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课程概述
Information Retrieval (IR) and Retrieval Augmented Generation (RAG) are only effective if the information retrieved from a database as a result of a query is relevant to the query and its application.
Too often, queries return semantically similar results but don’t answer the question posed. They may also return irrelevant material which can distract the LLM from the correct results.
This course teaches advanced retrieval techniques to improve the relevancy of retrieved results.
The techniques covered include:
1. Query Expansion: Expanding user queries improves information retrieval by including related concepts and keywords. Utilizing an LLM makes this traditional technique even more effective. Another form of expansion has the LLM suggest a possible answer to the query which is then included in the query.
2. Cross-encoder reranking: Reranking retrieval results to select the results most relevant to your query improves your results.
3. Training and utilizing Embedding Adapters: Adding an adapter layer to reshape embeddings can improve retrieval by emphasizing elements relevant to your application.
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1 - Advanced Retrieval for AI with Chroma 的 5 个评论(共 5 个)
创建者 CHRISTOPHER C
•Oct 2, 2024
This guy explains in a way that everyone can easily understand vector concepts.
创建者 Akarsh
•Aug 20, 2024
awesome
创建者 AmitBiswas
•Dec 20, 2024
Nice course
创建者 Santhosh
•Jan 8, 2025
good
创建者 Shiba B D
•Oct 15, 2024
They just show you a very simple RAG, not sure why even the title says advanced. Not at all useful