General
17 cheat sheets.
Sentence Transformers
Comprehensive reference for the sentence-transformers Python library — embeddings, similarity, clustering, retrieval, fine-tuning, and popular models (BGE, E5, GTE, Nomic, Jina).
weaviate-client
Store, search, and manage vector embeddings with the Weaviate Python client. Covers collections, CRUD, vector/hybrid/BM25 search, multi-tenancy, generative search, and batch import.
unstructured
Extract structured text from PDFs, Word docs, HTML, images, and more with the unstructured library. Covers partitioning, chunking, cleaning, metadata, and pipeline integrations.
TruLens
Evaluate and monitor LLM applications with TruLens. Covers the RAG triad, feedback functions, TruChain, TruLlama, custom evaluators, the dashboard, and CI integration.
transformers
Load and run pre-trained models for NLP, vision, and audio with the Hugging Face Transformers library. Covers pipelines, AutoModel, tokenisation, generation, fine-tuning, and device placement.
ragas
Measure and improve RAG pipeline quality with ragas. Covers faithfulness, answer relevancy, context precision, context recall, dataset format, LLM judges, and CI integration.
qdrant-client
Store and search vector embeddings with the Qdrant Python client. Covers collections, CRUD, filtered vector search, payload indexing, batch upsert, sparse/dense hybrid search, and integrations.
notebooklm-py
Automate Google NotebookLM from Python with the unofficial notebooklm-py library. Covers authentication, notebook and source management, summaries, FAQ generation, and audio podcast creation.
LlamaIndex
Build RAG pipelines and LLM-powered data applications with LlamaIndex. Covers document loading, indexing, query engines, custom LLMs and embeddings, persistent storage, and agents.
LangSmith
Trace, debug, evaluate, and monitor LLM applications with LangSmith. Covers tracing setup, datasets, evaluators, prompt hub, comparing runs, and CI integration.
LangChain
Build LLM-powered pipelines with LangChain. Covers LCEL chains, chat models, prompts, output parsers, tools, agents, retrievers, memory, and streaming.
guidance
Interleave Python control flow with LLM generation and enforce structured output using guidance. Covers gen(), select(), chat blocks, regex constraints, JSON schemas, and token healing.
google-generativeai
Call Google's Gemini models from Python for text, multimodal, streaming, chat, function calling, and embeddings. Covers the genai SDK, safety settings, file API, and async usage.
crewAI
Orchestrate teams of role-playing AI agents with crewAI. Covers agents, tasks, crews, tools, LLM selection, memory, YAML config, and the kickoff lifecycle.
ChromaDB
Store and query vector embeddings locally or over a network with ChromaDB. Covers client types, collections, add, query, metadata filters, embedding functions, and LangChain/LlamaIndex integration.
AutoGen
Build multi-agent AI systems with Microsoft AutoGen. Covers agents, group chats, code execution, tool registration, async runtimes, and LLM configuration.
AI
Claude Code, Codex CLI, the Claude API, and prompt engineering — practical reference for building with and using large language models.