Core Concepts of Retrieval in LLMs
To understand the power of retrieval-augmented generation, you must first grasp how embeddings work. Embeddings are numerical representations of words, sentences, or even entire documents, mapped into a high-dimensional vector space. This process allows language models to capture the underlying meaning of text, not just its literal form. By translating language into vectors, embeddings enable machines to compare and reason about text in a way that is much closer to how humans understand relationships and context.
When two pieces of text are converted into vectors, their closeness in the vector space reflects how similar their meanings are. The more semantically related two texts are, the closer their vectors will be.
Vectors can capture nuanced relationships, such as synonyms or paraphrases, by being positioned near each other. This allows retrieval systems to find relevant information even if the exact query wording does not appear in the documents.
By measuring the distance or angle between the query vector and document vectorsβusing metrics like cosine similarityβretrieval systems can rank documents by how well they match the user's intent.
The retrieval pipeline in a retrieval-augmented generation system typically starts with a user query. This query is first embedded into a vector using an embedding model. The system then searches a database of pre-computed document vectors to find those most similar to the query vector. These candidate documents are selected based on their semantic similarity to the query, and are then passed to the language model for further processing or answer generation. This approach enables language models to access and leverage external knowledge efficiently, providing up-to-date and contextually relevant responses.
In LLM retrieval, a vector representation is a numerical encoding of text where each dimension captures some aspect of its meaning. These vectors allow for efficient comparison and retrieval based on semantic content rather than exact word matches.
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To understand the power of retrieval-augmented generation, you must first grasp how embeddings work. Embeddings are numerical representations of words, sentences, or even entire documents, mapped into a high-dimensional vector space. This process allows language models to capture the underlying meaning of text, not just its literal form. By translating language into vectors, embeddings enable machines to compare and reason about text in a way that is much closer to how humans understand relationships and context.
When two pieces of text are converted into vectors, their closeness in the vector space reflects how similar their meanings are. The more semantically related two texts are, the closer their vectors will be.
Vectors can capture nuanced relationships, such as synonyms or paraphrases, by being positioned near each other. This allows retrieval systems to find relevant information even if the exact query wording does not appear in the documents.
By measuring the distance or angle between the query vector and document vectorsβusing metrics like cosine similarityβretrieval systems can rank documents by how well they match the user's intent.
The retrieval pipeline in a retrieval-augmented generation system typically starts with a user query. This query is first embedded into a vector using an embedding model. The system then searches a database of pre-computed document vectors to find those most similar to the query vector. These candidate documents are selected based on their semantic similarity to the query, and are then passed to the language model for further processing or answer generation. This approach enables language models to access and leverage external knowledge efficiently, providing up-to-date and contextually relevant responses.
In LLM retrieval, a vector representation is a numerical encoding of text where each dimension captures some aspect of its meaning. These vectors allow for efficient comparison and retrieval based on semantic content rather than exact word matches.
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