NLP Cloud
The NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, grammar and spelling correction, keywords and keyphrases extraction, chatbot, product description and ad generation, intent classification, text generation, image generation, blog post generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API.
This example goes over how to use LangChain to interact with NLP Cloud models.
%pip install --upgrade --quiet nlpcloud
# get a token: https://docs.nlpcloud.com/#authentication
from getpass import getpass
NLPCLOUD_API_KEY = getpass()
········
import os
os.environ["NLPCLOUD_API_KEY"] = NLPCLOUD_API_KEY
from langchain.chains import LLMChain
from langchain_community.llms import NLPCloud
from langchain_core.prompts import PromptTemplate
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
llm = NLPCloud()
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.run(question)
' Justin Bieber was born in 1994, so the team that won the Super Bowl that year was the San Francisco 49ers.'
Related
- LLM conceptual guide
- LLM how-to guides