Merge pull request #227 from ggozad/feat/docling-doc-compression
Fix large storage overflow by changing docling_document_json (str) to docling_document (compressed bytes)
This commit is contained in:
Yiorgis Gozadinos
GitHub
commit
119ca84da8
16 changed files with 380 additions and 80 deletions
13
CHANGELOG.md
13
CHANGELOG.md
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@ -1,6 +1,19 @@
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# Changelog
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## [Unreleased]
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### Fixed
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- **Large Document Storage Overflow**: Fixed "byte array offset overflow" panic when vacuuming/rebuilding databases with many large PDF documents ([#225](https://github.com/ggozad/haiku.rag/issues/225))
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- Root cause: Arrow's 32-bit string column offsets limited to ~2GB per fragment
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- Changed `docling_document_json` (string) to `docling_document` (bytes) with `large_binary` Arrow type (64-bit offsets)
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- Added gzip compression for DoclingDocument JSON (~1.4x compression ratio)
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- Migration automatically compresses existing documents in batches to avoid memory issues
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- **Breaking**: Migration is destructive - all table version history is lost after upgrade
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### Changed
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- **Dependencies**: Updated lancedb 0.26.0 → 0.26.1, docling 2.65.0 → 2.67.0
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## [0.24.2] - 2026-01-08
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### Fixed
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@ -17,6 +17,7 @@ import httpx
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from haiku.rag.config import AppConfig, Config
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from haiku.rag.converters import get_converter
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from haiku.rag.reranking import get_reranker
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from haiku.rag.store.compression import compress_json
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from haiku.rag.store.engine import Store
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from haiku.rag.store.models.chunk import Chunk, SearchResult
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from haiku.rag.store.models.document import Document
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@ -372,7 +373,7 @@ class HaikuRAG:
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embedded_chunks = await embed_chunks(chunks, self._config)
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# Store markdown export as content for better display/readability
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# The original content is preserved in docling_document_json
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# The original content is preserved in docling_document
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stored_content = docling_document.export_to_markdown()
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# Create document model
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@ -381,7 +382,7 @@ class HaikuRAG:
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uri=uri,
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title=title,
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metadata=metadata or {},
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docling_document_json=docling_document.model_dump_json(),
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docling_document=compress_json(docling_document.model_dump_json()),
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docling_version=docling_document.version,
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)
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@ -417,7 +418,7 @@ class HaikuRAG:
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uri=uri,
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title=title,
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metadata=metadata or {},
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docling_document_json=docling_document.model_dump_json(),
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docling_document=compress_json(docling_document.model_dump_json()),
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docling_version=docling_document.version,
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)
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@ -550,7 +551,9 @@ class HaikuRAG:
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# Update existing document and rechunk
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existing_doc.content = docling_document.export_to_markdown()
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existing_doc.metadata = metadata
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existing_doc.docling_document_json = docling_document.model_dump_json()
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existing_doc.docling_document = compress_json(
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docling_document.model_dump_json()
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)
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existing_doc.docling_version = docling_document.version
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if title is not None:
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existing_doc.title = title
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@ -564,7 +567,7 @@ class HaikuRAG:
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uri=uri,
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title=title,
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metadata=metadata,
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docling_document_json=docling_document.model_dump_json(),
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docling_document=compress_json(docling_document.model_dump_json()),
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docling_version=docling_document.version,
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)
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return await self._store_document_with_chunks(document, embedded_chunks)
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@ -657,7 +660,9 @@ class HaikuRAG:
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# Update existing document and rechunk
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existing_doc.content = docling_document.export_to_markdown()
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existing_doc.metadata = metadata
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existing_doc.docling_document_json = docling_document.model_dump_json()
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existing_doc.docling_document = compress_json(
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docling_document.model_dump_json()
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)
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existing_doc.docling_version = docling_document.version
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if title is not None:
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existing_doc.title = title
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@ -671,7 +676,7 @@ class HaikuRAG:
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uri=url,
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title=title,
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metadata=metadata,
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docling_document_json=docling_document.model_dump_json(),
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docling_document=compress_json(docling_document.model_dump_json()),
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docling_version=docling_document.version,
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)
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return await self._store_document_with_chunks(document, embedded_chunks)
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@ -788,7 +793,9 @@ class HaikuRAG:
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# Store docling data if provided
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if docling_document is not None:
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existing_doc.content = docling_document.export_to_markdown()
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existing_doc.docling_document_json = docling_document.model_dump_json()
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existing_doc.docling_document = compress_json(
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docling_document.model_dump_json()
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)
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existing_doc.docling_version = docling_document.version
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elif content is not None:
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existing_doc.content = content
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@ -798,7 +805,9 @@ class HaikuRAG:
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# DoclingDocument provided without chunks - chunk and embed using primitives
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if docling_document is not None:
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existing_doc.content = docling_document.export_to_markdown()
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existing_doc.docling_document_json = docling_document.model_dump_json()
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existing_doc.docling_document = compress_json(
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docling_document.model_dump_json()
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)
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existing_doc.docling_version = docling_document.version
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new_chunks = await self.chunk(docling_document)
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@ -810,7 +819,9 @@ class HaikuRAG:
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# Content provided without chunks - convert, chunk, and embed using primitives
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existing_doc.content = content # type: ignore[assignment]
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converted_docling = await self.convert(existing_doc.content)
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existing_doc.docling_document_json = converted_docling.model_dump_json()
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existing_doc.docling_document = compress_json(
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converted_docling.model_dump_json()
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)
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existing_doc.docling_version = converted_docling.version
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new_chunks = await self.chunk(converted_docling)
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@ -1485,7 +1496,7 @@ class HaikuRAG:
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uri=doc.uri,
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title=doc.title,
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metadata=json.dumps(doc.metadata),
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docling_document_json=doc.docling_document_json,
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docling_document=doc.docling_document,
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docling_version=doc.docling_version,
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created_at=doc.created_at.isoformat() if doc.created_at else now,
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updated_at=now,
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@ -1523,7 +1534,7 @@ class HaikuRAG:
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embedded_chunks = await embed_chunks(chunks, self._config)
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# Update document fields
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doc.docling_document_json = docling_document.model_dump_json()
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doc.docling_document = compress_json(docling_document.model_dump_json())
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doc.docling_version = docling_document.version
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# Prepare chunks with document_id and order
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@ -1602,7 +1613,7 @@ class HaikuRAG:
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chunks = await self.chunk(docling_document)
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embedded_chunks = await embed_chunks(chunks, self._config)
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doc.docling_document_json = docling_document.model_dump_json()
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doc.docling_document = compress_json(docling_document.model_dump_json())
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doc.docling_version = docling_document.version
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# Prepare chunks with document_id and order
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11
haiku_rag_slim/haiku/rag/store/compression.py
Normal file
11
haiku_rag_slim/haiku/rag/store/compression.py
Normal file
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@ -0,0 +1,11 @@
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import gzip
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def compress_json(json_str: str) -> bytes:
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"""Compress a JSON string with gzip."""
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return gzip.compress(json_str.encode("utf-8"))
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def decompress_json(data: bytes) -> str:
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"""Decompress gzip-compressed data to a JSON string."""
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return gzip.decompress(data).decode("utf-8")
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@ -8,6 +8,7 @@ from typing import Any
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from uuid import uuid4
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import lancedb
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import pyarrow as pa
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from lancedb.pydantic import LanceModel, Vector
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from pydantic import Field
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@ -24,12 +25,33 @@ class DocumentRecord(LanceModel):
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uri: str | None = None
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title: str | None = None
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metadata: str = Field(default="{}")
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docling_document_json: str | None = None
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docling_document: bytes | None = None
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docling_version: str | None = None
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created_at: str = Field(default_factory=lambda: "")
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updated_at: str = Field(default_factory=lambda: "")
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def get_documents_arrow_schema() -> pa.Schema:
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"""Generate Arrow schema for documents table with large_binary for docling_document.
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LanceDB maps Python `bytes` to Arrow's `binary` type, which uses 32-bit offsets
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and is limited to ~2GB per column in a fragment. When many large documents
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(with embedded page images) are grouped in a single fragment, this limit is
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exceeded, causing "byte array offset overflow" panics.
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This function overrides the default mapping to use `large_binary` instead,
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which has 64-bit offsets and no practical size limit.
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"""
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base_schema = DocumentRecord.to_arrow_schema()
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fields = []
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for field in base_schema:
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if field.name == "docling_document":
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fields.append(pa.field("docling_document", pa.large_binary()))
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else:
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fields.append(field)
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return pa.schema(fields)
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def create_chunk_model(vector_dim: int):
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"""Create a ChunkRecord model with the specified vector dimension.
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@ -281,7 +303,7 @@ class Store:
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self.documents_table = self.db.open_table("documents")
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else:
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self.documents_table = self.db.create_table(
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"documents", schema=DocumentRecord
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"documents", schema=get_documents_arrow_schema()
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)
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# Create or get chunks table
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@ -4,6 +4,8 @@ from typing import TYPE_CHECKING
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from cachetools import LRUCache
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from pydantic import BaseModel, Field
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from haiku.rag.store.compression import decompress_json
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if TYPE_CHECKING:
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from docling_core.types.doc.document import DoclingDocument
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@ -11,13 +13,16 @@ if TYPE_CHECKING:
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_docling_document_cache: LRUCache[str, "DoclingDocument"] = LRUCache(maxsize=100)
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def _get_cached_docling_document(document_id: str, json_str: str) -> "DoclingDocument":
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def _get_cached_docling_document(
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document_id: str, compressed_data: bytes
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) -> "DoclingDocument":
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"""Get or parse DoclingDocument with LRU caching by document ID."""
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if document_id in _docling_document_cache:
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return _docling_document_cache[document_id]
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from docling_core.types.doc.document import DoclingDocument
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json_str = decompress_json(compressed_data)
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doc = DoclingDocument.model_validate_json(json_str)
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_docling_document_cache[document_id] = doc
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return doc
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@ -38,7 +43,7 @@ class Document(BaseModel):
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uri: str | None = None
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title: str | None = None
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metadata: dict = {}
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docling_document_json: str | None = None
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docling_document: bytes | None = None
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docling_version: str | None = None
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created_at: datetime = Field(default_factory=datetime.now)
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updated_at: datetime = Field(default_factory=datetime.now)
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@ -51,13 +56,14 @@ class Document(BaseModel):
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Returns:
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The parsed DoclingDocument, or None if not stored or no ID.
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"""
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if self.docling_document_json is None:
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if self.docling_document is None:
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return None
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# No caching for documents without ID
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if self.id is None:
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from docling_core.types.doc.document import DoclingDocument
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return DoclingDocument.model_validate_json(self.docling_document_json)
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json_str = decompress_json(self.docling_document)
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return DoclingDocument.model_validate_json(json_str)
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return _get_cached_docling_document(self.id, self.docling_document_json)
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return _get_cached_docling_document(self.id, self.docling_document)
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@ -2,7 +2,7 @@ import json
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from datetime import datetime
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from uuid import uuid4
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from haiku.rag.store.engine import DocumentRecord, Store
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from haiku.rag.store.engine import DocumentRecord, Store, get_documents_arrow_schema
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from haiku.rag.store.models.document import Document
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@ -35,7 +35,7 @@ class DocumentRepository:
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uri=record.uri,
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title=record.title,
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metadata=json.loads(record.metadata),
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docling_document_json=record.docling_document_json,
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docling_document=record.docling_document,
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docling_version=record.docling_version,
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created_at=datetime.fromisoformat(record.created_at)
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if record.created_at
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@ -61,7 +61,7 @@ class DocumentRepository:
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uri=entity.uri,
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title=entity.title,
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metadata=json.dumps(entity.metadata),
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docling_document_json=entity.docling_document_json,
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docling_document=entity.docling_document,
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docling_version=entity.docling_version,
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created_at=now,
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updated_at=now,
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@ -111,7 +111,7 @@ class DocumentRepository:
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"uri": entity.uri,
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"title": entity.title,
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"metadata": json.dumps(entity.metadata),
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"docling_document_json": entity.docling_document_json,
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"docling_document": entity.docling_document,
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"docling_version": entity.docling_version,
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"updated_at": now,
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},
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@ -198,5 +198,5 @@ class DocumentRepository:
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# Drop and recreate table to clear all data
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self.store.db.drop_table("documents")
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self.store.documents_table = self.store.db.create_table(
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"documents", schema=DocumentRecord
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"documents", schema=get_documents_arrow_schema()
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)
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@ -69,6 +69,9 @@ from haiku.rag.store.upgrades.v0_20_0 import (
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from haiku.rag.store.upgrades.v0_23_1 import (
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upgrade_contextualize_chunks as upgrade_0_23_1_contextualize,
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)
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from haiku.rag.store.upgrades.v0_25_0 import (
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upgrade_compress_docling_document as upgrade_0_25_0_compress,
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)
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upgrades.append(upgrade_0_9_3_order)
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upgrades.append(upgrade_0_9_3_fts)
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@ -76,3 +79,4 @@ upgrades.append(upgrade_0_10_1_add_title)
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upgrades.append(upgrade_0_19_6_embeddings)
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upgrades.append(upgrade_0_20_0_docling)
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upgrades.append(upgrade_0_23_1_contextualize)
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upgrades.append(upgrade_0_25_0_compress)
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|
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200
haiku_rag_slim/haiku/rag/store/upgrades/v0_25_0.py
Normal file
200
haiku_rag_slim/haiku/rag/store/upgrades/v0_25_0.py
Normal file
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@ -0,0 +1,200 @@
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import json
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import logging
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from datetime import timedelta
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import pyarrow as pa
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from lancedb.pydantic import LanceModel
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from pydantic import Field
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from haiku.rag.store.compression import compress_json, decompress_json
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from haiku.rag.store.engine import Store
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from haiku.rag.store.upgrades import Upgrade
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logger = logging.getLogger(__name__)
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BATCH_SIZE = 10
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def _apply_compress_docling_document(store: Store) -> None: # pragma: no cover
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"""Migrate docling_document_json (str) to docling_document (compressed bytes)."""
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class DocumentRecordV4(LanceModel):
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id: str
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content: str
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uri: str | None = None
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title: str | None = None
|
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metadata: str = Field(default="{}")
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docling_document: bytes | None = None
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docling_version: str | None = None
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created_at: str = Field(default_factory=lambda: "")
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updated_at: str = Field(default_factory=lambda: "")
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def get_documents_arrow_schema_v4() -> pa.Schema:
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"""Generate Arrow schema with large_binary for docling_document."""
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base_schema = DocumentRecordV4.to_arrow_schema()
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fields = []
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for field in base_schema:
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if field.name == "docling_document":
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fields.append(pa.field("docling_document", pa.large_binary()))
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else:
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fields.append(field)
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return pa.schema(fields)
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def migrate_row(row: dict) -> DocumentRecordV4:
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"""Migrate a single row, compressing docling_document."""
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docling_json = row.get("docling_document_json") or row.get("docling_document")
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docling_bytes: bytes | None = None
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if docling_json:
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if isinstance(docling_json, str):
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docling_bytes = compress_json(docling_json)
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elif isinstance(docling_json, bytes):
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try:
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decompress_json(docling_json)
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docling_bytes = docling_json # Already compressed
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except Exception:
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docling_bytes = compress_json(docling_json.decode("utf-8"))
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metadata_raw = row.get("metadata")
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metadata_str = (
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metadata_raw
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if isinstance(metadata_raw, str)
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else json.dumps(metadata_raw or {})
|
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)
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return DocumentRecordV4(
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id=row.get("id") or "",
|
||||
content=row.get("content", ""),
|
||||
uri=row.get("uri"),
|
||||
title=row.get("title"),
|
||||
metadata=metadata_str,
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docling_document=docling_bytes,
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||||
docling_version=row.get("docling_version"),
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||||
created_at=row.get("created_at", ""),
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||||
updated_at=row.get("updated_at", ""),
|
||||
)
|
||||
|
||||
# First pass: collect document IDs to process
|
||||
try:
|
||||
ids = [
|
||||
row["id"]
|
||||
for row in store.documents_table.search()
|
||||
.select(["id"])
|
||||
.to_arrow()
|
||||
.to_pylist()
|
||||
]
|
||||
except Exception:
|
||||
ids = []
|
||||
|
||||
if not ids:
|
||||
# No documents, just recreate table with new schema
|
||||
try:
|
||||
store.db.drop_table("documents")
|
||||
except Exception:
|
||||
pass
|
||||
store.documents_table = store.db.create_table(
|
||||
"documents", schema=get_documents_arrow_schema_v4()
|
||||
)
|
||||
return
|
||||
|
||||
# Create staging table with new schema
|
||||
try:
|
||||
store.db.drop_table("documents_v4_staging")
|
||||
except Exception:
|
||||
pass
|
||||
staging_table = store.db.create_table(
|
||||
"documents_v4_staging", schema=get_documents_arrow_schema_v4()
|
||||
)
|
||||
|
||||
# Migrate in batches: read from old, compress, write to staging
|
||||
total_docs = len(ids)
|
||||
total_batches = (total_docs + BATCH_SIZE - 1) // BATCH_SIZE
|
||||
logger.info("Compressing %d documents in %d batches", total_docs, total_batches)
|
||||
|
||||
for batch_num, i in enumerate(range(0, len(ids), BATCH_SIZE), 1):
|
||||
batch_ids = ids[i : i + BATCH_SIZE]
|
||||
id_list = ", ".join(f"'{id}'" for id in batch_ids)
|
||||
|
||||
batch = (
|
||||
store.documents_table.search()
|
||||
.where(f"id IN ({id_list})")
|
||||
.to_arrow()
|
||||
.to_pylist()
|
||||
)
|
||||
|
||||
migrated_batch = [migrate_row(row) for row in batch]
|
||||
if migrated_batch:
|
||||
staging_table.add(migrated_batch)
|
||||
|
||||
logger.info(
|
||||
"Compressed batch %d/%d (%d documents)",
|
||||
batch_num,
|
||||
total_batches,
|
||||
len(migrated_batch),
|
||||
)
|
||||
|
||||
# Replace old table with staging table
|
||||
try:
|
||||
store.db.drop_table("documents")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
store.documents_table = store.db.create_table(
|
||||
"documents", schema=get_documents_arrow_schema_v4()
|
||||
)
|
||||
|
||||
# Copy from staging to final table in batches
|
||||
staging_ids = [
|
||||
row["id"]
|
||||
for row in staging_table.search().select(["id"]).to_arrow().to_pylist()
|
||||
]
|
||||
|
||||
logger.info("Copying %d documents to new table", len(staging_ids))
|
||||
|
||||
for batch_num, i in enumerate(range(0, len(staging_ids), BATCH_SIZE), 1):
|
||||
batch_ids = staging_ids[i : i + BATCH_SIZE]
|
||||
id_list = ", ".join(f"'{id}'" for id in batch_ids)
|
||||
|
||||
batch = (
|
||||
staging_table.search().where(f"id IN ({id_list})").to_arrow().to_pylist()
|
||||
)
|
||||
records = [
|
||||
DocumentRecordV4(
|
||||
id=row["id"],
|
||||
content=row["content"],
|
||||
uri=row["uri"],
|
||||
title=row["title"],
|
||||
metadata=row["metadata"],
|
||||
docling_document=row["docling_document"],
|
||||
docling_version=row["docling_version"],
|
||||
created_at=row["created_at"],
|
||||
updated_at=row["updated_at"],
|
||||
)
|
||||
for row in batch
|
||||
]
|
||||
if records:
|
||||
store.documents_table.add(records)
|
||||
logger.info("Copied batch %d/%d", batch_num, total_batches)
|
||||
|
||||
# Cleanup staging table
|
||||
try:
|
||||
store.db.drop_table("documents_v4_staging")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Vacuum all tables (destructive migration, no history preserved)
|
||||
logger.info("Vacuuming database")
|
||||
for table in [store.documents_table, store.chunks_table, store.settings_table]:
|
||||
try:
|
||||
table.optimize(cleanup_older_than=timedelta(seconds=0))
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
logger.info("Migration complete")
|
||||
|
||||
|
||||
upgrade_compress_docling_document = Upgrade(
|
||||
version="0.25.0",
|
||||
apply=_apply_compress_docling_document,
|
||||
description="Compress docling_document with gzip and use large_binary type",
|
||||
)
|
||||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
name = "haiku.rag-slim"
|
||||
description = "Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling - Minimal dependencies"
|
||||
version = "0.24.2"
|
||||
version = "0.25.0"
|
||||
authors = [{ name = "Yiorgis Gozadinos", email = "ggozadinos@gmail.com" }]
|
||||
license = { text = "MIT" }
|
||||
readme = { file = "README.md", content-type = "text/markdown" }
|
||||
|
|
@ -24,7 +24,7 @@ classifiers = [
|
|||
dependencies = [
|
||||
"docling-core==2.57.0",
|
||||
"httpx>=0.28.1",
|
||||
"lancedb==0.26.0",
|
||||
"lancedb==0.26.1",
|
||||
"pathspec>=0.12.1",
|
||||
"pydantic>=2.12.5",
|
||||
"pydantic-ai-slim[openai,fastmcp,logfire,ag-ui]>=1.39.0",
|
||||
|
|
@ -37,7 +37,7 @@ dependencies = [
|
|||
|
||||
[project.optional-dependencies]
|
||||
# Document processing
|
||||
docling = ["docling==2.65.0", "opencv-python-headless>=4.12.0.88"]
|
||||
docling = ["docling==2.67.0", "opencv-python-headless>=4.12.0.88"]
|
||||
# Embedding providers
|
||||
voyageai = ["voyageai>=0.3.7"]
|
||||
# Rerankers
|
||||
|
|
|
|||
15
tests/store/test_compression.py
Normal file
15
tests/store/test_compression.py
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
from haiku.rag.store.compression import compress_json, decompress_json
|
||||
|
||||
|
||||
class TestJsonCompression:
|
||||
def test_compress_json_roundtrip(self):
|
||||
json_str = '{"key": "value", "number": 42, "nested": {"a": 1}}'
|
||||
compressed = compress_json(json_str)
|
||||
decompressed = decompress_json(compressed)
|
||||
assert decompressed == json_str
|
||||
|
||||
def test_compress_json_with_unicode(self):
|
||||
json_str = '{"message": "Hello, 世界! 🌍"}'
|
||||
compressed = compress_json(json_str)
|
||||
decompressed = decompress_json(compressed)
|
||||
assert decompressed == json_str
|
||||
|
|
@ -8,6 +8,7 @@ from datasets import Dataset
|
|||
|
||||
from haiku.rag.client import HaikuRAG
|
||||
from haiku.rag.config import Config
|
||||
from haiku.rag.store.compression import decompress_json
|
||||
from haiku.rag.store.models.chunk import Chunk
|
||||
from haiku.rag.store.models.document import Document
|
||||
|
||||
|
|
@ -790,13 +791,15 @@ async def test_client_create_document_stores_docling_json(temp_db_path):
|
|||
)
|
||||
|
||||
assert doc.id is not None
|
||||
assert doc.docling_document_json is not None
|
||||
assert doc.docling_document is not None
|
||||
assert doc.docling_version is not None
|
||||
|
||||
# Verify JSON is valid and can be parsed
|
||||
import json
|
||||
|
||||
parsed = json.loads(doc.docling_document_json)
|
||||
from haiku.rag.store.compression import decompress_json
|
||||
|
||||
parsed = json.loads(decompress_json(doc.docling_document))
|
||||
assert "version" in parsed
|
||||
assert parsed["version"] == doc.docling_version
|
||||
|
||||
|
|
@ -824,7 +827,8 @@ async def test_client_import_document_stores_docling_data(temp_db_path):
|
|||
|
||||
assert doc.id is not None
|
||||
assert "Content from docling document" in doc.content
|
||||
assert doc.docling_document_json == docling_doc.model_dump_json()
|
||||
assert doc.docling_document is not None
|
||||
assert decompress_json(doc.docling_document) == docling_doc.model_dump_json()
|
||||
assert doc.docling_version == docling_doc.version
|
||||
|
||||
|
||||
|
|
@ -840,13 +844,13 @@ async def test_client_create_document_from_file_stores_docling_json(temp_db_path
|
|||
assert isinstance(doc, Document)
|
||||
|
||||
assert doc.id is not None
|
||||
assert doc.docling_document_json is not None
|
||||
assert doc.docling_document is not None
|
||||
assert doc.docling_version is not None
|
||||
|
||||
# Verify the stored document also has the JSON
|
||||
retrieved = await client.get_document_by_id(doc.id)
|
||||
assert retrieved is not None
|
||||
assert retrieved.docling_document_json == doc.docling_document_json
|
||||
assert retrieved.docling_document == doc.docling_document
|
||||
assert retrieved.docling_version == doc.docling_version
|
||||
|
||||
|
||||
|
|
@ -857,17 +861,17 @@ async def test_client_update_document_stores_docling_json(temp_db_path):
|
|||
# Create initial document
|
||||
doc = await client.create_document(content="Initial content")
|
||||
assert doc.id is not None
|
||||
original_json = doc.docling_document_json
|
||||
original_json = doc.docling_document
|
||||
|
||||
# Update content via update_document
|
||||
updated_doc = await client.update_document(
|
||||
document_id=doc.id, content="New content via fields update"
|
||||
)
|
||||
|
||||
assert updated_doc.docling_document_json is not None
|
||||
assert updated_doc.docling_document is not None
|
||||
assert updated_doc.docling_version is not None
|
||||
# JSON should be different because content changed
|
||||
assert updated_doc.docling_document_json != original_json
|
||||
assert updated_doc.docling_document != original_json
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
|
|
@ -879,7 +883,7 @@ async def test_client_update_document_with_custom_chunks_no_docling_json(
|
|||
# Create initial document
|
||||
doc = await client.create_document(content="Initial content")
|
||||
assert doc.id is not None
|
||||
original_json = doc.docling_document_json
|
||||
original_json = doc.docling_document
|
||||
|
||||
# Update with custom chunks
|
||||
custom_chunks = [Chunk(content="Custom chunk", order=0)]
|
||||
|
|
@ -888,7 +892,7 @@ async def test_client_update_document_with_custom_chunks_no_docling_json(
|
|||
)
|
||||
|
||||
# Docling JSON should remain unchanged (no conversion when custom chunks provided)
|
||||
assert updated_doc.docling_document_json == original_json
|
||||
assert updated_doc.docling_document == original_json
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
|
|
@ -942,7 +946,11 @@ async def test_client_update_document_with_docling_rechunks(temp_db_path):
|
|||
|
||||
# Content should be extracted from docling document
|
||||
assert "Completely different text" in updated_doc.content
|
||||
assert updated_doc.docling_document_json == docling_doc.model_dump_json()
|
||||
assert updated_doc.docling_document is not None
|
||||
assert (
|
||||
decompress_json(updated_doc.docling_document)
|
||||
== docling_doc.model_dump_json()
|
||||
)
|
||||
assert updated_doc.docling_version == docling_doc.version
|
||||
|
||||
# Chunks should be regenerated
|
||||
|
|
@ -981,7 +989,11 @@ async def test_client_update_document_docling_with_chunks(temp_db_path):
|
|||
|
||||
# Content should be extracted from docling (since content wasn't provided)
|
||||
assert "Text from docling" in updated_doc.content
|
||||
assert updated_doc.docling_document_json == docling_doc.model_dump_json()
|
||||
assert updated_doc.docling_document is not None
|
||||
assert (
|
||||
decompress_json(updated_doc.docling_document)
|
||||
== docling_doc.model_dump_json()
|
||||
)
|
||||
|
||||
# Custom chunks should be used (not rechunked from docling)
|
||||
chunks = await client.chunk_repository.get_by_document_id(doc.id)
|
||||
|
|
@ -1001,7 +1013,7 @@ async def test_client_file_update_stores_docling_json(temp_db_path):
|
|||
# Create initial document
|
||||
doc1 = await client.create_document_from_source(temp_path)
|
||||
assert isinstance(doc1, Document)
|
||||
original_json = doc1.docling_document_json
|
||||
original_json = doc1.docling_document
|
||||
original_version = doc1.docling_version
|
||||
|
||||
# Modify file
|
||||
|
|
@ -1013,8 +1025,8 @@ async def test_client_file_update_stores_docling_json(temp_db_path):
|
|||
assert doc2.id == doc1.id # Same document
|
||||
|
||||
# Docling JSON should be updated
|
||||
assert doc2.docling_document_json is not None
|
||||
assert doc2.docling_document_json != original_json
|
||||
assert doc2.docling_document is not None
|
||||
assert doc2.docling_document != original_json
|
||||
assert doc2.docling_version == original_version # Version stays same
|
||||
|
||||
|
||||
|
|
@ -1038,7 +1050,7 @@ async def test_client_visualize_chunk_no_bounding_boxes(temp_db_path):
|
|||
)
|
||||
|
||||
assert doc.id is not None
|
||||
assert doc.docling_document_json is not None
|
||||
assert doc.docling_document is not None
|
||||
|
||||
chunks = await client.chunk_repository.get_by_document_id(doc.id)
|
||||
assert len(chunks) >= 1
|
||||
|
|
@ -1095,7 +1107,7 @@ async def test_client_visualize_chunk_with_pdf(temp_db_path):
|
|||
doc = await client.create_document_from_source(pdf_path)
|
||||
assert isinstance(doc, Document)
|
||||
assert doc.id is not None
|
||||
assert doc.docling_document_json is not None
|
||||
assert doc.docling_document is not None
|
||||
|
||||
chunks = await client.chunk_repository.get_by_document_id(doc.id)
|
||||
assert len(chunks) > 0
|
||||
|
|
@ -1345,7 +1357,7 @@ async def test_client_create_document_with_html_format(temp_db_path):
|
|||
)
|
||||
|
||||
assert doc.id is not None
|
||||
assert doc.docling_document_json is not None
|
||||
assert doc.docling_document is not None
|
||||
|
||||
# Verify the DoclingDocument has proper structure
|
||||
docling_doc = doc.get_docling_document()
|
||||
|
|
|
|||
|
|
@ -613,7 +613,7 @@ This is paragraph four about topic C.
|
|||
)
|
||||
|
||||
assert doc.id is not None
|
||||
assert doc.docling_document_json is not None
|
||||
assert doc.docling_document is not None
|
||||
|
||||
# Get chunks which should have doc_item_refs
|
||||
chunks = await client.chunk_repository.get_by_document_id(doc.id)
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ def vcr_cassette_dir():
|
|||
return str(Path(__file__).parent / "cassettes" / "test_converters")
|
||||
|
||||
|
||||
def create_mock_docling_document_json(name: str = "test") -> dict:
|
||||
def create_mock_docling_document(name: str = "test") -> dict:
|
||||
"""Create a minimal valid DoclingDocument JSON structure for mocking."""
|
||||
return {
|
||||
"schema_name": "DoclingDocument",
|
||||
|
|
@ -476,7 +476,7 @@ class TestDoclingServeConverter:
|
|||
@pytest.mark.asyncio
|
||||
async def test_convert_text_success(self, converter):
|
||||
"""Test successful text conversion via docling-serve async workflow."""
|
||||
doc_json = create_mock_docling_document_json("test")
|
||||
doc_json = create_mock_docling_document("test")
|
||||
submit_resp, poll_resp, result_resp = create_async_workflow_mocks(doc_json)
|
||||
|
||||
with patch("httpx.AsyncClient") as mock_client_class:
|
||||
|
|
@ -498,7 +498,7 @@ class TestDoclingServeConverter:
|
|||
config.providers.docling_serve.api_key = "test-key"
|
||||
converter = DoclingServeConverter(config)
|
||||
|
||||
doc_json = create_mock_docling_document_json("test")
|
||||
doc_json = create_mock_docling_document("test")
|
||||
submit_resp, poll_resp, result_resp = create_async_workflow_mocks(doc_json)
|
||||
|
||||
with patch("httpx.AsyncClient") as mock_client_class:
|
||||
|
|
@ -527,7 +527,7 @@ class TestDoclingServeConverter:
|
|||
config.processing.conversion_options.images_scale = 3.0
|
||||
converter = DoclingServeConverter(config)
|
||||
|
||||
doc_json = create_mock_docling_document_json("test")
|
||||
doc_json = create_mock_docling_document("test")
|
||||
submit_resp, poll_resp, result_resp = create_async_workflow_mocks(doc_json)
|
||||
|
||||
with patch("httpx.AsyncClient") as mock_client_class:
|
||||
|
|
@ -622,7 +622,7 @@ class TestDoclingServeConverter:
|
|||
@pytest.mark.asyncio
|
||||
async def test_convert_file_pdf(self, converter):
|
||||
"""Test converting PDF file via docling-serve async workflow."""
|
||||
doc_json = create_mock_docling_document_json("test")
|
||||
doc_json = create_mock_docling_document("test")
|
||||
submit_resp, poll_resp, result_resp = create_async_workflow_mocks(doc_json)
|
||||
|
||||
with patch("httpx.AsyncClient") as mock_client_class:
|
||||
|
|
@ -645,7 +645,7 @@ class TestDoclingServeConverter:
|
|||
@pytest.mark.asyncio
|
||||
async def test_convert_file_text(self, converter):
|
||||
"""Test converting text file (reads locally, sends to docling-serve)."""
|
||||
doc_json = create_mock_docling_document_json("test")
|
||||
doc_json = create_mock_docling_document("test")
|
||||
submit_resp, poll_resp, result_resp = create_async_workflow_mocks(doc_json)
|
||||
|
||||
with patch("httpx.AsyncClient") as mock_client_class:
|
||||
|
|
@ -734,7 +734,7 @@ class TestDoclingServeConverterPictureDescription:
|
|||
config.prompts.picture_description = "Test prompt for picture description"
|
||||
converter = DoclingServeConverter(config)
|
||||
|
||||
doc_json = create_mock_docling_document_json("test")
|
||||
doc_json = create_mock_docling_document("test")
|
||||
submit_resp, poll_resp, result_resp = create_async_workflow_mocks(doc_json)
|
||||
|
||||
with patch("httpx.AsyncClient") as mock_client_class:
|
||||
|
|
@ -767,7 +767,7 @@ class TestDoclingServeConverterPictureDescription:
|
|||
"""Test that picture description is disabled by default."""
|
||||
converter = DoclingServeConverter(config)
|
||||
|
||||
doc_json = create_mock_docling_document_json("test")
|
||||
doc_json = create_mock_docling_document("test")
|
||||
submit_resp, poll_resp, result_resp = create_async_workflow_mocks(doc_json)
|
||||
|
||||
with patch("httpx.AsyncClient") as mock_client_class:
|
||||
|
|
|
|||
|
|
@ -70,9 +70,11 @@ def test_document_get_docling_document():
|
|||
|
||||
import json
|
||||
|
||||
from haiku.rag.store.compression import compress_json
|
||||
|
||||
document = Document(
|
||||
content="Test content",
|
||||
docling_document_json=json.dumps(doc_json),
|
||||
docling_document=compress_json(json.dumps(doc_json)),
|
||||
docling_version="1.3.0",
|
||||
)
|
||||
|
||||
|
|
@ -88,7 +90,7 @@ def test_document_get_docling_document_none():
|
|||
"""Test get_docling_document returns None when not stored."""
|
||||
document = Document(content="Test content")
|
||||
|
||||
assert document.docling_document_json is None
|
||||
assert document.docling_document is None
|
||||
assert document.get_docling_document() is None
|
||||
|
||||
|
||||
|
|
@ -118,13 +120,15 @@ def test_document_get_docling_document_caching():
|
|||
|
||||
import json
|
||||
|
||||
json_str = json.dumps(doc_json)
|
||||
from haiku.rag.store.compression import compress_json
|
||||
|
||||
compressed = compress_json(json.dumps(doc_json))
|
||||
|
||||
# Clear cache to get clean state
|
||||
_docling_document_cache.clear()
|
||||
|
||||
document = Document(
|
||||
id="test-doc-id", content="Test content", docling_document_json=json_str
|
||||
id="test-doc-id", content="Test content", docling_document=compressed
|
||||
)
|
||||
|
||||
# First call - not in cache
|
||||
|
|
@ -157,13 +161,15 @@ def test_document_get_docling_document_no_id_no_cache():
|
|||
|
||||
import json
|
||||
|
||||
json_str = json.dumps(doc_json)
|
||||
from haiku.rag.store.compression import compress_json
|
||||
|
||||
compressed = compress_json(json.dumps(doc_json))
|
||||
|
||||
# Clear cache
|
||||
_docling_document_cache.clear()
|
||||
|
||||
# Document without ID
|
||||
document = Document(content="Test content", docling_document_json=json_str)
|
||||
document = Document(content="Test content", docling_document=compressed)
|
||||
|
||||
doc1 = document.get_docling_document()
|
||||
doc2 = document.get_docling_document()
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ async def test_rebuild_full(qa_corpus: Dataset, temp_db_path):
|
|||
async with HaikuRAG(temp_db_path, create=True) as client:
|
||||
doc = await client.create_document(content=qa_corpus["document_extracted"][0])
|
||||
assert doc.id is not None
|
||||
assert doc.docling_document_json is not None
|
||||
assert doc.docling_document is not None
|
||||
|
||||
chunks_before = await client.chunk_repository.get_by_document_id(doc.id)
|
||||
assert len(chunks_before) > 0
|
||||
|
|
@ -23,7 +23,7 @@ async def test_rebuild_full(qa_corpus: Dataset, temp_db_path):
|
|||
# Verify DoclingDocument JSON is preserved after rebuild
|
||||
doc_after = await client.document_repository.get_by_id(doc.id)
|
||||
assert doc_after is not None
|
||||
assert doc_after.docling_document_json is not None
|
||||
assert doc_after.docling_document is not None
|
||||
assert doc_after.docling_version is not None
|
||||
|
||||
chunks_after = await client.chunk_repository.get_by_document_id(doc.id)
|
||||
|
|
@ -40,7 +40,7 @@ async def test_rebuild_embed_only(qa_corpus: Dataset, temp_db_path):
|
|||
async with HaikuRAG(temp_db_path, create=True) as client:
|
||||
doc = await client.create_document(content=qa_corpus["document_extracted"][0])
|
||||
assert doc.id is not None
|
||||
original_docling_json = doc.docling_document_json
|
||||
original_docling_json = doc.docling_document
|
||||
|
||||
chunks_before = await client.chunk_repository.get_by_document_id(doc.id)
|
||||
assert len(chunks_before) > 0
|
||||
|
|
@ -57,7 +57,7 @@ async def test_rebuild_embed_only(qa_corpus: Dataset, temp_db_path):
|
|||
# DoclingDocument JSON should be unchanged (embed-only doesn't touch documents)
|
||||
doc_after = await client.document_repository.get_by_id(doc.id)
|
||||
assert doc_after is not None
|
||||
assert doc_after.docling_document_json == original_docling_json
|
||||
assert doc_after.docling_document == original_docling_json
|
||||
|
||||
chunks_after = await client.chunk_repository.get_by_document_id(doc.id)
|
||||
chunk_ids_after = {c.id for c in chunks_after}
|
||||
|
|
@ -112,7 +112,7 @@ async def test_rebuild_rechunk(qa_corpus: Dataset, temp_db_path):
|
|||
async with HaikuRAG(temp_db_path, create=True) as client:
|
||||
doc = await client.create_document(content=qa_corpus["document_extracted"][0])
|
||||
assert doc.id is not None
|
||||
assert doc.docling_document_json is not None
|
||||
assert doc.docling_document is not None
|
||||
|
||||
# Set a fake URI to simulate a document that came from a file
|
||||
doc.uri = "file:///nonexistent/path.txt"
|
||||
|
|
@ -133,7 +133,7 @@ async def test_rebuild_rechunk(qa_corpus: Dataset, temp_db_path):
|
|||
doc_after = await client.document_repository.get_by_id(doc.id)
|
||||
assert doc_after is not None
|
||||
assert doc_after.content == content_before
|
||||
assert doc_after.docling_document_json is not None
|
||||
assert doc_after.docling_document is not None
|
||||
assert doc_after.docling_version is not None
|
||||
|
||||
chunks_after = await client.chunk_repository.get_by_document_id(doc.id)
|
||||
|
|
|
|||
26
uv.lock
26
uv.lock
|
|
@ -741,7 +741,7 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "docling"
|
||||
version = "2.65.0"
|
||||
version = "2.67.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "accelerate" },
|
||||
|
|
@ -773,9 +773,9 @@ dependencies = [
|
|||
{ name = "tqdm" },
|
||||
{ name = "typer" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/bb/dc/d42fb4dbe39578ff81d9c52b7a1faaf115c014a38a6e335e6db7ced80658/docling-2.65.0.tar.gz", hash = "sha256:64c6feccf808e19f6100aa9e2a6c71c992cd66e897cdfb33ccf15d0b62873b50", size = 257037, upload-time = "2025-12-15T16:56:16.609Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a7/eb/1900eaf576ab935d5b630a5ba6eb0e7063a2e1d36542df2f9ba572505d57/docling-2.67.0.tar.gz", hash = "sha256:d8c1992bbc090cee8c3f602fa63ac0c3127a65e3cabdf509d23ebb4e14feaf38", size = 259923, upload-time = "2026-01-09T08:37:37.984Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/94/71/ba084670cc4cc3679f7ab650a32ff3ddbf61ad7a1eb559104ee819c028cf/docling-2.65.0-py3-none-any.whl", hash = "sha256:02b00096e2785c08924c9fa89034b918d90f0ee6625aacf20435d1d847c89d09", size = 275389, upload-time = "2025-12-15T16:56:15.003Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/8d/99/fa9ac2cffaa77cf46b6414d0a32490b2580c8f3753ab1c65d983ee27f3a0/docling-2.67.0-py3-none-any.whl", hash = "sha256:d3ea4c0d7a64fce4b8f8b38415e075f6637384eaadb27acbf5621673e0da7669", size = 277794, upload-time = "2026-01-09T08:37:36.52Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1362,7 +1362,7 @@ requires-dist = [
|
|||
|
||||
[[package]]
|
||||
name = "haiku-rag-slim"
|
||||
version = "0.24.2"
|
||||
version = "0.25.0"
|
||||
source = { editable = "haiku_rag_slim" }
|
||||
dependencies = [
|
||||
{ name = "docling-core" },
|
||||
|
|
@ -1421,10 +1421,10 @@ zeroentropy = [
|
|||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "cohere", marker = "extra == 'cohere'", specifier = ">=5.20.1" },
|
||||
{ name = "docling", marker = "extra == 'docling'", specifier = "==2.65.0" },
|
||||
{ name = "docling", marker = "extra == 'docling'", specifier = "==2.67.0" },
|
||||
{ name = "docling-core", specifier = "==2.57.0" },
|
||||
{ name = "httpx", specifier = ">=0.28.1" },
|
||||
{ name = "lancedb", specifier = "==0.26.0" },
|
||||
{ name = "lancedb", specifier = "==0.26.1" },
|
||||
{ name = "mxbai-rerank", marker = "extra == 'mxbai'", specifier = ">=0.1.6" },
|
||||
{ name = "opencv-python-headless", marker = "extra == 'docling'", specifier = ">=4.12.0.88" },
|
||||
{ name = "pathspec", specifier = ">=0.12.1" },
|
||||
|
|
@ -1842,7 +1842,7 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "lancedb"
|
||||
version = "0.26.0"
|
||||
version = "0.26.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "deprecation" },
|
||||
|
|
@ -1854,12 +1854,12 @@ dependencies = [
|
|||
{ name = "tqdm" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a8/91/fe585b2181bd61efc65e1da410ae8ab7b29a26f156e4ca7d7d616b1234de/lancedb-0.26.0-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:3a0d435fff1392f056c173f695f71d495c691c555daa9802c056ea23f6a3900e", size = 41174270, upload-time = "2025-12-16T17:16:30.699Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ce/fc/e47e092f4fc97a8810b37dbee07996689bca42f0817f3f3c38d7fb51dd9d/lancedb-0.26.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a2206320fd0f33c01e264960afd768987646133cf152c4d3a8b7faf81b3017bf", size = 42936720, upload-time = "2025-12-16T17:24:43.527Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b5/d7/323897d22a7c00ef1dc4f5b76df1a11df549fe887d8e05d689c2224e47b8/lancedb-0.26.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7ca0322cb4b62d526748f6f29e5b43cce4251c7f693e111897eb1f77e7f1ec2b", size = 45846184, upload-time = "2025-12-16T17:27:33.802Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/3a/0b/7671c94b27a5aa267b9f1d6db759c9e08070cb8f783828ade04da9dc7d79/lancedb-0.26.0-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:7f2b8d69a647265b8753576b501354333c3edfd47d12ec9f47e665e8574c92fe", size = 42954293, upload-time = "2025-12-16T17:24:30.335Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/52/2e/9f720d6ae7bd3a94d096f320a0ec2f277735423af9d16cf5c61c4a70e6ca/lancedb-0.26.0-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:8e5cc334686a389cf2f28d1c239d13a205098ed98f3914226d3966858e58b957", size = 45896935, upload-time = "2025-12-16T17:27:30.156Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/00/0e/4b292c24a9e25ee2cd081d2da930fcdc672ee0eea531fc453c19c73addb5/lancedb-0.26.0-cp39-abi3-win_amd64.whl", hash = "sha256:2fc9b48a11f526de87388002eb3838329db7279241eefb3166c1c6c3b194a3cf", size = 50615000, upload-time = "2025-12-16T17:53:34.409Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/45/b5/110651418ceb1fa4ff2eb74ce4bad911ecf49dc765b134f0201d5564aab8/lancedb-0.26.1-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:b1c4389134ede49e4be0497b9719f573f447e627426bb9e6fc1b642db11fb22d", size = 43416143, upload-time = "2026-01-02T17:57:07.232Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/81/8a/b48a14281d7875e5bfccf22d911d9e1fa019c1fe7b805d290a4449e3cf60/lancedb-0.26.1-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:07abd18e0aa4730442d0361bab4491ad469de14f9087c3542e56ca6d7fcda473", size = 45302392, upload-time = "2026-01-02T18:04:55.963Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/4b/d0/8f6bc531f290206c7a0061236928710506598a2591ff1fcaea477fc52e7f/lancedb-0.26.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:df8eb631519c6ede9975099bea187ea25a09e4617a421fe19e5e1613651cd62f", size = 48372676, upload-time = "2026-01-02T18:08:12.373Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f5/13/d8db83335ddf28afe1fb814ca995da7f67826f337d547e54471d7d425dd1/lancedb-0.26.1-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:2941c9f8aa22244002307c4da5d19f12ab77dcb0569eb4f8a48b60e9c4fdee79", size = 45318771, upload-time = "2026-01-02T18:04:26.429Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/08/94/10e9d4b5ba49eeba72024d310dc42e0c24feb8d5676f48e989198121a8a0/lancedb-0.26.1-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:0d5f125b98836a49095c492085f5ecf3a78906fafcab59c367d9347eb372a4cc", size = 48425627, upload-time = "2026-01-02T18:11:57.443Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/17/5d/d7a834ce8dd9c5e6ef7a0e308c7de5f87bb8f04c0944a1bea617d9d42dc7/lancedb-0.26.1-cp39-abi3-win_amd64.whl", hash = "sha256:9338d34c6e7472c97e49fd6b2638b29d3d087e8b002d92cafdbb46a8b0b1480e", size = 53214501, upload-time = "2026-01-02T22:34:06.836Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
|
|||
Loading…
Reference in a new issue