Large Language Models (LLMs) like OpenAI’s GPT-3, Google’s BERT, and Meta’s LLaMA are revolutionizing various sectors with their ability to generate a wide array of text?—?from marketing copy and data science scripts to poetry. Even though ChatGPT’s intuitive interface has managed ...
LangChainis a programming framework, available in both Python and JavaScript, that application developers use to compose new AI applications from basic building blocks. The framework supports stringing together a number of different components using a straightforward, low-code expression syntax. Using Lan...
In the space of local LLMs, I first ran into LMStudio. While the app itself is easy to use, I liked the simplicity and maneuverability that Ollama provides.
You can consume predictions from this model by using the azure-ai-inference package with Python. To install this package, you need the following prerequisites: Python 3.8 or later installed, including pip. The endpoint URL. To construct the client library, you need to pass in t...
We will use LangChain to create a sample RAG application and the RAGAS framework for evaluation. RAGAS is open-source, has out-of-the-box support for all the above metrics, supports custom evaluation prompts, and has integrations with frameworks such as LangChain, LlamaIndex, and observability...
Model name: Meta-Llama-3.1-405B-Instruct Model type: chat-completions Model provider name: Meta Create a chat completion request The following example shows how you can create a basic chat completions request to the model. Python Kopírovať from azure.ai.inference.models import SystemMessage...
In this post, we’ll walk through how to use LlamaIndex and LangChain to implement the storage and retrieval of this contextual data for an LLM to use. We’ll solve a context-specific problem with RAG by using LlamaIndex, and then we’ll deploy our solution easily to Heroku. Before we...
This sample shows how to create two AKS-hosted chat applications that use OpenAI, LangChain, ChromaDB, and Chainlit using Python and deploy them to an AKS environment built in Terraform. - Azure-Samples/aks-openai-chainlit-terraform
On Windows, use:set OPENAI_API_KEY='your-api-key-here' Now your API key is available in your script, and you can access it using the os module in Python. Method 2: Using an .env file If you prefer a more permanent solution, you can use a .env file to store your environment vari...
You can consume predictions from this model by using the azure-ai-inference package with Python. To install this package, you need the following prerequisites: Python 3.8 or later installed, including pip. The endpoint URL. To construct the client library, you need to pass in the endpoint URL...