TreeIndex

TreeIndex is a vectorless semantic indexing SDK that converts large text into searchable knowledge trees.

It is inspired by PageIndex, with a simple npm-first developer workflow and bring-your-own-LLM setup.

How It Works

1. Provide large source text.
2. TreeIndex incrementally builds semantic nodes (topics + subtopics).
3. For a query, it retrieves relevant node IDs.
4. It gathers grounded source snippets from those nodes.
5. It generates an answer from the retrieved context.

Installation

npm install treeindex

Quick Start

1) Initialize

import { TreeIndex } from "treeindex";

const treeIndex = new TreeIndex({
    baseURL: "https://api.openai.com/v1",
    apiKey: process.env.OPENAI_API_KEY,
    model: "gpt-5.1",
});

2) First-time indexing flow (no stored tree yet)

Use this flow when indexing a document for the first time.

// Load source text
treeIndex.loadData(largeText);

// Build tree from loaded text
const tree = await treeIndex.generateTree();

// Persist the tree in your DB/storage layer
// TreeIndex does not store trees for you.
await saveTreeToDatabase(tree);

// Load the same tree into SDK state for retrieval/answering
treeIndex.loadTree(tree);

// Generate answer
const answer = await treeIndex.generateAnswer("What are assets vs liabilities?");
console.log(answer);

3) Existing tree flow (already stored in your DB)

Use this flow when the tree already exists in your storage.

const storedTree = await fetchTreeFromDatabase(documentId);

// loadData is still needed for node text slicing in findNodes()/generateAnswer()
treeIndex.loadData(largeText);
treeIndex.loadTree(storedTree);

const answer = await treeIndex.generateAnswer("What are assets vs liabilities?");
console.log(answer);

4) Custom answer generation pipeline (advanced)

If you want full control over answer generation logic:

treeIndex.loadData(largeText);
treeIndex.loadTree(storedTree);

const relevantNodeIds = await treeIndex.retrieveRelevantNodes("What are assets vs liabilities?");
const foundNodes = treeIndex.findNodes(relevantNodeIds);

const context = foundNodes.map((n) => n.data).join("\n");

// Your own generation call (any model/provider)
const answer = await myCustomGenerator({
    query: "What are assets vs liabilities?",
    context,
});

console.log(answer);

Why TreeIndex?

PageIndex-style workflows hit practical friction:

• tree generation can fail on weakly structured input
• local setup can feel heavy
• provider customization can be harder than expected
• only supports PDF input

TreeIndex focuses on a simpler developer experience:

• install with npm and start quickly
• designed to still produce a tree even when source text is poorly structured (with potential accuracy trade-offs)
• bring your own API key and model
• straightforward JavaScript and TypeScript integration

Supported Providers

TreeIndex works with OpenAI-compatible chat endpoints by supplying the provider base URL and model.

Provider	Typical baseURL
OpenAI	https://api.openai.com/v1
Gemini	https://generativelanguage.googleapis.com/v1beta/openai/
Anthropic	https://api.anthropic.com/v1
Grok (xAI)	https://api.x.ai/v1
Ollama	http://localhost:11434/v1
OpenRouter	https://openrouter.ai/api/v1

API Reference

new TreeIndex(options)

type TreeIndexOptions = {
    baseURL: string;
    apiKey: string;
    model: string;
};

Creates a TreeIndex instance backed by your chosen provider/model.

loadData(data: string): void

Loads source text to index.

generateTree(): Promise<TreeNode[]>

Builds or extends the semantic knowledge tree from loaded data.

type TreeNode = {
    nodeId: string;
    title: string;
    summary: string;
    stringSubset: [number, number];
    nodes: TreeNode[];
};

loadTree(tree: TreeNode[]): void

Loads an existing tree (for reuse or persisted state).

retrieveRelevantNodes(query: string): Promise<string[]>

Returns node IDs that are semantically relevant to the query.

findNodes(nodeIds: string[]): FoundNode[]

Returns matched nodes with extracted source snippets.

type FoundNode = {
    nodeId: string;
    title: string;
    summary: string;
    data: string; // extracted from loaded data using stringSubset
};

generateAnswer(query: string): Promise<string>

Generates an answer grounded in retrieved node data.

Why Vectorless?

TreeIndex intentionally avoids embedding-first infrastructure:

• more accurate retrieval on long, complex documents where embeddings may struggle to capture nuance
• no embedding pipeline setup required
• no vector database hosting cost
• semantic tree remains human-readable and inspectable
• similar chunks (in vector approach) are not always relevant

Limitations

• QUALITY DEPENDS ON MODEL CAPABILITY, ONLY BEST REASONING MODELS WILL WORK WELL
• long documents may require multiple recursive indexing passes and much more time
• malformed model JSON responses can reduce retrieval quality
• provider/model feature support may vary
• tree persistence is not handled by TreeIndex; you must store and load trees in your own database/storage
• project is early stage and API surface may evolve

Contributing

Issues and pull requests are welcome.

Please open an issue first so implementation details can be aligned early.

License

MIT