# Session Log

## Session: 2025-02-27

### Overview
Initial project setup and implementation of core functionality for semantic similarity search using Jina AI's APIs.

### Key Activities
1. Created the core `JinaSimilarity` class in jina_similarity.py with the following features:
   - Token counting using tiktoken
   - Embedding generation using Jina AI's Embeddings API
   - Similarity computation using cosine similarity
   - Error handling for token limit violations

2. Implemented the markdown segmenter in markdown_segmenter.py:
   - Segmentation of markdown documents using Jina AI's Segmenter API
   - Command-line interface for easy usage

3. Developed a test script (test_similarity.py) with:
   - Command-line argument parsing
   - File reading functionality
   - Verbose output option for debugging
   - Error handling

4. Created sample files for testing:
   - sample_chunk.txt: Contains a paragraph about pangrams
   - sample_query.txt: Contains a question about pangrams

### Insights
- Jina AI's embedding model (jina-embeddings-v3) provides high-quality embeddings for semantic search
- The token limit of 8,192 tokens is sufficient for most use cases, but longer documents need segmentation
- Normalizing embeddings simplifies similarity computation (dot product equals cosine similarity)
- Separating segmentation from similarity computation provides better modularity

### Challenges
- Ensuring proper error handling for API failures
- Managing token limits for large documents
- Balancing between chunking granularity and semantic coherence

### Next Steps
1. Add tiktoken to requirements.txt
2. Implement caching for embeddings to reduce API calls
3. Add batch processing capabilities for multiple chunks/queries
4. Create comprehensive documentation and usage examples
5. Develop integration tests for reliability testing

## Session: 2025-02-27 (Update)

### Overview
Created memory bank for the project to maintain persistent knowledge about the codebase and development progress.

### Key Activities
1. Created the `.note/` directory to store memory bank files
2. Created the following memory bank files:
   - project_overview.md: Purpose, goals, and high-level architecture
   - current_focus.md: Active work, recent changes, and next steps
   - development_standards.md: Coding conventions and patterns
   - decision_log.md: Key decisions with rationale
   - code_structure.md: Codebase organization with module descriptions
   - session_log.md: History of development sessions
   - interfaces.md: Component interfaces and API documentation

### Insights
- The project has a clear structure with well-defined components
- The use of Jina AI's APIs provides powerful semantic search capabilities
- The modular design allows for easy extension and maintenance
- Some improvements are needed, such as adding tiktoken to requirements.txt

### Next Steps
1. Update requirements.txt to include all dependencies (tiktoken)
2. Implement caching mechanism for embeddings
3. Add batch processing capabilities
4. Create comprehensive documentation
5. Develop integration tests

## Session: 2025-02-27 (Update 2)

### Overview
Expanded the project scope to build a comprehensive intelligent research system with an 8-stage pipeline.

### Key Activities
1. Defined the overall architecture for the intelligent research system:
   - 8-stage pipeline from query acceptance to report generation
   - Multiple search sources (Google, Serper, Jina Search, Google Scholar, arXiv)
   - Semantic processing using Jina AI's APIs

2. Updated the memory bank to reflect the broader vision:
   - Revised project_overview.md with the complete research system goals
   - Updated current_focus.md with next steps for each pipeline stage
   - Enhanced code_structure.md with planned project organization
   - Added new decisions to decision_log.md

### Insights
- The modular pipeline architecture allows for incremental development
- Jina AI's suite of APIs provides a consistent approach to semantic processing
- Multiple search sources will provide more comprehensive research results
- The current similarity components fit naturally into stages 6-7 of the pipeline

### Next Steps
1. Begin implementing the query processing module (stage 1)
2. Design the data structures for passing information between pipeline stages
3. Create a project roadmap with milestones for each stage
4. Prioritize development of core components for an end-to-end MVP

## Session: 2025-02-27 (Update 3)

### Overview
Planned the implementation of the Query Processing Module with LiteLLM integration and Gradio UI.

### Key Activities
1. Researched LiteLLM integration:
   - Explored LiteLLM documentation and usage patterns
   - Investigated integration with Gradio for UI development
   - Identified configuration requirements and best practices

2. Developed implementation plan:
   - Prioritized Query Processing Module with LiteLLM integration
   - Planned Gradio UI implementation for user interaction
   - Outlined configuration structure for API keys and settings
   - Established a sequence for implementing remaining modules

3. Updated memory bank:
   - Revised current_focus.md with new implementation plan
   - Added immediate and future steps for development

### Insights
- LiteLLM provides a unified interface to multiple LLM providers, simplifying integration
- Gradio offers an easy way to create interactive UIs for AI applications
- The modular approach allows for incremental development and testing
- Existing similarity components can be integrated into the pipeline at a later stage

### Next Steps
1. Update requirements.txt with new dependencies (litellm, gradio, etc.)
2. Create configuration structure for secure API key management
3. Implement LiteLLM interface for query enhancement and classification
4. Develop the query processor with structured output
5. Build the Gradio UI for user interaction

## Session: 2025-02-27 (Update 4)

### Overview
Implemented module-specific model configuration and created the Jina AI Reranker module.

### Key Activities
1. Enhanced configuration structure:
   - Added support for module-specific model assignments
   - Configured different models for different tasks
   - Added detailed endpoint configurations for various providers

2. Updated LLMInterface:
   - Modified to support module-specific model configurations
   - Added support for different endpoint types (OpenAI, Azure, Ollama)
   - Implemented method delegation to use appropriate models for each task

3. Created Jina AI Reranker module:
   - Implemented document reranking using Jina AI's Reranker API
   - Added support for reranking documents with metadata
   - Configured to use the "jina-reranker-v2-base-multilingual" model

### Insights
- Using different models for different tasks allows for optimizing performance and cost
- Jina's reranker provides a specialized solution for document ranking
- The modular approach allows for easy swapping of components and models

### Next Steps
1. Implement the remaining query processing components
2. Create the Gradio UI for user interaction
3. Test the full system with end-to-end workflows

## Session: 2025-02-27 (Update 5)

### Overview
Added support for OpenRouter and Groq as LLM providers and configured the system to use Groq for testing.

### Key Activities
1. **Jina Reranker API Integration**:
   - Updated the `rerank` method in the JinaReranker class to match the expected API request format
   - Modified the request payload to send an array of plain string documents instead of objects
   - Enhanced response processing to handle both current and older API response formats
   - Added detailed logging for API requests and responses for better debugging

2. **Testing Improvements**:
   - Created a simplified test script (`test_simple_reranker.py`) to isolate and test the reranker functionality
   - Updated the main test script to focus on core functionality without complex dependencies
   - Implemented JSON result saving for better analysis of reranker output
   - Added proper error handling in tests to provide clear feedback on issues

3. **Code Quality Enhancements**:
   - Improved error handling throughout the reranker implementation
   - Added informative debug messages at key points in the execution flow
   - Ensured backward compatibility with previous API response formats
   - Documented the expected request and response structures

### Insights and Learnings
- The Jina Reranker API expects documents as an array of plain strings, not objects with a "text" field
- The reranker response format includes a "document" field in the results which may contain either the text directly or an object with a "text" field
- Proper error handling and debug output are crucial for diagnosing issues with external API integrations
- Isolating components for testing makes debugging much more efficient

### Challenges
- Adapting to changes in the Jina Reranker API response format
- Ensuring backward compatibility with older response formats
- Debugging nested API response structures
- Managing environment variables and configuration consistently across test scripts

### Next Steps
1. **Expand Testing**: Develop more comprehensive test cases for the reranker with diverse document types
2. **Integration**: Ensure the reranker is properly integrated with the result collector for end-to-end functionality
3. **Documentation**: Update API documentation to reflect the latest changes to the reranker implementation
4. **UI Integration**: Add reranker configuration options to the Gradio interface

## Session: 2025-02-27 - Report Generation Module Planning

### Overview
In this session, we focused on planning the Report Generation module, designing a comprehensive implementation approach, and making key decisions about document scraping, storage, and processing.

### Key Activities
1. **Designed a Phased Implementation Plan**:
   - Created a four-phase implementation plan for the Report Generation module
   - Phase 1: Document Scraping and Storage
   - Phase 2: Document Prioritization and Chunking
   - Phase 3: Report Generation
   - Phase 4: Advanced Features
   - Documented the plan in the memory bank for future reference

2. **Made Key Design Decisions**:
   - Decided to use Jina Reader for web scraping due to its clean content extraction capabilities
   - Chose SQLite for document storage to ensure persistence and efficient querying
   - Designed a database schema with Documents and Metadata tables
   - Planned a token budget management system to handle context window limitations
   - Decided on a map-reduce approach for processing large document collections

3. **Addressed Context Window Limitations**:
   - Evaluated Groq's Llama 3.3 70B Versatile model's 128K context window
   - Designed document prioritization strategies based on relevance scores
   - Planned chunking strategies for handling long documents
   - Considered alternative models with larger context windows for future implementation

4. **Updated Documentation**:
   - Added the implementation plan to the memory bank
   - Updated the decision log with rationale for key decisions
   - Revised the current focus to reflect the new implementation priorities
   - Added a new session log entry to document the planning process

### Insights
- A phased implementation approach allows for incremental development and testing
- SQLite provides a good balance of simplicity and functionality for document storage
- Jina Reader integrates well with our existing Jina components (embeddings, reranker)
- The map-reduce pattern enables processing of unlimited document collections despite context window limitations
- Document prioritization is crucial for ensuring the most relevant content is included in reports

### Challenges
- Managing the 128K context window limitation with potentially large document collections
- Balancing between document coverage and report quality
- Ensuring efficient web scraping without overwhelming target websites
- Designing a flexible architecture that can accommodate different models and approaches

### Next Steps
1. Begin implementing Phase 1 of the Report Generation module:
   - Set up the SQLite database with the designed schema
   - Implement the Jina Reader integration for web scraping
   - Create the document processing pipeline
   - Develop URL validation and normalization functionality
   - Add caching and deduplication for scraped content

2. Plan for Phase 2 implementation:
   - Design the token budget management system
   - Develop document prioritization algorithms
   - Create chunking strategies for long documents

## Session: 2025-02-27 - Report Generation Module Implementation (Phase 1)

### Overview
In this session, we implemented Phase 1 of the Report Generation module, focusing on document scraping and SQLite storage. We created the necessary components for scraping web pages, storing their content in a SQLite database, and retrieving documents for report generation.

### Key Activities
1. **Created Database Manager**:
   - Implemented a SQLite database manager with tables for documents and metadata
   - Added full CRUD operations for documents
   - Implemented transaction handling for data integrity
   - Created methods for document search and retrieval
   - Used aiosqlite for asynchronous database operations

2. **Implemented Document Scraper**:
   - Created a document scraper with Jina Reader API integration
   - Added fallback mechanism using BeautifulSoup for when Jina API fails
   - Implemented URL validation and normalization
   - Added content conversion to Markdown format
   - Implemented token counting using tiktoken
   - Created metadata extraction from HTML content
   - Added document deduplication using content hashing

3. **Developed Report Generator Base**:
   - Created the basic structure for the report generation process
   - Implemented methods to process search results by scraping URLs
   - Integrated with the database manager and document scraper
   - Set up the foundation for future phases

4. **Created Test Script**:
   - Developed a test script to verify functionality
   - Tested document scraping, storage, and retrieval
   - Verified search functionality within the database
   - Ensured proper error handling and fallback mechanisms

### Insights
- The fallback mechanism for document scraping is crucial, as the Jina Reader API may not always be available or may fail for certain URLs
- Asynchronous processing significantly improves performance when scraping multiple URLs
- Content hashing is an effective way to prevent duplicate documents in the database
- Storing metadata separately from document content provides flexibility for future enhancements
- The SQLite database provides a good balance of simplicity and functionality for document storage

### Challenges
- Handling different HTML structures across websites for metadata extraction
- Managing asynchronous operations and error handling
- Ensuring proper transaction handling for database operations
- Balancing between clean content extraction and preserving important information

### Next Steps
1. **Integration with Search Execution**:
   - Connect the report generation module to the search execution pipeline
   - Implement automatic processing of search results

2. **Begin Phase 2 Implementation**:
   - Develop document prioritization based on relevance scores
   - Implement chunking strategies for long documents
   - Create token budget management system

3. **Testing and Refinement**:
   - Create more comprehensive tests for edge cases
   - Refine error handling and logging
   - Optimize performance for large numbers of documents

## Session: 2025-02-27 (Update)

### Overview
Implemented Phase 3 of the Report Generation module, focusing on report synthesis using LLMs with a map-reduce approach.

### Key Activities
1. **Created Report Synthesis Module**:
   - Implemented the `ReportSynthesizer` class for generating reports using Groq's Llama 3.3 70B model
   - Created a map-reduce approach for processing document chunks:
     - Map phase: Extract key information from individual chunks
     - Reduce phase: Synthesize extracted information into a coherent report
   - Added support for different query types (factual, exploratory, comparative)
   - Implemented automatic query type detection based on query text
   - Added citation generation and reference management

2. **Updated Report Generator**:
   - Integrated the new report synthesis module with the existing report generator
   - Replaced the placeholder report generation with the new LLM-based synthesis
   - Added proper error handling and logging throughout the process

3. **Created Test Scripts**:
   - Developed a dedicated test script for the report synthesis functionality
   - Implemented tests with both sample data and real URLs
   - Added support for mock data to avoid API dependencies during testing
   - Verified end-to-end functionality from document scraping to report generation

4. **Fixed LLM Integration Issues**:
   - Corrected the model name format for Groq provider by prefixing it with 'groq/'
   - Improved error handling for API failures
   - Added proper logging for the map-reduce process

### Insights
- The map-reduce approach is effective for processing large amounts of document data
- Different query types benefit from specialized report templates
- Groq's Llama 3.3 70B model produces high-quality reports with good coherence and factual accuracy
- Proper citation management is essential for creating trustworthy reports
- Automatic query type detection works well for common query patterns

### Challenges
- Managing API errors and rate limits with external LLM providers
- Ensuring consistent formatting across different report sections
- Balancing between report comprehensiveness and token usage
- Handling edge cases where document chunks contain irrelevant information

### Next Steps
1. Implement support for alternative models with larger context windows
2. Develop progressive report generation for very large research tasks
3. Create visualization components for data mentioned in reports
4. Add interactive elements to the generated reports
5. Implement report versioning and comparison

## Session: 2025-02-27 (Update 2)

### Overview
Successfully tested the end-to-end query to report pipeline with a specific query about the environmental and economic impact of electric vehicles, and fixed an issue with the Jina reranker integration.

### Key Activities
1. **Fixed Jina Reranker Integration**:
   - Corrected the import statement in query_to_report.py to use the proper function name (get_jina_reranker)
   - Updated the reranker call to properly format the results for the JinaReranker
   - Implemented proper extraction of text from search results for reranking
   - Added mapping of reranked indices back to the original results

2. **Created EV Query Test Script**:
   - Developed a dedicated test script (test_ev_query.py) for testing the pipeline with a query about electric vehicles
   - Configured the script to use 7 results per search engine for a comprehensive report
   - Added proper error handling and result display

3. **Tested End-to-End Pipeline**:
   - Successfully executed the full query to report workflow
   - Verified that all components (query processor, search executor, reranker, report generator) work together seamlessly
   - Generated a comprehensive report on the environmental and economic impact of electric vehicles

4. **Identified Report Detail Configuration Options**:
   - Documented multiple ways to adjust the level of detail in generated reports
   - Identified parameters that can be modified to control report comprehensiveness
   - Created a plan for implementing customizable report detail levels

### Insights
- The end-to-end pipeline successfully connects all major components of the system
- The Jina reranker significantly improves the relevance of search results for report generation
- The map-reduce approach effectively processes document chunks into a coherent report
- Some document sources (like ScienceDirect and ResearchGate) may require special handling due to access restrictions

### Challenges
- Handling API errors and access restrictions for certain document sources
- Ensuring proper formatting of data between different components
- Managing the processing of a large number of document chunks efficiently

### Next Steps
1. **Implement Customizable Report Detail Levels**:
   - Develop a system to allow users to select different levels of detail for generated reports
   - Integrate the customizable detail levels into the report generator
   - Test the new feature with various query types

2. **Add Support for Alternative Models**:
   - Research and implement support for alternative models with larger context windows
   - Test the new models with the report generation pipeline

3. **Develop Progressive Report Generation**:
   - Design and implement a system for progressive report generation
   - Test the new feature with very large research tasks

4. **Create Visualization Components**:
   - Develop visualization components for data mentioned in reports
   - Integrate the visualization components into the report generator

5. **Add Interactive Elements**:
   - Develop interactive elements for the generated reports
   - Integrate the interactive elements into the report generator

## Session: 2025-02-28

### Overview
Implemented customizable report detail levels for the Report Generation Module, allowing users to select different levels of detail for generated reports.

### Key Activities
1. **Created Report Detail Levels Module**:
   - Implemented a new module `report_detail_levels.py` with an enum for detail levels (Brief, Standard, Detailed, Comprehensive)
   - Created a `ReportDetailLevelManager` class to manage detail level configurations
   - Defined specific parameters for each detail level (num_results, token_budget, chunk_size, overlap_size, model)
   - Added methods to validate and retrieve detail level configurations

2. **Updated Report Synthesis Module**:
   - Modified the `ReportSynthesizer` class to accept and use detail level parameters
   - Updated synthesis templates to adapt based on the selected detail level
   - Adjusted the map-reduce process to handle different levels of detail
   - Implemented model selection based on detail level requirements

3. **Enhanced Report Generator**:
   - Added methods to set and get detail levels in the `ReportGenerator` class
   - Updated the document preparation process to use detail level configurations
   - Modified the report generation workflow to incorporate detail level settings
   - Implemented validation for detail level parameters

4. **Updated Query to Report Script**:
   - Added command-line arguments for detail level selection
   - Implemented a `--list-detail-levels` option to display available options
   - Updated the main workflow to pass detail level parameters to the report generator
   - Added documentation for the new parameters

5. **Created Test Scripts**:
   - Updated `test_ev_query.py` to support detail level selection
   - Created a new `test_detail_levels.py` script to generate reports with all detail levels for comparison
   - Added metrics collection (timing, report size, word count) for comparison

### Insights
- Different detail levels significantly affect report length, depth, and generation time
- The brief level is useful for quick summaries, while comprehensive provides exhaustive information
- Using different models for different detail levels offers a good balance between speed and quality
- Configuring multiple parameters (num_results, token_budget, etc.) together creates a coherent detail level experience

### Challenges
- Ensuring that the templates produce appropriate output for each detail level
- Balancing between speed and quality for different detail levels
- Managing token budgets effectively across different detail levels
- Ensuring backward compatibility with existing code

### Next Steps
1. Conduct thorough testing of the detail level features with various query types
2. Gather user feedback on the quality and usefulness of reports at different detail levels
3. Refine the detail level configurations based on testing and feedback
4. Implement progressive report generation for very large research tasks
5. Develop visualization components for data mentioned in reports

## Session: 2025-02-28 - Enhanced Report Detail Levels

### Overview
In this session, we enhanced the report detail levels to focus more on analytical depth rather than just adding additional sections. We improved the document chunk processing to extract more meaningful information from each chunk for detailed and comprehensive reports.

### Key Activities
1. **Enhanced Template Modifiers for Detailed and Comprehensive Reports**:
   - Rewrote the template modifiers to focus on analytical depth, evidence density, and perspective diversity
   - Added explicit instructions to prioritize depth over breadth
   - Emphasized multi-layered analysis, causal relationships, and interconnections
   - Added instructions for exploring second and third-order effects

2. **Improved Document Chunk Processing**:
   - Created a new `_get_extraction_prompt` method that provides different extraction prompts based on detail level
   - For DETAILED reports: Added focus on underlying principles, causal relationships, and different perspectives
   - For COMPREHENSIVE reports: Added focus on multi-layered analysis, complex causal networks, and theoretical frameworks
   - Modified the `map_document_chunks` method to pass the detail level parameter

3. **Enhanced MapReduce Approach**:
   - Updated the map phase to use detail-level-specific extraction prompts
   - Ensured the detail level parameter is passed throughout the process
   - Maintained the efficient processing of document chunks while improving the quality of extraction

### Insights
- The MapReduce approach is well-suited for LLM-based report generation, allowing processing of more information than would fit in a single context window
- Different extraction prompts for different detail levels significantly affect the quality and depth of the extracted information
- Focusing on analytical depth rather than additional sections provides more value to the end user
- The enhanced prompts guide the LLM to provide deeper analysis of causal relationships, underlying mechanisms, and interconnections

### Challenges
- Balancing between depth and breadth in detailed reports
- Ensuring that the extraction prompts extract the most relevant information for each detail level
- Managing the increased processing time for detailed and comprehensive reports with enhanced extraction

### Next Steps
1. Conduct thorough testing of the enhanced detail level features with various query types
2. Compare the analytical depth and quality of reports generated with the new prompts
3. Gather user feedback on the improved reports at different detail levels
4. Explore parallel processing for the map phase to reduce overall report generation time
5. Further refine the detail level configurations based on testing and feedback

## Session: 2025-02-28 - Gradio UI Enhancements and Future Planning

### Overview
In this session, we fixed issues in the Gradio UI for report generation and planned future enhancements to improve search quality and user experience.

### Key Activities
1. **Fixed Gradio UI for Report Generation**:
   - Updated the `generate_report` method in the Gradio UI to properly process queries and generate structured queries
   - Integrated the `QueryProcessor` to create structured queries from user input
   - Fixed method calls and parameter passing to the `execute_search` method
   - Implemented functionality to process `<thinking>` tags in the generated report
   - Added support for custom model selection in the UI
   - Updated the interfaces documentation to include ReportGenerator and ReportDetailLevelManager interfaces

2. **Planned Future Enhancements**:
   - **Multiple Query Variation Generation**:
     - Designed an approach to generate several similar queries with different keywords for better search coverage
     - Planned modifications to the QueryProcessor and SearchExecutor to handle multiple queries
     - Estimated this as a moderate difficulty task (3-4 days of work)
   
   - **Threshold-Based Reranking with Larger Document Sets**:
     - Developed a plan to process more initial documents and use reranking to select the most relevant ones
     - Designed new detail level configuration parameters for initial and final result counts
     - Estimated this as an easy to moderate difficulty task (2-3 days of work)
   
   - **UI Progress Indicators**:
     - Identified the need for chunk processing progress indicators in the UI
     - Planned modifications to report_synthesis.py to add logging during document processing
     - Estimated this as a simple enhancement (15-30 minutes of work)

### Insights
- The modular architecture of the system makes it easy to extend with new features
- Providing progress indicators during report generation would significantly improve user experience
- Generating multiple query variations could substantially improve search coverage and result quality
- Using a two-stage approach (fetch more, then filter) for document retrieval would likely improve report quality

### Challenges
- Balancing between fetching enough documents for comprehensive coverage and maintaining performance
- Ensuring proper deduplication when using multiple query variations
- Managing the increased API usage that would result from processing more queries and documents

### Next Steps
1. Implement the chunk processing progress indicators as a quick win
2. Begin work on the multiple query variation generation feature
3. Test the current implementation with various query types to identify any remaining issues
4. Update the documentation to reflect the new features and future plans

## Session: 2025-02-28: Google Gemini Integration and Reference Formatting

### Overview
Fixed the integration of Google Gemini models with LiteLLM, and fixed reference formatting issues.

### Key Activities
1. **Fixed Google Gemini Integration**:
   - Updated the model format to `gemini/gemini-2.0-flash` in config.yaml
   - Modified message formatting for Gemini models in LLM interface
   - Added proper handling for the 'gemini' provider in environment variable setup

2. **Fixed Reference Formatting Issues**:
   - Enhanced the instructions for reference formatting to ensure URLs are included
   - Added a recovery mechanism for truncated references
   - Improved context preparation to better extract URLs for references

3. **Converted LLM Interface Methods to Async**:
   - Made `generate_completion`, `classify_query`, and `enhance_query` methods async
   - Updated dependent code to properly await these methods
   - Fixed runtime errors related to async/await patterns

### Key Insights
- Gemini models require special message formatting (using 'user' and 'model' roles instead of 'system' and 'assistant')
- References were getting cut off due to token limits, requiring a separate generation step
- The async conversion was necessary to properly handle async LLM calls throughout the codebase

### Challenges
- Ensuring that the templates produce appropriate output for each detail level
- Balancing between speed and quality for different detail levels
- Managing token budgets effectively across different detail levels
- Ensuring backward compatibility with existing code

### Next Steps
1. Continue testing with Gemini models to ensure stable operation
2. Consider adding more robust error handling for LLM provider-specific issues
3. Improve the reference formatting further if needed

## Session: 2025-02-28: Fixing Reference Formatting and Async Implementation

### Overview
Fixed reference formatting issues with Gemini models and updated the codebase to properly handle async methods.

### Key Activities
1. **Enhanced Reference Formatting**:
   - Improved instructions to emphasize including URLs for each reference
   - Added duplicate URL fields in the context to ensure URLs are captured
   - Updated the reference generation prompt to explicitly request URLs
   - Added a separate reference generation step to handle truncated references

2. **Fixed Async Implementation**:
   - Converted all LLM interface methods to async for proper handling
   - Updated QueryProcessor's generate_search_queries method to be async
   - Modified query_to_report.py to correctly await async methods
   - Fixed runtime errors related to async/await patterns

3. **Updated Gradio Interface**:
   - Modified the generate_report method to properly handle async operations
   - Updated the report button click handler to correctly pass parameters
   - Fixed the parameter order in the lambda function for async execution
   - Improved error handling in the UI

### Key Insights
- Async/await patterns need to be consistently applied throughout the codebase
- Reference formatting requires explicit instructions to include URLs
- Gradio's interface needs special handling for async functions

### Challenges
- Ensuring that all async methods are properly awaited
- Balancing between detailed instructions and token limits for reference generation
- Managing the increased processing time for async operations

### Next Steps
1. Continue testing with Gemini models to ensure stable operation
2. Consider adding more robust error handling for LLM provider-specific issues
3. Improve the reference formatting further if needed
4. Update documentation to reflect the changes made to the LLM interface
5. Consider adding more unit tests for the async methods

## Session: 2025-02-28: Fixed NoneType Error in Report Synthesis

### Issue
Encountered an error during report generation:
```
TypeError: 'NoneType' object is not subscriptable
```

The error occurred in the `map_document_chunks` method of the `ReportSynthesizer` class when trying to slice a title that was `None`.

### Changes Made
1. Fixed the chunk counter in `map_document_chunks` method:
   - Used a separate counter for individual chunks instead of using the batch index
   - Added a null check for chunk titles with a fallback to 'Untitled'

2. Added defensive code in `synthesize_report` method:
   - Added code to ensure all chunks have a title before processing
   - Added null checks for title fields

3. Updated the `DocumentProcessor` class:
   - Modified `process_documents_for_report` to ensure all chunks have a title
   - Updated `chunk_document_by_sections`, `chunk_document_fixed_size`, and `chunk_document_hierarchical` methods to handle None titles
   - Added default 'Untitled' value for all title fields

### Testing
The changes were tested with a report generation task that previously failed, and the error was resolved.

### Next Steps
1. Consider adding more comprehensive null checks throughout the codebase
2. Add unit tests to verify proper handling of missing or null fields
3. Implement better error handling and recovery mechanisms
