How to Fix 'token limit exceeded during development' in LlamaIndex (TypeScript)

What the error means

If you’re seeing token limit exceeded during development in a LlamaIndex TypeScript app, it usually means one of your prompts, retrieved chunks, or chat history is too large for the model context window. In practice, this shows up when you stuff too much text into a QueryEngine, pass a giant document into an embedding or synthesis step, or keep appending conversation state without trimming it.

The exact failure often looks like a model-side context error wrapped by LlamaIndex, for example:

• •Error: 400 This model's maximum context length is 8192 tokens...
• •ContextWindowExceededError
• •Token limit exceeded
• •LLM request failed due to input token overflow

The Most Common Cause

The #1 cause is passing too much raw text into the query pipeline instead of chunking it first. In TypeScript projects, I see this most often when someone loads a document and calls index.asQueryEngine() without setting sensible chunk sizes or retrieval limits.

Here’s the broken pattern versus the fixed pattern.

// BROKEN
import { Document, VectorStoreIndex } from "llamaindex";

const hugeText = await Bun.file("./policies.txt").text();

const doc = new Document({ text: hugeText });
const index = await VectorStoreIndex.fromDocuments([doc]);

const queryEngine = index.asQueryEngine();
const response = await queryEngine.query({
  query: "Summarize the claims exclusions",
});

console.log(response.toString());

// FIXED
import { Document, VectorStoreIndex, Settings } from "llamaindex";

Settings.chunkSize = 512;
Settings.chunkOverlap = 50;

const hugeText = await Bun.file("./policies.txt").text();
const doc = new Document({ text: hugeText });

const index = await VectorStoreIndex.fromDocuments([doc]);

const queryEngine = index.asQueryEngine({
  similarityTopK: 3,
});

const response = await queryEngine.query({
  query: "Summarize the claims exclusions",
});

console.log(response.toString());

If you’re using a chat model directly, the same principle applies. Don’t send the full policy PDF, all chat turns, and every retrieved node in one shot.

Other Possible Causes

1. Chat memory is growing without bounds

If you keep appending every user message to history, your prompt grows until the model rejects it.

// BAD: unbounded chat history
messages.push({ role: "user", content: userInput });
messages.push({ role: "assistant", content: assistantReply });

Use a sliding window or summary buffer.

// BETTER: keep only recent turns
messages = messages.slice(-8);

2. Retrieval is returning too many nodes

A high similarityTopK can easily blow past token limits if each chunk is large.

const queryEngine = index.asQueryEngine({
  similarityTopK: 10,
});

Reduce it and tune chunking together.

const queryEngine = index.asQueryEngine({
  similarityTopK: 3,
});

3. Your chunk size is too large

Large chunks reduce retrieval precision and increase prompt size during synthesis.

import { Settings } from "llamaindex";

Settings.chunkSize = 2048;
Settings.chunkOverlap = 200;

For most RAG workloads, start smaller.

Settings.chunkSize = 512;
Settings.chunkOverlap = 50;

4. You are stuffing raw documents into prompts

This happens when people bypass retrieval and manually concatenate document text into a prompt template.

const prompt = `
Policy:
${policyText}

Question:
${question}
`;

Instead, retrieve only relevant passages and pass those to the LLM.

const nodes = await retriever.retrieve(question);
const context = nodes.map((n) => n.node.getContent()).join("\n\n");

How to Debug It

1. •

   Check the actual token-heavy inputs

   • •Log document length, retrieved node count, and chat history size.
   • •If you see one giant string being passed around, that’s your problem.
2. •

   Print retrieved chunks before synthesis

   • •Inspect what queryEngine is sending to the LLM.
   • •If you’re getting 8–10 chunks back for simple questions, lower similarityTopK.
3. •

   Temporarily shrink everything

   • •Set Settings.chunkSize = 256.
   • •Set similarityTopK = 1.
   • •Trim chat history to the last 2–4 messages.
   • •If the error disappears, you’ve confirmed a context-size issue.
4. •

   Check which call fails

   • •If failure happens during indexing, your source document or embedding batch is too large.
   • •If failure happens during querying, your retrieved context or prompt template is too large.
   • •If failure happens during chat completion, your message history is not being trimmed.

Prevention

• •Keep chunk sizes conservative unless you have measured evidence that larger chunks help.
• •Cap retrieval aggressively for user-facing queries; start with similarityTopK: 2 or 3.
• •Add token budgeting early:
  • •document chunks
  • •retrieved context
  • •system prompt
  • •conversation memory

A good rule in production RAG systems is simple: never let any single layer assume it can use “most of the context window.” Budget tokens explicitly at each step, especially in TypeScript apps where it’s easy to compose too much data into one request.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.