如何使用 AI 和 Redis 构建语言处理管道

redis_client.hset(f"article_id:{article_id}",mapping={'title': each_line['title']})
redis_client.hset(f"article_id:{article_id}",mapping={'publication_date':each_line['publish_time']})

rediscluster_client.set(f”paragraphs:{article_id}“,” “.join(article_body))

gb = GB('KeysReader')
gb.filter(filter_language)
gb.flatmap(parse_paragraphs)
gb.map(spellcheck_sentences)
gb.foreach(save_sentences)
gb.count()
gb.register('paragraphs:*',keyTypes=['string','hash'], mode="async_local")

bg = GearsBuilder('KeysReader')
bg.foreach(process_item)
bg.count()
bg.register('sentence:*',  mode="async_local",onRegistered=OnRegisteredAutomata)

'XADD', 'edges_matched_{%s}' % shard_id, '*','source',f'{source_entity_id}','destination',f'{destination_entity_id}','source_name',source_canonical_name,'destination_name',destination_canonical_name,'rank',1,'year',year)

zincrby(f'edges_scored:{source_entity_id}:{destination_entity_id}',1, sentence_key)

"GRAPH.QUERY", "cord19medical","""MERGE (source: entity { id: '%s', label :'entity', name: '%s'}) 
         ON CREATE SET source.rank=1
         ON MATCH SET source.rank=(source.rank+1)
         MERGE (destination: entity { id: '%s', label: 'entity', name: '%s' })
         ON CREATE SET destination.rank=1
         ON MATCH SET destination.rank=(destination.rank+1)
         MERGE (source)-[r:related]->(destination)
         ON CREATE SET r.rank=1, r.year=%s
         ON MATCH SET r.rank=(r.rank+1)
         ON CREATE SET r.rank=1
         ON MATCH SET r.rank=(r.rank+1)""" % (source_id ,source_name,destination_id,destination_name,year))

"WITH $ids as ids MATCH (e:entity)-[r]->(t:entity) where (e.id in ids) and (r.year in $years) RETURN DISTINCT e.id, t.id, max(r.rank), r.year ORDER BY r.rank DESC LIMIT $limits"""

"""WITH $ids as ids MATCH (e:entity) where (e.id in ids) RETURN DISTINCT e.id,e.name,max(e.rank)"""

app.py uses zrangebyscore(f"edges_scored:{edges_query}",'-inf','inf',0,5)

import httpimport
    with httpimport.remote_repo(['stop_words'], "https://raw.githubusercontent.com/explosion/spaCy/master/spacy/lang/en/"):
        import stop_words
    from stop_words import STOP_WORDS

    with httpimport.remote_repo(['utils'], "https://raw.githubusercontent.com/redis-developer/the-pattern-automata/main/automata/"):
        import utils
    from utils import loadAutomata, find_matches

get "bertqa{5M5}_PMC140314.xml:{5M5}:44_When air samples collected?"

PMC140314.xml:{5M5}:44

gb = GB('KeysReader')
gb.map(qa_cached_keymiss)
gb.register(prefix='bertqa*', commands=['get'], eventTypes=['keymiss'], mode="async_local")

rconn.sadd('processed_docs_stage3_queue', summary_key)

summary_processor_t5.py

redis_client.hset(f"article_id:{article_id}",mapping={'summary': output})

apt install docker-compose
brew install pyenv-virtualenv 

git clone --recurse-submodules https://github.com/redis-developer/the-pattern.git
cd the-pattern
./start.sh
cd ./the-pattern-platform/
source ~/venv_cord19/bin/activate #or create new venv
pip install -r requirements.txt
bash cluster_pipeline.sh

redis-cli -p 9001 -h 127.0.0.1 GRAPH.QUERY cord19medical "MATCH (n:entity) RETURN count(n) as entity_count" 
redis-cli -p 9001 -h 127.0.0.1 GRAPH.QUERY cord19medical "MATCH (e:entity)-[r]->(t:entity) RETURN count(r) as edge_count"

curl -i -H "Content-Type: application/json" -X POST -d '{"search":"How does temperature and humidity affect the transmission of 2019-nCoV"}' https://:8080/gsearch

cd the-pattern-ui
npm install 
npm install -g @angular/cli@9
ng serve

cd the-pattern-api/qasearch/
sh start.sh

curl -H "Content-Type: application/json" -X POST -d '{"search":"How does temperature and humidity affect the transmission of 2019-nCoV?"}' https://:8080/qasearch

cd the-pattern-bart-summary
# source same enviroment with gears-cli (no other dependencies required)
source ~/venv_cord19/bin/activate 
gears-cli run --host 127.0.0.1 --port 30001 tokenizer_gears_for_sum.py --requirements requirements.txt

sudo apt update
    sudo apt install nvidia-340
    lspci | grep -i nvidia
    sudo apt-get install linux-headers-$(uname -r)
    distribution=$(. /etc/os-release;echo $ID$VERSION_ID | sed -e 's/\.//g')
    wget https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/cuda-$distribution.pin
    sudo mv cuda-$distribution.pin /etc/apt/preferences.d/cuda-repository-pin-600
    sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/7fa2af80.pub
    echo "deb http://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda.list
    sudo apt-get update
    sudo apt-get -y install cuda-drivers
    # install anaconda
    curl -O https://repo.anaconda.com/archive/Anaconda3-2020.11-Linux-x86_64.sh
    sh Anaconda3-2020.11-Linux-x86_64.sh
    source ~/.bashrc
    conda create -n thepattern_env python=3.8
    conda activate thepattern_env
    conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11 -c pytorch

git clone https://raw.githubusercontent.com/redis-developer/the-pattern-bart-summary.git
#start tmux 
conda activate thepattern_env
pip3 install -r requirements.txt
python3 summary_processor_t5.py

bg = GearsBuilder('KeysReader')
bg.foreach(process_item)
bg.count()
bg.register('sentence:*',  mode="async_local",onRegistered=OnRegisteredAutomata)