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data-retrieval-and-display

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ksm26 / Prompt-Compression-and-Query-Optimization

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Enhance the performance and cost-efficiency of large-scale Retrieval Augmented Generation (RAG) applications. Learn to integrate vector search with traditional database operations and apply techniques like prefiltering, postfiltering, projection, and prompt compression.

mongodb projection query-optimization search-relevance data-retrieval performance-optimization reranking data-security vector-search developer-advocacy query-processing vector-search-engine database-operations prefiltering cost-efficiency data-retrieval-and-display prompt-compression rag-applications postfiltering large-scale-applications
  • Updated Jul 23, 2024
  • Jupyter Notebook

radman-s / REST_API

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This project demonstrates the usage of the JSONPlaceholder API by making HTTP requests and retrieving data from various endpoints. It is implemented in Python using the requests library.

session-management python-best-practices jsonplaceholder-api-integration data-retrieval-and-display http-requests-and-response-handling modularity-and-code-organization error-handling-and-user-input-validation api-testing-and-exploration
  • Updated Jun 26, 2023
  • Python

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