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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 #29121 Fixes #29086
473 lines
15 KiB
Python
473 lines
15 KiB
Python
import hashlib
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import os
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import struct
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import subprocess
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import sys
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import textwrap
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from types import SimpleNamespace
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from unittest.mock import AsyncMock, MagicMock
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import pytest
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sys.path.insert(0, os.path.abspath("../../.."))
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from litellm.caching.valkey_semantic_cache import ValkeySemanticCache
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_REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
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def _make_cache(sync_client=None, async_client=None, similarity_threshold=0.8):
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return ValkeySemanticCache(
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similarity_threshold=similarity_threshold,
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index_name="test_index",
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sync_client=sync_client or MagicMock(),
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async_client=async_client or AsyncMock(),
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)
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def _search_result(distance, response='{"content": "Paris"}'):
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return SimpleNamespace(
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docs=[SimpleNamespace(response=response, vector_distance=str(distance))]
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)
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def test_build_valkey_url_prefers_valkey_env(monkeypatch):
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monkeypatch.setenv("REDIS_HOST", "redis-host")
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monkeypatch.setenv("REDIS_PORT", "6379")
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monkeypatch.setenv("REDIS_PASSWORD", "rpass")
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monkeypatch.setenv("VALKEY_HOST", "valkey-host")
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monkeypatch.setenv("VALKEY_PORT", "6380")
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monkeypatch.setenv("VALKEY_PASSWORD", "vpass")
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assert (
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ValkeySemanticCache._build_valkey_url(None, None, None)
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== "redis://:vpass@valkey-host:6380"
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)
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def test_build_valkey_url_supports_passwordless(monkeypatch):
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monkeypatch.delenv("REDIS_PASSWORD", raising=False)
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monkeypatch.delenv("VALKEY_PASSWORD", raising=False)
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monkeypatch.setenv("VALKEY_HOST", "valkey-host")
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monkeypatch.setenv("VALKEY_PORT", "6380")
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assert (
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ValkeySemanticCache._build_valkey_url(None, None, None)
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== "redis://valkey-host:6380"
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)
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def test_build_valkey_url_falls_back_to_redis_env(monkeypatch):
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monkeypatch.delenv("VALKEY_HOST", raising=False)
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monkeypatch.delenv("VALKEY_PORT", raising=False)
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monkeypatch.delenv("VALKEY_PASSWORD", raising=False)
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monkeypatch.setenv("REDIS_HOST", "redis-host")
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monkeypatch.setenv("REDIS_PORT", "6379")
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monkeypatch.setenv("REDIS_PASSWORD", "rpass")
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assert (
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ValkeySemanticCache._build_valkey_url(None, None, None)
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== "redis://:rpass@redis-host:6379"
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)
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def test_build_valkey_url_requires_host_and_port(monkeypatch):
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for var in (
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"VALKEY_HOST",
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"VALKEY_PORT",
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"VALKEY_PASSWORD",
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"REDIS_HOST",
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"REDIS_PORT",
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"REDIS_PASSWORD",
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):
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monkeypatch.delenv(var, raising=False)
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with pytest.raises(ValueError, match="Missing required Valkey configuration"):
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ValkeySemanticCache._build_valkey_url(None, None, None)
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def test_build_valkey_url_uses_rediss_scheme_when_ssl(monkeypatch):
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monkeypatch.setenv("VALKEY_HOST", "valkey-host")
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monkeypatch.setenv("VALKEY_PORT", "6379")
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monkeypatch.setenv("VALKEY_PASSWORD", "vpass")
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assert (
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ValkeySemanticCache._build_valkey_url(None, None, None, ssl=True)
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== "rediss://:vpass@valkey-host:6379"
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)
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assert ValkeySemanticCache._build_valkey_url(
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"h", "6379", None, ssl=False
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).startswith("redis://")
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def test_init_requires_similarity_threshold():
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with pytest.raises(ValueError, match="similarity_threshold must be provided"):
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ValkeySemanticCache(sync_client=MagicMock(), async_client=AsyncMock())
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def test_init_rejects_cluster_startup_nodes():
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with pytest.raises(ValueError, match="cluster-mode-enabled"):
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ValkeySemanticCache(
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similarity_threshold=0.8,
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startup_nodes=[{"host": "shard1", "port": 6379}],
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)
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def test_cache_dispatch_rejects_cluster_for_valkey_semantic():
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from litellm.caching.caching import Cache
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from litellm.types.caching import LiteLLMCacheType
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with pytest.raises(ValueError, match="cluster-mode-enabled"):
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Cache(
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type=LiteLLMCacheType.VALKEY_SEMANTIC,
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host="valkey-host",
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port="6379",
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similarity_threshold=0.8,
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redis_startup_nodes=[{"host": "shard1", "port": 6379}],
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)
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def test_scope_tag_is_deterministic_hex():
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tag = ValkeySemanticCache._scope_tag("model:gpt-4o::abc-123")
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assert tag == hashlib.sha256(b"model:gpt-4o::abc-123").hexdigest()
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assert len(tag) == 64
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assert ValkeySemanticCache._scope_tag("a") != ValkeySemanticCache._scope_tag("b")
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def test_embedding_to_bytes_is_little_endian_float32():
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assert ValkeySemanticCache._embedding_to_bytes([1.0, 0.0]) == struct.pack(
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"<2f", 1.0, 0.0
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)
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def test_set_cache_stores_scoped_doc_with_embedding(monkeypatch):
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sync_client = MagicMock()
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cache = _make_cache(sync_client=sync_client)
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cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])
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cache.set_cache(
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key="cache-key",
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value={"content": "Paris"},
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messages=[{"role": "user", "content": "What is the capital of France?"}],
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)
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sync_client.ft.return_value.create_index.assert_called_once()
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assert sync_client.hset.call_count == 1
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doc_key, kwargs = (
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sync_client.hset.call_args.args[0],
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sync_client.hset.call_args.kwargs,
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)
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mapping = kwargs["mapping"]
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scope = ValkeySemanticCache._scope_tag("cache-key")
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assert mapping[ValkeySemanticCache.CACHE_KEY_FIELD_NAME] == scope
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assert mapping["prompt"] == "What is the capital of France?"
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assert mapping["response"] == "{'content': 'Paris'}"
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assert mapping["embedding"] == struct.pack("<3f", 0.1, 0.2, 0.3)
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assert doc_key.startswith(f"test_index:{scope}:")
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def test_set_cache_applies_ttl():
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sync_client = MagicMock()
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cache = _make_cache(sync_client=sync_client)
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cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])
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cache.set_cache(
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key="cache-key",
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value={"content": "Paris"},
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messages=[{"role": "user", "content": "What is the capital of France?"}],
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ttl=60,
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)
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sync_client.expire.assert_called_once()
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assert sync_client.expire.call_args.args[1] == 60
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def test_set_cache_skips_ttl_when_absent():
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sync_client = MagicMock()
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cache = _make_cache(sync_client=sync_client)
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cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])
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cache.set_cache(
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key="cache-key",
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value={"content": "Paris"},
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messages=[{"role": "user", "content": "What is the capital of France?"}],
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)
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sync_client.expire.assert_not_called()
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def test_get_cache_returns_hit_above_threshold():
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sync_client = MagicMock()
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sync_client.ft.return_value.search.return_value = _search_result(0.1)
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cache = _make_cache(sync_client=sync_client, similarity_threshold=0.8)
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cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])
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metadata = {}
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result = cache.get_cache(
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key="cache-key",
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messages=[{"role": "user", "content": "capital of France?"}],
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metadata=metadata,
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)
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assert result == {"content": "Paris"}
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assert metadata["semantic-similarity"] == pytest.approx(0.9)
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def test_get_cache_misses_below_threshold():
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sync_client = MagicMock()
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sync_client.ft.return_value.search.return_value = _search_result(0.5)
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cache = _make_cache(sync_client=sync_client, similarity_threshold=0.8)
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cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])
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metadata = {}
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result = cache.get_cache(
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key="cache-key",
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messages=[{"role": "user", "content": "capital of Germany?"}],
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metadata=metadata,
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)
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assert result is None
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assert metadata["semantic-similarity"] == pytest.approx(0.5)
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def test_get_cache_misses_when_no_docs():
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sync_client = MagicMock()
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sync_client.ft.return_value.search.return_value = SimpleNamespace(docs=[])
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cache = _make_cache(sync_client=sync_client)
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cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])
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metadata = {}
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result = cache.get_cache(
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key="cache-key",
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messages=[{"role": "user", "content": "capital of France?"}],
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metadata=metadata,
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)
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assert result is None
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assert metadata["semantic-similarity"] == 0.0
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def test_get_cache_query_filters_by_scope_tag():
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sync_client = MagicMock()
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sync_client.ft.return_value.search.return_value = _search_result(0.1)
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cache = _make_cache(sync_client=sync_client)
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cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])
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cache.get_cache(
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key="cache-key",
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messages=[{"role": "user", "content": "capital of France?"}],
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metadata={},
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)
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query = sync_client.ft.return_value.search.call_args.args[0]
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scope = ValkeySemanticCache._scope_tag("cache-key")
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assert scope in query.query_string()
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assert "KNN 1 @embedding" in query.query_string()
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def _async_ft(search_distance):
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search_obj = SimpleNamespace(
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search=AsyncMock(return_value=_search_result(search_distance)),
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create_index=AsyncMock(),
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)
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return MagicMock(return_value=search_obj)
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@pytest.mark.asyncio
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async def test_async_set_and_get_roundtrip():
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async_client = AsyncMock()
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async_client.ft = _async_ft(0.05)
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cache = _make_cache(async_client=async_client, similarity_threshold=0.8)
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cache._get_async_embedding = AsyncMock(return_value=[0.1, 0.2, 0.3])
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await cache.async_set_cache(
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key="cache-key",
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value={"content": "Paris"},
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messages=[{"role": "user", "content": "What is the capital of France?"}],
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ttl=30,
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)
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async_client.hset.assert_awaited_once()
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async_client.expire.assert_awaited_once()
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assert async_client.expire.call_args.args[1] == 30
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metadata = {}
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result = await cache.async_get_cache(
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key="cache-key",
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messages=[{"role": "user", "content": "capital city of France"}],
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metadata=metadata,
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)
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assert result == {"content": "Paris"}
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assert metadata["semantic-similarity"] == pytest.approx(0.95)
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@pytest.mark.asyncio
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async def test_async_get_cache_misses_below_threshold():
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async_client = AsyncMock()
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async_client.ft = _async_ft(0.4)
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cache = _make_cache(async_client=async_client, similarity_threshold=0.8)
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cache._get_async_embedding = AsyncMock(return_value=[0.1, 0.2, 0.3])
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metadata = {}
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result = await cache.async_get_cache(
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key="cache-key",
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messages=[{"role": "user", "content": "capital of Germany?"}],
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metadata=metadata,
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)
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assert result is None
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assert metadata["semantic-similarity"] == pytest.approx(0.6)
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def test_ensure_index_swallows_already_exists():
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sync_client = MagicMock()
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sync_client.ft.return_value.create_index.side_effect = Exception(
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"Index test_index already exists."
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)
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cache = _make_cache(sync_client=sync_client)
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cache._ensure_index_sync(3)
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assert cache._index_dim == 3
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def test_ensure_index_reraises_unexpected_error():
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sync_client = MagicMock()
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sync_client.ft.return_value.create_index.side_effect = Exception(
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"connection refused"
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)
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cache = _make_cache(sync_client=sync_client)
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with pytest.raises(Exception, match="connection refused"):
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cache._ensure_index_sync(3)
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_FT_INFO_ATTRS_DIM_1536 = [
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[b"identifier", b"litellm_cache_key", b"type", b"TAG"],
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[
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b"identifier",
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b"embedding",
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b"type",
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b"VECTOR",
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b"index",
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[b"capacity", 10240, b"dimensions", 1536, b"distance_metric", b"COSINE"],
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],
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]
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def test_extract_index_dim_parses_nested_ft_info():
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info = {"attributes": _FT_INFO_ATTRS_DIM_1536}
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assert ValkeySemanticCache._extract_index_dim(info) == 1536
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assert ValkeySemanticCache._extract_index_dim({"attributes": []}) is None
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def test_ensure_index_raises_on_dimension_mismatch():
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sync_client = MagicMock()
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sync_client.ft.return_value.create_index.side_effect = Exception(
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"Index test_index already exists."
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)
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sync_client.ft.return_value.info.return_value = {
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"attributes": _FT_INFO_ATTRS_DIM_1536
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}
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cache = _make_cache(sync_client=sync_client)
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with pytest.raises(
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ValueError, match="already exists with embedding dimension 1536"
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):
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cache._ensure_index_sync(768)
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assert cache._index_dim is None
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def test_ensure_index_accepts_matching_existing_dimension():
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sync_client = MagicMock()
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sync_client.ft.return_value.create_index.side_effect = Exception(
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"Index test_index already exists."
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)
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sync_client.ft.return_value.info.return_value = {
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"attributes": _FT_INFO_ATTRS_DIM_1536
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}
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cache = _make_cache(sync_client=sync_client)
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cache._ensure_index_sync(1536)
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assert cache._index_dim == 1536
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def test_init_builds_only_missing_client_from_url():
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sync_client = MagicMock()
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cache = ValkeySemanticCache(
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similarity_threshold=0.8,
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redis_url="redis://valkey-host:6380",
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sync_client=sync_client,
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)
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assert cache.sync_client is sync_client
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assert cache.async_client is not None and cache.async_client is not sync_client
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def test_init_uses_both_injected_clients_without_connection_info(monkeypatch):
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for var in ("VALKEY_HOST", "VALKEY_PORT", "REDIS_HOST", "REDIS_PORT"):
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monkeypatch.delenv(var, raising=False)
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sync_client = MagicMock()
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async_client = AsyncMock()
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cache = ValkeySemanticCache(
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similarity_threshold=0.8,
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sync_client=sync_client,
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async_client=async_client,
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)
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assert cache.sync_client is sync_client
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assert cache.async_client is async_client
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def test_cache_dispatches_valkey_semantic_type():
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from litellm.caching.caching import Cache
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from litellm.types.caching import LiteLLMCacheType
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cache = Cache(
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type=LiteLLMCacheType.VALKEY_SEMANTIC,
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host="valkey-host",
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port="6380",
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similarity_threshold=0.8,
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)
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assert isinstance(cache.cache, ValkeySemanticCache)
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@pytest.mark.asyncio
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async def test_index_info_uses_valkey_ft_info():
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# The /health/readiness endpoint calls _index_info() on any
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# RedisSemanticCache instance; since ValkeySemanticCache subclasses it,
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# the inherited RedisVL implementation (which reads self.llmcache) would
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# break. This override must query valkey-search FT.INFO instead.
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async_client = AsyncMock()
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info_namespace = SimpleNamespace(info=AsyncMock(return_value={"num_docs": 3}))
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async_client.ft = MagicMock(return_value=info_namespace)
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cache = _make_cache(async_client=async_client)
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result = await cache._index_info()
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assert result == {"num_docs": 3}
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async_client.ft.assert_called_once_with("test_index")
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def test_importing_caching_does_not_require_redis():
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# redis is an optional dependency (extra_proxy), so the base SDK can be
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# installed without it. Selecting valkey-semantic needs redis, but merely
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# importing litellm.caching.caching must not, or `import litellm` breaks for
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# every base-SDK user. This runs in a subprocess with redis blocked so the
|
|
# check is not polluted by redis already being imported in this session.
|
|
code = textwrap.dedent("""
|
|
import sys
|
|
for name in ("redis", "redis.asyncio", "redis.commands",
|
|
"redis.commands.search"):
|
|
sys.modules[name] = None
|
|
import litellm.caching.caching # must not import redis at module top
|
|
from litellm.types.caching import LiteLLMCacheType
|
|
assert LiteLLMCacheType.VALKEY_SEMANTIC == "valkey-semantic"
|
|
print("ok")
|
|
""")
|
|
result = subprocess.run(
|
|
[sys.executable, "-c", code],
|
|
capture_output=True,
|
|
text=True,
|
|
env={**os.environ, "PYTHONPATH": _REPO_ROOT},
|
|
)
|
|
assert result.returncode == 0, result.stderr
|
|
assert "ok" in result.stdout
|