Principle:Princeton nlp Tree of thought llm LLM API Wrapping
| Knowledge Sources | |
|---|---|
| Domains | API_Design, NLP, Infrastructure |
| Last Updated | 2026-02-14 03:30 GMT |
Overview
An abstraction layer that wraps external LLM API calls with retry logic, batching, and token usage tracking to provide a simple prompt-in/completions-out interface.
Description
LLM API Wrapping addresses the practical challenges of making reliable, high-volume calls to external language model services. Raw API calls can fail due to rate limits, server errors, or network issues. Additionally, generating many completions per prompt (e.g., n=100) may exceed per-request limits. This principle encapsulates:
- Retry with exponential backoff: Automatically retries failed API calls with increasing delays.
- Batching: Splits large n requests into batches of at most 20 to stay within API limits.
- Token tracking: Accumulates prompt and completion token counts across all calls for cost estimation.
- Unified interface: Provides a single function signature that all downstream code calls, abstracting away the chat message format.
Usage
Use this principle in any system that makes repeated LLM API calls during search or generation, especially when reliability, cost tracking, and large sample counts are needed. It is the foundational layer through which all LLM interactions pass in the Tree of Thoughts framework.
Theoretical Basis
The wrapper follows a layered architecture:
# Abstract pattern
def llm_call(prompt, model, temperature, max_tokens, n, stop):
messages = format_messages(prompt)
outputs = []
while n > 0:
batch = min(n, MAX_BATCH)
n -= batch
response = retry_with_backoff(api_call(messages, n=batch))
outputs.extend(extract_completions(response))
track_tokens(response.usage)
return outputs
The exponential backoff strategy waits seconds after the -th failure, preventing thundering herd effects on the API endpoint.