AI Agent

Implementing Local RAG with Groq and Llama 3: Phidata Framework Application and Performance Showcase

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Implementing Local RAG with Groq and Llama 3: Phidata Framework Application and Performance Showcase

Phidata is a framework designed for building AI agents with memory, knowledge, and tools. https://www.phidata.com/ https://github.com/phidatahq/phidata https://docs.phidata.com/introduction Three Aspects of Phidata Enhancing LLM Functionality: Memory: Phidata stores chat history in a database, allowing large language models to support longer conversations, thereby better understanding and tracking the context of dialogues. Knowledge: By storing business-relevant information in … Read more

Phidata: A Framework for Multi-Modal Agents

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Phidata: A Framework for Multi-Modal Agents

More AI Open Source Tools: https://www.aiinn.cn/ Phidata is a framework for building multi-modal agents. Using Phidata, you can: build multi-modal agents with memory, knowledge, tools, and reasoning. Establish a team of agents that can collaborate to solve problems. Chat with your agents using a beautiful Agent UI. 16200 Stars 2200 Forks 28 Issues 82 Contributors … Read more

Phidata: 8.3K Stars! Create AI Agents with Long-Term Memory Using GPT-4o

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Phidata: 8.3K Stars! Create AI Agents with Long-Term Memory Using GPT-4o

Project Overview Phidata is an open-source framework designed to build automated assistants (intelligent agents) with memory, knowledge, and tool capabilities. This framework addresses the limitations of existing large language models (LLMs) in terms of context and their inability to perform actions by adding a database to store chat history, a vector database to store business … Read more

Creating AI Agents with Memory and Tools Using Phidata

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Creating AI Agents with Memory and Tools Using Phidata

Aitrainee | Official Account: AI Trainee 🌟Phidata adds memory, knowledge, and tools to LLMs. ⭐️ Phidata:https://git.new/phidata Phidata is a framework for building autonomous assistants (also known as agents) that have long-term memory, contextual knowledge, and can perform actions through function calls. Recommended a tutorial video from YouTuber WorldofAl: Why Choose Phidata? Problem: Large Language Models … Read more

Unlocking Efficient Work: Building Multimodal Assistants with Phidata

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Unlocking Efficient Work: Building Multimodal Assistants with Phidata

Exploring the World of Multimodal Agents: Introduction to the Phidata Framework With the development of artificial intelligence technology, the application of multimodal agents is becoming increasingly widespread. Phidata, as a powerful framework, allows users to build multimodal agents with memory, knowledge, tools, and reasoning capabilities. This article will delve into the features, application scenarios, and … Read more

Phidata vs Langchain: A Comparative Framework for Smart Agents

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Follow us on WeChat: “Full Stack AI Knowledge” Set as a “Star”, bringing you new insights every day When building smart agents, Phidata and Langchain are undoubtedly the focal points in the industry. Both aim to enhance the performance of large language models (LLMs), but each has its unique focus and advantages. This article provides … Read more

Phidata: Framework for Building AI Assistants Using LLM Function Calls

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Phidata: Framework for Building AI Assistants Using LLM Function Calls

  Phidata: A framework for building AI assistants using LLM function calls, allowing LLMs to intelligently choose actions based on responses by executing functions. The assistant has built-in memory, knowledge, storage, and tools, making it easy to build various applications such as knowledge assistants, data assistants, Python assistants, customer assistants, research assistants, marketing assistants, travel assistants, … Read more

Exploring AI Development Frameworks with PhiData

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Exploring AI Development Frameworks with PhiData

The development of AI Agents or AI Assistants is booming, with various Agents emerging. How do we develop an Agent or Assistant? Through the PhiData project, we can glimpse the foundational components and frameworks for Agent development. Project Positioning 🎯 The slogan of PhiData is “Building memory, knowledge, and tools for AI assistants.” The capabilities … Read more

Comparison of Technical Architecture Between AutoGen and phiData Frameworks

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Comparison of Technical Architecture Between AutoGen and phiData Frameworks

In the current development frameworks for distributed AI systems, AutoGen led by Microsoft and phiData represent two different technical paths. Architectural Design Philosophy AutoGen adopts an event-driven distributed architecture, achieving decoupling and collaboration of complex tasks through dynamic Agent orchestration. Its core is a decentralized event bus that supports asynchronous message passing and state synchronization … Read more