Merge branch 'master' into eleanor-gptdraw

1 files changed, 19 insertions, 14 deletions

diff --git a/src/client/views/nodes/chatbot/tools/RAGTool.ts b/src/client/views/nodes/chatbot/tools/RAGTool.ts
index 4cc2f26ff..482069f36 100644
--- a/src/client/views/nodes/chatbot/tools/RAGTool.ts
+++ b/src/client/views/nodes/chatbot/tools/RAGTool.ts
@@ -1,20 +1,26 @@
 import { Networking } from '../../../../Network';
-import { RAGChunk } from '../types/types';
+import { Observation, RAGChunk } from '../types/types';
+import { ParametersType } from './ToolTypes';
 import { Vectorstore } from '../vectorstore/Vectorstore';
 import { BaseTool } from './BaseTool';
 
-export class RAGTool extends BaseTool {
+const ragToolParams = [
+    {
+        name: 'hypothetical_document_chunk',
+        type: 'string',
+        description: "A detailed prompt representing an ideal chunk to embed and compare against document vectors to retrieve the most relevant content for answering the user's query.",
+        required: true,
+    },
+] as const;
+
+type RAGToolParamsType = typeof ragToolParams;
+
+export class RAGTool extends BaseTool<RAGToolParamsType> {
     constructor(private vectorstore: Vectorstore) {
         super(
             'rag',
             'Perform a RAG search on user documents',
-            {
-                hypothetical_document_chunk: {
-                    type: 'string',
-                    description: "A detailed prompt representing an ideal chunk to embed and compare against document vectors to retrieve the most relevant content for answering the user's query.",
-                    required: 'true',
-                },
-            },
+            ragToolParams,
             `
             When using the RAG tool, the structure must adhere to the format described in the ReAct prompt. Below are additional guidelines specifically for RAG-based responses:
 
@@ -51,18 +57,17 @@ export class RAGTool extends BaseTool {
                 </follow_up_questions>
             </answer>
             `,
-
             `Performs a RAG (Retrieval-Augmented Generation) search on user documents and returns a set of document chunks (text or images) to provide a grounded response based on user documents.`
         );
     }
 
-    async execute(args: { hypothetical_document_chunk: string }): Promise<unknown> {
+    async execute(args: ParametersType<RAGToolParamsType>): Promise<Observation[]> {
         const relevantChunks = await this.vectorstore.retrieve(args.hypothetical_document_chunk);
-        const formatted_chunks = await this.getFormattedChunks(relevantChunks);
-        return formatted_chunks;
+        const formattedChunks = await this.getFormattedChunks(relevantChunks);
+        return formattedChunks;
     }
 
-    async getFormattedChunks(relevantChunks: RAGChunk[]): Promise<unknown> {
+    async getFormattedChunks(relevantChunks: RAGChunk[]): Promise<Observation[]> {
         try {
             const { formattedChunks } = await Networking.PostToServer('/formatChunks', { relevantChunks });