Enteric Reservoir of Knowledge
The goal of this experiment is to develop a semantic indexing and retrieval system for the cognetics folder, using state-of-the-art embedding techniques to enable advanced, context-aware search capabilities.
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Corpus Selection
We focus exclusively on documents and code within thecogneticsfolder to ensure domain specificity and relevance. -
Preprocessing and Chunking
Files are parsed and split into smaller, semantically coherent chunks to optimize embedding generation and improve retrieval granularity. -
Embedding Generation
Each chunk is transformed into a high-dimensional vector embedding using Ollama’s embedding model. These embeddings capture semantic and contextual information in numerical form, allowing meaningful similarity comparisons. -
Vector Storage
The embeddings are stored in a vector database optimized for efficient nearest neighbor search, enabling rapid retrieval of semantically related chunks. -
Semantic Search and Retrieval
Queries are converted into embeddings and matched against the stored vectors, returning the most semantically relevant chunks. This approach enables nuanced search beyond keyword matching.
By applying Ollama embeddings to the cognetics corpus, this experiment illustrates how embedding-based vector search can facilitate precise and context-aware retrieval in specialized technical domains. This system provides a foundation for enhanced knowledge discovery, summarization, and intelligent querying within domain-specific repositories.