重构: df-ai-core trait下沉拆crate+导入历史项目批量扫描
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@@ -5,6 +5,7 @@ edition = "2021"
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[dependencies]
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df-core = { path = "../df-core" }
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df-ai-core = { path = "../df-ai-core" }
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serde = { workspace = true }
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serde_json = { workspace = true }
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tokio = { workspace = true, features = ["sync", "time"] }
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@@ -13,6 +13,9 @@ pub mod retry;
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use provider::LlmProvider;
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// df-ai-core 直接暴露，供需要直接引用 trait crate 的下游（可选）。
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pub use df_ai_core;
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/// 按 provider_type 构建 LLM Provider 实例（统一选择逻辑，消除调用方重复 match）
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///
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/// `anthropic` 协议走 AnthropicCompatProvider（GLM 订阅端点 / Claude 官方），
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@@ -1,231 +1,15 @@
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//! LLM Provider trait — 统一的 LLM 调用抽象
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//! LLM Provider trait + 数据结构 re-export
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//!
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//! 支持 OpenAI 兼容 API（覆盖 OpenAI / GLM / DeepSeek / Claude 兼容模式），
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//! 含 function calling / tool use 能力。
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//! trait 与数据结构定义已下沉到 df-ai-core crate（零 HTTP 依赖，轻消费方
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//! 如 df-ideas 可直接依赖 trait 不引入 reqwest/eventsource-stream）。
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//! 本文件保留为 df-ai 的 re-export 入口，使 `df_ai::provider::*` 路径不变
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//! —— df-ai 内部模块（openai_compat / anthropic_compat / context / ai_tools）
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//! 与外部消费方（df-nodes / src-tauri）的 `use df_ai::provider::LlmProvider`
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//! 等引用全部透明继续可用（编译期验证）。
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//!
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//! 留在 df-ai 的部分：
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//! - `OpenAICompatProvider` / `AnthropicCompatProvider`（HTTP impl，见 openai_compat.rs / anthropic_compat.rs）
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//! - `build_provider()` 工厂（见 lib.rs，按协议选 impl）
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//! - `ContextManager` / `AiToolRegistry` / `StreamCollector`（业务逻辑）
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use std::pin::Pin;
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use async_trait::async_trait;
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use futures::Stream;
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use serde::{Deserialize, Serialize};
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// ============================================================
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// 核心数据结构
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// ============================================================
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/// LLM 调用请求
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct CompletionRequest {
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/// 模型名称
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pub model: String,
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/// 提示消息列表
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pub messages: Vec<ChatMessage>,
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/// 温度（0.0 ~ 2.0）
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pub temperature: Option<f32>,
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/// 最大生成 token 数
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pub max_tokens: Option<u32>,
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/// 是否流式输出
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pub stream: bool,
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/// 可调用的工具定义
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#[serde(skip_serializing_if = "Option::is_none")]
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pub tools: Option<Vec<ToolDefinition>>,
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/// 工具调用策略: "auto" | "none" | {"type":"function","name":"xxx"}
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#[serde(skip_serializing_if = "Option::is_none")]
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pub tool_choice: Option<serde_json::Value>,
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}
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/// 聊天消息
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ChatMessage {
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pub role: MessageRole,
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pub content: String,
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/// 工具调用 ID（role=Tool 时必填）
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#[serde(skip_serializing_if = "Option::is_none")]
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pub tool_call_id: Option<String>,
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/// AI 发起的工具调用列表（role=Assistant 时可能有）
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#[serde(skip_serializing_if = "Option::is_none")]
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pub tool_calls: Option<Vec<ToolCall>>,
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/// 生成该消息的 model（仅 assistant 消息有，消息级 model 追溯）
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#[serde(default, skip_serializing_if = "Option::is_none")]
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pub model: Option<String>,
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/// 消息状态（UX-09 编辑重生成）：None/"active" 正常可见；
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/// "truncated" 软删（编辑某条 user 消息后其后续消息标记，保留 DB 可追溯但不进 LLM 上下文、前端视图过滤）
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/// 默认 None（向前兼容老 JSON 反序列化）。落库随 messages JSON 序列化，无需独立列。
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#[serde(default, skip_serializing_if = "Option::is_none")]
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pub status: Option<String>,
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}
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impl ChatMessage {
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pub fn system(content: impl Into<String>) -> Self {
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Self { role: MessageRole::System, content: content.into(), tool_call_id: None, tool_calls: None, model: None, status: None }
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}
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pub fn user(content: impl Into<String>) -> Self {
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Self { role: MessageRole::User, content: content.into(), tool_call_id: None, tool_calls: None, model: None, status: None }
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}
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pub fn assistant(content: impl Into<String>) -> Self {
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Self { role: MessageRole::Assistant, content: content.into(), tool_call_id: None, tool_calls: None, model: None, status: None }
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}
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pub fn assistant_with_tools(content: impl Into<String>, tool_calls: Vec<ToolCall>) -> Self {
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Self { role: MessageRole::Assistant, content: content.into(), tool_call_id: None, tool_calls: Some(tool_calls), model: None, status: None }
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}
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pub fn tool_result(call_id: impl Into<String>, content: impl Into<String>) -> Self {
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Self { role: MessageRole::Tool, content: content.into(), tool_call_id: Some(call_id.into()), tool_calls: None, model: None, status: None }
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}
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/// 是否处于 active 态（status 为 None 或 "active"）。truncated 返回 false。
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pub fn is_active(&self) -> bool {
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!matches!(self.status.as_deref(), Some("truncated"))
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}
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}
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/// 消息角色
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#[derive(Debug, Clone, Serialize, Deserialize)]
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#[serde(rename_all = "lowercase")]
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pub enum MessageRole {
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System,
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User,
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Assistant,
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Tool,
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}
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/// 工具定义
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ToolDefinition {
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#[serde(rename = "type")]
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pub tool_type: String,
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pub function: ToolFunction,
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}
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impl ToolDefinition {
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pub fn function(name: impl Into<String>, description: impl Into<String>, parameters: serde_json::Value) -> Self {
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Self {
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tool_type: "function".into(),
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function: ToolFunction { name: name.into(), description: description.into(), parameters },
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}
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}
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}
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/// 函数定义
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ToolFunction {
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pub name: String,
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pub description: String,
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pub parameters: serde_json::Value,
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}
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/// 工具调用（AI 发起）
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ToolCall {
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pub id: String,
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#[serde(rename = "type")]
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pub call_type: String,
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pub function: ToolCallFunction,
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}
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impl ToolCall {
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pub fn new(id: impl Into<String>, name: impl Into<String>, arguments: impl Into<String>) -> Self {
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Self {
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id: id.into(),
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call_type: "function".into(),
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function: ToolCallFunction { name: name.into(), arguments: arguments.into() },
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}
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}
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}
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/// 工具调用函数部分
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ToolCallFunction {
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pub name: String,
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pub arguments: String,
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}
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/// LLM 调用响应
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct CompletionResponse {
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/// 生成的文本
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pub text: String,
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/// 使用的模型
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pub model: String,
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/// 消耗的 token 数
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pub usage: TokenUsage,
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/// AI 发起的工具调用（如有）
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#[serde(skip_serializing_if = "Option::is_none")]
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pub tool_calls: Option<Vec<ToolCall>>,
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}
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/// Token 用量
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#[derive(Debug, Clone, Default, Serialize, Deserialize)]
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pub struct TokenUsage {
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pub prompt_tokens: u32,
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pub completion_tokens: u32,
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pub total_tokens: u32,
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}
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/// 流式输出的 chunk
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct StreamChunk {
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/// 增量文本
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pub delta: String,
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/// 是否结束
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pub finished: bool,
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/// 工具调用增量（如有）
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#[serde(skip_serializing_if = "Option::is_none")]
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pub tool_calls: Option<Vec<ToolCallDelta>>,
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/// Token 用量（流末 chunk 携带，由 provider 解析自 SSE usage 事件）
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#[serde(skip_serializing_if = "Option::is_none")]
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pub usage: Option<TokenUsage>,
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/// provider 流式错误事件（如 Anthropic SSE `type=="error"`）。
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/// 非空表示流中途出错，不应视为正常完成（finished 路径），由 stream_llm 转 AiError。
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#[serde(skip)]
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pub error: Option<String>,
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}
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/// 工具调用增量（流式中的片段）
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ToolCallDelta {
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/// 索引
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pub index: u32,
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/// 工具调用 ID（仅第一个 chunk 有）
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#[serde(skip_serializing_if = "Option::is_none")]
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pub id: Option<String>,
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/// 函数名片段
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#[serde(skip_serializing_if = "Option::is_none")]
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pub function_name: Option<String>,
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/// 函数参数片段
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#[serde(skip_serializing_if = "Option::is_none")]
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pub function_arguments: Option<String>,
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}
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/// 异步流类型别名
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pub type StreamResult = Pin<Box<dyn Stream<Item = anyhow::Result<StreamChunk>> + Send>>;
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/// LLM Provider trait
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#[async_trait]
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pub trait LlmProvider: Send + Sync {
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/// 同步调用
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async fn complete(&self, request: CompletionRequest) -> anyhow::Result<CompletionResponse>;
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/// 流式调用（返回异步流）
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async fn stream(
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&self,
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request: CompletionRequest,
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) -> anyhow::Result<StreamResult>;
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/// 文本嵌入：批量文本 → 语义向量(供知识库向量检索)
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///
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/// 默认实现返回 Err(协议不支持)。OpenAI 兼容协议覆盖实现(/v1/embeddings);
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/// Anthropic 无 embedding API,保持默认。
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async fn embed(&self, _model: &str, _texts: Vec<String>) -> anyhow::Result<Vec<Vec<f32>>> {
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anyhow::bail!("该 Provider 不支持 embedding({})", self.name())
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}
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/// Provider 名称
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fn name(&self) -> &str;
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/// 实际请求端点（含 base_url + 关键路径，如 chat completions / messages）。
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/// 默认回落 `name()`，provider 实现覆盖返真实 URL，供 401/网络错误诊断打印
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/// —— 旧路径只能近似打印 provider_type，看不到实际请求端点。
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fn endpoint(&self) -> String {
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self.name().to_string()
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}
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}
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pub use df_ai_core::provider::*;
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