test/live-tests#66
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…LLM` querying with strategy-based execution, parallel model handling, multimodal message building, and weighted model scoring
…coding functions for `matplotlib` integration
…, performance, and fallback options
…on with key validation and model listing
…` models with endpoint, key validation, and model listing
… list_models, and validate_key methods
…orting text and multimodal queries with validation
… validation and supported models listing
…point with query, list_models, and key validation
…Ollama` models with query and validation
…th query, list_models, and key validation
…PI` with query, list_models, and key validation
…providers, manage `API` keys, and handle queries
…odels with iteration-based escalation
…order until success, with optional success tracking
…ghest-performance models with fallback to lower tiers
…l models for balanced usage
… provider/model pairs with iteration-aware selection
…ace` and `ProviderManager` with strategy-based model selection, remove `Groq`-specific hardcoding, and unify image handling
…ine to comply with `PEP8` formatting
…add new `AI/LLM` packages; update `setup.py` to read `README` safely and include additional dependencies
… license, and examples to `v0.1.3`
…arams (strategy, models, iterations, interactivity, timeout)
…d plot generation and validation system
…otlib plot mappings for common chart types
…te and grouped categorical plots
…ouped data handling
…riable visualization
…ation, validation, and retrieval
… switch to registry-based unified plotgen `API` using `Basic/Smart` generators
…filing and variable-type detection
…egation and recommendation refinement
…t generation based on dataframe schema
… into structured visualization recommendations
…ting model querying, parsing, and ensemble scoring
… for external access
…gic to dedicated `recommender` module - Removed the inlined `VisualizationRecommender` class from `suggestions.py` and imported it from `visual_suggestion.recommender` - Simplified `suggestions.py` to serve as a lightweight interface for visualization recommendation - Updated `__init__.py` imports to correctly expose `VisualizationRecommender` from its new package - Enhanced `recommender()` function to support: - `StrategyName` parameter for configurable model selection strategy - `selected_models`, `interactive`, and `timeout` arguments for flexible runtime behavior - Improved module clarity and reduced redundancy by centralizing model, API, and ensemble logic under the recommender system
…eration with inline image streaming
… synthesis with streaming support
…tgen extensions, and message orchestration
…ining chat, audio, file, and realtime clients
…tool OpenAI function calling (plotgen, explainer, recommender)
…t` assistant with user instruction injection
…me session keys via `OpenAI API`
…ents for chat and audio responses
…nd `SmartPlotGenerator`, replacing deprecated `PlotGenerator`
… `PlotExplainer` and explainer `API` coverage
…icPlotGenerator`, `SmartPlotGenerator`, and `plotgen` integration tests
…`VisualizationRecommender` initialization, parsing, and `LLM` integration
…est_suggestions` into `test/unit` for clearer test structure organization
…ner` run with `Groq` and `OpenAI` models
…nd custom plot generation examples
…g with multiple `LLM` providers
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This pull request introduces major enhancements to the PlotSense package, focusing on expanding AI model integration, improving documentation, and updating dependencies. The most significant changes include adding support for multiple large language model (LLM) providers (Groq, Anthropic, Gemini, Azure OpenAI), updating the documentation to reflect new features and workflows, and increasing the minimum required version for several dependencies. Additionally, the package metadata and licensing have been updated, and new modules and test files have been added to the codebase.
AI Model Integration
GroqProvider,AnthropicProvider,GeminiProvider, andAzureOpenAIProvider, each with methods for querying, listing models, and validating API keys. This enables PlotSense to leverage a broader range of AI models for plot suggestions and explanations. [1] [2] [3] [4] [5]core/enums/strategy.pyto support flexible model selection strategies.Documentation and API Usability
PKG-INFO) to reflect new features, including one-click plot generation, expanded explanation workflow, combined suggest-plot-explain pipeline, and improved sample code and outputs. [1] [2] [3]Dependency and Metadata Updates
matplotlibto 3.8.0 and added new dependencies:python-dotenv,groq,anthropic,openai,google-genai, andrequests. Updated license to Apache License 2.0 and expanded author information. [1] [2] [3]Codebase Structure
Miscellaneous
LICENCEandNOTICEfiles to the source distribution for compliance.These updates collectively make PlotSense more powerful, flexible, and easier to use for AI-powered data visualization and explanation workflows.