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You're using AI agents with RAG systems to work with PDFs. It works, but it's wasteful and expensive:
The Painful Truth
A 100-page technical manual takes 10-30 seconds to convert (if using multi-GPU)
Your token costs are 10-50x higher than necessary
You generate embeddings for content your agent will never use
API calls are slow because you're passing entire document context
Storage costs balloon as you keep full markdown versions
Example: A 500-page user manual for a support chatbot:
Traditional: Convert 500 pages → 2-3 million tokens → $30-50 per conversation
PyStreamPDF: Find 2-3 relevant pages → 50-150k tokens → $0.30-1.50 per conversation
Why This Happens
Current tools force you into a bad workflow:
Traditional RAG Workflow (Wasteful):
1. Convert entire PDF to markdown (100% of pages)
2. Generate embeddings for everything (100% indexed)
3. Store complete representation (100% stored)
4. Retrieve small portions on demand (use ~1%)
Result: You're processing and paying for 100x more than you use
PyStreamPDF: A Better Way
Instead of converting everything, PyStreamPDF finds and converts only what matters:
PyStreamPDF Workflow (Efficient):
1. Analyze PDF structure (no conversion needed)
2. Find relevant pages intelligently (5-10% identified)
3. Convert only selected sections to markdown (5-10% processed)
4. Optimize context for your AI system
Try it - pip install pystreamPDF
Would love to see built-in OCR support for scanned PDFs so the selective retrieval works on image-based documents too, not just text-extractable ones. That would really widen the use cases.
Would love to see a built-in visualization of the document structure it detects, like a tree view of sections and tables after analysis. Right now you have to trust the token savings, but seeing what got parsed and skipped would make debugging retrieval way easier.
The selective retrieval approach actually delivers on the token savings, dropped my processing bill noticeably on a stack of dense reports. Took a bit of fiddling to get the structure analysis dialed in for messy scans.
Would love to see a built-in caching layer for frequently accessed PDFs so repeat queries don't have to reprocess the same documents every time. That could push cost savings even further for teams running the same reports daily.
the token savings look great but it would be really useful to have a built-in visualization of the extracted document structure, something like an interactive outline tree so you can verify which sections were selected before sending them to the LLM
Tried it on a 200-page research PDF and was honestly surprised how much the token usage dropped compared to my usual pipeline. The structure analysis picking out section headers cleanly saved me a ton of cleanup work.
About PyStreamPDF on Product Hunt
“Intelligence engine for PDFs. Selective retrieval”
PyStreamPDF was submitted on Product Hunt and earned 14 upvotes and 8 comments, placing #95 on the daily leaderboard. Intelligence engine for PDFs. Selective retrieval, structure analysis, token-efficient RAG. 10-50x cost reduction for document processing. - Mullassery/PyStreamPDF
PyStreamPDF was featured in Artificial Intelligence (474.1k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 133.5k products, making this a competitive space to launch in.
Who hunted PyStreamPDF?
PyStreamPDF was hunted by Georgi Mullassery. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Georgi Mullassery