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| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
| cb01529d8b |
Go: implement provider: Voyage AI (#14811)
### What problem does this PR solve? Add a Go driver for Voyage AI (https://voyageai.com), one of the unchecked providers on the umbrella tracking issue #14736. Voyage AI is **embed + rerank only** — no chat, no streaming, no `/v1/models` endpoint. It's the first provider in the Go layer of this shape. Until this PR, a tenant who configured `voyage` as a model provider in the Go layer fell through to the default branch of `internal/entity/models/factory.go` and got the dummy driver. ### What this PR includes - New `internal/entity/models/voyage.go` with a `VoyageModel` implementing the `ModelDriver` interface. - New `conf/models/voyage.json` with 6 embedding models (`voyage-3.5`, `voyage-3.5-lite`, `voyage-3-large`, `voyage-code-3`, `voyage-law-2`, `voyage-finance-2`) and 2 rerank models (`rerank-2`, `rerank-2-lite`). - `factory.go`: route `"voyage"` to `NewVoyageModel`. - `internal/entity/models/voyage_test.go`: 19 unit tests. ### How the driver works - **Embed**: `POST /v1/embeddings`. Response is OpenAI-shaped (`{data: [{embedding, index, object, text}], model, usage}`). Driver reorders by `index`, rejects duplicate / out-of-range / missing slots, and short-circuits empty input without an HTTP call. - **Rerank**: `POST /v1/rerank`. Voyage uses **`top_k`** as the request param name (not `top_n` like Aliyun/SiliconFlow); the driver translates `RerankConfig.TopN` → `top_k`. Response is Cohere-shaped (`{data: [{relevance_score, index}], model}`), so the existing `RerankResponse{Data: []RerankResult{Index, RelevanceScore}}` shape fits cleanly. - **`ListModels`**: returns a hardcoded list of `voyageKnownModels`. Voyage does **not** expose `/v1/models` (probed live, returns 404), so the driver synthesizes the list from the same set the config ships. New upstream models are added by extending one slice. - **`CheckConnection`**: pings a 1-input embed call against `voyage-3.5`. Without `/v1/models`, this is the cheapest way to verify the API key + network path before a tenant tries a real workload. - **`ChatWithMessages` / `ChatStreamlyWithSender` / `Balance` / `TranscribeAudio` / `AudioSpeech` / `OCRFile`**: all return `"no such method"`. Voyage does not host any of these surfaces. No interface change. No new dependencies. ### How was this tested? **19 unit tests** in `internal/entity/models/voyage_test.go` — all pass on go 1.25: ``` $ go test -vet=off -run TestVoyage -count=1 ./internal/entity/models/... ok ragflow/internal/entity/models 0.036s ``` Coverage: Name; Embed (happy path, reorder, empty-input, missing key/model, duplicate index, out-of-range index, missing slot); Rerank (happy path with `top_k` assertion, default-to-len-documents, empty documents, out-of-range index); ListModels (static list, missing key); CheckConnection (happy, 401); chat methods sentinels; Balance sentinel; audio/OCR sentinels. `go build ./internal/entity/models/...` exits 0. **Live integration test** against `api.voyageai.com`: ``` === RUN TestVoyageLiveSmoke [OK] Name() = "voyage" [OK] ListModels (static): 8 models -> [voyage-3.5 voyage-3.5-lite voyage-3-large voyage-code-3 voyage-law-2 voyage-finance-2 rerank-2 rerank-2-lite] [OK] CheckConnection [OK] Embed vectors=3 dim=1024 indices=[0 1 2] [OK] Embed(empty) -> 0 vectors [OK] Rerank results=3 scores=[0.8125 0.59765625 0.39453125] [OK] ChatWithMessages returns voyage, no such method [OK] Balance returns voyage, no such method VOYAGE LIVE SMOKE PASSED --- PASS: TestVoyageLiveSmoke (0.81s) ``` What the live run proves on the wire: - Auth (`Bearer <key>`) accepted by `api.voyageai.com`. - Embed `voyage-3.5` on 3 inputs returns 3 vectors at dim 1024 with `index` field preserved as `[0, 1, 2]` — the reorder-by-index code is exercised on real data. - Empty input short-circuits without an HTTP call (mock server would have been hit if it did). - Rerank `rerank-2` on 3 docs returns 3 real `relevance_score` floats `[0.8125, 0.598, 0.395]`. The `top_k` translation works on the live wire. - All sentinel methods return the documented `"no such method"` strings. ### Note on PR history This branch was previously named for LocalAI Embed work which is now consolidated into PR #14813. The branch was reset to `upstream/main` and rebuilt for Voyage. Diff against `main` is a clean +838 lines across 4 files. ### Type of change - [x] New Feature (non-breaking change which adds functionality) Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com> |
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| 30d1c1dc28 |
Fix go compilation (#14900)
### What problem does this PR solve? As title ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) Signed-off-by: Jin Hai <haijin.chn@gmail.com> |
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| 0a4b733b2a |
Go: implement Rerank in LocalAI driver (#14813)
### What problem does this PR solve? The LocalAI Go driver landed in #14809 and Embed landed in #14811. `Rerank` was left as a stub that returns `"not implemented"`. This PR fills the gap. LocalAI exposes a public rerank endpoint at `<tenant-url>/v1/rerank` with a Cohere-shaped request and response (`{model, query, documents, top_n}` → `{results: [{index, relevance_score}]}`). The Python side has had `LocalAIRerank` in `rag/llm/rerank_model.py` for a long time. Until this PR, a tenant who wanted to use LocalAI for reranking in the Go layer got `"not implemented"`. ### What this PR includes - `conf/models/localai.json`: add `"rerank": "rerank"` under `url_suffix` so the driver can build the URL from config. This matches the `URLSuffix.Rerank` field already used by aliyun and siliconflow. - `internal/entity/models/localai.go`: replace the `Rerank` stub with a real implementation that POSTs to `/v1/rerank`. Adds local request/response types `localAIRerankRequest` and `localAIRerankResponse`. No factory change. No interface change. ### How the implementation works - Validate the model name and resolve the tenant-supplied base URL with the existing `resolveBaseURL` helper. - Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so the call has a clear deadline. Same pattern `ChatWithMessages`, `ListModels`, and `Embed` already use in this file. - Only set the `Authorization` header when a non-empty API key was supplied. LocalAI accepts an empty key by default, so this preserves the optional-auth contract. - Default `top_n` to `len(documents)` when `rerankConfig.TopN == 0`, matching the existing Aliyun and SiliconFlow rerank implementations. - Validate every `results[].index` against `len(documents)`. If the upstream returns an out-of-range index, fail clearly instead of silently writing past the slice. - An empty `documents` slice returns `&RerankResponse{}` with no HTTP call. - Non-200 responses propagate the upstream status line and body. ### Note on stacking This PR builds on #14809 (LocalAI driver) and #14811 (LocalAI Embed). Until both merge, this PR's diff on GitHub will include all three commits. After #14809 and #14811 land on `main`, GitHub will auto-reduce this PR to only the `Rerank` changes (one commit, ~99 line diff in `localai.go` plus 1 line in `localai.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 `LocalAIModel` still matches the `ModelDriver` interface. - Pattern parity with the existing Aliyun Rerank (`internal/entity/models/aliyun.go`) and SiliconFlow Rerank (`internal/entity/models/siliconflow.go`) implementations. Closes #14812 Depends on #14809, #14811 Tracking: #14736 Co-authored-by: Jin Hai <haijin.chn@gmail.com> |
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| b18640d228 |
Go: fix OCR command (#14891)
### 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 | +----------------------------------------------------------+ ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com> |
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| bf90c8948a |
Go: implement ListModels in ZhipuAI driver (#14886)
### What problem does this PR solve? Fixes #14884 The ZhipuAI Go driver in `internal/entity/models/zhipu-ai.go` had a stub `ListModels` method that always returned `"zhipu-ai, no such method"`. The DeepSeek, Gitee, NVIDIA, OpenAI, SiliconFlow, and OpenRouter drivers in the same package already implement `ListModels` against the OpenAI-compatible `/models` endpoint, and the model picker UI relies on it. This PR brings ZhipuAI in line with that pattern. ### Changes - `internal/entity/models/zhipu-ai.go`: implement `ZhipuAIModel.ListModels`. - Resolve region with default fallback. - GET `${BaseURL[region]}/${URLSuffix.Models}` (resolves to `https://open.bigmodel.cn/api/paas/v4/models` with the default region). - Send `Authorization: Bearer <api_key>` when an API key is configured. Omit the header when the key is empty, so an unauthenticated caller gets a clear `401` from upstream. - Surface non-200 responses with the upstream status line and body, matching the other Go drivers. - Parse the response via the package-level `DSModelList` / `DSModel` types already used by DeepSeek, Gitee, and SiliconFlow. - When the response includes `owned_by`, render the entry as `id@owned_by`, matching the convention of Gitee and SiliconFlow. - `conf/models/zhipu-ai.json`: add `"models": "models"` to `url_suffix`. ### Type of change - [x] New Feature (non-breaking change which adds functionality) |
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| 8b53960819 |
Go: implement provider: LongCat (#14809)
### What problem does this PR solve? Add a Go driver for LongCat (Meituan, https://longcat.chat), one of the unchecked providers on the umbrella tracking issue #14736. LongCat exposes an OpenAI-compatible REST API at `https://api.longcat.chat/openai/v1` with three public chat models including `LongCat-Flash-Thinking`, a reasoning model that returns chain-of-thought in `reasoning_content` (OpenAI o-series shape). Until this PR, a tenant who configured `longcat` as a model provider in the Go layer fell through to the default branch of `internal/entity/models/factory.go` and got the dummy driver. ### What this PR includes - New `internal/entity/models/longcat.go` with a `LongCatModel` implementing the `ModelDriver` interface. - New `conf/models/longcat.json` with the 3 public chat models (Flash-Chat, Flash-Lite, Flash-Thinking) and `url_suffix` for `chat` and `models`. - `factory.go`: route `"longcat"` to `NewLongCatModel`. Method coverage: - `ChatWithMessages`: `POST /openai/v1/chat/completions`, non-streaming - `ChatStreamlyWithSender`: SSE stream against the same endpoint - `ListModels` / `CheckConnection`: `GET /openai/v1/models` - **Reasoning extraction**: `message.reasoning_content` (non-stream) and `delta.reasoning_content` (stream) flow into `ChatResponse.ReasonContent` / the sender's second arg. Matches the OpenAI o-series convention also used by kimi-k2.6 and DeepSeek-R1. - **`reasoning_effort` propagation**: `ChatConfig.Effort` → request body `reasoning_effort` (LongCat-Flash-Thinking honors it; non-reasoning models ignore it). - `Embed` / `Rerank` / `Balance` / `TranscribeAudio` / `AudioSpeech` / `OCRFile` return `"no such method"` (LongCat does not expose any of these surfaces). No interface change. No new dependencies. ### How was this tested? **21 unit tests** in `internal/entity/models/longcat_test.go` — all pass: ``` $ go test -vet=off -run TestLongCat -count=1 -v ./internal/entity/models/... === RUN TestLongCatName --- PASS: TestLongCatName (0.00s) === RUN TestLongCatChatHappyPath --- PASS: TestLongCatChatHappyPath (0.00s) === RUN TestLongCatChatExtractsReasoningContent --- PASS: TestLongCatChatExtractsReasoningContent (0.00s) === RUN TestLongCatChatPropagatesReasoningEffort --- PASS: TestLongCatChatPropagatesReasoningEffort (0.00s) === RUN TestLongCatChatOmitsReasoningEffortWhenUnset --- PASS: TestLongCatChatOmitsReasoningEffortWhenUnset (0.00s) === RUN TestLongCatChatRequiresAPIKey --- PASS: TestLongCatChatRequiresAPIKey (0.00s) === RUN TestLongCatChatRequiresMessages --- PASS: TestLongCatChatRequiresMessages (0.00s) === RUN TestLongCatChatRejectsHTTPError --- PASS: TestLongCatChatRejectsHTTPError (0.00s) === RUN TestLongCatStreamHappyPath --- PASS: TestLongCatStreamHappyPath (0.00s) === RUN TestLongCatStreamExtractsReasoningContent --- PASS: TestLongCatStreamExtractsReasoningContent (0.00s) === RUN TestLongCatStreamRejectsExplicitFalse --- PASS: TestLongCatStreamRejectsExplicitFalse (0.00s) === RUN TestLongCatStreamRequiresSender --- PASS: TestLongCatStreamRequiresSender (0.00s) === RUN TestLongCatStreamFailsWithoutTerminal --- PASS: TestLongCatStreamFailsWithoutTerminal (0.00s) === RUN TestLongCatListModelsHappyPath --- PASS: TestLongCatListModelsHappyPath (0.00s) === RUN TestLongCatListModelsRequiresAPIKey --- PASS: TestLongCatListModelsRequiresAPIKey (0.00s) === RUN TestLongCatCheckConnectionDelegatesToListModels --- PASS: TestLongCatCheckConnectionDelegatesToListModels (0.00s) === RUN TestLongCatEmbedReturnsNoSuchMethod --- PASS: TestLongCatEmbedReturnsNoSuchMethod (0.00s) === RUN TestLongCatRerankReturnsNoSuchMethod --- PASS: TestLongCatRerankReturnsNoSuchMethod (0.00s) === RUN TestLongCatBalanceReturnsNoSuchMethod --- PASS: TestLongCatBalanceReturnsNoSuchMethod (0.00s) === RUN TestLongCatAudioOCRReturnNoSuchMethod --- PASS: TestLongCatAudioOCRReturnNoSuchMethod (0.00s) PASS ok ragflow/internal/entity/models 0.020s ``` `go build ./internal/entity/models/...` exits 0 on go 1.25. **Live integration test** against `api.longcat.chat`: ``` === RUN TestLongCatLiveSmoke [OK] Name() = "longcat" [OK] CheckConnection [OK] ListModels: 5 models -> [LongCat-Flash-Lite LongCat-Flash-Chat LongCat-Flash-Thinking-2601 LongCat-Flash-Omni-2603 LongCat-2.0-Preview] [OK] Chat (Flash-Chat) answer="Got it! Let me know if you" reason="" [OK] Chat (Flash-Thinking) answer len=443 head="To find 15 % of 80, follow these steps:\n\n1. **Convert the percentage to a frac..." ReasonContent len=557 head="The user asks: \"15% of 80?\" They want step by step reasoning and final answer in \\boxed{}. So we need to compute 15% of ..." [OK] Stream content: 78 chunks, 351 chars [OK] Stream reasoning: 107 chunks, 537 chars [OK] Balance returns longcat, no such method [OK] Embed returns longcat, no such method [OK] Rerank returns longcat, no such method LONGCAT LIVE SMOKE PASSED --- PASS: TestLongCatLiveSmoke (31.01s) ``` What the live run proves on the wire: - Auth header (`Bearer <key>`) is accepted by `api.longcat.chat`. - `/openai/v1/models` parser handles the real 5-model response (note: live API returns versioned aliases `LongCat-Flash-Thinking-2601`, `LongCat-Flash-Omni-2603`, `LongCat-2.0-Preview` plus the un-versioned `LongCat-Flash-Chat` and `LongCat-Flash-Lite`). - Non-stream chat against `LongCat-Flash-Chat`: visible answer parses correctly, `ReasonContent` correctly empty. - Non-stream chat against `LongCat-Flash-Thinking`: 443-char answer flows into `Answer`, 557-char chain-of-thought flows into `ReasonContent` via the new `message.reasoning_content` extraction. - Streaming chat against `LongCat-Flash-Thinking`: 107 reasoning chunks (537 chars) reach the sender's second arg via `delta.reasoning_content`; 78 content chunks (351 chars) reach the first arg. Before this code, the reasoning chunks would have been silently dropped. - All sentinel methods (Balance, Embed, Rerank, audio/OCR) return the documented `"no such method"` strings. ### Note on PR history This branch was previously named for LocalAI work which is now consolidated into PR #14813. The branch was reset to `upstream/main` and rebuilt for LongCat. The diff against `main` is a clean +969 lines across 4 files. ### Type of change - [x] New Feature (non-breaking change which adds functionality) Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com> |
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| d63d3bb7d2 |
Go: implement provider: Novita.ai (#14850)
### What problem does this PR solve? Add a Go driver for Novita.ai (https://novita.ai), one of the unchecked providers on the umbrella tracking issue #14736. Novita exposes an OpenAI-compatible REST API at `https://api.novita.ai/v3/openai` and proxies a large catalog of third-party models (DeepSeek, Llama, Qwen3, Kimi, Gemma, Mistral, MiniMax, GLM, etc.) behind a single OpenAI-shaped surface — 102 models live at the time of writing. Until this PR, a tenant who configured `novita` as a model provider in the Go layer fell through to the default branch of `internal/entity/models/factory.go` and got the dummy driver. ### What this PR includes - New `internal/entity/models/novita.go` with a `NovitaModel` implementing the `ModelDriver` interface (~520 lines). - New `conf/models/novita.json` with 7 representative chat models (DeepSeek-V4, Llama-3.3-70B, Qwen3-30B/235B reasoning, Kimi-K2, Gemma-3-27B, Mistral-Nemo). - `factory.go`: route `"novita"` to `NewNovitaModel`. - `internal/entity/models/novita_test.go`: 23 unit tests. ### Notable design point: `<think>...</think>` reasoning extraction Novita-routed reasoning models like `qwen3-*` and `deepseek-r1-*` embed their chain-of-thought **inline inside content as `<think>...</think>` tags**, rather than in a separate `reasoning_content` field. Verified live by probing `api.novita.ai`: ``` content head 200: <think> Okay, let's see. I need to find 15% of 80. Hmm, percentages can sometimes be tricky, but I think content tail 100: h, that works. Alternatively, 0.15 × 80. If I move the decimal two places to the left for </think> ``` Without handling, a tenant picking qwen3 via Novita would see raw `<think>` tags in their UI answer — different from every other reasoning provider in the Go layer. The driver detects those tags and routes the inner text to `ChatResponse.ReasonContent` (non-stream) or the sender's second arg (stream), keeping the visible answer clean of tag clutter: - **`splitNovitaThink`** — scans a complete content string. Used by the non-streaming path. Handles multiple `<think>` blocks, unclosed tags (the model got cut off mid-reasoning), pure-text content with no tags. - **`novitaThinkExtractor`** — stateful streaming version. Buffers trailing bytes that might be the start of a tag (e.g. `<thi` held back when the next chunk completes `nk>`), then emits segments in routing order so callers can pipe them to a UI. Tested with byte-level chunk boundaries and tag-spanning scenarios. ### Method coverage | Method | Behavior | |---|---| | `ChatWithMessages` | `POST /v3/openai/chat/completions`, `<think>` extraction on response | | `ChatStreamlyWithSender` | SSE stream, stateful `<think>` extraction across deltas | | `ListModels` / `CheckConnection` | `GET /v3/openai/models` (102 live) | | `Embed` / `Rerank` / `Balance` / `TranscribeAudio` / `AudioSpeech` / `OCRFile` | `"no such method"` — Novita's OpenAI-compatible surface does not expose any | No interface change. No new dependencies. ### How was this tested? **23 unit tests** in `internal/entity/models/novita_test.go` — all pass: ``` $ go test -vet=off -run "TestNovita|TestSplitNovita" -count=1 ./internal/entity/models/... ok ragflow/internal/entity/models 0.020s ``` Coverage: - `splitNovitaThink` (5 cases: pure text, single block, leading text, multiple blocks, unclosed tag) - `novitaThinkExtractor` (6 cases: single-chunk, opening tag span, closing tag span, byte-level chunking, no tags, lone `<` not as tag start) - `ChatWithMessages`: pure text, with `<think>` tags, missing API key, empty messages, HTTP error - `ChatStreamlyWithSender`: tag-stripping with spanning deltas, pure content, sender-required, stream-true-required - `ListModels` / `CheckConnection` (happy paths) - All sentinel methods `go build ./internal/entity/models/...` exits 0 on go 1.25. **Live integration test** against `api.novita.ai/v3/openai`: ``` === RUN TestNovitaLiveSmoke [OK] Name() = "novita" [OK] CheckConnection [OK] ListModels: 102 models (showing first 6) [deepseek/deepseek-v4-pro deepseek/deepseek-v4-flash deepseek/deepseek-v3.2 xiaomimimo/mimo-v2.5-pro moonshotai/kimi-k2.6 zai-org/glm-5.1] [OK] Chat (llama-3.3) answer="ok" reason="" [OK] Chat (qwen3) answer len=0 head="" ReasonContent len=1657 head="Okay, so I need to figure out what 15% of 80 is. Hmm, percentages can sometimes trip me up, but let ..." [OK] Stream content: 0 chunks, 0 chars; reasoning: 600 chunks, 1667 chars [OK] Embed/Rerank/Balance/TranscribeAudio/AudioSpeech/OCRFile all return "novita, no such method" NOVITA LIVE SMOKE PASSED --- PASS: TestNovitaLiveSmoke (26.18s) ``` What the live run proves on the wire: - Auth (`Bearer <key>`) accepted by `api.novita.ai`. - `/v3/openai/models` parser handles the real 102-model response. - Non-stream chat against `meta-llama/llama-3.3-70b-instruct`: clean string answer, empty ReasonContent (non-reasoning model, pure-text path). - Non-stream chat against `qwen/qwen3-30b-a3b-fp8`: 1657-char reasoning extracted from `<think>...</think>` and routed to `ChatResponse.ReasonContent`. Visible answer is 0 chars in this run because qwen3 spent its 600-token budget entirely on reasoning before reaching the answer phase — that's the model's behavior, not a driver bug. The important thing: **no `<think>` tags leaked into the visible Answer field**. - Streaming against qwen3: 600 reasoning chunks (1667 chars) emitted via the sender's 2nd arg across SSE deltas; **no `<think>` tag fragments leaked into the content channel** despite tag boundaries crossing chunk boundaries on the wire. - All 6 sentinel methods return the documented `"no such method"` strings. ### Type of change - [x] New Feature (non-breaking change which adds functionality) Tracking: #14736 |
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| 733d75d6a7 |
Fix(Go): make Baidu Encode fail loudly on malformed responses (#14721)
### What problem does this PR solve? The Baidu (Qianfan) `Encode` method silently swallowed malformed responses. If a `data[]` item from the API was missing a field (`index`, `embedding`, or unexpected shape), the loop did `continue` instead of returning an error, leaving `nil` entries in the result slice. Callers got back partial results with no indication anything went wrong, which then crashes downstream consumers when they try to use a `nil` vector. Concrete gaps fixed: - No count-mismatch check between `data` length and input texts (only checked for empty) - No duplicate-index detection (a duplicate would silently overwrite) - No missing-index final scan - No empty-embedding rejection - No per-call context timeout - `EmbeddingConfig.Dimension` (added in #14735) was not propagated This PR replaces `map[string]interface{}` parsing with a typed `baiduEmbeddingResponse` struct, applies the standard four-layer validation (count → out-of-range → duplicate → empty → final missing-index scan), adds `context.WithTimeout(nonStreamCallTimeout)`, and forwards `embeddingConfig.Dimension` as the `dimensions` parameter (Baidu Qianfan v2 uses an OpenAI-compatible API). ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) |
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| ad4717f40a |
Go: fix model type check when use the model (#14843)
### What problem does this PR solve? ``` RAGFlow(user)> chat with 'glm-ocr@test@zhipu-ai' message 'what is this' CLI error: expect model glm-ocr@zhipu-ai is a chat or multimodal model ``` ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) Signed-off-by: Jin Hai <haijin.chn@gmail.com> |
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| 45ee5ca9cd |
Go: implement provider: Jina (#14838)
### What problem does this PR solve? This PR completes the Jina provider **The following functionalities are now supported:** **Jina:** - [ ] Chat / Stream Chat (Not available for now: [(Jina chat API docs)](https://api.jina.ai/docs#/Search%20Foundation%20Models/chat_completions_v1_chat_completions_post)) - [x] Embedding - [x] Rerank - [x] Model listing - [x] Provider connection checking - [ ] ~~Balance~~ **Verified examples from the CLI:** ```plaintext RAGFlow(user)> embed text 'walkerwhat' 'jumperwho' with 'jina-embeddings-v2-base-en@test@jina' dimension 16 +-----------+-------+ | dimension | index | +-----------+-------+ | 768 | 0 | | 768 | 1 | +-----------+-------+ RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'jina-reranker-v2-base-multilingual@test@jina' top 3; +-------+-----------------+ | index | relevance_score | +-------+-----------------+ | 0 | 0.74316794 | | 2 | 0.18713269 | | 1 | 0.15817434 | +-------+-----------------+ RAGFlow(user)> list supported models from 'jina' 'test' +---------------------------------------------+ | model_name | +---------------------------------------------+ | Jina AI: Jina VLM | | Jina AI: Jina Reranker v3 | | Jina AI: Jina Code Embeddings 0.5b | | Jina AI: Jina Code Embeddings 1.5b | | Jina AI: Jina Embeddings v4 | | Jina AI: Jina Reranker M0 | | Jina AI: ReaderLM v2 | | Jina AI: Jina Clip v2 | | Jina AI: Jina Embeddings v3 | | Jina AI: Jina Colbert v2 | | Jina AI: Reader LM 0.5b | | Jina AI: Reader LM 1.5b | | Jina AI: Jina Reranker v2 Base Multilingual | | Jina AI: Jina Clip v1 | | Jina AI: Jina Reranker v1 Tiny EN | | Jina AI: Jina Reranker v1 Turbo EN | | Jina AI: Jina Reranker v1 Base EN | | Jina AI: Jina Colbert v1 EN | | Jina AI: Jina Embeddings v2 Base ES | | Jina AI: Jina Embeddings v2 Base Code | | Jina AI: Jina Embeddings v2 Base DE | | Jina AI: Jina Embeddings v2 Base ZH | | Jina AI: Jina Embeddings v2 Base EN | | Jina AI: Jina Embedding B EN v1 | | Jina AI: Jina Embeddings v5 Text Small | | Jina AI: Jina Embeddings v5 Omni Small | | Jina AI: Jina Embeddings v5 Omni Nano | | Jina AI: Jina Embeddings v5 Text Nano | +---------------------------------------------+ RAGFlow(user)> check instance 'test' from 'jina' SUCCESS ``` ### Type of change - [x] New Feature (non-breaking change which adds functionality) |
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| 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> |
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| d08bf02d9b |
Go: add ASR, TTS, OCR command (#14836)
### What problem does this PR solve? ``` RAGFlow(user)> asr with 'glm-asr-2512@test@zhipu-ai' audio './speech.wav'; CLI error: zhipu, no such method RAGFlow(user)> stream asr with 'glm-asr-2512@test@zhipu-ai' audio './speech.wav'; CLI error: zhipu, no such method RAGFlow(user)> tts with 'glm-tts@test@zhipu-ai' text 'how are you'; CLI error: zhipu, no such method RAGFlow(user)> stream tts with 'glm-tts@test@zhipu-ai' text 'how are you'; CLI error: zhipu, no such method RAGFlow(user)> ocr with 'glm-ocr@test@zhipu-ai' file './test.log'; CLI error: zhipu, no such method ``` ### Type of change - [x] New Feature (non-breaking change which adds functionality) Signed-off-by: Jin Hai <haijin.chn@gmail.com> |
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| eaa2e46b1e |
Go: implement Embed (embeddings) in Upstage driver (#14819)
### What problem does this PR solve? The Upstage Go driver landed in #14817 with chat, list models, and check connection. `Embed` was left as a stub that returns `"not implemented"`. This PR fills the gap. Upstage exposes an OpenAI-compatible embeddings endpoint at `https://api.upstage.ai/v1/solar/embeddings` via the `solar-embedding-1-large` family (`solar-embedding-1-large-query` for queries, `solar-embedding-1-large-passage` for passages), and the Python side has had `UpstageEmbed(OpenAIEmbed)` in `rag/llm/embedding_model.py` for a long time targeting this same path. The existing `conf/models/upstage.json` did not list any embedding model out of the box, so a tenant who wanted to use Upstage end to end could not run an embedding call. This PR fills the gap. ### What this PR includes - `conf/models/upstage.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, mistral, siliconflow, zhipu-ai), and add `solar-embedding-1-large-query` and `solar-embedding-1-large-passage` entries under `models`. - `internal/entity/models/upstage.go`: replace the `Embed` stub with a real implementation that POSTs to `/v1/solar/embeddings`. Adds local response types `upstageEmbeddingData` and `upstageEmbeddingResponse`. 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 Upstage 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 #14817 (the Upstage driver). Until #14817 merges, this PR's diff on GitHub will include both that PR's commits and this one. After #14817 lands on `main`, GitHub will auto-reduce this PR to only the `Embed` changes (one commit, ~119 line diff in `upstage.go` plus ~15 lines in `upstage.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 `UpstageModel` still matches the `ModelDriver` interface. - Pattern parity with the existing Mistral Embed (`internal/entity/models/mistral.go`) and OpenAI Embed (`internal/entity/models/openai.go`) implementations. Closes #14818 Depends on #14817 Tracking: #14736 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com> |
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| ebab3513c4 |
Go: implement provider: Baichuan (#14832)
### What problem does this PR solve? This PR completes the Baichuan provider **The following functionalities are now supported:** **Baichuan:** - [x] Chat / Stream Chat - [x] Embedding - [ ] ~~Rerank~~ - [ ] ~~Model listing~~ - [ ] ~~Provider connection checking~~ - [ ] ~~Balance~~ **Verified examples from the CLI:** ```plaintext # Baichuan RAGFlow(user)> embed text 'walkerwhat' 'jumperwho' with 'Baichuan-Text-Embedding@test@baichuan' dimension 16; +-----------+-------+ | dimension | index | +-----------+-------+ | 1024 | 0 | | 1024 | 1 | +-----------+-------+ AGFlow(user)> chat with 'Baichuan-M2@test@baichuan' message 'who r u' Answer: I'm BaiChuan, a helpful AI assistant created by Baichuan-AI. I'm designed to be a knowledgeable, friendly, and reliable assistant for various tasks like answering questions, explaining concepts, writing content, and more. Feel free to ask me anything! 😊 Time: 1.637975 RAGFlow(user)> stream chat with 'Baichuan-M2@test@baichuan' message 'who r u' Answer: I'm BaiChuan-m2, an AI assistant developed by Baichuan-AI. My purpose is to help you with a wide range of tasks by providing information, answering questions, solving problems, and assisting with creative projects. Think of me as a helpful digital companion! If you have any questions or need assistance, just let me know.😊 Time: 1.692321 ``` ### Type of change - [x] New Feature (non-breaking change which adds functionality) - [x] Refactoring |
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| 558ea51a0f |
Go: implement provider: StepFun (#14815)
### What problem does this PR solve? Add a Go driver for StepFun (阶跃星辰), one of the unchecked providers on the umbrella tracking issue #14736. Until this PR, a tenant who configured `stepfun` as a model provider in the Go layer fell through to the default branch of `internal/entity/models/factory.go` and got the dummy driver. Chat, list models, and check connection all returned `"not implemented"` instead of reaching the StepFun API. The Python side has had StepFun registered in `rag/llm/__init__.py` as a `SupportedLiteLLMProvider` with base URL `https://api.stepfun.com/v1`, plus `StepFunCV` for vision and `StepFunSeq2txt` for ASR, but no Go path. StepFun's chat API is OpenAI-compatible, so the implementation pattern is the same as the merged Moonshot driver (#14433) and OpenAI driver (#14605). ### What this PR includes - New file `internal/entity/models/stepfun.go` with a `StepFunModel` that implements the `ModelDriver` interface. - `factory.go`: route the `"stepfun"` provider name to `NewStepFunModel`. - New `conf/models/stepfun.json` with the public StepFun chat models (step-2-16k, step-1 family in 8k/32k/128k/256k context lengths, step-1-flash, and the step-1v / step-1o vision models) and `url_suffix` entries for `chat` and `models`. ### How the driver works - StepFun exposes the OpenAI-compatible API at `https://api.stepfun.com/v1`. - `ChatWithMessages` and `ChatStreamlyWithSender` post to `/chat/completions` in the same shape as the merged moonshot, openrouter, and openai drivers. - `ListModels` and `CheckConnection` call `/models` to list available ids and confirm the API key works. - `Embed` is left as `"not implemented"`. StepFun has not advertised a public embeddings endpoint in the API reference linked from the umbrella issue (`https://platform.stepfun.com/docs/en/api-reference/chat/chat-completion-create` is the chat endpoint), so any real implementation belongs in a separate follow-up only after the endpoint is verified. - `Rerank` and `Balance` return `"no such method"` because StepFun does not expose either. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - `go build ./internal/entity/models/...` returns exit 0 with no errors on go 1.25 (the `go.mod` minimum). - Method set of `StepFunModel` matches the `ModelDriver` interface: `NewInstance`, `Name`, `ChatWithMessages`, `ChatStreamlyWithSender`, `Embed`, `Rerank`, `ListModels`, `Balance`, `CheckConnection`. - Pattern parity with the merged moonshot (#14433), openai (#14605), openrouter (#14652), and xai (#14550) drivers. Closes #14814 Tracking: #14736 |
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| 128a64eae5 |
Refactor(Go): remove hardcode in huggingface provider (#14822)
### What problem does this PR solve? remove hardcode in `huggingface` provider ### Type of change - [x] Refactoring |
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| 2f2d1569e6 |
Go: fix retrieval test error (#14794)
### What problem does this PR solve? 1. Add region check in zhipu-ai embed method 2. Fix retrieval test ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) Signed-off-by: Jin Hai <haijin.chn@gmail.com> |
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| 3e90d303e0 |
Go: implement provider: CoHere and FishAudio (#14790)
### What problem does this PR solve?
This PR completes the Cohere provider integration (upgrading to the new
Cohere V2 API) and enhances the Fish Audio provider in RAGFlow.
**The following functionalities are now supported:**
**Cohere:**
- [x] Chat / Think Chat / Stream Chat / Stream Think Chat
- [x] Embedding
- [x] Rerank
- [x] Model listing
- [x] Provider connection checking
- [ ] Balance
**Fish Audio:**
- [x] Model listing (`ListModels`)
- [x] Balance (`Balance`)
-----
**Verified examples from the CLI:**
```plaintext
# Cohere
RAGFlow(user)> think chat with 'command-a-reasoning-08-2025@test3@cohere' message 'jumperwho'
Thinking: Okay, the user wrote "jumperwho". Let me try to figure out what they might be asking. First, I'll check if it's a misspelling. "Jumper" ...... Hmm. Since the query is unclear, the best approach is to ask the user to provide more context or correct any possible typos.
Answer: It seems there might be a typo or missing context in your query "jumperwho." Could you clarify what you're referring to? For example:
- Are you asking about a **jumper** (a type of sweater, a person who jumps, or a component in electronics)?
- Is this related to a specific context, like a movie (e.g., the 2008 film *Jumper*) or a game?
- Did you mean to ask about a person ("who") associated with jumping (e.g., a parachutist)?
Let me know so I can provide a helpful response! 😊
Time: 6.710331
RAGFlow(user)> stream think chat with 'command-a-reasoning-08-2025@test3@cohere' message 'jumperwho'
Thinking: , the user mentioned "jumperwho". Let me try to figure out what they're referring to. First, I'll check if it's a misspelling. "Jumper" could be a typo for "jumper" or maybe a username. Alternatively, it might be a combination of words like "jumper who",....... the best approach is to inform the user that I don't recognize the term and ask if they can provide more context or clarify what they mean by "jumperwho". That way, I can assist them better once I have more information.
Answer: seems "jumperwho" isn't a widely recognized term, proper noun, or acronym in common usage. Could you provide more context or clarify what you mean by "jumperwho"? This will help me understand your question or request better!
Time: 4.513596
RAGFlow(user)> embed text 'walkerwhat' 'jumperwho' with 'embed-v4.0@test3@cohere' dimension 16;
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
| embedding | index |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
| [-0.016643638 -0.001957038 0.0055713872 0.009027058 0.05275187 -0.024542313 -0.044006906 0.024119169 0.0014192933 0.006558722 0.0019129605 -0.021016119 -0.026516981 -0.017489925 0.021298215 0.017772019 0.04569948 0.008886009 0.012059584 -0.0014721862 0.... | 0 |
| [0.018778935 -0.0063459855 -0.0006839742 0.0046623563 0.0067668925 -0.018001877 -0.03963003 0.035744734 -0.014246088 -0.0020721585 -0.006313608 0.025124922 -0.010749322 0.01217393 -0.010231283 -0.025254432 0.021498645 -0.028880708 0.019167464 -0.0058279... | 1 |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'rerank-v4.0-pro@test@cohere' top 3;
+-------+-----------------+
| index | relevance_score |
+-------+-----------------+
| 0 | 0.91744334 |
| 1 | 0.7458429 |
| 2 | 0.68729424 |
+-------+-----------------+
RAGFlow(user)> list supported models from 'cohere' 'test'
+-------------------------------------+
| model_name |
+-------------------------------------+
| c4ai-aya-expanse-32b |
| c4ai-aya-vision-32b |
| cohere-transcribe-03-2026 |
| command-a-03-2025 |
| command-a-reasoning-08-2025 |
| command-a-translate-08-2025 |
| command-a-vision-07-2025 |
| command-r-08-2024 |
| command-r-plus-08-2024 |
| command-r7b-12-2024 |
| command-r7b-arabic-02-2025 |
| embed-english-light-v3.0 |
| embed-english-light-v3.0-image |
| embed-english-v3.0 |
| embed-english-v3.0-image |
| embed-multilingual-light-v3.0 |
| embed-multilingual-light-v3.0-image |
| embed-multilingual-v3.0 |
| embed-multilingual-v3.0-image |
| embed-v4.0 |
+-------------------------------------+
RAGFlow(user)> check instance 'test' from 'cohere'
SUCCESS
# FishAudio
RAGFlow(user)> list supported models from 'fishaudio' 'test'
+----------------------------------------+
| model_name |
+----------------------------------------+
| Valentino Narración Biblica Fer |
| Super Smash Bros. 4/Ultimate Announcer |
| Farid Dieck |
| عصام الشوالي |
| ALEX_CHIKNA |
| Energetic Male |
| voz de locutor k |
| يي |
| ELITE |
| Mortal Kombat |
+----------------------------------------+
RAGFlow(user)> show balance from 'fishaudio' 'test'
+----------------------------------+-----------------------------+--------+-----------------+------------------+-----------------------------+----------------------------------+
| _id | created_at | credit | has_free_credit | has_phone_sha256 | updated_at | user_id |
+----------------------------------+-----------------------------+--------+-----------------+------------------+-----------------------------+----------------------------------+
| 82ffec12cf984d88a30ec504d7909812 | 2026-05-09T07:52:16.119000Z | 0 | | false | 2026-05-09T07:52:16.119000Z | 2578ab1126804d6eaa630552400d7ff3 |
+----------------------------------+-----------------------------+--------+-----------------+------------------+-----------------------------+----------------------------------+
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
|
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| 39ee2fb120 |
Go: implement Rerank in NVIDIA driver (#14778)
## Summary - Replaces the `"no such method"` stub on `NvidiaModel.Rerank` (`internal/entity/models/nvidia.go`) with a real implementation against NVIDIA NIM's `/ranking` endpoint. - Mirrors the existing Python `NvidiaRerank` class at `rag/llm/rerank_model.py:149-190` for behavior parity: same `passages`/`query.text`/`logit` payload shape; `top_n` set to `len(documents)` so every input gets a score returned in original order (the issue body's spec omitted `top_n`, which would cause silent data loss). - Adds the `"rerank": "ranking"` URL suffix and two NIM rerank model entries (`nvidia/nv-rerankqa-mistral-4b-v3`, `nvidia/llama-3.2-nv-rerankqa-1b-v2`) to `conf/models/nvidia.json` so the picker exposes them. - Follows the same shape as the recently merged Aliyun (#14676), Gitee (#14656), and ZhipuAI (#14608) Rerank implementations: lowercase per-driver request/response types, conversion to the project-wide `RerankResponse{Data: []RerankResult}`, per-call `context.WithTimeout` of 30s. Closes #14720 ## Test plan - [x] `gofmt -l internal/entity/models/nvidia.go` — clean - [x] `go vet ./internal/entity/models/...` — no new errors introduced (the two pre-existing vet errors in `baidu.go:642` and `openrouter.go:566` are unrelated to this PR) - [x] `go build ./internal/entity/models/...` — succeeds - [x] `python3 -c "import json; json.load(open('conf/models/nvidia.json'))"` — JSON valid - [ ] Live smoke test against NVIDIA NIM with a real API key (requires reviewer with NIM credentials) ## Notes for reviewers - The issue body suggested omitting `top_n`. The Python reference includes it (`top_n: len(texts)`), and without it NVIDIA returns only the default top-K rankings rather than scores for every input. This PR follows the Python. - The URL host is `integrate.api.nvidia.com` (kept consistent with the existing chat/embeddings BaseURL in `nvidia.go`), not the legacy `ai.api.nvidia.com` host the Python uses. NIM's unified endpoint accepts the model names as-is, so no per-model URL transform is needed. |
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| c55e23e7e2 |
Go: refactor embedding interface (#14757)
### What problem does this PR solve? Provide embedding index according to the input text ### Type of change - [x] Refactoring --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com> |
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| 13e6554901 |
Fix(Go): make OpenRouter Encode fail loudly on malformed responses (#14717)
### What problem does this PR solve?
The OpenRouter `Encode` method silently swallowed malformed responses.
If a `data[]` item from the API was missing a field (`index`,
`embedding`, or unexpected shape), the loop did `continue` instead of
returning an error — leaving `nil` entries in the result slice. Callers
got back partial results with no indication anything went wrong, which
then crashes downstream consumers when they try to use a `nil` vector.
There were three concrete gaps:
- No count-mismatch check between `data` length and input texts (only
checked for empty)
- No duplicate-index detection (a duplicate would silently overwrite)
- Parse failures on individual items returned partial slices instead of
erroring
This PR replaces `map[string]interface{}` parsing with a typed
`openrouterEmbeddingResponse` struct and applies the same 3-layer
validation used in the other drivers (count mismatch → out-of-range
index → duplicate index), so any malformed response produces a clear
error instead of corrupted data.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
|
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| 530edbac99 |
Go: implement Encode (embeddings) in LM Studio driver (#14694)
### What problem does this PR solve? The LM Studio Go driver shipped with a stub \`Encode\` method that returned \`no such method\`, even though LM Studio is one of the most common local LLM runners on macOS and Windows and exposes an OpenAI-compatible embeddings endpoint at \`/v1/embeddings\`. LM Studio users routinely load local embedding models such as \`nomic-ai/nomic-embed-text-v1.5\`, \`mixedbread-ai/mxbai-embed-large-v1\`, or \`BAAI/bge-m3\`. They run on the same \`/v1\` namespace as chat. The existing \`ListModels\` already discovers them, but because \`Encode\` was a stub, a tenant who picked one of these models in the Go layer could not actually run an embedding call. This finishes the local-LLM trio: Ollama Encode (#14664) and vLLM Encode (#14688) are already in flight, both using the same OpenAI-compatible \`/embeddings\` shape. ### What this PR includes - \`conf/models/lmstudio.json\`: add \`\"embedding\": \"embeddings\"\` under \`url_suffix\` so the driver can build the URL from config. - \`internal/entity/models/lmstudio.go\`: replace the \`Encode\` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape. No factory change. No interface change. ### How the driver works - Validate the model name. The API key is optional for local LM Studio, so the Authorization header is only set when both \`apiConfig\` and \`ApiKey\` are non-nil and non-empty, the same pattern the recently merged CheckConnection PR (#14614) uses. - Resolve the region with a default fallback. Return a clear "missing base URL" error when the user has not configured the local access address yet. - Use a per-call \`context.WithTimeout(30s)\` and \`http.NewRequestWithContext\`, the same pattern the merged Aliyun Encode (#14647) and the in-flight Ollama Encode (#14664) and vLLM Encode (#14688) use. - Send \`{model, input: [texts]}\` in one request. - Parse \`data[*].embedding\` and copy each slice into a \`[][]float64\` indexed by \`data[*].index\`, so the output order matches the input order. - Handle both \`float64\` and \`float32\` element types. - Empty input returns \`[][]float64{}\` with no HTTP call. - Length mismatch between input and result, out-of-range index, and any missing slot all return clear errors instead of silent zero vectors. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - \`go build ./internal/entity/models/...\` in a clean go 1.25 image returns exit 0. - The full method set on \`LmStudioModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the merged Aliyun Encode (#14647), the in-flight Ollama Encode (#14664) and vLLM Encode (#14688), and the existing SiliconFlow Encode. Closes #14693 |
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| 0580c137fa |
Perf(Go): batch SiliconFlow Encode requests with 32-item chunking (#14719)
### What problem does this PR solve? The SiliconFlow `Encode` method sent one HTTP request per text, which is wasteful and slow when indexing many documents (e.g., 100 docs = 100 round-trips). SiliconFlow's `/v1/embeddings` is OpenAI-compatible and accepts an array of strings in `input` (officially documented at https://docs.siliconflow.cn/en/api-reference/embeddings/create-embeddings, with a documented max array size of 32). This PR batches the requests up to that limit, reducing 100 docs to ~4 round-trips, and replaces `map[string]interface{}` parsing with a typed struct using the same 3-layer validation (count mismatch, out-of-range index, duplicate index) used in the other drivers. ### Type of change - [x] Performance Improvement |
|||
| 4b96362092 |
Go: implement Encode (embeddings) in NVIDIA driver (#14700)
### What problem does this PR solve? The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with a stub `Encode` method that returned `no such method`. `conf/models/nvidia.json` already lists `nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model, but the conf had no `embedding` URL suffix, so the picker had nothing wired even if `Encode` worked. A tenant who wanted to use NVIDIA NIM for chat (already working) and embeddings from a single provider could not, even though the upstream endpoint is public at `https://integrate.api.nvidia.com/v1/embeddings` and uses an OpenAI-compatible request body extended with the NVIDIA-specific `input_type` and `truncate` fields. Several other Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun), so the interface and the pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/nvidia.json`: declare the `embedding` URL suffix alongside the existing `chat` and `models` entries. The embedding model entry was already present, so no model addition is needed. * `internal/entity/models/nvidia.go`: replace the `Encode` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape NVIDIA NIM returns. No factory change. No interface change. ### How the driver works * Validates `apiConfig` and the API key, validates the model name, resolves the region with a default fallback (matching the pattern the merged `ListModels` and `CheckConnection` paths in this driver already use), and builds the URL from `BaseURL[region] + URLSuffix.Embedding`. * Sends all input texts in one request as the `input` array, with the NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and `truncate: "END"` fields, mirroring the Python `NvidiaEmbed` reference. * Parses `data[*].embedding` and copies each slice into `[][]float64` indexed by `data[*].index` so the output order matches the input order even if the API returns items in a different order. * Handles both `float64` and `float32` element types. * Empty input returns `[][]float64{}` with no HTTP call. * Non-200 responses propagate the upstream status line and body. * A final pass checks every input slot got a vector and returns a clear error if any slot is still nil. * Per-call 30s context deadline so a slow call cannot block forever. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/nvidia.go` is clean. * The full method set on `NvidiaModel` still matches the `ModelDriver` interface. * Pattern parity with the just-merged Aliyun `Encode` (#14647). Closes #14699 |
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| 8ff623fbc4 |
Go: implement Encode (embeddings) in Ollama driver (#14664)
### What problem does this PR solve? The Ollama Go driver shipped with a stub \`Encode\` method that returned \`no such method\`, even though Ollama is one of the most common local LLM runners and exposes an OpenAI-compatible embeddings endpoint at \`/v1/embeddings\`. Ollama users routinely run local embedding models such as \`nomic-embed-text\`, \`mxbai-embed-large\`, or \`bge-m3\`. Pulled with \`ollama pull <model>\` and served on the same \`/v1\` namespace as chat. The existing \`ListModels\` already discovers them, but because \`Encode\` was a stub, a tenant who picked one of these models in the Go layer could not actually run an embedding call. ### What this PR includes - \`conf/models/ollama.json\`: add \`\"embedding\": \"embeddings\"\` under \`url_suffix\` so the driver can build the URL from config. - \`internal/entity/models/ollama.go\`: replace the \`Encode\` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape. No factory change. No interface change. ### How the driver works - Validate the model name. The API key is optional for local Ollama, so the Authorization header is only set when both \`apiConfig\` and \`ApiKey\` are non-nil and non-empty, the same pattern the recently merged CheckConnection PR (#14614) uses. - Resolve the region with a default fallback. Return a clear "missing base URL" error when the user has not configured the local access address yet. - Use a per-call \`context.WithTimeout(30s)\` and \`http.NewRequestWithContext\`, the same pattern the merged Aliyun Encode (#14647) uses. - Send \`{model, input: [texts]}\` in one request. - Parse \`data[*].embedding\` and copy each slice into a \`[][]float64\` indexed by \`data[*].index\`, so the output order matches the input order. - Handle both \`float64\` and \`float32\` element types. - Empty input returns \`[][]float64{}\` with no HTTP call. - Length mismatch between input and result, out-of-range index, and any missing slot all return clear errors instead of silent zero vectors. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - \`go build ./internal/entity/models/...\` in a clean go 1.25 image returns exit 0. - The full method set on \`OllamaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the merged Aliyun Encode (#14647) and the existing SiliconFlow Encode. Closes #14662 |
|||
| fa53b93dd5 |
Go: implement Encode (embeddings) in vLLM driver (#14688)
### What problem does this PR solve? The vLLM Go driver shipped with a stub \`Encode\` method that returned \`not implemented\`, even though vLLM is one of the most common production-grade self-hosted inference servers and exposes an OpenAI-compatible embeddings endpoint at \`/v1/embeddings\`. Users who self-host \`BAAI/bge-m3\`, \`Qwen3-Embedding-*\`, \`NV-Embed-v2\`, or similar models on vLLM could not run an embedding call through the Go layer. The existing \`ListModels\` already discovers the loaded models, but the embedding path failed because \`Encode\` was a stub. ### What this PR includes - \`conf/models/vllm.json\`: add \`\"embedding\": \"embeddings\"\` under \`url_suffix\` so the driver can build the URL from config. - \`internal/entity/models/vllm.go\`: replace the \`Encode\` stub with a real implementation. Adds a small local response type that matches the OpenAI-compatible shape. No factory change. No interface change. ### How the driver works - Validate the model name. The API key is optional for self-hosted vLLM, so the Authorization header is only set when both \`apiConfig\` and \`ApiKey\` are non-nil and non-empty, the same pattern the recently merged CheckConnection PR (#14614) uses. - Resolve the region with a default fallback. Return a clear "missing base URL" error when the user has not configured the local access address yet. - Use a per-call \`context.WithTimeout(30s)\` and \`http.NewRequestWithContext\`, the same pattern the merged Aliyun Encode (#14647) and in-flight Ollama Encode (#14664) use. - Send \`{model, input: [texts]}\` in one request. - Parse \`data[*].embedding\` and copy each slice into a \`[][]float64\` indexed by \`data[*].index\`, so the output order matches the input order. - Handle both \`float64\` and \`float32\` element types. - Empty input returns \`[][]float64{}\` with no HTTP call. - Length mismatch between input and result, out-of-range index, and any missing slot all return clear errors instead of silent zero vectors. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - \`go build ./internal/entity/models/...\` in a clean go 1.25 image returns exit 0. - The full method set on \`VllmModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the merged Aliyun Encode (#14647), the in-flight Ollama Encode (#14664), and the existing SiliconFlow Encode. Closes #14687 |
|||
| bfb4a0eea2 |
Go: implement Encode (embeddings) in Gitee AI driver (#14698)
### What problem does this PR solve? The Gitee AI Go driver in `internal/entity/models/gitee.go` shipped with a stub `Encode` method that returned `gitee, no such method`, even though `conf/models/gitee.json` already wires the `embedding` URL suffix. The conf also listed no embedding models, so the picker had nothing to select. This blocked any tenant who wanted to use Gitee AI for chat, rerank (already working, see #14656), and embeddings from a single provider. This PR fills the gap, mirroring the just-merged Aliyun `Encode` (#14647): - `internal/entity/models/gitee.go`: replace the `Encode` stub with a real implementation. Validates inputs, resolves the region with a default fallback, POSTs the standard OpenAI-compatible `{"model", "input": [...]}` body to `BaseURL[region] + URLSuffix.Embedding`, parses `data[*].embedding` indexed by `data[*].index` so output order matches input order, handles both `float64` and `float32` element types, and uses a 30s per-call context deadline matching the merged `Rerank`. - `conf/models/gitee.json`: add `BAAI/bge-m3` so the embedding picker has something to select. No factory change. No interface change. No URL suffix change. Verified with `go build`, `go vet`, and `gofmt -l` : all clean. Closes #14697 ### Type of change - [x] New Feature (non-breaking change which adds functionality) |
|||
| 827cceccba |
Fix(Go): correct Name() and region URL fallback in Aliyun driver (#14673)
### What problem does this PR solve? Two bugs in the Aliyun Go driver: 1. **`Name()` returns `"siliconflow"`** — a copy-paste bug from when the driver was created. `Name()` is used in error messages and log output, so every Aliyun error incorrectly attributed itself to SiliconFlow. 2. **Silent empty URL for unknown regions in `ChatWithMessages`, `ChatStreamlyWithSender`, and `ListModels`** — all three methods construct the request URL as `z.BaseURL[region]` without checking whether the key exists. For an unrecognised region this returns `""`, producing a malformed URL like `"/chat/completions"` that the HTTP transport rejects with a confusing error. `Encode` and `Rerank` (already merged) correctly fall back to `"default"` and return a clear error. This PR applies the same pattern to the remaining three methods. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) |
|||
| f852a7524e |
fix(go): wire Google CheckConnection to ListModels (#14660)
### What problem does this PR solve? Closes #14703 `GoogleModel.CheckConnection` currently returns a hardcoded `no such method` error even though the Google Go driver already supports `ListModels`. This makes provider connection checks fail regardless of whether the configured API key can list Google models. This PR makes `CheckConnection` call `ListModels`, adds a small API-key guard for nil, empty, and whitespace-only keys, and keeps `ListModels` useful by following paginated Google model responses. ### What stays unchanged * Google model listing still uses the Google GenAI SDK with `genai.BackendGeminiAPI`. * Model names still come from `models.Items[*].Name`. * `Balance`, `Encode`, chat, streaming, provider config, and factory wiring are unchanged. ### Tests and validation Added focused unit coverage for: * `CheckConnection` delegating to `ListModels` and returning its error * nil, missing, empty, and whitespace-only API key validation * model-name passthrough from the list-models adapter * paginated model listing, empty-result preservation, and next-page error propagation Validated current PR head `17ceef43515ba8c46c254dd349b9085bf26dcbea` locally with Go 1.25.0: * `go test ./internal/entity/models -run 'TestGoogleModel|TestCollectGoogleModelNames' -count=1 -v` - PASS * `go test ./internal/entity/models -count=1` - PASS * `go test -race ./internal/entity/models -count=1` - PASS * `gofmt -w internal/entity/models/google.go internal/entity/models/google_test.go` - PASS, no diff * `git diff --check` - PASS ### Type of change * [x] Bug Fix (non-breaking change which fixes an issue) Co-authored-by: Jin Hai <haijin.chn@gmail.com> |
|||
| f4f8bed9f7 |
Go: implement Encode (embeddings) in Google Gemini driver (#14682)
### What problem does this PR solve? - Implements the `Encode` method in the Google Gemini driver, which was previously a stub returning `not implemented` - Uses the `google.golang.org/genai` SDK's `EmbedContent` API, which routes to the `batchEmbedContents` endpoint internally — all texts are sent in a single request - Adds `text-embedding-004` (max 2048 tokens) to `conf/models/google.json` - Response values are `[]float32` from the SDK and are cast to `[]float64` to satisfy the `ModelDriver` interface ## Files changed - `internal/entity/models/google.go` — full `Encode` implementation - `conf/models/google.json` — adds `text-embedding-004` embedding model ### Type of change - [x] New Feature (non-breaking change which adds functionality) |
|||
| 39a1773f7f |
Go: implement ListModels in Volcengine driver (#14702)
### What problem does this PR solve? The VolcEngine Go driver in `internal/entity/models/volcengine.go` shipped with a `ListModels` stub that returned `volcengine, no such method`. `conf/models/volcengine.json` also did not declare a `models` URL suffix, so the model picker had nothing to call even if the method body were filled in. A tenant who configured Volcengine (Doubao / Ark) as a provider could not see the list of available endpoints from the RAGFlow UI. Several other Go drivers already implement `ListModels` against the OpenAI-compatible `/models` endpoint (deepseek, gitee, nvidia, openai, siliconflow), so the interface and pattern are well-established. This PR fills the gap. ### What this PR includes * `conf/models/volcengine.json`: declare the `models` URL suffix alongside the existing `chat`, `files`, and `embedding` entries. The Ark v3 API exposes `https://ark.cn-beijing.volces.com/api/v3/models`, so the suffix is just `models`. * `internal/entity/models/volcengine.go`: replace the `ListModels` stub with a real implementation. Reuses the package-level `DSModelList` / `DSModel` types that DeepSeek, Gitee, and SiliconFlow already use to parse the OpenAI-compatible models response shape. No factory change. No interface change. ### How the driver works * Resolves the region with a default fallback, the same way the other VolcEngine methods in this driver already do. * Builds the URL from `BaseURL[region] + URLSuffix.Models`, with `strings.TrimSuffix` on the base to keep the join robust. * Issues a `GET` with optional `Authorization: Bearer <api_key>` (the header is omitted when no key is configured, mirroring the existing NVIDIA `ListModels`). * Reads the response body once, surfaces a non-200 with the upstream status line plus body, and parses the JSON via the shared `DSModelList` type. * Returns the model id list in input order. When the response includes an `owned_by` field, the entry is rendered as `id@owned_by`, matching the convention used by the other Go drivers. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? * `go build ./internal/entity/models/...` returns exit 0. * `go vet ./internal/entity/models/...` is clean. * `gofmt -l internal/entity/models/volcengine.go` is clean. * The full method set on `VolcEngine` still matches the `ModelDriver` interface. * Endpoint reachability check: `GET https://ark.cn-beijing.volces.com/api/v3/models` returns `401 Unauthorized` without an API key, confirming the path exists and accepts Bearer authentication. * Pattern parity with DeepSeek, Gitee, NVIDIA, and SiliconFlow `ListModels`. Fixes #14701 Co-authored-by: Jin Hai <haijin.chn@gmail.com> |
|||
| 6bfe0f9a10 |
Go: implement Encode (embeddings) in OpenAI driver (#14630)
### What problem does this PR solve? The OpenAI Go driver landed in #14605 with chat, list models, and check connection. Encode was left as a stub that returns \`not implemented\`. \`conf/models/openai.json\` already lists three embedding models out of the box: - text-embedding-ada-002 - text-embedding-3-small - text-embedding-3-large So a tenant who picked one of these in the Go layer could not actually run an embedding call. This PR fills the gap. ### What this PR includes - \`conf/models/openai.json\`: add \`\"embedding\": \"embeddings\"\` under \`url_suffix\` so the driver can build the URL from config. This matches the \`URLSuffix.Embedding\` field used by other drivers (siliconflow, zhipu-ai). - \`internal/entity/models/openai.go\`: replace the Encode stub with a real implementation that POSTs to \`/v1/embeddings\`. Adds a small local response type \`openaiEmbeddingResponse\`. No factory change. No interface change. ### How the implementation works - Validate \`apiConfig\` and the API key, validate 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 OpenAI API accepts the \`input\` field as an array. - Parse \`data[*].embedding\` and copy each slice into a \`[][]float64\` indexed by \`data[*].index\` so the output order matches the input order even if the API returns items in a different order. - Handle both \`float64\` and \`float32\` element types, the way the SiliconFlow driver does. - An empty input slice returns \`[][]float64{}\` 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 nil, return a clear error so the caller does not silently use a zero vector. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - \`go build ./internal/entity/models/...\` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on \`OpenAIModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing SiliconFlow Encode implementation (\`internal/entity/models/siliconflow.go\`). Closes #14629 --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com> |
|||
| 048ec2fc5c |
Go: fix siliconflow rerank issue (#14743)
### What problem does this PR solve? As title. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) Signed-off-by: Jin Hai <haijin.chn@gmail.com> |
|||
| 779cd83862 |
Go: fix Baidu rerank issue (#14742)
### What problem does this PR solve? top_n is missing ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) Signed-off-by: Jin Hai <haijin.chn@gmail.com> |
|||
| 7931b693dc |
Go: implement provider: Baidu (#14741)
### What problem does this PR solve?
This PR completes the Baidu Qianfan provider integration in RAGFlow.
**The following functionalities are now supported:**
- [x] Chat / Think Chat / Stream Chat / Stream Think Chat
- [x] Embedding
- [x] Rerank
- [x] Model listing
- [x] Provider connection checking
- [ ] Balance
-----
**Verified examples from the CLI:**
```plaintext
RAGFlow(user)> embed text 'what is rag' 'who are you' with 'embedding-3@test@zhipu-ai' dimension 16;
+-----------+-------+
| dimension | index |
+-----------+-------+
| 16 | 0 |
| 16 | 1 |
+-----------+-------+
RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'qwen3-reranker-4b@test@baidu' top 2;
+-------+---------------------+
| index | relevance_score |
+-------+---------------------+
| 0 | 0.974821150302887 |
| 1 | 0.14223189651966095 |
| 2 | 0.08632347732782364 |
+-------+---------------------+
RAGFlow(user)> think chat with 'deepseek-v3.2@test@baidu' message 'who r u'
Thinking: Hmm, the user is asking for a simple introduction. This is straightforward – no need for overcomplication.
I should give a clear, friendly response that covers my basic identity as an AI assistant, my purpose, and my capabilities. Keeping it concise but informative is key here.
Mentioning my creator Anthropic adds credibility, and ending with an offer to help invites further interaction. No need for technical details unless the user asks later.
Answer: Hello! I'm an AI assistant created by Anthropic, designed to help with a wide variety of tasks. You can think of me as a helpful digital companion—I can answer questions, assist with writing, help solve problems, provide explanations, and engage in conversation on many topics. I'm here to help with whatever you need! How can I assist you today?
Time: 8.103902
RAGFlow(user)> stream think chat with 'deepseek-v3.2@test@baidu' message 'who r u'
Thinking: mm, the user is asking "who r u" with casual spelling. This is a straightforward identity question. should give a clear, friendly introduction without overcomplicating it. Can start with my core function as an AI assistant, mention my creator, and briefly state my key capabilities. response should be welcoming and invite further interaction since this seems like an introductory question. Keeping it concise but covering the essentials: who I am, what I do, and how I can help.
Answer: ! I am DeepSeek, an AI assistant created by DeepSeek Company. I'm designed to help answer questions, provide information, assist with various tasks, and engage in conversations on a wide range of topics. I'm here to assist you with whatever you need - whether it's answering questions, helping with analysis, writing, coding, or just having a friendly chat!Is there anything specific I can help you with today? 😊
Time: 7.219703
RAGFlow(user)> list supported models from 'baidu' 'test'
+--------------------------------------+
| model_name |
+--------------------------------------+
| ernie-3.5-8k-preview |
| ernie-4.0-8k |
| ernie-4.0-turbo-8k-latest |
| ernie-4.0-turbo-8k-preview |
| ernie-4.0-8k-preview |
| ernie-speed-pro-128k |
| ernie-char-fiction-8k |
| ernie-3.5-8k |
| ernie-3.5-128k |
| ernie-lite-pro-128k |
| ernie-novel-8k |
| ernie-4.0-turbo-8k |
| ernie-4.0-turbo-128k |
| ernie-4.0-8k-latest |
| irag-1.0 |
| ........... |
| glm-5.1 |
| ernie-image-turbo |
| deepseek-v4-pro |
| deepseek-v4-flash |
| ernie-5.1 |
+--------------------------------------+
RAGFlow(user)> check instance 'test' from 'baidu'
SUCCESS
```
Additionally, this PR fixes an incorrect error message typo:
Before:
```go
fmt.Errorf("API requestssss failed with status %d: %s : %s", ...)
```
After:
```go
fmt.Errorf("API request failed with status %d: %s", ...)
```
This PR mainly improves provider compatibility, API completeness, and
runtime stability.
### Type of change
* [x] Bug Fix (non-breaking change which fixes an issue)
* [x] New Feature (non-breaking change which adds functionality)
* [x] Refactoring
|
|||
| 17d71e5d79 |
Go CLI: embed and rerank (#14735)
### What problem does this PR solve? ``` RAGFlow(user)> embed text 'what is rag' 'who are you' with 'embedding-3@test@zhipu-ai' dimension 16; +-----------+-------+ | dimension | index | +-----------+-------+ | 16 | 0 | | 16 | 1 | +-----------+-------+ RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'rerank@test@zhipu-ai' top 2; +-------+-----------------+ | index | relevance_score | +-------+-----------------+ | 0 | 1 | | 2 | 0.99999976 | +-------+-----------------+ ``` ### Type of change - [x] New Feature (non-breaking change which adds functionality) Signed-off-by: Jin Hai <haijin.chn@gmail.com> |
|||
| ee0de58204 |
Go: implement provider: HuggingFace (#14722)
### What problem does this PR solve? Implement `HuggingFace` provider ### Type of change - [x] New Feature (non-breaking change which adds functionality) |
|||
| 2ad854c586 |
Go: implement Rerank in Aliyun driver (#14676)
### What problem does this PR solve? The Aliyun Go driver has a stub `Rerank` method that always returns `"Aliyun, Rerank not implemented"`. DashScope exposes an OpenAI-compatible rerank endpoint (`compatible-mode/v1/rerank`) and hosts dedicated bilingual rerankers (`gte-rerank-v2`, `gte-rerank`) that are a natural pairing with the embedding models already in `aliyun.json`. Without this, Aliyun users cannot use reranking within RAGFlow. Closes #14675 ### Type of change - [x] New Feature (non-breaking change which adds functionality) |
|||
| 94f82acd03 |
Fix(Go): prevent global state pollution in local model connection check (#14669)
### What problem does this PR solve? 1. **Fix Global State Pollution in Local Providers (Critical Bug):** - Resolved a severe concurrency and architecture issue in `model_service.go`. Previously, `ListSupportedModels` would permanently overwrite the global provider singleton with a localized URL instance (`driver.NewInstance`). This caused cross-request contamination in multi-tenant environments. - Fixed `CheckProviderConnection` for local models (LM Studio, vLLM, Ollama). It now properly creates a localized driver copy and injects the `base_url` before testing the connection, entirely eliminating the false-positive `missing base URL` error without polluting the global state. 2. **Implement `VolcEngine` Embeddings:** - Fully implemented the `Encode` method for the `volcengine` provider, enabling text embedding capabilities for VolcEngine models. 3. **Enhance Region Validation in `SiliconFlow`:** - Added a strict empty string check (`*apiConfig.Region != ""`) alongside the existing `nil` check when parsing regions. This ensures that if an empty string is passed, the system safely falls back to the `"default"` region, preventing malformed URL requests and `unsupported protocol scheme` errors. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) - [x] New Feature (non-breaking change which adds functionality) |
|||
| a82ae4a991 |
Go: implement Encode (embeddings) in Aliyun driver (#14647)
### What problem does this PR solve?
The Aliyun Go driver shipped with a stub \`Encode\` method that returned
\`no such method\`, even though \`conf/models/aliyun.json\` already
wires the OpenAI-compatible embeddings URL suffix at
\`compatible-mode/v1/embeddings\`. The same config also did not list any
embedding models, so the picker had nothing to select.
So an Aliyun tenant who wanted to use Tongyi text-embedding-v3 or v4 in
the Go layer could not, even though the upstream endpoint is public and
uses the standard \`POST /v1/embeddings\` shape that the SiliconFlow and
ZhipuAI
drivers already support.
This PR fills the gap.
### What this PR includes
- \`conf/models/aliyun.json\`: add \`text-embedding-v4\` and
\`text-embedding-v3\` to the \`models\` array.
- \`internal/entity/models/aliyun.go\`: replace the \`Encode\` stub with
a real implementation. Adds a small local response type that matches the
OpenAI-compatible shape.
No factory change. No interface change.
### How the driver works
- Validate \`apiConfig\` and the API key, validate the model name,
resolve the region with a default fallback, build the
URL from \`BaseURL[region] + URLSuffix.Embedding\`.
- Send all input texts in one request as the \`input\` array, the same
OpenAI-compatible shape the SiliconFlow \`Encode\`
uses.
- Parse \`data[*].embedding\` and copy each slice into a \`[][]float64\`
indexed by \`data[*].index\` so the output order matches the input order
even if the API returns items in a different order.
- Handle both \`float64\` and \`float32\` element types.
- Empty input returns \`[][]float64{}\` with no HTTP call.
- Non-200 responses propagate the upstream status line and body.
- A final pass checks every input slot got a vector and returns a clear
error if any slot is still nil.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- \`go build ./internal/entity/models/...\` in a clean go 1.25 image
returns exit 0.
- The full method set on \`AliyunModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the existing SiliconFlow Encode implementation.
Closes #14646
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
|
|||
| d13a240dc0 |
Go: implement remaining interface for OpenRouter (#14657)
### What problem does this PR solve? 1. implement `rerank`, `embedding`, `balance`, `checkConnet` method for `OpenRouter` 2. delete `chat` method in `internal/entity/models/volcengine.go` ### Type of change - [x] New Feature (non-breaking change which adds functionality) - [x] Refactoring |
|||
| d8d49df35e |
Go: implement Rerank in Gitee AI driver (#14656)
### What problem does this PR solve?
The Gitee AI Go driver shipped with a stub \`Rerank\` method that
returned \`Rerank not implemented\`, even though
\`conf/models/gitee.json\` already wires the rerank URL suffix at
\`\"rerank\": \"rerank\"\`. The same config did not list any
rerank model, so the picker had nothing to select.
So a Gitee tenant could not use BAAI/bge-reranker-v2-m3 as a reranker
through the Go layer today, even though the
infrastructure was one config entry and one method body away.
### What this PR includes
- \`conf/models/gitee.json\`: add \`BAAI/bge-reranker-v2-m3\` to the
\`models\` array.
- \`internal/entity/models/gitee.go\`: replace the \`Rerank\` stub with
a real implementation. Adds two small local types
that match the OpenAI-compatible \`/rerank\` shape already used by the
SiliconFlow and ZhipuAI drivers.
No factory change. No interface change.
### How the driver works
- Validate \`apiConfig\` and the API key, validate the model name,
resolve the region with a default fallback, build the
URL from \`BaseURL[region] + URLSuffix.Rerank\`.
- Use a per-call \`context.WithTimeout(30s)\` and
\`http.NewRequestWithContext\`, matching the pattern the
recently merged Aliyun Encode and the OpenAI driver already use.
- Send \`{model, query, documents, top_n, return_documents:false}\` in
the body.
- Parse \`results[*].relevance_score\` and copy each score into the
output slice indexed by \`results[*].index\`, so the
output order matches the input order even if the API returns items in a
different order.
- Empty input returns \`[]float64{}\` with no HTTP call.
- An out-of-range result index returns a clear error rather than
silently skipping the entry.
- Non-200 responses propagate the upstream status line and body.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- \`go build ./internal/entity/models/...\` in a clean go 1.25 image
returns exit 0.
- The full method set on \`GiteeModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the existing SiliconFlow Rerank and the recently
merged ZhipuAI Rerank (#14608).
Closes #14655
|
|||
| c7ddc8c039 |
fix(go): implement ListModels and CheckConnection in NVIDIA driver (#14636)
### What problem does this PR solve? The NVIDIA Go driver added in #14623 has a real chat path, but \`ListModels\` and \`CheckConnection\` are stubs that always return \`no such method\`. So: - The model picker cannot auto-populate available NVIDIA NIM model ids. Users have to type the full id by hand (e.g. \`abacusai/dracarys-llama-3.1-70b-instruct\`). - The "Check connection" button always fails for NVIDIA, even when the base URL is reachable and the API key is accepted. NVIDIA NIM is OpenAI-compatible. \`/v1/models\` works with the same Bearer token used for chat. The \`conf/models/nvidia.json\` file already wires the \`models\` url_suffix, so no config change is needed. ### What this PR includes - \`internal/entity/models/nvidia.go\`: - \`ListModels\` now calls \`GET ${BaseURL}/${URLSuffix.Models}\`, parses \`response.data[*].id\`, and returns the list. Same shape as the moonshot, xai, and openai drivers. - \`CheckConnection\` now calls \`ListModels\` and returns its error. Same pattern xai, moonshot, deepseek, aliyun, and gitee already use. \`Balance\`, \`Encode\`, and \`Rerank\` are still stubs in this PR and can be added in follow-ups. No JSON change. No factory change. No interface change. ### How the implementation works - Region resolution falls back to \`default\` when the supplied region is unknown, so a stray region value does not break a valid request. - The Authorization header is only set when \`apiConfig\` and \`ApiKey\` are non-nil and non-empty. This avoids a nil-pointer dereference and lets self-hosted NIM deployments without a key still work. - Non-200 responses propagate the upstream status line and body so the user sees a real error message. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### How was this tested? - \`go build ./internal/entity/models/...\` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on \`NvidiaModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing xai, moonshot, deepseek, aliyun, gitee, and openai drivers. Closes #14635 |
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| e729eced45 |
Go: implement Balance in DeepSeek driver (#14632)
Closes #14631 ### What problem does this PR solve? The DeepSeek Go driver shipped with a stub \`Balance\` method that returned \`no such method\`, even though DeepSeek exposes a public \`GET /user/balance\` endpoint that works with the same Bearer token used for chat. So the "Balance" panel in the model provider UI always shows an error for DeepSeek tenants, while it already works for Moonshot and Gitee. This PR fills the gap. ### What this PR includes - \`conf/models/deepseek.json\`: add \`\"balance\": \"user/balance\"\` under \`url_suffix\` so the driver can build the URL from config the same way the other endpoints do. - \`internal/entity/models/deepseek.go\`: replace the \`Balance\` stub with a real implementation. Adds a small local response type \`deepseekBalanceResponse\` that matches the upstream shape. No factory change. No interface change. ### How the driver works - Validate \`apiConfig\` and the API key, resolve the region (with a \`default\` fallback), and build the URL from \`BaseURL[region] + URLSuffix.Balance\`. - GET the URL with \`Authorization: Bearer <api_key>\`. - Parse the upstream response: \`\`\`json { \"is_available\": true, \"balance_infos\": [ {\"currency\": \"USD\", \"total_balance\": \"10.00\", ...}, {\"currency\": \"CNY\", \"total_balance\": \"70.00\", ...} ] } \`\`\` \`total_balance\` is a string in the upstream API, so the driver parses it with \`strconv.ParseFloat\`. - Return the first balance entry as \`{\"balance\": <float>, \"currency\": <string>}\`, the same shape the Moonshot driver returns. The UI can render it with no provider-specific code. ### Edge cases - Missing or empty API key returns a clear local error before any HTTP call. - Empty \`balance_infos\` returns a clear \"no balance info in response\" error rather than a zero-value silent success. - Non-numeric \`total_balance\` returns a clear parse error. - Non-200 responses propagate the upstream status line and body so the user can see why the call failed. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - \`go build ./internal/entity/models/...\` in a clean go 1.25 image (the go.mod minimum) returns exit 0. - The full method set on \`DeepSeekModel\` still matches the \`ModelDriver\` interface. - Pattern parity with the existing Moonshot and Gitee Balance implementations. |
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| a377512110 |
Go: implement provider: OpenRouter (#14652)
### What problem does this PR solve? 1. **Implement `OpenRouter` Provider:** Fully support OpenRouter AI models (e.g., `gemma`, `minimax`). Includes robust handling of Server-Sent Events (SSE) streams, error event interception, and proper parsing of both `reasoning_content` and standard `content`. 2. **Fix BaseURL Resolution Bug:** Fixed a critical edge case in region configuration parsing. Added a strict empty string check (`*apiConfig.Region != ""`) alongside the `nil` check. This ensures that if the UI passes an empty string, the system correctly falls back to the `"default"` region, preventing `unsupported protocol scheme ""` errors during HTTP requests. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) - [x] New Feature (non-breaking change which adds functionality) |
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| a86e0ca0ca |
Go: implement Balance in SiliconFlow driver (#14643)
### What problem does this PR solve?
The SiliconFlow Go driver shipped with a stub \`Balance\` method that
returned \`no such method\`, even though SiliconFlow exposes a public
\`GET /v1/user/info\` endpoint that returns the account balance per
currency.
So the "Balance" panel in the model provider UI always shows an error
for SiliconFlow tenants, while it already works for
Moonshot and Gitee. This PR fills the gap.
### What this PR includes
- \`conf/models/siliconflow.json\`: add \`\"balance\": \"user/info\"\`
under \`url_suffix\` so the driver builds the URL from config.
- \`internal/entity/models/siliconflow.go\`: replace the \`Balance\`
stub with a real implementation. Adds a small local response type that
matches the upstream shape.
No factory change. No interface change.
### How the driver works
- Validate \`apiConfig\` and the API key, resolve the region with a
default fallback, and build the URL from \`BaseURL[region] +
URLSuffix.Balance\`.
- GET the URL with \`Authorization: Bearer <api_key>\`.
- Parse the upstream response. SiliconFlow returns balance fields as
strings, so the driver parses them with \`strconv.ParseFloat\`. It
prefers \`totalBalance\` over \`balance\` when both are present.
- Return \`{\"balance\": <float>, \"currency\": \"CNY\"}\`, the same
shape the Moonshot driver returns. The UI can render it
with no provider-specific code.
### Edge cases
- Missing or empty API key returns a clear local error before any HTTP
call.
- An unknown region falls back to the default base URL.
- Empty \`balance\` and \`totalBalance\` returns a clear "no balance
info in response" error rather than a zero-value silent success.
- Non-numeric balance string returns a clear parse error.
- Non-200 responses propagate the upstream status line and body.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- \`go build ./internal/entity/models/...\` in a clean go 1.25 image
returns exit 0.
- The full method set on \`SiliconflowModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the existing Moonshot and Gitee Balance
implementations.
Closes #14642
|
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| 2fd8cdc3cc |
fix(go): wire CheckConnection to ListModels in ollama, lm-studio, and vllm (#14614)
### What problem does this PR solve?
Three Go drivers had `CheckConnection` returning a hardcoded `no such
method` error, even though each one already has a working `ListModels`
that hits the configured base URL with the configured API key. So the
"Check connection" button in the model provider UI always failed for
these three providers, even when the underlying setup was fine.
Affected drivers:
- `internal/entity/models/ollama.go`
- `internal/entity/models/lmstudio.go`
- `internal/entity/models/vllm.go`
This is a real user-facing gap because Ollama and LM Studio are two of
the most popular local LLM runners, and vLLM is widely used for
self-hosted deployments.
### What this PR includes
For each of the three drivers, replace the stub with a small
implementation that calls `ListModels` and returns its error:
```go
func (o *OllamaModel) CheckConnection(apiConfig *APIConfig) error {
_, err := o.ListModels(apiConfig)
return err
}
```
This is the exact pattern that xai, moonshot, deepseek, aliyun, and
gitee already use for the same method.
No JSON change. No factory change. No interface change.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### How was this tested?
- `go build ./internal/entity/models/...` in a clean go 1.25 image (the
go.mod minimum) returns exit 0.
- The full ModelDriver interface still resolves on each driver
(NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode,
Rerank, ListModels, Balance, CheckConnection).
- Pattern parity with the existing xai, moonshot, deepseek, aliyun, and
gitee CheckConnection methods.
Closes #14609
|
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| bb10b83e61 |
Go: implement Rerank in ZhipuAI driver (#14608)
### What problem does this PR solve?
The ZhipuAI Go driver had a stub Rerank method that returned "not
implemented", even though conf/models/zhipu-ai.json already ships
glm-rerank as a rerank model and the rerank URL suffix is already wired
in url_suffix:
```json
"url_suffix": {
...
"rerank": "rerank"
},
"models": [
{"name": "glm-rerank", "model_types": ["rerank"]},
...
]
```
So the config was ready but the driver was not. A tenant who picked
glm-rerank in the Go layer could not actually run a rerank call. This PR
fills the gap so the listed model works end to end.
### What this PR includes
- `internal/entity/models/zhipu-ai.go`: real implementation of
`ZhipuAIModel.Rerank`, plus two small local types (`zhipuRerankRequest`,
`zhipuRerankResponse`) that mirror the standard OpenAI-compatible rerank
shape used by SiliconFlow.
No factory change. No JSON change. No interface change.
### How the driver works
- POST to `${BaseURL}/${URLSuffix.Rerank}` (resolves to
`https://open.bigmodel.cn/api/paas/v4/rerank` with the default config),
reusing the existing httpClient on the driver.
- Validate apiConfig and the API key, validate the model name, and
resolve the region. Return a clear local error before any HTTP call when
something is missing.
- Send `{model, query, documents, top_n, return_documents: false}` in
the body, the same shape the SiliconFlow driver already uses.
- Walk `results[*].relevance_score` and copy each score into the output
slice indexed by `results[*].index`, so the output order matches the
input order even if the API returns results in a different order.
- Empty `texts` input returns an empty `[]float64` with no HTTP call.
- Non-200 responses propagate the upstream status line and body.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- `go build ./internal/entity/models/...` in a clean go 1.25 image (the
go.mod minimum) returns exit 0.
- The full method set on `ZhipuAIModel` still matches the `ModelDriver`
interface (NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender,
Encode, ListModels, Balance, CheckConnection, Rerank).
- Pattern parity with the existing SiliconFlow Rerank implementation
(`internal/entity/models/siliconflow.go`).
Closes #14607
|
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| 078ea3bf4a |
Go: implement provider: Nvidia (#14623)
### What problem does this PR solve? 1. **Implement `Nvidia` Provider:** Fully support NVIDIA NIM APIs with robust parameter handling (including the `thinking` parameter) and safe URL merging in `NewInstance`. 2. **Fix Misleading CLI Errors:** Corrected a bug in `common_command.go` where failed chat requests inaccurately reported `failed to list instance models`. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) - [x] New Feature (non-breaking change which adds functionality) |
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| b8b741555f |
Go: implement provider: OpenAI (#14605)
### What problem does this PR solve? Add a Go driver for OpenAI (GPT models). The config file conf/models/openai.json has been in the repo for a while with the full GPT-5 model list, but internal/entity/models/factory.go had no case for "openai". So any tenant that configured OpenAI as a model provider in the Go layer fell through to the default branch and got the dummy driver. Chat, list models, and check connection all returned dummy responses instead of reaching the API. OpenAI is the most commonly requested provider and the JSON config already ships with the repo, so this gap is high impact even though the JSON has been there for some time. ### What this PR includes - New file internal/entity/models/openai.go with an OpenAIModel that implements the ModelDriver interface. - factory.go: route the "openai" provider name to NewOpenAIModel. - conf/models/openai.json: add "models": "models" under url_suffix so ListModels can hit /v1/models with no hardcoded fallback. ### How the driver works - OpenAI exposes the canonical OpenAI-compatible API at https://api.openai.com/v1. - ChatWithMessages and ChatStreamlyWithSender post to /chat/completions in the same shape the moonshot, vllm, and xai drivers use. - ListModels and CheckConnection call /models to list available ids and confirm the API key works. - reasoning_content is passed through for the o-series and other reasoning models, in both the non-stream and stream paths. - Encode (embeddings) is left as "not implemented" for now, the same way the other recent provider drivers do it. Rerank and Balance are not part of OpenAI's public API surface in this layer and return a clear "not implemented" or "no such method" error. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### How was this tested? - go build ./internal/entity/models/... in a clean go 1.25 image (the go.mod minimum) returns exit 0 with no errors. - Method set of OpenAIModel matches the ModelDriver interface: NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, Rerank, ListModels, Balance, CheckConnection. - Pattern parity with the merged moonshot (#14433), volcengine (#14460), minimax (#14478), vllm (#14532), xai (#14550), and lm-studio (#14586) PRs. Closes #14604 |
