𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻
Most RAG systems fail before they even start.
You think building a RAG system is simple. A user uploads a PDF, you create embeddings, and you get answers.
That is a mistake.
Between the upload button and the vector database, there are 15 critical steps. If you skip one, your system gives wrong answers or wastes your money.
Here is the production-grade roadmap for document ingestion:
• File Hashing: Never hash the filename. Hash the actual file content. This stops your system from processing the same file twice if someone renames it.
• Smart Parsing: Use the right tool for the job.
- Simple text: pdf-parse (Free)
- Mixed content: Unstructured (Balanced)
- Complex tables/layouts: LlamaParse (High quality)
- Enterprise forms: Azure Document Intelligence (Best for scans)
• Text Cleaning: Remove the junk. Headers, footers, watermarks, and page numbers create noise. If you embed "Confidential" on every page, your AI will think every answer is a secret.
• Metadata Extraction: Add context like department, section, or version. This helps your system find the right document without searching everything.
• Smart Chunking: This is the most important part.
- Size: Aim for 1000 to 1500 tokens.
- Overlap: Use 200 tokens of overlap to keep context.
- Boundaries: Never break a sentence in the middle.
• Chunk Hashing and Deduplication: Hash every chunk. When a file changes, compare the new hashes to the old ones.
• Incremental Ingestion: Do not re-embed everything. If a 1000-page document changes by only one page, only embed that one new chunk. This saves you massive amounts of money on API costs.
The difference between a hobby project and a production system is the work you do before the embedding step.
A naive system re-embeds everything every time. A smart system only processes what changed.
Stop building soup. Build a foundation.
Source: https://dev.to/surajrkhonde/phase-1-document-ingestion-the-hidden-complexity-before-embeddings-4d32
Optional learning community: https://t.me/GyaanSetuAi