{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-bragai--brag-langchain","slug":"bragai--brag-langchain","name":"bRAG-langchain","type":"framework","url":"https://bragai.dev","page_url":"https://unfragile.ai/bragai--brag-langchain","categories":["rag-knowledge"],"tags":["agentic-rag","ai","chatbot","llm","machine-learning","python","rag","retrieval-augmented-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-bragai--brag-langchain__cap_0","uri":"capability://memory.knowledge.two.phase.rag.pipeline.assembly.with.lcel.orchestration","name":"two-phase rag pipeline assembly with lcel orchestration","description":"Constructs a complete Retrieval-Augmented Generation pipeline using LangChain Expression Language (LCEL) that separates indexing (one-time document embedding and vector store population) from query execution (per-request retrieval and LLM synthesis). The rag_chain in full_basic_rag.ipynb assembles retriever, prompt templates, and LLM into a single composable expression, enabling declarative pipeline definition without imperative control flow.","intents":["I want to build a RAG chatbot that separates document indexing from query handling","I need a production-ready boilerplate that handles both embedding and inference phases","I want to understand how to compose retrieval and generation steps into a single chain"],"best_for":["developers building their first RAG application","teams migrating from custom RAG implementations to LangChain patterns","builders prototyping knowledge-base chatbots with minimal setup"],"limitations":["LCEL abstractions add ~50-200ms latency per chain step due to serialization overhead","No built-in distributed execution — single-machine only without external orchestration","Vector store selection is fixed at pipeline creation time; runtime switching requires pipeline reconstruction"],"requires":["Python 3.11.11+","LangChain 0.1.0+","Vector store (ChromaDB, Pinecone, or compatible)","LLM API credentials (OpenAI, Anthropic, or local Ollama)"],"input_types":["documents (PDF, markdown, plain text)","user queries (natural language strings)"],"output_types":["LLM-generated responses (text)","retrieved context chunks (text with metadata)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bragai--brag-langchain__cap_1","uri":"capability://search.retrieval.multi.query.retrieval.with.llm.generated.query.variants","name":"multi-query retrieval with llm-generated query variants","description":"Generates multiple semantically-diverse query variants from a single user question using an LLM, then retrieves documents against all variants in parallel, unions the results, and deduplicates to improve recall. Implemented in Notebook 2 via LLM prompt templates that instruct the model to generate alternative phrasings, followed by concurrent retriever calls and result aggregation.","intents":["I want to retrieve more relevant documents by querying from multiple angles","I need to handle queries that might be phrased differently than my training documents","I want to reduce false negatives in retrieval without increasing latency significantly"],"best_for":["RAG systems with sparse or domain-specific document collections","applications where query reformulation improves recall (e.g., legal, medical docs)","teams willing to trade extra LLM calls for better retrieval coverage"],"limitations":["Increases LLM API costs by 2-5x per query (one call for variant generation, N calls for retrieval)","Adds 300-800ms latency for variant generation before parallel retrieval begins","Deduplication logic is simple string/embedding matching — may miss semantic duplicates"],"requires":["LLM with function-calling or prompt-following capability","Parallel retriever execution support (LangChain RunnableParallel)","Vector store supporting batch similarity search"],"input_types":["user query (natural language string)"],"output_types":["deduplicated document chunks (list of text with metadata)","query variants (list of strings)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bragai--brag-langchain__cap_10","uri":"capability://text.generation.language.prompt.engineering.and.template.management.for.rag.synthesis","name":"prompt engineering and template management for rag synthesis","description":"Manages LLM prompts using LangChain PromptTemplate, enabling parameterized prompt construction with context injection, variable substitution, and format specification. Notebooks demonstrate prompts for retrieval evaluation, query generation, answer synthesis, and re-ranking, with explicit separation of system instructions, context, and user input.","intents":["I want to manage prompts for different RAG stages (retrieval, synthesis, evaluation)","I need to inject retrieved context into prompts dynamically","I want to experiment with different prompt formulations without code changes"],"best_for":["teams iterating on prompt quality for RAG systems","applications requiring different prompts for different query types","builders learning prompt engineering for LLM-based retrieval"],"limitations":["Prompt quality is highly empirical; no principled optimization method provided","Template variables must be manually specified; no automatic variable detection","Prompt injection vulnerabilities possible if user input is not sanitized"],"requires":["LangChain PromptTemplate","LLM API (OpenAI, Anthropic, etc.)"],"input_types":["template string with variable placeholders","variable values (context, query, etc.)"],"output_types":["formatted prompt string (ready for LLM)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bragai--brag-langchain__cap_11","uri":"capability://text.generation.language.jupyter.notebook.based.progressive.learning.curriculum","name":"jupyter notebook-based progressive learning curriculum","description":"Provides five structured Jupyter notebooks (Notebooks 1-5) that progressively introduce RAG techniques from basic setup to advanced retrieval and self-correction. Each notebook builds on the previous, introducing new techniques (multi-query, routing, advanced indexing, re-ranking) with executable code, explanations, and reference links. The progression enables learners to understand RAG incrementally rather than all-at-once.","intents":["I want to learn RAG step-by-step with executable examples","I need to understand how basic RAG evolves into advanced techniques","I want reference implementations for each RAG pattern"],"best_for":["developers new to RAG seeking structured learning","teams onboarding engineers to RAG architecture","educators teaching RAG concepts with hands-on examples"],"limitations":["Notebooks require running locally or in Jupyter environment; not suitable for production deployment","Progression assumes familiarity with Python and LangChain; steep learning curve for beginners","Notebooks are not version-pinned; API changes in dependencies may break examples"],"requires":["Jupyter notebook environment (local or cloud)","Python 3.11.11+","All dependencies in requirements.txt"],"input_types":["notebook cells (Python code)"],"output_types":["executed code output, visualizations, learned patterns"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bragai--brag-langchain__cap_12","uri":"capability://memory.knowledge.production.boilerplate.rag.chatbot.full.basic.rag.ipynb","name":"production boilerplate rag chatbot (full_basic_rag.ipynb)","description":"Provides a self-contained, production-ready RAG chatbot implementation in full_basic_rag.ipynb that can be adapted to custom documents, LLMs, and vector stores. The boilerplate includes document loading, embedding, vector store setup, retrieval chain assembly, and inference loop, enabling developers to fork and customize without building from scratch.","intents":["I want a working RAG chatbot I can customize for my documents","I need a starting point that handles all RAG components end-to-end","I want to avoid building RAG infrastructure from scratch"],"best_for":["developers building RAG MVPs quickly","teams prototyping knowledge-base chatbots","builders with custom documents who need a starting point"],"limitations":["Boilerplate is single-file; scaling to multiple documents or users requires refactoring","No built-in persistence for conversation history; requires external storage","No authentication or access control; suitable for internal use only"],"requires":["Python 3.11.11+","Custom documents (PDF, markdown, text)","LLM API key (OpenAI, Anthropic, or local Ollama)","Vector store (ChromaDB for local, Pinecone for cloud)"],"input_types":["documents (PDF, markdown, text)","user queries (natural language strings)"],"output_types":["chatbot responses (text with retrieved context)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bragai--brag-langchain__cap_2","uri":"capability://planning.reasoning.semantic.and.logical.routing.with.runnablebranch","name":"semantic and logical routing with runnablebranch","description":"Routes incoming queries to different retrieval or processing paths based on semantic classification or logical rules using LangChain's RunnableBranch construct. Notebook 3 demonstrates routing via LLM classification (e.g., 'is this a factual question or a reasoning task?') and conditional branching to specialized chains (e.g., HyDE for hypothetical document expansion, RAG-Fusion for multi-perspective retrieval).","intents":["I want to handle different query types with specialized retrieval strategies","I need to route complex reasoning questions to a different pipeline than factual lookups","I want to apply different re-ranking or synthesis strategies based on query intent"],"best_for":["RAG systems handling heterogeneous query types (factual, reasoning, creative)","teams building agentic RAG with dynamic strategy selection","applications requiring conditional logic without explicit if-else chains"],"limitations":["Routing classification adds 200-500ms latency per query for LLM inference","RunnableBranch requires explicit condition definition — no automatic strategy discovery","Routing errors (misclassification) cascade to downstream chains; no fallback recovery built-in"],"requires":["LLM capable of classification or structured output","Multiple specialized retrieval/synthesis chains defined upfront","LangChain RunnableBranch or equivalent conditional execution"],"input_types":["user query (natural language string)"],"output_types":["LLM response routed through selected chain (text or structured data)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bragai--brag-langchain__cap_3","uri":"capability://memory.knowledge.advanced.document.indexing.with.multi.vector.and.parent.document.retrieval","name":"advanced document indexing with multi-vector and parent-document retrieval","description":"Implements sophisticated indexing strategies (Notebook 4) including MultiVectorRetriever for storing summaries/questions alongside full documents, InMemoryByteStore for metadata caching, and Parent Document Retriever for retrieving larger context chunks while querying against smaller summaries. These patterns decouple the retrieval unit (summary) from the context unit (full document), improving both precision and context quality.","intents":["I want to retrieve document summaries for relevance but return full documents for context","I need to index large documents without losing granular retrieval precision","I want to cache document metadata and relationships without re-embedding"],"best_for":["RAG systems with long documents (books, research papers, legal contracts)","teams needing fine-grained retrieval with rich context","applications where document structure (chapters, sections) should inform retrieval"],"limitations":["Requires storing multiple representations per document (summary + full text + metadata), increasing storage 2-3x","Parent-child relationships must be defined upfront; dynamic restructuring is expensive","InMemoryByteStore is not persistent — requires external storage integration for production"],"requires":["Vector store supporting metadata filtering (ChromaDB, Pinecone)","LLM for generating document summaries or questions","Persistent key-value store for parent-child mappings (optional but recommended)"],"input_types":["documents with hierarchical structure (text with sections/chapters)"],"output_types":["retrieved parent documents (full text with metadata)","retrieval scores and summary references"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bragai--brag-langchain__cap_4","uri":"capability://search.retrieval.retrieval.re.ranking.with.cross.encoder.models.and.crag","name":"retrieval re-ranking with cross-encoder models and crag","description":"Applies learned re-ranking to retrieval results using cross-encoder models (e.g., Cohere Rerank API) that score document-query pairs jointly, improving ranking quality beyond embedding-based similarity. Notebook 5 integrates CohereRerank and demonstrates Corrective RAG (CRAG) with LangGraph, which evaluates retrieval quality and iteratively refines queries or retrieves additional documents if confidence is low.","intents":["I want to improve ranking of retrieved documents beyond embedding similarity","I need to detect when retrieval fails and automatically correct the query","I want to implement self-correcting RAG that validates and refines results"],"best_for":["RAG systems requiring high precision ranking (e.g., customer support, QA)","teams building self-correcting agents with retrieval validation","applications where retrieval errors significantly impact downstream quality"],"limitations":["Cross-encoder re-ranking adds 200-500ms per query (API latency + inference)","Cohere Rerank API requires paid subscription; no free tier for production use","CRAG with LangGraph adds complexity — requires state management and graph definition","Iterative refinement can loop indefinitely if query quality is fundamentally poor"],"requires":["Cohere API key (for CohereRerank) or local cross-encoder model","LangGraph for CRAG state management and graph execution","Initial retrieval results to re-rank (vector search prerequisite)"],"input_types":["retrieved documents (list of text chunks with scores)","user query (natural language string)"],"output_types":["re-ranked documents (list with updated scores)","retrieval quality assessment (confidence score)","refined query (if CRAG triggers refinement)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bragai--brag-langchain__cap_5","uri":"capability://search.retrieval.hyde.hypothetical.document.embeddings.query.expansion","name":"hyde (hypothetical document embeddings) query expansion","description":"Generates hypothetical documents that would answer the user's query, embeds those hypothetical documents, and uses their embeddings to retrieve real documents. Implemented in Notebook 3, HyDE leverages the LLM's generative capability to imagine relevant document content, then uses those imagined embeddings as retrieval queries, often improving recall for questions where the phrasing differs significantly from document content.","intents":["I want to retrieve documents using hypothetical content as a retrieval signal","I need to handle queries where the answer phrasing differs from document phrasing","I want to improve retrieval for open-ended or creative questions"],"best_for":["RAG systems with domain-specific or technical documents","applications where query-document vocabulary mismatch is common","teams exploring generative retrieval approaches"],"limitations":["Adds LLM inference cost (one call per query for hypothetical document generation)","Hypothetical documents may contain hallucinations that mislead retrieval","Requires embedding model compatible with document embeddings (same dimensionality/model)"],"requires":["LLM capable of generating coherent multi-sentence text","Embedding model (same as document indexing)","Vector store for similarity search"],"input_types":["user query (natural language string)"],"output_types":["retrieved documents (list of text chunks)","hypothetical document (generated text used for retrieval)"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bragai--brag-langchain__cap_6","uri":"capability://search.retrieval.rag.fusion.with.reciprocal.rank.fusion.rrf.result.aggregation","name":"rag-fusion with reciprocal rank fusion (rrf) result aggregation","description":"Combines multi-query retrieval with Reciprocal Rank Fusion (RRF), a rank aggregation algorithm that merges results from multiple retrievers by computing harmonic mean of reciprocal ranks. Notebook 3 demonstrates RAG-Fusion, which generates query variants, retrieves from each, and uses RRF to produce a unified ranked list without requiring relevance scores to be comparable across retrievers.","intents":["I want to merge results from multiple retrieval strategies without score normalization","I need a principled way to combine multi-query retrieval results","I want to improve ranking by aggregating diverse retrieval perspectives"],"best_for":["RAG systems combining multiple retrieval methods (BM25 + semantic, multi-query, etc.)","teams needing rank aggregation without score calibration","applications where retriever scores are incomparable (different models/APIs)"],"limitations":["RRF assumes equal retriever quality — no weighting mechanism for high-confidence retrievers","Requires all retrievers to return ranked lists; incompatible with unranked result sets","RRF parameter (k) must be tuned empirically; no principled selection method"],"requires":["Multiple retrieval methods or query variants","Ranked results from each retriever (with position information)","RRF implementation (LangChain provides this)"],"input_types":["multiple ranked result lists (from different retrievers or queries)"],"output_types":["merged ranked result list (documents with RRF scores)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bragai--brag-langchain__cap_7","uri":"capability://planning.reasoning.self.rag.with.iterative.retrieval.validation.and.refinement","name":"self-rag with iterative retrieval validation and refinement","description":"Implements Self-Retrieval-Augmented Generation (Self-RAG) using LangGraph, where the LLM generates responses, evaluates whether retrieval is needed, validates retrieved documents, and iteratively refines answers. Notebook 5 demonstrates this pattern with explicit LLM-based evaluation steps that determine if initial retrieval was sufficient or if additional retrieval/refinement is required.","intents":["I want the LLM to decide when retrieval is necessary rather than always retrieving","I need to validate that retrieved documents actually support the generated answer","I want to implement iterative refinement where the LLM can request additional retrieval"],"best_for":["RAG systems where not all queries require retrieval (some are answerable from LLM knowledge)","applications requiring high answer quality with explicit validation","teams building agentic RAG with self-correction loops"],"limitations":["Adds 2-4x LLM calls per query (generation + validation + potential refinement)","Iterative loops can be expensive and slow; requires careful termination conditions","LLM evaluation of retrieval quality is imperfect — may miss subtle relevance issues"],"requires":["LLM capable of structured evaluation (e.g., 'is retrieval needed?' yes/no)","LangGraph for state machine and iterative execution","Retriever for on-demand document fetching"],"input_types":["user query (natural language string)"],"output_types":["final answer (text)","retrieval decisions (yes/no per iteration)","validation scores (relevance assessment)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bragai--brag-langchain__cap_8","uri":"capability://data.processing.analysis.document.loading.and.embedding.with.multi.format.support","name":"document loading and embedding with multi-format support","description":"Loads documents from multiple formats (PDF, markdown, plain text) using LangChain document loaders, chunks them using configurable splitters (recursive character splitting, semantic splitting), and embeds chunks using embedding models (OpenAI, Cohere, local models). Notebook 1 demonstrates the complete indexing pipeline from raw documents to vector store population, with support for metadata extraction and preservation.","intents":["I want to ingest documents in various formats into a vector store","I need to chunk documents intelligently while preserving semantic boundaries","I want to embed documents using different embedding models and store them"],"best_for":["teams building RAG systems from scratch","applications requiring multi-format document ingestion","builders experimenting with different chunking and embedding strategies"],"limitations":["Chunking strategy significantly impacts retrieval quality; no automatic optimization","Embedding API costs scale with document size; large corpora require careful batching","Metadata extraction is format-dependent; PDFs require special handling (OCR for scanned docs)"],"requires":["Document files (PDF, markdown, text)","LangChain document loaders and text splitters","Embedding model API key (OpenAI, Cohere) or local model","Vector store (ChromaDB, Pinecone, etc.)"],"input_types":["documents (PDF, markdown, plain text files)"],"output_types":["embedded chunks (vectors + text + metadata in vector store)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bragai--brag-langchain__cap_9","uri":"capability://memory.knowledge.vector.store.integration.with.chromadb.and.pinecone","name":"vector store integration with chromadb and pinecone","description":"Abstracts vector store operations (insert, search, delete, update) across multiple backends including ChromaDB (local/in-memory) and Pinecone (cloud). Notebook 1 demonstrates initialization, population, and querying of both stores, with support for metadata filtering and similarity search. The abstraction enables swapping vector stores without changing retrieval logic.","intents":["I want to choose between local and cloud vector storage for my RAG system","I need to store and retrieve embeddings with metadata filtering","I want to switch vector stores without rewriting retrieval code"],"best_for":["teams prototyping with local storage (ChromaDB) before scaling to cloud (Pinecone)","applications requiring flexible vector store selection","builders learning RAG without cloud infrastructure setup"],"limitations":["ChromaDB is not production-ready for high-concurrency scenarios; Pinecone required for scale","Metadata filtering capabilities vary between stores; complex filters may not be portable","Vector store switching requires re-embedding and re-indexing; no automatic migration"],"requires":["ChromaDB (local, no setup) or Pinecone API key (cloud)","Embedded vectors (from embedding model)","LangChain vector store wrappers"],"input_types":["embedded chunks (vectors + text + metadata)"],"output_types":["similarity search results (documents with scores)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Python 3.11.11+","LangChain 0.1.0+","Vector store (ChromaDB, Pinecone, or compatible)","LLM API credentials (OpenAI, Anthropic, or local Ollama)","LLM with function-calling or prompt-following capability","Parallel retriever execution support (LangChain RunnableParallel)","Vector store supporting batch similarity search","LangChain PromptTemplate","LLM API (OpenAI, Anthropic, etc.)","Jupyter notebook environment (local or cloud)"],"failure_modes":["LCEL abstractions add ~50-200ms latency per chain step due to serialization overhead","No built-in distributed execution — single-machine only without external orchestration","Vector store selection is fixed at pipeline creation time; runtime switching requires pipeline reconstruction","Increases LLM API costs by 2-5x per query (one call for variant generation, N calls for retrieval)","Adds 300-800ms latency for variant generation before parallel retrieval begins","Deduplication logic is simple string/embedding matching — may miss semantic duplicates","Prompt quality is highly empirical; no principled optimization method provided","Template variables must be manually specified; no automatic variable detection","Prompt injection vulnerabilities possible if user input is not sanitized","Notebooks require running locally or in Jupyter environment; not suitable for production deployment","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5746723042462245,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:29.527Z","last_commit":"2025-11-22T07:26:48Z"},"community":{"stars":4094,"forks":493,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=bragai--brag-langchain","compare_url":"https://unfragile.ai/compare?artifact=bragai--brag-langchain"}},"signature":"XV5A16t4dbC3V4KcTP08GN+iLYQ6/CkvzCaHmD20wpSkaANZlbDZj+03e1PUzEPNEy+t7Rp4a9Mz19OJYVASAg==","signedAt":"2026-06-22T09:48:46.314Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/bragai--brag-langchain","artifact":"https://unfragile.ai/bragai--brag-langchain","verify":"https://unfragile.ai/api/v1/verify?slug=bragai--brag-langchain","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"}}