98fa16bfc3
Full-stack Amazon AI Transcreation Platform with: - FastAPI backend (async, PostgreSQL, Redis, Celery) with 11 DB tables - JWT auth (SSO-ready abstract provider pattern) - 6-agent pipeline orchestrator with deterministic modules - Next.js 14 frontend with Amazon branding (Ember fonts, orange/dark theme) - Job wizard, monitoring HUD, output review, admin screens - 154 TM/reference files imported, 12 locales configured - Docker Compose for all services Agents 2-5 (TM retrieval, ranker, transcreator, compliance) are stubs pending Phase 3 LLM integration. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
"""Base agent abstract class for the transcreation pipeline."""
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from abc import ABC, abstractmethod
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from typing import Any
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from app.pipeline.contracts import PipelineContext
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class BaseAgent(ABC):
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"""Abstract base class for pipeline agents.
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Each agent in the pipeline implements this interface:
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1. get_system_prompt() - Returns the system prompt for the LLM
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2. build_user_message() - Builds the user message from pipeline context
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3. parse_response() - Parses the LLM response into structured data
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4. run() - Orchestrates the agent's execution
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"""
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name: str = "base_agent"
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description: str = ""
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@abstractmethod
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def get_system_prompt(self) -> str:
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"""Return the system prompt for this agent's LLM call."""
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...
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@abstractmethod
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def build_user_message(self, context: PipelineContext) -> str:
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"""Build the user message from pipeline context.
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Args:
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context: The current pipeline context.
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Returns:
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The user message string to send to the LLM.
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"""
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...
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@abstractmethod
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def parse_response(self, response: str, context: PipelineContext) -> Any:
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"""Parse the LLM response into structured data.
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Args:
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response: The raw LLM response text.
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context: The current pipeline context.
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Returns:
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Structured data appropriate for this agent's output.
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"""
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...
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@abstractmethod
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async def run(self, context: PipelineContext) -> PipelineContext:
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"""Execute this agent and return the updated pipeline context.
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Args:
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context: The current pipeline context.
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Returns:
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Updated pipeline context with this agent's results.
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"""
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...
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