sqless 3.1.1

A schema-flexible, zero-abstraction SQLite interface supporting Relational tables, JSON tables, Full-Text Search, and Semantic Search.

sqless

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A schema-flexible, zero-abstraction SQLite interface supporting Relational tables, JSON tables, Full-Text Search, and Semantic Search.

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Why sqless is special

• Schema-free – automatically adjusts the table schema to fit the input data structure.
• High-performance – executes SQL directly, close to raw SQLite speed, faster than many ORMs.
• Multi-file sharding – store data across multiple SQLite files easily.
• SQL-safe – semantic parsing, parameter binding, and identifier validation prevent SQL injection.
• Multi-table type – supports Relational, JSON, Full-Text Search (FTS), and Vector tables.

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⚡ Performance Test

• Unit: Seconds (lower is better).
• Percentages indicate the performance difference compared to other libraries.
  • ↑: How much faster sqless is than the library (i.e., how much slower the library is).
  • ↓: How much slower sqless is than raw SQLite (i.e., the overhead of sqless).

Name	Init	Write	Update	Read	Category
sqlalchemy	0.022 (↑12%)	5.572 (↑94%)	3.218 (↑99%)	58.345 (↑97%)	Static schema
dataset	0.030 (↑33%)	10.337 (↑97%)	1.290 (↑98%)	46.209 (↑96%)	Flex schema
pony	0.030 (↑34%)	2.221 (↑84%)	0.679 (↑96%)	10.283 (↑84%)	Static schema
sqless	0.020	0.359	0.030	1.676	Flex schema
raw sqlite	0.013 (↓51%)	0.260 (↓38%)	0.023 (↓32%)	1.406 (↓19%)	Static schema

See benchmark/cmp_with_other_orms.py for details.

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🚀 Quick Start

import sqless

db = sqless.DB("your.db")
table = db['users']

table.upsert({
    'U1': {'name': 'Tom', 'age': 14},
    'U2': {'name': 'Jerry', 'age': 12},
})

for item in table.iter('age < 15'):
    print(item)

table['U1']['age'] = 15   # ❌ does NOT update database
table['U1'] = {'age':15}  # ✅ updates database
print(table['U1']) # {'key': 'U1', 'name': 'Tom', 'age': 15}

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🧠 Table Types

Table type is determined by prefix:

Type	Prefix	Features
Relational	none	low-flexible schema, partial updates
JSON	json_	high-flexible schema, full replacement
FTS	fts_	full-text search
Vector	vec_	semantic search

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🔹 Relational Table

table = db['users']
table['U1'] = {'name': 'Tom', 'age': 14}
table['U1'] = {'age': 15}
print(table['U1']) # {'key': 'U1', 'name': 'Tom', 'age': 15}

• Partial updates: updating one field does not affect others.

See examples/hello-sqless.py for details.

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🔹 JSON Table

table = db['json_users']
table['U1'] = {'name': 'Tom', 'age': 14}
table['U1'] = {'age': 15}
print(table['U1']) # {'age': 15}

• Full replacement: writing field A will overwrite old fields.

See examples/json-table-example.py for details.

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🔹 FTS Table (Full-Text Search)

table = db['fts_docs']

table.upsert({
    "A": "SQLite supports full text search"
})

results = table.search('"SQLite"')

See examples/fts-table-example.py for details.

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🔹 Vector Table (Semantic Search)

Requirements:

1. pip install sqless[vec]
2. Set SILICON_API_KEY environment variable (uses BAAI/bge-m3 embedding model).
3. Register for SiliconFlow free API if needed: https://cloud.siliconflow.cn/i/szt2CkYN

Usage:

table = db['vec_docs']

table.upsert({
    "A": "Deep learning is part of machine learning"
})

results = table.search("large language model", k=2)

See examples/vec-table-example.py for details.

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📁 Multi-file DB

dbs = sqless.DBS(folder="db_folder")

table = dbs["app-Asia-users"]
table.upsert({...})

Mapping rule:

app-Asia-users
↓
app-Asia.sqlite / users

See examples/multi-files-sqless.py for details.

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🗂 DB Operations

dir(db)
db.list_tables()
del db['users']
db.close()

See examples/hello-sqless.py for details.

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Safe WHERE Expressions

sqless supports safe, parameterized WHERE parsing:

• Logical operators: AND, OR, NOT
• Comparison operators: =, ==, !=, <, >, <=, >=, like, ilike, is, in
• Parentheses: ( and )
• Sorting: ORDER BY <column> [ASC|DESC]
• SQL injection safe: forbidden characters ; -- /* */ blocked
• Values automatically bound with ? placeholders

Example

# Filter age in [10,12,14] or name like 'Tom%', order by age ascending
where_str = "age in [10,12,14] OR name like 'Tom%' order by age asc"
success, sql, params = parse_where(where_str)
print(sql)    # where age in (?,?,?) or name like ? order by age asc
print(params) # [10, 12, 14, 'Tom%']

Notes:

• in requires a list value: [10,12,14]
• is null works, e.g., age is null
• Column names must be valid identifiers (letters, digits, underscores, Chinese characters; cannot start with a digit)
• Complex SQL functions or subqueries are not supported

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🧭 Use Cases

• Local database tools
• Data analysis
• Lightweight services
• AI applications (RAG / semantic search)

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🔄 Migration / Changes

New in this version:

• vec_ table (semantic search)
• fts_ table (full-text search)
• json_ table (high-flexible schema)

Default table type remains Relational (low-flexible schema).

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🔹 Legacy Server (Deprecated)

sqless --host 127.0.0.1 --port 12239 --secret xxx

• Auto creates directories: www/, db/, fs/
• Access via: http://127.0.0.1:12239/index.html

⚠️ Will be removed in future versions. Current version still compatible.