* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
* fix(caching): pass only metadata to valkey semantic async embedding
ValkeySemanticCache async get/set passed **kwargs into _get_async_embedding,
which raised TypeError on cache_key and other fields and silently skipped
all cache writes. Match redis-semantic by forwarding metadata only.
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(caching): add async_get_cache embedding call regression test
Mirror the async_set_cache spy test so async_get_cache passing **kwargs
into _get_async_embedding is caught by a real signature, not AsyncMock.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Shivam Rawat <shivamrawat@Shivams-MacBook-Pro.local>
Co-authored-by: Cursor <cursoragent@cursor.com>
Adds a "valkey-semantic" cache type so semantic prompt caching can run
against Valkey clusters (for example AWS ElastiCache for Valkey) using the
valkey-search module.
The existing "redis-semantic" backend cannot drive valkey-search. RedisVL
gates the connection on a RediSearch module version that valkey-search does
not report, and its SemanticCache index declares the prompt as a TEXT field,
which valkey-search does not implement. ValkeySemanticCache therefore talks to
valkey-search directly over redis-py: it builds a vector index from the field
types valkey-search supports (TAG for caller scope, VECTOR for the prompt
embedding) and runs KNN queries for retrieval. Prompt extraction, embedding
generation, and cached-response parsing are reused from RedisSemanticCache
since those are backend agnostic. The redis dependency is imported lazily in
the cache dispatch so importing litellm without redis installed still works.
It also fixes semantic-cache scope keys so similarity matching works across
reworded prompts. get_cache_key() hashed messages / prompt / input into the
litellm_cache_key that every semantic backend filters its KNN search on, so a
paraphrase landed in a different bucket and never matched, even far above the
similarity threshold. For semantic cache types the prompt-bearing params are
now excluded from the scope key and the server-set tenant identity
(user_api_key, team, org) is appended instead, restoring embedding matching
within a tenant while keeping cache entries scoped to the authenticated
key / team / org. The three semantic backends share this key, so the same
change fixes redis-semantic and qdrant-semantic.
Connections resolve from VALKEY_HOST / VALKEY_PORT / VALKEY_PASSWORD, falling
back to REDIS_* for drop-in compatibility, and passwordless clusters (IAM or
no-auth) are supported.
Resolves#29121Fixes#29086
