Build a Smart Conversational AI with Memory Using Cognee and Free Hugging Face Models
Explore how to create a smart conversational AI agent with memory by integrating Cognee and free Hugging Face models. This tutorial covers setup, learning, reasoning, and conversation capabilities.
Setting Up the Environment and Libraries
This tutorial guides you through building an intelligent conversational AI agent equipped with persistent memory, leveraging free, open-source tools such as Cognee and Hugging Face models. The setup begins by installing essential libraries including Cognee, Transformers, Torch, and Sentence-Transformers. These components are vital for tokenization, model loading, asynchronous operations, and memory management.
Configuring Cognee for Memory Storage
Cognee is configured to use the 'sentence-transformers/all-MiniLM-L6-v2' embedding model, ensuring efficient and lightweight embedding storage. The configuration attempts to set parameters directly, and if that fails, environment variables are used as a fallback, guaranteeing robust setup.
Loading Hugging Face Language Models
The HuggingFaceLLM class handles loading and running lightweight Hugging Face models such as DialoGPT-medium or DistilGPT2. It automatically detects GPU availability to optimize performance. The class includes a method to generate context-aware responses based on input prompts.
Creating an Advanced AI Agent with Memory
The AdvancedAIAgent class is the core component that combines Cognee's memory capabilities with Hugging Face's language models. It supports:
- Initializing memory and model
- Learning from individual texts with domain tagging
- Batch learning from multiple documents
- Querying knowledge with optional domain filters
- Performing advanced reasoning by synthesizing answers from retrieved knowledge
- Engaging in conversational interactions that allow learning and responding naturally
Learning and Querying Knowledge
The agent can learn from texts and documents tagged by domain, storing enhanced content with timestamps. Queries can be filtered by domain to retrieve relevant knowledge snippets. If Cognee search fails, a manual memory fallback ensures information is retained.
Reasoning and Response Generation
Using retrieved knowledge, the agent synthesizes answers by feeding context and questions into the Hugging Face model, producing natural, informative responses. It also extracts key points from knowledge to summarize answers effectively.
Conversational Interface
The agent processes user inputs to detect learning commands, questions, or general conversation. It stores new information, performs reasoning to answer questions, or generates friendly conversational replies, maintaining a history of interactions.
Demonstration and Usage
A main asynchronous function demonstrates the agent's capabilities:
- Multi-domain learning from sample documents
- Knowledge retrieval and reasoning with test questions
- Conversational interactions simulating teaching and querying
- Summarizing knowledge domains and conversation history
The tutorial highlights the use of free Hugging Face models with optional GPU acceleration, enabling advanced AI functionalities without paid APIs.
This approach empowers developers to build capable AI agents that learn, reason, remember, and converse naturally using accessible open-source tools.
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