can any provide me pipe-line how i process large documents
#processing large document
clever obsidian
distant brookBOT
Hi @clever obsidian. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
Here’s a concise, battle‑tested pipeline for processing large documents, with options depending on your goals (RAG, summaries, extraction, or zero‑prep agentic retrieval). I’ve linked each step to concrete examples.
- Ingest and parse
- PDFs to text and/or images:
- Simple text extraction with pypdf/textract; check token length early (tiktoken) (Summarizing long documents).
- Page-wise loading for very large PDFs to keep memory stable (Financial doc loading).
- If visuals/tables matter, render pages to images and keep both text and images (Pinecone 4o vision parsing).
- Chunking strategy
- Token-aware chunking by sentence/section to reduce hallucinations and support citations (Model selection guide — agentic RAG flow).
- For summaries/extractions, split into manageable token windows and process piecewise (Summarizing long documents; Entity extraction).
- If you need zero preprocessing, consider hierarchical “agentic” navigation: load big slices, ask model which chunks to drill into, repeat to paragraphs, then answer + verify (Model selection guide — 1 & 2, Agentic RAG flow, Implementation).
- Enrichment (optional)
- Vision transcription/descriptions of charts/tables using GPT‑4o on page images; store results with page metadata (Pinecone 4o vision parsing).
- Embeddings and storage (RAG path)
- Compute embeddings for chunks; index into your store of choice:
- Elasticsearch bulk indexing for vectors + text fields (Elasticsearch RAG indexing).
- Redis hybrid search; batch embeddings and pipeline writes for speed (Redis hybrid VSS).
- Pinecone if you’re already using it for RAG; pair with 4o vision output when visuals matter (Pinecone 4o vision parsing).
- Retrieval and generation
- Retrieve top‑k chunks by semantic similarity; optionally re‑rank and add rule filters; generate with a grounded prompt (Parse PDFs for RAG — wrap‑up/optimizations).
- For extremely long source docs, the agentic drill‑down offers dynamic granularity and fine‑grained citations without a vector DB (Agentic RAG flow).
- Summarization or structured extraction (alternative paths)
- Summarize piecewise and stitch for controllable detail (Summarizing long documents).
- Run entity/figure/date extraction per chunk and combine results (Entity extraction).
- Throughput and cost
- Use the Batch API for large, non‑urgent jobs (embeddings, summarization/extraction) at ~50% lower cost and higher rate limits; prepare .jsonl and retrieve results after completion (Batch API guide).
- Productionize with concurrent, staged pipelines and worker pools per phase (chunk → extract → resolve), with rate limiting and batch writes (Concurrency in ingestion).
- Handling very large uploads in ChatGPT Enterprise (FYI)
- Up to ~110k tokens are “stuffed” into context; the rest go to a private vector store, including images for multimedia PDFs. This affects what’s available without retrieval calls (Handling files based on size).
Starter templates you can copy:
- RAG with PDFs: parse → clean/chunk → embed → index → retrieve → generate (Parse PDFs for RAG).
- Agentic zero‑prep: hierarchical navigation, chunk selection, answer + verify (Model selection guide, Implementation).
- High‑volume extraction/summarization: chunk → batch API → merge results (Batch API, Summarizing long documents, Entity extraction).