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base service class

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This commit is contained in:
Adam Wilson
2025-07-17 20:32:13 -06:00
parent f3dd8e9208
commit c1b4a130f9
4 changed files with 73 additions and 97 deletions
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src/text_generation/dependency_injection_container.py
+16 -10
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@@ -5,16 +5,16 @@ from src.text_generation.adapters.prompt_template_repository import PromptTempla
from src.text_generation.adapters.text_generation_foundation_model import TextGenerationFoundationModel
from src.text_generation.entrypoints.http_api_controller import HttpApiController
from src.text_generation.entrypoints.server import RestApiServer
from src.text_generation.services.guidelines.abstract_security_guidelines_service import AbstractSecurityGuidelinesService
from src.text_generation.services.guidelines.chain_of_thought_security_guidelines_service import ChainOfThoughtSecurityGuidelinesService
from src.text_generation.services.guidelines.rag_context_security_guidelines_configuration_builder import RetrievalAugmentedGenerationSecurityGuidelinesConfigurationBuilder
from src.text_generation.services.guidelines.rag_context_security_guidelines_service import RagContextSecurityGuidelinesService, RetrievalAugmentedGenerationContextSecurityGuidelinesService
from src.text_generation.services.guardrails.generated_text_guardrail_service import GeneratedTextGuardrailService
from src.text_generation.services.guardrails.reflexion_security_guidelines_service import ReflexionSecurityGuardrailsService
from src.text_generation.services.guidelines.rag_context_security_guidelines_service import RetrievalAugmentedGenerationContextSecurityGuidelinesService
from src.text_generation.services.logging.json_web_traffic_logging_service import JSONWebTrafficLoggingService
from src.text_generation.services.nlp.prompt_template_service import PromptTemplateService
from src.text_generation.services.nlp.semantic_similarity_service import SemanticSimilarityService
from src.text_generation.services.nlp.text_generation_completion_service import TextGenerationCompletionService
from src.text_generation.services.nlp.retrieval_augmented_generation_completion_service import RetrievalAugmentedGenerationCompletionService
from src.text_generation.services.guardrails.generated_text_guardrail_service import GeneratedTextGuardrailService
from src.text_generation.services.guidelines.rag_context_security_guidelines_configuration_builder import RetrievalAugmentedGenerationSecurityGuidelinesConfigurationBuilder
from src.text_generation.services.utilities.response_processing_service import ResponseProcessingService
@@ -71,14 +71,20 @@ class DependencyInjectionContainer(containers.DeclarativeContainer):
semantic_similarity_service=semantic_similarity_service
)
# Register security guideline services
chain_of_thought_guidelines = providers.Factory(
ChainOfThoughtSecurityGuidelinesService
)
ChainOfThoughtSecurityGuidelinesService,
foundation_model=foundation_model,
response_processing_service=response_processing_service,
prompt_template_service=prompt_template_service
).provides(AbstractSecurityGuidelinesService)
rag_context_guidelines = providers.Factory(
RetrievalAugmentedGenerationContextSecurityGuidelinesService,
embedding_model=embedding_model
)
RagContextSecurityGuidelinesService,
foundation_model=foundation_model,
response_processing_service=response_processing_service,
prompt_template_service=prompt_template_service
).provides(AbstractSecurityGuidelinesService)
reflexion_guardrails = providers.Factory(
ReflexionSecurityGuardrailsService
src/text_generation/services/guidelines/base_security_guidelines_service.py
+48
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@@ -0,0 +1,48 @@
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePassthrough
from src.text_generation.common.constants import Constants
from src.text_generation.ports.abstract_foundation_model import AbstractFoundationModel
from src.text_generation.services.guidelines.abstract_security_guidelines_service import AbstractSecurityGuidelinesService
from src.text_generation.services.nlp.abstract_prompt_template_service import AbstractPromptTemplateService
from src.text_generation.services.utilities.abstract_response_processing_service import AbstractResponseProcessingService
class BaseSecurityGuidelinesService(AbstractSecurityGuidelinesService):
"""Base service for security guidelines implementations."""
def __init__(
self,
foundation_model: AbstractFoundationModel,
response_processing_service: AbstractResponseProcessingService,
prompt_template_service: AbstractPromptTemplateService):
super().__init__()
self.constants = Constants()
self.foundation_model_pipeline = foundation_model.create_pipeline()
self.response_processing_service = response_processing_service
self.prompt_template_service = prompt_template_service
def _create_chain(self, prompt_template: PromptTemplate):
return (
{ f"{self.constants.INPUT_VARIABLE_TOKEN}": RunnablePassthrough() }
| prompt_template
| self.foundation_model_pipeline
| StrOutputParser()
| self.response_processing_service.process_text_generation_output
)
def _get_template_id(self) -> str:
"""Get the template ID for the specific implementation."""
raise NotImplementedError("Subclasses must implement _get_template_id()")
def apply_guidelines(self, user_prompt: str) -> str:
if not user_prompt:
raise ValueError(f"Parameter 'user_prompt' cannot be empty or None")
try:
template_id = self._get_template_id()
prompt_template: PromptTemplate = self.prompt_template_service.get(id=template_id)
chain = self._create_chain(prompt_template)
return chain.invoke(user_prompt)
except Exception as e:
raise e
src/text_generation/services/guidelines/chain_of_thought_security_guidelines_service.py
+4 -43
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@@ -1,46 +1,7 @@
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePassthrough
from src.text_generation.services.guidelines.base_security_guidelines_service import BaseSecurityGuidelinesService
from src.text_generation.common.constants import Constants
from src.text_generation.ports.abstract_foundation_model import AbstractFoundationModel
from src.text_generation.services.guidelines.abstract_security_guidelines_service import AbstractSecurityGuidelinesService
from src.text_generation.services.nlp.abstract_prompt_template_service import AbstractPromptTemplateService
from src.text_generation.services.nlp.prompt_template_service import PromptTemplateService
from src.text_generation.services.utilities.abstract_response_processing_service import AbstractResponseProcessingService
class ChainOfThoughtSecurityGuidelinesService(
AbstractSecurityGuidelinesService):
class ChainOfThoughtSecurityGuidelinesService(BaseSecurityGuidelinesService):
"""Service for zero-shot chain-of-thought security guidelines."""
def __init__(
self,
foundation_model: AbstractFoundationModel,
response_processing_service: AbstractResponseProcessingService,
prompt_template_service: AbstractPromptTemplateService):
super().__init__()
self.constants = Constants()
self.foundation_model_pipeline = foundation_model.create_pipeline()
self.response_processing_service = response_processing_service
self.prompt_template_service: PromptTemplateService = prompt_template_service
def _create_chain(self, prompt_template: PromptTemplate):
return (
{ f"{self.constants.INPUT_VARIABLE_TOKEN}": RunnablePassthrough() }
| prompt_template
| self.foundation_model_pipeline
| StrOutputParser()
| self.response_processing_service.process_text_generation_output
)
def apply_guidelines(self, user_prompt: str) -> str:
if not user_prompt:
raise ValueError(f"Parameter 'user_prompt' cannot be empty or None")
try:
template_id = self.constants.PromptTemplateIds.PHI_3_MINI_4K_INSTRUCT_ZERO_SHOT_CHAIN_OF_THOUGHT
prompt_template: PromptTemplate = self.prompt_template_service.get(id=template_id)
chain = self._create_chain(prompt_template)
return chain.invoke(user_prompt)
except Exception as e:
raise e
def _get_template_id(self) -> str:
return self.constants.PromptTemplateIds.PHI_3_MINI_4K_INSTRUCT_ZERO_SHOT_CHAIN_OF_THOUGHT
src/text_generation/services/guidelines/rag_context_security_guidelines_service.py
+5 -44
View File
@@ -1,46 +1,7 @@
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePassthrough
from src.text_generation.services.guidelines.base_security_guidelines_service import BaseSecurityGuidelinesService
from src.text_generation.common.constants import Constants
from src.text_generation.ports.abstract_foundation_model import AbstractFoundationModel
from src.text_generation.services.guidelines.abstract_security_guidelines_service import AbstractSecurityGuidelinesService
from src.text_generation.services.nlp.abstract_prompt_template_service import AbstractPromptTemplateService
from src.text_generation.services.nlp.prompt_template_service import PromptTemplateService
from src.text_generation.services.utilities.abstract_response_processing_service import AbstractResponseProcessingService
class RagContextSecurityGuidelinesService(BaseSecurityGuidelinesService):
"""Service for RAG context security guidelines."""
class RetrievalAugmentedGenerationContextSecurityGuidelinesService(
AbstractSecurityGuidelinesService):
"""Implementation of RAG context security guidelines service."""
def __init__(
self,
foundation_model: AbstractFoundationModel,
response_processing_service: AbstractResponseProcessingService,
prompt_template_service: AbstractPromptTemplateService):
super().__init__()
self.constants = Constants()
self.foundation_model_pipeline = foundation_model.create_pipeline()
self.response_processing_service = response_processing_service
self.prompt_template_service: PromptTemplateService = prompt_template_service
def _create_chain(self, prompt_template: PromptTemplate):
return (
{ f"{self.constants.INPUT_VARIABLE_TOKEN}": RunnablePassthrough() }
| prompt_template
| self.foundation_model_pipeline
| StrOutputParser()
| self.response_processing_service.process_text_generation_output
)
def apply_guidelines(self, user_prompt: str) -> str:
if not user_prompt:
raise ValueError(f"Parameter 'user_prompt' cannot be empty or None")
try:
template_id = self.constants.PromptTemplateIds.PHI_3_MINI_4K_INSTRUCT_FEW_SHOT_EXAMPLES
prompt_template: PromptTemplate = self.prompt_template_service.get(id=template_id)
chain = self._create_chain(prompt_template)
return chain.invoke(user_prompt)
except Exception as e:
raise e
def _get_template_id(self) -> str:
return self.constants.PromptTemplateIds.RAG_CONTEXT_SECURITY_GUIDELINES