harn-vm 0.7.22

//! LLM call option extraction — parses the `(prompt, system, options)`
//! argument shape every high-level builtin accepts into the canonical
//! `LlmCallOptions` struct, including provider-specific warnings.

use std::collections::BTreeMap;

use crate::value::{VmError, VmValue};

use super::{
    opt_bool, opt_float, opt_int, opt_str, resolve_api_key, vm_messages_to_json, vm_resolve_model,
    vm_resolve_provider, vm_value_dict_to_json, vm_value_to_json,
};

pub(crate) fn extract_json(text: &str) -> String {
    crate::stdlib::json::extract_json_from_text(text)
}

pub(crate) fn expects_structured_output(opts: &crate::llm::api::LlmCallOptions) -> bool {
    opts.response_format.as_deref() == Some("json")
        || opts.json_schema.is_some()
        || opts.output_schema.is_some()
}

/// Three-way resolution of `tool_search.mode` against the provider's
/// native capability. Kept as a private enum so the option-parse path
/// reads linearly; the `Client` variant feeds the harn#70 fallback
/// injection, the `Native` variant feeds the phase-1 Anthropic path
/// (and phase-2 OpenAI path via harn#71).
enum ToolSearchResolution {
    Native,
    Client,
}

/// Read the `provider_overrides.force_native_tool_search` escape hatch
/// (bool). Set to true when a user is pointed at a proxied OpenAI-compat
/// endpoint (self-hosted router, enterprise gateway) whose model ID
/// Harn cannot parse but that is known to forward `tool_search` +
/// `defer_loading` unchanged.
fn provider_overrides_force_native(
    options: Option<&BTreeMap<String, VmValue>>,
    provider: &str,
) -> bool {
    let Some(options) = options else { return false };
    let Some(VmValue::Dict(overrides)) = options.get(provider) else {
        return false;
    };
    matches!(
        overrides.get("force_native_tool_search"),
        Some(VmValue::Bool(true))
    )
}

/// Decide which wire shape this (provider, model) pair should emit for
/// the native tool-search meta-tool. Anthropic + Claude → Anthropic
/// shape; anything else → OpenAI shape. For `provider: "mock"` we
/// inspect the model string so conformance tests can spoof either
/// backend without HTTP.
fn classify_native_shape(
    provider: &str,
    model: &str,
) -> crate::llm::provider::NativeToolSearchShape {
    use crate::llm::provider::NativeToolSearchShape;
    if provider == "anthropic" {
        return NativeToolSearchShape::Anthropic;
    }
    if provider == "mock"
        && crate::llm::providers::anthropic::claude_model_supports_tool_search(model)
    {
        return NativeToolSearchShape::Anthropic;
    }
    NativeToolSearchShape::OpenAi
}

/// Extract all LLM call options from the standard (prompt, system, options) args.
pub(crate) fn extract_llm_options(
    args: &[VmValue],
) -> Result<crate::llm::api::LlmCallOptions, VmError> {
    use crate::llm::api::{LlmCallOptions, ThinkingConfig, ToolSearchMode, ToolSearchVariant};
    use crate::llm::provider::{provider_supports_defer_loading, provider_tool_search_variants};
    use crate::llm::tools::{extract_deferred_tool_names, vm_tools_to_native};

    let prompt = args.first().map(|a| a.display()).unwrap_or_default();
    let system = args.get(1).and_then(|a| {
        if matches!(a, VmValue::Nil) {
            None
        } else {
            Some(a.display())
        }
    });
    let options = args.get(2).and_then(|a| a.as_dict()).cloned();

    let provider = vm_resolve_provider(&options);
    let model = vm_resolve_model(&options, &provider);
    let api_key = resolve_api_key(&provider)?;

    // Apply providers.toml model_defaults as fallbacks for unspecified params
    // (e.g. presence_penalty=1.5 for Qwen to avoid repetition loops).
    let model_defaults = crate::llm_config::model_params(&model);
    let default_float =
        |key: &str| -> Option<f64> { model_defaults.get(key).and_then(|v| v.as_float()) };
    let default_int =
        |key: &str| -> Option<i64> { model_defaults.get(key).and_then(|v| v.as_integer()) };

    let max_tokens = opt_int(&options, "max_tokens").unwrap_or(16384);
    let temperature = opt_float(&options, "temperature").or_else(|| default_float("temperature"));
    let top_p = opt_float(&options, "top_p").or_else(|| default_float("top_p"));
    let top_k = opt_int(&options, "top_k").or_else(|| default_int("top_k"));
    let stop = opt_str_list(&options, "stop");
    let seed = opt_int(&options, "seed");
    let frequency_penalty =
        opt_float(&options, "frequency_penalty").or_else(|| default_float("frequency_penalty"));
    let presence_penalty =
        opt_float(&options, "presence_penalty").or_else(|| default_float("presence_penalty"));
    let response_format = opt_str(&options, "response_format");
    let timeout = opt_int(&options, "timeout").map(|t| t as u64);
    let idle_timeout = opt_int(&options, "idle_timeout").map(|t| t as u64);
    let cache = opt_bool(&options, "cache");
    let stream = options
        .as_ref()
        .and_then(|o| o.get("stream"))
        .map(|v| v.is_truthy())
        .unwrap_or_else(|| {
            std::env::var("HARN_LLM_STREAM")
                .map(|v| v != "0" && v.to_lowercase() != "false")
                .unwrap_or(true)
        });
    let output_validation = opt_str(&options, "output_validation");

    let thinking = options
        .as_ref()
        .and_then(|o| o.get("thinking"))
        .and_then(|v| match v {
            VmValue::Bool(true) => Some(ThinkingConfig::Enabled),
            VmValue::Dict(d) => {
                let budget = d
                    .get("budget_tokens")
                    .and_then(|b| b.as_int())
                    .unwrap_or(10000);
                Some(ThinkingConfig::WithBudget(budget))
            }
            _ if v.is_truthy() => Some(ThinkingConfig::Enabled),
            _ => None,
        });

    let json_schema = options
        .as_ref()
        .and_then(|o| o.get("schema"))
        .and_then(|v| v.as_dict())
        .map(vm_value_dict_to_json);
    let output_schema = options
        .as_ref()
        .and_then(|o| o.get("output_schema").or_else(|| o.get("schema")))
        .and_then(|v| v.as_dict())
        .map(vm_value_dict_to_json);

    // Reject the deprecated `transcript` option key. Conversation
    // lifecycle is expressed through `session_id` + the explicit
    // `agent_session_*` builtins; there is no opaque transcript dict to
    // pass around anymore.
    if options.as_ref().and_then(|o| o.get("transcript")).is_some() {
        return Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
            "llm_call / agent_loop: the `transcript` option was removed. \
                 Open or open-and-resume a session with agent_session_open(id) \
                 and pass `session_id: id` instead.",
        ))));
    }

    // Message source precedence: options.messages > prompt.
    let messages_val = options.as_ref().and_then(|o| o.get("messages")).cloned();
    let messages = if let Some(VmValue::List(msg_list)) = &messages_val {
        vm_messages_to_json(msg_list)?
    } else {
        vec![serde_json::json!({"role": "user", "content": prompt})]
    };

    let tools_val = options.as_ref().and_then(|o| o.get("tools")).cloned();
    let mut native_tools = if let Some(tools) = &tools_val {
        Some(vm_tools_to_native(tools, &provider)?)
    } else {
        None
    };

    // tool_search option parsing: three shapes accepted.
    //   - shorthand string: "bm25" | "regex" (mode: auto)
    //   - bool: true (defaults to bm25/auto), false (no tool_search)
    //   - dict: { variant, mode, always_loaded }
    // Unset / false / nil all leave tool_search absent — tools ship eagerly.
    let mut tool_search = parse_tool_search_option(options.as_ref())?;

    if let Some(cfg) = tool_search.as_mut() {
        // Resolve tool_search against the active provider now. Three
        // possible outcomes:
        //   - native: prepend the provider's meta-tool (Anthropic path
        //     for Claude 4.0+; OpenAI Responses-API path for GPT 5.4+).
        //   - client: keep native_tools as-is so the agent loop can
        //     strip deferred tools per-turn and inject the synthetic
        //     `__harn_tool_search` dispatchable.
        //   - error: explicit native mode on a provider that cannot
        //     satisfy it.
        let native_variants = provider_tool_search_variants(&provider, &model);
        let model_based_native =
            provider_supports_defer_loading(&provider, &model) && !native_variants.is_empty();
        // Escape hatch for proxied OpenAI-compat providers whose model
        // ID Harn cannot parse. The override forces the OpenAI
        // Responses-API shape; user asserts the endpoint forwards
        // `tool_search` + `defer_loading` unchanged.
        let forced = provider_overrides_force_native(options.as_ref(), &provider);
        let provider_has_native = model_based_native || forced;
        // If the forced path is active, use OpenAI's default variants
        // so the injection below picks the right shape.
        let effective_variants: Vec<String> = if forced && native_variants.is_empty() {
            vec!["hosted".to_string(), "client".to_string()]
        } else {
            native_variants
        };
        let variant_supported = |v: &str| effective_variants.iter().any(|x| x == v);
        let resolution = match cfg.mode {
            ToolSearchMode::Native => {
                if !provider_has_native {
                    return Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
                        format!(
                            "tool_search: provider \"{provider}\" does not expose native \
                         tool-search for model \"{model}\". Set \
                         `tool_search: {{ mode: \"client\" }}` to use the client-executed \
                         fallback, or omit tool_search to ship tools eagerly."
                        ),
                    ))));
                }
                ToolSearchResolution::Native
            }
            ToolSearchMode::Client => ToolSearchResolution::Client,
            ToolSearchMode::Auto => {
                if provider_has_native {
                    ToolSearchResolution::Native
                } else {
                    ToolSearchResolution::Client
                }
            }
        };

        // Pre-flight (applies to both native and client): all-deferred
        // tool lists leave the model with no starting point. Anthropic
        // returns HTTP 400 on this and we match the diagnostic for
        // consistency across modes.
        if let Some(tools) = native_tools.as_ref() {
            let deferred = extract_deferred_tool_names(tools);
            let total_user_tools = tools.len();
            if total_user_tools > 0 && deferred.len() == total_user_tools {
                return Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
                    "tool_search: all tools have defer_loading set. At least \
                     one tool must be non-deferred so the model has somewhere \
                     to start. (Matches Anthropic's 400 on the same condition.)",
                ))));
            }
        }

        match resolution {
            ToolSearchResolution::Native => {
                // Classify the native wire shape for this provider so
                // the injection and response parser agree on what to
                // emit / look for. Anthropic path emits the
                // `tool_search_tool_*_20251119` meta-tool; OpenAI path
                // emits `{"type": "tool_search"}`. For the "mock"
                // provider we infer from the model string so
                // conformance tests can exercise both paths without
                // HTTP. See `provider_native_tool_search_shape`.
                let shape = classify_native_shape(&provider, &model);
                match shape {
                    crate::llm::provider::NativeToolSearchShape::Anthropic => {
                        // Anthropic exposes {bm25, regex}. Variant
                        // names are documented in
                        // `effective_variants`; fall back to element 0
                        // with a warn if the user asked for something
                        // this model doesn't support.
                        if !variant_supported(cfg.variant.as_short()) {
                            crate::events::log_warn(
                                "llm.tool_search",
                                &format!(
                                    "provider \"{provider}\" model \"{model}\" does not support \
                                     tool_search variant \"{}\"; falling back to \"{}\"",
                                    cfg.variant.as_short(),
                                    effective_variants[0],
                                ),
                            );
                        }
                        let effective_variant = if variant_supported(cfg.variant.as_short()) {
                            cfg.variant
                        } else {
                            match effective_variants[0].as_str() {
                                "regex" => ToolSearchVariant::Regex,
                                _ => ToolSearchVariant::Bm25,
                            }
                        };
                        crate::llm::tools::apply_tool_search_native_injection_typed(
                            &mut native_tools,
                            shape,
                            effective_variant.as_short(),
                            "hosted",
                        );
                    }
                    crate::llm::provider::NativeToolSearchShape::OpenAi => {
                        // OpenAI Responses API exposes hosted + client
                        // modes. When the user picked `mode: "native"`
                        // they meant "let OpenAI handle the search on
                        // their side" — the hosted mode. Users who want
                        // Harn to execute the search locally should
                        // write `mode: "client"`, which flows through
                        // the harn#70 synthetic-tool path below (same
                        // ergonomics across every provider).
                        crate::llm::tools::apply_tool_search_native_injection_typed(
                            &mut native_tools,
                            shape,
                            cfg.variant.as_short(),
                            "hosted",
                        );
                    }
                }
            }
            ToolSearchResolution::Client => {
                // Client mode: capture the deferred tool bodies into
                // cfg.deferred_bodies (so the agent loop can re-surface
                // them), inject the synthetic search tool, and hide the
                // deferred tools from the initial payload. The agent
                // loop is responsible for promoting hits back onto
                // `opts.native_tools` across turns; for single-shot
                // `llm_call` the model still sees the synthetic tool
                // but without multi-turn continuity it degrades to one
                // query + one batch of suggestions.
                if let Some(list) = native_tools.as_ref() {
                    for tool in list {
                        let is_deferred = tool
                            .get("defer_loading")
                            .and_then(|v| v.as_bool())
                            .unwrap_or(false);
                        if !is_deferred {
                            continue;
                        }
                        let name = tool
                            .get("name")
                            .and_then(|v| v.as_str())
                            .or_else(|| {
                                tool.get("function")
                                    .and_then(|f| f.get("name"))
                                    .and_then(|v| v.as_str())
                            })
                            .unwrap_or("")
                            .to_string();
                        if name.is_empty() {
                            continue;
                        }
                        // Strip `defer_loading` from the stored copy —
                        // providers that don't support the flag will
                        // reject it when the tool is later promoted.
                        let mut cloned = tool.clone();
                        if let Some(obj) = cloned.as_object_mut() {
                            obj.remove("defer_loading");
                        }
                        if let Some(function) =
                            cloned.get_mut("function").and_then(|v| v.as_object_mut())
                        {
                            function.remove("defer_loading");
                        }
                        cfg.deferred_bodies.insert(name, cloned);
                    }
                }
                crate::llm::tools::apply_tool_search_client_injection(
                    &mut native_tools,
                    &provider,
                    cfg,
                );
            }
        }
    }

    let tool_choice = options
        .as_ref()
        .and_then(|o| o.get("tool_choice"))
        .map(vm_value_to_json);

    let provider_overrides = options
        .as_ref()
        .and_then(|o| o.get(&provider))
        .and_then(|v| v.as_dict())
        .map(vm_value_dict_to_json);

    let prefill = options
        .as_ref()
        .and_then(|o| o.get("prefill"))
        .and_then(|v| {
            if matches!(v, VmValue::Nil) {
                None
            } else {
                let s = v.display();
                if s.is_empty() {
                    None
                } else {
                    Some(s)
                }
            }
        });

    let opts = LlmCallOptions {
        provider,
        model,
        api_key,
        messages,
        system,
        transcript_summary: None,
        max_tokens,
        temperature,
        top_p,
        top_k,
        stop,
        seed,
        frequency_penalty,
        presence_penalty,
        response_format,
        json_schema,
        output_schema,
        output_validation,
        thinking,
        tools: tools_val,
        native_tools,
        tool_choice,
        tool_search,
        cache,
        timeout,
        idle_timeout,
        stream,
        provider_overrides,
        prefill,
    };

    validate_options(&opts);
    Ok(opts)
}

/// Parse the `tool_search` option into a ToolSearchConfig.
///
/// Accepts:
///   - `nil` / absent / `false` → None (no tool_search engaged)
///   - `true` → default (bm25 + auto)
///   - `"bm25"` | `"regex"` → that variant + auto
///   - `{ variant?, mode?, always_loaded? }` → explicit
fn parse_tool_search_option(
    options: Option<&BTreeMap<String, VmValue>>,
) -> Result<Option<crate::llm::api::ToolSearchConfig>, VmError> {
    use crate::llm::api::{
        ToolSearchConfig, ToolSearchMode, ToolSearchStrategy, ToolSearchVariant,
    };

    let raw = match options.and_then(|o| o.get("tool_search")) {
        Some(v) => v,
        None => return Ok(None),
    };

    let variant_from_short = |s: &str| -> Result<ToolSearchVariant, VmError> {
        match s {
            "bm25" => Ok(ToolSearchVariant::Bm25),
            "regex" => Ok(ToolSearchVariant::Regex),
            other => Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
                format!("tool_search.variant: expected \"bm25\" or \"regex\", got \"{other}\""),
            )))),
        }
    };
    let mode_from_short = |s: &str| -> Result<ToolSearchMode, VmError> {
        match s {
            "auto" => Ok(ToolSearchMode::Auto),
            "native" => Ok(ToolSearchMode::Native),
            "client" => Ok(ToolSearchMode::Client),
            other => Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
                format!(
                "tool_search.mode: expected \"auto\" | \"native\" | \"client\", got \"{other}\""
            ),
            )))),
        }
    };
    let strategy_from_short = |s: &str| -> Result<ToolSearchStrategy, VmError> {
        match s {
            "bm25" => Ok(ToolSearchStrategy::Bm25),
            "regex" => Ok(ToolSearchStrategy::Regex),
            "semantic" => Ok(ToolSearchStrategy::Semantic),
            "host" => Ok(ToolSearchStrategy::Host),
            other => Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
                format!(
                "tool_search.strategy: expected \"bm25\" | \"regex\" | \"semantic\" | \"host\", got \"{other}\""
            ),
            )))),
        }
    };

    match raw {
        VmValue::Nil => Ok(None),
        VmValue::Bool(false) => Ok(None),
        VmValue::Bool(true) => Ok(Some(ToolSearchConfig::default_bm25_auto())),
        VmValue::String(s) => Ok(Some(ToolSearchConfig {
            variant: variant_from_short(s.as_ref())?,
            mode: ToolSearchMode::Auto,
            always_loaded: Vec::new(),
            strategy: None,
            budget_tokens: None,
            name: None,
            include_stub_listing: false,
            deferred_bodies: std::collections::BTreeMap::new(),
        })),
        VmValue::Dict(d) => {
            let variant = match d.get("variant") {
                Some(VmValue::String(s)) => variant_from_short(s.as_ref())?,
                Some(_) => {
                    return Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
                        "tool_search.variant: expected a string",
                    ))));
                }
                None => ToolSearchVariant::Bm25,
            };
            let mode = match d.get("mode") {
                Some(VmValue::String(s)) => mode_from_short(s.as_ref())?,
                Some(_) => {
                    return Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
                        "tool_search.mode: expected a string",
                    ))));
                }
                None => ToolSearchMode::Auto,
            };
            let always_loaded = match d.get("always_loaded") {
                Some(VmValue::List(list)) => list.iter().map(|v| v.display()).collect(),
                Some(_) => {
                    return Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
                        "tool_search.always_loaded: expected a list of tool names",
                    ))));
                }
                None => Vec::new(),
            };
            let strategy = match d.get("strategy") {
                Some(VmValue::String(s)) => Some(strategy_from_short(s.as_ref())?),
                Some(_) => {
                    return Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
                        "tool_search.strategy: expected a string",
                    ))));
                }
                None => None,
            };
            let budget_tokens = match d.get("budget_tokens") {
                Some(VmValue::Int(n)) => Some(*n),
                Some(VmValue::Nil) | None => None,
                Some(_) => {
                    return Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
                        "tool_search.budget_tokens: expected an integer",
                    ))));
                }
            };
            let name = match d.get("name") {
                Some(VmValue::String(s)) => {
                    let s = s.as_ref().trim();
                    if s.is_empty() {
                        None
                    } else {
                        Some(s.to_string())
                    }
                }
                Some(VmValue::Nil) | None => None,
                Some(_) => {
                    return Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
                        "tool_search.name: expected a string",
                    ))));
                }
            };
            let include_stub_listing = match d.get("include_stub_listing") {
                Some(VmValue::Bool(b)) => *b,
                Some(VmValue::Nil) | None => false,
                Some(_) => {
                    return Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
                        "tool_search.include_stub_listing: expected a bool",
                    ))));
                }
            };
            Ok(Some(ToolSearchConfig {
                variant,
                mode,
                always_loaded,
                strategy,
                budget_tokens,
                name,
                include_stub_listing,
                deferred_bodies: std::collections::BTreeMap::new(),
            }))
        }
        _ => Err(VmError::Thrown(VmValue::String(std::rc::Rc::from(
            "tool_search: expected bool, string (\"bm25\"/\"regex\"), or dict \
             ({variant, mode, strategy, always_loaded, budget_tokens, name, include_stub_listing})",
        )))),
    }
}

pub(crate) fn opt_str_list(
    options: &Option<BTreeMap<String, VmValue>>,
    key: &str,
) -> Option<Vec<String>> {
    let val = options.as_ref()?.get(key)?;
    match val {
        VmValue::List(list) => {
            let strs: Vec<String> = list.iter().map(|v| v.display()).collect();
            if strs.is_empty() {
                None
            } else {
                Some(strs)
            }
        }
        _ => None,
    }
}

/// Emit warnings for options not supported by the target provider.
fn validate_options(opts: &crate::llm::api::LlmCallOptions) {
    let p = opts.provider.as_str();
    let warn = |param: &str| {
        crate::events::log_warn(
            "llm",
            &format!("\"{param}\" is not supported by provider \"{p}\", ignoring"),
        );
    };

    match p {
        "anthropic" => {
            if opts.seed.is_some() {
                warn("seed");
            }
            if opts.frequency_penalty.is_some() {
                warn("frequency_penalty");
            }
            if opts.presence_penalty.is_some() {
                warn("presence_penalty");
            }
        }
        "openai" | "openrouter" | "huggingface" | "local" => {
            if opts.top_k.is_some() {
                warn("top_k");
            }
            if opts.thinking.is_some() {
                warn("thinking");
            }
            if opts.cache {
                warn("cache");
            }
        }
        "ollama" => {
            if opts.frequency_penalty.is_some() {
                warn("frequency_penalty");
            }
            if opts.presence_penalty.is_some() {
                warn("presence_penalty");
            }
            if opts.cache {
                warn("cache");
            }
        }
        _ => {}
    }
}