#rag
18 snippets tagged with #rag
RAG Pipeline (Retrieve + Augment + Generate)
Minimal RAG implementation: embed a query, retrieve top-k chunks, inject into prompt.
Best for: document Q&A
LangChain RAG Chain Pipeline
Build a retrieval-augmented generation chain with LangChain using vector store retrieval and prompt templates.
Best for: Document Q&A
RAG Pipeline Implementation
Build a retrieval-augmented generation pipeline that grounds LLM answers in your own documents.
Best for: Grounding LLM answers in private documents
Build a RAG Pipeline with LangChain
Implement retrieval-augmented generation using LangChain, embeddings, and a vector store.
Best for: Knowledge base Q&A
ChromaDB Vector Database Operations
Store and query vector embeddings using ChromaDB for semantic search and RAG applications.
Best for: semantic search
Text Chunking Strategies for RAG
Implement different text chunking strategies for RAG pipelines — fixed, recursive, and semantic.
Best for: RAG pipeline preprocessing
Rag Evaluation
AI/ML technique: rag-evaluation
Best for: machine learning
RAG with FAISS and LangChain Python
Build a local RAG pipeline using FAISS vector store and LangChain for document Q&A.
Best for: document Q&A
ChromaDB Persistent Vector Store
Create, persist, and query a ChromaDB vector store for semantic document retrieval.
Best for: local vector DB
LlamaIndex Document Query Engine
Index and query documents with LlamaIndex's VectorStoreIndex for fast semantic search.
Best for: document RAG
Cohere Reranker for RAG Precision
Improve RAG retrieval quality by reranking candidate documents with Cohere's rerank API.
Best for: RAG precision improvement
LangChain Recursive Text Splitter
Split long documents into overlapping chunks optimised for LLM context windows.
Best for: PDF ingestion
Haystack Question Answering Pipeline
Build a document retrieval and Q&A pipeline using Haystack 2.0 with OpenAI backend.
Best for: enterprise RAG
RAG Evaluation with RAGAS
Evaluate RAG pipeline quality using RAGAS metrics: faithfulness, context recall, and answer relevance.
Best for: RAG evaluation
Multimodal RAG with Images and Text
Build a multimodal RAG pipeline that retrieves and answers questions about image+text documents.
Best for: visual document Q&A
LangChain RAG Retrieval Chain
Build a full RAG pipeline with source citations using LangChain's create_retrieval_chain.
Best for: RAG with citations
Semantic Chunking for RAG Documents
Split documents into semantically coherent chunks using embedding similarity for better RAG retrieval.
Best for: RAG optimization
RAG Retrieval Quality Metrics
Compute precision@k, recall@k, and MRR metrics to evaluate vector retrieval quality for RAG.
Best for: retrieval benchmarking