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Remove obsolete migrations

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This commit is contained in:
Yiorgis Gozadinos 2026-01-12 12:07:49 +02:00
parent 119ca84da8
commit 061095e855
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5 changed files with 4 additions and 253 deletions
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4
CHANGELOG.md
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@ -14,6 +14,10 @@
- **Dependencies**: Updated lancedb 0.26.0 → 0.26.1, docling 2.65.0 → 2.67.0 - **Dependencies**: Updated lancedb 0.26.0 → 0.26.1, docling 2.65.0 → 2.67.0
### Removed
- **Legacy Migrations**: Removed obsolete database migration files (`v0_9_3.py`, `v0_10_1.py`, `v0_19_6.py`). These migrations were for versions prior to 0.20.0 and are no longer needed since the current release requires a database rebuild anyway.
## [0.24.2] - 2026-01-08 ## [0.24.2] - 2026-01-08
### Fixed ### Fixed

12
haiku_rag_slim/haiku/rag/store/upgrades/__init__.py
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@ -55,14 +55,6 @@ def run_pending_upgrades(store: Store, from_version: str, to_version: str) -> No
# Import upgrade modules AFTER Upgrade class is defined to avoid circular imports # Import upgrade modules AFTER Upgrade class is defined to avoid circular imports
# ruff: noqa: E402, I001 # ruff: noqa: E402, I001
from haiku.rag.store.upgrades.v0_9_3 import upgrade_fts_phrase as upgrade_0_9_3_fts
from haiku.rag.store.upgrades.v0_9_3 import upgrade_order as upgrade_0_9_3_order
from haiku.rag.store.upgrades.v0_10_1 import (
upgrade_add_title as upgrade_0_10_1_add_title,
)
from haiku.rag.store.upgrades.v0_19_6 import (
upgrade_embeddings_model_config as upgrade_0_19_6_embeddings,
)
from haiku.rag.store.upgrades.v0_20_0 import ( from haiku.rag.store.upgrades.v0_20_0 import (
upgrade_add_docling_document as upgrade_0_20_0_docling, upgrade_add_docling_document as upgrade_0_20_0_docling,
) )
@ -73,10 +65,6 @@ from haiku.rag.store.upgrades.v0_25_0 import (
upgrade_compress_docling_document as upgrade_0_25_0_compress, upgrade_compress_docling_document as upgrade_0_25_0_compress,
) )
upgrades.append(upgrade_0_9_3_order)
upgrades.append(upgrade_0_9_3_fts)
upgrades.append(upgrade_0_10_1_add_title)
upgrades.append(upgrade_0_19_6_embeddings)
upgrades.append(upgrade_0_20_0_docling) upgrades.append(upgrade_0_20_0_docling)
upgrades.append(upgrade_0_23_1_contextualize) upgrades.append(upgrade_0_23_1_contextualize)
upgrades.append(upgrade_0_25_0_compress) upgrades.append(upgrade_0_25_0_compress)

64
haiku_rag_slim/haiku/rag/store/upgrades/v0_10_1.py
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@ -1,64 +0,0 @@
import json
from lancedb.pydantic import LanceModel
from pydantic import Field
from haiku.rag.store.engine import Store
from haiku.rag.store.upgrades import Upgrade
def _apply_add_document_title(store: Store) -> None: # pragma: no cover
"""Add a nullable 'title' column to the documents table."""
# Read existing rows using Arrow for schema-agnostic access
try:
docs_arrow = store.documents_table.search().to_arrow()
rows = docs_arrow.to_pylist()
except Exception:
rows = []
class DocumentRecordV2(LanceModel):
id: str
content: str
uri: str | None = None
title: str | None = None
metadata: str = Field(default="{}")
created_at: str = Field(default_factory=lambda: "")
updated_at: str = Field(default_factory=lambda: "")
# Drop and recreate documents table with the new schema
try:
store.db.drop_table("documents")
except Exception:
pass
store.documents_table = store.db.create_table("documents", schema=DocumentRecordV2)
# Reinsert previous rows with title=None
if rows:
backfilled = []
for row in rows:
backfilled.append(
DocumentRecordV2(
id=row.get("id"),
content=row.get("content", ""),
uri=row.get("uri"),
title=None,
metadata=(
row.get("metadata")
if isinstance(row.get("metadata"), str)
else json.dumps(row.get("metadata") or {})
),
created_at=row.get("created_at", ""),
updated_at=row.get("updated_at", ""),
)
)
store.documents_table.add(backfilled)
upgrade_add_title = Upgrade(
version="0.10.1",
apply=_apply_add_document_title,
description="Add nullable 'title' column to documents table",
)

65
haiku_rag_slim/haiku/rag/store/upgrades/v0_19_6.py
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@ -1,65 +0,0 @@
import json
import logging
from haiku.rag.store.engine import SettingsRecord, Store
from haiku.rag.store.upgrades import Upgrade
logger = logging.getLogger(__name__)
def _apply_embeddings_model_config(store: Store) -> None: # pragma: no cover
"""Migrate embeddings config from flat to nested EmbeddingModelConfig structure."""
results = list(
store.settings_table.search()
.where("id = 'settings'")
.limit(1)
.to_pydantic(SettingsRecord)
)
if not results or not results[0].settings:
return
settings = json.loads(results[0].settings)
embeddings = settings.get("embeddings", {})
# Check if already migrated (model is a dict with nested structure)
if isinstance(embeddings.get("model"), dict):
return
# Migrate from flat structure to nested EmbeddingModelConfig
old_provider = embeddings.get("provider", "ollama")
old_model = embeddings.get("model", "qwen3-embedding:4b")
old_vector_dim = embeddings.get("vector_dim", 2560)
logger.info(
"Migrating embeddings config to new nested structure: "
"embeddings.{provider,model,vector_dim} -> embeddings.model.{provider,name,vector_dim}"
)
# Create new nested structure
settings["embeddings"] = {
"model": {
"provider": old_provider,
"name": old_model,
"vector_dim": old_vector_dim,
}
}
store.settings_table.update(
where="id = 'settings'",
values={"settings": json.dumps(settings)},
)
logger.info(
"Embeddings config migrated: provider=%s, name=%s, vector_dim=%d",
old_provider,
old_model,
old_vector_dim,
)
upgrade_embeddings_model_config = Upgrade(
version="0.19.6",
apply=_apply_embeddings_model_config,
description="Migrate embeddings config to nested EmbeddingModelConfig structure",
)

112
haiku_rag_slim/haiku/rag/store/upgrades/v0_9_3.py
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@ -1,112 +0,0 @@
import json
from lancedb.pydantic import LanceModel, Vector
from pydantic import Field
from haiku.rag.store.engine import Store
from haiku.rag.store.upgrades import Upgrade
def _infer_vector_dim(store: Store) -> int: # pragma: no cover
"""Infer vector dimension from existing data; fallback to embedder config."""
try:
arrow = store.chunks_table.search().limit(1).to_arrow()
rows = arrow.to_pylist()
if rows:
vec = rows[0].get("vector")
if isinstance(vec, list) and vec:
return len(vec)
except Exception:
pass
# Fallback to configured embedder vector dim
return getattr(store.embedder, "_vector_dim", 1024)
def _apply_chunk_order(store: Store) -> None: # pragma: no cover
"""Add integer 'order' column to chunks and backfill from metadata."""
vector_dim = _infer_vector_dim(store)
class ChunkRecordV2(LanceModel):
id: str
document_id: str
content: str
metadata: str = Field(default="{}")
order: int = Field(default=0)
vector: Vector(vector_dim) = Field( # type: ignore
default_factory=lambda: [0.0] * vector_dim
)
# Read existing chunks
try:
chunks_arrow = store.chunks_table.search().to_arrow()
rows = chunks_arrow.to_pylist()
except Exception:
rows = []
new_chunk_records: list[ChunkRecordV2] = []
for row in rows:
md_raw = row.get("metadata") or "{}"
try:
md = json.loads(md_raw) if isinstance(md_raw, str) else md_raw
except Exception:
md = {}
# Extract and normalize order
order_val = 0
try:
if isinstance(md, dict) and "order" in md:
order_val = int(md["order"]) # type: ignore[arg-type]
except Exception:
order_val = 0
if isinstance(md, dict) and "order" in md:
md = {k: v for k, v in md.items() if k != "order"}
vec = row.get("vector") or [0.0] * vector_dim
new_chunk_records.append(
ChunkRecordV2(
id=row.get("id"),
document_id=row.get("document_id"),
content=row.get("content", ""),
metadata=json.dumps(md),
order=order_val,
vector=vec,
)
)
# Recreate chunks table with new schema
try:
store.db.drop_table("chunks")
except Exception:
pass
store.chunks_table = store.db.create_table("chunks", schema=ChunkRecordV2)
store.chunks_table.create_fts_index("content", replace=True)
if new_chunk_records:
store.chunks_table.add(new_chunk_records)
upgrade_order = Upgrade(
version="0.9.3",
apply=_apply_chunk_order,
description="Add 'order' column to chunks and backfill from metadata",
)
def _apply_fts_phrase_support(store: Store) -> None: # pragma: no cover
"""Recreate FTS index with phrase query support and no stop-word removal."""
try:
store.chunks_table.create_fts_index(
"content", replace=True, with_position=True, remove_stop_words=False
)
except Exception:
pass
upgrade_fts_phrase = Upgrade(
version="0.9.3",
apply=_apply_fts_phrase_support,
description="Enable FTS phrase queries (with positions) and keep stop-words",
)