LLM ProviderPython integration
LLMSlim + OpenAI
Compress Python application context before an OpenAI SDK request.
LLMSlim is provider-agnostic Python preprocessing. Use RAG provenance for retrieved content.
1. Package Installation
Install LLMSlim and the official OpenAI SDK using your package manager:
terminal
pip install llmslim2. Architecture & Execution Flow
STEP 01
1. Retrieve or construct application context.
STEP 02
2. Label provenance accurately and compress with LLMSlim.
STEP 03
3. Send the resulting text to the provider SDK.
STEP 04
4. Apply application-level policy and output validation.
3. Production Code Pattern
Complete, runnable implementation wrapper for OpenAI:
openai_app.py
from llmslim import compress_documents
results = compress_documents(retrieved_docs, query=user_question, target_ratio=0.4)
context = '\n\n'.join(item.compressed_text for item in results)
# Pass context to your OpenAI SDK request.4. Production Deployment Best Practices
Use LLMSlim in your Python application before constructing the provider request. Measure target workloads before choosing a compression ratio.
5. Key Optimization Tips
- 01.Use compress_documents() for retrieved documents; it defaults to RAG provenance.
- 02.Leave system messages unchanged by default in compress_chat_messages().
- 03.Do not treat compression as a complete prompt-injection defense.
6. Performance Metrics & Benchmark Matrix
| Metric Dimension | Uncompressed Payload | LLMSlim Compressed | Recorded Impact |
|---|---|---|---|
| v0.4.0 release gate | N/A | 489 tests passed | 90.93% coverage; 375 schemas across 18 catalogs |
7. Frequently Asked Questions
Does LLMSlim guarantee provider output correctness?
No. It reduces context according to its scoring and provenance rules; validate outputs in your application.
8. Troubleshooting & Diagnostics
Issue: Unexpected compression result
Solution: Check target_ratio, role labels, and whether the input is long enough to benefit from compression.