Files
ragflow/internal/entity/models/mistral.go

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Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
//
// Copyright 2026 The InfiniFlow Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
package models
import (
"bytes"
"context"
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
"encoding/base64"
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
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"encoding/json"
"fmt"
"io"
"net/http"
"strings"
"time"
)
// MistralModel implements ModelDriver for Mistral AI.
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
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type MistralModel struct {
baseModel BaseModel
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
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}
// NewMistralModel creates a new Mistral model instance.
func NewMistralModel(baseURL map[string]string, urlSuffix URLSuffix) *MistralModel {
return &MistralModel{
baseModel: BaseModel{
BaseURL: baseURL,
URLSuffix: urlSuffix,
httpClient: NewDriverHTTPClient(),
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
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},
}
}
func (m *MistralModel) NewInstance(baseURL map[string]string) ModelDriver {
return NewMistralModel(baseURL, m.baseModel.URLSuffix)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
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}
func (m *MistralModel) Name() string {
return "mistral"
}
// ChatWithMessages sends multiple messages with roles and returns the response.
func (m *MistralModel) ChatWithMessages(modelName string, messages []Message, apiConfig *APIConfig, chatModelConfig *ChatConfig) (*ChatResponse, error) {
if err := m.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
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}
if len(messages) == 0 {
return nil, fmt.Errorf("messages is empty")
}
baseURL, err := m.baseModel.GetBaseURL(apiConfig)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
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if err != nil {
return nil, err
}
baseURL = strings.TrimSuffix(baseURL, "/")
url := fmt.Sprintf("%s/%s", baseURL, m.baseModel.URLSuffix.Chat)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
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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,
}
// Note: do NOT propagate chatModelConfig.Stream into the request body
// here. ChatWithMessages parses a single JSON response, so stream must
// always be off for this code path.
if chatModelConfig != nil {
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
}
}
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 := m.baseModel.httpClient.Do(req)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
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("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")
}
fix(mistral): handle structured content from magistral reasoning models (#14805) ### What problem does this PR solve? `MistralModel.ChatWithMessages` (in the driver merged via #14807) assumes that `choices[0].message.content` from `/v1/chat/completions` is always a string and falls through to `return nil, fmt.Errorf("invalid content format")` on anything else. That assumption breaks for the **magistral reasoning family** (`magistral-small-*`, `magistral-medium-*`). When the model needs a chain-of-thought to answer, Mistral returns `content` as a **structured array of typed parts**: ```json "content": [ {"type": "thinking", "thinking": [{"type": "text", "text": "Combined speed is 150 mph. 300 / 150 = 2 hours."}], "closed": true}, {"type": "text", "text": "They will meet after **2 hours**."} ] ``` Concretely, this is what the live API returns today (probed against `api.mistral.ai/v1`): ``` $ curl -H "Authorization: Bearer <key>" -H "Content-Type: application/json" \ -X POST https://api.mistral.ai/v1/chat/completions \ -d '{"model":"magistral-medium-latest", "messages":[{"role":"user","content":"two trains 60mph and 90mph, 300mi apart, when do they meet? step by step."}], "max_tokens":1024}' HTTP 200 { "choices":[{"message":{ "role":"assistant", "content":[ {"type":"thinking","thinking":[{"type":"text","text":"Okay, let's see..."}],"closed":true}, {"type":"text","text":"To determine when the two trains meet..."} ]}}] } ``` With the current driver, every call like that returns the generic `"invalid content format"` error. Trivial prompts that happen to fit in a string answer still succeed, so the breakage is **non-deterministic from the tenant's POV**: same model, same provider, sometimes works, sometimes 500s with no useful error. A secondary issue: `conf/models/mistral.json` does not include any magistral model. The picker hid the broken path, which is why this wasn't caught during #14807's review. ### What this PR includes - New helper `extractMistralContent(raw interface{}) (answer, reasonContent string, err error)` in `internal/entity/models/mistral.go`, which normalizes both shapes Mistral can return: - `string` → historical path. `Answer = content`, `ReasonContent = ""`. Preserves behavior for every non-reasoning model (`mistral-large-*`, `mistral-small-*`, `ministral-*`, `codestral-*`, `pixtral-*`, `open-mistral-nemo`). - `[]interface{}` → walk the parts. Concatenate every `{"type":"text", "text":...}` part into `Answer`; concatenate the inner text inside every `{"type":"thinking", "thinking":[...]}` part into `ReasonContent`. - `ChatWithMessages` now calls the helper instead of doing the raw `.(string)` cast. - Unknown part types are **skipped, not failed**. Mistral has been adding new content variants quickly (audio chunks, citations, etc.); this driver should not 500 every call when a new part type appears. - `conf/models/mistral.json`: add `magistral-medium-latest` and `magistral-small-latest`. Both are visible in `/v1/models` today. No interface change. No factory change. No new dependencies. ### How was this tested? **Unit tests** — 5 new tests in `internal/entity/models/mistral_test.go` on top of the 27 already shipped via #14807: - `TestMistralChatHandlesStringContent` — regression net for the historical path - `TestMistralChatExtractsReasoningFromStructuredContent` — the fixture body is a trimmed copy of the actual `magistral-medium-latest` response captured above; asserts both `Answer` and `ReasonContent` are populated correctly - `TestMistralChatHandlesStructuredContentWithoutThinking` — `magistral-*` with a trivial answer returns a structured shape that has only a `text` part; `ReasonContent` must stay empty - `TestMistralChatIgnoresUnknownContentPartTypes` — `audio_url` and `future_part_type` parts are skipped, `text` parts still flow through - `TestExtractMistralContent` — table-driven unit coverage of the helper for string, empty string, nil, empty array, text-only, thinking+text, unsupported root type ``` $ go test -vet=off -run "TestMistral|TestExtractMistralContent" -count=1 -v ./internal/entity/models/... === RUN TestMistralChatHandlesStringContent --- PASS: TestMistralChatHandlesStringContent (0.00s) === RUN TestMistralChatExtractsReasoningFromStructuredContent --- PASS: TestMistralChatExtractsReasoningFromStructuredContent (0.00s) === RUN TestMistralChatHandlesStructuredContentWithoutThinking --- PASS: TestMistralChatHandlesStructuredContentWithoutThinking (0.00s) === RUN TestMistralChatIgnoresUnknownContentPartTypes --- PASS: TestMistralChatIgnoresUnknownContentPartTypes (0.00s) === RUN TestExtractMistralContent === RUN TestExtractMistralContent/plain_string === RUN TestExtractMistralContent/empty_string === RUN TestExtractMistralContent/nil === RUN TestExtractMistralContent/empty_array === RUN TestExtractMistralContent/text_only === RUN TestExtractMistralContent/thinking_then_text === RUN TestExtractMistralContent/unknown_root_type --- PASS: TestExtractMistralContent (0.00s) PASS ok ragflow/internal/entity/models 0.046s ``` All 32 Mistral tests pass on go 1.25. `go build ./internal/entity/models/...` exits 0. **Live integration test** — driver exercised against `api.mistral.ai/v1` with the patched code: ``` === RUN TestMistralMagistralSmoke [OK] "magistral-small-latest" present upstream [OK] "magistral-medium-latest" present upstream [OK trivial] Answer="7" ReasonContent="" [OK reasoning] Answer len=797 head="To determine when the two trains meet, we can follow these steps:\n\n1. **Identify..." ReasonContent len=1069 head="Okay, let's see. There are two trains, one going 60 mph and the other going 90 mph. They're moving towards each other, s..." MAGISTRAL SMOKE PASSED --- PASS: TestMistralMagistralSmoke (18.09s) PASS ok ragflow/internal/entity/models 18.112s ``` What the live run proves on the wire: - `magistral-small-latest` with a trivial prompt still uses the string-content shape; the regression-net path is exercised against the real server, not just the mock. - `magistral-medium-latest` with a reasoning prompt uses the structured-array shape; the new code path extracts a 1069-character reasoning trace into `ChatResponse.ReasonContent` and a 797-character visible answer into `ChatResponse.Answer`. Before this fix, the same call returned `"invalid content format"` and the caller saw nothing. The smoke-test file itself is not committed (live tests live outside the PR diff, same convention used for prior provider PRs). ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
2026-05-20 21:19:04 -10:00
content, reasonContent, err := extractMistralContent(messageMap["content"])
if err != nil {
return nil, err
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
}
return &ChatResponse{
Answer: &content,
fix(mistral): handle structured content from magistral reasoning models (#14805) ### What problem does this PR solve? `MistralModel.ChatWithMessages` (in the driver merged via #14807) assumes that `choices[0].message.content` from `/v1/chat/completions` is always a string and falls through to `return nil, fmt.Errorf("invalid content format")` on anything else. That assumption breaks for the **magistral reasoning family** (`magistral-small-*`, `magistral-medium-*`). When the model needs a chain-of-thought to answer, Mistral returns `content` as a **structured array of typed parts**: ```json "content": [ {"type": "thinking", "thinking": [{"type": "text", "text": "Combined speed is 150 mph. 300 / 150 = 2 hours."}], "closed": true}, {"type": "text", "text": "They will meet after **2 hours**."} ] ``` Concretely, this is what the live API returns today (probed against `api.mistral.ai/v1`): ``` $ curl -H "Authorization: Bearer <key>" -H "Content-Type: application/json" \ -X POST https://api.mistral.ai/v1/chat/completions \ -d '{"model":"magistral-medium-latest", "messages":[{"role":"user","content":"two trains 60mph and 90mph, 300mi apart, when do they meet? step by step."}], "max_tokens":1024}' HTTP 200 { "choices":[{"message":{ "role":"assistant", "content":[ {"type":"thinking","thinking":[{"type":"text","text":"Okay, let's see..."}],"closed":true}, {"type":"text","text":"To determine when the two trains meet..."} ]}}] } ``` With the current driver, every call like that returns the generic `"invalid content format"` error. Trivial prompts that happen to fit in a string answer still succeed, so the breakage is **non-deterministic from the tenant's POV**: same model, same provider, sometimes works, sometimes 500s with no useful error. A secondary issue: `conf/models/mistral.json` does not include any magistral model. The picker hid the broken path, which is why this wasn't caught during #14807's review. ### What this PR includes - New helper `extractMistralContent(raw interface{}) (answer, reasonContent string, err error)` in `internal/entity/models/mistral.go`, which normalizes both shapes Mistral can return: - `string` → historical path. `Answer = content`, `ReasonContent = ""`. Preserves behavior for every non-reasoning model (`mistral-large-*`, `mistral-small-*`, `ministral-*`, `codestral-*`, `pixtral-*`, `open-mistral-nemo`). - `[]interface{}` → walk the parts. Concatenate every `{"type":"text", "text":...}` part into `Answer`; concatenate the inner text inside every `{"type":"thinking", "thinking":[...]}` part into `ReasonContent`. - `ChatWithMessages` now calls the helper instead of doing the raw `.(string)` cast. - Unknown part types are **skipped, not failed**. Mistral has been adding new content variants quickly (audio chunks, citations, etc.); this driver should not 500 every call when a new part type appears. - `conf/models/mistral.json`: add `magistral-medium-latest` and `magistral-small-latest`. Both are visible in `/v1/models` today. No interface change. No factory change. No new dependencies. ### How was this tested? **Unit tests** — 5 new tests in `internal/entity/models/mistral_test.go` on top of the 27 already shipped via #14807: - `TestMistralChatHandlesStringContent` — regression net for the historical path - `TestMistralChatExtractsReasoningFromStructuredContent` — the fixture body is a trimmed copy of the actual `magistral-medium-latest` response captured above; asserts both `Answer` and `ReasonContent` are populated correctly - `TestMistralChatHandlesStructuredContentWithoutThinking` — `magistral-*` with a trivial answer returns a structured shape that has only a `text` part; `ReasonContent` must stay empty - `TestMistralChatIgnoresUnknownContentPartTypes` — `audio_url` and `future_part_type` parts are skipped, `text` parts still flow through - `TestExtractMistralContent` — table-driven unit coverage of the helper for string, empty string, nil, empty array, text-only, thinking+text, unsupported root type ``` $ go test -vet=off -run "TestMistral|TestExtractMistralContent" -count=1 -v ./internal/entity/models/... === RUN TestMistralChatHandlesStringContent --- PASS: TestMistralChatHandlesStringContent (0.00s) === RUN TestMistralChatExtractsReasoningFromStructuredContent --- PASS: TestMistralChatExtractsReasoningFromStructuredContent (0.00s) === RUN TestMistralChatHandlesStructuredContentWithoutThinking --- PASS: TestMistralChatHandlesStructuredContentWithoutThinking (0.00s) === RUN TestMistralChatIgnoresUnknownContentPartTypes --- PASS: TestMistralChatIgnoresUnknownContentPartTypes (0.00s) === RUN TestExtractMistralContent === RUN TestExtractMistralContent/plain_string === RUN TestExtractMistralContent/empty_string === RUN TestExtractMistralContent/nil === RUN TestExtractMistralContent/empty_array === RUN TestExtractMistralContent/text_only === RUN TestExtractMistralContent/thinking_then_text === RUN TestExtractMistralContent/unknown_root_type --- PASS: TestExtractMistralContent (0.00s) PASS ok ragflow/internal/entity/models 0.046s ``` All 32 Mistral tests pass on go 1.25. `go build ./internal/entity/models/...` exits 0. **Live integration test** — driver exercised against `api.mistral.ai/v1` with the patched code: ``` === RUN TestMistralMagistralSmoke [OK] "magistral-small-latest" present upstream [OK] "magistral-medium-latest" present upstream [OK trivial] Answer="7" ReasonContent="" [OK reasoning] Answer len=797 head="To determine when the two trains meet, we can follow these steps:\n\n1. **Identify..." ReasonContent len=1069 head="Okay, let's see. There are two trains, one going 60 mph and the other going 90 mph. They're moving towards each other, s..." MAGISTRAL SMOKE PASSED --- PASS: TestMistralMagistralSmoke (18.09s) PASS ok ragflow/internal/entity/models 18.112s ``` What the live run proves on the wire: - `magistral-small-latest` with a trivial prompt still uses the string-content shape; the regression-net path is exercised against the real server, not just the mock. - `magistral-medium-latest` with a reasoning prompt uses the structured-array shape; the new code path extracts a 1069-character reasoning trace into `ChatResponse.ReasonContent` and a 797-character visible answer into `ChatResponse.Answer`. Before this fix, the same call returned `"invalid content format"` and the caller saw nothing. The smoke-test file itself is not committed (live tests live outside the PR diff, same convention used for prior provider PRs). ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
2026-05-20 21:19:04 -10:00
ReasonContent: &reasonContent,
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
}, nil
}
fix(mistral): handle structured content from magistral reasoning models (#14805) ### What problem does this PR solve? `MistralModel.ChatWithMessages` (in the driver merged via #14807) assumes that `choices[0].message.content` from `/v1/chat/completions` is always a string and falls through to `return nil, fmt.Errorf("invalid content format")` on anything else. That assumption breaks for the **magistral reasoning family** (`magistral-small-*`, `magistral-medium-*`). When the model needs a chain-of-thought to answer, Mistral returns `content` as a **structured array of typed parts**: ```json "content": [ {"type": "thinking", "thinking": [{"type": "text", "text": "Combined speed is 150 mph. 300 / 150 = 2 hours."}], "closed": true}, {"type": "text", "text": "They will meet after **2 hours**."} ] ``` Concretely, this is what the live API returns today (probed against `api.mistral.ai/v1`): ``` $ curl -H "Authorization: Bearer <key>" -H "Content-Type: application/json" \ -X POST https://api.mistral.ai/v1/chat/completions \ -d '{"model":"magistral-medium-latest", "messages":[{"role":"user","content":"two trains 60mph and 90mph, 300mi apart, when do they meet? step by step."}], "max_tokens":1024}' HTTP 200 { "choices":[{"message":{ "role":"assistant", "content":[ {"type":"thinking","thinking":[{"type":"text","text":"Okay, let's see..."}],"closed":true}, {"type":"text","text":"To determine when the two trains meet..."} ]}}] } ``` With the current driver, every call like that returns the generic `"invalid content format"` error. Trivial prompts that happen to fit in a string answer still succeed, so the breakage is **non-deterministic from the tenant's POV**: same model, same provider, sometimes works, sometimes 500s with no useful error. A secondary issue: `conf/models/mistral.json` does not include any magistral model. The picker hid the broken path, which is why this wasn't caught during #14807's review. ### What this PR includes - New helper `extractMistralContent(raw interface{}) (answer, reasonContent string, err error)` in `internal/entity/models/mistral.go`, which normalizes both shapes Mistral can return: - `string` → historical path. `Answer = content`, `ReasonContent = ""`. Preserves behavior for every non-reasoning model (`mistral-large-*`, `mistral-small-*`, `ministral-*`, `codestral-*`, `pixtral-*`, `open-mistral-nemo`). - `[]interface{}` → walk the parts. Concatenate every `{"type":"text", "text":...}` part into `Answer`; concatenate the inner text inside every `{"type":"thinking", "thinking":[...]}` part into `ReasonContent`. - `ChatWithMessages` now calls the helper instead of doing the raw `.(string)` cast. - Unknown part types are **skipped, not failed**. Mistral has been adding new content variants quickly (audio chunks, citations, etc.); this driver should not 500 every call when a new part type appears. - `conf/models/mistral.json`: add `magistral-medium-latest` and `magistral-small-latest`. Both are visible in `/v1/models` today. No interface change. No factory change. No new dependencies. ### How was this tested? **Unit tests** — 5 new tests in `internal/entity/models/mistral_test.go` on top of the 27 already shipped via #14807: - `TestMistralChatHandlesStringContent` — regression net for the historical path - `TestMistralChatExtractsReasoningFromStructuredContent` — the fixture body is a trimmed copy of the actual `magistral-medium-latest` response captured above; asserts both `Answer` and `ReasonContent` are populated correctly - `TestMistralChatHandlesStructuredContentWithoutThinking` — `magistral-*` with a trivial answer returns a structured shape that has only a `text` part; `ReasonContent` must stay empty - `TestMistralChatIgnoresUnknownContentPartTypes` — `audio_url` and `future_part_type` parts are skipped, `text` parts still flow through - `TestExtractMistralContent` — table-driven unit coverage of the helper for string, empty string, nil, empty array, text-only, thinking+text, unsupported root type ``` $ go test -vet=off -run "TestMistral|TestExtractMistralContent" -count=1 -v ./internal/entity/models/... === RUN TestMistralChatHandlesStringContent --- PASS: TestMistralChatHandlesStringContent (0.00s) === RUN TestMistralChatExtractsReasoningFromStructuredContent --- PASS: TestMistralChatExtractsReasoningFromStructuredContent (0.00s) === RUN TestMistralChatHandlesStructuredContentWithoutThinking --- PASS: TestMistralChatHandlesStructuredContentWithoutThinking (0.00s) === RUN TestMistralChatIgnoresUnknownContentPartTypes --- PASS: TestMistralChatIgnoresUnknownContentPartTypes (0.00s) === RUN TestExtractMistralContent === RUN TestExtractMistralContent/plain_string === RUN TestExtractMistralContent/empty_string === RUN TestExtractMistralContent/nil === RUN TestExtractMistralContent/empty_array === RUN TestExtractMistralContent/text_only === RUN TestExtractMistralContent/thinking_then_text === RUN TestExtractMistralContent/unknown_root_type --- PASS: TestExtractMistralContent (0.00s) PASS ok ragflow/internal/entity/models 0.046s ``` All 32 Mistral tests pass on go 1.25. `go build ./internal/entity/models/...` exits 0. **Live integration test** — driver exercised against `api.mistral.ai/v1` with the patched code: ``` === RUN TestMistralMagistralSmoke [OK] "magistral-small-latest" present upstream [OK] "magistral-medium-latest" present upstream [OK trivial] Answer="7" ReasonContent="" [OK reasoning] Answer len=797 head="To determine when the two trains meet, we can follow these steps:\n\n1. **Identify..." ReasonContent len=1069 head="Okay, let's see. There are two trains, one going 60 mph and the other going 90 mph. They're moving towards each other, s..." MAGISTRAL SMOKE PASSED --- PASS: TestMistralMagistralSmoke (18.09s) PASS ok ragflow/internal/entity/models 18.112s ``` What the live run proves on the wire: - `magistral-small-latest` with a trivial prompt still uses the string-content shape; the regression-net path is exercised against the real server, not just the mock. - `magistral-medium-latest` with a reasoning prompt uses the structured-array shape; the new code path extracts a 1069-character reasoning trace into `ChatResponse.ReasonContent` and a 797-character visible answer into `ChatResponse.Answer`. Before this fix, the same call returned `"invalid content format"` and the caller saw nothing. The smoke-test file itself is not committed (live tests live outside the PR diff, same convention used for prior provider PRs). ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
2026-05-20 21:19:04 -10:00
func extractMistralContent(raw interface{}) (string, string, error) {
switch v := raw.(type) {
case string:
return v, "", nil
case []interface{}:
var answer, reasoning strings.Builder
for _, part := range v {
pm, ok := part.(map[string]interface{})
if !ok {
continue
}
switch pm["type"] {
case "text":
if t, ok := pm["text"].(string); ok {
answer.WriteString(t)
}
case "thinking":
// thinking is an array of inner text parts; concatenate
// any inner element with a non-empty text field.
inner, ok := pm["thinking"].([]interface{})
if !ok {
continue
}
for _, sub := range inner {
sm, ok := sub.(map[string]interface{})
if !ok {
continue
}
if t, ok := sm["text"].(string); ok {
reasoning.WriteString(t)
}
}
}
}
return answer.String(), reasoning.String(), nil
case nil:
return "", "", nil
default:
return "", "", fmt.Errorf("mistral: unsupported content type %T", raw)
}
}
// ChatStreamlyWithSender sends messages and streams the response
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
func (m *MistralModel) ChatStreamlyWithSender(modelName string, messages []Message, apiConfig *APIConfig, chatModelConfig *ChatConfig, sender func(*string, *string) error) error {
if err := m.baseModel.APIConfigCheck(apiConfig); err != nil {
return err
}
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
if sender == nil {
return fmt.Errorf("sender is required")
}
if len(messages) == 0 {
return fmt.Errorf("messages is empty")
}
baseURL, err := m.baseModel.GetBaseURL(apiConfig)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
if err != nil {
return err
}
baseURL = strings.TrimSuffix(baseURL, "/")
url := fmt.Sprintf("%s/%s", baseURL, m.baseModel.URLSuffix.Chat)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
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 chatModelConfig != nil {
if chatModelConfig.Stream != nil && !*chatModelConfig.Stream {
return fmt.Errorf("stream must be true in ChatStreamlyWithSender")
}
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
}
}
jsonData, err := json.Marshal(reqBody)
if err != nil {
return fmt.Errorf("failed to marshal request: %w", err)
}
req, err := http.NewRequestWithContext(context.Background(), "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 := m.baseModel.httpClient.Do(req)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
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))
}
sawTerminal := false
done, err := ParseSSEStream[map[string]interface{}](resp.Body, func(event map[string]interface{}) error {
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
choices, ok := event["choices"].([]interface{})
if !ok || len(choices) == 0 {
return nil
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
}
firstChoice, ok := choices[0].(map[string]interface{})
if !ok {
return nil
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
}
delta, ok := firstChoice["delta"].(map[string]interface{})
if !ok {
return nil
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
}
content, ok := delta["content"].(string)
if ok && content != "" {
if err := sender(&content, nil); err != nil {
return err
}
}
finishReason, ok := firstChoice["finish_reason"].(string)
if ok && finishReason != "" {
sawTerminal = true
}
return nil
})
if err != nil {
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
return fmt.Errorf("failed to scan response body: %w", err)
}
if !done && !sawTerminal {
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
return fmt.Errorf("mistral: stream ended before [DONE] or finish_reason")
}
endOfStream := "[DONE]"
if err := sender(&endOfStream, nil); err != nil {
return err
}
return nil
}
type mistralEmbeddingData struct {
Embedding []float64 `json:"embedding"`
Object string `json:"object"`
Index int `json:"index"`
}
type mistralEmbeddingResponse struct {
Data []mistralEmbeddingData `json:"data"`
Model string `json:"model"`
Object string `json:"object"`
}
// Embed turns a list of texts into embedding vectors
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
func (m *MistralModel) Embed(modelName *string, texts []string, apiConfig *APIConfig, embeddingConfig *EmbeddingConfig) ([]EmbeddingData, error) {
if err := m.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
}
if len(texts) == 0 {
return []EmbeddingData{}, nil
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
}
if modelName == nil || *modelName == "" {
return nil, fmt.Errorf("model name is required")
}
baseURL, err := m.baseModel.GetBaseURL(apiConfig)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
if err != nil {
return nil, err
}
baseURL = strings.TrimSuffix(baseURL, "/")
url := fmt.Sprintf("%s/%s", baseURL, m.baseModel.URLSuffix.Embedding)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
reqBody := map[string]interface{}{
"model": *modelName,
"input": texts,
}
if embeddingConfig != nil && embeddingConfig.Dimension > 0 {
reqBody["output_dimension"] = embeddingConfig.Dimension
}
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
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 := m.baseModel.httpClient.Do(req)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
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("Mistral embeddings API error: %s, body: %s", resp.Status, string(body))
}
var parsed mistralEmbeddingResponse
if err = json.Unmarshal(body, &parsed); err != nil {
return nil, fmt.Errorf("failed to parse response: %w", err)
}
embeddings := make([]EmbeddingData, len(texts))
filled := make([]bool, len(texts))
for _, item := range parsed.Data {
if item.Index < 0 || item.Index >= len(texts) {
return nil, fmt.Errorf("mistral: response index %d out of range for %d inputs", item.Index, len(texts))
}
if filled[item.Index] {
return nil, fmt.Errorf("mistral: duplicate embedding index %d in response", item.Index)
}
embeddings[item.Index] = EmbeddingData{
Embedding: item.Embedding,
Index: item.Index,
}
filled[item.Index] = true
}
for i, ok := range filled {
if !ok {
return nil, fmt.Errorf("mistral: missing embedding for input index %d", i)
}
}
return embeddings, nil
}
// ListModels returns the list of model ids visible to the API key.
func (m *MistralModel) ListModels(apiConfig *APIConfig) ([]ListModelResponse, error) {
if err := m.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
}
baseURL, err := m.baseModel.GetBaseURL(apiConfig)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
if err != nil {
return nil, err
}
baseURL = strings.TrimSuffix(baseURL, "/")
url := fmt.Sprintf("%s/%s", baseURL, m.baseModel.URLSuffix.Models)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
ctx, cancel := context.WithTimeout(context.Background(), nonStreamCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, "GET", url, nil)
if err != nil {
return nil, fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
resp, err := m.baseModel.httpClient.Do(req)
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
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("API request failed with status %d: %s", resp.StatusCode, string(body))
}
// Parse response
var modelList ModelList
if err = json.Unmarshal(body, &modelList); err != nil {
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
return nil, fmt.Errorf("failed to parse response: %w", err)
}
if modelList.Models == nil {
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
return nil, fmt.Errorf("invalid models list format")
}
return ParseListModel(modelList), nil
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
}
// Balance is not exposed by the Mistral API, so this returns "no such method".
func (m *MistralModel) Balance(apiConfig *APIConfig) (map[string]interface{}, error) {
return nil, fmt.Errorf("%s, no such method", m.Name())
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
}
// CheckConnection runs a lightweight ListModels call to verify the API key.
func (m *MistralModel) CheckConnection(apiConfig *APIConfig) error {
_, err := m.ListModels(apiConfig)
if err != nil {
return err
}
return nil
}
// Rerank calculates similarity scores between query and documents
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
func (m *MistralModel) Rerank(modelName *string, query string, documents []string, apiConfig *APIConfig, rerankConfig *RerankConfig) (*RerankResponse, error) {
return nil, fmt.Errorf("%s, no such method", m.Name())
Go: implement Embed (embeddings) in Mistral driver (#14807) ### What problem does this PR solve? The Mistral Go driver landed in #14805 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. `conf/models/mistral.json` did not list any embedding model out of the box, so a tenant who wanted to use Mistral end to end (chat + embeddings) could not run an embedding call. This PR adds `mistral-embed` to the config and a real `/v1/embeddings` implementation. ### What this PR includes - `conf/models/mistral.json`: add `"embedding": "embeddings"` under `url_suffix` so the driver can build the URL from config (matches the `URLSuffix.Embedding` field already used by openai, siliconflow, zhipu-ai), and add a `mistral-embed` entry under `models` (1024-dimensional vectors, 8192 max input tokens). - `internal/entity/models/mistral.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/embeddings`. Adds local response types `mistralEmbeddingData` and `mistralEmbeddingResponse`. No factory change. No interface change. ### How the implementation works - Validate `apiConfig`, the API key, and the model name. Use the existing `baseURLForRegion` helper so an unknown region fails fast with a clear error. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern as `ChatWithMessages` and `ListModels` already use in this file. - Send all input texts in one request. The Mistral API accepts the `input` field as an array. - Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. - An empty input slice returns `[]EmbeddingData{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. - A final pass checks that every input slot got a vector. If any slot is still empty, return a clear error so the caller does not silently use a zero vector. ### Note on stacking This PR builds on #14805 (the Mistral driver). Until #14805 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14805 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8 lines in `mistral.json`). ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the `go.mod` minimum). - The full method set on `MistralModel` still matches the `ModelDriver` interface. - Pattern parity with the existing OpenAI Embed implementation (`internal/entity/models/openai.go`). Closes #14806 Depends on #14805 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com>
2026-05-11 23:45:48 -10:00
}
// TranscribeAudio transcribe audio
func (m *MistralModel) TranscribeAudio(modelName *string, file *string, apiConfig *APIConfig, asrConfig *ASRConfig) (*ASRResponse, error) {
return nil, fmt.Errorf("%s, no such method", m.Name())
}
func (m *MistralModel) TranscribeAudioWithSender(modelName *string, file *string, apiConfig *APIConfig, asrConfig *ASRConfig, sender func(*string, *string) error) error {
return fmt.Errorf("%s, no such method", m.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 (m *MistralModel) AudioSpeech(modelName *string, audioContent *string, apiConfig *APIConfig, ttsConfig *TTSConfig) (*TTSResponse, error) {
return nil, fmt.Errorf("%s, no such method", m.Name())
}
func (m *MistralModel) AudioSpeechWithSender(modelName *string, audioContent *string, apiConfig *APIConfig, ttsConfig *TTSConfig, sender func(*string, *string) error) error {
return fmt.Errorf("%s, no such method", m.Name())
}
// OCRFile OCR file
func (m *MistralModel) OCRFile(modelName *string, content []byte, urls *string, apiConfig *APIConfig, ocrConfig *OCRConfig) (*OCRFileResponse, error) {
if err := m.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
}
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
if (urls == nil || *urls == "") && (content == nil || len(content) == 0) {
return nil, fmt.Errorf("file url or content is required")
}
resolvedBaseURL, err := m.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
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
}
url := fmt.Sprintf("%s/%s", resolvedBaseURL, m.baseModel.URLSuffix.OCR)
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
var docURL string
if urls != nil && *urls != "" {
docURL = *urls
} else {
mimeType := http.DetectContentType(content)
base64Str := base64.StdEncoding.EncodeToString(content)
docURL = fmt.Sprintf("data:%s;base64,%s", mimeType, base64Str)
}
reqData := map[string]interface{}{
"model": *modelName,
"document": map[string]interface{}{
"type": "document_url",
"document_url": docURL,
},
}
jsonData, err := json.Marshal(reqData)
if err != nil {
return nil, fmt.Errorf("failed to marshal json payload: %w", err)
}
ctx, cancel := context.WithTimeout(context.Background(), 120*time.Second)
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 := m.baseModel.httpClient.Do(req)
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
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("Mistral OCR API error: %s, body: %s", resp.Status, string(body))
}
var mistralResp struct {
Pages []struct {
Index int `json:"index"`
Markdown string `json:"markdown"`
} `json:"pages"`
}
if err = json.Unmarshal(body, &mistralResp); err != nil {
return nil, fmt.Errorf("failed to parse response json: %w", err)
}
var fullMarkdown strings.Builder
for _, page := range mistralResp.Pages {
fullMarkdown.WriteString(page.Markdown)
fullMarkdown.WriteString("\n\n")
}
resultText := strings.TrimSpace(fullMarkdown.String())
return &OCRFileResponse{
Text: &resultText,
}, nil
Go: add file parse command (#14892) ### What problem does this PR solve? ``` RAGFlow(user)> ocr with 'hunyuanocr@test@gitee' file './picture.png' +----------------------------------------------------------+ | text | +----------------------------------------------------------+ | 生活不是等待风暴过去,而是学会在雨中翩翩起舞。 ——佚名 | +----------------------------------------------------------+ RAGFlow(user)> list 'test@gitee' tasks; +---------+----------------------------------+ | status | task_id | +---------+----------------------------------+ | success | C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5 | +---------+----------------------------------+ RAGFlow(user)> show 'test@gitee' task 'C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5'; +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ | content | index | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ | # PDF 1: Purpose of RAGFlow RAGFlow is an open source Retrieval-Augmented Generation (RAG) engine designed to turn raw documents into reliable context for large language models.Its purpose is to make it practical to build an Al assistant that can ans... | 1 | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ ``` ### Type of change - [x] New Feature (non-breaking change which adds functionality) --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com>
2026-05-15 12:29:52 +08:00
}
func (m *MistralModel) ParseFile(modelName *string, content []byte, url *string, apiConfig *APIConfig, parseFileConfig *ParseFileConfig) (*ParseFileResponse, error) {
return nil, fmt.Errorf("%s, no such method", m.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 (m *MistralModel) ListTasks(apiConfig *APIConfig) ([]ListTaskStatus, error) {
return nil, fmt.Errorf("%s, no such method", m.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 (m *MistralModel) ShowTask(taskID string, apiConfig *APIConfig) (*TaskResponse, error) {
return nil, fmt.Errorf("%s, no such method", m.Name())
}