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Building a Semantic Search System with Spring AI and PGVector
- Semantic search enhances traditional keyword-based search by understanding the meaning and context of queries rather than relying on exact keyword matches.
- The article discusses implementing semantic search using Spring AI and PGVector by integrating Ollama for generating embeddings that represent the semantic meaning of text.
- Dependencies for the project include spring-ai-ollama-spring-boot-starter, spring-ai-pgvector-store-spring-boot-starter, spring-boot-docker-compose, and spring-ai-spring-boot-docker-compose.
- Setting up the environment involves configuring Docker with PostgreSQL, PGVector, and Ollama, each serving specific purposes in the application.
- Application properties are configured to interact with PGVector and Ollama, specifying settings for AI models, database connectivity, and Docker Compose integration.
- A record class for movies is created to store movie data as embeddings in PGVector for semantic search, and a MovieStoragePipeline class handles the storage process.
- The Semantic Search Controller processes user queries to find similar movie descriptions based on vector-based similarity search, enabling intuitive search experiences.
- The application, once configured, automatically launches Docker containers and initializes the vector store to enable semantic searches based on textual queries.
- Running queries like 'dystopian future' demonstrates the application's ability to perform semantic searches and retrieve conceptually matching documents.
- In conclusion, the article showcases the integration of Spring AI, PGVector, and Ollama to build a semantic search system, empowering effective document retrieval with natural language processing capabilities.
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