LangChain RAG Template

Implements a document loader abstraction that ingests content from diverse sources (files, APIs, databases) and normalizes them into a common Document object representation. The template demonstrates loader patterns for PDFs, text files, and web content, with each loader handling format-specific parsing before standardizing metadata and content fields for downstream processing.

Implements multiple text splitting strategies (character-based, token-based, recursive) that break documents into chunks optimized for embedding and retrieval. The template demonstrates how chunk size, overlap, and splitting logic affect retrieval quality, with recursive splitting preserving semantic boundaries by splitting on delimiters (paragraphs, sentences) before falling back to character-level splits.

Implements query preprocessing and augmentation strategies (query expansion, decomposition, rewriting) that improve retrieval by reformulating user queries into forms better suited for vector search. The template demonstrates techniques like generating multiple query variants, decomposing complex queries into sub-queries, and rewriting queries to match document terminology.

Generates final answers using an LLM conditioned on retrieved context, with explicit mechanisms for source attribution and grounding. The template demonstrates prompt patterns that encourage the LLM to cite sources, avoid hallucination, and acknowledge when information is not in the retrieved context. Includes techniques for validating that generated answers are grounded in retrieved documents.

Demonstrates production-ready RAG patterns including caching, batching, async processing, and scaling considerations. The template shows how to optimize for latency and throughput through techniques like embedding caching, batch indexing, and asynchronous retrieval, with guidance on deploying RAG systems to handle production workloads.

Demonstrates how to customize RAG systems for specific domains (code, legal, medical) through domain-specific chunking, embedding model selection, prompt engineering, and evaluation metrics. The template shows how to adapt generic RAG patterns to domain requirements, including handling domain-specific document structures and terminology.

Wraps embedding model APIs (OpenAI, Hugging Face, local models) behind a unified interface that converts text chunks into dense vector representations. The template shows how to instantiate different embedding models, handle batch processing, and manage embedding costs/latency tradeoffs, with support for both cloud-based and locally-hosted embeddings.

Builds searchable vector indices from embedded chunks using vector database abstractions (in-memory, FAISS, Pinecone, Chroma). The template demonstrates index creation, persistence, and similarity search with configurable retrieval strategies (k-nearest neighbors, similarity thresholds). Supports both dense vector search and hybrid approaches combining vector and keyword matching.

Implements retrieval strategies that rank indexed documents by relevance to a query, supporting k-nearest neighbor search, similarity thresholds, and hybrid approaches combining dense and sparse (keyword) retrieval. The template demonstrates how retrieval strategy affects answer quality, with advanced techniques like maximal marginal relevance (MMR) reducing redundancy in retrieved results.

Assembles retrieved documents into a formatted context string and constructs prompts that guide the LLM to generate answers grounded in retrieved content. The template demonstrates prompt engineering patterns (system prompts, few-shot examples, explicit instructions to cite sources) that improve answer quality and factuality by constraining generation to retrieved context.

Chains together document loading, chunking, embedding, indexing, retrieval, and generation into a complete RAG pipeline. The template demonstrates both offline (indexing) and online (query) phases with explicit separation of concerns, showing how to compose individual components into a working system and handle the data flow between stages.

Implements sophisticated text splitting approaches (parent-child chunking, sliding windows with semantic boundaries, document-specific strategies) that improve retrieval quality by preserving context and reducing information loss. The template demonstrates how different chunking strategies affect retrieval performance, with techniques like keeping parent documents alongside child chunks for context.

Implements evaluation frameworks that measure retrieval quality through metrics like precision@k, recall, mean reciprocal rank (MRR), and normalized discounted cumulative gain (NDCG). The template demonstrates how to construct evaluation datasets with ground-truth relevant documents, run retrieval experiments, and compare different strategies quantitatively.

Combines vector similarity search (dense retrieval) with keyword-based search (sparse retrieval, BM25) to improve recall on queries with specific entities or keywords. The template demonstrates how to weight and merge results from both approaches, with techniques like reciprocal rank fusion (RRF) for combining ranked lists from different retrievers.