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06e36d24f4
### 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>
(1). Deploy RAGFlow services and images
https://ragflow.io/docs/build_docker_image
(2). Configure the required environment for testing
Install Python dependencies (including test dependencies):
uv sync --python 3.13 --only-group test --no-default-groups --frozen
Activate the environment:
source .venv/bin/activate
Install SDK:
uv pip install sdk/python
Modify the .env file: Add the following code:
COMPOSE_PROFILES=${COMPOSE_PROFILES},tei-cpu
TEI_MODEL=BAAI/bge-small-en-v1.5
RAGFLOW_IMAGE=infiniflow/ragflow:v0.26.4 #Replace with the image you are using
Start the container（wait two minutes）:
docker compose -f docker/docker-compose.yml up -d
(3). Test Elasticsearch
a) Run sdk tests against Elasticsearch:
export HTTP_API_TEST_LEVEL=p2
export HOST_ADDRESS=http://127.0.0.1:9380 # Ensure that this port is the API port mapped to your localhost
pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api
b) Run http api tests against Elasticsearch:
pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api
(4). Test Infinity
Modify the .env file:
DOC_ENGINE=${DOC_ENGINE:-infinity}
Start the container:
docker compose -f docker/docker-compose.yml down -v
docker compose -f docker/docker-compose.yml up -d
a) Run sdk tests against Infinity:
DOC_ENGINE=infinity pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api
b) Run http api tests against Infinity:
DOC_ENGINE=infinity pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api