# Vector store insert Node

### Overview

The Vector Store Insert Node enables the addition of documents to vector stores for semantic search and retrieval. It supports both existing collections and custom configurations, with options for smart document insertion to prevent duplicates. Documents are automatically converted into embeddings using the specified model before storage.

Usage cost: 0.2 credit / document

### Configuration

#### Settings

1. **Documents Configuration**
   * Documents to Insert\*: Select documents to add to the vector store
   * Smart Insertion: Enable to prevent duplicate documents using content-based IDs (the generated ID is a 32 characters string).
2. **Storage Configuration**

   * Mode Selection:
     * Use Existing Collection: Insert into a pre-configured collection
     * Custom Configuration: Create and insert into a new collection

   **For Existing Collection:**

   * Vector Store Collection\*: Select target collection

   **For Custom Configuration:**

   * Knowledge Source\*: Select vector store provider
   * Embeddings Model\*: Choose model for document embedding
   * Collection Name\*: Define name for new collection

#### Output Ports

* `affected_ids` (string\[]): IDs of documents successfully inserted

### Best Practices

1. **Document Preparation**
   * Format documents consistently
   * Clean and preprocess text before insertion
2. **Collection Management**
   * Use descriptive collection names
   * Plan collection structure beforehand
   * Consider using smart insertion for production
3. **Vector Store Considerations**
   * Milvus/Zilliz: Supports automatic collection creation
   * Chroma: Supports automatic collection creation
   * Pinecone: Requires manual collection setup
   * Qdrant: Requires manual collection setup

### Common Issues

* Document format mismatches
* Embedding model rate limits
* Vector store connection issues
* Large batch processing timeouts


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