semantic-search

Semantic search retrieves documents by converting text into vector embeddings and matching query vectors to contextually relevant information in indexed collections, enabling meaning-based information lookup rather than exact keyword matching.

Yes, you need a running ChromaDB instance to perform vector-based document retrieval. This Skill depends on ChromaDB to store and query vector embeddings generated via the gemini-embedding-001 or Ollama models.

Yes, you can apply metadata filters to narrow down semantic search results. This allows you to restrict information lookup to specific topics or document collections within your research database.

The best way to find relevant tech articles is using vector-based semantic search over indexed document repositories. You describe the topic in natural language, and the system retrieves contextually relevant documents.

Yes, this semantic search approach works with Ollama embeddings and the Google genai gemini-embedding-001 model. It uses these dependencies to generate vector embeddings for retrieving contextually relevant information.