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https://github.com/n8n-io/n8n.git
synced 2025-03-05 20:50:17 -08:00
add retrieve-as-tool mode
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parent
87e2f2d154
commit
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@ -228,7 +228,7 @@ export class VectorStorePGVector extends createVectorStoreNode({
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testedBy: 'postgresConnectionTest',
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},
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],
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operationModes: ['load', 'insert', 'retrieve'],
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operationModes: ['load', 'insert', 'retrieve', 'retrieve-as-tool'],
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},
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sharedFields,
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insertFields,
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@ -65,7 +65,7 @@ export class VectorStorePinecone extends createVectorStoreNode({
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required: true,
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},
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],
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operationModes: ['load', 'insert', 'retrieve', 'update'],
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operationModes: ['load', 'insert', 'retrieve', 'update', 'retrieve-as-tool'],
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},
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methods: { listSearch: { pineconeIndexSearch } },
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retrieveFields,
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@ -55,7 +55,7 @@ export class VectorStoreSupabase extends createVectorStoreNode({
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required: true,
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},
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],
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operationModes: ['load', 'insert', 'retrieve', 'update'],
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operationModes: ['load', 'insert', 'retrieve', 'update', 'retrieve-as-tool'],
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},
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methods: {
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listSearch: { supabaseTableNameSearch },
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@ -26,10 +26,16 @@ import { N8nJsonLoader } from '@utils/N8nJsonLoader';
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import { getConnectionHintNoticeField } from '@utils/sharedFields';
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import { processDocument } from './processDocuments';
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import { DynamicTool } from 'langchain/tools';
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type NodeOperationMode = 'insert' | 'load' | 'retrieve' | 'update';
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type NodeOperationMode = 'insert' | 'load' | 'retrieve' | 'update' | 'retrieve-as-tool';
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const DEFAULT_OPERATION_MODES: NodeOperationMode[] = ['load', 'insert', 'retrieve'];
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const DEFAULT_OPERATION_MODES: NodeOperationMode[] = [
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'load',
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'insert',
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'retrieve',
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'retrieve-as-tool',
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];
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interface NodeMeta {
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displayName: string;
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@ -100,10 +106,16 @@ function getOperationModeOptions(args: VectorStoreNodeConstructorArgs): INodePro
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action: 'Add documents to vector store',
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},
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{
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name: 'Retrieve Documents (For Agent/Chain)',
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name: 'Retrieve Documents (As Vector Store for AI Agent)',
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value: 'retrieve',
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description: 'Retrieve documents from vector store to be used with AI nodes',
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action: 'Retrieve documents for AI processing',
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description: 'Retrieve documents from vector store to be used as vector store with AI nodes',
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action: 'Retrieve documents for AI processing as Vector Store',
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},
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{
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name: 'Retrieve Documents (As Tool for AI Agent)',
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value: 'retrieve-as-tool',
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description: 'Retrieve documents from vector store to be used as tool with AI nodes',
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action: 'Retrieve documents for AI processing as Tool',
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},
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{
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name: 'Update Documents',
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@ -150,6 +162,10 @@ export const createVectorStoreNode = (args: VectorStoreNodeConstructorArgs) =>
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const mode = parameters?.mode;
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const inputs = [{ displayName: "Embedding", type: "${NodeConnectionType.AiEmbedding}", required: true, maxConnections: 1}]
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if (mode === 'retrieve-as-tool') {
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return inputs;
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}
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if (['insert', 'load', 'update'].includes(mode)) {
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inputs.push({ displayName: "", type: "${NodeConnectionType.Main}"})
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}
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@ -163,6 +179,11 @@ export const createVectorStoreNode = (args: VectorStoreNodeConstructorArgs) =>
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outputs: `={{
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((parameters) => {
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const mode = parameters?.mode ?? 'retrieve';
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if (mode === 'retrieve-as-tool') {
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return [{ displayName: "Tool", type: "${NodeConnectionType.AiTool}"}]
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}
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if (mode === 'retrieve') {
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return [{ displayName: "Vector Store", type: "${NodeConnectionType.AiVectorStore}"}]
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}
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@ -186,6 +207,37 @@ export const createVectorStoreNode = (args: VectorStoreNodeConstructorArgs) =>
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},
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},
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},
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{
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displayName: 'Name',
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name: 'toolName',
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type: 'string',
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default: '',
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required: true,
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description: 'Name of the vector store',
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placeholder: 'e.g. company_knowledge_base',
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validateType: 'string-alphanumeric',
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displayOptions: {
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show: {
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mode: ['retrieve-as-tool'],
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},
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},
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},
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{
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displayName: 'Description',
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name: 'toolDescription',
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type: 'string',
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default: '',
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required: true,
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typeOptions: { rows: 2 },
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description:
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'Explain to the LLM what this tool does, a good, specific description would allow LLMs to produce expected results much more often',
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placeholder: `e.g. ${args.meta.description}`,
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displayOptions: {
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show: {
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mode: ['retrieve-as-tool'],
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},
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},
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},
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...args.sharedFields,
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...transformDescriptionForOperationMode(args.insertFields ?? [], 'insert'),
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// Prompt and topK are always used for the load operation
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@ -211,7 +263,7 @@ export const createVectorStoreNode = (args: VectorStoreNodeConstructorArgs) =>
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description: 'Number of top results to fetch from vector store',
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displayOptions: {
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show: {
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mode: ['load'],
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mode: ['load', 'retrieve-as-tool'],
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},
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},
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},
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@ -223,7 +275,7 @@ export const createVectorStoreNode = (args: VectorStoreNodeConstructorArgs) =>
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description: 'Whether or not to include document metadata',
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displayOptions: {
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show: {
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mode: ['load'],
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mode: ['load', 'retrieve-as-tool'],
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},
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},
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},
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@ -401,7 +453,7 @@ export const createVectorStoreNode = (args: VectorStoreNodeConstructorArgs) =>
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}
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async supplyData(this: ISupplyDataFunctions, itemIndex: number): Promise<SupplyData> {
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const mode = this.getNodeParameter('mode', 0) as 'load' | 'insert' | 'retrieve';
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const mode = this.getNodeParameter('mode', 0) as NodeOperationMode;
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const filter = getMetadataFiltersValues(this, itemIndex);
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const embeddings = (await this.getInputConnectionData(
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NodeConnectionType.AiEmbedding,
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@ -415,9 +467,54 @@ export const createVectorStoreNode = (args: VectorStoreNodeConstructorArgs) =>
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};
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}
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if (mode === 'retrieve-as-tool') {
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const toolDescription = this.getNodeParameter('toolDescription', itemIndex) as string;
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const toolName = this.getNodeParameter('toolName', itemIndex) as string;
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const topK = this.getNodeParameter('topK', itemIndex, 4) as number;
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const includeDocumentMetadata = this.getNodeParameter(
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'includeDocumentMetadata',
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itemIndex,
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true,
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) as boolean;
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const vectorStoreTool = new DynamicTool({
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name: toolName,
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description: toolDescription,
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func: async (input) => {
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const vectorStore = await args.getVectorStoreClient(
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this,
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filter,
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embeddings,
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itemIndex,
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);
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const embeddedPrompt = await embeddings.embedQuery(input);
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const documents = await vectorStore.similaritySearchVectorWithScore(
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embeddedPrompt,
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topK,
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filter,
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);
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return documents
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.map((document) => {
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if (includeDocumentMetadata) {
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return { type: 'text', text: JSON.stringify(document[0]) };
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}
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return {
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type: 'text',
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text: JSON.stringify({ pageContent: document[0].pageContent }),
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};
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})
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.filter((document) => !!document);
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},
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});
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return {
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response: logWrapper(vectorStoreTool, this),
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};
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}
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throw new NodeOperationError(
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this.getNode(),
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'Only the "retrieve" operation mode is supported to supply data',
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'Only the "retrieve" and "retrieve-as-tool" operation mode is supported to supply data',
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);
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}
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};
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