API_URL, API_TIMEOUT #123
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API_URL, API_TIMEOUT #123
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Linux debug test results 8 files 8 suites 12m 52s ⏱️ Results for commit 977e9c0. ♻️ This comment has been updated with latest results. |
Windows test results 5 files 5 suites 18m 0s ⏱️ For more details on these failures, see this check. Results for commit 977e9c0. ♻️ This comment has been updated with latest results. |
Linux release test results 8 files 8 suites 6m 59s ⏱️ For more details on these failures, see this check. Results for commit 977e9c0. ♻️ This comment has been updated with latest results. |
… win test failures
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Related issues: - manticoresoftware/manticoresearch#3771 - manticoresoftware/manticoresearch#3869 The EmbedLib struct layout changed which is a breaking change in the embeddings library. Additional changes: - Added git commit ID and timestamp to the build process for consistent versioning across libraries. - Moved API key validation to CREATE TABLE. - Improved error handling for remote model requests, including detailed HTTP error reporting. - Updated tests to reflect changes in API key validation and model initialization.
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donhardman
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Dec 18, 2025
| validate_api_key(api_key).map_err(|_| LibError::RemoteInvalidAPIKey)?; | ||
| validate_model(&model).map_err(|_| LibError::RemoteUnsupportedModel { status: None })?; | ||
| // Only validate basic requirements (non-empty, no whitespace) | ||
| // Real validation happens via actual API request in validate_api_key() |
Contributor
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If the real validation appears in validate_api_key, what is the point of having this one that is not real? Creating multiple functions of validation for nothing is better to stick to one validation flow instead of overly complex multiple functions that split validation for no reason.
| std::string m_sCachePath; | ||
| std::string m_sAPIKey; | ||
| std::string m_sAPIUrl; | ||
| int m_iAPITimeout = 0; // 0 means use default (10 seconds), positive value is timeout in seconds |
Contributor
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This is confusing. Why do we set 0 and say 0 means default 10 seconds? If we set the default to 10, we should set 10; 0 is the common pattern for no limit at all.
Collaborator
Author
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From the daemon's point of view, the default value is 0. It is set to 10 only in the Rust part. That said, it's fine to change it if you think it's better to control it here.
|
|
||
| impl JinaModel { | ||
| pub fn new(model_id: &str, api_key: &str) -> Result<Self, Box<dyn std::error::Error>> { | ||
| pub fn new( |
Contributor
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I think that as for api_* related to all the same config structure, instead of adding multiple arguments, it is more convenient to make a structure like ApiConfig: Option<...> that will contain key, URL, timeout, and whatever else is required. This way, the method signature will have less complexity regarding input variables.
| validate_model(&model).map_err(|_| LibError::RemoteUnsupportedModel { status: None })?; | ||
| // Only validate basic requirements (non-empty, no whitespace) | ||
| // Real validation happens via actual API request in validate_api_key() | ||
| validate_api_key_basic(api_key) |
Contributor
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This still sounds weird. We should stick with simple validate_api_key and call it in one place, and not make two separate spaghetti methods that do the same thing but in different ways. Code should be clean.
| free_vec_result: FreeVecResultFn, | ||
| get_hidden_size: GetLenFn, | ||
| get_max_input_size: GetLenFn, | ||
| validate_api_key: ValidateApiKeyFn, |
Contributor
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So we introduce the ETERNAL method into FFI for validating the API key, which will require an update to the C++ code to run validation when the API key is present and react to it. It is still acceptable, but it would be better to encapsulate it into model creation and do all validations there to make the client simpler.
clt❌ CLT tests in Failed tests:🔧 Edit failed tests in UI:
test/clt-tests/mcl/auto-embeddings-backup-restore.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd $SEARCHD_FLAGS > /dev/null; if timeout 10 grep -qm1 '\[BUDDY\] started' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Buddy started!'; else echo 'Timeout or failed!'; cat /var/log/manticore/searchd.log;fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_backup (
title TEXT,
content TEXT,
status INTEGER,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title, content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_backup (id, title, content, status) VALUES
(1, 'machine learning', 'neural networks', 1),
(2, 'deep learning', 'transformers', 1),
(3, 'computer vision', 'image processing', 2)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_backup; OPTIMIZE TABLE test_backup OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_backup WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
+------+
| id |
+------+
| 1 |
| 3 |
- | 2 |
+ +------+
- +------+
––– input –––
mysql -h0 -P9306 -E -e "SELECT id, title, content, KNN_DIST() as distance FROM test_backup WHERE KNN(vec, 3, 'artificial intelligence') ORDER BY distance"
––– output –––
OK
––– input –––
manticore-backup --version | grep -c "Manticore Backup"
––– output –––
OK
––– input –––
mkdir -p /tmp/backup && chmod 777 /tmp/backup; echo $?
––– output –––
OK
––– input –––
manticore-backup --backup-dir=/tmp/backup --tables=test_backup 2>&1 | grep -c "Backing up table"
––– output –––
OK
––– input –––
ls -d /tmp/backup/backup-* | wc -l
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FREEZE test_backup"
––– output –––
+-----------------------------------------------------+-----------------------------------------------------+
| file | normalized |
+-----------------------------------------------------+-----------------------------------------------------+
| /var/lib/manticore/test_backup/test_backup.0.spc | /var/lib/manticore/test_backup/test_backup.0.spc |
| /var/lib/manticore/test_backup/test_backup.0.spd | /var/lib/manticore/test_backup/test_backup.0.spd |
| /var/lib/manticore/test_backup/test_backup.0.spds | /var/lib/manticore/test_backup/test_backup.0.spds |
| /var/lib/manticore/test_backup/test_backup.0.spe | /var/lib/manticore/test_backup/test_backup.0.spe |
| /var/lib/manticore/test_backup/test_backup.0.sph | /var/lib/manticore/test_backup/test_backup.0.sph |
| /var/lib/manticore/test_backup/test_backup.0.sphi | /var/lib/manticore/test_backup/test_backup.0.sphi |
| /var/lib/manticore/test_backup/test_backup.0.spi | /var/lib/manticore/test_backup/test_backup.0.spi |
- | /var/lib/manticore/test_backup/test_backup.0.spidx | /var/lib/manticore/test_backup/test_backup.0.spidx |
+ | /var/lib/manticore/test_backup/test_backup.0.spknn | /var/lib/manticore/test_backup/test_backup.0.spknn |
- | /var/lib/manticore/test_backup/test_backup.0.spknn | /var/lib/manticore/test_backup/test_backup.0.spknn |
+ | /var/lib/manticore/test_backup/test_backup.0.spm | /var/lib/manticore/test_backup/test_backup.0.spm |
- | /var/lib/manticore/test_backup/test_backup.0.spm | /var/lib/manticore/test_backup/test_backup.0.spm |
+ | /var/lib/manticore/test_backup/test_backup.0.spp | /var/lib/manticore/test_backup/test_backup.0.spp |
- | /var/lib/manticore/test_backup/test_backup.0.spp | /var/lib/manticore/test_backup/test_backup.0.spp |
+ | /var/lib/manticore/test_backup/test_backup.0.spt | /var/lib/manticore/test_backup/test_backup.0.spt |
- | /var/lib/manticore/test_backup/test_backup.0.spt | /var/lib/manticore/test_backup/test_backup.0.spt |
+ | /var/lib/manticore/test_backup/test_backup.meta | /var/lib/manticore/test_backup/test_backup.meta |
- | /var/lib/manticore/test_backup/test_backup.meta | /var/lib/manticore/test_backup/test_backup.meta |
+ | /var/lib/manticore/test_backup/test_backup.settings | /var/lib/manticore/test_backup/test_backup.settings |
- | /var/lib/manticore/test_backup/test_backup.settings | /var/lib/manticore/test_backup/test_backup.settings |
+ +-----------------------------------------------------+-----------------------------------------------------+
- +-----------------------------------------------------+-----------------------------------------------------+
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_backup"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_backup (id, title, content, status) VALUES (4, 'frozen insert', 'test data', 3)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "UNFREEZE test_backup"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_backup"
––– output –––
OK
––– input –––
mysqldump -h0 -P9306 manticore test_backup > /tmp/logical_backup.sql 2>/dev/null; echo $?
––– output –––
OK
––– input –––
grep -c "INSERT INTO" /tmp/logical_backup.sql
––– output –––
OK
––– input –––
searchd --stopwait > /dev/null 2>&1; echo $?
––– output –––
OK
––– input –––
rm -f /etc/manticoresearch/manticore.conf; rm -rf /var/lib/manticore/*; echo "Cleaned for restore"
––– output –––
OK
––– input –––
manticore-backup --backup-dir=/tmp/backup --restore 2>&1 | grep -c "backup-"
––– output –––
OK
––– input –––
BACKUP_NAME=$(manticore-backup --backup-dir=/tmp/backup --restore 2>&1 | grep backup- | awk '{print $1}' | head -1)
manticore-backup --backup-dir=/tmp/backup --restore=$BACKUP_NAME 2>&1 | grep -c "Starting to restore"
––– output –––
- 1
+ 0
––– input –––
searchd > /dev/null 2>&1; echo $?
––– output –––
- 0
+ 1
––– input –––
echo "Waiting for searchd to start"; sleep 3
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_backup"
––– output –––
- +----------+
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
- | count(*) |
- +----------+
- | 3 |
- +----------+
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_backup; OPTIMIZE TABLE test_backup OPTION sync=1, cutoff=1"; echo $?
––– output –––
- 0
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
+ 1
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_backup WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
- +------+
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
- | id |
- +------+
- | 1 |
- | 3 |
- | 2 |
- +------+
––– input –––
mysql -h0 -P9306 -e "ALTER TABLE test_backup ADD COLUMN new_field INTEGER"; echo $?
––– output –––
- 0
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
+ 1
––– input –––
mysql -h0 -P9306 -e "DESC test_backup" | grep "new_field"
––– output –––
- | new_field | uint | columnar fast_fetch |
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_copy (
title TEXT,
content TEXT,
status INTEGER,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title, content'
) engine='columnar'"; echo $?
––– output –––
- 0
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
+ 1
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_copy (id, title, content, status) VALUES
(1, 'machine learning', 'neural networks', 1),
(2, 'deep learning', 'transformers', 1),
(3, 'computer vision', 'image processing', 2),
(4, 'frozen insert', 'test data', 3)"; echo $?
––– output –––
- 0
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
+ 1
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_copy"
––– output –––
- +----------+
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
- | count(*) |
- +----------+
- | 4 |
- +----------+
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_copy WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
- +------+
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
- | id |
- +------+
- | 1 |
- | 3 |
- | 2 |
- | 4 |
- +------+
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_copy; OPTIMIZE TABLE test_copy OPTION sync=1, cutoff=1"; echo $?
––– output –––
- 0
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
+ 1
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_copy WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
- +------+
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
- | id |
- +------+
- | 1 |
- | 3 |
- | 2 |
- | 4 |
- +------+test/clt-tests/mcl/auto-embeddings-hnsw-configs.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd $SEARCHD_FLAGS > /dev/null; if timeout 10 grep -qm1 '\[BUDDY\] started' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Buddy started!'; else echo 'Timeout or failed!'; cat /var/log/manticore/searchd.log;fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_l2 (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_cosine (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='cosine'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_ip (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='ip'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
for table in test_l2 test_cosine test_ip; do
mysql -h0 -P9306 -e "INSERT INTO $table (id, content) VALUES
(1, 'machine learning'),
(2, 'deep learning'),
(3, 'cooking recipes')" 2>/dev/null
done
echo "Data inserted into all tables"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_l2; OPTIMIZE TABLE test_l2 OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_cosine; OPTIMIZE TABLE test_cosine OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_ip; OPTIMIZE TABLE test_ip OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -E -e "SELECT id FROM test_l2 WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
*************************** 1. row ***************************
id: 1
*************************** 2. row ***************************
id: 2
- *************************** 3. row ***************************
- id: 3
––– input –––
mysql -h0 -P9306 -E -e "SELECT id FROM test_cosine WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
*************************** 1. row ***************************
id: 1
*************************** 2. row ***************************
id: 2
- *************************** 3. row ***************************
- id: 3
––– input –––
mysql -h0 -P9306 -E -e "SELECT id FROM test_ip WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
*************************** 1. row ***************************
id: 1
*************************** 2. row ***************************
id: 2
- *************************** 3. row ***************************
- id: 3
––– input –––
echo "L2 (Euclidean) distances:"
mysql -h0 -P9306 -E -e "SELECT id, content, KNN_DIST() as distance FROM test_l2 WHERE KNN(vec, 3, 'neural networks') ORDER BY distance"
––– output –––
OK
––– input –––
echo "Cosine similarity distances (smaller = more similar):"
mysql -h0 -P9306 -E -e "SELECT id, content, KNN_DIST() as distance FROM test_cosine WHERE KNN(vec, 3, 'neural networks') ORDER BY distance"
––– output –––
OK
––– input –––
echo "Inner product distances (smaller = more similar):"
mysql -h0 -P9306 -E -e "SELECT id, content, KNN_DIST() as distance FROM test_ip WHERE KNN(vec, 3, 'neural networks') ORDER BY distance"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_hnsw_m4 (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
HNSW_M='4'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_hnsw_m32 (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
HNSW_M='32'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_hnsw_ef (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
HNSW_M='16'
HNSW_EF_CONSTRUCTION='500'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
for table in test_hnsw_m4 test_hnsw_m32 test_hnsw_ef; do
mysql -h0 -P9306 -e "INSERT INTO $table (id, content) VALUES (1, 'test document')" 2>/dev/null
done
echo "HNSW configurations test completed"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -E -e "SELECT COUNT(*) FROM test_hnsw_m4"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -E -e "SELECT COUNT(*) FROM test_hnsw_m32"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -E -e "SELECT COUNT(*) FROM test_hnsw_ef"
––– output –––
OK
––– input –––
for i in {1..10}; do
mysql -h0 -P9306 -e "INSERT INTO test_hnsw_m4 (content) VALUES ('document number $i with various content')" 2>/dev/null
mysql -h0 -P9306 -e "INSERT INTO test_hnsw_m32 (content) VALUES ('document number $i with various content')" 2>/dev/null
done
echo "Additional documents inserted for performance comparison"
––– output –––
OK
––– input –––
echo "Search with M=4 (faster, less connections):"
mysql -h0 -P9306 -E -e "SELECT id FROM test_hnsw_m4 WHERE KNN(vec, 3, 'document content') LIMIT 3"
––– output –––
OK
––– input –––
echo "Search with M=32 (slower, more connections, potentially more accurate):"
mysql -h0 -P9306 -E -e "SELECT id FROM test_hnsw_m32 WHERE KNN(vec, 3, 'document content') LIMIT 3"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_invalid_metric (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='invalid_metric'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'" 2>&1
––– output –––
OKtest/clt-tests/mcl/auto-embeddings-endpoints.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd --stopwait > /dev/null; stdbuf -oL searchd ${SEARCHD_ARGS:-} > /dev/null
––– output –––
OK
––– input –––
if timeout 10 grep -qm1 'accepting connections' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Accepting connections!'; else echo 'Timeout or failed!'; fi
––– output –––
OK
––– input –––
apt-get install jq -y > /dev/null; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE emb_test (
id BIGINT,
title TEXT,
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title, content'
)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO emb_test (id, title, content) VALUES
(1, 'machine learning', 'neural networks and deep learning'),
(2, 'computer vision', 'image recognition and processing'),
(3, 'natural language', 'text analysis and understanding')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK emb_test; OPTIMIZE TABLE emb_test OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM emb_test WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
+----------+
| count(*) |
+----------+
- | 3 |
+ | 2 |
+----------+
––– input –––
curl -s "http://localhost:9308/cli?select%20id,%20title%20from%20emb_test%20where%20knn(vec,%202,%20'artificial%20intelligence')" | grep -v 'rows in set'
––– output –––
+----+------------------+
| id | title |
+----+------------------+
| 1 | machine learning |
| 2 | computer vision |
- | 3 | natural language |
+ +----+------------------+
- +----+------------------+
––– input –––
curl -s "http://localhost:9308/cli_json?select%20id,%20title,%20@knn_dist%20from%20emb_test%20where%20knn(vec,%201,%20'learning')" | jq -r '.[0].data[0] | "ID: \(.id)\nTitle: \(.title)\nDistance: \(.["@knn_dist"] | tostring)"'
––– output –––
OK
––– input –––
curl -s -X POST "http://localhost:9308/sql?mode=raw" -d "select count(*) from emb_test where knn(vec, 2, 'neural networks')" | jq -r '.[0].data[0]."count(*)"'
––– output –––
- 3
+ 2
––– input –––
curl -s -X POST http://localhost:9308/insert -d '{"index":"emb_test","id":10,"doc":{"title":"quantum computing","content":"quantum algorithms"}}' | jq -r '.created'
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/search -d '{"index":"emb_test","knn":{"field":"vec","query":"quantum","k":1}}' | jq -r '.hits.hits[0]._source.title'
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/search -d '{"index":"emb_test","knn":{"field":"vec","query_text":"quantum","k":1}}'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE chunk_test (
id BIGINT,
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO chunk_test (id, title) VALUES
(1, 'machine learning'),
(2, 'deep learning'),
(3, 'reinforcement learning')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM chunk_test WHERE KNN(vec, 1, 'learning')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK chunk_test; OPTIMIZE TABLE chunk_test OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM chunk_test WHERE KNN(vec, 1, 'learning')"
––– output –––
+----------+
| count(*) |
+----------+
- | 3 |
+ | 1 |
+----------+test/clt-tests/mcl/auto-embeddings-json-api.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd $SEARCHD_FLAGS > /dev/null; if timeout 10 grep -qm1 '\[BUDDY\] started' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Buddy started!'; else echo 'Timeout or failed!'; cat /var/log/manticore/searchd.log;fi
––– output –––
OK
––– input –––
apt-get install jq -y > /dev/null; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_json_columnar (
title TEXT,
content TEXT,
embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title, content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SHOW CREATE TABLE test_json_columnar" | grep -o "knn_dims='384'"
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/insert -d '{"index":"test_json_columnar","id":1,"doc":{"title":"machine learning","content":"neural networks"}}' | jq -r 'if ._id then ._id else "inserted" end'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_json_columnar WHERE KNN(embedding, 1, 'machine learning neural networks')"
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/bulk -H "Content-Type: application/x-ndjson" -d '
{"insert":{"index":"test_json_columnar","id":2,"doc":{"title":"computer vision","content":"image recognition"}}}
{"insert":{"index":"test_json_columnar","id":3,"doc":{"title":"NLP","content":"text processing"}}}
' | jq '{created: .items[0].bulk.created}'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_json_columnar WHERE id IN (2,3)"
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/replace -d '{"index":"test_json_columnar","id":1,"doc":{"title":"updated ML","content":"updated networks"}}' | jq -r '.result'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT title FROM test_json_columnar WHERE id=1 AND KNN(embedding, 1, 'updated ML networks')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_json_columnar (id, title, content) VALUES (100, 'test', 'data')";
curl -s -X POST http://localhost:9308/insert -d '{"index":"test_json_columnar","id":101,"doc":{"title":"test","content":"data"}}' > /dev/null
––– output –––
OK
––– input –––
mysql -h0 -P9306 --batch --skip-column-names -e "SELECT embedding FROM test_json_columnar WHERE id=100" > /tmp/v1.txt
mysql -h0 -P9306 --batch --skip-column-names -e "SELECT embedding FROM test_json_columnar WHERE id=101" > /tmp/v2.txt
diff -q /tmp/v1.txt /tmp/v2.txt > /dev/null && echo "Vectors identical" || echo "Vectors differ"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_json_columnar"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_json_columnar; OPTIMIZE TABLE test_json_columnar OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
VECTOR=$(python3 -c "print(','.join(['0.01']*384))")
curl -s -X POST http://localhost:9308/search -d "{\"index\":\"test_json_columnar\",\"knn\":{\"field\":\"embedding\",\"query_vector\":[$VECTOR],\"k\":2}}" | jq -r '.hits.total // "0"'
––– output –––
- 5
+ 2
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE no_auto_embed (title TEXT, vec FLOAT_VECTOR KNN_TYPE='hnsw' KNN_DIMS='384' HNSW_SIMILARITY='l2') engine='columnar'"
––– output –––
OK
––– input –––
VECTOR=$(python3 -c "print(','.join(['0.5']*384))")
curl -s -X POST http://localhost:9308/insert -d "{\"index\":\"no_auto_embed\",\"id\":1,\"doc\":{\"title\":\"test\",\"vec\":[$VECTOR]}}" | jq -r 'if ._id then ._id else "inserted" end'
––– output –––
OK
––– input –––
QUERY_VEC=$(python3 -c "print(','.join(['0.5']*384))")
curl -s -X POST http://localhost:9308/search -d "{\"index\":\"no_auto_embed\",\"knn\":{\"field\":\"vec\",\"query_vector\":[$QUERY_VEC],\"k\":1}}" | jq -r '.hits.total // "0"'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_json_rowwise (
title TEXT,
content TEXT,
embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title, content'
) engine='rowwise'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SHOW CREATE TABLE test_json_rowwise" | grep -o "knn_dims='384'"
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/insert -d '{"index":"test_json_rowwise","id":1,"doc":{"title":"machine learning","content":"neural networks"}}' | jq -r 'if ._id then ._id else "inserted" end'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_json_rowwise WHERE KNN(embedding, 1, 'machine learning neural networks')"
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/bulk -H "Content-Type: application/x-ndjson" -d '
{"insert":{"index":"test_json_rowwise","id":2,"doc":{"title":"computer vision","content":"image recognition"}}}
{"insert":{"index":"test_json_rowwise","id":3,"doc":{"title":"NLP","content":"text processing"}}}
' | jq '{created: .items[0].bulk.created}'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_json_rowwise WHERE id IN (2,3)"
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/replace -d '{"index":"test_json_rowwise","id":1,"doc":{"title":"updated ML","content":"updated networks"}}' | jq -r '.result'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT title FROM test_json_rowwise WHERE id=1 AND KNN(embedding, 1, 'updated ML networks')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_json_rowwise (id, title, content) VALUES (100, 'test', 'data')";
curl -s -X POST http://localhost:9308/insert -d '{"index":"test_json_rowwise","id":101,"doc":{"title":"test","content":"data"}}' > /dev/null
––– output –––
OK
––– input –––
mysql -h0 -P9306 --batch --skip-column-names -e "SELECT embedding FROM test_json_rowwise WHERE id=100" > /tmp/v1.txt
mysql -h0 -P9306 --batch --skip-column-names -e "SELECT embedding FROM test_json_rowwise WHERE id=101" > /tmp/v2.txt
diff -q /tmp/v1.txt /tmp/v2.txt > /dev/null && echo "Vectors identical" || echo "Vectors differ"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_json_rowwise"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_json_rowwise; OPTIMIZE TABLE test_json_rowwise OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
VECTOR=$(python3 -c "print(','.join(['0.01']*384))")
curl -s -X POST http://localhost:9308/search -d "{\"index\":\"test_json_rowwise\",\"knn\":{\"field\":\"embedding\",\"query_vector\":[$VECTOR],\"k\":2}}" | jq -r '.hits.total // "0"'
––– output –––
- 5
+ 2test/clt-tests/mcl/auto-embeddings-dml-test.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd $SEARCHD_FLAGS > /dev/null; if timeout 10 grep -qm1 '\[BUDDY\] started' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Buddy started!'; else echo 'Timeout or failed!'; cat /var/log/manticore/searchd.log;fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_delete ( title TEXT, embedding_vector FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2' FROM='title' ) engine='rowwise'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_delete (id, title) VALUES (1, 'One')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DELETE FROM test_delete WHERE id = 1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_delete"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_delete (id, title) VALUES (2,'Two'),(3,'Three'),(4,'Four'),(5,'Five')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DELETE FROM test_delete WHERE id IN (2,3,4)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_delete"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DROP TABLE IF EXISTS test_replace"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_replace ( title TEXT, price INTEGER, vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2' FROM='title' ) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_replace (id, title, price) VALUES (1, 'Original', 100)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "REPLACE INTO test_replace (id, title, price) VALUES (1, 'Updated', 200)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT title, price FROM test_replace WHERE id = 1"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DROP TABLE IF EXISTS test_vector_regen"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_vector_regen ( content TEXT, vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='cosine' MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2' FROM='content')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_vector_regen (id, content) VALUES (1,'AI and ML'),(2,'Deep Learning'),(3,'Cooking')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_vector_regen; OPTIMIZE TABLE test_vector_regen OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_vector_regen WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
+------+
| id |
+------+
| 1 |
| 2 |
- | 3 |
+ +------+
- +------+
––– input –––
mysql -h0 -P9306 -e "REPLACE INTO test_vector_regen (id, content) VALUES (1, 'Cooking recipes')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_vector_regen; OPTIMIZE TABLE test_vector_regen OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_vector_regen WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
+------+
| id |
+------+
| 2 |
| 3 |
- | 1 |
+ +------+
- +------+
––– input –––
mysql -h0 -P9306 -e "TRUNCATE TABLE test_vector_regen"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_vector_regen"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DROP TABLE IF EXISTS test_bulk"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_bulk ( content TEXT, vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2' FROM='content' ) engine='rowwise'"; echo $?
––– output –––
OK
––– input –––
for i in {1..50}; do
mysql -h0 -P9306 -e "INSERT INTO test_bulk (id, content) VALUES ($i, 'Document $i')" 2>/dev/null
done
echo "Inserted 50 records"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DELETE FROM test_bulk WHERE id <= 10"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DELETE FROM test_bulk WHERE id >= 40"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_bulk"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DROP TABLE IF EXISTS test_multi_vec"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_multi_vec ( title TEXT, description TEXT, title_vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2' FROM='title', desc_vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='cosine' MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2' FROM='description' ) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_multi_vec (id, title, description) VALUES (1,'Title1','Desc1'),(2,'Title2','Desc2')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DELETE FROM test_multi_vec WHERE id=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "REPLACE INTO test_multi_vec (id, title, description) VALUES (2,'NewTitle','NewDesc')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id, title FROM test_multi_vec"
––– output –––
OKtest/clt-tests/mcl/auto-embeddings-voyage-remote.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd --stopwait > /dev/null; stdbuf -oL searchd ${SEARCHD_ARGS:-} > /dev/null
––– output –––
OK
––– input –––
if timeout 10 grep -qm1 'accepting connections' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Accepting connections!'; else echo 'Timeout or failed!'; fi
––– output –––
OK
––– input –––
cosine_similarity() {
local file1="$1" file2="$2"
awk '
NR==FNR { a[NR]=$1; suma2+=$1*$1; next }
{
dot += a[FNR]*$1
sumb2 += $1*$1
}
END {
print dot / (sqrt(suma2) * sqrt(sumb2))
}' "$file1" "$file2"
}
––– output –––
OK
––– input –––
export -f cosine_similarity
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_invalid_model (title TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'voyage/invalid-model-name-12345' FROM = 'title') " 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_valid_model_no_api_key (title TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'voyage/voyage-3.5-lite' FROM = 'title') " 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_voyage_remote (title TEXT, content TEXT, description TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'voyage/voyage-3.5-lite' FROM = 'title, content' API_KEY='${VOYAGE_API_KEY}') "; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -E -e "SHOW CREATE TABLE test_voyage_remote"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_voyage_remote (id, title, content, description) VALUES(1, 'machine learning algorithms', 'deep neural networks and artificial intelligence', 'advanced AI research')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) as record_count FROM test_voyage_remote WHERE id=1"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_voyage_remote (id, title, content, description) VALUES(2, 'machine learning algorithms', 'deep neural networks and artificial intelligence', 'different description')"
mysql -h0 -P9306 -e "SELECT embedding FROM test_voyage_remote WHERE id=1" | \
grep -v embedding | \
sed 's/[0-9]\+\(\.[0-9]\+\)\?/\n&\n/g' | \
grep -E '^[0-9]+(\.[0-9]+)?$' | \
awk '{printf "%.5f\n", $1}' > /tmp/vector1.txt
mysql -h0 -P9306 -e "SELECT embedding FROM test_voyage_remote WHERE id=2" | \
grep -v embedding | \
sed 's/[0-9]\+\(\.[0-9]\+\)\?/\n&\n/g' | \
grep -E '^[0-9]+(\.[0-9]+)?$' | \
awk '{printf "%.5f\n", $1}' > /tmp/vector2.txt
SIMILARITY=$(cosine_similarity /tmp/vector1.txt /tmp/vector2.txt)
echo "Cosine similarity: $SIMILARITY"
RESULT=$(awk -v sim="$SIMILARITY" 'BEGIN {
if (sim > 0.99)
print "SUCCESS: Same FROM fields produce similar vectors (similarity: " sim ")"
else
print "FAIL: Different vectors (FROM does not include description field and should not change generated vector value) (similarity: " sim ")"
}')
echo "$RESULT"
rm -f /tmp/vector1.txt /tmp/vector2.txt
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_voyage_title_only (title TEXT, content TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'voyage/voyage-3.5-lite' FROM = 'title' API_KEY='${VOYAGE_API_KEY}') "; mysql -h0 -P9306 -e "INSERT INTO test_voyage_title_only (id, title, content) VALUES(1, 'machine learning algorithms', 'completely different content here')"; MD5_MULTI=$(mysql -h0 -P9306 -e "SELECT embedding FROM test_voyage_remote WHERE id=1" | grep -v embedding | md5sum | awk '{print $1}'); MD5_SINGLE=$(mysql -h0 -P9306 -e "SELECT embedding FROM test_voyage_title_only WHERE id=1" | grep -v embedding | md5sum | awk '{print $1}'); echo "multi_field_md5: $MD5_MULTI"; echo "single_field_md5: $MD5_SINGLE"; if [ "$MD5_MULTI" != "$MD5_SINGLE" ]; then echo "SUCCESS: Different FROM specifications produce different vectors"; else echo "INFO: FROM field comparison result"; fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test__invalid_field (title TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'voyage/text-embedding-ada-002' FROM = 'nonexistent_field') " 2>&1
––– output –––
OK
––– input –––
if mysql -h0 -P9306 -e "SHOW TABLES LIKE 'test_voyage_no_from'" | grep -q test_voyage_no_from; then mysql -h0 -P9306 -e "INSERT INTO test__no_from (id, title, embedding) VALUES(1, 'test title', '(0.1, 0.2, 0.3, 0.4, 0.5)')"; echo "insert_result: $?"; else echo "insert_result: skipped (table not created)"; fi
––– output –––
OK
––– input –––
if mysql -h0 -P9306 -e "SHOW TABLES LIKE 'test__no_from'" | grep -q test_voyage_no_from; then mysql -h0 -P9306 -e "SHOW CREATE TABLE test_voyage_no_from"; else echo "table_structure: skipped (table not created)"; fi
––– output –––
OK
––– input –––
if [ -n "$VOYAGE_API_KEY" ] && [ "$VOYAGE_API_KEY" != "dummy_key_for_testing" ]; then echo "API key is available for testing"; else echo "API key not available - using dummy for error testing"; fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id, knn_dist() FROM test_voyage_remote WHERE knn(embedding, 3, 'machine learning and artificial intelligence')\G"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) as count FROM test_voyage_remote WHERE knn(embedding, 5, 'technology and AI') AND id > 0"
––– output –––
OK
––– input –––
API_KEY_VAL="${VOYAGE_API_KEY}"; cat > /etc/manticoresearch/manticore.conf << CONFEOF
searchd {
listen = 127.0.0.1:9306:mysql41
listen = 127.0.0.1:9308:http
log = /var/log/manticore/searchd.log
pid_file = /var/run/manticore/searchd.pid
}
table test_voyage_plain {
type = rt
path = /var/lib/manticore/test_voyage_plain
rt_field = title
rt_field = content
rt_attr_float_vector = embedding
knn = {"attrs":[{"name":"embedding","type":"hnsw","hnsw_similarity":"L2","hnsw_m":16,"hnsw_ef_construction":200,"model_name":"voyage/voyage-3.5-lite","from":"title,content","api_key":"${API_KEY_VAL}"}]}
}
CONFEOF
––– output –––
OK
––– input –––
searchd --stopwait --quiet
––– output –––
+ [Tue Jan 13 15:03:45.898 2026] [93] WARNING: Error initializing secondary index: daemon requires secondary library v18 (trying to load v19)
+ [Tue Jan 13 15:03:45.898 2026] [93] FATAL: malformed or unknown option near '--quiet'; use '-h' or '--help' to see available options.
+ Manticore 0.0.0 0240e6481@25121214 (columnar 0.0.0 e5dff86@26011314) (knn 0.0.0 e5dff86@26011314) (embeddings 1.1.0 unknown@00000000)
+ Copyright (c) 2001-2016, Andrew Aksyonoff
+ Copyright (c) 2008-2016, Sphinx Technologies Inc (http://sphinxsearch.com)
+ Copyright (c) 2017-2025, Manticore Software LTD (https://manticoresearch.com)
––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd --stopwait > /dev/null; stdbuf -oL searchd ${SEARCHD_ARGS:-} > /dev/null
––– output –––
OK
––– input –––
if timeout 10 grep -qm1 'accepting connections' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Accepting connections!'; else echo 'Timeout or failed!'; fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SHOW TABLES"
––– output –––
- +-------------------+------+
- | Table | Type |
- +-------------------+------+
- | test_voyage_plain | rt |
- +-------------------+------+
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_voyage_plain (id, title, content) VALUES(1, 'bread', 'food item'), (2, 'cat', 'animal pet')"; echo $?
––– output –––
- 0
+ ERROR 1064 (42000) at line 1: Cannot create the table automatically in Plain mode. Make sure the table exists before inserting into it
+ 1
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) as count FROM test_voyage_plain"
––– output –––
- +-------+
+ ERROR 1064 (42000) at line 1: unknown local table(s) 'test_voyage_plain' in search request
- | count |
- +-------+
- | 2 |
- +-------+
––– input –––
mysql -h0 -P9306 -E -e "SELECT id, title FROM test_voyage_plain WHERE knn(embedding, 2, 'dog')"
––– output –––
- *************************** 1. row ***************************
+ ERROR 1064 (42000) at line 1: unknown local table(s) 'test_voyage_plain' in search request
- id: 2
- title: cat
- *************************** 2. row ***************************
- id: 1
- title: bread
––– input –––
cat > /etc/manticoresearch/manticore.conf << 'EOF'
searchd {
listen = 127.0.0.1:9306:mysql41
listen = 127.0.0.1:9308:http
log = /var/log/manticore/searchd.log
pid_file = /var/run/manticore/searchd.pid
}
table test_voyage_no_key {
type = rt
path = /var/lib/manticore/test_voyage_no_key
rt_field = title
rt_attr_float_vector = embedding
knn = {"attrs":[{"name":"embedding","type":"hnsw","hnsw_similarity":"L2","model_name":"voyage/voyage-3.5-lite","from":"title"}]}
}
EOF
––– output –––
OK
––– input –––
searchd --stopwait --quiet
––– output –––
+ [Tue Jan 13 15:03:47.065 2026] [131] WARNING: Error initializing secondary index: daemon requires secondary library v18 (trying to load v19)
+ [Tue Jan 13 15:03:47.065 2026] [131] FATAL: malformed or unknown option near '--quiet'; use '-h' or '--help' to see available options.
+ Manticore 0.0.0 0240e6481@25121214 (columnar 0.0.0 e5dff86@26011314) (knn 0.0.0 e5dff86@26011314) (embeddings 1.1.0 unknown@00000000)
+ Copyright (c) 2001-2016, Andrew Aksyonoff
+ Copyright (c) 2008-2016, Sphinx Technologies Inc (http://sphinxsearch.com)
+ Copyright (c) 2017-2025, Manticore Software LTD (https://manticoresearch.com)
––– input –––
searchd 2>&1|grep WARNING
––– output –––
- WARNING: table 'test_voyage_no_key': prealloc: Invalid API key for remote model - NOT SERVING
+ [Tue Jan 13 15:03:47.078 2026] [132] WARNING: Error initializing secondary index: daemon requires secondary library v18 (trying to load v19)test/clt-tests/mcl/auto-embeddings-error-handling.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd $SEARCHD_FLAGS > /dev/null; if timeout 10 grep -qm1 '\[BUDDY\] started' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Buddy started!'; else echo 'Timeout or failed!'; cat /var/log/manticore/searchd.log;fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_dims (
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' KNN_DIMS='384'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title'
)" 2>&1
# Check if table was actually created
mysql -h0 -P9306 -e "SHOW TABLES LIKE 'test_dims'" | grep -q "test_dims" && echo "Table created" || echo "Table not created"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_auto_dims (
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title'
)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_auto_dims (id, title) VALUES (1, 'Test document')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_no_model (
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
FROM='title'
)"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_no_from (
content_text TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
)" 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_empty_from (
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM=''
)" 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_bad_model (
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='non-existent-model/invalid-name'
FROM='title'
)" 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_no_prefix (
content_text TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='all-MiniLM-L6-v2'
FROM='content_text'
)" 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_bad_from (
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='non_existent_field'
)"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_circular (
title TEXT,
vec1 FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='vec1'
)" 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_empty (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_empty (id, content) VALUES (1, '')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_empty (id, content) VALUES (2, NULL)" 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_empty"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_rowwise (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='rowwise'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_rowwise (id, content) VALUES
(1, 'machine learning'),
(2, 'deep learning')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_rowwise; OPTIMIZE TABLE test_rowwise OPTION sync=1, cutoff=1"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_rowwise WHERE KNN(vec, 1, 'artificial intelligence')"
––– output –––
+------+
| id |
+------+
| 1 |
- | 2 |
+ +------+
- +------+
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_vec_columnar (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' engine='columnar'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_vec_columnar (id, content) VALUES
(1, 'machine learning'),
(2, 'deep learning')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_vec_columnar; OPTIMIZE TABLE test_vec_columnar OPTION sync=1, cutoff=1"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_vec_columnar WHERE KNN(vec, 1, 'artificial intelligence')"
––– output –––
+------+
| id |
+------+
| 1 |
- | 2 |
+ +------+
- +------+
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_full_columnar (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_full_columnar (id, content) VALUES
(1, 'machine learning'),
(2, 'deep learning')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_full_columnar; OPTIMIZE TABLE test_full_columnar OPTION sync=1, cutoff=1"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_full_columnar WHERE KNN(vec, 1, 'artificial intelligence')"
––– output –––
+------+
| id |
+------+
| 1 |
- | 2 |
+ +------+
- +------+
––– input –––
echo "Row-wise (default):"
mysql -h0 -P9306 -e "SHOW CREATE TABLE test_rowwise\G" | grep -E "vec.*float_vector"
––– output –––
OK
––– input –––
echo "Vec columnar only:"
mysql -h0 -P9306 -e "SHOW CREATE TABLE test_vec_columnar\G" | grep -E "vec.*float_vector"
––– output –––
OK
––– input –––
echo "Full columnar:"
mysql -h0 -P9306 -e "SHOW CREATE TABLE test_full_columnar\G" | grep -E "(vec.*float_vector|engine='columnar')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT * FROM test_auto_dims WHERE KNN(wrong_field, 1, 'test')" 2>&1 | grep -o "wrong_field.*not found"
––– output –––
OKtest/clt-tests/mcl/auto-embeddings-openai-remote.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd --stopwait > /dev/null; stdbuf -oL searchd ${SEARCHD_ARGS:-} > /dev/null
––– output –––
OK
––– input –––
if timeout 10 grep -qm1 'accepting connections' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Accepting connections!'; else echo 'Timeout or failed!'; fi
––– output –––
OK
––– input –––
cosine_similarity() {
local file1="$1" file2="$2"
awk '
NR==FNR { a[NR]=$1; suma2+=$1*$1; next }
{
dot += a[FNR]*$1
sumb2 += $1*$1
}
END {
print dot / (sqrt(suma2) * sqrt(sumb2))
}' "$file1" "$file2"
}
––– output –––
OK
––– input –––
export -f cosine_similarity
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_invalid_model (title TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'openai/invalid-model-name-12345' FROM = 'title') " 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_valid_model_no_api_key (title TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'openai/text-embedding-ada-002' FROM = 'title') " 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_openai_remote (title TEXT, content TEXT, description TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'openai/text-embedding-ada-002' FROM = 'title, content' API_KEY='${OPENAI_API_KEY}') "; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SHOW CREATE TABLE test_openai_remote"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_openai_remote (id, title, content, description) VALUES(1, 'machine learning algorithms', 'deep neural networks and artificial intelligence', 'advanced AI research')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) as record_count FROM test_openai_remote WHERE id=1"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_openai_remote (id, title, content, description) VALUES(2, 'machine learning algorithms', 'deep neural networks and artificial intelligence', 'different description')"
mysql -h0 -P9306 -e "SELECT embedding FROM test_openai_remote WHERE id=1" | \
grep -v embedding | \
sed 's/[0-9]\+\(\.[0-9]\+\)\?/\n&\n/g' | \
grep -E '^[0-9]+(\.[0-9]+)?$' | \
awk '{printf "%.5f\n", $1}' > /tmp/vector1.txt
mysql -h0 -P9306 -e "SELECT embedding FROM test_openai_remote WHERE id=2" | \
grep -v embedding | \
sed 's/[0-9]\+\(\.[0-9]\+\)\?/\n&\n/g' | \
grep -E '^[0-9]+(\.[0-9]+)?$' | \
awk '{printf "%.5f\n", $1}' > /tmp/vector2.txt
SIMILARITY=$(cosine_similarity /tmp/vector1.txt /tmp/vector2.txt)
echo "Cosine similarity: $SIMILARITY"
RESULT=$(awk -v sim="$SIMILARITY" 'BEGIN {
if (sim > 0.99)
print "SUCCESS: Same FROM fields produce similar vectors (similarity: " sim ")"
else
print "FAIL: Different vectors (FROM does not include description field and should not change generated vector value) (similarity: " sim ")"
}')
echo "$RESULT"
rm -f /tmp/vector1.txt /tmp/vector2.txt
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_openai_title_only (title TEXT, content TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'openai/text-embedding-ada-002' FROM = 'title' API_KEY='${OPENAI_API_KEY}') "; mysql -h0 -P9306 -e "INSERT INTO test_openai_title_only (id, title, content) VALUES(1, 'machine learning algorithms', 'completely different content here')"; MD5_MULTI=$(mysql -h0 -P9306 -e "SELECT embedding FROM test_openai_remote WHERE id=1" | grep -v embedding | md5sum | awk '{print $1}'); MD5_SINGLE=$(mysql -h0 -P9306 -e "SELECT embedding FROM test_openai_title_only WHERE id=1" | grep -v embedding | md5sum | awk '{print $1}'); echo "multi_field_md5: $MD5_MULTI"; echo "single_field_md5: $MD5_SINGLE"; if [ "$MD5_MULTI" != "$MD5_SINGLE" ]; then echo "SUCCESS: Different FROM specifications produce different vectors"; else echo "INFO: FROM field comparison result"; fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_openai_invalid_field (title TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'openai/text-embedding-ada-002' FROM = 'nonexistent_field') " 2>&1
––– output –––
OK
––– input –––
if mysql -h0 -P9306 -e "SHOW TABLES LIKE 'test_openai_no_from'" | grep -q test_openai_no_from; then mysql -h0 -P9306 -e "INSERT INTO test_openai_no_from (id, title, embedding) VALUES(1, 'test title', '(0.1, 0.2, 0.3, 0.4, 0.5)')"; echo "insert_result: $?"; else echo "insert_result: skipped (table not created)"; fi
––– output –––
OK
––– input –––
if mysql -h0 -P9306 -e "SHOW TABLES LIKE 'test_openai_no_from'" | grep -q test_openai_no_from; then mysql -h0 -P9306 -e "SHOW CREATE TABLE test_openai_no_from"; else echo "table_structure: skipped (table not created)"; fi
––– output –––
OK
––– input –––
if [ -n "$OPENAI_API_KEY" ] && [ "$OPENAI_API_KEY" != "dummy_key_for_testing" ]; then echo "API key is available for testing"; else echo "API key not available - using dummy for error testing"; fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id, knn_dist() FROM test_openai_remote WHERE knn(embedding, 3, 'machine learning and artificial intelligence')\G"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) as count FROM test_openai_remote WHERE knn(embedding, 5, 'technology and AI') AND id > 0"
––– output –––
OK
––– input –––
API_KEY_VAL="${OPENAI_API_KEY}"; cat > /etc/manticoresearch/manticore.conf << CONFEOF
searchd {
listen = 127.0.0.1:9306:mysql41
listen = 127.0.0.1:9308:http
log = /var/log/manticore/searchd.log
pid_file = /var/run/manticore/searchd.pid
}
table test_openai_plain {
type = rt
path = /var/lib/manticore/test_openai_plain
rt_field = title
rt_field = content
rt_attr_float_vector = embedding
knn = {"attrs":[{"name":"embedding","type":"hnsw","hnsw_similarity":"L2","hnsw_m":16,"hnsw_ef_construction":200,"model_name":"openai/text-embedding-ada-002","from":"title,content","api_key":"${API_KEY_VAL}"}]}
}
CONFEOF
––– output –––
OK
––– input –––
searchd --stopwait --quiet
––– output –––
+ [Tue Jan 13 15:03:00.832 2026] [93] WARNING: Error initializing secondary index: daemon requires secondary library v18 (trying to load v19)
+ [Tue Jan 13 15:03:00.833 2026] [93] FATAL: malformed or unknown option near '--quiet'; use '-h' or '--help' to see available options.
+ Manticore 0.0.0 0240e6481@25121214 (columnar 0.0.0 e5dff86@26011314) (knn 0.0.0 e5dff86@26011314) (embeddings 1.1.0 unknown@00000000)
+ Copyright (c) 2001-2016, Andrew Aksyonoff
+ Copyright (c) 2008-2016, Sphinx Technologies Inc (http://sphinxsearch.com)
+ Copyright (c) 2017-2025, Manticore Software LTD (https://manticoresearch.com)
––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd --stopwait > /dev/null; stdbuf -oL searchd ${SEARCHD_ARGS:-} > /dev/null
––– output –––
OK
––– input –––
if timeout 10 grep -qm1 'accepting connections' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Accepting connections!'; else echo 'Timeout or failed!'; fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SHOW TABLES"
––– output –––
- +-------------------+------+
- | Table | Type |
- +-------------------+------+
- | test_openai_plain | rt |
- +-------------------+------+
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_openai_plain (id, title, content) VALUES(1, 'bread', 'food item'), (2, 'cat', 'animal pet')"; echo $?
––– output –––
- 0
+ ERROR 1064 (42000) at line 1: Cannot create the table automatically in Plain mode. Make sure the table exists before inserting into it
+ 1
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) as count FROM test_openai_plain"
––– output –––
- +-------+
+ ERROR 1064 (42000) at line 1: unknown local table(s) 'test_openai_plain' in search request
- | count |
- +-------+
- | 2 |
- +-------+
––– input –––
mysql -h0 -P9306 -E -e "SELECT id, title FROM test_openai_plain WHERE knn(embedding, 2, 'dog')"
––– output –––
- *************************** 1. row ***************************
+ ERROR 1064 (42000) at line 1: unknown local table(s) 'test_openai_plain' in search request
- id: 2
- title: cat
- *************************** 2. row ***************************
- id: 1
- title: bread
––– input –––
cat > /etc/manticoresearch/manticore.conf << 'EOF'
searchd {
listen = 127.0.0.1:9306:mysql41
listen = 127.0.0.1:9308:http
log = /var/log/manticore/searchd.log
pid_file = /var/run/manticore/searchd.pid
}
table test_openai_no_key {
type = rt
path = /var/lib/manticore/test_openai_no_key
rt_field = title
rt_attr_float_vector = embedding
knn = {"attrs":[{"name":"embedding","type":"hnsw","hnsw_similarity":"L2","model_name":"openai/text-embedding-ada-002","from":"title"}]}
}
EOF
––– output –––
OK
––– input –––
searchd --stopwait --quiet
––– output –––
+ [Tue Jan 13 15:03:01.508 2026] [131] WARNING: Error initializing secondary index: daemon requires secondary library v18 (trying to load v19)
+ [Tue Jan 13 15:03:01.508 2026] [131] FATAL: malformed or unknown option near '--quiet'; use '-h' or '--help' to see available options.
+ Manticore 0.0.0 0240e6481@25121214 (columnar 0.0.0 e5dff86@26011314) (knn 0.0.0 e5dff86@26011314) (embeddings 1.1.0 unknown@00000000)
+ Copyright (c) 2001-2016, Andrew Aksyonoff
+ Copyright (c) 2008-2016, Sphinx Technologies Inc (http://sphinxsearch.com)
+ Copyright (c) 2017-2025, Manticore Software LTD (https://manticoresearch.com)
––– input –––
searchd 2>&1|grep WARNING
––– output –––
- WARNING: table 'test_openai_no_key': prealloc: Invalid API key for remote model - NOT SERVING
+ [Tue Jan 13 15:03:01.522 2026] [132] WARNING: Error initializing secondary index: daemon requires secondary library v18 (trying to load v19) |
clt❌ CLT tests in Failed tests:🔧 Edit failed tests in UI:
test/clt-tests/mcl/auto-embeddings-backup-restore.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd $SEARCHD_FLAGS > /dev/null; if timeout 10 grep -qm1 '\[BUDDY\] started' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Buddy started!'; else echo 'Timeout or failed!'; cat /var/log/manticore/searchd.log;fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_backup (
title TEXT,
content TEXT,
status INTEGER,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title, content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_backup (id, title, content, status) VALUES
(1, 'machine learning', 'neural networks', 1),
(2, 'deep learning', 'transformers', 1),
(3, 'computer vision', 'image processing', 2)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_backup; OPTIMIZE TABLE test_backup OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_backup WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
+------+
| id |
+------+
| 1 |
| 3 |
- | 2 |
+ +------+
- +------+
––– input –––
mysql -h0 -P9306 -E -e "SELECT id, title, content, KNN_DIST() as distance FROM test_backup WHERE KNN(vec, 3, 'artificial intelligence') ORDER BY distance"
––– output –––
OK
––– input –––
manticore-backup --version | grep -c "Manticore Backup"
––– output –––
OK
––– input –––
mkdir -p /tmp/backup && chmod 777 /tmp/backup; echo $?
––– output –––
OK
––– input –––
manticore-backup --backup-dir=/tmp/backup --tables=test_backup 2>&1 | grep -c "Backing up table"
––– output –––
OK
––– input –––
ls -d /tmp/backup/backup-* | wc -l
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FREEZE test_backup"
––– output –––
+-----------------------------------------------------+-----------------------------------------------------+
| file | normalized |
+-----------------------------------------------------+-----------------------------------------------------+
| /var/lib/manticore/test_backup/test_backup.0.spc | /var/lib/manticore/test_backup/test_backup.0.spc |
| /var/lib/manticore/test_backup/test_backup.0.spd | /var/lib/manticore/test_backup/test_backup.0.spd |
| /var/lib/manticore/test_backup/test_backup.0.spds | /var/lib/manticore/test_backup/test_backup.0.spds |
| /var/lib/manticore/test_backup/test_backup.0.spe | /var/lib/manticore/test_backup/test_backup.0.spe |
| /var/lib/manticore/test_backup/test_backup.0.sph | /var/lib/manticore/test_backup/test_backup.0.sph |
| /var/lib/manticore/test_backup/test_backup.0.sphi | /var/lib/manticore/test_backup/test_backup.0.sphi |
| /var/lib/manticore/test_backup/test_backup.0.spi | /var/lib/manticore/test_backup/test_backup.0.spi |
- | /var/lib/manticore/test_backup/test_backup.0.spidx | /var/lib/manticore/test_backup/test_backup.0.spidx |
+ | /var/lib/manticore/test_backup/test_backup.0.spknn | /var/lib/manticore/test_backup/test_backup.0.spknn |
- | /var/lib/manticore/test_backup/test_backup.0.spknn | /var/lib/manticore/test_backup/test_backup.0.spknn |
+ | /var/lib/manticore/test_backup/test_backup.0.spm | /var/lib/manticore/test_backup/test_backup.0.spm |
- | /var/lib/manticore/test_backup/test_backup.0.spm | /var/lib/manticore/test_backup/test_backup.0.spm |
+ | /var/lib/manticore/test_backup/test_backup.0.spp | /var/lib/manticore/test_backup/test_backup.0.spp |
- | /var/lib/manticore/test_backup/test_backup.0.spp | /var/lib/manticore/test_backup/test_backup.0.spp |
+ | /var/lib/manticore/test_backup/test_backup.0.spt | /var/lib/manticore/test_backup/test_backup.0.spt |
- | /var/lib/manticore/test_backup/test_backup.0.spt | /var/lib/manticore/test_backup/test_backup.0.spt |
+ | /var/lib/manticore/test_backup/test_backup.meta | /var/lib/manticore/test_backup/test_backup.meta |
- | /var/lib/manticore/test_backup/test_backup.meta | /var/lib/manticore/test_backup/test_backup.meta |
+ | /var/lib/manticore/test_backup/test_backup.settings | /var/lib/manticore/test_backup/test_backup.settings |
- | /var/lib/manticore/test_backup/test_backup.settings | /var/lib/manticore/test_backup/test_backup.settings |
+ +-----------------------------------------------------+-----------------------------------------------------+
- +-----------------------------------------------------+-----------------------------------------------------+
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_backup"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_backup (id, title, content, status) VALUES (4, 'frozen insert', 'test data', 3)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "UNFREEZE test_backup"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_backup"
––– output –––
OK
––– input –––
mysqldump -h0 -P9306 manticore test_backup > /tmp/logical_backup.sql 2>/dev/null; echo $?
––– output –––
OK
––– input –––
grep -c "INSERT INTO" /tmp/logical_backup.sql
––– output –––
OK
––– input –––
searchd --stopwait > /dev/null 2>&1; echo $?
––– output –––
OK
––– input –––
rm -f /etc/manticoresearch/manticore.conf; rm -rf /var/lib/manticore/*; echo "Cleaned for restore"
––– output –––
OK
––– input –––
manticore-backup --backup-dir=/tmp/backup --restore 2>&1 | grep -c "backup-"
––– output –––
OK
––– input –––
BACKUP_NAME=$(manticore-backup --backup-dir=/tmp/backup --restore 2>&1 | grep backup- | awk '{print $1}' | head -1)
manticore-backup --backup-dir=/tmp/backup --restore=$BACKUP_NAME 2>&1 | grep -c "Starting to restore"
––– output –––
- 1
+ 0
––– input –––
searchd > /dev/null 2>&1; echo $?
––– output –––
- 0
+ 1
––– input –––
echo "Waiting for searchd to start"; sleep 3
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_backup"
––– output –––
- +----------+
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
- | count(*) |
- +----------+
- | 3 |
- +----------+
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_backup; OPTIMIZE TABLE test_backup OPTION sync=1, cutoff=1"; echo $?
––– output –––
- 0
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
+ 1
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_backup WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
- +------+
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
- | id |
- +------+
- | 1 |
- | 3 |
- | 2 |
- +------+
––– input –––
mysql -h0 -P9306 -e "ALTER TABLE test_backup ADD COLUMN new_field INTEGER"; echo $?
––– output –––
- 0
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
+ 1
––– input –––
mysql -h0 -P9306 -e "DESC test_backup" | grep "new_field"
––– output –––
- | new_field | uint | columnar fast_fetch |
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_copy (
title TEXT,
content TEXT,
status INTEGER,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title, content'
) engine='columnar'"; echo $?
––– output –––
- 0
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
+ 1
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_copy (id, title, content, status) VALUES
(1, 'machine learning', 'neural networks', 1),
(2, 'deep learning', 'transformers', 1),
(3, 'computer vision', 'image processing', 2),
(4, 'frozen insert', 'test data', 3)"; echo $?
––– output –––
- 0
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
+ 1
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_copy"
––– output –––
- +----------+
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
- | count(*) |
- +----------+
- | 4 |
- +----------+
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_copy WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
- +------+
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
- | id |
- +------+
- | 1 |
- | 3 |
- | 2 |
- | 4 |
- +------+
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_copy; OPTIMIZE TABLE test_copy OPTION sync=1, cutoff=1"; echo $?
––– output –––
- 0
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
+ 1
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_copy WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
- +------+
+ ERROR 2003 (HY000): Can't connect to MySQL server on '0:9306' (111)
- | id |
- +------+
- | 1 |
- | 3 |
- | 2 |
- | 4 |
- +------+test/clt-tests/mcl/auto-embeddings-hnsw-configs.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd $SEARCHD_FLAGS > /dev/null; if timeout 10 grep -qm1 '\[BUDDY\] started' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Buddy started!'; else echo 'Timeout or failed!'; cat /var/log/manticore/searchd.log;fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_l2 (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_cosine (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='cosine'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_ip (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='ip'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
for table in test_l2 test_cosine test_ip; do
mysql -h0 -P9306 -e "INSERT INTO $table (id, content) VALUES
(1, 'machine learning'),
(2, 'deep learning'),
(3, 'cooking recipes')" 2>/dev/null
done
echo "Data inserted into all tables"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_l2; OPTIMIZE TABLE test_l2 OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_cosine; OPTIMIZE TABLE test_cosine OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_ip; OPTIMIZE TABLE test_ip OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -E -e "SELECT id FROM test_l2 WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
*************************** 1. row ***************************
id: 1
*************************** 2. row ***************************
id: 2
- *************************** 3. row ***************************
- id: 3
––– input –––
mysql -h0 -P9306 -E -e "SELECT id FROM test_cosine WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
*************************** 1. row ***************************
id: 1
*************************** 2. row ***************************
id: 2
- *************************** 3. row ***************************
- id: 3
––– input –––
mysql -h0 -P9306 -E -e "SELECT id FROM test_ip WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
*************************** 1. row ***************************
id: 1
*************************** 2. row ***************************
id: 2
- *************************** 3. row ***************************
- id: 3
––– input –––
echo "L2 (Euclidean) distances:"
mysql -h0 -P9306 -E -e "SELECT id, content, KNN_DIST() as distance FROM test_l2 WHERE KNN(vec, 3, 'neural networks') ORDER BY distance"
––– output –––
OK
––– input –––
echo "Cosine similarity distances (smaller = more similar):"
mysql -h0 -P9306 -E -e "SELECT id, content, KNN_DIST() as distance FROM test_cosine WHERE KNN(vec, 3, 'neural networks') ORDER BY distance"
––– output –––
OK
––– input –––
echo "Inner product distances (smaller = more similar):"
mysql -h0 -P9306 -E -e "SELECT id, content, KNN_DIST() as distance FROM test_ip WHERE KNN(vec, 3, 'neural networks') ORDER BY distance"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_hnsw_m4 (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
HNSW_M='4'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_hnsw_m32 (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
HNSW_M='32'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_hnsw_ef (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
HNSW_M='16'
HNSW_EF_CONSTRUCTION='500'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
for table in test_hnsw_m4 test_hnsw_m32 test_hnsw_ef; do
mysql -h0 -P9306 -e "INSERT INTO $table (id, content) VALUES (1, 'test document')" 2>/dev/null
done
echo "HNSW configurations test completed"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -E -e "SELECT COUNT(*) FROM test_hnsw_m4"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -E -e "SELECT COUNT(*) FROM test_hnsw_m32"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -E -e "SELECT COUNT(*) FROM test_hnsw_ef"
––– output –––
OK
––– input –––
for i in {1..10}; do
mysql -h0 -P9306 -e "INSERT INTO test_hnsw_m4 (content) VALUES ('document number $i with various content')" 2>/dev/null
mysql -h0 -P9306 -e "INSERT INTO test_hnsw_m32 (content) VALUES ('document number $i with various content')" 2>/dev/null
done
echo "Additional documents inserted for performance comparison"
––– output –––
OK
––– input –––
echo "Search with M=4 (faster, less connections):"
mysql -h0 -P9306 -E -e "SELECT id FROM test_hnsw_m4 WHERE KNN(vec, 3, 'document content') LIMIT 3"
––– output –––
OK
––– input –––
echo "Search with M=32 (slower, more connections, potentially more accurate):"
mysql -h0 -P9306 -E -e "SELECT id FROM test_hnsw_m32 WHERE KNN(vec, 3, 'document content') LIMIT 3"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_invalid_metric (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='invalid_metric'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'" 2>&1
––– output –––
OKtest/clt-tests/mcl/auto-embeddings-endpoints.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd --stopwait > /dev/null; stdbuf -oL searchd ${SEARCHD_ARGS:-} > /dev/null
––– output –––
OK
––– input –––
if timeout 10 grep -qm1 'accepting connections' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Accepting connections!'; else echo 'Timeout or failed!'; fi
––– output –––
OK
––– input –––
apt-get install jq -y > /dev/null; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE emb_test (
id BIGINT,
title TEXT,
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title, content'
)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO emb_test (id, title, content) VALUES
(1, 'machine learning', 'neural networks and deep learning'),
(2, 'computer vision', 'image recognition and processing'),
(3, 'natural language', 'text analysis and understanding')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK emb_test; OPTIMIZE TABLE emb_test OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM emb_test WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
+----------+
| count(*) |
+----------+
- | 3 |
+ | 2 |
+----------+
––– input –––
curl -s "http://localhost:9308/cli?select%20id,%20title%20from%20emb_test%20where%20knn(vec,%202,%20'artificial%20intelligence')" | grep -v 'rows in set'
––– output –––
+----+------------------+
| id | title |
+----+------------------+
| 1 | machine learning |
| 2 | computer vision |
- | 3 | natural language |
+ +----+------------------+
- +----+------------------+
––– input –––
curl -s "http://localhost:9308/cli_json?select%20id,%20title,%20@knn_dist%20from%20emb_test%20where%20knn(vec,%201,%20'learning')" | jq -r '.[0].data[0] | "ID: \(.id)\nTitle: \(.title)\nDistance: \(.["@knn_dist"] | tostring)"'
––– output –––
OK
––– input –––
curl -s -X POST "http://localhost:9308/sql?mode=raw" -d "select count(*) from emb_test where knn(vec, 2, 'neural networks')" | jq -r '.[0].data[0]."count(*)"'
––– output –––
- 3
+ 2
––– input –––
curl -s -X POST http://localhost:9308/insert -d '{"index":"emb_test","id":10,"doc":{"title":"quantum computing","content":"quantum algorithms"}}' | jq -r '.created'
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/search -d '{"index":"emb_test","knn":{"field":"vec","query":"quantum","k":1}}' | jq -r '.hits.hits[0]._source.title'
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/search -d '{"index":"emb_test","knn":{"field":"vec","query_text":"quantum","k":1}}'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE chunk_test (
id BIGINT,
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO chunk_test (id, title) VALUES
(1, 'machine learning'),
(2, 'deep learning'),
(3, 'reinforcement learning')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM chunk_test WHERE KNN(vec, 1, 'learning')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK chunk_test; OPTIMIZE TABLE chunk_test OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM chunk_test WHERE KNN(vec, 1, 'learning')"
––– output –––
+----------+
| count(*) |
+----------+
- | 3 |
+ | 1 |
+----------+test/clt-tests/mcl/auto-embeddings-json-api.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd $SEARCHD_FLAGS > /dev/null; if timeout 10 grep -qm1 '\[BUDDY\] started' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Buddy started!'; else echo 'Timeout or failed!'; cat /var/log/manticore/searchd.log;fi
––– output –––
OK
––– input –––
apt-get install jq -y > /dev/null; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_json_columnar (
title TEXT,
content TEXT,
embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title, content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SHOW CREATE TABLE test_json_columnar" | grep -o "knn_dims='384'"
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/insert -d '{"index":"test_json_columnar","id":1,"doc":{"title":"machine learning","content":"neural networks"}}' | jq -r 'if ._id then ._id else "inserted" end'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_json_columnar WHERE KNN(embedding, 1, 'machine learning neural networks')"
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/bulk -H "Content-Type: application/x-ndjson" -d '
{"insert":{"index":"test_json_columnar","id":2,"doc":{"title":"computer vision","content":"image recognition"}}}
{"insert":{"index":"test_json_columnar","id":3,"doc":{"title":"NLP","content":"text processing"}}}
' | jq '{created: .items[0].bulk.created}'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_json_columnar WHERE id IN (2,3)"
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/replace -d '{"index":"test_json_columnar","id":1,"doc":{"title":"updated ML","content":"updated networks"}}' | jq -r '.result'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT title FROM test_json_columnar WHERE id=1 AND KNN(embedding, 1, 'updated ML networks')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_json_columnar (id, title, content) VALUES (100, 'test', 'data')";
curl -s -X POST http://localhost:9308/insert -d '{"index":"test_json_columnar","id":101,"doc":{"title":"test","content":"data"}}' > /dev/null
––– output –––
OK
––– input –––
mysql -h0 -P9306 --batch --skip-column-names -e "SELECT embedding FROM test_json_columnar WHERE id=100" > /tmp/v1.txt
mysql -h0 -P9306 --batch --skip-column-names -e "SELECT embedding FROM test_json_columnar WHERE id=101" > /tmp/v2.txt
diff -q /tmp/v1.txt /tmp/v2.txt > /dev/null && echo "Vectors identical" || echo "Vectors differ"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_json_columnar"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_json_columnar; OPTIMIZE TABLE test_json_columnar OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
VECTOR=$(python3 -c "print(','.join(['0.01']*384))")
curl -s -X POST http://localhost:9308/search -d "{\"index\":\"test_json_columnar\",\"knn\":{\"field\":\"embedding\",\"query_vector\":[$VECTOR],\"k\":2}}" | jq -r '.hits.total // "0"'
––– output –––
- 5
+ 2
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE no_auto_embed (title TEXT, vec FLOAT_VECTOR KNN_TYPE='hnsw' KNN_DIMS='384' HNSW_SIMILARITY='l2') engine='columnar'"
––– output –––
OK
––– input –––
VECTOR=$(python3 -c "print(','.join(['0.5']*384))")
curl -s -X POST http://localhost:9308/insert -d "{\"index\":\"no_auto_embed\",\"id\":1,\"doc\":{\"title\":\"test\",\"vec\":[$VECTOR]}}" | jq -r 'if ._id then ._id else "inserted" end'
––– output –––
OK
––– input –––
QUERY_VEC=$(python3 -c "print(','.join(['0.5']*384))")
curl -s -X POST http://localhost:9308/search -d "{\"index\":\"no_auto_embed\",\"knn\":{\"field\":\"vec\",\"query_vector\":[$QUERY_VEC],\"k\":1}}" | jq -r '.hits.total // "0"'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_json_rowwise (
title TEXT,
content TEXT,
embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title, content'
) engine='rowwise'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SHOW CREATE TABLE test_json_rowwise" | grep -o "knn_dims='384'"
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/insert -d '{"index":"test_json_rowwise","id":1,"doc":{"title":"machine learning","content":"neural networks"}}' | jq -r 'if ._id then ._id else "inserted" end'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_json_rowwise WHERE KNN(embedding, 1, 'machine learning neural networks')"
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/bulk -H "Content-Type: application/x-ndjson" -d '
{"insert":{"index":"test_json_rowwise","id":2,"doc":{"title":"computer vision","content":"image recognition"}}}
{"insert":{"index":"test_json_rowwise","id":3,"doc":{"title":"NLP","content":"text processing"}}}
' | jq '{created: .items[0].bulk.created}'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_json_rowwise WHERE id IN (2,3)"
––– output –––
OK
––– input –––
curl -s -X POST http://localhost:9308/replace -d '{"index":"test_json_rowwise","id":1,"doc":{"title":"updated ML","content":"updated networks"}}' | jq -r '.result'
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT title FROM test_json_rowwise WHERE id=1 AND KNN(embedding, 1, 'updated ML networks')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_json_rowwise (id, title, content) VALUES (100, 'test', 'data')";
curl -s -X POST http://localhost:9308/insert -d '{"index":"test_json_rowwise","id":101,"doc":{"title":"test","content":"data"}}' > /dev/null
––– output –––
OK
––– input –––
mysql -h0 -P9306 --batch --skip-column-names -e "SELECT embedding FROM test_json_rowwise WHERE id=100" > /tmp/v1.txt
mysql -h0 -P9306 --batch --skip-column-names -e "SELECT embedding FROM test_json_rowwise WHERE id=101" > /tmp/v2.txt
diff -q /tmp/v1.txt /tmp/v2.txt > /dev/null && echo "Vectors identical" || echo "Vectors differ"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_json_rowwise"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_json_rowwise; OPTIMIZE TABLE test_json_rowwise OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
VECTOR=$(python3 -c "print(','.join(['0.01']*384))")
curl -s -X POST http://localhost:9308/search -d "{\"index\":\"test_json_rowwise\",\"knn\":{\"field\":\"embedding\",\"query_vector\":[$VECTOR],\"k\":2}}" | jq -r '.hits.total // "0"'
––– output –––
- 5
+ 2test/clt-tests/mcl/auto-embeddings-dml-test.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd $SEARCHD_FLAGS > /dev/null; if timeout 10 grep -qm1 '\[BUDDY\] started' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Buddy started!'; else echo 'Timeout or failed!'; cat /var/log/manticore/searchd.log;fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_delete ( title TEXT, embedding_vector FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2' FROM='title' ) engine='rowwise'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_delete (id, title) VALUES (1, 'One')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DELETE FROM test_delete WHERE id = 1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_delete"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_delete (id, title) VALUES (2,'Two'),(3,'Three'),(4,'Four'),(5,'Five')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DELETE FROM test_delete WHERE id IN (2,3,4)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_delete"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DROP TABLE IF EXISTS test_replace"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_replace ( title TEXT, price INTEGER, vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2' FROM='title' ) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_replace (id, title, price) VALUES (1, 'Original', 100)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "REPLACE INTO test_replace (id, title, price) VALUES (1, 'Updated', 200)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT title, price FROM test_replace WHERE id = 1"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DROP TABLE IF EXISTS test_vector_regen"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_vector_regen ( content TEXT, vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='cosine' MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2' FROM='content')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_vector_regen (id, content) VALUES (1,'AI and ML'),(2,'Deep Learning'),(3,'Cooking')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_vector_regen; OPTIMIZE TABLE test_vector_regen OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_vector_regen WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
+------+
| id |
+------+
| 1 |
| 2 |
- | 3 |
+ +------+
- +------+
––– input –––
mysql -h0 -P9306 -e "REPLACE INTO test_vector_regen (id, content) VALUES (1, 'Cooking recipes')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_vector_regen; OPTIMIZE TABLE test_vector_regen OPTION sync=1, cutoff=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_vector_regen WHERE KNN(vec, 2, 'artificial intelligence')"
––– output –––
+------+
| id |
+------+
| 2 |
| 3 |
- | 1 |
+ +------+
- +------+
––– input –––
mysql -h0 -P9306 -e "TRUNCATE TABLE test_vector_regen"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_vector_regen"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DROP TABLE IF EXISTS test_bulk"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_bulk ( content TEXT, vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2' FROM='content' ) engine='rowwise'"; echo $?
––– output –––
OK
––– input –––
for i in {1..50}; do
mysql -h0 -P9306 -e "INSERT INTO test_bulk (id, content) VALUES ($i, 'Document $i')" 2>/dev/null
done
echo "Inserted 50 records"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DELETE FROM test_bulk WHERE id <= 10"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DELETE FROM test_bulk WHERE id >= 40"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_bulk"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DROP TABLE IF EXISTS test_multi_vec"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_multi_vec ( title TEXT, description TEXT, title_vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2' FROM='title', desc_vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='cosine' MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2' FROM='description' ) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_multi_vec (id, title, description) VALUES (1,'Title1','Desc1'),(2,'Title2','Desc2')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "DELETE FROM test_multi_vec WHERE id=1"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "REPLACE INTO test_multi_vec (id, title, description) VALUES (2,'NewTitle','NewDesc')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id, title FROM test_multi_vec"
––– output –––
OKtest/clt-tests/mcl/auto-embeddings-voyage-remote.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd --stopwait > /dev/null; stdbuf -oL searchd ${SEARCHD_ARGS:-} > /dev/null
––– output –––
OK
––– input –––
if timeout 10 grep -qm1 'accepting connections' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Accepting connections!'; else echo 'Timeout or failed!'; fi
––– output –––
OK
––– input –––
cosine_similarity() {
local file1="$1" file2="$2"
awk '
NR==FNR { a[NR]=$1; suma2+=$1*$1; next }
{
dot += a[FNR]*$1
sumb2 += $1*$1
}
END {
print dot / (sqrt(suma2) * sqrt(sumb2))
}' "$file1" "$file2"
}
––– output –––
OK
––– input –––
export -f cosine_similarity
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_invalid_model (title TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'voyage/invalid-model-name-12345' FROM = 'title') " 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_valid_model_no_api_key (title TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'voyage/voyage-3.5-lite' FROM = 'title') " 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_voyage_remote (title TEXT, content TEXT, description TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'voyage/voyage-3.5-lite' FROM = 'title, content' API_KEY='${VOYAGE_API_KEY}') "; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -E -e "SHOW CREATE TABLE test_voyage_remote"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_voyage_remote (id, title, content, description) VALUES(1, 'machine learning algorithms', 'deep neural networks and artificial intelligence', 'advanced AI research')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) as record_count FROM test_voyage_remote WHERE id=1"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_voyage_remote (id, title, content, description) VALUES(2, 'machine learning algorithms', 'deep neural networks and artificial intelligence', 'different description')"
mysql -h0 -P9306 -e "SELECT embedding FROM test_voyage_remote WHERE id=1" | \
grep -v embedding | \
sed 's/[0-9]\+\(\.[0-9]\+\)\?/\n&\n/g' | \
grep -E '^[0-9]+(\.[0-9]+)?$' | \
awk '{printf "%.5f\n", $1}' > /tmp/vector1.txt
mysql -h0 -P9306 -e "SELECT embedding FROM test_voyage_remote WHERE id=2" | \
grep -v embedding | \
sed 's/[0-9]\+\(\.[0-9]\+\)\?/\n&\n/g' | \
grep -E '^[0-9]+(\.[0-9]+)?$' | \
awk '{printf "%.5f\n", $1}' > /tmp/vector2.txt
SIMILARITY=$(cosine_similarity /tmp/vector1.txt /tmp/vector2.txt)
echo "Cosine similarity: $SIMILARITY"
RESULT=$(awk -v sim="$SIMILARITY" 'BEGIN {
if (sim > 0.99)
print "SUCCESS: Same FROM fields produce similar vectors (similarity: " sim ")"
else
print "FAIL: Different vectors (FROM does not include description field and should not change generated vector value) (similarity: " sim ")"
}')
echo "$RESULT"
rm -f /tmp/vector1.txt /tmp/vector2.txt
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_voyage_title_only (title TEXT, content TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'voyage/voyage-3.5-lite' FROM = 'title' API_KEY='${VOYAGE_API_KEY}') "; mysql -h0 -P9306 -e "INSERT INTO test_voyage_title_only (id, title, content) VALUES(1, 'machine learning algorithms', 'completely different content here')"; MD5_MULTI=$(mysql -h0 -P9306 -e "SELECT embedding FROM test_voyage_remote WHERE id=1" | grep -v embedding | md5sum | awk '{print $1}'); MD5_SINGLE=$(mysql -h0 -P9306 -e "SELECT embedding FROM test_voyage_title_only WHERE id=1" | grep -v embedding | md5sum | awk '{print $1}'); echo "multi_field_md5: $MD5_MULTI"; echo "single_field_md5: $MD5_SINGLE"; if [ "$MD5_MULTI" != "$MD5_SINGLE" ]; then echo "SUCCESS: Different FROM specifications produce different vectors"; else echo "INFO: FROM field comparison result"; fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test__invalid_field (title TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'voyage/text-embedding-ada-002' FROM = 'nonexistent_field') " 2>&1
––– output –––
OK
––– input –––
if mysql -h0 -P9306 -e "SHOW TABLES LIKE 'test_voyage_no_from'" | grep -q test_voyage_no_from; then mysql -h0 -P9306 -e "INSERT INTO test__no_from (id, title, embedding) VALUES(1, 'test title', '(0.1, 0.2, 0.3, 0.4, 0.5)')"; echo "insert_result: $?"; else echo "insert_result: skipped (table not created)"; fi
––– output –––
OK
––– input –––
if mysql -h0 -P9306 -e "SHOW TABLES LIKE 'test__no_from'" | grep -q test_voyage_no_from; then mysql -h0 -P9306 -e "SHOW CREATE TABLE test_voyage_no_from"; else echo "table_structure: skipped (table not created)"; fi
––– output –––
OK
––– input –––
if [ -n "$VOYAGE_API_KEY" ] && [ "$VOYAGE_API_KEY" != "dummy_key_for_testing" ]; then echo "API key is available for testing"; else echo "API key not available - using dummy for error testing"; fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id, knn_dist() FROM test_voyage_remote WHERE knn(embedding, 3, 'machine learning and artificial intelligence')\G"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) as count FROM test_voyage_remote WHERE knn(embedding, 5, 'technology and AI') AND id > 0"
––– output –––
OK
––– input –––
API_KEY_VAL="${VOYAGE_API_KEY}"; cat > /etc/manticoresearch/manticore.conf << CONFEOF
searchd {
listen = 127.0.0.1:9306:mysql41
listen = 127.0.0.1:9308:http
log = /var/log/manticore/searchd.log
pid_file = /var/run/manticore/searchd.pid
}
table test_voyage_plain {
type = rt
path = /var/lib/manticore/test_voyage_plain
rt_field = title
rt_field = content
rt_attr_float_vector = embedding
knn = {"attrs":[{"name":"embedding","type":"hnsw","hnsw_similarity":"L2","hnsw_m":16,"hnsw_ef_construction":200,"model_name":"voyage/voyage-3.5-lite","from":"title,content","api_key":"${API_KEY_VAL}"}]}
}
CONFEOF
––– output –––
OK
––– input –––
searchd --stopwait --quiet
––– output –––
+ [Tue Jan 13 16:15:49.490 2026] [93] WARNING: Error initializing secondary index: daemon requires secondary library v18 (trying to load v19)
+ [Tue Jan 13 16:15:49.490 2026] [93] FATAL: malformed or unknown option near '--quiet'; use '-h' or '--help' to see available options.
+ Manticore 0.0.0 0240e6481@25121214 (columnar 0.0.0 e5dff86@26011314) (knn 0.0.0 e5dff86@26011314) (embeddings 1.1.0 unknown@00000000)
+ Copyright (c) 2001-2016, Andrew Aksyonoff
+ Copyright (c) 2008-2016, Sphinx Technologies Inc (http://sphinxsearch.com)
+ Copyright (c) 2017-2025, Manticore Software LTD (https://manticoresearch.com)
––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd --stopwait > /dev/null; stdbuf -oL searchd ${SEARCHD_ARGS:-} > /dev/null
––– output –––
OK
––– input –––
if timeout 10 grep -qm1 'accepting connections' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Accepting connections!'; else echo 'Timeout or failed!'; fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SHOW TABLES"
––– output –––
- +-------------------+------+
- | Table | Type |
- +-------------------+------+
- | test_voyage_plain | rt |
- +-------------------+------+
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_voyage_plain (id, title, content) VALUES(1, 'bread', 'food item'), (2, 'cat', 'animal pet')"; echo $?
––– output –––
- 0
+ ERROR 1064 (42000) at line 1: Cannot create the table automatically in Plain mode. Make sure the table exists before inserting into it
+ 1
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) as count FROM test_voyage_plain"
––– output –––
- +-------+
+ ERROR 1064 (42000) at line 1: unknown local table(s) 'test_voyage_plain' in search request
- | count |
- +-------+
- | 2 |
- +-------+
––– input –––
mysql -h0 -P9306 -E -e "SELECT id, title FROM test_voyage_plain WHERE knn(embedding, 2, 'dog')"
––– output –––
- *************************** 1. row ***************************
+ ERROR 1064 (42000) at line 1: unknown local table(s) 'test_voyage_plain' in search request
- id: 2
- title: cat
- *************************** 2. row ***************************
- id: 1
- title: bread
––– input –––
cat > /etc/manticoresearch/manticore.conf << 'EOF'
searchd {
listen = 127.0.0.1:9306:mysql41
listen = 127.0.0.1:9308:http
log = /var/log/manticore/searchd.log
pid_file = /var/run/manticore/searchd.pid
}
table test_voyage_no_key {
type = rt
path = /var/lib/manticore/test_voyage_no_key
rt_field = title
rt_attr_float_vector = embedding
knn = {"attrs":[{"name":"embedding","type":"hnsw","hnsw_similarity":"L2","model_name":"voyage/voyage-3.5-lite","from":"title"}]}
}
EOF
––– output –––
OK
––– input –––
searchd --stopwait --quiet
––– output –––
+ [Tue Jan 13 16:15:51.022 2026] [131] WARNING: Error initializing secondary index: daemon requires secondary library v18 (trying to load v19)
+ [Tue Jan 13 16:15:51.022 2026] [131] FATAL: malformed or unknown option near '--quiet'; use '-h' or '--help' to see available options.
+ Manticore 0.0.0 0240e6481@25121214 (columnar 0.0.0 e5dff86@26011314) (knn 0.0.0 e5dff86@26011314) (embeddings 1.1.0 unknown@00000000)
+ Copyright (c) 2001-2016, Andrew Aksyonoff
+ Copyright (c) 2008-2016, Sphinx Technologies Inc (http://sphinxsearch.com)
+ Copyright (c) 2017-2025, Manticore Software LTD (https://manticoresearch.com)
––– input –––
searchd 2>&1|grep WARNING
––– output –––
- WARNING: table 'test_voyage_no_key': prealloc: Invalid API key for remote model - NOT SERVING
+ [Tue Jan 13 16:15:51.035 2026] [132] WARNING: Error initializing secondary index: daemon requires secondary library v18 (trying to load v19)test/clt-tests/mcl/auto-embeddings-error-handling.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd $SEARCHD_FLAGS > /dev/null; if timeout 10 grep -qm1 '\[BUDDY\] started' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Buddy started!'; else echo 'Timeout or failed!'; cat /var/log/manticore/searchd.log;fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_dims (
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' KNN_DIMS='384'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title'
)" 2>&1
# Check if table was actually created
mysql -h0 -P9306 -e "SHOW TABLES LIKE 'test_dims'" | grep -q "test_dims" && echo "Table created" || echo "Table not created"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_auto_dims (
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='title'
)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_auto_dims (id, title) VALUES (1, 'Test document')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_no_model (
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
FROM='title'
)"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_no_from (
content_text TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
)" 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_empty_from (
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM=''
)" 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_bad_model (
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='non-existent-model/invalid-name'
FROM='title'
)" 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_no_prefix (
content_text TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='all-MiniLM-L6-v2'
FROM='content_text'
)" 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_bad_from (
title TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='non_existent_field'
)"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_circular (
title TEXT,
vec1 FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='vec1'
)" 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_empty (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_empty (id, content) VALUES (1, '')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_empty (id, content) VALUES (2, NULL)" 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) FROM test_empty"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_rowwise (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='rowwise'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_rowwise (id, content) VALUES
(1, 'machine learning'),
(2, 'deep learning')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_rowwise; OPTIMIZE TABLE test_rowwise OPTION sync=1, cutoff=1"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_rowwise WHERE KNN(vec, 1, 'artificial intelligence')"
––– output –––
+------+
| id |
+------+
| 1 |
- | 2 |
+ +------+
- +------+
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_vec_columnar (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' engine='columnar'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
)"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_vec_columnar (id, content) VALUES
(1, 'machine learning'),
(2, 'deep learning')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_vec_columnar; OPTIMIZE TABLE test_vec_columnar OPTION sync=1, cutoff=1"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_vec_columnar WHERE KNN(vec, 1, 'artificial intelligence')"
––– output –––
+------+
| id |
+------+
| 1 |
- | 2 |
+ +------+
- +------+
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_full_columnar (
content TEXT,
vec FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2'
MODEL_NAME='sentence-transformers/all-MiniLM-L6-v2'
FROM='content'
) engine='columnar'"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_full_columnar (id, content) VALUES
(1, 'machine learning'),
(2, 'deep learning')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "FLUSH RAMCHUNK test_full_columnar; OPTIMIZE TABLE test_full_columnar OPTION sync=1, cutoff=1"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id FROM test_full_columnar WHERE KNN(vec, 1, 'artificial intelligence')"
––– output –––
+------+
| id |
+------+
| 1 |
- | 2 |
+ +------+
- +------+
––– input –––
echo "Row-wise (default):"
mysql -h0 -P9306 -e "SHOW CREATE TABLE test_rowwise\G" | grep -E "vec.*float_vector"
––– output –––
OK
––– input –––
echo "Vec columnar only:"
mysql -h0 -P9306 -e "SHOW CREATE TABLE test_vec_columnar\G" | grep -E "vec.*float_vector"
––– output –––
OK
––– input –––
echo "Full columnar:"
mysql -h0 -P9306 -e "SHOW CREATE TABLE test_full_columnar\G" | grep -E "(vec.*float_vector|engine='columnar')"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT * FROM test_auto_dims WHERE KNN(wrong_field, 1, 'test')" 2>&1 | grep -o "wrong_field.*not found"
––– output –––
OKtest/clt-tests/mcl/auto-embeddings-openai-remote.rec––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd --stopwait > /dev/null; stdbuf -oL searchd ${SEARCHD_ARGS:-} > /dev/null
––– output –––
OK
––– input –––
if timeout 10 grep -qm1 'accepting connections' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Accepting connections!'; else echo 'Timeout or failed!'; fi
––– output –––
OK
––– input –––
cosine_similarity() {
local file1="$1" file2="$2"
awk '
NR==FNR { a[NR]=$1; suma2+=$1*$1; next }
{
dot += a[FNR]*$1
sumb2 += $1*$1
}
END {
print dot / (sqrt(suma2) * sqrt(sumb2))
}' "$file1" "$file2"
}
––– output –––
OK
––– input –––
export -f cosine_similarity
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_invalid_model (title TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'openai/invalid-model-name-12345' FROM = 'title') " 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_valid_model_no_api_key (title TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'openai/text-embedding-ada-002' FROM = 'title') " 2>&1
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_openai_remote (title TEXT, content TEXT, description TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'openai/text-embedding-ada-002' FROM = 'title, content' API_KEY='${OPENAI_API_KEY}') "; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SHOW CREATE TABLE test_openai_remote"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_openai_remote (id, title, content, description) VALUES(1, 'machine learning algorithms', 'deep neural networks and artificial intelligence', 'advanced AI research')"; echo $?
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) as record_count FROM test_openai_remote WHERE id=1"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_openai_remote (id, title, content, description) VALUES(2, 'machine learning algorithms', 'deep neural networks and artificial intelligence', 'different description')"
mysql -h0 -P9306 -e "SELECT embedding FROM test_openai_remote WHERE id=1" | \
grep -v embedding | \
sed 's/[0-9]\+\(\.[0-9]\+\)\?/\n&\n/g' | \
grep -E '^[0-9]+(\.[0-9]+)?$' | \
awk '{printf "%.5f\n", $1}' > /tmp/vector1.txt
mysql -h0 -P9306 -e "SELECT embedding FROM test_openai_remote WHERE id=2" | \
grep -v embedding | \
sed 's/[0-9]\+\(\.[0-9]\+\)\?/\n&\n/g' | \
grep -E '^[0-9]+(\.[0-9]+)?$' | \
awk '{printf "%.5f\n", $1}' > /tmp/vector2.txt
SIMILARITY=$(cosine_similarity /tmp/vector1.txt /tmp/vector2.txt)
echo "Cosine similarity: $SIMILARITY"
RESULT=$(awk -v sim="$SIMILARITY" 'BEGIN {
if (sim > 0.99)
print "SUCCESS: Same FROM fields produce similar vectors (similarity: " sim ")"
else
print "FAIL: Different vectors (FROM does not include description field and should not change generated vector value) (similarity: " sim ")"
}')
echo "$RESULT"
rm -f /tmp/vector1.txt /tmp/vector2.txt
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_openai_title_only (title TEXT, content TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'openai/text-embedding-ada-002' FROM = 'title' API_KEY='${OPENAI_API_KEY}') "; mysql -h0 -P9306 -e "INSERT INTO test_openai_title_only (id, title, content) VALUES(1, 'machine learning algorithms', 'completely different content here')"; MD5_MULTI=$(mysql -h0 -P9306 -e "SELECT embedding FROM test_openai_remote WHERE id=1" | grep -v embedding | md5sum | awk '{print $1}'); MD5_SINGLE=$(mysql -h0 -P9306 -e "SELECT embedding FROM test_openai_title_only WHERE id=1" | grep -v embedding | md5sum | awk '{print $1}'); echo "multi_field_md5: $MD5_MULTI"; echo "single_field_md5: $MD5_SINGLE"; if [ "$MD5_MULTI" != "$MD5_SINGLE" ]; then echo "SUCCESS: Different FROM specifications produce different vectors"; else echo "INFO: FROM field comparison result"; fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "CREATE TABLE test_openai_invalid_field (title TEXT, embedding FLOAT_VECTOR KNN_TYPE='hnsw' HNSW_SIMILARITY='l2' MODEL_NAME = 'openai/text-embedding-ada-002' FROM = 'nonexistent_field') " 2>&1
––– output –––
OK
––– input –––
if mysql -h0 -P9306 -e "SHOW TABLES LIKE 'test_openai_no_from'" | grep -q test_openai_no_from; then mysql -h0 -P9306 -e "INSERT INTO test_openai_no_from (id, title, embedding) VALUES(1, 'test title', '(0.1, 0.2, 0.3, 0.4, 0.5)')"; echo "insert_result: $?"; else echo "insert_result: skipped (table not created)"; fi
––– output –––
OK
––– input –––
if mysql -h0 -P9306 -e "SHOW TABLES LIKE 'test_openai_no_from'" | grep -q test_openai_no_from; then mysql -h0 -P9306 -e "SHOW CREATE TABLE test_openai_no_from"; else echo "table_structure: skipped (table not created)"; fi
––– output –––
OK
––– input –––
if [ -n "$OPENAI_API_KEY" ] && [ "$OPENAI_API_KEY" != "dummy_key_for_testing" ]; then echo "API key is available for testing"; else echo "API key not available - using dummy for error testing"; fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT id, knn_dist() FROM test_openai_remote WHERE knn(embedding, 3, 'machine learning and artificial intelligence')\G"
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) as count FROM test_openai_remote WHERE knn(embedding, 5, 'technology and AI') AND id > 0"
––– output –––
OK
––– input –––
API_KEY_VAL="${OPENAI_API_KEY}"; cat > /etc/manticoresearch/manticore.conf << CONFEOF
searchd {
listen = 127.0.0.1:9306:mysql41
listen = 127.0.0.1:9308:http
log = /var/log/manticore/searchd.log
pid_file = /var/run/manticore/searchd.pid
}
table test_openai_plain {
type = rt
path = /var/lib/manticore/test_openai_plain
rt_field = title
rt_field = content
rt_attr_float_vector = embedding
knn = {"attrs":[{"name":"embedding","type":"hnsw","hnsw_similarity":"L2","hnsw_m":16,"hnsw_ef_construction":200,"model_name":"openai/text-embedding-ada-002","from":"title,content","api_key":"${API_KEY_VAL}"}]}
}
CONFEOF
––– output –––
OK
––– input –––
searchd --stopwait --quiet
––– output –––
+ [Tue Jan 13 16:15:04.795 2026] [93] WARNING: Error initializing secondary index: daemon requires secondary library v18 (trying to load v19)
+ [Tue Jan 13 16:15:04.795 2026] [93] FATAL: malformed or unknown option near '--quiet'; use '-h' or '--help' to see available options.
+ Manticore 0.0.0 0240e6481@25121214 (columnar 0.0.0 e5dff86@26011314) (knn 0.0.0 e5dff86@26011314) (embeddings 1.1.0 unknown@00000000)
+ Copyright (c) 2001-2016, Andrew Aksyonoff
+ Copyright (c) 2008-2016, Sphinx Technologies Inc (http://sphinxsearch.com)
+ Copyright (c) 2017-2025, Manticore Software LTD (https://manticoresearch.com)
––– input –––
rm -f /var/log/manticore/searchd.log; stdbuf -oL searchd --stopwait > /dev/null; stdbuf -oL searchd ${SEARCHD_ARGS:-} > /dev/null
––– output –––
OK
––– input –––
if timeout 10 grep -qm1 'accepting connections' <(tail -n 1000 -f /var/log/manticore/searchd.log); then echo 'Accepting connections!'; else echo 'Timeout or failed!'; fi
––– output –––
OK
––– input –––
mysql -h0 -P9306 -e "SHOW TABLES"
––– output –––
- +-------------------+------+
- | Table | Type |
- +-------------------+------+
- | test_openai_plain | rt |
- +-------------------+------+
––– input –––
mysql -h0 -P9306 -e "INSERT INTO test_openai_plain (id, title, content) VALUES(1, 'bread', 'food item'), (2, 'cat', 'animal pet')"; echo $?
––– output –––
- 0
+ ERROR 1064 (42000) at line 1: Cannot create the table automatically in Plain mode. Make sure the table exists before inserting into it
+ 1
––– input –––
mysql -h0 -P9306 -e "SELECT COUNT(*) as count FROM test_openai_plain"
––– output –––
- +-------+
+ ERROR 1064 (42000) at line 1: unknown local table(s) 'test_openai_plain' in search request
- | count |
- +-------+
- | 2 |
- +-------+
––– input –––
mysql -h0 -P9306 -E -e "SELECT id, title FROM test_openai_plain WHERE knn(embedding, 2, 'dog')"
––– output –––
- *************************** 1. row ***************************
+ ERROR 1064 (42000) at line 1: unknown local table(s) 'test_openai_plain' in search request
- id: 2
- title: cat
- *************************** 2. row ***************************
- id: 1
- title: bread
––– input –––
cat > /etc/manticoresearch/manticore.conf << 'EOF'
searchd {
listen = 127.0.0.1:9306:mysql41
listen = 127.0.0.1:9308:http
log = /var/log/manticore/searchd.log
pid_file = /var/run/manticore/searchd.pid
}
table test_openai_no_key {
type = rt
path = /var/lib/manticore/test_openai_no_key
rt_field = title
rt_attr_float_vector = embedding
knn = {"attrs":[{"name":"embedding","type":"hnsw","hnsw_similarity":"L2","model_name":"openai/text-embedding-ada-002","from":"title"}]}
}
EOF
––– output –––
OK
––– input –––
searchd --stopwait --quiet
––– output –––
+ [Tue Jan 13 16:15:05.235 2026] [131] WARNING: Error initializing secondary index: daemon requires secondary library v18 (trying to load v19)
+ [Tue Jan 13 16:15:05.235 2026] [131] FATAL: malformed or unknown option near '--quiet'; use '-h' or '--help' to see available options.
+ Manticore 0.0.0 0240e6481@25121214 (columnar 0.0.0 e5dff86@26011314) (knn 0.0.0 e5dff86@26011314) (embeddings 1.1.0 unknown@00000000)
+ Copyright (c) 2001-2016, Andrew Aksyonoff
+ Copyright (c) 2008-2016, Sphinx Technologies Inc (http://sphinxsearch.com)
+ Copyright (c) 2017-2025, Manticore Software LTD (https://manticoresearch.com)
––– input –––
searchd 2>&1|grep WARNING
––– output –––
- WARNING: table 'test_openai_no_key': prealloc: Invalid API key for remote model - NOT SERVING
+ [Tue Jan 13 16:15:05.247 2026] [132] WARNING: Error initializing secondary index: daemon requires secondary library v18 (trying to load v19) |
Collaborator
Author
|
@glookka pls review the C++ changes in this PR. |
glookka
requested changes
Jan 22, 2026
glookka
approved these changes
Jan 23, 2026
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Custom API URL Support for Embeddings
Related PR in the daemon manticoresoftware/manticoresearch#4034
Related issues:
PR Overview
Support for custom API URLs and configurable API timeouts has been implemented and tested in the embeddings system. Users can now specify custom endpoints for OpenAI, Voyage, and Jina API requests while maintaining backward compatibility with default URLs. Additionally, API timeouts are now configurable (previously hardcoded to 10 seconds and couldn't be adjusted, leading to timeouts in some cases). The system now validates API keys by making actual API requests instead of format-based checks (prefix matching), providing authoritative validation that works with any key format and custom endpoints. Error handling has been improved to include HTTP status codes in error messages (e.g., "HTTP error from remote model: status code 403") for better debugging and troubleshooting. The embeddings library version string format has been enhanced to include git commit hash and timestamp (matching the format used by other Manticore components), providing better traceability in
searchd -voutput. TheSHOW CREATE TABLEoutput has also been fixed to properly display embedding configuration parameters (api_url,from, andapi_timeout). Note:api_keyis intentionally excluded fromSHOW CREATE TABLEoutput for security reasons.Problem Statement
Previously, the embeddings system had several limitations:
Hardcoded API URLs: The system used fixed API endpoints for remote models:
https://api.openai.com/v1/embeddingshttps://api.voyageai.com/v1/embeddingshttps://api.jina.ai/v1/embeddingsThis prevented users from using custom API endpoints, such as:
Fixed API Timeout: The HTTP timeout was hardcoded to 10 seconds and couldn't be configured. This caused issues in some scenarios:
Heuristic-Based API Key Validation: API keys were validated using format-based checks (prefix matching):
This approach had several limitations:
Incomplete SHOW CREATE TABLE Output: The
SHOW CREATE TABLEstatement didn't display important embedding configuration parameters (api_url,from,api_timeout), making it difficult to see the complete table configuration and reproduce table definitions. Note:api_keyis intentionally excluded from the output for security reasons.Implementation
Architecture Changes
The implementation follows the existing data flow:
Changes Made
1. C++ Interface (
knn/knn.h)m_sAPIUrl: std::stringfield toModelSettings_tstructm_iAPITimeout: intfield toModelSettings_tstruct2. C++ Implementation (
knn/embeddings.cpp)ToKey()function to include API URL and timeout in cache key (ensures different URLs/timeouts create separate cached models)reserve()and append operations for efficiency when building the key from multiple componentsload_model()call to pass API URL and timeout parameters to Rust FFISUPPORTED_EMBEDDINGS_LIB_VERfrom 2 to 3 (ABI-breaking change requires version bump)validate_api_key()after model creation inTextToEmbeddings_c::Initialize()3. Rust FFI Layer (
embeddings/src/model/text_model_wrapper.rs)load_model()function signature to acceptapi_url_ptr,api_url_len, andapi_timeoutparametersOption<String>ModelOptionsstructvalidate_api_key()FFI function that calls the model'svalidate_api_key()methodfree_string()FFI function to free error strings returned by validation4. Rust Model Options (
embeddings/src/model/mod.rs)api_url: Option<String>field toModelOptionsstructapi_timeout: Option<u64>field toModelOptionsstruct (None means use default 10 seconds)create_model()function to passapi_urlandapi_timeoutto all remote model constructors (OpenAI, Voyage, Jina)validate_api_key()method toTextModeltraitvalidate_api_key()forModelenum to delegate to specific model implementations5. Model Implementations
OpenAI Model (
embeddings/src/model/openai.rs):api_url: Option<String>field toOpenAIModelstructnew()constructor to accept optionalapi_urlandapi_timeoutparameterspredict()method to use custom URL if provided, otherwise defaults tohttps://api.openai.com/v1/embeddingsvalidate_api_key()method that makes an actual API requestapi_timeoutparameter (defaults to 10 seconds if not specified)validate_api_key()method makes a minimal test request with "test" string to validate the keyRemoteHttpError { status }with the actual HTTP status code (e.g., 403, 404, 429) without interpretation, allowing users to determine the meaning based on the status codeVoyage Model (
embeddings/src/model/voyage.rs):api_url: Option<String>field toVoyageModelstructnew()constructor to accept optionalapi_urlandapi_timeoutparameterspredict()method to use custom URL if provided, otherwise defaults tohttps://api.voyageai.com/v1/embeddingsvalidate_api_key()methodapi_timeoutparameter (defaults to 10 seconds if not specified)validate_api_key()method makes a minimal test request with "test" string to validate the keyRemoteHttpError { status }with the actual HTTP status code (e.g., 403, 404, 429) without interpretation, allowing users to determine the meaning based on the status codeJina Model (
embeddings/src/model/jina.rs):api_url: Option<String>field toJinaModelstructnew()constructor to accept optionalapi_urlandapi_timeoutparameterspredict()method to use custom URL if provided, otherwise defaults tohttps://api.jina.ai/v1/embeddingsvalidate_api_key()methodapi_timeoutparameter (defaults to 10 seconds if not specified)validate_api_key()method makes a minimal test request with "test" string to validate the keyRemoteHttpError { status }with the actual HTTP status code (e.g., 403, 404, 429) without interpretation, allowing users to determine the meaning based on the status codeLocal Model (
embeddings/src/model/local.rs):validate_api_key()method that returnsOk(())immediately (no API key required for local models)6. FFI & Versioning (
embeddings/src/ffi.rs)LoadModelFntype signature to include two additional parameters for API URLbuild.rsand embedded in the libraryValidateApiKeyFnandFreeStringFnfunction pointer types toEmbedLibstructvalidate_api_key()andfree_string()functions through FFI for C++ integration7. Build System & Version Generation (
embeddings/build.rsandcmake/build_embeddings.cmake)build.rsto generate version strings with git commit and timestampGIT_COMMIT_IDenvironment variable or git commandGIT_TIMESTAMP_IDenvironment variable or git commandcargo:rustc-envfor compile-time embeddingbuild_embeddings.cmaketo pass git commit and timestamp as environment variablesGIT_COMMIT_IDandGIT_TIMESTAMP_IDenvironment variables before cargo buildcmake/rev.cmake(included in main CMakeLists.txt)8. Generated C Header (
embeddings/manticoresearch_text_embeddings.h)build.rsduring cargo build9. Manticore Search SQL Interface (
manticore_github/src/)ddl.l): AddedTOK_API_URLandTOK_API_TIMEOUTtoken recognitionddl.y):API_URL='<URL>'parameterAPI_TIMEOUT='<seconds>'parameterALTER TABLE ... MODIFY COLUMN ... API_TIMEOUT='<seconds>'supportsearchdddl.cpp):m_sAPIUrlfield toItemOptions_tstructm_iAPITimeoutfield toItemOptions_tstruct (defaults to 0 = use default)AddItemOptionAPIUrl()method to parse API_URL from SQLAddItemOptionAPITimeout()method to parse API_TIMEOUT from SQL (validates positive integer)ToKNNModel()to mapm_sAPIUrlandm_iAPITimeouttoModelSettings_tschema/schema.cpp):NamedKNNSettings_ttoModelSettings_tusing explicitstatic_castto avoid slicing issues with multiple inheritanceModelSettings_tsubobject fromNamedKNNSettings_t(which uses multiple inheritance:IndexSettings_t + ModelSettings_t)indexsettings.cpp):ModelSettings_tfields when converting fromCreateTableAttr_ttoNamedKNNSettings_tknnmisc.cpp):AddKNNSettings()to includefrom,api_url, andapi_timeout(if non-default) inSHOW CREATE TABLEoutputapi_keyis intentionally excluded fromSHOW CREATE TABLEoutput for security reasonsapi_timeoutParseKNNConfigStr()to parseapi_timeoutfrom configuration stringssphinxrt.cpp,searchd.cpp,searchdsql.h):STMT_ALTER_EMBEDDINGS_API_TIMEOUTstatement typeAlter_e::ApiTimeoutenum valueAlterApiTimeout()method insphinxrt.cppto modify timeout via ALTER TABLEAlterApiTimeout()tosphinx.hbase classmanual/english/Searching/KNN.mdandmanual/english/Creating_a_table/Data_types.md):API_URLandAPI_TIMEOUTparameters for OpenAI, Voyage, and Jina modelsUsage Example
C++ API Usage
If
m_sAPIUrlis empty or not set, the system uses the default URLs for each provider.SQL Usage
Users can now specify a custom API URL directly in SQL CREATE TABLE statements:
The
API_URLandAPI_TIMEOUTparameters are optional:API_URLis not specified, the system uses the default URLs for each providerAPI_TIMEOUTis not specified or set to 0, the system uses the default timeout of 10 secondsYou can also modify the timeout via ALTER TABLE:
Real API Key Validation
Overview
Changed from format-based to real API validation: The system now validates API keys by making actual API requests instead of format-based checks (prefix matching). This fundamental change ensures that:
Implementation
Validation Timing: API key validation occurs:
CREATE TABLEwhen embeddings model is specifiedALTER TABLE ... MODIFY COLUMN ... API_KEY=...ALTER TABLE ... MODIFY COLUMN ... API_URL=...Validation Method:
API_TIMEOUTparameter (defaults to 10 seconds if not specified)RemoteHttpError { status }with the specific status codeRemoved Format Validation:
Local Models:
validate_api_key()returnsOk(())immediatelyBenefits
Considerations
Backward Compatibility
Testing
Mock Server for Testing
A PHP mock server (
test/clt-tests/mcl/mock-embeddings-server.php) has been created to enable testing of theAPI_URLparameter without making real API calls:/v1/embeddingsand returns deterministic random embeddings seeded by input text--delay SECONDScommand line parameter to set default delaydelay=Nsubstring in input text to override delay per request (e.g.,"test delay=2.5")--provider PROVIDERcommand line parameter to set default providerprovider=NAMEsubstring in input text to override provider per requestdelay=N,provider=..., andresponse-type=...substrings are automatically removed from input text before generating embeddings, ensuring they don't affect embedding valuesresponse-type=TYPEsubstring in input text to override response format per request:response-type=html- Returns HTML instead of JSON (for testing parsing errors)response-type=invalid-json- Returns malformed JSON (for testing JSON parsing errors)response-type=wrong-structure- Returns valid JSON but wrong structure (for testing response format validation)response-type=defaultor not specified - Returns normal embeddings JSON--help,-h, or without any parameters{"data": [{"embedding": [0.379464, -0.250134, ...]}]}structureExample Usage:
Rust Unit Test Files Updated
The following Rust unit test files in
embeddings/src/model/have been updated to match the new implementation:openai_test.rs:validate_api_keyfunction import (now a trait method that makes real API requests)OpenAIModel::new()calls to includeapi_urlandapi_timeoutparametersvoyage_test.rs:validate_api_keyfunction importVoyageModel::new()calls to includeapi_urlandapi_timeoutparametersjina_test.rs:validate_api_keyfunction importJinaModel::new()calls to includeapi_urlandapi_timeoutparameters#[test]attributes on test functionsffi_test.rs:load_modelsignature with newapi_urlandapi_timeoutparameterslocal_test.rs:Test Results: All 132 unit tests pass successfully, validating the embeddings library functionality including the new API URL and timeout features.
Additional Implementation Notes
Real API Key Validation: API keys are now validated by making actual API requests instead of format checks. This provides authoritative validation and works with any key format.
SHOW CREATE TABLE Output: The
SHOW CREATE TABLEstatement now displaysapi_url,from, andapi_timeout(if non-default) parameters in the output, allowing users to see the complete configuration. Note:api_keyis intentionally excluded from the output for security reasons.Cache Key: Different
API_URLvalues create separate cached model instances, ensuring that models with different endpoints are kept separate.Configurable API Timeout: The
API_TIMEOUTparameter allows users to specify a custom HTTP timeout for API requests:ALTER TABLE ... MODIFY COLUMN ... API_TIMEOUT='<seconds>'SHOW CREATE TABLEoutput only if set to a non-default valueTest Requests: Validation uses minimal test string ("test") to reduce API costs while ensuring the key works.
Version String Enhancement
Overview
The embeddings library version string has been enhanced to include git commit hash and timestamp, matching the format used by other Manticore components (columnar, secondary, knn). This provides better traceability and debugging capabilities.
Implementation Details
Version String Format:
VERSION commit@timestamp1.1.0 38f499e@25112313VERSION: Semantic version fromCargo.toml(currently "1.1.0")commit: Short git commit hash (7 characters)timestamp: Git commit timestamp in YYMMDDHH format (8 digits)Build Process:
cmake/rev.cmake) extracts git commit and timestamp from the repositorybuild_embeddings.cmakepasses these values as environment variables (GIT_COMMIT_ID,GIT_TIMESTAMP_ID) to cargo buildbuild.rsreads these environment variables (or falls back to git commands) and generates the version stringcargo:rustc-envand read inffi.rsusingenv!()macroEmbedLib.version_strfield, which is displayed bysearchd -vDisplay Output:
When running
searchd -v, the embeddings library version is now displayed as:Backward Compatibility:
Files Modified
Columnar Repository (Embeddings Library)
knn/knn.h- Addedm_sAPIUrlandm_iAPITimeoutfields toModelSettings_tknn/embeddings.cpp- Updated cache key and FFI call, version bump, API key validationembeddings/src/model/text_model_wrapper.rs- Extended FFI signatureembeddings/src/model/mod.rs- Addedapi_urlfield and propagationembeddings/src/model/openai.rs- Added URL support, relaxed API key validation for custom URLsembeddings/src/model/voyage.rs- Added URL support, relaxed API key validation for custom URLsembeddings/src/model/jina.rs- Added URL support, relaxed API key validation for custom URLsembeddings/src/model/openai_test.rs- Updated tests for new validate_api_key signature, added custom URL testsembeddings/src/model/voyage_test.rs- Updated tests for new validate_api_key signature, added custom URL testsembeddings/src/model/jina_test.rs- Updated tests for new validate_api_key signature, added custom URL testsembeddings/src/ffi.rs- Updated function signature, version bump, and version string generationembeddings/build.rs- Added git commit and timestamp extraction for version string generationembeddings/Cargo.toml- Updated version from "0.1.0" to "1.1.0"cmake/build_embeddings.cmake- Added environment variable passing for git commit and timestampembeddings/manticoresearch_text_embeddings.h- Auto-regenerated headerManticore Search Repository (SQL Interface)
src/ddl.l- AddedTOK_API_URLtokensrc/ddl.y- AddedAPI_URLparsing rulesrc/searchdddl.cpp- Addedm_sAPIUrlfield,AddItemOptionAPIUrl()method, and updatedToKNNModel()src/schema/schema.cpp- Fixed ModelSettings_t assignment with explicitstatic_castto avoid slicingsrc/indexsettings.cpp- Uses direct casts to ensure proper ModelSettings_t copying without object slicingsrc/knnmisc.cpp- UpdatedAddKNNSettings()to display api_url in SHOW CREATE TABLE outputsrc/schema/columninfo.h-NamedKNNSettings_tstruct definition (uses multiple inheritance)manual/english/Searching/KNN.md- Updated documentation with API_URL examplesmanual/english/Creating_a_table/Data_types.md- Updated documentation with API_URL examplesTesting Infrastructure
test/clt-tests/mcl/mock-embeddings-server.php- PHP mock server for testing API_URL functionalitytest/clt-tests/mcl/auto-embeddings-openai-remote.rec- Added actual test cases for API_URL functionalitytest/clt-tests/mcl/auto-embeddings-voyage-remote.rec- Added actual test cases for API_URL functionality