Strongly Typed AI Pipelines - Redpanda Connect

Redpanda Data
4 Sept 202404:56

TLDRThis demo showcases Redpanda Connect's integration with OpenAI, highlighting new features like structured output support using JSON schema. It demonstrates how to create a data pipeline that processes emails by pulling schemas from Redpanda's registry, ensuring outputs adhere to specified formats. The pipeline categorizes emails and extracts sender information, allowing for centralized management of schemas. As emails flow through the system, their enriched versions—categorized and complete with sender details—are produced, illustrating the simplicity of building robust data pipelines with Redpanda Connect.

Takeaways

  • 📊 Redpanda Connect now integrates with OpenAI, enabling text generation via its APIs.
  • 📝 New structured output support ensures LLM responses adhere to specified JSON schemas.
  • 📜 Redpanda's schema registry allows centralized management and updates of data schemas.
  • 📧 The demo pipeline processes emails from a topic, categorizing them based on their content.
  • 🔄 Emails are formatted as simple JSON objects containing an email field.
  • 🔍 The OpenAI processor categorizes emails and extracts sender information.
  • 📈 The output structure is enriched with categories and senders as defined by the schema.
  • ⚙️ Users can dynamically fetch schemas from the registry or use fixed schemas in pipelines.
  • 🧪 The pipeline verifies schema adherence, even if the prompt format is incorrect.
  • 🎉 Running the pipeline demonstrates its efficiency in categorizing emails accurately.

Q & A

  • What is Redpanda Connect?

    -Redpanda Connect is a platform that facilitates the creation of data pipelines, integrating features from Redpanda and OpenAI.

  • What new feature has been added to Redpanda Connect?

    -Redpanda Connect has introduced an OpenAI processor that allows for text generation using OpenAI's APIs and structured outputs based on JSON schema.

  • How does Redpanda Connect ensure the compliance of outputs with schemas?

    -It ensures compliance by allowing users to specify a JSON schema that the language model's output must adhere to.

  • What role does the schema registry play in this process?

    -The schema registry provides centralized management and updates for schemas, ensuring that data pipelines use consistent and valid schemas.

  • Can users create their own schemas in Redpanda Connect?

    -Yes, users can add fixed schemas within their pipelines or dynamically fetch schemas from the schema registry.

  • What is the purpose of categorizing emails in the pipeline?

    -The categorization helps in organizing emails by their types, enabling easier processing and management of email data.

  • What kind of data does the pipeline extract from emails?

    -The pipeline extracts the sender information and categorizes the email based on predefined categories.

  • What happens if the output format doesn't match the specified schema?

    -The system is designed to adhere strictly to the schema provided, ensuring that even if the prompt specifies the wrong format, the output will conform to the schema.

  • What is demonstrated at the end of the pipeline run?

    -The output shows categorized emails along with the extracted sender information, illustrating how the pipeline processes and enriches the original emails.

  • How does Redpanda Connect simplify the creation of data pipelines?

    -Redpanda Connect allows users to easily spin up data pipelines with structured outputs, ensuring that data integrity is maintained at every stage.

Outlines

00:00

🐼 Red Panda Connect Demo with OpenAI

This paragraph introduces a demo of Red Panda Connect, highlighting its integration with OpenAI's capabilities. It discusses two new features: the OpenAI processor in Red Panda Connect, which can generate text using OpenAI's APIs, and the recent addition of support for structured outputs from the OpenAI API, allowing the specification of a JSON schema to ensure the output adheres to it. The paragraph also mentions Red Panda's announcement of support for JSON schema within its schema registry, which aids in centralized management and updates of schemas used in data pipelines. The demo showcases a pipeline that pulls schemas from Red Panda and uses them with OpenAI to categorize emails and extract sender information, all while ensuring the output conforms to the schema registered in the schema registry.

Mindmap

Keywords

💡Redpanda Connect

Redpanda Connect is a platform that integrates with Redpanda, an event streaming platform. In the context of the video, it is used to create data pipelines that can process and categorize emails by leveraging the capabilities of OpenAI's APIs. It is highlighted for its new features, such as the OpenAI processor and support for JSON schema within its schema registry.

💡OpenAI Processor

The OpenAI Processor is a component within Redpanda Connect that enables the generation of text using OpenAI's APIs. It is capable of producing structured outputs that adhere to a specified JSON schema, ensuring that the output from the language model is formatted correctly and consistently.

💡Structured Outputs

Structured Outputs refer to the formatted data that the OpenAI API generates based on a predefined JSON schema. This feature is crucial for ensuring that the data produced by the AI aligns with the expected format, which is essential for downstream processing in data pipelines.

💡JSON Schema

JSON Schema is a powerful tool for validating the structure of JSON data. In the video, it is used to define the format of the data that will be processed by Redpanda Connect and OpenAI. It helps in ensuring that the data conforms to a specific structure, which is vital for maintaining data integrity and interoperability in data pipelines.

💡Schema Registry

The Schema Registry mentioned in the video is a service that manages and stores the definitions of data structures in JSON format. Redpanda has recently announced support for JSON schema within its schema registry, which allows for centralized management and updates of schemas used across different topics in the registry.

💡Data Pipelines

Data Pipelines are the processes or workflows that move and transform data from one place to another. In the video, Redpanda Connect is used to create data pipelines that can pull emails from a topic, process them through an OpenAI processor, and then categorize and extract information like the sender.

💡Categorize Email

Categorize Email refers to the process of sorting emails into different categories based on their content. In the demo, the OpenAI processor is instructed to categorize emails and extract the sender, which is then used to enrich the original email data with additional metadata.

💡Magic Byte

The term 'magic byte' in the video refers to a specific byte or set of bytes used to identify the schema format of the data. It is part of the schema registry format and is used to decode the schema ID and payload of the JSON object.

💡Decode

Decode, in the context of the video, refers to the process of converting encoded data into a readable format. The JSON schema is decoded to understand the structure of the email data, which is then used to process and categorize the emails correctly.

💡Re-encode

Re-encode is the process of converting data back into a specific format after it has been processed. In the video, after the emails are categorized and enriched with metadata, they are re-encoded using the subject value schema for the output topic, which is the categorized emails topic.

💡Consumer

A consumer, in the context of data streaming, is an application or service that receives and processes the data. In the video, a consumer is started to read messages from the output categorized emails topic, and it is configured to use the schema registry to decode the messages, allowing for the visualization of the actual decoded messages.

Highlights

Redpanda Connect integrates with OpenAI for text generation.

New support for structured outputs from OpenAI APIs using JSON schema.

Allows specification of a JSON schema to ensure LLM output adheres to it.

Recent announcements include JSON schema support in Redpanda's schema registry.

Demo showcases pulling schemas from Redpanda to ensure compliance in responses.

Centralized management of schemas enhances data pipeline reliability.

Example pipeline processes emails and categorizes them using AI.

Hand-generated emails are formatted in JSON schema for processing.

Output includes categorized email data with sender extraction.

Structured outputs ensure correct data format at every pipeline stage.

Demonstrates simple setup of data pipelines with Redpanda Connect.

Allows merging of dynamic and fixed schemas in the pipeline.

Shows real-time consumption of categorized email messages.

Categorization includes enum definitions for precise matching.

The demo highlights the flexibility of using a schema registry.

Effective categorization improves data organization and management.

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