Source code for gnes.router.reduce

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from typing import List
import numpy as np

from .base import BaseReduceRouter, BaseTopkReduceRouter, BaseEmbedReduceRouter
from ..proto import blob2array


[docs]class DocFillReducer(BaseReduceRouter): """ Gather all documents raw content from multiple shards. This is only useful when you have - multiple doc-indexer and docs are spreaded over multiple shards. - require full-doc retrieval with the original content, not just an doc id Ideally, only each doc can only belong to one shard. """
[docs] def apply(self, msg: 'gnes_pb2.Message', accum_msgs: List['gnes_pb2.Message'], *args, **kwargs): final_docs = [] for idx in range(len(msg.response.search.topk_results)): # get result from all shards, some may return None, we only take the first non-None doc final_docs.append([m.response.search.topk_results[idx] for m in accum_msgs if m.response.search.topk_results[idx].doc.WhichOneof('raw_data') is not None][0]) msg.response.search.ClearField('topk_results') msg.response.search.topk_results.extend(final_docs) super().apply(msg, accum_msgs)
[docs]class DocTopkReducer(BaseTopkReduceRouter): """ Gather all docs by their doc_id, result in a topk doc list """
[docs] def get_key(self, x: 'gnes_pb2.Response.QueryResponse.ScoredResult') -> str: return x.doc.doc_id
[docs] def set_key(self, x: 'gnes_pb2.Response.QueryResponse.ScoredResult', k: str): x.doc.doc_id = k
[docs]class Chunk2DocTopkReducer(BaseTopkReduceRouter): """ Gather all chunks by their doc_id, result in a topk doc list. This is almost always useful, as the final result should be group by doc_id not chunk """
[docs] def get_key(self, x: 'gnes_pb2.Response.QueryResponse.ScoredResult') -> str: return x.chunk.doc_id
[docs] def set_key(self, x: 'gnes_pb2.Response.QueryResponse.ScoredResult', k: str): x.doc.doc_id = k
[docs]class ChunkTopkReducer(BaseTopkReduceRouter): """ Gather all chunks by their chunk_id from all shards, aka doc_id-offset, result in a topk chunk list """
[docs] def get_key(self, x: 'gnes_pb2.Response.QueryResponse.ScoredResult') -> str: return '%d-%d' % (x.chunk.doc_id, x.chunk.offset)
[docs] def set_key(self, x: 'gnes_pb2.Response.QueryResponse.ScoredResult', k: str): x.chunk.doc_id, x.chunk.offset = map(int, k.split('-'))
[docs]class ConcatEmbedRouter(BaseEmbedReduceRouter): """ Gather all embeddings from multiple encoders and concat them on a specific axis. In default, concat will happen on the last axis. chunk_idx, doc_idx denote index in for loop used in BaseEmbedReduceRouter """
[docs] def reduce_embedding(self, accum_msgs: List['gnes_pb2.Message'], msg_type: str, chunk_idx: int, doc_idx: int): if msg_type == 'query': return np.concatenate([blob2array(m.request.search.query.chunks[chunk_idx].embedding) for m in accum_msgs], axis=1) elif msg_type == 'index': return np.concatenate([blob2array(m.request.index.docs[doc_idx].chunks[chunk_idx].embedding) for m in accum_msgs], axis=1) else: self.logger.error('dont know how to handle %s' % msg_type)
[docs]class AvgEmbedRouter(BaseEmbedReduceRouter): """ Gather all embeddings from multiple encoders and do average on a specific axis. In default, average will happen on the first axis. chunk_idx, doc_idx denote index in for loop used in BaseEmbedReduceRouter """
[docs] def reduce_embedding(self, accum_msgs: List['gnes_pb2.Message'], msg_type: str, chunk_idx: int, doc_idx: int): if msg_type == 'query': return np.mean([blob2array(m.request.search.query.chunks[chunk_idx].embedding) for m in accum_msgs], axis=0) elif msg_type == 'index': return np.mean([blob2array(m.request.index.docs[doc_idx].chunks[chunk_idx].embedding) for m in accum_msgs], axis=0) else: self.logger.error('dont know how to handle %s' % msg_type)