### Summary
This PR adds **aimlapi.com** as a model provider, so a RAGFlow user can
enter one API key in the model settings and use AIMLAPI's models across
the app. AIMLAPI ([aimlapi.com](https://aimlapi.com)) is an
OpenAI-compatible aggregator that serves 700+ models (LLM, embedding,
vision, TTS, ASR) from many providers behind a single API.
The change mirrors the repo's existing "add provider" pattern (e.g.
FuturMix / OpenRouter): provider logic lives in the same files those
providers use, and shared / UI files get only registration entries.
**Backend**
- `conf/llm_factories.json` — the `aimlapi.com` factory entry.
- `rag/llm/__init__.py`, `rag/llm/{chat,embedding,cv}_model.py` —
LiteLLM adapters (chat, embedding, image2text) with a production base
URL, overridable via `AIMLAPI_API_URL`.
- `rag/llm/model_meta.py` — an `AIMLAPI` model-meta so the provider
lists its full `/v1/models` catalog dynamically (classified by the
endpoint `type`), the same way OpenRouter does.
- `api/apps/restful_apis/aimlapi_api.py` — an optional "Get API key"
flow using AIMLAPI's agent-authorization (OAuth 2.0 Device Authorization
Grant, RFC 8628). The device code is kept server-side (Redis); only the
issued key reaches the browser.
**Frontend (`web/`)**
- Provider registration (constant, icon allowlist, brand logo), the
model picker (`LIST_MODEL_PROVIDERS` + a `buildLocalConfig` entry), and
the "Get API key" button in the provider dialog. Locales added to `en`
and `zh`.
**Configuration** — production defaults are compiled in; endpoints and
the partner id are overridable through `AIMLAPI_*` environment
variables, so the same build works across environments.
**Testing** — the `web` build passes; chat, embedding and dynamic model
listing were smoke-tested against the live API.
### What problem does this PR solve?
Adds first-class support for **Mistral OCR** (`POST /v1/ocr`) as a
document parser, and fixes the long-standing bug where selecting
`mistral-ocr-latest` fails with `Can't find model for
<tenant>/image2text/mistral-ocr-latest`.
`mistral-ocr-latest` is Mistral's dedicated document-OCR endpoint, not a
vision-chat (`image2text`) model, but the catalog tagged it `image2text`
— so it resolved to the `CvModel` registry, which has no `Mistral`
entry, and there was no `OcrModel` entry either. This PR registers it
correctly and wires it end to end.
Closes#17056Closes#5782Closes#7075
**What it does**
1. **`MistralParser` + `MistralOcrModel`**
(`deepdoc/parser/mistral_parser.py`, `rag/llm/ocr_model.py`) — a proper
`OcrModel` factory `Mistral OCR`, mirroring the SoMark cloud-OCR
template. Tables stay inline as HTML; the page range maps to Mistral's
native `pages` selector (absolute page indices, billed per selected
page, so multi-task documents do not re-OCR the whole file); documents
over the inline limit go through the `/v1/files` signed-URL flow with
cleanup.
2. **Removes the `image2text` mis-tag** for `mistral-ocr-latest` from
the `Mistral` factory in `conf/llm_factories.json` (it now lives only in
the `Mistral OCR` factory, typed `ocr`). This is what closes the `Can't
find model` path.
3. **`MistralCV`** (`rag/llm/cv_model.py`) — a thin `GptV4` subclass
over Mistral's OpenAI-compatible endpoint, registering a `Mistral` entry
in the `CvModel` registry so Mistral vision models (`pixtral-*`) become
usable as `image2text` at all.
4. **Figure description** — Mistral-OCR-extracted figures are captioned
using the tenant's configured `image2text` model (any provider),
matching MinerU/deepdoc behaviour.
5. **Wires the parser into every chunking method** (`naive`, `paper`,
`book`, `laws`, `manual`, `one`, `presentation`) and the `rag/flow` DAG
path. This also fixes a related latent gap where those chunkers
forwarded only `mineru_llm_name`, so any model-based OCR provider
selected on a non-`naive` method silently fell through.
**Notes on the API contract** (verified against the live Mistral API):
`pages` is a selector (returns absolute `index`, bills only the
requested pages); `include_blocks: true` returns per-block bounding
boxes usable for chunk highlighting and figure cropping; large files use
`POST /v1/files` → signed URL → OCR → `DELETE`.
**Testing**: new unit tests cover the response→sections contract (both
the 2-tuple `naive` path and the typed 3-tuple DAG path), the
position-tag rescale, the HTTP client incl. upload failure/cleanup
paths, `parse_pdf` page-range threading, registry registration, env
config, the suffix normalization, the factory catalog entry, `MistralCV`
registration, and figure-description injection. Verified end to end
against the live Mistral API on real PDFs (table extraction,
page-selector cost avoidance, figure captioning).
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Fix Infinity compatibility issues in knowledge compilation.
This change:
- Stores compilation source ID lists as JSON arrays in Infinity.
- Parses JSON array fields when reading compiled documents.
- Uses `json_contains` for filtering JSON array fields.
- Adds the missing `name` column to the Infinity mapping.
- Updates dataset navigation KNN search to use the unified
`MatchDenseExpr` interface.
- Handles unavailable embeddings without querying an invalid `q_0_vec`
field.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## Summary
- Add tool call support for SiliconFlow, Qiniu, Huawei Cloud, and Jina
while consolidating shared OpenAI-compatible helpers used by Aliyun
- Handle streaming tool call deltas and advertise tool support for
eligible provider models
- Add cross-provider tool call tests and enable Qiniu connection
verification through the models endpoint
#16990
### Summary
As title
verif ied from CLI
```
RAGFlow(api/default)> asr with 'paraformer@test@funasr' audio './internal/test.wav' param '{"language": "en"}'
+----------------------------------------------------------------------------------------------------------------------+
| text |
+----------------------------------------------------------------------------------------------------------------------+
| The examination and testimony of the experts enabled the commission to conclude that five shots may have been fired. |
+----------------------------------------------------------------------------------------------------------------------+
```
---
<img width="2876" height="1377" alt="image"
src="https://github.com/user-attachments/assets/4cee62c6-1c71-43ce-b398-4d9fbff33c3c"
/>
```bash
INFO: 127.0.0.1:51910 - "POST /v1/audio/transcriptions HTTP/1.1" 200 OK██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2.19it/s]
INFO: 127.0.0.1:60934 - "GET /v1/models HTTP/1.1" 200 OK%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2.10it/s]
```
---------
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
## What this PR does
Adds support for Alibaba Cloud's hosted Fun-ASR-Flash snapshots to the
existing Tongyi-Qianwen speech-to-text provider.
- registers `fun-asr-flash-2026-06-15` as a speech-to-text model;
- routes only `fun-asr-flash*` models to the documented workspace-native
multimodal-generation endpoint;
- supports local audio through size-checked data URIs as well as
URL/data-URI inputs;
- uses the documented SSE response mode for incremental streaming
transcription;
- closes the streamed HTTP response on completion, failure, or early
consumer cancellation;
- preserves the existing `dashscope.MultiModalConversation` path for all
other Qwen audio models;
- keeps RAGFlow's existing synchronous and streaming adapter interfaces.
## Why
Fun-ASR-Flash does not use the legacy Qwen audio request shape currently
used by `QWenSeq2txt`. Its synchronous API expects `input_audio` at:
`/api/v1/services/aigc/multimodal-generation/generation`
Without a narrowly scoped adapter path, the hosted model cannot be
selected successfully through RAGFlow's Tongyi-Qianwen speech-to-text
provider.
Closes#16843.
## Compatibility
The new behavior is gated by the `fun-asr-flash` model-name prefix.
Existing Qwen audio models continue through the original code path
unchanged.
## Validation
- `pytest test/unit_test/rag/llm/test_sequence2txt_model.py`: 10 passed
- Ruff check: passed
- Ruff format check: passed
- `llm_factories.json` validation: passed
- Real hosted-API validation with WAV audio
- Real RAGFlow upload/indexing validation with MP3 audio
The unit tests cover the native Fun-ASR-Flash request, regression
behavior for the legacy Qwen path, SSE streaming, and early response
cleanup.
## Documentation
- https://help.aliyun.com/document_detail/2979031.html
- https://help.aliyun.com/document_detail/2869541.html
### Why a dedicated adapter path is necessary (official evidence)
Alibaba Cloud's [Fun-ASR RESTful API
reference](https://help.aliyun.com/en/model-studio/fun-asr-recorded-speech-recognition-http-api)
makes the incompatibilities with RAGFlow's existing Qwen audio path
explicit:
| Adapter change | Official API requirement | Why the existing path is
insufficient |
| --- | --- | --- |
| Call the workspace-native HTTP endpoint | The Fun-ASR-Flash
synchronous section states that SDK calls are not supported and
specifies `POST /api/v1/services/aigc/multimodal-generation/generation`.
| The existing adapter calls `dashscope.MultiModalConversation`, so a
direct HTTP path is required. |
| Use the `input_audio` message shape | `input.messages`, `content`,
`type: input_audio`, `input_audio`, and `input_audio.data` are
documented as required for an audio request. | The existing Qwen path
sends the legacy `audio` content shape, which does not match this API
contract. |
| Send `parameters.format` | The request schema marks `parameters` and
`format` as **Required**, and says the value must match the actual audio
format. | The legacy request has no Fun-ASR-Flash `parameters.format`
field, so the adapter must derive and send it. |
| Encode local files as Data URIs | `input_audio.data` accepts either a
public URL or a Base64 Data URI; the reference gives the exact
`data:{MIME_TYPE};base64,...` form. | RAGFlow supplies local file paths,
which the remote API cannot read directly. |
| Parse `output.text` | The documented non-streaming response returns
the accumulated transcription in `output.text`. | The legacy Qwen
response parser reads `output.choices[].message.content`, so a separate
response parser is required. |
| Enforce the Base64 input limit | The reference requires the
Base64-encoded audio to remain within the 10 MB input limit. | The
adapter checks encoded size before reading/sending local audio and
directs oversized inputs to the existing public-URL path. |
| Use SSE for streaming | The reference specifies `X-DashScope-SSE:
enable` and documents intermediate and final SSE events. | The adapter
parses those events instead of wrapping one blocking response as a
synthetic stream. |
| Release streamed responses | Streaming responses must be closed when
iteration completes or stops early. | A `finally` cleanup releases the
HTTP response on completion, errors, and consumer cancellation. |
`sample_rate` is documented as **Optional**. The implementation omits it
instead of declaring a fixed value that may not match remote or
compressed audio.
The [official speech-to-text model
list](https://help.aliyun.com/en/model-studio/asr-model/) separately
confirms that `fun-asr-flash-2026-06-15` is an offline HTTP model with a
five-minute audio limit.
---------
Signed-off-by: LauraGPT <LauraGPT@users.noreply.github.com>
Co-authored-by: openhands <openhands@all-hands.dev>
Co-authored-by: LauraGPT <LauraGPT@users.noreply.github.com>
### Summary
We have updated our model driver to work with go.
It is based on OpenAI-API-Compatible model provider.
Draft
#15519
Our old model provider
#13425
### Summary
Adds FunASR as a self-hosted speech-to-text provider through its
OpenAI-compatible `/v1/audio/transcriptions` endpoint.
This is a focused replacement for #15526 by @Rene0422 and relates to
#15448. The unrelated Markdown parser changes from the previous branch
are intentionally removed so this PR contains only the FunASR provider
integration.
- register FunASR as a `SPEECH2TEXT` factory;
- add `FunASRSeq2txt` with `sensevoice` and `http://localhost:8000/v1`
defaults, an optional API key, URL normalization, and inherited
transcription handling;
- wire FunASR into the current local-provider schema with a prefilled
local URL and official documentation link;
- discover the server's `/v1/models` dynamically and expose every
returned model as speech-to-text in the model picker;
- use RAGFlow's existing default provider icon fallback instead of
referencing a missing `funasr` asset;
- list FunASR in the supported-provider documentation;
- add focused backend and frontend regression tests.
### Validation
- focused backend pytest suite -> `7 passed`
- real CPU `funasr-server` + RAGFlow provider smoke test -> discovered
`fun-asr-nano`, `sensevoice`, and `paraformer`; transcribed a real WAV
as `我现在在录一段测试音频` (`10` tokens, `0.504s`)
- `ruff check` and `ruff format --check` on the changed Python files
- `python3 -m py_compile` on the provider and its test
- JSON parse and a semantic assertion for exactly one enabled FunASR
`SPEECH2TEXT` factory
- focused frontend Jest test -> `2 passed`
- ESLint and Prettier on all changed TypeScript files
- `npm run build` -> production build succeeded (`14,181` modules
transformed)
- `git diff --check`
### Deployment
Run FunASR separately and point the RAGFlow provider at it:
```bash
pip install funasr
funasr-server --device cuda --model sensevoice
```
The API key remains optional because the stock local server does not
require authentication. A key can still be supplied when the endpoint is
protected by a gateway.
---------
Signed-off-by: LauraGPT <LauraGPT@users.noreply.github.com>
Co-authored-by: LauraGPT <LauraGPT@users.noreply.github.com>
### Summary
1. Add hooks for server init
2. Add hooks for router init
3. Add jaeger and otel related config
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Closes https://github.com/infiniflow/ragflow/issues/14571.
Adds CAJAL as a first-class local scientific-writing option in RAGFlow:
- registers `agnuxo/cajal-4b-p2pclaw` as a known Ollama chat model with
a 32K context setting
- adds a built-in “CAJAL scientific paper agent” template under the
existing agent template catalog
- preconfigures the agent for grounded scientific writing: retrieval
first, citation traceability, LaTeX-ready output, and explicit
limitations when evidence is missing
- adds unit coverage to ensure the template normalizes through RAGFlow’s
production template loader, keeps graph form data in sync, and exposes
the Ollama model option
Behavior/evidence gathered for the requested model:
- Hugging Face model metadata for `Agnuxo/CAJAL-4B-P2PCLAW` reports
`pipeline_tag=text-generation` and tags including `gguf`, `llama.cpp`,
`vllm`, `scientific-research`, `papers`, `academic-writing`, `latex`,
and `license:apache-2.0`.
- The model card documents CAJAL as a 4B scientific paper generation
model with 32K context, local inference, LaTeX/citation specialization,
and CPU-only support around 5 tok/s on Ryzen 7 5800X.
- Local CPU generation could not be completed on this machine because
the advertised Ollama model name is not currently resolvable from
Ollama’s registry: both
`https://registry.ollama.ai/v2/agnuxo/cajal-4b-p2pclaw/manifests/latest`
and
`https://registry.ollama.ai/v2/library/agnuxo/cajal-4b-p2pclaw/manifests/latest`
returned `404 Not Found`; the Hugging Face repo tree currently exposes
an 8.4 GB `model.safetensors` but no GGUF artifact in `main`. The
template therefore targets the documented Ollama model name for users
who have the local CAJAL deployment/model file available.
Verification run locally:
```bash
python3 -m pytest test/test_cajal_template_unit.py -q
# 3 passed in 0.34s
python3 - <<'PY'
import json, glob
for f in sorted(glob.glob('agent/templates/*.json') + ['conf/llm_factories.json']):
with open(f, encoding='utf-8') as fp: json.load(fp)
print('json_ok')
PY
# json_ok
python3 -m ruff check test/test_cajal_template_unit.py
# All checks passed!
git diff --check
```
`uv run pytest
test/testcases/test_web_api/test_agent_app/test_cajal_template_unit.py
-q` was also attempted first, but dependency setup failed before test
collection while building `ormsgpack==1.5.0` from uv with a package
metadata parse error. Clearing uv’s `ormsgpack` cache and retrying
reproduced the same build failure, so the focused unit test was run with
the system Python environment instead.
### Type of change
- [ ] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
---------
Co-authored-by: sxxtony <sxxtony@users.noreply.github.com>
Co-authored-by: yzc <yzc@users.noreply.github.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
## Summary
Fixes#15215 — attachments uploaded to an agent were not reaching the
LLM.
When a user uploads a file in an agent chat, `canvas.run` parses it into
the `sys.files` global (text content for documents, `data:image/...`
URIs
for images — see `agent/canvas.py:752-768`). But the LLM/Agent
component's
`_prepare_prompt_variables` only substitutes variables the user's prompt
template explicitly references via `{var}` placeholders. The default
prompt is `[{"role": "user", "content": "{sys.query}"}]` with no
`{sys.files}`, so the parsed attachment content never reaches the model.
In the reporter's logs, this is why the agent saw only the bare query
`附件 摘要 attachment summary` and went searching the dataset instead of
reading the uploaded PDF.
## Fix
`agent/component/llm.py` — added `_collect_sys_files()` and an
auto-injection step in `_prepare_prompt_variables`:
- If `sys.files` is non-empty **and** neither `sys_prompt` nor any entry
in `prompts` already contains `{sys.files}` (no double-injection),
split the entries into text vs. `data:image/...` URIs.
- Image URIs are merged into `self.imgs`, which the existing logic uses
to switch the chat model to `IMAGE2TEXT` and pass `images=...` to
`async_chat`.
- Text content is appended to the last `user` role message in `msg`,
mirroring how `dialog_service.async_chat_solo` handles attachments for
the non-agent chat path (`api/db/services/dialog_service.py:318-321`).
Both `LLM._invoke_async` and `Agent._invoke_async` (tool-using) go
through `_prepare_prompt_variables`, so plain LLM nodes and Agent nodes
are fixed in both streaming and non-streaming paths.
## Test plan
- [ ] Upload a PDF attachment to an agent with the default `{sys.query}`
prompt and ask "summarize the attachment" — the model should answer
from the file content rather than searching the knowledge base.
- [ ] Upload an image attachment to an agent and ask about its contents
—
the model should switch to the vision-capable LLM and answer from
the image.
- [ ] Verify that an agent whose prompt **does** include `{sys.files}`
still works and does **not** include the file content twice.
- [ ] Verify that an agent run with no attachments behaves unchanged.
- [ ] Run `uv run pytest` to make sure no existing tests regress.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
---------
Co-authored-by: yzc <yuzhichang@gmail.com>
## Summary
Add support for **"New API"** as a model provider, enabling connection
to [New API](https://github.com/QuantumNous/new-api) /
[one-api](https://github.com/songquanpeng/one-api) compatible gateways
that aggregate multiple LLM backends behind a unified OpenAI-compatible
`/v1` endpoint.
### Features
- **All model types**: Chat, Embedding, Rerank, Image2Text, TTS,
Speech2Text
- **List Models discovery**: `NewAPI(OpenAIAPICompatible)` class in
`model_meta.py` queries the gateway's `/v1/models` to auto-discover
available models via the native `GET /api/v1/providers/<name>/models`
endpoint
- **Model parameter editing**: Pencil icon on each discovered model row
to edit `model_type`, `max_tokens`, and `features` (e.g. tool call
support) before submitting
- **Custom model addition**: "Add Custom Model" button at the bottom of
the List Models dropdown for models not returned by the API
- **Gear icon settings**: Enabled the Settings gear button on provider
instances to manage models on existing instances (viewMode)
- **viewMode credential passthrough**: Fixed List Models in viewMode —
merges `initialValues` credentials when `api_key`/`base_url` fields are
hidden by `hideWhenInstanceExists`
### Changes
**Backend** (8 files):
- `rag/llm/chat_model.py` — `NewAPIChat(Base)` class
- `rag/llm/embedding_model.py` — `NewAPIEmbed(OpenAIEmbed)` class (no
auto `/v1` append)
- `rag/llm/rerank_model.py` — `NewAPIRerank(Base)` class (uses `/rerank`
endpoint)
- `rag/llm/cv_model.py` — `NewAPICv(GptV4)` class
- `rag/llm/tts_model.py` — `NewAPITTS(OpenAITTS)` class
- `rag/llm/sequence2txt_model.py` — `NewAPISeq2txt(GPTSeq2txt)` class
- `rag/llm/model_meta.py` — `NewAPI(OpenAIAPICompatible)` class for List
Models discovery
- `conf/llm_factories.json` — New API factory entry with all model type
tags
**Frontend** (8 files + 1 new SVG):
- `web/src/assets/svg/llm/new-api.svg` — New API logo icon
- `web/src/constants/llm.ts` — `LLMFactory.NewAPI` enum + `IconMap`
entry
- `web/src/components/svg-icon.tsx` — `NewAPI` added to `svgIcons`
-
`web/src/pages/user-setting/setting-model/modal/provider-modal/field-config/local-llm-configs.ts`
— New API `buildLocalConfig`
-
`web/src/pages/user-setting/setting-model/modal/provider-modal/constants.ts`
— `LIST_MODEL_PROVIDERS` includes NewAPI
- `web/src/pages/user-setting/setting-model/components/used-model.tsx` —
Enable Settings gear button
-
`web/src/pages/user-setting/setting-model/modal/provider-modal/hooks/use-list-models-picker.ts`
— viewMode credential merge + model editing state/handlers
-
`web/src/pages/user-setting/setting-model/modal/provider-modal/hooks/use-list-models-options.tsx`
— Pencil edit icon per model row
-
`web/src/pages/user-setting/setting-model/modal/provider-modal/index.tsx`
— `AddCustomModelDialog` import + edit dialog rendering
**Note on Go implementation**: A Go model driver (`NewAPIModel`
delegating to `OpenAIModel`) has been prepared but is deferred until the
Go runtime is enabled in a future release (current v0.26.0 images use
`API_PROXY_SCHEME=python` and do not compile Go binaries). Will submit
as a follow-up PR.
## Related
- Depends on: #15996 (provider instance API improvements — server-side
credential lookup, idempotent `add_model`, security fixes — required for
viewMode gear icon and batch model submission)
## Test plan
- [ ] Add New API provider with api_key and base_url pointing to an
OpenAI-compatible gateway
- [ ] Click "List Models" — should discover and display available models
from `/v1/models`
- [ ] Click pencil icon on a model — should open edit dialog to change
model_type, max_tokens, features
- [ ] Select multiple models and click OK — should add all selected
models
- [ ] Click gear icon on the added instance — should open viewMode with
List Models working
- [ ] In viewMode, select new models including pre-existing ones, click
OK — should succeed (requires #15996)
- [ ] Verify all model types work: create a Chat assistant, Embedding
KB, Rerank setting
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Tim Wang <wanghualoong@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
### What problem does this PR solve?
1. Remove unused file
2. Remove duplicate models
3. Resort the function order
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
## Summary
Migrate PaddleOCR integration from the deprecated synchronous HTTP API
to the new asynchronous Job API (`submit → poll → fetch`), aligning with
PaddleOCR 3.6.0+ architecture.
## Changes
### Python (`deepdoc/parser/paddleocr_parser.py`)
- Replace synchronous `requests.post()` with async Job API flow (submit
→ poll → fetch)
- Authentication: `token {token}` → `Bearer {token}`
- File transfer: base64 JSON body → multipart file upload
- Polling: exponential backoff (initial 3s, ×1.5, max 15s, timeout
controlled by `request_timeout`)
- Result: fetch full JSONL from result URL, preserving `prunedResult`
with bbox info for crop functionality
- Rename `api_url` → `base_url` (backward compatible: `api_url` still
accepted as fallback)
### Python (`rag/llm/ocr_model.py`)
- Prefer `paddleocr_base_url` / `PADDLEOCR_BASE_URL`, fallback to
`paddleocr_api_url` / `PADDLEOCR_API_URL`
### Go (`internal/entity/models/paddleocr.go`)
- Add `Client-Platform: ragflow` header to submit and poll requests
- Change polling from fixed 3s to exponential backoff (initial 3s, ×1.5,
max 15s)
### Python (`common/constants.py`)
- Add `PADDLEOCR_BASE_URL` to env keys and default config
## Backward Compatibility
- Old env var `PADDLEOCR_API_URL` still works (used as fallback)
- Frontend field `paddleocr_api_url` still works (backend reads it as
fallback)
- No user-facing configuration changes required for existing setups
## Why not use the `paddleocr` SDK package directly?
RAGFlow's `_transfer_to_sections()` relies on `prunedResult` (containing
`block_bbox`, `block_label`, `parsing_res_list`) from the raw API
response for PDF crop functionality. The SDK's public `parse_document()`
API only returns `DocParsingResult` with `markdown_text`, discarding the
bbox data. Therefore we implement the async Job API flow directly via
HTTP, following the same logic as the SDK internally.
### What problem does this PR solve?
This PR fixes Go admin server startup failure caused by duplicate model
aliases in conf/all_models.json.
The model provider loader builds a global lookup table from both model
name and alias values. Some aliases duplicated another model's name or
another
alias, for example amazon.titan-embed-text-v1, which caused startup to
fail with a duplicate alias error. This PR removes conflicting duplicate
aliases
while keeping all model definitions intact.
