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  The forced migration to OpenAI's Assistants API v2 has been a bumpy ride for many engineering teams. While the new   File Search   tool offers significantly better retrieval accuracy than the deprecated v1 "Retrieval" tool, it has introduced two critical issues: substantial, unexpected monthly costs and persistent "Assistant ID not found" errors during integration. If you recently checked your usage dashboard and saw a spike in "Vector Store" storage fees, or if your application is throwing 404s on Assistants that clearly exist in the dashboard, this guide is for you. The Root Cause: Why v2 is Breaking Your Budget and Builds To fix these issues, we must look at the architectural shift between v1 and v2. The Cost Trap: Orphaned Vector Stores In v1, file retrieval was a "black box." You uploaded a file, attached it to an assistant, and OpenAI handled the indexing. In v2, OpenAI exposed the infrastructure via  Vector Stores . A Vector Store is...

  You have successfully uploaded a file. You have the   file_id . You created an Assistant with the   file_search   tool enabled. Yet, when you query the Assistant about the document, it apologizes and claims it doesn't have access to that information, or worse, it hallucinates an answer. This is the most common frustration with the OpenAI Assistants API v2. The issue is rarely with the file itself. It usually stems from a misunderstanding of how the  v2 Vector Store architecture  decouples files from Assistants, or how the  run  orchestration handles tool selection. This guide provides a rigorous root cause analysis and a production-grade Python solution to ensure your RAG (Retrieval-Augmented Generation) pipeline actually retrieves data. The Root Cause: Why "Attached" Doesn't Mean "Indexed" In the deprecated v1 API, you simply attached a file to an Assistant. In v2, OpenAI introduced a strictly managed RAG pipeline involving  Vector Stores ...