feat: Add Anthropic Messages API support

core/http/app.go

@@ -205,6 +205,7 @@ func API(application *application.Application) (*echo.Echo, error) {
 
 	routes.RegisterLocalAIRoutes(e, requestExtractor, application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig(), application.GalleryService(), opcache, application.TemplatesEvaluator(), application)
 	routes.RegisterOpenAIRoutes(e, requestExtractor, application)
+	routes.RegisterAnthropicRoutes(e, requestExtractor, application)
 	if !application.ApplicationConfig().DisableWebUI {
 		routes.RegisterUIAPIRoutes(e, application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig(), application.GalleryService(), opcache, application)
 		routes.RegisterUIRoutes(e, application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig(), application.GalleryService())

core/http/endpoints/anthropic/messages.go

@@ -0,0 +1,310 @@
+package anthropic
+
+import (
+	"encoding/json"
+	"fmt"
+
+	"github.com/google/uuid"
+	"github.com/labstack/echo/v4"
+	"github.com/mudler/LocalAI/core/backend"
+	"github.com/mudler/LocalAI/core/config"
+	"github.com/mudler/LocalAI/core/http/middleware"
+	"github.com/mudler/LocalAI/core/schema"
+	"github.com/mudler/LocalAI/core/templates"
+	"github.com/mudler/LocalAI/pkg/model"
+	"github.com/mudler/xlog"
+)
+
+// MessagesEndpoint is the Anthropic Messages API endpoint
+// https://docs.anthropic.com/claude/reference/messages_post
+// @Summary Generate a message response for the given messages and model.
+// @Param request body schema.AnthropicRequest true "query params"
+// @Success 200 {object} schema.AnthropicResponse "Response"
+// @Router /v1/messages [post]
+func MessagesEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator *templates.Evaluator, appConfig *config.ApplicationConfig) echo.HandlerFunc {
+	return func(c echo.Context) error {
+		id := uuid.New().String()
+
+		input, ok := c.Get(middleware.CONTEXT_LOCALS_KEY_LOCALAI_REQUEST).(*schema.AnthropicRequest)
+		if !ok || input.Model == "" {
+			return sendAnthropicError(c, 400, "invalid_request_error", "model is required")
+		}
+
+		cfg, ok := c.Get(middleware.CONTEXT_LOCALS_KEY_MODEL_CONFIG).(*config.ModelConfig)
+		if !ok || cfg == nil {
+			return sendAnthropicError(c, 400, "invalid_request_error", "model configuration not found")
+		}
+
+		if input.MaxTokens <= 0 {
+			return sendAnthropicError(c, 400, "invalid_request_error", "max_tokens is required and must be greater than 0")
+		}
+
+		xlog.Debug("Anthropic Messages endpoint configuration read", "config", cfg)
+
+		// Convert Anthropic messages to OpenAI format for internal processing
+		openAIMessages := convertAnthropicToOpenAIMessages(input)
+
+		// Create an OpenAI-compatible request for internal processing
+		openAIReq := &schema.OpenAIRequest{
+			PredictionOptions: schema.PredictionOptions{
+				BasicModelRequest: schema.BasicModelRequest{Model: input.Model},
+				Temperature:       input.Temperature,
+				TopK:              input.TopK,
+				TopP:              input.TopP,
+				Maxtokens:         &input.MaxTokens,
+			},
+			Messages: openAIMessages,
+			Stream:   input.Stream,
+			Context:  input.Context,
+			Cancel:   input.Cancel,
+		}
+
+		// Set stop sequences
+		if len(input.StopSequences) > 0 {
+			openAIReq.Stop = input.StopSequences
+		}
+
+		// Merge config settings
+		if input.Temperature != nil {
+			cfg.Temperature = input.Temperature
+		}
+		if input.TopK != nil {
+			cfg.TopK = input.TopK
+		}
+		if input.TopP != nil {
+			cfg.TopP = input.TopP
+		}
+		cfg.Maxtokens = &input.MaxTokens
+		if len(input.StopSequences) > 0 {
+			cfg.StopWords = append(cfg.StopWords, input.StopSequences...)
+		}
+
+		// Template the prompt
+		predInput := evaluator.TemplateMessages(*openAIReq, openAIReq.Messages, cfg, nil, false)
+		xlog.Debug("Anthropic Messages - Prompt (after templating)", "prompt", predInput)
+
+		if input.Stream {
+			return handleAnthropicStream(c, id, input, cfg, ml, predInput)
+		}
+
+		return handleAnthropicNonStream(c, id, input, cfg, ml, predInput, openAIReq)
+	}
+}
+
+func handleAnthropicNonStream(c echo.Context, id string, input *schema.AnthropicRequest, cfg *config.ModelConfig, ml *model.ModelLoader, predInput string, openAIReq *schema.OpenAIRequest) error {
+	images := []string{}
+	for _, m := range openAIReq.Messages {
+		images = append(images, m.StringImages...)
+	}
+
+	predFunc, err := backend.ModelInference(
+		input.Context, predInput, openAIReq.Messages, images, nil, nil, ml, cfg, nil, nil, nil, "", "", nil, nil, nil)
+	if err != nil {
+		xlog.Error("Anthropic model inference failed", "error", err)
+		return sendAnthropicError(c, 500, "api_error", fmt.Sprintf("model inference failed: %v", err))
+	}
+
+	prediction, err := predFunc()
+	if err != nil {
+		xlog.Error("Anthropic prediction failed", "error", err)
+		return sendAnthropicError(c, 500, "api_error", fmt.Sprintf("prediction failed: %v", err))
+	}
+
+	result := backend.Finetune(*cfg, predInput, prediction.Response)
+	stopReason := "end_turn"
+
+	resp := &schema.AnthropicResponse{
+		ID:         fmt.Sprintf("msg_%s", id),
+		Type:       "message",
+		Role:       "assistant",
+		Model:      input.Model,
+		StopReason: &stopReason,
+		Content: []schema.AnthropicContentBlock{
+			{Type: "text", Text: result},
+		},
+		Usage: schema.AnthropicUsage{
+			InputTokens:  prediction.Usage.Prompt,
+			OutputTokens: prediction.Usage.Completion,
+		},
+	}
+
+	if respData, err := json.Marshal(resp); err == nil {
+		xlog.Debug("Anthropic Response", "response", string(respData))
+	}
+
+	return c.JSON(200, resp)
+}
+
+func handleAnthropicStream(c echo.Context, id string, input *schema.AnthropicRequest, cfg *config.ModelConfig, ml *model.ModelLoader, predInput string) error {
+	c.Response().Header().Set("Content-Type", "text/event-stream")
+	c.Response().Header().Set("Cache-Control", "no-cache")
+	c.Response().Header().Set("Connection", "keep-alive")
+
+	// Create OpenAI messages for inference
+	openAIMessages := convertAnthropicToOpenAIMessages(input)
+
+	images := []string{}
+	for _, m := range openAIMessages {
+		images = append(images, m.StringImages...)
+	}
+
+	// Send message_start event
+	messageStart := schema.AnthropicStreamEvent{
+		Type: "message_start",
+		Message: &schema.AnthropicStreamMessage{
+			ID:      fmt.Sprintf("msg_%s", id),
+			Type:    "message",
+			Role:    "assistant",
+			Content: []schema.AnthropicContentBlock{},
+			Model:   input.Model,
+			Usage:   schema.AnthropicUsage{InputTokens: 0, OutputTokens: 0},
+		},
+	}
+	sendAnthropicSSE(c, messageStart)
+
+	// Send content_block_start event
+	contentBlockStart := schema.AnthropicStreamEvent{
+		Type:         "content_block_start",
+		Index:        0,
+		ContentBlock: &schema.AnthropicContentBlock{Type: "text", Text: ""},
+	}
+	sendAnthropicSSE(c, contentBlockStart)
+
+	// Stream content deltas
+	tokenCallback := func(token string, usage backend.TokenUsage) bool {
+		delta := schema.AnthropicStreamEvent{
+			Type:  "content_block_delta",
+			Index: 0,
+			Delta: &schema.AnthropicStreamDelta{
+				Type: "text_delta",
+				Text: token,
+			},
+		}
+		sendAnthropicSSE(c, delta)
+		return true
+	}
+
+	predFunc, err := backend.ModelInference(
+		input.Context, predInput, openAIMessages, images, nil, nil, ml, cfg, nil, nil, tokenCallback, "", "", nil, nil, nil)
+	if err != nil {
+		xlog.Error("Anthropic stream model inference failed", "error", err)
+		return sendAnthropicError(c, 500, "api_error", fmt.Sprintf("model inference failed: %v", err))
+	}
+
+	prediction, err := predFunc()
+	if err != nil {
+		xlog.Error("Anthropic stream prediction failed", "error", err)
+		return sendAnthropicError(c, 500, "api_error", fmt.Sprintf("prediction failed: %v", err))
+	}
+
+	// Send content_block_stop event
+	contentBlockStop := schema.AnthropicStreamEvent{
+		Type:  "content_block_stop",
+		Index: 0,
+	}
+	sendAnthropicSSE(c, contentBlockStop)
+
+	// Send message_delta event with stop_reason
+	stopReason := "end_turn"
+	messageDelta := schema.AnthropicStreamEvent{
+		Type: "message_delta",
+		Delta: &schema.AnthropicStreamDelta{
+			StopReason: &stopReason,
+		},
+		Usage: &schema.AnthropicUsage{
+			OutputTokens: prediction.Usage.Completion,
+		},
+	}
+	sendAnthropicSSE(c, messageDelta)
+
+	// Send message_stop event
+	messageStop := schema.AnthropicStreamEvent{
+		Type: "message_stop",
+	}
+	sendAnthropicSSE(c, messageStop)
+
+	return nil
+}
+
+func sendAnthropicSSE(c echo.Context, event schema.AnthropicStreamEvent) {
+	data, err := json.Marshal(event)
+	if err != nil {
+		xlog.Error("Failed to marshal SSE event", "error", err)
+		return
+	}
+	fmt.Fprintf(c.Response().Writer, "event: %s\ndata: %s\n\n", event.Type, string(data))
+	c.Response().Flush()
+}
+
+func sendAnthropicError(c echo.Context, statusCode int, errorType, message string) error {
+	resp := schema.AnthropicErrorResponse{
+		Type: "error",
+		Error: schema.AnthropicError{
+			Type:    errorType,
+			Message: message,
+		},
+	}
+	return c.JSON(statusCode, resp)
+}
+
+func convertAnthropicToOpenAIMessages(input *schema.AnthropicRequest) []schema.Message {
+	var messages []schema.Message
+
+	// Add system message if present
+	if input.System != "" {
+		messages = append(messages, schema.Message{
+			Role:          "system",
+			StringContent: input.System,
+			Content:       input.System,
+		})
+	}
+
+	// Convert Anthropic messages to OpenAI format
+	for _, msg := range input.Messages {
+		openAIMsg := schema.Message{
+			Role: msg.Role,
+		}
+
+		// Handle content (can be string or array of content blocks)
+		switch content := msg.Content.(type) {
+		case string:
+			openAIMsg.StringContent = content
+			openAIMsg.Content = content
+		case []interface{}:
+			// Handle array of content blocks
+			var textContent string
+			var stringImages []string
+
+			for _, block := range content {
+				if blockMap, ok := block.(map[string]interface{}); ok {
+					blockType, _ := blockMap["type"].(string)
+					switch blockType {
+					case "text":
+						if text, ok := blockMap["text"].(string); ok {
+							textContent += text
+						}
+					case "image":
+						// Handle image content
+						if source, ok := blockMap["source"].(map[string]interface{}); ok {
+							if sourceType, ok := source["type"].(string); ok && sourceType == "base64" {
+								if data, ok := source["data"].(string); ok {
+									mediaType, _ := source["media_type"].(string)
+									// Format as data URI
+									dataURI := fmt.Sprintf("data:%s;base64,%s", mediaType, data)
+									stringImages = append(stringImages, dataURI)
+								}
+							}
+						}
+					}
+				}
+			}
+			openAIMsg.StringContent = textContent
+			openAIMsg.Content = textContent
+			openAIMsg.StringImages = stringImages
+		}
+
+		messages = append(messages, openAIMsg)
+	}
+
+	return messages
+}