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| Author | SHA1 | Message | Date | |
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tmimmanuel
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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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tmimmanuel
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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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tmimmanuel
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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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tmimmanuel
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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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Haruko386
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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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tmimmanuel
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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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tmimmanuel
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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> |
||
|
Haruko386
|
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 |
||
|
tmimmanuel
|
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 |
||
|
Haruko386
|
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
|
||
|
Jack Storment
|
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 |
||
|
Panda Dev
|
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> |
||
|
Haruko386
|
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
|
||
|
Haruko386
|
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) |
||
|
Haruko386
|
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) |
||
|
Haruko386
|
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) |
||
|
Panda Dev
|
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 |
||
|
Haruko386
|
dd7a0ce1d3 |
Go: implement provider: lm-studio (#14586)
### What problem does this PR solve? implement `lm-studio` provider ### Type of change - [x] New Feature (non-breaking change which adds functionality) - [x] Refactoring |
||
|
Jack Storment
|
c2ad672c09 |
Go: implement provider: xAI (#14550)
Closes #14552 ### What problem does this PR solve? Add a Go driver for xAI (Grok models). The config file conf/models/xai.json has been in the repo since the early Go provider work, but internal/entity/models/factory.go had no case for "xai". So any xAI request fell through to the dummy driver and never reached the API. This PR adds the missing driver and wires it up. ### What this PR includes - New file internal/entity/models/xai.go with an XAIModel that implements the ModelDriver interface. - factory.go: route the "xai" provider name to NewXAIModel. ### How the driver works - xAI exposes an OpenAI-compatible API at https://api.x.ai/v1. - ChatWithMessages and ChatStreamlyWithSender post to /chat/completions in the same shape the moonshot and deepseek drivers use. - ListModels and CheckConnection call /models to confirm the API key works and to list available model ids. - reasoning_content is passed through for grok-3-mini and other xAI reasoning models, both in the non-stream and stream paths. - Encode, Rerank, and Balance are not part of the public xAI API at the moment, so they 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 XAIModel matches the ModelDriver interface: NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode, Rerank, ListModels, Balance, CheckConnection. - Pattern parity with the merged moonshot (#14433), volcengine (#14460), minimax (#14478), and vllm (#14532) PRs. --------- Co-authored-by: Jin Hai <haijin.chn@gmail.com> |
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Jin Hai
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bb05a8bd7e |
Update create model instance command (#14441)
### What problem does this PR solve?
1. support command:
```
RAGFlow(user)> create provider 'vllm' instance 'test' key 'test-key' url 'base-url' region 'abc';
SUCCESS
RAGFlow(user)> list instances from 'vllm';
+----------+----------------------------------------+----------------------------------+--------------+----------------------------------+--------+
| apiKey | extra | id | instanceName | providerID | status |
+----------+----------------------------------------+----------------------------------+--------------+----------------------------------+--------+
| test-key | {"base_url":"base-url","region":"abc"} | 40213c89430311f1a7cf38a74640adcc | test | b4d40e6142d311f1a4f938a74640adcc | enable |
+----------+----------------------------------------+----------------------------------+--------------+----------------------------------+--------+
```
2. support add vllm model
```
RAGFlow(user)> add model 'Qwen/Qwen2-0.5B' to provider 'vllm' instance 'test' with tokens 131072 chat;
SUCCESS
```
3. add vllm chat
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
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Jin Hai
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819257f257 |
Go: add volcengine (#14409)
### What problem does this PR solve? 1. Refactor server_main 2. Add volcengine ### Type of change - [x] Refactoring --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com> |
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Jin Hai
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965717c4fb |
Go: add new provider: google (#14395)
### What problem does this PR solve? As title. ### 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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Jin Hai
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c3eac4103a |
Go: aliyun model provider (#14379)
### What problem does this PR solve? As title. ### 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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Jin Hai
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1c244df90d |
Go: add gitee and siliconflow as model provider (#14336)
### What problem does this PR solve? As title ### 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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Jin Hai
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2b029882d7 |
Go: add new provider minimax (#14296)
### What problem does this PR solve? 1. Add new provider minimax 2. Add new command: CHECK INSTANCE 'instance_name' FROM 'provider_name'; ``` RAGFlow(user)> check instance 'test' from 'minimax'; SUCCESS ``` ### 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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Jin Hai
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74b44e1aa3 |
Go: add balance command (#14262)
### What problem does this PR solve? ``` RAGFlow(user)> list supported models from 'moonshot' 'test'; +---------------------------------+ | model_name | +---------------------------------+ | moonshot-v1-32k-vision-preview | | kimi-k2.6 | | moonshot-v1-8k | | moonshot-v1-auto | | moonshot-v1-128k | | moonshot-v1-32k | | kimi-k2.5 | | moonshot-v1-8k-vision-preview | | moonshot-v1-128k-vision-preview | +---------------------------------+ RAGFlow(user)> show balance from 'moonshot' 'test'; +---------+----------+ | balance | currency | +---------+----------+ | 0 | CNY | +---------+----------+ ``` ### 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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Jin Hai
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e48d75987c |
Go: add stream / think chat (#14242)
### What problem does this PR solve? 1. Supports stream and non-stream chat 2. Supports think and non-think chat 3. List supported models from DeepSeek service. (This command can be used to verify the API validity) ### 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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Jin Hai
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af2ed416a7 |
Add extra field to model instance (#14203)
### What problem does this PR solve? Now each model support region with different URL ### 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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Jin Hai
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6c29128de1 |
Refactor model provider and command (#13887)
### What problem does this PR solve? Introduce 5 new tables, including model groups and provider instance. ### Type of change - [x] New Feature (non-breaking change which adds functionality) - [x] Refactoring --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com> |