藍+85CD
24432f881c
* refactor(test): use `granite4:350m-h` instead of `granite3:2b`, `qwen3:0.6b` * refactor(test): add 1b-h model, update test snapshot * refactor: simplify embed test * chore: revert changes * fix: test * fix: test * fix: try another seed * fix: test
73 lines
2.3 KiB
Markdown
73 lines
2.3 KiB
Markdown
# Contributing
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## Before
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Before creating a Pull Request, please contact us at [issues](https://github.com/moeru-ai/xsai/issues) or [discussions](https://github.com/moeru-ai/xsai/discussions) to confirm that the feature in question is one we need.
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When creating a Pull Request, please make sure you have read and agreed to it:
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- Allow us (xsAI Maintainers) to modify your code at any time.
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- Usually, this is to make it simpler, but there may be exceptions.
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- Allow us (xsAI Maintainers) to create new Pull Request with your code and close the old ones while adding the `Co-authored-by` trailers.
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We will do our best to try to explain and discuss the possible implementation approaches as much detailed as possible before modifications and Pull Request state changes,
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but still, sometimes it may be much more efficient for us to directly use actual code to address our opinions.
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## Testing
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We weren't supposed to connect to OpenAI's paid APIs while testing, so we chose some local services to use for test:
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### embed, {generate,stream}Text, {generate,stream}Object, tool
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It uses [Ollama](https://github.com/ollama/ollama).
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The model used may change as needed, currently `granite3.3:2b` (chat completion, tool calling), `qwen3:0.6b` (reasoning), and `all-minilm` (embedding).
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```bash
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ollama pull all-minilm
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ollama pull granite4:350m-h
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ollama pull granite4:1b-h
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ollama pull qwen3:0.6b
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ollama serve
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```
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### generateSpeech
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It uses [openai-edge-tts](https://github.com/travisvn/openai-edge-tts).
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You can quickly start one via `docker` or `docker-compose`:
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```bash
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docker run --rm -p 5050:5050 -e PORT=5050 travisvn/openai-edge-tts:latest
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```
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```yaml
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services:
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openai-edge-tts:
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image: travisvn/openai-edge-tts:latest
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ports:
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- 5050:5050
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```
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### generateTranscription
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It uses [whisper.cpp](https://github.com/ggerganov/whisper.cpp) and the `ggml-large-v3-turbo-q5_0` model.
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If you're using Nix, you can do this:
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```nix
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{ pkgs, ... }: {
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home.packages = with pkgs; [ openai-whisper-cpp ];
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}
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```
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Then store the model files in the directory you want and start the server:
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```bash
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mkdir models
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cd models
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whisper-cpp-download-ggml-model large-v3-turbo-q5_0
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cd ..
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whisper-cpp-server --host 127.0.0.1 --port 9010 -nt -m models/ggml-large-v3-turbo-q5_0.bin --request-path /audio/transcriptions --inference-path ""
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```
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