refactor: rename the application package to docsgpt

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.

tests/scripts/test_grant_admin.py

@@ -20,13 +20,13 @@ import pytest
 sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
 
 from scripts import grant_admin  # noqa: E402
-from application.storage.db.repositories.auth_events import (  # noqa: E402
+from docsgpt.storage.db.repositories.auth_events import (  # noqa: E402
     AuthEventsRepository,
 )
-from application.storage.db.repositories.user_roles import (  # noqa: E402
+from docsgpt.storage.db.repositories.user_roles import (  # noqa: E402
     UserRolesRepository,
 )
-from application.storage.db.repositories.users import UsersRepository  # noqa: E402
+from docsgpt.storage.db.repositories.users import UsersRepository  # noqa: E402
 
 
 @pytest.fixture

tests/scripts/test_prefetch_models.py

@@ -6,8 +6,8 @@ from unittest.mock import MagicMock, patch
 
 import pytest
 
-from application.scripts import prefetch_models
-from application.vectorstore.model_registry import GRANITE_311M, MPNET, OPENAI_ADA_002
+from docsgpt.scripts import prefetch_models
+from docsgpt.vectorstore.model_registry import GRANITE_311M, MPNET, OPENAI_ADA_002
 
 
 @pytest.fixture

tests/scripts/test_reembed.py

@@ -4,7 +4,7 @@ from unittest.mock import MagicMock, patch
 
 import pytest
 
-from application.scripts import reembed
+from docsgpt.scripts import reembed
 
 
 def paginating_cursor(chunk_rows, *, graph_rows=(), graph_table=("graph_nodes",)):
@@ -323,7 +323,7 @@ class TestFaissSourceDiscovery:
             "indexes/src-b/index.faiss",
         ]
         with patch(
-            "application.storage.storage_creator.StorageCreator.get_storage",
+            "docsgpt.storage.storage_creator.StorageCreator.get_storage",
             return_value=storage,
         ):
             assert reembed.list_source_ids("faiss") == ["src-a", "src-b"]
@@ -332,7 +332,7 @@ class TestFaissSourceDiscovery:
         storage = MagicMock()
         storage.list_files.side_effect = OSError("permission denied")
         with patch(
-            "application.storage.storage_creator.StorageCreator.get_storage",
+            "docsgpt.storage.storage_creator.StorageCreator.get_storage",
             return_value=storage,
         ):
             with pytest.raises(reembed.ReembedError, match="permission denied"):
@@ -453,7 +453,7 @@ class TestEmbedsInProcess:
     """A batch job should not round-trip every chunk through the broker."""
 
     def test_delegation_is_turned_off_for_the_run(self, monkeypatch):
-        from application.core.settings import settings
+        from docsgpt.core.settings import settings
 
         monkeypatch.setattr(settings, "EMBEDDINGS_DELEGATE_TO_WORKER", True, raising=False)
         monkeypatch.setattr(settings, "VECTOR_STORE", "pgvector", raising=False)
@@ -469,14 +469,14 @@ class TestRecordsTheModel:
     """``sources.model`` is what the boot mismatch check reads."""
 
     def test_a_re_embedded_source_is_stamped(self, monkeypatch):
-        from application.core.settings import settings
+        from docsgpt.core.settings import settings
 
         monkeypatch.setattr(settings, "EMBEDDINGS_NAME", "new/model", raising=False)
         conn = MagicMock()
         session = MagicMock()
         session.__enter__ = MagicMock(return_value=conn)
         session.__exit__ = MagicMock(return_value=False)
-        with patch("application.storage.db.session.db_session", return_value=session):
+        with patch("docsgpt.storage.db.session.db_session", return_value=session):
             reembed.record_source_model("src-1")
         params = conn.execute.call_args.args[1]
         assert params == {"model": "new/model", "id": "src-1"}
@@ -506,7 +506,7 @@ class TestRecordsTheModel:
 class TestThePinIsResolved:
     """The script must embed with the model the installation is pinned to.
 
-    ``resolve_embeddings_pin`` runs in ``application.app``, which this script
+    ``resolve_embeddings_pin`` runs in ``docsgpt.app``, which this script
     never imports. An install pinned in ``app_metadata`` with no
     ``EMBEDDINGS_NAME`` in the environment -- every stock Kubernetes
     deployment, whose manifests carry no embedding config -- would otherwise
@@ -518,7 +518,7 @@ class TestThePinIsResolved:
     def test_main_resolves_the_pin_before_reading_the_store(self):
         order = []
         with patch(
-            "application.storage.db.embeddings_pin.resolve_embeddings_pin",
+            "docsgpt.storage.db.embeddings_pin.resolve_embeddings_pin",
             side_effect=lambda *a, **k: order.append("pin"),
         ), patch.object(reembed.settings, "VECTOR_STORE", "pgvector", create=True), patch.object(
             reembed, "run", side_effect=lambda *a, **k: (order.append("run"), 0)[1]
@@ -528,7 +528,7 @@ class TestThePinIsResolved:
 
     def test_an_unsupported_store_still_resolved_the_pin_first(self):
         with patch(
-            "application.storage.db.embeddings_pin.resolve_embeddings_pin"
+            "docsgpt.storage.db.embeddings_pin.resolve_embeddings_pin"
         ) as pin, patch.object(reembed.settings, "VECTOR_STORE", "qdrant", create=True):
             assert reembed.main([]) == 2
         pin.assert_called_once()

tests/scripts/test_reembed_pgvector_live.py

@@ -11,9 +11,9 @@ from unittest.mock import patch
 
 import pytest
 
-from application.scripts import reembed
-from application.vectorstore import pgvector as pgvector_module
-from application.vectorstore.pgvector import PGVectorStore
+from docsgpt.scripts import reembed
+from docsgpt.vectorstore import pgvector as pgvector_module
+from docsgpt.vectorstore.pgvector import PGVectorStore
 
 pytestmark = pytest.mark.integration
 
@@ -68,7 +68,7 @@ def live_dsn(postgresql, monkeypatch):
         pytest.skip(f"pgvector extension unavailable: {exc}")
 
     dsn = _dsn(postgresql.info)
-    from application.core import settings as settings_module
+    from docsgpt.core import settings as settings_module
 
     settings = settings_module.settings
     monkeypatch.setattr(settings, "VECTOR_STORE", "pgvector", raising=False)
@@ -83,7 +83,7 @@ def live_dsn(postgresql, monkeypatch):
 def _seed(dsn, source_id, texts, embeddings):
     """Create the schema and insert ``texts`` embedded by ``embeddings``."""
     with patch(
-        "application.vectorstore.base.BaseVectorStore._get_embeddings",
+        "docsgpt.vectorstore.base.BaseVectorStore._get_embeddings",
         return_value=embeddings,
     ):
         store = PGVectorStore(source_id=source_id, connection_string=dsn)
@@ -139,7 +139,7 @@ class TestReembedPgvectorLive:
 
         new_model = _Embeddings(9.0)
         with patch(
-            "application.vectorstore.base.BaseVectorStore._get_embeddings",
+            "docsgpt.vectorstore.base.BaseVectorStore._get_embeddings",
             return_value=new_model,
         ):
             seen, written = reembed.reembed_pgvector("src-a", batch_size=2, dry_run=False)
@@ -154,7 +154,7 @@ class TestReembedPgvectorLive:
         _seed(live_dsn, "src-b", TEXTS, _Embeddings(1.0))
         new_model = _Embeddings(9.0)
         with patch(
-            "application.vectorstore.base.BaseVectorStore._get_embeddings",
+            "docsgpt.vectorstore.base.BaseVectorStore._get_embeddings",
             return_value=new_model,
         ):
             seen, written = reembed.reembed_pgvector("src-b", batch_size=2, dry_run=True)
@@ -167,7 +167,7 @@ class TestReembedPgvectorLive:
         _seed(live_dsn, "src-c", TEXTS, _Embeddings(1.0))
         new_model = _Embeddings(9.0)
         with patch(
-            "application.vectorstore.base.BaseVectorStore._get_embeddings",
+            "docsgpt.vectorstore.base.BaseVectorStore._get_embeddings",
             return_value=new_model,
         ):
             reembed.reembed_pgvector("src-c", batch_size=2, dry_run=False)
@@ -177,7 +177,7 @@ class TestReembedPgvectorLive:
         _seed(live_dsn, "src-d", TEXTS, _Embeddings(1.0))
         _seed(live_dsn, "src-e", TEXTS, _Embeddings(1.0))
         with patch(
-            "application.vectorstore.base.BaseVectorStore._get_embeddings",
+            "docsgpt.vectorstore.base.BaseVectorStore._get_embeddings",
             return_value=_Embeddings(9.0),
         ):
             reembed.reembed_pgvector("src-d", batch_size=64, dry_run=False)
@@ -196,7 +196,7 @@ GRAPH_SOURCE = "11111111-2222-3333-4444-555555555555"
 
 def _seed_graph_node(dsn, source_id, name, seed):
     """Insert one graph node carrying a name embedding at ``seed``."""
-    from application.graphrag.store import GraphStore
+    from docsgpt.graphrag.store import GraphStore
 
     store = PGVectorStore(source_id=source_id, connection_string=dsn)
     conn = store._get_connection()
@@ -240,7 +240,7 @@ class TestGraphNodeReembedLive:
     """
 
     def test_node_names_are_re_embedded(self, live_dsn, monkeypatch):
-        from application.core import settings as settings_module
+        from docsgpt.core import settings as settings_module
 
         monkeypatch.setattr(
             settings_module.settings, "GRAPHRAG_ENABLED", True, raising=False
@@ -251,7 +251,7 @@ class TestGraphNodeReembedLive:
         assert all(v[0] == pytest.approx(1.0) for _, v in _node_vectors(live_dsn, GRAPH_SOURCE))
 
         with patch(
-            "application.vectorstore.base.BaseVectorStore._get_embeddings",
+            "docsgpt.vectorstore.base.BaseVectorStore._get_embeddings",
             return_value=_Embeddings(9.0),
         ):
             reembed.reembed_pgvector(GRAPH_SOURCE, batch_size=64, dry_run=False)
@@ -263,7 +263,7 @@ class TestGraphNodeReembedLive:
         )
 
     def test_graph_is_left_alone_when_graphrag_is_off(self, live_dsn, monkeypatch):
-        from application.core import settings as settings_module
+        from docsgpt.core import settings as settings_module
 
         monkeypatch.setattr(
             settings_module.settings, "GRAPHRAG_ENABLED", False, raising=False
@@ -272,7 +272,7 @@ class TestGraphNodeReembedLive:
         _seed_graph_node(live_dsn, GRAPH_SOURCE, "Alpha", 1.0)
 
         with patch(
-            "application.vectorstore.base.BaseVectorStore._get_embeddings",
+            "docsgpt.vectorstore.base.BaseVectorStore._get_embeddings",
             return_value=_Embeddings(9.0),
         ):
             reembed.reembed_pgvector(GRAPH_SOURCE, batch_size=64, dry_run=False)
@@ -280,7 +280,7 @@ class TestGraphNodeReembedLive:
         assert all(v[0] == pytest.approx(1.0) for _, v in _node_vectors(live_dsn, GRAPH_SOURCE))
 
     def test_dry_run_leaves_node_vectors_untouched(self, live_dsn, monkeypatch):
-        from application.core import settings as settings_module
+        from docsgpt.core import settings as settings_module
 
         monkeypatch.setattr(
             settings_module.settings, "GRAPHRAG_ENABLED", True, raising=False
@@ -289,7 +289,7 @@ class TestGraphNodeReembedLive:
         _seed_graph_node(live_dsn, GRAPH_SOURCE, "Alpha", 1.0)
 
         with patch(
-            "application.vectorstore.base.BaseVectorStore._get_embeddings",
+            "docsgpt.vectorstore.base.BaseVectorStore._get_embeddings",
             return_value=_Embeddings(9.0),
         ):
             reembed.reembed_pgvector(GRAPH_SOURCE, batch_size=64, dry_run=True)

tests/scripts/test_verify_offline.py

@@ -4,7 +4,7 @@ import sys
 import types
 from unittest.mock import patch
 
-from application.scripts import verify_offline
+from docsgpt.scripts import verify_offline
 
 
 def _fake_tiktoken(monkeypatch):
@@ -17,8 +17,8 @@ class TestVerify:
     def test_passes_when_every_check_passes(self, monkeypatch, capsys):
         _fake_tiktoken(monkeypatch)
         counter = types.SimpleNamespace(name="org/model", count=lambda text: 4)
-        with patch("application.parser.tokenization.get_token_counter", return_value=counter), \
-                patch("application.vectorstore.embeddings_local.EmbeddingsWrapper") as wrapper, \
+        with patch("docsgpt.parser.tokenization.get_token_counter", return_value=counter), \
+                patch("docsgpt.vectorstore.embeddings_local.EmbeddingsWrapper") as wrapper, \
                 patch.object(verify_offline, "is_available", return_value=False):
             wrapper.return_value.embed_query.return_value = [0.0] * 768
             assert verify_offline.verify(["ibm-granite/granite-embedding-311m-multilingual-r2"]) is True
@@ -30,8 +30,8 @@ class TestVerify:
         """A cache miss makes chunking silently use cl100k; that is a failed check."""
         _fake_tiktoken(monkeypatch)
         counter = types.SimpleNamespace(name="cl100k_base", count=lambda text: 4)
-        with patch("application.parser.tokenization.get_token_counter", return_value=counter), \
-                patch("application.vectorstore.embeddings_local.EmbeddingsWrapper") as wrapper, \
+        with patch("docsgpt.parser.tokenization.get_token_counter", return_value=counter), \
+                patch("docsgpt.vectorstore.embeddings_local.EmbeddingsWrapper") as wrapper, \
                 patch.object(verify_offline, "is_available", return_value=False):
             wrapper.return_value.embed_query.return_value = [0.0] * 768
             assert verify_offline.verify(["ibm-granite/granite-embedding-311m-multilingual-r2"]) is False