Python RAG Ingestion Engineer

I’m building a desktop-first retrieval-augmented generation (RAG) tool and I need a senior Python engineer to take full ownership of the document-ingestion layer. The current prototype already embeds and queries local language models, but the pipeline that feeds them is still thin. Your focus will be to ingest and normalise three document types—plain text, PDFs, and Word files—so they can be chunked, vectorised, and indexed reliably.

Here is the landscape you’ll be stepping into:

• Stack: Python 3.11, LangChain, FastAPI, SQLite for local mode, Postgres in the cloud, Git for workflow, and a mix of GGUF local models plus an OpenAI fallback.
• Environment: Windows is the primary target, so you should be comfortable with PyInstaller or similar packaging to keep the desktop app self-contained.
• Goals: clean parsing, robust metadata capture, deduplication on re-ingest, and a schema that stays in sync between SQLite and Postgres.

What I will look for first in your message is relevant experience—examples of RAG or vector-based search projects you’ve shipped, any libraries you prefer for PDF or DOCX parsing, and lessons learned from packaging Python on Windows. A concise paragraph linking to repos or write-ups is perfect; no formal proposal deck needed.

Acceptance criteria
1. All supported files can be dropped into a watch folder and appear searchable inside the app within 60 s on a typical laptop.
2. Embeddings are stored with full path, hash, and timestamp to prevent duplicates.
3. Switching the persistence backend (SQLite ↔ Postgres) requires only a config flag.
4. The Windows installer bundles every dependency and starts up offline.

If this lines up with what you do every day, let’s talk.