Langchain-Beam is an innovative library that seamlessly integrates Large Language Models (LLMs) as PTransforms within Apache Beam pipelines, leveraging the power of LangChain for enhanced data processing and transformation. This powerful tool allows developers to harness the capabilities of language models directly in their Beam workflows, making data handling more efficient and effective.

Motivation

The Apache Beam programming model simplifies the development of batch and streaming data processing pipelines. It operates similarly to designing UI components in frameworks like Flutter or React Native, providing a clear and declarative way to define data processing logic. Langchain-Beam aims to expand this abstraction by incorporating the advanced functionalities of LLMs, such as generation, classification, completion, and reasoning, enabling users to process data more intelligently. Powered by LangChain, this library offers a unified interface for connecting to various LLM providers, making it a versatile choice for developers.

How It Works

With Langchain-Beam, you can easily include sophisticated LLM capabilities into your Apache Beam pipelines. Here’s a breakdown of how to utilize the library:

1. Create Model Options: Define the modelOptions according to the model provider being used. Customize parameters like temperature and max tokens to suit your needs.
2. Define the Instruction Prompt: Craft an instructionPrompt that instructs the model on how to process each PCollection input element effectively.
3. Apply the LangchainBeam PTransform: Pass your modelOptions and instructionPrompt to the LangchainModelHandler, which can then be applied in the pipeline through LangchainBeam.run(modelHandler).

Example Code

Here's a simple example demonstrating the core functionalities:

// Define an instruction prompt for processing the element
String prompt = "Categorize the product review as Positive or Negative and output your response in this JSON format: {review : {input_element}, feedback: {positive or negative}}";

// Set up model options with the chosen model and its parameters
OpenAiModelOptions modelOptions = OpenAiModelOptions.builder()
        .modelName("gpt-4o-mini")
        .apiKey(OPENAI_API_KEY)
        .build();

// Initialize LangchainModelHandler with model options and prompt
LangchainModelHandler handler = new LangchainModelHandler(modelOptions, prompt);

// Create a new pipeline
Pipeline p = Pipeline.create();

// Apply data transformations
p.apply(TextIO.read().from("/home/ganesh/Downloads/product_reviews.csv")) // Load data
        .apply(LangchainBeam.run(handler))  // Execute the model handler
        .apply(ParDo.of(new DoFn<String, Void>() {
            @ProcessElement
            public void processElement(@Element String output) {
                System.out.println("Model Output: " + output);  // Output model results
            }
        }));

p.run();  // Execute the pipeline

Whether you're looking to enhance your data processing capabilities or build intelligent applications, Langchain-Beam empowers you to harness the full potential of LLMs within the robust framework of Apache Beam.