🕳️ Attention Sinks in LLMs for endless fluency
In LangChain, we can also extract information from the conversation as facts and store these by integrating a knowledge graph as the memory.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
Overall, RAG and RALMs overcome the limits of language models’ memory by grounding responses in external information.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
LlamaIndex focuses on advanced retrieval rather than on the broader aspects of LLM apps.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
Text embeddings are a critical piece of many pipelines, from search, to RAG, to vector databases and more. Most embedding models are BERT/Transformer-based and typically have short context lengths (e.g., 512). That’s only about two pages of text, but documents can be very long – books, legal cases, TV screenplays, code repositories, etc can be tens... See more