WeKnora deployment source with the reviewed wiki identity, summary and recovery fixes

internal/models/embedding/azure_openai.go

@@ -0,0 +1,199 @@
+package embedding
+
+import (
+	"bytes"
+	"context"
+	"encoding/json"
+	"fmt"
+	"io"
+	"net/http"
+	"time"
+
+	"github.com/Tencent/WeKnora/internal/logger"
+	"github.com/Tencent/WeKnora/internal/models/requestbudget"
+	secutils "github.com/Tencent/WeKnora/internal/utils"
+)
+
+// AzureOpenAIEmbedder implements text vectorization using Azure OpenAI API
+type AzureOpenAIEmbedder struct {
+	apiKey                    string
+	baseURL                   string
+	modelName                 string
+	truncatePromptTokens      int
+	dimensions                int
+	modelID                   string
+	apiVersion                string
+	httpClient                *http.Client
+	maxRetries                int
+	customHeaders             map[string]string
+	supportsDimensionOverride bool
+	EmbedderPooler
+}
+
+// SetCustomHeaders 设置用户自定义 HTTP 请求头（类似 OpenAI Python SDK 的 extra_headers）。
+func (e *AzureOpenAIEmbedder) SetCustomHeaders(headers map[string]string) {
+	e.customHeaders = headers
+}
+
+func (e *AzureOpenAIEmbedder) SetSupportsDimensionOverride(supported bool) {
+	e.supportsDimensionOverride = supported
+}
+
+type azureOpenAIEmbedRequest struct {
+	Model          string   `json:"model"`
+	Input          []string `json:"input"`
+	EncodingFormat string   `json:"encoding_format,omitempty"`
+	Dimensions     int      `json:"dimensions,omitempty"`
+}
+
+// NewAzureOpenAIEmbedder creates a new Azure OpenAI embedder
+func NewAzureOpenAIEmbedder(apiKey, baseURL, modelName string,
+	truncatePromptTokens int, dimensions int, modelID string,
+	apiVersion string, pooler EmbedderPooler,
+) (*AzureOpenAIEmbedder, error) {
+	if baseURL == "" {
+		return nil, fmt.Errorf("Azure resource endpoint (base URL) is required")
+	}
+	if modelName == "" {
+		return nil, fmt.Errorf("deployment name (model name) is required")
+	}
+	if apiVersion == "" {
+		apiVersion = "2024-10-21"
+	}
+	if truncatePromptTokens == 0 {
+		truncatePromptTokens = 511
+	}
+
+	if err := validateEmbeddingBaseURL(baseURL); err != nil {
+		return nil, err
+	}
+
+	return &AzureOpenAIEmbedder{
+		apiKey:               apiKey,
+		baseURL:              baseURL,
+		modelName:            modelName,
+		truncatePromptTokens: truncatePromptTokens,
+		dimensions:           dimensions,
+		modelID:              modelID,
+		apiVersion:           apiVersion,
+		httpClient:           newEmbeddingHTTPClient(60 * time.Second),
+		maxRetries:           3,
+		EmbedderPooler:       pooler,
+	}, nil
+}
+
+func (e *AzureOpenAIEmbedder) Embed(ctx context.Context, text string) ([]float32, error) {
+	for range 3 {
+		embeddings, err := e.BatchEmbed(ctx, []string{text})
+		if err != nil {
+			return nil, err
+		}
+		if len(embeddings) > 0 {
+			return embeddings[0], nil
+		}
+	}
+	return nil, fmt.Errorf("no embedding returned")
+}
+
+func (e *AzureOpenAIEmbedder) BatchEmbed(ctx context.Context, texts []string) ([][]float32, error) {
+	reqBody := azureOpenAIEmbedRequest{
+		Model:          e.modelName,
+		Input:          texts,
+		EncodingFormat: "float",
+	}
+	if e.supportsDimensionsParam() {
+		reqBody.Dimensions = e.dimensions
+	}
+
+	jsonData, err := json.Marshal(reqBody)
+	if err != nil {
+		return nil, fmt.Errorf("marshal request: %w", err)
+	}
+
+	logger.GetLogger(ctx).Debugf("AzureOpenAIEmbedder BatchEmbed: model=%s, input_count=%d",
+		e.modelName, len(texts))
+
+	resp, err := e.doRequestWithRetry(ctx, jsonData)
+	if err != nil {
+		return nil, fmt.Errorf("send request: %w", err)
+	}
+	if resp.Body != nil {
+		defer resp.Body.Close()
+	}
+
+	body, err := io.ReadAll(resp.Body)
+	if err != nil {
+		return nil, fmt.Errorf("read response: %w", err)
+	}
+
+	if resp.StatusCode != http.StatusOK {
+		bodyStr := string(body)
+		if len(bodyStr) > 1000 {
+			bodyStr = bodyStr[:1000] + "... (truncated)"
+		}
+		return nil, fmt.Errorf("Azure Embedding API error: Http Status %s, Response: %s", resp.Status, bodyStr)
+	}
+
+	var response OpenAIEmbedResponse
+	if err := json.Unmarshal(body, &response); err != nil {
+		return nil, fmt.Errorf("unmarshal response: %w", err)
+	}
+
+	embeddings := make([][]float32, 0, len(response.Data))
+	for _, data := range response.Data {
+		embeddings = append(embeddings, data.Embedding)
+	}
+	return embeddings, nil
+}
+
+func (e *AzureOpenAIEmbedder) doRequestWithRetry(ctx context.Context, jsonData []byte) (*http.Response, error) {
+	ctx = requestbudget.WithModel(ctx, e.modelID)
+	url := fmt.Sprintf("%s/openai/deployments/%s/embeddings?api-version=%s",
+		e.baseURL, e.modelName, e.apiVersion)
+
+	var resp *http.Response
+	var err error
+
+	for i := 0; i <= e.maxRetries; i++ {
+		if err := requestbudget.Check(ctx); err != nil {
+			return nil, err
+		}
+		if i > 0 {
+			backoffTime := time.Duration(1<<uint(i-1)) * time.Second
+			if backoffTime > 10*time.Second {
+				backoffTime = 10 * time.Second
+			}
+			select {
+			case <-time.After(backoffTime):
+			case <-ctx.Done():
+				return nil, ctx.Err()
+			}
+		}
+
+		req, reqErr := http.NewRequestWithContext(ctx, "POST", url, bytes.NewReader(jsonData))
+		if reqErr != nil {
+			err = reqErr
+			continue
+		}
+		req.Header.Set("Content-Type", "application/json")
+		req.Header.Set("api-key", e.apiKey)
+		secutils.ApplyCustomHeaders(req, e.customHeaders)
+
+		resp, err = e.httpClient.Do(req)
+		if stopProviderRetry(ctx, err) {
+			return nil, err
+		}
+		if err == nil {
+			return resp, nil
+		}
+	}
+	return nil, err
+}
+
+func (e *AzureOpenAIEmbedder) supportsDimensionsParam() bool {
+	return e.supportsDimensionOverride && e.dimensions > 0
+}
+
+func (e *AzureOpenAIEmbedder) GetModelName() string { return e.modelName }
+func (e *AzureOpenAIEmbedder) GetDimensions() int   { return e.dimensions }
+func (e *AzureOpenAIEmbedder) GetModelID() string   { return e.modelID }