72 lines
3.2 KiB
Markdown
72 lines
3.2 KiB
Markdown
# Current Focus: Intelligent Research System Development
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## Latest Update (2025-02-27)
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We are currently developing an intelligent research system that automates the process of finding, filtering, and synthesizing information from various sources. The system is designed to be modular, allowing different components to utilize specific LLM models and endpoints based on their requirements.
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### Recent Progress
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1. **Configuration Enhancements**:
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- Implemented module-specific model assignments in the configuration
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- Added support for different LLM providers and endpoints
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- Added configuration for Jina AI's reranker
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- Added support for OpenRouter and Groq as LLM providers
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- Configured the system to use Groq's Llama 3.1 and 3.3 models for testing
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2. **LLM Interface Updates**:
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- Enhanced the LLMInterface to support different models for different modules
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- Implemented dynamic model switching based on the module and function
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- Added support for Groq and OpenRouter providers
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- Added special handling for provider-specific requirements
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- Modified the query enhancement prompt to return only the enhanced query text without explanations
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3. **Document Ranking Module**:
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- Created a new JinaReranker class that uses Jina AI's Reranker API
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- Implemented document reranking with metadata support
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- Configured to use the "jina-reranker-v2-base-multilingual" model
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4. **Search Execution Module**:
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- Fixed the Serper API integration for both regular search and Scholar search
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- Streamlined the search execution process by removing redundant Google search handler
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- Added query truncation to handle long queries (Serper API has a 2048 character limit)
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- Enhanced error handling for API requests
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- Improved result processing and deduplication
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- Created comprehensive test scripts for all search handlers
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### Current Tasks
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1. **Report Generation Module Development**:
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- Designing the report synthesis pipeline
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- Implementing result summarization using Groq's Llama 3.3 70B Versatile model
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- Creating formatting and export options
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2. **Gradio UI Development**:
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- Designing the user interface for query input
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- Implementing result display components
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- Creating configuration options in the UI
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### Next Steps
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1. **Integrate Search Execution with Query Processor**:
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- Ensure seamless flow from query processing to search execution
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- Test end-to-end pipeline with various query types
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- Fine-tune result scoring and filtering
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2. **Build the Report Generation Module**:
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- Implement report synthesis using Groq's Llama 3.3 70B Versatile model
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- Create formatting and export options
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- Develop citation and reference management
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3. **Comprehensive System Testing**:
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- Test the complete pipeline from query to report
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- Evaluate performance with different query types and domains
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- Optimize for speed and accuracy
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### Technical Notes
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- Using LiteLLM for unified LLM interface across different providers
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- Implementing a modular architecture for flexibility and maintainability
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- Using Jina AI's reranker for improved document ranking
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- Using Groq's Llama 3.1 and 3.3 models for fast inference during testing
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- Managing API keys securely through environment variables and configuration files
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