feat: add OpenAI/Anthropic tools support with tool emulation
- Parse tools/tool_choice from OpenAI and Anthropic requests - Inject tool definitions into system prompt via toolemulation - Parse action blocks (```json action) from model responses - Retry logic for forced tool_choice (any/required) - Return proper tool_calls / tool_use in responses - Support streaming tools via collect-and-replay pattern - Add tool history projection (assistant tool_calls + tool results) - Model ID normalization: use official names (Qwen3.6-Plus, etc.) - Fix resolveSessionMode to use Fresh mode when tools present
This commit is contained in:
@@ -9,6 +9,7 @@ import (
|
||||
"time"
|
||||
|
||||
"lingma-ipc-proxy/internal/service"
|
||||
"lingma-ipc-proxy/internal/toolemulation"
|
||||
)
|
||||
|
||||
type Server struct {
|
||||
@@ -18,11 +19,13 @@ type Server struct {
|
||||
}
|
||||
|
||||
type anthropicRequest struct {
|
||||
Model string `json:"model"`
|
||||
MaxTokens int `json:"max_tokens,omitempty"`
|
||||
System any `json:"system,omitempty"`
|
||||
Messages []rawMessage `json:"messages"`
|
||||
Stream bool `json:"stream,omitempty"`
|
||||
Model string `json:"model"`
|
||||
MaxTokens int `json:"max_tokens,omitempty"`
|
||||
System any `json:"system,omitempty"`
|
||||
Messages []rawMessage `json:"messages"`
|
||||
Stream bool `json:"stream,omitempty"`
|
||||
Tools any `json:"tools,omitempty"`
|
||||
ToolChoice any `json:"tool_choice,omitempty"`
|
||||
}
|
||||
|
||||
type openAIChatRequest struct {
|
||||
@@ -31,11 +34,15 @@ type openAIChatRequest struct {
|
||||
Stream bool `json:"stream,omitempty"`
|
||||
MaxTokens int `json:"max_tokens,omitempty"`
|
||||
MaxCompletionTokens int `json:"max_completion_tokens,omitempty"`
|
||||
Tools any `json:"tools,omitempty"`
|
||||
ToolChoice any `json:"tool_choice,omitempty"`
|
||||
}
|
||||
|
||||
type rawMessage struct {
|
||||
Role string `json:"role"`
|
||||
Content any `json:"content"`
|
||||
Role string `json:"role"`
|
||||
Content any `json:"content"`
|
||||
ToolCalls []any `json:"tool_calls,omitempty"`
|
||||
ToolCallID string `json:"tool_call_id,omitempty"`
|
||||
}
|
||||
|
||||
type modelResponse struct {
|
||||
@@ -170,13 +177,26 @@ func (s *Server) handleAnthropicMessages(w http.ResponseWriter, r *http.Request)
|
||||
return
|
||||
}
|
||||
|
||||
content := []map[string]any{{"type": "text", "text": result.Text}}
|
||||
stopReason := "end_turn"
|
||||
if len(result.ToolCalls) > 0 {
|
||||
for _, tc := range result.ToolCalls {
|
||||
content = append(content, map[string]any{
|
||||
"type": "tool_use",
|
||||
"id": tc.ID,
|
||||
"name": tc.Name,
|
||||
"input": tc.Arguments,
|
||||
})
|
||||
}
|
||||
stopReason = "tool_use"
|
||||
}
|
||||
writeJSON(w, http.StatusOK, map[string]any{
|
||||
"id": fmt.Sprintf("msg_%d", time.Now().UnixNano()),
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"content": []map[string]any{{"type": "text", "text": result.Text}},
|
||||
"content": content,
|
||||
"model": result.Model,
|
||||
"stop_reason": "end_turn",
|
||||
"stop_reason": stopReason,
|
||||
"stop_sequence": nil,
|
||||
"usage": map[string]any{
|
||||
"input_tokens": result.InputTokens,
|
||||
@@ -224,6 +244,27 @@ func (s *Server) handleOpenAIChatCompletions(w http.ResponseWriter, r *http.Requ
|
||||
}
|
||||
|
||||
created := time.Now().Unix()
|
||||
message := map[string]any{
|
||||
"role": "assistant",
|
||||
"content": result.Text,
|
||||
}
|
||||
finishReason := "stop"
|
||||
if len(result.ToolCalls) > 0 {
|
||||
toolCalls := make([]map[string]any, 0, len(result.ToolCalls))
|
||||
for _, tc := range result.ToolCalls {
|
||||
argsJSON, _ := json.Marshal(tc.Arguments)
|
||||
toolCalls = append(toolCalls, map[string]any{
|
||||
"id": tc.ID,
|
||||
"type": "function",
|
||||
"function": map[string]any{
|
||||
"name": tc.Name,
|
||||
"arguments": string(argsJSON),
|
||||
},
|
||||
})
|
||||
}
|
||||
message["tool_calls"] = toolCalls
|
||||
finishReason = "tool_calls"
|
||||
}
|
||||
writeJSON(w, http.StatusOK, map[string]any{
|
||||
"id": fmt.Sprintf("chatcmpl-%d", time.Now().UnixNano()),
|
||||
"object": "chat.completion",
|
||||
@@ -231,12 +272,9 @@ func (s *Server) handleOpenAIChatCompletions(w http.ResponseWriter, r *http.Requ
|
||||
"model": result.Model,
|
||||
"choices": []map[string]any{
|
||||
{
|
||||
"index": 0,
|
||||
"message": map[string]any{
|
||||
"role": "assistant",
|
||||
"content": result.Text,
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"message": message,
|
||||
"finish_reason": finishReason,
|
||||
},
|
||||
},
|
||||
"usage": map[string]any{
|
||||
@@ -254,17 +292,73 @@ func (s *Server) handleAnthropicStream(w http.ResponseWriter, r *http.Request, r
|
||||
return
|
||||
}
|
||||
|
||||
model := strings.TrimSpace(req.Model)
|
||||
if model == "" {
|
||||
model = "lingma"
|
||||
}
|
||||
msgID := fmt.Sprintf("msg_%d", time.Now().UnixNano())
|
||||
|
||||
if len(req.Tools) > 0 {
|
||||
result, err := s.svc.Generate(r.Context(), req)
|
||||
if err != nil {
|
||||
writeAnthropicError(w, http.StatusInternalServerError, "api_error", err.Error())
|
||||
return
|
||||
}
|
||||
streamingHeaders(w)
|
||||
_ = writeSSEEvent(w, flusher, "message_start", map[string]any{
|
||||
"type": "message_start",
|
||||
"message": map[string]any{
|
||||
"id": msgID, "type": "message", "role": "assistant", "content": []any{},
|
||||
"model": model, "stop_reason": nil, "stop_sequence": nil,
|
||||
"usage": map[string]any{"input_tokens": 0, "output_tokens": 0},
|
||||
},
|
||||
})
|
||||
_ = writeSSEEvent(w, flusher, "content_block_start", map[string]any{
|
||||
"type": "content_block_start", "index": 0,
|
||||
"content_block": map[string]any{"type": "text", "text": ""},
|
||||
})
|
||||
if result.Text != "" {
|
||||
_ = writeSSEEvent(w, flusher, "content_block_delta", map[string]any{
|
||||
"type": "content_block_delta", "index": 0,
|
||||
"delta": map[string]any{"type": "text_delta", "text": result.Text},
|
||||
})
|
||||
}
|
||||
_ = writeSSEEvent(w, flusher, "content_block_stop", map[string]any{
|
||||
"type": "content_block_stop", "index": 0,
|
||||
})
|
||||
for i, tc := range result.ToolCalls {
|
||||
_ = writeSSEEvent(w, flusher, "content_block_start", map[string]any{
|
||||
"type": "content_block_start", "index": i + 1,
|
||||
"content_block": map[string]any{"type": "tool_use", "id": tc.ID, "name": tc.Name, "input": map[string]any{}},
|
||||
})
|
||||
argsJSON, _ := json.Marshal(tc.Arguments)
|
||||
_ = writeSSEEvent(w, flusher, "content_block_delta", map[string]any{
|
||||
"type": "content_block_delta", "index": i + 1,
|
||||
"delta": map[string]any{"type": "input_json_delta", "partial_json": string(argsJSON)},
|
||||
})
|
||||
_ = writeSSEEvent(w, flusher, "content_block_stop", map[string]any{
|
||||
"type": "content_block_stop", "index": i + 1,
|
||||
})
|
||||
}
|
||||
stopReason := "end_turn"
|
||||
if len(result.ToolCalls) > 0 {
|
||||
stopReason = "tool_use"
|
||||
}
|
||||
_ = writeSSEEvent(w, flusher, "message_delta", map[string]any{
|
||||
"type": "message_delta",
|
||||
"delta": map[string]any{"stop_reason": stopReason, "stop_sequence": nil},
|
||||
"usage": map[string]any{"output_tokens": result.OutputTokens},
|
||||
})
|
||||
_ = writeSSEEvent(w, flusher, "message_stop", map[string]any{"type": "message_stop"})
|
||||
return
|
||||
}
|
||||
|
||||
events, done, err := s.svc.GenerateStream(r.Context(), req)
|
||||
if err != nil {
|
||||
writeAnthropicError(w, http.StatusInternalServerError, "api_error", err.Error())
|
||||
return
|
||||
}
|
||||
|
||||
model := strings.TrimSpace(req.Model)
|
||||
if model == "" {
|
||||
model = "lingma"
|
||||
}
|
||||
msgID := fmt.Sprintf("msg_%d", time.Now().UnixNano())
|
||||
streamingHeaders(w)
|
||||
if err := writeSSEEvent(w, flusher, "message_start", map[string]any{
|
||||
"type": "message_start",
|
||||
@@ -383,18 +477,65 @@ func (s *Server) handleOpenAIStream(w http.ResponseWriter, r *http.Request, req
|
||||
return
|
||||
}
|
||||
|
||||
events, done, err := s.svc.GenerateStream(r.Context(), req)
|
||||
if err != nil {
|
||||
writeOpenAIError(w, http.StatusInternalServerError, "api_error", err.Error())
|
||||
return
|
||||
}
|
||||
|
||||
model := strings.TrimSpace(req.Model)
|
||||
if model == "" {
|
||||
model = "lingma"
|
||||
}
|
||||
chatID := fmt.Sprintf("chatcmpl-%d", time.Now().UnixNano())
|
||||
created := time.Now().Unix()
|
||||
|
||||
if len(req.Tools) > 0 {
|
||||
result, err := s.svc.Generate(r.Context(), req)
|
||||
if err != nil {
|
||||
writeOpenAIError(w, http.StatusInternalServerError, "api_error", err.Error())
|
||||
return
|
||||
}
|
||||
streamingHeaders(w)
|
||||
_ = writeOpenAIChunk(w, flusher, map[string]any{
|
||||
"id": chatID, "object": "chat.completion.chunk", "created": created, "model": model,
|
||||
"choices": []map[string]any{{"index": 0, "delta": map[string]any{"role": "assistant"}, "finish_reason": nil}},
|
||||
})
|
||||
if result.Text != "" {
|
||||
_ = writeOpenAIChunk(w, flusher, map[string]any{
|
||||
"id": chatID, "object": "chat.completion.chunk", "created": created, "model": model,
|
||||
"choices": []map[string]any{{"index": 0, "delta": map[string]any{"content": result.Text}, "finish_reason": nil}},
|
||||
})
|
||||
}
|
||||
for i, tc := range result.ToolCalls {
|
||||
argsJSON, _ := json.Marshal(tc.Arguments)
|
||||
_ = writeOpenAIChunk(w, flusher, map[string]any{
|
||||
"id": chatID, "object": "chat.completion.chunk", "created": created, "model": model,
|
||||
"choices": []map[string]any{{
|
||||
"index": 0,
|
||||
"delta": map[string]any{
|
||||
"tool_calls": []map[string]any{{
|
||||
"index": i, "id": tc.ID, "type": "function",
|
||||
"function": map[string]any{"name": tc.Name, "arguments": string(argsJSON)},
|
||||
}},
|
||||
},
|
||||
"finish_reason": nil,
|
||||
}},
|
||||
})
|
||||
}
|
||||
finishReason := "stop"
|
||||
if len(result.ToolCalls) > 0 {
|
||||
finishReason = "tool_calls"
|
||||
}
|
||||
_ = writeOpenAIChunk(w, flusher, map[string]any{
|
||||
"id": chatID, "object": "chat.completion.chunk", "created": created, "model": model,
|
||||
"choices": []map[string]any{{"index": 0, "delta": map[string]any{}, "finish_reason": finishReason}},
|
||||
})
|
||||
_, _ = fmt.Fprint(w, "data: [DONE]\n\n")
|
||||
flusher.Flush()
|
||||
return
|
||||
}
|
||||
|
||||
events, done, err := s.svc.GenerateStream(r.Context(), req)
|
||||
if err != nil {
|
||||
writeOpenAIError(w, http.StatusInternalServerError, "api_error", err.Error())
|
||||
return
|
||||
}
|
||||
|
||||
streamingHeaders(w)
|
||||
if err := writeOpenAIChunk(w, flusher, map[string]any{
|
||||
"id": chatID,
|
||||
@@ -493,22 +634,41 @@ func normalizeAnthropicRequest(req anthropicRequest) (service.ChatRequest, error
|
||||
messages := make([]service.ChatMessage, 0, len(req.Messages))
|
||||
for _, message := range req.Messages {
|
||||
role := strings.ToLower(strings.TrimSpace(message.Role))
|
||||
text := strings.TrimSpace(extractText(message.Content))
|
||||
if role != "user" && role != "assistant" {
|
||||
continue
|
||||
switch role {
|
||||
case "user":
|
||||
text, toolResults := extractAnthropicUserContent(message.Content)
|
||||
for _, tr := range toolResults {
|
||||
prompt := toolemulation.ActionOutputPrompt(tr.ToolUseID, tr.Content)
|
||||
if prompt != "" {
|
||||
messages = append(messages, service.ChatMessage{Role: "user", Text: prompt})
|
||||
}
|
||||
}
|
||||
if text != "" {
|
||||
messages = append(messages, service.ChatMessage{Role: role, Text: text})
|
||||
}
|
||||
case "assistant":
|
||||
text, calls := extractAnthropicAssistantContent(message.Content)
|
||||
projected := toolemulation.AssistantToolCallsToText(text, calls)
|
||||
if projected != "" {
|
||||
messages = append(messages, service.ChatMessage{Role: role, Text: projected})
|
||||
}
|
||||
}
|
||||
if text == "" {
|
||||
continue
|
||||
}
|
||||
messages = append(messages, service.ChatMessage{Role: role, Text: text})
|
||||
}
|
||||
if len(messages) == 0 {
|
||||
return service.ChatRequest{}, fmt.Errorf("no user or assistant messages found")
|
||||
}
|
||||
|
||||
toolChoice := toolemulation.ToolChoice{Mode: "auto"}
|
||||
if req.ToolChoice != nil {
|
||||
toolChoice = toolemulation.ExtractToolChoice(req.ToolChoice)
|
||||
}
|
||||
|
||||
return service.ChatRequest{
|
||||
Model: strings.TrimSpace(req.Model),
|
||||
System: strings.TrimSpace(extractText(req.System)),
|
||||
Messages: messages,
|
||||
Model: strings.TrimSpace(req.Model),
|
||||
System: strings.TrimSpace(extractText(req.System)),
|
||||
Messages: messages,
|
||||
Tools: toolemulation.ExtractAnthropicTools(req.Tools),
|
||||
ToolChoice: toolChoice,
|
||||
}, nil
|
||||
}
|
||||
|
||||
@@ -517,24 +677,41 @@ func normalizeOpenAIRequest(req openAIChatRequest) (service.ChatRequest, error)
|
||||
systemParts := make([]string, 0, 2)
|
||||
for _, message := range req.Messages {
|
||||
role := strings.ToLower(strings.TrimSpace(message.Role))
|
||||
text := strings.TrimSpace(extractText(message.Content))
|
||||
if text == "" {
|
||||
continue
|
||||
}
|
||||
switch role {
|
||||
case "system":
|
||||
systemParts = append(systemParts, text)
|
||||
case "user", "assistant":
|
||||
messages = append(messages, service.ChatMessage{Role: role, Text: text})
|
||||
text := strings.TrimSpace(extractText(message.Content))
|
||||
if text != "" {
|
||||
systemParts = append(systemParts, text)
|
||||
}
|
||||
case "user":
|
||||
text := strings.TrimSpace(extractText(message.Content))
|
||||
if text != "" {
|
||||
messages = append(messages, service.ChatMessage{Role: role, Text: text})
|
||||
}
|
||||
case "assistant":
|
||||
text := strings.TrimSpace(extractText(message.Content))
|
||||
calls := extractOpenAIToolCalls(message.ToolCalls)
|
||||
projected := toolemulation.AssistantToolCallsToText(text, calls)
|
||||
if projected != "" {
|
||||
messages = append(messages, service.ChatMessage{Role: role, Text: projected})
|
||||
}
|
||||
case "tool":
|
||||
output := strings.TrimSpace(extractText(message.Content))
|
||||
prompt := toolemulation.ActionOutputPrompt(message.ToolCallID, output)
|
||||
if prompt != "" {
|
||||
messages = append(messages, service.ChatMessage{Role: "user", Text: prompt})
|
||||
}
|
||||
}
|
||||
}
|
||||
if len(messages) == 0 {
|
||||
return service.ChatRequest{}, fmt.Errorf("no user or assistant messages found")
|
||||
}
|
||||
return service.ChatRequest{
|
||||
Model: strings.TrimSpace(req.Model),
|
||||
System: strings.Join(systemParts, "\n\n"),
|
||||
Messages: messages,
|
||||
Model: strings.TrimSpace(req.Model),
|
||||
System: strings.Join(systemParts, "\n\n"),
|
||||
Messages: messages,
|
||||
Tools: toolemulation.ExtractTools(req.Tools),
|
||||
ToolChoice: toolemulation.ExtractToolChoice(req.ToolChoice),
|
||||
}, nil
|
||||
}
|
||||
|
||||
@@ -681,3 +858,117 @@ func (s *Server) release() {
|
||||
default:
|
||||
}
|
||||
}
|
||||
|
||||
func extractOpenAIToolCalls(raw []any) []toolemulation.ToolCall {
|
||||
if len(raw) == 0 {
|
||||
return nil
|
||||
}
|
||||
out := make([]toolemulation.ToolCall, 0, len(raw))
|
||||
for _, item := range raw {
|
||||
m, ok := item.(map[string]any)
|
||||
if !ok {
|
||||
continue
|
||||
}
|
||||
id := stringFromAny(m["id"])
|
||||
fn, ok := m["function"].(map[string]any)
|
||||
if !ok {
|
||||
continue
|
||||
}
|
||||
name := stringFromAny(fn["name"])
|
||||
if name == "" {
|
||||
continue
|
||||
}
|
||||
argsRaw := stringFromAny(fn["arguments"])
|
||||
var args map[string]any
|
||||
if argsRaw != "" {
|
||||
_ = json.Unmarshal([]byte(argsRaw), &args)
|
||||
}
|
||||
out = append(out, toolemulation.ToolCall{
|
||||
ID: id,
|
||||
Name: name,
|
||||
Arguments: args,
|
||||
})
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
type anthropicToolResult struct {
|
||||
ToolUseID string
|
||||
Content string
|
||||
}
|
||||
|
||||
func extractAnthropicUserContent(content any) (string, []anthropicToolResult) {
|
||||
text := extractText(content)
|
||||
items, ok := content.([]any)
|
||||
if !ok {
|
||||
return text, nil
|
||||
}
|
||||
var results []anthropicToolResult
|
||||
var textParts []string
|
||||
for _, item := range items {
|
||||
m, ok := item.(map[string]any)
|
||||
if !ok {
|
||||
continue
|
||||
}
|
||||
switch stringFromAny(m["type"]) {
|
||||
case "text":
|
||||
if t := stringFromAny(m["text"]); t != "" {
|
||||
textParts = append(textParts, t)
|
||||
}
|
||||
case "tool_result":
|
||||
toolUseID := stringFromAny(m["tool_use_id"])
|
||||
resultText := extractText(m["content"])
|
||||
if resultText != "" {
|
||||
results = append(results, anthropicToolResult{
|
||||
ToolUseID: toolUseID,
|
||||
Content: resultText,
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
if len(textParts) > 0 {
|
||||
text = strings.Join(textParts, "\n")
|
||||
}
|
||||
return text, results
|
||||
}
|
||||
|
||||
func extractAnthropicAssistantContent(content any) (string, []toolemulation.ToolCall) {
|
||||
text := extractText(content)
|
||||
items, ok := content.([]any)
|
||||
if !ok {
|
||||
return text, nil
|
||||
}
|
||||
calls := make([]toolemulation.ToolCall, 0, len(items))
|
||||
var textParts []string
|
||||
for _, item := range items {
|
||||
m, ok := item.(map[string]any)
|
||||
if !ok {
|
||||
continue
|
||||
}
|
||||
switch stringFromAny(m["type"]) {
|
||||
case "text":
|
||||
if t := stringFromAny(m["text"]); t != "" {
|
||||
textParts = append(textParts, t)
|
||||
}
|
||||
case "tool_use":
|
||||
id := stringFromAny(m["id"])
|
||||
name := stringFromAny(m["name"])
|
||||
if name == "" {
|
||||
continue
|
||||
}
|
||||
var args map[string]any
|
||||
if rawInput, ok := m["input"].(map[string]any); ok {
|
||||
args = rawInput
|
||||
}
|
||||
calls = append(calls, toolemulation.ToolCall{
|
||||
ID: id,
|
||||
Name: name,
|
||||
Arguments: args,
|
||||
})
|
||||
}
|
||||
}
|
||||
if len(textParts) > 0 {
|
||||
text = strings.Join(textParts, "\n")
|
||||
}
|
||||
return text, calls
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user