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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"
Go: implement Encode (embeddings) in NVIDIA driver (#14700) ### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699
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"context"
"encoding/json"
"fmt"
"io"
"net/http"
"strings"
)
// NvidiaModel implements ModelDriver for Nvidia
type NvidiaModel struct {
baseModel BaseModel
}
// NewNvidiaModel creates a new Nvidia model instance
func NewNvidiaModel(baseURL map[string]string, urlSuffix URLSuffix) *NvidiaModel {
return &NvidiaModel{
baseModel: BaseModel{
BaseURL: baseURL,
URLSuffix: urlSuffix,
httpClient: NewDriverHTTPClient(),
},
}
}
func (n NvidiaModel) NewInstance(baseURL map[string]string) ModelDriver {
return NewNvidiaModel(baseURL, n.baseModel.URLSuffix)
}
func (n NvidiaModel) Name() string {
return "nvidia"
}
func (n *NvidiaModel) ChatWithMessages(modelName string, messages []Message, apiConfig *APIConfig, chatModelConfig *ChatConfig) (*ChatResponse, error) {
if err := n.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
}
if len(messages) == 0 {
return nil, fmt.Errorf("messages is empty")
}
resolvedBaseURL, err := n.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
}
baseURL := resolvedBaseURL
if baseURL == "" {
baseURL = resolvedBaseURL
}
url := fmt.Sprintf("%s/%s", baseURL, n.baseModel.URLSuffix.Chat)
apiMessages := make([]map[string]interface{}, len(messages))
for i, msg := range messages {
apiMessages[i] = map[string]interface{}{
"role": msg.Role,
"content": msg.Content,
}
}
reqBody := map[string]interface{}{
"model": modelName,
"messages": apiMessages,
"stream": false,
}
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 := n.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))
}
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 reasonContent != "" && reasonContent[0] == '\n' {
reasonContent = reasonContent[1:]
}
}
chatResponse := &ChatResponse{
Answer: &content,
ReasonContent: &reasonContent,
}
return chatResponse, nil
}
func (n *NvidiaModel) ChatStreamlyWithSender(modelName string, messages []Message, apiConfig *APIConfig, modelConfig *ChatConfig, sender func(*string, *string) error) error {
if err := n.baseModel.APIConfigCheck(apiConfig); err != nil {
return err
}
if sender == nil {
return fmt.Errorf("sender is required")
}
if len(messages) == 0 {
return fmt.Errorf("messages is empty")
}
resolvedBaseURL, err := n.baseModel.GetBaseURL(apiConfig)
if err != nil {
return err
}
baseURL := resolvedBaseURL
if baseURL == "" {
baseURL = resolvedBaseURL
}
url := fmt.Sprintf("%s/%s", baseURL, n.baseModel.URLSuffix.Chat)
apiMessages := make([]map[string]interface{}, len(messages))
for i, msg := range messages {
apiMessages[i] = map[string]interface{}{
"role": msg.Role,
"content": msg.Content,
}
}
reqBody := map[string]interface{}{
"model": modelName,
"messages": apiMessages,
"stream": true,
}
if modelConfig != nil {
if modelConfig.Stream != nil {
reqBody["stream"] = *modelConfig.Stream
}
if modelConfig.MaxTokens != nil {
reqBody["max_tokens"] = *modelConfig.MaxTokens
}
if modelConfig.Temperature != nil {
reqBody["temperature"] = *modelConfig.Temperature
}
if modelConfig.DoSample != nil {
reqBody["do_sample"] = *modelConfig.DoSample
}
if modelConfig.TopP != nil {
reqBody["top_p"] = *modelConfig.TopP
}
if modelConfig.Stop != nil {
reqBody["stop"] = *modelConfig.Stop
}
if modelConfig.Thinking != nil {
if *modelConfig.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 := n.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))
}
if _, err := ParseSSEStream[map[string]interface{}](resp.Body, func(event map[string]interface{}) error {
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)
}
endOfStream := "[DONE]"
if err = sender(&endOfStream, nil); err != nil {
return err
}
return nil
}
Go: implement Encode (embeddings) in NVIDIA driver (#14700) ### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699
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type nvidiaEmbeddingResponse struct {
Data []struct {
Index int `json:"index"`
Embedding []float64 `json:"embedding"`
Go: implement Encode (embeddings) in NVIDIA driver (#14700) ### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699
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} `json:"data"`
}
func (n NvidiaModel) Embed(modelName *string, texts []string, apiConfig *APIConfig, embeddingConfig *EmbeddingConfig) ([]EmbeddingData, error) {
if err := n.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
Go: implement Encode (embeddings) in NVIDIA driver (#14700) ### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699
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}
if len(texts) == 0 {
return []EmbeddingData{}, nil
Go: implement Encode (embeddings) in NVIDIA driver (#14700) ### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699
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}
if modelName == nil || *modelName == "" {
return nil, fmt.Errorf("model name is required")
}
resolvedBaseURL, err := n.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
Go: implement Encode (embeddings) in NVIDIA driver (#14700) ### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699
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}
baseURL := resolvedBaseURL
Go: implement Encode (embeddings) in NVIDIA driver (#14700) ### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699
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if baseURL == "" {
baseURL = resolvedBaseURL
Go: implement Encode (embeddings) in NVIDIA driver (#14700) ### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699
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}
url := fmt.Sprintf("%s/%s", strings.TrimSuffix(baseURL, "/"), n.baseModel.URLSuffix.Embedding)
Go: implement Encode (embeddings) in NVIDIA driver (#14700) ### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699
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reqBody := map[string]interface{}{
"model": *modelName,
"input": texts,
"input_type": "query",
"encoding_format": "float",
"truncate": "END",
}
if embeddingConfig != nil && 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)
Go: implement Encode (embeddings) in NVIDIA driver (#14700) ### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699
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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 := n.baseModel.httpClient.Do(req)
Go: implement Encode (embeddings) in NVIDIA driver (#14700) ### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699
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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("Nvidia embeddings API error: %s, body: %s", resp.Status, string(body))
}
var parsed nvidiaEmbeddingResponse
if err = json.Unmarshal(body, &parsed); err != nil {
return nil, fmt.Errorf("failed to parse response: %w", err)
}
var embeddings []EmbeddingData
for _, dataElem := range parsed.Data {
var embeddingData EmbeddingData
embeddingData.Embedding = dataElem.Embedding
embeddingData.Index = dataElem.Index
embeddings = append(embeddings, embeddingData)
Go: implement Encode (embeddings) in NVIDIA driver (#14700) ### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699
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}
return embeddings, nil
}
Go: implement Rerank in NVIDIA driver (#14778) ## Summary - Replaces the `"no such method"` stub on `NvidiaModel.Rerank` (`internal/entity/models/nvidia.go`) with a real implementation against NVIDIA NIM's `/ranking` endpoint. - Mirrors the existing Python `NvidiaRerank` class at `rag/llm/rerank_model.py:149-190` for behavior parity: same `passages`/`query.text`/`logit` payload shape; `top_n` set to `len(documents)` so every input gets a score returned in original order (the issue body's spec omitted `top_n`, which would cause silent data loss). - Adds the `"rerank": "ranking"` URL suffix and two NIM rerank model entries (`nvidia/nv-rerankqa-mistral-4b-v3`, `nvidia/llama-3.2-nv-rerankqa-1b-v2`) to `conf/models/nvidia.json` so the picker exposes them. - Follows the same shape as the recently merged Aliyun (#14676), Gitee (#14656), and ZhipuAI (#14608) Rerank implementations: lowercase per-driver request/response types, conversion to the project-wide `RerankResponse{Data: []RerankResult}`, per-call `context.WithTimeout` of 30s. Closes #14720 ## Test plan - [x] `gofmt -l internal/entity/models/nvidia.go` — clean - [x] `go vet ./internal/entity/models/...` — no new errors introduced (the two pre-existing vet errors in `baidu.go:642` and `openrouter.go:566` are unrelated to this PR) - [x] `go build ./internal/entity/models/...` — succeeds - [x] `python3 -c "import json; json.load(open('conf/models/nvidia.json'))"` — JSON valid - [ ] Live smoke test against NVIDIA NIM with a real API key (requires reviewer with NIM credentials) ## Notes for reviewers - The issue body suggested omitting `top_n`. The Python reference includes it (`top_n: len(texts)`), and without it NVIDIA returns only the default top-K rankings rather than scores for every input. This PR follows the Python. - The URL host is `integrate.api.nvidia.com` (kept consistent with the existing chat/embeddings BaseURL in `nvidia.go`), not the legacy `ai.api.nvidia.com` host the Python uses. NIM's unified endpoint accepts the model names as-is, so no per-model URL transform is needed.
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// nvidiaRerankRequest mirrors the NIM /ranking request shape:
// query is an object with a "text" field, passages is an array of
// objects each with a "text" field. truncate=END matches the Python
// NvidiaRerank reference at rag/llm/rerank_model.py.
type nvidiaRerankRequest struct {
Model string `json:"model"`
Query nvidiaRerankText `json:"query"`
Passages []nvidiaRerankText `json:"passages"`
Truncate string `json:"truncate,omitempty"`
TopN int `json:"top_n"`
}
type nvidiaRerankText struct {
Text string `json:"text"`
}
// nvidiaRerankResponse maps the NIM rankings array. Each entry pairs
// the original passage index with a logit score; the caller uses the
// index to restore original input order.
type nvidiaRerankResponse struct {
Rankings []struct {
Index int `json:"index"`
Logit float64 `json:"logit"`
} `json:"rankings"`
}
// Rerank scores documents against the query using an NVIDIA NIM
// reranking model. Mirrors the Python NvidiaRerank class in
// rag/llm/rerank_model.py for payload shape (passages/query/logit).
// Defaults top_n to len(documents) so the API returns a score per
// input; callers may shrink it via RerankConfig.TopN, in which case
// only the top RerankConfig.TopN entries come back. Returned
// RerankResult entries are in the API's ranking order; callers that
// need original-input order should sort by Index. Same return-shape
// contract as the Aliyun and ZhipuAI Rerank drivers.
func (n NvidiaModel) Rerank(modelName *string, query string, documents []string, apiConfig *APIConfig, rerankConfig *RerankConfig) (*RerankResponse, error) {
if err := n.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
}
Go: implement Rerank in NVIDIA driver (#14778) ## Summary - Replaces the `"no such method"` stub on `NvidiaModel.Rerank` (`internal/entity/models/nvidia.go`) with a real implementation against NVIDIA NIM's `/ranking` endpoint. - Mirrors the existing Python `NvidiaRerank` class at `rag/llm/rerank_model.py:149-190` for behavior parity: same `passages`/`query.text`/`logit` payload shape; `top_n` set to `len(documents)` so every input gets a score returned in original order (the issue body's spec omitted `top_n`, which would cause silent data loss). - Adds the `"rerank": "ranking"` URL suffix and two NIM rerank model entries (`nvidia/nv-rerankqa-mistral-4b-v3`, `nvidia/llama-3.2-nv-rerankqa-1b-v2`) to `conf/models/nvidia.json` so the picker exposes them. - Follows the same shape as the recently merged Aliyun (#14676), Gitee (#14656), and ZhipuAI (#14608) Rerank implementations: lowercase per-driver request/response types, conversion to the project-wide `RerankResponse{Data: []RerankResult}`, per-call `context.WithTimeout` of 30s. Closes #14720 ## Test plan - [x] `gofmt -l internal/entity/models/nvidia.go` — clean - [x] `go vet ./internal/entity/models/...` — no new errors introduced (the two pre-existing vet errors in `baidu.go:642` and `openrouter.go:566` are unrelated to this PR) - [x] `go build ./internal/entity/models/...` — succeeds - [x] `python3 -c "import json; json.load(open('conf/models/nvidia.json'))"` — JSON valid - [ ] Live smoke test against NVIDIA NIM with a real API key (requires reviewer with NIM credentials) ## Notes for reviewers - The issue body suggested omitting `top_n`. The Python reference includes it (`top_n: len(texts)`), and without it NVIDIA returns only the default top-K rankings rather than scores for every input. This PR follows the Python. - The URL host is `integrate.api.nvidia.com` (kept consistent with the existing chat/embeddings BaseURL in `nvidia.go`), not the legacy `ai.api.nvidia.com` host the Python uses. NIM's unified endpoint accepts the model names as-is, so no per-model URL transform is needed.
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if len(documents) == 0 {
return &RerankResponse{}, nil
}
if modelName == nil || *modelName == "" {
return nil, fmt.Errorf("model name is required")
}
resolvedBaseURL, err := n.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
Go: implement Rerank in NVIDIA driver (#14778) ## Summary - Replaces the `"no such method"` stub on `NvidiaModel.Rerank` (`internal/entity/models/nvidia.go`) with a real implementation against NVIDIA NIM's `/ranking` endpoint. - Mirrors the existing Python `NvidiaRerank` class at `rag/llm/rerank_model.py:149-190` for behavior parity: same `passages`/`query.text`/`logit` payload shape; `top_n` set to `len(documents)` so every input gets a score returned in original order (the issue body's spec omitted `top_n`, which would cause silent data loss). - Adds the `"rerank": "ranking"` URL suffix and two NIM rerank model entries (`nvidia/nv-rerankqa-mistral-4b-v3`, `nvidia/llama-3.2-nv-rerankqa-1b-v2`) to `conf/models/nvidia.json` so the picker exposes them. - Follows the same shape as the recently merged Aliyun (#14676), Gitee (#14656), and ZhipuAI (#14608) Rerank implementations: lowercase per-driver request/response types, conversion to the project-wide `RerankResponse{Data: []RerankResult}`, per-call `context.WithTimeout` of 30s. Closes #14720 ## Test plan - [x] `gofmt -l internal/entity/models/nvidia.go` — clean - [x] `go vet ./internal/entity/models/...` — no new errors introduced (the two pre-existing vet errors in `baidu.go:642` and `openrouter.go:566` are unrelated to this PR) - [x] `go build ./internal/entity/models/...` — succeeds - [x] `python3 -c "import json; json.load(open('conf/models/nvidia.json'))"` — JSON valid - [ ] Live smoke test against NVIDIA NIM with a real API key (requires reviewer with NIM credentials) ## Notes for reviewers - The issue body suggested omitting `top_n`. The Python reference includes it (`top_n: len(texts)`), and without it NVIDIA returns only the default top-K rankings rather than scores for every input. This PR follows the Python. - The URL host is `integrate.api.nvidia.com` (kept consistent with the existing chat/embeddings BaseURL in `nvidia.go`), not the legacy `ai.api.nvidia.com` host the Python uses. NIM's unified endpoint accepts the model names as-is, so no per-model URL transform is needed.
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}
baseURL := resolvedBaseURL
Go: implement Rerank in NVIDIA driver (#14778) ## Summary - Replaces the `"no such method"` stub on `NvidiaModel.Rerank` (`internal/entity/models/nvidia.go`) with a real implementation against NVIDIA NIM's `/ranking` endpoint. - Mirrors the existing Python `NvidiaRerank` class at `rag/llm/rerank_model.py:149-190` for behavior parity: same `passages`/`query.text`/`logit` payload shape; `top_n` set to `len(documents)` so every input gets a score returned in original order (the issue body's spec omitted `top_n`, which would cause silent data loss). - Adds the `"rerank": "ranking"` URL suffix and two NIM rerank model entries (`nvidia/nv-rerankqa-mistral-4b-v3`, `nvidia/llama-3.2-nv-rerankqa-1b-v2`) to `conf/models/nvidia.json` so the picker exposes them. - Follows the same shape as the recently merged Aliyun (#14676), Gitee (#14656), and ZhipuAI (#14608) Rerank implementations: lowercase per-driver request/response types, conversion to the project-wide `RerankResponse{Data: []RerankResult}`, per-call `context.WithTimeout` of 30s. Closes #14720 ## Test plan - [x] `gofmt -l internal/entity/models/nvidia.go` — clean - [x] `go vet ./internal/entity/models/...` — no new errors introduced (the two pre-existing vet errors in `baidu.go:642` and `openrouter.go:566` are unrelated to this PR) - [x] `go build ./internal/entity/models/...` — succeeds - [x] `python3 -c "import json; json.load(open('conf/models/nvidia.json'))"` — JSON valid - [ ] Live smoke test against NVIDIA NIM with a real API key (requires reviewer with NIM credentials) ## Notes for reviewers - The issue body suggested omitting `top_n`. The Python reference includes it (`top_n: len(texts)`), and without it NVIDIA returns only the default top-K rankings rather than scores for every input. This PR follows the Python. - The URL host is `integrate.api.nvidia.com` (kept consistent with the existing chat/embeddings BaseURL in `nvidia.go`), not the legacy `ai.api.nvidia.com` host the Python uses. NIM's unified endpoint accepts the model names as-is, so no per-model URL transform is needed.
2026-05-11 11:21:16 +02:00
if baseURL == "" {
baseURL = resolvedBaseURL
Go: implement Rerank in NVIDIA driver (#14778) ## Summary - Replaces the `"no such method"` stub on `NvidiaModel.Rerank` (`internal/entity/models/nvidia.go`) with a real implementation against NVIDIA NIM's `/ranking` endpoint. - Mirrors the existing Python `NvidiaRerank` class at `rag/llm/rerank_model.py:149-190` for behavior parity: same `passages`/`query.text`/`logit` payload shape; `top_n` set to `len(documents)` so every input gets a score returned in original order (the issue body's spec omitted `top_n`, which would cause silent data loss). - Adds the `"rerank": "ranking"` URL suffix and two NIM rerank model entries (`nvidia/nv-rerankqa-mistral-4b-v3`, `nvidia/llama-3.2-nv-rerankqa-1b-v2`) to `conf/models/nvidia.json` so the picker exposes them. - Follows the same shape as the recently merged Aliyun (#14676), Gitee (#14656), and ZhipuAI (#14608) Rerank implementations: lowercase per-driver request/response types, conversion to the project-wide `RerankResponse{Data: []RerankResult}`, per-call `context.WithTimeout` of 30s. Closes #14720 ## Test plan - [x] `gofmt -l internal/entity/models/nvidia.go` — clean - [x] `go vet ./internal/entity/models/...` — no new errors introduced (the two pre-existing vet errors in `baidu.go:642` and `openrouter.go:566` are unrelated to this PR) - [x] `go build ./internal/entity/models/...` — succeeds - [x] `python3 -c "import json; json.load(open('conf/models/nvidia.json'))"` — JSON valid - [ ] Live smoke test against NVIDIA NIM with a real API key (requires reviewer with NIM credentials) ## Notes for reviewers - The issue body suggested omitting `top_n`. The Python reference includes it (`top_n: len(texts)`), and without it NVIDIA returns only the default top-K rankings rather than scores for every input. This PR follows the Python. - The URL host is `integrate.api.nvidia.com` (kept consistent with the existing chat/embeddings BaseURL in `nvidia.go`), not the legacy `ai.api.nvidia.com` host the Python uses. NIM's unified endpoint accepts the model names as-is, so no per-model URL transform is needed.
2026-05-11 11:21:16 +02:00
}
url := fmt.Sprintf("%s/%s", strings.TrimSuffix(baseURL, "/"), n.baseModel.URLSuffix.Rerank)
Go: implement Rerank in NVIDIA driver (#14778) ## Summary - Replaces the `"no such method"` stub on `NvidiaModel.Rerank` (`internal/entity/models/nvidia.go`) with a real implementation against NVIDIA NIM's `/ranking` endpoint. - Mirrors the existing Python `NvidiaRerank` class at `rag/llm/rerank_model.py:149-190` for behavior parity: same `passages`/`query.text`/`logit` payload shape; `top_n` set to `len(documents)` so every input gets a score returned in original order (the issue body's spec omitted `top_n`, which would cause silent data loss). - Adds the `"rerank": "ranking"` URL suffix and two NIM rerank model entries (`nvidia/nv-rerankqa-mistral-4b-v3`, `nvidia/llama-3.2-nv-rerankqa-1b-v2`) to `conf/models/nvidia.json` so the picker exposes them. - Follows the same shape as the recently merged Aliyun (#14676), Gitee (#14656), and ZhipuAI (#14608) Rerank implementations: lowercase per-driver request/response types, conversion to the project-wide `RerankResponse{Data: []RerankResult}`, per-call `context.WithTimeout` of 30s. Closes #14720 ## Test plan - [x] `gofmt -l internal/entity/models/nvidia.go` — clean - [x] `go vet ./internal/entity/models/...` — no new errors introduced (the two pre-existing vet errors in `baidu.go:642` and `openrouter.go:566` are unrelated to this PR) - [x] `go build ./internal/entity/models/...` — succeeds - [x] `python3 -c "import json; json.load(open('conf/models/nvidia.json'))"` — JSON valid - [ ] Live smoke test against NVIDIA NIM with a real API key (requires reviewer with NIM credentials) ## Notes for reviewers - The issue body suggested omitting `top_n`. The Python reference includes it (`top_n: len(texts)`), and without it NVIDIA returns only the default top-K rankings rather than scores for every input. This PR follows the Python. - The URL host is `integrate.api.nvidia.com` (kept consistent with the existing chat/embeddings BaseURL in `nvidia.go`), not the legacy `ai.api.nvidia.com` host the Python uses. NIM's unified endpoint accepts the model names as-is, so no per-model URL transform is needed.
2026-05-11 11:21:16 +02:00
topN := len(documents)
if rerankConfig != nil && rerankConfig.TopN > 0 && rerankConfig.TopN < topN {
topN = rerankConfig.TopN
}
passages := make([]nvidiaRerankText, len(documents))
for i, doc := range documents {
passages[i] = nvidiaRerankText{Text: doc}
}
reqBody := nvidiaRerankRequest{
Model: *modelName,
Query: nvidiaRerankText{Text: query},
Passages: passages,
Truncate: "END",
TopN: topN,
}
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)
Go: implement Rerank in NVIDIA driver (#14778) ## Summary - Replaces the `"no such method"` stub on `NvidiaModel.Rerank` (`internal/entity/models/nvidia.go`) with a real implementation against NVIDIA NIM's `/ranking` endpoint. - Mirrors the existing Python `NvidiaRerank` class at `rag/llm/rerank_model.py:149-190` for behavior parity: same `passages`/`query.text`/`logit` payload shape; `top_n` set to `len(documents)` so every input gets a score returned in original order (the issue body's spec omitted `top_n`, which would cause silent data loss). - Adds the `"rerank": "ranking"` URL suffix and two NIM rerank model entries (`nvidia/nv-rerankqa-mistral-4b-v3`, `nvidia/llama-3.2-nv-rerankqa-1b-v2`) to `conf/models/nvidia.json` so the picker exposes them. - Follows the same shape as the recently merged Aliyun (#14676), Gitee (#14656), and ZhipuAI (#14608) Rerank implementations: lowercase per-driver request/response types, conversion to the project-wide `RerankResponse{Data: []RerankResult}`, per-call `context.WithTimeout` of 30s. Closes #14720 ## Test plan - [x] `gofmt -l internal/entity/models/nvidia.go` — clean - [x] `go vet ./internal/entity/models/...` — no new errors introduced (the two pre-existing vet errors in `baidu.go:642` and `openrouter.go:566` are unrelated to this PR) - [x] `go build ./internal/entity/models/...` — succeeds - [x] `python3 -c "import json; json.load(open('conf/models/nvidia.json'))"` — JSON valid - [ ] Live smoke test against NVIDIA NIM with a real API key (requires reviewer with NIM credentials) ## Notes for reviewers - The issue body suggested omitting `top_n`. The Python reference includes it (`top_n: len(texts)`), and without it NVIDIA returns only the default top-K rankings rather than scores for every input. This PR follows the Python. - The URL host is `integrate.api.nvidia.com` (kept consistent with the existing chat/embeddings BaseURL in `nvidia.go`), not the legacy `ai.api.nvidia.com` host the Python uses. NIM's unified endpoint accepts the model names as-is, so no per-model URL transform is needed.
2026-05-11 11:21:16 +02:00
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 := n.baseModel.httpClient.Do(req)
Go: implement Rerank in NVIDIA driver (#14778) ## Summary - Replaces the `"no such method"` stub on `NvidiaModel.Rerank` (`internal/entity/models/nvidia.go`) with a real implementation against NVIDIA NIM's `/ranking` endpoint. - Mirrors the existing Python `NvidiaRerank` class at `rag/llm/rerank_model.py:149-190` for behavior parity: same `passages`/`query.text`/`logit` payload shape; `top_n` set to `len(documents)` so every input gets a score returned in original order (the issue body's spec omitted `top_n`, which would cause silent data loss). - Adds the `"rerank": "ranking"` URL suffix and two NIM rerank model entries (`nvidia/nv-rerankqa-mistral-4b-v3`, `nvidia/llama-3.2-nv-rerankqa-1b-v2`) to `conf/models/nvidia.json` so the picker exposes them. - Follows the same shape as the recently merged Aliyun (#14676), Gitee (#14656), and ZhipuAI (#14608) Rerank implementations: lowercase per-driver request/response types, conversion to the project-wide `RerankResponse{Data: []RerankResult}`, per-call `context.WithTimeout` of 30s. Closes #14720 ## Test plan - [x] `gofmt -l internal/entity/models/nvidia.go` — clean - [x] `go vet ./internal/entity/models/...` — no new errors introduced (the two pre-existing vet errors in `baidu.go:642` and `openrouter.go:566` are unrelated to this PR) - [x] `go build ./internal/entity/models/...` — succeeds - [x] `python3 -c "import json; json.load(open('conf/models/nvidia.json'))"` — JSON valid - [ ] Live smoke test against NVIDIA NIM with a real API key (requires reviewer with NIM credentials) ## Notes for reviewers - The issue body suggested omitting `top_n`. The Python reference includes it (`top_n: len(texts)`), and without it NVIDIA returns only the default top-K rankings rather than scores for every input. This PR follows the Python. - The URL host is `integrate.api.nvidia.com` (kept consistent with the existing chat/embeddings BaseURL in `nvidia.go`), not the legacy `ai.api.nvidia.com` host the Python uses. NIM's unified endpoint accepts the model names as-is, so no per-model URL transform is needed.
2026-05-11 11:21:16 +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("Nvidia rerank API error: %s, body: %s", resp.Status, string(body))
}
var parsed nvidiaRerankResponse
if err = json.Unmarshal(body, &parsed); err != nil {
return nil, fmt.Errorf("failed to parse response: %w", err)
}
rerankResponse := RerankResponse{Data: make([]RerankResult, 0, len(parsed.Rankings))}
for _, r := range parsed.Rankings {
if r.Index < 0 || r.Index >= len(documents) {
return nil, fmt.Errorf("unexpected rerank index %d for %d inputs", r.Index, len(documents))
}
rerankResponse.Data = append(rerankResponse.Data, RerankResult{
Index: r.Index,
RelevanceScore: r.Logit,
})
}
return &rerankResponse, nil
}
// TranscribeAudio transcribe audio
func (n *NvidiaModel) TranscribeAudio(modelName *string, file *string, apiConfig *APIConfig, asrConfig *ASRConfig) (*ASRResponse, error) {
return nil, fmt.Errorf("%s, no such method", n.Name())
}
func (n *NvidiaModel) TranscribeAudioWithSender(modelName *string, file *string, apiConfig *APIConfig, asrConfig *ASRConfig, sender func(*string, *string) error) error {
return fmt.Errorf("%s, no such method", n.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 (n *NvidiaModel) AudioSpeech(modelName *string, audioContent *string, apiConfig *APIConfig, ttsConfig *TTSConfig) (*TTSResponse, error) {
return nil, fmt.Errorf("%s, no such method", n.Name())
}
func (n *NvidiaModel) AudioSpeechWithSender(modelName *string, audioContent *string, apiConfig *APIConfig, ttsConfig *TTSConfig, sender func(*string, *string) error) error {
return fmt.Errorf("%s, no such method", n.Name())
}
// OCRFile OCR file
func (n *NvidiaModel) OCRFile(modelName *string, content []byte, url *string, apiConfig *APIConfig, ocrConfig *OCRConfig) (*OCRFileResponse, error) {
return nil, fmt.Errorf("%s, no such method", n.Name())
}
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 (n *NvidiaModel) ParseFile(modelName *string, content []byte, url *string, apiConfig *APIConfig, parseFileConfig *ParseFileConfig) (*ParseFileResponse, error) {
return nil, fmt.Errorf("%s, no such method", n.Name())
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
}
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +02:00
// ListModels calls /v1/models on the configured NVIDIA NIM base URL
// and returns the list of available model ids. The endpoint is
// OpenAI-compatible, so the parsing follows the same shape used by
// the moonshot, xai, and openai drivers.
func (n NvidiaModel) ListModels(apiConfig *APIConfig) ([]ListModelResponse, error) {
if err := n.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +02:00
}
resolvedBaseURL, err := n.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +02:00
}
baseURL := resolvedBaseURL
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +02:00
if baseURL == "" {
baseURL = resolvedBaseURL
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +02:00
}
url := fmt.Sprintf("%s/%s", baseURL, n.baseModel.URLSuffix.Models)
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +02:00
ctx, cancel := context.WithTimeout(context.Background(), nonStreamCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, "GET", url, nil)
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +02:00
if err != nil {
return nil, fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +02:00
resp, err := n.baseModel.httpClient.Do(req)
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +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("Nvidia models API error: %s, body: %s", resp.Status, string(body))
}
// Parse response
var modelList ModelList
if err = json.Unmarshal(body, &modelList); err != nil {
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +02:00
return nil, fmt.Errorf("failed to parse response: %w", err)
}
if modelList.Models == nil {
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +02:00
return nil, fmt.Errorf("invalid models list format")
}
return ParseListModel(modelList), nil
}
func (n NvidiaModel) Balance(apiConfig *APIConfig) (map[string]interface{}, error) {
return nil, fmt.Errorf("no such method")
}
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +02:00
// CheckConnection verifies that the configured NVIDIA NIM base URL
// is reachable and that the API key is accepted, by issuing a
// lightweight ListModels call. Mirrors the pattern used by the xai,
// moonshot, deepseek, aliyun, and gitee drivers.
func (n NvidiaModel) CheckConnection(apiConfig *APIConfig) error {
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636) ### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### 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 \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635
2026-05-08 06:04:28 +02:00
_, err := n.ListModels(apiConfig)
return err
}
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
func (n *NvidiaModel) ListTasks(apiConfig *APIConfig) ([]ListTaskStatus, error) {
return nil, fmt.Errorf("%s, no such method", n.Name())
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
}
func (n *NvidiaModel) ShowTask(taskID string, apiConfig *APIConfig) (*TaskResponse, error) {
return nil, fmt.Errorf("%s, no such method", n.Name())
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
}