16 articles with this tag
Go beyond basic similarity search with ParentDocumentRetriever, MultiQueryRetriever, EnsembleRetriever, HyDE, and 6 more LangChain retrieval strategies — with code for each.
Use LangChain with ChromaDB for persistent local embeddings — setup, metadata filtering, similarity search, and collection management with full Python code.
Build a production LangChain customer support agent: KB ingestion, intent classification, RAG retrieval, escalation logic, and feedback collection in Python.
Learn 10 LangChain document compressors that slash context length, cut LLM costs, and keep RAG pipelines fast and accurate in production.
A practical guide to LangChain's S3FileLoader, NotionDirectoryLoader, YoutubeLoader, TwitterTweetLoader, and building custom API loaders with real code examples.
Build a complete LangChain document Q&A system that loads PDF, HTML, and DOCX files with PyPDFLoader, RecursiveCharacterTextSplitter, and a full retrieval pipeline.
Master LangChain document transformers to preprocess documents for RAG — splitters, filters, embeddings, and redundancy removal in Python.
Master LangChain's Indexing API with RecordManager for deduplication, incremental sync, and deletion cleanup in production vector store pipelines.
Set up LanceDB as a serverless, open-source vector database with LangChain. Covers local and cloud modes, IVF_PQ indexing, ANN search, and a full RAG example.
Deploy cloud-native RAG with LangChain and Pinecone Serverless. Complete guide covering setup, upsert, query, namespaces, metadata filtering, and cost estimates.
Build a complete RAG pipeline with LangChain, Chroma, and OpenAI embeddings — document loading, chunking, vector storage, and retrieval in one guide.
Improve RAG relevance with LangChain rerankers — CohereRerank, CrossEncoderReranker, FlashrankRerank, RankGPT, and more, with BEIR benchmark results and code.
Combine multiple retrievers in LangChain using EnsembleRetriever, BM25 fusion, and Reciprocal Rank Fusion to build higher-accuracy RAG pipelines.
A practical guide to all 10 LangChain text splitters — Recursive, Markdown, Code, HTML, Semantic, Token — with comparison table and chunking best practices.
Master LangChain's 5 core retriever types — SimilaritySearch, MMR, ContextualCompression, MultiVectorRetriever, and SelfQueryRetriever — with code, benchmarks, and guidance.
LangChain vs LlamaIndex: an honest 2026 comparison of RAG capabilities, indexing strategies, query engines, community size, and when to choose each framework.
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