deepwiki-rs 0.9.8

deepwiki-rs(also known as Litho) is a high-performance automatic generation engine for C4 architecture documentation, developed using Rust. It can intelligently analyze project structures, identify core components, parse dependency relationships, and leverage large language models (LLMs) to automatically generate professional architecture documentation.
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use anyhow::Result;
use schemars::JsonSchema;
use serde::{Deserialize, Serialize};

use crate::generator::context::GeneratorContext;
use crate::llm::client::utils::estimate_token_usage;

pub struct AgentExecuteParams {
    pub prompt_sys: String,
    pub prompt_user: String,
    pub cache_scope: String,
    pub log_tag: String,
}

pub async fn prompt(context: &GeneratorContext, params: AgentExecuteParams) -> Result<String> {
    let prompt_sys = &params.prompt_sys;
    let prompt_user = &params.prompt_user;
    let cache_scope = &params.cache_scope;
    let log_tag = &params.log_tag;

    let prompt_key = format!("{}|{}|reply-prompt", prompt_sys, prompt_user);
    // 尝试从缓存获取 - 直接使用prompt作为key，CacheManager会自动计算hash
    if let Some(cached_reply) = context
        .cache_manager
        .read()
        .await
        .get::<serde_json::Value>(cache_scope, &prompt_key)
        .await?
    {
        println!("   ✅ 使用缓存的AI分析结果: {}", log_tag);
        return Ok(cached_reply.to_string());
    }

    println!("   🤖 正在进行AI分析: {}", log_tag);

    let reply = context
        .llm_client
        .prompt_without_react(prompt_sys, prompt_user)
        .await
        .map_err(|e| anyhow::anyhow!("AI分析失败: {}", e))?;

    // 估算token使用情况
    let input_text = format!("{} {}", prompt_sys, prompt_user);
    let token_usage = estimate_token_usage(&input_text, &reply);

    // 缓存结果 - 使用带token信息的方法
    context
        .cache_manager
        .write()
        .await
        .set_with_tokens(cache_scope, &prompt_key, &reply, token_usage)
        .await?;

    Ok(reply)
}

pub async fn prompt_with_tools(
    context: &GeneratorContext,
    params: AgentExecuteParams,
) -> Result<String> {
    let prompt_sys = &params.prompt_sys;
    let prompt_user = &params.prompt_user;
    let log_tag = &params.log_tag;

    println!("   🤖 正在进行AI分析: {}", log_tag);

    let reply = context
        .llm_client
        .prompt(prompt_sys, prompt_user)
        .await
        .map_err(|e| anyhow::anyhow!("AI分析失败: {}", e))?;

    Ok(reply)
}

pub async fn extract<T>(context: &GeneratorContext, params: AgentExecuteParams) -> Result<T>
where
    T: JsonSchema + for<'a> Deserialize<'a> + Serialize + Send + Sync + 'static,
{
    let prompt_sys = &params.prompt_sys;
    let prompt_user = &params.prompt_user;
    let cache_scope = &params.cache_scope;
    let log_tag = &params.log_tag;

    let prompt_key = format!("{}|{}", prompt_sys, prompt_user);
    // 尝试从缓存获取 - 直接使用prompt作为key，CacheManager会自动计算hash
    if let Some(cached_reply) = context
        .cache_manager
        .read()
        .await
        .get::<T>(cache_scope, &prompt_key)
        .await?
    {
        println!("   ✅ 使用缓存的AI分析结果: {}", log_tag);
        return Ok(cached_reply);
    }

    println!("   🤖 正在进行AI分析: {}", log_tag);

    let reply = context
        .llm_client
        .extract::<T>(prompt_sys, prompt_user)
        .await
        .map_err(|e| anyhow::anyhow!("AI分析失败: {}", e))?;

    // 估算token使用情况
    let input_text = format!("{} {}", prompt_sys, prompt_user);
    let output_text = serde_json::to_string(&reply).unwrap_or_default();
    let token_usage = estimate_token_usage(&input_text, &output_text);

    // 缓存结果 - 使用带token信息的方法
    context
        .cache_manager
        .write()
        .await
        .set_with_tokens(cache_scope, &prompt_key, &reply, token_usage)
        .await?;

    Ok(reply)
}