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

internal/models/embedding/zhipu.go

@@ -0,0 +1,264 @@
+package embedding
+
+import (
+	"bytes"
+	"context"
+	"encoding/json"
+	"fmt"
+	"io"
+	"net/http"
+	"time"
+
+	"github.com/Tencent/WeKnora/internal/logger"
+	"github.com/Tencent/WeKnora/internal/models/provider"
+	"github.com/Tencent/WeKnora/internal/models/requestbudget"
+	secutils "github.com/Tencent/WeKnora/internal/utils"
+)
+
+// ZhipuEmbedder implements text vectorization functionality using Zhipu AI API
+type ZhipuEmbedder struct {
+	apiKey                    string
+	baseURL                   string
+	modelName                 string
+	truncatePromptTokens      int
+	dimensions                int
+	modelID                   string
+	httpClient                *http.Client
+	timeout                   time.Duration
+	maxRetries                int
+	customHeaders             map[string]string
+	supportsDimensionOverride bool
+	EmbedderPooler
+}
+
+// ZhipuEmbedRequest represents a Zhipu embedding request
+type ZhipuEmbedRequest struct {
+	Model                string   `json:"model"`
+	Input                []string `json:"input"`
+	Dimensions           int      `json:"dimensions,omitempty"`
+	TruncatePromptTokens int      `json:"truncate_prompt_tokens,omitempty"`
+}
+
+// ZhipuEmbedResponse represents a Zhipu embedding response
+type ZhipuEmbedResponse struct {
+	Data []struct {
+		Embedding []float32 `json:"embedding"`
+		Index     int       `json:"index"`
+	} `json:"data"`
+	Model string `json:"model"`
+}
+
+// NewZhipuEmbedder creates a new Zhipu embedder
+func NewZhipuEmbedder(apiKey, baseURL, modelName string,
+	truncatePromptTokens int, dimensions int, modelID string, pooler EmbedderPooler,
+) (*ZhipuEmbedder, error) {
+	if baseURL == "" {
+		baseURL = provider.ZhipuEmbeddingBaseURL
+	}
+
+	if modelName == "" {
+		return nil, fmt.Errorf("model name is required")
+	}
+
+	if truncatePromptTokens == 0 {
+		truncatePromptTokens = 511
+	}
+
+	timeout := 60 * time.Second
+
+	if err := validateEmbeddingBaseURL(baseURL); err != nil {
+		return nil, err
+	}
+
+	return &ZhipuEmbedder{
+		apiKey:               apiKey,
+		baseURL:              baseURL,
+		modelName:            modelName,
+		httpClient:           newEmbeddingHTTPClient(timeout),
+		truncatePromptTokens: truncatePromptTokens,
+		EmbedderPooler:       pooler,
+		dimensions:           dimensions,
+		modelID:              modelID,
+		timeout:              timeout,
+		maxRetries:           3, // Maximum retry count
+	}, nil
+}
+
+// SetCustomHeaders sets custom HTTP headers for the embedder
+func (e *ZhipuEmbedder) SetCustomHeaders(headers map[string]string) {
+	e.customHeaders = headers
+}
+
+func (e *ZhipuEmbedder) SetSupportsDimensionOverride(supported bool) {
+	e.supportsDimensionOverride = supported
+}
+
+// Embed converts text to vector
+func (e *ZhipuEmbedder) 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 *ZhipuEmbedder) doRequestWithRetry(ctx context.Context, jsonData []byte) (*http.Response, error) {
+	ctx = requestbudget.WithModel(ctx, e.modelID)
+	var resp *http.Response
+	var err error
+	url := e.baseURL + "/embeddings"
+
+	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
+			}
+			logger.GetLogger(ctx).
+				Infof("ZhipuEmbedder retrying request (%d/%d), waiting %v", i, e.maxRetries, backoffTime)
+
+			select {
+			case <-time.After(backoffTime):
+			case <-ctx.Done():
+				return nil, ctx.Err()
+			}
+		}
+
+		var req *http.Request
+		req, err = http.NewRequestWithContext(ctx, "POST", url, bytes.NewReader(jsonData))
+		if err != nil {
+			logger.GetLogger(ctx).Errorf("ZhipuEmbedder failed to create request: %v", err)
+			continue
+		}
+		req.Header.Set("Content-Type", "application/json")
+		req.Header.Set("Authorization", "Bearer "+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
+		}
+
+		logger.GetLogger(ctx).Errorf("ZhipuEmbedder request failed (attempt %d/%d): %v", i+1, e.maxRetries+1, err)
+	}
+
+	return nil, err
+}
+
+func (e *ZhipuEmbedder) BatchEmbed(ctx context.Context, texts []string) ([][]float32, error) {
+	// Create request body
+	reqBody := ZhipuEmbedRequest{
+		Model:                e.modelName,
+		Input:                texts,
+		TruncatePromptTokens: e.truncatePromptTokens,
+	}
+	if e.supportsDimensionsParam() {
+		reqBody.Dimensions = e.dimensions
+	}
+
+	jsonData, err := json.Marshal(reqBody)
+	if err != nil {
+		logger.GetLogger(ctx).Errorf("ZhipuEmbedder BatchEmbed marshal request error: %v", err)
+		return nil, fmt.Errorf("marshal request: %w", err)
+	}
+
+	// Log request details for debugging
+	logger.GetLogger(ctx).Debugf("ZhipuEmbedder BatchEmbed: model=%s, input_count=%d, truncate_tokens=%d",
+		e.modelName, len(texts), e.truncatePromptTokens)
+
+	// Check for invalid input lengths and log details
+	hasInvalidLength := false
+	for i, text := range texts {
+		textLen := len(text)
+		textPreview := text
+		if len(textPreview) > 200 {
+			textPreview = textPreview[:200] + "..."
+		}
+
+		// Log warning if length is outside valid range [1, 8192]
+		if textLen == 0 || textLen > 8192 {
+			hasInvalidLength = true
+			logger.GetLogger(ctx).Errorf("ZhipuEmbedder BatchEmbed input[%d]: INVALID length=%d (must be [1, 8192]), preview=%s",
+				i, textLen, textPreview)
+		} else {
+			logger.GetLogger(ctx).Debugf("ZhipuEmbedder BatchEmbed input[%d]: length=%d, preview=%s",
+				i, textLen, textPreview)
+		}
+	}
+
+	if hasInvalidLength {
+		logger.GetLogger(ctx).Errorf("ZhipuEmbedder BatchEmbed: Found invalid input lengths, this will likely cause API error")
+	}
+
+	// Send request (passing jsonData instead of constructing http.Request)
+	resp, err := e.doRequestWithRetry(ctx, jsonData)
+	if err != nil {
+		logger.GetLogger(ctx).Errorf("ZhipuEmbedder BatchEmbed send request error: %v", err)
+		return nil, fmt.Errorf("send request: %w", err)
+	}
+	if resp.Body != nil {
+		defer resp.Body.Close()
+	}
+
+	// Read response
+	body, err := io.ReadAll(resp.Body)
+	if err != nil {
+		logger.GetLogger(ctx).Errorf("ZhipuEmbedder BatchEmbed read response error: %v", err)
+		return nil, fmt.Errorf("read response: %w", err)
+	}
+
+	if resp.StatusCode != http.StatusOK {
+		// Log detailed error response from OpenAI API
+		bodyStr := string(body)
+		if len(bodyStr) > 1000 {
+			bodyStr = bodyStr[:1000] + "... (truncated)"
+		}
+		logger.GetLogger(ctx).Errorf("ZhipuEmbedder BatchEmbed API error: Http Status %s, Response Body: %s", resp.Status, bodyStr)
+		return nil, fmt.Errorf("BatchEmbed API error: Http Status %s, Response: %s", resp.Status, bodyStr)
+	}
+
+	// Parse response
+	var response ZhipuEmbedResponse
+	if err := json.Unmarshal(body, &response); err != nil {
+		logger.GetLogger(ctx).Errorf("ZhipuEmbedder BatchEmbed unmarshal response error: %v", err)
+		return nil, fmt.Errorf("unmarshal response: %w", err)
+	}
+
+	// Extract embedding vectors
+	embeddings := make([][]float32, 0, len(response.Data))
+	for _, data := range response.Data {
+		embeddings = append(embeddings, data.Embedding)
+	}
+
+	return embeddings, nil
+}
+
+// GetModelName returns the model name
+func (e *ZhipuEmbedder) GetModelName() string {
+	return e.modelName
+}
+
+func (e *ZhipuEmbedder) supportsDimensionsParam() bool {
+	return e.supportsDimensionOverride && e.dimensions > 0
+}
+
+// GetDimensions returns the vector dimensions
+func (e *ZhipuEmbedder) GetDimensions() int {
+	return e.dimensions
+}
+
+// GetModelID returns the model ID
+func (e *ZhipuEmbedder) GetModelID() string {
+	return e.modelID
+}