Building an AI Document Intelligence System: Architecture, LangChain, and Production Lessons
How I built a provider-agnostic RAG platform for document Q&A - and what years of delivering document-heavy digital services taught me about doing it properly. The problem: documents don't answer their own questions Every organisation I've worked with - public sector, regulated enterprise, or otherwise - has the same quiet bottleneck: information locked inside PDFs, DOCX files, and scanned…
A provider-agnostic RAG (Retrieval-Augmented Generation) platform named AI-DocumentIntelligence was created to tackle the issue of documents not being able to answer their own questions. This platform enables organizations to upload, process, and perform natural-language Q&A on PDFs, DOCX files, and scanned reports.
The main components of the AI-DocumentIntelligence platform include:
1. Ingestion of PDF, DOCX, or TXT documents
2. Chunking of documents into semantically meaningful pieces
3. Embedding those chunks into PostgreSQL using pgvector
4. Answering questions using natural-language queries about the content, citing back to source chunks
The platform is built using a technology stack that is straightforward and familiar to most teams:
- Frontend: React 18 + TypeScript
- Backend: Node.js + Express + TypeScript
- AI/Orchestration: LangChain
- Vector store: PostgreSQL + pgvector
- LLMs: OpenAI or Anthropic Claude (configurable)
To switch between LLM providers, the system relies on a configuration flag, LLM_PROVIDER, rather than a hard-coded architectural decision. This allows for easy swaps in case of cost, compliance, or availability reasons without having to rewrite the code.
The architecture of the platform consists of a React UI layer, an Express API layer, and a Document Processor layer. The Document Processor layer is responsible for chunking and embedding documents, and for answering queries using LangChain and the selected LLM.
The chunking and embedding pipeline is executed once per document upload, while query time involves embedding the question, performing a similarity search against pgvector, passing the top-k chunks to the selected LLM, and returning an answer with the chat history for that document.
The chunking process uses LangChain's recursive splitter, which is tuned specifically for document Q&A. The overlap between chunks is crucial to ensure that answers are complete and not cut off across chunk boundaries. An overlap of 150 characters on a 1000-character chunk proved to be the best balance in the author's experience.
Debugging and testing are made easier by separating retrieval and generation, allowing for the validation of retrieval without incurring API costs. This approach is especially valuable when iterating on a budget or using free embedding models.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
