nlm-utils 0.1.2

Common utilities used by all nlm-* libraries.

About

This repo contains the utils for nlmatics projects. Any modules/funcs used across two repos should be listed here.

model_client

This module provides clients to access nlp models from model server.

EncoderClient with DPR

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"])

EncoderClient with SIF

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"])

ClassificationClient used to get possible answer type of a qa

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']}

ClassificationClient used for QA

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'}},
  {}]}

ClassificationClient used for boolean (yes/no) question answering

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']}

lazy cache

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)

cache agent

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")

Key for the cache

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

utils (planning)

Functions can be shared across multiple repos.

• read_config(config_file)

Credits 2020-2024

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.