Analytical MCP Server

# Exa-Based NER Implementation Plan ## Overview This document outlines the approach for replacing the Hugging Face dependency with Exa for advanced named entity recognition in the Analytical MCP Server. ## Current Implementation The current implementation uses: 1. Hugging Face API for advanced NER 2. Natural.js for basic NER as a fallback 3. Rule-based approach as a final fallback ## New Implementation Strategy ### 1. Remove Hugging Face Dependencies - Remove `@huggingface/inference` package - Remove Hugging Face API key from config - Remove Hugging Face-specific NER methods ### 2. Implement Exa-Based NER - Integrate with existing `exaResearch` utility - Create a new method `recognizeWithExa` that: 1. Uses Exa search capabilities to get relevant context about potential entities 2. Extracts entities from search results using the existing fact extraction 3. Transforms the extracted entities to match the required `RecognizedEntity` interface ### 3. Maintain Fallback Mechanisms - Keep natural.js NER as first fallback - Keep rule-based approach as final fallback - Ensure smooth degradation of capabilities when Exa is unavailable ### 4. Update Configuration - Remove Hugging Face API key references - Ensure Exa API key configuration is properly utilized - Update feature flags if needed ## Implementation Steps ### Step 1: Update Package.json - Remove `@huggingface/inference` from dependencies - Update any related type definitions ### Step 2: Update Configuration - Remove Hugging Face API key from config.ts - Update any related environment variable processing - Adjust feature flags to support advanced NER with Exa ### Step 3: Update Advanced NER Implementation - Modify `advanced_ner.ts` to remove Hugging Face - Add new Exa-based recognition method - Update the main recognition flow to use Exa instead of Hugging Face ### Step 4: Create Integration Tests - Create test cases for Exa-based NER - Ensure fallback mechanisms are properly tested - Verify entity recognition quality ### Step 5: Documentation Updates - Update documentation to reflect changes - Add examples of using Exa-based NER - Update environment variable requirements ## Advantages of Exa-Based Approach 1. Consolidates external API dependencies (only need Exa API key) 2. Leverages existing robust Exa integration (rate limiting, caching, etc.) 3. Provides more contextual entity recognition through search capabilities 4. Maintains compatibility with existing code through identical interfaces ## Challenges and Mitigations 1. **Challenge**: Exa search might not be as specialized for NER as Hugging Face models **Mitigation**: Enhance post-processing of search results to improve entity recognition 2. **Challenge**: Different confidence scoring mechanisms between Hugging Face and Exa **Mitigation**: Calibrate confidence scores to maintain consistency 3. **Challenge**: Potential performance differences **Mitigation**: Leverage existing caching in Exa research to optimize performance ## Implementation Timeline - Package and config updates: 1 hour - Advanced NER implementation: 2-3 hours - Testing and refinement: 1-2 hours - Documentation: 1 hour - Total estimated time: 5-7 hours