Build semantic search that understands meaning.
Create search that finds relevant results by meaning, not keywords.
Architecture
1. Document ingestion
2. Embedding generation
3. Vector storage
4. Query embedding
5. Similarity search
Code Example
def semantic_search(query, vectorstore, k=5):
query_embedding = embeddings.embed_query(query)
results = vectorstore.similarity_search_by_vector(query_embedding, k=k)
return results
Benefits
✅ Better relevance
✅ Handles synonyms
✅ Multi-language support
Conclusion
Semantic search improves search quality!