feat/plotchat-sdk#65
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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
…lors, and NaN handling
…h seaborn integration
… and numeric validation
…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`
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This pull request introduces significant updates to the PlotSense package, focusing on expanding LLM provider integrations, updating documentation, and refining the API and dependency management. The most important changes are grouped below:
Provider Integrations
LLMProviderinterface for querying, model listing, and API key validation. This enables seamless support for multiple AI backends. [1] [2] [3] [4] [5]StrategyNameenum to support different model selection strategies.API and Dependency Updates
requires.txtandPKG-INFOto require newer versions ofmatplotliband added support forgroq,anthropic,openai,google-genai,python-dotenv, andrequests. [1] [2]__init__.pyto expose new generator classes (BasicPlotGenerator,SmartPlotGenerator).Documentation and Usage Improvements
PKG-INFO, including clearer installation instructions, new sample outputs, combined workflow examples, and a feedback form link. [1] [2] [3] [4]Packaging and Source Management
SOURCES.txtandtop_level.txtto include new modules for plot generation, recommender system, and test files, ensuring all new features are properly packaged. [1] [2] [3]These changes collectively modernize PlotSense, making it more extensible and easier to use with a variety of AI providers, while improving documentation and packaging for end users and contributors.