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LLMSlim
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OpenAI
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.

terminalbash
pip install llmslim

2. Architecture & execution flow

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1. Retrieve or construct application context.

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2. Label provenance accurately and compress with LLMSlim.

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3. Send the resulting text to the provider SDK.

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4. Apply application-level policy and output validation.

3. Production code pattern

openai_app.pypython
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. 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

  • Use compress_documents() for retrieved documents; it defaults to RAG provenance.
  • Leave system messages unchanged by default in compress_chat_messages().
  • Do not treat compression as a complete prompt-injection defense.

6. Performance metrics

MetricUncompressedLLMSlimImpact
v0.4.0 release gateN/A489 tests passed90.93% coverage; 375 schemas across 18 catalogs

Frequently asked questions

Troubleshooting