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Nappai Documentation

Data Batch Chain

The Data Batch Chain component is designed to efficiently process data in batches. This means it takes a set of data, processes it according to specific rules, and outputs the processed data. This is particularly useful in scenarios where handling large volumes of data at once can enhance performance and organization.

Inputs

  • Inherited Inputs: The component inherits its inputs from a base class called BaseDataBatchChain. While specific inputs are not detailed, they are essential for the component’s operation and are predefined in the base class.

Outputs

The component produces an output named “Data,” which represents the result of processing a batch of data. This output can be used in workflows to further analyze, store, or visualize the processed data.

Usage Example

Imagine you have a large dataset that needs to be processed to extract meaningful insights. By using the Data Batch Chain component, you can input the dataset, and it will process the data in batches, outputting a refined dataset ready for analysis or reporting.

Templates

Currently, there are no specific templates where the Data Batch Chain component is used. However, it can be integrated into custom workflows within the Nappai system.

  • libSQLRetrieverTool: Tool for interacting with libSQL Retriever.
  • Embedding Similarity: Computes similarity between two embedding vectors.
  • SQLite: Interacts with SQLite databases.
  • Tool Calling Agent: Uses tools for various tasks.
  • OpenAI Tools Agent: Utilizes OpenAI’s language models for text generation.
  • Cassandra: Vector Store with search capabilities.
  • Supabase: Vector Store with search capabilities.
  • Milvus: Vector Store with search capabilities.
  • MongoDB Atlas: Vector Store with search capabilities.
  • Pinecone: Vector Store with search capabilities.
  • Qdrant: Vector Store with search capabilities.
  • Astra DB: Vector Store with search capabilities.
  • PGVector: Vector Store with search capabilities.
  • Redis: Vector Store with search capabilities.
  • Vectara: Vector Store with search capabilities.
  • Weaviate: Vector Store with search capabilities.
  • Upstash: Vector Store with search capabilities.
  • Couchbase: Vector Store with search capabilities.
  • FAISS: Vector Store with search capabilities.
  • Deeplake Database: Vector Store with search capabilities.
  • Chroma DB: Vector Store with search capabilities.
  • Update Data: Dynamically update or append data.
  • Runnable Data Batch: Processes list of data and run runnables.
  • Natural Language Text Splitter: Splits text based on natural language boundaries.
  • Recursive Character Text Splitter: Splits text while keeping related text together.
  • CharacterTextSplitter: Splits text by number of characters.
  • Language Recursive Text Splitter: Splits text into chunks based on language.
  • Semantic Text Splitter: Splits text into semantically meaningful chunks.
  • Data Retrieval Chain: Batches data and retrieves information from a vector store.
  • API Request: Makes HTTP requests to URLs with customizable headers and body.

Tips and Best Practices

  • Ensure that the data inputted into the component is well-organized to maximize processing efficiency.
  • Familiarize yourself with the inherited inputs from BaseDataBatchChain to optimize the component’s configuration.

Security Considerations

When processing sensitive data, ensure that appropriate data protection measures are in place to prevent unauthorized access or data breaches.