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refactor: rename the application package to docsgpt

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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.
This commit is contained in:
Alex committed 2026-09-07 10:20:43 +01:00
1 parent 3e7f1f91b4
commit 574f96341e
984 files changed
+9847 -9682
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tests/agents/test_base_agent.py
+40 -40
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@@ -2,14 +2,14 @@ from contextlib import contextmanager
from unittest.mock import Mock, patch
import pytest
from application.agents.classic_agent import ClassicAgent
from application.llm.anthropic import AnthropicLLM
from application.llm.docsgpt_provider import DocsGPTAPILLM
from application.llm.google_ai import GoogleLLM
from application.llm.groq import GroqLLM
from application.llm.novita import NovitaLLM
from application.llm.open_router import OpenRouterLLM
from application.llm.openai import OpenAILLM
from docsgpt.agents.classic_agent import ClassicAgent
from docsgpt.llm.anthropic import AnthropicLLM
from docsgpt.llm.docsgpt_provider import DocsGPTAPILLM
from docsgpt.llm.google_ai import GoogleLLM
from docsgpt.llm.groq import GroqLLM
from docsgpt.llm.novita import NovitaLLM
from docsgpt.llm.open_router import OpenRouterLLM
from docsgpt.llm.openai import OpenAILLM
@pytest.mark.unit
@@ -229,7 +229,7 @@ class TestBaseAgentTools:
mock_llm_creator,
mock_llm_handler_creator,
):
from application.storage.db.repositories.user_tools import UserToolsRepository
from docsgpt.storage.db.repositories.user_tools import UserToolsRepository
repo = UserToolsRepository(pg_conn)
repo.create(user_id="test_user", name="tool1", status=True)
@@ -240,13 +240,13 @@ class TestBaseAgentTools:
yield pg_conn
monkeypatch.setattr(
"application.agents.tool_executor.db_readonly", _use_pg_conn
"docsgpt.agents.tool_executor.db_readonly", _use_pg_conn
)
agent = ClassicAgent(**agent_base_params)
tools = agent._get_user_tools("test_user")
from application.agents.default_tools import loaded_default_tools
from docsgpt.agents.default_tools import loaded_default_tools
assert len(tools) == 2 + len(loaded_default_tools())
assert "0" in tools
@@ -263,7 +263,7 @@ class TestBaseAgentTools:
mock_llm_creator,
mock_llm_handler_creator,
):
from application.storage.db.repositories.user_tools import UserToolsRepository
from docsgpt.storage.db.repositories.user_tools import UserToolsRepository
repo = UserToolsRepository(pg_conn)
repo.create(user_id="test_user", name="tool1", status=True)
@@ -274,13 +274,13 @@ class TestBaseAgentTools:
yield pg_conn
monkeypatch.setattr(
"application.agents.tool_executor.db_readonly", _use_pg_conn
"docsgpt.agents.tool_executor.db_readonly", _use_pg_conn
)
agent = ClassicAgent(**agent_base_params)
tools = agent._get_user_tools("test_user")
from application.agents.default_tools import loaded_default_tools
from docsgpt.agents.default_tools import loaded_default_tools
assert len(tools) == 1 + len(loaded_default_tools())
names = {t["name"] for t in tools.values()}
@@ -295,8 +295,8 @@ class TestBaseAgentTools:
mock_llm_creator,
mock_llm_handler_creator,
):
from application.storage.db.repositories.agents import AgentsRepository
from application.storage.db.repositories.user_tools import UserToolsRepository
from docsgpt.storage.db.repositories.agents import AgentsRepository
from docsgpt.storage.db.repositories.user_tools import UserToolsRepository
tool_row = UserToolsRepository(pg_conn).create(
user_id="alice", name="api_tool"
@@ -316,13 +316,13 @@ class TestBaseAgentTools:
yield pg_conn
monkeypatch.setattr(
"application.agents.tool_executor.db_readonly", _use_pg_conn
"docsgpt.agents.tool_executor.db_readonly", _use_pg_conn
)
agent = ClassicAgent(**agent_base_params)
tools = agent._get_tools("api_key_123")
from application.agents.default_tools import loaded_default_tools
from docsgpt.agents.default_tools import loaded_default_tools
# Agent-bound: exactly agents.tools, no defaults.
assert set(tools) == {tool_id}
@@ -573,7 +573,7 @@ class TestBaseAgentToolExecution:
):
agent = ClassicAgent(**agent_base_params)
from application.agents.tool_executor import PERSISTED_RESULT_MAX_LEN
from docsgpt.agents.tool_executor import PERSISTED_RESULT_MAX_LEN
agent.tool_calls = [
{
@@ -833,7 +833,7 @@ class TestCalculateContextTokens:
messages = [{"role": "user", "content": "hello"}]
with patch(
"application.api.answer.services.compression.token_counter.TokenCounter"
"docsgpt.api.answer.services.compression.token_counter.TokenCounter"
) as MockTC:
MockTC.count_message_tokens.return_value = 42
result = agent._calculate_current_context_tokens(messages)
@@ -862,7 +862,7 @@ class TestCheckContextLimit:
with patch.object(agent, "_calculate_current_context_tokens", return_value=100):
with patch(
"application.core.model_utils.get_token_limit", return_value=10000
"docsgpt.core.model_utils.get_token_limit", return_value=10000
):
result = agent._check_context_limit(messages)
assert result is False
@@ -879,7 +879,7 @@ class TestCheckContextLimit:
# threshold = 10000 * 0.8 = 8000; tokens = 8001 → True
with patch.object(agent, "_calculate_current_context_tokens", return_value=8001):
with patch(
"application.core.model_utils.get_token_limit", return_value=10000
"docsgpt.core.model_utils.get_token_limit", return_value=10000
):
result = agent._check_context_limit(messages)
assert result is True
@@ -913,7 +913,7 @@ class TestValidateContextSize:
agent = ClassicAgent(**agent_base_params)
with patch.object(agent, "_calculate_current_context_tokens", return_value=10000):
with patch(
"application.core.model_utils.get_token_limit", return_value=10000
"docsgpt.core.model_utils.get_token_limit", return_value=10000
):
# Should not raise
agent._validate_context_size([{"role": "user", "content": "x"}])
@@ -925,7 +925,7 @@ class TestValidateContextSize:
agent = ClassicAgent(**agent_base_params)
with patch.object(agent, "_calculate_current_context_tokens", return_value=100):
with patch(
"application.core.model_utils.get_token_limit", return_value=10000
"docsgpt.core.model_utils.get_token_limit", return_value=10000
):
agent._validate_context_size([])
assert agent.current_token_count == 100
@@ -937,7 +937,7 @@ class TestValidateContextSize:
# 8500 / 10000 = 85% → above 80% threshold but below 100%
with patch.object(agent, "_calculate_current_context_tokens", return_value=8500):
with patch(
"application.core.model_utils.get_token_limit", return_value=10000
"docsgpt.core.model_utils.get_token_limit", return_value=10000
):
agent._validate_context_size([])
assert agent.current_token_count == 8500
@@ -955,7 +955,7 @@ class TestTruncateTextMiddle:
self, agent_base_params, mock_llm_creator, mock_llm_handler_creator
):
agent = ClassicAgent(**agent_base_params)
with patch("application.utils.num_tokens_from_string", return_value=5):
with patch("docsgpt.utils.num_tokens_from_string", return_value=5):
result = agent._truncate_text_middle("short", max_tokens=100)
assert result == "short"
@@ -968,7 +968,7 @@ class TestTruncateTextMiddle:
def fake_tokens(text):
return len(text) // 4
with patch("application.utils.num_tokens_from_string", side_effect=fake_tokens):
with patch("docsgpt.utils.num_tokens_from_string", side_effect=fake_tokens):
result = agent._truncate_text_middle(long_text, max_tokens=50)
assert "[... content truncated to fit context limit ...]" in result
assert len(result) < len(long_text)
@@ -977,7 +977,7 @@ class TestTruncateTextMiddle:
self, agent_base_params, mock_llm_creator, mock_llm_handler_creator
):
agent = ClassicAgent(**agent_base_params)
with patch("application.utils.num_tokens_from_string", return_value=100):
with patch("docsgpt.utils.num_tokens_from_string", return_value=100):
result = agent._truncate_text_middle("some text", max_tokens=0)
assert result == ""
@@ -1020,7 +1020,7 @@ class TestTruncateHistoryToFit:
{"prompt": "q1", "response": "a1"},
{"prompt": "q2", "response": "a2"},
]
with patch("application.utils.num_tokens_from_string", return_value=5):
with patch("docsgpt.utils.num_tokens_from_string", return_value=5):
result = agent._truncate_history_to_fit(history, 10000)
assert len(result) == 2
@@ -1035,7 +1035,7 @@ class TestTruncateHistoryToFit:
{"prompt": "new", "response": "new_ans"},
]
# Each message = 10 tokens (prompt + response), budget = 15 → only 1 fits
with patch("application.utils.num_tokens_from_string", return_value=5):
with patch("docsgpt.utils.num_tokens_from_string", return_value=5):
result = agent._truncate_history_to_fit(history, 15)
assert len(result) == 1
assert result[0]["prompt"] == "new"
@@ -1060,7 +1060,7 @@ class TestTruncateHistoryToFit:
],
}
]
with patch("application.utils.num_tokens_from_string", return_value=3):
with patch("docsgpt.utils.num_tokens_from_string", return_value=3):
result = agent._truncate_history_to_fit(history, 100)
assert len(result) == 1
@@ -1080,8 +1080,8 @@ class TestBuildMessagesAdvanced:
agent = ClassicAgent(**agent_base_params)
with patch(
"application.core.model_utils.get_token_limit", return_value=100000
), patch("application.utils.num_tokens_from_string", return_value=10):
"docsgpt.core.model_utils.get_token_limit", return_value=100000
), patch("docsgpt.utils.num_tokens_from_string", return_value=10):
messages = agent._build_messages("System prompt", "query")
system_content = messages[0]["content"]
@@ -1099,8 +1099,8 @@ class TestBuildMessagesAdvanced:
return len(text)
with patch(
"application.core.model_utils.get_token_limit", return_value=200
), patch("application.utils.num_tokens_from_string", side_effect=fake_tokens):
"docsgpt.core.model_utils.get_token_limit", return_value=200
), patch("docsgpt.utils.num_tokens_from_string", side_effect=fake_tokens):
with patch.object(agent, "_truncate_text_middle", return_value="truncated"):
with patch.object(agent, "_truncate_history_to_fit", return_value=[]):
messages = agent._build_messages("sys", "A" * 500)
@@ -1127,8 +1127,8 @@ class TestBuildMessagesAdvanced:
agent = ClassicAgent(**agent_base_params)
with patch(
"application.core.model_utils.get_token_limit", return_value=100000
), patch("application.utils.num_tokens_from_string", return_value=5):
"docsgpt.core.model_utils.get_token_limit", return_value=100000
), patch("docsgpt.utils.num_tokens_from_string", return_value=5):
messages = agent._build_messages("sys", "q")
tool_msgs = [m for m in messages if m["role"] == "tool"]
@@ -1391,7 +1391,7 @@ class TestBaseAgentContextBudget:
def _agent_with_limit(params, monkeypatch, limit):
agent = ClassicAgent(**params)
monkeypatch.setattr(
"application.core.model_utils.get_token_limit",
"docsgpt.core.model_utils.get_token_limit",
lambda *a, **k: limit,
)
return agent
@@ -1489,7 +1489,7 @@ class TestBaseAgentDocumentsInUserTurn:
]
agent = ClassicAgent(**agent_base_params)
monkeypatch.setattr(
"application.core.model_utils.get_token_limit", lambda *a, **k: 1500
"docsgpt.core.model_utils.get_token_limit", lambda *a, **k: 1500
)
messages = agent._build_messages("short system", "What is Python?")
user = messages[-1]["content"]
@@ -1531,7 +1531,7 @@ class TestBaseAgentDocumentBudgetOrdering:
]
agent = ClassicAgent(**agent_base_params)
monkeypatch.setattr(
"application.core.model_utils.get_token_limit", lambda *a, **k: 4000
"docsgpt.core.model_utils.get_token_limit", lambda *a, **k: 4000
)
huge_question = "please explain this in detail " * 900
user = agent._build_messages("short system", huge_question)[-1]["content"]