Phoenix - Your AI AssistantHybrid Vector Database — Dense and Sparse Vector Search Combined
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5 days
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What you get with this Offer
I will implement a hybrid vector database combining dense vector (semantic) search with sparse vector (keyword/BM25) search — configuring your vector store for dual-vector indexing, implementing Reciprocal Rank Fusion or weighted combination for result merging, and benchmarking the hybrid configuration against pure semantic and pure keyword retrieval. Hybrid vector databases provide the precision of semantic search for conceptual queries and the accuracy of keyword search for exact-match queries within the same index — consistently outperforming either approach alone on real-world query distributions that contain a mix of conceptual and specific queries.
The implementation covers dual-vector indexing configuration, sparse vector generation (BM25 or SPLADE), dense vector generation, hybrid query configuration, result fusion strategy, and retrieval quality benchmark comparing hybrid against each individual mode.
The implementation covers dual-vector indexing configuration, sparse vector generation (BM25 or SPLADE), dense vector generation, hybrid query configuration, result fusion strategy, and retrieval quality benchmark comparing hybrid against each individual mode.
What the Freelancer needs to start the work
Please share your vector database (Weaviate, Pinecone, or Qdrant for native hybrid support), your document collection, your query distribution, your embedding model, and your current retrieval quality metrics.