1. Vector search → "Find documents similar to this embedding"
2. Graph traversal → "Walk relationships between entities"
3. Full-text search → "Rank documents containing these keywords"
1. Vector search → "Find documents similar to this embedding"
2. Graph traversal → "Walk relationships between entities"
3. Full-text search → "Rank documents containing these keywords"
1. Vector search → "Find documents similar to this embedding"
2. Graph traversal → "Walk relationships between entities"
3. Full-text search → "Rank documents containing these keywords"
Your app → Pinecone client (proprietary API) → Neo4j client (Cypher) → Elasticsearch (JSON DSL) → custom code to merge results
Your app → Pinecone client (proprietary API) → Neo4j client (Cypher) → Elasticsearch (JSON DSL) → custom code to merge results
Your app → Pinecone client (proprietary API) → Neo4j client (Cypher) → Elasticsearch (JSON DSL) → custom code to merge results
Your app → VelesDB + VelesQL (one language, one result set)
Your app → VelesDB + VelesQL (one language, one result set)
Your app → VelesDB + VelesQL (one language, one result set)
pip install velesdb numpy
pip install velesdb numpy
pip install velesdb numpy
import velesdb
import numpy as np db = velesdb.Database("./velesql_demo")
import velesdb
import numpy as np db = velesdb.Database("./velesql_demo")
import velesdb
import numpy as np db = velesdb.Database("./velesql_demo")
# Create a vector collection
db.execute_query("CREATE COLLECTION articles (dimension = 384, metric = 'cosine')") # Create a graph collection with schema
db.execute_query(""" CREATE GRAPH COLLECTION knowledge (dimension = 384, metric = 'cosine') SCHEMALESS
""") # Inspect what we have
results = db.execute_query("SHOW COLLECTIONS")
for r in results: print(f" {r['payload']['name']} ({r['payload']['type']})")
# Create a vector collection
db.execute_query("CREATE COLLECTION articles (dimension = 384, metric = 'cosine')") # Create a graph collection with schema
db.execute_query(""" CREATE GRAPH COLLECTION knowledge (dimension = 384, metric = 'cosine') SCHEMALESS
""") # Inspect what we have
results = db.execute_query("SHOW COLLECTIONS")
for r in results: print(f" {r['payload']['name']} ({r['payload']['type']})")
# Create a vector collection
db.execute_query("CREATE COLLECTION articles (dimension = 384, metric = 'cosine')") # Create a graph collection with schema
db.execute_query(""" CREATE GRAPH COLLECTION knowledge (dimension = 384, metric = 'cosine') SCHEMALESS
""") # Inspect what we have
results = db.execute_query("SHOW COLLECTIONS")
for r in results: print(f" {r['payload']['name']} ({r['payload']['type']})")
articles (vector) knowledge (graph)
articles (vector) knowledge (graph)
articles (vector) knowledge (graph)
info = db.execute_query("DESCRIBE COLLECTION articles")
print(info[0]['payload'])
# {'name': 'articles', 'type': 'vector', 'dimension': 384, 'metric': 'Cosine', 'point_count': 0}
info = db.execute_query("DESCRIBE COLLECTION articles")
print(info[0]['payload'])
# {'name': 'articles', 'type': 'vector', 'dimension': 384, 'metric': 'Cosine', 'point_count': 0}
info = db.execute_query("DESCRIBE COLLECTION articles")
print(info[0]['payload'])
# {'name': 'articles', 'type': 'vector', 'dimension': 384, 'metric': 'Cosine', 'point_count': 0}
def make_vector(): """Generate a random 384-dim vector for demonstration.""" v = np.random.rand(384).astype(np.float32) return (v / np.linalg.norm(v)).tolist() np.random.seed(42) documents = [ (1, "Vector databases for AI applications", "tutorial"), (2, "Graph neural networks explained", "guide"), (3, "Building RAG pipelines with Python", "tutorial"), (4, "Knowledge graphs in healthcare", "research"), (5, "Hybrid search strategies for production", "tutorial"),
] for doc_id, title, category in documents: v = make_vector() db.execute_query( "INSERT INTO articles (id, vector, title, category) VALUES ($id, $v, $title, $cat)", {"id": doc_id, "v": v, "title": title, "cat": category} ) print(f"Inserted {len(documents)} documents")
def make_vector(): """Generate a random 384-dim vector for demonstration.""" v = np.random.rand(384).astype(np.float32) return (v / np.linalg.norm(v)).tolist() np.random.seed(42) documents = [ (1, "Vector databases for AI applications", "tutorial"), (2, "Graph neural networks explained", "guide"), (3, "Building RAG pipelines with Python", "tutorial"), (4, "Knowledge graphs in healthcare", "research"), (5, "Hybrid search strategies for production", "tutorial"),
] for doc_id, title, category in documents: v = make_vector() db.execute_query( "INSERT INTO articles (id, vector, title, category) VALUES ($id, $v, $title, $cat)", {"id": doc_id, "v": v, "title": title, "cat": category} ) print(f"Inserted {len(documents)} documents")
def make_vector(): """Generate a random 384-dim vector for demonstration.""" v = np.random.rand(384).astype(np.float32) return (v / np.linalg.norm(v)).tolist() np.random.seed(42) documents = [ (1, "Vector databases for AI applications", "tutorial"), (2, "Graph neural networks explained", "guide"), (3, "Building RAG pipelines with Python", "tutorial"), (4, "Knowledge graphs in healthcare", "research"), (5, "Hybrid search strategies for production", "tutorial"),
] for doc_id, title, category in documents: v = make_vector() db.execute_query( "INSERT INTO articles (id, vector, title, category) VALUES ($id, $v, $title, $cat)", {"id": doc_id, "v": v, "title": title, "cat": category} ) print(f"Inserted {len(documents)} documents")
db.execute_query( """INSERT INTO articles (id, vector, title, category) VALUES ($id1, $v1, 'Sparse embeddings 101', 'tutorial'), ($id2, $v2, 'Local-first AI architectures', 'guide')""", {"id1": 6, "v1": make_vector(), "id2": 7, "v2": make_vector()}
)
db.execute_query( """INSERT INTO articles (id, vector, title, category) VALUES ($id1, $v1, 'Sparse embeddings 101', 'tutorial'), ($id2, $v2, 'Local-first AI architectures', 'guide')""", {"id1": 6, "v1": make_vector(), "id2": 7, "v2": make_vector()}
)
db.execute_query( """INSERT INTO articles (id, vector, title, category) VALUES ($id1, $v1, 'Sparse embeddings 101', 'tutorial'), ($id2, $v2, 'Local-first AI architectures', 'guide')""", {"id1": 6, "v1": make_vector(), "id2": 7, "v2": make_vector()}
)
query_vector = make_vector() results = db.execute_query( """SELECT id, title, similarity() AS score FROM articles WHERE vector NEAR $q ORDER BY similarity() DESC LIMIT 3""", {"q": query_vector}
) for r in results: print(f" [{r['score']:.3f}] {r['payload']['title']}")
query_vector = make_vector() results = db.execute_query( """SELECT id, title, similarity() AS score FROM articles WHERE vector NEAR $q ORDER BY similarity() DESC LIMIT 3""", {"q": query_vector}
) for r in results: print(f" [{r['score']:.3f}] {r['payload']['title']}")
query_vector = make_vector() results = db.execute_query( """SELECT id, title, similarity() AS score FROM articles WHERE vector NEAR $q ORDER BY similarity() DESC LIMIT 3""", {"q": query_vector}
) for r in results: print(f" [{r['score']:.3f}] {r['payload']['title']}")
[0.821] Vector databases for AI applications [0.779] Building RAG pipelines with Python [0.752] Hybrid search strategies for production
[0.821] Vector databases for AI applications [0.779] Building RAG pipelines with Python [0.752] Hybrid search strategies for production
[0.821] Vector databases for AI applications [0.779] Building RAG pipelines with Python [0.752] Hybrid search strategies for production
results = db.execute_query( """SELECT id, title, similarity() AS score FROM articles WHERE vector NEAR $q AND category = 'tutorial' ORDER BY similarity() DESC LIMIT 5""", {"q": query_vector}
) for r in results: print(f" [{r['score']:.3f}] {r['payload']['title']}")
results = db.execute_query( """SELECT id, title, similarity() AS score FROM articles WHERE vector NEAR $q AND category = 'tutorial' ORDER BY similarity() DESC LIMIT 5""", {"q": query_vector}
) for r in results: print(f" [{r['score']:.3f}] {r['payload']['title']}")
results = db.execute_query( """SELECT id, title, similarity() AS score FROM articles WHERE vector NEAR $q AND category = 'tutorial' ORDER BY similarity() DESC LIMIT 5""", {"q": query_vector}
) for r in results: print(f" [{r['score']:.3f}] {r['payload']['title']}")
# IN: filter by a set of values
results = db.execute_query( "SELECT id, title FROM articles WHERE category IN ('tutorial', 'research') LIMIT 10"
) # BETWEEN: range filtering
results = db.execute_query( "SELECT id, title FROM articles WHERE price BETWEEN 10 AND 100 LIMIT 10"
) # ILIKE: case-insensitive pattern matching
results = db.execute_query( "SELECT id, title FROM articles WHERE title ILIKE '%ai%' LIMIT 10"
) # IS NULL / IS NOT NULL
results = db.execute_query( "SELECT id, title FROM articles WHERE category IS NOT NULL LIMIT 10"
) # NOT + compound conditions
results = db.execute_query( "SELECT id, title FROM articles WHERE NOT (category = 'draft') AND price > 50 LIMIT 10"
)
# IN: filter by a set of values
results = db.execute_query( "SELECT id, title FROM articles WHERE category IN ('tutorial', 'research') LIMIT 10"
) # BETWEEN: range filtering
results = db.execute_query( "SELECT id, title FROM articles WHERE price BETWEEN 10 AND 100 LIMIT 10"
) # ILIKE: case-insensitive pattern matching
results = db.execute_query( "SELECT id, title FROM articles WHERE title ILIKE '%ai%' LIMIT 10"
) # IS NULL / IS NOT NULL
results = db.execute_query( "SELECT id, title FROM articles WHERE category IS NOT NULL LIMIT 10"
) # NOT + compound conditions
results = db.execute_query( "SELECT id, title FROM articles WHERE NOT (category = 'draft') AND price > 50 LIMIT 10"
)
# IN: filter by a set of values
results = db.execute_query( "SELECT id, title FROM articles WHERE category IN ('tutorial', 'research') LIMIT 10"
) # BETWEEN: range filtering
results = db.execute_query( "SELECT id, title FROM articles WHERE price BETWEEN 10 AND 100 LIMIT 10"
) # ILIKE: case-insensitive pattern matching
results = db.execute_query( "SELECT id, title FROM articles WHERE title ILIKE '%ai%' LIMIT 10"
) # IS NULL / IS NOT NULL
results = db.execute_query( "SELECT id, title FROM articles WHERE category IS NOT NULL LIMIT 10"
) # NOT + compound conditions
results = db.execute_query( "SELECT id, title FROM articles WHERE NOT (category = 'draft') AND price > 50 LIMIT 10"
)
results = db.execute_query( "SELECT id, title FROM articles WHERE title MATCH 'graph' LIMIT 5"
) for r in results: print(f" {r['payload']['title']}")
results = db.execute_query( "SELECT id, title FROM articles WHERE title MATCH 'graph' LIMIT 5"
) for r in results: print(f" {r['payload']['title']}")
results = db.execute_query( "SELECT id, title FROM articles WHERE title MATCH 'graph' LIMIT 5"
) for r in results: print(f" {r['payload']['title']}")
results = db.execute_query( """SELECT id, title, similarity() AS score FROM articles WHERE vector NEAR $q AND title MATCH 'AI' ORDER BY similarity() DESC LIMIT 5""", {"q": query_vector}
) for r in results: print(f" [{r['score']:.3f}] {r['payload']['title']}")
results = db.execute_query( """SELECT id, title, similarity() AS score FROM articles WHERE vector NEAR $q AND title MATCH 'AI' ORDER BY similarity() DESC LIMIT 5""", {"q": query_vector}
) for r in results: print(f" [{r['score']:.3f}] {r['payload']['title']}")
results = db.execute_query( """SELECT id, title, similarity() AS score FROM articles WHERE vector NEAR $q AND title MATCH 'AI' ORDER BY similarity() DESC LIMIT 5""", {"q": query_vector}
) for r in results: print(f" [{r['score']:.3f}] {r['payload']['title']}")
results = db.execute_query( """SELECT id, title, similarity() AS score FROM articles WHERE vector NEAR $q AND title MATCH 'AI' LIMIT 5 USING FUSION(strategy = 'rrf', k = 60)""", {"q": query_vector}
)
results = db.execute_query( """SELECT id, title, similarity() AS score FROM articles WHERE vector NEAR $q AND title MATCH 'AI' LIMIT 5 USING FUSION(strategy = 'rrf', k = 60)""", {"q": query_vector}
)
results = db.execute_query( """SELECT id, title, similarity() AS score FROM articles WHERE vector NEAR $q AND title MATCH 'AI' LIMIT 5 USING FUSION(strategy = 'rrf', k = 60)""", {"q": query_vector}
)
results = db.execute_query( """LET relevance = similarity() SELECT id, title, relevance AS score FROM articles WHERE vector NEAR $q ORDER BY relevance DESC LIMIT 3""", {"q": query_vector}
) for r in results: print(f" [{r['score']:.3f}] {r['payload']['title']}")
results = db.execute_query( """LET relevance = similarity() SELECT id, title, relevance AS score FROM articles WHERE vector NEAR $q ORDER BY relevance DESC LIMIT 3""", {"q": query_vector}
) for r in results: print(f" [{r['score']:.3f}] {r['payload']['title']}")
results = db.execute_query( """LET relevance = similarity() SELECT id, title, relevance AS score FROM articles WHERE vector NEAR $q ORDER BY relevance DESC LIMIT 3""", {"q": query_vector}
) for r in results: print(f" [{r['score']:.3f}] {r['payload']['title']}")
results = db.execute_query( """LET hybrid = 0.7 * vector_score + 0.3 * bm25_score SELECT id, title, hybrid AS score FROM articles WHERE vector NEAR $q AND title MATCH 'search' ORDER BY hybrid DESC LIMIT 5""", {"q": query_vector}
)
results = db.execute_query( """LET hybrid = 0.7 * vector_score + 0.3 * bm25_score SELECT id, title, hybrid AS score FROM articles WHERE vector NEAR $q AND title MATCH 'search' ORDER BY hybrid DESC LIMIT 5""", {"q": query_vector}
)
results = db.execute_query( """LET hybrid = 0.7 * vector_score + 0.3 * bm25_score SELECT id, title, hybrid AS score FROM articles WHERE vector NEAR $q AND title MATCH 'search' ORDER BY hybrid DESC LIMIT 5""", {"q": query_vector}
)
LET base = 0.6 * vector_score + 0.4 * bm25_score
LET boosted = base * 1.5
SELECT *, boosted AS final_score
FROM docs
WHERE vector NEAR $v AND content MATCH 'AI'
ORDER BY boosted DESC
LIMIT 10
LET base = 0.6 * vector_score + 0.4 * bm25_score
LET boosted = base * 1.5
SELECT *, boosted AS final_score
FROM docs
WHERE vector NEAR $v AND content MATCH 'AI'
ORDER BY boosted DESC
LIMIT 10
LET base = 0.6 * vector_score + 0.4 * bm25_score
LET boosted = base * 1.5
SELECT *, boosted AS final_score
FROM docs
WHERE vector NEAR $v AND content MATCH 'AI'
ORDER BY boosted DESC
LIMIT 10
import json # Insert nodes with _labels (required for MATCH pattern matching)
db.execute_query( "INSERT NODE INTO knowledge (id = 1, payload = '%s')" % json.dumps({"name": "Alice", "role": "engineer", "_labels": ["Person"]})
)
db.execute_query( "INSERT NODE INTO knowledge (id = 2, payload = '%s')" % json.dumps({"name": "Bob", "role": "researcher", "_labels": ["Person"]})
)
db.execute_query( "INSERT NODE INTO knowledge (id = 3, payload = '%s')" % json.dumps({"name": "Acme Corp", "industry": "tech", "_labels": ["Company"]})
) # Add vectors to nodes (needed for combined vector + graph queries)
coll = db.get_collection("knowledge")
coll.upsert(1, vector=make_vector(), payload={"name": "Alice", "role": "engineer", "_labels": ["Person"]})
coll.upsert(2, vector=make_vector(), payload={"name": "Bob", "role": "researcher", "_labels": ["Person"]})
coll.upsert(3, vector=make_vector(), payload={"name": "Acme Corp", "industry": "tech", "_labels": ["Company"]}) # Insert edges
db.execute_query("INSERT EDGE INTO knowledge (source = 1, target = 2, label = 'KNOWS')")
db.execute_query("INSERT EDGE INTO knowledge (source = 1, target = 3, label = 'WORKS_AT')")
db.execute_query("INSERT EDGE INTO knowledge (source = 2, target = 3, label = 'WORKS_AT')")
import json # Insert nodes with _labels (required for MATCH pattern matching)
db.execute_query( "INSERT NODE INTO knowledge (id = 1, payload = '%s')" % json.dumps({"name": "Alice", "role": "engineer", "_labels": ["Person"]})
)
db.execute_query( "INSERT NODE INTO knowledge (id = 2, payload = '%s')" % json.dumps({"name": "Bob", "role": "researcher", "_labels": ["Person"]})
)
db.execute_query( "INSERT NODE INTO knowledge (id = 3, payload = '%s')" % json.dumps({"name": "Acme Corp", "industry": "tech", "_labels": ["Company"]})
) # Add vectors to nodes (needed for combined vector + graph queries)
coll = db.get_collection("knowledge")
coll.upsert(1, vector=make_vector(), payload={"name": "Alice", "role": "engineer", "_labels": ["Person"]})
coll.upsert(2, vector=make_vector(), payload={"name": "Bob", "role": "researcher", "_labels": ["Person"]})
coll.upsert(3, vector=make_vector(), payload={"name": "Acme Corp", "industry": "tech", "_labels": ["Company"]}) # Insert edges
db.execute_query("INSERT EDGE INTO knowledge (source = 1, target = 2, label = 'KNOWS')")
db.execute_query("INSERT EDGE INTO knowledge (source = 1, target = 3, label = 'WORKS_AT')")
db.execute_query("INSERT EDGE INTO knowledge (source = 2, target = 3, label = 'WORKS_AT')")
import json # Insert nodes with _labels (required for MATCH pattern matching)
db.execute_query( "INSERT NODE INTO knowledge (id = 1, payload = '%s')" % json.dumps({"name": "Alice", "role": "engineer", "_labels": ["Person"]})
)
db.execute_query( "INSERT NODE INTO knowledge (id = 2, payload = '%s')" % json.dumps({"name": "Bob", "role": "researcher", "_labels": ["Person"]})
)
db.execute_query( "INSERT NODE INTO knowledge (id = 3, payload = '%s')" % json.dumps({"name": "Acme Corp", "industry": "tech", "_labels": ["Company"]})
) # Add vectors to nodes (needed for combined vector + graph queries)
coll = db.get_collection("knowledge")
coll.upsert(1, vector=make_vector(), payload={"name": "Alice", "role": "engineer", "_labels": ["Person"]})
coll.upsert(2, vector=make_vector(), payload={"name": "Bob", "role": "researcher", "_labels": ["Person"]})
coll.upsert(3, vector=make_vector(), payload={"name": "Acme Corp", "industry": "tech", "_labels": ["Company"]}) # Insert edges
db.execute_query("INSERT EDGE INTO knowledge (source = 1, target = 2, label = 'KNOWS')")
db.execute_query("INSERT EDGE INTO knowledge (source = 1, target = 3, label = 'WORKS_AT')")
db.execute_query("INSERT EDGE INTO knowledge (source = 2, target = 3, label = 'WORKS_AT')")
# Find people who know other people
results = db.execute_query( "SELECT * FROM knowledge WHERE MATCH (a:Person)-[:KNOWS]->(b) LIMIT 10"
)
for r in results: print(f" {r['payload']['name']} ({r['payload']['role']})")
# Find people who know other people
results = db.execute_query( "SELECT * FROM knowledge WHERE MATCH (a:Person)-[:KNOWS]->(b) LIMIT 10"
)
for r in results: print(f" {r['payload']['name']} ({r['payload']['role']})")
# Find people who know other people
results = db.execute_query( "SELECT * FROM knowledge WHERE MATCH (a:Person)-[:KNOWS]->(b) LIMIT 10"
)
for r in results: print(f" {r['payload']['name']} ({r['payload']['role']})")
Alice (engineer)
Alice (engineer)
Alice (engineer)
# Find everyone who works at a company
results = db.execute_query( "SELECT * FROM knowledge WHERE MATCH (p:Person)-[:WORKS_AT]->(c:Company) LIMIT 10"
)
for r in results: print(f" {r['payload']['name']}")
# Find everyone who works at a company
results = db.execute_query( "SELECT * FROM knowledge WHERE MATCH (p:Person)-[:WORKS_AT]->(c:Company) LIMIT 10"
)
for r in results: print(f" {r['payload']['name']}")
# Find everyone who works at a company
results = db.execute_query( "SELECT * FROM knowledge WHERE MATCH (p:Person)-[:WORKS_AT]->(c:Company) LIMIT 10"
)
for r in results: print(f" {r['payload']['name']}")
Bob Alice
Bob Alice
Bob Alice
# Find people who work at a company AND are similar to a query vector
results = db.execute_query( """SELECT * FROM knowledge WHERE vector NEAR $v AND MATCH (p:Person)-[:WORKS_AT]->(c:Company) LIMIT 5""", {"q": query_vector}
) # Graph pattern + scalar filter
results = db.execute_query( """SELECT * FROM knowledge WHERE MATCH (a:Person)-[:WORKS_AT]->(b) AND role = 'engineer' LIMIT 10"""
)
for r in results: print(f" {r['payload']['name']} - {r['payload']['role']}")
# Find people who work at a company AND are similar to a query vector
results = db.execute_query( """SELECT * FROM knowledge WHERE vector NEAR $v AND MATCH (p:Person)-[:WORKS_AT]->(c:Company) LIMIT 5""", {"q": query_vector}
) # Graph pattern + scalar filter
results = db.execute_query( """SELECT * FROM knowledge WHERE MATCH (a:Person)-[:WORKS_AT]->(b) AND role = 'engineer' LIMIT 10"""
)
for r in results: print(f" {r['payload']['name']} - {r['payload']['role']}")
# Find people who work at a company AND are similar to a query vector
results = db.execute_query( """SELECT * FROM knowledge WHERE vector NEAR $v AND MATCH (p:Person)-[:WORKS_AT]->(c:Company) LIMIT 5""", {"q": query_vector}
) # Graph pattern + scalar filter
results = db.execute_query( """SELECT * FROM knowledge WHERE MATCH (a:Person)-[:WORKS_AT]->(b) AND role = 'engineer' LIMIT 10"""
)
for r in results: print(f" {r['payload']['name']} - {r['payload']['role']}")
edges = db.execute_query("SELECT EDGES FROM knowledge WHERE source = 1")
for e in edges: print(f" {e['payload']['label']}: {e['payload']['source']} -> {e['payload']['target']}")
edges = db.execute_query("SELECT EDGES FROM knowledge WHERE source = 1")
for e in edges: print(f" {e['payload']['label']}: {e['payload']['source']} -> {e['payload']['target']}")
edges = db.execute_query("SELECT EDGES FROM knowledge WHERE source = 1")
for e in edges: print(f" {e['payload']['label']}: {e['payload']['source']} -> {e['payload']['target']}")
KNOWS: 1 -> 2 WORKS_AT: 1 -> 3
KNOWS: 1 -> 2 WORKS_AT: 1 -> 3
KNOWS: 1 -> 2 WORKS_AT: 1 -> 3
db.execute_query( """INSERT EDGE INTO knowledge (source = 1, target = 2, label = 'COLLABORATES') WITH PROPERTIES (project = 'VelesDB', since = 2024)"""
)
db.execute_query( """INSERT EDGE INTO knowledge (source = 1, target = 2, label = 'COLLABORATES') WITH PROPERTIES (project = 'VelesDB', since = 2024)"""
)
db.execute_query( """INSERT EDGE INTO knowledge (source = 1, target = 2, label = 'COLLABORATES') WITH PROPERTIES (project = 'VelesDB', since = 2024)"""
)
# Fast search (autocomplete, suggestions)
results = db.execute_query( "SELECT id, title FROM articles WHERE vector NEAR $q LIMIT 5 WITH (mode = 'fast')", {"q": query_vector}
) # Accurate search (production queries)
results = db.execute_query( "SELECT id, title FROM articles WHERE vector NEAR $q LIMIT 5 WITH (mode = 'accurate')", {"q": query_vector}
) # Custom HNSW parameters
results = db.execute_query( """SELECT id, title FROM articles WHERE vector NEAR $q LIMIT 5 WITH (ef_search = 256, rerank = true)""", {"q": query_vector}
)
# Fast search (autocomplete, suggestions)
results = db.execute_query( "SELECT id, title FROM articles WHERE vector NEAR $q LIMIT 5 WITH (mode = 'fast')", {"q": query_vector}
) # Accurate search (production queries)
results = db.execute_query( "SELECT id, title FROM articles WHERE vector NEAR $q LIMIT 5 WITH (mode = 'accurate')", {"q": query_vector}
) # Custom HNSW parameters
results = db.execute_query( """SELECT id, title FROM articles WHERE vector NEAR $q LIMIT 5 WITH (ef_search = 256, rerank = true)""", {"q": query_vector}
)
# Fast search (autocomplete, suggestions)
results = db.execute_query( "SELECT id, title FROM articles WHERE vector NEAR $q LIMIT 5 WITH (mode = 'fast')", {"q": query_vector}
) # Accurate search (production queries)
results = db.execute_query( "SELECT id, title FROM articles WHERE vector NEAR $q LIMIT 5 WITH (mode = 'accurate')", {"q": query_vector}
) # Custom HNSW parameters
results = db.execute_query( """SELECT id, title FROM articles WHERE vector NEAR $q LIMIT 5 WITH (ef_search = 256, rerank = true)""", {"q": query_vector}
)
# Update a field
db.execute_query("UPDATE articles SET category = 'advanced' WHERE id = 2") # Delete a row (WHERE is mandatory for safety)
db.execute_query("DELETE FROM articles WHERE id = 7") # Upsert (insert or update)
db.execute_query( "UPSERT INTO articles (id, vector, title, category) VALUES ($id, $v, 'Updated article', 'guide')", {"id": 2, "v": make_vector()}
)
# Update a field
db.execute_query("UPDATE articles SET category = 'advanced' WHERE id = 2") # Delete a row (WHERE is mandatory for safety)
db.execute_query("DELETE FROM articles WHERE id = 7") # Upsert (insert or update)
db.execute_query( "UPSERT INTO articles (id, vector, title, category) VALUES ($id, $v, 'Updated article', 'guide')", {"id": 2, "v": make_vector()}
)
# Update a field
db.execute_query("UPDATE articles SET category = 'advanced' WHERE id = 2") # Delete a row (WHERE is mandatory for safety)
db.execute_query("DELETE FROM articles WHERE id = 7") # Upsert (insert or update)
db.execute_query( "UPSERT INTO articles (id, vector, title, category) VALUES ($id, $v, 'Updated article', 'guide')", {"id": 2, "v": make_vector()}
)
# UNION: combine tech and finance products, remove duplicates
results = db.execute_query(""" SELECT id, title FROM articles WHERE category = 'tutorial' UNION SELECT id, title FROM articles WHERE category = 'guide'
""") # INTERSECT: find items in both result sets
results = db.execute_query(""" SELECT id, title FROM articles WHERE category = 'tutorial' INTERSECT SELECT id, title FROM articles WHERE vector NEAR $q LIMIT 10
""", {"q": query_vector}) # EXCEPT: items in first set but not in second
results = db.execute_query(""" SELECT id FROM articles WHERE category = 'tutorial' EXCEPT SELECT id FROM articles WHERE title MATCH 'graph'
""")
# UNION: combine tech and finance products, remove duplicates
results = db.execute_query(""" SELECT id, title FROM articles WHERE category = 'tutorial' UNION SELECT id, title FROM articles WHERE category = 'guide'
""") # INTERSECT: find items in both result sets
results = db.execute_query(""" SELECT id, title FROM articles WHERE category = 'tutorial' INTERSECT SELECT id, title FROM articles WHERE vector NEAR $q LIMIT 10
""", {"q": query_vector}) # EXCEPT: items in first set but not in second
results = db.execute_query(""" SELECT id FROM articles WHERE category = 'tutorial' EXCEPT SELECT id FROM articles WHERE title MATCH 'graph'
""")
# UNION: combine tech and finance products, remove duplicates
results = db.execute_query(""" SELECT id, title FROM articles WHERE category = 'tutorial' UNION SELECT id, title FROM articles WHERE category = 'guide'
""") # INTERSECT: find items in both result sets
results = db.execute_query(""" SELECT id, title FROM articles WHERE category = 'tutorial' INTERSECT SELECT id, title FROM articles WHERE vector NEAR $q LIMIT 10
""", {"q": query_vector}) # EXCEPT: items in first set but not in second
results = db.execute_query(""" SELECT id FROM articles WHERE category = 'tutorial' EXCEPT SELECT id FROM articles WHERE title MATCH 'graph'
""")
# Unique categories
results = db.execute_query("SELECT DISTINCT category FROM articles") # DISTINCT with vector search (dedup by payload value after search)
results = db.execute_query( "SELECT DISTINCT category FROM articles WHERE vector NEAR $q LIMIT 20", {"q": query_vector}
)
# Unique categories
results = db.execute_query("SELECT DISTINCT category FROM articles") # DISTINCT with vector search (dedup by payload value after search)
results = db.execute_query( "SELECT DISTINCT category FROM articles WHERE vector NEAR $q LIMIT 20", {"q": query_vector}
)
# Unique categories
results = db.execute_query("SELECT DISTINCT category FROM articles") # DISTINCT with vector search (dedup by payload value after search)
results = db.execute_query( "SELECT DISTINCT category FROM articles WHERE vector NEAR $q LIMIT 20", {"q": query_vector}
)
import time # Insert an event with a timestamp
now = int(time.time())
db.execute_query( "INSERT INTO articles (id, vector, title, created_at) VALUES ($id, $v, 'Fresh article', $ts)", {"id": 100, "v": make_vector(), "ts": now}
) # Query using NOW() and INTERVAL
results = db.execute_query( "SELECT id, title FROM articles WHERE created_at > NOW() - INTERVAL '24 hours' LIMIT 10"
)
import time # Insert an event with a timestamp
now = int(time.time())
db.execute_query( "INSERT INTO articles (id, vector, title, created_at) VALUES ($id, $v, 'Fresh article', $ts)", {"id": 100, "v": make_vector(), "ts": now}
) # Query using NOW() and INTERVAL
results = db.execute_query( "SELECT id, title FROM articles WHERE created_at > NOW() - INTERVAL '24 hours' LIMIT 10"
)
import time # Insert an event with a timestamp
now = int(time.time())
db.execute_query( "INSERT INTO articles (id, vector, title, created_at) VALUES ($id, $v, 'Fresh article', $ts)", {"id": 100, "v": make_vector(), "ts": now}
) # Query using NOW() and INTERVAL
results = db.execute_query( "SELECT id, title FROM articles WHERE created_at > NOW() - INTERVAL '24 hours' LIMIT 10"
)
plan = db.execute_query( "EXPLAIN SELECT * FROM articles WHERE vector NEAR $q LIMIT 5", {"q": query_vector}
) print(plan[0]['payload']['tree'])
plan = db.execute_query( "EXPLAIN SELECT * FROM articles WHERE vector NEAR $q LIMIT 5", {"q": query_vector}
) print(plan[0]['payload']['tree'])
plan = db.execute_query( "EXPLAIN SELECT * FROM articles WHERE vector NEAR $q LIMIT 5", {"q": query_vector}
) print(plan[0]['payload']['tree'])
Query Plan:
├─ VectorSearch
│ ├─ Collection: articles
│ ├─ ef_search: 100
│ └─ Candidates: 5
└─ Limit: 5 Estimated cost: 0.101ms
Index used: HNSW
Query Plan:
├─ VectorSearch
│ ├─ Collection: articles
│ ├─ ef_search: 100
│ └─ Candidates: 5
└─ Limit: 5 Estimated cost: 0.101ms
Index used: HNSW
Query Plan:
├─ VectorSearch
│ ├─ Collection: articles
│ ├─ ef_search: 100
│ └─ Candidates: 5
└─ Limit: 5 Estimated cost: 0.101ms
Index used: HNSW
# Create an index on category
db.execute_query("CREATE INDEX ON articles (category)") # Analyze collection statistics for the query optimizer
db.execute_query("ANALYZE articles")
# Create an index on category
db.execute_query("CREATE INDEX ON articles (category)") # Analyze collection statistics for the query optimizer
db.execute_query("ANALYZE articles")
# Create an index on category
db.execute_query("CREATE INDEX ON articles (category)") # Analyze collection statistics for the query optimizer
db.execute_query("ANALYZE articles")
memory = db.agent_memory(384) # Store some facts via the SDK
memory.semantic.store(1, "User prefers Python", make_vector())
memory.semantic.store(2, "User works in healthcare", make_vector()) # Query them with VelesQL
results = db.execute_query( """SELECT * FROM _semantic_memory WHERE vector NEAR $q LIMIT 5 WITH (mode = 'accurate')""", {"q": make_vector()}
) for r in results: print(f" {r['payload']['content']}")
memory = db.agent_memory(384) # Store some facts via the SDK
memory.semantic.store(1, "User prefers Python", make_vector())
memory.semantic.store(2, "User works in healthcare", make_vector()) # Query them with VelesQL
results = db.execute_query( """SELECT * FROM _semantic_memory WHERE vector NEAR $q LIMIT 5 WITH (mode = 'accurate')""", {"q": make_vector()}
) for r in results: print(f" {r['payload']['content']}")
memory = db.agent_memory(384) # Store some facts via the SDK
memory.semantic.store(1, "User prefers Python", make_vector())
memory.semantic.store(2, "User works in healthcare", make_vector()) # Query them with VelesQL
results = db.execute_query( """SELECT * FROM _semantic_memory WHERE vector NEAR $q LIMIT 5 WITH (mode = 'accurate')""", {"q": make_vector()}
) for r in results: print(f" {r['payload']['content']}") - VelesDB on GitHub (~3MB binary, source-available under Elastic License 2.0)
- Documentation and examples
- VelesQL Specification
