Merge branch 'main' into anydoc-support

Conflicts, and how each was taken:

- application/core/settings.py — ours. The renamed OCR_ENABLED /
  OCR_ATTACHMENTS_ENABLED / OCR_MIN_CHARS_PER_PAGE accept main's
  DOCLING_OCR_* spellings as AliasChoices, so nothing is dropped.
- application/Dockerfile — both. Main's install layers plus the
  INSTALL_DOCLING build arg.
- application/parser/file/constants.py — both imports.
- deployment/docker-compose.yaml — both. The INSTALL_DOCLING /
  INSTALL_TESSERACT build args on backend and worker, and main's
  -Q docsgpt,parsing,embeddings, which query embedding needs.
- tests/conftest.py — theirs. Both sides fixed the same pytest-postgresql
  9.0.0 autocommit= breakage; main's spelling is the one already on main.
- application/requirements.txt — the comments claimed different reasons
  torch is in core. Main's is the true one now: it removed
  sentence-transformers, so docling is torch's only remaining consumer.

Two things the merge broke without conflicting:

- onnxruntime. This branch moved it out of core into the docling extra;
  main meanwhile made it the runtime local embeddings execute on
  (fastembed). Git took the deletion, leaving fastembed with no pinned
  runtime in a repo that pins everything. Restored to core, and no longer
  pinned twice from the extra.
- The frontend copy of ATTACHMENT_PARSER_EXTENSIONS. The backend list is
  derived and picked up the anydoc suffixes; the hand-kept frontend mirror
  did not, so the composer would refuse files the API accepts.
  tests/parser/file/test_constants.py is what caught it.

ruff, pytest (9897 passed), frontend build and docs build all pass. The
image build is unverified: no Docker daemon on this machine.

docs/content/Deploying/Development-Environment.mdx

@@ -76,18 +76,16 @@ To run the DocsGPT backend locally, you'll need to set up a Python environment a
          venv/Scripts/activate
         ```
 
-3.  **Download Embedding Model:**
+3.  **Embedding Model (no action needed):**
 
-    The backend requires an embedding model. Download the `mpnet-base-v2` model and place it in the `models/` directory within the project root. You can use the following script:
+    The embedding model is downloaded automatically the first time you ingest a document, and cached for subsequent runs. Set `EMBEDDINGS_CACHE_DIR` to control where.
+
+    For an offline or air-gapped machine, fetch it ahead of time instead:
 
     ```bash
-    wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
-    unzip mpnet-base-v2.zip -d model
-    rm mpnet-base-v2.zip
+    python -m application.scripts.prefetch_models
     ```
 
-    Alternatively, you can manually download the zip file from [here](https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip), unzip it, and place the extracted folder in `models/`.
-
 4.  **Install Backend Dependencies:**
 
     Navigate to the root of your DocsGPT repository and install the required Python packages:

docs/content/Deploying/DocsGPT-Settings.mdx

@@ -59,8 +59,9 @@ Here are some of the most fundamental settings you'll likely want to configure:
 
 - **`EMBEDDINGS_NAME`**: This setting defines which embedding model DocsGPT will use to generate vector embeddings for your documents. Embeddings are numerical representations of text that allow DocsGPT to understand the semantic meaning of your documents for efficient search and retrieval.
 
-  - **Default value:** `huggingface_sentence-transformers/all-mpnet-base-v2` (a good general-purpose embedding model).
-  - **Other options:** You can explore other embedding models from Hugging Face Sentence Transformers or other providers if needed.
+  - **Default value:** leave it unset and DocsGPT picks for you at first boot, recording the choice so it never changes underneath you: a fresh install is pinned to `ibm-granite/granite-embedding-311m-multilingual-r2` (multilingual, 32k context, same 768 dimensions), and an install that already has sources is pinned to `huggingface_sentence-transformers/all-mpnet-base-v2` so its index stays readable. Setting it here overrides that pin.
+  - **Other options:** Any FastEmbed built-in model, or any Hugging Face repository shipping an ONNX export. See [Embeddings](/Models/embeddings).
+  - **Changing it on an existing index requires re-embedding** — same-width models swap without any error and silently degrade retrieval. Run `python -m application.scripts.reembed`.
 
 - **`API_KEY`**: Required for most cloud-based LLM providers. This is your authentication key to access the LLM provider's API. You'll need to obtain this key from your chosen provider's platform.
 
@@ -93,7 +94,7 @@ LLM_PROVIDER=openai # Using OpenAI compatible API format for local models
 API_KEY=None      # API Key is not needed for local Ollama
 LLM_NAME=llama3.2:1b
 OPENAI_BASE_URL=http://host.docker.internal:11434/v1 # Default Ollama API URL within Docker
-EMBEDDINGS_NAME=huggingface_sentence-transformers/all-mpnet-base-v2 # You can also run embeddings locally if needed
+EMBEDDINGS_NAME=ibm-granite/granite-embedding-311m-multilingual-r2 # runs locally; see Models/embeddings for alternatives
 ```
 
 In this case, even though you are using Ollama locally, `LLM_PROVIDER` is set to `openai` because Ollama (and many other local inference engines) are designed to be API-compatible with OpenAI. `OPENAI_BASE_URL` points DocsGPT to the local Ollama server.
@@ -468,10 +469,13 @@ See [Embeddings](/Models/embeddings) for full guidance.
 
 | Setting | Default | Description |
 | --- | --- | --- |
-| `EMBEDDINGS_NAME` | `huggingface_sentence-transformers/all-mpnet-base-v2` | The embedding model. |
+| `EMBEDDINGS_NAME` | `huggingface_sentence-transformers/all-mpnet-base-v2` | The embedding model. New installs use `ibm-granite/granite-embedding-311m-multilingual-r2`. Changing it on a populated index requires `application.scripts.reembed`. |
 | `EMBEDDINGS_BASE_URL` | unset | Base URL of a remote OpenAI-compatible embeddings server. Setting it routes all embedding calls there. |
 | `EMBEDDINGS_KEY` | unset | Optional bearer token for the remote embeddings server. |
 | `EMBEDDINGS_MAX_INPUT_TOKENS` | unset | Truncate each remote embedding input to N tokens (guards servers that reject oversized inputs). |
+| `EMBEDDINGS_DELEGATE_TO_WORKER` | `true` | Embed queries on the Celery worker instead of loading a model in the API. Requires a worker consuming `EMBEDDINGS_QUEUE`; set `false` to run the API standalone. Ignored when `EMBEDDINGS_BASE_URL` is set. |
+| `EMBEDDINGS_QUEUE` | `embeddings` | Queue the query-embedding task is routed to. A worker started with an explicit `-Q` must list it. |
+| `EMBEDDINGS_DELEGATE_TIMEOUT` | `60` | Seconds the API waits for the worker's vector before failing the search. |
 
 ## Tools Settings