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Unfortunately, most current chatbots are limited due to their reliance on static training data.
Data outputted by these systems can be obsolete, limiting our ability to gain real-time information for our queries.
To overcome these issues, advanced techniques like Retrieval-Augmented Generation (RAG) have emerged.
However, as mentioned previously, these chatbots face several challenges.
Furthermore, without real-time data integration, chatbots may experience hallucinations and inaccuracies.
This approach significantly improves contextual understanding, accuracy, and relevance in AI models.
Some popular vector databases are Pinecone, Weaviate, Milvus, Neo4j, and Qdrant.
They can process high-dimensional data for RAG systems that require complex vector operations.
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