mod messages;
mod system;
mod thinking;
mod tools;
use crate::config::constants::models;
use crate::config::core::{AnthropicConfig, AnthropicPromptCacheSettings};
use crate::config::types::ReasoningEffortLevel;
use crate::llm::provider::{LLMError, LLMRequest};
use crate::llm::providers::anthropic_types::{
AnthropicOutputConfig, AnthropicOutputFormat, AnthropicRequest, CacheControl,
};
use serde_json::Value;
use super::capabilities::{resolve_model_name, supports_effort};
use super::prompt_cache::{get_messages_cache_ttl, get_tools_cache_ttl};
use messages::{build_messages, hoist_largest_user_message};
use system::{SystemPromptBuildResult, build_system_prompt};
use thinking::build_thinking_config;
use tools::{build_tool_choice, build_tools};
#[cfg(test)]
pub use messages::tool_result_blocks;
pub struct RequestBuilderContext<'a> {
pub prompt_cache_enabled: bool,
pub prompt_cache_settings: &'a AnthropicPromptCacheSettings,
pub anthropic_config: &'a AnthropicConfig,
pub model: &'a str,
}
pub fn convert_to_anthropic_format(
request: &LLMRequest,
ctx: &RequestBuilderContext,
) -> Result<Value, LLMError> {
let resolved_model = resolve_model_name(&request.model, ctx.model);
let tools_ttl = if ctx.prompt_cache_enabled {
get_tools_cache_ttl(ctx.prompt_cache_settings)
} else {
"5m"
};
let messages_ttl = if ctx.prompt_cache_enabled {
get_messages_cache_ttl(ctx.prompt_cache_settings)
} else {
"5m"
};
let tools_cache_control =
if ctx.prompt_cache_enabled && ctx.prompt_cache_settings.cache_tool_definitions {
Some(CacheControl {
control_type: "ephemeral".to_string(),
ttl: Some(tools_ttl.to_string()),
})
} else {
None
};
let system_cache_control =
if ctx.prompt_cache_enabled && ctx.prompt_cache_settings.cache_system_messages {
Some(CacheControl {
control_type: "ephemeral".to_string(),
ttl: Some(tools_ttl.to_string()),
})
} else {
None
};
let max_breakpoints = if ctx.prompt_cache_enabled {
ctx.prompt_cache_settings.max_breakpoints as usize
} else {
0
};
let mut breakpoints_remaining = max_breakpoints;
let tools_breakpoints_before = breakpoints_remaining;
let tools = build_tools(request, &tools_cache_control, &mut breakpoints_remaining)?;
let tools_breakpoints_used = tools_breakpoints_before.saturating_sub(breakpoints_remaining);
let SystemPromptBuildResult {
system_value,
breakpoints_used,
has_uncached_runtime_context,
} = build_system_prompt(request, &system_cache_control, breakpoints_remaining);
breakpoints_remaining = breakpoints_remaining.saturating_sub(breakpoints_used);
let messages_cache_control =
if ctx.prompt_cache_enabled && ctx.prompt_cache_settings.cache_user_messages {
Some(CacheControl {
control_type: "ephemeral".to_string(),
ttl: Some(messages_ttl.to_string()),
})
} else {
None
};
let mut messages_to_process = request.messages.clone();
if let Some(settings) = &request.coding_agent_settings
&& settings.long_context_optimization
&& messages_to_process.len() > 1
{
hoist_largest_user_message(&mut messages_to_process);
}
let messages_breakpoints_before = breakpoints_remaining;
let messages = build_messages(
request,
&messages_to_process,
&messages_cache_control,
ctx.prompt_cache_settings,
&mut breakpoints_remaining,
)?;
let messages_breakpoints_used =
messages_breakpoints_before.saturating_sub(breakpoints_remaining);
let explicit_breakpoints_used = max_breakpoints.saturating_sub(breakpoints_remaining);
let (thinking_val, reasoning_val) =
build_thinking_config(request, ctx.anthropic_config, ctx.model);
let final_tool_choice = build_tool_choice(request, &thinking_val);
let adaptive_effort =
if resolved_model == models::anthropic::CLAUDE_OPUS_4_6 && request.effort.is_none() {
request
.reasoning_effort
.map(|effort| effort_from_reasoning_for_adaptive(effort).to_string())
} else {
None
};
let effort_value = if supports_effort(resolved_model, ctx.model) {
request
.effort
.as_ref()
.map(|effort| effort.to_ascii_lowercase())
.or_else(|| {
adaptive_effort
.as_ref()
.map(|effort| effort.to_ascii_lowercase())
})
.or_else(|| {
let eff = ctx.anthropic_config.effort.as_str();
if eff.eq_ignore_ascii_case("high") {
None
} else {
Some(eff.to_ascii_lowercase())
}
})
} else {
None
};
let output_format =
request
.output_format
.as_ref()
.map(|schema| AnthropicOutputFormat::JsonSchema {
schema: schema.clone(),
});
let output_config = if effort_value.is_some() || output_format.is_some() {
Some(AnthropicOutputConfig {
effort: effort_value,
format: output_format,
})
} else {
None
};
let effective_temperature = if thinking_val.is_some() {
None
} else {
request.temperature
};
let top_level_cache_control = if ctx.prompt_cache_enabled
&& explicit_breakpoints_used < max_breakpoints
&& !has_uncached_runtime_context
{
let ttl = if messages_breakpoints_used > 0 {
messages_ttl
} else if breakpoints_used > 0 || tools_breakpoints_used > 0 {
tools_ttl
} else {
messages_ttl
};
Some(CacheControl {
control_type: "ephemeral".to_string(),
ttl: Some(ttl.to_string()),
})
} else {
None
};
let anthropic_request = AnthropicRequest {
model: resolved_model.to_string(),
max_tokens: request
.max_tokens
.unwrap_or(if thinking_val.is_some() { 16000 } else { 4096 }),
cache_control: top_level_cache_control,
messages,
system: system_value,
temperature: effective_temperature,
tools,
tool_choice: final_tool_choice,
thinking: thinking_val,
reasoning: reasoning_val,
output_config,
context_management: request.context_management.clone(),
stream: request.stream,
};
serde_json::to_value(anthropic_request).map_err(|e| LLMError::Provider {
message: format!("Serialization error: {}", e),
metadata: None,
})
}
fn effort_from_reasoning_for_adaptive(effort: ReasoningEffortLevel) -> &'static str {
match effort {
ReasoningEffortLevel::None | ReasoningEffortLevel::Minimal | ReasoningEffortLevel::Low => {
"low"
}
ReasoningEffortLevel::Medium => "medium",
ReasoningEffortLevel::High => "high",
ReasoningEffortLevel::XHigh => "max",
}
}