Overview
This node provides embeddings generation using a TensorFlow model via the LangChain library. It produces 512-dimensional vector embeddings suitable for use in vector stores or machine learning workflows. This node is beneficial when you want to convert text data into numerical vectors for similarity search, clustering, or as input features for AI models. For example, it can be used to embed documents for semantic search or to preprocess text data before feeding it into a recommendation system.
Properties
| Name | Meaning |
|---|---|
| The TensorFlow model we use for generating embeddings is using 512-dimensional embeddings. Please make sure to use the same dimensionality for your vector store. Be aware that running this model with high-dimensional embeddings may result in high CPU usage on the machine. | Informational notice explaining that the embeddings generated are 512-dimensional vectors and advising users to ensure compatibility with their vector store. It also warns about potential high CPU usage when running this model with high-dimensional embeddings. |
Output
- The node outputs data under the output named "Embeddings" with the output type
"ai_embedding". - The
jsonoutput field contains the generated embedding vectors as 512-dimensional numerical arrays. - There is no indication of binary data output; the output consists purely of JSON-formatted embeddings.
Dependencies
- Requires the
@tensorflow/tfjs-backend-cpupackage for TensorFlow CPU backend support. - Uses the
langchainlibrary's TensorFlow embeddings implementation. - No explicit API keys or external service credentials are required since the embeddings are generated locally using TensorFlow.
Troubleshooting
- High CPU Usage: Running the model with embeddings of high dimensionality can cause significant CPU load. Users should monitor resource consumption and consider limiting concurrency or running on machines with sufficient CPU capacity.
- Dimensionality Mismatch: If the vector store expects embeddings of a different size than 512 dimensions, the stored vectors will not be compatible. Ensure the vector store configuration matches the 512-dimensional output.
- Missing TensorFlow Backend: If the TensorFlow CPU backend package is not installed or properly loaded, the node may fail to initialize. Verify that dependencies are correctly installed.