Honestly you're better off rolling your own (but avoid LangChain like the plague). The actual implementation is simple but the devil is in the details - specifically how you chunk your documents to generate vector embeddings. Every time I've tried to apply general purpose RAG tools to specific types of documents like medical records, internal knowledge base, case law, datasheets, and legislation, it's been a mess.

Best case scenario you can come up with a chunking strategy specific to your use case that will make it work: stuff like grouping all the paragraphs/tables about a register together or grouping tables of physical properties in a datasheet with the table title or grouping the paragraphs in a PCB layout guideline together into a single unit. You also have to figure out how much overlap to allow between the different types of chunks and how many dimensions you need in the output vectors. You then have to link chunks together so that when your RAG matches the register description, it knows to include the chunk with the actual documentation so that the LLM can actually use the documentation chunk instead of just the description chunk. I've had to train many a classifier to get this part even remotely usable in nontrivial use cases like caselaw.

Worst case scenario you have to finetune your own embedding model because the colloquialisms the general purpose ones are trained on have little overlap with how terms of art and jargon used in the documents (this is especially bad for legal and highly technical texts IME). This generally requires thousands of examples created by an expert in the field.