### Summary
Add Querit Contents as a built-in page content tool for RAGFlow Agents
and Canvas workflows.
Querit Contents crawls one or more known URLs and returns their page
content and optional metadata. It complements the existing Querit Search
tool: Search discovers relevant pages, while Contents retrieves pages
already selected by an Agent or workflow.
This integration supports two usage modes:
- A standalone `QueritContents` node in Canvas workflows.
- An embedded content tool available to RAGFlow Agents.
Port the wiki_incremental dataset-level merge and make its rewrite
barrier durable and concurrency-safe. Wiki pages merge replace-only; the
barrier persists a monotonic numeric generation, and a scheduler-backed
per-dataset lock closes the cross-process TOCTOU window. Adds the
Compiler Plan toggle (frontend) with Mode A grouping.
## What
This pull request adds **MWS GPT Model Hub** as a built-in model
provider in RAGFlow.
The integration allows users to configure an MWS project endpoint and
token, discover the models available to that project, and use supported
MWS models for chat completion, embeddings, and reranking.
Co-authored-by: ilarionov_n <ilarionov_n@promis.ru>
Ports dataset knowledge compilation (wiki/graph/tree/mindmap) to the Go
scheduler with a status contract, aligns wiki storage/retrieval with
Python, sizes prompts by content_length, and resolves embedding batch
size from provider capability.
Ports the dataset knowledge compilation (wiki/graph/tree/mindmap) to the
Go scheduler with a status contract, aligns wiki storage/retrieval with
Python, and sizes prompts by content_length.
## Summary
Improve the UX of the **"Delimiter for text"** field on the dataset
configuration page. The field is a single string with a backtick-based
mini-syntax, but both the tooltip and the surrounding UI failed to
surface what delimiters the backend would actually derive from a given
value — leaving users to discover by trial-and-error that the same
string produces different splits depending on file type (see #7436,
#4704, #9680).
### 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.
