llm runtime_error ai_generated true

Warning: Using cached embedding from version 1.0.0, but current model is version 2.0.0. Embedding may be stale.

ID: llm/llm-caching-stale-embedding

Also available as: JSON · Markdown · 中文
85%Fix Rate
84%Confidence
1Evidence
2024-05-01First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
langchain 0.1.10 active — — —
langchain 0.1.11 active — — —
chromadb 0.4.22 active — — —

Root Cause

Embedding cache from a previous model version is reused after model update, causing dimension or semantic drift.

generic

中文

模型更新后，来自旧版本的嵌入缓存被重用，导致维度或语义漂移。

Official Documentation

https://python.langchain.com/docs/modules/data_connection/retrievers/

Workarounds

  1. 95% success Clear embedding cache and re-index: chroma_collection.delete(where={}); then re-embed all documents with new model version 
    Clear embedding cache and re-index: chroma_collection.delete(where={}); then re-embed all documents with new model version
  2. 80% success Pin embedding model version in config: EMBEDDING_MODEL = 'text-embedding-3-small@v1' (if version pinning is supported) 
    Pin embedding model version in config: EMBEDDING_MODEL = 'text-embedding-3-small@v1' (if version pinning is supported)

中文步骤

  1. Clear embedding cache and re-index: chroma_collection.delete(where={}); then re-embed all documents with new model version
  2. Pin embedding model version in config: EMBEDDING_MODEL = 'text-embedding-3-small@v1' (if version pinning is supported)

Dead Ends

Common approaches that don't work:

  1. Ignoring warning and continuing to use cache 95% fail

    Stale embeddings cause retrieval failures or inaccurate results, leading to silent data issues.

  2. Disabling caching entirely 70% fail

    Increases latency and API costs without solving root cause of version mismatch.