Results for “build”

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

Build and query AI-powered knowledge bases from claude-mem observations, enabling focused conversational sessions on specific topics.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.

Builds a local RAG memory system that indexes session logs and notes into ChromaDB for semantic recall across agent restarts.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Build or modify a RAG retrieval pipeline with vector store search, corpus routing, citation rendering, and book fidelity enforcement.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines with observability and security.

Guides building production-grade AI features with LLM integration patterns, RAG architecture, prompt engineering, and cost optimization.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents with vector search, multimodal AI, and enterprise integrations.

Guides building LLM applications on the Dify platform, covering visual workflows, knowledge bases, agents, and API deployment.

Build a persistent, compounding knowledge base that combines a wiki, knowledge graph, and raw source storage for structured retrieval with provenance.

Build AI agents, multi-agent teams, and agentic workflows using the Agno framework, with reference files covering agents, teams, workflows, memory, knowledge, and deployment.

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

Builds and debugs n8n workflows, covering nodes, RAG with vector stores, the REST API, Code node scripts, expressions, and Docker hosting.

Build search applications with vector, hybrid, and semantic search using the Azure AI Search SDK for TypeScript.

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

Build RAG pipelines that combine web search and LLMs for research, fact-checking, and grounded responses using the inference.sh CLI.

Extract text, tables, and structured data from documents using Azure Document Intelligence. Process invoices, receipts, IDs, forms, or build custom document models.

Build and train graph neural networks for drug discovery, protein modeling, and molecular science using PyTorch-native tools.

Build complex AI systems with declarative programming, optimize prompts automatically, and create modular RAG systems and agents using Stanford NLP's DSPy framework.

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.

Builds production RAG and semantic search systems with Qdrant, covering collection setup, vector indexing, filtered and hybrid search, and integration with LangChain and LlamaIndex.

Build production RAG and semantic search systems with a high-performance vector database written in Rust, supporting hybrid search, filtering, and horizontal scaling.

Builds a configurable scraping agent that collects data from APIs, HTML, or RSS, enriches it with Gemini AI scoring, and stores results in Notion, Google Sheets, Supabase, or local files.

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.