deepwiki-rs 1.2.6

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);
    // Try to get from cache - Use prompt directly as key, CacheManager will automatically calculate hash
    if let Some(cached_reply) = context
        .cache_manager
        .read()
        .await
        .get::<serde_json::Value>(cache_scope, &prompt_key)
        .await?
    {
        let msg = context.config.target_language.msg_cache_hit().replace("{}", log_tag);
        println!("{}", msg);
        return Ok(cached_reply.to_string());
    }

    let msg = context.config.target_language.msg_ai_analyzing().replace("{}", log_tag);
    println!("{}", msg);

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

    // Estimate token usage
    let input_text = format!("{} {}", prompt_sys, prompt_user);
    let token_usage = estimate_token_usage(&input_text, &reply);

    // Cache result - Use method with token information
    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 cache_scope = &params.cache_scope;
    let log_tag = &params.log_tag;

    let prompt_key = format!("{}|{}|reply-prompt+tool", prompt_sys, prompt_user);
    // Try to get from cache - Use prompt directly as key, CacheManager will automatically calculate hash
    if let Some(cached_reply) = context
        .cache_manager
        .read()
        .await
        .get::<serde_json::Value>(cache_scope, &prompt_key)
        .await?
    {
        let msg = context.config.target_language.msg_cache_hit().replace("{}", log_tag);
        println!("{}", msg);
        return Ok(cached_reply.to_string());
    }

    let msg = context.config.target_language.msg_ai_analyzing().replace("{}", log_tag);
    println!("{}", msg);

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

    // Estimate token usage
    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);

    // Cache result - Use method with token information
    context
        .cache_manager
        .write()
        .await
        .set_with_tokens(cache_scope, &prompt_key, &reply, token_usage)
        .await?;

    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);
    // Try to get from cache - Use prompt directly as key, CacheManager will automatically calculate hash
    if let Some(cached_reply) = context
        .cache_manager
        .read()
        .await
        .get::<T>(cache_scope, &prompt_key)
        .await?
    {
        let msg = context.config.target_language.msg_cache_hit().replace("{}", log_tag);
        println!("{}", msg);
        return Ok(cached_reply);
    }

    let msg = context.config.target_language.msg_ai_analyzing().replace("{}", log_tag);
    println!("{}", msg);

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

    // Estimate token usage
    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);

    // Cache result - Use method with token information
    context
        .cache_manager
        .write()
        .await
        .set_with_tokens(cache_scope, &prompt_key, &reply, token_usage)
        .await?;

    Ok(reply)
}