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Phase 12 done... loading via adaptive collection, yadisk or local

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idchlife
2026-02-10 22:19:27 +03:00
parent e9dd28ad55
commit 1e6ab247b9
5 changed files with 154 additions and 113 deletions
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12
services/rag/langchain/PLANNING.md
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@@ -78,9 +78,9 @@ Chosen data folder: relatve ./../../../data - from the current folder
During enrichment, we should use adaptive collection from the helpers, for loading documents. We should not use directly local filesystem, but use adaptive collection as a wrapper.
- [ ] Adaptive file in helper now has filename in it, so tests should be adjusted for this
- [ ] Add conditional usage of adaptive collection in the enrichment stage. .env has now variable ENRICHMENT_SOURCE with 2 possible values: yadisk, local
- [ ] With local source, use env variable for local filesystem adaptive collection: ENRICHMENT_LOCAL_PATH
- [ ] With yadisk source, use env variable for YADISK_TOKEN for token for auth within Yandex Disk, ENRICHMENT_YADISK_PATH for path on the Yandex Disk system
- [ ] We still will need filetypes that we will need to skip, so while iterating over files we need to check their extension and skip them.
- [ ] Adaptive files has filename in them, so it should be used when extracting metadata
- [x] Adaptive file in helper now has filename in it, so tests should be adjusted for this
- [x] Add conditional usage of adaptive collection in the enrichment stage. .env has now variable ENRICHMENT_SOURCE with 2 possible values: yadisk, local
- [x] With local source, use env variable for local filesystem adaptive collection: ENRICHMENT_LOCAL_PATH
- [x] With yadisk source, use env variable for YADISK_TOKEN for token for auth within Yandex Disk, ENRICHMENT_YADISK_PATH for path on the Yandex Disk system
- [x] We still will need filetypes that we will need to skip, so while iterating over files we need to check their extension and skip them.
- [x] Adaptive files has filename in them, so it should be used when extracting metadata

237
services/rag/langchain/enrichment.py
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@@ -3,7 +3,7 @@
import os
import hashlib
from pathlib import Path
from typing import List, Dict, Any
from typing import List, Tuple
from dotenv import load_dotenv
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
@@ -37,9 +37,15 @@ from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from loguru import logger
import sqlite3
from helpers import extract_russian_event_names, extract_years_from_text
from helpers import (
LocalFilesystemAdaptiveCollection,
YandexDiskAdaptiveCollection,
_AdaptiveCollection,
_AdaptiveFile,
extract_russian_event_names,
extract_years_from_text,
)
# Load environment variables
load_dotenv()
@@ -48,6 +54,27 @@ load_dotenv()
# Define the path to the data directory
DATA_DIR = Path("../../../data").resolve()
DB_PATH = Path("document_tracking.db").resolve()
ENRICHMENT_SOURCE = os.getenv("ENRICHMENT_SOURCE", "local").lower()
ENRICHMENT_LOCAL_PATH = os.getenv("ENRICHMENT_LOCAL_PATH")
ENRICHMENT_YADISK_PATH = os.getenv("ENRICHMENT_YADISK_PATH")
YADISK_TOKEN = os.getenv("YADISK_TOKEN")
SUPPORTED_EXTENSIONS = {
".pdf",
".docx",
".doc",
".pptx",
".xlsx",
".xls",
".jpg",
".jpeg",
".png",
".gif",
".bmp",
".tiff",
".webp",
".odt",
}
Base = declarative_base()
@@ -91,19 +118,17 @@ class DocumentEnricher:
hash_sha256.update(chunk)
return hash_sha256.hexdigest()
def _is_document_processed(self, file_path: str) -> bool:
"""Check if a document has already been processed."""
file_hash = self._get_file_hash(file_path)
def _is_document_hash_processed(self, file_hash: str) -> bool:
"""Check if a document hash has already been processed."""
existing = self.session.query(ProcessedDocument).filter_by(
file_hash=file_hash
).first()
return existing is not None
def _mark_document_processed(self, file_path: str):
def _mark_document_processed(self, file_identifier: str, file_hash: str):
"""Mark a document as processed in the database."""
file_hash = self._get_file_hash(file_path)
doc_record = ProcessedDocument(
file_path=file_path,
file_path=file_identifier,
file_hash=file_hash
)
self.session.add(doc_record)
@@ -142,77 +167,88 @@ class DocumentEnricher:
return None
return UnstructuredODTLoader(file_path, **{"strategy": "hi_res", "languages": ["rus"]})
else:
# For text files and unsupported formats, try to load as text
try:
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
return None, content # Return content directly for text processing
except UnicodeDecodeError:
logger.warning(f"Could not decode file as text: {file_path}")
return None, None
return None
def load_and_split_documents(self, file_paths: List[str]) -> List[Document]:
"""Load documents from file paths and split them appropriately."""
all_docs = []
def _load_one_adaptive_file(
self, adaptive_file: _AdaptiveFile
) -> Tuple[List[Document], str | None]:
"""Load and split one adaptive file by using its local working callback."""
loaded_docs: List[Document] = []
file_hash: str | None = None
source_identifier = adaptive_file.local_path
extension = adaptive_file.extension.lower()
for file_path in file_paths:
if self._is_document_processed(file_path):
logger.info(f"Skipping already processed document: {file_path}")
continue
def process_local_file(local_file_path: str):
nonlocal loaded_docs, file_hash
logger.info(f"Processing document: {file_path}")
# Get the appropriate loader for the file extension
loader = self._get_loader_for_extension(file_path)
file_hash = self._get_file_hash(local_file_path)
if self._is_document_hash_processed(file_hash):
logger.info(f"Skipping already processed document hash for: {source_identifier}")
return
loader = self._get_loader_for_extension(local_file_path)
if loader is None:
# For unsupported formats that we tried to load as text
logger.warning(f"No loader available for file: {source_identifier}")
return
docs = loader.load()
for doc in docs:
doc.metadata["source"] = source_identifier
doc.metadata["filename"] = adaptive_file.filename
doc.metadata["file_path"] = source_identifier
doc.metadata["file_size"] = os.path.getsize(local_file_path)
doc.metadata["file_extension"] = extension
if "page" in doc.metadata:
doc.metadata["page_number"] = doc.metadata["page"]
split_docs = self.text_splitter.split_documents(docs)
for chunk in split_docs:
years = extract_years_from_text(chunk.page_content)
events = extract_russian_event_names(chunk.page_content)
chunk.metadata["years"] = years
chunk.metadata["events"] = events
loaded_docs = split_docs
adaptive_file.work_with_file_locally(process_local_file)
return loaded_docs, file_hash
def load_and_split_documents(
self, adaptive_collection: _AdaptiveCollection, recursive: bool = True
) -> Tuple[List[Document], List[Tuple[str, str]]]:
"""Load documents from adaptive collection and split them appropriately."""
all_docs: List[Document] = []
processed_file_records: dict[str, str] = {}
for adaptive_file in adaptive_collection.iterate(recursive=recursive):
if adaptive_file.extension.lower() not in SUPPORTED_EXTENSIONS:
logger.debug(
f"Skipping unsupported file extension for {adaptive_file.filename}: {adaptive_file.extension}"
)
continue
logger.info(f"Processing document: {adaptive_file.local_path}")
try:
# Load the document(s)
docs = loader.load()
# Add metadata to each document
for doc in docs:
# Extract metadata from the original file
doc.metadata["source"] = file_path
doc.metadata["filename"] = Path(file_path).name
doc.metadata["file_path"] = file_path
doc.metadata["file_size"] = os.path.getsize(file_path)
# Add page number if available in original metadata
if "page" in doc.metadata:
doc.metadata["page_number"] = doc.metadata["page"]
# Add file extension as metadata
doc.metadata["file_extension"] = Path(file_path).suffix
# Split documents if they are too large
split_docs = self.text_splitter.split_documents(docs)
# Extract additional metadata from each chunk.
for chunk in split_docs:
years = extract_years_from_text(chunk.page_content)
events = extract_russian_event_names(chunk.page_content)
chunk.metadata["years"] = years
chunk.metadata["events"] = events
# Add to the collection
all_docs.extend(split_docs)
split_docs, file_hash = self._load_one_adaptive_file(adaptive_file)
if split_docs:
all_docs.extend(split_docs)
if file_hash:
processed_file_records[adaptive_file.local_path] = file_hash
except Exception as e:
logger.error(f"Error processing {file_path}: {str(e)}")
logger.error(f"Error processing {adaptive_file.local_path}: {str(e)}")
continue
return all_docs
return all_docs, list(processed_file_records.items())
def enrich_and_store(self, file_paths: List[str]):
def enrich_and_store(self, adaptive_collection: _AdaptiveCollection):
"""Load, enrich, and store documents in the vector store."""
logger.info(f"Starting enrichment process for {len(file_paths)} files...")
logger.info("Starting enrichment process...")
# Load and split documents
documents = self.load_and_split_documents(file_paths)
documents, processed_file_records = self.load_and_split_documents(
adaptive_collection
)
if not documents:
logger.info("No new documents to process.")
@@ -225,55 +261,58 @@ class DocumentEnricher:
self.vector_store.add_documents(documents)
# Only mark documents as processed after successful insertion to vector store
processed_file_paths = set()
for doc in documents:
if 'source' in doc.metadata:
processed_file_paths.add(doc.metadata['source'])
for file_identifier, file_hash in processed_file_records:
self._mark_document_processed(file_identifier, file_hash)
for file_path in processed_file_paths:
self._mark_document_processed(file_path)
logger.info(f"Successfully added {len(documents)} document chunks to vector store and marked {len(processed_file_paths)} files as processed.")
logger.info(
f"Successfully added {len(documents)} document chunks to vector store and marked {len(processed_file_records)} files as processed."
)
except Exception as e:
logger.error(f"Error adding documents to vector store: {str(e)}")
raise
def get_all_documents_from_data_dir(data_dir: str = str(DATA_DIR)) -> List[str]:
"""Get all supported document file paths from the data directory."""
supported_extensions = {
'.pdf', '.docx', '.doc', '.pptx', '.xlsx', '.xls',
'.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff',
'.webp', '.odt'
}
file_paths = []
for root, dirs, files in os.walk(data_dir):
for file in files:
if Path(file).suffix.lower() in supported_extensions:
file_paths.append(os.path.join(root, file))
return file_paths
def get_enrichment_adaptive_collection(
data_dir: str = str(DATA_DIR),
) -> _AdaptiveCollection:
"""Create adaptive collection based on environment source configuration."""
source = ENRICHMENT_SOURCE
if source == "local":
local_path = ENRICHMENT_LOCAL_PATH or data_dir
logger.info(f"Using local adaptive collection from path: {local_path}")
return LocalFilesystemAdaptiveCollection(local_path)
if source == "yadisk":
if not YADISK_TOKEN:
raise ValueError("YADISK_TOKEN must be set when ENRICHMENT_SOURCE=yadisk")
if not ENRICHMENT_YADISK_PATH:
raise ValueError(
"ENRICHMENT_YADISK_PATH must be set when ENRICHMENT_SOURCE=yadisk"
)
logger.info(
f"Using Yandex Disk adaptive collection from path: {ENRICHMENT_YADISK_PATH}"
)
return YandexDiskAdaptiveCollection(
token=YADISK_TOKEN,
base_dir=ENRICHMENT_YADISK_PATH,
)
raise ValueError(
f"Unsupported ENRICHMENT_SOURCE='{source}'. Allowed values: local, yadisk"
)
def run_enrichment_process(vector_store, data_dir: str = str(DATA_DIR)):
"""Run the full enrichment process."""
logger.info(f"Starting document enrichment from directory: {data_dir}")
# Get all supported documents from the data directory
file_paths = get_all_documents_from_data_dir(data_dir)
if not file_paths:
logger.warning(f"No supported documents found in {data_dir}")
return
logger.info(f"Found {len(file_paths)} documents to process")
logger.info("Starting document enrichment process")
adaptive_collection = get_enrichment_adaptive_collection(data_dir=data_dir)
# Initialize the document enricher
enricher = DocumentEnricher(vector_store)
# Run the enrichment process
enricher.enrich_and_store(file_paths)
enricher.enrich_and_store(adaptive_collection)
logger.info("Document enrichment process completed!")

10
services/rag/langchain/helpers.py
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@@ -118,9 +118,10 @@ class _AdaptiveFile(ABC):
local_path: str
filename: str
def __init__(self, filename: str, extension: str):
def __init__(self, filename: str, extension: str, local_path: str):
self.filename = filename
self.extension = extension
self.local_path = local_path
# This method allows to work with file locally, and lambda should be provided for this.
# Why separate method? For possible cleanup after work is done. And to download file, if needed
@@ -138,11 +139,8 @@ class _AdaptiveCollection(ABC):
class LocalFilesystemAdaptiveFile(_AdaptiveFile):
local_path: str
def __init__(self, filename: str, extension: str, local_path: str):
super().__init__(filename, extension)
self.local_path = local_path
super().__init__(filename, extension, local_path)
def work_with_file_locally(self, func: Callable[[str], None]):
func(self.local_path)
@@ -173,7 +171,7 @@ class YandexDiskAdaptiveFile(_AdaptiveFile):
remote_path: str
def __init__(self, filename: str, extension: str, remote_path: str, token: str):
super().__init__(filename, extension)
super().__init__(filename, extension, remote_path)
self.token = token
self.remote_path = remote_path

7
services/rag/langchain/test/test_local_filesystem_adaptive_collection.py
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@@ -13,7 +13,7 @@ class TestLocalFilesystemAdaptiveCollection(unittest.TestCase):
collection = LocalFilesystemAdaptiveCollection(str(self.samples_dir))
files = list(collection.iterate(recursive=False))
file_names = sorted(Path(file.local_path).name for file in files)
file_names = sorted(file.filename for file in files)
self.assertEqual(file_names, ["root.txt"])
self.assertTrue(all(isinstance(file, LocalFilesystemAdaptiveFile) for file in files))
@@ -33,7 +33,9 @@ class TestLocalFilesystemAdaptiveCollection(unittest.TestCase):
def test_work_with_file_locally_provides_existing_path(self):
target_path = self.samples_dir / "root.txt"
adaptive_file = LocalFilesystemAdaptiveFile(target_path.suffix, str(target_path))
adaptive_file = LocalFilesystemAdaptiveFile(
target_path.name, target_path.suffix, str(target_path)
)
observed = {}
@@ -44,6 +46,7 @@ class TestLocalFilesystemAdaptiveCollection(unittest.TestCase):
adaptive_file.work_with_file_locally(callback)
self.assertEqual(adaptive_file.filename, "root.txt")
self.assertEqual(observed["path"], str(target_path))
self.assertEqual(observed["content"], "root file")

1
services/rag/langchain/test/test_yandex_disk_adaptive_collection.py
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@@ -31,6 +31,7 @@ class TestYandexDiskAdaptiveCollection(unittest.TestCase):
self.skipTest(f"Yandex Disk request failed and needs manual verification: {exc}")
for item in files:
self.assertTrue(item.filename)
logger.info(f"Yandex file found during test iteration: {item.local_path}")
self.assertIsInstance(files, list)