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ragflow/internal/entity/models/zhipu-ai.go

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//
// Copyright 2026 The InfiniFlow Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
package models
import (
"bytes"
"context"
"encoding/base64"
"encoding/json"
"fmt"
"io"
"mime/multipart"
"net/http"
"os"
"path/filepath"
"ragflow/internal/common"
"strings"
)
// ZhipuAIModel implements ModelDriver for Zhipu AI
type ZhipuAIModel struct {
baseModel BaseModel
}
// NewZhipuAIModel creates a new Zhipu AI model instance
func NewZhipuAIModel(baseURL map[string]string, urlSuffix URLSuffix) *ZhipuAIModel {
return &ZhipuAIModel{
baseModel: BaseModel{
BaseURL: baseURL,
URLSuffix: urlSuffix,
httpClient: NewDriverHTTPClient(),
},
}
}
func (z *ZhipuAIModel) NewInstance(baseURL map[string]string) ModelDriver {
return NewZhipuAIModel(baseURL, z.baseModel.URLSuffix)
}
func (z *ZhipuAIModel) Name() string {
return "zhipu"
}
// ChatWithMessages sends multiple messages with roles and returns response
func (z *ZhipuAIModel) ChatWithMessages(modelName string, messages []Message, apiConfig *APIConfig, chatModelConfig *ChatConfig) (*ChatResponse, error) {
if err := z.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
}
if len(messages) == 0 {
return nil, fmt.Errorf("messages is empty")
}
baseURL, err := z.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
}
url := fmt.Sprintf("%s/%s", baseURL, z.baseModel.URLSuffix.Chat)
// Convert messages to the format expected by API
apiMessages := make([]map[string]interface{}, len(messages))
for i, msg := range messages {
apiMessages[i] = map[string]interface{}{
"role": msg.Role,
"content": msg.Content,
}
}
// Build request body
reqBody := map[string]interface{}{
"model": modelName,
"messages": apiMessages,
"stream": false,
"temperature": 1,
}
if chatModelConfig != nil {
if chatModelConfig.Stream != nil {
reqBody["stream"] = *chatModelConfig.Stream
}
if chatModelConfig.MaxTokens != nil {
reqBody["max_tokens"] = *chatModelConfig.MaxTokens
}
if chatModelConfig.Temperature != nil {
reqBody["temperature"] = *chatModelConfig.Temperature
}
if chatModelConfig.TopP != nil {
reqBody["top_p"] = *chatModelConfig.TopP
}
if chatModelConfig.Stop != nil {
reqBody["stop"] = *chatModelConfig.Stop
}
if chatModelConfig.Thinking != nil {
if *chatModelConfig.Thinking {
reqBody["thinking"] = map[string]interface{}{
"type": "enabled",
}
} else {
reqBody["thinking"] = map[string]interface{}{
"type": "disabled",
}
}
}
}
jsonData, err := json.Marshal(reqBody)
if err != nil {
return nil, fmt.Errorf("failed to marshal request: %w", err)
}
ctx, cancel := context.WithTimeout(context.Background(), nonStreamCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewBuffer(jsonData))
if err != nil {
return nil, fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
resp, err := z.baseModel.httpClient.Do(req)
if err != nil {
return nil, fmt.Errorf("failed to send request: %w", err)
}
defer resp.Body.Close()
body, err := io.ReadAll(resp.Body)
if err != nil {
return nil, fmt.Errorf("failed to read response: %w", err)
}
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("API request failed with status %d: %s", resp.StatusCode, string(body))
}
// Parse response
var result map[string]interface{}
if err = json.Unmarshal(body, &result); err != nil {
return nil, fmt.Errorf("failed to parse response: %w", err)
}
choices, ok := result["choices"].([]interface{})
if !ok || len(choices) == 0 {
return nil, fmt.Errorf("no choices in response")
}
firstChoice, ok := choices[0].(map[string]interface{})
if !ok {
return nil, fmt.Errorf("invalid choice format")
}
messageMap, ok := firstChoice["message"].(map[string]interface{})
if !ok {
return nil, fmt.Errorf("invalid message format")
}
content, ok := messageMap["content"].(string)
if !ok {
return nil, fmt.Errorf("invalid content format")
}
var reasonContent string
if chatModelConfig != nil && chatModelConfig.Thinking != nil && *chatModelConfig.Thinking {
reasonContent, ok = messageMap["reasoning_content"].(string)
if !ok {
return nil, fmt.Errorf("invalid content format")
}
// if first char of reasonContent is \n remove the '\n'
if reasonContent != "" && reasonContent[0] == '\n' {
reasonContent = reasonContent[1:]
}
}
chatResponse := &ChatResponse{
Answer: &content,
ReasonContent: &reasonContent,
}
return chatResponse, nil
}
// ChatStreamlyWithSender sends messages and streams response via sender function (best performance, no channel)
func (z *ZhipuAIModel) ChatStreamlyWithSender(modelName string, messages []Message, apiConfig *APIConfig, chatModelConfig *ChatConfig, sender func(*string, *string) error) error {
if err := z.baseModel.APIConfigCheck(apiConfig); err != nil {
return err
}
if len(messages) == 0 {
return fmt.Errorf("messages is empty")
}
baseURL, err := z.baseModel.GetBaseURL(apiConfig)
if err != nil {
return err
}
url := fmt.Sprintf("%s/%s", baseURL, z.baseModel.URLSuffix.Chat)
// Convert messages to API format
apiMessages := make([]map[string]interface{}, len(messages))
for i, msg := range messages {
apiMessages[i] = map[string]interface{}{
"role": msg.Role,
"content": msg.Content,
}
}
// Build request body with streaming enabled
reqBody := map[string]interface{}{
"model": modelName,
"messages": apiMessages,
"stream": true,
"temperature": 1,
}
if chatModelConfig != nil {
if chatModelConfig.Stream != nil {
reqBody["stream"] = *chatModelConfig.Stream
}
if chatModelConfig.MaxTokens != nil {
reqBody["max_tokens"] = *chatModelConfig.MaxTokens
}
if chatModelConfig.Temperature != nil {
reqBody["temperature"] = *chatModelConfig.Temperature
}
if chatModelConfig.DoSample != nil {
reqBody["do_sample"] = *chatModelConfig.DoSample
}
if chatModelConfig.TopP != nil {
reqBody["top_p"] = *chatModelConfig.TopP
}
if chatModelConfig.Stop != nil {
reqBody["stop"] = *chatModelConfig.Stop
}
if chatModelConfig.Thinking != nil {
if *chatModelConfig.Thinking {
reqBody["thinking"] = map[string]interface{}{
"type": "enabled",
}
} else {
reqBody["thinking"] = map[string]interface{}{
"type": "disabled",
}
}
}
}
jsonData, err := json.Marshal(reqBody)
if err != nil {
return fmt.Errorf("failed to marshal request: %w", err)
}
ctx, cancel := context.WithTimeout(context.Background(), streamCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewBuffer(jsonData))
if err != nil {
return fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
resp, err := z.baseModel.httpClient.Do(req)
if err != nil {
return fmt.Errorf("failed to send request: %w", err)
}
defer resp.Body.Close()
if resp.StatusCode != http.StatusOK {
body, _ := io.ReadAll(resp.Body)
return fmt.Errorf("API request failed with status %d: %s", resp.StatusCode, string(body))
}
// SSE parsing: read line by line
if _, err := ParseSSEStream[map[string]interface{}](resp.Body, func(event map[string]interface{}) error {
common.Info(fmt.Sprintf("%v", event))
choices, ok := event["choices"].([]interface{})
if !ok || len(choices) == 0 {
return nil
}
firstChoice, ok := choices[0].(map[string]interface{})
if !ok {
return nil
}
delta, ok := firstChoice["delta"].(map[string]interface{})
if !ok {
return nil
}
reasoningContent, ok := delta["reasoning_content"].(string)
if ok && reasoningContent != "" {
if err := sender(nil, &reasoningContent); err != nil {
return err
}
}
content, ok := delta["content"].(string)
if ok && content != "" {
if err := sender(&content, nil); err != nil {
return err
}
}
return nil
}); err != nil {
return fmt.Errorf("failed to scan response body: %w", err)
}
// Send [DONE] marker for OpenAI compatibility
endOfStream := "[DONE]"
if err = sender(&endOfStream, nil); err != nil {
return err
}
return nil
}
type zhipuEmbeddingResponse struct {
Data []zhipuEmbeddingData `json:"data"`
Model string `json:"model"`
Object string `json:"object"`
Usage zhipuUsage `json:"usage"`
}
type zhipuEmbeddingData struct {
Embedding []float64 `json:"embedding"`
Index int `json:"index"`
Object string `json:"object"`
}
type zhipuUsage struct {
CompletionTokens int `json:"completion_tokens"`
PromptTokens int `json:"prompt_tokens"`
TotalTokens int `json:"total_tokens"`
}
// Encode encodes a list of texts into embeddings
func (z *ZhipuAIModel) Embed(modelName *string, texts []string, apiConfig *APIConfig, embeddingConfig *EmbeddingConfig) ([]EmbeddingData, error) {
if err := z.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
}
if len(texts) == 0 {
return []EmbeddingData{}, nil
}
if modelName == nil || *modelName == "" {
return nil, fmt.Errorf("model name is required")
}
baseURL, err := z.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
}
url := fmt.Sprintf("%s/%s", baseURL, z.baseModel.URLSuffix.Embedding)
reqBody := map[string]interface{}{}
reqBody["model"] = modelName
reqBody["input"] = texts
if embeddingConfig.Dimension > 0 {
reqBody["dimensions"] = embeddingConfig.Dimension
}
jsonData, err := json.Marshal(reqBody)
if err != nil {
return nil, fmt.Errorf("failed to marshal request: %w", err)
}
ctx, cancel := context.WithTimeout(context.Background(), nonStreamCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewBuffer(jsonData))
if err != nil {
return nil, fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
resp, err := z.baseModel.httpClient.Do(req)
if err != nil {
return nil, fmt.Errorf("failed to send request: %w", err)
}
body, err := io.ReadAll(resp.Body)
resp.Body.Close()
if err != nil {
return nil, fmt.Errorf("failed to read response: %w", err)
}
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("API request failed with status %d: %s", resp.StatusCode, string(body))
}
// Parse response
var zhipuResp zhipuEmbeddingResponse
if err = json.Unmarshal(body, &zhipuResp); err != nil {
return nil, fmt.Errorf("failed to parse response: %w", err)
}
var embeddings []EmbeddingData
for _, dataElem := range zhipuResp.Data {
var embeddingData EmbeddingData
embeddingData.Embedding = dataElem.Embedding
embeddingData.Index = dataElem.Index
embeddings = append(embeddings, embeddingData)
}
return embeddings, nil
}
func (z *ZhipuAIModel) ListModels(apiConfig *APIConfig) ([]ListModelResponse, error) {
if err := z.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
}
baseURL, err := z.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
}
url := fmt.Sprintf("%s/%s", baseURL, z.baseModel.URLSuffix.Models)
ctx, cancel := context.WithTimeout(context.Background(), nonStreamCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, "GET", url, nil)
if err != nil {
return nil, fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
resp, err := z.baseModel.httpClient.Do(req)
if err != nil {
return nil, fmt.Errorf("failed to send request: %w", err)
}
defer resp.Body.Close()
body, err := io.ReadAll(resp.Body)
if err != nil {
return nil, fmt.Errorf("failed to read response: %w", err)
}
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("ZhipuAI models API error: %s, body: %s", resp.Status, string(body))
}
// Parse response
var modelList ModelList
if err = json.Unmarshal(body, &modelList); err != nil {
return nil, fmt.Errorf("failed to parse response: %w", err)
}
return ParseListModel(modelList), nil
}
func (z *ZhipuAIModel) Balance(apiConfig *APIConfig) (map[string]interface{}, error) {
return nil, fmt.Errorf("%s, no such method", z.Name())
}
func (z *ZhipuAIModel) CheckConnection(apiConfig *APIConfig) error {
if err := z.baseModel.APIConfigCheck(apiConfig); err != nil {
return err
}
baseURL, err := z.baseModel.GetBaseURL(apiConfig)
if err != nil {
return err
}
url := fmt.Sprintf("%s/%s", baseURL, z.baseModel.URLSuffix.Files)
ctx, cancel := context.WithTimeout(context.Background(), nonStreamCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, "GET", url, nil)
if err != nil {
return fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
resp, err := z.baseModel.httpClient.Do(req)
if err != nil {
return fmt.Errorf("failed to send request: %w", err)
}
defer resp.Body.Close()
body, err := io.ReadAll(resp.Body)
if err != nil {
return fmt.Errorf("failed to read response: %w", err)
}
if resp.StatusCode != http.StatusOK {
return fmt.Errorf("API request failed with status %d: %s", resp.StatusCode, string(body))
}
return nil
}
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
// zhipuRerankRequest is the request body for the ZhipuAI rerank
// endpoint. The shape matches the standard OpenAI-compatible rerank
// API also used by SiliconFlow.
type zhipuRerankRequest struct {
Model string `json:"model"`
Query string `json:"query"`
Documents []string `json:"documents"`
TopN int `json:"top_n"`
ReturnDocuments bool `json:"return_documents"`
}
// zhipuRerankResponse is the response shape for the ZhipuAI rerank
// endpoint.
type zhipuRerankResponse struct {
Created int64 `json:"created"`
ID string `json:"id"`
RequestID string `json:"request_id"`
Usage struct {
CompletionTokens int `json:"completion_tokens"`
PromptTokens int `json:"prompt_tokens"`
TotalTokens int `json:"total_tokens"`
} `json:"usage"`
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
Results []struct {
Index int `json:"index"`
RelevanceScore float64 `json:"relevance_score"`
} `json:"results"`
}
type zhipuOCRResponse struct {
MarkdownResults *string `json:"md_results"`
}
// Rerank calculates similarity scores between query and documents using
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
// the ZhipuAI /rerank endpoint (e.g. glm-rerank). The result is one
// score per input text, in the same order the documents were given.
func (z *ZhipuAIModel) Rerank(modelName *string, query string, documents []string, apiConfig *APIConfig, rerankConfig *RerankConfig) (*RerankResponse, error) {
if err := z.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
}
if len(documents) == 0 {
return &RerankResponse{}, nil
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
}
if modelName == nil || *modelName == "" {
return nil, fmt.Errorf("model name is required")
}
baseURL, err := z.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
}
url := fmt.Sprintf("%s/%s", baseURL, z.baseModel.URLSuffix.Rerank)
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
var topN = rerankConfig.TopN
if rerankConfig.TopN == 0 {
topN = len(documents)
}
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
reqBody := zhipuRerankRequest{
Model: *modelName,
Query: query,
Documents: documents,
TopN: topN,
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
ReturnDocuments: false,
}
jsonData, err := json.Marshal(reqBody)
if err != nil {
return nil, fmt.Errorf("failed to marshal request: %w", err)
}
ctx, cancel := context.WithTimeout(context.Background(), nonStreamCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewBuffer(jsonData))
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
if err != nil {
return nil, fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
resp, err := z.baseModel.httpClient.Do(req)
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
if err != nil {
return nil, fmt.Errorf("failed to send request: %w", err)
}
defer resp.Body.Close()
body, err := io.ReadAll(resp.Body)
if err != nil {
return nil, fmt.Errorf("failed to read response: %w", err)
}
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("ZhipuAI rerank API error: %s, body: %s", resp.Status, string(body))
}
var zhipuRerankResp zhipuRerankResponse
if err = json.Unmarshal(body, &zhipuRerankResp); err != nil {
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
return nil, fmt.Errorf("failed to parse response: %w", err)
}
var rerankResponse RerankResponse
for _, result := range zhipuRerankResp.Results {
rerankResult := RerankResult{
Index: result.Index,
RelevanceScore: result.RelevanceScore,
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
}
rerankResponse.Data = append(rerankResponse.Data, rerankResult)
Go: implement Rerank in ZhipuAI driver (#14608) ### What problem does this PR solve? The ZhipuAI Go driver had a stub Rerank method that returned "not implemented", even though conf/models/zhipu-ai.json already ships glm-rerank as a rerank model and the rerank URL suffix is already wired in url_suffix: ```json "url_suffix": { ... "rerank": "rerank" }, "models": [ {"name": "glm-rerank", "model_types": ["rerank"]}, ... ] ``` So the config was ready but the driver was not. A tenant who picked glm-rerank in the Go layer could not actually run a rerank call. This PR fills the gap so the listed model works end to end. ### What this PR includes - `internal/entity/models/zhipu-ai.go`: real implementation of `ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`, `zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank shape used by SiliconFlow. No factory change. No JSON change. No interface change. ### How the driver works - POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to `https://open.bigmodel.cn/api/paas/v4/rerank` with the default config), reusing the existing httpClient on the driver. - Validate apiConfig and the API key, validate the model name, and resolve the region. Return a clear local error before any HTTP call when something is missing. - Send `{model, query, documents, top_n, return_documents: false}` in the body, the same shape the SiliconFlow driver already uses. - Walk `results[*].relevance_score` and copy each score into the output slice indexed by `results[*].index`, so the output order matches the input order even if the API returns results in a different order. - Empty `texts` input returns an empty `[]float64` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on `ZhipuAIModel` still matches the `ModelDriver` interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, ListModels, Balance, CheckConnection, Rerank). - Pattern parity with the existing SiliconFlow Rerank implementation (`internal/entity/models/siliconflow.go`). Closes #14607
2026-05-07 11:56:30 +02:00
}
return &rerankResponse, nil
}
// TranscribeAudio transcribe audio
func (z *ZhipuAIModel) TranscribeAudio(modelName *string, file *string, apiConfig *APIConfig, asrConfig *ASRConfig) (*ASRResponse, error) {
if err := z.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
}
if modelName == nil || *modelName == "" {
return nil, fmt.Errorf("model name is required")
}
if file == nil || *file == "" {
return nil, fmt.Errorf("file is required")
}
if z.baseModel.URLSuffix.ASR == "" {
return nil, fmt.Errorf("zhipu-ai: ASR URL suffix is not configured")
}
baseURL, err := z.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
}
url := fmt.Sprintf("%s/%s", baseURL, strings.TrimLeft(z.baseModel.URLSuffix.ASR, "/"))
var body bytes.Buffer
writer := multipart.NewWriter(&body)
if err := writer.WriteField("model", *modelName); err != nil {
return nil, fmt.Errorf("failed to write model field: %w", err)
}
if err := writer.WriteField("stream", "false"); err != nil {
return nil, fmt.Errorf("failed to write stream field: %w", err)
}
if err := writeZhipuASRParams(writer, asrConfig); err != nil {
return nil, err
}
fix(codeql): close remaining 44 CodeQL alerts post-merge (#16408) ## Summary After #16407 merged, 44 of the original 93 CodeQL alerts were still open on the default branch. This PR closes the remaining ones by: 1. **Moving 32 existing `// codeql[...]` directives** so they sit on the line **immediately before** the suppressed statement. The original multi-line suppression blocks had the directive as the first line, with the rationale on subsequent lines. After line shifts (refactors, linter reformat), the directive ended up several lines above the alert location — CodeQL only recognizes the suppression when it appears on the line directly above. (32 alerts across 27 files.) 2. **Adding 9 new `// codeql[...]` suppressions** for alerts that had no suppression in the preceding lines at all — mostly real-fixes that CodeQL conservatively still flags (filepath.Base, bounded slice sizes, model-identifier strings, the MD5-legacy-migration lookup in `conversation_service.py`). ## Files changed - `api/db/services/conversation_service.py` — add `py/weak-sensitive-data-hashing` suppression (MD5 for backward-compat legacy row lookup; not used for auth) - `api/db/services/llm_service.py` — 3× `py/clear-text-logging-sensitive-data` suppressions on the lines that log `llm_name` in warnings/info - `common/misc_utils.py` — 2× `py/clear-text-logging-sensitive-data` suppressions on the redacted `current_url` log sites - `internal/agent/component/invoke.go` — moved existing `go/request-forgery` directive - `internal/agent/sandbox/ssh.go` — moved existing `go/command-injection` directive - `internal/agent/tool/retrieval_service.go` — added `go/uncontrolled-allocation-size` suppression (`topN` is bounded to 1024 above) - `internal/cli/common_command.go` — moved 2× `go/disabled-certificate-check` directives - `internal/cli/user_command.go` — added `go/clear-text-logging` suppression (filepath.Base already strips user-identifying path) - `internal/dao/pipeline_operation_log.go` — moved 2× `go/sql-injection` directives - `internal/dao/user_canvas.go` — added `go/sql-injection` suppression in `GetList` (the new `userCanvasOrderClause` call path) - `internal/engine/infinity/chunk.go` — moved existing `go/unsafe-quoting` directive - `internal/entity/models/*` — moved `go/path-injection` directives (15 files) - `internal/handler/oauth_login.go` — moved existing `go/cookie-httponly-not-set` directive - `internal/handler/tenant.go` — moved existing `go/path-injection` directive - `internal/service/deep_researcher.go` — moved existing `go/unsafe-quoting` directive - `internal/service/dataset.go` — added `go/uncontrolled-allocation-size` suppression (`n` bounded to 1024 above) - `internal/service/file.go` — moved existing `go/request-forgery` directive - `internal/service/langfuse.go` — moved 2× `go/request-forgery` directives - `internal/utility/mcp_client.go` — moved 3× `go/request-forgery` directives - `internal/utility/smtp.go` — moved existing `go/email-injection` directive - `rag/prompts/generator.py` — added `py/clear-text-logging-sensitive-data` suppression - `web/.../use-provider-fields.tsx` — added `js/prototype-pollution-utility` suppression (FORBIDDEN_KEYS guard is on the line above) ## Why the previous PR left alerts open `// codeql[query-id] explanation` must be on the line **immediately before** the suppressed statement per the [GitHub CodeQL suppression spec](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/customizing-code-scanning-with-codeql/suppressing-code-scanning-alerts). The original suppression blocks were 4-5 lines, with the directive as the **first** line. After linter reformat / line shifts, the directive ended up too far above the actual alert line to be recognized. The fix is to put the directive on the line directly above the suppressed statement, with the rationale above it. ## Test plan - All 9 modified Python files `ast.parse` clean - All 4 modified Go files `gofmt` clean - 36/44 expected alert suppressions in place - 8 remaining CodeQL alerts are the originals (#3485851828, #3485851831, #3485869759, #3485869766, #3485869768, #3485869771, #3485885962, #3485895527) which were resolved by the corresponding commit comments; these should close on the next scan when the suppression comments match the alert lines. 🤖 Generated with [Claude Code](https://claude.com/claude-code)
2026-06-27 20:49:06 +08:00
// codeql[go/path-injection] False positive: *file is the audio file path the caller passes in to upload. The user (or operator-supplied pipeline) explicitly chose this path, and the OS access check enforces permissions anyway.
audioFile, err := os.Open(*file)
if err != nil {
return nil, fmt.Errorf("failed to open audio file: %w", err)
}
defer audioFile.Close()
part, err := writer.CreateFormFile("file", filepath.Base(*file))
if err != nil {
return nil, fmt.Errorf("failed to create multipart file: %w", err)
}
if _, err = io.Copy(part, audioFile); err != nil {
return nil, fmt.Errorf("failed to copy audio data: %w", err)
}
if err = writer.Close(); err != nil {
return nil, fmt.Errorf("failed to close multipart writer: %w", err)
}
ctx, cancel := context.WithTimeout(context.Background(), longOpCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, http.MethodPost, url, &body)
if err != nil {
return nil, fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
req.Header.Set("Content-Type", writer.FormDataContentType())
resp, err := z.baseModel.httpClient.Do(req)
if err != nil {
return nil, fmt.Errorf("failed to send request: %w", err)
}
defer resp.Body.Close()
respBody, err := io.ReadAll(resp.Body)
if err != nil {
return nil, fmt.Errorf("failed to read response body: %w", err)
}
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("ZhipuAI ASR API error: %s, body: %s", resp.Status, string(respBody))
}
var result struct {
Text string `json:"text"`
}
if err = json.Unmarshal(respBody, &result); err != nil {
return nil, fmt.Errorf("failed to parse response: %w", err)
}
return &ASRResponse{Text: result.Text}, nil
}
func writeZhipuASRParams(writer *multipart.Writer, asrConfig *ASRConfig) error {
if asrConfig == nil || asrConfig.Params == nil {
return nil
}
for key, value := range asrConfig.Params {
switch key {
case "model", "stream", "file", "file_base64":
continue
}
if err := writeZhipuASRField(writer, key, value); err != nil {
return err
}
}
return nil
}
func writeZhipuASRField(writer *multipart.Writer, key string, value interface{}) error {
switch v := value.(type) {
case nil:
return nil
case []string:
for _, item := range v {
if err := writer.WriteField(key, item); err != nil {
return fmt.Errorf("failed to write field %s: %w", key, err)
}
}
return nil
case []interface{}:
for _, item := range v {
if err := writer.WriteField(key, fmt.Sprint(item)); err != nil {
return fmt.Errorf("failed to write field %s: %w", key, err)
}
}
return nil
default:
if err := writer.WriteField(key, fmt.Sprint(v)); err != nil {
return fmt.Errorf("failed to write field %s: %w", key, err)
}
return nil
}
}
func (z *ZhipuAIModel) TranscribeAudioWithSender(modelName *string, file *string, apiConfig *APIConfig, asrConfig *ASRConfig, sender func(*string, *string) error) error {
return fmt.Errorf("%s, no such method", z.Name())
}
Go: implement PaddleOCR provider and implement ASR for CoHere (#14954) ### What problem does this PR solve? This PR implement implement OCR for Baidu and Mistral, implement PaddleOCR provider and implement ASR for CoHere **Verified examples from the CLI:** ``` RAGFlow(user)> ocr with 'mistral-ocr-2512@test@mistral' file './internal/text.jpg' +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | text | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Parallel to these organizational innovations there were significant complementary technical innovations (e.g., improved methods of manufacturing cast-iron pipe and of coating interiors for pressure maintenance, and newer paving and construction material... | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ RAGFlow(user)> ocr with 'paddleocr-vl-0.9b@test@baidu' file './internal/text.jpg' +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | text | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Parallel to these organizational innovations there were significant complementary technical innovations (e.g., improved methods of manufacturing cast-iron pipe and of coating interiors for pressure maintenance, and newer paving and construction material... | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ # PaddleOCR RAGFlow(user)> ocr with 'PaddleOCR-VL-1.5@test@paddleocr' file './internal/test.pdf' +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | text | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | # Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation Bingxin Ke Nando Metzger Photogra Anton Obukhov Rodrigo Caye Daudt netry and Remote Sensing, Shengyu Huang Konrad Schindler ETH Zürich <div style="text-align: c... | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ # Cohere RAGFlow(user)> asr with 'cohere-transcribe-03-2026@test@cohere' audio './internal/test.wav' param '{"language": "en"}' +-----------------------------------------------------------------------------------------------------------------------+ | text | +-----------------------------------------------------------------------------------------------------------------------+ | The examination and testimony of the experts enabled the Commission to conclude that five shots may have been fired. | +-----------------------------------------------------------------------------------------------------------------------+ ``` ### Type of change - [x] New Feature (non-breaking change which adds functionality) - [x] Refactoring
2026-05-15 18:41:43 +08:00
// AudioSpeech convert text to audio
func (z *ZhipuAIModel) AudioSpeech(modelName *string, audioContent *string, apiConfig *APIConfig, ttsConfig *TTSConfig) (*TTSResponse, error) {
if err := z.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
}
reqBody, url, err := z.buildTTSRequest(modelName, audioContent, apiConfig, ttsConfig, false)
if err != nil {
return nil, err
}
jsonData, err := json.Marshal(reqBody)
if err != nil {
return nil, fmt.Errorf("failed to marshal request: %w", err)
}
ctx, cancel := context.WithTimeout(context.Background(), longOpCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, http.MethodPost, url, bytes.NewBuffer(jsonData))
if err != nil {
return nil, fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
resp, err := z.baseModel.httpClient.Do(req)
if err != nil {
return nil, fmt.Errorf("failed to send request: %w", err)
}
defer resp.Body.Close()
body, err := io.ReadAll(resp.Body)
if err != nil {
return nil, fmt.Errorf("failed to read response body: %w", err)
}
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("ZhipuAI TTS API error: %s, body: %s", resp.Status, string(body))
}
return &TTSResponse{Audio: body}, nil
}
func (z *ZhipuAIModel) AudioSpeechWithSender(modelName *string, audioContent *string, apiConfig *APIConfig, ttsConfig *TTSConfig, sender func(*string, *string) error) error {
if err := z.baseModel.APIConfigCheck(apiConfig); err != nil {
return err
}
if sender == nil {
return fmt.Errorf("sender is required")
}
reqBody, url, err := z.buildTTSRequest(modelName, audioContent, apiConfig, ttsConfig, true)
if err != nil {
return err
}
jsonData, err := json.Marshal(reqBody)
if err != nil {
return fmt.Errorf("failed to marshal request: %w", err)
}
ctx, cancel := context.WithTimeout(context.Background(), streamCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, http.MethodPost, url, bytes.NewBuffer(jsonData))
if err != nil {
return fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
resp, err := z.baseModel.httpClient.Do(req)
if err != nil {
return fmt.Errorf("failed to send request: %w", err)
}
defer resp.Body.Close()
if resp.StatusCode != http.StatusOK {
body, _ := io.ReadAll(resp.Body)
return fmt.Errorf("ZhipuAI stream TTS API error: %s, body: %s", resp.Status, string(body))
}
buf := make([]byte, 32*1024)
for {
n, err := resp.Body.Read(buf)
if n > 0 {
chunk := string(buf[:n])
if errSend := sender(&chunk, nil); errSend != nil {
return errSend
}
}
if err != nil {
if err == io.EOF {
break
}
return fmt.Errorf("error reading ZhipuAI binary audio stream: %w", err)
}
}
return nil
}
func (z *ZhipuAIModel) buildTTSRequest(modelName *string, audioContent *string, apiConfig *APIConfig, ttsConfig *TTSConfig, stream bool) (map[string]interface{}, string, error) {
if modelName == nil || *modelName == "" {
return nil, "", fmt.Errorf("model name is required")
}
if audioContent == nil || *audioContent == "" {
return nil, "", fmt.Errorf("audio content is empty")
}
if z.baseModel.URLSuffix.TTS == "" {
return nil, "", fmt.Errorf("zhipu-ai: TTS URL suffix is not configured")
}
baseURL, err := z.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, "", err
}
reqBody := map[string]interface{}{
"model": *modelName,
"input": *audioContent,
"stream": stream,
}
if ttsConfig != nil {
for key, value := range ttsConfig.Params {
switch key {
case "model", "input", "stream", "response_format":
continue
}
reqBody[key] = value
}
if ttsConfig.Format != "" {
reqBody["response_format"] = ttsConfig.Format
}
}
url := fmt.Sprintf("%s/%s", baseURL, strings.TrimLeft(z.baseModel.URLSuffix.TTS, "/"))
return reqBody, url, nil
}
// OCRFile OCR file
func (z *ZhipuAIModel) OCRFile(modelName *string, content []byte, fileURL *string, apiConfig *APIConfig, ocrConfig *OCRConfig) (*OCRFileResponse, error) {
if err := z.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
}
if modelName == nil || *modelName == "" {
return nil, fmt.Errorf("model name is required")
}
if (fileURL == nil || *fileURL == "") && len(content) == 0 {
return nil, fmt.Errorf("file url or content is required")
}
baseURL, err := z.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
}
if z.baseModel.URLSuffix.OCR == "" {
return nil, fmt.Errorf("zhipu-ai: no OCR URL suffix configured")
}
file := ""
if fileURL != nil && *fileURL != "" {
file = *fileURL
} else {
mimeType := http.DetectContentType(content)
if len(content) > 4 && string(content[:4]) == "%PDF" {
mimeType = "application/pdf"
}
file = fmt.Sprintf("data:%s;base64,%s", mimeType, base64.StdEncoding.EncodeToString(content))
}
reqBody := map[string]interface{}{
"model": *modelName,
"file": file,
}
jsonData, err := json.Marshal(reqBody)
if err != nil {
return nil, fmt.Errorf("failed to marshal request: %w", err)
}
url := fmt.Sprintf("%s/%s", baseURL, strings.TrimPrefix(z.baseModel.URLSuffix.OCR, "/"))
ctx, cancel := context.WithTimeout(context.Background(), longOpCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewBuffer(jsonData))
if err != nil {
return nil, fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
resp, err := z.baseModel.httpClient.Do(req)
if err != nil {
return nil, fmt.Errorf("failed to send request: %w", err)
}
defer resp.Body.Close()
body, err := io.ReadAll(resp.Body)
if err != nil {
return nil, fmt.Errorf("failed to read response: %w", err)
}
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("ZhipuAI OCR API error: %s, body: %s", resp.Status, string(body))
}
var zhipuResp zhipuOCRResponse
if err = json.Unmarshal(body, &zhipuResp); err != nil {
return nil, fmt.Errorf("failed to parse response: %w", err)
}
if zhipuResp.MarkdownResults == nil {
return nil, fmt.Errorf("ZhipuAI OCR API response missing md_results")
}
return &OCRFileResponse{Text: zhipuResp.MarkdownResults}, nil
}
Go: add file parse command (#14892) ### What problem does this PR solve? ``` RAGFlow(user)> ocr with 'hunyuanocr@test@gitee' file './picture.png' +----------------------------------------------------------+ | text | +----------------------------------------------------------+ | 生活不是等待风暴过去,而是学会在雨中翩翩起舞。 ——佚名 | +----------------------------------------------------------+ RAGFlow(user)> list 'test@gitee' tasks; +---------+----------------------------------+ | status | task_id | +---------+----------------------------------+ | success | C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5 | +---------+----------------------------------+ RAGFlow(user)> show 'test@gitee' task 'C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5'; +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ | content | index | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ | # PDF 1: Purpose of RAGFlow RAGFlow is an open source Retrieval-Augmented Generation (RAG) engine designed to turn raw documents into reliable context for large language models.Its purpose is to make it practical to build an Al assistant that can ans... | 1 | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ ``` ### Type of change - [x] New Feature (non-breaking change which adds functionality) --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com>
2026-05-15 12:29:52 +08:00
// ParseFile parse file
func (z *ZhipuAIModel) ParseFile(modelName *string, content []byte, url *string, apiConfig *APIConfig, parseFileConfig *ParseFileConfig) (*ParseFileResponse, error) {
return nil, fmt.Errorf("%s, no such method", z.Name())
}
func (z *ZhipuAIModel) ListTasks(apiConfig *APIConfig) ([]ListTaskStatus, error) {
return nil, fmt.Errorf("%s, no such method", z.Name())
}
func (z *ZhipuAIModel) ShowTask(taskID string, apiConfig *APIConfig) (*TaskResponse, error) {
return nil, fmt.Errorf("%s, no such method", z.Name())
}