This repo contains the utils for nlmatics projects. Any modules/funcs used across two repos should be listed here.
This module provides clients to access nlp models from model server.
from nlm_utils.model_client import EncoderClient
model_server_url = <suppy model server url>
encoder = EncoderClient(
model="dpr-context",
url=model_server_url,
)
encoder(["sales was 20 million dollars"])
from nlm_utils.model_client import EncoderClient
model_server_url = <suppy model server url>
encoder = EncoderClient(
model="dpr-question",
url=model_server_url,
)
encoder(["how much was sales"])
from nlm_utils.model_client import EncoderClient
model_server_url = <suppy model server url>
encoder = EncoderClient(
model="sif",
url=model_server_url,
)
encoder(["sales was 20 million dollars"])
from nlm_utils.model_client.classification import ClassificationClient
model_server_url = <suppy model server url>
qa_type_client = ClassificationClient(
model="roberta",
task="qa_type",
url=serverUrl,
retry=1,
)
qa_type_client(["What is the name of the company"])
returns
{'predictions': ['HUM:gr']}
from nlm_utils.model_client.classification import ClassificationClient
model_server_url = <suppy model server url>
qa_client = ClassificationClient(
model='roberta',
task="roberta-qa",
host=model_server_url,
port=80,
)
qa_client(["wht is the listing symbol of common stock or shares"], ["Our common stock is listed on the NYSE under the symbol 'MSFT'."])
returns
{'answers': [{'0': {'end_byte': 60,
'end_logit': 16,
'probability': 0.9999986487212269,
'start_byte': 57,
'start_logit': 14,
'text': 'MSFT'}},
{}]}
from nlm_utils.model_client.classification import ClassificationClient
model_server_url = <suppy model server url>
boolq_client = ClassificationClient(
model="roberta",
task="boolq",
url=model_server_url,
retry=1,
)
sentences = ["it is snowing outside"]
question = ["is it snowing"]
boolq_client(question, sentences)
returns
{'predictions': ['True']}
This module provides lazy cache for different types of data. Cache can be configured to saved to different stroage
- Files
- Memory
- Redis
- MongoDB
- Google Cloud (planning)
Usage
# import Cache module
from nlm_utils.cache import Cache
# init cache with FileAgent
cache = Cache("FileAgent")
# apply cache on function
@cache
def func1(args):
pass
# specify cache_key
func1(args, cache_key="cache_key")
# force_overwrite_cache
func1(args, overwrite=True)
# do not read and write cache
func1(args, no_cache=True)
Currently, cache support following agents
# file
cache = Cache("FileAgent", path=".cache", collection="collection")
# memory
cache = Cache("MemoryAgent", prefix="prefix")
# Mongodb
cache = Cache("MongodbAgent", db="cache", collection="cache")
# Redis
cache = Cache("RedisAgent", prefix="collection")
By default, cache layer will detect the arguments and generate the cache automaticly.
You can also specify the cache_key or include uid as a attribute in the argument.
The cache can be force overwrite by passing in overwrite argument.
Cache will also block the I/O if writing cache is happening (lock) -- planning
Functions can be shared across multiple repos.
- read_config(config_file)
The code was written by the following while working at Nlmatics Corp.
- The initial skeleton and model clients were written by Suhail Kandanur.
- Reshav Abraham wrote the nlp_client.
- Yi Zhang refactored the code and created the core framework.
- Ambika Sukla wrote the value parser added code and prompts for flan-t5, encoder and openai models.
- Tom Liu wrote yolo client and made several bug fixes.
- Kiran Panicker wrote the location parser, search summarization prompts for openai and made several bug fixes.