{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"langchain-rag-template","slug":"langchain-rag-template","name":"LangChain RAG Template","type":"template","url":"https://github.com/langchain-ai/rag-from-scratch","page_url":"https://unfragile.ai/langchain-rag-template","categories":["rag-knowledge","documentation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"langchain-rag-template__cap_0","uri":"capability://data.processing.analysis.multi.source.document.loading.with.format.agnostic.ingestion","name":"multi-source document loading with format-agnostic ingestion","description":"Loads documents from diverse sources (files, APIs, databases) and normalizes them into a unified document representation. The template demonstrates pluggable loader patterns that abstract source-specific logic, enabling developers to extend support for new document types by implementing a common interface without modifying core pipeline code.","intents":["I need to ingest documents from multiple sources (PDFs, web pages, databases) into a single RAG pipeline","I want to add support for a custom document source without rewriting my entire indexing pipeline","I need to handle different document formats (markdown, HTML, plain text) uniformly"],"best_for":["teams building knowledge bases from heterogeneous data sources","developers prototyping RAG systems with evolving data requirements","enterprises migrating legacy document stores to LLM-powered search"],"limitations":["No built-in handling for streaming large documents — requires manual chunking before loading","Format detection is manual; no automatic MIME-type inference","Loader implementations are synchronous; high-volume ingestion requires external orchestration"],"requires":["Python 3.9+","LangChain library (0.1.0+)","Document source credentials (API keys, database connection strings, file system access)"],"input_types":["file paths (PDF, TXT, MD, HTML)","API endpoints (REST, GraphQL)","database connection strings","raw text content"],"output_types":["LangChain Document objects with metadata","structured document collections with source attribution"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_1","uri":"capability://data.processing.analysis.semantic.text.chunking.with.configurable.splitting.strategies","name":"semantic text chunking with configurable splitting strategies","description":"Splits documents into semantically coherent chunks using multiple strategies (character-based, token-based, recursive splitting) with configurable overlap and chunk size parameters. The template demonstrates how different chunking strategies impact retrieval quality, allowing developers to experiment with recursive splitting (which preserves semantic boundaries) versus fixed-size splitting for different document types.","intents":["I need to split long documents into chunks that fit within my LLM's context window","I want to preserve semantic meaning when chunking — avoid splitting sentences or paragraphs mid-thought","I need to experiment with different chunk sizes and overlap to optimize retrieval quality for my domain"],"best_for":["developers optimizing RAG retrieval quality through chunk size tuning","teams working with domain-specific documents (legal, medical, technical) where semantic boundaries matter","researchers evaluating how chunking strategies affect downstream generation quality"],"limitations":["Recursive splitting adds computational overhead (~50-200ms per document depending on size)","No automatic optimal chunk size detection — requires manual experimentation and evaluation","Overlap parameter can create redundant embeddings, increasing vector store size and query latency"],"requires":["Python 3.9+","LangChain TextSplitter classes","Token counter for accurate token-based splitting (tiktoken or equivalent)"],"input_types":["raw text documents","LangChain Document objects with metadata"],"output_types":["list of text chunks with preserved metadata","chunk-to-source-document mapping for citation"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_10","uri":"capability://search.retrieval.hybrid.search.combining.dense.and.sparse.retrieval","name":"hybrid search combining dense and sparse retrieval","description":"Combines dense vector similarity search with sparse keyword-based search (BM25, TF-IDF) to improve recall by capturing both semantic and lexical relevance. The template demonstrates how to weight and merge results from both retrieval methods, showing trade-offs between semantic understanding and exact term matching.","intents":["I need to improve recall by combining semantic search with keyword matching","I want to handle domain-specific terminology that may not be captured by semantic embeddings","I need to balance semantic understanding with exact term matching for different query types"],"best_for":["teams building search systems for specialized domains (legal, medical, technical) with domain-specific vocabulary","developers optimizing for high recall where missing relevant documents is costly","researchers evaluating how hybrid search improves over pure semantic or pure keyword search"],"limitations":["Hybrid search requires maintaining both dense and sparse indexes — doubles storage overhead","Merging results from different retrieval methods requires tuning weight parameters; no automatic optimal weighting","Sparse search (BM25) requires preprocessing (tokenization, stemming) that may not be optimal for all domains"],"requires":["Python 3.9+","Vector store for dense retrieval","BM25 or TF-IDF index for sparse retrieval (e.g., Elasticsearch, Whoosh, or in-memory implementation)","Weighting strategy for combining dense and sparse scores"],"input_types":["user query (string)","query embedding (vector for dense search)","tokenized query (for sparse search)"],"output_types":["merged result list combining dense and sparse results","combined relevance scores","per-method scores for debugging and tuning"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_11","uri":"capability://search.retrieval.query.expansion.and.reformulation.for.improved.retrieval","name":"query expansion and reformulation for improved retrieval","description":"Expands or reformulates user queries to improve retrieval by generating multiple query variants, decomposing complex queries into sub-queries, or using LLM-based query rewriting. The template demonstrates how query expansion increases recall by retrieving documents relevant to different phrasings of the same intent.","intents":["I need to improve retrieval for ambiguous or complex queries by generating multiple search variants","I want to decompose multi-part questions into sub-queries that can be answered separately","I need to reformulate queries using domain-specific terminology to improve retrieval precision"],"best_for":["teams building search systems where query ambiguity or complexity reduces retrieval quality","developers working with specialized domains where terminology variation is high","researchers studying how query reformulation affects downstream answer quality"],"limitations":["Query expansion increases retrieval latency (multiple queries instead of one)","Expanded queries may retrieve irrelevant documents if expansion is too aggressive","No automatic optimal expansion strategy — requires domain-specific tuning or learning"],"requires":["Python 3.9+","LLM for query rewriting (or rule-based expansion templates)","Vector store or search index for executing expanded queries","Optional: query classification to determine when expansion is beneficial"],"input_types":["original user query (string)","optional: query metadata (type, complexity, domain)"],"output_types":["expanded query list (multiple query variants)","merged retrieval results from all expanded queries","per-query result attribution for debugging"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_12","uri":"capability://search.retrieval.metadata.filtering.and.faceted.search.for.refined.retrieval","name":"metadata filtering and faceted search for refined retrieval","description":"Filters retrieved documents by metadata (source, date, category, author) to refine results and enable faceted search. The template demonstrates how to construct metadata filters, apply them during retrieval, and combine filtering with semantic search for more precise results.","intents":["I need to filter search results by document source, date, or category","I want to enable faceted search where users can refine results by multiple metadata dimensions","I need to restrict retrieval to specific document subsets (e.g., only recent documents, only from trusted sources)"],"best_for":["teams building search systems with rich metadata (news, research papers, documentation)","developers implementing faceted search interfaces where users refine results interactively","organizations with access control requirements where retrieval must respect document permissions"],"limitations":["Metadata filtering capability varies across vector stores (FAISS has limited filtering vs. Weaviate's rich filtering)","Overly restrictive filters may eliminate all results; requires fallback strategies","Metadata must be maintained through entire pipeline; metadata loss breaks filtering"],"requires":["Python 3.9+","Vector store supporting metadata filtering (Weaviate, Pinecone, Chroma, or custom implementation)","Structured metadata attached to documents (source, date, category, etc.)","Filter query language or builder for constructing filter expressions"],"input_types":["user query (string)","filter criteria (dict or filter expression: {source: 'wikipedia', date_range: [2023, 2024]})"],"output_types":["filtered retrieval results matching both semantic and metadata criteria","facet counts (number of results per metadata value for UI display)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_13","uri":"capability://memory.knowledge.domain.specific.rag.customization.and.fine.tuning","name":"domain-specific rag customization and fine-tuning","description":"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.","intents":["I need to build a RAG system optimized for my specific domain (code, legal, medical)","I want to understand how to customize chunking, embeddings, and prompts for domain-specific requirements","I need to evaluate RAG quality using domain-specific metrics and evaluation datasets"],"best_for":["teams building RAG systems for specialized domains with unique requirements","developers optimizing RAG for domain-specific document types and terminology","engineers tuning RAG parameters for specific industries (legal, medical, financial)"],"limitations":["Domain customization requires domain expertise; no one-size-fits-all approach","Domain-specific evaluation datasets are expensive to create","Optimal parameters vary by domain; transfer learning from other domains is limited","Domain-specific embeddings or models may not be available; may require fine-tuning"],"requires":["Python 3.9+","LangChain library with customization modules","Domain expertise for customization decisions","Domain-specific evaluation dataset","Optional: domain-specific embedding models or fine-tuned LLMs"],"input_types":["domain-specific documents","domain-specific queries","domain-specific evaluation dataset"],"output_types":["customized RAG system optimized for domain","domain-specific evaluation metrics"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_2","uri":"capability://data.processing.analysis.vector.embedding.generation.with.pluggable.embedding.providers","name":"vector embedding generation with pluggable embedding providers","description":"Converts text chunks into dense vector embeddings using pluggable embedding providers (OpenAI, Hugging Face, local models). The template abstracts embedding provider selection, allowing developers to swap embedding models without changing retrieval or indexing code, and demonstrates how embedding quality directly impacts retrieval relevance.","intents":["I need to convert text chunks into embeddings for semantic similarity search","I want to experiment with different embedding models (OpenAI, open-source) without rewriting my pipeline","I need to use local embeddings for privacy or cost reasons instead of cloud-based APIs"],"best_for":["teams evaluating embedding model trade-offs (cost vs. quality vs. latency)","organizations with data privacy requirements preferring local embeddings","developers building multi-modal RAG systems requiring specialized embedding models"],"limitations":["Embedding generation is synchronous and can be slow for large document collections (100k+ chunks)","Different embedding models produce incompatible vector spaces — switching models requires re-embedding entire corpus","No built-in batch processing for cost optimization with cloud APIs; requires manual batching"],"requires":["Python 3.9+","LangChain Embeddings interface","API credentials for cloud providers (OpenAI, Cohere) OR local model weights (Hugging Face)","Sufficient GPU memory for local embeddings (varies by model, typically 2-8GB)"],"input_types":["text chunks (strings)","lists of text chunks for batch processing"],"output_types":["dense vectors (1D numpy arrays or lists)","embedding metadata (model name, dimension, timestamp)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_3","uri":"capability://memory.knowledge.vector.store.indexing.and.persistence.with.multiple.backend.support","name":"vector store indexing and persistence with multiple backend support","description":"Indexes embedded text chunks into vector stores (FAISS, Chroma, Pinecone, Weaviate) with configurable persistence strategies. The template demonstrates how to initialize vector stores, add embeddings with metadata, and persist indexes for reuse across sessions, abstracting backend-specific APIs behind a common interface.","intents":["I need to store embeddings in a searchable index for fast semantic retrieval","I want to persist my vector index so I don't re-embed documents on every application restart","I need to choose between local (FAISS) and cloud (Pinecone) vector stores based on scale and latency requirements"],"best_for":["teams building production RAG systems requiring persistent knowledge bases","developers prototyping with local vector stores (FAISS) before migrating to cloud backends","organizations needing multi-tenant vector stores with access control (Weaviate, Pinecone)"],"limitations":["Local vector stores (FAISS) require in-memory loading for queries — not suitable for >10M embeddings on single machine","No built-in index versioning or rollback — updating indexes requires careful state management","Metadata filtering capabilities vary significantly across backends (FAISS has limited filtering vs. Weaviate's rich filtering)"],"requires":["Python 3.9+","LangChain VectorStore interface","Backend-specific dependencies (faiss-cpu, chromadb, pinecone-client, weaviate-client)","Sufficient disk space for local indexes or cloud credentials for managed backends"],"input_types":["text chunks with embeddings","metadata dictionaries (source, timestamp, document ID)","batch lists of documents for bulk indexing"],"output_types":["indexed vector store object","persistence artifacts (FAISS index files, database records)","retrieval interface for semantic search"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_4","uri":"capability://search.retrieval.semantic.similarity.retrieval.with.configurable.search.strategies","name":"semantic similarity retrieval with configurable search strategies","description":"Retrieves semantically similar documents from the vector store using multiple strategies: dense similarity search (cosine, L2), sparse keyword search, and hybrid retrieval combining both. The template demonstrates how to configure retrieval parameters (k results, similarity threshold, reranking) and shows trade-offs between recall and latency.","intents":["I need to find the most relevant documents for a user query using semantic similarity","I want to combine semantic search with keyword matching to improve recall for domain-specific terms","I need to tune retrieval parameters (number of results, similarity threshold) to balance relevance and latency"],"best_for":["developers building search-heavy RAG applications where retrieval quality directly impacts answer quality","teams working with specialized vocabularies (medical, legal, technical) where keyword search complements semantic search","researchers evaluating retrieval strategies and their impact on downstream generation"],"limitations":["Dense similarity search can miss relevant documents with different vocabulary (semantic gap problem)","Hybrid retrieval requires tuning weight between dense and sparse components — no automatic optimal weighting","Retrieval latency scales with vector store size; >10M embeddings may require approximate nearest neighbor algorithms"],"requires":["Python 3.9+","Populated vector store with embeddings","Query embedding (generated using same embedding model as indexed documents)","Optional: BM25 or TF-IDF index for keyword search component"],"input_types":["user query (string)","query embedding (vector)","retrieval parameters (k, similarity_threshold, search_type)"],"output_types":["list of Document objects ranked by relevance","similarity scores for each result","metadata including source and chunk position"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_5","uri":"capability://text.generation.language.context.assembly.and.prompt.construction.with.source.attribution","name":"context assembly and prompt construction with source attribution","description":"Assembles retrieved documents into a coherent context string formatted for LLM consumption, with configurable templates and source attribution. The template demonstrates how to structure context (document ordering, separator formatting, metadata inclusion) and how to construct prompts that guide the LLM to generate answers grounded in retrieved sources.","intents":["I need to format retrieved documents into a context block that the LLM can use to answer questions","I want to include source citations in generated answers so users can verify information","I need to experiment with different prompt templates and context formatting to improve answer quality"],"best_for":["developers building RAG systems where answer traceability and source attribution are critical","teams optimizing prompt engineering for domain-specific RAG applications","researchers studying how context formatting and prompt structure affect LLM generation quality"],"limitations":["Context length is bounded by LLM context window — large result sets require truncation or summarization","Prompt template quality directly impacts generation quality but requires manual tuning per domain","Source attribution requires maintaining document metadata through entire pipeline — metadata loss breaks citation chain"],"requires":["Python 3.9+","Retrieved Document objects with metadata (source, page number, chunk index)","Prompt template (string with placeholders for context and query)","Target LLM context window size for context truncation"],"input_types":["list of retrieved Document objects","user query (string)","prompt template (string with {context} and {question} placeholders)"],"output_types":["formatted prompt string ready for LLM consumption","context metadata mapping (chunk index to source document)","source attribution list for answer verification"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_6","uri":"capability://text.generation.language.llm.based.answer.generation.with.retrieval.augmented.prompting","name":"llm-based answer generation with retrieval-augmented prompting","description":"Generates answers using an LLM (OpenAI, Anthropic, local models) with context from retrieved documents, implementing the generation phase of RAG. The template shows how to invoke LLMs with augmented prompts, handle streaming responses, and extract structured answers from unstructured LLM output.","intents":["I need to generate natural language answers to user questions using retrieved context","I want to use different LLM providers (OpenAI, Anthropic, local) without changing my RAG pipeline","I need to stream LLM responses for real-time user feedback instead of waiting for complete generation"],"best_for":["developers building conversational RAG systems where answer quality depends on both retrieval and generation","teams evaluating LLM trade-offs (cost, latency, quality) for their specific domain","organizations requiring local LLM inference for data privacy or cost reasons"],"limitations":["LLM generation quality depends on prompt quality and retrieved context — poor retrieval cannot be compensated by better prompting","Streaming responses add latency overhead (~100-500ms) compared to batch generation","No built-in hallucination detection — LLMs may generate plausible-sounding but incorrect answers even with context"],"requires":["Python 3.9+","LangChain LLM interface","API credentials for cloud LLMs (OpenAI, Anthropic) OR local model weights and inference server (Ollama, vLLM)","Sufficient GPU memory for local inference (varies by model, typically 4-40GB)"],"input_types":["augmented prompt string (query + context)","LLM parameters (temperature, max_tokens, top_p)","optional: chat history for multi-turn conversations"],"output_types":["generated answer text","token usage statistics","optional: streaming token chunks for real-time display"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_7","uri":"capability://search.retrieval.advanced.retrieval.optimization.with.reranking.and.diversity","name":"advanced retrieval optimization with reranking and diversity","description":"Optimizes retrieval results using reranking (cross-encoder models that score query-document pairs) and diversity-aware selection (maximal marginal relevance) to improve answer quality. The template demonstrates how reranking can improve precision by re-scoring initial retrieval results, and how diversity selection reduces redundancy in retrieved context.","intents":["I need to improve retrieval precision by reranking initial results using more sophisticated scoring","I want to reduce redundancy in retrieved documents so context focuses on diverse information","I need to balance relevance and diversity — retrieve highly relevant documents that cover different aspects"],"best_for":["teams optimizing RAG systems where retrieval precision directly impacts answer quality","developers building search systems where result diversity improves user experience","researchers evaluating advanced retrieval techniques beyond basic similarity search"],"limitations":["Reranking adds computational overhead (~100-500ms per query depending on result set size and model)","Cross-encoder models require separate inference infrastructure; cannot be embedded in vector store","Diversity optimization requires defining similarity metric for redundancy detection — no universal optimal metric"],"requires":["Python 3.9+","Initial retrieval results from vector store","Reranker model (cross-encoder from Hugging Face or commercial API)","Optional: diversity metric implementation (cosine similarity, Jaccard, custom)"],"input_types":["user query (string)","initial retrieval results (list of Document objects with scores)","reranking parameters (k for top-k results, diversity weight)"],"output_types":["reranked Document list with updated scores","diversity-optimized subset of results","reranking scores for confidence estimation"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_8","uri":"capability://planning.reasoning.evaluation.framework.for.rag.quality.metrics","name":"evaluation framework for rag quality metrics","description":"Provides evaluation utilities to measure RAG system quality across multiple dimensions: retrieval quality (precision, recall, NDCG), generation quality (BLEU, ROUGE, semantic similarity), and end-to-end metrics (answer correctness, source attribution accuracy). The template demonstrates how to construct evaluation datasets and compute metrics to guide optimization.","intents":["I need to measure whether my RAG system is retrieving relevant documents for test queries","I want to evaluate answer quality beyond subjective assessment — use automated metrics","I need to track how changes to chunking, embedding, or retrieval strategies affect overall system performance"],"best_for":["teams iterating on RAG systems where metrics guide optimization decisions","researchers comparing RAG architectures and techniques using standardized benchmarks","developers building production systems requiring quality gates before deployment"],"limitations":["Automated metrics (BLEU, ROUGE) correlate imperfectly with human judgment — require manual evaluation for validation","Evaluation datasets must be manually created or sourced; no automatic ground truth generation","Metrics are domain-specific; metrics optimized for one domain may not transfer to another"],"requires":["Python 3.9+","Evaluation dataset with queries, expected answers, and source documents","Metric libraries (scikit-learn for precision/recall, rouge-score, sentence-transformers for semantic similarity)","Baseline or reference answers for comparative evaluation"],"input_types":["test queries (list of strings)","retrieved documents (list of Document objects per query)","generated answers (list of strings)","ground truth answers and relevant documents (for metric computation)"],"output_types":["retrieval metrics (precision@k, recall@k, NDCG, MRR)","generation metrics (BLEU, ROUGE, semantic similarity scores)","end-to-end metrics (answer correctness, citation accuracy)","metric aggregations and statistical summaries"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__cap_9","uri":"capability://text.generation.language.multi.turn.conversation.with.memory.management","name":"multi-turn conversation with memory management","description":"Extends RAG to multi-turn conversations by maintaining conversation history and using it to reformulate queries or provide context for follow-up questions. The template demonstrates how to manage conversation state, integrate history into retrieval, and prevent context window overflow through summarization or history truncation.","intents":["I need to build conversational RAG where follow-up questions reference previous context","I want to reformulate user queries using conversation history to improve retrieval","I need to manage conversation memory efficiently so context window doesn't overflow with long histories"],"best_for":["teams building conversational AI systems where multi-turn interaction is core","developers creating domain-specific chatbots that maintain context across questions","researchers studying how conversation history affects retrieval and generation quality"],"limitations":["Conversation history increases context length, reducing space for retrieved documents or limiting conversation length","No automatic optimal history length — requires manual tuning or heuristic truncation","History management adds complexity; requires careful state handling to prevent information loss or token waste"],"requires":["Python 3.9+","Conversation history storage (in-memory list, database, or external state store)","Query reformulation strategy (using LLM to rewrite queries with history context)","Context window management (tracking token usage across history + context + generation)"],"input_types":["user message (string)","conversation history (list of message tuples: (role, content))","optional: conversation metadata (user ID, session ID, timestamp)"],"output_types":["reformulated query for retrieval","retrieved context documents","generated answer","updated conversation history"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langchain-rag-template__headline","uri":"capability://data.processing.analysis.rag.framework.for.building.retrieval.augmented.generation.applications","name":"rag framework for building retrieval-augmented generation applications","description":"This artifact provides a comprehensive reference implementation for developing RAG applications using LangChain, featuring document loading, text processing, and advanced retrieval techniques in a step-by-step format.","intents":["best RAG framework","RAG framework for educational purposes","how to build RAG applications","RAG implementation guide","RAG system tutorial"],"best_for":["developers looking to learn RAG","educators teaching RAG concepts"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LangChain library (0.1.0+)","Document source credentials (API keys, database connection strings, file system access)","LangChain TextSplitter classes","Token counter for accurate token-based splitting (tiktoken or equivalent)","Vector store for dense retrieval","BM25 or TF-IDF index for sparse retrieval (e.g., Elasticsearch, Whoosh, or in-memory implementation)","Weighting strategy for combining dense and sparse scores","LLM for query rewriting (or rule-based expansion templates)","Vector store or search index for executing expanded queries"],"failure_modes":["No built-in handling for streaming large documents — requires manual chunking before loading","Format detection is manual; no automatic MIME-type inference","Loader implementations are synchronous; high-volume ingestion requires external orchestration","Recursive splitting adds computational overhead (~50-200ms per document depending on size)","No automatic optimal chunk size detection — requires manual experimentation and evaluation","Overlap parameter can create redundant embeddings, increasing vector store size and query latency","Hybrid search requires maintaining both dense and sparse indexes — doubles storage overhead","Merging results from different retrieval methods requires tuning weight parameters; no automatic optimal weighting","Sparse search (BM25) requires preprocessing (tokenization, stemming) that may not be optimal for all domains","Query expansion increases retrieval latency (multiple queries instead of one)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=langchain-rag-template","compare_url":"https://unfragile.ai/compare?artifact=langchain-rag-template"}},"signature":"7hvoUeQg0rZDsluXraGkh8BK1SByvQYcAe1KmvDT2YCQoFmfC9+2DcJySHY8CXFewbhkFIARtmF+hHeSc57KAg==","signedAt":"2026-06-21T10:41:56.503Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langchain-rag-template","artifact":"https://unfragile.ai/langchain-rag-template","verify":"https://unfragile.ai/api/v1/verify?slug=langchain-rag-template","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}