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68 lines
1.6 KiB
Markdown
68 lines
1.6 KiB
Markdown
# Data query example
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Example of integration with HuggingFace Inference API with help of [langchaingo](https://github.com/tmc/langchaingo).
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## Setup
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Download the LocalAI and start the API:
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```bash
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# Clone LocalAI
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git clone https://github.com/go-skynet/LocalAI
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cd LocalAI/examples/langchain-huggingface
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docker-compose up -d
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```
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Node: Ensure you've set `HUGGINGFACEHUB_API_TOKEN` environment variable, you can generate it
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on [Settings / Access Tokens](https://huggingface.co/settings/tokens) page of HuggingFace site.
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This is an example `.env` file for LocalAI:
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```ini
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MODELS_PATH=/models
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CONTEXT_SIZE=512
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HUGGINGFACEHUB_API_TOKEN=hg_123456
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```
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## Using remote models
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Now you can use any remote models available via HuggingFace API, for example let's enable using of
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[gpt2](https://huggingface.co/gpt2) model in `gpt-3.5-turbo.yaml` config:
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```yml
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name: gpt-3.5-turbo
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parameters:
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model: gpt2
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top_k: 80
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temperature: 0.2
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top_p: 0.7
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context_size: 1024
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backend: "langchain-huggingface"
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stopwords:
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- "HUMAN:"
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- "GPT:"
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roles:
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user: " "
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system: " "
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template:
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completion: completion
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chat: gpt4all
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```
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Here is you can see in field `parameters.model` equal `gpt2` and `backend` equal `langchain-huggingface`.
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## How to use
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```shell
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# Now API is accessible at localhost:8080
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curl http://localhost:8080/v1/models
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# {"object":"list","data":[{"id":"gpt-3.5-turbo","object":"model"}]}
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curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
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"model": "gpt-3.5-turbo",
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"prompt": "A long time ago in a galaxy far, far away",
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"temperature": 0.7
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}'
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``` |