Vector Query Node in Giselle
The Vector Query Node is a powerful component in Giselle designed to search and retrieve information from connected vector store data sources. Its primary function is to execute a query (such as a question or a search term) against a vectorized dataset and return the most relevant results. The Vector Query Node works with the following vector store nodes, making it a crucial building block for creating Retrieval-Augmented Generation (RAG) applications directly within your Giselle workflows:- GitHub Vector Store Node: Search through vectorized GitHub repository content including code, issues, and pull requests.
- Document Vector Store Node: Search through your uploaded documents such as PDFs, text files, and markdown files.
How to Use the Vector Query Node
Setting up a Vector Query Node involves adding it to your canvas, connecting a data source, and running a query.1. Add a Vector Query Node
From the node toolbar at the bottom of the canvas, select the Vector Query Node, which is identifiable by its magnifying glass icon, and place it in your workspace.2. Connect a Data Source
The Vector Query Node requires an input from a vector store data source to function. You must connect theOutput of a vector store node (GitHub Vector Store Node or Document Vector Store Node) to the Input of the Vector Query Node. Until a data source is connected, the node will indicate that it is waiting for a connection.
3. Write and Run a Query
Once a data source is connected, you can configure the Vector Query Node:- Enter a Query: In the configuration panel, type your question or search term into the “Query” input field.
- Run the Query: Click the Run Query button to execute the search against the connected data source.
4. Review the Results
After the query runs, the results will be displayed directly in the node’s panel. The results typically include:- A summary of the number of results found.
- A list of relevant “chunks” of text retrieved from the source documents.
- A similarity score (e.g., 58%) for each chunk, indicating its relevance to your query.
Core Use Case: Retrieval-Augmented Generation (RAG)
The primary purpose of the Vector Query Node is to enable Retrieval-Augmented Generation (RAG) workflows. RAG enhances the capabilities of Large Language Models (LLMs) by providing them with relevant, up-to-date information from your own data sources before they generate a response. A typical RAG workflow in Giselle looks like this:- Data Source (Vector Store Node): Ingests and vectorizes your content from a GitHub repository or uploaded documents, making it searchable.
- Vector Query Node: Takes a user’s question (the query) and retrieves the most relevant text chunks from the vector store.
- Generator Node: Receives the original question and the retrieved information from the Vector Query Node’s output. It uses this combined context to generate a more accurate, detailed, and factually grounded answer.
Output of the Vector Query Node
TheResult output of the Vector Query Node contains the collection of retrieved data chunks. This structured data can be passed to subsequent nodes—most commonly a Generator Node—to serve as dynamic context for text generation.
For more information on how to set up a data source for querying, see our documentation on the GitHub Vector Store Node and Document Vector Store Node.