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Stock Price Retrieval Improvements

This document summarizes the improvements made to ensure the AI system properly retrieves and presents current stock prices rather than outdated information.

1. Root Cause Analysis

Issues Identified:

  1. Search Configuration: Search configuration was commented out in config.toml, causing default values to be used
  2. Query Refinement: Stock price queries were not specifically optimized for financial data retrieval
  3. Result Processing: The system was returning raw search content instead of extracting specific stock prices
  4. Engine Selection: Google search engine was failing, but fallback engines were not properly configured

2. Configuration Improvements

Enabled Search Configuration:

  • Uncommented and configured the [search] section in config.toml
  • Set primary engine to DuckDuckGo with Google as fallback
  • Reduced retry delay to 30 seconds and max retries to 2
  • Confirmed search configuration is properly loaded

Configuration Verification:

  • Search engine order: ['duckduckgo', 'google', 'baidu', 'bing']
  • Language: en, Country: us
  • Proper fallback mechanism in place

3. Query Refinement Enhancements

Specific Stock Symbol Mapping:

  • Apple/AAPL queries -> "AAPL stock price"
  • Microsoft/MSFT queries -> "MSFT stock price"
  • Google/GOOG queries -> "GOOGL stock price"
  • Generic stock queries -> "current stock price [query]"

Benefits:

  • More targeted search queries
  • Better results from financial websites
  • Reduced noise from general search results

4. Stock Price Extraction Logic

Pattern Matching:

  • $[0-9]+\.?[0-9]* - Matches $175.42 format
  • [0-9]+\.?[0-9]*\s*usd - Matches 175.42 USD format
  • price[:\s]*\$?[0-9]+\.?[0-9]* - Matches price: $175.42 or price: 175.42

Validation:

  • Price range validation (0 < price < 10000)
  • Multiple pattern matching for robust extraction
  • First valid price extraction from search results

Response Formatting:

  • Clear, concise stock price responses
  • Fallback to raw content when extraction fails
  • Source attribution for transparency

5. Implementation Files

Modified Files:

  1. config/config.toml - Enabled and configured search settings
  2. app/agent/manus.py - Enhanced stock price extraction in _get_current_information and added _extract_stock_price method
  3. web_ui.py - Enhanced stock price extraction in web UI with extract_stock_price_from_results function

New Test Files:

  1. test_search_config.py - Verifies search configuration loading
  2. test_stock_price_extraction.py - Tests stock price pattern matching
  3. test_current_stock_price.py - Tests current stock price retrieval
  4. test_complete_stock_functionality.py - Comprehensive functionality testing

6. Testing Results

Pattern Matching Tests:

  • Successfully identified stock prices in various formats
  • Validated price range checking
  • Confirmed multiple pattern matching works correctly

Query Refinement Tests:

  • Apple stock queries correctly mapped to "AAPL stock price"
  • Microsoft stock queries correctly mapped to "MSFT stock price"
  • Google stock queries correctly mapped to "GOOGL stock price"
  • Generic queries properly refined

Configuration Tests:

  • Search configuration properly loaded
  • Search engine order correctly configured
  • Fallback mechanisms verified

7. Key Features

Automatic Detection:

  • Stock price queries automatically identified
  • Specific company mapping for major stocks
  • Pattern matching for price extraction

Proactive Tool Usage:

  • No need for explicit user prompting
  • Immediate web search execution for stock queries
  • Direct response generation with extracted prices

Error Handling:

  • Graceful fallback to raw content when extraction fails
  • Clear error messaging when search fails
  • Multiple search engine fallbacks

Response Quality:

  • Clear, concise stock price responses
  • Source attribution for transparency
  • Fallback to detailed content when needed

8. Benefits

Accuracy Improvements:

  • Eliminates outdated stock price information
  • Ensures current data through direct web search
  • Provides specific stock prices rather than general information

Performance Enhancements:

  • Faster response times for stock price queries
  • Reduced model inference for straightforward requests
  • Better resource utilization through direct tool usage

User Experience:

  • More reliable stock price information
  • Clear, concise responses
  • Better error handling and communication

9. Future Improvements

Potential Enhancements:

  • Integration with financial APIs for real-time data
  • Enhanced result filtering and ranking
  • Multi-source information verification
  • Caching of recent stock prices
  • Improved handling of international stocks

10. Verification

Tests Performed:

  • Search configuration loading
  • Stock price pattern matching
  • Query refinement logic
  • Search engine functionality
  • Complete integration testing

Results:

  • All core functionality verified
  • Pattern matching working correctly
  • Query refinement properly implemented
  • Search configuration properly loaded
  • Error handling functioning

These improvements ensure that the AI system will now proactively identify stock price queries and automatically execute web search tools to obtain current stock data, extract the specific prices, and present them in a clear, concise format rather than relying on internal knowledge or generating fabricated information.