Strongly Typed AI Pipelines - Redpanda Connect

Redpanda Data
4 Sept 202404:56

TLDRThis demo showcases Redpanda Connect's integration with OpenAI, utilizing new features for structured outputs and JSON schema support. It demonstrates a pipeline that pulls emails from an 'emails' topic, processes them with an OpenAI processor to categorize and extract sender information, and ensures the output adheres to a specified JSON schema. The result is an enriched 'categorized emails' topic with emails tagged by category and sender, highlighting the ease of creating data pipelines with schema adherence in Redpanda Connect.

Takeaways

  • 🐼 Redpanda Connect has introduced new features that integrate with OpenAI, allowing for the generation of text using OpenAI's APIs.
  • 📄 Support for structured outputs from the OpenAI API has been added, enabling the specification of a JSON schema to ensure the LM's output adheres to it.
  • 🔍 Redpanda has announced support for JSON schema within its schema registry, enhancing centralized management and updates of schemas.
  • 🚀 Redpanda Connect can pull schemas from Redpanda and provide them to OpenAI to ensure the LLM responds using registered schemas.
  • 📨 The demo showcases a pipeline that pulls emails from an 'emails' topic, processes them, and ensures they are formatted according to a JSON schema.
  • 🔑 The JSON schema for the emails includes a simple object with a single 'email' field, which is the payload of the email.
  • 🤖 The OpenAI processor categorizes the email and extracts the sender, using structured outputs in JSON schema format.
  • 🗄️ The response output is merged back into the original object and re-encoded using the subject value schema for the output topic.
  • 📈 The categorized emails are enriched with the category and sender, ensuring data integrity at every stage of the pipeline.
  • 🛠️ Redpanda Connect allows for both dynamic fetching of schemas from the schema registry and the use of fixed schemas within the pipeline.

Q & A

  • What is the main feature of Redpanda Connect demonstrated in the transcript?

    -The main feature demonstrated is the use of Redpanda Connect's open AI processor to generate text using open AI APIs, with a focus on structured outputs that adhere to a specified JSON schema.

  • How does Redpanda Connect's open AI processor interact with JSON schemas?

    -Redpanda Connect's open AI processor ensures that the LM's output follows the exact schema specified in the JSON schema, providing centralized management and updates through the schema registry.

  • What is the purpose of using schemas in Redpanda Connect's data pipelines?

    -Using schemas in Redpanda Connect's data pipelines helps ensure that the data conforms to a predefined structure, which is essential for consistency and compatibility with various topics in the registry.

  • Can you explain the process of the demo pipeline that pulls emails from the 'emails' topic?

    -The demo pipeline decodes JSON formatted emails, runs them through an open AI processor to categorize the email and extract the sender, and then re-encodes the enriched data into the 'categorized emails' topic using the subject value schema.

  • What is the significance of the JSON schema support in Redpanda Connect?

    -JSON schema support in Redpanda Connect is significant as it allows for the creation of data pipelines that are structured and consistent, ensuring that the data at every stage of the pipeline is correct and符合预定义的schema.

  • How does Redpanda Connect handle dynamic schema fetching from the schema registry?

    -Redpanda Connect can dynamically fetch schemas from the schema registry, which can be used within pipelines to ensure that the data conforms to the latest schema definitions.

  • What is the benefit of using structured outputs in Redpanda Connect's pipelines?

    -Using structured outputs in Redpanda Connect's pipelines ensures that the data is correctly formatted and consistent, which simplifies data management and processing.

  • How does the demo show the adherence to the schema provided to the open AI processor?

    -The demo shows adherence to the schema by comparing the schema sent to open AI with the prompt given to the LM, verifying that the output matches the schema and not the potentially incorrect format mentioned in the prompt.

  • What is the role of the consumer in the demo that reads from the 'categorized emails' topic?

    -The consumer in the demo reads from the 'categorized emails' topic and uses the schema registry to decode the messages, allowing the actual decoded messages to be viewed.

  • How does Redpanda Connect ensure that the pipeline has the correct data at every stage?

    -Redpanda Connect ensures that the pipeline has the correct data at every stage by using JSON schema validation and structured outputs, which enforce data conformity and consistency throughout the pipeline.

  • What is the final outcome of the demo pipeline with structured outputs?

    -The final outcome of the demo pipeline is that each email is categorized and the sender is extracted, with the enriched data being output in a structured JSON format that matches the schema defined in the schema registry.

Outlines

00:00

🐼 Red Panda Connect and OpenAI Integration

This paragraph introduces a demo of Red Panda Connect, highlighting two new features. The first feature is the integration with OpenAI, allowing text generation using OpenAI's APIs. The second feature is the support for structured outputs from the OpenAI API, which ensures that the language model's output adheres to a specified JSON schema. Red Panda has also announced support for JSON schema within its schema registry, enabling centralized management and updates of schemas used in data pipelines. The demo showcases a pipeline that pulls email schemas from Red Panda, processes emails through an OpenAI processor for categorization and extraction of the sender, and then merges the results back into the original JSON object. The output is structured as JSON schema and is encoded for the output topic, demonstrating the simplicity of setting up data pipelines with Red Panda Connect and ensuring data integrity at each stage.

Mindmap

Keywords

💡Redpanda Connect

Redpanda Connect is a platform that integrates with Redpanda, an event streaming platform, to create data pipelines. In the context of the video, Redpanda Connect is used to demonstrate how it can pull schemas from Redpanda and utilize them with OpenAI's API to ensure that the data processed adheres to a specified schema. This is crucial for maintaining data integrity and consistency across different stages of a data pipeline.

💡Open AI Processor

The Open AI Processor is a feature within Redpanda Connect that enables the generation of text using OpenAI's APIs. It plays a central role in the video by showcasing how it can process structured outputs from the OpenAI API, adhering to a specified JSON schema. This feature is vital for ensuring that the AI's output is not only relevant but also correctly formatted according to predefined schemas.

💡Structured Outputs

Structured Outputs refer to the feature that allows OpenAI's API to generate responses that strictly follow a specified JSON schema. In the video, this is highlighted as a recently added capability that enhances the precision of AI-generated text by ensuring it aligns with the expected data structure, which is particularly useful for data pipeline management and ensuring data consistency.

💡JSON Schema

JSON Schema is a powerful tool for validating the structure of JSON data. In the video, JSON Schema is used within Redpanda's schema registry to define the structure of data, such as emails, that will be processed by Redpanda Connect and OpenAI. The script mentions a simple JSON object with an 'email' field, which is an example of how JSON Schema can be used to standardize data for processing.

💡Schema Registry

The Schema Registry is a component within Redpanda that allows for the centralized management and storage of schemas. In the context of the video, it is used in conjunction with Redpanda Connect to fetch and apply schemas to data pipelines, ensuring that the data being processed conforms to the registered schemas. This registry is essential for maintaining consistency and version control of data schemas across different topics.

💡Data Pipelines

Data Pipelines are the processes or workflows through which data moves and is transformed from one stage to another. In the video, Redpanda Connect is used to create a data pipeline that pulls emails, processes them using OpenAI's API, and then categorizes and extracts information like the sender. This demonstrates how data pipelines can be efficiently set up and managed using Redpanda Connect.

💡Categorization

Categorization in the video refers to the process of classifying emails into different categories using the OpenAI processor within Redpanda Connect. The script mentions that the AI is trained to understand different categories and can extract relevant information such as the sender from the email content, which is then used to enrich the data within the pipeline.

💡Email Processing

Email Processing is the act of managing and manipulating email data within a data pipeline. The video demonstrates a scenario where Redpanda Connect pulls emails from an 'emails' topic, processes them through the OpenAI processor to categorize and extract the sender, and then outputs the enriched data to a 'categorized emails' topic. This showcases the practical application of data pipelines in handling email data.

💡Magic Byte

The term 'Magic Byte' in the video refers to a specific format used in the schema registry to identify the schema ID and the payload. It is mentioned in the context of decoding the JSON schema for the email data. The 'Magic Byte' integer ID helps in identifying the correct schema to be applied to the data, which is crucial for ensuring that the data is decoded and processed accurately.

💡Consumer

A Consumer, in the context of the video, is an entity that consumes or reads data from a data pipeline's output. The script describes starting a consumer from the 'categorized emails' topic and configuring it to use the schema registry to decode the messages. This allows the consumer to view the actual decoded messages, demonstrating how data can be consumed and interpreted after being processed through a pipeline.

💡Structured Data

Structured Data refers to information that is organized into a specific format, such as a table with rows and columns, making it easily readable and processable by machines. In the video, the emphasis is on ensuring that the data flowing through the Redpanda Connect pipeline is structured and conforms to the JSON schemas, which is essential for accurate AI processing and data analysis.

Highlights

Redpanda Connect demo showcasing integration with OpenAI's API.

Introduction of an OpenAI processor in Redpanda Connect for text generation.

New feature for structured outputs from the OpenAI API, adhering to a specified JSON schema.

Redpanda's support for JSON schema within its schema registry.

Centralized management and updates of schemas in Redpanda's schema registry.

Data pipelines in Redpanda Connect utilize schemas from the registry for consistency.

Example pipeline pulls schemas from Redpanda and uses them with OpenAI API.

Pipeline processes emails formatted in JSON schema from an 'emails' topic.

Schema includes a simple JSON object with a single 'email' field for the payload.

Decoding of schema registry format including a magic byte, integer ID, and payload.

OpenAI processor categorizes emails and extracts sender information.

Structured output from OpenAI is in JSON schema format.

Schema registry is used to fetch the actual JSON schema for structured outputs.

Support for both dynamic fetching and fixed schema within the pipeline.

Merging of structured output back into the original object.

Re-encoding of the output using the subject value schema for the 'categorized emails' topic.

Categorized emails enriched with category and sender information.

Schema sent to OpenAI includes a singular 'category' string field.

Pipeline verifies adherence to schema rather than prompt instructions.

Demonstration of a consumer using schema registry to decode messages from the output.

Pipeline categorizes each email and extracts sender information accurately.

Simplicity of setting up data pipelines in Redpanda Connect with structured outputs.

Ensuring correct data at every stage of the pipeline with structured outputs.