{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"llamaindex-starter","slug":"llamaindex-starter","name":"LlamaIndex Starter","type":"template","url":"https://github.com/run-llama/llama_index_starter_pack","page_url":"https://unfragile.ai/llamaindex-starter","categories":["rag-knowledge","documentation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"llamaindex-starter__cap_0","uri":"capability://memory.knowledge.document.q.a.template.with.rag.pipeline","name":"document q&a template with rag pipeline","description":"Pre-configured template implementing retrieval-augmented generation (RAG) for question-answering over document collections. Uses LlamaIndex's document ingestion pipeline to parse files (PDF, TXT, Markdown), chunk them with configurable strategies, embed chunks via vector stores, and retrieve relevant context before passing to an LLM for answer generation. Abstracts away index construction, retrieval configuration, and prompt engineering boilerplate.","intents":["I want to quickly build a Q&A system over my company's documentation without writing RAG infrastructure from scratch","I need to understand how to structure document parsing, chunking, and retrieval for a production LLM application","I'm prototyping a customer support chatbot that answers questions from a knowledge base"],"best_for":["developers new to RAG patterns looking for working reference implementations","teams building internal knowledge base Q&A systems","founders prototyping document-based AI features for MVP validation"],"limitations":["Template assumes single-document or small-scale collections; scaling to millions of documents requires custom index partitioning strategy","Default chunking strategy (fixed size) may not preserve semantic boundaries in domain-specific documents; requires manual tuning for technical/legal content","No built-in handling for document versioning or incremental updates; full re-indexing required for document changes","Retrieval quality depends heavily on embedding model choice and chunk size; template provides no automated optimization guidance"],"requires":["Python 3.8+","LlamaIndex library (latest version)","API key for LLM provider (OpenAI, Anthropic, or local Ollama instance)","Vector store backend (Pinecone, Weaviate, Chroma, or in-memory SimpleVectorStore)","Document files in supported formats (PDF, TXT, Markdown, DOCX)"],"input_types":["documents (PDF, TXT, Markdown, DOCX)","natural language queries (text)"],"output_types":["text (LLM-generated answers with optional source citations)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llamaindex-starter__cap_1","uri":"capability://text.generation.language.multi.turn.conversational.chat.with.document.context","name":"multi-turn conversational chat with document context","description":"Template implementing stateful conversation over documents using LlamaIndex's chat engine, which maintains conversation history while retrieving relevant document context for each turn. Handles context window management by summarizing or filtering conversation history, retrieves fresh context from the document index per query, and passes both history and context to the LLM to generate contextually-aware responses that reference previous turns.","intents":["I want to build a chatbot that remembers conversation history while staying grounded in document facts","I need to understand how to manage token budgets when combining long conversation histories with document retrieval","I'm building a customer support agent that can reference both previous customer messages and company documentation"],"best_for":["developers building conversational AI systems over knowledge bases","teams implementing multi-turn support chatbots with document grounding","builders creating interactive data exploration interfaces"],"limitations":["Conversation history grows unbounded; template does not implement automatic summarization or pruning, leading to token budget exhaustion on long conversations","No built-in session persistence; conversation state is in-memory and lost on process restart without external database integration","Context retrieval happens per-turn without conversation-level optimization; may retrieve redundant or conflicting information across turns","No native support for multi-user sessions or conversation branching; requires custom state management for complex interaction patterns"],"requires":["Python 3.8+","LlamaIndex library with chat engine support","LLM API key (OpenAI, Anthropic, or local model)","Indexed document collection (from Document Q&A template or custom setup)","Optional: external database for conversation persistence (PostgreSQL, MongoDB, etc.)"],"input_types":["natural language user messages (text)","conversation history (structured message list)"],"output_types":["text (LLM-generated responses with optional source citations)","structured conversation logs (JSON or database records)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llamaindex-starter__cap_10","uri":"capability://data.processing.analysis.evaluation.and.benchmarking.of.rag.pipeline.quality","name":"evaluation and benchmarking of rag pipeline quality","description":"Template providing utilities to evaluate RAG system quality across multiple dimensions: retrieval quality (precision, recall, NDCG), answer quality (relevance, factuality, citation accuracy), and end-to-end performance. Includes evaluation datasets, metrics computation, and comparison tools to measure impact of configuration changes. Supports both automated metrics (embedding-based similarity) and human evaluation workflows.","intents":["I want to measure how well my RAG system retrieves relevant documents and generates accurate answers","I need to compare different configurations (chunking, retrieval method, LLM) to find the best setup","I'm building a system where I need to track quality metrics over time as documents are updated"],"best_for":["data scientists optimizing RAG systems for production deployment","teams implementing quality assurance for RAG applications","developers building systems where answer quality is critical"],"limitations":["Automated metrics (BLEU, ROUGE, embedding similarity) don't always correlate with human judgment; requires manual evaluation for validation","Evaluation requires ground-truth datasets with human annotations; creating these datasets is expensive and time-consuming","No built-in statistical significance testing; requires custom code to determine if improvements are meaningful","Evaluation metrics are task-specific; metrics for Q&A may not apply to summarization or extraction tasks"],"requires":["Python 3.8+","LlamaIndex library with evaluation support","Evaluation dataset with ground-truth answers or relevance judgments","LLM API key for automated evaluation metrics","Optional: human evaluators for manual quality assessment"],"input_types":["RAG pipeline (LlamaIndex query engine)","evaluation dataset (queries with ground-truth answers)","configuration parameters (for comparison)"],"output_types":["evaluation metrics (precision, recall, NDCG, BLEU, ROUGE, etc.)","comparison reports (configuration A vs. B performance)","quality dashboards (metrics over time)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llamaindex-starter__cap_11","uri":"capability://automation.workflow.cost.and.latency.optimization.for.llm.calls","name":"cost and latency optimization for llm calls","description":"Template providing utilities to monitor and optimize LLM API costs and latency in RAG pipelines. Tracks token usage per component (retrieval, synthesis, tool calls), identifies bottlenecks, and suggests optimizations (smaller models, caching, batching). Implements caching strategies (semantic caching, exact-match caching) to reduce redundant LLM calls, and provides cost estimation before execution.","intents":["I want to understand where my RAG system is spending money and tokens on LLM calls","I need to optimize my system to reduce latency without sacrificing quality","I'm building a production system where I need to predict and control API costs"],"best_for":["teams deploying RAG systems at scale with cost constraints","developers optimizing latency-sensitive applications","builders implementing cost-aware AI features in consumer products"],"limitations":["Cost optimization often requires trade-offs with quality; smaller models or aggressive caching may reduce answer quality","Caching strategies are application-specific; semantic caching requires careful tuning of similarity thresholds","Latency optimization is hardware-dependent; improvements on one infrastructure may not transfer to another","No built-in cost prediction for complex agentic workflows; cost estimation is approximate for multi-step reasoning"],"requires":["Python 3.8+","LlamaIndex library with cost tracking support","LLM API key with usage tracking (OpenAI, Anthropic, etc.)","Optional: caching backend (Redis, in-memory, or custom)"],"input_types":["RAG pipeline (LlamaIndex query engine)","usage patterns (queries, documents, configuration)"],"output_types":["cost reports (token usage, API costs per component)","latency profiles (execution time per component)","optimization recommendations (caching, model selection, batching)","cost estimates (predicted costs for future usage)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llamaindex-starter__cap_2","uri":"capability://data.processing.analysis.structured.data.extraction.from.unstructured.documents","name":"structured data extraction from unstructured documents","description":"Template using LlamaIndex's structured output capabilities (via Pydantic schema definitions) to extract typed data from documents. Defines a Pydantic model representing desired output structure (e.g., invoice fields, entity lists), passes documents through LlamaIndex's extraction pipeline which uses the LLM to parse content and map it to the schema, and returns validated structured objects. Handles schema validation, type coercion, and optional field handling automatically.","intents":["I need to extract structured information (entities, relationships, fields) from PDFs or text documents at scale","I want to convert unstructured documents into database records with type safety and validation","I'm building a document processing pipeline that feeds extracted data into downstream systems"],"best_for":["data engineers building document-to-database ETL pipelines","teams automating invoice, receipt, or form processing","builders extracting knowledge graphs or entity relationships from text"],"limitations":["Extraction accuracy depends on LLM capability and schema clarity; complex nested structures or ambiguous fields may have high error rates","No built-in validation of extracted data against business rules; requires post-processing validation logic","Batch extraction of large document collections is slow (sequential LLM calls); no native parallelization or async support in template","Schema changes require reprocessing all documents; no incremental extraction or schema versioning support"],"requires":["Python 3.8+","LlamaIndex library with structured output support","Pydantic library for schema definition","LLM API key (OpenAI GPT-4 recommended for complex extraction, Anthropic Claude also supported)","Unstructured documents (PDF, TXT, images with OCR support)"],"input_types":["unstructured documents (PDF, TXT, Markdown, images)","Pydantic schema definitions (Python classes)"],"output_types":["structured data (Pydantic model instances, JSON, CSV)","validation reports (field-level error logs)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llamaindex-starter__cap_3","uri":"capability://planning.reasoning.multi.document.agent.with.tool.based.reasoning","name":"multi-document agent with tool-based reasoning","description":"Template implementing an agentic loop where an LLM reasons over multiple documents and tools to answer complex queries. Uses LlamaIndex's agent framework to define tools (document search, calculation, external API calls), implements a ReAct-style loop where the agent plans actions, executes tools, observes results, and refines its approach. Manages context across multiple document indexes and tool invocations, handling tool selection, parameter binding, and result integration into the reasoning loop.","intents":["I need to build an AI system that can search multiple documents, cross-reference information, and synthesize answers from different sources","I want to create an agent that can use tools (APIs, calculators, databases) alongside document retrieval to answer complex questions","I'm implementing a research assistant that can reason over multiple knowledge bases and external data sources"],"best_for":["developers building autonomous research or analysis agents","teams implementing complex query systems that require multi-step reasoning","builders creating enterprise knowledge workers that combine documents with external tools"],"limitations":["Agent reasoning is non-deterministic and can hallucinate tool calls or misinterpret results; requires careful prompt engineering and tool definition","No built-in cost control; agents may make many LLM calls and tool invocations, leading to unpredictable API costs","Debugging agent behavior is difficult; reasoning traces are opaque and require manual inspection of intermediate steps","Tool integration requires custom code for each external system; no standardized tool marketplace or discovery mechanism in template","Agent loops can get stuck in infinite reasoning cycles; requires manual timeout and max-iteration limits"],"requires":["Python 3.8+","LlamaIndex library with agent framework","LLM API key (GPT-4 or Claude recommended for complex reasoning)","Multiple document indexes (from other templates or custom setup)","Tool definitions (custom functions or API integrations)","Optional: external APIs or databases for tool execution"],"input_types":["natural language queries (text)","tool definitions (Python functions with type hints)","document indexes (LlamaIndex Index objects)"],"output_types":["text (agent-generated answers with reasoning trace)","structured results (tool outputs, intermediate findings)","execution logs (tool calls, reasoning steps)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llamaindex-starter__cap_4","uri":"capability://data.processing.analysis.configurable.document.chunking.and.indexing.strategy","name":"configurable document chunking and indexing strategy","description":"Template exposing LlamaIndex's chunking and indexing configuration options (chunk size, overlap, separator strategy, node post-processors) as configurable parameters. Allows developers to experiment with different chunking strategies (fixed-size, semantic, hierarchical) and index types (vector, keyword, tree-based) without code changes. Includes utilities to evaluate chunking quality and measure retrieval performance across configurations.","intents":["I want to tune document chunking parameters to optimize retrieval quality for my specific domain","I need to understand how chunk size and overlap affect both retrieval accuracy and token costs","I'm comparing different indexing strategies (vector vs. keyword vs. hybrid) to find the best fit for my use case"],"best_for":["data scientists optimizing RAG systems for domain-specific content","teams evaluating trade-offs between retrieval quality and latency","developers building production RAG systems that require parameter tuning"],"limitations":["No automated hyperparameter optimization; tuning is manual and requires domain expertise to evaluate quality","Evaluation metrics are not built-in; requires custom code to measure retrieval precision/recall or downstream task performance","Chunking strategy changes require full re-indexing; no incremental updates or strategy migration tools","Template does not provide guidance on optimal chunk sizes for different document types or domains"],"requires":["Python 3.8+","LlamaIndex library with configurable chunking support","Document collection for testing","Optional: evaluation dataset with ground-truth relevance judgments"],"input_types":["documents (PDF, TXT, Markdown, etc.)","configuration parameters (chunk size, overlap, separator, index type)"],"output_types":["indexed documents (LlamaIndex Index objects)","performance metrics (retrieval quality, latency, token usage)","configuration reports (parameter settings and results)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llamaindex-starter__cap_5","uri":"capability://image.visual.multi.modal.document.indexing.with.image.and.text.extraction","name":"multi-modal document indexing with image and text extraction","description":"Template supporting indexing of multi-modal documents (PDFs with images, scanned documents, mixed text/image content) using LlamaIndex's image extraction and OCR capabilities. Automatically extracts images from documents, generates descriptions or embeddings for images, indexes both text and image content separately, and enables retrieval that matches queries against both text and visual content. Handles image-to-text mapping to preserve document structure.","intents":["I need to index documents with mixed text and image content (diagrams, charts, scanned pages) and retrieve based on visual or textual queries","I want to extract and index images from PDFs alongside text to enable comprehensive document search","I'm building a system that can answer questions about document content that includes charts, tables, or diagrams"],"best_for":["teams processing scanned documents or PDFs with heavy visual content","builders creating search systems for technical documentation with diagrams","organizations digitizing paper-based knowledge bases with mixed content"],"limitations":["Image extraction and OCR quality depends on document quality; scanned documents with poor resolution may have high error rates","Image embedding models are separate from text embeddings; cross-modal retrieval requires careful similarity metric design","No built-in image description generation; requires external vision model (GPT-4V, Claude) for semantic image understanding","Indexing multi-modal documents is slower and more resource-intensive than text-only indexing","Image-to-text mapping can be lossy; spatial relationships and layout information may not be preserved"],"requires":["Python 3.8+","LlamaIndex library with image extraction support","OCR library (Tesseract, PyPDF2, or cloud-based OCR service)","Image embedding model (CLIP, or custom vision model)","LLM API key for image description (GPT-4V or Claude recommended)","Multi-modal documents (PDFs with images, scanned documents)"],"input_types":["multi-modal documents (PDF with images, scanned documents, mixed content)","natural language queries (text or image-based)"],"output_types":["indexed documents with image metadata (LlamaIndex Index with image nodes)","retrieval results (text and image content with relevance scores)","extracted images with descriptions (image files and text descriptions)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llamaindex-starter__cap_6","uri":"capability://search.retrieval.hybrid.retrieval.combining.vector.and.keyword.search","name":"hybrid retrieval combining vector and keyword search","description":"Template implementing hybrid retrieval that combines semantic vector search with keyword/BM25 search to improve recall and precision. Uses LlamaIndex's retriever composition to run both vector and keyword queries in parallel, ranks results using configurable fusion strategies (RRF, weighted scoring), and returns a merged result set. Enables fallback to keyword search when vector search fails and vice versa, improving robustness across different query types.","intents":["I want to improve retrieval quality by combining semantic and keyword-based search to handle both conceptual and exact-match queries","I need a more robust retrieval system that doesn't fail when vector embeddings don't capture query intent","I'm building a search system that needs to handle both natural language queries and specific keyword searches"],"best_for":["teams building production RAG systems requiring high recall","developers optimizing search quality for domain-specific vocabularies","builders creating search systems that must handle diverse query types"],"limitations":["Hybrid retrieval is slower than single-method retrieval (2x latency for parallel queries); requires optimization for real-time applications","Fusion strategy tuning is manual; no automated method for finding optimal weights or ranking strategies","Keyword search quality depends on document preprocessing (tokenization, stemming); requires careful configuration for non-English languages","No built-in handling of query expansion or synonym matching; keyword search may miss semantically equivalent terms"],"requires":["Python 3.8+","LlamaIndex library with hybrid retriever support","Vector store backend (Pinecone, Weaviate, Chroma, etc.)","BM25 or keyword search backend (built-in or external)","Indexed document collection"],"input_types":["natural language queries (text)"],"output_types":["ranked retrieval results (merged from vector and keyword search)","relevance scores (per retrieval method and fused score)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llamaindex-starter__cap_7","uri":"capability://search.retrieval.metadata.filtering.and.faceted.retrieval","name":"metadata filtering and faceted retrieval","description":"Template using LlamaIndex's metadata filtering capabilities to enable retrieval constrained by document metadata (date ranges, categories, source, author, etc.). Defines metadata schemas, attaches metadata to indexed nodes, and uses metadata filters in retrieval queries to narrow search space. Supports complex filter expressions (AND, OR, NOT) and enables faceted search where users can filter by multiple metadata dimensions simultaneously.","intents":["I want to enable users to filter search results by document properties (date, category, source) without re-indexing","I need to implement faceted search where users can narrow results across multiple dimensions","I'm building a system that must respect document access controls or organizational boundaries during retrieval"],"best_for":["teams building search systems with complex filtering requirements","developers implementing multi-tenant RAG systems with access control","builders creating faceted search interfaces for large document collections"],"limitations":["Metadata filtering reduces retrieval speed; complex filter expressions may require full index scans","No built-in metadata extraction; metadata must be manually attached or extracted via separate pipeline","Filter expressions are vector-store-specific; moving between backends may require rewriting filter logic","No automatic metadata indexing or optimization; requires manual tuning for high-cardinality metadata fields"],"requires":["Python 3.8+","LlamaIndex library with metadata filtering support","Vector store with metadata filtering capability (Pinecone, Weaviate, Chroma, etc.)","Indexed documents with metadata attached"],"input_types":["natural language queries (text)","metadata filter expressions (structured filter objects)"],"output_types":["filtered retrieval results (documents matching query and metadata constraints)","facet counts (number of results per metadata value)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llamaindex-starter__cap_8","uri":"capability://text.generation.language.query.transformation.and.expansion.for.improved.retrieval","name":"query transformation and expansion for improved retrieval","description":"Template implementing query preprocessing techniques (expansion, rewriting, decomposition) to improve retrieval quality. Uses LlamaIndex's query transformation modules to generate multiple query variants (synonyms, paraphrases, sub-questions), retrieves results for each variant, and merges results. Handles query decomposition for complex multi-part questions, enabling the system to retrieve context for each sub-question separately before synthesis.","intents":["I want to improve retrieval for complex or ambiguous queries by breaking them into sub-questions","I need to handle query variations (synonyms, paraphrases) to improve recall without modifying the index","I'm building a system that can understand implicit context in user queries and expand them automatically"],"best_for":["teams optimizing retrieval quality for complex or ambiguous queries","developers building systems that must handle diverse query phrasings","builders creating conversational search systems with implicit context"],"limitations":["Query transformation adds latency (multiple LLM calls per query); not suitable for real-time applications without caching","Transformation quality depends on LLM capability; may generate irrelevant or contradictory query variants","No built-in evaluation of transformation quality; requires manual inspection or downstream task metrics","Query decomposition can fail for open-ended or exploratory queries that don't have clear sub-questions"],"requires":["Python 3.8+","LlamaIndex library with query transformation support","LLM API key (GPT-4 or Claude recommended for complex transformations)","Indexed document collection"],"input_types":["natural language queries (text)"],"output_types":["transformed queries (list of query variants)","merged retrieval results (combined from all query variants)","query decomposition (sub-questions and their results)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llamaindex-starter__cap_9","uri":"capability://text.generation.language.response.synthesis.with.source.attribution.and.citations","name":"response synthesis with source attribution and citations","description":"Template implementing response synthesis that generates LLM answers while tracking and attributing source documents. Uses LlamaIndex's response synthesizer to combine retrieved context with LLM generation, maintains source-to-content mappings, and generates citations or footnotes in the final response. Supports multiple synthesis modes (refine, compact, tree-summarize) with different trade-offs between quality and token usage.","intents":["I want to generate answers that include citations to source documents for transparency and fact-checking","I need to synthesize answers from multiple retrieved documents while maintaining source attribution","I'm building a system where users can verify answer sources by clicking on citations"],"best_for":["teams building RAG systems requiring transparency and auditability","developers creating research or knowledge work tools with source tracking","builders implementing systems where answer provenance is critical (legal, medical, compliance)"],"limitations":["Citation accuracy depends on LLM's ability to track sources; hallucinated citations are possible","Different synthesis modes have different citation quality; tree-summarize may lose fine-grained source tracking","No built-in validation that citations actually support the claims made; requires manual review","Citation format is not standardized; requires custom code to format citations for different output formats (HTML, Markdown, PDF)"],"requires":["Python 3.8+","LlamaIndex library with response synthesizer support","LLM API key (GPT-4 or Claude recommended for accurate source tracking)","Retrieved context with source metadata"],"input_types":["retrieved documents with metadata (LlamaIndex Node objects)","user query (text)"],"output_types":["synthesized response with citations (text with source references)","source attribution metadata (document IDs, page numbers, excerpts)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llamaindex-starter__headline","uri":"capability://rag.knowledge.llamaindex.starter.templates","name":"llamaindex starter templates","description":"A collection of starter templates for LlamaIndex designed to simplify common use cases like document Q&A, chat with data, and structured data extraction, making it easier for developers to implement RAG solutions.","intents":["best RAG framework for document Q&A","LlamaIndex templates for chat with data","starter templates for structured data extraction","multi-document agent templates for LlamaIndex","how to use LlamaIndex for data extraction"],"best_for":["developers looking for quick setup"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["rag-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","LlamaIndex library (latest version)","API key for LLM provider (OpenAI, Anthropic, or local Ollama instance)","Vector store backend (Pinecone, Weaviate, Chroma, or in-memory SimpleVectorStore)","Document files in supported formats (PDF, TXT, Markdown, DOCX)","LlamaIndex library with chat engine support","LLM API key (OpenAI, Anthropic, or local model)","Indexed document collection (from Document Q&A template or custom setup)","Optional: external database for conversation persistence (PostgreSQL, MongoDB, etc.)","LlamaIndex library with evaluation support"],"failure_modes":["Template assumes single-document or small-scale collections; scaling to millions of documents requires custom index partitioning strategy","Default chunking strategy (fixed size) may not preserve semantic boundaries in domain-specific documents; requires manual tuning for technical/legal content","No built-in handling for document versioning or incremental updates; full re-indexing required for document changes","Retrieval quality depends heavily on embedding model choice and chunk size; template provides no automated optimization guidance","Conversation history grows unbounded; template does not implement automatic summarization or pruning, leading to token budget exhaustion on long conversations","No built-in session persistence; conversation state is in-memory and lost on process restart without external database integration","Context retrieval happens per-turn without conversation-level optimization; may retrieve redundant or conflicting information across turns","No native support for multi-user sessions or conversation branching; requires custom state management for complex interaction patterns","Automated metrics (BLEU, ROUGE, embedding similarity) don't always correlate with human judgment; requires manual evaluation for validation","Evaluation requires ground-truth datasets with human annotations; creating these datasets is expensive and time-consuming","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":0.75,"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=llamaindex-starter","compare_url":"https://unfragile.ai/compare?artifact=llamaindex-starter"}},"signature":"6pW55ZrpO9Niv79khz3YOqVaejWedESeLWB97mQ8oapEX4vhUS4HGgc7oHDijEa8cfNkIJiel0h5al5veRqYDA==","signedAt":"2026-06-21T16:01:04.474Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llamaindex-starter","artifact":"https://unfragile.ai/llamaindex-starter","verify":"https://unfragile.ai/api/v1/verify?slug=llamaindex-starter","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"}}