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**Vectorless** is a reasoning-native document engine written in Rust. It compiles documents into navigable trees, then dispatches **multiple agents** to find exactly what's relevant across your **PDFs, Markdown, reports, contracts**. No embeddings, no chunking, no approximate nearest neighbors. Every retrieval is a **reasoning** act.
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**Vectorless** is a document understanding engine for AI. It compiles documents into structured trees of meaning, then dispatches multiple agents to reason through headings, sections, and paragraphs — evaluating how each part relates to the whole. The problem it solves is not "where to look", but "what does this mean in context". Every answer is a reasoning act, not a retrieval result.
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Light up a star and shine with us! ⭐
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## Three Rules
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-**Reason, don't vector.**Retrieval is a reasoning act, not a similarity computation.
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-**Reason, don't vector.**Understanding is reasoning, not similarity.
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-**Model fails, we fail.** No heuristic fallbacks, no silent degradation.
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-**No thought, no answer.** Only reasoned output counts as an answer.
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## Why Vectorless
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Traditional RAG systems split documents into chunks, embed them into vectors, and retrieve by similarity. Vectorless takes a different approach: it preserves document structure as a navigable tree and lets agents reason through it.
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|| Embedding-Based RAG | Vectorless |
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|---|---|---|
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|**Indexing**| Chunk → embed → vector store | Parse → compile → document tree |
-**Rust Core** — The entire engine (indexing, retrieval, agent, storage) is implemented in Rust for performance and reliability. Python SDK via PyO3 bindings and a CLI are also provided.
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-**Multi-Agent Retrieval** — Every query is handled by multiple cooperating agents: an Orchestrator plans and evaluates, Workers navigate documents. Each retrieval is a reasoning act — not a similarity score, but a sequence of LLM decisions about where to look, what to read, and when to stop.
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-**Zero Vectors** — No embedding model, no vector store, no similarity search. This eliminates a class of failure modes: wrong chunk boundaries, stale embeddings, and similarity-score false positives.
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-**Tree Navigation** — Documents are compiled into hierarchical trees that preserve the original structure — headings, sections, paragraphs, lists. Workers navigate this tree the way a human would: scan the table of contents, jump to the relevant section, read the passage.
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-**Document-Exact Output** — Returns original text passages from the source document. No synthesis, no rewriting, no hallucinated content. What you get is what was written.
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-**Multi-Document Orchestration** — Query across multiple documents with a single call. The Orchestrator dispatches Workers, evaluates evidence, and fuses results. When one document is insufficient, it replans and expands the search scope.
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-**Query Understanding** — Every query passes through LLM-based intent classification, concept extraction, and strategy selection. Complex queries are decomposed into sub-queries. The system adapts its navigation strategy based on whether the query is factual, analytical, comparative, or navigational.
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-**Checkpointable Pipeline** — The 8-stage compile pipeline writes checkpoints at each stage. If indexing is interrupted (LLM rate limit, network failure), it resumes from the last completed stage — no wasted work.
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-**Incremental Updates** — Content fingerprinting detects changes at the node level. Re-indexing a modified document only recompiles the changed sections and their dependents.
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## Supported Documents
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-**PDF** — Full text extraction with page metadata
Vectorless is a reasoning-native document intelligence engine written in Rust — **no vector database, no embeddings, no similarity search**.
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Vectorless is a document understanding engine for AI. It compiles documents into structured trees of meaning, then dispatches multiple agents to reason through headings, sections, and paragraphs — evaluating how each part relates to the whole. The problem it solves is not "where to look", but "what does this mean in context". Every answer is a reasoning act, not a retrieval result.
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{/* truncate */}
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## Why Vectorless?
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Traditional RAG systems rely on vector embeddings and similarity search. This approach loses document structure, requires a vector database, and often returns chunks that lack context.
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Understanding a document requires more than finding keywords — it requires navigating structure, cross-referencing sections, and evaluating whether the evidence is sufficient. Vectorless agents do exactly this: they reason through documents the way a human expert would.
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Vectorless takes a different path:
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Key capabilities:
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-**Hierarchical Semantic Trees** — Documents are parsed into a tree of sections, preserving structure and relationships.
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-**LLM Agent Navigation** — Queries are resolved by agents that navigate the tree using commands (ls, cd, cat, find, grep), making every decision through LLM reasoning.
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-**Zero Infrastructure** — No vector DB, no embedding models, no similarity search. Just an LLM API key.
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-**Zero Infrastructure** — Just an LLM API key, nothing else to deploy.
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# Introduction
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**Vectorless** is a reasoning-native document intelligence engine written in Rust — **no vector database, no embeddings, no similarity search**.
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It transforms documents into hierarchical semantic trees and uses LLMs to navigate the structure, retrieving the most relevant content through deep contextual understanding instead of vector math.
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**Vectorless** is a document understanding engine for AI. It compiles documents into structured trees of meaning, then dispatches multiple agents to reason through headings, sections, and paragraphs — evaluating how each part relates to the whole. The problem it solves is not "where to look", but "what does this mean in context". Every answer is a reasoning act, not a retrieval result.
-**Hierarchical Semantic Trees** — Preserves document structure, not flat chunks
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-**LLM-Powered Agent Navigation** — Worker agents navigate the tree using commands (ls, cd, cat, find, grep), making every retrieval decision through LLM reasoning
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-**LLM-Powered Agent Navigation** — Worker agents navigate the tree using commands (ls, cd, cat, find, grep), making every decision through LLM reasoning
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-**Cross-Reference Resolution** — Automatically resolves "see Section 2.1", "Appendix G" references during indexing
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-**Synonym Expansion** — LLM-generated synonyms for indexed keywords improve recall for differently-worded queries
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-**Orchestrator Supervisor Loop** — Multi-document queries are coordinated by an LLM supervisor that dispatches Workers, evaluates evidence, and replans when needed
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-**Cross-Document Graph** — Automatic relationship discovery between documents via shared keywords
<pclassName={styles.subTitle}>Document Understanding Engine for AI</p>
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<divclassName={styles.featureList}>
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<divclassName={styles.featureItem}>
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return(
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<Layout
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title={`${siteConfig.title}`}
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description="Reasoning-based document engine. No embeddings, no chunking. Multiple agents navigate your documents to find exactly what's relevant.">
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description="Document understanding engine for AI. Agents reason through your documents — navigating structure, reading passages, cross-referencing across sections.">
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