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88 lines
2.2 KiB
Markdown
88 lines
2.2 KiB
Markdown
# GPT4All REST API
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This directory contains the source code to run and build docker images that run a FastAPI app
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for serving inference from GPT4All models. The API matches the OpenAI API spec.
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## Tutorial
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The following tutorial assumes that you have checked out this repo and cd'd into it.
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### Starting the app
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First change your working directory to `gpt4all/gpt4all-api`.
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Now you can build the FastAPI docker image. You only have to do this on initial build or when you add new dependencies to the requirements.txt file:
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```bash
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DOCKER_BUILDKIT=1 docker build -t gpt4all_api --progress plain -f gpt4all_api/Dockerfile.buildkit .
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```
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Then, start the backend with:
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```bash
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docker compose up --build
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```
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This will run both the API and locally hosted GPU inference server. If you want to run the API without the GPU inference server, you can run:
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```bash
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docker compose up --build gpt4all_api
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```
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To run the API with the GPU inference server, you will need to include environment variables (like the `MODEL_ID`). Edit the `.env` file and run
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```bash
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docker compose --env-file .env up --build
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```
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#### Spinning up your app
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Run `docker compose up` to spin up the backend. Monitor the logs for errors in-case you forgot to set an environment variable above.
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#### Development
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Run
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```bash
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docker compose up --build
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```
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and edit files in the `app` directory. The api will hot-reload on changes.
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You can run the unit tests with
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```bash
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make test
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```
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#### Viewing API documentation
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Once the FastAPI ap is started you can access its documentation and test the search endpoint by going to:
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```
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localhost:80/docs
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```
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This documentation should match the OpenAI OpenAPI spec located at https://github.com/openai/openai-openapi/blob/master/openapi.yaml
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#### Running inference
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```python
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import openai
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openai.api_base = "http://localhost:4891/v1"
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openai.api_key = "not needed for a local LLM"
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def test_completion():
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model = "gpt4all-j-v1.3-groovy"
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prompt = "Who is Michael Jordan?"
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response = openai.Completion.create(
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model=model,
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prompt=prompt,
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max_tokens=50,
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temperature=0.28,
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top_p=0.95,
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n=1,
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echo=True,
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stream=False
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)
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assert len(response['choices'][0]['text']) > len(prompt)
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print(response)
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```
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