diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 7c771a6..4183de0 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -27,7 +27,7 @@ jobs: - name: Install uv run: curl -LsSf https://astral.sh/uv/install.sh | sh - name: Install dependencies - run: uv sync --group lint + run: uv sync --group code-quality - name: Run linter run: uv run ruff check - name: Run formatter diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 75400a9..9687dbf 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.1 + rev: v0.12.12 hooks: # Run the linter - id: ruff @@ -16,7 +16,7 @@ repos: language: system - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.6.0 + rev: v6.0.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer diff --git a/app/config.py b/app/config.py index 423d9f9..b2270dc 100644 --- a/app/config.py +++ b/app/config.py @@ -1,15 +1,13 @@ -from pathlib import Path from functools import lru_cache +from pathlib import Path -from pydantic import AnyHttpUrl, ValidationInfo, field_validator, SecretStr +from pydantic import AnyHttpUrl, SecretStr, field_validator from pydantic_settings import BaseSettings, SettingsConfigDict -from app.utils.config_utils import EncryptedField, EnvironmentType +from app.utils.config_utils import EnvironmentType class Settings(BaseSettings): - # FERNET_DECRYPTOR: FernetDecryptorField = Field("MASTER_KEY") - PROJECT_NAME: str = "MCP Server" API_V1_STR: str = "/api/v1" VERSION: str = "0.0.1" @@ -39,17 +37,12 @@ class Settings(BaseSettings): RAW_XML_PATH: str = "raw.xml" XML_SAMPLE_SIZE: int = 1000 - @field_validator("*", mode="after") - def _decryptor(cls, v, validation_info: ValidationInfo, *args, **kwargs): - if isinstance(v, EncryptedField): - return v.get_decrypted_value(validation_info.data["FERNET_DECRYPTOR"]) - return v - @field_validator("BACKEND_CORS_ORIGINS", mode="after") + @classmethod def assemble_cors_origins(cls, v: str | list[str]) -> list[str] | str: if isinstance(v, str) and not v.startswith("["): return [i.strip() for i in v.split(",")] - elif isinstance(v, (list, str)): + if isinstance(v, (list, str)): return v raise ValueError(v) @@ -61,7 +54,7 @@ def assemble_cors_origins(cls, v: str | list[str]) -> list[str] | str: @lru_cache def get_settings() -> Settings: - return Settings() + return Settings() # type: ignore[call-arg settings = get_settings() diff --git a/app/main.py b/app/main.py index 0e8a857..168c1eb 100644 --- a/app/main.py +++ b/app/main.py @@ -2,9 +2,8 @@ from fastmcp import FastMCP -from app.mcp.v1.mcp import mcp_router from app.config import settings - +from app.mcp.v1.mcp import mcp_router print("SETUP -> Setting up the app", file=sys.stderr) mcp = FastMCP(settings.PROJECT_NAME) diff --git a/app/mcp/v1/mcp.py b/app/mcp/v1/mcp.py index e59fd95..c698502 100644 --- a/app/mcp/v1/mcp.py +++ b/app/mcp/v1/mcp.py @@ -1,6 +1,6 @@ from fastmcp import FastMCP -from app.mcp.v1.tools import xml_reader, es_reader, ch_reader, duckdb_reader +from app.mcp.v1.tools import ch_reader, duckdb_reader, es_reader, xml_reader mcp_router = FastMCP(name="Main MCP") diff --git a/app/mcp/v1/tools/ch_reader.py b/app/mcp/v1/tools/ch_reader.py index 7d6cb23..802de15 100644 --- a/app/mcp/v1/tools/ch_reader.py +++ b/app/mcp/v1/tools/ch_reader.py @@ -1,12 +1,13 @@ from typing import Any + from fastmcp import FastMCP -from app.schemas.record import RecordType, IntervalType, HealthRecordSearchParams +from app.schemas.record import HealthRecordSearchParams, IntervalType, RecordType from app.services.health.clickhouse import ( get_health_summary_from_ch, - search_health_records_from_ch, get_statistics_by_type_from_ch, get_trend_data_from_ch, + search_health_records_from_ch, ) ch_reader_router = FastMCP(name="CH Reader MCP") @@ -16,7 +17,8 @@ def get_health_summary_ch() -> dict[str, Any]: """ Get a summary of Apple Health data from ClickHouse. - The function returns total record count, record type breakdown, and (optionally) a date range aggregation. + The function returns total record count, record type breakdown, and + (optionally) a date range aggregation. Notes for LLM: - IMPORTANT - Do not guess, autofill, or assume any missing data. @@ -38,9 +40,11 @@ def search_health_records_ch(params: HealthRecordSearchParams) -> dict[str, Any] - params: HealthRecordSearchParams object containing all search/filter parameters. Notes for LLMs: - - This function should return a list of health record documents (dicts) matching the search criteria. + - This function should return a list of health record documents (dicts) + matching the search criteria. - Each document in the list should represent a single health record as stored in ClickHouse. - - If an error occurs, the function should return a list with a single dict containing an 'error' key and the error message. + - If an error occurs, the function should return a list with a single dict + containing an 'error' key and the error message. - Use this to retrieve structured health data for further analysis, filtering, or display. - Example source_name: "Rob’s iPhone", "Polar Flow", "Sync Solver". - Example date_from/date_to: "2020-01-01T00:00:00+00:00" @@ -61,7 +65,8 @@ def get_statistics_by_type_ch(record_type: RecordType | str) -> dict[str, Any]: Get comprehensive statistics for a specific health record type from ClickHouse. Parameters: - - record_type: The type of health record to analyze. Use RecordType for most frequent types. Use str if that type is beyond RecordType scope. + - record_type: The type of health record to analyze. Use RecordType for + most frequent types. Use str if that type is beyond RecordType scope. Returns: - record_type: The analyzed record type @@ -76,12 +81,17 @@ def get_statistics_by_type_ch(record_type: RecordType | str) -> dict[str, Any]: Notes for LLMs: - This function provides comprehensive statistical analysis for any health record type. - - The value_statistics object contains all basic statistics (count, min, max, avg, sum) for the 'value' field. + - The value_statistics object contains all basic statistics (count, min, + max, avg, sum) for the 'value' field. - The sources breakdown shows which devices/apps contributed data for this record type. - - Example types: "HKQuantityTypeIdentifierStepCount", "HKQuantityTypeIdentifierBodyMassIndex", "HKQuantityTypeIdentifierHeartRate", etc. - - Use this function to understand the distribution, range, and trends of specific health metrics. - - The function is useful for health analysis, identifying outliers, and understanding data quality. - - date_range key for query is commented, since it contained hardcoded from date, but you can use it anyway if you replace startDate with your data. + - Example types: "HKQuantityTypeIdentifierStepCount", + "HKQuantityTypeIdentifierBodyMassIndex", "HKQuantityTypeIdentifierHeartRate", etc. + - Use this function to understand the distribution, range, and trends of + specific health metrics. + - The function is useful for health analysis, identifying outliers, and + understanding data quality. + - date_range key for query is commented, since it contained hardcoded from + date, but you can use it anyway if you replace startDate with your data. - IMPORTANT - Do not guess, autofill, or assume any missing data. - When asked for medical advice, ask the user whether he wants to use DuckDB, ClickHouse or Elasticsearch. @@ -100,7 +110,8 @@ def get_trend_data_ch( date_to: str | None = None, ) -> dict[str, Any]: """ - Get trend data for a specific health record type over time using ClickHouse date histogram aggregation. + Get trend data for a specific health record type over time using ClickHouse + date histogram aggregation. Parameters: - record_type: The type of health record to analyze (e.g., "HKQuantityTypeIdentifierStepCount") @@ -120,7 +131,8 @@ def get_trend_data_ch( Notes for LLMs: - Use this to analyze trends, patterns, and seasonal variations in health data - The function automatically handles date filtering if date_from/date_to are provided - - IMPORTANT - interval must be one of: "day", "week", "month", or "year". Do not use other values. + - IMPORTANT - interval must be one of: "day", "week", "month", or "year". + Do not use other values. - Do not guess, autofill, or assume any missing data. - When asked for medical advice, ask the user whether he wants to use DuckDB, ClickHouse or Elasticsearch. diff --git a/app/mcp/v1/tools/duckdb_reader.py b/app/mcp/v1/tools/duckdb_reader.py index 70576c4..ea2514c 100644 --- a/app/mcp/v1/tools/duckdb_reader.py +++ b/app/mcp/v1/tools/duckdb_reader.py @@ -1,12 +1,13 @@ from typing import Any + from fastmcp import FastMCP -from app.schemas.record import RecordType, IntervalType, HealthRecordSearchParams +from app.schemas.record import HealthRecordSearchParams, IntervalType, RecordType from app.services.health.duckdb_queries import ( get_health_summary_from_duckdb, - search_health_records_from_duckdb, get_statistics_by_type_from_duckdb, get_trend_data_from_duckdb, + search_health_records_from_duckdb, ) duckdb_reader_router = FastMCP(name="CH Reader MCP") @@ -16,7 +17,8 @@ def get_health_summary_duckdb() -> list[dict[str, Any]]: """ Get a summary of Apple Health data from DuckDB. - The function returns total record count, record type breakdown, and (optionally) a date range aggregation. + The function returns total record count, record type breakdown, and + (optionally) a date range aggregation. Notes for LLM: - IMPORTANT - Do not guess, autofill, or assume any missing data. @@ -38,9 +40,11 @@ def search_health_records_duckdb(params: HealthRecordSearchParams) -> list[dict[ - params: HealthRecordSearchParams object containing all search/filter parameters. Notes for LLMs: - - This function should return a list of health record documents (dicts) matching the search criteria. + - This function should return a list of health record documents (dicts) + matching the search criteria. - Each document in the list should represent a single health record as stored in ClickHouse. - - If an error occurs, the function should return a list with a single dict containing an 'error' key and the error message. + - If an error occurs, the function should return a list with a single dict + containing an 'error' key and the error message. - Use this to retrieve structured health data for further analysis, filtering, or display. - Example source_name: "Rob’s iPhone", "Polar Flow", "Sync Solver". - Example date_from/date_to: "2020-01-01T00:00:00+00:00" @@ -61,7 +65,8 @@ def get_statistics_by_type_duckdb(record_type: RecordType | str) -> list[dict[st Get comprehensive statistics for a specific health record type from DuckDB. Parameters: - - record_type: The type of health record to analyze. Use RecordType for most frequent types. Use str if that type is beyond RecordType scope. + - record_type: The type of health record to analyze. Use RecordType for + most frequent types. Use str if that type is beyond RecordType scope. Returns: - record_type: The analyzed record type @@ -76,12 +81,17 @@ def get_statistics_by_type_duckdb(record_type: RecordType | str) -> list[dict[st Notes for LLMs: - This function provides comprehensive statistical analysis for any health record type. - - The value_statistics object contains all basic statistics (count, min, max, avg, sum) for the 'value' field. + - The value_statistics object contains all basic statistics (count, min, + max, avg, sum) for the 'value' field. - The sources breakdown shows which devices/apps contributed data for this record type. - - Example types: "HKQuantityTypeIdentifierStepCount", "HKQuantityTypeIdentifierBodyMassIndex", "HKQuantityTypeIdentifierHeartRate", etc. - - Use this function to understand the distribution, range, and trends of specific health metrics. - - The function is useful for health analysis, identifying outliers, and understanding data quality. - - date_range key for query is commented, since it contained hardcoded from date, but you can use it anyway if you replace startDate with your data. + - Example types: "HKQuantityTypeIdentifierStepCount", + "HKQuantityTypeIdentifierBodyMassIndex", "HKQuantityTypeIdentifierHeartRate", etc. + - Use this function to understand the distribution, range, and trends of + specific health metrics. + - The function is useful for health analysis, identifying outliers, and + understanding data quality. + - date_range key for query is commented, since it contained hardcoded from + date, but you can use it anyway if you replace startDate with your data. - IMPORTANT - Do not guess, autofill, or assume any missing data. - When asked for medical advice, ask the user whether he wants to use DuckDB, ClickHouse or Elasticsearch. @@ -100,7 +110,8 @@ def get_trend_data_duckdb( date_to: str | None = None, ) -> list[dict[str, Any]]: """ - Get trend data for a specific health record type over time using DuckDB date histogram aggregation. + Get trend data for a specific health record type over time using DuckDB + date histogram aggregation. Parameters: - record_type: The type of health record to analyze (e.g., "HKQuantityTypeIdentifierStepCount") @@ -120,7 +131,8 @@ def get_trend_data_duckdb( Notes for LLMs: - Use this to analyze trends, patterns, and seasonal variations in health data - The function automatically handles date filtering if date_from/date_to are provided - - IMPORTANT - interval must be one of: "day", "week", "month", or "year". Do not use other values. + - IMPORTANT - interval must be one of: "day", "week", "month", or "year". + Do not use other values. - Do not guess, autofill, or assume any missing data. - When asked for medical advice, ask the user whether he wants to use DuckDB, ClickHouse or Elasticsearch. diff --git a/app/mcp/v1/tools/es_reader.py b/app/mcp/v1/tools/es_reader.py index 3e52990..36dc638 100644 --- a/app/mcp/v1/tools/es_reader.py +++ b/app/mcp/v1/tools/es_reader.py @@ -1,12 +1,13 @@ from typing import Any + from fastmcp import FastMCP -from app.schemas.record import RecordType, IntervalType, HealthRecordSearchParams +from app.schemas.record import HealthRecordSearchParams, IntervalType, RecordType from app.services.health.elasticsearch import ( get_health_summary_from_es, - search_health_records_logic, get_statistics_by_type_logic, get_trend_data_logic, + search_health_records_logic, ) es_reader_router = FastMCP(name="ES Reader MCP") @@ -16,7 +17,8 @@ def get_health_summary_es() -> dict[str, Any]: """ Get a summary of Apple Health data from Elasticsearch. - The function returns total record count, record type breakdown, and (optionally) a date range aggregation. + The function returns total record count, record type breakdown, and + (optionally) a date range aggregation. Notes for LLM: - IMPORTANT - Do not guess, auto-fill, or assume any missing data. @@ -38,9 +40,11 @@ def search_health_records_es(params: HealthRecordSearchParams) -> list[dict[str, - params: HealthRecordSearchParams object containing all search/filter parameters. Notes for LLMs: - - This function should return a list of health record documents (dicts) matching the search criteria. + - This function should return a list of health record documents (dicts) + matching the search criteria. - Each document in the list should represent a single health record as stored in Elasticsearch. - - If an error occurs, the function should return a list with a single dict containing an 'error' key and the error message. + - If an error occurs, the function should return a list with a single dict + containing an 'error' key and the error message. - Use this to retrieve structured health data for further analysis, filtering, or display. - Example source_name: "Rob’s iPhone", "Polar Flow", "Sync Solver". - Example date_from/date_to: "2020-01-01T00:00:00+00:00" @@ -61,7 +65,8 @@ def get_statistics_by_type_es(record_type: RecordType | str) -> dict[str, Any]: Get comprehensive statistics for a specific health record type from Elasticsearch. Parameters: - - record_type: The type of health record to analyze. Use RecordType for most frequent types. Use str if that type is beyond RecordType scope. + - record_type: The type of health record to analyze. Use RecordType for + most frequent types. Use str if that type is beyond RecordType scope. Returns: - record_type: The analyzed record type @@ -76,12 +81,17 @@ def get_statistics_by_type_es(record_type: RecordType | str) -> dict[str, Any]: Notes for LLMs: - This function provides comprehensive statistical analysis for any health record type. - - The value_statistics object contains all basic statistics (count, min, max, avg, sum) for the 'value' field. + - The value_statistics object contains all basic statistics (count, min, + max, avg, sum) for the 'value' field. - The sources breakdown shows which devices/apps contributed data for this record type. - - Example types: "HKQuantityTypeIdentifierStepCount", "HKQuantityTypeIdentifierBodyMassIndex", "HKQuantityTypeIdentifierHeartRate", etc. - - Use this function to understand the distribution, range, and trends of specific health metrics. - - The function is useful for health analysis, identifying outliers, and understanding data quality. - - date_range key for query is commented, since it contained hardcoded from date, but you can use it anyway if you replace startDate with your data. + - Example types: "HKQuantityTypeIdentifierStepCount", + "HKQuantityTypeIdentifierBodyMassIndex", "HKQuantityTypeIdentifierHeartRate", etc. + - Use this function to understand the distribution, range, and trends of + specific health metrics. + - The function is useful for health analysis, identifying outliers, and + understanding data quality. + - date_range key for query is commented, since it contained hardcoded from + date, but you can use it anyway if you replace startDate with your data. - IMPORTANT - Do not guess, auto-fill, or assume any missing data. - When asked for medical advice, ask the user whether he wants to use DuckDB, ClickHouse or Elasticsearch. @@ -100,7 +110,8 @@ def get_trend_data_es( date_to: str | None = None, ) -> dict[str, Any]: """ - Get trend data for a specific health record type over time using Elasticsearch date histogram aggregation. + Get trend data for a specific health record type over time using + Elasticsearch date histogram aggregation. Parameters: - record_type: The type of health record to analyze (e.g., "HKQuantityTypeIdentifierStepCount") @@ -120,7 +131,8 @@ def get_trend_data_es( Notes for LLMs: - Use this to analyze trends, patterns, and seasonal variations in health data - The function automatically handles date filtering if date_from/date_to are provided - - IMPORTANT - interval must be one of: "day", "week", "month", or "year". Do not use other values. + - IMPORTANT - interval must be one of: "day", "week", "month", or "year". + Do not use other values. - Do not guess, auto-fill, or assume any missing data. - When asked for medical advice, ask the user whether he wants to use DuckDB, ClickHouse or Elasticsearch. diff --git a/app/mcp/v1/tools/xml_reader.py b/app/mcp/v1/tools/xml_reader.py index 27e8100..06d1c98 100644 --- a/app/mcp/v1/tools/xml_reader.py +++ b/app/mcp/v1/tools/xml_reader.py @@ -3,7 +3,7 @@ from fastmcp import FastMCP from app.schemas.record import RecordType -from app.services.health.direct_xml import analyze_xml_structure, search_xml, get_records_by_type +from app.services.health.direct_xml import analyze_xml_structure, get_records_by_type, search_xml xml_reader_router = FastMCP(name="XML Reader MCP") @@ -11,12 +11,14 @@ @xml_reader_router.tool def get_xml_structure() -> dict[str, Any]: """ - Analyze the structure and metadata of an Apple Health XML export file without loading the entire content. + Analyze the structure and metadata of an Apple Health XML export file + without loading the entire content. Returns: - file_size_mb: Size of the file in megabytes - root_elements: List of unique root-level XML tags - - record_types: List of unique health record types (see RecordType for most frequent types, but may include others) + - record_types: List of unique health record types (see RecordType for + most frequent types, but may include others) - workout_types: List of unique workout types - sources: List of unique data sources (device/app names) @@ -35,19 +37,22 @@ def get_xml_structure() -> dict[str, Any]: @xml_reader_router.tool def search_xml_content(query: str = "", max_results: int = 50) -> str: """ - Search for specific content in the Apple Health XML file and return matching records as XML text. + Search for specific content in the Apple Health XML file and return + matching records as XML text. Parameters: - query: Text to search for in any attribute value - max_results: Maximum number of matching records to return (default: 50) Returns: - - A string containing up to max_results XML elements that match the query, or a message if no matches are found. + - A string containing up to max_results XML elements that match the + query, or a message if no matches are found. Notes for LLMs: - Searches both Record and Workout elements - Useful for finding all records containing a specific value, device, or type - - This function streams the file for memory efficiency and does not load the entire file into memory. + - This function streams the file for memory efficiency and does not load + the entire file into memory. - If filename is not provided, the file set by set_xml_file will be used. - Do not guess, auto-fill, or assume any missing data. - When asked for medical advice, try to use my data from ElasticSearch first. @@ -64,16 +69,19 @@ def get_xml_by_type(record_type: RecordType | str = "", limit: int = 20) -> str: Get all records of a specific health record type from the Apple Health XML file. Parameters: - - record_type: The type of health record to retrieve (use RecordType for most frequent types, or any string for custom/rare types) + - record_type: The type of health record to retrieve (use RecordType for + most frequent types, or any string for custom/rare types) - limit: Maximum number of records to return (default: 20) Returns: - - A string containing up to limit XML elements of the specified type, or a message if no records are found. + - A string containing up to limit XML elements of the specified type, or + a message if no records are found. Notes for LLMs: - Use this to extract all records of a given type for further analysis or conversion - RecordType contains only the most frequent types; other types may appear as strings - - This function streams the file for memory efficiency and does not load the entire file into memory. + - This function streams the file for memory efficiency and does not load + the entire file into memory. - If filename is not provided, the file set by set_xml_file will be used. - Do not guess, auto-fill, or assume any missing data. - When asked for medical advice, try to use my data from ElasticSearch first. diff --git a/app/schemas/record.py b/app/schemas/record.py index 64a7b69..fe0c940 100644 --- a/app/schemas/record.py +++ b/app/schemas/record.py @@ -1,4 +1,5 @@ from typing import Literal + from pydantic import BaseModel RecordType = Literal[ diff --git a/app/services/health/clickhouse.py b/app/services/health/clickhouse.py index 1ce4126..af1b086 100644 --- a/app/services/health/clickhouse.py +++ b/app/services/health/clickhouse.py @@ -1,7 +1,7 @@ from typing import Any +from app.schemas.record import HealthRecordSearchParams, IntervalType, RecordType from app.services.ch_client import CHClient -from app.schemas.record import RecordType, IntervalType, HealthRecordSearchParams from app.services.health.sql_helpers import fill_query ch = CHClient() @@ -19,7 +19,9 @@ def search_health_records_from_ch(params: HealthRecordSearchParams) -> dict[str, def get_statistics_by_type_from_ch(record_type: RecordType | str) -> dict[str, Any]: return ch.inquire( - f"SELECT type, COUNT(*), AVG(numerical), SUM(numerical), MIN(numerical), MAX(numerical) FROM {ch.db_name}.{ch.table_name} WHERE type = '{record_type}' GROUP BY type" + f"SELECT type, COUNT(*), AVG(value), SUM(value), MIN(value), " + f"MAX(value) FROM {ch.db_name}.{ch.table_name} WHERE type = '{record_type}' " + f"GROUP BY type", ) @@ -31,7 +33,9 @@ def get_trend_data_from_ch( ) -> dict[str, Any]: return ch.inquire(f""" SELECT toStartOfInterval(startDate, INTERVAL 1 {interval}) AS interval, - AVG(numerical), MIN(numerical), MAX(numerical), COUNT(*) FROM {ch.db_name}.{ch.table_name} - WHERE type = '{record_type}' {f"AND startDate >= '{date_from}'" if date_from else ""} {f"AND startDate <= '{date_to}'" if date_to else ""} + AVG(value), MIN(value), MAX(value), COUNT(*) FROM {ch.db_name}.{ch.table_name} + WHERE type = '{record_type}' + {f"AND startDate >= '{date_from}'" if date_from else ""} + {f"AND startDate <= '{date_to}'" if date_to else ""} GROUP BY interval ORDER BY interval ASC """) diff --git a/app/services/health/direct_xml.py b/app/services/health/direct_xml.py index 920bd11..828e7f5 100644 --- a/app/services/health/direct_xml.py +++ b/app/services/health/direct_xml.py @@ -2,6 +2,7 @@ from pathlib import Path from typing import Any, Iterator from xml.etree.ElementTree import Element + from app.config import settings @@ -69,7 +70,10 @@ def search_xml(query: str = "", max_results: int = 50) -> str: break if not results: return f"No matches found for query: '{query}'" - return f"""Search Results for '{query}' (showing up to {max_results} results):\n\n{chr(10).join(results)}\n\nTotal matches found: {len(results)}""" + return ( + f"""Search Results for '{query}' (showing up to {max_results} results):\n\n""" + f"""{chr(10).join(results)}\n\nTotal matches found: {len(results)}""" + ) def get_records_by_type(record_type: str = "", limit: int = 20) -> str: @@ -82,4 +86,7 @@ def get_records_by_type(record_type: str = "", limit: int = 20) -> str: break if not results: return f"No records found of type: '{record_type}'" - return f"""Records of type '{record_type}' (showing up to {limit} results):\n\n{chr(10).join(results)}\n\nTotal records found: {len(results)}""" + return ( + f"""Records of type '{record_type}' (showing up to {limit} results):\n\n""" + f"""{chr(10).join(results)}\n\nTotal records found: {len(results)}""" + ) diff --git a/app/services/health/duckdb_queries.py b/app/services/health/duckdb_queries.py index 9434a02..4676631 100644 --- a/app/services/health/duckdb_queries.py +++ b/app/services/health/duckdb_queries.py @@ -2,7 +2,7 @@ import duckdb -from app.schemas.record import RecordType, IntervalType, HealthRecordSearchParams +from app.schemas.record import HealthRecordSearchParams, IntervalType, RecordType from app.services.duckdb_client import DuckDBClient from app.services.health.sql_helpers import fill_query @@ -11,7 +11,7 @@ def get_health_summary_from_duckdb() -> list[dict[str, Any]]: response = duckdb.sql( - f"SELECT type, COUNT(*) AS count FROM read_parquet('{client.parquetpath}') GROUP BY ALL" + f"SELECT type, COUNT(*) AS count FROM read_parquet('{client.parquetpath}') GROUP BY ALL", ) return client.format_response(response) @@ -45,8 +45,11 @@ def get_trend_data_from_duckdb( ) -> list[dict[str, Any]]: result = duckdb.sql(f""" SELECT time_bucket(INTERVAL '1 {interval}', startDate) AS interval, - AVG(value) AS average, MIN(value) AS min, MAX(value) AS max, COUNT(*) AS count FROM read_parquet('{client.parquetpath}') - WHERE type = '{record_type}' {f"AND startDate >= '{date_from}'" if date_from else ""} {f"AND startDate <= '{date_to}'" if date_to else ""} + AVG(value) AS average, MIN(value) AS min, MAX(value) AS max, COUNT(*) AS count + FROM read_parquet('{client.parquetpath}') + WHERE type = '{record_type}' + {f"AND startDate >= '{date_from}'" if date_from else ""} + {f"AND startDate <= '{date_to}'" if date_to else ""} GROUP BY interval ORDER BY interval ASC """) return client.format_response(result) diff --git a/app/services/health/elasticsearch.py b/app/services/health/elasticsearch.py index 5aa5e2d..cf73d93 100644 --- a/app/services/health/elasticsearch.py +++ b/app/services/health/elasticsearch.py @@ -1,7 +1,8 @@ from typing import Any + from app.config import settings +from app.schemas.record import HealthRecordSearchParams, IntervalType, RecordType from app.services.es_client import ESClient -from app.schemas.record import RecordType, IntervalType, HealthRecordSearchParams es_client = ESClient() @@ -23,7 +24,7 @@ def _run_es_query(query: dict) -> Any: return es_client.engine.search(index=settings.ES_INDEX, body=query) -def get_health_summary_from_es(): +def get_health_summary_from_es() -> dict[str, Any]: query = { "size": 0, "aggs": { @@ -107,7 +108,7 @@ def get_trend_data_logic( "max_value": {"max": {"field": "value"}}, "count": {"value_count": {"field": "value"}}, }, - } + }, }, } response = _run_es_query(query) @@ -121,6 +122,6 @@ def get_trend_data_logic( "min_value": bucket["min_value"]["value"], "max_value": bucket["max_value"]["value"], "count": bucket["count"]["value"], - } + }, ) return {"record_type": record_type, "interval": interval, "trend_data": trend_data} diff --git a/app/utils/auth.py b/app/utils/auth.py index 0d09033..ba61c40 100644 --- a/app/utils/auth.py +++ b/app/utils/auth.py @@ -1,6 +1,6 @@ from requests import PreparedRequest -from requests.structures import CaseInsensitiveDict from requests.auth import AuthBase +from requests.structures import CaseInsensitiveDict class BearerAuth(AuthBase): diff --git a/app/utils/config_utils.py b/app/utils/config_utils.py index 5a9ab21..b59a865 100644 --- a/app/utils/config_utils.py +++ b/app/utils/config_utils.py @@ -1,6 +1,7 @@ import os from enum import Enum from typing import Any, Callable, Generator, Protocol + from cryptography.fernet import Fernet from pydantic import ValidationInfo @@ -51,7 +52,7 @@ def get_decrypted_value(self, decryptor: Decryptor) -> str: class FernetDecryptorField(str): - def __get_pydantic_json_schema__(cls, field_schema: dict[str, Any]) -> None: + def __get_pydantic_json_schema__(self, field_schema: dict[str, Any]) -> None: field_schema.update(type="str", writeOnly=True) @classmethod diff --git a/pyproject.toml b/pyproject.toml index 2d1d143..4a9a28a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,10 +34,10 @@ dev = [ "pytest-asyncio>=1.0.0", "pytest-cov>=6.2.1", ] -lint = [ - "pre-commit>=4.2.0", - "ruff>=0.12.1", - "ty>=0.0.1a12", +code-quality = [ + "pre-commit>=4.3.0", + "ruff>=0.12.12", + "ty>=0.0.1a20", ] [tool.pytest.ini_options] @@ -47,13 +47,27 @@ asyncio_default_fixture_loop_scope = "session" line-length = 100 target-version = "py313" -[tool.mypy] -python_version = "3.13" -ignore_missing_imports = true -disallow_untyped_defs = false -exclude = [ - "alembic/", - "migrations/", +[tool.ruff.lint] +select = [ + "I", # isort + "F", # pyflakes + "FAST", # FastApi + "ANN", # flake8-annotations + "ASYNC", # flake8-async + "COM", # flake8-commas + "T10", # flake8-debugger + "PT", # flake8-pytest-style + "RET", # flake8-return + "SIM", # flake8-simplify + "N", # pep8-naming + "E", # pycodestyle errors + "W", # pycodestyle warnings +] +ignore = [ + "ANN002", # missing-type-args + "ANN003", # missing-type-kwargs + "ANN204", # missing-return-type-special-method + "ANN401", # any-type ] [project.scripts] diff --git a/scripts/clickhouse_importer.py b/scripts/clickhouse_importer.py index c6288bb..a8db6d6 100644 --- a/scripts/clickhouse_importer.py +++ b/scripts/clickhouse_importer.py @@ -55,9 +55,8 @@ def run(self) -> bool: if result: print("Inserted data into chdb correctly") return True - else: - print("Error during data indexing") - return False + print("Error during data indexing") + return False if __name__ == "__main__": diff --git a/scripts/cryptography/decrypt_setting.py b/scripts/cryptography/decrypt_setting.py deleted file mode 100644 index 52c3269..0000000 --- a/scripts/cryptography/decrypt_setting.py +++ /dev/null @@ -1,14 +0,0 @@ -import os -import sys - -from cryptography.fernet import Fernet - - -def main(): - setting = sys.argv[1] - fernet = Fernet(os.environ["MASTER_KEY"]) - print(fernet.decrypt(setting.encode("utf-8"))) - - -if __name__ == "__main__": - main() diff --git a/scripts/cryptography/encrypt_setting.py b/scripts/cryptography/encrypt_setting.py deleted file mode 100644 index 66b65a5..0000000 --- a/scripts/cryptography/encrypt_setting.py +++ /dev/null @@ -1,11 +0,0 @@ -import os -import sys - -from cryptography.fernet import Fernet - -if __name__ == "__main__": - if "MASTER_KEY" not in os.environ: - print("Set MASTER_KEY env variable to encrypt") - - f = Fernet(os.environ["MASTER_KEY"]) - print(f.encrypt(sys.argv[1].encode("utf-8"))) diff --git a/scripts/cryptography/generate_master_key.py b/scripts/cryptography/generate_master_key.py deleted file mode 100644 index f707734..0000000 --- a/scripts/cryptography/generate_master_key.py +++ /dev/null @@ -1,9 +0,0 @@ -from cryptography.fernet import Fernet - - -def main(): - print(Fernet.generate_key()) - - -if __name__ == "__main__": - main() diff --git a/scripts/duckdb_importer.py b/scripts/duckdb_importer.py index fc44f60..b39929c 100644 --- a/scripts/duckdb_importer.py +++ b/scripts/duckdb_importer.py @@ -12,7 +12,7 @@ def __init__(self): XMLExporter.__init__(self) DuckDBClient.__init__(self) - def exportxml(self): + def exportxml(self) -> None: chunkfiles = [] for i, docs in enumerate(self.parse_xml(), 1): df: pl.DataFrame = pl.DataFrame(docs) diff --git a/scripts/xml2es.py b/scripts/xml2es.py index 8ff9038..8a37d4a 100644 --- a/scripts/xml2es.py +++ b/scripts/xml2es.py @@ -1,8 +1,8 @@ +import sys import xml.etree.ElementTree as ET -from typing import Any from collections.abc import Generator -import sys from datetime import datetime +from typing import Any from elasticsearch import NotFoundError, helpers @@ -64,7 +64,8 @@ def index_to_es(self, documents: list[dict[str, Any]]) -> None: def delete_index(self) -> None: try: resp = self.es.engine.delete_by_query( - index=self.es.index, body={"query": {"match_all": {}}} + index=self.es.index, + body={"query": {"match_all": {}}}, ) print(f"Deleted {resp.get('deleted', 0)} documents from '{self.es.index}'") except NotFoundError: diff --git a/start.py b/start.py index 0610d54..2e87b76 100644 --- a/start.py +++ b/start.py @@ -3,11 +3,11 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