PageIndex retweeted
If you are feeling brave, I just pushed a v0 Recursive Decision Model module for @DSPyOSS available now on my fork rawwerks/dspy
uv add "dspy[typesafe] @ git+github.com/rawwerks/dspy"
from dspy.experimental import RDM
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Just as AlphaGo helped ignite the modern AI wave, self-improving agents may shape its future; hierarchical search is a fundamental building block for the next stage of AI development.
my personal faves are:
AutoResearch for Feature Extraction
Use LLMs to generate Jev questions's who's probabilities are fed into a classic ML algorithm like logistic regression to predict a label.
Hierarchical Classification
Traverse a classification taxonomy using Jev and beam sesarch. Jev is great for graph traversal.
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Inspired by @EGafni’s Twitter thread on combining Jev with PageIndex.
We show how to build long-document search with @typesafeai Jev + PageIndex.
No vector database. No embeddings.
Open source: github.com/VectifyAI/jev-doc…
🧵👇
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PageIndex fixes both. It turns the document into a tree of sections, each with a title and a summary.
Jev then makes one small choice per level: a section, then a page inside it. Every choice has only a handful of options, however long the document.
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Huge thanks to the amazing Jev team — @CompleteSkeptic, @EGafni, and @typesafeai.
Check out the repo for the full implementation and details:
github.com/VectifyAI/jev-doc…
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Wow, this can be a perfect model for RAG, with string/number/integer types.
Not another Jev.
TypeLLM adds type-safe generation to LLMs — more types (string, number, boolean, enum), vision, dependent fields and thinking.
Playground is open to all with $5 credit.
API is rolling out to early-access users in the coming days.
typellm.ai/dashboard/playgro…
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Not too late to scales the retrieval with the model!
if this takes off, then i was 4 years too early 😭 x.lingyaoai.com/jerryjliu0/status/1590…
only OGs remember gpt tree index
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Block references are now live for all PageIndex Cloud users. Every citation points to the exact block, not just the page.
Docs → docs.pageindex.ai/sdk/chat#c…
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As we scale up PageIndex Cloud, we’re introducing a new, simpler pricing model:
• $0.01 per page indexed
• $0.001 per active page / month
• Unlimited retrieval on active pages
Simple usage-based billing. No monthly commitment.
Get started with $10 in free credits.
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An illustration of three ways to ask a document:
• Vector DB: Split into chunks and search by vector similarity
• PageIndex: Navigate a document tree and retrieve by node relevance
• Full context: Put the whole document into the context— through an LLM provider’s file API
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LLMs can solve the Navier–Stokes problem, but they’re still very poor at understanding PDFs. GPT-6 Astra scores just 32.2% on the GDP-PDF benchmark: surgehq.ai/benchmarks/gdp-pd…
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PageIndex is built on this principle: a persistent semantic representation of knowledge, with retrieval accuracy that improves as the LLM scales.
General coding agents now significantly outperform purpose-built data agents. What is left for systems researchers to solve? We break it down in our latest paper.
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A simple visualization of how PageIndex works.
Index: generate a tree index for each document.
Retrieve: agentically tree search with LLM
Learn more: pageindex.ai/developer
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