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Vector Search Token Budget Planner

This planner estimates the token budget for a vector-search workflow that retrieves document chunks and inserts them into a model prompt. It combines fixed prompt tokens, user-query tokens, the number and size of retrieved chunks, metadata overhead, reserved output tokens, and an optional safety margin.

The result helps determine whether a retrieval configuration fits within a model context window before implementation or tuning. It also reports remaining context capacity and the maximum number of same-sized chunks that could fit under the current assumptions. Tokenization varies by model and content, so chunk-token estimates should come from the intended tokenizer. The planner treats all retrieved chunks as equal-sized averages and does not account for prompt compression, deduplication, or variable metadata unless those effects are reflected in the entered values.

Inputs

tokens
tokens
tokens
chunks
tokens
tokens
tokens
%
Result
—
Planned token budget
Tokens before safety margin—
Remaining context capacity—
Maximum chunks at this size—
Context window used—

1. Enter the context window
Use the maximum context supported by the selected model and API configuration.

2. Add fixed and query tokens
Include system instructions, tool definitions, examples, conversation history, and the expected user query.

3. Describe retrieval payload
Enter the number of chunks and average content plus metadata tokens per chunk.

4. Reserve output capacity
Keep enough tokens for the response rather than allowing retrieved context to consume the entire window.

5. Apply a safety margin
Use margin for tokenizer variation and longer-than-average chunks, then review remaining capacity and maximum chunks.

Retrieved tokens = chunks × (chunk tokens + metadata tokens) Base tokens = fixed tokens + query tokens + retrieved tokens + output reserve Planned budget = base tokens × (1 + safety margin) Remaining context = context window − planned budget

The maximum-chunk result holds all other values constant and uses the entered average tokens per chunk. The safety margin applies to the full base budget.

What the result means

The main result is the estimated total context allocation after applying the selected safety margin.

A negative remaining value means the prompt plan exceeds the context window and should be reduced or compressed.

Given: 128,000-token context, 1,800 fixed tokens, 120 query tokens, 12 chunks, 650 content tokens and 35 metadata tokens per chunk, 1,200 output tokens, and a 10% margin.

Calculation: Retrieved tokens = 12 × (650 + 35) = 8,220. Base tokens = 1,800 + 120 + 8,220 + 1,200 = 11,340. Planned budget = 11,340 × 1.10 = 12,474 tokens.

Result: The plan uses about 12,474 tokens, leaving 115,526 tokens in the context window. With the same fixed costs and margin, up to 165 chunks of this average size could fit.

Why reserve output tokens?

Most model APIs share one context limit across input and generated output. Reserving output capacity prevents retrieval content from leaving too little room for the answer.

How should chunk size be measured?

Tokenize representative chunks with the model’s tokenizer and use an average or conservative percentile. Character or word counts are only rough proxies.

Does a larger context window always improve retrieval quality?

No. Adding more chunks can introduce irrelevant or conflicting evidence and increase cost and latency. Retrieval quality and ranking still matter.

What belongs in fixed prompt tokens?

Include system instructions, examples, tool schemas, conversation history, and any text always sent with the request. Omitting them overstates available retrieval capacity.

What should I do when the plan exceeds the window?

Reduce chunk count or size, trim metadata and history, compress retrieved text, lower output reserve only when safe, or select a larger-context model.