If you don’t really want your content to be used to train our future robot overlords, here are the steps that will stop ethical AI…
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Wondering how your current Linux system will handle AI and ML-related workloads? Well, now you can find out. Primate Labs, makers of the hugely popular Geekbench software, has released a new preview build of their machine-learning benchmarking tool, Ge…
All the members of this AI-focused alliance are set to collaborate with each other by being part of an open community that allows both developers and researchers to further “responsible innovation” in AI.
The post AI Alliance for Open Innovation Formed…
Development of open-source AI models just got a major boost with the launch of the AI Alliance, a super-team made up of companies, developers, scientists, academics, and spearheaded by Meta and IBM, owners of Red Hat. While governments around the world…
To create a custom LLM chat agent, import libraries from the modules to build ChatModel. Design and run the agent to get the answer from the internet.
To create a custom LLM agent in LangChain, import libraries to build the prompt template, output parser, LLM, and Output parser before executing the agent.
Creating a custom agent with Tool Retrieval is useful when we have a huge number of tools and the agent has to find which one is right for the job.
To create a custom agent in LangChain, install the modules to get libraries and set up the tool for the agent. Test the agent to get answers from the internet.
To configure max iteration behavior in LangChain, install the modules for configuring agents and setting the limit for max iterations while initializing them.
To build a customized conversational memory in LangChain, install the modules to import libraries for using memory with ai_prefix and human-prefix arguments.
To add multiple memory classes in LangChain, install the modules for importing multiple memory libraries to configure the conversation and run the model.
To Add memory in an agent using LangChain, install the modules to import libraries for configuring the memory with an agent to store the previous messages.
To add a custom memory type in LangChain, install the modules to import libraries like spaCy for designing a custom memory and run it to test the performance.
To get started with agents in LangChain, install the modules for building the chat model or agent and use it to build the conversation interface by calling it.
To implement the self-ask with the search chain in LangChain, install the modules and configure the model using the self-ask agent before extracting information.
To implement ReAct logic in LangChain, install the modules for building agents and environments for LCEL and chat models to test the ReAct logic.
To adjust the contrast of an image in PyTorch, use the “adjust_contrast()” method and provide the input image and contrast factor as an argument.
To adjust the brightness of an image in PyTorch, use the “adjust_brightness()” method and provide the input image and brightness factor as an argument.
To adjust the saturation of an image in PyTorch, use the “adjust_saturation()” method and provide the input image and saturation factor as an argument.
To use transformation chains from LangChain, install the required modules to load data, transform it into smaller chunks, and display them on the screen.