BatchPredictor: Efficient Long-Context Processing with Parallel Chunk Processing and Answer Synthesis

[Siddharth-rll]

Description of the feature request:

This implementation adds a BatchPredictor class that enables efficient processing of multiple questions against long transcripts using the Gemini API. The feature includes transcript chunking, parallel processing, answer synthesis, context caching, and automatic question generation capabilities—all designed to work within API token limits while maintaining response quality.

What problem are you trying to solve with this feature?

This feature addresses several critical challenges in processing long-form content with LLMs:

1. Context length limitations: By intelligently chunking transcripts while preserving semantic coherence, it enables processing of documents far exceeding model context windows.

2. API efficiency: The implementation reduces API costs and latency through parallel processing, context caching, and batched requests with appropriate rate limiting.

3. Information fragmentation: The answer synthesis mechanism ensures users receive coherent, comprehensive responses even when relevant information is scattered across different parts of a long document.

4. Redundant processing: Context caching eliminates repeated API calls for similar questions, significantly improving response time for repeated or similar queries.

5. Manual question formulation: The automatic question generation capability helps users extract insights from content without needing to manually craft questions.

Any other information you'd like to share?

Here's my repo link: https://github.com/ZoroZoro95/GoogleDeepmindGsoc2025 please let me know if you would like to see a notebook format of the example implementation and what changes i can make.

[Siddharth-rll]

@markmcd @Giom-V please review the external repo https://github.com/ZoroZoro95/GoogleDeepmindGsoc2025