Your Data Swiss Army Knife
This comprehensive toolkit transforms the way you manage and interact with your data. Segment, format, extract, validate, and communicate with ease and efficiency. Simplify complex flows, ensure uncompromised data quality, and streamline the exchange of information for superior AI performance. Put the right data tools to work for you.
Our platform empowers you to get the most from your data. We’ve designed and developed practical tools for common use cases, ensuring your data is prepared and optimized for maximum compatibility with LLMs.
LLM Tools
Customize your data through fine-tuning and embedding techniques, ensuring your LLM aligns perfectly with your specific needs.
Retrieval-Augmented Generation: RAG is a framework that combines information retrieval with text generation. It retrieves relevant documents from a knowledge base in response to a query, and then uses these documents to inform the generation of a response. RAG can improve the accuracy and reliability of LLM responses by grounding them on factual information. It can also help to mitigate the problem of hallucinations, where LLMs generate text that is not supported by evidence.
- Fine Tune: fine-tuning is a technique used to improve the performance of a pre-trained model on a specific task. This would involve taking a pre-trained model, such as GPT-3, and then training it on a dataset of text and code that is specific to the task you want it to perform. This can improve the accuracy and efficiency of the LLM for your particular use case.
- Embeddings: embeddings are a way of representing data points as vectors. This can be useful for LLMs because it allows them to process data more efficiently. For example, an LLM could be trained on a dataset of text where each word is represented as an embedding. This would allow the LLM to understand the relationships between words and to generate more coherent text.
Data Management Tools
Prepare and streamline data for LLM interaction. Segment, format, extract, and target the right data for your tailored AI Flow.
- Segmentation: Divides data into smaller segments based on a specified separator or condition. This is useful when working with large files that need to be processed individually by an LLM (Large Language Model).
- Formatter: Transforms data from one format to another. For example, a data converter could take a table generated by GPT and transform it into an Excel sheet.
- Extractor: Retrieves specific data elements or content from a source, such as a document, website, or API response.
- Locator: Identifies a specific web element on a page using various targeting strategies (e.g., ID, class name).
Data Control Functions
Manage the flow and integrity of your data. Iterate and validate data for optimal LLM use.
- Iterator: Allows for iterations over a predefined count or a length dynamically adjusted based on previous outputs.
For example: In an Excel sheet containing a customer list with additional parameters, an iterator can be used to cycle through each customer (row) and generate personalized emails using an LLM (Large Language Model). - Validator: Assesses whether data conforms to expected formats, data types, or business rules. (e.g., ensuring an email address has the correct format)
Communication Tools
Interact with the outside world. Compose and send emails, and make targeted web requests to gather or transmit information.
- Email: Allows the user to compose and transmit an electronic message to designated recipients, providing options for subject line, message content, and attachments.
- Http Request: Executes an HTTP request (GET, POST, PUT, DELETE, etc.) to a specified web address (URL).
