Results for “vector”

Index local folders and search documents with BM25, vector, or hybrid queries, plus AI answers with citations and a web UI.

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

Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

Index folders of PDFs and other documents into LanceDB for vector search, then query them with semantic search, page filters, verbatim quotes, and cross-document aggregation.

Expert guidance for architecting, developing, and operating SurrealDB 3, covering SurrealQL, multi-model data modeling, vector search, security, deployment, performance tuning, SDK integration, and ecosystem tools.

Provides a standardized interface for ingesting raw data across domains such as genomics, network analysis, document review, and spatial mapping, converting it into semantic vectors for agent use.

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

Build LLM-powered applications with modular components for chains, agents, memory, and retrieval, supporting Python and JavaScript frameworks.

Build production-ready LLM applications, RAG systems, and intelligent agents with architecture design, model selection, and cost controls.

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

Build LLM-powered applications with agents, chains, and RAG using a framework that supports multiple providers and 500+ integrations.

Augments nearest-neighbour search with graph Laplacian features to retrieve items based on both semantic similarity and structural role.

Diagnoses Qdrant search quality issues by isolating causes like HNSW approximation, quantization, embedding model, or search pipeline problems.

Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.

Builds a Streamlit and FastAPI RAG application that lets users upload documents and query them with natural language through LM Studio.

Designs and implements RAG pipelines, covering document chunking, embedding strategies, hybrid search, answer synthesis with source attribution, and evaluation using RAGAS metrics.

Manage a multi-layered knowledge system for ingesting, organizing, syncing, and retrieving knowledge across local files, MCP memory, vector stores, and Git repos.

Guides selection of Qdrant search strategies including hybrid search, reranking, relevance feedback, MMR, and discovery APIs to improve retrieval quality.

Provides persistent, self-hosted semantic memory for AI agents via MCP, storing worklogs, decisions, and notes in PostgreSQL with pgvector for meaning-based retrieval across sessions.

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.