Index a batch of documents. TODO: Implement batch indexing - Prepare indexed documents - Check for duplicates

Check if content meets quality criteria. TODO: Implement quality checking - Check word count vs character count ratio

TODO: Load ImageNet class labels Load the 1000 ImageNet class names for mapping predictions. Options: 1. Load from

TODO: Log feature importance to MLflow Steps: 1. Extract feature importances from model 2. Create DataFrame with fea

Measure the impact of quantization on quality and performance. Args: original_model: Original unquantized model

Normalize whitespace. TODO: Implement whitespace normalization - Replace multiple spaces with single space - Replace

TODO: Plot confusion matrix

TODO: Plot ROC curve

Combine results using Reciprocal Rank Fusion. Args: dense_results: Results from dense retrieval sparse_resul

Remove control characters. TODO: Implement control character removal - Keep newlines and tabs - Remove other control

Remove duplicate documents. TODO: Implement duplicate removal - Use content hashing - Compare similar documents

Rerank candidates using cross-encoder. Args: query: Search query candidates: Retrieved documents Re

Rewrite query using conversation history. Args: question: Current question history: Conversation history

TODO: Save validation report to file Steps: 1. Create reports directory 2. Generate timestamped filename 3.

Internal method to search the vector database. Args: query_embedding: Query vector top_k: Number of results

Split text into sentences. Args: text: Input text Returns: List of sentences TODO: Impleme

Upsert vectors to database. TODO: Implement vector upsert - Format data for vector database - Perform upsert operati

Upsert vectors to Pinecone. TODO: Implement Pinecone upsert - Format documents for Pinecone - Perform upsert

Upsert vectors to ChromaDB. TODO: Implement ChromaDB upsert - Format documents for ChromaDB - Perform add/update

Validate API key and return metadata. TODO: Implement key validation - Hash provided key - Look up in database/cache

TODO: Add data source to pipeline Args: source: DataSource instance to add

Add token to buffer. TODO: Implement token addition - Add token to buffer - Check if should flush - Return b

Aggregate metrics for Grafana dashboard. Returns: Dictionary of aggregated metrics TODO: 1. Query Prometheus for recent met

Send alert when component becomes unhealthy. Args: component_health: Component health status TODO: 1. C

Analyze costs and suggest optimizations. TODO: Implement cost analysis - Find high-cost customers - Calculate cost p

Apply quantization to reduce model size and memory. Args: model: PyTorch model to quantize method: Quantizat

Calculate similarities between query and multiple documents efficiently. Args: query_embedding: Query embedding (1D array) d

Generate text for multiple prompts in a batch. Args: prompts: List of input prompts **generation_kwargs: Gen

Process multiple queries in batch. Args: questions: List of questions filters: Optional filters Ret

Benchmark different batch sizes to find optimal throughput. Args: prompts: Test prompts engine: LLM engine

Benchmark LLM inference latency across batch sizes. Args: prompts: List of test prompts batch_sizes: Batch sizes to test

TODO: Build Docker image with model Steps: 1. Create Dockerfile with model 2. Build image 3. Tag appropriate

TODO: Build Docker image containing the new model Steps: 1. Get model information from previous task 2. Create Dockerfile (or use te

Calculate GPU compute cost. TODO: Implement compute cost calculation - Get hourly rate - Multiply by hours -

Calculate cost estimate for LLM usage. Args: prompt_tokens: Number of input tokens completion_tokens: Number of generated to

Calculate similarity between two embeddings. Args: embedding1: First embedding embedding2: Second embedding metric:

TODO: Calculate latency percentiles Steps to implement: 1. Sort latencies 2. Calculate p50, p95, p99 3. Calculate min, max, mean

Calculate similarity between query and document embeddings. Args: query_embedding: Query vector doc_embedding: Document vect

Calculate storage cost. TODO: Implement storage cost calculation - Get storage rate - Multiply by GB-months

Calculate streaming performance metrics. TODO: Implement metrics calculation - Time to first token (TTFT) - Tokens per second (throu

Calculate cost for token processing. TODO: Implement token cost calculation - Get pricing for model - Calculate inpu

TODO: Auto-calculate total_tokens Sum prompt_tokens and completion_tokens

Generate response for a chat conversation. Args: messages: List of message dicts with 'role' and 'content'

OpenAI-compatible chat completion endpoint. Args: request: Chat request with messages api_key: API key llm: Chat LLM

Check if budget threshold reached. TODO: Implement budget checking - Compare current cost to budget - Check alert th

Check if current GPU is compatible with the model. Args: model_name: Model to check Returns: Dictionary with compatibil

TODO: Check if model is healthy Steps to implement: 1. Verify model is loaded 2. Run test inference 3. Check device availability

TODO: Check MLflow Model Registry for newly promoted models Steps: 1. Connect to MLflow Model Registry 2. Check for models in "Produ

Split text into chunks. Args: text: Input text metadata: Optional metadata to attach Returns:

Split text into fixed-size chunks. Args: text: Input text metadata: Optional metadata Returns:

Recursively split text. Args: text: Input text metadata: Optional metadata Returns: Lis

Split text semantically. Args: text: Input text metadata: Optional metadata Returns: Li

Split markdown text. Args: text: Markdown text metadata: Optional metadata Returns: Lis

Clean text content. TODO: Implement text cleaning pipeline - Extract and preserve code blocks - Remove URLs if confi

TODO: Cleanup after each test Steps: 1. Run test (yield) 2. Clean up any resources 3. Reset state Example implementation:

Track resources that need cleanup. TODO: Implement cleanup tracker - Track resources created during test - Clean up after test -

TODO: Clear all cache entries

Clear the embedding cache. TODO: Clear the cache dictionary and log statistics

TODO: Create test client Steps to implement: 1. Import your FastAPI app 2. Create TestClient 3. Return client for use in tests

Benchmark different embedding models. Args: texts: Test texts models: Model names to compare task: Task type (retrie

TODO: Compare multiple models

Compare quality between original and quantized models. Args: original_outputs: Outputs from original model quantized_outputs

Compare RAG performance vs baseline LLM. Args: test_questions: List of test questions ground_truth_answers: Expected answers

Individual component health check.

TODO: Establish connection to data source Steps: 1. Validate configuration 2. Establish connection 3. Handle

TODO: Validate file path or URL exists For local files: Check file exists For remote files: Check URL is accessible

TODO: Establish database connection Steps: 1. Parse connection string 2. Create connection using appropriate library

TODO: Validate API access Steps: 1. Setup authentication headers 2. Test connection with health endpoint 3.

Count tokens in text. Args: text: Input text tokenizer: Tokenizer to use Returns: Number of tokens TODO: I

Count tokens in text. Args: text: Input text tokenizer: Optional tokenizer Returns: Token count TODO: Impl

Create FastAPI endpoint for health checks. Returns: FastAPI app with health endpoints TODO: 1. Create FastAPI app 2. Ad

Factory function to create appropriate indexer. TODO: Implement indexer factory - Support different database types - Pass configurat

Create FastAPI endpoint for Prometheus metrics. TODO: Implement metrics endpoint - Return Prometheus metrics format - Set correct co

TODO: Create ML model based on configuration Steps: 1. Select model class based on model_type 2. Initialize with hyp

TODO: Create sklearn Pipeline for preprocessing Steps: 1. Identify numerical and categorical columns 2. Create separ

Create FastAPI StreamingResponse for SSE. TODO: Implement SSE response creation - Set proper content-type header (text/event-stream)

TODO: Perform cross-validation Steps: 1. Setup k-fold cross-validation 2. Train model on each fold 3. Collec

TODO: Decide whether deployment is successful or needs rollback Decision Logic: 1. Check health check results 2. If all passed → pro

TODO: Decide whether to promote model to Staging/Production Decision logic: 1. Check if model meets minimum performance threshold 2.

Delete documents by IDs. TODO: Implement deletion - Delete from vector database - Return count of deleted documents

Delete documents from Pinecone. TODO: Implement Pinecone deletion

Delete documents from ChromaDB. TODO: Implement ChromaDB deletion

Delete documents matching metadata filter. TODO: Implement filtered deletion - Query documents matching filter - Del

TODO: Deploy to Kubernetes Steps: 1. Update deployment manifest with new image 2. Apply deployment 3. Wait f

TODO: Deploy new model to Kubernetes using rolling update strategy Steps: 1. Get image information from previous task 2. Update Kube

Detect potential memory bottlenecks in the configuration. Args: engine: LLM engine to analyze Returns:

TODO: Close connection to data source Steps: 1. Close connections 2. Release resources 3. Log disconnection

No persistent connection for CSV files

TODO: Close database connection Steps: 1. Commit any pending transactions 2. Close connection 3. Set connect

No persistent connection for API calls

Process request with rate limiting. TODO: Implement middleware dispatch - Get client ID - Try to consume token

Log request and response. TODO: Implement logging - Generate request ID - Log incoming request - Measure dur

Authenticate request. TODO: Implement authentication - Skip public endpoints - Extract API key from header -

Track request cost. TODO: Implement cost tracking - Process request - Extract token usage from response - Ca

Encode text(s) into embeddings. Args: texts: Single text or list of texts batch_size: Batch size for encodin

Efficiently encode large batches of texts. Args: texts: List of texts to encode batch_size: Batch size for e

TODO: Encode categorical variables Steps: 1. Identify categorical columns 2. Apply encoding strategy 3. Save

Encode a document. Args: document: Document text normalize: Normalize embedding Returns:

Encode text and/or image into shared embedding space. Args: text: Optional text input image: Optional image