AnythingLLM PDF upload failing or processing hanging? Compress your PDF instantly using the tool below — fixes hosted CPU limits, NGINX upload caps, and embedding errors.
👉 Fix: Compress your file below the required limit using the tool above.
AnythingLLM upload failures have three common causes: CPU exhaustion on the hosted tier, NGINX's default 1MB upload cap on Docker deployments, or the embedder model running out of resources on large local files. Compressing the PDF addresses all three by reducing the processing load.
Three common causes: (1) CPU exhaustion on the hosted Starter tier for files over 10k words, (2) NGINX's default 1MB upload cap on Docker deployments, (3) embedder model running out of RAM on large local files. Compress the PDF to reduce load.Source: GitHub ↗
Add or increase the client_max_body_size directive in your NGINX config (e.g. client_max_body_size 100M;) and reload NGINX. This raises the upload cap for your Docker deployment.Source: GitHub ↗
Large PDFs overwhelm the embedder model, causing the process to hang or crash. Compress the PDF, split it into chapters, or convert it to Markdown for faster, more reliable embedding.Source: GitHub ↗
Yes, for large documents. Markdown is faster to parse and embed than PDF, and produces better AI responses. Use a PDF-to-Markdown converter for text-heavy research papers and documentation.Source: GitHub ↗
Split the PDF into smaller chapters (under 10k words each for hosted Starter), compress each part, and upload separately. This avoids CPU exhaustion, NGINX limits, and embedder memory issues simultaneously.Source: GitHub ↗