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Challenge

Build an Automated Document Factory with LangChain & RunPod Inference Runtime

Manual financial document indexing generates immense operational friction and processing delay. Build an Automated Document Factory processing engine using LangChain chains deployed on RunPod GPU serverless infrastructure to parse, extract, and index thousands of financial invoices per minute.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Construct an end-to-end document processing pipeline using LangChain agents scaled horizontally across serverless RunPod GPU instances.

Delivery guide

How work is evaluated

Evaluation

Measures document field extraction accuracy, throughput (docs/sec), and GPU worker invocation efficiency.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

total_match_test

Extracted invoice total must match target precisely.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

field_extraction_accuracy

Percentage of accurately extracted JSON fields. • target: 0.99 • range: 0.9-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Build modular document parsing chains using LangChain and LangGraph

  • Deploy custom OCR visual-LLM inference runtimes on RunPod serverless GPUs

  • Implement automated document queue balancing and worker scaling

  • Achieve 99% accuracy on key-value document payload extractions

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of 500 scanned financial invoices and structural key-value ground truth extractions.

Challenge setup

Measures document field extraction accuracy, throughput (docs/sec), and GPU worker invocation efficiency.

Challenge input

Image/PDF payload containing financial invoice

LangChain

Provides robust abstractions for chains and document loaders.

RunPod

Enables cost-effective serverless GPU scaling.

Evaluated output

JSON object with extracted total, vendor, line items, and tax ID

Evaluation checks
  • Extracted invoice total must match target precisely.
  • Percentage of accurately extracted JSON fields. • target: 0.99 • range: 0.9-1
Configured evaluation evidence
  • Benchmark: DocVQA Financial Benchmark
  • Field Extraction Accuracy target: 0.99
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Langchain · Required
  • LangChain · Optional
  • RunPod · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional

Evaluation contract

  • total_match_test · Weight 1
  • field_extraction_accuracy · Weight 1

Metrics

  • field_extraction_accuracy · Target 0.99

Benchmarks

  • DocVQA Financial Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: Image/PDF payload containing financial invoice

    Output: JSON object with extracted total, vendor, line items, and tax ID

    Execution harness: Python

Evaluation tools

  • LangChain · Workflow Framework
  • RunPod · Inference Infrastructure

Recipe freeze marker

No recipe freeze time is recorded.

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