I’m building a document chat system using RAG with large language models. The main issue I’m running into is extracting structured data from PDF tables that have complex layouts.
I’ve tested several Python libraries including LlamaIndex’s SimpleDirectoryReader and the unstructured package. Both tools extract the text content but completely lose the table structure.
For example, when processing a specifications table, SimpleDirectoryReader gives me: "Component ESP32S3Dx ESP32S3Rx Memory 128KB 256KB 512KB 128KB 256KB 512KB"
The problem is that these outputs don’t preserve the relationship between product models and their corresponding specifications. For instance, ESP32S3Dx should map to the first set of values “128KB 256KB 512KB” but this connection gets lost in the extracted text.
What Python libraries or approaches would work better for maintaining table structure when parsing PDFs for RAG applications?
the camelot + pdfplumber combo works well, but you’re missing a key step - run ghostscript preprocessing before extraction. most PDFs have weird encoding that breaks table parsers. I normalize everything with gs first, then use camelot for grid detection. this’ll keep your ESP32 example’s column mapping clean instead of getting that messy concatenated text. if you want to try something different, layoutparser has solid deep learning models for complex table layouts.
Hit this nightmare on three projects. You’re treating tables like regular text when they need special handling upfront.
What worked: pymupdf4llm. Built for RAG workflows and gets table semantics. Instead of dumping everything into text blobs, it keeps table structure in markdown.
This preserves relationships when chunking for vector databases. Your LLM knows 128KB belongs to ESP32S3Dx instead of guessing.
Trick I learned the hard way - extract tables and text separately, then merge with context markers. I give tables special chunk types so retrieval knows what it’s handling.
Tested AWS Textract for messy scanned PDFs. Expensive but handles complex layouts that break everything else. Worth it if your PDFs are consistently formatted.
Had the same issue last year building a contract analysis system. Game changer was switching to pdfplumber + tabula-py. Pdfplumber’s great at finding table boundaries and keeping cell relationships intact, while tabula handles complex multi-page stuff. What really worked was a two-stage process: extract tables as dataframes first, then convert to a standard format before hitting your RAG pipeline. For your ESP32 example, you’d get clean Component-Memory mapping that actually makes sense. Also worth trying PyMuPDF (fitz) - their table detection got way better recently. Outputs straight to CSV or JSON, which makes RAG preprocessing much easier. Pro tip: process tables separately from regular text and tag them properly in your vector store. That way the LLM knows it’s dealing with structured data instead of just random text.
Been wrestling with this exact problem for months on a financial document processing project. Burned through countless hours with basic text extraction tools before finding success with camelot-py and pandas for post-processing. Camelot’s great at detecting table grids even when borders are invisible or partially missing - super common in PDFs. The key was preprocessing PDFs with pdf2image first to clean up scanning artifacts that mess with table detection. For your ESP32 specs, camelot would keep the column-row relationships that SimpleDirectoryReader destroys. Azure Form Recognizer API also worked well for consistently formatted documents - their table extraction is surprisingly accurate for technical specs. Main lesson: treat table extraction as a separate preprocessing step instead of trying to handle it in your RAG pipeline. Convert tables to structured JSON or CSV first, then create embeddings that preserve those relationships.
I’ve been through this hell on multiple enterprise projects. The problem isn’t just finding the right extraction library - it’s building something that works with messy real-world PDFs.
What saved my sanity was automating everything with Latenode. Instead of fighting Python scripts and manual preprocessing, I built a workflow that chains multiple extraction methods. OCR cleanup hits first, then table detection with several algorithms, structure validation, and finally reformatting for RAG.
You can test different extraction approaches in parallel. Pdfplumber might crush clean tables but choke on scanned docs. Latenode routes different PDF types to the right extraction methods automatically. No more 2am debugging sessions.
For your ESP32 example, you’d get clean component-spec mappings since the workflow validates relationships before hitting your vector store. You can even add human review for weird edge cases.
Built this for a client processing thousands of technical datasheets daily. Zero manual work now.