQuestionQueryEngine",
|
||||
"label": "SubQuestionQueryEngine",
|
||||
"type": "SubQuestionQueryEngine"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 982.7583030231563,
|
||||
"y": 349.50858200305896
|
||||
"x": 43.95604951980056,
|
||||
"y": -783.0024679245387
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||||
},
|
||||
"dragging": false
|
||||
},
|
||||
|
|
@ -750,8 +536,8 @@
|
|||
"height": 529,
|
||||
"id": "chatOpenAI_LlamaIndex_1",
|
||||
"position": {
|
||||
"x": -846.9087470244615,
|
||||
"y": 23.446501495097493
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||||
"x": -446.80851289432655,
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||||
"y": 246.8790997755625
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||||
},
|
||||
"type": "customNode",
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||||
"data": {
|
||||
|
|
@ -884,8 +670,8 @@
|
|||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": -846.9087470244615,
|
||||
"y": 23.446501495097493
|
||||
"x": -446.80851289432655,
|
||||
"y": 246.8790997755625
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
|
|
@ -894,8 +680,8 @@
|
|||
"height": 334,
|
||||
"id": "openAIEmbedding_LlamaIndex_1",
|
||||
"position": {
|
||||
"x": -437.3136244622061,
|
||||
"y": 329.99986619821175
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||||
"x": -37.812177549447284,
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||||
"y": 577.9112529482311
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
|
|
@ -952,17 +738,370 @@
|
|||
"selected": false,
|
||||
"dragging": false,
|
||||
"positionAbsolute": {
|
||||
"x": -437.3136244622061,
|
||||
"y": 329.99986619821175
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||||
"x": -37.812177549447284,
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||||
"y": 577.9112529482311
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||||
}
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 382,
|
||||
"id": "queryEngine_0",
|
||||
"position": {
|
||||
"x": 416.2466817793368,
|
||||
"y": -600.1335182096643
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||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "queryEngine_0",
|
||||
"label": "Query Engine",
|
||||
"version": 2,
|
||||
"name": "queryEngine",
|
||||
"type": "QueryEngine",
|
||||
"baseClasses": ["QueryEngine", "BaseQueryEngine"],
|
||||
"tags": ["LlamaIndex"],
|
||||
"category": "Engine",
|
||||
"description": "Simple query engine built to answer question over your data, without memory",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Return Source Documents",
|
||||
"name": "returnSourceDocuments",
|
||||
"type": "boolean",
|
||||
"optional": true,
|
||||
"id": "queryEngine_0-input-returnSourceDocuments-boolean"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Vector Store Retriever",
|
||||
"name": "vectorStoreRetriever",
|
||||
"type": "VectorIndexRetriever",
|
||||
"id": "queryEngine_0-input-vectorStoreRetriever-VectorIndexRetriever"
|
||||
},
|
||||
{
|
||||
"label": "Response Synthesizer",
|
||||
"name": "responseSynthesizer",
|
||||
"type": "ResponseSynthesizer",
|
||||
"description": "ResponseSynthesizer is responsible for sending the query, nodes, and prompt templates to the LLM to generate a response. See <a target=\"_blank\" href=\"https://ts.llamaindex.ai/modules/low_level/response_synthesizer\">more</a>",
|
||||
"optional": true,
|
||||
"id": "queryEngine_0-input-responseSynthesizer-ResponseSynthesizer"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"vectorStoreRetriever": "{{pineconeLlamaIndex_1.data.instance}}",
|
||||
"responseSynthesizer": "",
|
||||
"returnSourceDocuments": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "queryEngine_0-output-queryEngine-QueryEngine|BaseQueryEngine",
|
||||
"name": "queryEngine",
|
||||
"label": "QueryEngine",
|
||||
"description": "Simple query engine built to answer question over your data, without memory",
|
||||
"type": "QueryEngine | BaseQueryEngine"
|
||||
}
|
||||
],
|
||||
"outputs": {},
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||||
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|
||||
},
|
||||
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|
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||||
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||||
},
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||||
{
|
||||
"width": 300,
|
||||
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|
||||
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||||
"position": {
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||||
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||||
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||||
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|
||||
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|
||||
"name": "queryEngineToolLlamaIndex",
|
||||
"type": "QueryEngineTool",
|
||||
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|
||||
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|
||||
"category": "Tools",
|
||||
"description": "Tool used to invoke query engine",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Tool Name",
|
||||
"name": "toolName",
|
||||
"type": "string",
|
||||
"description": "Tool name must be small capital letter with underscore. Ex: my_tool",
|
||||
"id": "queryEngineToolLlamaIndex_2-input-toolName-string"
|
||||
},
|
||||
{
|
||||
"label": "Tool Description",
|
||||
"name": "toolDesc",
|
||||
"type": "string",
|
||||
"rows": 4,
|
||||
"id": "queryEngineToolLlamaIndex_2-input-toolDesc-string"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
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||||
{
|
||||
"label": "Base QueryEngine",
|
||||
"name": "baseQueryEngine",
|
||||
"type": "BaseQueryEngine",
|
||||
"id": "queryEngineToolLlamaIndex_2-input-baseQueryEngine-BaseQueryEngine"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"baseQueryEngine": "{{queryEngine_0.data.instance}}",
|
||||
"toolName": "apple_tool",
|
||||
"toolDesc": "A SEC Form 10K filing describing the financials of Apple Inc (APPL) for the 2022 time period."
|
||||
},
|
||||
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|
||||
{
|
||||
"id": "queryEngineToolLlamaIndex_2-output-queryEngineToolLlamaIndex-QueryEngineTool",
|
||||
"name": "queryEngineToolLlamaIndex",
|
||||
"label": "QueryEngineTool",
|
||||
"description": "Tool used to invoke query engine",
|
||||
"type": "QueryEngineTool"
|
||||
}
|
||||
],
|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
{
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
"id": "queryEngineToolLlamaIndex_1",
|
||||
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|
||||
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|
||||
"name": "queryEngineToolLlamaIndex",
|
||||
"type": "QueryEngineTool",
|
||||
"baseClasses": ["QueryEngineTool"],
|
||||
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|
||||
"category": "Tools",
|
||||
"description": "Tool used to invoke query engine",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Tool Name",
|
||||
"name": "toolName",
|
||||
"type": "string",
|
||||
"description": "Tool name must be small capital letter with underscore. Ex: my_tool",
|
||||
"id": "queryEngineToolLlamaIndex_1-input-toolName-string"
|
||||
},
|
||||
{
|
||||
"label": "Tool Description",
|
||||
"name": "toolDesc",
|
||||
"type": "string",
|
||||
"rows": 4,
|
||||
"id": "queryEngineToolLlamaIndex_1-input-toolDesc-string"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Base QueryEngine",
|
||||
"name": "baseQueryEngine",
|
||||
"type": "BaseQueryEngine",
|
||||
"id": "queryEngineToolLlamaIndex_1-input-baseQueryEngine-BaseQueryEngine"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"baseQueryEngine": "{{queryEngine_1.data.instance}}",
|
||||
"toolName": "tesla_tool",
|
||||
"toolDesc": "A SEC Form 10K filing describing the financials of Tesla Inc (TSLA) for the 2022 time period."
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "queryEngineToolLlamaIndex_1-output-queryEngineToolLlamaIndex-QueryEngineTool",
|
||||
"name": "queryEngineToolLlamaIndex",
|
||||
"label": "QueryEngineTool",
|
||||
"description": "Tool used to invoke query engine",
|
||||
"type": "QueryEngineTool"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
"x": 771.5434180813253,
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||||
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||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 382,
|
||||
"id": "queryEngine_1",
|
||||
"position": {
|
||||
"x": 411.8632262885343,
|
||||
"y": -68.91392354277994
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||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "queryEngine_1",
|
||||
"label": "Query Engine",
|
||||
"version": 2,
|
||||
"name": "queryEngine",
|
||||
"type": "QueryEngine",
|
||||
"baseClasses": ["QueryEngine", "BaseQueryEngine"],
|
||||
"tags": ["LlamaIndex"],
|
||||
"category": "Engine",
|
||||
"description": "Simple query engine built to answer question over your data, without memory",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Return Source Documents",
|
||||
"name": "returnSourceDocuments",
|
||||
"type": "boolean",
|
||||
"optional": true,
|
||||
"id": "queryEngine_1-input-returnSourceDocuments-boolean"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Vector Store Retriever",
|
||||
"name": "vectorStoreRetriever",
|
||||
"type": "VectorIndexRetriever",
|
||||
"id": "queryEngine_1-input-vectorStoreRetriever-VectorIndexRetriever"
|
||||
},
|
||||
{
|
||||
"label": "Response Synthesizer",
|
||||
"name": "responseSynthesizer",
|
||||
"type": "ResponseSynthesizer",
|
||||
"description": "ResponseSynthesizer is responsible for sending the query, nodes, and prompt templates to the LLM to generate a response. See <a target=\"_blank\" href=\"https://ts.llamaindex.ai/modules/low_level/response_synthesizer\">more</a>",
|
||||
"optional": true,
|
||||
"id": "queryEngine_1-input-responseSynthesizer-ResponseSynthesizer"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"vectorStoreRetriever": "{{pineconeLlamaIndex_0.data.instance}}",
|
||||
"responseSynthesizer": "",
|
||||
"returnSourceDocuments": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "queryEngine_1-output-queryEngine-QueryEngine|BaseQueryEngine",
|
||||
"name": "queryEngine",
|
||||
"label": "QueryEngine",
|
||||
"description": "Simple query engine built to answer question over your data, without memory",
|
||||
"type": "QueryEngine | BaseQueryEngine"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 411.8632262885343,
|
||||
"y": -68.91392354277994
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 484,
|
||||
"id": "subQuestionQueryEngine_0",
|
||||
"position": {
|
||||
"x": 1204.489328490966,
|
||||
"y": 347.2090726754211
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "subQuestionQueryEngine_0",
|
||||
"label": "Sub Question Query Engine",
|
||||
"version": 2,
|
||||
"name": "subQuestionQueryEngine",
|
||||
"type": "SubQuestionQueryEngine",
|
||||
"baseClasses": ["SubQuestionQueryEngine", "BaseQueryEngine"],
|
||||
"tags": ["LlamaIndex"],
|
||||
"category": "Engine",
|
||||
"description": "Breaks complex query into sub questions for each relevant data source, then gather all the intermediate reponses and synthesizes a final response",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Return Source Documents",
|
||||
"name": "returnSourceDocuments",
|
||||
"type": "boolean",
|
||||
"optional": true,
|
||||
"id": "subQuestionQueryEngine_0-input-returnSourceDocuments-boolean"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "QueryEngine Tools",
|
||||
"name": "queryEngineTools",
|
||||
"type": "QueryEngineTool",
|
||||
"list": true,
|
||||
"id": "subQuestionQueryEngine_0-input-queryEngineTools-QueryEngineTool"
|
||||
},
|
||||
{
|
||||
"label": "Chat Model",
|
||||
"name": "model",
|
||||
"type": "BaseChatModel_LlamaIndex",
|
||||
"id": "subQuestionQueryEngine_0-input-model-BaseChatModel_LlamaIndex"
|
||||
},
|
||||
{
|
||||
"label": "Embeddings",
|
||||
"name": "embeddings",
|
||||
"type": "BaseEmbedding_LlamaIndex",
|
||||
"id": "subQuestionQueryEngine_0-input-embeddings-BaseEmbedding_LlamaIndex"
|
||||
},
|
||||
{
|
||||
"label": "Response Synthesizer",
|
||||
"name": "responseSynthesizer",
|
||||
"type": "ResponseSynthesizer",
|
||||
"description": "ResponseSynthesizer is responsible for sending the query, nodes, and prompt templates to the LLM to generate a response. See <a target=\"_blank\" href=\"https://ts.llamaindex.ai/modules/low_level/response_synthesizer\">more</a>",
|
||||
"optional": true,
|
||||
"id": "subQuestionQueryEngine_0-input-responseSynthesizer-ResponseSynthesizer"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"queryEngineTools": ["{{queryEngineToolLlamaIndex_2.data.instance}}", "{{queryEngineToolLlamaIndex_1.data.instance}}"],
|
||||
"model": "{{chatOpenAI_LlamaIndex_1.data.instance}}",
|
||||
"embeddings": "{{openAIEmbedding_LlamaIndex_1.data.instance}}",
|
||||
"responseSynthesizer": "{{compactrefineLlamaIndex_0.data.instance}}",
|
||||
"returnSourceDocuments": true
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "subQuestionQueryEngine_0-output-subQuestionQueryEngine-SubQuestionQueryEngine|BaseQueryEngine",
|
||||
"name": "subQuestionQueryEngine",
|
||||
"label": "SubQuestionQueryEngine",
|
||||
"description": "Breaks complex query into sub questions for each relevant data source, then gather all the intermediate reponses and synthesizes a final response",
|
||||
"type": "SubQuestionQueryEngine | BaseQueryEngine"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 1204.489328490966,
|
||||
"y": 347.2090726754211
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 82,
|
||||
"id": "stickyNote_0",
|
||||
"position": {
|
||||
"x": 35.90892935132143,
|
||||
"y": -936.1282632923861
|
||||
"x": 1208.1786832265154,
|
||||
"y": 238.26647262900994
|
||||
},
|
||||
"type": "stickyNote",
|
||||
"data": {
|
||||
|
|
@ -987,13 +1126,14 @@
|
|||
],
|
||||
"inputAnchors": [],
|
||||
"inputs": {
|
||||
"note": "Query previously upserted documents with corresponding metadata key value pair - \n{ source: \"apple\"}"
|
||||
"note": "Break questions into subqueries, then retrieve corresponding context using queryengine tools"
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "stickyNote_0-output-stickyNote-StickyNote",
|
||||
"name": "stickyNote",
|
||||
"label": "StickyNote",
|
||||
"description": "Add a sticky note",
|
||||
"type": "StickyNote"
|
||||
}
|
||||
],
|
||||
|
|
@ -1002,8 +1142,8 @@
|
|||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 35.90892935132143,
|
||||
"y": -936.1282632923861
|
||||
"x": 1208.1786832265154,
|
||||
"y": 238.26647262900994
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
|
|
@ -1012,8 +1152,8 @@
|
|||
"height": 82,
|
||||
"id": "stickyNote_1",
|
||||
"position": {
|
||||
"x": 37.74909394815296,
|
||||
"y": -215.17456133022054
|
||||
"x": 416.8958270395809,
|
||||
"y": -179.9680840754678
|
||||
},
|
||||
"type": "stickyNote",
|
||||
"data": {
|
||||
|
|
@ -1038,13 +1178,14 @@
|
|||
],
|
||||
"inputAnchors": [],
|
||||
"inputs": {
|
||||
"note": "Query previously upserted documents with corresponding metadata key value pair - \n{ source: \"tesla\"}"
|
||||
"note": "Query previously upserted documents with corresponding metadata key value pair - \n{ source: \"<company>\"}"
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "stickyNote_1-output-stickyNote-StickyNote",
|
||||
"name": "stickyNote",
|
||||
"label": "StickyNote",
|
||||
"description": "Add a sticky note",
|
||||
"type": "StickyNote"
|
||||
}
|
||||
],
|
||||
|
|
@ -1053,59 +1194,8 @@
|
|||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 37.74909394815296,
|
||||
"y": -215.17456133022054
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 163,
|
||||
"id": "stickyNote_2",
|
||||
"position": {
|
||||
"x": 984.9543031068163,
|
||||
"y": 171.04264459503852
|
||||
},
|
||||
"type": "stickyNote",
|
||||
"data": {
|
||||
"id": "stickyNote_2",
|
||||
"label": "Sticky Note",
|
||||
"version": 1,
|
||||
"name": "stickyNote",
|
||||
"type": "StickyNote",
|
||||
"baseClasses": ["StickyNote"],
|
||||
"category": "Utilities",
|
||||
"description": "Add a sticky note",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "",
|
||||
"name": "note",
|
||||
"type": "string",
|
||||
"rows": 1,
|
||||
"placeholder": "Type something here",
|
||||
"optional": true,
|
||||
"id": "stickyNote_2-input-note-string"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [],
|
||||
"inputs": {
|
||||
"note": "Break questions into subqueries, then retrieve corresponding context using queryengine tool.\n\nThis implementation does not contains memory, we can use OpenAI Agent to function call this flow"
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "stickyNote_2-output-stickyNote-StickyNote",
|
||||
"name": "stickyNote",
|
||||
"label": "StickyNote",
|
||||
"type": "StickyNote"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 984.9543031068163,
|
||||
"y": 171.04264459503852
|
||||
"x": 416.8958270395809,
|
||||
"y": -179.9680840754678
|
||||
},
|
||||
"dragging": false
|
||||
}
|
||||
|
|
@ -1128,20 +1218,60 @@
|
|||
"id": "openAIEmbedding_LlamaIndex_0-openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding-pineconeLlamaIndex_1-pineconeLlamaIndex_1-input-embeddings-BaseEmbedding_LlamaIndex"
|
||||
},
|
||||
{
|
||||
"source": "pineconeLlamaIndex_1",
|
||||
"sourceHandle": "pineconeLlamaIndex_1-output-vectorStore-Pinecone|VectorStoreIndex",
|
||||
"target": "queryEngineToolLlamaIndex_0",
|
||||
"targetHandle": "queryEngineToolLlamaIndex_0-input-vectorStoreIndex-VectorStoreIndex",
|
||||
"source": "openAIEmbedding_LlamaIndex_0",
|
||||
"sourceHandle": "openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding",
|
||||
"target": "pineconeLlamaIndex_0",
|
||||
"targetHandle": "pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex",
|
||||
"type": "buttonedge",
|
||||
"id": "pineconeLlamaIndex_1-pineconeLlamaIndex_1-output-vectorStore-Pinecone|VectorStoreIndex-queryEngineToolLlamaIndex_0-queryEngineToolLlamaIndex_0-input-vectorStoreIndex-VectorStoreIndex"
|
||||
"id": "openAIEmbedding_LlamaIndex_0-openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding-pineconeLlamaIndex_0-pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex"
|
||||
},
|
||||
{
|
||||
"source": "chatOpenAI_LlamaIndex_0",
|
||||
"sourceHandle": "chatOpenAI_LlamaIndex_0-output-chatOpenAI_LlamaIndex-ChatOpenAI|BaseChatModel_LlamaIndex|BaseLLM",
|
||||
"target": "pineconeLlamaIndex_0",
|
||||
"targetHandle": "pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex",
|
||||
"type": "buttonedge",
|
||||
"id": "chatOpenAI_LlamaIndex_0-chatOpenAI_LlamaIndex_0-output-chatOpenAI_LlamaIndex-ChatOpenAI|BaseChatModel_LlamaIndex|BaseLLM-pineconeLlamaIndex_0-pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex"
|
||||
},
|
||||
{
|
||||
"source": "pineconeLlamaIndex_1",
|
||||
"sourceHandle": "pineconeLlamaIndex_1-output-retriever-Pinecone|VectorIndexRetriever",
|
||||
"target": "queryEngine_0",
|
||||
"targetHandle": "queryEngine_0-input-vectorStoreRetriever-VectorIndexRetriever",
|
||||
"type": "buttonedge",
|
||||
"id": "pineconeLlamaIndex_1-pineconeLlamaIndex_1-output-retriever-Pinecone|VectorIndexRetriever-queryEngine_0-queryEngine_0-input-vectorStoreRetriever-VectorIndexRetriever"
|
||||
},
|
||||
{
|
||||
"source": "queryEngine_0",
|
||||
"sourceHandle": "queryEngine_0-output-queryEngine-QueryEngine|BaseQueryEngine",
|
||||
"target": "queryEngineToolLlamaIndex_2",
|
||||
"targetHandle": "queryEngineToolLlamaIndex_2-input-baseQueryEngine-BaseQueryEngine",
|
||||
"type": "buttonedge",
|
||||
"id": "queryEngine_0-queryEngine_0-output-queryEngine-QueryEngine|BaseQueryEngine-queryEngineToolLlamaIndex_2-queryEngineToolLlamaIndex_2-input-baseQueryEngine-BaseQueryEngine"
|
||||
},
|
||||
{
|
||||
"source": "pineconeLlamaIndex_0",
|
||||
"sourceHandle": "pineconeLlamaIndex_0-output-vectorStore-Pinecone|VectorStoreIndex",
|
||||
"target": "queryEngineToolLlamaIndex_1",
|
||||
"targetHandle": "queryEngineToolLlamaIndex_1-input-vectorStoreIndex-VectorStoreIndex",
|
||||
"sourceHandle": "pineconeLlamaIndex_0-output-retriever-Pinecone|VectorIndexRetriever",
|
||||
"target": "queryEngine_1",
|
||||
"targetHandle": "queryEngine_1-input-vectorStoreRetriever-VectorIndexRetriever",
|
||||
"type": "buttonedge",
|
||||
"id": "pineconeLlamaIndex_0-pineconeLlamaIndex_0-output-vectorStore-Pinecone|VectorStoreIndex-queryEngineToolLlamaIndex_1-queryEngineToolLlamaIndex_1-input-vectorStoreIndex-VectorStoreIndex"
|
||||
"id": "pineconeLlamaIndex_0-pineconeLlamaIndex_0-output-retriever-Pinecone|VectorIndexRetriever-queryEngine_1-queryEngine_1-input-vectorStoreRetriever-VectorIndexRetriever"
|
||||
},
|
||||
{
|
||||
"source": "queryEngine_1",
|
||||
"sourceHandle": "queryEngine_1-output-queryEngine-QueryEngine|BaseQueryEngine",
|
||||
"target": "queryEngineToolLlamaIndex_1",
|
||||
"targetHandle": "queryEngineToolLlamaIndex_1-input-baseQueryEngine-BaseQueryEngine",
|
||||
"type": "buttonedge",
|
||||
"id": "queryEngine_1-queryEngine_1-output-queryEngine-QueryEngine|BaseQueryEngine-queryEngineToolLlamaIndex_1-queryEngineToolLlamaIndex_1-input-baseQueryEngine-BaseQueryEngine"
|
||||
},
|
||||
{
|
||||
"source": "queryEngineToolLlamaIndex_2",
|
||||
"sourceHandle": "queryEngineToolLlamaIndex_2-output-queryEngineToolLlamaIndex-QueryEngineTool",
|
||||
"target": "subQuestionQueryEngine_0",
|
||||
"targetHandle": "subQuestionQueryEngine_0-input-queryEngineTools-QueryEngineTool",
|
||||
"type": "buttonedge",
|
||||
"id": "queryEngineToolLlamaIndex_2-queryEngineToolLlamaIndex_2-output-queryEngineToolLlamaIndex-QueryEngineTool-subQuestionQueryEngine_0-subQuestionQueryEngine_0-input-queryEngineTools-QueryEngineTool"
|
||||
},
|
||||
{
|
||||
"source": "queryEngineToolLlamaIndex_1",
|
||||
|
|
@ -1151,14 +1281,6 @@
|
|||
"type": "buttonedge",
|
||||
"id": "queryEngineToolLlamaIndex_1-queryEngineToolLlamaIndex_1-output-queryEngineToolLlamaIndex-QueryEngineTool-subQuestionQueryEngine_0-subQuestionQueryEngine_0-input-queryEngineTools-QueryEngineTool"
|
||||
},
|
||||
{
|
||||
"source": "queryEngineToolLlamaIndex_0",
|
||||
"sourceHandle": "queryEngineToolLlamaIndex_0-output-queryEngineToolLlamaIndex-QueryEngineTool",
|
||||
"target": "subQuestionQueryEngine_0",
|
||||
"targetHandle": "subQuestionQueryEngine_0-input-queryEngineTools-QueryEngineTool",
|
||||
"type": "buttonedge",
|
||||
"id": "queryEngineToolLlamaIndex_0-queryEngineToolLlamaIndex_0-output-queryEngineToolLlamaIndex-QueryEngineTool-subQuestionQueryEngine_0-subQuestionQueryEngine_0-input-queryEngineTools-QueryEngineTool"
|
||||
},
|
||||
{
|
||||
"source": "chatOpenAI_LlamaIndex_1",
|
||||
"sourceHandle": "chatOpenAI_LlamaIndex_1-output-chatOpenAI_LlamaIndex-ChatOpenAI|BaseChatModel_LlamaIndex|BaseLLM",
|
||||
|
|
@ -1182,22 +1304,6 @@
|
|||
"targetHandle": "subQuestionQueryEngine_0-input-responseSynthesizer-ResponseSynthesizer",
|
||||
"type": "buttonedge",
|
||||
"id": "compactrefineLlamaIndex_0-compactrefineLlamaIndex_0-output-compactrefineLlamaIndex-CompactRefine|ResponseSynthesizer-subQuestionQueryEngine_0-subQuestionQueryEngine_0-input-responseSynthesizer-ResponseSynthesizer"
|
||||
},
|
||||
{
|
||||
"source": "openAIEmbedding_LlamaIndex_0",
|
||||
"sourceHandle": "openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding",
|
||||
"target": "pineconeLlamaIndex_0",
|
||||
"targetHandle": "pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex",
|
||||
"type": "buttonedge",
|
||||
"id": "openAIEmbedding_LlamaIndex_0-openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding-pineconeLlamaIndex_0-pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex"
|
||||
},
|
||||
{
|
||||
"source": "chatOpenAI_LlamaIndex_0",
|
||||
"sourceHandle": "chatOpenAI_LlamaIndex_0-output-chatOpenAI_LlamaIndex-ChatOpenAI|BaseChatModel_LlamaIndex|BaseLLM",
|
||||
"target": "pineconeLlamaIndex_0",
|
||||
"targetHandle": "pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex",
|
||||
"type": "buttonedge",
|
||||
"id": "chatOpenAI_LlamaIndex_0-chatOpenAI_LlamaIndex_0-output-chatOpenAI_LlamaIndex-ChatOpenAI|BaseChatModel_LlamaIndex|BaseLLM-pineconeLlamaIndex_0-pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue