54 lines
2.7 KiB
Markdown
54 lines
2.7 KiB
Markdown
# Language Learning App API Architecture
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This is a HTTP API, written in Python, using the Fastapi framework.
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The code should be organised using both Domain Driven Design and Hexagonal Architecture principles.
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## Domain
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The `domain` directory contains the core logic of my application, not related to specific implementation details.
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This is where all of the logic around the actual language learning lives.
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### Models
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In `app/domain/models` contains the core Domain Entities, i.e. classes that represent objects for core domain processes.
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### Services
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The `app/domain/services` directory contains modules that encapsulate the "orchestration" or "choreography" of other components of the system to achieve complex, domain actions.
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For example:
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- `TextGenerationService` details the step-by-step process of how a series of text is synthesised, audio is generated, parts of speech tagged, timed transcripts generated, etc. which is then used by the learner for language learning.
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## Outbound
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The `app/outbound` directory contains modules that allow this project to communicate with external systems.
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### Repositories and persistence
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This project uses SQLAlchemy for persistence and a postgresql database for storage. This is the same in local, deployed, and test environments.
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Notably the `app/outbound/postgres` directory contains two modules:
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- `entities` contains definitions of tables in the database. These are how the Domain Entities are serialised, but this code specifically is used for serialisation to, and de-serialisation from, persisted data and into Domain Entities from the `models` module.
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- `repositories` module contains classes which manage transactions (e.g. CRUD operations) with the psotgresql database, managed through sqlite. Code in the repositories module are what allow Models to be persisted via Entities, allowing all other code to be free of implementation details.
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### API Clients
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Other modules in the wider `app/outbound` directory are about communicating with specific third-party APIs, notably through HTTP APIs authenticated with an authentication token.
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These modules are modelled as Classes.
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Example Api Clients in their own modules are:
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- `AnthropicClient` to communicate with Anthorpic's LLM, i.e. Claude, to generate text and synthesis.
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- `GeminiClient` to communicate with Google's Gemini for text-to-speech generation
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- `DeepgramClient` for timestamped speech-to-text transcription
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## Deploymnet
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The application has not been deployed yet, but local development should mimic the deployed environment as much as possible.
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It will be deployed on a VPS using containerisation technologies (docker, podman). At the root of the projec there is a `docker-compose.yaml` file which will describe each dependency (e.g. database, queue, storage).
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