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Merge branch 'main' into feature/LlamaIndex

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# Conflicts:
#	packages/components/nodes/agents/ConversationalAgent/ConversationalAgent.ts
#	packages/components/nodes/agents/ConversationalRetrievalAgent/ConversationalRetrievalAgent.ts
#	packages/components/nodes/agents/OpenAIAssistant/OpenAIAssistant.ts
#	packages/components/nodes/agents/OpenAIFunctionAgent/OpenAIFunctionAgent.ts
#	packages/components/nodes/chains/ConversationChain/ConversationChain.ts
#	packages/components/nodes/chains/ConversationalRetrievalQAChain/ConversationalRetrievalQAChain.ts
#	packages/components/nodes/memory/BufferMemory/BufferMemory.ts
#	packages/components/nodes/memory/BufferWindowMemory/BufferWindowMemory.ts
#	packages/components/nodes/memory/ConversationSummaryMemory/ConversationSummaryMemory.ts
#	packages/components/nodes/memory/DynamoDb/DynamoDb.ts
#	packages/components/nodes/memory/MongoDBMemory/MongoDBMemory.ts
#	packages/components/nodes/memory/MotorheadMemory/MotorheadMemory.ts
#	packages/components/nodes/memory/RedisBackedChatMemory/RedisBackedChatMemory.ts
#	packages/components/nodes/memory/UpstashRedisBackedChatMemory/UpstashRedisBackedChatMemory.ts
#	packages/components/nodes/memory/ZepMemory/ZepMemory.ts
#	packages/components/src/utils.ts
#	packages/server/marketplaces/chatflows/Long Term Memory.json
#	packages/server/src/index.ts
#	packages/server/src/utils/index.ts
This commit is contained in:
Henry
2024-01-24 03:00:31 +00:00
parent 40f8371de9 764efcccb7
commit 8189f945cc
468 changed files with 13562 additions and 3588 deletions
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packages/components/nodes/vectorstores/Astra/Astra.ts
+182
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import { flatten } from 'lodash'
import { Embeddings } from 'langchain/embeddings/base'
import { Document } from 'langchain/document'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData } from '../../../src/utils'
import { AstraDBVectorStore, AstraLibArgs } from '@langchain/community/vectorstores/astradb'
import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
class Astra_VectorStores implements INode {
label: string
name: string
version: number
description: string
type: string
icon: string
category: string
badge: string
baseClasses: string[]
inputs: INodeParams[]
credential: INodeParams
outputs: INodeOutputsValue[]
constructor() {
this.label = 'Astra'
this.name = 'Astra'
this.version = 1.0
this.type = 'Astra'
this.icon = 'astra.svg'
this.category = 'Vector Stores'
this.description = `Upsert embedded data and perform similarity or mmr search upon query using DataStax Astra DB, a serverless vector database that’s perfect for managing mission-critical AI workloads`
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
this.badge = 'NEW'
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['AstraDBApi']
}
this.inputs = [
{
label: 'Document',
name: 'document',
type: 'Document',
list: true,
optional: true
},
{
label: 'Embeddings',
name: 'embeddings',
type: 'Embeddings'
},
{
label: 'Vector Dimension',
name: 'vectorDimension',
type: 'number',
placeholder: '1536',
optional: true,
description: 'Dimension used for storing vector embedding'
},
{
label: 'Similarity Metric',
name: 'similarityMetric',
type: 'string',
placeholder: 'cosine',
optional: true,
description: 'cosine | euclidean | dot_product'
},
{
label: 'Top K',
name: 'topK',
description: 'Number of top results to fetch. Default to 4',
placeholder: '4',
type: 'number',
additionalParams: true,
optional: true
}
]
addMMRInputParams(this.inputs)
this.outputs = [
{
label: 'Astra Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Astra Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...getBaseClasses(AstraDBVectorStore)]
}
]
}
//@ts-ignore
vectorStoreMethods = {
async upsert(nodeData: INodeData, options: ICommonObject): Promise<void> {
const docs = nodeData.inputs?.document as Document[]
const embeddings = nodeData.inputs?.embeddings as Embeddings
const vectorDimension = nodeData.inputs?.vectorDimension as number
const similarityMetric = nodeData.inputs?.similarityMetric as 'cosine' | 'euclidean' | 'dot_product' | undefined
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const expectedSimilarityMetric = ['cosine', 'euclidean', 'dot_product']
if (similarityMetric && !expectedSimilarityMetric.includes(similarityMetric)) {
throw new Error(`Invalid Similarity Metric should be one of 'cosine' | 'euclidean' | 'dot_product'`)
}
const clientConfig = {
token: credentialData?.applicationToken,
endpoint: credentialData?.dbEndPoint
}
const astraConfig: AstraLibArgs = {
...clientConfig,
collection: credentialData.collectionName ?? 'flowise_test',
collectionOptions: {
vector: {
dimension: vectorDimension ?? 1536,
metric: similarityMetric ?? 'cosine'
}
}
}
const flattenDocs = docs && docs.length ? flatten(docs) : []
const finalDocs = []
for (let i = 0; i < flattenDocs.length; i += 1) {
if (flattenDocs[i] && flattenDocs[i].pageContent) {
finalDocs.push(new Document(flattenDocs[i]))
}
}
try {
await AstraDBVectorStore.fromDocuments(finalDocs, embeddings, astraConfig)
} catch (e) {
throw new Error(e)
}
}
}
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
const docs = nodeData.inputs?.document as Document[]
const embeddings = nodeData.inputs?.embeddings as Embeddings
const vectorDimension = nodeData.inputs?.vectorDimension as number
const similarityMetric = nodeData.inputs?.similarityMetric as 'cosine' | 'euclidean' | 'dot_product' | undefined
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const expectedSimilarityMetric = ['cosine', 'euclidean', 'dot_product']
if (similarityMetric && !expectedSimilarityMetric.includes(similarityMetric)) {
throw new Error(`Invalid Similarity Metric should be one of 'cosine' | 'euclidean' | 'dot_product'`)
}
const clientConfig = {
token: credentialData?.applicationToken,
endpoint: credentialData?.dbEndPoint
}
const astraConfig: AstraLibArgs = {
...clientConfig,
collection: credentialData.collectionName ?? 'flowise_test',
collectionOptions: {
vector: {
dimension: vectorDimension ?? 1536,
metric: similarityMetric ?? 'cosine'
}
}
}
const flattenDocs = docs && docs.length ? flatten(docs) : []
const finalDocs = []
for (let i = 0; i < flattenDocs.length; i += 1) {
if (flattenDocs[i] && flattenDocs[i].pageContent) {
finalDocs.push(new Document(flattenDocs[i]))
}
}
const vectorStore = await AstraDBVectorStore.fromExistingIndex(embeddings, astraConfig)
return resolveVectorStoreOrRetriever(nodeData, vectorStore)
}
}
module.exports = { nodeClass: Astra_VectorStores }
packages/components/nodes/vectorstores/Astra/astra.svg
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<svg width="1200" height="1200" viewBox="0 0 1200 1200" fill="none" xmlns="http://www.w3.org/2000/svg">
<rect width="1200" height="1200" fill="black"/>
<g clip-path="url(#clip0_102_1968)">
<path d="M508.819 464.97H267.001V737.697H508.819L569.566 690.526V512.14L508.819 464.97ZM313.864 512.14H522.703V690.575H313.864V512.14Z" fill="white"/>
<path d="M917.531 514.121V468H696.425L636.389 514.121V577.447L696.425 623.568H889.124V688.545H648.348V734.667H875.409L935.444 688.545V623.568L875.409 577.447H682.709V514.121H917.531Z" fill="white"/>
</g>
<defs>
<clipPath id="clip0_102_1968">
<rect width="668.444" height="266.667" fill="white" transform="translate(267 468)"/>
</clipPath>
</defs>
</svg>
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packages/components/nodes/vectorstores/Elasticsearch/Elasticsearch.ts
+20 -7
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@@ -183,13 +183,26 @@ const prepareConnectionOptions = (
} else if (cloudId) {
let username = getCredentialParam('username', credentialData, nodeData)
let password = getCredentialParam('password', credentialData, nodeData)
elasticSearchClientOptions = {
cloud: {
id: cloudId
},
auth: {
username: username,
password: password
if (cloudId.startsWith('http')) {
elasticSearchClientOptions = {
node: cloudId,
auth: {
username: username,
password: password
},
tls: {
rejectUnauthorized: false
}
}
} else {
elasticSearchClientOptions = {
cloud: {
id: cloudId
},
auth: {
username: username,
password: password
}
}
}
}
packages/components/nodes/vectorstores/Faiss/faiss.svg
+9 -10
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@@ -1,10 +1,9 @@
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-chart-dots-3" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
<path d="M5 7m-2 0a2 2 0 1 0 4 0a2 2 0 1 0 -4 0"></path>
<path d="M16 15m-2 0a2 2 0 1 0 4 0a2 2 0 1 0 -4 0"></path>
<path d="M18 6m-3 0a3 3 0 1 0 6 0a3 3 0 1 0 -6 0"></path>
<path d="M6 18m-3 0a3 3 0 1 0 6 0a3 3 0 1 0 -6 0"></path>
<path d="M9 17l5 -1.5"></path>
<path d="M6.5 8.5l7.81 5.37"></path>
<path d="M7 7l8 -1"></path>
</svg>
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M5 10C5 10.7956 5.31607 11.5587 5.87868 12.1213C6.44129 12.6839 7.20435 13 8 13C8.79565 13 9.55871 12.6839 10.1213 12.1213C10.6839 11.5587 11 10.7956 11 10C11 9.20435 10.6839 8.44129 10.1213 7.87868C9.55871 7.31607 8.79565 7 8 7C7.20435 7 6.44129 7.31607 5.87868 7.87868C5.31607 8.44129 5 9.20435 5 10Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M20 19.5C20 20.163 20.2634 20.7989 20.7322 21.2678C21.2011 21.7366 21.837 22 22.5 22C23.163 22 23.7989 21.7366 24.2678 21.2678C24.7366 20.7989 25 20.163 25 19.5C25 18.837 24.7366 18.2011 24.2678 17.7322C23.7989 17.2634 23.163 17 22.5 17C21.837 17 21.2011 17.2634 20.7322 17.7322C20.2634 18.2011 20 18.837 20 19.5Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
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<path d="M5 23C5 24.0609 5.42143 25.0783 6.17157 25.8284C6.92172 26.5786 7.93913 27 9 27C10.0609 27 11.0783 26.5786 11.8284 25.8284C12.5786 25.0783 13 24.0609 13 23C13 21.9391 12.5786 20.9217 11.8284 20.1716C11.0783 19.4214 10.0609 19 9 19C7.93913 19 6.92172 19.4214 6.17157 20.1716C5.42143 20.9217 5 21.9391 5 23Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M13 22L20 20" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
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<path d="M11 9.5L18.5 8.5" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
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packages/components/nodes/vectorstores/InMemory/memory.svg
+19 -7
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@@ -1,7 +1,19 @@
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-device-sd-card" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
<path d="M7 21h10a2 2 0 0 0 2 -2v-14a2 2 0 0 0 -2 -2h-6.172a2 2 0 0 0 -1.414 .586l-3.828 3.828a2 2 0 0 0 -.586 1.414v10.172a2 2 0 0 0 2 2z"></path>
<path d="M13 6v2"></path>
<path d="M16 6v2"></path>
<path d="M10 7v1"></path>
</svg>
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
<g clip-path="url(#clip0_121_17940)">
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</g>
<defs>
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<rect width="32" height="32" fill="white"/>
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packages/components/nodes/vectorstores/Milvus/Milvus.ts
+11 -1
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@@ -65,6 +65,14 @@ class Milvus_VectorStores implements INode {
name: 'milvusCollection',
type: 'string'
},
{
label: 'Milvus Text Field',
name: 'milvusTextField',
type: 'string',
placeholder: 'langchain_text',
optional: true,
additionalParams: true
},
{
label: 'Milvus Filter',
name: 'milvusFilter',
@@ -150,6 +158,7 @@ class Milvus_VectorStores implements INode {
const address = nodeData.inputs?.milvusServerUrl as string
const collectionName = nodeData.inputs?.milvusCollection as string
const milvusFilter = nodeData.inputs?.milvusFilter as string
const textField = nodeData.inputs?.milvusTextField as string
// embeddings
const embeddings = nodeData.inputs?.embeddings as Embeddings
@@ -169,7 +178,8 @@ class Milvus_VectorStores implements INode {
// init MilvusLibArgs
const milVusArgs: MilvusLibArgs = {
url: address,
collectionName: collectionName
collectionName: collectionName,
textField: textField
}
if (milvusUser) milVusArgs.username = milvusUser
packages/components/nodes/vectorstores/MongoDBAtlas/MongoDBAtlas.ts
+5 -12
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@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
import { Document } from 'langchain/document'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
class MongoDBAtlas_VectorStores implements INode {
label: string
@@ -24,9 +25,9 @@ class MongoDBAtlas_VectorStores implements INode {
this.label = 'MongoDB Atlas'
this.name = 'mongoDBAtlas'
this.version = 1.0
this.description = `Upsert embedded data and perform similarity search upon query using MongoDB Atlas, a managed cloud mongodb database`
this.description = `Upsert embedded data and perform similarity or mmr search upon query using MongoDB Atlas, a managed cloud mongodb database`
this.type = 'MongoDB Atlas'
this.icon = 'mongodb.png'
this.icon = 'mongodb.svg'
this.category = 'Vector Stores'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
this.badge = 'NEW'
@@ -95,6 +96,7 @@ class MongoDBAtlas_VectorStores implements INode {
optional: true
}
]
addMMRInputParams(this.inputs)
this.outputs = [
{
label: 'MongoDB Retriever',
@@ -162,9 +164,6 @@ class MongoDBAtlas_VectorStores implements INode {
let textKey = nodeData.inputs?.textKey as string
let embeddingKey = nodeData.inputs?.embeddingKey as string
const embeddings = nodeData.inputs?.embeddings as Embeddings
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const output = nodeData.outputs?.output as string
let mongoDBConnectUrl = getCredentialParam('mongoDBConnectUrl', credentialData, nodeData)
@@ -181,13 +180,7 @@ class MongoDBAtlas_VectorStores implements INode {
embeddingKey
})
if (output === 'retriever') {
return vectorStore.asRetriever(k)
} else if (output === 'vectorStore') {
;(vectorStore as any).k = k
return vectorStore
}
return vectorStore
return resolveVectorStoreOrRetriever(nodeData, vectorStore)
}
}
packages/components/nodes/vectorstores/MongoDBAtlas/MongoDBSearchBase.ts
+1 -1
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@@ -31,7 +31,7 @@ export abstract class MongoDBSearchBase {
protected constructor() {
this.type = 'MongoDB Atlas'
this.icon = 'mongodb.png'
this.icon = 'mongodb.svg'
this.category = 'Vector Stores'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
this.badge = 'DEPRECATING'
packages/components/nodes/vectorstores/MongoDBAtlas/mongodb.png
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packages/components/nodes/vectorstores/MongoDBAtlas/mongodb.svg
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<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M15 29C15 29.5523 15.4477 30 16 30C16.5523 30 17 29.5523 17 29H15ZM17 29V26.5H15V29H17Z" fill="#01EC64"/>
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<path d="M16 25V12" stroke="white" stroke-width="1.5" stroke-linecap="round"/>
</svg>
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packages/components/nodes/vectorstores/OpenSearch/OpenSearch.ts
+1 -1
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@@ -24,7 +24,7 @@ class OpenSearch_VectorStores implements INode {
this.name = 'openSearch'
this.version = 1.0
this.type = 'OpenSearch'
this.icon = 'opensearch.png'
this.icon = 'opensearch.svg'
this.category = 'Vector Stores'
this.description = `Upsert embedded data and perform similarity search upon query using OpenSearch, an open-source, all-in-one vector database`
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
packages/components/nodes/vectorstores/OpenSearch/OpenSearch_Upsert.ts
+1 -1
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@@ -24,7 +24,7 @@ class OpenSearchUpsert_VectorStores implements INode {
this.name = 'openSearchUpsertDocument'
this.version = 1.0
this.type = 'OpenSearch'
this.icon = 'opensearch.png'
this.icon = 'opensearch.svg'
this.category = 'Vector Stores'
this.description = 'Upsert documents to OpenSearch'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
packages/components/nodes/vectorstores/OpenSearch/OpenSearch_existing.ts
+1 -1
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@@ -22,7 +22,7 @@ class OpenSearch_Existing_VectorStores implements INode {
this.name = 'openSearchExistingIndex'
this.version = 1.0
this.type = 'OpenSearch'
this.icon = 'opensearch.png'
this.icon = 'opensearch.svg'
this.category = 'Vector Stores'
this.description = 'Load existing index from OpenSearch (i.e: Document has been upserted)'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
packages/components/nodes/vectorstores/OpenSearch/opensearch.png
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packages/components/nodes/vectorstores/OpenSearch/opensearch.svg
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<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
<rect width="32" height="32" fill="white"/>
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packages/components/nodes/vectorstores/Pinecone/Pinecone.ts
+8 -20
View File
@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
import { Document } from 'langchain/document'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
class Pinecone_VectorStores implements INode {
label: string
@@ -23,11 +24,11 @@ class Pinecone_VectorStores implements INode {
constructor() {
this.label = 'Pinecone'
this.name = 'pinecone'
this.version = 1.0
this.version = 2.0
this.type = 'Pinecone'
this.icon = 'pinecone.png'
this.icon = 'pinecone.svg'
this.category = 'Vector Stores'
this.description = `Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database`
this.description = `Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database`
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
this.badge = 'NEW'
this.credential = {
@@ -79,6 +80,7 @@ class Pinecone_VectorStores implements INode {
optional: true
}
]
addMMRInputParams(this.inputs)
this.outputs = [
{
label: 'Pinecone Retriever',
@@ -103,11 +105,9 @@ class Pinecone_VectorStores implements INode {
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
const pineconeEnv = getCredentialParam('pineconeEnv', credentialData, nodeData)
const client = new Pinecone({
apiKey: pineconeApiKey,
environment: pineconeEnv
apiKey: pineconeApiKey
})
const pineconeIndex = client.Index(index)
@@ -140,17 +140,12 @@ class Pinecone_VectorStores implements INode {
const pineconeMetadataFilter = nodeData.inputs?.pineconeMetadataFilter
const docs = nodeData.inputs?.document as Document[]
const embeddings = nodeData.inputs?.embeddings as Embeddings
const output = nodeData.outputs?.output as string
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
const pineconeEnv = getCredentialParam('pineconeEnv', credentialData, nodeData)
const client = new Pinecone({
apiKey: pineconeApiKey,
environment: pineconeEnv
apiKey: pineconeApiKey
})
const pineconeIndex = client.Index(index)
@@ -175,14 +170,7 @@ class Pinecone_VectorStores implements INode {
const vectorStore = await PineconeStore.fromExistingIndex(embeddings, obj)
if (output === 'retriever') {
const retriever = vectorStore.asRetriever(k)
return retriever
} else if (output === 'vectorStore') {
;(vectorStore as any).k = k
return vectorStore
}
return vectorStore
return resolveVectorStoreOrRetriever(nodeData, vectorStore)
}
}
packages/components/nodes/vectorstores/Pinecone/Pinecone_Existing.ts
+2 -4
View File
@@ -23,7 +23,7 @@ class Pinecone_Existing_VectorStores implements INode {
this.name = 'pineconeExistingIndex'
this.version = 1.0
this.type = 'Pinecone'
this.icon = 'pinecone.png'
this.icon = 'pinecone.svg'
this.category = 'Vector Stores'
this.description = 'Load existing index from Pinecone (i.e: Document has been upserted)'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
@@ -95,11 +95,9 @@ class Pinecone_Existing_VectorStores implements INode {
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
const pineconeEnv = getCredentialParam('pineconeEnv', credentialData, nodeData)
const client = new Pinecone({
apiKey: pineconeApiKey,
environment: pineconeEnv
apiKey: pineconeApiKey
})
const pineconeIndex = client.Index(index)
packages/components/nodes/vectorstores/Pinecone/Pinecone_LlamaIndex.ts
+3 -11
View File
@@ -34,7 +34,7 @@ class PineconeLlamaIndex_VectorStores implements INode {
this.name = 'pineconeLlamaIndex'
this.version = 1.0
this.type = 'Pinecone'
this.icon = 'pinecone.png'
this.icon = 'pinecone.svg'
this.category = 'Vector Stores'
this.description = `Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database`
this.baseClasses = [this.type, 'VectorIndexRetriever']
@@ -106,12 +106,10 @@ class PineconeLlamaIndex_VectorStores implements INode {
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
const pineconeEnv = getCredentialParam('pineconeEnv', credentialData, nodeData)
const pcvs = new PineconeVectorStore({
indexName,
apiKey: pineconeApiKey,
environment: pineconeEnv,
namespace: pineconeNamespace
})
@@ -150,12 +148,10 @@ class PineconeLlamaIndex_VectorStores implements INode {
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
const pineconeEnv = getCredentialParam('pineconeEnv', credentialData, nodeData)
const obj: PineconeParams = {
indexName,
apiKey: pineconeApiKey,
environment: pineconeEnv
apiKey: pineconeApiKey
}
if (pineconeNamespace) obj.namespace = pineconeNamespace
@@ -186,7 +182,6 @@ class PineconeLlamaIndex_VectorStores implements INode {
type PineconeParams = {
indexName: string
apiKey: string
environment: string
namespace?: string
chunkSize?: number
queryFilter?: object
@@ -197,7 +192,6 @@ class PineconeVectorStore implements VectorStore {
db?: Pinecone
indexName: string
apiKey: string
environment: string
chunkSize: number
namespace?: string
queryFilter?: object
@@ -205,7 +199,6 @@ class PineconeVectorStore implements VectorStore {
constructor(params: PineconeParams) {
this.indexName = params?.indexName
this.apiKey = params?.apiKey
this.environment = params?.environment
this.namespace = params?.namespace ?? ''
this.chunkSize = params?.chunkSize ?? Number.parseInt(process.env.PINECONE_CHUNK_SIZE ?? '100')
this.queryFilter = params?.queryFilter ?? {}
@@ -214,8 +207,7 @@ class PineconeVectorStore implements VectorStore {
private async getDb(): Promise<Pinecone> {
if (!this.db) {
this.db = new Pinecone({
apiKey: this.apiKey,
environment: this.environment
apiKey: this.apiKey
})
}
return Promise.resolve(this.db)
packages/components/nodes/vectorstores/Pinecone/Pinecone_Upsert.ts
+2 -4
View File
@@ -25,7 +25,7 @@ class PineconeUpsert_VectorStores implements INode {
this.name = 'pineconeUpsert'
this.version = 1.0
this.type = 'Pinecone'
this.icon = 'pinecone.png'
this.icon = 'pinecone.svg'
this.category = 'Vector Stores'
this.description = 'Upsert documents to Pinecone'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
@@ -96,11 +96,9 @@ class PineconeUpsert_VectorStores implements INode {
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
const pineconeEnv = getCredentialParam('pineconeEnv', credentialData, nodeData)
const client = new Pinecone({
apiKey: pineconeApiKey,
environment: pineconeEnv
apiKey: pineconeApiKey
})
const pineconeIndex = client.Index(index)
packages/components/nodes/vectorstores/Pinecone/pinecone.png
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packages/components/nodes/vectorstores/Pinecone/pinecone.svg
+12
View File
@@ -0,0 +1,12 @@
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
<circle cx="14" cy="28" r="1.5" fill="black"/>
<path d="M16 16L17 11M17 11L14 12.5M17 11L19 13.5" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M19.5 24L21.95 27.6568M21.95 27.6568L23.0106 24.4749M21.95 27.6568L18.768 27.3033" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M22 12.5L25.6516 10.1213M25.6516 10.1213L22.4696 9.06066M25.6516 10.1213L25.298 13.3033" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M12 10.4095L9.22528 7.54574M9.22528 7.54574L8.72713 10.8626M9.22528 7.54574L12.4207 7.3473" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M9.24639 22.7722L6.1657 25.2398M6.1657 25.2398L6.38213 21.8927M6.1657 25.2398L9.24639 26.1113" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M23 19L27 20M27 20L25.5 17M27 20L24.5 22" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M9 16.5L5 16M5 16L6.5 19M5 16L7.5 14" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M17.5 8L18.5 3M18.5 3L15.5 4.5M18.5 3L20.5 5.5" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M14.5 24L15.5 19M15.5 19L12.5 20.5M15.5 19L17.5 21.5" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
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packages/components/nodes/vectorstores/Postgres/Postgres.ts
+11 -2
View File
@@ -24,7 +24,7 @@ class Postgres_VectorStores implements INode {
constructor() {
this.label = 'Postgres'
this.name = 'postgres'
this.version = 1.0
this.version = 2.0
this.type = 'Postgres'
this.icon = 'postgres.svg'
this.category = 'Vector Stores'
@@ -60,6 +60,13 @@ class Postgres_VectorStores implements INode {
name: 'database',
type: 'string'
},
{
label: 'SSL Connection',
name: 'sslConnection',
type: 'boolean',
default: false,
optional: false
},
{
label: 'Port',
name: 'port',
@@ -117,6 +124,7 @@ class Postgres_VectorStores implements INode {
const docs = nodeData.inputs?.document as Document[]
const embeddings = nodeData.inputs?.embeddings as Embeddings
const additionalConfig = nodeData.inputs?.additionalConfig as string
const sslConnection = nodeData.inputs?.sslConnection as boolean
let additionalConfiguration = {}
if (additionalConfig) {
@@ -134,7 +142,8 @@ class Postgres_VectorStores implements INode {
port: nodeData.inputs?.port as number,
username: user,
password: password,
database: nodeData.inputs?.database as string
database: nodeData.inputs?.database as string,
ssl: sslConnection
}
const args = {
packages/components/nodes/vectorstores/Postgres/Postgres_Exisiting.ts
+11 -2
View File
@@ -23,7 +23,7 @@ class Postgres_Existing_VectorStores implements INode {
constructor() {
this.label = 'Postgres Load Existing Index'
this.name = 'postgresExistingIndex'
this.version = 1.0
this.version = 2.0
this.type = 'Postgres'
this.icon = 'postgres.svg'
this.category = 'Vector Stores'
@@ -52,6 +52,13 @@ class Postgres_Existing_VectorStores implements INode {
name: 'database',
type: 'string'
},
{
label: 'SSL Connection',
name: 'sslConnection',
type: 'boolean',
default: false,
optional: false
},
{
label: 'Port',
name: 'port',
@@ -109,6 +116,7 @@ class Postgres_Existing_VectorStores implements INode {
const output = nodeData.outputs?.output as string
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const sslConnection = nodeData.inputs?.sslConnection as boolean
let additionalConfiguration = {}
if (additionalConfig) {
@@ -126,7 +134,8 @@ class Postgres_Existing_VectorStores implements INode {
port: nodeData.inputs?.port as number,
username: user,
password: password,
database: nodeData.inputs?.database as string
database: nodeData.inputs?.database as string,
ssl: sslConnection
}
const args = {
packages/components/nodes/vectorstores/Postgres/Postgres_Upsert.ts
+11 -2
View File
@@ -24,7 +24,7 @@ class PostgresUpsert_VectorStores implements INode {
constructor() {
this.label = 'Postgres Upsert Document'
this.name = 'postgresUpsert'
this.version = 1.0
this.version = 2.0
this.type = 'Postgres'
this.icon = 'postgres.svg'
this.category = 'Vector Stores'
@@ -59,6 +59,13 @@ class PostgresUpsert_VectorStores implements INode {
name: 'database',
type: 'string'
},
{
label: 'SSL Connection',
name: 'sslConnection',
type: 'boolean',
default: false,
optional: false
},
{
label: 'Port',
name: 'port',
@@ -117,6 +124,7 @@ class PostgresUpsert_VectorStores implements INode {
const output = nodeData.outputs?.output as string
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const sslConnection = nodeData.inputs?.sslConnection as boolean
let additionalConfiguration = {}
if (additionalConfig) {
@@ -134,7 +142,8 @@ class PostgresUpsert_VectorStores implements INode {
port: nodeData.inputs?.port as number,
username: user,
password: password,
database: nodeData.inputs?.database as string
database: nodeData.inputs?.database as string,
ssl: sslConnection
}
const args = {
packages/components/nodes/vectorstores/Qdrant/Qdrant.ts
+30 -2
View File
@@ -149,9 +149,12 @@ class Qdrant_VectorStores implements INode {
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const qdrantApiKey = getCredentialParam('qdrantApiKey', credentialData, nodeData)
const port = Qdrant_VectorStores.determinePortByUrl(qdrantServerUrl)
const client = new QdrantClient({
url: qdrantServerUrl,
apiKey: qdrantApiKey
apiKey: qdrantApiKey,
port: port
})
const flattenDocs = docs && docs.length ? flatten(docs) : []
@@ -198,9 +201,12 @@ class Qdrant_VectorStores implements INode {
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const qdrantApiKey = getCredentialParam('qdrantApiKey', credentialData, nodeData)
const port = Qdrant_VectorStores.determinePortByUrl(qdrantServerUrl)
const client = new QdrantClient({
url: qdrantServerUrl,
apiKey: qdrantApiKey
apiKey: qdrantApiKey,
port: port
})
const dbConfig: QdrantLibArgs = {
@@ -242,6 +248,28 @@ class Qdrant_VectorStores implements INode {
}
return vectorStore
}
/**
* Determine the port number from the given URL.
*
* The problem is when not doing this the qdrant-client.js will fall back on 6663 when you enter a port 443 and 80.
* See: https://stackoverflow.com/questions/59104197/nodejs-new-url-urlhttps-myurl-com80-lists-the-port-as-empty
* @param qdrantServerUrl the url to get the port from
*/
static determinePortByUrl(qdrantServerUrl: string): number {
const parsedUrl = new URL(qdrantServerUrl)
let port = parsedUrl.port ? parseInt(parsedUrl.port) : 6663
if (parsedUrl.protocol === 'https:' && parsedUrl.port === '') {
port = 443
}
if (parsedUrl.protocol === 'http:' && parsedUrl.port === '') {
port = 80
}
return port
}
}
module.exports = { nodeClass: Qdrant_VectorStores }
packages/components/nodes/vectorstores/Redis/Redis.ts
+3 -3
View File
@@ -148,10 +148,10 @@ class Redis_VectorStores implements INode {
}
}
const redisClient = createClient({ url: redisUrl })
await redisClient.connect()
try {
const redisClient = createClient({ url: redisUrl })
await redisClient.connect()
const storeConfig: RedisVectorStoreConfig = {
redisClient: redisClient,
indexName: indexName
packages/components/nodes/vectorstores/Supabase/Supabase.ts
+5 -13
View File
@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { SupabaseLibArgs, SupabaseVectorStore } from 'langchain/vectorstores/supabase'
import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
class Supabase_VectorStores implements INode {
label: string
@@ -23,11 +24,11 @@ class Supabase_VectorStores implements INode {
constructor() {
this.label = 'Supabase'
this.name = 'supabase'
this.version = 1.0
this.version = 2.0
this.type = 'Supabase'
this.icon = 'supabase.svg'
this.category = 'Vector Stores'
this.description = 'Upsert embedded data and perform similarity search upon query using Supabase via pgvector extension'
this.description = 'Upsert embedded data and perform similarity or mmr search upon query using Supabase via pgvector extension'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
this.badge = 'NEW'
this.credential = {
@@ -81,6 +82,7 @@ class Supabase_VectorStores implements INode {
optional: true
}
]
addMMRInputParams(this.inputs)
this.outputs = [
{
label: 'Supabase Retriever',
@@ -135,9 +137,6 @@ class Supabase_VectorStores implements INode {
const queryName = nodeData.inputs?.queryName as string
const embeddings = nodeData.inputs?.embeddings as Embeddings
const supabaseMetadataFilter = nodeData.inputs?.supabaseMetadataFilter
const output = nodeData.outputs?.output as string
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const supabaseApiKey = getCredentialParam('supabaseApiKey', credentialData, nodeData)
@@ -157,14 +156,7 @@ class Supabase_VectorStores implements INode {
const vectorStore = await SupabaseVectorStore.fromExistingIndex(embeddings, obj)
if (output === 'retriever') {
const retriever = vectorStore.asRetriever(k)
return retriever
} else if (output === 'vectorStore') {
;(vectorStore as any).k = k
return vectorStore
}
return vectorStore
return resolveVectorStoreOrRetriever(nodeData, vectorStore)
}
}
packages/components/nodes/vectorstores/Vectara/Vectara.ts
+37 -6
View File
@@ -1,5 +1,5 @@
import { flatten } from 'lodash'
import { VectaraStore, VectaraLibArgs, VectaraFilter, VectaraContextConfig, VectaraFile } from 'langchain/vectorstores/vectara'
import { VectaraStore, VectaraLibArgs, VectaraFilter, VectaraContextConfig, VectaraFile, MMRConfig } from 'langchain/vectorstores/vectara'
import { Document } from 'langchain/document'
import { Embeddings } from 'langchain/embeddings/base'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
@@ -22,7 +22,7 @@ class Vectara_VectorStores implements INode {
constructor() {
this.label = 'Vectara'
this.name = 'vectara'
this.version = 1.0
this.version = 2.0
this.type = 'Vectara'
this.icon = 'vectara.png'
this.category = 'Vector Stores'
@@ -82,7 +82,9 @@ class Vectara_VectorStores implements INode {
label: 'Lambda',
name: 'lambda',
description:
'Improves retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.',
'Enable hybrid search to improve retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.' +
'A value of 0.0 means that only neural search is used, while a value of 1.0 means that only keyword-based search is used. Defaults to 0.0 (neural only).',
default: 0.0,
type: 'number',
additionalParams: true,
optional: true
@@ -90,8 +92,30 @@ class Vectara_VectorStores implements INode {
{
label: 'Top K',
name: 'topK',
description: 'Number of top results to fetch. Defaults to 4',
placeholder: '4',
description: 'Number of top results to fetch. Defaults to 5',
placeholder: '5',
type: 'number',
additionalParams: true,
optional: true
},
{
label: 'MMR K',
name: 'mmrK',
description: 'Number of top results to fetch for MMR. Defaults to 50',
placeholder: '50',
type: 'number',
additionalParams: true,
optional: true
},
{
label: 'MMR diversity bias',
name: 'mmrDiversityBias',
step: 0.1,
description:
'The diversity bias to use for MMR. This is a value between 0.0 and 1.0' +
'Values closer to 1.0 optimize for the most diverse results.' +
'Defaults to 0 (MMR disabled)',
placeholder: '0.0',
type: 'number',
additionalParams: true,
optional: true
@@ -191,7 +215,9 @@ class Vectara_VectorStores implements INode {
const lambda = nodeData.inputs?.lambda as number
const output = nodeData.outputs?.output as string
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const k = topK ? parseFloat(topK) : 5
const mmrK = nodeData.inputs?.mmrK as number
const mmrDiversityBias = nodeData.inputs?.mmrDiversityBias as number
const vectaraArgs: VectaraLibArgs = {
apiKey: apiKey,
@@ -208,6 +234,11 @@ class Vectara_VectorStores implements INode {
if (sentencesBefore) vectaraContextConfig.sentencesBefore = sentencesBefore
if (sentencesAfter) vectaraContextConfig.sentencesAfter = sentencesAfter
vectaraFilter.contextConfig = vectaraContextConfig
const mmrConfig: MMRConfig = {}
mmrConfig.enabled = mmrDiversityBias > 0
mmrConfig.mmrTopK = mmrK
mmrConfig.diversityBias = mmrDiversityBias
vectaraFilter.mmrConfig = mmrConfig
const vectorStore = new VectaraStore(vectaraArgs)
packages/components/nodes/vectorstores/VectorStoreUtils.ts
+75
View File
@@ -0,0 +1,75 @@
import { INodeData } from '../../src'
export const resolveVectorStoreOrRetriever = (nodeData: INodeData, vectorStore: any) => {
const output = nodeData.outputs?.output as string
const searchType = nodeData.outputs?.searchType as string
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
if (output === 'retriever') {
if ('mmr' === searchType) {
const fetchK = nodeData.inputs?.fetchK as string
const lambda = nodeData.inputs?.lambda as string
const f = fetchK ? parseInt(fetchK) : 20
const l = lambda ? parseFloat(lambda) : 0.5
return vectorStore.asRetriever({
searchType: 'mmr',
k: k,
searchKwargs: {
fetchK: f,
lambda: l
}
})
} else {
// "searchType" is "similarity"
return vectorStore.asRetriever(k)
}
} else if (output === 'vectorStore') {
;(vectorStore as any).k = k
return vectorStore
}
}
export const addMMRInputParams = (inputs: any[]) => {
const mmrInputParams = [
{
label: 'Search Type',
name: 'searchType',
type: 'options',
default: 'similarity',
options: [
{
label: 'Similarity',
name: 'similarity'
},
{
label: 'Max Marginal Relevance',
name: 'mmr'
}
],
additionalParams: true,
optional: true
},
{
label: 'Fetch K (for MMR Search)',
name: 'fetchK',
description: 'Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR',
placeholder: '20',
type: 'number',
additionalParams: true,
optional: true
},
{
label: 'Lambda (for MMR Search)',
name: 'lambda',
description:
'Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR',
placeholder: '0.5',
type: 'number',
additionalParams: true,
optional: true
}
]
inputs.push(...mmrInputParams)
}
packages/components/nodes/vectorstores/Weaviate/Weaviate.ts
+5 -13
View File
@@ -5,6 +5,7 @@ import { Document } from 'langchain/document'
import { Embeddings } from 'langchain/embeddings/base'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
class Weaviate_VectorStores implements INode {
label: string
@@ -23,12 +24,12 @@ class Weaviate_VectorStores implements INode {
constructor() {
this.label = 'Weaviate'
this.name = 'weaviate'
this.version = 1.0
this.version = 2.0
this.type = 'Weaviate'
this.icon = 'weaviate.png'
this.category = 'Vector Stores'
this.description =
'Upsert embedded data and perform similarity search upon query using Weaviate, a scalable open-source vector database'
'Upsert embedded data and perform similarity or mmr search using Weaviate, a scalable open-source vector database'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
this.badge = 'NEW'
this.credential = {
@@ -107,6 +108,7 @@ class Weaviate_VectorStores implements INode {
optional: true
}
]
addMMRInputParams(this.inputs)
this.outputs = [
{
label: 'Weaviate Retriever',
@@ -174,9 +176,6 @@ class Weaviate_VectorStores implements INode {
const weaviateTextKey = nodeData.inputs?.weaviateTextKey as string
const weaviateMetadataKeys = nodeData.inputs?.weaviateMetadataKeys as string
const embeddings = nodeData.inputs?.embeddings as Embeddings
const output = nodeData.outputs?.output as string
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const weaviateApiKey = getCredentialParam('weaviateApiKey', credentialData, nodeData)
@@ -199,14 +198,7 @@ class Weaviate_VectorStores implements INode {
const vectorStore = await WeaviateStore.fromExistingIndex(embeddings, obj)
if (output === 'retriever') {
const retriever = vectorStore.asRetriever(k)
return retriever
} else if (output === 'vectorStore') {
;(vectorStore as any).k = k
return vectorStore
}
return vectorStore
return resolveVectorStoreOrRetriever(nodeData, vectorStore)
}
}
packages/components/nodes/vectorstores/Zep/Zep.ts
+8 -16
View File
@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
import { Document } from 'langchain/document'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
class Zep_VectorStores implements INode {
label: string
@@ -23,12 +24,12 @@ class Zep_VectorStores implements INode {
constructor() {
this.label = 'Zep'
this.name = 'zep'
this.version = 1.0
this.version = 2.0
this.type = 'Zep'
this.icon = 'zep.png'
this.icon = 'zep.svg'
this.category = 'Vector Stores'
this.description =
'Upsert embedded data and perform similarity search upon query using Zep, a fast and scalable building block for LLM apps'
'Upsert embedded data and perform similarity or mmr search upon query using Zep, a fast and scalable building block for LLM apps'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
this.badge = 'NEW'
this.credential = {
@@ -88,6 +89,7 @@ class Zep_VectorStores implements INode {
optional: true
}
]
addMMRInputParams(this.inputs)
this.outputs = [
{
label: 'Zep Retriever',
@@ -144,9 +146,6 @@ class Zep_VectorStores implements INode {
const zepMetadataFilter = nodeData.inputs?.zepMetadataFilter
const dimension = nodeData.inputs?.dimension as number
const embeddings = nodeData.inputs?.embeddings as Embeddings
const output = nodeData.outputs?.output as string
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const apiKey = getCredentialParam('apiKey', credentialData, nodeData)
@@ -165,14 +164,7 @@ class Zep_VectorStores implements INode {
const vectorStore = await ZepExistingVS.fromExistingIndex(embeddings, zepConfig)
if (output === 'retriever') {
const retriever = vectorStore.asRetriever(k)
return retriever
} else if (output === 'vectorStore') {
;(vectorStore as any).k = k
return vectorStore
}
return vectorStore
return resolveVectorStoreOrRetriever(nodeData, vectorStore)
}
}
@@ -210,7 +202,7 @@ class ZepExistingVS extends ZepVectorStore {
this.args = args
}
async initalizeCollection(args: IZepConfig & Partial<ZepFilter>) {
async initializeCollection(args: IZepConfig & Partial<ZepFilter>) {
this.client = await ZepClient.init(args.apiUrl, args.apiKey)
try {
this.collection = await this.client.document.getCollection(args.collectionName)
@@ -259,7 +251,7 @@ class ZepExistingVS extends ZepVectorStore {
const newfilter = {
where: { and: ANDFilters }
}
await this.initalizeCollection(this.args!).catch((err) => {
await this.initializeCollection(this.args!).catch((err) => {
console.error('Error initializing collection:', err)
throw err
})
packages/components/nodes/vectorstores/Zep/Zep_Existing.ts
+1 -1
View File
@@ -24,7 +24,7 @@ class Zep_Existing_VectorStores implements INode {
this.name = 'zepExistingIndex'
this.version = 1.0
this.type = 'Zep'
this.icon = 'zep.png'
this.icon = 'zep.svg'
this.category = 'Vector Stores'
this.description = 'Load existing index from Zep (i.e: Document has been upserted)'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
packages/components/nodes/vectorstores/Zep/Zep_Upsert.ts
+1 -1
View File
@@ -24,7 +24,7 @@ class Zep_Upsert_VectorStores implements INode {
this.name = 'zepUpsert'
this.version = 1.0
this.type = 'Zep'
this.icon = 'zep.png'
this.icon = 'zep.svg'
this.category = 'Vector Stores'
this.description = 'Upsert documents to Zep'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
packages/components/nodes/vectorstores/Zep/zep.png
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packages/components/nodes/vectorstores/Zep/zep.svg
+19
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@@ -0,0 +1,19 @@
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