Learn about ColBERT, a new way of scoring passage relevance using a BERT language model that substantially solves the problems with dense passage retrieval.
Retrieval augmented generation is by now of generative artificial intelligence applications. Supplementing your application prompt with relevant context retrieved from a vector database can dramatically increase accuracy and reduce hallucinations. This means that increasing relevance in vector search results has a direct correlation to the quality of your
cosine similarity Unfortunately, off-the-shelf models struggle with unusual terms, including names, that are not commonly in their training data. DPR also tends to be hypersensitive to chunking strategy, which can cause a relevant passage to be missed if it’s surrounded by a lot of irrelevant information. All of this creates a burden on the application developer to “get it right the first time,” because a mistake usually results in the need to rebuild the index from scratch.
application: ingestion and retrieval. Ingestion Because each document chunk will have multiple embeddings associated with it, I’ll need two tables: CREATE TABLE chunks ); CREATE TABLE colbert_embeddings ); CREATE INDEX colbert_ann ON colbert_embeddings WITH OPTIONS={ 'similarity_function': 'DOT_PRODUCT' }; After installing the ColBERT library and downloading , I can load documents into these tables: pip install colbert-ai the pretrained BERT checkpoint from colbert.infra.
applications that puts them in control of the step-up to new functionality. Instead of having to keep up with the myriad changes in techniques and libraries, you have a single stream, so you can focus on building your application. You can use
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