mirror of
https://github.com/tiennm99/DocsGPT.git
synced 2026-10-05 14:14:39 +00:00
Alex
574f96341e
The backend import package is now docsgpt, the name it will carry on PyPI; application was far too generic to install into anyone's site-packages. git mv plus a mechanical rewrite of every import, dotted string and path reference: 734 Python files, the compose files, Dockerfile, workflows, docs, setup scripts, devcontainer, k8s manifests, vscode config, pytest and coverage config, .gitignore. Behaviour is unchanged. Kept for one release: - A top-level application package whose meta-path finder resolves application.x.y to the already-imported docsgpt.x.y object, so old imports and entry points (celery -A application.app.celery, uvicorn application.asgi:asgi_app) keep working with a FutureWarning. - Celery registers every application.* task name as an alias of its docsgpt.* task on start-up, so messages queued by the previous release still run. The redbeat key prefix moves to redbeat:docsgpt:v2: so schedule entries the previous release wrote are left unread instead of firing twice. The backend image builds from the repository root (docker build -f docsgpt/Dockerfile .) so it can ship the alias package; a root .dockerignore allow-lists docsgpt/ and application/ and keeps caches, local data, .env files, the sample index files and the Dockerfile out. Compose and the image workflows point at the new context.
21 lines
723 B
YAML
21 lines
723 B
YAML
provider: openai
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defaults:
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supports_tools: true
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supports_structured_output: true
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attachments: [image]
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context_window: 400000
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models:
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- id: gpt-5.5
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display_name: GPT-5.5
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description: Flagship frontier model for complex reasoning, coding, and agentic work with a 1M-token context window
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context_window: 1050000
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api_flavor: responses
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reasoning_effort: medium
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- id: gpt-5.4-mini
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display_name: GPT-5.4 Mini
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description: Cost-efficient GPT-5.4-class model for high-volume coding, computer use, and subagent workloads
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- id: gpt-5.4-nano
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display_name: GPT-5.4 Nano
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description: Cheapest GPT-5.4-class model, optimized for simple high-volume tasks where speed and cost matter most
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