In today's video, I will be showcasing Phidata - a toolkit for building AI Assistants using function calling.
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[Link's Used]:
Github Repo: github.com/phidatahq/phidata
Ashpreet's Original Video: / ashpreetbedi
Docs: docs.phidata.com/introduction
Phidata is a cutting-edge framework designed for creating autonomous AI assistants (aka Agents) that have long-term memory, contextual knowledge, and the ability to perform actions using function calling. This enables more dynamic and context-aware interactions, paving the way for advanced AI applications.
*Why Phidata?*
LLMs often face challenges due to limited context and inability to take actions. Phidata addresses these issues by incorporating:
- *Memory:* Stores chat history in a database, allowing long-term conversations.
- *Knowledge:* Uses a vector database to provide LLMs with business context.
- *Tools:* Enables LLMs to execute actions like pulling data from APIs, sending emails, or querying databases.
*How It Works*
1. *Create an Assistant:* Begin by defining your autonomous assistant.
2. *Add Tools, Knowledge, and Storage:* Integrate functions, vector databases, and storage to enhance capabilities.
3. *Serve Your AI Application:* Utilize platforms like Streamlit, FastAPI, or Django to deploy your AI assistant.
*Key Highlights*
- Build AI assistants that remember past interactions.
- Equip your AI with relevant business context.
- Empower your AI to perform real-world actions seamlessly.
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*Additional Tags and Keywords:*
AI, Autonomous Assistants, Phidata, Long-term Memory, Contextual Knowledge, AI Tools, Machine Learning, Vector Database, Function Calling, Streamlit, FastAPI, Django, AI Development, Advanced AI Applications
*Hashtags:*
#ai #Phidata #AutonomousAssistants #machinelearning #aidevelopment #TechInnovation
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