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PyStreamPDF
Intelligence engine for PDFs. Selective retrieval
Intelligence engine for PDFs. Selective retrieval, structure analysis, token-efficient RAG. 10-50x cost reduction for document processing. - Mullassery/PyStreamPDF
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
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
On the analytics side, PyStreamPDF competes within Artificial Intelligence and GitHub — topics that collectively have 515.5k followers on Product Hunt. The dashboard above tracks how PyStreamPDF performed against the three products that launched closest to it on the same day.
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.
For a complete overview of PyStreamPDF including community comment highlights and product details, visit the product overview.
Georgi Mullassery