API Reference (Python)
The complete Python API for LLM Context Forge. All critical operations are available from the root package.
TokenCounter
Provides exact, deterministic token counting for a specified model.
from llm_context_forge import TokenCounter
counter = TokenCounter("gpt-4o")
Methods
count(text: str) -> int
Calculates the exact token count for the given text.
fits_in_window(text: str, reserve_output: int = 0) -> bool
Returns True if the text token count + reserve_output is less than or equal to the model's context window.
DocumentChunker
Splits text into token-safe chunks based on an intelligent strategy.
from llm_context_forge import DocumentChunker, ChunkStrategy
chunker = DocumentChunker("gpt-4o")
Methods
chunk(text: str, strategy: ChunkStrategy, max_tokens: int, overlap_tokens: int = 0) -> list[str]
Splits text using strategy so that no chunk exceeds max_tokens. overlap_tokens are duplicated between chunks.
ContextWindow
Provides priority-based packing for LLM context assembly.
from llm_context_forge import ContextWindow, Priority
window = ContextWindow("claude-3-5-sonnet")
Methods
add_block(content: str, priority: Priority, block_id: str = None) -> None
Adds text to the window queue. Priority.CRITICAL (0) blocks will raise a ContextOverflowError if they don't fit.
assemble(max_tokens: int = None) -> str
Packs the blocks in priority order until max_tokens (or the model's window) is reached. Returns the combined string.
usage() -> UsageStats
Returns an object containing tokens_used, included (list of block IDs), and excluded (list of block IDs).
CostCalculator
Calculates pricing estimates for prompts and completions.
from llm_context_forge import CostCalculator
calc = CostCalculator("gpt-4o")
Methods
estimate_prompt(text: str) -> CostEstimate
Returns an object containing the .usd cost of the input.
compare_models(texts: list[str], models: list[str]) -> list[ComparisonReport]
Calculates the total cost of texts across all specified models and returns a list sorted by cheapest first.