feat: add validation and cleaning methods for unique combinations and extracted values

feat: enhance LLMClient with structured output capabilities

feat: update UniqueIndicesCombinator to use structured output from LLM

feat: enhance app.py with session state management and extraction strategy selection

feat: enhance architecture and developer documentation for clarity and detail

feat: enhance field and unique indices descriptions handling in app and agents

feat: update unique indices combinator to return array of objects

Add extraction strategy selection and unique indices handling in app.py and FieldMapperAgent. Enhance Planner to accommodate new parameters for execution plans. Update AzureDIService to process tables and save extracted content in both markdown and JSON formats for improved traceability.

Refactor FieldMapperAgent to process entire documents at once, improving efficiency by eliminating page-by-page analysis. Update logging to reflect changes in document processing. Enhance AzureDIService to save extracted content to timestamped files for better traceability.

Refactor AzureDIService to enhance document analysis logging and update table extraction logic. Temporarily disable table extraction and improve content type logging. Update TableAgent to reflect changes in context handling.

Add cost tracking functionality across various components, including Executor, Planner, and FieldMapperAgent. Integrate CostTracker to monitor LLM and document intelligence costs, enhancing logging for cost-related metrics and providing detailed cost breakdowns in the user interface.

Update Dockerfile to use new app entry point and enhance requirements.txt with additional dependencies. Remove obsolete streamlit_app.py file.