TODO: Get value from cache

Initialize data source Args: source_name: Name of the data source config: Configuration dictionary with sour

Initialize vector indexer. Args: collection_name: Name of vector collection batch_size: Documents per batch

Initialize rate limiter. TODO: Implement initialization - Store rate limit configuration - Initialize client buckets

Format data as Server-Sent Event. SSE format: - event: event_name - data: json_data - id: event_id - (blank line to end even

Process a RAG query. Args: question: User question filters: Optional metadata filters for retrieval

TODO: Execute ingestion pipeline Steps: 1. Connect to all data sources 2. Fetch data from each source 3. Com

Convert to dictionary.

Initialize the LLM server. Args: config: LLM configuration object with model settings TODO: Initialize the foll

Initialize RAG pipeline. Args: retriever: Document retriever instance llm_server: LLM server instance

Convert text chunks to Chunk objects. Args: texts: List of text chunks metadata: Metadata to attach

Default RAG prompt template. Returns: Prompt template string TODO: Create effective RAG prompt: 1. Syst

Internal method for complete generation. TODO: Implement non-streaming logic: 1. Generate text synchronously 2. Wait

Internal method for streaming generation. TODO: Implement streaming logic: 1. Generate unique request ID 2. Create a

Recursively split text. Args: text: Text to split separators: Remaining separators to try Returns:

Check Airflow scheduler health. Returns: ComponentHealth object TODO: 1. Check if scheduler is running

Check Airflow webserver health. Returns: ComponentHealth object TODO: 1. Ping webserver endpoint

Check health of all components. Returns: Dictionary mapping component name to health status TODO: 1. Ru

Check MinIO object storage health. Returns: ComponentHealth object TODO: 1. Test MinIO API endpoint

Check MLflow tracking server health. Returns: ComponentHealth object TODO: 1. Ping MLflow API endpoint

Check model serving endpoints health. Returns: ComponentHealth object TODO: 1. Get list of deployed mod

Check PostgreSQL database health. Returns: ComponentHealth object TODO: 1. Test database connection

Check Redis cache health. Returns: ComponentHealth object TODO: 1. Test Redis connection 2. Ver

Asynchronously initialize the LLM engine and load the model. This is a separate method to allow for async initialization in the API

TODO: Load the pre-trained model Steps: 1. Check if model_name is valid 2. Load model from torchvision.models

TODO: Run inference and return predictions Args: image_bytes: Raw image bytes top_k: Number of top predictio

TODO: Register model in MLflow Model Registry Steps: 1. Connect to MLflow Model Registry 2. Register model with version 3. Add m

Initialize preprocessor Args: config: Preprocessing configuration

Initialize data validator Args: config: Validation configuration including expectations

Initialize ingestion pipeline Args: config: Pipeline configuration with data sources

Initialize model trainer Args: config: Training configuration including model type, hyperparameters, etc.

Initialize evaluator

Initialize component health. Args: component_name: Name of the component status: Health status m

Initialize health checker. Args: config: Configuration with component endpoints and thresholds TODO: 1.

Initialize model metrics.

Initialize data quality metrics.

Initialize pipeline metrics.

Initialize metrics exporter. Args: port: Port to serve metrics on TODO: 1. Initialize Prometheus HTTP s

Initialize the optimizer. Args: config: LLM configuration object TODO: 1. Store configuration 2

Initialize text loader. Args: encoding: Force specific encoding (None = auto-detect)

Initialize PDF loader. Args: ocr_enabled: Whether to use OCR for scanned PDFs

Initialize web loader. Args: timeout: Request timeout in seconds user_agent: Custom user agent string

Initialize directory loader. Args: file_types: List of extensions to load (None = all) recursive: Whether to

Initialize Pinecone indexer. Args: api_key: Pinecone API key environment: Pinecone environment i

Initialize ChromaDB indexer. Args: persist_directory: Directory for ChromaDB storage collection_name: Collec

Initialize async indexer. Args: base_indexer: Base synchronous indexer max_concurrent: Max concurrent batche

Initialize text cleaner. Args: remove_urls: Whether to remove URLs remove_emails: Whether to remove email ad

Initialize document filter. Args: min_length: Minimum character count max_length: Maximum character count

Initialize document processor. Args: cleaner: Text cleaner instance filter: Document filter instance

Initialize authentication. TODO: Implement initialization - Load API keys (from database/config) - Set up key valida

Initialize cost tracking. Args: app: FastAPI application cost_per_1k_tokens: Pricing per model

Initialize buffer. Args: buffer_size: Number of tokens to buffer flush_timeout: Max seconds to wait before f

Initialize fixed-size chunker. Args: chunk_size: Size of each chunk (in characters or tokens) chunk_overlap:

Initialize recursive chunker. Args: chunk_size: Target chunk size chunk_overlap: Overlap between chunks

Initialize semantic chunker. Args: embedding_model: Model for computing embeddings breakpoint_threshold: Sim

Initialize markdown chunker. Args: chunk_size: Target chunk size chunk_overlap: Overlap between chunks

Initialize the vector retriever. Args: vector_db_client: Vector database client instance embedding_model: Em

Initialize hybrid retriever. Args: dense_retriever: Vector-based retriever sparse_retriever: Keyword-based r

Initialize reranker. Args: base_retriever: First-stage retriever reranker_model: Cross-encoder for reranking

Initialize multi-query retriever. Args: base_retriever: Underlying retriever query_generator: LLM to generat

Initialize conversational RAG. Args: retriever: Document retriever llm_server: LLM server config

Initialize embedding model. Args: model_name: Hugging Face model name device: Device to use (cuda/cpu)

Initialize multi-modal embeddings. Args: text_model: Text embedding model image_model: Optional image embedd

Initialize metrics collector. Args: model_name: Name of the LLM model cost_per_1k_tokens: Cost configuration

Initialize GPU monitor. Args: collector: MetricsCollector instance update_interval: Update interval in secon

Initialize cost calculator. Args: pricing_config: Pricing configuration dict

Initialize cost tracker. Args: calculator: CostCalculator instance

Initialize budget monitor. Args: budgets: Dict of budgets {customer_id: budget_usd}

TODO: Initialize cache Args: max_size: Maximum number of entries ttl_seconds: Time-to-live for cache entries

Initialize the model inference class TODO: Implement initialization Args: model_name: Name of the model to load

TODO: Post-initialization processing - Validate content is not empty - Add default metadata if missing - Generate ID

Build context string from retrieved documents. Args: retrieved_docs: Retrieved documents max_tokens: Maximum

Build database-specific filter expression. Args: filters: Dictionary of filters Returns: Filter exp

Build complete prompt with context and question. Args: question: User question context: Retrieved context

Calculate confidence score for the answer. Args: answer: Generated answer retrieved_docs: Retrieved document

Calculate readability score. TODO: Implement Flesch reading ease or similar - Count words, sentences, syllables - Ca

Check which documents already exist. TODO: Implement duplicate checking - Query vector database for IDs - Return set

Check if NVIDIA GPU available. TODO: Implement NVIDIA check - Try importing pynvml - Initialize NVML - Retur

Attempt to consume one token. TODO: Implement token consumption - Get current tokens - If >= 1, consume and return T

TODO: Create image preprocessing transforms ImageNet models expect: - Image size: 224x224 - Normalized with mean=[0.

Detect file encoding. TODO: Implement encoding detection - Try common encodings (utf-8, latin-1, etc.) - Use chardet

Detect text language. TODO: Implement language detection - Use langdetect or similar library - Return ISO language c

Extract headers and their positions. Args: text: Markdown text Returns: List of (level, text, posit

Extract metadata from HTML. TODO: Implement metadata extraction - Get title from <title> tag - Get description from

Extract keywords from text. TODO: Implement keyword extraction - Use TF-IDF or similar - Filter stop words -

Extract PDF metadata. TODO: Implement metadata extraction - Get title, author, subject from PDF info - Get creation/

Format a single document for context. Args: doc: Document to format index: Document index Returns:

Generate unique ID for document. TODO: Implement ID generation - Use content hash or metadata - Ensure uniqueness

Generate query variations using LLM. Args: query: Original query Returns: List of query variations

Generate cache key for text. Args: text: Input text Returns: Cache key (hash) TODO: Create

Get unique client identifier. TODO: Implement client identification - Check for API key in headers - Fall back to IP

Get query instruction prefix for model. Returns: Instruction string or empty string TODO: Return instruction ba

Get current token count for client. TODO: Implement token bucket logic - Get bucket for client (create if new) - Cal