3 Commits

Author	SHA1	Message	Date
tmimmanuel	85d0b46d8e	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)	2026-05-21 15:33:14 +08:00
Haruko386	bf41d35729	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
tmimmanuel	7d3836907a	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>	2026-05-12 17:45:48 +08:00