Vector Database Setup & Embedding Pipeline for AI Applications

I will design and build a complete vector database and embedding pipeline for your AI application — covering document ingestion and chunking, embedding generation using OpenAI Embeddings or an open-source alternative, vector store setup and indexing (Pinecone, Chroma, Weaviate, or Qdrant), metadata filtering configuration, similarity search API, and integration with your LangChain or LlamaIndex orchestration layer. The vector data layer is the most technically nuanced component of any RAG or semantic search system; poorly designed chunking, embedding mismatches, and misconfigured retrieval parameters are the most common causes of RAG systems that retrieve the wrong context and produce hallucinated answers despite having the right documents indexed.

The build covers chunking strategy selection and implementation (fixed-size, semantic, or recursive with overlap), embedding model selection and benchmarking for your content type, vector store schema design with metadata fields for filtering, upsert pipeline for initial data load and incremental updates, retrieval API with configurable top-k and similarity threshold, and a retrieval quality test against 20 sample queries measuring precision and recall before handover.

This service is essential for teams building RAG chatbots, semantic search systems, document Q&A tools, or any AI application where retrieval quality from a vector store directly determines the usefulness of generated responses.