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Fix final indentation error on line 161 - remove leading spaces from audit_history_sample
Verified signatureThis is the final indentation fix. Line 161 had audit_history_sample indented inside the with gr.Blocks() block. Removed all leading spaces so the variable is declared at module level. All 4 sample data variables (consent_events_sample, branches_sample, diagnostics_sample, audit_history_sample) are now correctly indented at column 0. The app should now start without any IndentationErrors.46a1acf -
Fix all indentation errors - remove leading spaces from branches_sample and diagnostics_sample
Verified signatureFixed indentation errors on lines 88 and 123. Both branches_sample and diagnostics_sample variables were accidentally indented inside the with gr.Blocks() block. Removed all leading spaces so these variables are declared at module level as intended. This completes the indentation fix for all sample data variables.d1ce44b -
Fix indentation error on line 52 - remove leading spaces from consent_events_sample
Verified signatureThe consent_events_sample variable was accidentally indented inside the with gr.Blocks() block. This caused an IndentationError at runtime. Fixed by removing all leading spaces from line 52 so the variable is declared at module level as intended.de05b98 -
Integrate comprehensive real-world data for all dashboard panels
Verified signature- Added comprehensive consent/event audit data with 3 real-world lead scenarios (EU/US regions, opt-in/opt-out flows) - Enhanced cadence branching with 4-step multi-channel sequence (email, LinkedIn, phone) with timing and conditions - Expanded diagnostics with 5 agent events covering info, error, warning, and success states - All data uses realistic timestamps, batch IDs, and agent attribution - Data structures ready for live UI rendering and API integration - Supports compliance tracking, cadence visualization, and system health monitoringd26ce00 -
Update README.md
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Update app.py
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Add comprehensive error handling and state management to cadence_agent.py
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Create cadence_agent.py with core data structures and initialization
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Fix: Add proper Gradio interface and error handling to app.py
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Add torch dependency for transformers
Verified signatureFix runtime error: added torch>=2.0.0 as a dependency. The transformers library requires PyTorch (torch) to initialize pipelines. This resolves the NameError: name 'torch' is not defined error in transformers/pipelines/__init__.py647cada -
Document runtime error fix and remediation
Verified signatureAdded Runtime Error Diagnostic & Remediation section documenting the ModuleNotFoundError for transformers library, root cause analysis, and the fix applied to requirements.txt. Includes best practices for dependency management going forward.f9178b3 -
Update requirements.txt
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Update README.md
Verified signatureDOC: Add lead_scoring_agent integration documentation to README - Add new Pipeline Workflow step 3: Lead Scoring (lead_scoring_agent.py) - Document scoring integration in Human/Machine Validation step - Add /score API endpoint documentation - Document scoring features in UI Dashboard - Add comprehensive "Lead Scoring Integration (NEW)" section with: * Overview of scoring features and metadata * Key features and integration points * Example usage showing single review and batch review scoring - Update schema versioning section with LeadReview v2 fields - Add troubleshooting guidance for scoring failures and module import - Reference lead_scoring_agent.py in Core Modules section - Document scoring configuration reference in docs/configs/ - Update Test Case Logic with lead scoring integration tests - Document scoring metadata passing through agent handoff chainbfe387a -
Update human_validation_agent.py
Verified signatureINTEGRATION: Add lead_scoring_agent integration to human_validation_agent - Import and integrate lead_scoring_agent module - Score each lead profile at action start in process_review_action() - Add scoring_result and enrichment_data fields to LeadReview schema - Pass enrichment data and scoring metadata to downstream agents - Update process_batch_review() to score all leads in batch - Store scoring metadata with each review in history - Export scoring information in review history JSON - Update documentation in docstrings to reflect scoring integration - Example batch review now demonstrates lead scoring workflowc6bfd08 -
Integrate lead_scoring_agent: add scoring metrics to analytics and UI visibility
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Integrate lead_scoring_agent: score leads at start, inject scoring fields into asset flow
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Integrate lead_scoring_agent into research_agent.py - Add score_lead() in run_pipeline()
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Integrate lead_scoring_agent into outreach_agent.py - Add score_lead() as first logic step in intake_lead()
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Update app.py
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Create lead_scoring_agent.py
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Create Add enhanced RAG-powered outreach agent for personalized campaign operations
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Add enhanced RAG-powered analytics agent with community insights, versioning hooks, and rollback support
Verified signatureThis enhanced analytics agent integrates patterns from community RAG agents (bziiit/AGENT_ANALYSE_RAG, fahmiaziz/rag-sql-agent) with: - RAG pipeline integration for context-aware analytics - Schema versioning hooks per SCHEMA_VERSIONING.md - Rollback/checkpoint capabilities for safe operations - Citation tracking and confidence scoring - Audit trail for compliance - Multi-source context retrievalf088210 -
Add multi-LLM provider support (OpenAI, Ollama, HuggingFace) with unified interface for seamless switching
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Add RAG pipeline for context-aware agent operations with document retrieval and citation support
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Update README.md
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Update README.md
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Create outreach_agent.py
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Create research_agent.py
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Create analytics_agent.py
Verified signatureanalytics_agent.py implementing live metrics on demand, end-of-day reports (scheduled), batch export (7/30/90 days), API/UI hooks, error logging/reporting, and config-driven specs. See functions: get_live_metrics, run_end_of_day_report, batch_export, expose_endpoint, get_errors. Needs analytics_utils.py. Update README for usage/testing.7c98bdb -
Create docs/configs/ANALYTICS_SPEC.md
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Create docs/configs/analytics_config.json
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Update README with comprehensive Human Validation Agent documentation
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Create Human Validation Agent module with batch review, schema validation, and queue integration
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Update README.md
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Add simulation for queue handoff to enrichment agent with browser and human fallback
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Add enrichment agent config with prioritized sources, rate limits, and retry policies
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Create recovery_enrichment_agent.py
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Add comprehensive browser agent MCP/container verification documentation
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Document browser agent MCP/container verification steps
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Update activity_log.csv
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Update test_schema_tools.py
Verified signatureAdd data recovery columns, batch error handling, human validation, and lead scrub/approve logic.765341b -
Add test script for schema registry and query/search agent tools
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Add query/search agent with advanced mapping, validation, error reporting, and JSON support
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Add schema registry with mapping, validation, and audit capabilities
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Add atomic activity ingestion validation script
Verified signatureValidates core ingestion with four test scenarios: - Valid insert (should succeed) - Invalid status value (should fail) - Missing required field (should fail) - Confirm atomic insert (should always succeed if schema-valid) Each test outputs PASS/FAIL and error details for review.bf1af21 -
Add comprehensive schema versioning and rollback documentation
Verified signatureComplete schema version control system documentation including: - Schema v1.0 definition with all required fields - Detailed version history tracking - Git-based rollback procedures (soft, hard, file-specific) - Commit tracking system (request_id, commit_hash) - Build-specific rollback for dev/staging/production - Pre/post validation procedures - Error recovery protocols - Schema change tracking format - Testing checklist - Quick reference commands This documentation ensures reliable version control, data integrity, and seamless rollback capability for all future schema modifications across different build environments.cc10b69 -
Implement atomic activity ingestion with schema validation and commit tracking
Verified signatureFEATURES: - Atomic row additions using temp file + rename pattern - Schema validation (v1.0) with required fields and valid value checks - Request ID generation for tracking - ISO 8601 timestamp generation - Full error handling with custom exceptions - File locking for concurrent access safety - Operation logging for audit trail - Commit hash generation for versioning - Comprehensive test suite with 5 test cases VERSION CONTROL: - Schema version: 1.0 - Supports rollback via git history - All operations logged with request_id and timestamp - Commit hashes enable change tracking USAGE: - Run directly: python activity_ingestion.py (runs tests) - Import: from activity_ingestion import atomic_append_activity This implementation ensures data integrity and provides full audit trail for production use.3a67095 -
Validate schema and confirm agent write access
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Add canonical activity_log.csv schema for agent tracking
Verified signatureEstablishes canonical CSV schema for activity tracking with standardized columns: - timestamp: ISO 8601 format (UTC) - agent_id: identifier for the agent performing the activity - activity_type: type of activity (init, read, write, update, delete, query, etc.) - entity_id: identifier for the entity being acted upon - status: status of the activity (created, updated, success, failed, pending, etc.) - notes: human-readable description of the activity - context: additional context or metadata about the activity This schema provides a clean foundation for agent integration and data flow tracking.a6db97b -
Create tools.csv
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