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package agent
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import (
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"bufio"
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"context"
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"encoding/json"
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"errors"
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"net/http"
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"strings"
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)
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// normalizeAnthropicURL 把用户填写的 Base URL 规范化为 Anthropic Messages 接口地址。
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// 兼容多种填写方式：https://api.anthropic.com、.../v1、.../v1/messages、.../v1/chat/completions。
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func normalizeAnthropicURL(baseURL string) string {
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trimmed := strings.TrimRight(strings.TrimSpace(baseURL), "/")
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if trimmed == "" {
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return "https://api.anthropic.com/v1/messages"
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}
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switch {
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case strings.HasSuffix(trimmed, "/v1/chat/completions"):
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return strings.TrimSuffix(trimmed, "/chat/completions") + "/messages"
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case strings.HasSuffix(trimmed, "/v1/messages"):
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return trimmed
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case strings.HasSuffix(trimmed, "/v1"):
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return trimmed + "/messages"
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default:
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return trimmed + "/v1/messages"
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}
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}
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// extractSystemPrompt 汇总消息中的 system 角色内容，Anthropic 要求 system 放在顶层字段。
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func extractSystemPrompt(messages []Message) string {
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var parts []string
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for _, message := range messages {
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if message.Role == "system" && message.Content != nil && strings.TrimSpace(*message.Content) != "" {
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parts = append(parts, *message.Content)
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}
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}
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return strings.Join(parts, "\n\n")
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}
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// toAnthropicTools 把内部工具定义转换为 Anthropic 的 tools 数组（input_schema 替代 parameters）。
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func toAnthropicTools(tools []ToolDefinition) []map[string]any {
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result := make([]map[string]any, 0, len(tools))
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for _, tool := range tools {
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schema := tool.Function.Parameters
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if schema == nil {
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schema = map[string]any{"type": "object"}
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}
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result = append(result, map[string]any{
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"name": tool.Function.Name,
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"description": tool.Function.Description,
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"input_schema": schema,
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})
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}
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return result
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}
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// toAnthropicMessages 把内部 Message 列表转换为 Anthropic messages 数组。
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// system 消息被过滤（走顶层 system 字段）；assistant 的 tool_use 与 user 的
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// tool_result 都以 content block 形式表达。
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// 注意：Anthropic 要求上一条 assistant 消息中所有 tool_use 的 tool_result
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// 必须放在紧邻的同一条 user 消息里，因此连续的 tool 结果消息需要合并。
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func toAnthropicMessages(messages []Message) []map[string]any {
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result := make([]map[string]any, 0, len(messages))
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for i := 0; i < len(messages); i++ {
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message := messages[i]
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switch message.Role {
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case "system":
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continue
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case "assistant":
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blocks := make([]map[string]any, 0, 1+len(message.ToolCalls))
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if message.Content != nil && strings.TrimSpace(*message.Content) != "" {
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blocks = append(blocks, map[string]any{"type": "text", "text": *message.Content})
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}
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for _, call := range message.ToolCalls {
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blocks = append(blocks, map[string]any{
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"type": "tool_use",
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"id": call.ID,
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"name": call.Function.Name,
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"input": parseJSONValue(call.Function.Arguments),
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})
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}
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if len(blocks) == 0 {
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blocks = append(blocks, map[string]any{"type": "text", "text": ""})
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}
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result = append(result, map[string]any{"role": "assistant", "content": blocks})
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case "tool":
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// 合并连续的 tool 消息：同一条 user 消息包含所有 tool_result 块
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blocks := []map[string]any{{
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"type": "tool_result",
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"tool_use_id": message.ToolCallID,
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"content": contentString(message.Content),
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}}
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for i+1 < len(messages) && messages[i+1].Role == "tool" {
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i++
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next := messages[i]
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blocks = append(blocks, map[string]any{
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"type": "tool_result",
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"tool_use_id": next.ToolCallID,
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"content": contentString(next.Content),
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})
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}
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result = append(result, map[string]any{"role": "user", "content": blocks})
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default: // user
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blocks := make([]map[string]any, 0, 1)
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if message.Content != nil && strings.TrimSpace(*message.Content) != "" {
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blocks = append(blocks, map[string]any{"type": "text", "text": *message.Content})
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}
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if len(blocks) == 0 {
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blocks = append(blocks, map[string]any{"type": "text", "text": ""})
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}
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result = append(result, map[string]any{"role": "user", "content": blocks})
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}
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}
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return result
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}
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// parseJSONValue 把工具参数 JSON 字符串解析为任意值；解析失败时回退为空对象。
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func parseJSONValue(raw string) any {
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var value any
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if err := json.Unmarshal([]byte(raw), &value); err != nil || value == nil {
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return map[string]any{}
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}
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return value
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}
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func contentString(content *string) string {
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if content == nil {
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return ""
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}
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return *content
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}
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// readAnthropicSSE 解析 Anthropic Messages API 的流式响应。
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// 事件格式为 `event: <type>` 与 `data: <json>` 两行一组。
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func readAnthropicSSE(ctx context.Context, resp *http.Response, ch chan<- StreamChunk) {
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scanner := bufio.NewScanner(resp.Body)
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scanner.Buffer(make([]byte, 64*1024), 1024*1024)
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// content block index -> 正在累积的 tool_use 状态
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type toolState struct {
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callIndex int
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id string
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name string
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}
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tools := make(map[int]*toolState)
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nextCallIndex := 0
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var eventType string
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for scanner.Scan() {
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line := scanner.Text()
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switch {
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case strings.HasPrefix(line, "event:"):
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eventType = strings.TrimSpace(strings.TrimPrefix(line, "event:"))
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continue
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case strings.HasPrefix(line, "data:"):
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default:
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continue
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}
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data := strings.TrimSpace(strings.TrimPrefix(line, "data:"))
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switch eventType {
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case "content_block_start":
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var ev struct {
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Index int `json:"index"`
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Block struct {
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Type string `json:"type"`
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ID string `json:"id"`
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Name string `json:"name"`
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} `json:"content_block"`
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}
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if err := json.Unmarshal([]byte(data), &ev); err != nil {
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continue
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}
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if ev.Block.Type == "tool_use" {
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state := &toolState{callIndex: nextCallIndex, id: ev.Block.ID, name: ev.Block.Name}
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nextCallIndex++
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tools[ev.Index] = state
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ch <- StreamChunk{ToolCalls: []ToolCallDelta{
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{Index: state.callIndex, ID: state.id, Name: state.name},
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}}
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}
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case "content_block_delta":
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var ev struct {
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Index int `json:"index"`
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Delta struct {
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Type string `json:"type"`
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Text string `json:"text"`
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PartialJSON string `json:"partial_json"`
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} `json:"delta"`
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}
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if err := json.Unmarshal([]byte(data), &ev); err != nil {
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continue
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}
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switch ev.Delta.Type {
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case "text_delta":
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ch <- StreamChunk{Content: ev.Delta.Text}
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case "input_json_delta":
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if state, ok := tools[ev.Index]; ok {
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ch <- StreamChunk{ToolCalls: []ToolCallDelta{
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{Index: state.callIndex, ArgumentsDelta: ev.Delta.PartialJSON},
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}}
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}
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}
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case "message_delta":
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var ev struct {
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Delta struct {
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StopReason string `json:"stop_reason"`
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} `json:"delta"`
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Usage *struct {
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OutputTokens int `json:"output_tokens"`
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} `json:"usage"`
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}
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if err := json.Unmarshal([]byte(data), &ev); err != nil {
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continue
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}
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if ev.Delta.StopReason != "" {
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ch <- StreamChunk{FinishReason: ev.Delta.StopReason}
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}
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if ev.Usage != nil {
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ch <- StreamChunk{Usage: &Usage{CompletionTokens: ev.Usage.OutputTokens}}
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}
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case "error":
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var ev struct {
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Error struct {
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Type string `json:"type"`
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Message string `json:"message"`
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} `json:"error"`
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}
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if err := json.Unmarshal([]byte(data), &ev); err != nil {
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continue
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}
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msg := strings.TrimSpace(ev.Error.Message)
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if msg == "" {
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msg = "Anthropic API 错误"
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}
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ch <- StreamChunk{Error: errors.New(msg)}
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}
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}
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if err := scanner.Err(); err != nil && ctx.Err() == nil {
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ch <- StreamChunk{Error: err}
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}
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}
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// compressAnthropic 使用 Anthropic 非流式接口执行对话压缩。
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func (c *LLMClient) compressAnthropic(ctx context.Context, messages []Message) (string, error) {
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body := map[string]any{
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"model": c.apiConfig.Model(),
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"max_tokens": c.cfg.CompactionTokens,
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"stream": false,
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"temperature": 0.2,
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// 关闭思考：推理模型（如 deepseek-v4-flash）默认先输出 thinking 块，
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// 会把 max_tokens 预算耗尽而拿不到 text 块，导致压缩被判为失败。
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"thinking": map[string]any{"type": "disabled"},
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"messages": toAnthropicMessages(messages),
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}
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if system := extractSystemPrompt(messages); system != "" {
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body["system"] = system
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}
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ctx, cancel := context.WithTimeout(ctx, c.cfg.RequestTimeout)
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defer cancel()
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payload, err := json.Marshal(body)
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if err != nil {
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return "", err
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}
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respBody, err := c.doSyncRequestWithRetry(ctx, true, normalizeAnthropicURL(c.apiConfig.BaseURL()), payload)
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if err != nil {
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return "", err
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}
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var result struct {
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Content []struct {
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Type string `json:"type"`
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Text string `json:"text"`
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Thinking string `json:"thinking"`
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} `json:"content"`
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}
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if err := json.Unmarshal(respBody, &result); err != nil {
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return "", err
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}
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fallback := ""
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for _, block := range result.Content {
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if block.Type == "text" && strings.TrimSpace(block.Text) != "" {
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return strings.TrimSpace(block.Text), nil
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}
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// 记录 thinking 作为兜底（仅当端点不支持 thinking:disabled 时才会出现）
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if block.Type == "thinking" && fallback == "" {
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fallback = strings.TrimSpace(block.Thinking)
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}
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}
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if fallback != "" {
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return fallback, nil
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}
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return "", errors.New("模型没有返回压缩摘要")
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}
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