63a0df2536
Phase 1: Dead Code Cleanup - Remove GeminiGroundedProvider import and property from linkedin_service.py - Remove fallback_provider property (gemini_provider imports) - Fix routers/linkedin.py edit endpoint to use llm_text_gen - Delete dead LinkedInImageEditor class - Remove dead _transform_gemini_sources from content_generator.py Phase 2: Research Infrastructure Alignment - Add user_id to _conduct_research() for pre-flight validation - Add validate_exa_research_operations() before Exa/Tavily calls - Pass user_id to provider.simple_search() for usage tracking - Inject research content into LLM prompts via _build_research_context() - Fix Google engine path to fallback to Exa - Add Exa → Tavily fallback on research failure Phase 3: Cosmetic Cleanup - Rename _generate_prompts_with_gemini → _generate_prompts_with_llm - Rename _build_gemini_prompt → _build_image_prompt - Rename _parse_gemini_response → _parse_llm_response - Remove all Gemini references from LinkedIn code (0 remaining) - Update docstrings and log messages Additional: - Research caching using existing ResearchCache - Shared ExaContentResearchProvider in services/research/ - Persona service uses llm_text_gen instead of gemini_structured_json_response - LinkedInWriter.tsx ChatMessage → ChatMsg type mapping fix - RegisterLinkedInActionsEnhanced.tsx content_format_rules typing fix
62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
"""
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Research Services Module for ALwrity
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This module provides research and grounding capabilities for content generation,
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replacing mock research with real-time industry information.
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Available Services:
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- GoogleSearchService: Real-time industry research using Google Custom Search API
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- ExaService: Competitor discovery and analysis using Exa API
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- ExaContentResearchProvider: Shared content research provider for LinkedIn/Blog
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- TavilyService: AI-powered web search with real-time information
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- Source ranking and credibility assessment
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- Content extraction and insight generation
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Core Module (v2.0):
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- ResearchEngine: Standalone AI research engine for any content tool
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- ResearchContext: Unified input schema for research requests
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- ParameterOptimizer: AI-driven parameter optimization
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Author: ALwrity Team
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Version: 2.1
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Last Updated: June 2026
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"""
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from .google_search_service import GoogleSearchService
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from .exa_service import ExaService
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from .exa_content_research import ExaContentResearchProvider, get_exa_content_provider
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from .tavily_service import TavilyService
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# Core Research Engine (v2.0)
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from .core import (
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ResearchEngine,
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ResearchContext,
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ResearchPersonalizationContext,
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ContentType,
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ResearchGoal,
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ResearchDepth,
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ProviderPreference,
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ParameterOptimizer,
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)
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__all__ = [
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# Legacy services (still used by blog writer)
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"GoogleSearchService",
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"ExaService",
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"TavilyService",
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# Shared content research provider
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"ExaContentResearchProvider",
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"get_exa_content_provider",
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# Core Research Engine (v2.0)
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"ResearchEngine",
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"ResearchContext",
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"ResearchPersonalizationContext",
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"ContentType",
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"ResearchGoal",
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"ResearchDepth",
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"ProviderPreference",
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"ParameterOptimizer",
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]
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