Results for “e-e-a-t”

Chat with your agent about projects, recommendations, and canonical papers in Paperzilla. Fetch feeds, read papers, leave feedback, and export data using the pz CLI.

Access AI-ready drug discovery datasets and benchmarks from Therapeutics Data Commons, covering ADME, toxicity, drug-target interactions, and molecular generation with standardized splits and evaluation metrics.

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

Creates and manages annotation configs and annotation queues on Arize, and applies human annotations to project spans via the Python SDK.

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

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.

Chunks a book into canonical retrieval units with heading-aware structure splitting, recursive token targets, and contextual prefixes for downstream RAG ingestion.

Designs persistent memory architectures for AI agents, covering cross-session knowledge retention, entity tracking, temporal validity, graph/vector retrieval, and memory consolidation.

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

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

Probe RAG applications for prompt injection via poisoned retrieved context and embedding manipulation.

Extract semantic content from documents, images, audio, and video using Azure AI Content Understanding SDK for Python.

Extract text, tables, and structured data from documents using Azure AI Document Intelligence SDK for .NET, with support for prebuilt and custom models.

Generate image captions, answer visual questions, and perform image-text retrieval using BLIP-2's Q-Former architecture with frozen vision encoders and LLMs.

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

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

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

Reference for configuring and using LangChain4j vector stores, covering setup, search, filtering, and ingestion.

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

Enables fast similarity search and clustering of dense vectors using FAISS, supporting billions of vectors, GPU acceleration, and various index types.