bugfix

internal/infra/llm/openai.go

@@ -12,8 +12,11 @@ import (
 	tiktoken "github.com/pkoukk/tiktoken-go"
 )
 
-// Compile-time assertion: OpenAIClient must satisfy LLMClient.
-var _ LLMClient = (*OpenAIClient)(nil)
+// Compile-time assertion: OpenAIClient must satisfy LLMClient + Embedder.
+var (
+	_ LLMClient = (*OpenAIClient)(nil)
+	_ Embedder  = (*OpenAIClient)(nil)
+)
 
 // OpenAIClient 通过官方 SDK 调用 Chat Completions API。
 // baseURL 改写后兼容 DeepSeek / Qwen / Ollama / vLLM / Moonshot 等 OpenAI-compatible 端点。
@@ -54,7 +57,7 @@ func toOpenAIMessages(msgs []Message) []openai.ChatCompletionMessageParamUnion {
 					parts = append(parts, openai.ImageContentPart(openai.ChatCompletionContentPartImageImageURLParam{
 						URL: dataURI,
 					}))
-				// document/PDF 不翻译，由上层能力检查拦截
+					// document/PDF 不翻译，由上层能力检查拦截
 				}
 			}
 			out = append(out, openai.UserMessage(parts))
@@ -199,3 +202,40 @@ func (c *OpenAIClient) CountTokens(_ context.Context, modelID string, req Reques
 	}
 	return total, nil
 }
+
+// EmbedDims returns the platform-standard embedding dimension. text-embedding-3-*
+// models honor the `dimensions` request param, so Embed always pins the output to
+// PlatformEmbeddingDims to match the fixed pgvector column width.
+func (c *OpenAIClient) EmbedDims(string) int { return PlatformEmbeddingDims }
+
+// Embed calls POST /embeddings once for the whole batch (input order preserved)
+// and converts the returned []float64 vectors to []float32 for pgvector storage.
+func (c *OpenAIClient) Embed(ctx context.Context, modelID string, inputs []string) ([][]float32, error) {
+	if len(inputs) == 0 {
+		return nil, nil
+	}
+	resp, err := c.sdk.Embeddings.New(ctx, openai.EmbeddingNewParams{
+		Model:          openai.EmbeddingModel(modelID),
+		Input:          openai.EmbeddingNewParamsInputUnion{OfArrayOfStrings: inputs},
+		Dimensions:     param.NewOpt(int64(PlatformEmbeddingDims)),
+		EncodingFormat: openai.EmbeddingNewParamsEncodingFormatFloat,
+	})
+	if err != nil {
+		return nil, fmt.Errorf("openai embeddings: %w", err)
+	}
+	if len(resp.Data) != len(inputs) {
+		return nil, fmt.Errorf("openai embeddings: got %d vectors for %d inputs", len(resp.Data), len(inputs))
+	}
+	out := make([][]float32, len(resp.Data))
+	for _, e := range resp.Data {
+		if e.Index < 0 || int(e.Index) >= len(out) {
+			return nil, fmt.Errorf("openai embeddings: out-of-range index %d", e.Index)
+		}
+		v := make([]float32, len(e.Embedding))
+		for i, f := range e.Embedding {
+			v[i] = float32(f)
+		}
+		out[e.Index] = v
+	}
+	return out, nil
+}