Results for “vector”

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.

Build search applications with full-text, vector, semantic, and hybrid search using the Azure AI Search SDK for .NET.

Guides scaling Qdrant vector search with time-based data rotation using shard rotation, collection rotation, or filter-and-delete strategies.

Search Azure AI Search indexes using the Python SDK for full-text, vector, hybrid, and semantic search with AI enrichment.

Guides scaling decisions for Qdrant vector databases when data volume exceeds single-node capacity, covering tenant scaling, time window rotation, vertical scaling, and horizontal sharding.

Retrieval-Augmented Generation pipelines — ingestion, chunking, embedding, vector stores, retrieval, evaluation. Use when building a RAG pipeline, choosing chunking strategies or embedding models, debugging retrieval quality or hallucinations, evaluating an existing RAG system, or scaling/migrating vector stores.

Builds Retrieval-Augmented Generation pipelines with vector stores, chunking strategies, and reranking

Guides vertical scaling decisions for Qdrant vector databases, covering when to scale up, how to resize nodes in Qdrant Cloud or self-hosted deployments, RAM sizing formulas, and when to switch to horizontal scaling.

Upgrade Qdrant version without interrupting application availability and ensuring data integrity.

Guides scaling Qdrant for multi-tenant workloads using payload partitioning, custom sharding, and tiered multitenancy.

Guides optimization of Qdrant query latency by tuning segments, memory, quantization, and search parameters.

Build and manage Supabase projects including Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, and Queues with security best practices.

Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.

Data pipeline specialist for AI system integration. Use when the user asks to talk to Hagrid, requests data integration, or needs embedding pipeline or vector DB setup.

Diagnoses and resolves slow Qdrant search performance issues including high latency, low throughput, and slow filtered searches.

Diagnoses Qdrant capacity needs and guides horizontal scaling decisions, including node count, shard count, replication factor, and resharding trade-offs.

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

Diagnoses and resolves slow Qdrant indexing and data ingestion by optimizing batching, sharding, HNSW parameters, and payload indexing strategies.

Guides scaling Qdrant query throughput (QPS) through performance tuning, horizontal scaling with read replicas, and disk I/O optimization.

Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task.

Guides selection of Qdrant deployment options: local mode, Docker, self-hosted, Qdrant Cloud, Hybrid Cloud, or Qdrant EDGE based on latency, control, and production needs.

Diagnoses and reduces Qdrant memory usage by analyzing resident memory, page cache, and providing optimization techniques like quantization, on-disk storage, and async_scorer.

Audits Kubernetes pods for container escape vectors by analyzing privileged mode, dangerous capabilities, host namespace sharing, and writable hostPath mounts using the Kubernetes Python client.

Expert guidance for Xata, the serverless data platform that combines PostgreSQL, Elasticsearch, and AI capabilities in a single API. Helps developers build applications with full-text search, vector similarity search, file attachments, and branching — all through a type-safe TypeScript SDK.

Security controls for RAG. Indirect prompt-injection via retrieved documents, PII detection/redaction (Microsoft Presidio, AWS Comprehend), multi-tenant isolation, ACL-aware retrieval with row-level/metadata filtering, data-leakage prevention, jailbreak hardening on retrieved context, GDPR right-to-be-forgotten in vector DBs. USE WHEN: user mentions "prompt injection RAG", "indirect prompt injection", "PII redaction", "Presidio", "ACL RAG", "row-level security", "multi-tenant RAG isolation", "GDPR vector DB", "right to be forgotten", "jailbreak", "data leakage RAG" DO NOT USE FOR: hallucination detection - use `rag-guardrails`; tenancy scaling patterns - use `rag-production`; audit tracing schema - use `rag-observability`

Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations, detecting attack vectors where attacker-controlled input reaches AI agents in CI/CD pipelines.

Scans GitHub Actions workflows and CI/CD pipeline configurations for supply chain attack vectors including unpinned actions, script injection via expressions, dependency confusion, and secrets exposure.

Navigate Oracle Cloud Infrastructure's Enterprise AI services: choose models, build agents with RAG and tools, estimate costs, secure access, and integrate with Oracle Database, APEX, and other platform services.

Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), feature flags (Flagship), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.