mirror of
https://github.com/farcasclaudiu/Flowise.git
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Merge pull request #774 from vectara/sentence-config
Add sentence config option to Vectara
This commit is contained in:
Henry Heng
@@ -1,6 +1,6 @@
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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { VectaraStore, VectaraLibArgs, VectaraFilter } from 'langchain/vectorstores/vectara'
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import { VectaraStore, VectaraLibArgs, VectaraFilter, VectaraContextConfig } from 'langchain/vectorstores/vectara'
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class VectaraExisting_VectorStores implements INode {
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class VectaraExisting_VectorStores implements INode {
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label: string
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label: string
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@@ -40,9 +40,27 @@ class VectaraExisting_VectorStores implements INode {
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additionalParams: true,
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additionalParams: true,
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optional: true
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optional: true
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},
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},
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{
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label: 'Sentences Before',
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name: 'sentencesBefore',
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description: 'Number of sentences to fetch before the matched sentence. Defaults to 2.',
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type: 'number',
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additionalParams: true,
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optional: true
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},
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{
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label: 'Sentences After',
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name: 'sentencesAfter',
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description: 'Number of sentences to fetch after the matched sentence. Defaults to 2.',
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type: 'number',
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additionalParams: true,
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optional: true
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},
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{
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{
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label: 'Lambda',
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label: 'Lambda',
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name: 'lambda',
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name: 'lambda',
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description:
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'Improves retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.',
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type: 'number',
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type: 'number',
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additionalParams: true,
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additionalParams: true,
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optional: true
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optional: true
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@@ -77,6 +95,8 @@ class VectaraExisting_VectorStores implements INode {
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const corpusId = getCredentialParam('corpusID', credentialData, nodeData)
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const corpusId = getCredentialParam('corpusID', credentialData, nodeData)
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const vectaraMetadataFilter = nodeData.inputs?.filter as string
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const vectaraMetadataFilter = nodeData.inputs?.filter as string
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const sentencesBefore = nodeData.inputs?.sentencesBefore as number
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const sentencesAfter = nodeData.inputs?.sentencesAfter as number
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const lambda = nodeData.inputs?.lambda as number
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const lambda = nodeData.inputs?.lambda as number
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const output = nodeData.outputs?.output as string
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const output = nodeData.outputs?.output as string
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const topK = nodeData.inputs?.topK as string
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const topK = nodeData.inputs?.topK as string
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@@ -92,6 +112,11 @@ class VectaraExisting_VectorStores implements INode {
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if (vectaraMetadataFilter) vectaraFilter.filter = vectaraMetadataFilter
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if (vectaraMetadataFilter) vectaraFilter.filter = vectaraMetadataFilter
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if (lambda) vectaraFilter.lambda = lambda
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if (lambda) vectaraFilter.lambda = lambda
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const vectaraContextConfig: VectaraContextConfig = {}
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if (sentencesBefore) vectaraContextConfig.sentencesBefore = sentencesBefore
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if (sentencesAfter) vectaraContextConfig.sentencesAfter = sentencesAfter
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vectaraFilter.contextConfig = vectaraContextConfig
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const vectorStore = new VectaraStore(vectaraArgs)
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const vectorStore = new VectaraStore(vectaraArgs)
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if (output === 'retriever') {
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if (output === 'retriever') {
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@@ -1,7 +1,7 @@
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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { Embeddings } from 'langchain/embeddings/base'
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import { Embeddings } from 'langchain/embeddings/base'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { VectaraStore, VectaraLibArgs, VectaraFilter } from 'langchain/vectorstores/vectara'
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import { VectaraStore, VectaraLibArgs, VectaraFilter, VectaraContextConfig } from 'langchain/vectorstores/vectara'
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import { Document } from 'langchain/document'
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import { Document } from 'langchain/document'
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import { flatten } from 'lodash'
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import { flatten } from 'lodash'
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@@ -49,9 +49,27 @@ class VectaraUpsert_VectorStores implements INode {
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additionalParams: true,
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additionalParams: true,
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optional: true
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optional: true
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},
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},
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{
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label: 'Sentences Before',
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name: 'sentencesBefore',
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description: 'Number of sentences to fetch before the matched sentence. Defaults to 2.',
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type: 'number',
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additionalParams: true,
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optional: true
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},
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{
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label: 'Sentences After',
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name: 'sentencesAfter',
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description: 'Number of sentences to fetch after the matched sentence. Defaults to 2.',
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type: 'number',
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additionalParams: true,
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optional: true
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},
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{
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{
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label: 'Lambda',
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label: 'Lambda',
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name: 'lambda',
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name: 'lambda',
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description:
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'Improves retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.',
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type: 'number',
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type: 'number',
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additionalParams: true,
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additionalParams: true,
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optional: true
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optional: true
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@@ -88,6 +106,8 @@ class VectaraUpsert_VectorStores implements INode {
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const docs = nodeData.inputs?.document as Document[]
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const docs = nodeData.inputs?.document as Document[]
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const embeddings = {} as Embeddings
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const embeddings = {} as Embeddings
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const vectaraMetadataFilter = nodeData.inputs?.filter as string
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const vectaraMetadataFilter = nodeData.inputs?.filter as string
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const sentencesBefore = nodeData.inputs?.sentencesBefore as number
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const sentencesAfter = nodeData.inputs?.sentencesAfter as number
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const lambda = nodeData.inputs?.lambda as number
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const lambda = nodeData.inputs?.lambda as number
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const output = nodeData.outputs?.output as string
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const output = nodeData.outputs?.output as string
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const topK = nodeData.inputs?.topK as string
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const topK = nodeData.inputs?.topK as string
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@@ -103,6 +123,11 @@ class VectaraUpsert_VectorStores implements INode {
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if (vectaraMetadataFilter) vectaraFilter.filter = vectaraMetadataFilter
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if (vectaraMetadataFilter) vectaraFilter.filter = vectaraMetadataFilter
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if (lambda) vectaraFilter.lambda = lambda
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if (lambda) vectaraFilter.lambda = lambda
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const vectaraContextConfig: VectaraContextConfig = {}
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if (sentencesBefore) vectaraContextConfig.sentencesBefore = sentencesBefore
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if (sentencesAfter) vectaraContextConfig.sentencesAfter = sentencesAfter
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vectaraFilter.contextConfig = vectaraContextConfig
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const flattenDocs = docs && docs.length ? flatten(docs) : []
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const flattenDocs = docs && docs.length ? flatten(docs) : []
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const finalDocs = []
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const finalDocs = []
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for (let i = 0; i < flattenDocs.length; i += 1) {
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for (let i = 0; i < flattenDocs.length; i += 1) {
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@@ -40,7 +40,7 @@
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"google-auth-library": "^9.0.0",
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"google-auth-library": "^9.0.0",
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"graphql": "^16.6.0",
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"graphql": "^16.6.0",
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"html-to-text": "^9.0.5",
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"html-to-text": "^9.0.5",
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"langchain": "^0.0.122",
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"langchain": "^0.0.126",
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"linkifyjs": "^4.1.1",
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"linkifyjs": "^4.1.1",
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"mammoth": "^1.5.1",
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"mammoth": "^1.5.1",
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"moment": "^2.29.3",
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"moment": "^2.29.3",
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@@ -1,186 +1,11 @@
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{
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{
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"description": "A simple LLM chain that uses Vectara to enable conversations with uploaded documents",
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"description": "A simple LLM chain that uses Vectara to enable conversations with uploaded documents",
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"nodes": [
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"nodes": [
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{
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"width": 300,
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"height": 408,
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"id": "vectaraUpsert_0",
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"position": { "x": 438, "y": 214 },
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"type": "customNode",
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"data": {
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"id": "vectaraUpsert_0",
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"label": "Vectara Upsert Document",
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"version": 1,
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"name": "vectaraUpsert",
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"type": "Vectara",
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"baseClasses": ["Vectara", "VectorStoreRetriever", "BaseRetriever"],
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"category": "Vector Stores",
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"description": "Upsert documents to Vectara",
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"inputParams": [
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{
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"label": "Connect Credential",
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"name": "credential",
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"type": "credential",
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"credentialNames": ["vectaraApi"],
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"id": "vectaraUpsert_0-input-credential-credential"
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},
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{
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"label": "Filter",
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"name": "filter",
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"type": "json",
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"additionalParams": true,
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"optional": true,
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"id": "vectaraUpsert_0-input-filter-json"
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},
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{
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"label": "Lambda",
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"name": "lambda",
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"type": "number",
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"additionalParams": true,
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"optional": true,
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"id": "vectaraUpsert_0-input-lambda-number"
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},
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{
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"label": "Top K",
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"name": "topK",
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"description": "Number of top results to fetch. Defaults to 4",
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"placeholder": "4",
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"type": "number",
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"additionalParams": true,
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"optional": true,
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"id": "vectaraUpsert_0-input-topK-number"
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}
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],
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"inputAnchors": [
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{
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"label": "Document",
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"name": "document",
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"type": "Document",
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"list": true,
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"id": "vectaraUpsert_0-input-document-Document"
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}
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],
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"inputs": {
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"document": ["{{pdfFile_0.data.instance}}"],
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"filter": "",
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"lambda": "",
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"topK": ""
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},
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"outputAnchors": [
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{
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"name": "output",
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"label": "Output",
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"type": "options",
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"options": [
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{
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"id": "vectaraUpsert_0-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever",
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"name": "retriever",
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"label": "Vectara Retriever",
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"type": "Vectara | VectorStoreRetriever | BaseRetriever"
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},
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{
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"id": "vectaraUpsert_0-output-vectorStore-Vectara|VectorStore",
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"name": "vectorStore",
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"label": "Vectara Vector Store",
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"type": "Vectara | VectorStore"
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}
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],
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"default": "retriever"
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}
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],
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"outputs": { "output": "retriever" },
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"selected": false
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},
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"selected": false,
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"dragging": false,
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"positionAbsolute": { "x": 438, "y": 214 }
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},
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{
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"width": 300,
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"height": 509,
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"id": "pdfFile_0",
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"position": { "x": 68.3013317598369, "y": 199.60454731299677 },
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"type": "customNode",
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"data": {
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"id": "pdfFile_0",
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"label": "Pdf File",
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"version": 1,
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"name": "pdfFile",
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"type": "Document",
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"baseClasses": ["Document"],
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"category": "Document Loaders",
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"description": "Load data from PDF files",
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"inputParams": [
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{
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"label": "Pdf File",
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"name": "pdfFile",
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"type": "file",
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"fileType": ".pdf",
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"id": "pdfFile_0-input-pdfFile-file"
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},
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{
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"label": "Usage",
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"name": "usage",
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"type": "options",
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"options": [
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{ "label": "One document per page", "name": "perPage" },
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{ "label": "One document per file", "name": "perFile" }
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],
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"default": "perPage",
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"id": "pdfFile_0-input-usage-options"
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},
|
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{
|
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"label": "Use Legacy Build",
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"name": "legacyBuild",
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"type": "boolean",
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"optional": true,
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"additionalParams": true,
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"id": "pdfFile_0-input-legacyBuild-boolean"
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},
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{
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"label": "Metadata",
|
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"name": "metadata",
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"type": "json",
|
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"optional": true,
|
|
||||||
"additionalParams": true,
|
|
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"id": "pdfFile_0-input-metadata-json"
|
|
||||||
}
|
|
||||||
],
|
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"inputAnchors": [
|
|
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{
|
|
||||||
"label": "Text Splitter",
|
|
||||||
"name": "textSplitter",
|
|
||||||
"type": "TextSplitter",
|
|
||||||
"optional": true,
|
|
||||||
"id": "pdfFile_0-input-textSplitter-TextSplitter"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"inputs": {
|
|
||||||
"textSplitter": "",
|
|
||||||
"usage": "perPage",
|
|
||||||
"legacyBuild": "",
|
|
||||||
"metadata": ""
|
|
||||||
},
|
|
||||||
"outputAnchors": [
|
|
||||||
{
|
|
||||||
"id": "pdfFile_0-output-pdfFile-Document",
|
|
||||||
"name": "pdfFile",
|
|
||||||
"label": "Document",
|
|
||||||
"type": "Document"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"outputs": {},
|
|
||||||
"selected": false
|
|
||||||
},
|
|
||||||
"selected": false,
|
|
||||||
"positionAbsolute": { "x": 68.3013317598369, "y": 199.60454731299677 },
|
|
||||||
"dragging": false
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
"width": 300,
|
"width": 300,
|
||||||
"height": 525,
|
"height": 525,
|
||||||
"id": "chatOpenAI_0",
|
"id": "chatOpenAI_0",
|
||||||
"position": { "x": 804.3889791707068, "y": 195.11620799951592 },
|
"position": { "x": 514.1088940275924, "y": 199.574479681537 },
|
||||||
"type": "customNode",
|
"type": "customNode",
|
||||||
"data": {
|
"data": {
|
||||||
"id": "chatOpenAI_0",
|
"id": "chatOpenAI_0",
|
||||||
@@ -211,10 +36,7 @@
|
|||||||
{ "label": "gpt-3.5-turbo", "name": "gpt-3.5-turbo" },
|
{ "label": "gpt-3.5-turbo", "name": "gpt-3.5-turbo" },
|
||||||
{ "label": "gpt-3.5-turbo-0613", "name": "gpt-3.5-turbo-0613" },
|
{ "label": "gpt-3.5-turbo-0613", "name": "gpt-3.5-turbo-0613" },
|
||||||
{ "label": "gpt-3.5-turbo-16k", "name": "gpt-3.5-turbo-16k" },
|
{ "label": "gpt-3.5-turbo-16k", "name": "gpt-3.5-turbo-16k" },
|
||||||
{
|
{ "label": "gpt-3.5-turbo-16k-0613", "name": "gpt-3.5-turbo-16k-0613" }
|
||||||
"label": "gpt-3.5-turbo-16k-0613",
|
|
||||||
"name": "gpt-3.5-turbo-16k-0613"
|
|
||||||
}
|
|
||||||
],
|
],
|
||||||
"default": "gpt-3.5-turbo",
|
"default": "gpt-3.5-turbo",
|
||||||
"optional": true,
|
"optional": true,
|
||||||
@@ -286,7 +108,7 @@
|
|||||||
"inputAnchors": [],
|
"inputAnchors": [],
|
||||||
"inputs": {
|
"inputs": {
|
||||||
"modelName": "gpt-3.5-turbo",
|
"modelName": "gpt-3.5-turbo",
|
||||||
"temperature": "0.2",
|
"temperature": "0.5",
|
||||||
"maxTokens": "",
|
"maxTokens": "",
|
||||||
"topP": "",
|
"topP": "",
|
||||||
"frequencyPenalty": "",
|
"frequencyPenalty": "",
|
||||||
@@ -306,14 +128,14 @@
|
|||||||
"selected": false
|
"selected": false
|
||||||
},
|
},
|
||||||
"selected": false,
|
"selected": false,
|
||||||
"positionAbsolute": { "x": 804.3889791707068, "y": 195.11620799951592 },
|
"positionAbsolute": { "x": 514.1088940275924, "y": 199.574479681537 },
|
||||||
"dragging": false
|
"dragging": false
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"width": 300,
|
"width": 300,
|
||||||
"height": 481,
|
"height": 481,
|
||||||
"id": "conversationalRetrievalQAChain_0",
|
"id": "conversationalRetrievalQAChain_0",
|
||||||
"position": { "x": 1160.4877473512795, "y": 259.2799138505109 },
|
"position": { "x": 900.4793407261002, "y": 205.9476004518217 },
|
||||||
"type": "customNode",
|
"type": "customNode",
|
||||||
"data": {
|
"data": {
|
||||||
"id": "conversationalRetrievalQAChain_0",
|
"id": "conversationalRetrievalQAChain_0",
|
||||||
@@ -410,11 +232,200 @@
|
|||||||
"selected": false
|
"selected": false
|
||||||
},
|
},
|
||||||
"selected": false,
|
"selected": false,
|
||||||
"positionAbsolute": { "x": 1160.4877473512795, "y": 259.2799138505109 },
|
"positionAbsolute": { "x": 900.4793407261002, "y": 205.9476004518217 },
|
||||||
"dragging": false
|
"dragging": false
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"width": 300,
|
||||||
|
"height": 509,
|
||||||
|
"id": "pdfFile_0",
|
||||||
|
"position": { "x": -210.44158723479913, "y": 236.6627524951051 },
|
||||||
|
"type": "customNode",
|
||||||
|
"data": {
|
||||||
|
"id": "pdfFile_0",
|
||||||
|
"label": "Pdf File",
|
||||||
|
"version": 1,
|
||||||
|
"name": "pdfFile",
|
||||||
|
"type": "Document",
|
||||||
|
"baseClasses": ["Document"],
|
||||||
|
"category": "Document Loaders",
|
||||||
|
"description": "Load data from PDF files",
|
||||||
|
"inputParams": [
|
||||||
|
{ "label": "Pdf File", "name": "pdfFile", "type": "file", "fileType": ".pdf", "id": "pdfFile_0-input-pdfFile-file" },
|
||||||
|
{
|
||||||
|
"label": "Usage",
|
||||||
|
"name": "usage",
|
||||||
|
"type": "options",
|
||||||
|
"options": [
|
||||||
|
{ "label": "One document per page", "name": "perPage" },
|
||||||
|
{ "label": "One document per file", "name": "perFile" }
|
||||||
|
],
|
||||||
|
"default": "perPage",
|
||||||
|
"id": "pdfFile_0-input-usage-options"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "Use Legacy Build",
|
||||||
|
"name": "legacyBuild",
|
||||||
|
"type": "boolean",
|
||||||
|
"optional": true,
|
||||||
|
"additionalParams": true,
|
||||||
|
"id": "pdfFile_0-input-legacyBuild-boolean"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "Metadata",
|
||||||
|
"name": "metadata",
|
||||||
|
"type": "json",
|
||||||
|
"optional": true,
|
||||||
|
"additionalParams": true,
|
||||||
|
"id": "pdfFile_0-input-metadata-json"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"inputAnchors": [
|
||||||
|
{
|
||||||
|
"label": "Text Splitter",
|
||||||
|
"name": "textSplitter",
|
||||||
|
"type": "TextSplitter",
|
||||||
|
"optional": true,
|
||||||
|
"id": "pdfFile_0-input-textSplitter-TextSplitter"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"inputs": { "textSplitter": "", "usage": "perPage", "legacyBuild": "", "metadata": "" },
|
||||||
|
"outputAnchors": [
|
||||||
|
{ "id": "pdfFile_0-output-pdfFile-Document", "name": "pdfFile", "label": "Document", "type": "Document" }
|
||||||
|
],
|
||||||
|
"outputs": {},
|
||||||
|
"selected": false
|
||||||
|
},
|
||||||
|
"selected": false,
|
||||||
|
"positionAbsolute": { "x": -210.44158723479913, "y": 236.6627524951051 },
|
||||||
|
"dragging": false
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"width": 300,
|
||||||
|
"height": 408,
|
||||||
|
"id": "vectaraUpsert_0",
|
||||||
|
"position": { "x": 172.06946164914868, "y": 373.11406233089934 },
|
||||||
|
"type": "customNode",
|
||||||
|
"data": {
|
||||||
|
"id": "vectaraUpsert_0",
|
||||||
|
"label": "Vectara Upsert Document",
|
||||||
|
"version": 1,
|
||||||
|
"name": "vectaraUpsert",
|
||||||
|
"type": "Vectara",
|
||||||
|
"baseClasses": ["Vectara", "VectorStoreRetriever", "BaseRetriever"],
|
||||||
|
"category": "Vector Stores",
|
||||||
|
"description": "Upsert documents to Vectara",
|
||||||
|
"inputParams": [
|
||||||
|
{
|
||||||
|
"label": "Connect Credential",
|
||||||
|
"name": "credential",
|
||||||
|
"type": "credential",
|
||||||
|
"credentialNames": ["vectaraApi"],
|
||||||
|
"id": "vectaraUpsert_0-input-credential-credential"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "Vectara Metadata Filter",
|
||||||
|
"name": "filter",
|
||||||
|
"description": "Filter to apply to Vectara metadata. Refer to the <a target=\"_blank\" href=\"https://docs.flowiseai.com/vector-stores/vectara\">documentation</a> on how to use Vectara filters with Flowise.",
|
||||||
|
"type": "string",
|
||||||
|
"additionalParams": true,
|
||||||
|
"optional": true,
|
||||||
|
"id": "vectaraUpsert_0-input-filter-string"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "Sentences Before",
|
||||||
|
"name": "sentencesBefore",
|
||||||
|
"description": "Number of sentences to fetch before the matched sentence. Defaults to 2.",
|
||||||
|
"type": "number",
|
||||||
|
"additionalParams": true,
|
||||||
|
"optional": true,
|
||||||
|
"id": "vectaraUpsert_0-input-sentencesBefore-number"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "Sentences After",
|
||||||
|
"name": "sentencesAfter",
|
||||||
|
"description": "Number of sentences to fetch after the matched sentence. Defaults to 2.",
|
||||||
|
"type": "number",
|
||||||
|
"additionalParams": true,
|
||||||
|
"optional": true,
|
||||||
|
"id": "vectaraUpsert_0-input-sentencesAfter-number"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "Lambda",
|
||||||
|
"name": "lambda",
|
||||||
|
"description": "Improves retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.",
|
||||||
|
"type": "number",
|
||||||
|
"additionalParams": true,
|
||||||
|
"optional": true,
|
||||||
|
"id": "vectaraUpsert_0-input-lambda-number"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "Top K",
|
||||||
|
"name": "topK",
|
||||||
|
"description": "Number of top results to fetch. Defaults to 4",
|
||||||
|
"placeholder": "4",
|
||||||
|
"type": "number",
|
||||||
|
"additionalParams": true,
|
||||||
|
"optional": true,
|
||||||
|
"id": "vectaraUpsert_0-input-topK-number"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"inputAnchors": [
|
||||||
|
{
|
||||||
|
"label": "Document",
|
||||||
|
"name": "document",
|
||||||
|
"type": "Document",
|
||||||
|
"list": true,
|
||||||
|
"id": "vectaraUpsert_0-input-document-Document"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"inputs": {
|
||||||
|
"document": ["{{pdfFile_0.data.instance}}"],
|
||||||
|
"filter": "",
|
||||||
|
"sentencesBefore": "",
|
||||||
|
"sentencesAfter": "",
|
||||||
|
"lambda": "",
|
||||||
|
"topK": ""
|
||||||
|
},
|
||||||
|
"outputAnchors": [
|
||||||
|
{
|
||||||
|
"name": "output",
|
||||||
|
"label": "Output",
|
||||||
|
"type": "options",
|
||||||
|
"options": [
|
||||||
|
{
|
||||||
|
"id": "vectaraUpsert_0-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever",
|
||||||
|
"name": "retriever",
|
||||||
|
"label": "Vectara Retriever",
|
||||||
|
"type": "Vectara | VectorStoreRetriever | BaseRetriever"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "vectaraUpsert_0-output-vectorStore-Vectara|VectorStore",
|
||||||
|
"name": "vectorStore",
|
||||||
|
"label": "Vectara Vector Store",
|
||||||
|
"type": "Vectara | VectorStore"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"default": "retriever"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"outputs": { "output": "retriever" },
|
||||||
|
"selected": false
|
||||||
|
},
|
||||||
|
"positionAbsolute": { "x": 172.06946164914868, "y": 373.11406233089934 },
|
||||||
|
"selected": false
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"edges": [
|
"edges": [
|
||||||
|
{
|
||||||
|
"source": "chatOpenAI_0",
|
||||||
|
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
|
||||||
|
"target": "conversationalRetrievalQAChain_0",
|
||||||
|
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
|
||||||
|
"type": "buttonedge",
|
||||||
|
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
|
||||||
|
"data": { "label": "" }
|
||||||
|
},
|
||||||
{
|
{
|
||||||
"source": "pdfFile_0",
|
"source": "pdfFile_0",
|
||||||
"sourceHandle": "pdfFile_0-output-pdfFile-Document",
|
"sourceHandle": "pdfFile_0-output-pdfFile-Document",
|
||||||
@@ -432,15 +443,6 @@
|
|||||||
"type": "buttonedge",
|
"type": "buttonedge",
|
||||||
"id": "vectaraUpsert_0-vectaraUpsert_0-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-vectorStoreRetriever-BaseRetriever",
|
"id": "vectaraUpsert_0-vectaraUpsert_0-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-vectorStoreRetriever-BaseRetriever",
|
||||||
"data": { "label": "" }
|
"data": { "label": "" }
|
||||||
},
|
|
||||||
{
|
|
||||||
"source": "chatOpenAI_0",
|
|
||||||
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
|
|
||||||
"target": "conversationalRetrievalQAChain_0",
|
|
||||||
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
|
|
||||||
"type": "buttonedge",
|
|
||||||
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
|
|
||||||
"data": { "label": "" }
|
|
||||||
}
|
}
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
|
|||||||
Reference in New Issue
Block a user