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from .commands import SearchCommands
class Search(SearchCommands):
"""
Create a client for talking to search.
It abstracts the API of the module and lets you just use the engine.
"""
class BatchIndexer(object):
"""
A batch indexer allows you to automatically batch
document indexing in pipelines, flushing it every N documents.
"""
def __init__(self, client, chunk_size=1000):
self.client = client
self.execute_command = client.execute_command
self.pipeline = client.pipeline(transaction=False, shard_hint=None)
self.total = 0
self.chunk_size = chunk_size
self.current_chunk = 0
def __del__(self):
if self.current_chunk:
self.commit()
def add_document(
self,
doc_id,
nosave=False,
score=1.0,
payload=None,
replace=False,
partial=False,
no_create=False,
**fields
):
"""
Add a document to the batch query
"""
self.client._add_document(
doc_id,
conn=self.pipeline,
nosave=nosave,
score=score,
payload=payload,
replace=replace,
partial=partial,
no_create=no_create,
**fields
)
self.current_chunk += 1
self.total += 1
if self.current_chunk >= self.chunk_size:
self.commit()
def add_document_hash(
self,
doc_id,
score=1.0,
replace=False,
):
"""
Add a hash to the batch query
"""
self.client._add_document_hash(
doc_id,
conn=self.pipeline,
score=score,
replace=replace,
)
self.current_chunk += 1
self.total += 1
if self.current_chunk >= self.chunk_size:
self.commit()
def commit(self):
"""
Manually commit and flush the batch indexing query
"""
self.pipeline.execute()
self.current_chunk = 0
def __init__(self, client, index_name="idx"):
"""
Create a new Client for the given index_name.
The default name is `idx`
If conn is not None, we employ an already existing redis connection
"""
self.client = client
self.index_name = index_name
self.execute_command = client.execute_command
self.pipeline = client.pipeline
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