{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-nirdiamant--rag_techniques","slug":"nirdiamant--rag_techniques","name":"RAG_Techniques","type":"repo","url":"https://amzn.to/4cvxqSw","page_url":"https://unfragile.ai/nirdiamant--rag_techniques","categories":["rag-knowledge"],"tags":["ai","embeddings","langchain","llama-index","llm","llms","nlp","openai","python","rag","retrieval-augmented-generation","tutorials","vector-database"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-nirdiamant--rag_techniques__cap_0","uri":"capability://memory.knowledge.foundational.rag.pipeline.implementation","name":"foundational-rag-pipeline-implementation","description":"Implements a standard RAG pipeline architecture with document ingestion, embedding generation, vector storage, semantic retrieval, and LLM-based generation. Uses a modular pattern where each stage (chunking, embedding, retrieval, generation) is independently configurable, allowing developers to swap components (e.g., different embedding models, vector databases, LLM providers) without rewriting the pipeline. The architecture follows a consistent interface across 40+ technique implementations, enabling pedagogical progression from simple RAG to advanced variants.","intents":["I need to understand the complete flow of a RAG system from raw documents to generated answers","I want to build a RAG application but need a reference architecture that shows best practices","I need to compare how different RAG techniques fit into a standard pipeline"],"best_for":["developers building their first RAG system","teams evaluating RAG frameworks and needing architectural reference","researchers prototyping new RAG techniques within a standardized pipeline"],"limitations":["Pipeline assumes synchronous processing — no built-in support for streaming or async document ingestion at scale","Standard pipeline doesn't handle multi-modal documents natively; multi-modal RAG is a separate technique","No built-in persistence layer — requires external vector database and document store configuration"],"requires":["Python 3.8+","LangChain or LlamaIndex framework installed","Vector database (Chroma, Pinecone, Weaviate, Milvus, etc.)","Embedding model API access (OpenAI, HuggingFace, local models)","LLM API access (OpenAI, Anthropic, local models via Ollama)"],"input_types":["text documents (PDF, markdown, plain text)","document paths or URLs","raw text content"],"output_types":["generated text responses","retrieved document chunks with relevance scores","structured metadata about retrieval process"],"categories":["memory-knowledge","rag-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_1","uri":"capability://data.processing.analysis.semantic.chunking.with.size.optimization","name":"semantic-chunking-with-size-optimization","description":"Implements intelligent document chunking strategies that go beyond fixed-size splitting by using semantic boundaries (sentence/paragraph breaks, code blocks) and configurable chunk size optimization. The technique analyzes document structure to preserve semantic coherence while optimizing for embedding model context windows and retrieval performance. Includes methods to test different chunk sizes against a query workload to empirically determine optimal chunk dimensions, with metrics tracking retrieval quality vs. computational cost tradeoffs.","intents":["I'm getting poor retrieval results and suspect my chunk size is wrong — how do I find the optimal size?","I need to chunk documents while preserving semantic meaning, not just splitting at arbitrary boundaries","I want to understand how chunk size affects both retrieval accuracy and latency in my RAG system"],"best_for":["teams optimizing RAG retrieval quality and cost","developers working with domain-specific documents (code, legal, medical) where semantic boundaries matter","practitioners tuning RAG systems for production deployment"],"limitations":["Semantic chunking adds preprocessing latency (typically 2-5x slower than fixed-size splitting) due to boundary detection","Optimal chunk size is workload-dependent — no universal best size; requires empirical testing with your specific queries","Doesn't handle overlapping chunks natively; overlap must be implemented as a separate post-processing step"],"requires":["Python 3.8+","Document content in text or structured format","Query dataset for empirical chunk size optimization","Embedding model for semantic similarity calculations","Vector database for retrieval testing"],"input_types":["raw text documents","structured documents (markdown, code files)","query workload for optimization"],"output_types":["chunked documents with metadata","chunk size optimization metrics (retrieval quality, latency)","recommended chunk size parameters"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_10","uri":"capability://planning.reasoning.self.correcting.rag.with.answer.validation","name":"self-correcting-rag-with-answer-validation","description":"Implements Self-RAG and Corrective RAG (CRAG) techniques where the system generates answers, then validates them against retrieved context and self-corrects if validation fails. The system uses learned or rule-based validators to assess whether generated answers are supported by retrieved context, and if validation fails, triggers retrieval refinement (new queries, different retrieval strategies) and regeneration. This approach creates a feedback loop within the generation process, enabling the system to detect and correct hallucinations or unsupported claims without requiring external feedback.","intents":["I want to detect when my RAG system generates unsupported answers and automatically correct them","I need to validate that generated answers are actually grounded in retrieved context","I want to implement self-correction without requiring user feedback or external validators"],"best_for":["high-stakes applications where answer correctness is critical","systems where hallucination detection is important","applications where self-correction can improve quality without user intervention"],"limitations":["Self-correction adds latency (validation + potential re-retrieval and regeneration, typically 1-3s per query)","Validator quality is critical — poor validators miss hallucinations or reject valid answers","Correction strategy must be carefully designed to avoid infinite loops or excessive iterations"],"requires":["Python 3.8+","LLM for answer generation and validation","Retriever for initial and refinement retrieval","Validator implementation (learned model or rule-based)","Correction strategy (query refinement, strategy switching, etc.)","LangChain or LlamaIndex for orchestration"],"input_types":["query","retrieved context","generated answer"],"output_types":["validated answer","validation score/confidence","correction trace (if answer was corrected)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_11","uri":"capability://image.visual.multi.modal.rag.with.image.and.text","name":"multi-modal-rag-with-image-and-text","description":"Extends RAG to handle multi-modal documents containing both text and images by using multi-modal embedding models that encode images and text into a shared embedding space, enabling retrieval across modalities. The system processes images (extracting text via OCR, generating captions, or using vision models) and text separately, embeds them into a unified space, and retrieves relevant content regardless of modality. This approach enables queries to find relevant images when asking text questions and vice versa, supporting richer document understanding.","intents":["I have documents with both text and images and need to retrieve relevant content across both modalities","I want to find images relevant to text queries and text relevant to image queries","I need to understand documents that combine text and visual information"],"best_for":["applications with rich media documents (technical documentation, research papers, product catalogs)","systems requiring cross-modal retrieval (find images for text queries)","teams building comprehensive document understanding systems"],"limitations":["Multi-modal embedding models are computationally expensive; inference is slower than text-only models","Image processing (OCR, captioning) adds preprocessing overhead and introduces errors","Multi-modal models have smaller context windows and fewer options than text-only models"],"requires":["Python 3.8+","Multi-modal embedding model (CLIP, LLaVA, or similar)","Image processing tools (OCR, vision model for captioning)","Vector database supporting multi-modal embeddings","LangChain or LlamaIndex with multi-modal support"],"input_types":["documents containing text and images","queries (text or image)"],"output_types":["retrieved text chunks and images","cross-modal relevance scores","unified ranked results across modalities"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_12","uri":"capability://data.processing.analysis.rag.evaluation.with.deepeval.framework","name":"rag-evaluation-with-deepeval-framework","description":"Provides a comprehensive evaluation framework (DeepEval) for assessing RAG system quality across multiple dimensions: retrieval quality (precision, recall, NDCG), answer quality (faithfulness, relevance, coherence), and end-to-end performance. The framework includes pre-built metrics, dataset management, and evaluation pipelines that can be integrated into development workflows. Developers can define evaluation criteria, run automated evaluations against test datasets, and track metrics over time to monitor RAG system quality and detect regressions.","intents":["I need to measure whether my RAG system is actually improving with changes I make","I want to evaluate retrieval quality, answer quality, and end-to-end performance systematically","I need to track RAG system quality over time and detect regressions"],"best_for":["teams building production RAG systems where quality monitoring is critical","developers iterating on RAG techniques and needing systematic evaluation","organizations requiring quality metrics for compliance or stakeholder reporting"],"limitations":["Evaluation requires labeled test datasets; creating high-quality evaluation sets is time-consuming","Some metrics (faithfulness, relevance) require LLM-based assessment, adding cost and latency","Metric selection is domain-dependent; no universal set of metrics works for all RAG applications"],"requires":["Python 3.8+","DeepEval framework installed","Test dataset with queries and expected answers/retrieved documents","LLM API access for LLM-based metrics","Vector database and retriever for evaluation"],"input_types":["test queries","expected retrieved documents or answers","RAG system outputs (retrieved chunks, generated answers)"],"output_types":["evaluation metrics (precision, recall, NDCG, faithfulness, relevance, etc.)","per-query evaluation results","aggregated performance reports"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_13","uri":"capability://data.processing.analysis.rag.benchmarking.with.test.datasets","name":"rag-benchmarking-with-test-datasets","description":"Provides standardized benchmark datasets and evaluation protocols for comparing RAG techniques and implementations. The repository includes curated test datasets with queries, expected answers, and ground-truth retrieved documents, enabling developers to benchmark their RAG systems against known baselines. Benchmarks cover different domains (general knowledge, technical documentation, research papers) and query types (factual, conceptual, reasoning), allowing developers to assess RAG performance across diverse scenarios and compare their implementations against published baselines.","intents":["I want to benchmark my RAG implementation against standard datasets to understand its performance","I need to compare different RAG techniques using the same evaluation dataset","I want to know how my RAG system performs on different query types and domains"],"best_for":["researchers comparing RAG techniques","developers evaluating RAG frameworks and implementations","teams establishing baseline performance before optimization"],"limitations":["Benchmark datasets may not reflect your specific domain or query distribution","Performance on benchmarks doesn't guarantee performance on production data","Benchmarks are static; they don't evolve with new RAG techniques or domains"],"requires":["Python 3.8+","RAG implementation to evaluate","Evaluation framework (DeepEval or similar)","Computational resources for running benchmarks (can be time-consuming)"],"input_types":["benchmark dataset (queries, expected answers, documents)"],"output_types":["benchmark results (metrics per query and aggregated)","comparison with baseline implementations","per-domain and per-query-type breakdowns"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_14","uri":"capability://tool.use.integration.dual.framework.implementation.with.langchain.and.llamaindex","name":"dual-framework-implementation-with-langchain-and-llamaindex","description":"Provides parallel implementations of all RAG techniques using both LangChain and LlamaIndex frameworks, showing how the same logical RAG concepts map to different framework abstractions. Each technique has implementations in both frameworks, allowing developers to understand RAG architecture independent of framework choice and to compare framework approaches. This dual-implementation strategy helps developers make informed framework choices and understand how to port RAG implementations between frameworks.","intents":["I want to understand RAG concepts independent of which framework I choose","I need to compare LangChain and LlamaIndex to decide which framework to use","I want to see how the same RAG technique is implemented differently in different frameworks"],"best_for":["developers evaluating RAG frameworks","teams migrating between LangChain and LlamaIndex","learners wanting to understand RAG concepts independent of framework"],"limitations":["Maintaining dual implementations increases maintenance burden; techniques may diverge between frameworks","Framework differences mean implementations aren't perfectly equivalent; some features may be framework-specific","Dual implementations may not cover all framework features; some advanced features may only be shown in one framework"],"requires":["Python 3.8+","Both LangChain and LlamaIndex installed","Understanding of both frameworks' abstractions and APIs"],"input_types":["RAG technique description"],"output_types":["LangChain implementation","LlamaIndex implementation","comparison of framework approaches"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_15","uri":"capability://code.generation.editing.production.ready.runnable.scripts.for.rag.techniques","name":"production-ready-runnable-scripts-for-rag-techniques","description":"Provides standalone, executable Python scripts for each RAG technique that can be run immediately without modification (with API keys configured). Scripts include all necessary imports, configuration, and error handling, demonstrating production-ready patterns. Each script is self-contained and can serve as a template for implementing the technique in production systems. Scripts include examples with real data, showing end-to-end execution from document loading through answer generation.","intents":["I want to quickly test a RAG technique without building from scratch","I need a production-ready template for implementing a specific RAG technique","I want to see a complete working example of a technique before integrating it into my system"],"best_for":["developers prototyping RAG techniques quickly","teams building production RAG systems and needing reference implementations","practitioners wanting to understand techniques through working code"],"limitations":["Scripts are examples; production deployment requires additional error handling, logging, monitoring","Scripts assume specific API keys and configurations; customization required for different environments","Scripts may not handle edge cases or scale to production data volumes without modification"],"requires":["Python 3.8+","API keys for LLM and embedding model providers","Vector database setup (local or cloud)","Required Python packages installed"],"input_types":["documents (provided in scripts)","queries (provided in scripts)"],"output_types":["generated answers","retrieved chunks","execution logs"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_2","uri":"capability://text.generation.language.query.transformation.and.enhancement","name":"query-transformation-and-enhancement","description":"Implements multiple query transformation techniques (query rewriting, expansion, decomposition) that improve retrieval by reformulating user queries into forms more likely to match relevant documents. Techniques include HyDE (Hypothetical Document Embeddings) which generates synthetic relevant documents from queries, HyPE which generates hypothetical passages, and multi-query expansion that creates semantically similar query variants. Each transformation is applied before retrieval to increase the likelihood of finding relevant chunks, with optional fusion of results from multiple query variants.","intents":["My RAG system misses relevant documents because the user's query wording doesn't match document content","I want to implement HyDE or similar techniques to improve retrieval without retraining my embedding model","I need to handle ambiguous or under-specified queries by generating multiple interpretations"],"best_for":["teams dealing with vocabulary mismatch between queries and documents","applications with domain-specific terminology where query expansion helps","systems where query quality is unpredictable (user-facing chatbots, search interfaces)"],"limitations":["Query transformation adds latency (HyDE requires generating synthetic documents via LLM, typically 500-2000ms per query)","Synthetic document generation quality depends on LLM capability — weaker models produce less useful transformations","Multiple query variants increase vector database load; fusion of multiple retrievals adds computational cost"],"requires":["Python 3.8+","LLM API access for query transformation (OpenAI, Anthropic, or local model)","Embedding model for encoding transformed queries","Vector database for retrieval","LangChain or LlamaIndex for orchestration"],"input_types":["user query (text string)","optional context about query domain"],"output_types":["transformed query variants","synthetic documents (for HyDE)","fused retrieval results from multiple queries"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_3","uri":"capability://memory.knowledge.contextual.chunk.enrichment.with.headers","name":"contextual-chunk-enrichment-with-headers","description":"Enhances retrieved chunks with contextual metadata by automatically generating or extracting chunk headers, parent document context, and hierarchical position information. When a chunk is retrieved, the system includes its semantic context (what section of the document it belongs to, what the surrounding chunks discuss) alongside the chunk content itself. This enrichment happens during indexing (headers are computed and stored with chunks) and retrieval (context is appended to retrieved chunks before passing to the LLM), improving the LLM's ability to understand chunk meaning without requiring larger context windows.","intents":["My LLM is confused about the context of retrieved chunks because it lacks surrounding information","I want to include document structure (sections, chapters) in my RAG system without increasing chunk size","I need to improve answer quality by giving the LLM more context about where each chunk comes from"],"best_for":["systems with structured documents (books, technical documentation, research papers)","applications where chunk meaning depends heavily on document structure","teams wanting to improve answer quality without increasing context window usage"],"limitations":["Header generation requires document structure analysis — works best with well-structured documents, struggles with unstructured text","Storing contextual metadata increases index size by 10-30% depending on context depth","Contextual compression (removing redundant context) requires additional LLM calls, adding latency"],"requires":["Python 3.8+","Structured or semi-structured documents with clear hierarchies","LLM for header generation (optional, can use rule-based extraction)","Vector database supporting metadata storage","LangChain or LlamaIndex for implementation"],"input_types":["chunked documents with structural metadata","document hierarchy information (sections, subsections)"],"output_types":["chunks enriched with contextual headers","chunks with parent/sibling context appended","metadata about chunk position in document hierarchy"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_4","uri":"capability://search.retrieval.fusion.retrieval.with.multi.strategy.ranking","name":"fusion-retrieval-with-multi-strategy-ranking","description":"Combines results from multiple retrieval strategies (dense semantic search, sparse BM25 keyword search, hypothetical document embeddings) using fusion algorithms (Reciprocal Rank Fusion, weighted scoring) to produce a unified ranked result set. Each retrieval strategy is executed independently, then results are merged using configurable fusion methods that balance semantic relevance (from dense retrieval) with keyword matching (from sparse retrieval). This approach captures both semantic and lexical relevance without requiring a single unified index.","intents":["I want to combine semantic and keyword-based retrieval to get better coverage of relevant documents","My dense retrieval misses documents with exact keyword matches that are relevant to the query","I need a retrieval strategy that handles both semantic queries and specific factual lookups"],"best_for":["applications requiring both semantic understanding and keyword precision (technical documentation, legal search)","systems where document relevance depends on both meaning and specific terminology","teams wanting to improve recall without sacrificing precision"],"limitations":["Fusion requires running multiple retrieval strategies, multiplying latency (typically 2-3x slower than single-strategy retrieval)","Fusion algorithm tuning is empirical — optimal weights depend on query distribution and document collection","Requires maintaining both dense (vector) and sparse (BM25) indices, doubling index storage and update complexity"],"requires":["Python 3.8+","Vector database supporting dense retrieval","BM25 or similar sparse retrieval implementation (Elasticsearch, Lucene, or library)","Fusion algorithm implementation (RRF, weighted scoring)","LangChain or LlamaIndex for orchestration"],"input_types":["user query (text)","fusion algorithm configuration (weights, strategy selection)"],"output_types":["fused ranked list of retrieved chunks","per-strategy scores for each result","combined relevance scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_5","uri":"capability://search.retrieval.intelligent.reranking.with.cross.encoders","name":"intelligent-reranking-with-cross-encoders","description":"Implements a two-stage retrieval pipeline where an initial retriever (fast, approximate) returns candidate chunks, then a cross-encoder reranker (slower, more accurate) scores and reorders results based on query-document relevance. The reranker uses transformer models that jointly encode the query and document to compute relevance scores, providing more accurate ranking than embedding-based similarity. This approach maintains retrieval speed (initial retrieval is still fast) while improving result quality through expensive but accurate reranking on a smaller candidate set.","intents":["My initial retrieval returns many candidates but the top results aren't always most relevant","I want to improve ranking quality without slowing down the initial retrieval step","I need to use more sophisticated relevance models without the computational cost of applying them to all documents"],"best_for":["production RAG systems where ranking quality significantly impacts answer quality","applications with large document collections where initial retrieval must be fast","teams willing to trade reranking latency for improved result quality"],"limitations":["Reranking adds latency (cross-encoder inference typically 50-200ms per query depending on candidate set size)","Cross-encoder models are computationally expensive; GPU acceleration recommended for production use","Reranking effectiveness depends on initial retriever quality — if initial retrieval misses relevant documents, reranking can't recover them"],"requires":["Python 3.8+","Initial retriever (vector database with embedding model)","Cross-encoder model (HuggingFace, Cohere, or custom-trained)","GPU recommended for production inference (CPU inference adds 200-500ms latency)","LangChain or LlamaIndex for pipeline orchestration"],"input_types":["query (text)","candidate chunks from initial retrieval","reranking model configuration"],"output_types":["reranked list of chunks","relevance scores from cross-encoder","top-k results after reranking"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_6","uri":"capability://memory.knowledge.hierarchical.index.construction.and.traversal","name":"hierarchical-index-construction-and-traversal","description":"Builds multi-level document indices where documents are recursively summarized into hierarchies (leaf chunks → summaries → higher-level summaries) and retrieval traverses this hierarchy top-down. The system first retrieves relevant high-level summaries, then recursively retrieves more detailed chunks from relevant branches, reducing the number of embeddings needed and improving retrieval efficiency. This approach is particularly effective for large document collections where flat indices become inefficient, enabling both faster retrieval and better handling of documents with varying levels of detail.","intents":["I have very large documents and need to retrieve relevant sections efficiently without embedding every chunk","I want to understand document structure hierarchically and retrieve at appropriate levels of detail","I need to reduce the number of embeddings computed during retrieval while maintaining quality"],"best_for":["systems with large document collections (100k+ chunks) where flat indices become inefficient","applications with hierarchically-structured documents (books, technical documentation, codebases)","teams optimizing for retrieval latency and embedding costs"],"limitations":["Hierarchical index construction requires recursive summarization, adding significant preprocessing time (10-50x slower than flat indexing)","Summary quality depends on summarization model capability — poor summaries degrade retrieval quality","Traversal strategy (how many levels to retrieve, when to stop) requires tuning; no universal optimal strategy"],"requires":["Python 3.8+","LLM for recursive summarization (OpenAI, Anthropic, or local model)","Vector database supporting hierarchical organization","Structured documents or documents that can be meaningfully hierarchized","LangChain or LlamaIndex for implementation"],"input_types":["documents (text or structured)","hierarchy configuration (summarization depth, chunk sizes at each level)"],"output_types":["hierarchical index structure","retrieved chunks at appropriate hierarchy levels","traversal path showing which summaries were used"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_7","uri":"capability://planning.reasoning.adaptive.retrieval.with.query.routing","name":"adaptive-retrieval-with-query-routing","description":"Implements dynamic retrieval strategies that adapt based on query characteristics, routing different query types to different retrieval methods. The system analyzes incoming queries to determine optimal retrieval strategy (e.g., simple keyword search for factual lookups, semantic search for conceptual questions, graph-based retrieval for relationship queries) and applies the appropriate method. This routing can be rule-based (query classification) or learned (trained classifier), enabling the system to use the most efficient and effective retrieval method for each query type without requiring all queries to use the same strategy.","intents":["Different types of queries need different retrieval strategies but I don't want to manually choose for each query","I want to optimize retrieval efficiency by using simple methods for simple queries and complex methods only when needed","I need to handle diverse query types (factual, conceptual, relational) with appropriate retrieval methods"],"best_for":["systems handling diverse query types (factual, conceptual, relational questions)","applications where query complexity varies widely","teams optimizing for both retrieval quality and latency across heterogeneous workloads"],"limitations":["Query routing requires classification overhead (typically 50-200ms per query for LLM-based routing)","Routing strategy effectiveness depends on query type distribution — requires empirical evaluation on representative queries","Maintaining multiple retrieval strategies increases system complexity and operational overhead"],"requires":["Python 3.8+","Query classifier (rule-based, ML model, or LLM-based)","Multiple retrieval implementations (keyword, semantic, graph-based, etc.)","Vector database and optional graph database","LangChain or LlamaIndex for orchestration"],"input_types":["user query (text)","optional query metadata or context"],"output_types":["query classification/routing decision","retrieved results using selected strategy","routing explanation (which strategy was used and why)"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_8","uri":"capability://planning.reasoning.retrieval.with.feedback.loops.and.iteration","name":"retrieval-with-feedback-loops-and-iteration","description":"Implements iterative retrieval where initial retrieval results are evaluated, and based on evaluation (relevance feedback, answer quality assessment), the system refines queries or retrieval parameters and retrieves again. The feedback loop can be explicit (user indicates whether results are relevant) or implicit (system evaluates answer quality and decides whether to retrieve more context). This approach enables the system to improve results through iteration without requiring perfect initial retrieval, particularly useful for complex queries that may need multiple retrieval rounds to gather sufficient context.","intents":["My initial retrieval doesn't find all relevant information and I want to iteratively refine until I have enough context","I want to implement user feedback loops where users indicate if results are relevant and the system refines","I need to handle complex queries that require multiple retrieval rounds to gather complete information"],"best_for":["interactive systems where users can provide relevance feedback","complex query scenarios requiring multiple retrieval rounds","applications where answer quality can be assessed and used to trigger refinement"],"limitations":["Iterative retrieval increases latency (each iteration adds retrieval + evaluation overhead, typically 500ms-2s per iteration)","Feedback loop termination requires clear stopping criteria; poorly designed criteria can lead to excessive iterations","Explicit user feedback requires user interaction; implicit feedback requires reliable quality assessment"],"requires":["Python 3.8+","Initial retriever (vector database)","Feedback mechanism (explicit user input or implicit quality assessment)","Iteration logic (query refinement, parameter adjustment)","Termination criteria (max iterations, quality threshold, etc.)","LangChain or LlamaIndex for orchestration"],"input_types":["initial query","feedback (relevance judgments, quality scores, or user input)"],"output_types":["refined queries","accumulated retrieved chunks across iterations","iteration history and feedback trace"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--rag_techniques__cap_9","uri":"capability://memory.knowledge.graph.based.rag.with.knowledge.graphs","name":"graph-based-rag-with-knowledge-graphs","description":"Implements RAG using knowledge graphs (GraphRAG, RAPTOR) where documents are converted into structured knowledge graphs with entities and relationships, and retrieval operates on graph structure rather than flat chunks. The system extracts entities and relationships from documents, builds a graph index, and retrieves relevant subgraphs based on query entities and relationship patterns. This approach enables relationship-aware retrieval (finding documents about related entities) and supports complex queries that depend on understanding connections between concepts, not just individual chunks.","intents":["I need to retrieve information about relationships between entities, not just individual facts","My queries involve understanding how concepts connect and I need retrieval that respects these connections","I want to leverage document structure and entity relationships to improve retrieval quality"],"best_for":["domains with rich entity relationships (knowledge bases, research papers, technical documentation)","applications requiring relationship-aware retrieval (recommendation, knowledge discovery)","systems where query understanding depends on entity and relationship extraction"],"limitations":["Knowledge graph construction requires entity and relationship extraction, adding significant preprocessing overhead (5-10x slower than flat chunking)","Extraction quality depends on NER and relation extraction models; errors propagate through the graph","Graph-based retrieval is more complex to implement and tune than flat retrieval; requires graph database expertise"],"requires":["Python 3.8+","NER and relation extraction models (spaCy, transformer-based, or LLM-based)","Graph database (Neo4j, ArangoDB, or similar)","Graph query language knowledge (Cypher, AQL, etc.)","LangChain or LlamaIndex with graph support"],"input_types":["documents (text)","entity and relationship extraction configuration"],"output_types":["knowledge graph with entities and relationships","retrieved subgraphs relevant to query","entity-relationship paths connecting query concepts"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","LangChain or LlamaIndex framework installed","Vector database (Chroma, Pinecone, Weaviate, Milvus, etc.)","Embedding model API access (OpenAI, HuggingFace, local models)","LLM API access (OpenAI, Anthropic, local models via Ollama)","Document content in text or structured format","Query dataset for empirical chunk size optimization","Embedding model for semantic similarity calculations","Vector database for retrieval testing","LLM for answer generation and validation"],"failure_modes":["Pipeline assumes synchronous processing — no built-in support for streaming or async document ingestion at scale","Standard pipeline doesn't handle multi-modal documents natively; multi-modal RAG is a separate technique","No built-in persistence layer — requires external vector database and document store configuration","Semantic chunking adds preprocessing latency (typically 2-5x slower than fixed-size splitting) due to boundary detection","Optimal chunk size is workload-dependent — no universal best size; requires empirical testing with your specific queries","Doesn't handle overlapping chunks natively; overlap must be implemented as a separate post-processing step","Self-correction adds latency (validation + potential re-retrieval and regeneration, typically 1-3s per query)","Validator quality is critical — poor validators miss hallucinations or reject valid answers","Correction strategy must be carefully designed to avoid infinite loops or excessive iterations","Multi-modal embedding models are computationally expensive; inference is slower than text-only models","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.769799750991111,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:58:24.501Z","last_commit":"2026-04-15T15:30:57Z"},"community":{"stars":27119,"forks":3256,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nirdiamant--rag_techniques","compare_url":"https://unfragile.ai/compare?artifact=nirdiamant--rag_techniques"}},"signature":"ZlJh1UZDy1j2E4SfZlTjOmDTYR51O65Rm6+n2P2/CXlMzlXSfi9DCYZLHblIPAD/zCCP6H5AuEV7n+EJXdGyDA==","signedAt":"2026-06-20T18:48:07.780Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nirdiamant--rag_techniques","artifact":"https://unfragile.ai/nirdiamant--rag_techniques","verify":"https://unfragile.ai/api/v1/verify?slug=nirdiamant--rag_techniques","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"}}