The following strategies can be used for API call cost management:
Basic Optimization Program
- Modular installation: Install only the required functional packages (e.g. text/image)
- caching mechanism: Implementing result reuse with the built-in openai_complete_if_cache
- Model Selection: Use gpt-4o-mini instead of the full version for non-critical questions and answers.
Advanced control methods
- Pretreatment filtration: Parsing the document structure locally before selecting key content for submission
- batch file: centralized document processing rather than single interactions
- hybrid search: Prioritize the use of keyword matching to reduce LLM calls
Cost monitoring
Recommendation:
- Setting Usage Alerts for API Keys
- utilization
max_tokensParameters limit the response length - Regular cleaning
rag_storageCache in the
Measured data shows that monthly API costs can be reduced by 40-60% through optimized configurations, which is especially effective when dealing with a large number of technical documents.
This answer comes from the articleRAG-Anything: an all-in-one RAG system that can handle graphic formsThe
May not be reproduced without permission:AI productivity tools " How can I control the cost of API calls when using it? What are some optimization suggestions?
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