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# Directory Structure
```
├── .dockerignore
├── .env.example
├── .github
│ ├── dependabot.yml
│ ├── FUNDING.yml
│ ├── ISSUE_TEMPLATE
│ │ ├── bug_report.md
│ │ ├── config.yml
│ │ ├── feature_request.md
│ │ ├── question.md
│ │ └── security_report.md
│ ├── pull_request_template.md
│ └── workflows
│ ├── claude-code-review.yml
│ └── claude.yml
├── .gitignore
├── .python-version
├── .vscode
│ ├── launch.json
│ └── settings.json
├── alembic
│ ├── env.py
│ ├── script.py.mako
│ └── versions
│ ├── 001_initial_schema.py
│ ├── 003_add_performance_indexes.py
│ ├── 006_rename_metadata_columns.py
│ ├── 008_performance_optimization_indexes.py
│ ├── 009_rename_to_supply_demand.py
│ ├── 010_self_contained_schema.py
│ ├── 011_remove_proprietary_terms.py
│ ├── 013_add_backtest_persistence_models.py
│ ├── 014_add_portfolio_models.py
│ ├── 08e3945a0c93_merge_heads.py
│ ├── 9374a5c9b679_merge_heads_for_testing.py
│ ├── abf9b9afb134_merge_multiple_heads.py
│ ├── adda6d3fd84b_merge_proprietary_terms_removal_with_.py
│ ├── e0c75b0bdadb_fix_financial_data_precision_only.py
│ ├── f0696e2cac15_add_essential_performance_indexes.py
│ └── fix_database_integrity_issues.py
├── alembic.ini
├── CLAUDE.md
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── DATABASE_SETUP.md
├── docker-compose.override.yml.example
├── docker-compose.yml
├── Dockerfile
├── docs
│ ├── api
│ │ └── backtesting.md
│ ├── BACKTESTING.md
│ ├── COST_BASIS_SPECIFICATION.md
│ ├── deep_research_agent.md
│ ├── exa_research_testing_strategy.md
│ ├── PORTFOLIO_PERSONALIZATION_PLAN.md
│ ├── PORTFOLIO.md
│ ├── SETUP_SELF_CONTAINED.md
│ └── speed_testing_framework.md
├── examples
│ ├── complete_speed_validation.py
│ ├── deep_research_integration.py
│ ├── llm_optimization_example.py
│ ├── llm_speed_demo.py
│ ├── monitoring_example.py
│ ├── parallel_research_example.py
│ ├── speed_optimization_demo.py
│ └── timeout_fix_demonstration.py
├── LICENSE
├── Makefile
├── MANIFEST.in
├── maverick_mcp
│ ├── __init__.py
│ ├── agents
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── circuit_breaker.py
│ │ ├── deep_research.py
│ │ ├── market_analysis.py
│ │ ├── optimized_research.py
│ │ ├── supervisor.py
│ │ └── technical_analysis.py
│ ├── api
│ │ ├── __init__.py
│ │ ├── api_server.py
│ │ ├── connection_manager.py
│ │ ├── dependencies
│ │ │ ├── __init__.py
│ │ │ ├── stock_analysis.py
│ │ │ └── technical_analysis.py
│ │ ├── error_handling.py
│ │ ├── inspector_compatible_sse.py
│ │ ├── inspector_sse.py
│ │ ├── middleware
│ │ │ ├── error_handling.py
│ │ │ ├── mcp_logging.py
│ │ │ ├── rate_limiting_enhanced.py
│ │ │ └── security.py
│ │ ├── openapi_config.py
│ │ ├── routers
│ │ │ ├── __init__.py
│ │ │ ├── agents.py
│ │ │ ├── backtesting.py
│ │ │ ├── data_enhanced.py
│ │ │ ├── data.py
│ │ │ ├── health_enhanced.py
│ │ │ ├── health_tools.py
│ │ │ ├── health.py
│ │ │ ├── intelligent_backtesting.py
│ │ │ ├── introspection.py
│ │ │ ├── mcp_prompts.py
│ │ │ ├── monitoring.py
│ │ │ ├── news_sentiment_enhanced.py
│ │ │ ├── performance.py
│ │ │ ├── portfolio.py
│ │ │ ├── research.py
│ │ │ ├── screening_ddd.py
│ │ │ ├── screening_parallel.py
│ │ │ ├── screening.py
│ │ │ ├── technical_ddd.py
│ │ │ ├── technical_enhanced.py
│ │ │ ├── technical.py
│ │ │ └── tool_registry.py
│ │ ├── server.py
│ │ ├── services
│ │ │ ├── __init__.py
│ │ │ ├── base_service.py
│ │ │ ├── market_service.py
│ │ │ ├── portfolio_service.py
│ │ │ ├── prompt_service.py
│ │ │ └── resource_service.py
│ │ ├── simple_sse.py
│ │ └── utils
│ │ ├── __init__.py
│ │ ├── insomnia_export.py
│ │ └── postman_export.py
│ ├── application
│ │ ├── __init__.py
│ │ ├── commands
│ │ │ └── __init__.py
│ │ ├── dto
│ │ │ ├── __init__.py
│ │ │ └── technical_analysis_dto.py
│ │ ├── queries
│ │ │ ├── __init__.py
│ │ │ └── get_technical_analysis.py
│ │ └── screening
│ │ ├── __init__.py
│ │ ├── dtos.py
│ │ └── queries.py
│ ├── backtesting
│ │ ├── __init__.py
│ │ ├── ab_testing.py
│ │ ├── analysis.py
│ │ ├── batch_processing_stub.py
│ │ ├── batch_processing.py
│ │ ├── model_manager.py
│ │ ├── optimization.py
│ │ ├── persistence.py
│ │ ├── retraining_pipeline.py
│ │ ├── strategies
│ │ │ ├── __init__.py
│ │ │ ├── base.py
│ │ │ ├── ml
│ │ │ │ ├── __init__.py
│ │ │ │ ├── adaptive.py
│ │ │ │ ├── ensemble.py
│ │ │ │ ├── feature_engineering.py
│ │ │ │ └── regime_aware.py
│ │ │ ├── ml_strategies.py
│ │ │ ├── parser.py
│ │ │ └── templates.py
│ │ ├── strategy_executor.py
│ │ ├── vectorbt_engine.py
│ │ └── visualization.py
│ ├── config
│ │ ├── __init__.py
│ │ ├── constants.py
│ │ ├── database_self_contained.py
│ │ ├── database.py
│ │ ├── llm_optimization_config.py
│ │ ├── logging_settings.py
│ │ ├── plotly_config.py
│ │ ├── security_utils.py
│ │ ├── security.py
│ │ ├── settings.py
│ │ ├── technical_constants.py
│ │ ├── tool_estimation.py
│ │ └── validation.py
│ ├── core
│ │ ├── __init__.py
│ │ ├── technical_analysis.py
│ │ └── visualization.py
│ ├── data
│ │ ├── __init__.py
│ │ ├── cache_manager.py
│ │ ├── cache.py
│ │ ├── django_adapter.py
│ │ ├── health.py
│ │ ├── models.py
│ │ ├── performance.py
│ │ ├── session_management.py
│ │ └── validation.py
│ ├── database
│ │ ├── __init__.py
│ │ ├── base.py
│ │ └── optimization.py
│ ├── dependencies.py
│ ├── domain
│ │ ├── __init__.py
│ │ ├── entities
│ │ │ ├── __init__.py
│ │ │ └── stock_analysis.py
│ │ ├── events
│ │ │ └── __init__.py
│ │ ├── portfolio.py
│ │ ├── screening
│ │ │ ├── __init__.py
│ │ │ ├── entities.py
│ │ │ ├── services.py
│ │ │ └── value_objects.py
│ │ ├── services
│ │ │ ├── __init__.py
│ │ │ └── technical_analysis_service.py
│ │ ├── stock_analysis
│ │ │ ├── __init__.py
│ │ │ └── stock_analysis_service.py
│ │ └── value_objects
│ │ ├── __init__.py
│ │ └── technical_indicators.py
│ ├── exceptions.py
│ ├── infrastructure
│ │ ├── __init__.py
│ │ ├── cache
│ │ │ └── __init__.py
│ │ ├── caching
│ │ │ ├── __init__.py
│ │ │ └── cache_management_service.py
│ │ ├── connection_manager.py
│ │ ├── data_fetching
│ │ │ ├── __init__.py
│ │ │ └── stock_data_service.py
│ │ ├── health
│ │ │ ├── __init__.py
│ │ │ └── health_checker.py
│ │ ├── persistence
│ │ │ ├── __init__.py
│ │ │ └── stock_repository.py
│ │ ├── providers
│ │ │ └── __init__.py
│ │ ├── screening
│ │ │ ├── __init__.py
│ │ │ └── repositories.py
│ │ └── sse_optimizer.py
│ ├── langchain_tools
│ │ ├── __init__.py
│ │ ├── adapters.py
│ │ └── registry.py
│ ├── logging_config.py
│ ├── memory
│ │ ├── __init__.py
│ │ └── stores.py
│ ├── monitoring
│ │ ├── __init__.py
│ │ ├── health_check.py
│ │ ├── health_monitor.py
│ │ ├── integration_example.py
│ │ ├── metrics.py
│ │ ├── middleware.py
│ │ └── status_dashboard.py
│ ├── providers
│ │ ├── __init__.py
│ │ ├── dependencies.py
│ │ ├── factories
│ │ │ ├── __init__.py
│ │ │ ├── config_factory.py
│ │ │ └── provider_factory.py
│ │ ├── implementations
│ │ │ ├── __init__.py
│ │ │ ├── cache_adapter.py
│ │ │ ├── macro_data_adapter.py
│ │ │ ├── market_data_adapter.py
│ │ │ ├── persistence_adapter.py
│ │ │ └── stock_data_adapter.py
│ │ ├── interfaces
│ │ │ ├── __init__.py
│ │ │ ├── cache.py
│ │ │ ├── config.py
│ │ │ ├── macro_data.py
│ │ │ ├── market_data.py
│ │ │ ├── persistence.py
│ │ │ └── stock_data.py
│ │ ├── llm_factory.py
│ │ ├── macro_data.py
│ │ ├── market_data.py
│ │ ├── mocks
│ │ │ ├── __init__.py
│ │ │ ├── mock_cache.py
│ │ │ ├── mock_config.py
│ │ │ ├── mock_macro_data.py
│ │ │ ├── mock_market_data.py
│ │ │ ├── mock_persistence.py
│ │ │ └── mock_stock_data.py
│ │ ├── openrouter_provider.py
│ │ ├── optimized_screening.py
│ │ ├── optimized_stock_data.py
│ │ └── stock_data.py
│ ├── README.md
│ ├── tests
│ │ ├── __init__.py
│ │ ├── README_INMEMORY_TESTS.md
│ │ ├── test_cache_debug.py
│ │ ├── test_fixes_validation.py
│ │ ├── test_in_memory_routers.py
│ │ ├── test_in_memory_server.py
│ │ ├── test_macro_data_provider.py
│ │ ├── test_mailgun_email.py
│ │ ├── test_market_calendar_caching.py
│ │ ├── test_mcp_tool_fixes_pytest.py
│ │ ├── test_mcp_tool_fixes.py
│ │ ├── test_mcp_tools.py
│ │ ├── test_models_functional.py
│ │ ├── test_server.py
│ │ ├── test_stock_data_enhanced.py
│ │ ├── test_stock_data_provider.py
│ │ └── test_technical_analysis.py
│ ├── tools
│ │ ├── __init__.py
│ │ ├── performance_monitoring.py
│ │ ├── portfolio_manager.py
│ │ ├── risk_management.py
│ │ └── sentiment_analysis.py
│ ├── utils
│ │ ├── __init__.py
│ │ ├── agent_errors.py
│ │ ├── batch_processing.py
│ │ ├── cache_warmer.py
│ │ ├── circuit_breaker_decorators.py
│ │ ├── circuit_breaker_services.py
│ │ ├── circuit_breaker.py
│ │ ├── data_chunking.py
│ │ ├── database_monitoring.py
│ │ ├── debug_utils.py
│ │ ├── fallback_strategies.py
│ │ ├── llm_optimization.py
│ │ ├── logging_example.py
│ │ ├── logging_init.py
│ │ ├── logging.py
│ │ ├── mcp_logging.py
│ │ ├── memory_profiler.py
│ │ ├── monitoring_middleware.py
│ │ ├── monitoring.py
│ │ ├── orchestration_logging.py
│ │ ├── parallel_research.py
│ │ ├── parallel_screening.py
│ │ ├── quick_cache.py
│ │ ├── resource_manager.py
│ │ ├── shutdown.py
│ │ ├── stock_helpers.py
│ │ ├── structured_logger.py
│ │ ├── tool_monitoring.py
│ │ ├── tracing.py
│ │ └── yfinance_pool.py
│ ├── validation
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── data.py
│ │ ├── middleware.py
│ │ ├── portfolio.py
│ │ ├── responses.py
│ │ ├── screening.py
│ │ └── technical.py
│ └── workflows
│ ├── __init__.py
│ ├── agents
│ │ ├── __init__.py
│ │ ├── market_analyzer.py
│ │ ├── optimizer_agent.py
│ │ ├── strategy_selector.py
│ │ └── validator_agent.py
│ ├── backtesting_workflow.py
│ └── state.py
├── PLANS.md
├── pyproject.toml
├── pyrightconfig.json
├── README.md
├── scripts
│ ├── dev.sh
│ ├── INSTALLATION_GUIDE.md
│ ├── load_example.py
│ ├── load_market_data.py
│ ├── load_tiingo_data.py
│ ├── migrate_db.py
│ ├── README_TIINGO_LOADER.md
│ ├── requirements_tiingo.txt
│ ├── run_stock_screening.py
│ ├── run-migrations.sh
│ ├── seed_db.py
│ ├── seed_sp500.py
│ ├── setup_database.sh
│ ├── setup_self_contained.py
│ ├── setup_sp500_database.sh
│ ├── test_seeded_data.py
│ ├── test_tiingo_loader.py
│ ├── tiingo_config.py
│ └── validate_setup.py
├── SECURITY.md
├── server.json
├── setup.py
├── tests
│ ├── __init__.py
│ ├── conftest.py
│ ├── core
│ │ └── test_technical_analysis.py
│ ├── data
│ │ └── test_portfolio_models.py
│ ├── domain
│ │ ├── conftest.py
│ │ ├── test_portfolio_entities.py
│ │ └── test_technical_analysis_service.py
│ ├── fixtures
│ │ └── orchestration_fixtures.py
│ ├── integration
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── README.md
│ │ ├── run_integration_tests.sh
│ │ ├── test_api_technical.py
│ │ ├── test_chaos_engineering.py
│ │ ├── test_config_management.py
│ │ ├── test_full_backtest_workflow_advanced.py
│ │ ├── test_full_backtest_workflow.py
│ │ ├── test_high_volume.py
│ │ ├── test_mcp_tools.py
│ │ ├── test_orchestration_complete.py
│ │ ├── test_portfolio_persistence.py
│ │ ├── test_redis_cache.py
│ │ ├── test_security_integration.py.disabled
│ │ └── vcr_setup.py
│ ├── performance
│ │ ├── __init__.py
│ │ ├── test_benchmarks.py
│ │ ├── test_load.py
│ │ ├── test_profiling.py
│ │ └── test_stress.py
│ ├── providers
│ │ └── test_stock_data_simple.py
│ ├── README.md
│ ├── test_agents_router_mcp.py
│ ├── test_backtest_persistence.py
│ ├── test_cache_management_service.py
│ ├── test_cache_serialization.py
│ ├── test_circuit_breaker.py
│ ├── test_database_pool_config_simple.py
│ ├── test_database_pool_config.py
│ ├── test_deep_research_functional.py
│ ├── test_deep_research_integration.py
│ ├── test_deep_research_parallel_execution.py
│ ├── test_error_handling.py
│ ├── test_event_loop_integrity.py
│ ├── test_exa_research_integration.py
│ ├── test_exception_hierarchy.py
│ ├── test_financial_search.py
│ ├── test_graceful_shutdown.py
│ ├── test_integration_simple.py
│ ├── test_langgraph_workflow.py
│ ├── test_market_data_async.py
│ ├── test_market_data_simple.py
│ ├── test_mcp_orchestration_functional.py
│ ├── test_ml_strategies.py
│ ├── test_optimized_research_agent.py
│ ├── test_orchestration_integration.py
│ ├── test_orchestration_logging.py
│ ├── test_orchestration_tools_simple.py
│ ├── test_parallel_research_integration.py
│ ├── test_parallel_research_orchestrator.py
│ ├── test_parallel_research_performance.py
│ ├── test_performance_optimizations.py
│ ├── test_production_validation.py
│ ├── test_provider_architecture.py
│ ├── test_rate_limiting_enhanced.py
│ ├── test_runner_validation.py
│ ├── test_security_comprehensive.py.disabled
│ ├── test_security_cors.py
│ ├── test_security_enhancements.py.disabled
│ ├── test_security_headers.py
│ ├── test_security_penetration.py
│ ├── test_session_management.py
│ ├── test_speed_optimization_validation.py
│ ├── test_stock_analysis_dependencies.py
│ ├── test_stock_analysis_service.py
│ ├── test_stock_data_fetching_service.py
│ ├── test_supervisor_agent.py
│ ├── test_supervisor_functional.py
│ ├── test_tool_estimation_config.py
│ ├── test_visualization.py
│ └── utils
│ ├── test_agent_errors.py
│ ├── test_logging.py
│ ├── test_parallel_screening.py
│ └── test_quick_cache.py
├── tools
│ ├── check_orchestration_config.py
│ ├── experiments
│ │ ├── validation_examples.py
│ │ └── validation_fixed.py
│ ├── fast_dev.sh
│ ├── hot_reload.py
│ ├── quick_test.py
│ └── templates
│ ├── new_router_template.py
│ ├── new_tool_template.py
│ ├── screening_strategy_template.py
│ └── test_template.py
└── uv.lock
```
# Files
--------------------------------------------------------------------------------
/maverick_mcp/agents/deep_research.py:
--------------------------------------------------------------------------------
```python
1 | """
2 | DeepResearchAgent implementation using 2025 LangGraph patterns.
3 |
4 | Provides comprehensive financial research capabilities with web search,
5 | content analysis, sentiment detection, and source validation.
6 | """
7 |
8 | from __future__ import annotations
9 |
10 | import asyncio
11 | import json
12 | import logging
13 | from collections.abc import Iterable
14 | from datetime import UTC, datetime
15 | from typing import Any
16 | from uuid import uuid4
17 |
18 | from langchain_core.language_models import BaseChatModel
19 | from langchain_core.messages import HumanMessage, SystemMessage
20 | from langchain_core.tools import BaseTool, tool
21 | from langgraph.checkpoint.memory import MemorySaver
22 | from langgraph.graph import END, START, StateGraph # type: ignore[import-untyped]
23 | from langgraph.types import Command # type: ignore[import-untyped]
24 |
25 | from maverick_mcp.agents.base import PersonaAwareAgent
26 | from maverick_mcp.agents.circuit_breaker import circuit_manager
27 | from maverick_mcp.config.settings import get_settings
28 | from maverick_mcp.exceptions import (
29 | WebSearchError,
30 | )
31 | from maverick_mcp.memory.stores import ConversationStore
32 | from maverick_mcp.utils.orchestration_logging import (
33 | get_orchestration_logger,
34 | log_agent_execution,
35 | log_method_call,
36 | log_performance_metrics,
37 | log_synthesis_operation,
38 | )
39 |
40 | try: # pragma: no cover - optional dependency
41 | from tavily import TavilyClient # type: ignore[import-not-found]
42 | except ImportError: # pragma: no cover
43 | TavilyClient = None # type: ignore[assignment]
44 |
45 | # Import moved to avoid circular dependency - will import where needed
46 | from maverick_mcp.workflows.state import DeepResearchState
47 |
48 | logger = logging.getLogger(__name__)
49 | settings = get_settings()
50 |
51 | # Global search provider cache and connection manager
52 | _search_provider_cache: dict[str, Any] = {}
53 |
54 |
55 | async def get_cached_search_provider(exa_api_key: str | None = None) -> Any | None:
56 | """Get cached Exa search provider to avoid repeated initialization delays."""
57 | cache_key = f"exa:{exa_api_key is not None}"
58 |
59 | if cache_key in _search_provider_cache:
60 | return _search_provider_cache[cache_key]
61 |
62 | logger.info("Initializing Exa search provider")
63 | provider = None
64 |
65 | # Initialize Exa provider with caching
66 | if exa_api_key:
67 | try:
68 | provider = ExaSearchProvider(exa_api_key)
69 | logger.info("Initialized Exa search provider")
70 | # Cache the provider
71 | _search_provider_cache[cache_key] = provider
72 | except ImportError as e:
73 | logger.warning(f"Failed to initialize Exa provider: {e}")
74 |
75 | return provider
76 |
77 |
78 | # Research depth levels optimized for quick searches
79 | RESEARCH_DEPTH_LEVELS = {
80 | "basic": {
81 | "max_sources": 3,
82 | "max_searches": 1, # Reduced for speed
83 | "analysis_depth": "summary",
84 | "validation_required": False,
85 | },
86 | "standard": {
87 | "max_sources": 5, # Reduced from 8
88 | "max_searches": 2, # Reduced from 4
89 | "analysis_depth": "detailed",
90 | "validation_required": False, # Disabled for speed
91 | },
92 | "comprehensive": {
93 | "max_sources": 10, # Reduced from 15
94 | "max_searches": 3, # Reduced from 6
95 | "analysis_depth": "comprehensive",
96 | "validation_required": False, # Disabled for speed
97 | },
98 | "exhaustive": {
99 | "max_sources": 15, # Reduced from 25
100 | "max_searches": 5, # Reduced from 10
101 | "analysis_depth": "exhaustive",
102 | "validation_required": True,
103 | },
104 | }
105 |
106 | # Persona-specific research focus areas
107 | PERSONA_RESEARCH_FOCUS = {
108 | "conservative": {
109 | "keywords": [
110 | "dividend",
111 | "stability",
112 | "risk",
113 | "debt",
114 | "cash flow",
115 | "established",
116 | ],
117 | "sources": [
118 | "sec filings",
119 | "annual reports",
120 | "rating agencies",
121 | "dividend history",
122 | ],
123 | "risk_focus": "downside protection",
124 | "time_horizon": "long-term",
125 | },
126 | "moderate": {
127 | "keywords": ["growth", "value", "balance", "diversification", "fundamentals"],
128 | "sources": ["financial statements", "analyst reports", "industry analysis"],
129 | "risk_focus": "risk-adjusted returns",
130 | "time_horizon": "medium-term",
131 | },
132 | "aggressive": {
133 | "keywords": ["growth", "momentum", "opportunity", "innovation", "expansion"],
134 | "sources": [
135 | "news",
136 | "earnings calls",
137 | "industry trends",
138 | "competitive analysis",
139 | ],
140 | "risk_focus": "upside potential",
141 | "time_horizon": "short to medium-term",
142 | },
143 | "day_trader": {
144 | "keywords": [
145 | "catalysts",
146 | "earnings",
147 | "news",
148 | "volume",
149 | "volatility",
150 | "momentum",
151 | ],
152 | "sources": ["breaking news", "social sentiment", "earnings announcements"],
153 | "risk_focus": "short-term risks",
154 | "time_horizon": "intraday to weekly",
155 | },
156 | }
157 |
158 |
159 | class WebSearchProvider:
160 | """Base class for web search providers with early abort mechanism."""
161 |
162 | def __init__(self, api_key: str):
163 | self.api_key = api_key
164 | self.rate_limiter = None # Implement rate limiting
165 | self._failure_count = 0
166 | self._max_failures = 3 # Abort after 3 consecutive failures
167 | self._is_healthy = True
168 | self.settings = get_settings()
169 | self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")
170 |
171 | def _calculate_timeout(
172 | self, query: str, timeout_budget: float | None = None
173 | ) -> float:
174 | """Calculate generous timeout for thorough research operations."""
175 | query_words = len(query.split())
176 |
177 | # Generous timeout calculation for thorough search operations
178 | if query_words <= 3:
179 | base_timeout = 30.0 # Simple queries - 30s for thorough results
180 | elif query_words <= 8:
181 | base_timeout = 45.0 # Standard queries - 45s for comprehensive search
182 | else:
183 | base_timeout = 60.0 # Complex queries - 60s for exhaustive search
184 |
185 | # Apply budget constraints if available
186 | if timeout_budget and timeout_budget > 0:
187 | # Use generous portion of available budget per search operation
188 | budget_timeout = max(
189 | timeout_budget * 0.6, 30.0
190 | ) # At least 30s, use 60% of budget
191 | calculated_timeout = min(base_timeout, budget_timeout)
192 |
193 | # Ensure minimum timeout (at least 30s for thorough search)
194 | calculated_timeout = max(calculated_timeout, 30.0)
195 | else:
196 | calculated_timeout = base_timeout
197 |
198 | # Final timeout with generous minimum for thorough search
199 | final_timeout = max(calculated_timeout, 30.0)
200 |
201 | return final_timeout
202 |
203 | def _record_failure(self, error_type: str = "unknown") -> None:
204 | """Record a search failure and check if provider should be disabled."""
205 | self._failure_count += 1
206 |
207 | # Use separate thresholds for timeout vs other failures
208 | timeout_threshold = getattr(
209 | self.settings.performance, "search_timeout_failure_threshold", 12
210 | )
211 |
212 | # Much more tolerant of timeout failures - they may be due to network/complexity
213 | if error_type == "timeout" and self._failure_count >= timeout_threshold:
214 | self._is_healthy = False
215 | logger.warning(
216 | f"Search provider {self.__class__.__name__} disabled after "
217 | f"{self._failure_count} consecutive timeout failures (threshold: {timeout_threshold})"
218 | )
219 | elif error_type != "timeout" and self._failure_count >= self._max_failures * 2:
220 | # Be more lenient for non-timeout failures (2x threshold)
221 | self._is_healthy = False
222 | logger.warning(
223 | f"Search provider {self.__class__.__name__} disabled after "
224 | f"{self._failure_count} total non-timeout failures"
225 | )
226 |
227 | logger.debug(
228 | f"Provider {self.__class__.__name__} failure recorded: "
229 | f"type={error_type}, count={self._failure_count}, healthy={self._is_healthy}"
230 | )
231 |
232 | def _record_success(self) -> None:
233 | """Record a successful search and reset failure count."""
234 | if self._failure_count > 0:
235 | logger.info(
236 | f"Search provider {self.__class__.__name__} recovered after "
237 | f"{self._failure_count} failures"
238 | )
239 | self._failure_count = 0
240 | self._is_healthy = True
241 |
242 | def is_healthy(self) -> bool:
243 | """Check if provider is healthy and should be used."""
244 | return self._is_healthy
245 |
246 | async def search(
247 | self, query: str, num_results: int = 10, timeout_budget: float | None = None
248 | ) -> list[dict[str, Any]]:
249 | """Perform web search and return results."""
250 | raise NotImplementedError
251 |
252 | async def get_content(self, url: str) -> dict[str, Any]:
253 | """Extract content from URL."""
254 | raise NotImplementedError
255 |
256 | async def search_multiple_providers(
257 | self,
258 | queries: list[str],
259 | providers: list[str] | None = None,
260 | max_results_per_query: int = 5,
261 | ) -> dict[str, list[dict[str, Any]]]:
262 | """Search using multiple providers and return aggregated results."""
263 | providers = providers or ["exa"] # Default to available providers
264 | results = {}
265 |
266 | for provider_name in providers:
267 | provider_results = []
268 | for query in queries:
269 | try:
270 | query_results = await self.search(query, max_results_per_query)
271 |
272 | provider_results.extend(query_results or [])
273 | except Exception as e:
274 | self.logger.warning(
275 | f"Search failed for provider {provider_name}, query '{query}': {e}"
276 | )
277 | continue
278 |
279 | results[provider_name] = provider_results
280 |
281 | return results
282 |
283 | def _timeframe_to_date(self, timeframe: str) -> str | None:
284 | """Convert timeframe string to date string."""
285 | from datetime import datetime, timedelta
286 |
287 | now = datetime.now()
288 |
289 | if timeframe == "1d":
290 | date = now - timedelta(days=1)
291 | elif timeframe == "1w":
292 | date = now - timedelta(weeks=1)
293 | elif timeframe == "1m":
294 | date = now - timedelta(days=30)
295 | else:
296 | # Invalid or unsupported timeframe, return None
297 | return None
298 |
299 | return date.strftime("%Y-%m-%d")
300 |
301 |
302 | class ExaSearchProvider(WebSearchProvider):
303 | """Exa search provider for comprehensive web search using MCP tools with financial optimization."""
304 |
305 | def __init__(self, api_key: str):
306 | super().__init__(api_key)
307 | # Store the API key for verification
308 | self._api_key_verified = bool(api_key)
309 |
310 | # Financial-specific domain preferences for better results
311 | self.financial_domains = [
312 | "sec.gov",
313 | "edgar.sec.gov",
314 | "investor.gov",
315 | "bloomberg.com",
316 | "reuters.com",
317 | "wsj.com",
318 | "ft.com",
319 | "marketwatch.com",
320 | "yahoo.com/finance",
321 | "finance.yahoo.com",
322 | "morningstar.com",
323 | "fool.com",
324 | "seekingalpha.com",
325 | "investopedia.com",
326 | "barrons.com",
327 | "cnbc.com",
328 | "nasdaq.com",
329 | "nyse.com",
330 | "finra.org",
331 | "federalreserve.gov",
332 | "treasury.gov",
333 | "bls.gov",
334 | ]
335 |
336 | # Domains to exclude for financial searches
337 | self.excluded_domains = [
338 | "facebook.com",
339 | "twitter.com",
340 | "x.com",
341 | "instagram.com",
342 | "tiktok.com",
343 | "reddit.com",
344 | "pinterest.com",
345 | "linkedin.com",
346 | "youtube.com",
347 | "wikipedia.org",
348 | ]
349 |
350 | logger.info("Initialized ExaSearchProvider with financial optimization")
351 |
352 | async def search(
353 | self, query: str, num_results: int = 10, timeout_budget: float | None = None
354 | ) -> list[dict[str, Any]]:
355 | """Search using Exa via async client for comprehensive web results with adaptive timeout."""
356 | return await self._search_with_strategy(
357 | query, num_results, timeout_budget, "auto"
358 | )
359 |
360 | async def search_financial(
361 | self,
362 | query: str,
363 | num_results: int = 10,
364 | timeout_budget: float | None = None,
365 | strategy: str = "hybrid",
366 | ) -> list[dict[str, Any]]:
367 | """
368 | Enhanced financial search with optimized queries and domain targeting.
369 |
370 | Args:
371 | query: Search query
372 | num_results: Number of results to return
373 | timeout_budget: Timeout budget in seconds
374 | strategy: Search strategy - 'hybrid', 'authoritative', 'comprehensive', or 'auto'
375 | """
376 | return await self._search_with_strategy(
377 | query, num_results, timeout_budget, strategy
378 | )
379 |
380 | async def _search_with_strategy(
381 | self, query: str, num_results: int, timeout_budget: float | None, strategy: str
382 | ) -> list[dict[str, Any]]:
383 | """Internal method to handle different search strategies."""
384 |
385 | # Check provider health before attempting search
386 | if not self.is_healthy():
387 | logger.warning("Exa provider is unhealthy - skipping search")
388 | raise WebSearchError("Exa provider disabled due to repeated failures")
389 |
390 | # Calculate adaptive timeout
391 | search_timeout = self._calculate_timeout(query, timeout_budget)
392 |
393 | try:
394 | # Use search-specific circuit breaker settings (more tolerant)
395 | circuit_breaker = await circuit_manager.get_or_create(
396 | "exa_search",
397 | failure_threshold=getattr(
398 | self.settings.performance,
399 | "search_circuit_breaker_failure_threshold",
400 | 8,
401 | ),
402 | recovery_timeout=getattr(
403 | self.settings.performance,
404 | "search_circuit_breaker_recovery_timeout",
405 | 30,
406 | ),
407 | )
408 |
409 | async def _search():
410 | # Use the async exa-py library for web search
411 | try:
412 | from exa_py import AsyncExa
413 |
414 | # Initialize AsyncExa client with API key
415 | async_exa_client = AsyncExa(api_key=self.api_key)
416 |
417 | # Configure search parameters based on strategy
418 | search_params = self._get_search_params(
419 | query, num_results, strategy
420 | )
421 |
422 | # Call Exa search with optimized parameters
423 | exa_response = await async_exa_client.search_and_contents(
424 | **search_params
425 | )
426 |
427 | # Convert Exa response to standard format with enhanced metadata
428 | results = []
429 | if exa_response and hasattr(exa_response, "results"):
430 | for result in exa_response.results:
431 | # Enhanced result processing with financial relevance scoring
432 | financial_relevance = self._calculate_financial_relevance(
433 | result
434 | )
435 |
436 | results.append(
437 | {
438 | "url": result.url or "",
439 | "title": result.title or "No Title",
440 | "content": (result.text or "")[:2000],
441 | "raw_content": (result.text or "")[
442 | :5000
443 | ], # Increased for financial content
444 | "published_date": result.published_date or "",
445 | "score": result.score
446 | if hasattr(result, "score")
447 | and result.score is not None
448 | else 0.7,
449 | "financial_relevance": financial_relevance,
450 | "provider": "exa",
451 | "author": result.author
452 | if hasattr(result, "author")
453 | and result.author is not None
454 | else "",
455 | "domain": self._extract_domain(result.url or ""),
456 | "is_authoritative": self._is_authoritative_source(
457 | result.url or ""
458 | ),
459 | }
460 | )
461 |
462 | # Sort results by financial relevance and score
463 | results.sort(
464 | key=lambda x: (x["financial_relevance"], x["score"]),
465 | reverse=True,
466 | )
467 | return results
468 |
469 | except ImportError:
470 | logger.error("exa-py library not available - cannot perform search")
471 | raise WebSearchError(
472 | "exa-py library required for ExaSearchProvider"
473 | )
474 | except Exception as e:
475 | logger.error(f"Error calling Exa API: {e}")
476 | raise e
477 |
478 | # Use adaptive timeout based on query complexity and budget
479 | result = await asyncio.wait_for(
480 | circuit_breaker.call(_search), timeout=search_timeout
481 | )
482 | self._record_success() # Record successful search
483 | logger.debug(
484 | f"Exa search completed in {search_timeout:.1f}s timeout window"
485 | )
486 | return result
487 |
488 | except TimeoutError:
489 | self._record_failure("timeout") # Record timeout as specific failure type
490 | query_snippet = query[:100] + ("..." if len(query) > 100 else "")
491 | logger.error(
492 | f"Exa search timeout after {search_timeout:.1f} seconds (failure #{self._failure_count}) "
493 | f"for query: '{query_snippet}'"
494 | )
495 | raise WebSearchError(
496 | f"Exa search timed out after {search_timeout:.1f} seconds"
497 | )
498 | except Exception as e:
499 | self._record_failure("error") # Record non-timeout failure
500 | logger.error(f"Exa search error (failure #{self._failure_count}): {e}")
501 | raise WebSearchError(f"Exa search failed: {str(e)}")
502 |
503 | def _get_search_params(
504 | self, query: str, num_results: int, strategy: str
505 | ) -> dict[str, Any]:
506 | """
507 | Generate optimized search parameters based on strategy and query type.
508 |
509 | Args:
510 | query: Search query
511 | num_results: Number of results
512 | strategy: Search strategy
513 |
514 | Returns:
515 | Dictionary of search parameters for Exa API
516 | """
517 | # Base parameters
518 | params = {
519 | "query": query,
520 | "num_results": num_results,
521 | "text": {"max_characters": 5000}, # Increased for financial content
522 | }
523 |
524 | # Strategy-specific optimizations
525 | if strategy == "authoritative":
526 | # Focus on authoritative financial sources
527 | # Note: Exa API doesn't allow both include_domains and exclude_domains with content
528 | params.update(
529 | {
530 | "include_domains": self.financial_domains[
531 | :10
532 | ], # Top authoritative sources
533 | "type": "auto", # Let Exa decide neural vs keyword
534 | "start_published_date": "2020-01-01", # Recent financial data
535 | }
536 | )
537 |
538 | elif strategy == "comprehensive":
539 | # Broad search across all financial sources
540 | params.update(
541 | {
542 | "exclude_domains": self.excluded_domains,
543 | "type": "neural", # Better for comprehensive understanding
544 | "start_published_date": "2018-01-01", # Broader historical context
545 | }
546 | )
547 |
548 | elif strategy == "hybrid":
549 | # Balanced approach with domain preferences
550 | params.update(
551 | {
552 | "exclude_domains": self.excluded_domains,
553 | "type": "auto", # Hybrid neural/keyword approach
554 | "start_published_date": "2019-01-01",
555 | # Use domain weighting rather than strict inclusion
556 | }
557 | )
558 |
559 | else: # "auto" or default
560 | # Standard search with basic optimizations
561 | params.update(
562 | {
563 | "exclude_domains": self.excluded_domains[:5], # Basic exclusions
564 | "type": "auto",
565 | }
566 | )
567 |
568 | # Add financial-specific query enhancements
569 | enhanced_query = self._enhance_financial_query(query)
570 | if enhanced_query != query:
571 | params["query"] = enhanced_query
572 |
573 | return params
574 |
575 | def _enhance_financial_query(self, query: str) -> str:
576 | """
577 | Enhance queries with financial context and terminology.
578 |
579 | Args:
580 | query: Original search query
581 |
582 | Returns:
583 | Enhanced query with financial context
584 | """
585 | # Financial keywords that improve search quality
586 | financial_terms = {
587 | "earnings",
588 | "revenue",
589 | "profit",
590 | "loss",
591 | "financial",
592 | "quarterly",
593 | "annual",
594 | "SEC",
595 | "10-K",
596 | "10-Q",
597 | "balance sheet",
598 | "income statement",
599 | "cash flow",
600 | "dividend",
601 | "stock",
602 | "share",
603 | "market cap",
604 | "valuation",
605 | }
606 |
607 | query_lower = query.lower()
608 |
609 | # Check if query already contains financial terms
610 | has_financial_context = any(term in query_lower for term in financial_terms)
611 |
612 | # Add context for company/stock queries
613 | if not has_financial_context:
614 | # Detect if it's a company or stock symbol query
615 | if any(
616 | indicator in query_lower
617 | for indicator in ["company", "corp", "inc", "$", "stock"]
618 | ):
619 | return f"{query} financial analysis earnings revenue"
620 | elif len(query.split()) <= 3 and query.isupper(): # Likely stock symbol
621 | return f"{query} stock financial performance earnings"
622 | elif "analysis" in query_lower or "research" in query_lower:
623 | return f"{query} financial data SEC filings"
624 |
625 | return query
626 |
627 | def _calculate_financial_relevance(self, result) -> float:
628 | """
629 | Calculate financial relevance score for a search result.
630 |
631 | Args:
632 | result: Exa search result object
633 |
634 | Returns:
635 | Financial relevance score (0.0 to 1.0)
636 | """
637 | score = 0.0
638 |
639 | # Domain-based scoring
640 | domain = self._extract_domain(result.url)
641 | if domain in self.financial_domains:
642 | if domain in ["sec.gov", "edgar.sec.gov", "federalreserve.gov"]:
643 | score += 0.4 # Highest authority
644 | elif domain in ["bloomberg.com", "reuters.com", "wsj.com", "ft.com"]:
645 | score += 0.3 # High-quality financial news
646 | else:
647 | score += 0.2 # Other financial sources
648 |
649 | # Content-based scoring
650 | if hasattr(result, "text") and result.text:
651 | text_lower = result.text.lower()
652 |
653 | # Financial terminology scoring
654 | financial_keywords = [
655 | "earnings",
656 | "revenue",
657 | "profit",
658 | "financial",
659 | "quarterly",
660 | "annual",
661 | "sec filing",
662 | "10-k",
663 | "10-q",
664 | "balance sheet",
665 | "income statement",
666 | "cash flow",
667 | "dividend",
668 | "market cap",
669 | "valuation",
670 | "analyst",
671 | "forecast",
672 | "guidance",
673 | "ebitda",
674 | "eps",
675 | "pe ratio",
676 | ]
677 |
678 | keyword_matches = sum(
679 | 1 for keyword in financial_keywords if keyword in text_lower
680 | )
681 | score += min(keyword_matches * 0.05, 0.3) # Max 0.3 from keywords
682 |
683 | # Title-based scoring
684 | if hasattr(result, "title") and result.title:
685 | title_lower = result.title.lower()
686 | if any(
687 | term in title_lower
688 | for term in ["financial", "earnings", "quarterly", "annual", "sec"]
689 | ):
690 | score += 0.1
691 |
692 | # Recency bonus for financial data
693 | if hasattr(result, "published_date") and result.published_date:
694 | try:
695 | from datetime import datetime
696 |
697 | # Handle different date formats
698 | date_str = str(result.published_date)
699 | if date_str and date_str != "":
700 | # Handle ISO format with Z
701 | if date_str.endswith("Z"):
702 | date_str = date_str.replace("Z", "+00:00")
703 |
704 | pub_date = datetime.fromisoformat(date_str)
705 | days_old = (datetime.now(UTC) - pub_date).days
706 |
707 | if days_old <= 30:
708 | score += 0.1 # Recent data bonus
709 | elif days_old <= 90:
710 | score += 0.05 # Somewhat recent bonus
711 | except (ValueError, AttributeError, TypeError):
712 | pass # Skip if date parsing fails
713 |
714 | return min(score, 1.0) # Cap at 1.0
715 |
716 | def _extract_domain(self, url: str) -> str:
717 | """Extract domain from URL."""
718 | try:
719 | from urllib.parse import urlparse
720 |
721 | return urlparse(url).netloc.lower().replace("www.", "")
722 | except Exception:
723 | return ""
724 |
725 | def _is_authoritative_source(self, url: str) -> bool:
726 | """Check if URL is from an authoritative financial source."""
727 | domain = self._extract_domain(url)
728 | authoritative_domains = [
729 | "sec.gov",
730 | "edgar.sec.gov",
731 | "federalreserve.gov",
732 | "treasury.gov",
733 | "bloomberg.com",
734 | "reuters.com",
735 | "wsj.com",
736 | "ft.com",
737 | ]
738 | return domain in authoritative_domains
739 |
740 |
741 | class TavilySearchProvider(WebSearchProvider):
742 | """Tavily search provider with sensible filtering for financial research."""
743 |
744 | def __init__(self, api_key: str):
745 | super().__init__(api_key)
746 | self.excluded_domains = {
747 | "facebook.com",
748 | "twitter.com",
749 | "x.com",
750 | "instagram.com",
751 | "reddit.com",
752 | }
753 |
754 | async def search(
755 | self, query: str, num_results: int = 10, timeout_budget: float | None = None
756 | ) -> list[dict[str, Any]]:
757 | if not self.is_healthy():
758 | raise WebSearchError("Tavily provider disabled due to repeated failures")
759 |
760 | timeout = self._calculate_timeout(query, timeout_budget)
761 | circuit_breaker = await circuit_manager.get_or_create(
762 | "tavily_search",
763 | failure_threshold=8,
764 | recovery_timeout=30,
765 | )
766 |
767 | async def _search() -> list[dict[str, Any]]:
768 | if TavilyClient is None:
769 | raise ImportError("tavily package is required for TavilySearchProvider")
770 |
771 | client = TavilyClient(api_key=self.api_key)
772 | response = await asyncio.get_event_loop().run_in_executor(
773 | None,
774 | lambda: client.search(query=query, max_results=num_results),
775 | )
776 | return self._process_results(response.get("results", []))
777 |
778 | return await circuit_breaker.call(_search, timeout=timeout)
779 |
780 | def _process_results(
781 | self, results: Iterable[dict[str, Any]]
782 | ) -> list[dict[str, Any]]:
783 | processed: list[dict[str, Any]] = []
784 | for item in results:
785 | url = item.get("url", "")
786 | if any(domain in url for domain in self.excluded_domains):
787 | continue
788 | processed.append(
789 | {
790 | "url": url,
791 | "title": item.get("title"),
792 | "content": item.get("content") or item.get("raw_content", ""),
793 | "raw_content": item.get("raw_content"),
794 | "published_date": item.get("published_date"),
795 | "score": item.get("score", 0.0),
796 | "provider": "tavily",
797 | }
798 | )
799 | return processed
800 |
801 |
802 | class ContentAnalyzer:
803 | """AI-powered content analysis for research results with batch processing capability."""
804 |
805 | def __init__(self, llm: BaseChatModel):
806 | self.llm = llm
807 | self._batch_size = 4 # Process up to 4 sources concurrently
808 |
809 | @staticmethod
810 | def _coerce_message_content(raw_content: Any) -> str:
811 | """Convert LLM response content to a string for JSON parsing."""
812 | if isinstance(raw_content, str):
813 | return raw_content
814 |
815 | if isinstance(raw_content, list):
816 | parts: list[str] = []
817 | for item in raw_content:
818 | if isinstance(item, dict):
819 | text_value = item.get("text")
820 | if isinstance(text_value, str):
821 | parts.append(text_value)
822 | else:
823 | parts.append(str(text_value))
824 | else:
825 | parts.append(str(item))
826 | return "".join(parts)
827 |
828 | return str(raw_content)
829 |
830 | async def analyze_content(
831 | self, content: str, persona: str, analysis_focus: str = "general"
832 | ) -> dict[str, Any]:
833 | """Analyze content with AI for insights, sentiment, and relevance."""
834 |
835 | persona_focus = PERSONA_RESEARCH_FOCUS.get(
836 | persona, PERSONA_RESEARCH_FOCUS["moderate"]
837 | )
838 |
839 | analysis_prompt = f"""
840 | Analyze this financial content from the perspective of a {persona} investor.
841 |
842 | Content to analyze:
843 | {content[:3000]} # Limit content length
844 |
845 | Focus Areas: {", ".join(persona_focus["keywords"])}
846 | Risk Focus: {persona_focus["risk_focus"]}
847 | Time Horizon: {persona_focus["time_horizon"]}
848 |
849 | Provide analysis in the following structure:
850 |
851 | 1. KEY_INSIGHTS: 3-5 bullet points of most important insights
852 | 2. SENTIMENT: Overall sentiment (bullish/bearish/neutral) with confidence (0-1)
853 | 3. RISK_FACTORS: Key risks identified relevant to {persona} investors
854 | 4. OPPORTUNITIES: Investment opportunities or catalysts identified
855 | 5. CREDIBILITY: Assessment of source credibility (0-1 score)
856 | 6. RELEVANCE: How relevant is this to {persona} investment strategy (0-1 score)
857 | 7. SUMMARY: 2-3 sentence summary for {persona} investors
858 |
859 | Format as JSON with clear structure.
860 | """
861 |
862 | try:
863 | response = await self.llm.ainvoke(
864 | [
865 | SystemMessage(
866 | content="You are a financial content analyst. Return only valid JSON."
867 | ),
868 | HumanMessage(content=analysis_prompt),
869 | ]
870 | )
871 |
872 | raw_content = self._coerce_message_content(response.content).strip()
873 | analysis = json.loads(raw_content)
874 |
875 | return {
876 | "insights": analysis.get("KEY_INSIGHTS", []),
877 | "sentiment": {
878 | "direction": analysis.get("SENTIMENT", {}).get(
879 | "direction", "neutral"
880 | ),
881 | "confidence": analysis.get("SENTIMENT", {}).get("confidence", 0.5),
882 | },
883 | "risk_factors": analysis.get("RISK_FACTORS", []),
884 | "opportunities": analysis.get("OPPORTUNITIES", []),
885 | "credibility_score": analysis.get("CREDIBILITY", 0.5),
886 | "relevance_score": analysis.get("RELEVANCE", 0.5),
887 | "summary": analysis.get("SUMMARY", ""),
888 | "analysis_timestamp": datetime.now(),
889 | }
890 |
891 | except Exception as e:
892 | logger.warning(f"AI content analysis failed: {e}, using fallback")
893 | return self._fallback_analysis(content, persona)
894 |
895 | def _fallback_analysis(self, content: str, persona: str) -> dict[str, Any]:
896 | """Fallback analysis using keyword matching."""
897 | persona_focus = PERSONA_RESEARCH_FOCUS.get(
898 | persona, PERSONA_RESEARCH_FOCUS["moderate"]
899 | )
900 |
901 | content_lower = content.lower()
902 |
903 | # Simple sentiment analysis
904 | positive_words = [
905 | "growth",
906 | "increase",
907 | "profit",
908 | "success",
909 | "opportunity",
910 | "strong",
911 | ]
912 | negative_words = ["decline", "loss", "risk", "problem", "concern", "weak"]
913 |
914 | positive_count = sum(1 for word in positive_words if word in content_lower)
915 | negative_count = sum(1 for word in negative_words if word in content_lower)
916 |
917 | if positive_count > negative_count:
918 | sentiment = "bullish"
919 | confidence = 0.6
920 | elif negative_count > positive_count:
921 | sentiment = "bearish"
922 | confidence = 0.6
923 | else:
924 | sentiment = "neutral"
925 | confidence = 0.5
926 |
927 | # Relevance scoring based on keywords
928 | keyword_matches = sum(
929 | 1 for keyword in persona_focus["keywords"] if keyword in content_lower
930 | )
931 | relevance_score = min(keyword_matches / len(persona_focus["keywords"]), 1.0)
932 |
933 | return {
934 | "insights": [f"Fallback analysis for {persona} investor perspective"],
935 | "sentiment": {"direction": sentiment, "confidence": confidence},
936 | "risk_factors": ["Unable to perform detailed risk analysis"],
937 | "opportunities": ["Unable to identify specific opportunities"],
938 | "credibility_score": 0.5,
939 | "relevance_score": relevance_score,
940 | "summary": f"Content analysis for {persona} investor using fallback method",
941 | "analysis_timestamp": datetime.now(),
942 | "fallback_used": True,
943 | }
944 |
945 | async def analyze_content_batch(
946 | self,
947 | content_items: list[tuple[str, str]],
948 | persona: str,
949 | analysis_focus: str = "general",
950 | ) -> list[dict[str, Any]]:
951 | """
952 | Analyze multiple content items in parallel batches for improved performance.
953 |
954 | Args:
955 | content_items: List of (content, source_identifier) tuples
956 | persona: Investor persona for analysis perspective
957 | analysis_focus: Focus area for analysis
958 |
959 | Returns:
960 | List of analysis results in same order as input
961 | """
962 | if not content_items:
963 | return []
964 |
965 | # Process items in batches to avoid overwhelming the LLM
966 | results = []
967 | for i in range(0, len(content_items), self._batch_size):
968 | batch = content_items[i : i + self._batch_size]
969 |
970 | # Create concurrent tasks for this batch
971 | tasks = [
972 | self.analyze_content(content, persona, analysis_focus)
973 | for content, _ in batch
974 | ]
975 |
976 | # Wait for all tasks in this batch to complete
977 | try:
978 | batch_results = await asyncio.gather(*tasks, return_exceptions=True)
979 |
980 | # Process results and handle exceptions
981 | for j, result in enumerate(batch_results):
982 | if isinstance(result, Exception):
983 | logger.warning(
984 | f"Batch analysis failed for item {i + j}: {result}"
985 | )
986 | # Use fallback for failed items
987 | content, source_id = batch[j]
988 | fallback_result = self._fallback_analysis(content, persona)
989 | fallback_result["source_identifier"] = source_id
990 | fallback_result["batch_processed"] = True
991 | results.append(fallback_result)
992 | elif isinstance(result, dict):
993 | enriched_result = dict(result)
994 | enriched_result["source_identifier"] = batch[j][1]
995 | enriched_result["batch_processed"] = True
996 | results.append(enriched_result)
997 | else:
998 | content, source_id = batch[j]
999 | fallback_result = self._fallback_analysis(content, persona)
1000 | fallback_result["source_identifier"] = source_id
1001 | fallback_result["batch_processed"] = True
1002 | results.append(fallback_result)
1003 |
1004 | except Exception as e:
1005 | logger.error(f"Batch analysis completely failed: {e}")
1006 | # Fallback for entire batch
1007 | for content, source_id in batch:
1008 | fallback_result = self._fallback_analysis(content, persona)
1009 | fallback_result["source_identifier"] = source_id
1010 | fallback_result["batch_processed"] = True
1011 | fallback_result["batch_error"] = str(e)
1012 | results.append(fallback_result)
1013 |
1014 | logger.info(
1015 | f"Batch content analysis completed: {len(content_items)} items processed "
1016 | f"in {(len(content_items) + self._batch_size - 1) // self._batch_size} batches"
1017 | )
1018 |
1019 | return results
1020 |
1021 | async def analyze_content_items(
1022 | self,
1023 | content_items: list[dict[str, Any]],
1024 | focus_areas: list[str],
1025 | ) -> dict[str, Any]:
1026 | """
1027 | Analyze content items for test compatibility.
1028 |
1029 | Args:
1030 | content_items: List of search result dictionaries with content/text field
1031 | focus_areas: List of focus areas for analysis
1032 |
1033 | Returns:
1034 | Dictionary with aggregated analysis results
1035 | """
1036 | if not content_items:
1037 | return {
1038 | "insights": [],
1039 | "sentiment_scores": [],
1040 | "credibility_scores": [],
1041 | }
1042 |
1043 | # For test compatibility, directly use LLM with test-compatible format
1044 | analyzed_results = []
1045 | for item in content_items:
1046 | content = item.get("text") or item.get("content") or ""
1047 | if content:
1048 | try:
1049 | # Direct LLM call for test compatibility
1050 | prompt = f"Analyze: {content[:500]}"
1051 | response = await self.llm.ainvoke(
1052 | [
1053 | SystemMessage(
1054 | content="You are a financial content analyst. Return only valid JSON."
1055 | ),
1056 | HumanMessage(content=prompt),
1057 | ]
1058 | )
1059 |
1060 | coerced_content = self._coerce_message_content(
1061 | response.content
1062 | ).strip()
1063 | analysis = json.loads(coerced_content)
1064 | analyzed_results.append(analysis)
1065 | except Exception as e:
1066 | logger.warning(f"Content analysis failed: {e}")
1067 | # Add fallback analysis
1068 | analyzed_results.append(
1069 | {
1070 | "insights": [
1071 | {"insight": "Analysis failed", "confidence": 0.1}
1072 | ],
1073 | "sentiment": {"direction": "neutral", "confidence": 0.5},
1074 | "credibility": 0.5,
1075 | }
1076 | )
1077 |
1078 | # Aggregate results
1079 | all_insights = []
1080 | sentiment_scores = []
1081 | credibility_scores = []
1082 |
1083 | for result in analyzed_results:
1084 | # Handle test format with nested insight objects
1085 | insights = result.get("insights", [])
1086 | if isinstance(insights, list):
1087 | for insight in insights:
1088 | if isinstance(insight, dict) and "insight" in insight:
1089 | all_insights.append(insight["insight"])
1090 | elif isinstance(insight, str):
1091 | all_insights.append(insight)
1092 | else:
1093 | all_insights.append(str(insight))
1094 |
1095 | sentiment = result.get("sentiment", {})
1096 | if sentiment:
1097 | sentiment_scores.append(sentiment)
1098 |
1099 | credibility = result.get(
1100 | "credibility_score", result.get("credibility", 0.5)
1101 | )
1102 | credibility_scores.append(credibility)
1103 |
1104 | return {
1105 | "insights": all_insights,
1106 | "sentiment_scores": sentiment_scores,
1107 | "credibility_scores": credibility_scores,
1108 | }
1109 |
1110 | async def _analyze_single_content(
1111 | self, content_item: dict[str, Any] | str, focus_areas: list[str] | None = None
1112 | ) -> dict[str, Any]:
1113 | """Analyze single content item - used by tests."""
1114 | if isinstance(content_item, dict):
1115 | content = content_item.get("text") or content_item.get("content") or ""
1116 | else:
1117 | content = content_item
1118 |
1119 | try:
1120 | result = await self.analyze_content(content, "moderate")
1121 | # Ensure test-compatible format
1122 | if "credibility_score" in result and "credibility" not in result:
1123 | result["credibility"] = result["credibility_score"]
1124 | return result
1125 | except Exception as e:
1126 | logger.warning(f"Single content analysis failed: {e}")
1127 | # Return fallback result
1128 | return {
1129 | "sentiment": {"direction": "neutral", "confidence": 0.5},
1130 | "credibility": 0.5,
1131 | "credibility_score": 0.5,
1132 | "insights": [],
1133 | "risk_factors": [],
1134 | "opportunities": [],
1135 | }
1136 |
1137 | async def _extract_themes(
1138 | self, content_items: list[dict[str, Any]]
1139 | ) -> list[dict[str, Any]]:
1140 | """Extract themes from content items - used by tests."""
1141 | if not content_items:
1142 | return []
1143 |
1144 | # Use LLM to extract structured themes
1145 | try:
1146 | content_text = "\n".join(
1147 | [item.get("text", item.get("content", "")) for item in content_items]
1148 | )
1149 |
1150 | prompt = f"""
1151 | Extract key themes from the following content and return as JSON:
1152 |
1153 | {content_text[:2000]}
1154 |
1155 | Return format: {{"themes": [{{"theme": "theme_name", "relevance": 0.9, "mentions": 10}}]}}
1156 | """
1157 |
1158 | response = await self.llm.ainvoke(
1159 | [
1160 | SystemMessage(
1161 | content="You are a theme extraction AI. Return only valid JSON."
1162 | ),
1163 | HumanMessage(content=prompt),
1164 | ]
1165 | )
1166 |
1167 | result = json.loads(
1168 | ContentAnalyzer._coerce_message_content(response.content)
1169 | )
1170 | return result.get("themes", [])
1171 |
1172 | except Exception as e:
1173 | logger.warning(f"Theme extraction failed: {e}")
1174 | # Fallback to simple keyword-based themes
1175 | themes = []
1176 | for item in content_items:
1177 | content = item.get("text") or item.get("content") or ""
1178 | if content:
1179 | content_lower = content.lower()
1180 | if "growth" in content_lower:
1181 | themes.append(
1182 | {"theme": "Growth", "relevance": 0.8, "mentions": 1}
1183 | )
1184 | if "earnings" in content_lower:
1185 | themes.append(
1186 | {"theme": "Earnings", "relevance": 0.7, "mentions": 1}
1187 | )
1188 | if "technology" in content_lower:
1189 | themes.append(
1190 | {"theme": "Technology", "relevance": 0.6, "mentions": 1}
1191 | )
1192 |
1193 | return themes
1194 |
1195 |
1196 | class DeepResearchAgent(PersonaAwareAgent):
1197 | """
1198 | Deep research agent using 2025 LangGraph patterns.
1199 |
1200 | Provides comprehensive financial research with web search, content analysis,
1201 | sentiment detection, and source validation.
1202 | """
1203 |
1204 | def __init__(
1205 | self,
1206 | llm: BaseChatModel,
1207 | persona: str = "moderate",
1208 | checkpointer: MemorySaver | None = None,
1209 | ttl_hours: int = 24, # Research results cached longer
1210 | exa_api_key: str | None = None,
1211 | default_depth: str = "standard",
1212 | max_sources: int | None = None,
1213 | research_depth: str | None = None,
1214 | enable_parallel_execution: bool = True,
1215 | parallel_config=None, # Type: ParallelResearchConfig | None
1216 | ):
1217 | """Initialize deep research agent."""
1218 |
1219 | # Import here to avoid circular dependency
1220 | from maverick_mcp.utils.parallel_research import (
1221 | ParallelResearchConfig,
1222 | ParallelResearchOrchestrator,
1223 | TaskDistributionEngine,
1224 | )
1225 |
1226 | # Store API key for immediate loading of search provider (pre-initialization)
1227 | self._exa_api_key = exa_api_key
1228 | self._search_providers_loaded = False
1229 | self.search_providers = []
1230 |
1231 | # Pre-initialize search providers immediately (async init will be called separately)
1232 | self._initialization_pending = True
1233 |
1234 | # Configuration
1235 | self.default_depth = research_depth or default_depth
1236 | self.max_sources = max_sources or RESEARCH_DEPTH_LEVELS.get(
1237 | self.default_depth, {}
1238 | ).get("max_sources", 10)
1239 | self.content_analyzer = ContentAnalyzer(llm)
1240 |
1241 | # Parallel execution configuration
1242 | self.enable_parallel_execution = enable_parallel_execution
1243 | self.parallel_config = parallel_config or ParallelResearchConfig(
1244 | max_concurrent_agents=settings.data_limits.max_parallel_agents,
1245 | timeout_per_agent=180, # 3 minutes per agent for thorough research
1246 | enable_fallbacks=False, # Disable fallbacks for speed
1247 | rate_limit_delay=0.5, # Reduced delay for faster execution
1248 | )
1249 | self.parallel_orchestrator = ParallelResearchOrchestrator(self.parallel_config)
1250 | self.task_distributor = TaskDistributionEngine()
1251 |
1252 | # Get research-specific tools
1253 | research_tools = self._get_research_tools()
1254 |
1255 | # Initialize base class
1256 | super().__init__(
1257 | llm=llm,
1258 | tools=research_tools,
1259 | persona=persona,
1260 | checkpointer=checkpointer or MemorySaver(),
1261 | ttl_hours=ttl_hours,
1262 | )
1263 |
1264 | # Initialize components
1265 | self.conversation_store = ConversationStore(ttl_hours=ttl_hours)
1266 |
1267 | @property
1268 | def web_search_provider(self):
1269 | """Compatibility property for tests - returns first search provider."""
1270 | return self.search_providers[0] if self.search_providers else None
1271 |
1272 | def _is_insight_relevant_for_persona(
1273 | self, insight: dict[str, Any], characteristics: dict[str, Any]
1274 | ) -> bool:
1275 | """Check if an insight is relevant for a given persona - used by tests."""
1276 | # Simple implementation for test compatibility
1277 | # In a real implementation, this would analyze the insight against persona characteristics
1278 | return True # Default permissive approach as mentioned in test comments
1279 |
1280 | async def initialize(self) -> None:
1281 | """Pre-initialize Exa search provider to eliminate lazy loading overhead during research."""
1282 | if not self._initialization_pending:
1283 | return
1284 |
1285 | try:
1286 | provider = await get_cached_search_provider(self._exa_api_key)
1287 | self.search_providers = [provider] if provider else []
1288 | self._search_providers_loaded = True
1289 | self._initialization_pending = False
1290 |
1291 | if not self.search_providers:
1292 | logger.warning(
1293 | "Exa search provider not available - research capabilities will be limited"
1294 | )
1295 | else:
1296 | logger.info("Pre-initialized Exa search provider")
1297 |
1298 | except Exception as e:
1299 | logger.error(f"Failed to pre-initialize Exa search provider: {e}")
1300 | self.search_providers = []
1301 | self._search_providers_loaded = True
1302 | self._initialization_pending = False
1303 |
1304 | logger.info(
1305 | f"DeepResearchAgent pre-initialized with {len(self.search_providers)} search providers, "
1306 | f"parallel execution: {self.enable_parallel_execution}"
1307 | )
1308 |
1309 | async def _ensure_search_providers_loaded(self) -> None:
1310 | """Ensure search providers are loaded - fallback to initialization if not pre-initialized."""
1311 | if self._search_providers_loaded:
1312 | return
1313 |
1314 | # Check if initialization was marked as needed
1315 | if hasattr(self, "_needs_initialization") and self._needs_initialization:
1316 | logger.info("Performing deferred initialization of search providers")
1317 | await self.initialize()
1318 | self._needs_initialization = False
1319 | else:
1320 | # Fallback to pre-initialization if not done during agent creation
1321 | logger.warning(
1322 | "Search providers not pre-initialized - falling back to lazy loading"
1323 | )
1324 | await self.initialize()
1325 |
1326 | def get_state_schema(self) -> type:
1327 | """Return DeepResearchState schema."""
1328 | return DeepResearchState
1329 |
1330 | def _get_research_tools(self) -> list[BaseTool]:
1331 | """Get tools specific to research capabilities."""
1332 | tools = []
1333 |
1334 | @tool
1335 | async def web_search_financial(
1336 | query: str,
1337 | num_results: int = 10,
1338 | provider: str = "auto",
1339 | strategy: str = "hybrid",
1340 | ) -> dict[str, Any]:
1341 | """
1342 | Search the web for financial information using optimized providers and strategies.
1343 |
1344 | Args:
1345 | query: Search query for financial information
1346 | num_results: Number of results to return (default: 10)
1347 | provider: Search provider to use ('auto', 'exa', 'tavily')
1348 | strategy: Search strategy ('hybrid', 'authoritative', 'comprehensive', 'auto')
1349 | """
1350 | return await self._perform_financial_search(
1351 | query, num_results, provider, strategy
1352 | )
1353 |
1354 | @tool
1355 | async def analyze_company_fundamentals(
1356 | symbol: str, depth: str = "standard"
1357 | ) -> dict[str, Any]:
1358 | """Research company fundamentals including financials, competitive position, and outlook."""
1359 | return await self._research_company_fundamentals(symbol, depth)
1360 |
1361 | @tool
1362 | async def analyze_market_sentiment(
1363 | topic: str, timeframe: str = "7d"
1364 | ) -> dict[str, Any]:
1365 | """Analyze market sentiment around a topic using news and social signals."""
1366 | return await self._analyze_market_sentiment_tool(topic, timeframe)
1367 |
1368 | @tool
1369 | async def validate_research_claims(
1370 | claims: list[str], sources: list[str]
1371 | ) -> dict[str, Any]:
1372 | """Validate research claims against multiple sources for fact-checking."""
1373 | return await self._validate_claims(claims, sources)
1374 |
1375 | tools.extend(
1376 | [
1377 | web_search_financial,
1378 | analyze_company_fundamentals,
1379 | analyze_market_sentiment,
1380 | validate_research_claims,
1381 | ]
1382 | )
1383 |
1384 | return tools
1385 |
1386 | async def _perform_web_search(
1387 | self, query: str, num_results: int, provider: str = "auto"
1388 | ) -> dict[str, Any]:
1389 | """Fallback web search across configured providers."""
1390 | await self._ensure_search_providers_loaded()
1391 |
1392 | if not self.search_providers:
1393 | return {
1394 | "error": "No search providers available",
1395 | "results": [],
1396 | "total_results": 0,
1397 | }
1398 |
1399 | aggregated_results: list[dict[str, Any]] = []
1400 | target = provider.lower()
1401 |
1402 | for provider_obj in self.search_providers:
1403 | provider_name = provider_obj.__class__.__name__.lower()
1404 | if target != "auto" and target not in provider_name:
1405 | continue
1406 |
1407 | try:
1408 | results = await provider_obj.search(query, num_results)
1409 | aggregated_results.extend(results)
1410 | if target != "auto":
1411 | break
1412 | except Exception as error: # pragma: no cover - fallback logging
1413 | logger.warning(
1414 | "Fallback web search failed for provider %s: %s",
1415 | provider_obj.__class__.__name__,
1416 | error,
1417 | )
1418 |
1419 | if not aggregated_results:
1420 | return {
1421 | "error": "Search failed",
1422 | "results": [],
1423 | "total_results": 0,
1424 | }
1425 |
1426 | truncated_results = aggregated_results[:num_results]
1427 | return {
1428 | "results": truncated_results,
1429 | "total_results": len(truncated_results),
1430 | "search_duration": 0.0,
1431 | "search_strategy": "fallback",
1432 | }
1433 |
1434 | async def _research_company_fundamentals(
1435 | self, symbol: str, depth: str = "standard"
1436 | ) -> dict[str, Any]:
1437 | """Convenience wrapper for company fundamental research used by tools."""
1438 |
1439 | session_id = f"fundamentals-{symbol}-{uuid4().hex}"
1440 | focus_areas = [
1441 | "fundamentals",
1442 | "financials",
1443 | "valuation",
1444 | "risk_management",
1445 | "growth_drivers",
1446 | ]
1447 |
1448 | return await self.research_comprehensive(
1449 | topic=f"{symbol} company fundamentals analysis",
1450 | session_id=session_id,
1451 | depth=depth,
1452 | focus_areas=focus_areas,
1453 | timeframe="180d",
1454 | use_parallel_execution=False,
1455 | )
1456 |
1457 | async def _analyze_market_sentiment_tool(
1458 | self, topic: str, timeframe: str = "7d"
1459 | ) -> dict[str, Any]:
1460 | """Wrapper used by the sentiment analysis tool."""
1461 |
1462 | session_id = f"sentiment-{uuid4().hex}"
1463 | return await self.analyze_market_sentiment(
1464 | topic=topic,
1465 | session_id=session_id,
1466 | timeframe=timeframe,
1467 | use_parallel_execution=False,
1468 | )
1469 |
1470 | async def _validate_claims(
1471 | self, claims: list[str], sources: list[str]
1472 | ) -> dict[str, Any]:
1473 | """Lightweight claim validation used for tool compatibility."""
1474 |
1475 | validation_results: list[dict[str, Any]] = []
1476 |
1477 | for claim in claims:
1478 | source_checks = []
1479 | for source in sources:
1480 | source_checks.append(
1481 | {
1482 | "source": source,
1483 | "status": "not_verified",
1484 | "confidence": 0.0,
1485 | "notes": "Automatic validation not available in fallback mode",
1486 | }
1487 | )
1488 |
1489 | validation_results.append(
1490 | {
1491 | "claim": claim,
1492 | "validated": False,
1493 | "confidence": 0.0,
1494 | "evidence": [],
1495 | "source_checks": source_checks,
1496 | }
1497 | )
1498 |
1499 | return {
1500 | "results": validation_results,
1501 | "summary": "Claim validation is currently using fallback heuristics.",
1502 | }
1503 |
1504 | async def _perform_financial_search(
1505 | self, query: str, num_results: int, provider: str, strategy: str
1506 | ) -> dict[str, Any]:
1507 | """
1508 | Perform optimized financial search with enhanced strategies.
1509 |
1510 | Args:
1511 | query: Search query
1512 | num_results: Number of results
1513 | provider: Search provider preference
1514 | strategy: Search strategy
1515 |
1516 | Returns:
1517 | Dictionary with search results and metadata
1518 | """
1519 | if not self.search_providers:
1520 | return {
1521 | "error": "No search providers available",
1522 | "results": [],
1523 | "total_results": 0,
1524 | }
1525 |
1526 | start_time = datetime.now()
1527 | all_results = []
1528 |
1529 | # Use Exa provider with financial optimization if available
1530 | exa_provider = None
1531 | for p in self.search_providers:
1532 | if isinstance(p, ExaSearchProvider):
1533 | exa_provider = p
1534 | break
1535 |
1536 | if exa_provider and (provider == "auto" or provider == "exa"):
1537 | try:
1538 | # Use the enhanced financial search method
1539 | results = await exa_provider.search_financial(
1540 | query, num_results, strategy=strategy
1541 | )
1542 |
1543 | # Add search metadata
1544 | for result in results:
1545 | result.update(
1546 | {
1547 | "search_strategy": strategy,
1548 | "search_timestamp": start_time.isoformat(),
1549 | "enhanced_query": query,
1550 | }
1551 | )
1552 |
1553 | all_results.extend(results)
1554 |
1555 | logger.info(
1556 | f"Financial search completed: {len(results)} results "
1557 | f"using strategy '{strategy}' in {(datetime.now() - start_time).total_seconds():.2f}s"
1558 | )
1559 |
1560 | except Exception as e:
1561 | logger.error(f"Enhanced financial search failed: {e}")
1562 | # Fallback to regular search if available
1563 | if hasattr(self, "_perform_web_search"):
1564 | return await self._perform_web_search(query, num_results, provider)
1565 | else:
1566 | return {
1567 | "error": f"Financial search failed: {str(e)}",
1568 | "results": [],
1569 | "total_results": 0,
1570 | }
1571 | else:
1572 | # Use regular search providers
1573 | try:
1574 | for provider_obj in self.search_providers:
1575 | if (
1576 | provider == "auto"
1577 | or provider.lower() in str(type(provider_obj)).lower()
1578 | ):
1579 | results = await provider_obj.search(query, num_results)
1580 | all_results.extend(results)
1581 | break
1582 | except Exception as e:
1583 | logger.error(f"Fallback search failed: {e}")
1584 | return {
1585 | "error": f"Search failed: {str(e)}",
1586 | "results": [],
1587 | "total_results": 0,
1588 | }
1589 |
1590 | # Sort by financial relevance and authority
1591 | all_results.sort(
1592 | key=lambda x: (
1593 | x.get("financial_relevance", 0),
1594 | x.get("is_authoritative", False),
1595 | x.get("score", 0),
1596 | ),
1597 | reverse=True,
1598 | )
1599 |
1600 | return {
1601 | "results": all_results[:num_results],
1602 | "total_results": len(all_results),
1603 | "search_strategy": strategy,
1604 | "search_duration": (datetime.now() - start_time).total_seconds(),
1605 | "enhanced_search": True,
1606 | }
1607 |
1608 | def _build_graph(self):
1609 | """Build research workflow graph with multi-step research process."""
1610 | workflow = StateGraph(DeepResearchState)
1611 |
1612 | # Core research workflow nodes
1613 | workflow.add_node("plan_research", self._plan_research)
1614 | workflow.add_node("execute_searches", self._execute_searches)
1615 | workflow.add_node("analyze_content", self._analyze_content)
1616 | workflow.add_node("validate_sources", self._validate_sources)
1617 | workflow.add_node("synthesize_findings", self._synthesize_findings)
1618 | workflow.add_node("generate_citations", self._generate_citations)
1619 |
1620 | # Specialized research nodes
1621 | workflow.add_node("sentiment_analysis", self._sentiment_analysis)
1622 | workflow.add_node("fundamental_analysis", self._fundamental_analysis)
1623 | workflow.add_node("competitive_analysis", self._competitive_analysis)
1624 |
1625 | # Quality control nodes
1626 | workflow.add_node("fact_validation", self._fact_validation)
1627 | workflow.add_node("source_credibility", self._source_credibility)
1628 |
1629 | # Define workflow edges
1630 | workflow.add_edge(START, "plan_research")
1631 | workflow.add_edge("plan_research", "execute_searches")
1632 | workflow.add_edge("execute_searches", "analyze_content")
1633 |
1634 | # Conditional routing based on research type
1635 | workflow.add_conditional_edges(
1636 | "analyze_content",
1637 | self._route_specialized_analysis,
1638 | {
1639 | "sentiment": "sentiment_analysis",
1640 | "fundamental": "fundamental_analysis",
1641 | "competitive": "competitive_analysis",
1642 | "validation": "validate_sources",
1643 | "synthesis": "synthesize_findings",
1644 | },
1645 | )
1646 |
1647 | # Specialized analysis flows
1648 | workflow.add_edge("sentiment_analysis", "validate_sources")
1649 | workflow.add_edge("fundamental_analysis", "validate_sources")
1650 | workflow.add_edge("competitive_analysis", "validate_sources")
1651 |
1652 | # Quality control flow
1653 | workflow.add_edge("validate_sources", "fact_validation")
1654 | workflow.add_edge("fact_validation", "source_credibility")
1655 | workflow.add_edge("source_credibility", "synthesize_findings")
1656 |
1657 | # Final steps
1658 | workflow.add_edge("synthesize_findings", "generate_citations")
1659 | workflow.add_edge("generate_citations", END)
1660 |
1661 | return workflow.compile(checkpointer=self.checkpointer)
1662 |
1663 | @log_method_call(component="DeepResearchAgent", include_timing=True)
1664 | async def research_comprehensive(
1665 | self,
1666 | topic: str,
1667 | session_id: str,
1668 | depth: str | None = None,
1669 | focus_areas: list[str] | None = None,
1670 | timeframe: str = "30d",
1671 | timeout_budget: float | None = None, # Total timeout budget in seconds
1672 | **kwargs,
1673 | ) -> dict[str, Any]:
1674 | """
1675 | Comprehensive research on a financial topic.
1676 |
1677 | Args:
1678 | topic: Research topic or company/symbol
1679 | session_id: Session identifier
1680 | depth: Research depth (basic/standard/comprehensive/exhaustive)
1681 | focus_areas: Specific areas to focus on
1682 | timeframe: Time range for research
1683 | timeout_budget: Total timeout budget in seconds (enables budget allocation)
1684 | **kwargs: Additional parameters
1685 |
1686 | Returns:
1687 | Comprehensive research results with analysis and citations
1688 | """
1689 | # Ensure search providers are loaded (cached for performance)
1690 | await self._ensure_search_providers_loaded()
1691 |
1692 | # Check if search providers are available
1693 | if not self.search_providers:
1694 | return {
1695 | "error": "Research functionality unavailable - no search providers configured",
1696 | "details": "Please configure EXA_API_KEY environment variable to enable research capabilities",
1697 | "topic": topic,
1698 | "available_functionality": "Limited to pre-existing data and basic analysis",
1699 | }
1700 |
1701 | start_time = datetime.now()
1702 | depth = depth or self.default_depth
1703 |
1704 | # Calculate timeout budget allocation for generous research timeouts
1705 | timeout_budgets = {}
1706 | if timeout_budget and timeout_budget > 0:
1707 | timeout_budgets = {
1708 | "search_budget": timeout_budget
1709 | * 0.50, # 50% for search operations (generous allocation)
1710 | "analysis_budget": timeout_budget * 0.30, # 30% for content analysis
1711 | "synthesis_budget": timeout_budget * 0.20, # 20% for result synthesis
1712 | "total_budget": timeout_budget,
1713 | "allocation_strategy": "comprehensive_research",
1714 | }
1715 | logger.info(
1716 | f"TIMEOUT_BUDGET_ALLOCATION: total={timeout_budget}s → "
1717 | f"search={timeout_budgets['search_budget']:.1f}s, "
1718 | f"analysis={timeout_budgets['analysis_budget']:.1f}s, "
1719 | f"synthesis={timeout_budgets['synthesis_budget']:.1f}s"
1720 | )
1721 |
1722 | # Initialize research state
1723 | initial_state = {
1724 | "messages": [HumanMessage(content=f"Research: {topic}")],
1725 | "persona": self.persona.name,
1726 | "session_id": session_id,
1727 | "timestamp": datetime.now(),
1728 | "research_topic": topic,
1729 | "research_depth": depth,
1730 | "focus_areas": focus_areas
1731 | or PERSONA_RESEARCH_FOCUS[self.persona.name.lower()]["keywords"],
1732 | "timeframe": timeframe,
1733 | "search_queries": [],
1734 | "search_results": [],
1735 | "analyzed_content": [],
1736 | "validated_sources": [],
1737 | "research_findings": [],
1738 | "sentiment_analysis": {},
1739 | "source_credibility_scores": {},
1740 | "citations": [],
1741 | "research_status": "planning",
1742 | "research_confidence": 0.0,
1743 | "source_diversity_score": 0.0,
1744 | "fact_validation_results": [],
1745 | "execution_time_ms": 0.0,
1746 | "api_calls_made": 0,
1747 | "cache_hits": 0,
1748 | "cache_misses": 0,
1749 | # Timeout budget allocation for intelligent time management
1750 | "timeout_budgets": timeout_budgets,
1751 | # Legacy fields
1752 | "token_count": 0,
1753 | "error": None,
1754 | "analyzed_stocks": {},
1755 | "key_price_levels": {},
1756 | "last_analysis_time": {},
1757 | "conversation_context": {},
1758 | }
1759 |
1760 | # Add additional parameters
1761 | initial_state.update(kwargs)
1762 |
1763 | # Set up orchestration logging
1764 | orchestration_logger = get_orchestration_logger("DeepResearchAgent")
1765 | orchestration_logger.set_request_context(
1766 | session_id=session_id,
1767 | research_topic=topic[:50], # Truncate for logging
1768 | research_depth=depth,
1769 | )
1770 |
1771 | # Check if parallel execution is enabled and requested
1772 | use_parallel = kwargs.get(
1773 | "use_parallel_execution", self.enable_parallel_execution
1774 | )
1775 |
1776 | orchestration_logger.info(
1777 | "🔍 RESEARCH_START",
1778 | execution_mode="parallel" if use_parallel else "sequential",
1779 | focus_areas=focus_areas[:3] if focus_areas else None,
1780 | timeframe=timeframe,
1781 | )
1782 |
1783 | if use_parallel:
1784 | orchestration_logger.info("🚀 PARALLEL_EXECUTION_SELECTED")
1785 | try:
1786 | result = await self._execute_parallel_research(
1787 | topic=topic,
1788 | session_id=session_id,
1789 | depth=depth,
1790 | focus_areas=focus_areas,
1791 | timeframe=timeframe,
1792 | initial_state=initial_state,
1793 | start_time=start_time,
1794 | **kwargs,
1795 | )
1796 | orchestration_logger.info("✅ PARALLEL_EXECUTION_SUCCESS")
1797 | return result
1798 | except Exception as e:
1799 | orchestration_logger.warning(
1800 | "⚠️ PARALLEL_FALLBACK_TRIGGERED",
1801 | error=str(e),
1802 | fallback_mode="sequential",
1803 | )
1804 | # Fall through to sequential execution
1805 |
1806 | # Execute research workflow (sequential)
1807 | orchestration_logger.info("🔄 SEQUENTIAL_EXECUTION_START")
1808 | try:
1809 | result = await self.graph.ainvoke(
1810 | initial_state,
1811 | config={
1812 | "configurable": {
1813 | "thread_id": session_id,
1814 | "checkpoint_ns": "deep_research",
1815 | }
1816 | },
1817 | )
1818 |
1819 | # Calculate execution time
1820 | execution_time = (datetime.now() - start_time).total_seconds() * 1000
1821 | result["execution_time_ms"] = execution_time
1822 |
1823 | return self._format_research_response(result)
1824 |
1825 | except Exception as e:
1826 | logger.error(f"Error in deep research: {e}")
1827 | return {
1828 | "status": "error",
1829 | "error": str(e),
1830 | "execution_time_ms": (datetime.now() - start_time).total_seconds()
1831 | * 1000,
1832 | "agent_type": "deep_research",
1833 | }
1834 |
1835 | # Workflow node implementations
1836 |
1837 | async def _plan_research(self, state: DeepResearchState) -> Command:
1838 | """Plan research strategy based on topic and persona."""
1839 | topic = state["research_topic"]
1840 | depth_config = RESEARCH_DEPTH_LEVELS[state["research_depth"]]
1841 | persona_focus = PERSONA_RESEARCH_FOCUS[self.persona.name.lower()]
1842 |
1843 | # Generate search queries based on topic and persona
1844 | search_queries = await self._generate_search_queries(
1845 | topic, persona_focus, depth_config
1846 | )
1847 |
1848 | return Command(
1849 | goto="execute_searches",
1850 | update={"search_queries": search_queries, "research_status": "searching"},
1851 | )
1852 |
1853 | async def _safe_search(
1854 | self,
1855 | provider: WebSearchProvider,
1856 | query: str,
1857 | num_results: int = 5,
1858 | timeout_budget: float | None = None,
1859 | ) -> list[dict[str, Any]]:
1860 | """Safely execute search with a provider, handling exceptions gracefully."""
1861 | try:
1862 | return await provider.search(
1863 | query, num_results=num_results, timeout_budget=timeout_budget
1864 | )
1865 | except Exception as e:
1866 | logger.warning(
1867 | f"Search failed for '{query}' with provider {type(provider).__name__}: {e}"
1868 | )
1869 | return [] # Return empty list on failure
1870 |
1871 | async def _execute_searches(self, state: DeepResearchState) -> Command:
1872 | """Execute web searches using available providers with timeout budget awareness."""
1873 | search_queries = state["search_queries"]
1874 | depth_config = RESEARCH_DEPTH_LEVELS[state["research_depth"]]
1875 |
1876 | # Calculate timeout budget per search operation
1877 | timeout_budgets = state.get("timeout_budgets", {})
1878 | search_budget = timeout_budgets.get("search_budget")
1879 |
1880 | if search_budget:
1881 | # Divide search budget across queries and providers
1882 | total_search_operations = len(
1883 | search_queries[: depth_config["max_searches"]]
1884 | ) * len(self.search_providers)
1885 | timeout_per_search = (
1886 | search_budget / max(total_search_operations, 1)
1887 | if total_search_operations > 0
1888 | else search_budget
1889 | )
1890 | logger.info(
1891 | f"SEARCH_BUDGET_ALLOCATION: {search_budget:.1f}s total → "
1892 | f"{timeout_per_search:.1f}s per search ({total_search_operations} operations)"
1893 | )
1894 | else:
1895 | timeout_per_search = None
1896 |
1897 | all_results = []
1898 |
1899 | # Create all search tasks for parallel execution with budget-aware timeouts
1900 | search_tasks = []
1901 | for query in search_queries[: depth_config["max_searches"]]:
1902 | for provider in self.search_providers:
1903 | # Create async task for each provider/query combination with timeout budget
1904 | search_tasks.append(
1905 | self._safe_search(
1906 | provider,
1907 | query,
1908 | num_results=5,
1909 | timeout_budget=timeout_per_search,
1910 | )
1911 | )
1912 |
1913 | # Execute all searches in parallel using asyncio.gather()
1914 | if search_tasks:
1915 | parallel_results = await asyncio.gather(
1916 | *search_tasks, return_exceptions=True
1917 | )
1918 |
1919 | # Process results and filter out exceptions
1920 | for result in parallel_results:
1921 | if isinstance(result, Exception):
1922 | # Log the exception but continue with other results
1923 | logger.warning(f"Search task failed: {result}")
1924 | elif isinstance(result, list):
1925 | all_results.extend(result)
1926 | elif result is not None:
1927 | all_results.append(result)
1928 |
1929 | # Deduplicate and limit results
1930 | unique_results = []
1931 | seen_urls = set()
1932 | for result in all_results:
1933 | if (
1934 | result["url"] not in seen_urls
1935 | and len(unique_results) < depth_config["max_sources"]
1936 | ):
1937 | unique_results.append(result)
1938 | seen_urls.add(result["url"])
1939 |
1940 | logger.info(
1941 | f"Search completed: {len(unique_results)} unique results from {len(all_results)} total"
1942 | )
1943 |
1944 | return Command(
1945 | goto="analyze_content",
1946 | update={"search_results": unique_results, "research_status": "analyzing"},
1947 | )
1948 |
1949 | async def _analyze_content(self, state: DeepResearchState) -> Command:
1950 | """Analyze search results using AI content analysis."""
1951 | search_results = state["search_results"]
1952 | analyzed_content = []
1953 |
1954 | # Analyze each piece of content
1955 | for result in search_results:
1956 | if result.get("content"):
1957 | analysis = await self.content_analyzer.analyze_content(
1958 | content=result["content"],
1959 | persona=self.persona.name.lower(),
1960 | analysis_focus=state["research_depth"],
1961 | )
1962 |
1963 | analyzed_content.append({**result, "analysis": analysis})
1964 |
1965 | return Command(
1966 | goto="validate_sources",
1967 | update={
1968 | "analyzed_content": analyzed_content,
1969 | "research_status": "validating",
1970 | },
1971 | )
1972 |
1973 | def _route_specialized_analysis(self, state: DeepResearchState) -> str:
1974 | """Route to specialized analysis based on research focus."""
1975 | focus_areas = state.get("focus_areas", [])
1976 |
1977 | if any(word in focus_areas for word in ["sentiment", "news", "social"]):
1978 | return "sentiment"
1979 | elif any(
1980 | word in focus_areas for word in ["fundamental", "financial", "earnings"]
1981 | ):
1982 | return "fundamental"
1983 | elif any(word in focus_areas for word in ["competitive", "market", "industry"]):
1984 | return "competitive"
1985 | else:
1986 | return "validation"
1987 |
1988 | async def _validate_sources(self, state: DeepResearchState) -> Command:
1989 | """Validate source credibility and filter results."""
1990 | analyzed_content = state["analyzed_content"]
1991 | validated_sources = []
1992 | credibility_scores = {}
1993 |
1994 | for content in analyzed_content:
1995 | # Calculate credibility score based on multiple factors
1996 | credibility_score = self._calculate_source_credibility(content)
1997 | credibility_scores[content["url"]] = credibility_score
1998 |
1999 | # Only include sources above credibility threshold
2000 | if credibility_score >= 0.6: # Configurable threshold
2001 | validated_sources.append(content)
2002 |
2003 | return Command(
2004 | goto="synthesize_findings",
2005 | update={
2006 | "validated_sources": validated_sources,
2007 | "source_credibility_scores": credibility_scores,
2008 | "research_status": "synthesizing",
2009 | },
2010 | )
2011 |
2012 | async def _synthesize_findings(self, state: DeepResearchState) -> Command:
2013 | """Synthesize research findings into coherent insights."""
2014 | validated_sources = state["validated_sources"]
2015 |
2016 | # Generate synthesis using LLM
2017 | synthesis_prompt = self._build_synthesis_prompt(validated_sources, state)
2018 |
2019 | synthesis_response = await self.llm.ainvoke(
2020 | [
2021 | SystemMessage(content="You are a financial research synthesizer."),
2022 | HumanMessage(content=synthesis_prompt),
2023 | ]
2024 | )
2025 |
2026 | raw_synthesis = ContentAnalyzer._coerce_message_content(
2027 | synthesis_response.content
2028 | )
2029 |
2030 | research_findings = {
2031 | "synthesis": raw_synthesis,
2032 | "key_insights": self._extract_key_insights(validated_sources),
2033 | "overall_sentiment": self._calculate_overall_sentiment(validated_sources),
2034 | "risk_assessment": self._assess_risks(validated_sources),
2035 | "investment_implications": self._derive_investment_implications(
2036 | validated_sources
2037 | ),
2038 | "confidence_score": self._calculate_research_confidence(validated_sources),
2039 | }
2040 |
2041 | return Command(
2042 | goto="generate_citations",
2043 | update={
2044 | "research_findings": research_findings,
2045 | "research_confidence": research_findings["confidence_score"],
2046 | "research_status": "completing",
2047 | },
2048 | )
2049 |
2050 | async def _generate_citations(self, state: DeepResearchState) -> Command:
2051 | """Generate proper citations for all sources."""
2052 | validated_sources = state["validated_sources"]
2053 |
2054 | citations = []
2055 | for i, source in enumerate(validated_sources, 1):
2056 | citation = {
2057 | "id": i,
2058 | "title": source.get("title", "Untitled"),
2059 | "url": source["url"],
2060 | "published_date": source.get("published_date"),
2061 | "author": source.get("author"),
2062 | "credibility_score": state["source_credibility_scores"].get(
2063 | source["url"], 0.5
2064 | ),
2065 | "relevance_score": source.get("analysis", {}).get(
2066 | "relevance_score", 0.5
2067 | ),
2068 | }
2069 | citations.append(citation)
2070 |
2071 | return Command(
2072 | goto="__end__",
2073 | update={"citations": citations, "research_status": "completed"},
2074 | )
2075 |
2076 | # Helper methods
2077 |
2078 | async def _generate_search_queries(
2079 | self, topic: str, persona_focus: dict[str, Any], depth_config: dict[str, Any]
2080 | ) -> list[str]:
2081 | """Generate search queries optimized for the research topic and persona."""
2082 |
2083 | base_queries = [
2084 | f"{topic} financial analysis",
2085 | f"{topic} investment research",
2086 | f"{topic} market outlook",
2087 | ]
2088 |
2089 | # Add persona-specific queries
2090 | persona_queries = [
2091 | f"{topic} {keyword}" for keyword in persona_focus["keywords"][:3]
2092 | ]
2093 |
2094 | # Add source-specific queries
2095 | source_queries = [
2096 | f"{topic} {source}" for source in persona_focus["sources"][:2]
2097 | ]
2098 |
2099 | all_queries = base_queries + persona_queries + source_queries
2100 | return all_queries[: depth_config["max_searches"]]
2101 |
2102 | def _calculate_source_credibility(self, content: dict[str, Any]) -> float:
2103 | """Calculate credibility score for a source."""
2104 | score = 0.5 # Base score
2105 |
2106 | # Domain credibility
2107 | url = content.get("url", "")
2108 | if any(domain in url for domain in [".gov", ".edu", ".org"]):
2109 | score += 0.2
2110 | elif any(
2111 | domain in url
2112 | for domain in [
2113 | "sec.gov",
2114 | "investopedia.com",
2115 | "bloomberg.com",
2116 | "reuters.com",
2117 | ]
2118 | ):
2119 | score += 0.3
2120 |
2121 | # Publication date recency
2122 | pub_date = content.get("published_date")
2123 | if pub_date:
2124 | try:
2125 | date_obj = datetime.fromisoformat(pub_date.replace("Z", "+00:00"))
2126 | days_old = (datetime.now() - date_obj).days
2127 | if days_old < 30:
2128 | score += 0.1
2129 | elif days_old < 90:
2130 | score += 0.05
2131 | except (ValueError, TypeError, AttributeError):
2132 | pass
2133 |
2134 | # Content analysis credibility
2135 | if "analysis" in content:
2136 | analysis_cred = content["analysis"].get("credibility_score", 0.5)
2137 | score = (score + analysis_cred) / 2
2138 |
2139 | return min(score, 1.0)
2140 |
2141 | def _build_synthesis_prompt(
2142 | self, sources: list[dict[str, Any]], state: DeepResearchState
2143 | ) -> str:
2144 | """Build synthesis prompt for final research output."""
2145 | topic = state["research_topic"]
2146 | persona = self.persona.name
2147 |
2148 | prompt = f"""
2149 | Synthesize comprehensive research findings on '{topic}' for a {persona} investor.
2150 |
2151 | Research Sources ({len(sources)} validated sources):
2152 | """
2153 |
2154 | for i, source in enumerate(sources, 1):
2155 | analysis = source.get("analysis", {})
2156 | prompt += f"\n{i}. {source.get('title', 'Unknown Title')}"
2157 | prompt += f" - Insights: {', '.join(analysis.get('insights', [])[:2])}"
2158 | prompt += f" - Sentiment: {analysis.get('sentiment', {}).get('direction', 'neutral')}"
2159 | prompt += f" - Credibility: {state['source_credibility_scores'].get(source['url'], 0.5):.2f}"
2160 |
2161 | prompt += f"""
2162 |
2163 | Please provide a comprehensive synthesis that includes:
2164 | 1. Executive Summary (2-3 sentences)
2165 | 2. Key Findings (5-7 bullet points)
2166 | 3. Investment Implications for {persona} investors
2167 | 4. Risk Considerations
2168 | 5. Recommended Actions
2169 | 6. Confidence Level and reasoning
2170 |
2171 | Tailor the analysis specifically for {persona} investment characteristics and risk tolerance.
2172 | """
2173 |
2174 | return prompt
2175 |
2176 | def _extract_key_insights(self, sources: list[dict[str, Any]]) -> list[str]:
2177 | """Extract and consolidate key insights from all sources."""
2178 | all_insights = []
2179 | for source in sources:
2180 | analysis = source.get("analysis", {})
2181 | insights = analysis.get("insights", [])
2182 | all_insights.extend(insights)
2183 |
2184 | # Simple deduplication (could be enhanced with semantic similarity)
2185 | unique_insights = list(dict.fromkeys(all_insights))
2186 | return unique_insights[:10] # Return top 10 insights
2187 |
2188 | def _calculate_overall_sentiment(
2189 | self, sources: list[dict[str, Any]]
2190 | ) -> dict[str, Any]:
2191 | """Calculate overall sentiment from all sources."""
2192 | sentiments = []
2193 | weights = []
2194 |
2195 | for source in sources:
2196 | analysis = source.get("analysis", {})
2197 | sentiment = analysis.get("sentiment", {})
2198 |
2199 | # Convert sentiment to numeric value
2200 | direction = sentiment.get("direction", "neutral")
2201 | if direction == "bullish":
2202 | sentiment_value = 1
2203 | elif direction == "bearish":
2204 | sentiment_value = -1
2205 | else:
2206 | sentiment_value = 0
2207 |
2208 | confidence = sentiment.get("confidence", 0.5)
2209 | credibility = source.get("credibility_score", 0.5)
2210 |
2211 | sentiments.append(sentiment_value)
2212 | weights.append(confidence * credibility)
2213 |
2214 | if not sentiments:
2215 | return {"direction": "neutral", "confidence": 0.5, "consensus": 0.5}
2216 |
2217 | # Weighted average sentiment
2218 | weighted_sentiment = sum(
2219 | s * w for s, w in zip(sentiments, weights, strict=False)
2220 | ) / sum(weights)
2221 |
2222 | # Convert back to direction
2223 | if weighted_sentiment > 0.2:
2224 | overall_direction = "bullish"
2225 | elif weighted_sentiment < -0.2:
2226 | overall_direction = "bearish"
2227 | else:
2228 | overall_direction = "neutral"
2229 |
2230 | # Calculate consensus (how much sources agree)
2231 | sentiment_variance = sum(weights) / len(sentiments) if sentiments else 0
2232 | consensus = 1 - sentiment_variance if sentiment_variance < 1 else 0
2233 |
2234 | return {
2235 | "direction": overall_direction,
2236 | "confidence": abs(weighted_sentiment),
2237 | "consensus": consensus,
2238 | "source_count": len(sentiments),
2239 | }
2240 |
2241 | def _assess_risks(self, sources: list[dict[str, Any]]) -> list[str]:
2242 | """Consolidate risk assessments from all sources."""
2243 | all_risks = []
2244 | for source in sources:
2245 | analysis = source.get("analysis", {})
2246 | risks = analysis.get("risk_factors", [])
2247 | all_risks.extend(risks)
2248 |
2249 | # Deduplicate and return top risks
2250 | unique_risks = list(dict.fromkeys(all_risks))
2251 | return unique_risks[:8]
2252 |
2253 | def _derive_investment_implications(
2254 | self, sources: list[dict[str, Any]]
2255 | ) -> dict[str, Any]:
2256 | """Derive investment implications based on research findings."""
2257 | opportunities = []
2258 | threats = []
2259 |
2260 | for source in sources:
2261 | analysis = source.get("analysis", {})
2262 | opps = analysis.get("opportunities", [])
2263 | risks = analysis.get("risk_factors", [])
2264 |
2265 | opportunities.extend(opps)
2266 | threats.extend(risks)
2267 |
2268 | return {
2269 | "opportunities": list(dict.fromkeys(opportunities))[:5],
2270 | "threats": list(dict.fromkeys(threats))[:5],
2271 | "recommended_action": self._recommend_action(sources),
2272 | "time_horizon": PERSONA_RESEARCH_FOCUS[self.persona.name.lower()][
2273 | "time_horizon"
2274 | ],
2275 | }
2276 |
2277 | def _recommend_action(self, sources: list[dict[str, Any]]) -> str:
2278 | """Recommend investment action based on research findings."""
2279 | overall_sentiment = self._calculate_overall_sentiment(sources)
2280 |
2281 | if (
2282 | overall_sentiment["direction"] == "bullish"
2283 | and overall_sentiment["confidence"] > 0.7
2284 | ):
2285 | if self.persona.name.lower() == "conservative":
2286 | return "Consider gradual position building with proper risk management"
2287 | else:
2288 | return "Consider initiating position with appropriate position sizing"
2289 | elif (
2290 | overall_sentiment["direction"] == "bearish"
2291 | and overall_sentiment["confidence"] > 0.7
2292 | ):
2293 | return "Exercise caution - consider waiting for better entry or avoiding"
2294 | else:
2295 | return "Monitor closely - mixed signals suggest waiting for clarity"
2296 |
2297 | def _calculate_research_confidence(self, sources: list[dict[str, Any]]) -> float:
2298 | """Calculate overall confidence in research findings."""
2299 | if not sources:
2300 | return 0.0
2301 |
2302 | # Factors that increase confidence
2303 | source_count_factor = min(
2304 | len(sources) / 10, 1.0
2305 | ) # More sources = higher confidence
2306 |
2307 | avg_credibility = sum(
2308 | source.get("credibility_score", 0.5) for source in sources
2309 | ) / len(sources)
2310 |
2311 | avg_relevance = sum(
2312 | source.get("analysis", {}).get("relevance_score", 0.5) for source in sources
2313 | ) / len(sources)
2314 |
2315 | # Diversity of sources (different domains)
2316 | unique_domains = len(
2317 | {source["url"].split("/")[2] for source in sources if "url" in source}
2318 | )
2319 | diversity_factor = min(unique_domains / 5, 1.0)
2320 |
2321 | # Combine factors
2322 | confidence = (
2323 | source_count_factor + avg_credibility + avg_relevance + diversity_factor
2324 | ) / 4
2325 |
2326 | return round(confidence, 2)
2327 |
2328 | def _format_research_response(self, result: dict[str, Any]) -> dict[str, Any]:
2329 | """Format research response for consistent output."""
2330 | return {
2331 | "status": "success",
2332 | "agent_type": "deep_research",
2333 | "persona": result.get("persona"),
2334 | "research_topic": result.get("research_topic"),
2335 | "research_depth": result.get("research_depth"),
2336 | "findings": result.get("research_findings", {}),
2337 | "sources_analyzed": len(result.get("validated_sources", [])),
2338 | "confidence_score": result.get("research_confidence", 0.0),
2339 | "citations": result.get("citations", []),
2340 | "execution_time_ms": result.get("execution_time_ms", 0.0),
2341 | "search_queries_used": result.get("search_queries", []),
2342 | "source_diversity": result.get("source_diversity_score", 0.0),
2343 | }
2344 |
2345 | # Specialized research analysis methods
2346 | async def _sentiment_analysis(self, state: DeepResearchState) -> Command:
2347 | """Perform specialized sentiment analysis."""
2348 | logger.info("Performing sentiment analysis")
2349 |
2350 | # For now, route to content analysis with sentiment focus
2351 | original_focus = state.get("focus_areas", [])
2352 | state["focus_areas"] = ["market_sentiment", "sentiment", "mood"]
2353 | result = await self._analyze_content(state)
2354 | state["focus_areas"] = original_focus # Restore original focus
2355 | return result
2356 |
2357 | async def _fundamental_analysis(self, state: DeepResearchState) -> Command:
2358 | """Perform specialized fundamental analysis."""
2359 | logger.info("Performing fundamental analysis")
2360 |
2361 | # For now, route to content analysis with fundamental focus
2362 | original_focus = state.get("focus_areas", [])
2363 | state["focus_areas"] = ["fundamentals", "financials", "valuation"]
2364 | result = await self._analyze_content(state)
2365 | state["focus_areas"] = original_focus # Restore original focus
2366 | return result
2367 |
2368 | async def _competitive_analysis(self, state: DeepResearchState) -> Command:
2369 | """Perform specialized competitive analysis."""
2370 | logger.info("Performing competitive analysis")
2371 |
2372 | # For now, route to content analysis with competitive focus
2373 | original_focus = state.get("focus_areas", [])
2374 | state["focus_areas"] = ["competitive_landscape", "market_share", "competitors"]
2375 | result = await self._analyze_content(state)
2376 | state["focus_areas"] = original_focus # Restore original focus
2377 | return result
2378 |
2379 | async def _fact_validation(self, state: DeepResearchState) -> Command:
2380 | """Perform fact validation on research findings."""
2381 | logger.info("Performing fact validation")
2382 |
2383 | # For now, route to source validation
2384 | return await self._validate_sources(state)
2385 |
2386 | async def _source_credibility(self, state: DeepResearchState) -> Command:
2387 | """Assess source credibility and reliability."""
2388 | logger.info("Assessing source credibility")
2389 |
2390 | # For now, route to source validation
2391 | return await self._validate_sources(state)
2392 |
2393 | async def research_company_comprehensive(
2394 | self,
2395 | symbol: str,
2396 | session_id: str,
2397 | include_competitive_analysis: bool = False,
2398 | **kwargs,
2399 | ) -> dict[str, Any]:
2400 | """
2401 | Comprehensive company research.
2402 |
2403 | Args:
2404 | symbol: Stock symbol to research
2405 | session_id: Session identifier
2406 | include_competitive_analysis: Whether to include competitive analysis
2407 | **kwargs: Additional parameters
2408 |
2409 | Returns:
2410 | Comprehensive company research results
2411 | """
2412 | topic = f"{symbol} company financial analysis and outlook"
2413 | if include_competitive_analysis:
2414 | kwargs["focus_areas"] = kwargs.get("focus_areas", []) + [
2415 | "competitive_analysis",
2416 | "market_position",
2417 | ]
2418 |
2419 | return await self.research_comprehensive(
2420 | topic=topic, session_id=session_id, **kwargs
2421 | )
2422 |
2423 | async def research_topic(
2424 | self,
2425 | query: str,
2426 | session_id: str,
2427 | focus_areas: list[str] | None = None,
2428 | timeframe: str = "30d",
2429 | **kwargs,
2430 | ) -> dict[str, Any]:
2431 | """
2432 | General topic research.
2433 |
2434 | Args:
2435 | query: Research query or topic
2436 | session_id: Session identifier
2437 | focus_areas: Specific areas to focus on
2438 | timeframe: Time range for research
2439 | **kwargs: Additional parameters
2440 |
2441 | Returns:
2442 | Research results for the given topic
2443 | """
2444 | return await self.research_comprehensive(
2445 | topic=query,
2446 | session_id=session_id,
2447 | focus_areas=focus_areas,
2448 | timeframe=timeframe,
2449 | **kwargs,
2450 | )
2451 |
2452 | async def analyze_market_sentiment(
2453 | self, topic: str, session_id: str, timeframe: str = "7d", **kwargs
2454 | ) -> dict[str, Any]:
2455 | """
2456 | Analyze market sentiment around a topic.
2457 |
2458 | Args:
2459 | topic: Topic to analyze sentiment for
2460 | session_id: Session identifier
2461 | timeframe: Time range for analysis
2462 | **kwargs: Additional parameters
2463 |
2464 | Returns:
2465 | Market sentiment analysis results
2466 | """
2467 | return await self.research_comprehensive(
2468 | topic=f"market sentiment analysis: {topic}",
2469 | session_id=session_id,
2470 | focus_areas=["sentiment", "market_mood", "investor_sentiment"],
2471 | timeframe=timeframe,
2472 | **kwargs,
2473 | )
2474 |
2475 | # Parallel Execution Implementation
2476 |
2477 | @log_method_call(component="DeepResearchAgent", include_timing=True)
2478 | async def _execute_parallel_research(
2479 | self,
2480 | topic: str,
2481 | session_id: str,
2482 | depth: str,
2483 | focus_areas: list[str] | None = None,
2484 | timeframe: str = "30d",
2485 | initial_state: dict[str, Any] | None = None,
2486 | start_time: datetime | None = None,
2487 | **kwargs,
2488 | ) -> dict[str, Any]:
2489 | """
2490 | Execute research using parallel subagent execution.
2491 |
2492 | Args:
2493 | topic: Research topic
2494 | session_id: Session identifier
2495 | depth: Research depth level
2496 | focus_areas: Specific focus areas
2497 | timeframe: Research timeframe
2498 | initial_state: Initial state for backward compatibility
2499 | start_time: Start time for execution measurement
2500 | **kwargs: Additional parameters
2501 |
2502 | Returns:
2503 | Research results in same format as sequential execution
2504 | """
2505 | orchestration_logger = get_orchestration_logger("ParallelExecution")
2506 | orchestration_logger.set_request_context(session_id=session_id)
2507 |
2508 | try:
2509 | # Generate research tasks using task distributor
2510 | orchestration_logger.info("🎯 TASK_DISTRIBUTION_START")
2511 | research_tasks = self.task_distributor.distribute_research_tasks(
2512 | topic=topic, session_id=session_id, focus_areas=focus_areas
2513 | )
2514 |
2515 | orchestration_logger.info(
2516 | "📋 TASKS_GENERATED",
2517 | task_count=len(research_tasks),
2518 | task_types=[t.task_type for t in research_tasks],
2519 | )
2520 |
2521 | # Execute tasks in parallel
2522 | orchestration_logger.info("🚀 PARALLEL_ORCHESTRATION_START")
2523 | research_result = (
2524 | await self.parallel_orchestrator.execute_parallel_research(
2525 | tasks=research_tasks,
2526 | research_executor=self._execute_subagent_task,
2527 | synthesis_callback=self._synthesize_parallel_results,
2528 | )
2529 | )
2530 |
2531 | # Log parallel execution metrics
2532 | log_performance_metrics(
2533 | "ParallelExecution",
2534 | {
2535 | "total_tasks": research_result.successful_tasks
2536 | + research_result.failed_tasks,
2537 | "successful_tasks": research_result.successful_tasks,
2538 | "failed_tasks": research_result.failed_tasks,
2539 | "parallel_efficiency": research_result.parallel_efficiency,
2540 | "execution_time": research_result.total_execution_time,
2541 | },
2542 | )
2543 |
2544 | # Convert parallel results to expected format
2545 | orchestration_logger.info("🔄 RESULT_FORMATTING_START")
2546 | formatted_result = await self._format_parallel_research_response(
2547 | research_result=research_result,
2548 | topic=topic,
2549 | session_id=session_id,
2550 | depth=depth,
2551 | initial_state=initial_state,
2552 | start_time=start_time,
2553 | )
2554 |
2555 | orchestration_logger.info(
2556 | "✅ PARALLEL_RESEARCH_COMPLETE",
2557 | result_confidence=formatted_result.get("confidence_score", 0.0),
2558 | sources_analyzed=formatted_result.get("sources_analyzed", 0),
2559 | )
2560 |
2561 | return formatted_result
2562 |
2563 | except Exception as e:
2564 | orchestration_logger.error("❌ PARALLEL_RESEARCH_FAILED", error=str(e))
2565 | raise # Re-raise to trigger fallback to sequential
2566 |
2567 | async def _execute_subagent_task(
2568 | self, task
2569 | ) -> dict[str, Any]: # Type: ResearchTask
2570 | """
2571 | Execute a single research task using specialized subagent.
2572 |
2573 | Args:
2574 | task: ResearchTask to execute
2575 |
2576 | Returns:
2577 | Research results from specialized subagent
2578 | """
2579 | with log_agent_execution(
2580 | task.task_type, task.task_id, task.focus_areas
2581 | ) as agent_logger:
2582 | agent_logger.info(
2583 | "🎯 SUBAGENT_ROUTING",
2584 | target_topic=task.target_topic[:50],
2585 | focus_count=len(task.focus_areas),
2586 | priority=task.priority,
2587 | )
2588 |
2589 | # Route to appropriate subagent based on task type
2590 | if task.task_type == "fundamental":
2591 | subagent = FundamentalResearchAgent(self)
2592 | return await subagent.execute_research(task)
2593 | elif task.task_type == "technical":
2594 | subagent = TechnicalResearchAgent(self)
2595 | return await subagent.execute_research(task)
2596 | elif task.task_type == "sentiment":
2597 | subagent = SentimentResearchAgent(self)
2598 | return await subagent.execute_research(task)
2599 | elif task.task_type == "competitive":
2600 | subagent = CompetitiveResearchAgent(self)
2601 | return await subagent.execute_research(task)
2602 | else:
2603 | # Default to fundamental analysis
2604 | agent_logger.warning("⚠️ UNKNOWN_TASK_TYPE", fallback="fundamental")
2605 | subagent = FundamentalResearchAgent(self)
2606 | return await subagent.execute_research(task)
2607 |
2608 | async def _synthesize_parallel_results(
2609 | self,
2610 | task_results, # Type: dict[str, ResearchTask]
2611 | ) -> dict[str, Any]:
2612 | """
2613 | Synthesize results from multiple parallel research tasks.
2614 |
2615 | Args:
2616 | task_results: Dictionary of task IDs to ResearchTask objects
2617 |
2618 | Returns:
2619 | Synthesized research insights
2620 | """
2621 | synthesis_logger = get_orchestration_logger("ResultSynthesis")
2622 |
2623 | log_synthesis_operation(
2624 | "parallel_research_synthesis",
2625 | len(task_results),
2626 | f"Synthesizing from {len(task_results)} research tasks",
2627 | )
2628 |
2629 | # Extract successful results
2630 | successful_results = {
2631 | task_id: task.result
2632 | for task_id, task in task_results.items()
2633 | if task.status == "completed" and task.result
2634 | }
2635 |
2636 | synthesis_logger.info(
2637 | "📊 SYNTHESIS_INPUT_ANALYSIS",
2638 | total_tasks=len(task_results),
2639 | successful_tasks=len(successful_results),
2640 | failed_tasks=len(task_results) - len(successful_results),
2641 | )
2642 |
2643 | if not successful_results:
2644 | synthesis_logger.warning("⚠️ NO_SUCCESSFUL_RESULTS")
2645 | return {
2646 | "synthesis": "No research results available for synthesis",
2647 | "confidence_score": 0.0,
2648 | }
2649 |
2650 | all_insights = []
2651 | all_risks = []
2652 | all_opportunities = []
2653 | sentiment_scores = []
2654 | credibility_scores = []
2655 |
2656 | # Aggregate results from all successful tasks
2657 | for task_id, task in task_results.items():
2658 | if task.status == "completed" and task.result:
2659 | task_type = task_id.split("_")[-1] if "_" in task_id else "unknown"
2660 | synthesis_logger.debug(
2661 | "🔍 PROCESSING_TASK_RESULT",
2662 | task_id=task_id,
2663 | task_type=task_type,
2664 | has_insights="insights" in task.result
2665 | if isinstance(task.result, dict)
2666 | else False,
2667 | )
2668 |
2669 | result = task.result
2670 |
2671 | # Extract insights
2672 | insights = result.get("insights", [])
2673 | all_insights.extend(insights)
2674 |
2675 | # Extract risks and opportunities
2676 | risks = result.get("risk_factors", [])
2677 | opportunities = result.get("opportunities", [])
2678 | all_risks.extend(risks)
2679 | all_opportunities.extend(opportunities)
2680 |
2681 | # Extract sentiment
2682 | sentiment = result.get("sentiment", {})
2683 | if sentiment:
2684 | sentiment_scores.append(sentiment)
2685 |
2686 | # Extract credibility
2687 | credibility = result.get("credibility_score", 0.5)
2688 | credibility_scores.append(credibility)
2689 |
2690 | # Calculate overall metrics
2691 | overall_sentiment = self._calculate_aggregated_sentiment(sentiment_scores)
2692 | average_credibility = (
2693 | sum(credibility_scores) / len(credibility_scores)
2694 | if credibility_scores
2695 | else 0.5
2696 | )
2697 |
2698 | # Generate synthesis using LLM
2699 | synthesis_prompt = self._build_parallel_synthesis_prompt(
2700 | task_results, all_insights, all_risks, all_opportunities, overall_sentiment
2701 | )
2702 |
2703 | try:
2704 | synthesis_response = await self.llm.ainvoke(
2705 | [
2706 | SystemMessage(
2707 | content="You are a financial research synthesizer. Combine insights from multiple specialized research agents."
2708 | ),
2709 | HumanMessage(content=synthesis_prompt),
2710 | ]
2711 | )
2712 |
2713 | synthesis_text = ContentAnalyzer._coerce_message_content(
2714 | synthesis_response.content
2715 | )
2716 | synthesis_logger.info("🧠 LLM_SYNTHESIS_SUCCESS")
2717 | except Exception as e:
2718 | synthesis_logger.warning(
2719 | "⚠️ LLM_SYNTHESIS_FAILED", error=str(e), fallback="text_fallback"
2720 | )
2721 | synthesis_text = self._generate_fallback_synthesis(
2722 | all_insights, overall_sentiment
2723 | )
2724 |
2725 | synthesis_result = {
2726 | "synthesis": synthesis_text,
2727 | "key_insights": list(dict.fromkeys(all_insights))[
2728 | :10
2729 | ], # Deduplicate and limit
2730 | "overall_sentiment": overall_sentiment,
2731 | "risk_assessment": list(dict.fromkeys(all_risks))[:8],
2732 | "investment_implications": {
2733 | "opportunities": list(dict.fromkeys(all_opportunities))[:5],
2734 | "threats": list(dict.fromkeys(all_risks))[:5],
2735 | "recommended_action": self._derive_parallel_recommendation(
2736 | overall_sentiment
2737 | ),
2738 | },
2739 | "confidence_score": average_credibility,
2740 | "task_breakdown": {
2741 | task_id: {
2742 | "type": task.task_type,
2743 | "status": task.status,
2744 | "execution_time": (task.end_time - task.start_time)
2745 | if task.start_time and task.end_time
2746 | else 0,
2747 | }
2748 | for task_id, task in task_results.items()
2749 | },
2750 | }
2751 |
2752 | synthesis_logger.info(
2753 | "✅ SYNTHESIS_COMPLETE",
2754 | insights_count=len(all_insights),
2755 | overall_confidence=average_credibility,
2756 | sentiment_direction=synthesis_result["overall_sentiment"]["direction"],
2757 | )
2758 |
2759 | return synthesis_result
2760 |
2761 | async def _format_parallel_research_response(
2762 | self,
2763 | research_result,
2764 | topic: str,
2765 | session_id: str,
2766 | depth: str,
2767 | initial_state: dict[str, Any] | None,
2768 | start_time: datetime | None,
2769 | ) -> dict[str, Any]:
2770 | """Format parallel research results to match expected sequential format."""
2771 |
2772 | if start_time is not None:
2773 | execution_time = (datetime.now() - start_time).total_seconds() * 1000
2774 | else:
2775 | execution_time = 0.0
2776 |
2777 | # Extract synthesis from research result
2778 | synthesis = research_result.synthesis or {}
2779 |
2780 | state_snapshot: dict[str, Any] = initial_state or {}
2781 |
2782 | # Create citations from task results
2783 | citations = []
2784 | all_sources = []
2785 | citation_id = 1
2786 |
2787 | for _task_id, task in research_result.task_results.items():
2788 | if task.status == "completed" and task.result:
2789 | sources = task.result.get("sources", [])
2790 | for source in sources:
2791 | citation = {
2792 | "id": citation_id,
2793 | "title": source.get("title", "Unknown Title"),
2794 | "url": source.get("url", ""),
2795 | "published_date": source.get("published_date"),
2796 | "author": source.get("author"),
2797 | "credibility_score": source.get("credibility_score", 0.5),
2798 | "relevance_score": source.get("relevance_score", 0.5),
2799 | "research_type": task.task_type,
2800 | }
2801 | citations.append(citation)
2802 | all_sources.append(source)
2803 | citation_id += 1
2804 |
2805 | return {
2806 | "status": "success",
2807 | "agent_type": "deep_research",
2808 | "execution_mode": "parallel",
2809 | "persona": state_snapshot.get("persona"),
2810 | "research_topic": topic,
2811 | "research_depth": depth,
2812 | "findings": synthesis,
2813 | "sources_analyzed": len(all_sources),
2814 | "confidence_score": synthesis.get("confidence_score", 0.0),
2815 | "citations": citations,
2816 | "execution_time_ms": execution_time,
2817 | "parallel_execution_stats": {
2818 | "total_tasks": len(research_result.task_results),
2819 | "successful_tasks": research_result.successful_tasks,
2820 | "failed_tasks": research_result.failed_tasks,
2821 | "parallel_efficiency": research_result.parallel_efficiency,
2822 | "task_breakdown": synthesis.get("task_breakdown", {}),
2823 | },
2824 | "search_queries_used": [], # Will be populated by subagents
2825 | "source_diversity": len({source.get("url", "") for source in all_sources})
2826 | / max(len(all_sources), 1),
2827 | }
2828 |
2829 | # Helper methods for parallel execution
2830 |
2831 | def _calculate_aggregated_sentiment(
2832 | self, sentiment_scores: list[dict[str, Any]]
2833 | ) -> dict[str, Any]:
2834 | """Calculate overall sentiment from multiple sentiment scores."""
2835 | if not sentiment_scores:
2836 | return {"direction": "neutral", "confidence": 0.5}
2837 |
2838 | # Convert sentiment directions to numeric values
2839 | numeric_scores = []
2840 | confidences = []
2841 |
2842 | for sentiment in sentiment_scores:
2843 | direction = sentiment.get("direction", "neutral")
2844 | confidence = sentiment.get("confidence", 0.5)
2845 |
2846 | if direction == "bullish":
2847 | numeric_scores.append(1 * confidence)
2848 | elif direction == "bearish":
2849 | numeric_scores.append(-1 * confidence)
2850 | else:
2851 | numeric_scores.append(0)
2852 |
2853 | confidences.append(confidence)
2854 |
2855 | # Calculate weighted average
2856 | avg_score = sum(numeric_scores) / len(numeric_scores)
2857 | avg_confidence = sum(confidences) / len(confidences)
2858 |
2859 | # Convert back to direction
2860 | if avg_score > 0.2:
2861 | direction = "bullish"
2862 | elif avg_score < -0.2:
2863 | direction = "bearish"
2864 | else:
2865 | direction = "neutral"
2866 |
2867 | return {
2868 | "direction": direction,
2869 | "confidence": avg_confidence,
2870 | "consensus": 1 - abs(avg_score) if abs(avg_score) < 1 else 0,
2871 | "source_count": len(sentiment_scores),
2872 | }
2873 |
2874 | def _build_parallel_synthesis_prompt(
2875 | self,
2876 | task_results: dict[str, Any], # Actually dict[str, ResearchTask]
2877 | all_insights: list[str],
2878 | all_risks: list[str],
2879 | all_opportunities: list[str],
2880 | overall_sentiment: dict[str, Any],
2881 | ) -> str:
2882 | """Build synthesis prompt for parallel research results."""
2883 | successful_tasks = [
2884 | task for task in task_results.values() if task.status == "completed"
2885 | ]
2886 |
2887 | prompt = f"""
2888 | Synthesize comprehensive research findings from {len(successful_tasks)} specialized research agents.
2889 |
2890 | Research Task Results:
2891 | """
2892 |
2893 | for task in successful_tasks:
2894 | if task.result:
2895 | prompt += f"\n{task.task_type.upper()} RESEARCH:"
2896 | prompt += f" - Status: {task.status}"
2897 | prompt += f" - Key Insights: {', '.join(task.result.get('insights', [])[:3])}"
2898 | prompt += f" - Sentiment: {task.result.get('sentiment', {}).get('direction', 'neutral')}"
2899 |
2900 | prompt += f"""
2901 |
2902 | AGGREGATED DATA:
2903 | - Total Insights: {len(all_insights)}
2904 | - Risk Factors: {len(all_risks)}
2905 | - Opportunities: {len(all_opportunities)}
2906 | - Overall Sentiment: {overall_sentiment.get("direction")} (confidence: {overall_sentiment.get("confidence", 0.5):.2f})
2907 |
2908 | Please provide a comprehensive synthesis that includes:
2909 | 1. Executive Summary (2-3 sentences)
2910 | 2. Key Findings from all research areas
2911 | 3. Investment Implications for {self.persona.name} investors
2912 | 4. Risk Assessment and Mitigation
2913 | 5. Recommended Actions based on parallel analysis
2914 | 6. Confidence Level and reasoning
2915 |
2916 | Focus on insights that are supported by multiple research agents and highlight any contradictions.
2917 | """
2918 |
2919 | return prompt
2920 |
2921 | def _generate_fallback_synthesis(
2922 | self, insights: list[str], sentiment: dict[str, Any]
2923 | ) -> str:
2924 | """Generate fallback synthesis when LLM synthesis fails."""
2925 | return f"""
2926 | Research synthesis generated from {len(insights)} insights.
2927 |
2928 | Overall sentiment: {sentiment.get("direction", "neutral")} with {sentiment.get("confidence", 0.5):.2f} confidence.
2929 |
2930 | Key insights identified:
2931 | {chr(10).join(f"- {insight}" for insight in insights[:5])}
2932 |
2933 | This is a fallback synthesis due to LLM processing limitations.
2934 | """
2935 |
2936 | def _derive_parallel_recommendation(self, sentiment: dict[str, Any]) -> str:
2937 | """Derive investment recommendation from parallel analysis."""
2938 | direction = sentiment.get("direction", "neutral")
2939 | confidence = sentiment.get("confidence", 0.5)
2940 |
2941 | if direction == "bullish" and confidence > 0.7:
2942 | return "Strong buy signal based on parallel analysis from multiple research angles"
2943 | elif direction == "bullish" and confidence > 0.5:
2944 | return "Consider position building with appropriate risk management"
2945 | elif direction == "bearish" and confidence > 0.7:
2946 | return "Exercise significant caution - multiple research areas show negative signals"
2947 | elif direction == "bearish" and confidence > 0.5:
2948 | return "Monitor closely - mixed to negative signals suggest waiting"
2949 | else:
2950 | return "Neutral stance recommended - parallel analysis shows mixed signals"
2951 |
2952 |
2953 | # Specialized Subagent Classes
2954 |
2955 |
2956 | class BaseSubagent:
2957 | """Base class for specialized research subagents."""
2958 |
2959 | def __init__(self, parent_agent: DeepResearchAgent):
2960 | self.parent = parent_agent
2961 | self.llm = parent_agent.llm
2962 | self.search_providers = parent_agent.search_providers
2963 | self.content_analyzer = parent_agent.content_analyzer
2964 | self.persona = parent_agent.persona
2965 | self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")
2966 |
2967 | async def execute_research(self, task) -> dict[str, Any]: # task: ResearchTask
2968 | """Execute research task - to be implemented by subclasses."""
2969 | raise NotImplementedError
2970 |
2971 | async def _safe_search(
2972 | self,
2973 | provider: WebSearchProvider,
2974 | query: str,
2975 | num_results: int = 5,
2976 | timeout_budget: float | None = None,
2977 | ) -> list[dict[str, Any]]:
2978 | """Safely execute search with a provider, handling exceptions gracefully."""
2979 | try:
2980 | return await provider.search(
2981 | query, num_results=num_results, timeout_budget=timeout_budget
2982 | )
2983 | except Exception as e:
2984 | self.logger.warning(
2985 | f"Search failed for '{query}' with provider {type(provider).__name__}: {e}"
2986 | )
2987 | return [] # Return empty list on failure
2988 |
2989 | async def _perform_specialized_search(
2990 | self,
2991 | topic: str,
2992 | specialized_queries: list[str],
2993 | max_results: int = 10,
2994 | timeout_budget: float | None = None,
2995 | ) -> list[dict[str, Any]]:
2996 | """Perform specialized web search for this subagent type."""
2997 | all_results = []
2998 |
2999 | # Create all search tasks for parallel execution
3000 | search_tasks = []
3001 | results_per_query = (
3002 | max_results // len(specialized_queries)
3003 | if specialized_queries
3004 | else max_results
3005 | )
3006 |
3007 | # Calculate timeout per search if budget provided
3008 | if timeout_budget:
3009 | total_searches = len(specialized_queries) * len(self.search_providers)
3010 | timeout_per_search = timeout_budget / max(total_searches, 1)
3011 | else:
3012 | timeout_per_search = None
3013 |
3014 | for query in specialized_queries:
3015 | for provider in self.search_providers:
3016 | # Create async task for each provider/query combination
3017 | search_tasks.append(
3018 | self._safe_search(
3019 | provider,
3020 | query,
3021 | num_results=results_per_query,
3022 | timeout_budget=timeout_per_search,
3023 | )
3024 | )
3025 |
3026 | # Execute all searches in parallel using asyncio.gather()
3027 | if search_tasks:
3028 | parallel_results = await asyncio.gather(
3029 | *search_tasks, return_exceptions=True
3030 | )
3031 |
3032 | # Process results and filter out exceptions
3033 | for result in parallel_results:
3034 | if isinstance(result, Exception):
3035 | # Log the exception but continue with other results
3036 | self.logger.warning(f"Search task failed: {result}")
3037 | elif isinstance(result, list):
3038 | all_results.extend(result)
3039 | elif result is not None:
3040 | all_results.append(result)
3041 |
3042 | # Deduplicate results
3043 | seen_urls = set()
3044 | unique_results = []
3045 | for result in all_results:
3046 | if result.get("url") not in seen_urls:
3047 | seen_urls.add(result["url"])
3048 | unique_results.append(result)
3049 |
3050 | return unique_results[:max_results]
3051 |
3052 | async def _analyze_search_results(
3053 | self, results: list[dict[str, Any]], analysis_focus: str
3054 | ) -> list[dict[str, Any]]:
3055 | """Analyze search results with specialized focus."""
3056 | analyzed_results = []
3057 |
3058 | for result in results:
3059 | if result.get("content"):
3060 | try:
3061 | analysis = await self.content_analyzer.analyze_content(
3062 | content=result["content"],
3063 | persona=self.persona.name.lower(),
3064 | analysis_focus=analysis_focus,
3065 | )
3066 |
3067 | # Add source credibility
3068 | credibility_score = self._calculate_source_credibility(result)
3069 | analysis["credibility_score"] = credibility_score
3070 |
3071 | analyzed_results.append(
3072 | {
3073 | **result,
3074 | "analysis": analysis,
3075 | "credibility_score": credibility_score,
3076 | }
3077 | )
3078 | except Exception as e:
3079 | self.logger.warning(
3080 | f"Content analysis failed for {result.get('url', 'unknown')}: {e}"
3081 | )
3082 |
3083 | return analyzed_results
3084 |
3085 | def _calculate_source_credibility(self, source: dict[str, Any]) -> float:
3086 | """Calculate credibility score for a source - reuse from parent."""
3087 | return self.parent._calculate_source_credibility(source)
3088 |
3089 |
3090 | class FundamentalResearchAgent(BaseSubagent):
3091 | """Specialized agent for fundamental financial analysis."""
3092 |
3093 | async def execute_research(self, task) -> dict[str, Any]: # task: ResearchTask
3094 | """Execute fundamental analysis research."""
3095 | self.logger.info(f"Executing fundamental research for: {task.target_topic}")
3096 |
3097 | # Generate fundamental-specific search queries
3098 | queries = self._generate_fundamental_queries(task.target_topic)
3099 |
3100 | # Perform specialized search
3101 | search_results = await self._perform_specialized_search(
3102 | topic=task.target_topic, specialized_queries=queries, max_results=8
3103 | )
3104 |
3105 | # Analyze results with fundamental focus
3106 | analyzed_results = await self._analyze_search_results(
3107 | search_results, analysis_focus="fundamental_analysis"
3108 | )
3109 |
3110 | # Extract fundamental-specific insights
3111 | insights = []
3112 | risks = []
3113 | opportunities = []
3114 | sources = []
3115 |
3116 | for result in analyzed_results:
3117 | analysis = result.get("analysis", {})
3118 | insights.extend(analysis.get("insights", []))
3119 | risks.extend(analysis.get("risk_factors", []))
3120 | opportunities.extend(analysis.get("opportunities", []))
3121 | sources.append(
3122 | {
3123 | "title": result.get("title", ""),
3124 | "url": result.get("url", ""),
3125 | "credibility_score": result.get("credibility_score", 0.5),
3126 | "published_date": result.get("published_date"),
3127 | "author": result.get("author"),
3128 | }
3129 | )
3130 |
3131 | return {
3132 | "research_type": "fundamental",
3133 | "insights": list(dict.fromkeys(insights))[:8], # Deduplicate
3134 | "risk_factors": list(dict.fromkeys(risks))[:6],
3135 | "opportunities": list(dict.fromkeys(opportunities))[:6],
3136 | "sentiment": self._calculate_fundamental_sentiment(analyzed_results),
3137 | "credibility_score": self._calculate_average_credibility(analyzed_results),
3138 | "sources": sources,
3139 | "focus_areas": [
3140 | "earnings",
3141 | "valuation",
3142 | "financial_health",
3143 | "growth_prospects",
3144 | ],
3145 | }
3146 |
3147 | def _generate_fundamental_queries(self, topic: str) -> list[str]:
3148 | """Generate fundamental analysis specific queries."""
3149 | return [
3150 | f"{topic} earnings report financial results",
3151 | f"{topic} revenue growth profit margins",
3152 | f"{topic} balance sheet debt ratio financial health",
3153 | f"{topic} valuation PE ratio price earnings",
3154 | f"{topic} cash flow dividend payout",
3155 | ]
3156 |
3157 | def _calculate_fundamental_sentiment(
3158 | self, results: list[dict[str, Any]]
3159 | ) -> dict[str, Any]:
3160 | """Calculate sentiment specific to fundamental analysis."""
3161 | sentiments = []
3162 | for result in results:
3163 | analysis = result.get("analysis", {})
3164 | sentiment = analysis.get("sentiment", {})
3165 | if sentiment:
3166 | sentiments.append(sentiment)
3167 |
3168 | if not sentiments:
3169 | return {"direction": "neutral", "confidence": 0.5}
3170 |
3171 | # Simple aggregation for now
3172 | bullish_count = sum(1 for s in sentiments if s.get("direction") == "bullish")
3173 | bearish_count = sum(1 for s in sentiments if s.get("direction") == "bearish")
3174 |
3175 | if bullish_count > bearish_count:
3176 | return {"direction": "bullish", "confidence": 0.7}
3177 | elif bearish_count > bullish_count:
3178 | return {"direction": "bearish", "confidence": 0.7}
3179 | else:
3180 | return {"direction": "neutral", "confidence": 0.5}
3181 |
3182 | def _calculate_average_credibility(self, results: list[dict[str, Any]]) -> float:
3183 | """Calculate average credibility of sources."""
3184 | if not results:
3185 | return 0.5
3186 |
3187 | credibility_scores = [r.get("credibility_score", 0.5) for r in results]
3188 | return sum(credibility_scores) / len(credibility_scores)
3189 |
3190 |
3191 | class TechnicalResearchAgent(BaseSubagent):
3192 | """Specialized agent for technical analysis research."""
3193 |
3194 | async def execute_research(self, task) -> dict[str, Any]: # task: ResearchTask
3195 | """Execute technical analysis research."""
3196 | self.logger.info(f"Executing technical research for: {task.target_topic}")
3197 |
3198 | queries = self._generate_technical_queries(task.target_topic)
3199 | search_results = await self._perform_specialized_search(
3200 | topic=task.target_topic, specialized_queries=queries, max_results=6
3201 | )
3202 |
3203 | analyzed_results = await self._analyze_search_results(
3204 | search_results, analysis_focus="technical_analysis"
3205 | )
3206 |
3207 | # Extract technical-specific insights
3208 | insights = []
3209 | risks = []
3210 | opportunities = []
3211 | sources = []
3212 |
3213 | for result in analyzed_results:
3214 | analysis = result.get("analysis", {})
3215 | insights.extend(analysis.get("insights", []))
3216 | risks.extend(analysis.get("risk_factors", []))
3217 | opportunities.extend(analysis.get("opportunities", []))
3218 | sources.append(
3219 | {
3220 | "title": result.get("title", ""),
3221 | "url": result.get("url", ""),
3222 | "credibility_score": result.get("credibility_score", 0.5),
3223 | "published_date": result.get("published_date"),
3224 | "author": result.get("author"),
3225 | }
3226 | )
3227 |
3228 | return {
3229 | "research_type": "technical",
3230 | "insights": list(dict.fromkeys(insights))[:8],
3231 | "risk_factors": list(dict.fromkeys(risks))[:6],
3232 | "opportunities": list(dict.fromkeys(opportunities))[:6],
3233 | "sentiment": self._calculate_technical_sentiment(analyzed_results),
3234 | "credibility_score": self._calculate_average_credibility(analyzed_results),
3235 | "sources": sources,
3236 | "focus_areas": [
3237 | "price_action",
3238 | "chart_patterns",
3239 | "technical_indicators",
3240 | "support_resistance",
3241 | ],
3242 | }
3243 |
3244 | def _generate_technical_queries(self, topic: str) -> list[str]:
3245 | """Generate technical analysis specific queries."""
3246 | return [
3247 | f"{topic} technical analysis chart pattern",
3248 | f"{topic} price target support resistance",
3249 | f"{topic} RSI MACD technical indicators",
3250 | f"{topic} breakout trend analysis",
3251 | f"{topic} volume analysis price movement",
3252 | ]
3253 |
3254 | def _calculate_technical_sentiment(
3255 | self, results: list[dict[str, Any]]
3256 | ) -> dict[str, Any]:
3257 | """Calculate sentiment specific to technical analysis."""
3258 | # Similar to fundamental but focused on technical indicators
3259 | sentiments = [
3260 | r.get("analysis", {}).get("sentiment", {})
3261 | for r in results
3262 | if r.get("analysis")
3263 | ]
3264 | sentiments = [s for s in sentiments if s]
3265 |
3266 | if not sentiments:
3267 | return {"direction": "neutral", "confidence": 0.5}
3268 |
3269 | bullish_count = sum(1 for s in sentiments if s.get("direction") == "bullish")
3270 | bearish_count = sum(1 for s in sentiments if s.get("direction") == "bearish")
3271 |
3272 | if bullish_count > bearish_count:
3273 | return {"direction": "bullish", "confidence": 0.6}
3274 | elif bearish_count > bullish_count:
3275 | return {"direction": "bearish", "confidence": 0.6}
3276 | else:
3277 | return {"direction": "neutral", "confidence": 0.5}
3278 |
3279 | def _calculate_average_credibility(self, results: list[dict[str, Any]]) -> float:
3280 | """Calculate average credibility of sources."""
3281 | if not results:
3282 | return 0.5
3283 | credibility_scores = [r.get("credibility_score", 0.5) for r in results]
3284 | return sum(credibility_scores) / len(credibility_scores)
3285 |
3286 |
3287 | class SentimentResearchAgent(BaseSubagent):
3288 | """Specialized agent for market sentiment analysis."""
3289 |
3290 | async def execute_research(self, task) -> dict[str, Any]: # task: ResearchTask
3291 | """Execute sentiment analysis research."""
3292 | self.logger.info(f"Executing sentiment research for: {task.target_topic}")
3293 |
3294 | queries = self._generate_sentiment_queries(task.target_topic)
3295 | search_results = await self._perform_specialized_search(
3296 | topic=task.target_topic, specialized_queries=queries, max_results=10
3297 | )
3298 |
3299 | analyzed_results = await self._analyze_search_results(
3300 | search_results, analysis_focus="sentiment_analysis"
3301 | )
3302 |
3303 | # Extract sentiment-specific insights
3304 | insights = []
3305 | risks = []
3306 | opportunities = []
3307 | sources = []
3308 |
3309 | for result in analyzed_results:
3310 | analysis = result.get("analysis", {})
3311 | insights.extend(analysis.get("insights", []))
3312 | risks.extend(analysis.get("risk_factors", []))
3313 | opportunities.extend(analysis.get("opportunities", []))
3314 | sources.append(
3315 | {
3316 | "title": result.get("title", ""),
3317 | "url": result.get("url", ""),
3318 | "credibility_score": result.get("credibility_score", 0.5),
3319 | "published_date": result.get("published_date"),
3320 | "author": result.get("author"),
3321 | }
3322 | )
3323 |
3324 | return {
3325 | "research_type": "sentiment",
3326 | "insights": list(dict.fromkeys(insights))[:8],
3327 | "risk_factors": list(dict.fromkeys(risks))[:6],
3328 | "opportunities": list(dict.fromkeys(opportunities))[:6],
3329 | "sentiment": self._calculate_market_sentiment(analyzed_results),
3330 | "credibility_score": self._calculate_average_credibility(analyzed_results),
3331 | "sources": sources,
3332 | "focus_areas": [
3333 | "market_sentiment",
3334 | "analyst_opinions",
3335 | "news_sentiment",
3336 | "social_sentiment",
3337 | ],
3338 | }
3339 |
3340 | def _generate_sentiment_queries(self, topic: str) -> list[str]:
3341 | """Generate sentiment analysis specific queries."""
3342 | return [
3343 | f"{topic} analyst rating recommendation upgrade downgrade",
3344 | f"{topic} market sentiment investor opinion",
3345 | f"{topic} news sentiment positive negative",
3346 | f"{topic} social sentiment reddit twitter discussion",
3347 | f"{topic} institutional investor sentiment",
3348 | ]
3349 |
3350 | def _calculate_market_sentiment(
3351 | self, results: list[dict[str, Any]]
3352 | ) -> dict[str, Any]:
3353 | """Calculate overall market sentiment."""
3354 | sentiments = [
3355 | r.get("analysis", {}).get("sentiment", {})
3356 | for r in results
3357 | if r.get("analysis")
3358 | ]
3359 | sentiments = [s for s in sentiments if s]
3360 |
3361 | if not sentiments:
3362 | return {"direction": "neutral", "confidence": 0.5}
3363 |
3364 | # Weighted by confidence
3365 | weighted_scores = []
3366 | total_confidence = 0
3367 |
3368 | for sentiment in sentiments:
3369 | direction = sentiment.get("direction", "neutral")
3370 | confidence = sentiment.get("confidence", 0.5)
3371 |
3372 | if direction == "bullish":
3373 | weighted_scores.append(1 * confidence)
3374 | elif direction == "bearish":
3375 | weighted_scores.append(-1 * confidence)
3376 | else:
3377 | weighted_scores.append(0)
3378 |
3379 | total_confidence += confidence
3380 |
3381 | if not weighted_scores:
3382 | return {"direction": "neutral", "confidence": 0.5}
3383 |
3384 | avg_score = sum(weighted_scores) / len(weighted_scores)
3385 | avg_confidence = total_confidence / len(sentiments)
3386 |
3387 | if avg_score > 0.3:
3388 | return {"direction": "bullish", "confidence": avg_confidence}
3389 | elif avg_score < -0.3:
3390 | return {"direction": "bearish", "confidence": avg_confidence}
3391 | else:
3392 | return {"direction": "neutral", "confidence": avg_confidence}
3393 |
3394 | def _calculate_average_credibility(self, results: list[dict[str, Any]]) -> float:
3395 | """Calculate average credibility of sources."""
3396 | if not results:
3397 | return 0.5
3398 | credibility_scores = [r.get("credibility_score", 0.5) for r in results]
3399 | return sum(credibility_scores) / len(credibility_scores)
3400 |
3401 |
3402 | class CompetitiveResearchAgent(BaseSubagent):
3403 | """Specialized agent for competitive and industry analysis."""
3404 |
3405 | async def execute_research(self, task) -> dict[str, Any]: # task: ResearchTask
3406 | """Execute competitive analysis research."""
3407 | self.logger.info(f"Executing competitive research for: {task.target_topic}")
3408 |
3409 | queries = self._generate_competitive_queries(task.target_topic)
3410 | search_results = await self._perform_specialized_search(
3411 | topic=task.target_topic, specialized_queries=queries, max_results=8
3412 | )
3413 |
3414 | analyzed_results = await self._analyze_search_results(
3415 | search_results, analysis_focus="competitive_analysis"
3416 | )
3417 |
3418 | # Extract competitive-specific insights
3419 | insights = []
3420 | risks = []
3421 | opportunities = []
3422 | sources = []
3423 |
3424 | for result in analyzed_results:
3425 | analysis = result.get("analysis", {})
3426 | insights.extend(analysis.get("insights", []))
3427 | risks.extend(analysis.get("risk_factors", []))
3428 | opportunities.extend(analysis.get("opportunities", []))
3429 | sources.append(
3430 | {
3431 | "title": result.get("title", ""),
3432 | "url": result.get("url", ""),
3433 | "credibility_score": result.get("credibility_score", 0.5),
3434 | "published_date": result.get("published_date"),
3435 | "author": result.get("author"),
3436 | }
3437 | )
3438 |
3439 | return {
3440 | "research_type": "competitive",
3441 | "insights": list(dict.fromkeys(insights))[:8],
3442 | "risk_factors": list(dict.fromkeys(risks))[:6],
3443 | "opportunities": list(dict.fromkeys(opportunities))[:6],
3444 | "sentiment": self._calculate_competitive_sentiment(analyzed_results),
3445 | "credibility_score": self._calculate_average_credibility(analyzed_results),
3446 | "sources": sources,
3447 | "focus_areas": [
3448 | "competitive_position",
3449 | "market_share",
3450 | "industry_trends",
3451 | "competitive_advantages",
3452 | ],
3453 | }
3454 |
3455 | def _generate_competitive_queries(self, topic: str) -> list[str]:
3456 | """Generate competitive analysis specific queries."""
3457 | return [
3458 | f"{topic} market share competitive position industry",
3459 | f"{topic} competitors comparison competitive advantage",
3460 | f"{topic} industry analysis market trends",
3461 | f"{topic} competitive landscape market dynamics",
3462 | f"{topic} industry outlook sector performance",
3463 | ]
3464 |
3465 | def _calculate_competitive_sentiment(
3466 | self, results: list[dict[str, Any]]
3467 | ) -> dict[str, Any]:
3468 | """Calculate sentiment specific to competitive positioning."""
3469 | sentiments = [
3470 | r.get("analysis", {}).get("sentiment", {})
3471 | for r in results
3472 | if r.get("analysis")
3473 | ]
3474 | sentiments = [s for s in sentiments if s]
3475 |
3476 | if not sentiments:
3477 | return {"direction": "neutral", "confidence": 0.5}
3478 |
3479 | # Focus on competitive strength indicators
3480 | bullish_count = sum(1 for s in sentiments if s.get("direction") == "bullish")
3481 | bearish_count = sum(1 for s in sentiments if s.get("direction") == "bearish")
3482 |
3483 | if bullish_count > bearish_count:
3484 | return {"direction": "bullish", "confidence": 0.6}
3485 | elif bearish_count > bullish_count:
3486 | return {"direction": "bearish", "confidence": 0.6}
3487 | else:
3488 | return {"direction": "neutral", "confidence": 0.5}
3489 |
3490 | def _calculate_average_credibility(self, results: list[dict[str, Any]]) -> float:
3491 | """Calculate average credibility of sources."""
3492 | if not results:
3493 | return 0.5
3494 | credibility_scores = [r.get("credibility_score", 0.5) for r in results]
3495 | return sum(credibility_scores) / len(credibility_scores)
3496 |
```