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#embeddings (11)

Concepts (1)

Retrieval-Augmented Generation (RAG)

Grounding LLM responses in chunks retrieved from an external corpus so the model reasons over real, citable sources instead of parametric memory alone.
llm vector-search ai grounding embeddings hallucination evaluation web-researched

Cheat sheets (10)

chromadb — Embedded Vector Database

Package-level reference for chromadb on PyPI — install variants, server/client split, embedding-function extras, and alternative vector stores.
pip package ai vector-search rag embeddings

qdrant-client — Python Client for Qdrant

Package-level reference for qdrant-client on PyPI — install variants, server version matching, gRPC vs HTTP, fastembed extras, and alternatives.
pip package ai vector-search rag embeddings

sentence-transformers — Embedding & Reranking Models

Package-level reference for the sentence-transformers library on PyPI — install, transformers/torch deps, model registry, and embedding alternatives.
pip package llm ai embeddings rag huggingface

weaviate-client — Python Client for Weaviate

Package-level reference for weaviate-client on PyPI — install variants, the v3 → v4 API split, gRPC, and alternative vector stores.
pip package ai vector-search rag embeddings

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

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

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