|
@@ -530,7 +530,6 @@ class IndexingRunner:
|
|
|
# chunk nodes by chunk size
|
|
|
indexing_start_at = time.perf_counter()
|
|
|
tokens = 0
|
|
|
- chunk_size = 10
|
|
|
if dataset_document.doc_form != IndexType.PARENT_CHILD_INDEX:
|
|
|
# create keyword index
|
|
|
create_keyword_thread = threading.Thread(
|
|
@@ -539,11 +538,22 @@ class IndexingRunner:
|
|
|
)
|
|
|
create_keyword_thread.start()
|
|
|
|
|
|
+ max_workers = 10
|
|
|
if dataset.indexing_technique == "high_quality":
|
|
|
- with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
|
|
|
+ with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
|
|
|
futures = []
|
|
|
- for i in range(0, len(documents), chunk_size):
|
|
|
- chunk_documents = documents[i : i + chunk_size]
|
|
|
+
|
|
|
+ # Distribute documents into multiple groups based on the hash values of page_content
|
|
|
+ # This is done to prevent multiple threads from processing the same document,
|
|
|
+ # Thereby avoiding potential database insertion deadlocks
|
|
|
+ document_groups: list[list[Document]] = [[] for _ in range(max_workers)]
|
|
|
+ for document in documents:
|
|
|
+ hash = helper.generate_text_hash(document.page_content)
|
|
|
+ group_index = int(hash, 16) % max_workers
|
|
|
+ document_groups[group_index].append(document)
|
|
|
+ for chunk_documents in document_groups:
|
|
|
+ if len(chunk_documents) == 0:
|
|
|
+ continue
|
|
|
futures.append(
|
|
|
executor.submit(
|
|
|
self._process_chunk,
|