Store a message in the Supabase messages table.

Start a timer

Helper to create mock Telegram update.

Simple text splitting as fallback. Args: text: Text to split Returns: List of chunk

Simple text splitting as fallback. Args: text: Text to split Returns: List of chunk

Simple text splitting as fallback. Args: text: Text to split Returns: List of chunk

Add episodes to the graph with a given prefix.

Add the footer to the page

Create a callable function for a specific tool. This allows us to execute database operations through the MCP server. Args:

Chunk document using simple rules. Args: content: Document content title: Document title

Close connection pool.

Create appropriate chunker based on configuration. Args: config: Chunking configuration Returns: Chunker instan

Display welcome message.

Generates the PowerPoint slides

Main entry point to generate the .json files

Get a ChromaDB client with the specified persistence directory. Args: persist_directory: Directory where ChromaDB will store its data

Get a ChromaDB client with the specified persistence directory. Args: persist_directory: Directory where ChromaDB will store its data

Returns the client configuration for the LLM call

Get embedding client configuration based on environment variables. Returns: Configured OpenAI-compatible client for embeddings

Get embedding model name from environment. Returns: Embedding model name

Returns the form session state key

Returns the ideation from prompt configuration

Get LLM model configuration based on environment variables. Args: model_choice: Optional override for model choice Retu

Returns the model to use based on the default model to use

Returns the model configurations

Returns the model configurations

Returns the response from the index or the chat

Get prompt enhancement flag condition

Search the knowledge graph. Args: input_data: Search parameters Returns: List of graph search results

Perform a image generation request

Abstract method for initializing the LLM

Create connection pool.

Make a request to the NWS API with proper error handling.

Returns the OpenAI API client and model configurations

Query the RAG agent with a question. Args: question: The question to ask max_results: Maximum number

Store a message in the Supabase messages table.

Store a message in the Supabase messages table.

Store a message in the Supabase messages table.

Store a message in the Supabase messages table.

Store a message in the Supabase messages table.

Store a message in the Supabase messages table.

Store a message in the Supabase messages table.

Store a message in the Supabase messages table.

Store a message in the Supabase messages table.

Store a message in the Supabase messages table.

Store a message in the Supabase messages table.

Store a message in the Supabase messages table.

Store a message in the Supabase messages table.

Store a message in Supabase.

Store a message in the Supabase messages table.

Update the side bar conversations from the database

Validate the question

Perform vector similarity search. Args: input_data: Search parameters Returns: List of matching chunks

Main agent that creates initial plan and final summary

Async context manager entry

Create a DocumentChunk object.

Create a DocumentChunk object.

Create a DocumentChunk object.

Create a DocumentChunk object.

Create a DocumentChunk object.

Create a DocumentChunk object.

Create DocumentChunk objects from text chunks. Args: chunks: List of chunk texts original_content: O

Create DocumentChunk objects from text chunks. Args: chunks: List of chunk texts original_content: O

Create DocumentChunk objects from text chunks. Args: chunks: List of chunk texts original_content: O

Create DocumentChunk objects from text chunks. Args: chunks: List of chunk texts original_content: O

Create a zero vector with the correct dimension.

Format season averages for display.

Generate a basic analysis when AI analysis fails.

Generate prediction using AI analysis of comprehensive team and player data.

Get team statistical leaders.

Generate hash for text.

Generate hash for text.

Generate hash for text.

Generate hash for text.

Process texts individually as fallback. Args: texts: List of texts to embed Returns:

Process texts individually as fallback. Args: texts: List of texts to embed Returns:

Process texts individually as fallback. Args: texts: List of texts to embed Returns:

Process texts individually as fallback. Args: texts: List of texts to embed Returns:

Simple fallback chunking when HybridChunker can't be used. This is used when: - No DoclingDocument is provided - Hyb

Simple fallback chunking when HybridChunker can't be used. This is used when: - No DoclingDocument is provided - Hyb

Simple intent matching using string matching. Args: text: Input text to classify Returns: Tuple of (intent, conf

Split a long section using LLM for semantic boundaries. Args: section: Section to split Returns

Split content on structural boundaries. Args: content: Content to split Returns: Li

Add document chunks to the knowledge graph. Args: chunks: List of document chunks document_title: Ti

Add the results containers to the page

Main interaction loop that processes user queries using the LLM and available tools. This function: 1. Sends the user query to the LLM

Perform a Allegro video generation request

Check API health.

Split text by separator first, then ensure chunks are within size limits. Args: text: The text to split

Chunk document using simple paragraph-based rules. Args: content: Document content title: Document title

Chunk document using simple paragraph-based rules. Args: content: Document content title: Document title

Clean up all resources including the exit stack.

Clean up server resources.