Large Language Models (LLMs) have become an essential tool for businesses that seek to leverage the power of natural language processing and artificial intelligence. Here are the top 10 use cases for using LLMs in business: Overall, LLMs have the potential to transform how businesses interact with customers, streamline internal processes, and gain a competitive …
import osfrom pprint import pprintfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import TokenTextSplitterfrom langchain.llms import OpenAIfrom langchain.chains import ChatVectorDBChainfrom langchain.document_loaders import UnstructuredURLLoader os.environ[“OPENAI_API_KEY”] = ‘your_open_api_key’ h2o_ai_wave_urls = [“https://github.com/h2oai/wave/releases”,“https://wave.h2o.ai/docs/installation”,“https://wave.h2o.ai/docs/getting-started”,“https://wave.h2o.ai/docs/examples”,“https://github.com/h2oai/wave/issues/693”,“https://github.com/h2oai/wave/blob/master/.github/CONTRIBUTING.md#development-setup”,“https://github.com/h2oai/wave/discussions/1897”,“https://github.com/h2oai/wave/discussions/1888”,“https://github.com/h2oai/wave/discussions/1885”,“https://github.com/h2oai/wave/discussions/1865”] collection_name = “h2o_wave_knowledgebase”local_directory = “kb-h2o-wave”persist_directory = os.path.join(os.getcwd(), local_directory) loader = UnstructuredURLLoader(urls=h2o_ai_wave_urls)kb_data = loader.load() text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=0)kb_doc = text_splitter.split_documents(kb_data) embeddings = OpenAIEmbeddings() kb_db = Chroma.from_documents(kb_doc,embeddings,collection_name=collection_name,persist_directory=persist_directory)kb_db.persist() kb_qa …
Example code for fine-tuning a language model using URL content and a vector database, and also an example logic to answer questions with the model using Python. Example Code: To fine-tune a language model using URL content and a vector database, we can follow these steps: Example Logic for Answering Questions: To use the fine-tuned …
Large language models, such as GPT-3, are created using a combination of machine learning techniques and massive amounts of training data. In general, the process of creating a large language model involves the following steps: Overall, creating a large language model is a complex process that involves a combination of data collection, preprocessing, training, and …
Introduction:Language modeling is an essential task in natural language processing. It involves training a model to predict the probability of a sequence of words given a preceding context. Large language models, such as the GPT-3, have achieved impressive performance on a variety of language tasks, including text generation, translation, and question-answering. However, these models can …
