[package]
name = "anyllm_optimize_cli"
description = "Offline runner for FFEC: token-usage/savings eval harness, parity, shadow-diff"
version.workspace = true
edition.workspace = true
license.workspace = true
repository.workspace = true

[[bin]]
name = "optimize-eval"
path = "src/main.rs"

# Opt-in prerequisite fetcher for the `onnx` scorer. Builds without the heavy ONNX
# Runtime stack (download + sha256 verify only); running it is an explicit operator step,
# never automatic. See src/model_fetch.rs.
[[bin]]
name = "optimize-model"
path = "src/model_fetch.rs"

[features]
default = []
# M3.6: wire the real LLMLingua2Pass scorer (--llmlingua2-model-dir) into the harness
# behind the frontier, instead of always defaulting to UniformScorer. Pulls in the same
# heavy ONNX Runtime deps as `anyllm_optimize_scorer/onnx` — opt-in only.
onnx = ["anyllm_optimize_scorer/onnx"]

# M3.6: exact OpenAI token counting for the eval harness's reported est_raw/est_comp
# columns (ALGO.md: tiktoken o200k_base for OpenAI; bytes/3.6 heuristic elsewhere, since
# Anthropic's tokenizer is unpublished). Opt-in only — pulls in tiktoken-rs's BPE tables.
tiktoken = ["dep:tiktoken-rs"]

[dependencies]
anyllm_optimize_core = { path = "../crates/optimize-core", version = "0.16.0" }
anyllm_optimize_passes = { path = "../crates/optimize-passes", version = "0.16.0" }
anyllm_optimize_scorer = { path = "../crates/optimize-scorer", version = "0.16.0" }
serde = { version = "1", features = ["derive"] }
serde_json = "1"
clap = { version = "4", features = ["derive", "env"] }
anyhow = "1"
reqwest = { version = "0.12", default-features = false, features = ["blocking", "json", "rustls-tls"] }
tiktoken-rs = { version = "0.9", optional = true }