Vectorize Node

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Overview

The Vectorize Node is a data transformation component that converts text data into vector embeddings. These embeddings enable semantic search, similarity comparisons, and other AI-powered applications that require numerical representations of text.

Node Type Information

Type	Description	Status
Batch Trigger	Starts the flow on a schedule or batch event. Ideal for periodic data processing.	❌ False
Event Trigger	Starts the flow based on external events (e.g., webhook, user interaction).	❌ False
Action	Executes a task or logic as part of the flow (e.g., API call, transformation).	✅ True

This node is an **Action** node that converts text data into vector embeddings for semantic processing and AI applications.

Features

Key Functionalities

Custom Embedding Model Selection: Choose from a variety of embedding models to fit your application's needs.
Parameter Configuration: Fine-tune embedding parameters for optimal performance and relevance.
Efficient Vectorization: Quickly convert large volumes of text data into numeric vector representations.
Semantic Retrieval Compatibility: Enable advanced search and comparison functionalities using semantic understanding.
Scalable Processing: Handle massive datasets effectively, ensuring seamless vectorization at scale.

Benefits

Enhanced Data Analysis: Leverage semantic vectors to improve search and analysis capabilities.
Streamlined Integration: Easily incorporate vectorized data into downstream applications and flow.
Improved Accuracy: Benefit from state-of-the-art embedding models for precise semantic representation.
Operational Flexibility: Configure and adapt vectorization settings to meet evolving requirements.
Future-Proof Design: Stay compatible with the latest embedding technologies and methodologies.

What Can You Build?

Build advanced search engines that can understand and retrieve information based on semantic meaning rather than keywords.
Create recommendation systems that provide more accurate suggestions by understanding the context and meaning of user preferences.
Develop chatbots and virtual assistants that can comprehend and respond to user queries with greater relevance and understanding.
Implement content categorization and organization systems that can automatically classify and sort large volumes of text data.

Setup

Select the Vectorize Node

Fill in the required parameters.
Build the desired flow
Deploy the Project
Click Setup on the workflow editor to get the automatically generated instruction and add it in your application.

Configuration Reference

Parameter	Description	Required	Example Value
Texts to vectorize	Enter the texts to be transformed into vectors.	Yes	`${{data}}`
Embedding Model Name	Select the model to convert the texts into vector representations.	Yes	`text-embedding-3-large`

Low-Code Example

- nodeId: vectorizeNode_448
  nodeType: vectorizeNode
  nodeName: Get Vectors
  values:
    inputText: "{{codeNode_234.output}}"
    embeddingModelName: {}
    generativeModelName:
      type: embedder/text
      nodeId: vectorizeNode
      model_name: text-embedding-ada-002
      provider_name: openai

Output

vectors: An array of arrays, each containing numerical values representing the vectorized form of the input data.

Example Output

{
    "vectors": [
      [
        -0.010908611,
        -0.013849079,
        -0.0080731595,
        -0.03575819,
        -0.004489464,
 
      ]
    ]
}

Troubleshooting

Common Issues

Problem	Solution
Invalid API Key	Ensure the API key is correct and has not expired.
Dynamic Content Not Loaded	Increase the `Wait for Page Load` time in the configuration.

Debugging

Check Lamatic Flow logs for error details.
Verify API Key.

VectorDB Node Branch Node

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