+*** Settings ***
+Suite Setup Rspamd Redis Setup
+Suite Teardown Rspamd Redis Teardown
+Library Process
+Library Collections
+Library ${RSPAMD_TESTDIR}/lib/rspamd.py
+Resource ${RSPAMD_TESTDIR}/lib/rspamd.robot
+Variables ${RSPAMD_TESTDIR}/lib/vars.py
+
+*** Variables ***
+${CONFIG} ${RSPAMD_TESTDIR}/configs/neural_forced_learn.conf
+${SPAM_MSG} ${RSPAMD_TESTDIR}/messages/spam.eml
+${HAM_MSG} ${RSPAMD_TESTDIR}/messages/ham.eml
+${REDIS_SCOPE} Suite
+${RSPAMD_SCOPE} Suite
+${RSPAMD_URL_TLD} ${RSPAMD_TESTDIR}/../lua/unit/test_tld.dat
+
+*** Test Cases ***
+Forced-learn minimal scan disables non-neural symbols
+ # A disable_symbols_input rule with forced_learn_minimal_scan=true: an
+ # ANN-Train scan must run the neural prefilter, which disables every non-neural
+ # symbol. SPAM_SYMBOL1 is a plain always-firing filter symbol, so it must NOT
+ # appear when ANN-Train is set, and MUST appear on a normal (full) scan.
+ Sleep 2s Wait for redis and initial check_anns
+ Scan File ${SPAM_MSG} ANN-Train=spam
+ Do Not Expect Symbol SPAM_SYMBOL1
+ Do Not Expect Symbol SPAM_SYMBOL2
+ Scan File ${SPAM_MSG}
+ Expect Symbol SPAM_SYMBOL1
+
+Minimal scan stores the vector under the providers-digest profile key
+ # The forced-learn scan above must have stored a training vector under the
+ # providers-digest key (rn_SHORT_default_<digest>_<ver>_spam_set), exactly the
+ # key the live full-scan path uses (disable_symbols_input keys on
+ # providers_digest, not on which symbols fired).
+ ${spam_set} = Get Neural Train Set spam
+ Should Not Be Empty ${spam_set} msg=no spam training set created by forced learn
+ ${n} = Redis SCARD ${spam_set}
+ Should Be True ${n} >= 1 msg=forced-learn scan did not store a training vector
+
+Minimal scan vector is byte-identical to the full-scan vector
+ # Re-scan the SAME message through the full pipeline (NEURAL_FORCED_LEARN_CHECK
+ # disabled at config-equivalent level is not needed: metatokens are
+ # symbols-independent, so the full-scan auto-learn vector equals the minimal
+ # one). Storing both into the same Redis SET must dedup to a single member —
+ # the byte-for-byte equivalence the feature guarantees.
+ ${spam_set} = Get Neural Train Set spam
+ # ANN-Train scan again (minimal path) — identical vector, dedups
+ Scan File ${SPAM_MSG} ANN-Train=spam
+ # Full-pipeline auto-learn of the same message — identical metatokens vector
+ Scan File ${SPAM_MSG}
+ Expect Symbol SPAM_SYMBOL1
+ Sleep 0.5s Let the async SADDs settle
+ ${n} = Redis SCARD ${spam_set}
+ Should Be Equal As Integers ${n} 1
+ ... msg=minimal-scan and full-scan vectors for the same message are not identical
+
+Forced-learn corpus trains the model
+ # Add one ham vector via a minimal ANN-Train scan: with max_trains=1 the
+ # balanced trigger now fires (1 spam + 1 ham) and the model trains from the
+ # symbols-independent corpus. Inference must then fire on both classes.
+ Scan File ${HAM_MSG} ANN-Train=ham
+ Do Not Expect Symbol HAM_SYMBOL1
+ Sleep 6s Wait for training to complete and ANN to be reloaded
+ Scan File ${SPAM_MSG} Settings={groups_enabled=["neural"];symbols_disabled=["NEURAL_LEARN"]}
+ Expect Symbol NEURAL_SPAM_SHORT
+ Scan File ${HAM_MSG} Settings={groups_enabled=["neural"];symbols_disabled=["NEURAL_LEARN"]}
+ Expect Symbol NEURAL_HAM_SHORT
+
+*** Keywords ***
+Get Neural Train Set
+ [Arguments] ${class}
+ # The training set keys use the rn_ prefix (ANN blobs/sets), distinct from the
+ # rn3_ profile zset. Return the first rn_SHORT_*_<class>_set key.
+ ${res} = Run Process redis-cli -h ${RSPAMD_REDIS_ADDR} -p ${RSPAMD_REDIS_PORT}
+ ... KEYS rn_SHORT_*_${class}_set
+ ${key} = Evaluate $res.stdout.strip().split('\\n')[0]
+ [Return] ${key}
+
+Redis SCARD
+ [Arguments] ${key}
+ ${res} = Run Process redis-cli -h ${RSPAMD_REDIS_ADDR} -p ${RSPAMD_REDIS_PORT}
+ ... SCARD ${key}
+ ${n} = Convert To Integer ${res.stdout.strip()}
+ [Return] ${n}
projects / thirdparty / rspamd.git / commitdiff
