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#rag (13)

Cheat sheets (13)

Haystack 2.x — Pipelines for LLM Applications

Build production-grade LLM pipelines with Haystack 2.x. Covers components, the pipeline graph, indexing and querying, retrievers, generators, RAG patterns, and evaluation.
python haystack llm rag ai pipelines deepset retrievers generators

Prompting

Prompt engineering patterns, RAG, evaluations, few-shot, chain-of-thought, and structured output — foundational techniques for extracting reliable, structured behavior from LLMs.
prompting rag llm prompt-engineering cot few-shot evals structured-output

RAG Implementation Checklist

End-to-end checklist and code for building reliable Retrieval-Augmented Generation pipelines — chunking, embedding, vector DBs, retrieval, and evaluation.
rag ai embeddings vector-db llm retrieval semantic-search

Sentence Transformers — Embeddings, Search & Fine-Tuning

Comprehensive reference for the sentence-transformers Python library — embeddings, similarity, clustering, retrieval, fine-tuning, and popular models (BGE, E5, GTE, Nomic, Jina).
python embeddings nlp sentence-transformers semantic-search vector-db rag clustering fine-tuning bge e5 gte nomic jina

ChromaDB — Embedded Vector Database

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.
python chromadb vector-database embeddings rag ai similarity-search

crewAI — Role-Based Agent Crews

Orchestrate teams of role-playing AI agents with crewAI. Covers agents, tasks, crews, tools, LLM selection, memory, YAML config, and the kickoff lifecycle.
python crewai agents llm ai multi-agent rag tools

LangChain — LLM Application Framework

Build LLM-powered pipelines with LangChain. Covers LCEL chains, chat models, prompts, output parsers, tools, agents, retrievers, memory, and streaming.
python langchain llm ai rag agents openai anthropic

LlamaIndex — Data Framework for LLMs

Build RAG pipelines and LLM-powered data applications with LlamaIndex. Covers document loading, indexing, query engines, custom LLMs and embeddings, persistent storage, and agents.
python llamaindex llm rag ai embeddings agents retrieval

qdrant-client — High-Performance Vector Database

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.
python qdrant vector-database embeddings rag search ai

ragas — RAG Evaluation Framework

Measure and improve RAG pipeline quality with ragas. Covers faithfulness, answer relevancy, context precision, context recall, dataset format, LLM judges, and CI integration.
python ragas rag evaluation llm ai retrieval metrics

TruLens — LLM App Evaluation

Evaluate and monitor LLM applications with TruLens. Covers the RAG triad, feedback functions, TruChain, TruLlama, custom evaluators, the dashboard, and CI integration.
python trulens rag evaluation llm ai observability feedback

unstructured — Document Parsing & Ingestion

Extract structured text from PDFs, Word docs, HTML, images, and more with the unstructured library. Covers partitioning, chunking, cleaning, metadata, and pipeline integrations.
python unstructured pdf parsing ingestion rag document etl

weaviate-client — Vector Database 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.
python weaviate vector-database embeddings rag search ai