Exploring Cache Augmented Generation (CAG) for Faster PDF Q&A

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Exploring Cache Augmented Generation (CAG) for Faster PDF Q&A: A Practical Journey

We’re always looking for ways to make technical documentation more accessible and interactive. Imagine users asking questions directly to a PDF and getting instant, accurate answers, even offline. That’s the promise that led me to explore Cache Augmented Generation (CAG), a technique designed to speed up interactions with large language models (LLMs).

The core idea behind CAG is appealing: pre-process your document (like a PDF guide) with an LLM once, creating a compact “KV cache.” This cache essentially stores the model’s “understanding” of the content. Later, when a user asks a question, the model leverages this cache, bypassing the need to re-read the entire document, leading to significantly faster response times. For local or edge applications, this could be a game-changer.

The Goal: Local, Fast Q&A on a Technical Guide

My objective was clear: use IBM’s Granite LLM to generate a KV cache for a specific PDF style guide on my local Mac. This would enable a proof-of-concept for rapid, offline Q&A capabilities based on that document.

Initial Attempt: Hitting an Unexpected Wall

I set up the environment, extracted the text from the PDF, and initiated the caching process. Instead of the expected cache file, the process halted with a baffling error: Invalid buffer size: 770.93 GB. This was not a standard out of memory warning; it pointed to a deeper incompatibility, likely related to how PyTorch’s MPS backend (for Apple Silicon GPUs) interacted with the model and the very long text sequence from the PDF.

• Key Takeaway: Integrating cutting-edge AI techniques often reveals system-specific limitations or edge cases not covered in standard documentation. Cross-platform compatibility, especially with specialized hardware acceleration like MPS, requires careful validation.

Seeking a Stable Environment: Moving to the Cloud

To isolate the issue, I shifted the experiment to Google Colab, leveraging its readily available NVIDIA GPUs and the more mature CUDA backend. This standardized environment is often helpful for debugging AI workloads.

Running the same process on Colab yielded a different, more conventional error: CUDA out of memory. The massive 770 GB request vanished, but the underlying truth remained – processing the entire PDF to generate the full KV cache required more GPU memory (VRAM) than the free Colab tier provided (~15 GB).

• Key Takeaway: Even in cloud environments, AI models require substantial memory. Generating representations (like a KV cache) for long sequences of text can easily exceed the VRAM available on standard or free-tier GPUs.

Resource Management Strategies: Optimization Efforts

With a clearer understanding that memory was the primary constraint, the focus shifted to optimization:

1. Model Quantization: I employed 4-bit quantization using bitsandbytes. This technique significantly reduces the model’s memory footprint by representing its weights with lower precision, freeing up VRAM.
2. Compute Type Adjustments: Addressed performance warnings by aligning the compute data type within bitsandbytes (setting bnb_4bit_compute_dtype=torch.float16).
3. Exploring Alternative Models: Briefly considered switching to inherently smaller models (like Gemma 2B or TinyLlama 1.1B), though this often involves a trade-off in capability.

While quantization noticeably reduced the base model footprint, the peak memory required during the forward pass over the entire PDF text still exceeded the limits.

• Key Takeaway: Standard LLM optimization techniques, such as quantization, are crucial but may not be sufficient when dealing with extremely long input sequences that drive up activation and cache memory requirements.

The Pragmatic Solution: Reducing Scope

The consistent OOM errors, even after optimization, pointed squarely at the input sequence length. If processing the whole document at once was not feasible within the available resources, the logical step was to reduce the scope.

I modified the input to include only the text from the first 43 pages of the guide, covering most core sections but excluding a lengthy appendix. I re-ran the process using the quantized Granite model on Colab.

Success! The process completed, generating the cache file: red-hat-supplementary-style-guide-1-43_ibm-granite_granite-3.2-2b-instruct_kvcache.pt.

Outcomes and Lessons Learned

While I didn’t achieve the initial goal of a full-document cache on readily available hardware, the journey provided valuable insights:

1. CAG Feasibility: The CAG technique can work, but generating the cache is memory-intensive and scales significantly with input length.
2. Resource Planning is Crucial: Accurately estimating VRAM requirements for specific models and input sequence lengths is vital, especially for full-document processing.
3. Environment Matters: Be prepared for potential backend-specific issues (like MPS vs. CUDA) that can affect feasibility.
4. Scope Management: When resources are constrained, reducing the processing scope (e.g., processing chunks or partial documents) is a viable, pragmatic workaround.
5. Alternatives Exist: This experience underscores why techniques like Retrieval-Augmented Generation (RAG), which process smaller, relevant text chunks on demand, are often preferred for Q&A over large documents, as they avoid the upfront memory cost of full caching.

Next Steps

With the partial cache generated, I can proceed to build the Q&A interface, understanding its knowledge limitations. This experiment also provides a strong case for exploring RAG as a more scalable alternative for handling the entire document effectively within typical resource constraints.