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 of LLMs (Large Language Models) are limited, and PhiData aims to enhance the response capabilities of long-term dialogue models (LLMs) by adding memory, knowledge, and practical tools.🚀
Project Address:
https://github.com/phidatahq/phidata
With 4.3K stars, it is used by several well-known organizations:
PhiData is positioned as a framework that enhances any long-term dialogue model with the capabilities of memory, knowledge, and practical tools, transforming it into an efficient AI assistant. This project is particularly suitable for applications requiring real-time data access.😊
Key Modules 🧩
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LLM Encapsulation (LLMs): Supports the use of mainstream large language models such as OpenAI, OpenAI-like (e.g., Dark Side of the Moon), Ollama, AWS Bedrock, Together, Mistral, Groq, Azure, Gemini, Claude, Cohere, etc., or access to model endpoints.
For example, OpenAI:
For example, Ollama:
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Instruction Prompts: Includes system_prompt to define the functions of the Agent or Assistant; user_prompt to submit user input requests.
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Memory Function: Allows the AI to save chat history for long-term dialogue. The built-in assistant.memory is used to save the dialogue history of the entire Agent or Assistant and the message history of the LLM. The dialogue history refers to the conversation records between the user and the LLM; the LLM’s message history refers to all instructions sent to the LLM, including system prompts, tool calls, etc.
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Knowledge Base: Stores business-related knowledge through a vector database, providing contextual support. For example, the knowledge base below is based on PgVector and stores PDF content:
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Practical Tools: Tools are functions that the Agent or Assistant can call, supporting operations such as web search, sending emails, database queries, and API calls, enhancing the AI’s autonomy and functionality. 🛠️
The tools currently supported by PhiData include:
Use Cases 🎬
PhiData can be used to build various AI applications, such as an AI that automatically extracts information from PDFs and answers related questions, or an AI that responds to queries based on a specific research paper library. It supports deployment through various means such as Streamlit, FastApi, or Django. 🌐
PhiData primarily targets AI engineers and developers, especially professionals looking to quickly build and deploy AI products with complex interactive capabilities. 👩💻👨💻
Usage Process 🧗♀️
Users can create and deploy an AI Assistant through simple steps:
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Create an Assistant instance.
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Add tools (such as function calls), knowledge (such as vector databases), and storage (such as databases) to the Assistant.
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Deploy your AI application using platforms like Streamlit, FastApi, or Django. 🚀
An example is as follows:
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Create a virtual environment.
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Install: Install PhiData using the Python package manager.
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Create an AI Assistant. For example, create an assistant.py file that includes an Assistant capable of web searching using DuckDuckGo.
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Run: Set environment variables such as the LLM API key and install necessary libraries, then run the python program.
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Deploy: Run the Assistant locally or on a server, enabling it to respond to queries or perform tasks programmatically. 🛠️ You can experience three demos developed with PhiData here:
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PDF Reading AI Assistant: https://pdf.aidev.run/
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Research Paper Inquiry AI Assistant: https://arxiv.aidev.run/
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Hacker News Exploration Assistant: https://hn.aidev.run/
Project Summary 🌈
The PhiData open-source project provides great convenience and efficiency for AI product development with its powerful functional integration and flexible deployment options. We hope this report helps you better understand the value and applications of PhiData. More importantly, it is crucial to understand the structure and framework of AI Agents or Assistants. 🌟
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