Dev

docs/docs/architecture.mdx

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+---
+sidebar_position: 3
+---
+
+# Architecture
+
+Vectorless transforms documents into hierarchical semantic trees and uses LLM-powered reasoning to navigate them. This page describes the end-to-end pipeline.
+
+## High-Level Flow
+
+```text
+┌──────────────┐     ┌──────────────┐     ┌──────────────┐
+│   Document   │────▶│    Index     │────▶│   Storage    │
+│  (PDF/MD)    │     │   Pipeline   │     │   (Disk)     │
+└──────────────┘     └──────────────┘     └──────┬───────┘
+                                                  │
+                     ┌──────────────┐     ┌──────▼───────┐
+                     │    Result    │◀────│   Retrieval  │
+                     │   (Answer)   │     │   Pipeline   │
+                     └──────────────┘     └──────────────┘
+```
+
+## Index Pipeline
+
+The indexing pipeline processes documents through ordered stages:
+
+| Stage | Priority | Description |
+|-------|----------|-------------|
+| **Parse** | 10 | Parse document into raw nodes (Markdown headings, PDF pages) |
+| **Build** | 20 | Construct arena-based tree with thinning and content merge |
+| **Validate** | 22 | Tree integrity checks |
+| **Split** | 25 | Split oversized leaf nodes (>4000 tokens) |
+| **Enhance** | 30 | Generate LLM summaries (Full, Selective, or Lazy strategy) |
+| **Enrich** | 40 | Calculate metadata, page ranges, resolve cross-references |
+| **Reasoning Index** | 45 | Build keyword-to-node mappings, synonym expansion, summary shortcuts |
+| **Optimize** | 60 | Final tree optimization |
+
+Each stage is independently configurable. The pipeline supports incremental re-indexing via content fingerprinting.
+
+## Tree Structure
+
+Each node in the tree contains:
+
+```text
+TreeNode
+├── title          — Section heading
+├── content        — Raw text (leaf nodes)
+├── summary        — LLM-generated summary
+├── structure      — Hierarchical index (e.g., "1.2.3")
+├── depth          — Tree depth (root = 0)
+├── references[]   — Resolved cross-references ("see Section 2.1" → NodeId)
+├── token_count    — Estimated token count
+└── page_range     — Start/end page (PDF)
+```
+
+## Retrieval Pipeline
+
+The retrieval pipeline consists of four phases:
+
+1. **Analyze** — Detect query complexity, extract keywords, decompose complex queries
+2. **Plan** — Select retrieval strategy and search algorithm
+3. **Search** — Execute tree traversal with Pilot guidance
+4. **Evaluate** — Score, deduplicate, and aggregate results
+
+### Pilot
+
+The Pilot is the core intelligence component. It provides LLM-guided navigation at key decision points:
+
+- **Fork points** — When multiple children exist, Pilot evaluates which path to follow
+- **Backtracking** — When a path yields insufficient results, Pilot suggests alternatives
+- **Binary pruning** — Quick relevance filter for nodes with many children
+
+### Search Algorithms
+
+| Algorithm | Description | Use Case |
+|-----------|-------------|----------|
+| **Beam Search** | Explores multiple paths with backtracking | General purpose (recommended) |
+| **MCTS** | Monte Carlo Tree Search with UCT selection | Complex multi-hop queries |
+| **Pure Pilot** | Greedy single-path, Pilot at every level | High-accuracy, higher token cost |
+| **ToC Navigator** | Table-of-contents based location | Broad queries ("what is this about?") |
+
+## Cross-Document Graph
+
+When multiple documents are indexed, Vectorless automatically builds a relationship graph based on shared keywords and Jaccard similarity. This graph enables cross-document retrieval with score boosting.
+
+## Zero Infrastructure
+
+The entire system requires only an LLM API key. No vector database, no embedding models, no additional infrastructure. Trees and metadata are persisted to the local filesystem in the workspace directory.

docs/docs/examples/batch-indexing.mdx

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+---
+sidebar_position: 3
+---
+
+# Batch Indexing
+
+Index multiple documents efficiently with progress tracking and error handling.
+
+## Python
+
+```python
+import asyncio
+from vectorless import Engine, IndexContext, IndexOptions
+
+async def main():
+    engine = Engine(
+        workspace="./workspace",
+        api_key="sk-...",
+        model="gpt-4o",
+    )
+
+    # Index a directory of documents
+    result = await engine.index(
+        IndexContext.from_dir("./documents/")
+    )
+
+    print(f"Indexed {len(result.items)} documents")
+    print(f"Failures: {len(result.failed)}")
+
+    for item in result.items:
+        print(f"  ✓ {item.name} ({item.format}) → {item.doc_id}")
+        if item.metrics:
+            m = item.metrics
+            print(f"    Nodes: {m.nodes_processed}, "
+                  f"Summaries: {m.summaries_generated}, "
+                  f"Time: {m.total_time_ms}ms")
+
+    for fail in result.failed:
+        print(f"  ✗ {fail.source}: {fail.error}")
+
+    # List all indexed documents
+    docs = await engine.list()
+    print(f"\nTotal indexed: {len(docs)} documents")
+
+asyncio.run(main())
+```
+
+## Rust
+
+```rust
+use vectorless::client::{Engine, EngineBuilder, IndexContext};
+
+#[tokio::main]
+async fn main() -> vectorless::Result<()> {
+    let engine = EngineBuilder::new()
+        .with_workspace("./workspace")
+        .with_key("sk-...")
+        .with_model("gpt-4o")
+        .build()
+        .await?;
+
+    // Index a directory
+    let result = engine.index(IndexContext::from_dir("./documents/")).await?;
+
+    println!("Indexed {} documents", result.items.len());
+    println!("Failures: {}", result.failed.len());
+
+    for item in &result.items {
+        println!("  ✓ {} ({:?}) → {}", item.name, item.format, item.doc_id);
+    }
+
+    // List all documents
+    let docs = engine.list().await?;
+    println!("Total indexed: {} documents", docs.len());
+
+    Ok(())
+}
+```
+
+## Error Handling
+
+Each item in the result is either successful or failed. Failures don't prevent other documents from being indexed:
+
+```python
+result = await engine.index(IndexContext.from_paths(mixed_paths))
+
+# Successful items
+for item in result.items:
+    process(item)
+
+# Failed items — handle gracefully
+for fail in result.failed:
+    print(f"Failed: {fail.source} — {fail.error}")
+```

docs/docs/examples/multi-document.mdx

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+---
+sidebar_position: 2
+---
+
+# Multi-Document Retrieval
+
+Query across multiple indexed documents using the cross-document strategy with graph-based score boosting.
+
+## Python
+
+```python
+import asyncio
+from vectorless import (
+    Engine, IndexContext, QueryContext,
+    IndexOptions, StrategyPreference
+)
+
+async def main():
+    engine = Engine(
+        workspace="./workspace",
+        api_key="sk-...",
+        model="gpt-4o",
+    )
+
+    # Index multiple documents
+    docs = ["./report-q1.pdf", "./report-q2.pdf", "./report-q3.pdf"]
+    doc_ids = []
+
+    for path in docs:
+        result = await engine.index(IndexContext.from_path(path))
+        doc_ids.append(result.doc_id)
+        print(f"Indexed: {path} → {result.doc_id}")
+
+    # Check the cross-document graph
+    graph = await engine.get_graph()
+    if graph:
+        print(f"\nGraph: {graph.node_count()} docs, {graph.edge_count()} edges")
+        for doc_id in doc_ids:
+            neighbors = graph.get_neighbors(doc_id)
+            for edge in neighbors:
+                print(f"  {doc_id[:8]}... → {edge.target_doc_id[:8]}... ({edge.weight:.2f})")
+
+    # Query across all documents
+    result = await engine.query(
+        QueryContext("Compare quarterly revenue trends")
+        .with_doc_ids(doc_ids)
+        .with_strategy(StrategyPreference.CROSS_DOCUMENT)
+    )
+
+    for item in result.items:
+        print(f"\n[{item.doc_id[:8]}...] Score: {item.score:.2f}")
+        print(item.content[:300])
+
+    # Or query entire workspace
+    result = await engine.query(
+        QueryContext("What documents discuss risk factors?")
+        .with_workspace()
+    )
+
+    print(f"\nFound in {len(result.items)} document(s)")
+
+    # Cleanup
+    for doc_id in doc_ids:
+        await engine.remove(doc_id)
+
+asyncio.run(main())
+```
+
+## Key Concepts
+
+### Document Graph
+
+After indexing, documents are connected in a graph based on shared keywords. The graph enables:
+
+- **Score boosting** — High-confidence results in one document boost neighbor documents
+- **Relationship discovery** — Automatically find related documents
+- **Cross-referencing** — Results from connected documents are surfaced together
+
+### Merge Strategies
+
+The cross-document strategy supports multiple merge modes:
+
+| Strategy | Description |
+|----------|-------------|
+| **TopK** | Return top-K results across all documents |
+| **BestPerDocument** | Best result from each document |
+| **WeightedByRelevance** | Weight by each document's best score |
+| **GraphBoosted** | Use graph connections to boost scores |

docs/docs/examples/quick-query.mdx

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+---
+sidebar_position: 1
+---
+
+# Quick Query Example
+
+This example demonstrates the basic index-and-query workflow with both Python and Rust.
+
+## Python
+
+```python
+import asyncio
+from vectorless import Engine, IndexContext, QueryContext, StrategyPreference
+
+async def main():
+    # 1. Create engine
+    engine = Engine(
+        workspace="./data",
+        api_key="sk-...",
+        model="gpt-4o",
+    )
+
+    # 2. Index a document
+    result = await engine.index(IndexContext.from_path("./report.pdf"))
+    doc_id = result.doc_id
+    print(f"Indexed document: {doc_id}")
+
+    # 3. Simple keyword query
+    answer = await engine.query(
+        QueryContext("revenue")
+        .with_doc_id(doc_id)
+        .with_strategy(StrategyPreference.KEYWORD)
+    )
+    print(f"Keyword result: {answer.single().content[:200]}")
+
+    # 4. Complex reasoning query
+    answer = await engine.query(
+        QueryContext("What are the main factors affecting performance?")
+        .with_doc_id(doc_id)
+        .with_strategy(StrategyPreference.HYBRID)
+    )
+    print(f"Score: {answer.single().score:.2f}")
+    print(f"Hybrid result: {answer.single().content[:200]}")
+
+    # 5. Cleanup
+    await engine.remove(doc_id)
+
+asyncio.run(main())
+```
+
+## Rust
+
+```rust
+use vectorless::client::{Engine, EngineBuilder, IndexContext, QueryContext};
+use vectorless::StrategyPreference;
+
+#[tokio::main]
+async fn main() -> vectorless::Result<()> {
+    // 1. Create engine
+    let engine = EngineBuilder::new()
+        .with_workspace("./data")
+        .with_key("sk-...")
+        .with_model("gpt-4o")
+        .build()
+        .await?;
+
+    // 2. Index a document
+    let result = engine.index(IndexContext::from_path("./report.pdf")).await?;
+    let doc_id = result.doc_id().unwrap().to_string();
+    println!("Indexed document: {}", doc_id);
+
+    // 3. Query with hybrid strategy
+    let answer = engine.query(
+        QueryContext::new("What are the main factors affecting performance?")
+            .with_doc_id(&doc_id)
+    ).await?;
+
+    if let Some(item) = answer.single() {
+        println!("Score: {:.2}", item.score);
+        println!("{}", item.content);
+    }
+
+    // 4. Cleanup
+    engine.remove(&doc_id).await?;
+
+    Ok(())
+}
+```