57 lines
1.3 KiB
Markdown
57 lines
1.3 KiB
Markdown
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# Usage
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## Running Server
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```bash
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# Locally
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minyma server run
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# Docker Quick Start
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make docker_build_local
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docker run \
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-p 5000:5000 \
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-e OPENAI_API_KEY=`cat openai_key` \
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-e DATA_PATH=/data \
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-v ./data:/data \
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minyma:latest
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```
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The server will now be accessible at `http://localhost:5000`
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## Normalizing & Loading Data
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Minyma is designed to be extensible. You can add normalizers and vector db's
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using the appropriate interfaces defined in `./minyma/normalizer.py` and
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`./minyma/vdb.py`. At the moment the only supported database is `chroma`
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and the only supported normalizer is the `pubmed` normalizer.
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To normalize data, you can use Minyma's `normalize` CLI command:
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```bash
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minyma normalize --filename ./pubmed_manuscripts.jsonl --normalizer pubmed --database chroma --datapath ./chroma
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```
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The above example does the following:
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- Uses the `pubmed` normalizer
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- Normalizes the `./pubmed_manuscripts.jsonl` raw dataset [0]
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- Loads the output into a `chroma` database and persists the data to the `./chroma` directory
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**NOTE:** The above dataset took about an hour to normalize on my MPB M2 Max
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[0] https://huggingface.co/datasets/TaylorAI/pubmed_author_manuscripts/tree/main
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# Development
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```bash
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# Initiate
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python3 -m venv venv
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. ./venv/bin/activate
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# Local Development
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pip install -e .
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# Creds
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export OPENAI_API_KEY=`cat openai_key`
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```
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