760b56bfd6
Covers: overview, setup, API reference, configuration, testing, deployment, and known quirks.
68 lines
1.5 KiB
Markdown
68 lines
1.5 KiB
Markdown
# Setup & Installation
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## Prerequisites
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- Python 3.12+
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- [uv](https://astral.sh/uv/) package manager
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- An LLM backend (Ollama, LM Studio, MiniMax, OpenAI, or any OpenAI/Anthropic-compatible API)
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## Quick Start
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```bash
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# Clone the repository
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git clone https://git.danhenry.dev/daniel/email-classifier.git
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cd email-classifier
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# Install dependencies
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uv sync
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# Start the server
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uv run uvicorn app.main:app --host 0.0.0.0 --port 7999
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```
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The API will be available at `http://localhost:7999`. Auto-generated API docs are at `http://localhost:7999/docs` (Swagger UI) and `http://localhost:7999/redoc`.
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## Environment Variables
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The service is configured entirely through environment variables. See [Configuration](configuration.md) for the full reference.
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A minimal `.env` file for local development with Ollama:
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```bash
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LLM_PROVIDER=openai
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LLM_BASE_URL=http://localhost:11434/v1
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LLM_API_KEY=none
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LLM_MODEL=qwen2.5-7b-instruct.q4_k_m
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LLM_TEMPERATURE=0.1
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```
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## Using Docker
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```bash
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# Build the image
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docker build -t email-classifier .
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# Run the container
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docker run -p 7999:7999 \
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-e LLM_PROVIDER=openai \
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-e LLM_BASE_URL=http://host.docker.internal:11434/v1 \
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-e LLM_API_KEY=none \
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-e LLM_MODEL=qwen2.5-7b-instruct.q4_k_m \
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email-classifier
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```
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## Dependency Management
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This project uses [uv](https://astral.sh/uv/) for dependency management. Do not use `pip` directly.
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```bash
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# Add a new dependency
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uv add <package>
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# Sync dependencies (after pulling changes)
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uv sync
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# Run with uv (recommended)
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uv run uvicorn app.main:app --reload
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```
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