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* fix(token_counter): stop re-downloading a HuggingFace tokenizer after a failed download Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(tokenizer): isolate HuggingFace tokenizer caches Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(token_counter): clear the traceback before re-raising a cached tokenizer load error Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(token_counter): retry failed HuggingFace tokenizer loads after the cache window Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(token_counter): start tokenizer retry TTL after a failure Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(utils): lock every HuggingFace tokenizer load and keep load failures as values Concurrent llama-2, llama-3 and cohere counts each started their own failed download because only the bundled tokenizers took a load lock. Every tokenizer kind now has one, so a burst during an outage makes a single attempt and two counts can no longer read the same expiring entry at once. A failed load is now remembered as a small value with the error message instead of the error object, so re-raising it no longer keeps the first failing request's frames alive. Adds tests for the concurrent outage and for fork-guard errors staying raised and unremembered. * fix(utils): keep Hugging Face tokenizer downloads unlocked and warn once per failed download Reverts the per-kind load lock so concurrent first downloads still run in parallel, as #47353 intends. A short lock around the failure cache read and write stops two counts racing on an expiring entry, and the tiktoken fallback warning now fires only when a download fails, not on every count that reuses the remembered failure. * fix(caching): read an entry another reader just expired as a cache miss Two threads reading the same expiring key in InMemoryCache could both pass the membership check, and the second then raised KeyError after the first removed it. get_cache now treats that key as a miss. This replaces the extra lock the tokenizer failure cache took, and the regression test forces the interleaving without timing. * fix(caching): treat a TTL another reader just removed as not expired InMemoryCache's expiry check read the TTL after a separate membership check, so a reader racing another reader's eviction raised KeyError. It now reads the TTL once and treats a missing one as not expired, and get_cache already reads the vanished entry as a miss. --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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|---|---|---|
| .. | ||
| __init__.py | ||
| _embedding_router.py | ||
| _internal_lru_cache.py | ||
| affinity_cache.py | ||
| azure_blob_cache.py | ||
| base_cache.py | ||
| caching.py | ||
| caching_handler.py | ||
| disk_cache.py | ||
| dual_cache.py | ||
| evicted_client_closer.py | ||
| gcs_cache.py | ||
| in_memory_cache.py | ||
| llm_caching_handler.py | ||
| qdrant_semantic_cache.py | ||
| Readme.md | ||
| redis_batch.py | ||
| redis_cache.py | ||
| redis_cluster_cache.py | ||
| redis_cluster_node_isolation.py | ||
| redis_semantic_cache.py | ||
| s3_cache.py | ||
| valkey_semantic_cache.py | ||
Caching on LiteLLM
LiteLLM supports multiple caching mechanisms. This allows users to choose the most suitable caching solution for their use case.
The following caching mechanisms are supported:
- RedisCache
- RedisSemanticCache
- QdrantSemanticCache
- InMemoryCache
- DiskCache
- S3Cache
- AzureBlobCache
- DualCache (updates both Redis and an in-memory cache simultaneously)
Folder Structure
litellm/caching/
├── base_cache.py
├── caching.py
├── caching_handler.py
├── disk_cache.py
├── dual_cache.py
├── in_memory_cache.py
├── qdrant_semantic_cache.py
├── redis_cache.py
├── redis_semantic_cache.py
├── s3_cache.py