diff --git a/docs/python-integration.md b/docs/python-integration.md index 40ae36c8f..b217f476d 100644 --- a/docs/python-integration.md +++ b/docs/python-integration.md @@ -165,7 +165,7 @@ for post, embedding in results: ## Working with alembic - +### Excluding managed tables The `vectorizer_relationship` generates a new SQLAlchemy model, that is available under the attribute that you specify. If you are using alembic's autogenerate functionality to generate migrations, you will need to exclude these models from the autogenerate process. These are added to a list in your metadata called `pgai_managed_tables` and you can exclude them by adding the following to your `env.py`: @@ -183,3 +183,49 @@ context.configure( ``` This should now prevent alembic from generating tables for these models when you run `alembic revision --autogenerate`. + + +### Creating vectorizers +pgai provides native Alembic operations for managing vectorizers. For them to work you need to run `register_operations` in your env.py file. Which registers the pgai operations under the global op context: + +```python +from pgai.alembic import register_operations + +register_operations() +``` + +Then you can use the `create_vectorizer` operation to create a vectorizer for your model. As well as the `drop_vectorizer` operation to remove it. + +```python +from alembic import op +from pgai.alembic.configuration import ( + OpenAIConfig, + CharacterTextSplitterConfig, + PythonTemplateConfig +) + + +def upgrade() -> None: + op.create_vectorizer( + source_table="blog", + target_table='blog_embeddings', + embedding=OpenAIConfig( + model='text-embedding-3-small', + dimensions=768 + ), + chunking=CharacterTextSplitterConfig( + chunk_column='content', + chunk_size=800, + chunk_overlap=400, + separator='.', + is_separator_regex=False + ), + formatting=PythonTemplateConfig(template='$title - $chunk') + ) + + +def downgrade() -> None: + op.drop_vectorizer(vectorizer_id=1, drop_all=True) +``` + +The `create_vectorizer` operation supports all configuration options available in the [SQL API](vectorizer-api-reference.md). diff --git a/projects/extension/Dockerfile b/projects/extension/Dockerfile index 470b297bb..6277e1b8c 100644 --- a/projects/extension/Dockerfile +++ b/projects/extension/Dockerfile @@ -52,6 +52,10 @@ WORKDIR /pgai COPY . . RUN just build install +RUN mkdir -p /docker-entrypoint-initdb.d && \ + echo "#!/bin/bash" > /docker-entrypoint-initdb.d/configure-timescaledb.sh && \ + echo "echo \"shared_preload_libraries = 'timescaledb'\" >> \${PGDATA}/postgresql.conf" >> /docker-entrypoint-initdb.d/configure-timescaledb.sh && \ + chmod +x /docker-entrypoint-initdb.d/configure-timescaledb.sh ############################################################################### # image for use in extension development diff --git a/projects/pgai/pgai/alembic/__init__.py b/projects/pgai/pgai/alembic/__init__.py new file mode 100644 index 000000000..10e096bbd --- /dev/null +++ b/projects/pgai/pgai/alembic/__init__.py @@ -0,0 +1,7 @@ +from pgai.alembic.operations import ( + CreateVectorizerOp, + DropVectorizerOp, + register_operations, +) + +__all__ = ["CreateVectorizerOp", "DropVectorizerOp", "register_operations"] diff --git a/projects/pgai/pgai/alembic/configuration.py b/projects/pgai/pgai/alembic/configuration.py new file mode 100644 index 000000000..5effb3f9d --- /dev/null +++ b/projects/pgai/pgai/alembic/configuration.py @@ -0,0 +1,206 @@ +"""This file is generated by the configuration generator +in generate/config_generator.py. Do not modify by hand.""" + +from dataclasses import dataclass +from datetime import datetime, timedelta +from typing import Any, ClassVar, TypeAlias + +JsonDict: TypeAlias = dict[str, Any] + + +def format_sql_params(params: dict[str, str | None | bool | list[str]]) -> str: + """Format dictionary of parameters into SQL argument string without any quoting.""" + formatted: list[str] = [] + for key, value in params.items(): + if value is None: + continue + elif isinstance(value, bool): + formatted.append(f"{key}=>{str(value).lower()}") + elif isinstance(value, list): + array_list = ",".join(f"E'{v}'" for v in value) + formatted.append(f"{key}=>ARRAY[{array_list}]") + elif isinstance(value, timedelta): + formatted.append(f"{key}=>'{value.seconds} seconds'") + else: + formatted.append(f"{key}=> '{value}'") + return ", ".join(formatted) + + +class SQLArgumentMixin: + arg_type: ClassVar[str] + function_name: ClassVar[str] + + def to_sql_argument(self) -> str: + params = {} + for field_name in self.__dataclass_fields__: # type: ignore + if field_name != "arg_type" and field_name != "function_name": + value = getattr(self, field_name) # type: ignore + if value is not None: + params[field_name] = value + + return f", {self.arg_type} => {self.function_name}({format_sql_params(params)})" # type: ignore + + +@dataclass +class ChunkingCharacterTextSplitterConfig(SQLArgumentMixin): + """Configuration for ai.chunking_character_text_splitter function.""" + + arg_type: ClassVar[str] = "chunking" + function_name: ClassVar[str] = "ai.chunking_character_text_splitter" + + chunk_column: str + chunk_size: int | None = None + chunk_overlap: int | None = None + separator: str | None = None + is_separator_regex: bool | None = None + + +@dataclass +class ChunkingRecursiveCharacterTextSplitterConfig(SQLArgumentMixin): + """Configuration for ai.chunking_recursive_character_text_splitter function.""" + + arg_type: ClassVar[str] = "chunking" + function_name: ClassVar[str] = "ai.chunking_recursive_character_text_splitter" + + chunk_column: str + chunk_size: int | None = None + chunk_overlap: int | None = None + separators: list[str] | None = None + is_separator_regex: bool | None = None + + +@dataclass +class EmbeddingOllamaConfig(SQLArgumentMixin): + """Configuration for ai.embedding_ollama function.""" + + arg_type: ClassVar[str] = "embedding" + function_name: ClassVar[str] = "ai.embedding_ollama" + + model: str + dimensions: int + base_url: str | None = None + options: dict[str, Any] | None = None + keep_alive: str | None = None + + +@dataclass +class EmbeddingOpenaiConfig(SQLArgumentMixin): + """Configuration for ai.embedding_openai function.""" + + arg_type: ClassVar[str] = "embedding" + function_name: ClassVar[str] = "ai.embedding_openai" + + model: str + dimensions: int + chat_user: str | None = None + api_key_name: str | None = None + + +@dataclass +class EmbeddingVoyageaiConfig(SQLArgumentMixin): + """Configuration for ai.embedding_voyageai function.""" + + arg_type: ClassVar[str] = "embedding" + function_name: ClassVar[str] = "ai.embedding_voyageai" + + model: str + dimensions: int + input_type: str | None = None + api_key_name: str | None = None + + +@dataclass +class FormattingPythonTemplateConfig(SQLArgumentMixin): + """Configuration for ai.formatting_python_template function.""" + + arg_type: ClassVar[str] = "formatting" + function_name: ClassVar[str] = "ai.formatting_python_template" + + template: str | None = None + + +@dataclass +class IndexingDefaultConfig(SQLArgumentMixin): + """Configuration for ai.indexing_default function.""" + + arg_type: ClassVar[str] = "indexing" + function_name: ClassVar[str] = "ai.indexing_default" + + +@dataclass +class IndexingDiskannConfig(SQLArgumentMixin): + """Configuration for ai.indexing_diskann function.""" + + arg_type: ClassVar[str] = "indexing" + function_name: ClassVar[str] = "ai.indexing_diskann" + + min_rows: int | None = None + storage_layout: str | None = None + num_neighbors: int | None = None + search_list_size: int | None = None + max_alpha: float | None = None + num_dimensions: int | None = None + num_bits_per_dimension: int | None = None + create_when_queue_empty: bool | None = None + + +@dataclass +class IndexingHnswConfig(SQLArgumentMixin): + """Configuration for ai.indexing_hnsw function.""" + + arg_type: ClassVar[str] = "indexing" + function_name: ClassVar[str] = "ai.indexing_hnsw" + + min_rows: int | None = None + opclass: str | None = None + m: int | None = None + ef_construction: int | None = None + create_when_queue_empty: bool | None = None + + +@dataclass +class IndexingNoneConfig(SQLArgumentMixin): + """Configuration for ai.indexing_none function.""" + + arg_type: ClassVar[str] = "indexing" + function_name: ClassVar[str] = "ai.indexing_none" + + +@dataclass +class ProcessingDefaultConfig(SQLArgumentMixin): + """Configuration for ai.processing_default function.""" + + arg_type: ClassVar[str] = "processing" + function_name: ClassVar[str] = "ai.processing_default" + + batch_size: int | None = None + concurrency: int | None = None + + +@dataclass +class SchedulingDefaultConfig(SQLArgumentMixin): + """Configuration for ai.scheduling_default function.""" + + arg_type: ClassVar[str] = "scheduling" + function_name: ClassVar[str] = "ai.scheduling_default" + + +@dataclass +class SchedulingNoneConfig(SQLArgumentMixin): + """Configuration for ai.scheduling_none function.""" + + arg_type: ClassVar[str] = "scheduling" + function_name: ClassVar[str] = "ai.scheduling_none" + + +@dataclass +class SchedulingTimescaledbConfig(SQLArgumentMixin): + """Configuration for ai.scheduling_timescaledb function.""" + + arg_type: ClassVar[str] = "scheduling" + function_name: ClassVar[str] = "ai.scheduling_timescaledb" + + schedule_interval: timedelta | None = None + initial_start: datetime | None = None + fixed_schedule: bool | None = None + timezone: str | None = None diff --git a/projects/pgai/pgai/alembic/generate/README.md b/projects/pgai/pgai/alembic/generate/README.md new file mode 100644 index 000000000..bff171de1 --- /dev/null +++ b/projects/pgai/pgai/alembic/generate/README.md @@ -0,0 +1,11 @@ +# dataclasses generation for CreateVectorizerParams +This module contains a script to generate dataclasses as params for the `CreateVectorizerParams` class. +If the interface for the extension is changed, running this script via uv run generate should update the dataclasses in configuration.py and vectorizer_params.py without needing to manually update them. + +## How to use +The script itself queries the pg_catalog table of a database with pgai installed it has hardcoded all the parameter functions in generate.py to avoid creating dataclasses for functions that are not intended to be used as vectorizer parameters. This also means that this list has to be kept up to date. + +To run the script first start a postgres database with pgai installed on post 5432 (e.g. the docker-compose in this folder) and then run the following command: +```bash +uv run generate +``` \ No newline at end of file diff --git a/projects/pgai/pgai/alembic/generate/__init__.py b/projects/pgai/pgai/alembic/generate/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/projects/pgai/pgai/alembic/generate/config_generator.py b/projects/pgai/pgai/alembic/generate/config_generator.py new file mode 100644 index 000000000..6d1376389 --- /dev/null +++ b/projects/pgai/pgai/alembic/generate/config_generator.py @@ -0,0 +1,135 @@ +# ruff: noqa: E501 +from pathlib import Path + +from jinja2 import Environment + +from pgai.alembic.generate.function_parser import PostgresFunction, VectorizerParameter + +TEMPLATE = '''"""This file is generated by the configuration generator +in generate/config_generator.py. Do not modify by hand.""" +from dataclasses import dataclass +from typing import ClassVar, Any, TypeAlias +from datetime import datetime, timedelta + +JsonDict: TypeAlias = dict[str, Any] + +def format_sql_params(params: dict[str, str | None | bool | list[str]]) -> str: + """Format dictionary of parameters into SQL argument string without any quoting.""" + formatted: list[str] = [] + for key, value in params.items(): + if value is None: + continue + elif isinstance(value, bool): + formatted.append(f"{key}=>{str(value).lower()}") + elif isinstance(value, list): + array_list = ",".join(f"E'{v}'" for v in value) + formatted.append(f"{key}=>ARRAY[{array_list}]") + elif isinstance(value, timedelta): + formatted.append(f"{key}=>'{value.seconds} seconds'") + else: + formatted.append(f"{key}=> '{value}'") + return ", ".join(formatted) + +class SQLArgumentMixin: + arg_type: ClassVar[str] + function_name: ClassVar[str] + + def to_sql_argument(self) -> str: + params = {} + for field_name in self.__dataclass_fields__: # type: ignore + if field_name != "arg_type" and field_name != "function_name": + value = getattr(self, field_name) # type: ignore + if value is not None: + params[field_name] = value + + return f", {self.arg_type} => {self.function_name}({format_sql_params(params)})" # type: ignore + +{% for function in functions %} + + +@dataclass +class {{ function.name|replace('ai.', '')|replace('_', ' ')|title|replace(' ', '') }}Config(SQLArgumentMixin): + """Configuration for {{ function.name }} function.""" + arg_type: ClassVar[str] = "{{ function.name|split('.')|last|split('_')|first }}" + function_name: ClassVar[str] = "{{ function.name }}" + + {% for param in function.parameters %} + {{ param.name }}: {{ param.python_type }}{% if param.is_required == False %} = {{ param.default_value }}{% endif %} + + {% endfor %} +{% endfor %} +''' + +VECTORIZER_TEMPLATE = '''"""This file is generated by the configuration generator +in generate/config_generator.py. Do not modify by hand.""" +from typing import Any +from dataclasses import dataclass + +from .configuration import SQLArgumentMixin + +# Dynamic imports based on parameter types +from .configuration import ( + {% for function in functions %} {{ function.name|replace('ai.', '')|replace('_', ' ')|title|replace(' ', '') }}Config{%- if not loop.last %}, + {% endif %}{% endfor %} + +) + + +@dataclass +class CreateVectorizerParams: + """Parameters for ai.create_vectorizer function""" + {% for param in parameters %} + {{ param.name }}: {{ param.python_type }}{% if not param.is_required %} = {{ param.default_value or 'None' }}{% endif %} + + {% endfor %} + + def to_sql(self) -> str: + """Convert parameters to SQL function call""" + parts = ["SELECT ai.create_vectorizer("] + + parts.append(f"'{self.source}'::regclass") + + # Handle all config objects that implement SQLArgumentMixin + for field_name, field_value in self.__dict__.items(): + if isinstance(field_value, SQLArgumentMixin): + parts.append(field_value.to_sql_argument()) + elif field_value is not None and field_name not in ('source',): + # Handle primitive types + if isinstance(field_value, bool): + parts.append(f", {field_name} => {str(field_value).lower()}") + elif isinstance(field_value, list): + values = ",".join(f"'{v}'" for v in field_value) # type: ignore + parts.append(f", {field_name} => ARRAY[{values}]") + else: + parts.append(f", {field_name} => '{field_value}'") + + parts.append(")") + return "\\n".join(parts) +''' + + +def generate_vectorizer_params( + parameters: list[VectorizerParameter], + output_file: Path, + functions: list[PostgresFunction], +) -> None: + """Generate CreateVectorizerParams class from metadata""" + env = Environment(trim_blocks=True, lstrip_blocks=True) + template = env.from_string(VECTORIZER_TEMPLATE) + + output = template.render(parameters=parameters, functions=functions) + output_file.write_text(output) + + +def generate_config_classes( + functions: list[PostgresFunction], output_file: Path +) -> None: + """Generate Python configuration classes from function metadata.""" + env = Environment(trim_blocks=True, lstrip_blocks=True) + + # Add custom filters + env.filters["split"] = lambda x, sep: x.split(sep) # type: ignore + + template = env.from_string(TEMPLATE) + output = template.render(functions=functions) + output_file.write_text(output) diff --git a/projects/pgai/pgai/alembic/generate/docker-compose.yaml b/projects/pgai/pgai/alembic/generate/docker-compose.yaml new file mode 100644 index 000000000..4e057ec52 --- /dev/null +++ b/projects/pgai/pgai/alembic/generate/docker-compose.yaml @@ -0,0 +1,12 @@ +name: pgai +services: + db: + image: timescale/timescaledb-ha:pg17 + environment: + POSTGRES_PASSWORD: postgres + ports: + - "5432:5432" + volumes: + - data:/home/postgres/pgdata/data +volumes: + data: \ No newline at end of file diff --git a/projects/pgai/pgai/alembic/generate/function_parser.py b/projects/pgai/pgai/alembic/generate/function_parser.py new file mode 100644 index 000000000..9b0d072a1 --- /dev/null +++ b/projects/pgai/pgai/alembic/generate/function_parser.py @@ -0,0 +1,204 @@ +from dataclasses import dataclass + +import psycopg + + +@dataclass +class PostgresParameter: + name: str + type_name: str + default_value: str | None = None + is_array: bool = False + is_required: bool = False + + @property + def python_type(self) -> str: + """Convert Postgres type to Python type.""" + type_mapping = { + "text": "str", + "name": "str", + "int4": "int", + "int8": "int", + "bool": "bool", + "boolean": "bool", + "jsonb": "dict[str, Any]", + "float8": "float", + "interval": "timedelta", + "timestamptz": "datetime", + "_text": "list[str]", + "_int4": "list[int]", + "_float8": "list[float]", + "regclass": "str", + } + base_type = type_mapping.get(self.type_name, "Any") + if self.is_array and not base_type.startswith("list"): + type_str = f"list[{base_type}]" + else: + type_str = base_type + if not self.is_required: + type_str = f"{type_str} | None" + return type_str + + +@dataclass +class PostgresFunction: + name: str + schema: str + parameters: list[PostgresParameter] + return_type: str + + +def get_function_metadata( + conn: psycopg.Connection, function_names: list[str] +) -> list[PostgresFunction]: + """Extract function metadata from Postgres catalog.""" + + query = """ + SELECT + p.proname, + n.nspname, + p.proargnames, + p.pronargdefaults, + string_to_array(array_to_string(p.proargtypes, ' '), ' ') as argtypes, + p.proallargtypes, + p.proargmodes, + t.typname as return_type, + p.oid, + p.proargnames IS NULL as null_argnames, + p.proargtypes IS NULL as null_argtypes + FROM pg_proc p + JOIN pg_namespace n ON p.pronamespace = n.oid + JOIN pg_type t ON p.prorettype = t.oid + WHERE n.nspname = 'ai' + AND p.proname = ANY(%s) + ORDER BY p.proname; + """ + + functions: list[PostgresFunction] = [] + with conn.cursor() as cur: + cur.execute(query, (function_names,)) + + results = cur.fetchall() + for row in results: + parameters: list[PostgresParameter] = [] + param_names = row[2] or [] # type: ignore + count_of_default_params = row[3] + param_count = len(param_names) # type: ignore + non_default_count = param_count - count_of_default_params + param_types = row[4] or [] # type: ignore + + for i, name in enumerate(param_names): # type: ignore + type_oid = int(param_types[i]) if i < len(param_types) else None # type: ignore + + # Get detailed type info + type_query = """ + SELECT t.typname, + (CASE WHEN t.typtype = 'b' THEN t.typname + WHEN t.typtype = 'a' THEN (SELECT at.typname + FROM pg_type at + WHERE at.oid = t.typelem) + ELSE t.typname + END) as base_type, + t.typtype = 'a' as is_array + FROM pg_type t + WHERE t.oid = %s + """ + cur.execute(type_query, (type_oid,)) + type_info = cur.fetchone() + + type_name = type_info[1] # type: ignore + is_array = type_info[2] # type: ignore + + default = None + # A parameter is required if it has no default value + is_required = i < non_default_count + + parameters.append( + PostgresParameter( + name=name, # type: ignore + type_name=type_name, # type: ignore + default_value=default, + is_array=is_array, # type: ignore + is_required=is_required, + ) + ) + + functions.append( + PostgresFunction( + name=f"ai.{row[0]}", + schema=row[1], + parameters=parameters, + return_type=row[7], + ) + ) + + return functions + + +@dataclass +class VectorizerParameter: + """Dataclass to specifically handle create_vectorizer function parameters""" + + name: str + python_type: str + accepted_configs: list[str] | None = None + is_required: bool = False + default_value: str | None = None + + +def read_create_vectorizer_metadata( + conn: psycopg.Connection, +) -> list[VectorizerParameter]: + """Extract and enhance metadata for the create_vectorizer function parameters""" + # First get basic function metadata + functions = get_function_metadata(conn, ["create_vectorizer"]) + if not functions: + raise ValueError("create_vectorizer function not found") + + vectorizer_fn = functions[0] + + # Map parameter names to their enhanced metadata + parameters: list[VectorizerParameter] = [] + + # Query to get all configuration functions for each argument type + config_functions_query = """ + SELECT p.proname + FROM pg_proc p + JOIN pg_namespace n ON p.pronamespace = n.oid + WHERE n.nspname = 'ai' + AND p.proname LIKE %s; + """ + + for param in vectorizer_fn.parameters: + # Skip internal parameters that shouldn't be exposed + if param.name.startswith("_"): + continue + + accepted_configs = None + python_type = param.python_type + + # For each parameter, check if there is corresponding config functions + with conn.cursor() as cur: + cur.execute(config_functions_query, (f"{param.name}_%",)) + config_functions = [row[0] for row in cur.fetchall()] + + if config_functions: + # Convert function names to config class names + accepted_configs = [ + f"{''.join(part.title() for part in name.split('_'))}Config" + for name in config_functions + ] + # Create union type of all possible configs + python_type = " | ".join(accepted_configs) + " | None" + + parameters.append( + VectorizerParameter( + name=param.name, + python_type=python_type, + accepted_configs=accepted_configs, + is_required=param.is_required, + default_value=param.default_value, + ) + ) + + return parameters diff --git a/projects/pgai/pgai/alembic/generate/generate.py b/projects/pgai/pgai/alembic/generate/generate.py new file mode 100644 index 000000000..c3a76e6e7 --- /dev/null +++ b/projects/pgai/pgai/alembic/generate/generate.py @@ -0,0 +1,69 @@ +from pathlib import Path + +import psycopg + +from pgai.alembic.generate.config_generator import ( + generate_config_classes, + generate_vectorizer_params, +) +from pgai.alembic.generate.function_parser import ( + get_function_metadata, + read_create_vectorizer_metadata, +) + +VECTORIZER_FUNCTIONS = [ + "embedding_openai", + "embedding_ollama", + "embedding_voyageai", + "chunking_character_text_splitter", + "chunking_recursive_character_text_splitter", + "formatting_python_template", + "indexing_diskann", + "indexing_hnsw", + "indexing_default", + "indexing_none", + "scheduling_default", + "scheduling_none", + "scheduling_timescaledb", + "processing_default", +] + + +def list_vectorizer_functions(conn: psycopg.Connection) -> list[str]: + """List all available vectorizer functions in the database.""" + query = """ + SELECT p.proname + FROM pg_proc p + JOIN pg_namespace n ON p.pronamespace = n.oid + WHERE n.nspname = 'ai' + AND p.proname = ANY(%s) + ORDER BY p.proname + """ + + with conn.cursor() as cur: + cur.execute(query, (VECTORIZER_FUNCTIONS,)) + return [row[0] for row in cur] + + +def generate_vectorizer_configs( + conn_str: str, output_file: Path, vectorizer_output_file: Path +) -> None: + """Generate all vectorizer configuration classes.""" + with psycopg.connect(conn_str) as conn: + available_functions = list_vectorizer_functions(conn) + functions = get_function_metadata(conn, available_functions) + generate_config_classes(functions, output_file) + vectorizer_params = read_create_vectorizer_metadata(conn) + generate_vectorizer_params(vectorizer_params, vectorizer_output_file, functions) + + +def generate_models(): + # Connect to database + conn_str = "postgresql://postgres:postgres@localhost:5432/postgres" + output_file = Path("../configuration.py") + vectorizer_output_file = Path("../vectorizer_params.py") + generate_vectorizer_configs(conn_str, output_file, vectorizer_output_file) + + +if __name__ == "__main__": + generate_models() diff --git a/projects/pgai/pgai/alembic/operations.py b/projects/pgai/pgai/alembic/operations.py new file mode 100644 index 000000000..996b9f22b --- /dev/null +++ b/projects/pgai/pgai/alembic/operations.py @@ -0,0 +1,65 @@ +from typing import Any + +from alembic.operations import MigrateOperation, Operations +from sqlalchemy import text + +from pgai.alembic.vectorizer_params import CreateVectorizerParams + + +class CreateVectorizerOp(MigrateOperation): + def __init__( + self, + **kw: dict[str, Any], + ): + self.params = CreateVectorizerParams( + **kw # type: ignore + ) + + @classmethod + def create_vectorizer(cls, operations: Operations, **kw: Any): + op = CreateVectorizerOp(**kw) # type: ignore + return operations.invoke(op) + + +class DropVectorizerOp(MigrateOperation): + def __init__(self, vectorizer_id: int | None, drop_all: bool): + self.vectorizer_id = vectorizer_id + self.drop_all = drop_all + + @classmethod + def drop_vectorizer( + cls, + operations: Operations, + vectorizer_id: int | None = None, + drop_all: bool = True, + ): + op = DropVectorizerOp(vectorizer_id, drop_all) + return operations.invoke(op) + + +def create_vectorizer(operations: Operations, operation: CreateVectorizerOp): + params = operation.params + operations.execute(params.to_sql()) + + +def drop_vectorizer(operations: Operations, operation: DropVectorizerOp): + connection = operations.get_bind() + vectorizer_id = operation.vectorizer_id + connection.execute( + text("SELECT ai.drop_vectorizer(:id, drop_all=>:drop_all)"), + {"id": vectorizer_id, "drop_all": operation.drop_all}, + ) + + +_operations_registered = False + + +def register_operations(): + global _operations_registered + + if not _operations_registered: + Operations.register_operation("create_vectorizer")(CreateVectorizerOp) + Operations.register_operation("drop_vectorizer")(DropVectorizerOp) + Operations.implementation_for(CreateVectorizerOp)(create_vectorizer) + Operations.implementation_for(DropVectorizerOp)(drop_vectorizer) + _operations_registered = True diff --git a/projects/pgai/pgai/alembic/vectorizer_params.py b/projects/pgai/pgai/alembic/vectorizer_params.py new file mode 100644 index 000000000..dc52f1cc1 --- /dev/null +++ b/projects/pgai/pgai/alembic/vectorizer_params.py @@ -0,0 +1,86 @@ +"""This file is generated by the configuration generator +in generate/config_generator.py. Do not modify by hand.""" + +from dataclasses import dataclass +from typing import Any + +# Dynamic imports based on parameter types +from .configuration import ( + ChunkingCharacterTextSplitterConfig, + ChunkingRecursiveCharacterTextSplitterConfig, + EmbeddingOllamaConfig, + EmbeddingOpenaiConfig, + EmbeddingVoyageaiConfig, + FormattingPythonTemplateConfig, + IndexingDefaultConfig, + IndexingDiskannConfig, + IndexingHnswConfig, + IndexingNoneConfig, + ProcessingDefaultConfig, + SchedulingDefaultConfig, + SchedulingNoneConfig, + SchedulingTimescaledbConfig, + SQLArgumentMixin, +) + + +@dataclass +class CreateVectorizerParams: + """Parameters for ai.create_vectorizer function""" + + source: str + destination: str | None = None + embedding: ( + EmbeddingOllamaConfig | EmbeddingOpenaiConfig | EmbeddingVoyageaiConfig | None + ) = None + chunking: ( + ChunkingCharacterTextSplitterConfig + | ChunkingRecursiveCharacterTextSplitterConfig + | None + ) = None + indexing: ( + IndexingDefaultConfig + | IndexingDiskannConfig + | IndexingHnswConfig + | IndexingNoneConfig + | None + ) = None + formatting: FormattingPythonTemplateConfig | None = None + scheduling: ( + SchedulingDefaultConfig + | SchedulingNoneConfig + | SchedulingTimescaledbConfig + | None + ) = None + processing: ProcessingDefaultConfig | None = None + target_schema: str | None = None + target_table: str | None = None + view_schema: str | None = None + view_name: str | None = None + queue_schema: str | None = None + queue_table: str | None = None + grant_to: Any | None = None + enqueue_existing: bool | None = None + + def to_sql(self) -> str: + """Convert parameters to SQL function call""" + parts = ["SELECT ai.create_vectorizer("] + + parts.append(f"'{self.source}'::regclass") + + # Handle all config objects that implement SQLArgumentMixin + for field_name, field_value in self.__dict__.items(): + if isinstance(field_value, SQLArgumentMixin): + parts.append(field_value.to_sql_argument()) + elif field_value is not None and field_name not in ("source",): + # Handle primitive types + if isinstance(field_value, bool): + parts.append(f", {field_name} => {str(field_value).lower()}") + elif isinstance(field_value, list): + values = ",".join(f"'{v}'" for v in field_value) # type: ignore + parts.append(f", {field_name} => ARRAY[{values}]") + else: + parts.append(f", {field_name} => '{field_value}'") + + parts.append(")") + return "\n".join(parts) diff --git a/projects/pgai/pyproject.toml b/projects/pgai/pyproject.toml index 455e8d24a..d1cb71404 100644 --- a/projects/pgai/pyproject.toml +++ b/projects/pgai/pyproject.toml @@ -46,6 +46,7 @@ classifiers = [ [project.optional-dependencies] sqlalchemy=[ "sqlalchemy>=2.0.36", + "alembic>=1.14.0", ] [project.urls] @@ -59,6 +60,8 @@ path = "pgai/__init__.py" [project.scripts] pgai = "pgai.cli:cli" +generate = "pgai.alembic.generate.generate:generate_models" + [tool.pytest.ini_options] addopts = [ @@ -118,5 +121,6 @@ dev-dependencies = [ "testcontainers==4.8.1", "build==1.2.2.post1", "twine==5.1.1", - "mitmproxy>=11.0.2", + "mitmproxy==10.3.0", + "Jinja2>=3.1.5", ] diff --git a/projects/pgai/tests/vectorizer/extensions/conftest.py b/projects/pgai/tests/vectorizer/extensions/conftest.py index cdb46f303..e9ec476f2 100644 --- a/projects/pgai/tests/vectorizer/extensions/conftest.py +++ b/projects/pgai/tests/vectorizer/extensions/conftest.py @@ -1,7 +1,9 @@ -from collections.abc import Generator +import tempfile +from collections.abc import Generator, Iterator from pathlib import Path import pytest +from alembic.config import Config from sqlalchemy import Engine, create_engine, text from testcontainers.postgres import PostgresContainer # type: ignore @@ -9,6 +11,71 @@ FIXTURES_DIR = Path(__file__).parent / "fixtures" +def load_template(template_path: str, **kwargs: str | int) -> str: + """Load a template file and substitute any provided values. + + Args: + template_path: Path relative to fixtures directory + **kwargs: Key-value pairs to substitute in template + + Returns: + str: Processed template content + """ + template_file = FIXTURES_DIR / template_path + with open(template_file) as f: + content = f.read() + + return content.format(**kwargs) if kwargs else content + + +@pytest.fixture +def alembic_dir() -> Iterator[Path]: + """Create and manage a temporary directory for Alembic migrations.""" + with tempfile.TemporaryDirectory() as temp_dir: + temp_path = Path(temp_dir) + migrations_dir = temp_path / "migrations" + versions_dir = migrations_dir / "versions" + + # Create directory structure + migrations_dir.mkdir() + versions_dir.mkdir() + + # Copy script.py.mako template + script_content = load_template("alembic/script.py.mako") + with open(migrations_dir / "script.py.mako", "w") as f: + f.write(script_content) + + yield temp_path + + +@pytest.fixture +def alembic_config(alembic_dir: Path, postgres_container: PostgresContainer) -> Config: + """Create a configured Alembic environment.""" + # Create alembic.ini from template + ini_path = alembic_dir / "alembic.ini" + ini_content = load_template( + "alembic/alembic.ini.template", + sqlalchemy_url=postgres_container.get_connection_url(), + ) + with open(ini_path, "w") as f: + f.write(ini_content) + + # Create env.py from template + env_path = alembic_dir / "migrations" / "env.py" + env_content = load_template("alembic/env.py.template") + with open(env_path, "w") as f: + f.write(env_content) + + # Configure and return + config = Config(ini_path) + config.set_main_option("script_location", str(alembic_dir / "migrations")) + + engine = create_engine(postgres_container.get_connection_url(driver="psycopg")) + config.attributes["connection"] = engine + + return config + + def drop_vectorizer_if_exists(id: int, engine: Engine): with engine.connect() as conn: vectorizer_exists = conn.execute( @@ -34,6 +101,7 @@ def initialized_engine( """ engine = create_engine(postgres_container.get_connection_url(driver="psycopg")) with engine.connect() as conn: + conn.execute(text("CREATE EXTENSION IF NOT EXISTS timescaledb;")) conn.execute(text("CREATE EXTENSION IF NOT EXISTS ai CASCADE;")) conn.commit() diff --git a/projects/pgai/tests/vectorizer/extensions/fixtures/alembic/alembic.ini.template b/projects/pgai/tests/vectorizer/extensions/fixtures/alembic/alembic.ini.template new file mode 100644 index 000000000..e5ac44de3 --- /dev/null +++ b/projects/pgai/tests/vectorizer/extensions/fixtures/alembic/alembic.ini.template @@ -0,0 +1,37 @@ +[alembic] +script_location = migrations +sqlalchemy.url = {sqlalchemy_url} + +[loggers] +keys = root,sqlalchemy,alembic + +[handlers] +keys = console + +[formatters] +keys = generic + +[logger_root] +level = WARN +handlers = console +qualname = + +[logger_sqlalchemy] +level = WARN +handlers = +qualname = sqlalchemy.engine + +[logger_alembic] +level = INFO +handlers = +qualname = alembic + +[handler_console] +class = StreamHandler +args = (sys.stdout,) +level = NOTSET +formatter = generic + +[formatter_generic] +format = %(levelname)-5.5s [%(name)s] %(message)s +datefmt = %H:%M:%S \ No newline at end of file diff --git a/projects/pgai/tests/vectorizer/extensions/fixtures/alembic/env.py.template b/projects/pgai/tests/vectorizer/extensions/fixtures/alembic/env.py.template new file mode 100644 index 000000000..f763ba6a6 --- /dev/null +++ b/projects/pgai/tests/vectorizer/extensions/fixtures/alembic/env.py.template @@ -0,0 +1,46 @@ +from logging.config import fileConfig +from alembic import context +from sqlalchemy import engine_from_config, pool +from pgai.alembic import register_operations + +register_operations() + +config = context.config +if config.config_file_name is not None: + fileConfig(config.config_file_name) + +target_metadata = None + +def run_migrations_online(): + connectable = context.config.attributes.get('connection', None) + if connectable is None: + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata + ) + + with context.begin_transaction(): + context.run_migrations() + +def run_migrations_offline(): + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True + ) + + with context.begin_transaction(): + context.run_migrations() + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() \ No newline at end of file diff --git a/projects/pgai/tests/vectorizer/extensions/fixtures/alembic/script.py.mako b/projects/pgai/tests/vectorizer/extensions/fixtures/alembic/script.py.mako new file mode 100644 index 000000000..e5365bdf4 --- /dev/null +++ b/projects/pgai/tests/vectorizer/extensions/fixtures/alembic/script.py.mako @@ -0,0 +1,21 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + +def upgrade(): + ${upgrades if upgrades else "pass"} + +def downgrade(): + ${downgrades if downgrades else "pass"} \ No newline at end of file diff --git a/projects/pgai/tests/vectorizer/extensions/fixtures/migrations/generic_vectorizer.py.template b/projects/pgai/tests/vectorizer/extensions/fixtures/migrations/generic_vectorizer.py.template new file mode 100644 index 000000000..0f93f6d11 --- /dev/null +++ b/projects/pgai/tests/vectorizer/extensions/fixtures/migrations/generic_vectorizer.py.template @@ -0,0 +1,58 @@ +"""Generic vectorizer migration template. + +Revision ID: {revision_id} +Revises: {revises} +Create Date: {create_date} +""" +from alembic import op +from sqlalchemy import Column, Integer, String, Text +from sqlalchemy import text +from pgai.alembic.configuration import ( + ChunkingCharacterTextSplitterConfig, + IndexingHnswConfig, + EmbeddingOllamaConfig, + EmbeddingOpenaiConfig, + ProcessingDefaultConfig, + FormattingPythonTemplateConfig, + ChunkingRecursiveCharacterTextSplitterConfig, + SchedulingTimescaledbConfig, + EmbeddingVoyageaiConfig, +) +import datetime + +# revision identifiers, used by Alembic +revision = '{revision_id}' +down_revision = {down_revision} +branch_labels = None +depends_on = None + + +def upgrade() -> None: + # Create a generic blog table that we can use for all tests + op.create_table( + 'blog', + Column('id', Integer, primary_key=True), + Column('title', String(200), nullable=False), + Column('content', Text, nullable=False), + schema='public' + ) + + # Create vectorizer using the provided configuration + {vectorizer_config} + + +def downgrade() -> None: + connection = op.get_bind() + # Look up the vectorizer ID + result = connection.execute( + text("SELECT id FROM ai.vectorizer WHERE source_table = 'blog'") + ).scalar() + + if result is not None: + print(f"Found vectorizer with ID: {{result}}") + op.drop_vectorizer(result, drop_all=True) + else: + print("No vectorizer found!") + + # Drop the blog table + op.drop_table('blog', schema='public') \ No newline at end of file diff --git a/projects/pgai/tests/vectorizer/extensions/test_alembic.py b/projects/pgai/tests/vectorizer/extensions/test_alembic.py new file mode 100644 index 000000000..5dac625c9 --- /dev/null +++ b/projects/pgai/tests/vectorizer/extensions/test_alembic.py @@ -0,0 +1,241 @@ +""" +The migration template for these tests lies in +fixtures/migrations/generic_vectorizer.py.template +Each test generates a different migration, runs it, and verifies the configuration +This doesn't test if the vectorizer behaves as expected, +but it validates that the create_vectorizer call works. + +If you add new configuration options, add a test for them here. +Either in an existing test case or a new one. +The config classes for migrations lie in pgai/alembic/configuration.py +If the migration and the validation definitions overlap, +it is possible to define a parent class in base.py +see e.g. CharacterTextSplitterConfig as an example +""" + +import textwrap +from datetime import timedelta +from pathlib import Path +from typing import Any + +from alembic.command import downgrade, upgrade +from alembic.config import Config +from sqlalchemy import Engine, text + +from pgai.alembic.configuration import ( + ChunkingCharacterTextSplitterConfig, + ChunkingRecursiveCharacterTextSplitterConfig, + EmbeddingOllamaConfig, + EmbeddingOpenaiConfig, + EmbeddingVoyageaiConfig, + FormattingPythonTemplateConfig, + IndexingHnswConfig, + ProcessingDefaultConfig, + SchedulingTimescaledbConfig, +) +from tests.vectorizer.extensions.conftest import load_template + + +def create_vectorizer_migration(migrations_dir: Path, vectorizer_config: str) -> None: + """Create migration script with the given vectorizer configuration""" + versions_dir = migrations_dir / "versions" + migration_content = load_template( + "migrations/generic_vectorizer.py.template", + revision_id="001", + revises="", + create_date="2024-03-19 10:00:00.000000", + down_revision="None", + vectorizer_config=vectorizer_config, + ) + + with open(versions_dir / "001_generic_vectorizer.py", "w") as f: + f.write(migration_content) + + +def create_vectorizer_config_code(**kwargs: Any) -> str: + """Convert configuration objects to valid Python code for the migration template""" + return textwrap.dedent(f""" + op.create_vectorizer( + source='public.blog', + **{kwargs} + ) + """).strip() + + +def test_openai_vectorizer( + alembic_config: Config, + initialized_engine: Engine, +): + """Test OpenAI vectorizer configuration""" + config = create_vectorizer_config_code( + embedding=EmbeddingOpenaiConfig( + model="text-embedding-3-small", + dimensions=768, + chat_user="test_user", + api_key_name="TEST_OPENAI_KEY", + ), + chunking=ChunkingCharacterTextSplitterConfig( + chunk_column="content", + chunk_size=256, + chunk_overlap=20, + separator="\n", + is_separator_regex=False, + ), + ) + + migrations_dir = Path(alembic_config.get_main_option("script_location")) # type: ignore + create_vectorizer_migration(migrations_dir, config) + + # Run upgrade + upgrade(alembic_config, "head") + + # Verify configuration + with initialized_engine.connect() as conn: + vectorizer = conn.execute(text("SELECT * FROM ai.vectorizer")).fetchone() + assert vectorizer is not None + config = str(dict(vectorizer._mapping)) # type: ignore + assert "text-embedding-3-small" in config + assert "768" in config + assert "test_user" in config + + # Run downgrade + downgrade(alembic_config, "base") + + +def test_ollama_vectorizer( + alembic_config: Config, + initialized_engine: Engine, +): + """Test Ollama vectorizer configuration""" + config = create_vectorizer_config_code( + embedding=EmbeddingOllamaConfig( + model="nomic-embed-text", + dimensions=768, + base_url="http://localhost:11434", + keep_alive="5m", + ), + chunking=ChunkingRecursiveCharacterTextSplitterConfig( + chunk_column="content", + chunk_size=300, + chunk_overlap=30, + separators=["\n\n", "\n", "; "], + is_separator_regex=False, + ), + formatting=FormattingPythonTemplateConfig( + template="Title: $title\nContent: $chunk" + ), + ) + + migrations_dir = Path(alembic_config.get_main_option("script_location")) # type: ignore + create_vectorizer_migration(migrations_dir, config) + + # Run upgrade + upgrade(alembic_config, "head") + + # Verify configuration + with initialized_engine.connect() as conn: + vectorizer = conn.execute(text("SELECT * FROM ai.vectorizer")).fetchone() + assert vectorizer is not None + config = str(dict(vectorizer._mapping)) # type: ignore + assert "nomic-embed-text" in config + assert "http://localhost:11434" in config + + # Run downgrade + downgrade(alembic_config, "base") + + +def test_voyage_vectorizer( + alembic_config: Config, + initialized_engine: Engine, +): + """Test VoyageAI vectorizer configuration""" + config = create_vectorizer_config_code( + embedding=EmbeddingVoyageaiConfig( + model="voyage-ai-1", + dimensions=256, + ), + chunking=ChunkingRecursiveCharacterTextSplitterConfig( + chunk_column="content", + ), + indexing=IndexingHnswConfig( + min_rows=10000, + opclass="vector_l1_ops", + m=32, + ef_construction=128, + create_when_queue_empty=True, + ), + scheduling=SchedulingTimescaledbConfig( + schedule_interval=timedelta(minutes=10), timezone="UTC", fixed_schedule=True + ), + processing=ProcessingDefaultConfig(batch_size=100, concurrency=2), + ) + + migrations_dir = Path(alembic_config.get_main_option("script_location")) # type: ignore + create_vectorizer_migration(migrations_dir, config) + + # Run upgrade + upgrade(alembic_config, "head") + + # Verify configuration + with initialized_engine.connect() as conn: + vectorizer = conn.execute(text("SELECT * FROM ai.vectorizer")).fetchone() + assert vectorizer is not None + config = str(dict(vectorizer._mapping)) # type: ignore + assert "voyage-ai-1" in config + assert "256" in config + assert "document" in config + assert "vector_l1_ops" in config + assert "32" in config # m parameter + assert "128" in config # ef_construction + + # Run downgrade + downgrade(alembic_config, "base") + + +def test_hnsw_vectorizer( + alembic_config: Config, + initialized_engine: Engine, +): + """Test HNSW vectorizer configuration""" + config = create_vectorizer_config_code( + embedding=EmbeddingOpenaiConfig( + model="text-embedding-3-small", + dimensions=768, + api_key_name="TEST_OPENAI_KEY", + ), + chunking=ChunkingCharacterTextSplitterConfig( + chunk_column="content", + chunk_size=200, + chunk_overlap=25, + separator=" ", + is_separator_regex=False, + ), + indexing=IndexingHnswConfig( + min_rows=50000, + opclass="vector_l1_ops", + m=16, + ef_construction=64, + create_when_queue_empty=True, + ), + scheduling=SchedulingTimescaledbConfig( + schedule_interval=timedelta(minutes=10), fixed_schedule=False + ), + ) + + migrations_dir = Path(alembic_config.get_main_option("script_location")) # type: ignore + create_vectorizer_migration(migrations_dir, config) + + # Run upgrade + upgrade(alembic_config, "head") + + # Verify configuration + with initialized_engine.connect() as conn: + vectorizer = conn.execute(text("SELECT * FROM ai.vectorizer")).fetchone() + assert vectorizer is not None + config = str(dict(vectorizer._mapping)) # type: ignore + assert "vector_l1_ops" in config + assert "16" in config # m parameter + assert "64" in config # ef_construction + + # Run downgrade + downgrade(alembic_config, "base") diff --git a/projects/pgai/tests/vectorizer/extensions/utils.py b/projects/pgai/tests/vectorizer/extensions/utils.py index a1f3553fa..0596d831f 100644 --- a/projects/pgai/tests/vectorizer/extensions/utils.py +++ b/projects/pgai/tests/vectorizer/extensions/utils.py @@ -1,6 +1,11 @@ +from pathlib import Path +from typing import Any + from click.testing import CliRunner +from sqlalchemy import Column from pgai.cli import vectorizer_worker +from tests.vectorizer.extensions.conftest import load_template def run_vectorizer_worker(db_url: str, vectorizer_id: int) -> None: @@ -17,3 +22,44 @@ def run_vectorizer_worker(db_url: str, vectorizer_id: int) -> None: ], catch_exceptions=False, ) + + +def create_vectorizer_migration( + migrations_dir: Path, + table_name: str, + table_columns: list[Column], # type: ignore + vectorizer_config: dict[str, Any], + revision_id: str = "001", + revises: str = "", + create_date: str = "2024-03-19 10:00:00.000000", + down_revision: str = "None", +) -> None: + """Create a vectorizer migration with the given configuration. + + Args: + migrations_dir: Directory where migration files are stored + table_name: Name of the source table to create + table_columns: List of SQLAlchemy Column objects defining table structure + vectorizer_config: Dictionary of vectorizer configuration options + revision_id: Migration revision ID (default: "001") + revises: What this migration revises (default: "") + create_date: Migration creation date (default: current timestamp) + down_revision: Previous revision (default: None) + """ + versions_dir = migrations_dir / "versions" + + migration_content = load_template( + "migrations/universal_vectorizer.py.template", + revision_id=revision_id, + revises=revises, + create_date=create_date, + down_revision=down_revision, + table_name=table_name, + table_columns=table_columns, # type: ignore + vectorizer_config=vectorizer_config, # type: ignore + ) + + with open( + versions_dir / f"{revision_id}_create_{table_name}_vectorizer.py", "w" + ) as f: + f.write(migration_content) diff --git a/projects/pgai/tests/vectorizer/test_required_annotation.py b/projects/pgai/tests/vectorizer/test_required_annotation.py new file mode 100644 index 000000000..022ae0974 --- /dev/null +++ b/projects/pgai/tests/vectorizer/test_required_annotation.py @@ -0,0 +1,55 @@ +from typing import Any + +import pytest +from pydantic import ValidationError + +from pgai.vectorizer.chunking import LangChainRecursiveCharacterTextSplitter + + +def test_langchain_splitter_requires_all_fields(): + # Test each field individually set to None + required_fields = [ + "chunk_size", + "chunk_column", + "chunk_overlap", + "is_separator_regex", + ] + + for field in required_fields: + # Create valid base params + valid_params: dict[str, Any] = { + "implementation": "recursive_character_text_splitter", + "separators": ["\n\n", "\n", " ", ""], + "chunk_size": 100, + "chunk_column": "text", + "chunk_overlap": 20, + "is_separator_regex": False, + } + + # Set the field being tested to None and assert it fails + valid_params[field] = None + + with pytest.raises(ValidationError): + LangChainRecursiveCharacterTextSplitter(**valid_params) + + +def test_langchain_splitter_accepts_valid_params(): + # Verify that valid parameters are accepted + valid_params = { + "implementation": "recursive_character_text_splitter", + "separators": ["\n\n", "\n", " ", ""], + "chunk_size": 100, + "chunk_column": "text", + "chunk_overlap": 20, + "is_separator_regex": False, + } + + # This should not raise any validation errors + splitter = LangChainRecursiveCharacterTextSplitter(**valid_params) # type: ignore + + # Verify all fields are set correctly + assert splitter.chunk_size == 100 + assert splitter.chunk_column == "text" + assert splitter.chunk_overlap == 20 + assert splitter.is_separator_regex is False + assert splitter.separators == ["\n\n", "\n", " ", ""] diff --git a/projects/pgai/uv.lock b/projects/pgai/uv.lock index 87f2b1751..2f6a87d71 100644 --- a/projects/pgai/uv.lock +++ b/projects/pgai/uv.lock @@ -4,23 +4,22 @@ resolution-markers = [ "python_full_version < '3.11'", "python_full_version == '3.11.*'", "python_full_version >= '3.12' and 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