I'm unhappy with my current full-text search for semantic retrieval

Got it — “full-text search” often feels blunt for semantic retrieval.

A few common reasons it disappoints:

• it matches keywords, not meaning
• it struggles with synonyms and paraphrases
• ranking can be noisy when terms are common
• it doesn’t handle long documents or nuanced intent well

If your goal is better semantic retrieval, typical upgrades are:

1. Hybrid search

   • Combine keyword search with embedding-based vector search.
   • This usually gives the best practical results.

2. Chunking

   • Split documents into smaller semantic chunks instead of indexing whole docs.
   • Improves recall and ranking precision.

3. Embeddings + reranking

   • Use vector search to fetch candidates.
   • Then rerank with a cross-encoder or LLM-based scorer.

4. Query understanding

   • Expand queries, detect intent, normalize entities, and use synonyms.

5. Metadata filtering

   • Apply filters like date, product, language, author, source before ranking.

6. Evaluation

   • Measure retrieval quality with a test set of real queries.
   • Often the issue is less the index and more the lack of tuning/evaluation.

If you want, I can help you choose a better architecture based on:

• your data type
• corpus size
• latency requirements
• whether you need exact keyword matches
• what search stack you’re using now

If you share your current setup, I can suggest a concrete replacement plan.