Vector Database Development

Complete retrieval infrastructure for RAG systems, the layer that determines whether your AI assistant returns accurate, grounded answers or plausibly-worded hallucinations. Document ingestion handles PDFs, Word documents, HTML, Confluence pages, SharePoint libraries, and database records; preprocessing removes boilerplate, headers, and noise that pollutes retrieval quality. Chunking strategy is chosen based on document structure: fixed-size windows (512-1024 tokens) for homogeneous text, paragraph-boundary chunking for structured reports, hierarchical chunking (chunk + parent document retrieval) for documents where individual chunks lack sufficient context. Embedding model selection balances accuracy and inference cost: text-embedding-3-large for high-accuracy domain-specific retrieval; text-embedding-3-small or all-MiniLM-L6-v2 for high-volume pipelines where cost matters. Vector store setup (Pinecone, Weaviate, Qdrant, or pgvector) based on your operational preferences and scale. Hybrid retrieval combines dense vector search with sparse BM25 keyword search using Reciprocal Rank Fusion; a cross-encoder re-ranking pass improves top-K relevance before context is assembled. RAGAS evaluation framework measures Faithfulness, Answer Relevance, Context Precision, and Context Recall against a curated query set before launch and on a scheduled basis in production.

Search applications that understand meaning rather than matching keywords, so a user searching for "ways to reduce employee turnover" finds content about "retention strategies" and "staff engagement programmes" because they mean the same thing, not because they share lexical tokens. The failure mode of keyword search (exact-match hits on irrelevant content, zero results when the query uses different terminology than the document) is replaced by cosine similarity on dense embeddings that capture semantic proximity. For knowledge bases, help centres, internal document search, and product catalogues, semantic search reduces failed-search rates by 30-50% versus BM25-only search. Implementation uses bi-encoder models (sentence-transformers, OpenAI text-embedding-3-large) for candidate retrieval via approximate nearest neighbor search (HNSW indexing in Qdrant, Weaviate, or pgvector) and an optional cross-encoder reranking pass for top-K results where higher precision justifies the added latency. Metadata filtering applies structured attribute constraints alongside semantic similarity, searching a product catalogue by category + semantic query simultaneously. Search result explanations surface the passage that matched so users understand why a result was returned.

Product, content, and document recommendation systems using vector similarity, matching items to users based on semantic proximity rather than requiring exact keyword overlap or collaborative filtering's need for dense interaction matrices. Item-to-item similarity recommendations use embeddings of product descriptions, images (via CLIP), or behavioral sequences to find semantically similar items, handling cold-start for new catalog additions that have no interaction history. User preference modelling encodes a user's interaction history into a preference vector using weighted aggregation or a learned user encoder; the preference vector is queried against the item vector index for real-time personalized ranking. Two-tower neural network architectures separate user and item encoding for high-volume serving, the item index is pre-computed; only the user query vector needs to be computed at inference time, enabling sub-50ms recommendation latency for catalogs of millions of items. An A/B testing framework with holdout groups validates lift in click-through rate, conversion rate, or session depth against a non-personalized baseline before full rollout.

Long-term memory architecture for AI assistants and agents that need continuity across sessions, the component that enables an AI to remember that a specific customer's integration issue was escalated to tier 2 last week, or that a user prefers concise responses and works in the healthcare domain. Memory items (key conversation turns, user preferences, resolved issues, established facts) are embedded and stored in a vector database; at the start of each new session, a similarity search against the current context retrieves the most relevant past memories rather than loading the entire history into context. Memory types handled separately: episodic memory (specific past interactions with timestamps), semantic memory (learned facts about users, accounts, or topics), and procedural memory (successful action sequences the agent has executed). Recency and relevance weighting controls whether a memory from 3 months ago gets retrieved when a newer but less relevant memory exists. For multi-user applications, strict isolation ensures that User A's memory is never retrievable in User B's session, a security requirement that cannot be an afterthought in production memory systems.

Vector indexing for images, audio, and mixed-modal content alongside text, enabling applications that need to search across content types in a unified semantic index rather than maintaining separate search systems per modality. CLIP (Contrastive Language-Image Pretraining) embeddings enable cross-modal retrieval: a text query for "red leather sofa in modern living room" returns visually matching product images from an e-commerce catalog, even when the product descriptions don't contain those exact words. Image-to-image similarity search handles content deduplication, visual product matching, and forensic content identification. ImageBind handles joint embedding across image, text, audio, depth, and IMU data for IoT and media applications that need unified search across sensor types. Multi-modal indexes store both the raw content metadata and the embedding in the vector store, enabling metadata filters (product category, color attribute, upload date) to apply alongside semantic similarity ranking. For high-volume image catalogs with 1M+ images, approximate nearest neighbor search maintains sub-100ms retrieval latency without exhaustive comparison.

Production-grade embedding infrastructure that keeps your vector index current as your data changes, the engineering component that most RAG prototypes skip and then struggle with in production when the source data evolves. Document preprocessing extracts clean text from PDFs (PyMuPDF, pdfplumber), Word documents (python-docx), HTML (BeautifulSoup with boilerplate removal), and Confluence/SharePoint via API; noise removal strips navigation elements, headers, footers, and formatting artifacts that dilute embedding quality. Batch embedding API calls handle rate limits with exponential backoff and queue-based throughput management, enabling processing of large document corpora without manual intervention. Incremental indexing detects content changes using hashing or timestamp comparison and only re-embeds and re-indexes documents that have changed. Deletion and update handling propagates document changes to the vector store immediately, including cascade deletion for chunked documents where all child chunks must be removed when the parent is deleted. Metadata schema design ensures that structured attributes (document type, author, department, creation date, access level) are stored alongside vectors for hybrid filtering at query time.