PDF Extraction Pipeline Optimization

# Project Title: AI-Powered PDF Datasheet Parameter Extraction and Pipeline Optimization

## Project Description

I have an existing Python-based AI application that extracts predefined technical parameter values from manufacturer datasheets in PDF format

The application processes technical datasheets containing parameter values, specifications, tables, diagrams, units, tolerances, and other relevant information. The parameters to be extracted are defined in structured Excel files, which contain parameter names, definitions, and extraction requirements.

The current implementation successfully processes PDFs of approximately 12 pages. I want to optimize and enhance the existing application so that it can efficiently process manufacturer datasheets of up to 50 pages while maintaining high extraction accuracy, reducing processing time, and minimizing LLM token consumption and API costs.

### Key Requirements

1. **PDF Parameter Extraction:** Extract predefined technical parameters according to the parameter names, definitions, and rules provided in Excel files.

2. **Large PDF Processing:** Scale the existing pipeline to handle datasheets of up to 50 pages while preserving relevant context across pages, tables, and diagrams.

3. **AI Pipeline Optimization:** Optimize the existing LangGraph workflow, state management, orchestration, and document-processing logic without unnecessarily rewriting the existing application.

4. **Azure AI Document Intelligence Integration:** Improve the preparation and processing of Azure AI Document Intelligence output before sending it to the LLM for parameter extraction.

5. **LLM Token and Cost Optimization:** Reduce unnecessary LLM calls, input tokens, processing latency, and Azure OpenAI costs without compromising extraction quality.

6. **Parameter Validation:** Preserve existing validation logic, including parameter definitions, units, tolerance rules, and other domain-specific extraction requirements.

7. **Structured Excel Output:** Ensure that extracted parameter values are validated and exported consistently into the required Excel format.

8. **Logging and Error Handling:** Improve error handling, execution logs, and workflow observability to identify bottlenecks and troubleshoot extraction failures.

9. **Performance Benchmarking:** Compare the existing implementation with the optimized pipeline using representative datasheets and known reference values.

### Current Technology Stack

- Python
- LangGraph
- Azure OpenAI
- Azure AI Document Intelligence
- Langfuse for tracing and observability
- Excel for parameter definitions and structured output

### Expected Deliverables

- Optimized implementation of the existing Python codebase.
- A reliable workflow capable of processing datasheets containing up to 50 pages.
- Reduced processing time, token consumption, and API costs.
- Extraction accuracy validated against known reference values.
- Benchmark results comparing the original and optimized implementations.
- Improved error handling, logging, and Langfuse tracing.
- Technical documentation describing the changes, optimization approach, and recommendations for future scaling.

### Important Constraints

The primary objective is to optimize the existing application rather than replace it with an unrelated implementation. Existing extraction rules, validation logic, parameter definitions, and output formats must be preserved.

Any performance improvements must be measured against the current implementation to ensure that speed and cost reductions do not negatively affect extraction accuracy.

I am looking for a developer with strong experience in Python, LLM-based document processing, LangGraph or LangChain, Azure OpenAI, document intelligence, prompt optimization, and performance profiling.