25213f2004
- eveai_chat becomes deprecated and should be replaced with SSE - Adaptation of STANDARD_RAG specialist - Base class definition allowing to realise specialists with crewai framework - Implementation of SPIN_SPECIALIST - Implementation of test app for testing specialists (test_specialist_client). Also serves as an example for future SSE-based client - Improvements to startup scripts to better handle and scale multiple connections - Small improvements to the interaction forms and views - Caching implementation improved and augmented with additional caches
331 lines
13 KiB
Python
331 lines
13 KiB
Python
from datetime import datetime as dt, timezone as tz
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from typing import Dict, Any, Optional
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from flask import current_app
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from sqlalchemy.exc import SQLAlchemyError
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from common.utils.config_field_types import TaggingFields
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from common.utils.database import Database
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from common.models.document import Catalog
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from common.models.user import Tenant
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from common.models.interaction import Interaction, Specialist, SpecialistRetriever
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from common.extensions import db, cache_manager
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from common.utils.celery_utils import current_celery
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from common.utils.business_event import BusinessEvent
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from common.utils.business_event_context import current_event
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from eveai_chat_workers.specialists.specialist_typing import SpecialistArguments
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from eveai_chat_workers.specialists.base_specialist import get_specialist_class
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from common.utils.execution_progress import ExecutionProgressTracker
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# Healthcheck task
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@current_celery.task(name='ping', queue='llm_interactions')
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def ping():
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return 'pong'
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class ArgumentPreparationError(Exception):
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"""Custom exception for argument preparation errors"""
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pass
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def validate_specialist_arguments(specialist_type: str, specialist_type_version:str, arguments: Dict[str, Any]) -> None:
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"""
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Validate specialist-specific arguments
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Args:
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specialist_type: Type of specialist
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specialist_type_version: Version of specialist type
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arguments: Arguments to validate (excluding retriever-specific arguments)
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Raises:
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ArgumentPreparationError: If validation fails
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"""
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specialist_config = cache_manager.specialists_config_cache.get_config(specialist_type, specialist_type_version)
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if not specialist_config:
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raise ArgumentPreparationError(f"Unknown specialist type: {specialist_type}")
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required_args = specialist_config.get('arguments', {})
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# Check for required arguments
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for arg_name, arg_config in required_args.items():
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if arg_config.get('required', False) and arg_name not in arguments:
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raise ArgumentPreparationError(f"Missing required argument '{arg_name}' for specialist")
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if arg_name in arguments:
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# Type checking
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expected_type = arg_config.get('type')
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if expected_type == 'str' and not isinstance(arguments[arg_name], str):
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raise ArgumentPreparationError(f"Argument '{arg_name}' must be a string")
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elif expected_type == 'int' and not isinstance(arguments[arg_name], int):
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raise ArgumentPreparationError(f"Argument '{arg_name}' must be an integer")
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def validate_retriever_arguments(retriever_type: str, retriever_type_version: str, arguments: Dict[str, Any],
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catalog_config: Optional[Dict[str, Any]] = None) -> None:
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"""
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Validate retriever-specific arguments
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Args:
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retriever_type: Type of retriever
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retriever_type_version: Version of retriever type
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arguments: Arguments to validate
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catalog_config: Optional catalog configuration for metadata validation
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Raises:
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ArgumentPreparationError: If validation fails
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"""
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retriever_config = cache_manager.retrievers_config_cache.get_config(retriever_type, retriever_type_version)
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if not retriever_config:
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raise ArgumentPreparationError(f"Unknown retriever type: {retriever_type}")
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# Validate standard retriever arguments
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required_args = retriever_config.get('arguments', {})
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for arg_name, arg_config in required_args.items():
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if arg_config.get('required', False) and arg_name not in arguments:
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raise ArgumentPreparationError(f"Missing required argument '{arg_name}' for retriever")
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# Only validate metadata filters if catalog configuration is provided
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if catalog_config and 'metadata_filters' in arguments:
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if 'tagging_fields' in catalog_config:
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tagging_fields = TaggingFields.from_dict(catalog_config['tagging_fields'])
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errors = tagging_fields.validate_argument_values(arguments['metadata_filters'])
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if errors:
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raise ArgumentPreparationError(f"Invalid metadata filters: {errors}")
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def is_retriever_id(key: str) -> bool:
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"""
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Check if a key represents a valid retriever ID.
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Valid formats: positive integers, including leading zeros
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Args:
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key: String to check
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Returns:
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bool: True if the key represents a valid retriever ID
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"""
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try:
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# Convert to int to handle leading zeros
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value = int(key)
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# Ensure it's a positive number
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return value > 0
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except ValueError:
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return False
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def prepare_arguments(specialist: Any, arguments: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Prepare complete argument dictionary for specialist execution with inheritance
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Args:
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specialist: Specialist model instance
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arguments: Dictionary containing:
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- Specialist arguments
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- Retriever-specific arguments keyed by retriever ID
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Returns:
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Dict containing prepared arguments with inheritance applied
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Raises:
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ArgumentPreparationError: If argument preparation or validation fails
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"""
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try:
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# Separate specialist arguments from retriever arguments
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retriever_args = {}
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specialist_args = {}
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for key, value in arguments.items():
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if isinstance(key, str) and is_retriever_id(key): # Retriever ID
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retriever_args[key] = value
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else:
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specialist_args[key] = value
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# Validate specialist arguments
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validate_specialist_arguments(specialist.type, specialist.type_version, specialist_args)
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# Get all retrievers associated with this specialist
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specialist_retrievers = (
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SpecialistRetriever.query
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.filter_by(specialist_id=specialist.id)
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.all()
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)
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# Process each retriever
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prepared_retriever_args = {}
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for spec_retriever in specialist_retrievers:
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retriever = spec_retriever.retriever
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retriever_id = str(retriever.id)
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# Get catalog configuration if it exists
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catalog_config = None
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if retriever.catalog_id:
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try:
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catalog = Catalog.query.get(retriever.catalog_id)
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if catalog:
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catalog_config = catalog.configuration
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except SQLAlchemyError:
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current_app.logger.warning(
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f"Could not fetch catalog {retriever.catalog_id} for retriever {retriever_id}"
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)
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# Start with specialist arguments (inheritance)
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inherited_args = specialist_args.copy()
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# Override with retriever-specific arguments if provided
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if retriever_id in retriever_args:
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inherited_args.update(retriever_args[retriever_id])
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# Always include the retriever type
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inherited_args['type'] = retriever.type
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inherited_args['type_version'] = retriever.type_version
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# Validate the combined arguments
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validate_retriever_arguments(
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retriever.type, retriever.type_version,
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inherited_args,
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catalog_config
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)
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prepared_retriever_args[retriever_id] = inherited_args
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# Construct final argument structure
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final_arguments = {
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**specialist_args,
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'retriever_arguments': prepared_retriever_args
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}
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return final_arguments
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except SQLAlchemyError as e:
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current_app.logger.error(f'Database error during argument preparation: {e}')
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raise ArgumentPreparationError(f"Database error: {str(e)}")
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except Exception as e:
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current_app.logger.error(f'Error during argument preparation: {e}')
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raise ArgumentPreparationError(str(e))
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@current_celery.task(name='execute_specialist', queue='llm_interactions', bind=True)
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def execute_specialist(self, tenant_id: int, specialist_id: int, arguments: Dict[str, Any],
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session_id: str, user_timezone: str) -> dict:
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"""
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Execute a specialist with given arguments
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Args:
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tenant_id: ID of the tenant
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specialist_id: ID of the specialist to use
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arguments: Dictionary containing all required arguments for specialist and retrievers
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session_id: Chat session ID
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user_timezone: User's timezone
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Returns:
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dict: {
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'result': Dict - Specialist execution result
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'interaction_id': int - Created interaction ID
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}
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"""
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task_id = self.request.id
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ept = ExecutionProgressTracker()
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ept.send_update(task_id, "EveAI Specialist Started", {})
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with BusinessEvent("Execute Specialist", tenant_id=tenant_id, chat_session_id=session_id) as event:
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current_app.logger.info(
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f'execute_specialist: Processing request for tenant {tenant_id} using specialist {specialist_id}')
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try:
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# Retrieve the tenant
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tenant = Tenant.query.get(tenant_id)
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if not tenant:
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raise Exception(f'Tenant {tenant_id} not found.')
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# Switch to correct database schema
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Database(tenant_id).switch_schema()
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# Ensure we have a session
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cached_session = cache_manager.chat_session_cache.get_cached_session(
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session_id,
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create_params={'timezone': user_timezone}
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)
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if cached_session:
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current_app.logger.debug(f"Cached Session successfully retrieved for {session_id}: {cached_session.id}")
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else:
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current_app.logger.debug(f"No Cached Session retrieved for {session_id}")
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# Get specialist from database
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specialist = Specialist.query.get_or_404(specialist_id)
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# Prepare complete arguments
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try:
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raw_arguments = prepare_arguments(specialist, arguments)
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# Convert the prepared arguments into a SpecialistArguments instance
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complete_arguments = SpecialistArguments.create(
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type_name=specialist.type,
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type_version=specialist.type_version,
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specialist_args={k: v for k, v in raw_arguments.items() if k != 'retriever_arguments'},
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retriever_args=raw_arguments.get('retriever_arguments', {})
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)
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except ValueError as e:
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current_app.logger.error(f'execute_specialist: Error preparing arguments: {e}')
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raise
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# Create new interaction record
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new_interaction = Interaction()
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new_interaction.chat_session_id = cached_session.id
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new_interaction.timezone = user_timezone
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new_interaction.question_at = dt.now(tz.utc)
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new_interaction.specialist_id = specialist.id
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new_interaction.specialist_arguments = complete_arguments.model_dump(mode='json')
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try:
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db.session.add(new_interaction)
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db.session.commit()
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event.update_attribute('interaction_id', new_interaction.id)
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except SQLAlchemyError as e:
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current_app.logger.error(f'execute_specialist: Error creating interaction: {e}')
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raise
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with current_event.create_span("Specialist invocation"):
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ept.send_update(task_id, "EveAI Specialist Start", {})
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# Initialize specialist instance
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specialist_class = get_specialist_class(specialist.type, specialist.type_version)
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specialist_instance = specialist_class(
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tenant_id=tenant_id,
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specialist_id=specialist_id,
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session_id=session_id,
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task_id=task_id,
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)
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# Execute specialist
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result = specialist_instance.execute(complete_arguments)
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# Update interaction record
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new_interaction.specialist_results = result.model_dump(mode='json') # Store complete result
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new_interaction.answer_at = dt.now(tz.utc)
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try:
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db.session.add(new_interaction)
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db.session.commit()
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except SQLAlchemyError as e:
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current_app.logger.error(f'execute_specialist: Error updating interaction: {e}')
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raise
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# Now that we have a complete interaction with an answer, add it to the cache
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cache_manager.chat_session_cache.add_completed_interaction(session_id, new_interaction)
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# Prepare response
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response = {
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'result': result.model_dump(),
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'interaction_id': new_interaction.id
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}
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ept.send_update(task_id, "EveAI Specialist Complete", response)
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return response
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except Exception as e:
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ept.send_update(task_id, "EveAI Specialist Error", {'Error': str(e)})
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current_app.logger.error(f'execute_specialist: Error executing specialist: {e}')
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raise
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def tasks_ping():
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return 'pong'
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