Vector Space Model
The Vector Space Model (VSM) is a statistical approach to information retrieval and text analysis that represents texts and other objects as vectors of identifier occurrences, often weighted, such as indexed terms, suitably displayed in a vector space.
Core Principles
- Representation: Documents are represented as sparse vectors where each dimension corresponds to a term in the vocabulary.
- Similarity: Similarity between documents is typically measured using Cosine Similarity, which calculates the cosine of the angle between two vectors.
- Weighting: Term frequencies are often weighted using TF-IDF (Term Frequency-Inverse Document Frequency) to reduce the impact of common, less informative words.
Evolution and Relation to Embeddings
While traditional VSM relies on discrete term counts, modern approaches utilize dense Vector Embeddings to capture semantic meaning beyond keyword overlap. This shift facilitates more accurate semantic search capabilities in local AI environments.
Recent developments highlight the integration of these retrieval models with open-source tools for cost-effective deployment:
- Summary Report: Open-Source AI Projects for Retrieval, Local LLMs, and Cost Savings outlines innovative GitHub projects enhancing AI development.
- These projects demonstrate practical implementations of retrieval-augmented generation workflows using Local LLMs, reducing reliance on expensive cloud APIs.
References
Summary Report: Open-Source AI Projects for Retrieval, Local LLMs, and Cost Savings