mirror of
https://gh-proxy.org/https://github.com/klinsmaya/SQLBot.git
synced 2026-11-08 01:06:59 +08:00
ulleo
3fd6fa867d
- Add extract_keywords() to LLMService with conversation context support, using process_stream + start_log/end_log for logging consistency - Add expand_with_terminology() to expand keywords with synonym matching - Add fusion scoring in calc_table_embedding(): α * vec_score + (1-α) * keyword_score - Add calc_keyword_score() with multi-level matching: table name exact (1.0), comment exact (0.9), comment substring (0.7), table name partial (0.25-0.5), field comment exact (0.5), field comment substring (0.4), field name partial (0.3) - Scoring considers keyword coverage rate to differentiate tables with different numbers of matched keywords - Extracted keywords persisted to ChatRecord for reuse across endpoints - get_terminology_template() supports comma-separated keyword input with per-keyword matching for higher precision - Add TABLE_EMBEDDING_ALPHA and TABLE_EMBEDDING_KEYWORD_ENABLED config
97 lines
2.7 KiB
Python
Executable File
97 lines
2.7 KiB
Python
Executable File
# alembic/env.py
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import sys
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from os.path import abspath, dirname
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sys.path.insert(0, dirname(dirname(abspath(__file__))))
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import os
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from alembic import context
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from sqlalchemy import engine_from_config, pool
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# this is the Alembic Config object, which provides
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# access to the values within the .ini file in use.
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config = context.config
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# Interpret the config file for Python logging.
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# This line sets up loggers basically.
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# add your model's MetaData object here
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# for 'autogenerate' support
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# from myapp import mymodel
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# target_metadata = mymodel.Base.metadata
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# target_metadata = None
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# from apps.system.models.user import SQLModel # noqa
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# from apps.settings.models.setting_models import SQLModel
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from apps.chat.models.chat_model import SQLModel
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#from apps.terminology.models.terminology_model import SQLModel
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#from sqlbot_xpack.custom_prompt.models.custom_prompt_model import SQLModel
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#from apps.data_training.models.data_training_model import SQLModel
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# from apps.dashboard.models.dashboard_model import SQLModel
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from common.core.config import settings # noqa
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#from apps.datasource.models.datasource import SQLModel
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#from apps.system.models.system_model import SQLModel
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target_metadata = SQLModel.metadata
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# other values from the config, defined by the needs of env.py,
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# can be acquired:
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# my_important_option = config.get_main_option("my_important_option")
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# ... etc.
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def get_url():
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return str(settings.SQLALCHEMY_DATABASE_URI)
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def run_migrations_offline():
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"""Run migrations in 'offline' mode.
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This configures the context with just a URL
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and not an Engine, though an Engine is acceptable
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here as well. By skipping the Engine creation
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we don't even need a DBAPI to be available.
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Calls to context.execute() here emit the given string to the
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script output.
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"""
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url = get_url()
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context.configure(
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url=url, target_metadata=target_metadata, literal_binds=True, compare_type=True
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)
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with context.begin_transaction():
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context.run_migrations()
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def run_migrations_online():
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"""Run migrations in 'online' mode.
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In this scenario we need to create an Engine
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and associate a connection with the context.
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"""
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configuration = config.get_section(config.config_ini_section)
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configuration["sqlalchemy.url"] = get_url()
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connectable = engine_from_config(
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configuration,
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prefix="sqlalchemy.",
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poolclass=pool.NullPool,
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)
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with connectable.connect() as connection:
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context.configure(
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connection=connection, target_metadata=target_metadata, compare_type=True
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)
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with context.begin_transaction():
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context.run_migrations()
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if context.is_offline_mode():
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run_migrations_offline()
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else:
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run_migrations_online()
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