Retrieval-Augmented Generation (RAG) is all fun and games until you feed it a PDF that seems hell-bent on sabotaging your efforts. Consider yourself lucky if you have not come across PDFs with split columns, cryptic tables, wandering footnotes, and images in weird places. As a developer who’s wrangled everything from mobile apps to VOIP systems, I thought I’d seen it all. Yet parsing certain PDFs for an AI pipeline did make me think that I should just stick to upper management now. In this article, I’ll share how I learned to stop worrying and love the PDF (well, almost). We’ll dive into why PDFs “hate” being parsed, and how to figure out tables, footnotes, and figures using open-source tools in a RAG pipeline. Feel free to laugh at the tiny anecdotes that I manage to sneak in, because if you don’t laugh, you might just cry over these PDFs. Let’s get into it!

Why PDF Parsing Is Hard (AKA “Why Do PDFs Hate Us?”)

PDFs were never designed with easy text extraction in mind. They’re a digital format optimized for how things look on a page, not how they’re structured logically. In fact, “PDFs represent content closer to how a printer thinks about putting ink on paper”. That means a PDF allows text in multiple columns, images floating around, footnotes stuck at the bottom, all positioned for visual perfection, not for extracting into neatly ordered text. This flexibility makes PDFs awesome for printing and design, but a royal pain in the a** when you need to programmatically retrieve information.

If you try to extract text from a complex PDF, you’ll quickly find nonsense ordering: e.g. the footer might appear before the page content, or a multi-column layout might read straight across columns mixing unrelated sentences.

Tables often turn into jumbled lists of numbers, losing the alignment that gives them meaning. Images/figures? They usually disappear entirely from a text dump, or you get a [Image] tag if you’re lucky. And footnotes or headers – those can get interleaved into the main text, creating bizarre, run-on sentences with citation numbers and references in places they absolutely shouldn’t be. In short, PDFs “are extremely messy under-the-hood so expecting perfect output is a fool’s errand”

I’ve personally seen footnotes from page 5 show up smack in the middle of a paragraph from page 1 during extraction. To be fair, the PDFs aren’t inherently evil, my tools just didn’t know any better.

Knowing this, we can’t just brute-force parse and hope for the best. We need a smarter strategy. Let’s talk about why this matters so much for RAG, and then break down solutions for the worst offenders: tables, footnotes, and figures.

Why Good PDF Parsing Matters for RAG

In a RAG pipeline, the garbage in, garbage out principle definitely applies, just like in your life. So follow me, and get the opposite of whatever “garbage in” is for your brain.

Getting back to the topic at hand, RAG involves retrieving relevant chunks of text from your documents (PDFs in our case), and feeding those to an LLM to get an answer. If the text chunks are messed up – say a table’s data is misaligned or a footer text is randomly inserted – the LLM may get confused or generate incorrect answers as it’s definitely not as smart as you, you super smart person. The quality of your answers is directly tied to how well you extracted and segmented the source content.

I learned this the hard way. In one of my AI assistant prototypes, I fed in a research paper PDF. The answer it gave was laughably wrong; it mentioned a reference that wasn’t relevant. Turns out, the PDF parser had glommed a footnote about a citation into the middle of a sentence. The LLM dutifully treated that as if it were part of the content. What do you know, not very smart AI.

By contrast, when parsing is done right, each chunk fed into the vector store or search index is a coherent piece of information – a paragraph, a table, a caption – without extraneous noise. This not only improves answer accuracy but also helps with embedding quality. (It’s easier to embed a clean paragraph than one with random page numbers or leftover artifacts.) Even if modern transformers can often cope with some mess, it’s not an excuse to make the model’s job harder. Clean data in means better results out, especially for beginners who don’t want to tear their hair out debugging why their shiny new “ChatPDF” clone is spitting nonsense.

So how do we go about taming the PDF in the wild? Let’s tackle the big three troublemakers individually and talk solutions, with an emphasis on open-source tools (no $25k API fees, thank you very much).

Tables: When Rows and Columns Turn to Spaghetti

Tables are probably the #1 nightmare for PDF parsing in RAG. On the page, I actually prefer them, they’re nice and easy to understand. But basic PDF text extractors will read them row by row (or sometimes column by column) with no clear separation of columns, resulting in data that looks like one big run-on sentence or a list of numbers with no context. For example, in a microcontroller spec sheet, a table might have columns for different model variants and rows for memory sizes. A naive parse might spit out: “STM32L475Vx STM32L475Rx Flash memory 256KB 512KB 1MB 256KB 512KB 1MB SRAM 128KB” which completely loses which number belongs to which model. The relationships between header and data are lost, making that text almost useless for question-answering.

Why is this so hard? Because PDFs don’t inherently know about table structure, they just know about text at X/Y coordinates. Merged cells, multi-line cells, or tables spanning multiple pages add extra chaos. I’ve seen tables in financial reports that made me squint, so imagine how an algorithm feels.

Open-Source Solutions for Tables: This is an evolving area, but here are some approaches I’ve tried or seen work:

• Option 1: Use a Table Extraction Library (Camelot or PDFPlumber). If your environment allows (i.e. you can install Java or Ghostscript if needed), Camelot is a popular open-source library that specifically targets tables. It has two modes: lattice (detect tables by drawing lines, good for PDF with visible table borders) and stream (detect by whitespace). Camelot can output each table as a CSV, JSON, or even a pandas DataFrame. It’s prettier than Sydney Sweeny when it works – you get structured data out. But Camelot can struggle with the really funky tables, it can mis-detect things like page titles as headers. Also, it requires Ghostscript (for PDF rendering) and can’t easily be installed in some restricted environments.
  
  An alternative is pdfplumber, a pure-Python library that lets you inspect PDF layout. It doesn’t magically give you a perfect table HTML, but you can use it to find text within certain coordinate boxes or detect lines that might form cell boundaries. I’ve used pdfplumber’s extract_table on simple tables with success – it returns a list of rows, each a list of cell text (with None for empty cells). For more complex stuff, you might combine pdfplumber with heuristics: e.g., identify the area of the page that contains the table and extract text from just that region. This can avoid picking up paragraph text or footnotes while you grab the table.
• Option 2: Turn the Table into an Image and OCR it. This sounds extreme, but a feel free to play around with a “hybrid” approach: “You can use a hybrid approach (non-OCR + OCR) … For tables: use img2table. Convert PDF to image and then use img2table. You can even get a DataFrame.”.
  Essentially, you render the PDF page (or portion) as an image (using something like PyMuPDF or PIL), then use img2table (an open-source project based on OpenCV) to detect table structures in that image. The upside is that this approach uses computer vision to “see” the table layout, which can be more robust for weird cases (especially if the PDF text extraction was failing or the table has a non-standard structure). The downside is OCR can introduce minor text recognition errors and it’s an extra step. But for many, this trade-off is worth it – you get the structure back. In my own tests, OCRing a table with Tesseract then parsing it sometimes preserved the rows/cols better than any direct text extraction did. It feels like using a sledgehammer, but hey, if the table is your enemy, sometimes a sledgehammer is warranted.
• Option 3: ML/DL Table Parsers (Advanced). If you’re feeling adventurous (or desperate), there are deep learning models specifically for table extraction. Microsoft’s Table Transformer (TATR) is one, and then there’s the new hotness like Meta’s “Nougat” model (Neural Optical Understanding for Academic Documents). These models treat the problem as an image-to-markup task: you give a PDF page image, and they output something like structured markup (HTML/LaTeX or JSON) that includes tables. For example, Nougat was designed to extract scientific papers into LaTeX, preserving equations and tables. There’s also Donut, which is an OCR-free model for docs. These can be very powerful – they essentially do what an expert human might, “reading” the page – but require GPU resources and setup. Not exactly plug-and-play for a beginner.
  

In practice, my playbook for tables is: try simple things first, escalate if needed. Start with Camelot or pdfplumber for quick wins on reasonably formatted tables. If the output is garbage or you hit limitations (e.g. Camelot can’t be installed), switch to the image+OCR approach (PyMuPDF to get an image, then img2table or even just Tesseract + custom parsing). Keep an eye on the cutting-edge stuff if you regularly face nasty tables – sometimes an ML model can save the day where traditional methods fail. Just remember to integrate whatever you do back into your RAG pipeline: the end goal is to have the table content in a useful form (markdown, CSV, JSON) that you can embed or let the LLM read. Which brings us to an important point: when adding tables to your knowledge base, consider storing some metadata. For example, I sometimes keep the table caption or title together with the extracted data so that the LLM has context of what the table represents (more on this later).

Before moving on, a quick sanity check: if a table is extremely large (say dozens of rows and columns), do you really need the entire table for QA? Sometimes summarizing or just indexing a few key columns might serve better – huge tables can bloat your embeddings and context window. Always align with your use case (for instance, if users will ask very specific data lookup questions, you need detailed table data; if they just need general trends, a summary might do).

Alright, let’s step off the table (carefully) and wade into another messy territory: footnotes and headers.

Footnotes & Headers

Who invited these guys? Footnotes, endnotes, headers, and footers – all the extra textual elements that publishers add for humans, but that confuse our poor AI pipeline. In PDFs, footnotes often appear at the bottom of the page in smaller font, while headers/footers (like page numbers, document titles, etc.) might repeat on every page. A basic text extraction will typically gather everything and toss it together, so you can end up with footnote text appearing mid-sentence or a page number smack in the middle of a paragraph in your output.

I’ve seen an extreme case where every chunk of text extracted from a report ended with “Confidential Draft – [Page X]” because the parser grabbed the footer every time. Talk about polluting the embeddings! Another time, I had Q&A output where the AI started spouting bibliographic references that were in the document’s footnotes section, clearly not the answer the user needed.

The obvious solution is: remove or isolate these elements so they don’t interfere with the main content. But how?

• Detect by Position: If you use a tool like pdfplumber or PyMuPDF that gives you coordinates for text, you can programmatically decide “ignore text that appears in the top/bottom margin of each page.” For instance, if you know the page height is 800 points and footnotes usually fall between 700-800, you could drop those.
  Similarly, if a header always occupies the first ~50 points of the page, ignore that region. This requires tuning per document layout, though. For varying layouts, a more dynamic approach: find repeated text lines that occur on many pages (like the document title or section name in a header) and filter them out. Many PDFs have consistent headers/footers that you can spot by frequency.
• Detect by Font Size/Style: Often, the main body text is one font size, and footnotes are smaller. A clever approach is to use pdfplumber’s character analysis. Then you can separate text by those sizes. This isn’t foolproof (what if a quote uses a smaller font?), but in structured documents it holds surprisingly well. I’ve used this trick: parse page by page, classify text segments into “main text vs. possible footnote” by font size and vertical position, then only keep main text for embedding. If needed, you could store the footnotes somewhere else (perhaps as reference material, or ignore entirely if they’re just citations).
• Leverage PDF Parsing Libraries: Some higher-level parsing libraries do this for you. For example, the Unstructured library and others can sometimes tag elements like “Footer” or separate out footers if they recognize a pattern. There’s also Dedoc (an open-source parser I recently discovered) which has a parameter specifically to need_header_footer_analysis=True to automatically remove headers and footers. Using such a library can save a ton of time – you hand it the PDF, and it gives you back a cleaned text or segmented elements, without the common header/footer noise. In LangChain, for instance, there’s a DedocPDFLoader that wraps this logic; you can tell it to detect multi-column layouts and strip headers/footers. Unstructured’s PDF loader by default tries to split content into elements (like Title, List, NarrativeText, Table, etc.), and it often ignores repetitive headers. It’s not perfect, but it helps. In my experience, Unstructured won’t explicitly label “this is a footnote,” but it might separate that text into its own element, which you could then drop if you detect it’s out-of-place.
• Skip Them and Pray: No, I’m not kidding. As noted earlier, sometimes if a tiny bit of footer sneaks through, the model might just ignore it, especially if it’s something like a page number or a citation in brackets. Modern LLMs have been trained on lots of text with references, so seeing “[12]” or a snippet of a citation might not throw them completely off. However, I don’t recommend relying on this. It’s better to minimize noise proactively, especially for an open-source model which might be less forgiving than something like GPT-4.

One more thing on footnotes: occasionally, footnotes contain actual useful info (like an explanatory note, not just a citation). If that’s the case, you might want to preserve them somewhere. One approach is to append footnote text to the end of the page’s content or as parenthetical text in the main content. But doing this systematically is hard. If the footnote is referenced by a superscript number in the text, an ideal solution would be to insert the footnote content at that point in text (or at least provide a reference). That is way beyond basic parsing – you’d need to identify the superscript link and merge content. It’s doable with some PDF libs (they can give you the annotation links), but usually not worth the effort unless you have a very special use case

In most scenarios for QA, footnotes and bibliography aren’t needed for answering questions – so I default to cutting them out to avoid confusion. The same goes for running heads or page numbers. My typical pipeline when using LangChain’s document loaders is: use Unstructured or Dedoc with header/footer removal if available, or do a preprocessing pass on the extracted text to regex out things like “^Page \d+ of \d+” or known header strings.

The difference can be huge. After cleaning up, I remember re-running that earlier faulty Q&A and the answer magically fixed itself (no more random references). 

Figures & Images: When Important Information Isn’t Text

Now, what about figures, images, charts, and graphs? These are the “blind spot” of many RAG pipelines, especially open-source ones. If your PDF has a crucial diagram or an infographic, a plain text extractor will likely skip it entirely (or give you an empty placeholder). This means your QA system might act like you with your tiny attention span, and miss out on important information that was conveyed visually.

For example, imagine a PDF report with a pie chart labeled with some stats, or a screenshot of an important quote (yes, I’ve seen people embed text as images in PDFs – why, just why?). If you ignore figures, a user’s question like “What does Figure 5 illustrate about revenue split?” will come up empty. Even more subtly, sometimes the text references an image: “as shown in the figure above, the trend is increasing”. Without the figure, the poor AI has no clue what that trend is.

Options for handling figures/images in open-source RAG:

• Extract Embedded Text via OCR: If the figure is something like a chart with labels, or an image of text, the first step is to see if any textual content can be OCR’ed. Tools like PyMuPDF can extract images from PDFs easily. You can iterate through the PDF pages, pull out each image, and run an OCR (Tesseract or any OCR engine) to get any text from it. This helps for things like scanned documents (where the entire page is an image) or diagrams with labels. It won’t give you the meaning of the chart, but at least you have the text that was on it.
• Use Captions and Surrounding Text: Often, figures in PDFs have captions or descriptive text right below or above them. Make sure your parser doesn’t miss captions – they’re usually in italics or smaller text, but they are part of the text layer. Unstructured will usually grab captions as separate elements (labeled as such). Even if you can’t parse the figure content, the caption alone can be gold. It might say “Figure 5: Revenue split by region (the Americas account for 40%…)”. That sentence contains the insight, which is way easier to embed than an actual pie chart image. So include captions in your chunks. You might attach the caption to the preceding paragraph or treat it as its own chunk with an identifier like “Figure 5: …”.
• Generate a Description of the Image: This is venturing beyond pure retrieval into a bit of generation, but you could use an image captioning model to describe the figure. There are open-source models like BLIP or CLIP+GPT variants that can provide a caption for an image. For instance, BLIP-2 or Microsoft’s Vision Encoder-Decoder can sometimes summarize a plot. This is essentially what GPT-4 Vision would do if you asked it about an image, though GPT-4V is closed. With open source, the reliability varies – describing a complex scientific chart might be too much for these models yet. But for a simple infographic or a photo, they could give a helpful blurb. If you choose to do this, you’ll be creating content that wasn’t explicitly in the PDF, so mark it clearly or put it in metadata (so you know it’s an AI-generated description).
• Multimodal Embeddings: There’s cutting-edge research on creating embeddings from images for search. Essentially, instead of parsing the PDF to text at all, you’d treat it as images and let a model embed those images (like screenshots) such that semantic search can happen. 
• What I typically do: In my projects, if an image contains something obviously important (like a graph or an equation), I will do a quick OCR on it and include any text I get as a note. I also always include the figure caption in the knowledge base, as mentioned. If the figure itself is crucial for answering questions, you might be stuck – an LLM can’t analyze a raw image without a specialized vision model. One workaround is to preemptively ask an AI (like GPT-4 with vision, if you have access) to summarize the figure and then include that summary as part of your data. For open-source only flows, maybe you generate a summary manually or via some script if possible. It’s a bit of a manual patch, but it can work if there are only a few key figures.

One more gotcha: vector images vs raster images. If a PDF has vector graphics (drawn shapes, etc.), some PDF extractors might actually be able to extract text from those if they contain text. But often they treat them as separate objects. PyMuPDF, for example, can give you a list of drawing commands for vector graphics, which is practically useless for us. So basically, vector diagrams might be invisible to text parsers. Converting the page to a bitmap image and doing OCR is a brute-force equalizer in that scenario.

To sum up on figures: decide how important they are for your application. If you’re doing, say, a medical paper Q&A and a lot of content is in charts, you’ll have to invest in some image analysis. If images are rare or not central, you might safely ignore them (just don’t forget captions, as they’re easy low-hanging fruit). The focus of RAG is usually on text, but as these AI agents become more “agentic” and multimodal, we should be ready to pull in visual data too.

Before leaving this topic, I’ll share a small victory: using the above strategies, I had a PDF with a complex schematic image that was crucial. I used an open-source captioning model to get a description, which was something like “a flowchart showing the steps from user input to output, involving validation and error handling.” I stored that. Later, a question came, “What does the diagram illustrate about the process flow?” My system actually retrieved the AI-generated description and the LLM gave a decent answer summarizing the flow. Was it as good as having the actual image? No, but it was good enough to be useful, and all done with open tools.

Building an Open-Source PDF Parsing Pipeline for RAG

We’ve looked at individual problems and tools, so let’s piece it together. How do you actually implement this in a semi-cohesive pipeline? Here’s a blueprint that I (humbly) recommend, based on experience and a lot of trial and error:

Initial Text Extraction – Use a Reliable Parser: Start with a good general-purpose PDF text parser for the bulk of the text. My go-to here is often Unstructured (with LangChain’s UnstructuredPDFLoader) or Dedoc. These handle a lot out of the box: they’ll get you the paragraphs, headings, etc., and skip obvious junk. Unstructured, for example, will use a layout detection model under the hood for things like tables and images – it might not give you structured tables, but it will label certain content as “Table” and you can decide what to do with it. It also can fall back to OCR automatically if the PDF has scanned pages, which is convenient (no need to manually check if text extract is empty, it’ll just OCR it). If you use Dedoc via LangChain, you can set parameters like is_one_column_document=”auto” (it will try to detect multi-column layouts) and need_header_footer_analysis=True to auto-remove headers/footers. In code, this might look like:

from langchain_community.document_loaders import DedocPDFLoader
loader = DedocPDFLoader(
    "mydoc.pdf", 
    split="page", 
    need_header_footer_analysis=True
)
docs = loader.load()

 This would give you a list of Document objects, one per page (you could also do the whole doc as one or other splits). Each Document.page_content now should be relatively clean text of that page. If you go the Unstructured route:

from langchain.document_loaders import UnstructuredPDFLoader
loader = UnstructuredPDFLoader("mydoc.pdf")
docs = loader.load()

By default, this loader will split by elements, I believe, so you might get multiple documents (one for each section, table, etc.). You might need to concatenate or handle those carefully. The key is: we have baseline text.

Secondary Extraction for Tables: Now, if your document might have important tables, decide on a strategy as discussed. If using Unstructured, possibly it already extracted table text (likely as a single blob of text per table). That text might be missing structure. You could try a quick fix: if the table isn’t huge, just include it as is. LLMs like GPT-4 are surprisingly good at parsing plain text tables if they’re consistent (they’ve been trained on Markdown and CSV data). But smaller models may not. If the table is critical or complex, I would run a dedicated table parser. For example:

import camelot
tables = camelot.read_pdf("mydoc.pdf", pages="all")
for i, table in enumerate(tables):
    csv = table.df.to_csv(index=False)
    # Save or use the CSV, and possibly store as Document for RAG
    table_doc = Document(page_content=str(csv), metadata={"source": f"table_{i}"})
    docs.append(table_doc)

This snippet uses Camelot to extract all tables and then converts each to CSV text. You might instead keep it as a DataFrame. But storing as CSV text is convenient – you can even format it nicely or keep it in Markdown table format for the LLM to read later. Make sure to tie it back to some metadata (like which page or section it came from) in case you need to trace it.

If Camelot is not an option, I might do:

from PIL import Image
import fitz  # PyMuPDF
import pytesseract

doc = fitz.open("mydoc.pdf")
for page_num in range(doc.page_count):
    page = doc.load_page(page_num)
    text = page.get_text()  # baseline text (we did above)
    # If we suspect a table on this page:
    pix = page.get_pixmap(dpi=150)
    img = Image.open(io.BytesIO(pix.png_data))
    table_text = pytesseract.image_to_string(img)
    # Here, optionally use img2table to structure it:
    from img2table.document import Image as Img2TableImage
    tables = Img2TableImage(img).extract_tables()
    # img2table returns structured tables, you could get DataFrames, etc.

The above is a bit pseudo-code-ish (and mixing approaches), but the point is: get table text via OCR if needed and structure it. This is an advanced step; beginners might skip directly to OCR if nothing else works: just feed the image’s text as one big chunk. It’s messy but at least the info is there.

1. Integrate Footnote Handling: After initial extraction, you might still have some footers/headers lingering (if your tool didn’t remove them). I usually post-process the text. For example, if I know the PDF had a footer like “ACME Corp Confidential”, just do doc.page_content = doc.page_content.replace(“ACME Corp Confidential”, “”). Or use regex to drop page numbers, etc. If using pdfplumber manually, you could have done the bbox trick to exclude the bottom region. Tailor this to your document layout. It’s worth it – those are easy gains in cleanliness.
   
2. Combine Text and Structured Data: Now you likely have a bunch of content pieces: cleaned paragraphs, maybe some table data, maybe some extracted captions or OCR’d bits from images. The next step is to assemble your LangChain Documents (or whichever format your RAG uses) for indexing. It could be as simple as appending the table data to the end of the page text, but I prefer keeping them separate with metadata. For example, I might have Document(page_content=”Table: [Markdown table here]”, metadata={“type”: “table”, “page”: 5, “caption”: “Revenue by Region”}). Regular text chunks have metadata={“type”: “text”, “page”: 5, …}. This way, I can later do a sanity check: if a table chunk is retrieved, I know I might need to format the answer differently or ensure the LLM can handle it.
   
   Also, chunking: You should split the text into reasonable chunks (LangChain’s TextSplitter) after you’ve cleaned it. Don’t split before removing footers, or you might end up with a footer in the middle of a chunk which is harder to detect. For tables, if they are small, keep the whole table as one chunk (so the model sees it whole). If a table is very large, you might split it by rows or groups of rows – but be cautious, you might break the structure.
   
3. Embedding and Indexing: Use your favorite embedding model to vectorize the chunks and store in a vector database (FAISS, Chroma, etc.). Tables in text form can be embedded too, though they may not embed meaningfully. If your table is mostly numbers, an embedding might not capture the nuance (embeddings are geared towards semantic meaning of words). You might rely more on keyword or structured lookup for such data. In some cases, I skip embedding big numeric tables and instead use a different approach (like direct search by value or something). But that’s situational. For completeness, you’ll embed everything that’s text.
   
4. Retrieval and Synthesis: Finally, during QA, you retrieve relevant chunks. If your parsing strategy worked, the chunks you get back should be relevant and internally coherent. For example, you might retrieve a chunk that is a paragraph of text and another chunk that is a table snippet, both related to the query. Now it’s up to the LLM to synthesize. If using an open-source LLM, you might need to prompt it a bit to use the table data properly (e.g., “If the answer requires data from a table, use the provided table content in your answer”). For a powerful LLM like GPT-4, it usually figures it out. I had an experience where the model was asked something like “Compare the revenues of region A and B”. I had the table of revenues in a chunk. GPT-4 picked the numbers out of the markdown table and wrote a comparison sentence. That felt like magic – but really, it’s because we provided the table in a semi-structured form that it could parse (Markdown). I can’t guarantee smaller open models would do as well, but it’s improving.
   

Deduplication Note: If you used multiple methods, there’s a chance you have duplicate info. For instance, Unstructured might have already extracted some table text, and then you separately OCR’d that table. You don’t want both in your index. That’s a good practice: if you know you’re handling tables separately, tell your base parser not to include table content if possible. Unstructured allows you to specify which element types to include/exclude. Or you can filter out chunks that look like they came from a table. The goal is to not confuse retrieval with two versions of the same content. In my pipeline, I usually drop the raw table text from the initial parse as soon as I replace it with a nicely formatted version.

A Note on Open-Source vs. Paid Solutions

I’ve focused on open-source tools here because, well, I love the freedom and community around them. But I’ll be honest: the big cloud offerings (Amazon Textract, Google Document AI, Azure Form Recognizer) are very good at this stuff. They use state-of-the-art models and have teams of PhDs working on PDF parsing. These services can detect tables, forms, do multi-column layout, label headings, etc., with high accuracy. They also integrate vision for images naturally. The catch? They cost money, sometimes a lot of money, and sending data to them might be a privacy issue for your project. For example, Adobe’s PDF Extract API was mentioned with a $25k upfront fee in a forum complaint – not exactly hobbyist-friendly, if you are thinking about trying out that API, I’d highly recommend buying me a coffee first 👀👉https://buymeacoffee.com/thecodecity.

For many of us, open-source is the pragmatic choice, either due to budget or the need to run on-premises. Just be aware of the gap. Open-source tools work and can be extremely powerful (plus you can hack them to your needs), but you might need to put in extra elbow grease to cover all the edge cases that commercial APIs handle out-of-the-box. As an example, open tools may not automatically classify “this is a header vs body vs footer” with ease, or they might crash on a 1000-page PDF (out-of-memory issues) where a cloud API would stream it.

That said, the open-source world is catching up rapidly. The community-driven projects are narrowing the gap. And they’re often more customizable. If you’re willing to script and tinker, you can achieve a lot. 

So my advice: if you have the resources and just need a solution, consider trying those cloud APIs (they often have free tiers or demos) to see the ideal output. It can be eye-opening; you’ll know what you’re aiming for. Then implement the closest approximation with open tools if you decide to go that route. It’s a great way to benchmark your pipeline.

Conclusion: Triumph (or at least Truce) Over Annoying PDFs

After wrestling with countless PDFs that “hated” me, I’ve come to a zen-like acceptance: PDF parsing will never be 100% perfect (jk, it probably will, very soon), but it can be good enough to get the job done. With a thoughtful combination of open-source libraries – from layout parsers to OCR and table extractors – you can extract the majority of useful information and make it digestible for an LLM. The key insights I’ve gained:

• As of October 2025, no single tool does it all. You’ll likely end up with a stack of tools: maybe PyMuPDF for quick text, Unstructured for smarter chunking, Camelot for tables, Tesseract for OCR, etc. That’s normal. Embrace the pipeline approach.
  
• Clean up the junk. Removing or segregating footers, headers, and other noise will boost your RAG results significantly. It’s worth spending time on this early on.
  
• Preserve structure when possible. If you can keep headings, lists, or table formatting, do it. In markdown or JSON form, structured data gives the LLM more to work with. Large language models actually like some structure – it provides hints (e.g., a markdown table is easier to read than a dense sentence of numbers).
  
• Test with real questions. The ultimate judge of your parsing is the end QA performance. I iteratively test my pipeline by asking actual questions and seeing if the answer is correct and sourced. If something consistently fails (like questions about a table are wrong), that’s a sign to improve parsing of that table or how the info is stored.
  
• Be ready to iterate. There will be PDFs that break your logic. When (not if) that happens, use it as a learning opportunity. Recently, I parsed a PDF that had two columns and footnotes and weird sidebar text – a trifecta of pain. The first run jumbled things. I went back and adjusted my script to detect the two columns (by splitting the page in half), and that solved it for that document. It might not be needed for others, so I keep such handling optional or data-driven.
  

In the end, while PDFs may never love being parsed, you can certainly stop them from ruining your day. There’s a strange satisfaction in turning a messy PDF into a knowledge source that your AI assistant can actually use effectively. It’s like taming a wild beast, not by force alone, but by understanding its nature (and maybe bribing it with some OCR treats).

Final thought: Don’t hesitate to share your experiences and solutions. The community around RAG and document parsing is growing, and many of us are figuring this out together in real-time. I’ve picked up some of the tricks mentioned here from forum posts, open-source contributors, and trial-and-error with open models. In that spirit, I hope this playbook gives you a head start (and saves you a few headaches) in dealing with those complex PDFs and hopefully you avoid hating your life. With the right approach, you’ll turn them into valuable data in your RAG pipeline. Happy parsing, and may your tables be ever aligned, your footnotes tamed, and your figures enlightening!

Sharing is caring!