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  • class OneCo_hybrid_RAG

    A class named OneCo_hybrid_RAG

    File: /tf/active/vicechatdev/OneCo_hybrid_RAG copy.py

    class oneco_hybrid_rag

  • class OneCo_hybrid_RAG_v1

    A class named OneCo_hybrid_RAG

    File: /tf/active/vicechatdev/OneCo_hybrid_RAG_old.py

    class oneco_hybrid_rag

  • class OneCo_hybrid_RAG_v2

    A class named OneCo_hybrid_RAG

    File: /tf/active/vicechatdev/OneCo_hybrid_RAG.py

    class oneco_hybrid_rag

  • class DocumentProcessor_v4

    Handles document processing and text extraction using llmsherpa (same approach as offline_docstore_multi_vice.py).

    File: /tf/active/vicechatdev/docchat/document_processor.py

    class documentprocessor

  • class DocChatEmbeddingFunction

    A custom ChromaDB embedding function that generates OpenAI embeddings with automatic text summarization for documents exceeding token limits.

    File: /tf/active/vicechatdev/docchat/document_indexer.py

    embeddings chromadb openai text-processing summarization

  • class DocumentProcessor_v8

    Process different document types for indexing

    File: /tf/active/vicechatdev/docchat/document_indexer.py

    class documentprocessor

  • class TextClusterer

    A class that clusters similar documents based on their embeddings using various clustering algorithms (K-means, Agglomerative, DBSCAN) and optionally generates summaries for each cluster.

    File: /tf/active/vicechatdev/chromadb-cleanup/src/clustering/text_clusterer.py

    clustering document-clustering embeddings machine-learning kmeans

  • function calculate_similarity

    Computes the cosine similarity between two embedding vectors, returning a normalized score between 0 and 1 that measures their directional alignment.

    File: /tf/active/vicechatdev/chromadb-cleanup/src/utils/similarity_utils.py

    cosine-similarity vector-comparison embeddings similarity-metric machine-learning

  • function build_similarity_matrix

    Computes a pairwise cosine similarity matrix for a collection of embedding vectors, where each cell (i,j) represents the similarity between embedding i and embedding j.

    File: /tf/active/vicechatdev/chromadb-cleanup/src/utils/similarity_utils.py

    embeddings similarity cosine-similarity matrix nlp

  • class AgentExecutor

    Agent-based script executor that generates standalone Python files, manages dependencies, and provides iterative debugging capabilities

    File: /tf/active/vicechatdev/vice_ai/agent_executor.py

    class agentexecutor

  • class ScriptExecutor

    A sandboxed Python script executor that safely runs user-provided Python code with timeout controls, security restrictions, and isolated execution environments for data analysis tasks.

    File: /tf/active/vicechatdev/vice_ai/script_executor.py

    sandbox script-execution security code-validation data-analysis

  • function validate_sheet_format

    Analyzes Excel sheet structure using multiple heuristics to classify it as tabular data, information sheet, or mixed format, returning quality metrics and extraction recommendations.

    File: /tf/active/vicechatdev/vice_ai/smartstat_service.py

    data-validation excel-processing sheet-classification data-quality heuristic-analysis

  • class SmartStatService

    Service for running SmartStat analysis sessions in Vice AI

    File: /tf/active/vicechatdev/vice_ai/smartstat_service.py

    class smartstatservice

  • class Config

    Configuration class that manages application-wide settings, directory structures, API keys, and operational parameters for a statistical analysis application.

    File: /tf/active/vicechatdev/vice_ai/smartstat_config.py

    configuration settings flask api-keys directory-management

  • function clean_nan_for_json

    Recursively traverses nested data structures (dicts, lists) and converts NaN, null, and invalid numeric values to None for safe JSON serialization.

    File: /tf/active/vicechatdev/vice_ai/data_analysis_service.py

    json-serialization data-cleaning nan-handling recursive data-preprocessing

  • class OneCo_hybrid_RAG_v3

    A class named OneCo_hybrid_RAG

    File: /tf/active/vicechatdev/vice_ai/hybrid_rag_engine.py

    class oneco_hybrid_rag

  • class StatisticalAgent

    LLM-powered statistical analysis agent

    File: /tf/active/vicechatdev/vice_ai/statistical_agent.py

    class statisticalagent

  • function clean_for_json_v14

    Recursively sanitizes Python objects to make them JSON-serializable by converting NumPy types to native Python types and handling NaN/Inf values.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/42b81361-ba7e-4d79-9598-3090af68384b/analysis_2.py

    json serialization numpy data-conversion type-conversion

  • function clean_for_json_v4

    Recursively traverses nested data structures (dicts, lists, arrays) and converts NaN and Inf float values to None for safe JSON serialization, while also converting NumPy types to native Python types.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/7372154d-807e-4723-a769-4668761944b5/analysis_2.py

    json serialization data-cleaning numpy nan-handling

  • function clean_for_json_v11

    Recursively sanitizes Python objects (dicts, lists, floats) to make them JSON-serializable by replacing NaN and infinity float values with None.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/f5da873e-41e6-4f34-b3e4-f7443d4d213b/analysis_4.py

    json serialization data-cleaning nan-handling infinity-handling

  • function clean_for_json_v5

    Recursively traverses nested data structures (dictionaries, lists) and sanitizes numeric values by converting NaN and Inf to None, and normalizing NumPy numeric types to native Python types for JSON serialization.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/e4e8cb00-c17d-4282-aa80-5af67f32952f/analysis_1.py

    data-cleaning json-serialization numpy data-preprocessing nan-handling

  • function clean_for_json_v1

    Recursively sanitizes nested data structures (dictionaries, lists, tuples) by converting NaN and Inf values to None and normalizing NumPy types to native Python types for JSON serialization.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/e4e8cb00-c17d-4282-aa80-5af67f32952f/project_1/analysis.py

    json serialization data-cleaning numpy nan-handling

  • function clean_for_json_v9

    Recursively sanitizes Python objects (dicts, lists, floats) to ensure they are JSON-serializable by converting NaN and infinity values to None and ensuring all dictionary keys are strings.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/c385e1f5-fbf6-4832-8fd4-78ef8b72fc53/project_2/analysis.py

    json serialization data-cleaning sanitization nan-handling

  • function clean_for_json_v10

    Recursively converts Python objects containing NumPy and Pandas data types into JSON-serializable native Python types.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/c385e1f5-fbf6-4832-8fd4-78ef8b72fc53/project_1/analysis.py

    json serialization data-conversion numpy pandas

  • function calculate_cv

    Calculates the coefficient of variation (CV) for a dataset, expressed as a percentage of the standard deviation relative to the mean.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d48d7789-9627-4e96-9f48-f90b687cd07d/analysis_1.py

    statistics coefficient-of-variation data-analysis variability dispersion

  • function clean_for_json_v13

    Recursively sanitizes Python objects to make them JSON-serializable by converting NumPy types to native Python types and handling NaN/Inf values.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d48d7789-9627-4e96-9f48-f90b687cd07d/analysis_1.py

    json serialization numpy data-cleaning type-conversion

  • function clean_for_json_v3

    Recursively converts Python objects (including NumPy and Pandas types) into JSON-serializable formats by handling special numeric types, NaN/Inf values, and nested data structures.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/f0a78968-1d2b-4fbe-a0c6-a372da2ce2a4/project_1/analysis.py

    json serialization data-conversion numpy pandas

  • function calculate_cv_v1

    Calculates the Coefficient of Variation (CV) for a dataset, expressed as a percentage. CV measures relative variability by dividing standard deviation by mean.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d1e252f5-950c-4ad7-b425-86b4b02c3c62/analysis_4.py

    statistics coefficient-of-variation variability dispersion data-analysis

  • function clean_for_json_v6

    Recursively traverses nested data structures (dicts, lists) and sanitizes floating-point values by replacing NaN and Inf with None, while also converting NumPy numeric types to native Python types.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d1e252f5-950c-4ad7-b425-86b4b02c3c62/analysis_4.py

    json serialization data-cleaning numpy nan-handling

  • function correlation_significance

    Calculates Pearson correlation coefficient and statistical significance (p-value) between two numeric arrays, handling NaN values automatically.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d1e252f5-950c-4ad7-b425-86b4b02c3c62/analysis_7.py

    statistics correlation pearson p-value significance-testing

  • function clean_for_json_v7

    Recursively traverses and sanitizes data structures (dicts, lists, numpy types) to ensure JSON serialization compatibility by converting numpy types to native Python types and handling NaN/Inf values.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d1e252f5-950c-4ad7-b425-86b4b02c3c62/analysis_1.py

    json serialization data-cleaning numpy pandas

  • function clean_for_json_v8

    Recursively traverses and converts a nested data structure (dicts, lists, numpy types, pandas NaN) into JSON-serializable Python primitives.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d1e252f5-950c-4ad7-b425-86b4b02c3c62/analysis_5.py

    json serialization data-cleaning numpy pandas

  • function clean_for_json_v2

    Recursively traverses nested data structures (dicts, lists) and sanitizes numeric values by converting NaN and Inf to None, and numpy types to native Python types for JSON serialization.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/e9b7c942-87b5-4a6f-865e-e7a0d62fb0a1/analysis_2.py

    json serialization data-cleaning numpy nan-handling

  • function detect_outliers_zscore

    Detects outliers in numerical data using the Z-score statistical method, identifying data points that deviate significantly from the mean.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/328d2f87-3367-495e-89f7-e633ff8c5b3d/analysis_2.py

    outlier-detection statistics data-cleaning anomaly-detection z-score

  • function clean_for_json_v15

    Recursively sanitizes Python objects to make them JSON-serializable by converting NumPy types to native Python types and handling NaN/Inf float values.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/290a39ea-3ae0-4301-8e2f-9d5c3bf80e6e/analysis_3.py

    json serialization data-cleaning numpy type-conversion

  • function clean_for_json_v12

    Recursively sanitizes Python objects to make them JSON-serializable by converting non-serializable types (NumPy types, pandas objects, tuples, NaN/Inf values) into JSON-compatible formats.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/290a39ea-3ae0-4301-8e2f-9d5c3bf80e6e/project_3/analysis.py

    json serialization data-cleaning numpy pandas

  • function clean_for_json

    Recursively traverses and sanitizes Python data structures (dicts, lists, tuples, numpy arrays) to ensure all values are JSON-serializable by converting numpy types, handling NaN/Inf values, and normalizing data types.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/f0b81d95-24d9-418a-8d9f-1b241684e64c/project_1/analysis.py

    json serialization data-cleaning numpy pandas

  • class AgentExecutor_v1

    Agent-based script executor that generates standalone Python files, manages dependencies, and provides iterative debugging capabilities

    File: /tf/active/vicechatdev/full_smartstat/agent_executor.py

    class agentexecutor

  • function test_enhanced_workflow

    A comprehensive test function that validates the EnhancedSQLWorkflow system by testing component initialization, request parsing, and data assessment capabilities.

    File: /tf/active/vicechatdev/full_smartstat/test_enhanced_workflow.py

    testing integration-test workflow sql data-analysis

  • class Config_v1

    Configuration class that centralizes all application settings including Flask configuration, directory paths, API keys, LLM model settings, and statistical analysis parameters.

    File: /tf/active/vicechatdev/full_smartstat/config.py

    configuration settings flask llm openai

  • function create_sample_data_v1

    Generates a synthetic dataset with 200 samples containing group-based measurements, quality scores, environmental data, and temporal information, then saves it to a CSV file.

    File: /tf/active/vicechatdev/full_smartstat/demo.py

    data-generation synthetic-data sample-data testing demonstration

  • function safe_json_dumps

    Safely serializes Python objects to JSON format, handling NaN values and datetime objects that would otherwise cause serialization errors.

    File: /tf/active/vicechatdev/full_smartstat/services.py

    json serialization data-conversion nan-handling datetime

  • function test_agent_executor

    Integration test function that validates the AgentExecutor's ability to generate and execute data analysis projects using synthetic test data.

    File: /tf/active/vicechatdev/full_smartstat/debug_agent.py

    testing integration-test agent-executor data-analysis pandas

  • class StatisticalAgent_v1

    LLM-powered statistical analysis agent

    File: /tf/active/vicechatdev/full_smartstat/statistical_agent.py

    class statisticalagent

  • class AgentExecutor_v2

    Agent-based script executor that generates standalone Python files, manages dependencies, and provides iterative debugging capabilities

    File: /tf/active/vicechatdev/smartstat/agent_executor.py

    class agentexecutor

  • class StatisticalAgent_v2

    LLM-powered statistical analysis agent

    File: /tf/active/vicechatdev/smartstat/statistical_agent.py

    class statisticalagent

  • class HashableJSON

    A JSON encoder extension that generates hashable string representations for a wide variety of Python objects, including those not normally JSON-serializable like sets, numpy arrays, and pandas DataFrames.

    File: /tf/active/vicechatdev/patches/util.py

    json hashing serialization memoization caching

  • function isscalar

    Checks if a value is a scalar type, None, or a datetime-related type.

    File: /tf/active/vicechatdev/patches/util.py

    validation type-checking scalar datetime numpy

  • function isnumeric

    Determines whether a given value can be converted to a numeric type (int or float), excluding strings and boolean types.

    File: /tf/active/vicechatdev/patches/util.py

    validation type-checking numeric data-validation type-conversion

  • function asarray

    Converts array-like objects (lists, pandas Series, objects with __array__ method) to NumPy ndarray format with optional strict validation.

    File: /tf/active/vicechatdev/patches/util.py

    array-conversion numpy data-processing type-conversion validation

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