Vectorize Node
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). | âś… True |
This node is an **Action** node that converts text data into vector embeddings for semantic processing and AI applications.
Features
Key Functionalities
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Custom Embedding Model Selection: Choose from a variety of embedding models to fit your application's needs.
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Parameter Configuration: Fine-tune embedding parameters for optimal performance and relevance.
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Efficient Vectorization: Quickly convert large volumes of text data into numeric vector representations.
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Semantic Retrieval Compatibility: Enable advanced search and comparison functionalities using semantic understanding.
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Scalable Processing: Handle massive datasets effectively, ensuring seamless vectorization at scale.
Benefits
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Enhanced Data Analysis: Leverage semantic vectors to improve search and analysis capabilities.
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Streamlined Integration: Easily incorporate vectorized data into downstream applications and flow.
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Improved Accuracy: Benefit from state-of-the-art embedding models for precise semantic representation.
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Operational Flexibility: Configure and adapt vectorization settings to meet evolving requirements.
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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.