The motivation of this thoughts that trying to discover the LLMs and Generative AI...

Quick learning vector database

This article explores chunking strategies to handle context limitations in Large Language Models (LLMs) like GPT. It covers preprocessing data, selecting chunk sizes, and how to maintain coherence in various content types such as unstructured, Markdown, and LaTeX.

Chunking strategies to overcome context limitation in LLM

An overview of how to implement long-term memory in a ChatGPT-based chatbot using a Vector Database (VectorDB) to store conversation history and retrieve the most relevant past interactions.

Storing long-term memory in ChatGPT using VectorDB

An exploration of query caching in Large Language Models (LLMs), focusing on how semantic vector databases can drastically improve efficiency and reduce computational costs by reusing cached answers for semantically similar queries.

Query caching for large language models

In spite of having taken the world by storm, Large Language Models(LLM) still has some limitations such as limited context window and a knowledge cutoff date. Retrieval-Augmented Generation(RAG) steps in to bridge this gap by allowing LLMs to access and utilize external knowledge sources beyond their training data. However, data is not text based only, it also can be image, audio, table in docs,...

Multimodal: in rag

An overview of vector databases and their application in LLM systems. This article covers the history and core concepts of vector databases, their use cases, implementation considerations with LLMs, and a comparison of popular vector database options.

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