Part 8/11:
The Role of Semantic Search in Knowledge Retrieval and QA
Semantic search acts like a library catalog. Instead of trying to encode all knowledge into the model, it:
Indexes Data using semantic or vector embeddings.
Matches Queries to relevant documents based on meaning, not just keywords.
Retrieves the most relevant documents instantly.
Summarizes or processes these documents with a GPT model to generate an answer.
This approach is akin to a library clerk finding the right books, rather than memorizing all details.
How this process works step-by-step:
Indexing: Convert your corpus into semantic vectors.
Querying: Generate a semantic embedding of your question.
Retrieval: Find documents with similar embeddings.