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authorA.J. Shulman <Shulman.aj@gmail.com>2025-07-07 14:39:06 -0400
committerA.J. Shulman <Shulman.aj@gmail.com>2025-07-07 14:39:06 -0400
commit9092494778abd55b6aa299fe06b4f70e7c7a767f (patch)
tree28aedb8db51224374e1a31d9557ffd28e1c7e8f9 /src/client/views/nodes/chatbot/vectorstore
parent86c666427ff8b9d516450a150af641570e00f2d2 (diff)
changes (seeing if they work)
Diffstat (limited to 'src/client/views/nodes/chatbot/vectorstore')
-rw-r--r--src/client/views/nodes/chatbot/vectorstore/Vectorstore.ts4
1 files changed, 2 insertions, 2 deletions
diff --git a/src/client/views/nodes/chatbot/vectorstore/Vectorstore.ts b/src/client/views/nodes/chatbot/vectorstore/Vectorstore.ts
index 72060973b..f10e889e2 100644
--- a/src/client/views/nodes/chatbot/vectorstore/Vectorstore.ts
+++ b/src/client/views/nodes/chatbot/vectorstore/Vectorstore.ts
@@ -790,10 +790,10 @@ export class Vectorstore {
* Retrieves the most relevant document chunks for a given query.
* Uses OpenAI for embedding the query and Pinecone for vector similarity matching.
* @param query The search query string.
- * @param topK The number of top results to return (default is 10).
+ * @param topK The number of top results to return (default is 15).
* @returns A list of document chunks that match the query.
*/
- async retrieve(query: string, topK: number = 10, docIds?: string[]): Promise<RAGChunk[]> {
+ async retrieve(query: string, topK: number = 15, docIds?: string[]): Promise<RAGChunk[]> {
console.log(`Retrieving chunks for query: ${query}`);
try {
// Generate an embedding for the query using OpenAI.