How can I implement semantic search using Generative AI for querying large text corpora without loss of accuracy

Question

With the help of proper code example can you tell me How can I implement semantic search using Generative AI for querying large text corpora without loss of accuracy?

Answer 1

Semantic search using Generative AI can be implemented with embedding models and vector databases, enabling accurate querying of large text corpora based on meaning rather than keyword matching.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• AI-Powered Embeddings – Uses OpenAI’s embedding models for meaning-based text representation.
• Vector Search with FAISS – Enables fast and accurate retrieval of semantically similar documents.
• Handles Large Corpora – Efficiently indexes and searches through massive text datasets.
• Context-Aware Results – Retrieves documents based on conceptual similarity rather than exact keywords.
• Scalable & Adaptable – Can be integrated into chatbots, search engines, and knowledge management systems.

Hence, semantic search with Generative AI enhances query accuracy by understanding intent and meaning, enabling fast, intelligent, and context-aware retrieval from large text corpora.