From 71426098db20343c29c41ecfb4b1409d09e5f1bd Mon Sep 17 00:00:00 2001 From: Egor Kraev Date: Thu, 16 Apr 2026 13:17:33 +0200 Subject: [PATCH 1/3] Replace motleycrew with local structured_output_with_retries; drop langchain MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Removes the `motleycrew` and `langchain-core` / `langchain-openai` runtime dependencies. The only thing the project used from motleycrew was its `structured_output_with_retries` helper; that helper is now built in-tree on top of `instructor` + `litellm` + `tenacity`, which is what motleycrew itself was wrapping. The old LangChain `BaseLanguageModel` argument is replaced with a plain LiteLLM model string (e.g. `openai/gpt-4o`). Also: * Purge leftover `storyline.schema_gen.*` imports in `models.py`, `converter.py`, and `validators.py` — the package was extracted from `storyline` but kept referencing the parent namespace, which broke imports on a clean install. * Pin `litellm == 1.82.6` (exact). Versions 1.82.7 and 1.82.8 were compromised by the TeamPCP supply-chain attack (March 2026) and must not be resolvable from the constraint. * Add an import-time shim for `instructor` >= 1.10, which eagerly imports every provider module at top level. When an optional provider's top-level symbol isn't available (e.g. `mistralai` 2.x no longer exposes top-level `Mistral`), `import instructor` fails even though we only use `from_litellm`. The shim retries the import and installs a stub for whatever name/module is reported missing, so future provider breakage is handled automatically. See 567-labs/instructor#2205. * New test suite (9 tests, all offline) covering the helper's success and retry paths and the generator's wiring to it. Uses a fake instructor client that honours the passed-in tenacity retry policy. Co-Authored-By: Claude Opus 4.6 (1M context) --- README.md | 67 +- example.py | 13 +- poetry.lock | 2961 ++++++++++++++++++ pyproject.toml | 14 +- schema_gen/__init__.py | 11 +- schema_gen/converter.py | 2 +- schema_gen/llm_generator.py | 115 +- schema_gen/models.py | 6 +- schema_gen/structured_output_with_retries.py | 174 + schema_gen/validators.py | 2 +- tests/__init__.py | 0 tests/conftest.py | 74 + tests/test_llm_generator.py | 140 + tests/test_structured_output_with_retries.py | 148 + 14 files changed, 3617 insertions(+), 110 deletions(-) create mode 100644 poetry.lock create mode 100644 schema_gen/structured_output_with_retries.py create mode 100644 tests/__init__.py create mode 100644 tests/conftest.py create mode 100644 tests/test_llm_generator.py create mode 100644 tests/test_structured_output_with_retries.py diff --git a/README.md b/README.md index 080467b..a1e3994 100644 --- a/README.md +++ b/README.md @@ -46,21 +46,19 @@ pip install -e . ### Basic Usage ```python -from langchain_openai import ChatOpenAI -from schema_gen import generate_schema_from_prompt +from schema_gen import generate_sqlalchemy_models_from_prompt -# Initialize LLM -llm = ChatOpenAI(model="gpt-4o") - -# Generate schema from natural language -schema = generate_schema_from_prompt( +# Generate schema from natural language. `model` is a LiteLLM model string; +# the corresponding provider API key must be set in the environment +# (e.g. OPENAI_API_KEY for openai/*, ANTHROPIC_API_KEY for anthropic/*). +schema = generate_sqlalchemy_models_from_prompt( prompt=""" Create a blog schema with: - Users who can write posts - Posts with title, content, and timestamps - Comments on posts """, - language_model=llm, + model="openai/gpt-4o", ) # Convert to SQLAlchemy ORM classes @@ -121,10 +119,14 @@ poetry run python example.py #### LLM Generator (`schema_gen.llm_generator`) -- **`generate_schema_from_prompt()`**: Main function that uses LLM with validation loop +- **`generate_sqlalchemy_models_from_prompt()`**: Main function that uses an LLM with validation loop - **`post_process_schema()`**: Validates and post-processes generated schemas - Automatic retry logic if Pydantic validation or ORM conversion fails +#### Structured Output Helper (`schema_gen.structured_output_with_retries`) + +- **`structured_output_with_retries()`**: Self-contained validation-loop helper built on [instructor](https://github.com/instructor-ai/instructor) + [litellm](https://github.com/BerriAI/litellm) + [tenacity](https://github.com/jd/tenacity). Forces the LLM to return a valid Pydantic instance and retries with the validation error text appended on failure. + #### Database Setup (`schema_gen.setup_db`) - **`create_database()`**: Create PostgreSQL database if it doesn't exist @@ -136,7 +138,7 @@ poetry run python example.py The validation loop ensures generated schemas are correct: 1. **Prompt Construction**: Natural language description is formatted for the LLM -2. **LLM Generation**: Uses `structured_output_with_retries` from MotleyCrew +2. **LLM Generation**: Uses the local `structured_output_with_retries` helper (instructor + litellm) 3. **Pydantic Validation**: Automatic validation of field types and constraints 4. **Post-Processing**: Additional validation: - Verify foreign key references point to existing tables @@ -150,27 +152,29 @@ The validation loop ensures generated schemas are correct: ### Main Functions -#### `generate_schema_from_prompt()` +#### `generate_sqlalchemy_models_from_prompt()` ```python -def generate_schema_from_prompt( +def generate_sqlalchemy_models_from_prompt( prompt: str, - language_model: BaseLanguageModel, - max_retries: int = 3, + model: str = "anthropic/claude-sonnet-4-5-20250929", + max_attempts: int = 3, ) -> DatabaseSchema: """ Generate a database schema from a natural language prompt. Args: prompt: Natural language description of the desired schema - language_model: LangChain LLM to use for generation - max_retries: Maximum number of retry attempts if validation fails + model: LiteLLM model string (e.g. "openai/gpt-4o", + "anthropic/claude-sonnet-4-5-20250929"). The relevant provider + API key must be set in the environment. + max_attempts: Maximum number of LLM calls before giving up. Returns: DatabaseSchema: Validated schema that can be converted to ORM classes Raises: - ValueError: If schema generation fails after max_retries + RuntimeError: If schema generation fails after max_attempts """ ``` @@ -205,27 +209,25 @@ class TableDefinition(BaseModel): ## LLM Providers -The library works with any LangChain-compatible LLM: +The library uses [LiteLLM](https://docs.litellm.ai/docs/providers) under the hood, so any provider LiteLLM supports works with the appropriate `provider/model` string and the corresponding API key in your environment: ### OpenAI ```python -from langchain_openai import ChatOpenAI - -llm = ChatOpenAI(model="gpt-4o") +generate_sqlalchemy_models_from_prompt(prompt, model="openai/gpt-4o") +# requires OPENAI_API_KEY ``` ### Anthropic Claude ```python -from langchain_anthropic import ChatAnthropic - -llm = ChatAnthropic(model="claude-sonnet-4-5-20250929") +generate_sqlalchemy_models_from_prompt(prompt, model="anthropic/claude-sonnet-4-5-20250929") +# requires ANTHROPIC_API_KEY ``` ### Other Providers -Any LangChain `BaseLanguageModel` implementation will work with the appropriate setup. +See the [LiteLLM provider docs](https://docs.litellm.ai/docs/providers) for the full list — Azure, Cohere, Gemini, Ollama, Bedrock, etc. ## Configuration @@ -243,11 +245,10 @@ Requires Python 3.11 or higher. ### Custom Validation ```python -from schema_gen import DatabaseSchema -from schema_gen.validators import post_process_schema +from schema_gen import DatabaseSchema, generate_sqlalchemy_models_from_prompt, post_process_schema # Generate schema -schema = generate_schema_from_prompt(prompt, llm) +schema = generate_sqlalchemy_models_from_prompt(prompt, model="openai/gpt-4o") # Additional custom validation schema = post_process_schema(schema) @@ -290,7 +291,7 @@ Create an e-commerce schema with: - Payments for orders """ -schema = generate_schema_from_prompt(prompt, llm) +schema = generate_sqlalchemy_models_from_prompt(prompt, model="openai/gpt-4o") ``` ### SaaS Application @@ -304,7 +305,7 @@ Create a multi-tenant SaaS schema with: - Usage metrics tracked per organization """ -schema = generate_schema_from_prompt(prompt, llm) +schema = generate_sqlalchemy_models_from_prompt(prompt, model="openai/gpt-4o") ``` ## License @@ -319,7 +320,7 @@ This library was extracted from the Storyline project for standalone use. Contri - **pydantic** (>=2.11.7) - Schema validation and type safety - **sqlalchemy** (>=2.0.36) - Database ORM and query building -- **langchain-core** - LLM interface abstraction -- **langchain-openai** - OpenAI integration -- **motleycrew** (>=0.3.4) - Validation loop with structured output +- **instructor** (>=1.6.0) - Structured output wrapper +- **litellm** (==1.82.6) - Multi-provider LLM client. Pinned to 1.82.6 because versions 1.82.7 and 1.82.8 were compromised by a supply-chain attack (TeamPCP, March 2026). +- **tenacity** (>=9.0.0) - Retry logic - **psycopg2-binary** (optional) - PostgreSQL support diff --git a/example.py b/example.py index 76bfa9c..5291743 100644 --- a/example.py +++ b/example.py @@ -16,7 +16,6 @@ import logging from sqlalchemy import create_engine, inspect from sqlalchemy.orm import Session -from langchain_openai import ChatOpenAI from schema_gen import generate_sqlalchemy_models_from_prompt @@ -35,16 +34,16 @@ def main(): print("LLM-Driven Database Schema Generation Example") print("=" * 80) - # Check for API key - api_key = os.getenv("OPENAI_API_KEY") - if not api_key: + # Check for API key (litellm reads this directly from the environment) + if not os.getenv("OPENAI_API_KEY"): print("\nError: OPENAI_API_KEY environment variable not set") print("Please set your OpenAI API key:") print(" export OPENAI_API_KEY='your-api-key-here'") return - # Initialize LLM - llm = ChatOpenAI(model="gpt-4o", api_key=api_key) + # LiteLLM model string (provider/model). Change provider prefix to switch + # providers, e.g. "anthropic/claude-sonnet-4-5-20250929". + model = "openai/gpt-4o" # Define schema prompt prompt = """ @@ -65,7 +64,7 @@ def main(): # Generate schema using validation loop pattern schema = generate_sqlalchemy_models_from_prompt( prompt=prompt, - language_model=llm, + model=model, ) print(f"\nGenerated schema with {len(schema.tables)} tables:") diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..4e7134d --- /dev/null +++ b/poetry.lock @@ -0,0 +1,2961 @@ +# This file is automatically @generated by Poetry 1.8.0 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.9" +files = [ + {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"}, + {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = 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packages = [{include = "schema_gen"}] python = ">=3.11,<4.0" pydantic = ">=2.11.7" sqlalchemy = ">=2.0.36" -langchain-core = "^0.3.0" -langchain-openai = "^0.2.0" -motleycrew = ">=0.3.4" +# Instructor 1.10+ eagerly imports all provider shims at top-level `import +# instructor`, which breaks when some optional providers (e.g. mistralai 2.x, +# which dropped top-level symbols) aren't fully installed. We use the latest +# stable release and shim missing provider symbols at import time; see the +# `_shim_missing_providers()` helper in schema_gen/structured_output_with_retries.py. +instructor = ">=1.15.0" +# Exact pin: versions 1.82.7 and 1.82.8 were compromised by the TeamPCP +# supply-chain attack (March 2026). Do NOT relax to a caret or range that +# allows those releases. 1.82.6 is the last confirmed-safe version. +litellm = "==1.82.6" +tenacity = ">=9.0.0" psycopg2-binary = {version = "^2.9.9", optional = true} [tool.poetry.extras] diff --git a/schema_gen/__init__.py b/schema_gen/__init__.py index d2c8686..fae4809 100644 --- a/schema_gen/__init__.py +++ b/schema_gen/__init__.py @@ -24,14 +24,13 @@ - create_database_schema: Convenience function to create schema in database Example: - >>> from langchain_anthropic import ChatAnthropic - >>> from schema_gen import DatabaseSchema, create_sqlalchemy_tables + >>> from schema_gen import generate_sqlalchemy_models_from_prompt, create_sqlalchemy_tables >>> from sqlalchemy import MetaData >>> - >>> llm = ChatAnthropic(model="claude-sonnet-4-5-20250929") - >>> structured_llm = llm.with_structured_output(DatabaseSchema) - >>> - >>> schema = structured_llm.invoke("Create a blog schema with users and posts") + >>> schema = generate_sqlalchemy_models_from_prompt( + ... "Create a blog schema with users and posts", + ... model="anthropic/claude-sonnet-4-5-20250929", + ... ) >>> metadata = MetaData() >>> tables = create_sqlalchemy_tables(schema, metadata) """ diff --git a/schema_gen/converter.py b/schema_gen/converter.py index 2e72473..dbe1db5 100644 --- a/schema_gen/converter.py +++ b/schema_gen/converter.py @@ -24,7 +24,7 @@ except ImportError: HAS_POSTGRESQL = False -from storyline.schema_gen.enums import SQLColumnType +from schema_gen.enums import SQLColumnType # Mapping from SQLColumnType enum to SQLAlchemy type classes SQLALCHEMY_TYPE_MAP = { diff --git a/schema_gen/llm_generator.py b/schema_gen/llm_generator.py index e65169d..b4ceab8 100644 --- a/schema_gen/llm_generator.py +++ b/schema_gen/llm_generator.py @@ -1,18 +1,48 @@ """LLM-driven SQLAlchemy ORM model generation with automatic retry logic.""" import logging -from typing import Optional -from langchain_core.language_models import BaseLanguageModel -from langchain_core.prompts import PromptTemplate +from pydantic import ValidationError -from storyline.schema_gen.models import DatabaseSchema - -from motleycrew.utils.structured_output_with_retries import structured_output_with_retries +from schema_gen.models import DatabaseSchema +from schema_gen.structured_output_with_retries import structured_output_with_retries logger = logging.getLogger(__name__) +DEFAULT_MODEL = "anthropic/claude-sonnet-4-5-20250929" + + +_SCHEMA_PROMPT_TEMPLATE = """You are a database schema designer. Generate a complete database schema based on the user's requirements: + +{prompt} + +For each table, specify: +- Table name and comment (description) +- Columns with types, constraints, and comments +- Foreign keys with proper references and ON DELETE/UPDATE actions +- Indexes for frequently queried columns +- Unique constraints EXACTLY for all primary keys and no other columns + +Use appropriate data types: +- INTEGER, BIGINT, SMALLINT for whole numbers +- STRING (with length), TEXT for strings +- FLOAT, NUMERIC (with precision/scale) for decimals +- BOOLEAN for true/false +- DATE, DATETIME, TIMESTAMP for dates/times +- JSON, JSONB for structured data +- UUID for unique identifiers + +Ensure referential integrity: +- Foreign keys reference existing tables +- Primary keys are defined +- Avoid circular non-nullable foreign key dependencies + +Don't insert uniqueness constraints except for primary keys. +Provide clear, descriptive comments for tables and all columns. +""" + + def post_process_schema(schema: DatabaseSchema) -> DatabaseSchema: """ Post-process function that validates DatabaseSchema by attempting ORM conversion. @@ -59,82 +89,55 @@ def post_process_schema(schema: DatabaseSchema) -> DatabaseSchema: def generate_sqlalchemy_models_from_prompt( prompt: str, - language_model: Optional[BaseLanguageModel] = None, + model: str = DEFAULT_MODEL, + max_attempts: int = 3, ) -> DatabaseSchema: """ Generate SQLAlchemy ORM model classes from a natural language prompt. - Uses MotleyCrew's structured_output_with_retries to generate a validated - database schema, then converts it to SQLAlchemy ORM classes. + Uses structured_output_with_retries (instructor + litellm + tenacity) to + generate a validated database schema, then converts it to SQLAlchemy ORM + classes. Args: prompt: Natural language description of the desired database schema - language_model: LLM to use for generation (optional) + model: LiteLLM model string (e.g. "anthropic/claude-sonnet-4-5-20250929", + "openai/gpt-4o"). The relevant provider API key must be set in + the environment (e.g. ANTHROPIC_API_KEY, OPENAI_API_KEY). + max_attempts: Maximum number of LLM calls before giving up. Returns: - A DatabaseSchema object containing the generated schema + A DatabaseSchema object containing the generated schema. Raises: - ValueError: If schema generation or conversion fails + RuntimeError: If schema generation fails after max_attempts. Example: - >>> llm = ChatAnthropic(model="claude-sonnet-4-5-20250929") >>> models = generate_sqlalchemy_models_from_prompt( ... "Create a blog with users, posts, and comments", - ... language_model=llm + ... model="anthropic/claude-sonnet-4-5-20250929", ... ).to_orm_classes() >>> User = models['User'] >>> Post = models['Post'] """ logger.info("Starting SQLAlchemy model generation from prompt") logger.info(f"User prompt: {prompt}") - logger.info( - f"Language model: {language_model.__class__.__name__ if language_model else 'None'}" - ) + logger.info(f"Model: {model}") - prompt_template = """You are a database schema designer. Generate a complete database schema based on the user's requirements: - - {prompt} - - For each table, specify: - - Table name and comment (description) - - Columns with types, constraints, and comments - - Foreign keys with proper references and ON DELETE/UPDATE actions - - Indexes for frequently queried columns - - Unique constraints EXACTLY for all primary keys and no other columns - - Use appropriate data types: - - INTEGER, BIGINT, SMALLINT for whole numbers - - STRING (with length), TEXT for strings - - FLOAT, NUMERIC (with precision/scale) for decimals - - BOOLEAN for true/false - - DATE, DATETIME, TIMESTAMP for dates/times - - JSON, JSONB for structured data - - UUID for unique identifiers - - Ensure referential integrity: - - Foreign keys reference existing tables - - Primary keys are defined - - Avoid circular non-nullable foreign key dependencies - - Don't insert uniqueness constraints except for primary keys. - Provide clear, descriptive comments for tables and all columns. - """ - - prompt_template = PromptTemplate.from_template(prompt_template).format(prompt=prompt) - logger.info("Generated prompt template for LLM") - - # Use structured_output_with_retries to generate and validate schema + rendered_prompt = _SCHEMA_PROMPT_TEMPLATE.format(prompt=prompt) + messages = [{"role": "user", "content": rendered_prompt}] logger.info("Calling structured_output_with_retries to generate schema") - schema_result = structured_output_with_retries( + + schema_result: DatabaseSchema = structured_output_with_retries( schema=DatabaseSchema, - prompt=prompt_template, - input_messages={"prompt": prompt}, - language_model=language_model, - post_process=post_process_schema, + messages=messages, + validation_callable=post_process_schema, + model=model, + max_attempts=max_attempts, + retry_exceptions=(ValidationError, ValueError), ) - # Convert the validated schema to ORM classes + # Convert the validated schema to ORM classes (also acts as a final sanity check) logger.info("Converting validated schema to ORM classes") orm_classes = schema_result.to_orm_classes() diff --git a/schema_gen/models.py b/schema_gen/models.py index 9b6824d..6d416ec 100644 --- a/schema_gen/models.py +++ b/schema_gen/models.py @@ -32,9 +32,9 @@ TypeDecorator, ) -from storyline.schema_gen.converter import HAS_POSTGRESQL, SQLALCHEMY_TYPE_MAP -from storyline.schema_gen.enums import SQLColumnType, ForeignKeyAction, IndexType -from storyline.schema_gen import validators +from schema_gen.converter import HAS_POSTGRESQL, SQLALCHEMY_TYPE_MAP +from schema_gen.enums import SQLColumnType, ForeignKeyAction, IndexType +from schema_gen import validators from sqlalchemy.orm import declarative_base, mapped_column, relationship, DeclarativeBase if HAS_POSTGRESQL: diff --git a/schema_gen/structured_output_with_retries.py b/schema_gen/structured_output_with_retries.py new file mode 100644 index 0000000..1c61201 --- /dev/null +++ b/schema_gen/structured_output_with_retries.py @@ -0,0 +1,174 @@ +import importlib +import re +import sys +import types +from typing import Any, Callable, Type, TypeVar + +import tenacity +from pydantic import BaseModel, ValidationError + + +# --------------------------------------------------------------------------- +# Import-time shim for `instructor`. +# +# instructor >=1.10 eagerly imports every provider shim (openai, anthropic, +# mistralai, bedrock, cohere, …) from `instructor.providers.__init__`. When +# an optional provider's top-level symbol isn't available at the expected +# path — e.g. mistralai 2.x split its package and no longer exposes a +# top-level `Mistral` class — `import instructor` raises ImportError even +# though we never touch that provider. See 567-labs/instructor#2205 (open) +# and PR #2206 (closed, not merged). +# +# We only need `instructor.from_litellm`, so we retry the import and, on +# each failure, install a dummy stub for whatever name or module is missing, +# then try again. As long as the shimmed provider is never actually called, +# this is safe. +# --------------------------------------------------------------------------- + +_CANNOT_IMPORT_NAME_RE = re.compile(r"cannot import name '([^']+)' from '([^']+)'") +_NO_MODULE_RE = re.compile(r"No module named '([^']+)'") + + +def _import_instructor_with_stubs(max_shims: int = 20): + """Import instructor, stubbing missing provider symbols discovered at runtime.""" + for _ in range(max_shims): + # Drop any partial instructor state so each retry is a clean import. + for name in [n for n in sys.modules if n == "instructor" or n.startswith("instructor.")]: + del sys.modules[name] + try: + return importlib.import_module("instructor") + except ImportError as exc: + msg = str(exc) + m = _CANNOT_IMPORT_NAME_RE.search(msg) + if m: + attr_name, module_name = m.group(1), m.group(2) + try: + target = importlib.import_module(module_name) + except ImportError: + target = types.ModuleType(module_name) + sys.modules[module_name] = target + setattr(target, attr_name, type(attr_name, (), {})) + continue + m = _NO_MODULE_RE.search(msg) + if m: + module_name = m.group(1) + sys.modules[module_name] = types.ModuleType(module_name) + continue + # Unknown ImportError shape — don't silently mask it. + raise + raise RuntimeError( + f"Could not import `instructor` after {max_shims} shim attempts — " + "unexpected ImportError pattern." + ) + + +instructor = _import_instructor_with_stubs() + +from litellm import completion # noqa: E402 (after the instructor shim) + +T = TypeVar("T") + + +def structured_output_with_retries( + schema: Type[T], + messages: list[dict], + validation_callable: Callable[[T], Any], + model: str = "anthropic/claude-3-5-sonnet-20241022", + max_attempts: int = 3, + retry_exceptions: tuple[Type[Exception], ...] = (ValidationError,), +) -> Any: + """ + Feed messages to an LLM, forcing it to return a valid instance of `schema`. + On each attempt, the result is passed to `validation_callable`. + If it raises, the exception text is appended to the conversation and the LLM retries. + Returns the return value of `validation_callable` on success. + + Args: + schema: Pydantic model class defining the expected output shape. + messages: OpenAI-style message list, e.g. [{"role": "user", "content": "..."}]. + May include image content blocks for vision models. + validation_callable: Called with the validated Pydantic instance. Its return value + is returned on success; any exception triggers a retry. + model: LiteLLM model string (supports all providers). + max_attempts: Maximum number of LLM calls before giving up. + retry_exceptions: Exception types that trigger a retry. Defaults to ValidationError + (covers Pydantic field/model validators). Add your own custom + exception classes to catch business-rule failures too. + """ + client = instructor.from_litellm(completion) + + # Wrap the Pydantic schema to also run the validation callable, + # so its exceptions are caught by Instructor's retry loop. + class WrappedSchema(schema): # type: ignore[valid-type] + _validation_result: Any = None + + def model_post_init(self, __context: Any) -> None: + # Store the callable's return value so we can retrieve it after .create() + object.__setattr__(self, "_validation_result", validation_callable(self)) + + try: + result: WrappedSchema = client.chat.completions.create( + model=model, + response_model=WrappedSchema, + messages=messages, + max_retries=tenacity.Retrying( + stop=tenacity.stop_after_attempt(max_attempts), + retry=tenacity.retry_if_exception_type(retry_exceptions), + # NOTE: no reraise=True — we want tenacity to raise RetryError + # on exhaustion (with __cause__ set to the last exception) so + # we can wrap it in our own RuntimeError below. With reraise, + # the original exception would bypass our handler. + ), + ) + return result._validation_result + + except tenacity.RetryError as e: + raise RuntimeError( + f"structured_output_with_retries failed after {max_attempts} attempts" + ) from e.__cause__ + + +# --------------------------------------------------------------------------- +# Example usage +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + from pydantic import field_validator + + class MovieReview(BaseModel): + title: str + rating: float + summary: str + + @field_validator("rating") + @classmethod + def rating_in_range(cls, v: float) -> float: + if not (0.0 <= v <= 10.0): + raise ValueError(f"Rating must be between 0 and 10, got {v}") + return v + + def validate_and_transform(review: MovieReview) -> dict: + # Custom business logic beyond Pydantic — also retried on failure + if len(review.summary) < 20: + raise ValueError("Summary is too short to be useful") + return { + "title": review.title.upper(), + "rating": review.rating, + "summary": review.summary, + } + + output = structured_output_with_retries( + schema=MovieReview, + messages=[ + { + "role": "user", + "content": "Review the movie Inception in one short sentence.", + } + ], + validation_callable=validate_and_transform, + model="anthropic/claude-3-5-sonnet-20241022", + max_attempts=3, + retry_exceptions=(ValidationError, ValueError), + ) + + print(output) diff --git a/schema_gen/validators.py b/schema_gen/validators.py index 3cdb278..445216f 100644 --- a/schema_gen/validators.py +++ b/schema_gen/validators.py @@ -16,7 +16,7 @@ def validate_type_parameters(type_params, column_type, column_name: str): Raises: ValueError: If type parameters don't match the column type requirements """ - from storyline.schema_gen.enums import SQLColumnType + from schema_gen.enums import SQLColumnType requires_length = column_type in {SQLColumnType.STRING, SQLColumnType.VARCHAR} requires_precision = column_type in {SQLColumnType.NUMERIC, SQLColumnType.DECIMAL} diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..cf9cb39 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,74 @@ +"""Shared test fixtures for schema_gen tests.""" + +from typing import Any, List + +import pytest + + +class _FakeCompletions: + """Stand-in for `instructor_client.chat.completions`.""" + + def __init__(self, script: List[Any]): + self._script = list(script) + self.calls = 0 + self.last_kwargs: dict | None = None + + def create(self, *, model, response_model, messages, max_retries, **_kwargs): + self.last_kwargs = { + "model": model, + "response_model": response_model, + "messages": messages, + } + + def _attempt(): + self.calls += 1 + if not self._script: + raise RuntimeError("test script exhausted before max_attempts reached") + step = self._script.pop(0) + if isinstance(step, BaseException): + raise step + # step is a kwargs dict — construct the response_model. + # Invalid payloads will raise ValidationError here; valid payloads + # will also invoke WrappedSchema.model_post_init, which runs the + # validation_callable and may raise whatever it likes. + return response_model(**step) + + # `max_retries` is the tenacity.Retrying instance passed in by the + # caller. Invoking it runs _attempt under the retry policy. + return max_retries(_attempt) + + +class FakeInstructorClient: + """Drop-in replacement for `instructor.from_litellm(completion)` return value. + + The `script` is a list of either: + - dict: kwargs passed to `response_model(**kwargs)`; invalid data raises + ValidationError, valid data triggers the validation_callable. + - Exception instance: raised directly in place of calling the model. + """ + + def __init__(self, script: List[Any]): + self.completions = _FakeCompletions(script) + # instructor exposes `client.chat.completions.create(...)` + self.chat = type("_FakeChat", (), {"completions": self.completions})() + + +@pytest.fixture +def patch_instructor(monkeypatch): + """Install a scripted fake instructor client. + + Usage in a test: + client = patch_instructor([{"name": "ok", "count": 1}]) + ... + assert client.completions.calls == 1 + """ + + def install(script: List[Any]) -> FakeInstructorClient: + client = FakeInstructorClient(script) + monkeypatch.setattr( + "schema_gen.structured_output_with_retries.instructor.from_litellm", + lambda _completion: client, + ) + return client + + return install diff --git a/tests/test_llm_generator.py b/tests/test_llm_generator.py new file mode 100644 index 0000000..4cfa592 --- /dev/null +++ b/tests/test_llm_generator.py @@ -0,0 +1,140 @@ +"""Tests for schema_gen.llm_generator.""" + +from pydantic import ValidationError + +from schema_gen import ( + DatabaseSchema, + generate_sqlalchemy_models_from_prompt, + post_process_schema, +) +from schema_gen.enums import SQLColumnType +from schema_gen.models import ColumnDefinition, TableDefinition, TypeParameter +from schema_gen import llm_generator + + +def _make_valid_schema() -> DatabaseSchema: + """Build a minimal but fully valid DatabaseSchema for post-processing tests.""" + return DatabaseSchema( + tables=[ + TableDefinition( + name="User", + columns=[ + ColumnDefinition( + name="id", + type=SQLColumnType.INTEGER, + primary_key=True, + nullable=False, + autoincrement=True, + ), + ColumnDefinition( + name="email", + type=SQLColumnType.STRING, + type_parameters=TypeParameter(length=255), + nullable=False, + ), + ], + ) + ] + ) + + +def test_post_process_schema_passthrough(): + """A valid schema is returned unchanged (same instance).""" + schema = _make_valid_schema() + returned = post_process_schema(schema) + assert returned is schema + # No mutation of the valid columns. + assert returned.tables[0].columns[0].nullable is False + assert returned.tables[0].columns[1].nullable is False + + +def test_post_process_schema_fixes_nullable_primary_key(): + """A primary key column with nullable=True must be corrected to nullable=False.""" + schema = DatabaseSchema( + tables=[ + TableDefinition( + name="User", + columns=[ + ColumnDefinition( + name="id", + type=SQLColumnType.INTEGER, + primary_key=True, + nullable=True, # the bug we're fixing + autoincrement=True, + ), + ColumnDefinition( + name="email", + type=SQLColumnType.STRING, + type_parameters=TypeParameter(length=255), + nullable=False, + ), + ], + ) + ] + ) + + returned = post_process_schema(schema) + + assert returned.tables[0].columns[0].nullable is False + # Non-PK column untouched. + assert returned.tables[0].columns[1].nullable is False + + +def test_generate_calls_structured_output_with_expected_args(monkeypatch): + """generate_sqlalchemy_models_from_prompt wires the right args to the helper.""" + captured: dict = {} + expected_schema = _make_valid_schema() + + def fake_structured_output( + *, schema, messages, validation_callable, model, max_attempts, retry_exceptions + ): + captured.update( + schema=schema, + messages=messages, + validation_callable=validation_callable, + model=model, + max_attempts=max_attempts, + retry_exceptions=retry_exceptions, + ) + return expected_schema + + monkeypatch.setattr( + llm_generator, "structured_output_with_retries", fake_structured_output + ) + + result = generate_sqlalchemy_models_from_prompt( + prompt="Create a blog with users and posts", + model="anthropic/claude-3-haiku-test", + max_attempts=5, + ) + + assert result is expected_schema + assert captured["schema"] is DatabaseSchema + assert captured["model"] == "anthropic/claude-3-haiku-test" + assert captured["max_attempts"] == 5 + assert captured["validation_callable"] is post_process_schema + assert ValidationError in captured["retry_exceptions"] + assert ValueError in captured["retry_exceptions"] + + # The prompt should be embedded into a single user-role message. + messages = captured["messages"] + assert isinstance(messages, list) and len(messages) == 1 + assert messages[0]["role"] == "user" + assert "Create a blog with users and posts" in messages[0]["content"] + + +def test_generate_uses_default_model_when_not_specified(monkeypatch): + """If `model` is not provided, the module-level DEFAULT_MODEL is used.""" + captured: dict = {} + + def fake_structured_output(*, model, **_kwargs): + captured["model"] = model + return _make_valid_schema() + + monkeypatch.setattr( + llm_generator, "structured_output_with_retries", fake_structured_output + ) + + generate_sqlalchemy_models_from_prompt(prompt="anything") + + assert captured["model"] == llm_generator.DEFAULT_MODEL diff --git a/tests/test_structured_output_with_retries.py b/tests/test_structured_output_with_retries.py new file mode 100644 index 0000000..6769290 --- /dev/null +++ b/tests/test_structured_output_with_retries.py @@ -0,0 +1,148 @@ +"""Tests for schema_gen.structured_output_with_retries.""" + +import pytest +from pydantic import BaseModel, ValidationError + +from schema_gen.structured_output_with_retries import structured_output_with_retries + + +class SampleSchema(BaseModel): + """Minimal Pydantic schema used by these tests.""" + + name: str + count: int + + +def test_success_returns_validation_callable_result(patch_instructor): + """On first-attempt success, the helper returns validation_callable's return value.""" + client = patch_instructor([{"name": "foo", "count": 1}]) + + result = structured_output_with_retries( + schema=SampleSchema, + messages=[{"role": "user", "content": "hi"}], + validation_callable=lambda s: {"upper_name": s.name.upper(), "count": s.count}, + max_attempts=3, + ) + + assert result == {"upper_name": "FOO", "count": 1} + assert client.completions.calls == 1 + # Sanity-check the args that reached the fake client. + assert client.completions.last_kwargs["model"] # whatever default + assert client.completions.last_kwargs["messages"] == [{"role": "user", "content": "hi"}] + + +def test_retry_on_validation_error_then_success(patch_instructor): + """ValidationError on attempt 1 triggers a retry; attempt 2 succeeds.""" + client = patch_instructor( + [ + # count=str triggers Pydantic ValidationError + {"name": "foo", "count": "not-an-int"}, + {"name": "bar", "count": 2}, + ] + ) + + result = structured_output_with_retries( + schema=SampleSchema, + messages=[{"role": "user", "content": "hi"}], + validation_callable=lambda s: s.model_dump(), + max_attempts=3, + ) + + assert result == {"name": "bar", "count": 2} + assert client.completions.calls == 2 + + +def test_retry_on_callable_value_error(patch_instructor): + """ValueError raised by validation_callable retries under the default retry_exceptions. + + Pydantic v2 wraps any ValueError raised from `model_post_init` into a + ValidationError, which is in the default retry_exceptions, so the retry + fires without the caller needing to add ValueError explicitly. + """ + callable_calls = {"n": 0} + + def rejects_first(s): + callable_calls["n"] += 1 + if callable_calls["n"] == 1: + raise ValueError("first payload is semantically wrong") + return s.model_dump() + + client = patch_instructor( + [ + {"name": "foo", "count": 1}, + {"name": "bar", "count": 2}, + ] + ) + + result = structured_output_with_retries( + schema=SampleSchema, + messages=[{"role": "user", "content": "hi"}], + validation_callable=rejects_first, + max_attempts=3, + ) + + assert result == {"name": "bar", "count": 2} + assert client.completions.calls == 2 + assert callable_calls["n"] == 2 + + +def test_exhausts_retries_raises_runtime_error(patch_instructor): + """After max_attempts failed attempts, RuntimeError wraps the final exception.""" + client = patch_instructor( + [ + {"name": "foo", "count": "bad1"}, + {"name": "foo", "count": "bad2"}, + {"name": "foo", "count": "bad3"}, + ] + ) + + with pytest.raises(RuntimeError) as exc_info: + structured_output_with_retries( + schema=SampleSchema, + messages=[{"role": "user", "content": "hi"}], + validation_callable=lambda s: s.model_dump(), + max_attempts=3, + ) + + assert "failed after 3 attempts" in str(exc_info.value) + assert client.completions.calls == 3 + # The underlying ValidationError should be chained. + assert isinstance(exc_info.value.__cause__, ValidationError) + + +def test_callable_exception_not_in_retry_exceptions_is_not_retried(patch_instructor): + """Exceptions not matching retry_exceptions bypass the retry loop and propagate. + + Uses a custom exception class (not ValueError/TypeError/AssertionError) so + Pydantic's model_post_init doesn't wrap it into a ValidationError. The + default retry_exceptions=(ValidationError,) should then leave this + exception untouched. + """ + + class _NonRetryable(Exception): + pass + + call_count = {"n": 0} + + def always_raises(s): + call_count["n"] += 1 + raise _NonRetryable("no good") + + client = patch_instructor( + [ + {"name": "foo", "count": 1}, + {"name": "bar", "count": 2}, # never reached + ] + ) + + with pytest.raises(_NonRetryable, match="no good"): + structured_output_with_retries( + schema=SampleSchema, + messages=[{"role": "user", "content": "hi"}], + validation_callable=always_raises, + max_attempts=3, + ) + + # Only one attempt — custom exception isn't in default retry_exceptions. + assert client.completions.calls == 1 + assert call_count["n"] == 1 From 3505f8b5a220aaa249512d225a13b282625e4ccf Mon Sep 17 00:00:00 2001 From: Egor Kraev Date: Sat, 18 Apr 2026 15:57:52 +0200 Subject: [PATCH 2/3] Address CodeRabbit review: upgrade litellm, fix docs, default to openai/gpt-4.1-mini - Upgrade litellm from ==1.82.6 to >=1.83.0 (compromised versions removed from PyPI) - Fix README instructor version (>=1.6.0 -> >=1.15.0) to match pyproject.toml - Remove redundant to_orm_classes() call in generate_sqlalchemy_models_from_prompt - Change default model from anthropic/claude-sonnet to openai/gpt-4.1-mini everywhere - Add integration test with real LLM call (pytest -m integration) - Configure pytest to skip integration tests by default Co-Authored-By: Claude Opus 4.6 (1M context) --- README.md | 31 ++--- example.py | 5 +- poetry.lock | 119 +++++++++++++++---- pyproject.toml | 11 +- schema_gen/__init__.py | 2 +- schema_gen/llm_generator.py | 15 +-- schema_gen/structured_output_with_retries.py | 4 +- tests/test_integration.py | 49 ++++++++ 8 files changed, 174 insertions(+), 62 deletions(-) create mode 100644 tests/test_integration.py diff --git a/README.md b/README.md index a1e3994..b68712f 100644 --- a/README.md +++ b/README.md @@ -50,7 +50,8 @@ from schema_gen import generate_sqlalchemy_models_from_prompt # Generate schema from natural language. `model` is a LiteLLM model string; # the corresponding provider API key must be set in the environment -# (e.g. OPENAI_API_KEY for openai/*, ANTHROPIC_API_KEY for anthropic/*). +# the corresponding provider API key must be set in the environment +# (e.g. OPENAI_API_KEY for openai/*). schema = generate_sqlalchemy_models_from_prompt( prompt=""" Create a blog schema with: @@ -58,7 +59,6 @@ schema = generate_sqlalchemy_models_from_prompt( - Posts with title, content, and timestamps - Comments on posts """, - model="openai/gpt-4o", ) # Convert to SQLAlchemy ORM classes @@ -157,7 +157,7 @@ The validation loop ensures generated schemas are correct: ```python def generate_sqlalchemy_models_from_prompt( prompt: str, - model: str = "anthropic/claude-sonnet-4-5-20250929", + model: str = "openai/gpt-4.1-mini", max_attempts: int = 3, ) -> DatabaseSchema: """ @@ -165,9 +165,9 @@ def generate_sqlalchemy_models_from_prompt( Args: prompt: Natural language description of the desired schema - model: LiteLLM model string (e.g. "openai/gpt-4o", - "anthropic/claude-sonnet-4-5-20250929"). The relevant provider - API key must be set in the environment. + model: LiteLLM model string (e.g. "openai/gpt-4.1-mini", + "openai/gpt-4o"). The relevant provider API key must be set in + the environment. max_attempts: Maximum number of LLM calls before giving up. Returns: @@ -211,30 +211,23 @@ class TableDefinition(BaseModel): The library uses [LiteLLM](https://docs.litellm.ai/docs/providers) under the hood, so any provider LiteLLM supports works with the appropriate `provider/model` string and the corresponding API key in your environment: -### OpenAI +### OpenAI (default) ```python +generate_sqlalchemy_models_from_prompt(prompt) # uses openai/gpt-4.1-mini generate_sqlalchemy_models_from_prompt(prompt, model="openai/gpt-4o") # requires OPENAI_API_KEY ``` -### Anthropic Claude - -```python -generate_sqlalchemy_models_from_prompt(prompt, model="anthropic/claude-sonnet-4-5-20250929") -# requires ANTHROPIC_API_KEY -``` - ### Other Providers -See the [LiteLLM provider docs](https://docs.litellm.ai/docs/providers) for the full list — Azure, Cohere, Gemini, Ollama, Bedrock, etc. +See the [LiteLLM provider docs](https://docs.litellm.ai/docs/providers) for the full list — Anthropic, Azure, Cohere, Gemini, Ollama, Bedrock, etc. ## Configuration ### Environment Variables -- **`OPENAI_API_KEY`**: Required for OpenAI models -- **`ANTHROPIC_API_KEY`**: Required for Anthropic models +- **`OPENAI_API_KEY`**: Required for OpenAI models (default provider) ### Python Version @@ -320,7 +313,7 @@ This library was extracted from the Storyline project for standalone use. Contri - **pydantic** (>=2.11.7) - Schema validation and type safety - **sqlalchemy** (>=2.0.36) - Database ORM and query building -- **instructor** (>=1.6.0) - Structured output wrapper -- **litellm** (==1.82.6) - Multi-provider LLM client. Pinned to 1.82.6 because versions 1.82.7 and 1.82.8 were compromised by a supply-chain attack (TeamPCP, March 2026). +- **instructor** (>=1.15.0) - Structured output wrapper +- **litellm** (>=1.83.0) - Multi-provider LLM client. Versions 1.82.7/1.82.8 were compromised (TeamPCP, March 2026) and removed from PyPI; 1.83.0+ uses a new secure CI/CD pipeline. - **tenacity** (>=9.0.0) - Retry logic - **psycopg2-binary** (optional) - PostgreSQL support diff --git a/example.py b/example.py index 5291743..59bbf79 100644 --- a/example.py +++ b/example.py @@ -41,9 +41,8 @@ def main(): print(" export OPENAI_API_KEY='your-api-key-here'") return - # LiteLLM model string (provider/model). Change provider prefix to switch - # providers, e.g. "anthropic/claude-sonnet-4-5-20250929". - model = "openai/gpt-4o" + # LiteLLM model string (provider/model). + model = "openai/gpt-4.1-mini" # Define schema prompt prompt = """ diff --git a/poetry.lock b/poetry.lock index 4e7134d..922d301 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.0 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -6,6 +6,7 @@ version = "2.6.1" description = "Happy Eyeballs for asyncio" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"}, {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"}, @@ -17,6 +18,7 @@ version = "3.13.5" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "aiohttp-3.13.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:02222e7e233295f40e011c1b00e3b0bd451f22cf853a0304c3595633ee47da4b"}, {file = "aiohttp-3.13.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bace460460ed20614fa6bc8cb09966c0b8517b8c58ad8046828c6078d25333b5"}, @@ -150,7 +152,7 @@ propcache = ">=0.2.0" yarl = ">=1.17.0,<2.0" [package.extras] -speedups = ["Brotli (>=1.2)", "aiodns (>=3.3.0)", "backports.zstd", "brotlicffi (>=1.2)"] +speedups = ["Brotli (>=1.2) ; platform_python_implementation == \"CPython\"", "aiodns (>=3.3.0)", "backports.zstd ; platform_python_implementation == \"CPython\" and python_version < \"3.14\"", "brotlicffi (>=1.2) ; platform_python_implementation != \"CPython\""] [[package]] name = "aiosignal" @@ -158,6 +160,7 @@ version = "1.4.0" description = "aiosignal: a list of registered asynchronous callbacks" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e"}, {file = "aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7"}, @@ -173,6 +176,7 @@ version = "0.0.4" description = "Document parameters, class attributes, return types, and variables inline, with Annotated." optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320"}, {file = "annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4"}, @@ -184,6 +188,7 @@ version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, @@ -195,6 +200,7 @@ version = "4.13.0" description = "High-level concurrency and networking framework on top of asyncio or Trio" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "anyio-4.13.0-py3-none-any.whl", hash = "sha256:08b310f9e24a9594186fd75b4f73f4a4152069e3853f1ed8bfbf58369f4ad708"}, {file = "anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc"}, @@ -213,6 +219,7 @@ version = "26.1.0" description = "Classes Without Boilerplate" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309"}, {file = "attrs-26.1.0.tar.gz", hash = "sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32"}, @@ -224,6 +231,7 @@ version = "2026.2.25" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"}, {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"}, @@ -235,6 +243,7 @@ version = "3.4.7" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"}, {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"}, @@ -373,6 +382,7 @@ version = "8.3.2" description = "Composable command line interface toolkit" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "click-8.3.2-py3-none-any.whl", hash = "sha256:1924d2c27c5653561cd2cae4548d1406039cb79b858b747cfea24924bbc1616d"}, {file = "click-8.3.2.tar.gz", hash = "sha256:14162b8b3b3550a7d479eafa77dfd3c38d9dc8951f6f69c78913a8f9a7540fd5"}, @@ -387,10 +397,12 @@ version = "0.4.6" description = "Cross-platform colored terminal text." optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" +groups = ["main", "dev"] files = [ {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] +markers = {main = "platform_system == \"Windows\"", dev = "sys_platform == \"win32\""} [[package]] name = "coverage" @@ -398,6 +410,7 @@ version = "7.13.5" description = "Code coverage measurement for Python" optional = false python-versions = ">=3.10" +groups = ["dev"] files = [ {file = "coverage-7.13.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e0723d2c96324561b9aa76fb982406e11d93cdb388a7a7da2b16e04719cf7ca5"}, {file = "coverage-7.13.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:52f444e86475992506b32d4e5ca55c24fc88d73bcbda0e9745095b28ef4dc0cf"}, @@ -508,7 +521,7 @@ files = [ ] [package.extras] -toml = ["tomli"] +toml = ["tomli ; python_full_version <= \"3.11.0a6\""] [[package]] name = "distro" @@ -516,6 +529,7 @@ version = "1.9.0" description = "Distro - an OS platform information API" optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2"}, {file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"}, @@ -527,13 +541,14 @@ version = "0.18.0" description = "Parse Python docstrings in reST, Google and Numpydoc format" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "docstring_parser-0.18.0-py3-none-any.whl", hash = "sha256:b3fcbed555c47d8479be0796ef7e19c2670d428d72e96da63f3a40122860374b"}, {file = "docstring_parser-0.18.0.tar.gz", hash = "sha256:292510982205c12b1248696f44959db3cdd1740237a968ea1e2e7a900eeb2015"}, ] [package.extras] -dev = ["pre-commit (>=2.16.0)", "pydoctor (>=25.4.0)", "pytest"] +dev = ["pre-commit (>=2.16.0) ; python_version >= \"3.9\"", "pydoctor (>=25.4.0)", "pytest"] docs = ["pydoctor (>=25.4.0)"] test = ["pytest"] @@ -543,6 +558,7 @@ version = "0.14.0" description = "Python bindings to Rust's UUID library." optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "fastuuid-0.14.0-cp310-cp310-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:6e6243d40f6c793c3e2ee14c13769e341b90be5ef0c23c82fa6515a96145181a"}, {file = "fastuuid-0.14.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:13ec4f2c3b04271f62be2e1ce7e95ad2dd1cf97e94503a3760db739afbd48f00"}, @@ -630,6 +646,7 @@ version = "3.28.0" description = "A platform independent file lock." optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "filelock-3.28.0-py3-none-any.whl", hash = "sha256:de9af6712788e7171df1b28b15eba2446c69721433fa427a9bee07b17820a9db"}, {file = "filelock-3.28.0.tar.gz", hash = "sha256:4ed1010aae813c4ee8d9c660e4792475ee60c4a0ba76073ceaf862bd317e3ca6"}, @@ -641,6 +658,7 @@ version = "1.8.0" description = "A list-like structure which implements collections.abc.MutableSequence" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "frozenlist-1.8.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b37f6d31b3dcea7deb5e9696e529a6aa4a898adc33db82da12e4c60a7c4d2011"}, {file = "frozenlist-1.8.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ef2b7b394f208233e471abc541cc6991f907ffd47dc72584acee3147899d6565"}, @@ -780,6 +798,7 @@ version = "2026.3.0" description = "File-system specification" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "fsspec-2026.3.0-py3-none-any.whl", hash = "sha256:d2ceafaad1b3457968ed14efa28798162f1638dbb5d2a6868a2db002a5ee39a4"}, {file = "fsspec-2026.3.0.tar.gz", hash = "sha256:1ee6a0e28677557f8c2f994e3eea77db6392b4de9cd1f5d7a9e87a0ae9d01b41"}, @@ -810,7 +829,7 @@ smb = ["smbprotocol"] ssh = ["paramiko"] test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"] test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"] -test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "backports-zstd", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas (<3.0.0)", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"] +test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "backports-zstd ; python_version < \"3.14\"", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas (<3.0.0)", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard ; python_version < \"3.14\""] tqdm = ["tqdm"] [[package]] @@ -819,6 +838,8 @@ version = "3.4.0" description = "Lightweight in-process concurrent programming" optional = false python-versions = ">=3.10" +groups = ["main"] +markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\"" files = [ {file = "greenlet-3.4.0-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:d18eae9a7fb0f499efcd146b8c9750a2e1f6e0e93b5a382b3481875354a430e6"}, {file = "greenlet-3.4.0-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:636d2f95c309e35f650e421c23297d5011716be15d966e6328b367c9fc513a82"}, @@ -891,6 +912,7 @@ version = "0.16.0" description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"}, {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"}, @@ -902,6 +924,8 @@ version = "1.4.3" description = "Fast transfer of large files with the Hugging Face Hub." optional = false python-versions = ">=3.8" +groups = ["main"] +markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\"" files = [ {file = "hf_xet-1.4.3-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:7551659ba4f1e1074e9623996f28c3873682530aee0a846b7f2f066239228144"}, {file = "hf_xet-1.4.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:bee693ada985e7045997f05f081d0e12c4c08bd7626dc397f8a7c487e6c04f7f"}, @@ -939,6 +963,7 @@ version = "1.0.9" description = "A minimal low-level HTTP client." optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55"}, {file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"}, @@ -960,6 +985,7 @@ version = "0.28.1" description = "The next generation HTTP client." optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"}, {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"}, @@ -972,7 +998,7 @@ httpcore = "==1.*" idna = "*" [package.extras] -brotli = ["brotli", "brotlicffi"] +brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""] cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"] http2 = ["h2 (>=3,<5)"] socks = ["socksio (==1.*)"] @@ -984,6 +1010,7 @@ version = "1.10.2" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" +groups = ["main"] files = [ {file = "huggingface_hub-1.10.2-py3-none-any.whl", hash = "sha256:c26c908767cc711493978dc0b4f5747ba7841602997cc98bfd628450a28cf9bc"}, {file = "huggingface_hub-1.10.2.tar.gz", hash = "sha256:4b276f820483b709dc86a53bcb8183ea496b8d8447c9f7f88a115a12b498a95f"}, @@ -1019,6 +1046,7 @@ version = "3.11" description = "Internationalized Domain Names in Applications (IDNA)" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea"}, {file = "idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902"}, @@ -1033,6 +1061,7 @@ version = "9.0.0" description = "Read metadata from Python packages" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "importlib_metadata-9.0.0-py3-none-any.whl", hash = "sha256:2d21d1cc5a017bd0559e36150c21c830ab1dc304dedd1b7ea85d20f45ef3edd7"}, {file = "importlib_metadata-9.0.0.tar.gz", hash = "sha256:a4f57ab599e6a2e3016d7595cfd72eb4661a5106e787a95bcc90c7105b831efc"}, @@ -1042,13 +1071,13 @@ files = [ zipp = ">=3.20" [package.extras] -check = ["pytest-checkdocs (>=2.14)", "pytest-ruff (>=0.2.1)"] +check = ["pytest-checkdocs (>=2.14)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] enabler = ["pytest-enabler (>=3.4)"] perf = ["ipython"] test = ["packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"] -type = ["pytest-mypy (>=1.0.1)"] +type = ["pytest-mypy (>=1.0.1) ; platform_python_implementation != \"PyPy\""] [[package]] name = "iniconfig" @@ -1056,6 +1085,7 @@ version = "2.3.0" description = "brain-dead simple config-ini parsing" optional = false python-versions = ">=3.10" +groups = ["dev"] files = [ {file = "iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12"}, {file = "iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730"}, @@ -1067,6 +1097,7 @@ version = "1.15.1" description = "structured outputs for llm" optional = false python-versions = "<4.0,>=3.9" +groups = ["main"] files = [ {file = "instructor-1.15.1-py3-none-any.whl", hash = "sha256:be81d17ba2b154a04ab4720808f24f9d6b598f80992f82eaf9cc79006099cf6c"}, {file = "instructor-1.15.1.tar.gz", hash = "sha256:c72406469d9025b742e83cf0c13e914b317db2089d08d889944e74fcd659ef94"}, @@ -1108,7 +1139,7 @@ test-docs = ["diskcache (>=5.6.3,<6.0.0)", "fastapi (>=0.109.2,<0.129.0)", "lite trafilatura = ["trafilatura (>=1.12.2,<3.0.0)"] vertexai = ["google-cloud-aiplatform (>=1.53.0,<2.0.0)", "jsonref (>=1.1.0,<2.0.0)"] writer = ["writer-sdk (>=2.2.0,<3.0.0)"] -xai = ["xai-sdk (>=0.2.0)"] +xai = ["xai-sdk (>=0.2.0) ; python_version >= \"3.10\""] [[package]] name = "jinja2" @@ -1116,6 +1147,7 @@ version = "3.1.6" description = "A very fast and expressive template engine." optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67"}, {file = "jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d"}, @@ -1133,6 +1165,7 @@ version = "0.13.0" description = "Fast iterable JSON parser." optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "jiter-0.13.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:2ffc63785fd6c7977defe49b9824ae6ce2b2e2b77ce539bdaf006c26da06342e"}, {file = "jiter-0.13.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4a638816427006c1e3f0013eb66d391d7a3acda99a7b0cf091eff4497ccea33a"}, @@ -1244,6 +1277,7 @@ version = "4.26.0" description = "An implementation of JSON Schema validation for Python" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "jsonschema-4.26.0-py3-none-any.whl", hash = "sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce"}, {file = "jsonschema-4.26.0.tar.gz", hash = "sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326"}, @@ -1251,7 +1285,7 @@ files = [ [package.dependencies] attrs = ">=22.2.0" -jsonschema-specifications = ">=2023.03.6" +jsonschema-specifications = ">=2023.3.6" referencing = ">=0.28.4" rpds-py = ">=0.25.0" @@ -1265,6 +1299,7 @@ version = "2025.9.1" description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe"}, {file = "jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d"}, @@ -1275,13 +1310,14 @@ referencing = ">=0.31.0" [[package]] name = "litellm" -version = "1.82.6" +version = "1.83.0" description = "Library to easily interface with LLM API providers" optional = false python-versions = "<4.0,>=3.9" +groups = ["main"] files = [ - {file = "litellm-1.82.6-py3-none-any.whl", hash = "sha256:164a3ef3e19f309e3cabc199bef3d2045212712fefdfa25fc7f75884a5b5b205"}, - {file = "litellm-1.82.6.tar.gz", hash = "sha256:2aa1c2da21fe940c33613aa447119674a3ad4d2ad5eb064e4d5ce5ee42420136"}, + {file = "litellm-1.83.0-py3-none-any.whl", hash = "sha256:88c536d339248f3987571493015784671ba3f193a328e1ea6780dbebaa2094a8"}, + {file = "litellm-1.83.0.tar.gz", hash = "sha256:860bebc76c4bb27b4cf90b4a77acd66dba25aced37e3db98750de8a1766bfb7a"}, ] [package.dependencies] @@ -1300,12 +1336,12 @@ tokenizers = "*" [package.extras] caching = ["diskcache (>=5.6.1,<6.0.0)"] -extra-proxy = ["a2a-sdk (>=0.3.22,<0.4.0)", "azure-identity (>=1.15.0,<2.0.0)", "azure-keyvault-secrets (>=4.8.0,<5.0.0)", "google-cloud-iam (>=2.19.1,<3.0.0)", "google-cloud-kms (>=2.21.3,<3.0.0)", "prisma (>=0.11.0,<0.12.0)", "redisvl (>=0.4.1,<0.5.0)", "resend (>=0.8.0)"] +extra-proxy = ["a2a-sdk (>=0.3.22,<0.4.0) ; 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python_version < \"3.14\"", "grpcio (>=1.75.0) ; python_version >= \"3.14\""] +mlflow = ["mlflow (>3.1.4) ; python_version >= \"3.10\""] +proxy = ["PyJWT (>=2.12.0,<3.0.0) ; python_version >= \"3.9\"", "apscheduler (>=3.10.4,<4.0.0)", "azure-identity (>=1.15.0,<2.0.0) ; python_version >= \"3.9\"", "azure-storage-blob (>=12.25.1,<13.0.0)", "backoff", "boto3 (>=1.40.76,<2.0.0)", "cryptography", "fastapi (>=0.120.1)", "fastapi-sso (>=0.16.0,<0.17.0)", "gunicorn (>=23.0.0,<24.0.0)", "litellm-enterprise (==0.1.35)", "litellm-proxy-extras (>=0.4.62,<0.5.0)", "mcp (>=1.25.0,<2.0.0) ; python_version >= \"3.10\"", "orjson (>=3.9.7,<4.0.0)", "polars (>=1.31.0,<2.0.0) ; python_version >= \"3.10\"", "pynacl (>=1.5.0,<2.0.0)", "pyroscope-io (>=0.8,<0.9) ; sys_platform != \"win32\"", "python-multipart (>=0.0.20)", "pyyaml (>=6.0.1,<7.0.0)", "rich (>=13.7.1,<14.0.0)", "rq", "soundfile (>=0.12.1,<0.13.0)", "uvicorn (>=0.32.1,<1.0.0)", "uvloop (>=0.21.0,<0.22.0) ; sys_platform != \"win32\"", "websockets (>=15.0.1,<16.0.0)"] +semantic-router = ["semantic-router (>=0.1.12) ; python_version >= \"3.9\" and python_version < \"3.14\""] utils = ["numpydoc"] [[package]] @@ -1314,6 +1350,7 @@ version = "4.0.0" description = "Python port of markdown-it. Markdown parsing, done right!" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "markdown_it_py-4.0.0-py3-none-any.whl", hash = "sha256:87327c59b172c5011896038353a81343b6754500a08cd7a4973bb48c6d578147"}, {file = "markdown_it_py-4.0.0.tar.gz", hash = "sha256:cb0a2b4aa34f932c007117b194e945bd74e0ec24133ceb5bac59009cda1cb9f3"}, @@ -1337,6 +1374,7 @@ version = "3.0.3" description = "Safely add untrusted strings to HTML/XML markup." optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "markupsafe-3.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2f981d352f04553a7171b8e44369f2af4055f888dfb147d55e42d29e29e74559"}, {file = "markupsafe-3.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e1c1493fb6e50ab01d20a22826e57520f1284df32f2d8601fdd90b6304601419"}, @@ -1435,6 +1473,7 @@ version = "0.1.2" description = "Markdown URL utilities" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"}, {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"}, @@ -1446,6 +1485,7 @@ version = "6.7.1" description = "multidict implementation" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "multidict-6.7.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:c93c3db7ea657dd4637d57e74ab73de31bccefe144d3d4ce370052035bc85fb5"}, {file = "multidict-6.7.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:974e72a2474600827abaeda71af0c53d9ebbc3c2eb7da37b37d7829ae31232d8"}, @@ -1601,6 +1641,7 @@ version = "2.32.0" description = "The official Python library for the openai API" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "openai-2.32.0-py3-none-any.whl", hash = "sha256:4dcc9badeb4bf54ad0d187453742f290226d30150890b7890711bda4f32f192f"}, {file = "openai-2.32.0.tar.gz", hash = "sha256:c54b27a9e4cb8d51f0dd94972ffd1a04437efeb259a9e60d8922b8bd26fe55e0"}, @@ -1628,6 +1669,7 @@ version = "26.1" description = "Core utilities for Python packages" optional = false python-versions = ">=3.8" +groups = ["main", "dev"] files = [ {file = "packaging-26.1-py3-none-any.whl", hash = "sha256:5d9c0669c6285e491e0ced2eee587eaf67b670d94a19e94e3984a481aba6802f"}, {file = "packaging-26.1.tar.gz", hash = "sha256:f042152b681c4bfac5cae2742a55e103d27ab2ec0f3d88037136b6bfe7c9c5de"}, @@ -1639,6 +1681,7 @@ version = "1.6.0" description = "plugin and hook calling mechanisms for python" optional = false python-versions = ">=3.9" +groups = ["dev"] files = [ {file = "pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746"}, {file = "pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3"}, @@ -1654,6 +1697,7 @@ version = "0.4.1" description = "Accelerated property cache" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "propcache-0.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7c2d1fa3201efaf55d730400d945b5b3ab6e672e100ba0f9a409d950ab25d7db"}, {file = "propcache-0.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1eb2994229cc8ce7fe9b3db88f5465f5fd8651672840b2e426b88cdb1a30aac8"}, @@ -1785,6 +1829,8 @@ version = "2.9.11" description = "psycopg2 - Python-PostgreSQL Database Adapter" optional = true python-versions = ">=3.9" +groups = ["main"] +markers = "extra == \"postgres\"" files = [ {file = "psycopg2-binary-2.9.11.tar.gz", hash = "sha256:b6aed9e096bf63f9e75edf2581aa9a7e7186d97ab5c177aa6c87797cd591236c"}, {file = "psycopg2_binary-2.9.11-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d6fe6b47d0b42ce1c9f1fa3e35bb365011ca22e39db37074458f27921dca40f2"}, @@ -1861,6 +1907,7 @@ version = "2.13.1" description = "Data validation using Python type hints" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "pydantic-2.13.1-py3-none-any.whl", hash = "sha256:9557ecc2806faaf6037f85b1fbd963d01e30511c48085f0d573650fdeaad378a"}, {file = "pydantic-2.13.1.tar.gz", hash = "sha256:a0f829b279ddd1e39291133fe2539d2aa46cc6b150c1706a270ff0879e3774d2"}, @@ -1874,7 +1921,7 @@ typing-inspection = ">=0.4.2" [package.extras] email = ["email-validator (>=2.0.0)"] -timezone = ["tzdata"] +timezone = ["tzdata ; python_version >= \"3.9\" and platform_system == \"Windows\""] [[package]] name = "pydantic-core" @@ -1882,6 +1929,7 @@ version = "2.46.1" description = "Core functionality for Pydantic validation and serialization" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "pydantic_core-2.46.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:84eb5414871fd0293c38d2075802f95030ff11a92cf2189942bf76fd181af77b"}, {file = "pydantic_core-2.46.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5c75fb25db086bf504c55730442e471c12bc9bfae817dd359b1a36bc93049d34"}, @@ -2014,6 +2062,7 @@ version = "2.20.0" description = "Pygments is a syntax highlighting package written in Python." optional = false python-versions = ">=3.9" +groups = ["main", "dev"] files = [ {file = "pygments-2.20.0-py3-none-any.whl", hash = "sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176"}, {file = "pygments-2.20.0.tar.gz", hash = "sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f"}, @@ -2028,6 +2077,7 @@ version = "8.4.2" description = "pytest: simple powerful testing with Python" optional = false python-versions = ">=3.9" +groups = ["dev"] files = [ {file = "pytest-8.4.2-py3-none-any.whl", hash = "sha256:872f880de3fc3a5bdc88a11b39c9710c3497a547cfa9320bc3c5e62fbf272e79"}, {file = "pytest-8.4.2.tar.gz", hash = "sha256:86c0d0b93306b961d58d62a4db4879f27fe25513d4b969df351abdddb3c30e01"}, @@ -2049,6 +2099,7 @@ version = "4.1.0" description = "Pytest plugin for measuring coverage." optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "pytest-cov-4.1.0.tar.gz", hash = "sha256:3904b13dfbfec47f003b8e77fd5b589cd11904a21ddf1ab38a64f204d6a10ef6"}, {file = "pytest_cov-4.1.0-py3-none-any.whl", hash = "sha256:6ba70b9e97e69fcc3fb45bfeab2d0a138fb65c4d0d6a41ef33983ad114be8c3a"}, @@ -2067,6 +2118,7 @@ version = "1.2.2" description = "Read key-value pairs from a .env file and set them as environment variables" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "python_dotenv-1.2.2-py3-none-any.whl", hash = "sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a"}, {file = "python_dotenv-1.2.2.tar.gz", hash = "sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3"}, @@ -2081,6 +2133,7 @@ version = "6.0.3" description = "YAML parser and emitter for Python" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "PyYAML-6.0.3-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:c2514fceb77bc5e7a2f7adfaa1feb2fb311607c9cb518dbc378688ec73d8292f"}, {file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c57bb8c96f6d1808c030b1687b9b5fb476abaa47f0db9c0101f5e9f394e97f4"}, @@ -2163,6 +2216,7 @@ version = "0.37.0" description = "JSON Referencing + Python" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "referencing-0.37.0-py3-none-any.whl", hash = "sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231"}, {file = "referencing-0.37.0.tar.gz", hash = "sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8"}, @@ -2179,6 +2233,7 @@ version = "2026.4.4" description = "Alternative regular expression module, to replace re." optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "regex-2026.4.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:74fa82dcc8143386c7c0392e18032009d1db715c25f4ba22d23dc2e04d02a20f"}, {file = "regex-2026.4.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a85b620a388d6c9caa12189233109e236b3da3deffe4ff11b84ae84e218a274f"}, @@ -2302,6 +2357,7 @@ version = "2.33.1" description = "Python HTTP for Humans." optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "requests-2.33.1-py3-none-any.whl", hash = "sha256:4e6d1ef462f3626a1f0a0a9c42dd93c63bad33f9f1c1937509b8c5c8718ab56a"}, {file = "requests-2.33.1.tar.gz", hash = "sha256:18817f8c57c6263968bc123d237e3b8b08ac046f5456bd1e307ee8f4250d3517"}, @@ -2323,6 +2379,7 @@ version = "14.3.4" description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal" optional = false python-versions = ">=3.8.0" +groups = ["main"] files = [ {file = "rich-14.3.4-py3-none-any.whl", hash = "sha256:07e7adb4690f68864777b1450859253bed81a99a31ac321ac1817b2313558952"}, {file = "rich-14.3.4.tar.gz", hash = "sha256:817e02727f2b25b40ef56f5aa2217f400c8489f79ca8f46ea2b70dd5e14558a9"}, @@ -2341,6 +2398,7 @@ version = "0.30.0" description = "Python bindings to Rust's persistent data structures (rpds)" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "rpds_py-0.30.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:679ae98e00c0e8d68a7fda324e16b90fd5260945b45d3b824c892cec9eea3288"}, {file = "rpds_py-0.30.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4cc2206b76b4f576934f0ed374b10d7ca5f457858b157ca52064bdfc26b9fc00"}, @@ -2465,6 +2523,7 @@ version = "1.5.4" description = "Tool to Detect Surrounding Shell" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686"}, {file = "shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de"}, @@ -2476,6 +2535,7 @@ version = "1.3.1" description = "Sniff out which async library your code is running under" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"}, {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"}, @@ -2487,6 +2547,7 @@ version = "2.0.49" description = "Database Abstraction Library" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "sqlalchemy-2.0.49-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:42e8804962f9e6f4be2cbaedc0c3718f08f60a16910fa3d86da5a1e3b1bfe60f"}, {file = "sqlalchemy-2.0.49-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cc992c6ed024c8c3c592c5fc9846a03dd68a425674900c70122c77ea16c5fb0b"}, @@ -2588,6 +2649,7 @@ version = "9.1.4" description = "Retry code until it succeeds" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "tenacity-9.1.4-py3-none-any.whl", hash = "sha256:6095a360c919085f28c6527de529e76a06ad89b23659fa881ae0649b867a9d55"}, {file = "tenacity-9.1.4.tar.gz", hash = "sha256:adb31d4c263f2bd041081ab33b498309a57c77f9acf2db65aadf0898179cf93a"}, @@ -2603,6 +2665,7 @@ version = "0.12.0" description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "tiktoken-0.12.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:3de02f5a491cfd179aec916eddb70331814bd6bf764075d39e21d5862e533970"}, {file = "tiktoken-0.12.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b6cfb6d9b7b54d20af21a912bfe63a2727d9cfa8fbda642fd8322c70340aad16"}, @@ -2676,6 +2739,7 @@ version = "0.22.2" description = "" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "tokenizers-0.22.2-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:544dd704ae7238755d790de45ba8da072e9af3eea688f698b137915ae959281c"}, {file = "tokenizers-0.22.2-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:1e418a55456beedca4621dbab65a318981467a2b188e982a23e117f115ce5001"}, @@ -2717,6 +2781,7 @@ version = "4.67.3" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, @@ -2738,6 +2803,7 @@ version = "0.24.1" description = "Typer, build great CLIs. Easy to code. Based on Python type hints." optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "typer-0.24.1-py3-none-any.whl", hash = "sha256:112c1f0ce578bfb4cab9ffdabc68f031416ebcc216536611ba21f04e9aa84c9e"}, {file = "typer-0.24.1.tar.gz", hash = "sha256:e39b4732d65fbdcde189ae76cf7cd48aeae72919dea1fdfc16593be016256b45"}, @@ -2755,6 +2821,7 @@ version = "4.15.0" description = "Backported and Experimental Type Hints for Python 3.9+" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548"}, {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"}, @@ -2766,6 +2833,7 @@ version = "0.4.2" description = "Runtime typing introspection tools" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"}, {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"}, @@ -2780,16 +2848,17 @@ version = "2.6.3" description = "HTTP library with thread-safe connection pooling, file post, and more." optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"}, {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"}, ] [package.extras] -brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"] +brotli = ["brotli (>=1.2.0) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=1.2.0.0) ; platform_python_implementation != \"CPython\""] h2 = ["h2 (>=4,<5)"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] -zstd = ["backports-zstd (>=1.0.0)"] +zstd = ["backports-zstd (>=1.0.0) ; python_version < \"3.14\""] [[package]] name = "yarl" @@ -2797,6 +2866,7 @@ version = "1.23.0" description = "Yet another URL library" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "yarl-1.23.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cff6d44cb13d39db2663a22b22305d10855efa0fa8015ddeacc40bc59b9d8107"}, {file = "yarl-1.23.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e4c53f8347cd4200f0d70a48ad059cabaf24f5adc6ba08622a23423bc7efa10d"}, @@ -2939,13 +3009,14 @@ version = "3.23.1" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "zipp-3.23.1-py3-none-any.whl", hash = "sha256:0b3596c50a5c700c9cb40ba8d86d9f2cc4807e9bedb06bcdf7fac85633e444dc"}, {file = "zipp-3.23.1.tar.gz", hash = "sha256:32120e378d32cd9714ad503c1d024619063ec28aad2248dc6672ad13edfa5110"}, ] [package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] enabler = ["pytest-enabler (>=2.2)"] @@ -2956,6 +3027,6 @@ type = ["pytest-mypy"] postgres = ["psycopg2-binary"] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = ">=3.11,<4.0" -content-hash = "8889d0a41db0345cdabc9a3696d97f9ba087f36ba1796f11bfd7a832c5adf796" +content-hash = "3692c0c75fa0fac92d01b52dd430292972e43941a1f0cd3dfd3f36f87a72aebf" diff --git a/pyproject.toml b/pyproject.toml index a9270c3..fcaedf3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,10 +16,9 @@ sqlalchemy = ">=2.0.36" # stable release and shim missing provider symbols at import time; see the # `_shim_missing_providers()` helper in schema_gen/structured_output_with_retries.py. instructor = ">=1.15.0" -# Exact pin: versions 1.82.7 and 1.82.8 were compromised by the TeamPCP -# supply-chain attack (March 2026). Do NOT relax to a caret or range that -# allows those releases. 1.82.6 is the last confirmed-safe version. -litellm = "==1.82.6" +# Versions 1.82.7 and 1.82.8 were compromised (TeamPCP supply-chain attack, +# March 2026) and removed from PyPI. 1.83.0+ uses a new secure CI/CD pipeline. +litellm = ">=1.83.0" tenacity = ">=9.0.0" psycopg2-binary = {version = "^2.9.9", optional = true} @@ -30,6 +29,10 @@ postgres = ["psycopg2-binary"] pytest = "^8.0.0" pytest-cov = "^4.1.0" +[tool.pytest.ini_options] +markers = ["integration: tests that call real LLM APIs (deselect with '-m \"not integration\"')"] +addopts = "-m 'not integration'" + [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/schema_gen/__init__.py b/schema_gen/__init__.py index fae4809..d46ddfa 100644 --- a/schema_gen/__init__.py +++ b/schema_gen/__init__.py @@ -29,7 +29,7 @@ >>> >>> schema = generate_sqlalchemy_models_from_prompt( ... "Create a blog schema with users and posts", - ... model="anthropic/claude-sonnet-4-5-20250929", + ... model="openai/gpt-4.1-mini", ... ) >>> metadata = MetaData() >>> tables = create_sqlalchemy_tables(schema, metadata) diff --git a/schema_gen/llm_generator.py b/schema_gen/llm_generator.py index b4ceab8..c914811 100644 --- a/schema_gen/llm_generator.py +++ b/schema_gen/llm_generator.py @@ -10,7 +10,7 @@ logger = logging.getLogger(__name__) -DEFAULT_MODEL = "anthropic/claude-sonnet-4-5-20250929" +DEFAULT_MODEL = "openai/gpt-4.1-mini" _SCHEMA_PROMPT_TEMPLATE = """You are a database schema designer. Generate a complete database schema based on the user's requirements: @@ -101,9 +101,9 @@ def generate_sqlalchemy_models_from_prompt( Args: prompt: Natural language description of the desired database schema - model: LiteLLM model string (e.g. "anthropic/claude-sonnet-4-5-20250929", + model: LiteLLM model string (e.g. "openai/gpt-4.1-mini", "openai/gpt-4o"). The relevant provider API key must be set in - the environment (e.g. ANTHROPIC_API_KEY, OPENAI_API_KEY). + the environment (e.g. OPENAI_API_KEY). max_attempts: Maximum number of LLM calls before giving up. Returns: @@ -115,7 +115,7 @@ def generate_sqlalchemy_models_from_prompt( Example: >>> models = generate_sqlalchemy_models_from_prompt( ... "Create a blog with users, posts, and comments", - ... model="anthropic/claude-sonnet-4-5-20250929", + ... model="openai/gpt-4.1-mini", ... ).to_orm_classes() >>> User = models['User'] >>> Post = models['Post'] @@ -137,11 +137,8 @@ def generate_sqlalchemy_models_from_prompt( retry_exceptions=(ValidationError, ValueError), ) - # Convert the validated schema to ORM classes (also acts as a final sanity check) - logger.info("Converting validated schema to ORM classes") - orm_classes = schema_result.to_orm_classes() - logger.info( - f"Successfully generated {len(orm_classes)} ORM model classes: {list(orm_classes.keys())}" + f"Successfully generated schema with {len(schema_result.tables)} tables: " + f"{[t.name for t in schema_result.tables]}" ) return schema_result diff --git a/schema_gen/structured_output_with_retries.py b/schema_gen/structured_output_with_retries.py index 1c61201..587014e 100644 --- a/schema_gen/structured_output_with_retries.py +++ b/schema_gen/structured_output_with_retries.py @@ -73,7 +73,7 @@ def structured_output_with_retries( schema: Type[T], messages: list[dict], validation_callable: Callable[[T], Any], - model: str = "anthropic/claude-3-5-sonnet-20241022", + model: str = "openai/gpt-4.1-mini", max_attempts: int = 3, retry_exceptions: tuple[Type[Exception], ...] = (ValidationError,), ) -> Any: @@ -166,7 +166,7 @@ def validate_and_transform(review: MovieReview) -> dict: } ], validation_callable=validate_and_transform, - model="anthropic/claude-3-5-sonnet-20241022", + model="openai/gpt-4.1-mini", max_attempts=3, retry_exceptions=(ValidationError, ValueError), ) diff --git a/tests/test_integration.py b/tests/test_integration.py new file mode 100644 index 0000000..a76c843 --- /dev/null +++ b/tests/test_integration.py @@ -0,0 +1,49 @@ +"""Integration tests that call a real LLM. + +Run with: + pytest -m integration + +Requires OPENAI_API_KEY in the environment. +""" + +import pytest +from sqlalchemy import create_engine, inspect + +from schema_gen import generate_sqlalchemy_models_from_prompt + + +pytestmark = pytest.mark.integration + + +@pytest.fixture +def in_memory_engine(): + engine = create_engine("sqlite:///:memory:") + yield engine + engine.dispose() + + +def test_full_cycle(in_memory_engine): + """Generate a schema from a prompt, convert to ORM, create tables in SQLite.""" + schema = generate_sqlalchemy_models_from_prompt( + prompt="Create a schema with a users table (id, username, email) " + "and a posts table (id, title, body, user_id foreign key to users).", + max_attempts=3, + ) + + # Schema should have at least 2 tables + table_names = [t.name.lower() for t in schema.tables] + assert len(schema.tables) >= 2 + assert any("user" in n for n in table_names) + assert any("post" in n for n in table_names) + + # Convert to ORM classes + orm_classes = schema.to_orm_classes() + assert len(orm_classes) >= 2 + + # Materialise tables in SQLite + Base = list(orm_classes.values())[0].__bases__[0] + Base.metadata.create_all(in_memory_engine) + + inspector = inspect(in_memory_engine) + db_tables = inspector.get_table_names() + assert len(db_tables) >= 2 From 1326e7e2882e023bcebcc3829eace7a98a48c782 Mon Sep 17 00:00:00 2001 From: Egor Kraev Date: Sun, 19 Apr 2026 21:34:44 +0200 Subject: [PATCH 3/3] Update README.md Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index b68712f..5eb94b4 100644 --- a/README.md +++ b/README.md @@ -50,7 +50,6 @@ from schema_gen import generate_sqlalchemy_models_from_prompt # Generate schema from natural language. `model` is a LiteLLM model string; # the corresponding provider API key must be set in the environment -# the corresponding provider API key must be set in the environment # (e.g. OPENAI_API_KEY for openai/*). schema = generate_sqlalchemy_models_from_prompt( prompt="""