need hybrid search keyword plus embeddings

Yes — hybrid search combines keyword-based search and embedding/vector search so you get both:

• Keyword search: exact term matching, filters, high precision for named entities, IDs, rare terms
• Embedding search: semantic similarity, paraphrases, concept matching, typo/wording variation

Common hybrid approach

1. Run a keyword retriever and a vector retriever in parallel
2. Merge results
3. Re-rank them using:
   • weighted score combination, or
   • a cross-encoder / reranker model

Typical scoring strategies

1) Weighted sum

final_score = a * keyword_score + b * vector_score

Good when both scores are normalized.

2) Reciprocal Rank Fusion (RRF)

Combines ranked lists without needing score normalization.

RRF = Σ 1 / (k + rank)

Often works very well in practice.

3) Rerank top-N

• Retrieve top 50 from keyword
• Retrieve top 50 from vector
• Union them
• Use a reranker to select final top 10

Best use cases

• Enterprise search
• Product search
• Document search
• RAG / chatbot retrieval
• Code search

Simple architecture

Query
  ├── Keyword search (BM25 / inverted index)
  ├── Vector search (embeddings / ANN)
  └── Merge + Rerank
         ↓
      Final results

If you're building this

Tools

• Elasticsearch / OpenSearch: supports hybrid setups
• Weaviate
• Pinecone + keyword layer
• Qdrant + external BM25
• Vespa
• Postgres + pgvector + full-text search

Practical recommendation

If you want strong quality:

• use BM25 + embeddings
• apply RRF
• then rerank with a cross-encoder if latency allows

If you want, I can give you:

1. a code example in Python,
2. a DB-specific implementation (Elasticsearch, OpenSearch, Postgres, etc.),
3. or a design for RAG hybrid retrieval.