lc/

chat.rs

use crate::config::Config;
use crate::database::ChatEntry;
use crate::model_metadata::MetadataExtractor;
use crate::provider::{
    ChatRequest, Message, MessageContent, OpenAIClient,
};
use crate::token_utils::TokenCounter;
use anyhow::Result;
use chrono::{DateTime, Utc};

pub async fn send_chat_request_with_validation(
    client: &LLMClient,
    model: &str,
    prompt: &str,
    history: &[ChatEntry],
    system_prompt: Option<&str>,
    max_tokens: Option<u32>,
    temperature: Option<f32>,
    provider_name: &str,
    tools: Option<Vec<crate::provider::Tool>>,
) -> Result<(String, Option<i32>, Option<i32>)> {
    crate::debug_log!("Sending chat request - provider: '{}', model: '{}', prompt length: {}, history entries: {}",
                      provider_name, model, prompt.len(), history.len());
    crate::debug_log!(
        "Request parameters - max_tokens: {:?}, temperature: {:?}",
        max_tokens,
        temperature
    );

    // Try to get model metadata for context validation
    crate::debug_log!(
        "Loading model metadata for provider '{}', model '{}'",
        provider_name,
        model
    );
    let model_metadata = get_model_metadata(provider_name, model).await;

    if let Some(ref metadata) = model_metadata {
        crate::debug_log!(
            "Found metadata for model '{}' - context_length: {:?}, max_output: {:?}",
            model,
            metadata.context_length,
            metadata.max_output_tokens
        );
    } else {
        crate::debug_log!("No metadata found for model '{}'", model);
    }

    // Create token counter
    crate::debug_log!("Creating token counter for model '{}'", model);
    let token_counter = match TokenCounter::new(model) {
        Ok(counter) => {
            crate::debug_log!("Successfully created token counter for model '{}'", model);
            Some(counter)
        }
        Err(e) => {
            crate::debug_log!(
                "Failed to create token counter for model '{}': {}",
                model,
                e
            );
            eprintln!(
                "Warning: Failed to create token counter for model '{}': {}",
                model, e
            );
            None
        }
    };

    let mut final_prompt = prompt.to_string();
    let mut final_history = history.to_vec();
    let mut input_tokens = None;

    // Validate context size if we have both metadata and token counter
    if let (Some(metadata), Some(ref counter)) = (&model_metadata, &token_counter) {
        if let Some(context_limit) = metadata.context_length {
            // Check if input exceeds context limit
            if counter.exceeds_context_limit(prompt, system_prompt, history, context_limit) {
                println!(
                    "⚠️  Input exceeds model context limit ({}k tokens). Truncating...",
                    context_limit / 1000
                );

                // Truncate to fit within context limit
                let (truncated_prompt, truncated_history) = counter.truncate_to_fit(
                    prompt,
                    system_prompt,
                    history,
                    context_limit,
                    metadata.max_output_tokens,
                );

                final_prompt = truncated_prompt;
                final_history = truncated_history;

                if final_history.len() < history.len() {
                    println!(
                        "📝 Truncated conversation history from {} to {} messages",
                        history.len(),
                        final_history.len()
                    );
                }

                if final_prompt.len() < prompt.len() {
                    println!(
                        "✂️  Truncated prompt from {} to {} characters",
                        prompt.len(),
                        final_prompt.len()
                    );
                }
            }

            // Calculate input tokens after potential truncation
            input_tokens = Some(counter.estimate_chat_tokens(
                &final_prompt,
                system_prompt,
                &final_history,
            ) as i32);
        }
    } else if let Some(ref counter) = token_counter {
        // No metadata available, but we can still count tokens
        input_tokens =
            Some(counter.estimate_chat_tokens(&final_prompt, system_prompt, &final_history) as i32);
    }

    // Build messages for the request
    let mut messages = Vec::new();

    // Add system prompt if provided
    if let Some(sys_prompt) = system_prompt {
        messages.push(Message {
            role: "system".to_string(),
            content_type: MessageContent::Text {
                content: Some(sys_prompt.to_string()),
            },
            tool_calls: None,
            tool_call_id: None,
        });
    }

    // Add conversation history
    for entry in &final_history {
        messages.push(Message::user(entry.question.clone()));
        messages.push(Message::assistant(entry.response.clone()));
    }

    // Add current prompt
    messages.push(Message::user(final_prompt));

    let request = ChatRequest {
        model: model.to_string(),
        messages: messages.clone(),
        max_tokens: max_tokens.or(Some(1024)),
        temperature: temperature.or(Some(0.7)),
        tools,
        stream: None, // Non-streaming request
    };

    crate::debug_log!(
        "Sending chat request with {} messages, max_tokens: {:?}, temperature: {:?}",
        messages.len(),
        request.max_tokens,
        request.temperature
    );

    // Send the request
    crate::debug_log!("Making API call to chat endpoint...");
    let response = client.chat(&request).await?;

    crate::debug_log!(
        "Received response from chat API ({} characters)",
        response.len()
    );

    // Calculate output tokens if we have a token counter
    let output_tokens = if let Some(ref counter) = token_counter {
        Some(counter.count_tokens(&response) as i32)
    } else {
        None
    };

    // Display token usage if available
    if let (Some(input), Some(output)) = (input_tokens, output_tokens) {
        println!(
            "📊 Token usage: {} input + {} output = {} total",
            input,
            output,
            input + output
        );

        // Show cost estimate if we have pricing info
        if let Some(metadata) = &model_metadata {
            if let (Some(input_price), Some(output_price)) =
                (metadata.input_price_per_m, metadata.output_price_per_m)
            {
                let input_cost = (input as f64 / 1_000_000.0) * input_price;
                let output_cost = (output as f64 / 1_000_000.0) * output_price;
                let total_cost = input_cost + output_cost;
                println!(
                    "💰 Estimated cost: ${:.6} (${:.6} input + ${:.6} output)",
                    total_cost, input_cost, output_cost
                );
            }
        }
    }

    Ok((response, input_tokens, output_tokens))
}

pub async fn send_chat_request_with_streaming(
    client: &LLMClient,
    model: &str,
    prompt: &str,
    history: &[ChatEntry],
    system_prompt: Option<&str>,
    max_tokens: Option<u32>,
    temperature: Option<f32>,
    provider_name: &str,
    tools: Option<Vec<crate::provider::Tool>>,
) -> Result<()> {
    crate::debug_log!("Sending streaming chat request - provider: '{}', model: '{}', prompt length: {}, history entries: {}",
                      provider_name, model, prompt.len(), history.len());
    crate::debug_log!(
        "Request parameters - max_tokens: {:?}, temperature: {:?}",
        max_tokens,
        temperature
    );

    // Try to get model metadata for context validation
    crate::debug_log!(
        "Loading model metadata for provider '{}', model '{}'",
        provider_name,
        model
    );
    let model_metadata = get_model_metadata(provider_name, model).await;

    if let Some(ref metadata) = model_metadata {
        crate::debug_log!(
            "Found metadata for model '{}' - context_length: {:?}, max_output: {:?}",
            model,
            metadata.context_length,
            metadata.max_output_tokens
        );
    } else {
        crate::debug_log!("No metadata found for model '{}'", model);
    }

    // Create token counter
    crate::debug_log!("Creating token counter for model '{}'", model);
    let token_counter = match TokenCounter::new(model) {
        Ok(counter) => {
            crate::debug_log!("Successfully created token counter for model '{}'", model);
            Some(counter)
        }
        Err(e) => {
            crate::debug_log!(
                "Failed to create token counter for model '{}': {}",
                model,
                e
            );
            eprintln!(
                "Warning: Failed to create token counter for model '{}': {}",
                model, e
            );
            None
        }
    };

    let mut final_prompt = prompt.to_string();
    let mut final_history = history.to_vec();

    // Validate context size if we have both metadata and token counter
    if let (Some(metadata), Some(ref counter)) = (&model_metadata, &token_counter) {
        if let Some(context_limit) = metadata.context_length {
            // Check if input exceeds context limit
            if counter.exceeds_context_limit(prompt, system_prompt, history, context_limit) {
                println!(
                    "⚠️  Input exceeds model context limit ({}k tokens). Truncating...",
                    context_limit / 1000
                );

                // Truncate to fit within context limit
                let (truncated_prompt, truncated_history) = counter.truncate_to_fit(
                    prompt,
                    system_prompt,
                    history,
                    context_limit,
                    metadata.max_output_tokens,
                );

                final_prompt = truncated_prompt;
                final_history = truncated_history;

                if final_history.len() < history.len() {
                    println!(
                        "📝 Truncated conversation history from {} to {} messages",
                        history.len(),
                        final_history.len()
                    );
                }

                if final_prompt.len() < prompt.len() {
                    println!(
                        "✂️  Truncated prompt from {} to {} characters",
                        prompt.len(),
                        final_prompt.len()
                    );
                }
            }
        }
    }

    // Build messages for the request
    let mut messages = Vec::new();

    // Add system prompt if provided
    if let Some(sys_prompt) = system_prompt {
        messages.push(Message {
            role: "system".to_string(),
            content_type: MessageContent::Text {
                content: Some(sys_prompt.to_string()),
            },
            tool_calls: None,
            tool_call_id: None,
        });
    }

    // Add conversation history
    for entry in &final_history {
        messages.push(Message::user(entry.question.clone()));
        messages.push(Message::assistant(entry.response.clone()));
    }

    // Add current prompt
    messages.push(Message::user(final_prompt));

    let request = ChatRequest {
        model: model.to_string(),
        messages: messages.clone(),
        max_tokens: max_tokens.or(Some(1024)),
        temperature: temperature.or(Some(0.7)),
        tools,
        stream: Some(true), // Enable streaming
    };

    crate::debug_log!(
        "Sending streaming chat request with {} messages, max_tokens: {:?}, temperature: {:?}",
        messages.len(),
        request.max_tokens,
        request.temperature
    );

    // Send the streaming request
    crate::debug_log!("Making streaming API call to chat endpoint...");
    client.chat_stream(&request).await?;

    Ok(())
}

async fn get_model_metadata(
    provider_name: &str,
    model_name: &str,
) -> Option<crate::model_metadata::ModelMetadata> {
    use std::fs;

    let filename = format!("models/{}.json", provider_name);

    if !std::path::Path::new(&filename).exists() {
        return None;
    }

    match fs::read_to_string(&filename) {
        Ok(json_content) => {
            match MetadataExtractor::extract_from_provider(provider_name, &json_content) {
                Ok(models) => models.into_iter().find(|m| m.id == model_name),
                Err(_) => None,
            }
        }
        Err(_) => None,
    }
}

pub async fn get_or_refresh_token(
    config: &mut Config,
    provider_name: &str,
    client: &OpenAIClient,
) -> Result<String> {
    // If provider is configured for Google SA JWT (Vertex AI), use JWT Bearer flow
    let provider = config.get_provider(provider_name)?.clone();
    let is_vertex = provider
        .endpoint
        .to_lowercase()
        .contains("aiplatform.googleapis.com")
        || provider.auth_type.as_deref() == Some("google_sa_jwt");

    // If we have a valid cached token, use it (30s skew)
    if let Some(cached_token) = config.get_cached_token(provider_name) {
        if Utc::now() < cached_token.expires_at {
            return Ok(cached_token.token.clone());
        }
    }

    if is_vertex {
        // Google OAuth 2.0 JWT Bearer flow
        let token_url = provider
            .token_url
            .clone()
            .unwrap_or_else(|| "https://oauth2.googleapis.com/token".to_string());

        // Parse Service Account JSON from api_key
        let api_key_raw = provider.api_key.clone().ok_or_else(|| {
            anyhow::anyhow!(
                "Service Account JSON not set for '{}'. Run lc k a {} and paste SA JSON.",
                provider_name,
                provider_name
            )
        })?;
        #[derive(serde::Deserialize)]
        struct GoogleSA {
            #[serde(rename = "type")]
            sa_type: String,
            client_email: String,
            private_key: String,
        }
        let sa: GoogleSA = serde_json::from_str(&api_key_raw)
            .map_err(|e| anyhow::anyhow!("Invalid Service Account JSON: {}", e))?;
        if sa.sa_type != "service_account" {
            anyhow::bail!("Provided key is not a service_account");
        }

        // Build JWT
        #[derive(serde::Serialize)]
        struct Claims<'a> {
            iss: &'a str,
            scope: &'a str,
            aud: &'a str,
            exp: i64,
            iat: i64,
        }
        let now = Utc::now().timestamp();
        let claims = Claims {
            iss: &sa.client_email,
            scope: "https://www.googleapis.com/auth/cloud-platform",
            aud: &token_url,
            iat: now,
            exp: now + 3600,
        };
        let header = jsonwebtoken::Header::new(jsonwebtoken::Algorithm::RS256);
        let key = jsonwebtoken::EncodingKey::from_rsa_pem(sa.private_key.as_bytes())
            .map_err(|e| anyhow::anyhow!("Failed to load RSA key: {}", e))?;
        let assertion = jsonwebtoken::encode(&header, &claims, &key)
            .map_err(|e| anyhow::anyhow!("JWT encode failed: {}", e))?;

        // Exchange for access token
        #[derive(serde::Deserialize)]
        struct GoogleTokenResp {
            access_token: String,
            expires_in: i64,
            #[allow(dead_code)]
            token_type: String,
        }
        let http = reqwest::Client::new();
        let resp = http
            .post(&token_url)
            .form(&[
                ("grant_type", "urn:ietf:params:oauth:grant-type:jwt-bearer"),
                ("assertion", assertion.as_str()),
            ])
            .send()
            .await
            .map_err(|e| anyhow::anyhow!("Token exchange error: {}", e))?;
        if !resp.status().is_success() {
            let status = resp.status();
            let txt = resp.text().await.unwrap_or_default();
            anyhow::bail!("Token exchange failed ({}): {}", status, txt);
        }
        let token_json: GoogleTokenResp = resp
            .json()
            .await
            .map_err(|e| anyhow::anyhow!("Failed to parse token response: {}", e))?;
        let expires_at = DateTime::from_timestamp(now + token_json.expires_in - 60, 0)
            .ok_or_else(|| anyhow::anyhow!("Invalid expires timestamp"))?;
        config.set_cached_token(
            provider_name.to_string(),
            token_json.access_token.clone(),
            expires_at,
        )?;
        config.save()?;
        return Ok(token_json.access_token);
    }

    // Fallback: GitHub-style token endpoint using existing client helper
    let token_url = match config.get_token_url(provider_name) {
        Some(url) => url.clone(),
        None => {
            let provider_config = config.get_provider(provider_name)?;
            return provider_config.api_key.clone().ok_or_else(|| {
                anyhow::anyhow!(
                    "No API key or token URL configured for provider '{}'",
                    provider_name
                )
            });
        }
    };

    let token_response = client.get_token_from_url(&token_url).await?;
    let expires_at = DateTime::from_timestamp(token_response.expires_at, 0).ok_or_else(|| {
        anyhow::anyhow!(
            "Invalid expires_at timestamp: {}",
            token_response.expires_at
        )
    })?;
    config.set_cached_token(
        provider_name.to_string(),
        token_response.token.clone(),
        expires_at,
    )?;
    config.save()?;
    Ok(token_response.token)
}


// All providers now use OpenAIClient with template-based transformations
pub type LLMClient = OpenAIClient;

// Hardcoded conversion functions removed - now using template-based transformations

pub async fn create_authenticated_client(
    config: &mut Config,
    provider_name: &str,
) -> Result<LLMClient> {
    crate::debug_log!(
        "Creating authenticated client for provider '{}'",
        provider_name
    );
    let provider_config = config.get_provider(provider_name)?.clone();

    crate::debug_log!(
        "Provider '{}' config - endpoint: {}, models_path: {}, chat_path: {}",
        provider_name,
        provider_config.endpoint,
        provider_config.models_path,
        provider_config.chat_path
    );

    // Normalize chat_path placeholders: support both {model} and legacy {model_name}
    let normalized_chat_path = provider_config.chat_path.replace("{model_name}", "{model}");
    let provider_config = crate::config::ProviderConfig {
        chat_path: normalized_chat_path,
        ..provider_config
    };

    // All providers now use OpenAIClient with template-based transformations
    // Check if this needs OAuth authentication (Vertex AI)
    let needs_oauth = provider_config
        .endpoint
        .contains("aiplatform.googleapis.com")
        || provider_config.auth_type.as_deref() == Some("google_sa_jwt");

    if needs_oauth {
        // OAuth authentication flow (Vertex AI)
        let temp_client = OpenAIClient::new_with_headers(
            provider_config.endpoint.clone(),
            provider_config.api_key.clone().unwrap_or_default(),
            provider_config.models_path.clone(),
            provider_config.chat_path.clone(),
            provider_config.headers.clone(),
        );

        let auth_token = get_or_refresh_token(config, provider_name, &temp_client).await?;

        // Create custom headers with Authorization
        let mut oauth_headers = provider_config.headers.clone();
        oauth_headers.insert(
            "Authorization".to_string(),
            format!("Bearer {}", auth_token),
        );

        let client = OpenAIClient::new_with_provider_config(
            provider_config.endpoint.clone(),
            auth_token,
            provider_config.models_path.clone(),
            provider_config.chat_path.clone(),
            oauth_headers,
            provider_config.clone(),
        );

        return Ok(client);
    }

    // Regular authentication flow (API key or token URL)
    let temp_client = OpenAIClient::new_with_headers(
        provider_config.endpoint.clone(),
        provider_config.api_key.clone().unwrap_or_default(),
        provider_config.models_path.clone(),
        provider_config.chat_path.clone(),
        provider_config.headers.clone(),
    );

    let auth_token = get_or_refresh_token(config, provider_name, &temp_client).await?;

    let client = OpenAIClient::new_with_provider_config(
        provider_config.endpoint.clone(),
        auth_token,
        provider_config.models_path.clone(),
        provider_config.chat_path.clone(),
        provider_config.headers.clone(),
        provider_config.clone(),
    );

    Ok(client)
}

// New function to handle tool execution loop
pub async fn send_chat_request_with_tool_execution(
    client: &LLMClient,
    model: &str,
    prompt: &str,
    history: &[ChatEntry],
    system_prompt: Option<&str>,
    max_tokens: Option<u32>,
    temperature: Option<f32>,
    _provider_name: &str,
    tools: Option<Vec<crate::provider::Tool>>,
    mcp_server_names: &[&str],
) -> Result<(String, Option<i32>, Option<i32>)> {
    use crate::provider::{ChatRequest, Message};
    use crate::token_utils::TokenCounter;

    let mut conversation_messages = Vec::new();
    let mut total_input_tokens = 0i32;
    let mut total_output_tokens = 0i32;

    // Create token counter for tracking usage
    let token_counter = TokenCounter::new(model).ok();

    // Add system prompt if provided
    if let Some(sys_prompt) = system_prompt {
        conversation_messages.push(Message {
            role: "system".to_string(),
            content_type: MessageContent::Text {
                content: Some(sys_prompt.to_string()),
            },
            tool_calls: None,
            tool_call_id: None,
        });
    }

    // Add conversation history
    for entry in history {
        conversation_messages.push(Message::user(entry.question.clone()));
        conversation_messages.push(Message::assistant(entry.response.clone()));
    }

    // Add current prompt
    conversation_messages.push(Message::user(prompt.to_string()));
    let max_iterations = 10; // Prevent infinite loops
    let mut iteration = 0;

    loop {
        iteration += 1;
        if iteration > max_iterations {
            anyhow::bail!(
                "Maximum tool execution iterations reached ({})",
                max_iterations
            );
        }

        crate::debug_log!("Tool execution iteration {}/{}", iteration, max_iterations);

        let request = ChatRequest {
            model: model.to_string(),
            messages: conversation_messages.clone(),
            max_tokens: max_tokens.or(Some(1024)),
            temperature: temperature.or(Some(0.7)),
            tools: tools.clone(),
            stream: None, // Non-streaming request for tool execution
        };

        // Make the API call
        let response = client.chat_with_tools(&request).await?;

        // Track token usage if we have a counter
        if let Some(ref counter) = token_counter {
            let input_tokens = counter.estimate_chat_tokens("", system_prompt, &[]) as i32;
            total_input_tokens += input_tokens;
        }

        if let Some(choice) = response.choices.first() {
            crate::debug_log!(
                "Response choice - tool_calls: {}, content: {}",
                choice.message.tool_calls.as_ref().map_or(0, |tc| tc.len()),
                choice
                    .message
                    .content
                    .as_ref()
                    .map_or("None", |c| if c.len() > 50 { &c[..50] } else { c })
            );

            // Check if the LLM made tool calls
            if let Some(tool_calls) = &choice.message.tool_calls {
                if !tool_calls.is_empty() {
                    crate::debug_log!(
                        "LLM made {} tool calls in iteration {}",
                        tool_calls.len(),
                        iteration
                    );

                    // Add the assistant's tool call message to conversation
                    conversation_messages
                        .push(Message::assistant_with_tool_calls(tool_calls.clone()));

                    // Execute each tool call
                    for (i, tool_call) in tool_calls.iter().enumerate() {
                        crate::debug_log!(
                            "Executing tool call {}/{}: {} with args: {}",
                            i + 1,
                            tool_calls.len(),
                            tool_call.function.name,
                            tool_call.function.arguments
                        );

                        // Find which MCP server has this function using daemon client
                        let daemon_client = crate::mcp_daemon::DaemonClient::new()?;
                        let mut tool_result = None;
                        for server_name in mcp_server_names {
                            // Parse arguments as JSON value instead of Vec<String>
                            let args_value: serde_json::Value =
                                serde_json::from_str(&tool_call.function.arguments)?;
                            match daemon_client
                                .call_tool(server_name, &tool_call.function.name, args_value)
                                .await
                            {
                                Ok(result) => {
                                    crate::debug_log!(
                                        "Tool call successful on server '{}': {}",
                                        server_name,
                                        serde_json::to_string(&result)
                                            .unwrap_or_else(|_| "invalid json".to_string())
                                    );
                                    tool_result = Some(format_tool_result(&result));
                                    break;
                                }
                                Err(e) => {
                                    crate::debug_log!(
                                        "Tool call failed on server '{}': {}",
                                        server_name,
                                        e
                                    );
                                    continue;
                                }
                            }
                        }

                        let result_content = tool_result.unwrap_or_else(|| {
                            format!(
                                "Error: Function '{}' not found on any MCP server",
                                tool_call.function.name
                            )
                        });

                        crate::debug_log!(
                            "Tool result for {}: {}",
                            tool_call.function.name,
                            if result_content.len() > 100 {
                                format!("{}...", &result_content[..100])
                            } else {
                                result_content.clone()
                            }
                        );

                        // Add tool result to conversation
                        conversation_messages
                            .push(Message::tool_result(tool_call.id.clone(), result_content));
                    }

                    // Continue the loop to get the LLM's response to the tool results
                    continue;
                } else {
                    // Empty tool_calls array - check if we have content (final answer)
                    if let Some(content) = &choice.message.content {
                        if !content.trim().is_empty() {
                            crate::debug_log!("LLM provided final answer with empty tool_calls after {} iterations: {}",
                                             iteration, if content.len() > 100 {
                                                 format!("{}...", &content[..100])
                                             } else {
                                                 content.clone()
                                             });

                            // Track output tokens
                            if let Some(ref counter) = token_counter {
                                total_output_tokens += counter.count_tokens(content) as i32;
                            }

                            // Exit immediately when LLM provides content (final answer)
                            return Ok((
                                content.clone(),
                                Some(total_input_tokens),
                                Some(total_output_tokens),
                            ));
                        }
                    }
                }
            } else if let Some(content) = &choice.message.content {
                // LLM provided a final answer without tool calls field
                crate::debug_log!(
                    "LLM provided final answer without tool_calls field after {} iterations: {}",
                    iteration,
                    if content.len() > 100 {
                        format!("{}...", &content[..100])
                    } else {
                        content.clone()
                    }
                );

                // Track output tokens
                if let Some(ref counter) = token_counter {
                    total_output_tokens += counter.count_tokens(content) as i32;
                }

                // Exit immediately when LLM provides content (final answer)
                return Ok((
                    content.clone(),
                    Some(total_input_tokens),
                    Some(total_output_tokens),
                ));
            } else {
                // LLM provided neither tool calls nor content - this shouldn't happen
                crate::debug_log!(
                    "LLM provided neither tool calls nor content in iteration {}",
                    iteration
                );
                anyhow::bail!(
                    "No content or tool calls in response from LLM in iteration {}",
                    iteration
                );
            }
        } else {
            anyhow::bail!("No response from API");
        }
    }
}

// Helper function to format tool result for display
fn format_tool_result(result: &serde_json::Value) -> String {
    if let Some(content_array) = result.get("content") {
        if let Some(content_items) = content_array.as_array() {
            let mut formatted = String::new();
            for item in content_items {
                if let Some(text) = item.get("text") {
                    if let Some(text_str) = text.as_str() {
                        formatted.push_str(text_str);
                        formatted.push('\n');
                    }
                }
            }
            return formatted.trim().to_string();
        }
    }

    // Fallback to pretty-printed JSON
    serde_json::to_string_pretty(result).unwrap_or_else(|_| "Error formatting result".to_string())
}

// Message-based versions of the chat functions for handling multimodal content

pub async fn send_chat_request_with_validation_messages(
    client: &LLMClient,
    model: &str,
    messages: &[Message],
    system_prompt: Option<&str>,
    max_tokens: Option<u32>,
    temperature: Option<f32>,
    provider_name: &str,
    tools: Option<Vec<crate::provider::Tool>>,
) -> Result<(String, Option<i32>, Option<i32>)> {
    crate::debug_log!(
        "Sending chat request with messages - provider: '{}', model: '{}', messages: {}",
        provider_name,
        model,
        messages.len()
    );

    // Build final messages including system prompt if needed
    let mut final_messages = Vec::new();

    // Add system prompt if provided and not already in messages
    if let Some(sys_prompt) = system_prompt {
        let has_system = messages.iter().any(|m| m.role == "system");
        if !has_system {
            final_messages.push(Message {
                role: "system".to_string(),
                content_type: MessageContent::Text {
                    content: Some(sys_prompt.to_string()),
                },
                tool_calls: None,
                tool_call_id: None,
            });
        }
    }

    // Add all provided messages
    final_messages.extend_from_slice(messages);

    let request = ChatRequest {
        model: model.to_string(),
        messages: final_messages,
        max_tokens: max_tokens.or(Some(1024)),
        temperature: temperature.or(Some(0.7)),
        tools,
        stream: None,
    };

    let response = client.chat(&request).await?;

    // For now, return None for token counts as we'd need to implement multimodal token counting
    Ok((response, None, None))
}

pub async fn send_chat_request_with_streaming_messages(
    client: &LLMClient,
    model: &str,
    messages: &[Message],
    system_prompt: Option<&str>,
    max_tokens: Option<u32>,
    temperature: Option<f32>,
    provider_name: &str,
    tools: Option<Vec<crate::provider::Tool>>,
) -> Result<()> {
    crate::debug_log!(
        "Sending streaming chat request with messages - provider: '{}', model: '{}', messages: {}",
        provider_name,
        model,
        messages.len()
    );

    // Build final messages including system prompt if needed
    let mut final_messages = Vec::new();

    // Add system prompt if provided and not already in messages
    if let Some(sys_prompt) = system_prompt {
        let has_system = messages.iter().any(|m| m.role == "system");
        if !has_system {
            final_messages.push(Message {
                role: "system".to_string(),
                content_type: MessageContent::Text {
                    content: Some(sys_prompt.to_string()),
                },
                tool_calls: None,
                tool_call_id: None,
            });
        }
    }

    // Add all provided messages
    final_messages.extend_from_slice(messages);

    let request = ChatRequest {
        model: model.to_string(),
        messages: final_messages,
        max_tokens: max_tokens.or(Some(1024)),
        temperature: temperature.or(Some(0.7)),
        tools,
        stream: Some(true),
    };

    client.chat_stream(&request).await?;

    Ok(())
}

pub async fn send_chat_request_with_tool_execution_messages(
    client: &LLMClient,
    model: &str,
    messages: &[Message],
    system_prompt: Option<&str>,
    max_tokens: Option<u32>,
    temperature: Option<f32>,
    provider_name: &str,
    tools: Option<Vec<crate::provider::Tool>>,
    mcp_server_names: &[&str],
) -> Result<(String, Option<i32>, Option<i32>)> {
    crate::debug_log!("Sending chat request with tool execution and messages - provider: '{}', model: '{}', messages: {}",
                      provider_name, model, messages.len());

    let mut conversation_messages = Vec::new();

    // Add system prompt if provided and not already in messages
    if let Some(sys_prompt) = system_prompt {
        let has_system = messages.iter().any(|m| m.role == "system");
        if !has_system {
            conversation_messages.push(Message {
                role: "system".to_string(),
                content_type: MessageContent::Text {
                    content: Some(sys_prompt.to_string()),
                },
                tool_calls: None,
                tool_call_id: None,
            });
        }
    }

    // Add all provided messages
    conversation_messages.extend_from_slice(messages);

    let max_iterations = 10;
    let mut iteration = 0;

    loop {
        iteration += 1;
        if iteration > max_iterations {
            anyhow::bail!(
                "Maximum tool execution iterations reached ({})",
                max_iterations
            );
        }

        let request = ChatRequest {
            model: model.to_string(),
            messages: conversation_messages.clone(),
            max_tokens: max_tokens.or(Some(1024)),
            temperature: temperature.or(Some(0.7)),
            tools: tools.clone(),
            stream: None,
        };

        let response = client.chat_with_tools(&request).await?;

        if let Some(choice) = response.choices.first() {
            if let Some(tool_calls) = &choice.message.tool_calls {
                if !tool_calls.is_empty() {
                    conversation_messages
                        .push(Message::assistant_with_tool_calls(tool_calls.clone()));

                    for tool_call in tool_calls {
                        let daemon_client = crate::mcp_daemon::DaemonClient::new()?;
                        let mut tool_result = None;

                        for server_name in mcp_server_names {
                            let args_value: serde_json::Value =
                                serde_json::from_str(&tool_call.function.arguments)?;
                            match daemon_client
                                .call_tool(server_name, &tool_call.function.name, args_value)
                                .await
                            {
                                Ok(result) => {
                                    tool_result = Some(format_tool_result(&result));
                                    break;
                                }
                                Err(_) => continue,
                            }
                        }

                        let result_content = tool_result.unwrap_or_else(|| {
                            format!(
                                "Error: Function '{}' not found on any MCP server",
                                tool_call.function.name
                            )
                        });

                        conversation_messages
                            .push(Message::tool_result(tool_call.id.clone(), result_content));
                    }

                    continue;
                }
            }

            if let Some(content) = &choice.message.content {
                return Ok((content.clone(), None, None));
            }
        }

        anyhow::bail!("No response from API");
    }
}