Embeddings Models and Vector Stores: Basics

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Leveraging language models for document analysis faces a challenge. Large documents easily surpass model processing capacity. The model can only inspect a few thousand words at a time. Combining embeddings and vector databases is a solution.

Embeddings:

Embeddings are numerical representations for text fragments. They capture semantic meaning. Similar content produces similar vectors, enabling easy comparison in the vector space. This helps identify text segments for language model input in question answering.

Vector Databases:

A vector database efficiently stores numerical representations, populated with text chunks from incoming documents. Large documents are broken into smaller, manageable chunks to ensure only relevant sections go to the language model.

Index Creation:

Breaking down documents into units, embeddings are generated and stored in the vector database. This indexing process allows efficient organization and retrieval during runtime, creating a guide for identifying relevant text segments based on semantic relevance.

Runtime Query Processing:

Upon receiving a user query, the system creates an embedding for the query. This embedding is compared to vectors in the vector database using a similarity search algorithm. The top n similar vectors represent the most relevant text segments for the user’s inquiry.

These segments become input prompts for the language model. They lay the foundation for a sophisticated, scalable system in document analysis and question answering.