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LLM ProviderGPT-4o & GPT-5 Ready
LLMSlim + OpenAI
Cut OpenAI API costs by 40-70% while preserving 100% instruction fidelity.
Surgically compress input system prompts and RAG contexts before dispatching requests to OpenAI GPT-4o, GPT-4o-mini, and flagship models.
1. Package Installation
Install LLMSlim and the official OpenAI SDK using your package manager:
terminal
pip install llmslim openai2. Architecture & Execution Flow
STEP 01
1. Application captures user input & RAG search context.
STEP 02
2. LLMSlim executes local TF-IDF & Priority Tier compression (< 30ms).
STEP 03
3. Token-dense compressed prompt payload dispatched to OpenAI Chat Completions API.
STEP 04
4. GPT model processes prefill context with 50%+ lower input token billing.
3. Production Code Pattern
Complete, runnable implementation wrapper for OpenAI:
openai_llmslim.py
from openai import OpenAI
from llmslim import compress
client = OpenAI()
def generate_answer(long_context: str, query: str) -> str:
# Compress verbose RAG context to 40% target token count
slim = compress(long_context, target_ratio=0.4, mode="auto")
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are a senior enterprise analyst."},
{"role": "user", "content": f"Context:\n{slim.compressed_text}\n\nQuery: {query}"}
]
)
return response.choices[0].message.content4. Production Deployment Best Practices
Deploy LLMSlim pre-dispatch compression directly inside your API client handlers or custom SDK wrapper functions.
5. Key Optimization Tips
- 01.Pre-compress static system directives once at application startup to eliminate repetitive system token billing.
- 02.Use target_ratio=0.4 for large document contexts and target_ratio=0.6 for dense technical instructions.
- 03.Enable preserve_code=True when system prompts contain JSON format schemas.
6. Performance Metrics & Benchmark Matrix
| Metric Dimension | Uncompressed Payload | LLMSlim Compressed | Recorded Impact |
|---|---|---|---|
| Input Token Volume | 4,200 tokens | 1,680 tokens | 60% Billed Token Reduction |
| Instruction Retention | 100.0% | 100.0% | Zero Rule Loss |
7. Frequently Asked Questions
Does LLMSlim work with OpenAI Structured Outputs (JSON Schema)?
Yes. Priority Tier 4 automatically locks JSON schema definition tags, guaranteeing structural syntax remains valid.
8. Troubleshooting & Diagnostics
Issue: OpenAI API returns 400 validation error due to empty prompt.
Solution: Ensure target_ratio is set between 0.2 and 0.8. Check that input string is non-empty before calling compress().