diff --git a/README-ZH.md b/README-ZH.md index 2805ef9bc..8750ebc7f 100644 --- a/README-ZH.md +++ b/README-ZH.md @@ -145,25 +145,40 @@ Flowise 支持不同的环境变量来配置您的实例。您可以在 `package ## 🌐 自托管 -### [Railway](https://docs.flowiseai.com/deployment/railway) +在您现有的基础设施中部署自托管的 Flowise，我们支持各种[部署](https://docs.flowiseai.com/configuration/deployment) -[![在 Railway 上部署](https://railway.app/button.svg)](https://railway.app/template/pn4G8S?referralCode=WVNPD9) +- [AWS](https://docs.flowiseai.com/deployment/aws) +- [Azure](https://docs.flowiseai.com/deployment/azure) +- [Digital Ocean](https://docs.flowiseai.com/deployment/digital-ocean) +- [GCP](https://docs.flowiseai.com/deployment/gcp) +-
+ 其他 -### [Render](https://docs.flowiseai.com/deployment/render) + - [Railway](https://docs.flowiseai.com/deployment/railway) -[![部署到 Render](https://render.com/images/deploy-to-render-button.svg)](https://docs.flowiseai.com/deployment/render) + [![在 Railway 上部署](https://railway.app/button.svg)](https://railway.app/template/pn4G8S?referralCode=WVNPD9) -### [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face) + - [Render](https://docs.flowiseai.com/deployment/render) -HuggingFace Spaces + [![部署到 Render](https://render.com/images/deploy-to-render-button.svg)](https://docs.flowiseai.com/deployment/render) -### [AWS](https://docs.flowiseai.com/deployment/aws) + - [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face) -### [Azure](https://docs.flowiseai.com/deployment/azure) + HuggingFace Spaces -### [DigitalOcean](https://docs.flowiseai.com/deployment/digital-ocean) + - [Elestio](https://elest.io/open-source/flowiseai) -### [GCP](https://docs.flowiseai.com/deployment/gcp) + [![Deploy](https://pub-da36157c854648669813f3f76c526c2b.r2.dev/deploy-on-elestio-black.png)](https://elest.io/open-source/flowiseai) + + - [Sealos](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise) + + [![部署到 Sealos](https://raw.githubusercontent.com/labring-actions/templates/main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise) + + - [RepoCloud](https://repocloud.io/details/?app_id=29) + + [![部署到 RepoCloud](https://d16t0pc4846x52.cloudfront.net/deploy.png)](https://repocloud.io/details/?app_id=29) + +
## 💻 云托管 diff --git a/README.md b/README.md index 25026237f..3e6b7e561 100644 --- a/README.md +++ b/README.md @@ -145,29 +145,40 @@ Flowise support different environment variables to configure your instance. You ## 🌐 Self Host -### [Railway](https://docs.flowiseai.com/deployment/railway) +Deploy Flowise self-hosted in your existing infrastructure, we support various [deployments](https://docs.flowiseai.com/configuration/deployment) -[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/pn4G8S?referralCode=WVNPD9) +- [AWS](https://docs.flowiseai.com/deployment/aws) +- [Azure](https://docs.flowiseai.com/deployment/azure) +- [Digital Ocean](https://docs.flowiseai.com/deployment/digital-ocean) +- [GCP](https://docs.flowiseai.com/deployment/gcp) +-
+ Others -### [Render](https://docs.flowiseai.com/deployment/render) + - [Railway](https://docs.flowiseai.com/deployment/railway) -[![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://docs.flowiseai.com/deployment/render) + [![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/pn4G8S?referralCode=WVNPD9) -### [Elestio](https://elest.io/open-source/flowiseai) + - [Render](https://docs.flowiseai.com/deployment/render) -[![Deploy](https://pub-da36157c854648669813f3f76c526c2b.r2.dev/deploy-on-elestio-black.png)](https://elest.io/open-source/flowiseai) + [![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://docs.flowiseai.com/deployment/render) -### [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face) + - [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face) -HuggingFace Spaces + HuggingFace Spaces -### [AWS](https://docs.flowiseai.com/deployment/aws) + - [Elestio](https://elest.io/open-source/flowiseai) -### [Azure](https://docs.flowiseai.com/deployment/azure) + [![Deploy](https://pub-da36157c854648669813f3f76c526c2b.r2.dev/deploy-on-elestio-black.png)](https://elest.io/open-source/flowiseai) -### [DigitalOcean](https://docs.flowiseai.com/deployment/digital-ocean) + - [Sealos](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise) -### [GCP](https://docs.flowiseai.com/deployment/gcp) + [![](https://raw.githubusercontent.com/labring-actions/templates/main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise) + + - [RepoCloud](https://repocloud.io/details/?app_id=29) + + [![Deploy on RepoCloud](https://d16t0pc4846x52.cloudfront.net/deploy.png)](https://repocloud.io/details/?app_id=29) + +
## 💻 Cloud Hosted diff --git a/docker/README.md b/docker/README.md index d3ad1c197..11b29cf38 100644 --- a/docker/README.md +++ b/docker/README.md @@ -1,6 +1,6 @@ # Flowise Docker Hub Image -Starts Flowise from [DockerHub Image](https://hub.docker.com/repository/docker/flowiseai/flowise/general) +Starts Flowise from [DockerHub Image](https://hub.docker.com/r/flowiseai/flowise) ## Usage diff --git a/packages/components/credentials/AstraApi.credential.ts b/packages/components/credentials/AstraApi.credential.ts new file mode 100644 index 000000000..a89a259f5 --- /dev/null +++ b/packages/components/credentials/AstraApi.credential.ts @@ -0,0 +1,34 @@ +import { INodeParams, INodeCredential } from '../src/Interface' + +class AstraDBApi implements INodeCredential { + label: string + name: string + version: number + description: string + inputs: INodeParams[] + + constructor() { + this.label = 'Astra DB API' + this.name = 'AstraDBApi' + this.version = 1.0 + this.inputs = [ + { + label: 'Astra DB Collection Name', + name: 'collectionName', + type: 'string' + }, + { + label: 'Astra DB Application Token', + name: 'applicationToken', + type: 'password' + }, + { + label: 'Astra DB Api Endpoint', + name: 'dbEndPoint', + type: 'string' + } + ] + } +} + +module.exports = { credClass: AstraDBApi } diff --git a/packages/components/credentials/ZapierNLAApi.credential.ts b/packages/components/credentials/LocalAIApi.credential.ts similarity index 51% rename from packages/components/credentials/ZapierNLAApi.credential.ts rename to packages/components/credentials/LocalAIApi.credential.ts index 72035660e..4aafe040d 100644 --- a/packages/components/credentials/ZapierNLAApi.credential.ts +++ b/packages/components/credentials/LocalAIApi.credential.ts @@ -1,24 +1,23 @@ import { INodeParams, INodeCredential } from '../src/Interface' -class ZapierNLAApi implements INodeCredential { +class LocalAIApi implements INodeCredential { label: string name: string version: number - description: string inputs: INodeParams[] constructor() { - this.label = 'Zapier NLA API' - this.name = 'zapierNLAApi' + this.label = 'LocalAI API' + this.name = 'localAIApi' this.version = 1.0 this.inputs = [ { - label: 'Zapier NLA Api Key', - name: 'zapierNLAApiKey', + label: 'LocalAI Api Key', + name: 'localAIApiKey', type: 'password' } ] } } -module.exports = { credClass: ZapierNLAApi } +module.exports = { credClass: LocalAIApi } diff --git a/packages/components/nodes/agents/OpenAIFunctionAgent/OpenAIFunctionAgent.ts b/packages/components/nodes/agents/OpenAIFunctionAgent/OpenAIFunctionAgent.ts index 135121d25..c21c887aa 100644 --- a/packages/components/nodes/agents/OpenAIFunctionAgent/OpenAIFunctionAgent.ts +++ b/packages/components/nodes/agents/OpenAIFunctionAgent/OpenAIFunctionAgent.ts @@ -112,7 +112,7 @@ const prepareAgent = ( const inputKey = memory.inputKey ? memory.inputKey : 'input' const prompt = ChatPromptTemplate.fromMessages([ - ['ai', systemMessage ? systemMessage : `You are a helpful AI assistant.`], + ['system', systemMessage ? systemMessage : `You are a helpful AI assistant.`], new MessagesPlaceholder(memoryKey), ['human', `{${inputKey}}`], new MessagesPlaceholder('agent_scratchpad') diff --git a/packages/components/nodes/chains/VectaraChain/VectaraChain.ts b/packages/components/nodes/chains/VectaraChain/VectaraChain.ts index 3799d062f..7d65c9cd0 100644 --- a/packages/components/nodes/chains/VectaraChain/VectaraChain.ts +++ b/packages/components/nodes/chains/VectaraChain/VectaraChain.ts @@ -69,22 +69,23 @@ class VectaraChain_Chains implements INode { options: [ { label: 'vectara-summary-ext-v1.2.0 (gpt-3.5-turbo)', - name: 'vectara-summary-ext-v1.2.0' + name: 'vectara-summary-ext-v1.2.0', + description: 'base summarizer, available to all Vectara users' }, { label: 'vectara-experimental-summary-ext-2023-10-23-small (gpt-3.5-turbo)', name: 'vectara-experimental-summary-ext-2023-10-23-small', - description: 'In beta, available to both Growth and Scale Vectara users' + description: `In beta, available to both Growth and Scale Vectara users` }, { label: 'vectara-summary-ext-v1.3.0 (gpt-4.0)', name: 'vectara-summary-ext-v1.3.0', - description: 'Only available to paying Scale Vectara users' + description: 'Only available to Scale Vectara users' }, { label: 'vectara-experimental-summary-ext-2023-10-23-med (gpt-4.0)', name: 'vectara-experimental-summary-ext-2023-10-23-med', - description: 'In beta, only available to paying Scale Vectara users' + description: `In beta, only available to Scale Vectara users` } ], default: 'vectara-summary-ext-v1.2.0' @@ -228,7 +229,7 @@ class VectaraChain_Chains implements INode { async run(nodeData: INodeData, input: string): Promise { const vectorStore = nodeData.inputs?.vectaraStore as VectaraStore - const responseLang = (nodeData.inputs?.responseLang as string) ?? 'auto' + const responseLang = (nodeData.inputs?.responseLang as string) ?? 'eng' const summarizerPromptName = nodeData.inputs?.summarizerPromptName as string const maxSummarizedResultsStr = nodeData.inputs?.maxSummarizedResults as string const maxSummarizedResults = maxSummarizedResultsStr ? parseInt(maxSummarizedResultsStr, 10) : 7 @@ -247,17 +248,31 @@ class VectaraChain_Chains implements INode { lexicalInterpolationConfig: { lambda: vectaraFilter?.lambda ?? 0.025 } })) + // Vectara reranker ID for MMR (https://docs.vectara.com/docs/api-reference/search-apis/reranking#maximal-marginal-relevance-mmr-reranker) + const mmrRerankerId = 272725718 + const mmrEnabled = vectaraFilter?.mmrConfig?.enabled + const data = { query: [ { query: input, start: 0, - numResults: topK, + numResults: mmrEnabled ? vectaraFilter?.mmrTopK : topK, + corpusKey: corpusKeys, contextConfig: { sentencesAfter: vectaraFilter?.contextConfig?.sentencesAfter ?? 2, sentencesBefore: vectaraFilter?.contextConfig?.sentencesBefore ?? 2 }, - corpusKey: corpusKeys, + ...(mmrEnabled + ? { + rerankingConfig: { + rerankerId: mmrRerankerId, + mmrConfig: { + diversityBias: vectaraFilter?.mmrConfig.diversityBias + } + } + } + : {}), summary: [ { summarizerPromptName, @@ -285,6 +300,14 @@ class VectaraChain_Chains implements INode { const documents = result.responseSet[0].document let rawSummarizedText = '' + // remove responses that are not in the topK (in case of MMR) + // Note that this does not really matter functionally due to the reorder citations, but it is more efficient + const maxResponses = mmrEnabled ? Math.min(responses.length, topK) : responses.length + if (responses.length > maxResponses) { + responses.splice(0, maxResponses) + } + + // Add metadata to each text response given its corresponding document metadata for (let i = 0; i < responses.length; i += 1) { const responseMetadata = responses[i].metadata const documentMetadata = documents[responses[i].documentIndex].metadata @@ -301,13 +324,13 @@ class VectaraChain_Chains implements INode { responses[i].metadata = combinedMetadata } + // Create the summarization response const summaryStatus = result.responseSet[0].summary[0].status if (summaryStatus.length > 0 && summaryStatus[0].code === 'BAD_REQUEST') { throw new Error( `BAD REQUEST: Too much text for the summarizer to summarize. Please try reducing the number of search results to summarize, or the context of each result by adjusting the 'summary_num_sentences', and 'summary_num_results' parameters respectively.` ) } - if ( summaryStatus.length > 0 && summaryStatus[0].code === 'NOT_FOUND' && @@ -316,8 +339,8 @@ class VectaraChain_Chains implements INode { throw new Error(`BAD REQUEST: summarizer ${summarizerPromptName} is invalid for this account.`) } + // Reorder citations in summary and create the list of returned source documents rawSummarizedText = result.responseSet[0].summary[0]?.text - let summarizedText = reorderCitations(rawSummarizedText) let summaryResponses = applyCitationOrder(responses, rawSummarizedText) diff --git a/packages/components/nodes/chatmodels/ChatLocalAI/ChatLocalAI.ts b/packages/components/nodes/chatmodels/ChatLocalAI/ChatLocalAI.ts index 18ed409bf..f2825d0d3 100644 --- a/packages/components/nodes/chatmodels/ChatLocalAI/ChatLocalAI.ts +++ b/packages/components/nodes/chatmodels/ChatLocalAI/ChatLocalAI.ts @@ -1,5 +1,5 @@ -import { INode, INodeData, INodeParams } from '../../../src/Interface' -import { getBaseClasses } from '../../../src/utils' +import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface' +import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils' import { OpenAIChat } from 'langchain/llms/openai' import { OpenAIChatInput } from 'langchain/chat_models/openai' import { BaseCache } from 'langchain/schema' @@ -14,6 +14,7 @@ class ChatLocalAI_ChatModels implements INode { category: string description: string baseClasses: string[] + credential: INodeParams inputs: INodeParams[] constructor() { @@ -25,6 +26,13 @@ class ChatLocalAI_ChatModels implements INode { this.category = 'Chat Models' this.description = 'Use local LLMs like llama.cpp, gpt4all using LocalAI' this.baseClasses = [this.type, 'BaseChatModel', ...getBaseClasses(OpenAIChat)] + this.credential = { + label: 'Connect Credential', + name: 'credential', + type: 'credential', + credentialNames: ['localAIApi'], + optional: true + } this.inputs = [ { label: 'Cache', @@ -79,13 +87,16 @@ class ChatLocalAI_ChatModels implements INode { ] } - async init(nodeData: INodeData): Promise { + async init(nodeData: INodeData, _: string, options: ICommonObject): Promise { const temperature = nodeData.inputs?.temperature as string const modelName = nodeData.inputs?.modelName as string const maxTokens = nodeData.inputs?.maxTokens as string const topP = nodeData.inputs?.topP as string const timeout = nodeData.inputs?.timeout as string const basePath = nodeData.inputs?.basePath as string + const credentialData = await getCredentialData(nodeData.credential ?? '', options) + const localAIApiKey = getCredentialParam('localAIApiKey', credentialData, nodeData) + const cache = nodeData.inputs?.cache as BaseCache const obj: Partial & BaseLLMParams & { openAIApiKey?: string } = { @@ -98,6 +109,7 @@ class ChatLocalAI_ChatModels implements INode { if (topP) obj.topP = parseFloat(topP) if (timeout) obj.timeout = parseInt(timeout, 10) if (cache) obj.cache = cache + if (localAIApiKey) obj.openAIApiKey = localAIApiKey const model = new OpenAIChat(obj, { basePath }) diff --git a/packages/components/nodes/embeddings/LocalAIEmbedding/LocalAIEmbedding.ts b/packages/components/nodes/embeddings/LocalAIEmbedding/LocalAIEmbedding.ts index 557e35d68..24efaf8c6 100644 --- a/packages/components/nodes/embeddings/LocalAIEmbedding/LocalAIEmbedding.ts +++ b/packages/components/nodes/embeddings/LocalAIEmbedding/LocalAIEmbedding.ts @@ -1,4 +1,5 @@ -import { INode, INodeData, INodeParams } from '../../../src/Interface' +import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface' +import { getCredentialData, getCredentialParam } from '../../../src/utils' import { OpenAIEmbeddings, OpenAIEmbeddingsParams } from 'langchain/embeddings/openai' class LocalAIEmbedding_Embeddings implements INode { @@ -10,6 +11,7 @@ class LocalAIEmbedding_Embeddings implements INode { category: string description: string baseClasses: string[] + credential: INodeParams inputs: INodeParams[] constructor() { @@ -21,6 +23,13 @@ class LocalAIEmbedding_Embeddings implements INode { this.category = 'Embeddings' this.description = 'Use local embeddings models like llama.cpp' this.baseClasses = [this.type, 'Embeddings'] + this.credential = { + label: 'Connect Credential', + name: 'credential', + type: 'credential', + credentialNames: ['localAIApi'], + optional: true + } this.inputs = [ { label: 'Base Path', @@ -37,15 +46,20 @@ class LocalAIEmbedding_Embeddings implements INode { ] } - async init(nodeData: INodeData): Promise { + async init(nodeData: INodeData, _: string, options: ICommonObject): Promise { const modelName = nodeData.inputs?.modelName as string const basePath = nodeData.inputs?.basePath as string + const credentialData = await getCredentialData(nodeData.credential ?? '', options) + const localAIApiKey = getCredentialParam('localAIApiKey', credentialData, nodeData) + const obj: Partial & { openAIApiKey?: string } = { modelName, openAIApiKey: 'sk-' } + if (localAIApiKey) obj.openAIApiKey = localAIApiKey + const model = new OpenAIEmbeddings(obj, { basePath }) return model diff --git a/packages/components/nodes/utilities/CustomFunction/CustomFunction.ts b/packages/components/nodes/utilities/CustomFunction/CustomFunction.ts index b358b24b3..37511e476 100644 --- a/packages/components/nodes/utilities/CustomFunction/CustomFunction.ts +++ b/packages/components/nodes/utilities/CustomFunction/CustomFunction.ts @@ -65,7 +65,7 @@ class CustomFunction_Utilities implements INode { inputVars = typeof functionInputVariablesRaw === 'object' ? functionInputVariablesRaw : JSON.parse(functionInputVariablesRaw) } catch (exception) { - throw new Error("Invalid JSON in the PromptTemplate's promptValues: " + exception) + throw new Error('Invalid JSON in the Custom Function Input Variables: ' + exception) } } diff --git a/packages/components/nodes/vectorstores/Astra/Astra.ts b/packages/components/nodes/vectorstores/Astra/Astra.ts new file mode 100644 index 000000000..865f10446 --- /dev/null +++ b/packages/components/nodes/vectorstores/Astra/Astra.ts @@ -0,0 +1,190 @@ +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' + +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 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 + } + ] + 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 { + 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 { + 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 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 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) + + if (output === 'retriever') { + const retriever = vectorStore.asRetriever(k) + return retriever + } else if (output === 'vectorStore') { + ;(vectorStore as any).k = k + return vectorStore + } + return vectorStore + } +} + +module.exports = { nodeClass: Astra_VectorStores } diff --git a/packages/components/nodes/vectorstores/Astra/astra.svg b/packages/components/nodes/vectorstores/Astra/astra.svg new file mode 100644 index 000000000..de58397d9 --- /dev/null +++ b/packages/components/nodes/vectorstores/Astra/astra.svg @@ -0,0 +1,12 @@ + + + + + + + + + + + + diff --git a/packages/components/nodes/vectorstores/Milvus/Milvus.ts b/packages/components/nodes/vectorstores/Milvus/Milvus.ts index 090f35f74..7566f8a8a 100644 --- a/packages/components/nodes/vectorstores/Milvus/Milvus.ts +++ b/packages/components/nodes/vectorstores/Milvus/Milvus.ts @@ -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 diff --git a/packages/components/nodes/vectorstores/Vectara/Vectara.ts b/packages/components/nodes/vectorstores/Vectara/Vectara.ts index 7460c5864..939a4ac3d 100644 --- a/packages/components/nodes/vectorstores/Vectara/Vectara.ts +++ b/packages/components/nodes/vectorstores/Vectara/Vectara.ts @@ -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) diff --git a/packages/components/package.json b/packages/components/package.json index a77d91e4b..57ab7b3df 100644 --- a/packages/components/package.json +++ b/packages/components/package.json @@ -19,6 +19,7 @@ "@aws-sdk/client-bedrock-runtime": "3.422.0", "@aws-sdk/client-dynamodb": "^3.360.0", "@aws-sdk/client-s3": "^3.427.0", + "@datastax/astra-db-ts": "^0.1.2", "@dqbd/tiktoken": "^1.0.7", "@elastic/elasticsearch": "^8.9.0", "@getzep/zep-js": "^0.9.0", @@ -26,6 +27,7 @@ "@gomomento/sdk-core": "^1.51.1", "@google-ai/generativelanguage": "^0.2.1", "@huggingface/inference": "^2.6.1", + "@langchain/community": "^0.0.16", "@langchain/google-genai": "^0.0.6", "@langchain/mistralai": "^0.0.6", "@notionhq/client": "^2.2.8", @@ -48,7 +50,7 @@ "faiss-node": "^0.2.2", "fast-json-patch": "^3.1.1", "form-data": "^4.0.0", - "google-auth-library": "^9.0.0", + "google-auth-library": "^9.4.0", "graphql": "^16.6.0", "html-to-text": "^9.0.5", "husky": "^8.0.3", diff --git a/packages/components/src/agents.ts b/packages/components/src/agents.ts index e30a0c43a..5e241d505 100644 --- a/packages/components/src/agents.ts +++ b/packages/components/src/agents.ts @@ -606,9 +606,18 @@ class ExceptionTool extends Tool { export const formatAgentSteps = (steps: AgentStep[]): BaseMessage[] => steps.flatMap(({ action, observation }) => { + const create_function_message = (observation: string, action: AgentAction) => { + let content: string + if (typeof observation !== 'string') { + content = JSON.stringify(observation) + } else { + content = observation + } + return new FunctionMessage(content, action.tool) + } if ('messageLog' in action && action.messageLog !== undefined) { const log = action.messageLog as BaseMessage[] - return log.concat(new FunctionMessage(observation, action.tool)) + return log.concat(create_function_message(observation, action)) } else { return [new AIMessage(action.log)] } diff --git a/packages/server/marketplaces/chatflows/Vectara LLM Chain Upload.json b/packages/server/marketplaces/chatflows/Vectara LLM Chain Upload.json index 6f0edeea9..1a440be70 100644 --- a/packages/server/marketplaces/chatflows/Vectara LLM Chain Upload.json +++ b/packages/server/marketplaces/chatflows/Vectara LLM Chain Upload.json @@ -350,12 +350,33 @@ { "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, "id": "vectara_0-input-topK-number" + }, + { + "label": "MMR K", + "name": "mmrK", + "description": "The number of results to rerank if MMR is enabled.", + "placeholder": "50", + "type": "number", + "additionalParams": true, + "optional": true, + "id": "vectara_0-input-mmrK-number" + }, + { + "label": "MMR Diversity Bias", + "name": "mmrDiversityBias", + "step": 0.1, + "description": "Diversity Bias parameter for MMR, if enabled. 0.0 means no diversiry bias, 1.0 means maximum diversity bias. Defaults to 0.0 (MMR disabled).", + "placeholder": "0.0", + "type": "number", + "additionalParams": true, + "optional": true, + "id": "vectara_0-input-mmrDiversityBias-number" } ], "inputAnchors": [ @@ -374,7 +395,9 @@ "sentencesBefore": "", "sentencesAfter": "", "lambda": "", - "topK": "" + "topK": "", + "mmrK": "", + "mmrDiversityBias": "" }, "outputAnchors": [ { diff --git a/packages/server/src/index.ts b/packages/server/src/index.ts index 8f5ab5dbd..94a3b538b 100644 --- a/packages/server/src/index.ts +++ b/packages/server/src/index.ts @@ -361,7 +361,8 @@ export class App { const chatflow = await this.AppDataSource.getRepository(ChatFlow).findOneBy({ id: req.params.id }) - if (chatflow && chatflow.chatbotConfig) { + if (!chatflow) return res.status(404).send(`Chatflow ${req.params.id} not found`) + if (chatflow.chatbotConfig) { try { const parsedConfig = JSON.parse(chatflow.chatbotConfig) return res.json(parsedConfig) @@ -369,7 +370,7 @@ export class App { return res.status(500).send(`Error parsing Chatbot Config for Chatflow ${req.params.id}`) } } - return res.status(404).send(`Chatbot Config for Chatflow ${req.params.id} not found`) + return res.status(200).send('OK') }) // Save chatflow @@ -521,7 +522,7 @@ export class App { res.status(404).send(`Chatflow ${chatflowid} not found`) return } - const chatId = (req.query?.chatId as string) ?? (await getChatId(chatflowid)) + const chatId = req.query?.chatId as string const memoryType = req.query?.memoryType as string | undefined const sessionId = req.query?.sessionId as string | undefined const chatType = req.query?.chatType as string | undefined @@ -545,7 +546,8 @@ export class App { return res.status(500).send('Error clearing chat messages') } - const deleteOptions: FindOptionsWhere = { chatflowid, chatId } + const deleteOptions: FindOptionsWhere = { chatflowid } + if (chatId) deleteOptions.chatId = chatId if (memoryType) deleteOptions.memoryType = memoryType if (sessionId) deleteOptions.sessionId = sessionId if (chatType) deleteOptions.chatType = chatType @@ -633,7 +635,7 @@ export class App { return res.json(result) }) - // Delete all chatmessages from chatflowid + // Delete all credentials from chatflowid this.app.delete('/api/v1/credentials/:id', async (req: Request, res: Response) => { const results = await this.AppDataSource.getRepository(Credential).delete({ id: req.params.id }) return res.json(results) @@ -1790,23 +1792,6 @@ export class App { } } -/** - * Get first chat message id - * @param {string} chatflowid - * @returns {string} - */ -export async function getChatId(chatflowid: string): Promise { - // first chatmessage id as the unique chat id - const firstChatMessage = await getDataSource() - .getRepository(ChatMessage) - .createQueryBuilder('cm') - .select('cm.id') - .where('chatflowid = :chatflowid', { chatflowid }) - .orderBy('cm.createdDate', 'ASC') - .getOne() - return firstChatMessage ? firstChatMessage.id : '' -} - let serverApp: App | undefined export async function getAllChatFlow(): Promise { diff --git a/packages/server/src/utils/index.ts b/packages/server/src/utils/index.ts index 7569d5414..dafe612c8 100644 --- a/packages/server/src/utils/index.ts +++ b/packages/server/src/utils/index.ts @@ -547,7 +547,11 @@ export const getVariableValue = ( variablePaths.forEach((path) => { const variableValue = variableDict[path] // Replace all occurrence - returnVal = returnVal.split(path).join(variableValue) + if (typeof variableValue === 'object') { + returnVal = returnVal.split(path).join(JSON.stringify(variableValue).replace(/"/g, '\\"')) + } else { + returnVal = returnVal.split(path).join(variableValue) + } }) return returnVal } diff --git a/packages/ui/src/ui-component/dialog/ExpandTextDialog.js b/packages/ui/src/ui-component/dialog/ExpandTextDialog.js index 0ef70e29e..f4fdb9f9e 100644 --- a/packages/ui/src/ui-component/dialog/ExpandTextDialog.js +++ b/packages/ui/src/ui-component/dialog/ExpandTextDialog.js @@ -67,7 +67,11 @@ const ExpandTextDialog = ({ show, dialogProps, onCancel, onConfirm }) => { useEffect(() => { if (executeCustomFunctionNodeApi.data) { - setCodeExecutedResult(executeCustomFunctionNodeApi.data) + if (typeof executeCustomFunctionNodeApi.data === 'object') { + setCodeExecutedResult(JSON.stringify(executeCustomFunctionNodeApi.data, null, 2)) + } else { + setCodeExecutedResult(executeCustomFunctionNodeApi.data) + } } }, [executeCustomFunctionNodeApi.data])