Zilliz RAG Knowledge Base

Build and manage RAG knowledge base with Zilliz vector database

Actions4

Overview

This node integrates with the Zilliz vector database to perform semantic search operations on a knowledge base. Specifically, the Semantic Search operation allows users to query a collection of documents using natural language or pre-computed query vectors, retrieving the most relevant documents based on vector similarity.

Common scenarios include:

Searching large document collections for relevant information using natural language queries.
Enhancing AI assistants by retrieving contextually relevant documents from a knowledge base.
Implementing semantic search features in applications where keyword matching is insufficient.

For example, a user can input a question in natural language, and the node will return the top matching documents from a specified Zilliz collection, including their metadata if desired.

Properties

Name	Meaning
Database Name	Name of the Zilliz database to use (default: "default").
Collection Name	Name of the Zilliz collection to search within. Must start with a letter and contain only letters, numbers, and underscores.
Query Text	Natural language query string for semantic search.
Query Vector	Pre-computed embedding vector representing the query (JSON array).
Search Options	Collection of options controlling the search behavior:
- Max Results	Maximum number of relevant documents to return (1-100).
- Similarity Threshold	Minimum similarity score (0-1) to filter results.
- Filter Expression	Additional filter conditions to apply during search (string expression).
- Include Metadata	Boolean flag indicating whether to include full document metadata in the results.

Output

The output JSON contains the following fields:

query: The original query text or indication that a vector query was used.
total_results: Number of documents returned after applying similarity threshold.
search_results: An array of matched documents, each containing:
- rank: Position in the result list (1-based).
- similarity_score: Similarity score between query and document vector.
- document_id: Unique identifier of the matched document.
- title: Document title.
- text_chunk: The text content of the matched chunk.
- chunk_index: Index of the chunk within the document.
- metadata (optional): Full metadata of the document if included.
context_text: Concatenated text chunks from all matched documents, separated by two newlines.
collection: The name of the collection searched.
database: The database name used.

No binary data output is produced by this operation.

Dependencies

Requires an active connection to a Zilliz vector database cluster.
Needs an API key credential and cluster endpoint configured in n8n credentials.
The node depends on the external ZillizClient library to interact with the Zilliz service.

Troubleshooting

Error: "Invalid JSON in Query Vector parameter"
Occurs if the provided query vector string is not valid JSON. Ensure the vector is a properly formatted JSON array.
Error: "Query vector required. Please compute embeddings for the query text using an embedding node."
Happens when neither a query vector nor a query text is provided. To fix, either supply a natural language query (which must be embedded beforehand) or provide a pre-computed query vector.
Error: "Either Query Text or Query Vector must be provided"
Indicates missing input for the search. Provide at least one of these inputs.
Empty or no results returned
Could be due to overly restrictive similarity threshold or filters. Try lowering the threshold or removing filters.
Collection or database not found
Verify that the specified database and collection names exist and are correctly spelled.