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Use Gemini 2.0 to speed up document extraction and lower costs
- Gemini 2.0 has been released by Google DeepMind, offering models like Gemini 2.0 Flash and Gemini 2.0 Pro.
- Gemini 2.0 supports up to 1 million input tokens, aiding tasks like image generation and creative writing.
- It revolutionizes document processing by chunking pdfs for RAG systems and extracting insights from pdfs.
- A multi-step approach using generative AI with Gemini 2.0 enhances document extraction by combining LLMs with structured rules.
- Modular extraction in the multi-step architecture improves accuracy and reduces cognitive load on the LLM.
- Externalized rule management in a multi-step approach enhances maintainability and version control.
- The hybrid approach combines LLM-powered extraction with a rules engine for better understanding and processing of data.
- Step 1 involves data extraction using Gemini based on a sample prompt and controlled generation.
- Step 2 focuses on feeding the extracted data into a rules engine for validation and rule enforcement.
- Step 3 integrates insights generated by the rules engine into existing data pipelines for automation and workflow enhancements.
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