{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-llamaindex","slug":"llamaindex","name":"LlamaIndex","type":"framework","url":"https://www.llamaindex.ai/","page_url":"https://unfragile.ai/llamaindex","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-llamaindex__cap_0","uri":"capability://data.processing.analysis.multi.format.document.ingestion.and.parsing","name":"multi-format document ingestion and parsing","description":"Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.","intents":["I need to load PDFs, CSVs, and web pages into a single pipeline without writing custom parsers","I want to preserve document metadata and structure while converting to a common format","I need to handle multiple file types in batch without conditional logic per format"],"best_for":["teams building RAG systems over heterogeneous data sources","developers prototyping document-based LLM applications quickly","enterprises migrating legacy document stores to LLM-powered search"],"limitations":["Parser accuracy varies by format — complex PDF layouts may lose structural information","Large files (>100MB) require streaming loaders to avoid memory exhaustion","Metadata extraction is best-effort and format-dependent; custom extraction logic often needed","No built-in OCR for scanned PDFs — requires external service integration"],"requires":["Python 3.8+","pypdf, python-docx, or other format-specific libraries depending on document types","For remote sources: network access and appropriate API credentials"],"input_types":["PDF files","Word documents (.docx)","HTML/Markdown text","CSV/JSON structured data","Code files (Python, JavaScript, etc.)","Database query results","Web URLs"],"output_types":["Document objects with content and metadata","Structured node representations","Indexed vector embeddings"],"categories":["data-processing-analysis","document-ingestion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_1","uri":"capability://data.processing.analysis.intelligent.document.chunking.and.node.splitting","name":"intelligent document chunking and node splitting","description":"Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.","intents":["I need to split long documents into chunks that fit my LLM's context window without losing semantic coherence","I want to preserve parent-child relationships between chunks for hierarchical retrieval","I need to avoid splitting in the middle of important semantic units (code functions, paragraphs, sections)"],"best_for":["RAG pipeline builders optimizing retrieval quality and context preservation","developers building hierarchical document retrieval systems","teams tuning chunk size for specific embedding models and LLM context limits"],"limitations":["Semantic splitting requires embedding calls upfront, adding latency (~100-500ms per document depending on size)","Recursive splitting may produce uneven chunk sizes if document structure is irregular","No automatic detection of optimal chunk size — requires manual tuning per domain/model","Metadata propagation to child nodes is manual; custom logic needed for complex hierarchies"],"requires":["Python 3.8+","tiktoken or similar tokenizer for token-aware splitting","For semantic splitting: embedding model API access (OpenAI, Hugging Face, local)"],"input_types":["Document objects","Raw text strings","Structured node hierarchies"],"output_types":["Node objects with chunk content, metadata, and parent/child relationships","Indexed node collections ready for embedding"],"categories":["data-processing-analysis","text-chunking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_10","uri":"capability://image.visual.multi.modal.document.understanding","name":"multi-modal document understanding","description":"Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.","intents":["I need to extract information from documents with images, charts, and tables","I want to understand visual content in PDFs and web pages for better retrieval","I need to preserve table structure and code formatting while extracting text"],"best_for":["teams building RAG systems over documents with rich visual content","developers processing technical documentation with code and diagrams","builders creating document understanding systems for financial reports, research papers"],"limitations":["Vision model API calls add significant latency (500ms-2s per image)","Table extraction accuracy varies by complexity — nested tables and merged cells often fail","Code extraction requires language-specific parsing — generic approaches lose structure","Multi-modal fusion is heuristic-based — no automatic optimization of modality weighting"],"requires":["Python 3.8+","Vision model API access (OpenAI GPT-4V, Claude, etc.) or local vision model","Table extraction library (e.g., pdf-plumber, camelot)","Code parser for language-specific extraction"],"input_types":["PDF documents with images and text","Web pages with mixed content","Documents with tables and code blocks"],"output_types":["Extracted text with image descriptions","Structured table data","Code blocks with language metadata","Unified document representation"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_11","uri":"capability://text.generation.language.streaming.and.real.time.response.generation","name":"streaming and real-time response generation","description":"Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.","intents":["I need to show users partial answers immediately instead of waiting for full generation","I want to stream retrieved documents as they're found instead of batching results","I need to implement cancellation so users can stop long-running queries"],"best_for":["teams building interactive chat interfaces with responsive UX","developers implementing real-time search with progressive result display","builders creating streaming APIs for RAG systems"],"limitations":["Streaming adds complexity to error handling — failures mid-stream are harder to recover from","Backpressure handling requires careful buffer management to avoid memory exhaustion","Streaming latency depends on network and LLM response time — can't be optimized below LLM latency","Client-side buffering and rendering adds complexity for web/mobile applications"],"requires":["Python 3.8+","LLM API with streaming support (OpenAI, Anthropic, etc.)","Async/await support in application code","Client-side streaming support (WebSocket, Server-Sent Events, etc.)"],"input_types":["Query text","Streaming configuration (chunk size, timeout, etc.)"],"output_types":["Token stream (generator/async iterator)","Partial response chunks","Real-time retrieval updates"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_12","uri":"capability://automation.workflow.cost.tracking.and.optimization.for.llm.operations","name":"cost tracking and optimization for llm operations","description":"Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.","intents":["I need to understand how much my RAG system costs to operate per query","I want to optimize costs by choosing cheaper models or batching operations","I need to track costs per user or project for billing and budget management"],"best_for":["teams deploying RAG systems at scale with cost concerns","developers optimizing LLM costs for production systems","builders implementing cost-aware query planning and model selection"],"limitations":["Cost tracking is approximate — actual costs depend on provider billing details","Cost optimization recommendations are heuristic-based and may not apply to all use cases","No automatic cost-quality tradeoff optimization — requires manual tuning","Cost data requires API access to provider billing APIs which may have latency"],"requires":["Python 3.8+","LLM API keys with cost tracking support","Optional: provider billing API access for real-time cost data"],"input_types":["LLM API calls with model and token counts","Embedding operations","Query execution traces"],"output_types":["Cost per query","Cost per session","Cost optimization recommendations","Cost reports and analytics"],"categories":["automation-workflow","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_13","uri":"capability://automation.workflow.customizable.pipeline.composition.and.workflow.orchestration","name":"customizable pipeline composition and workflow orchestration","description":"Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.","intents":["I need to build a custom RAG pipeline combining multiple retrieval and generation strategies","I want to implement complex workflows with conditional branching based on query type","I need to compose reusable pipeline components without writing boilerplate orchestration code"],"best_for":["teams building production RAG systems with custom requirements","developers implementing complex multi-stage retrieval and generation workflows","builders creating reusable RAG components for internal platforms"],"limitations":["Pipeline composition adds complexity — debugging multi-stage pipelines is harder than simple RAG","Dependency resolution can be opaque — unexpected execution order may occur","No automatic optimization of pipeline execution — manual tuning needed for performance","Error handling across pipeline stages requires careful design to avoid cascading failures"],"requires":["Python 3.8+","Understanding of pipeline composition patterns","Component implementations (retrievers, synthesizers, etc.)"],"input_types":["Pipeline configuration (declarative or programmatic)","Component definitions","Input data (queries, documents, etc.)"],"output_types":["Pipeline execution results","Execution traces and logs","Deployed pipeline APIs"],"categories":["automation-workflow","pipeline-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_2","uri":"capability://memory.knowledge.embedding.generation.and.vector.storage.abstraction","name":"embedding generation and vector storage abstraction","description":"Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.","intents":["I need to embed documents once and store them in a vector database for semantic search without vendor lock-in","I want to switch between cloud and local vector stores without rewriting my retrieval logic","I need to batch embed large document collections efficiently with automatic retry and error handling"],"best_for":["teams building production RAG systems with multi-vector-store support","developers prototyping with local embeddings before moving to cloud","enterprises requiring flexibility to switch vector database providers"],"limitations":["Embedding generation cost scales with document volume — no built-in deduplication or caching across runs","Vector store abstraction adds ~50-100ms latency per query due to interface indirection","Metadata filtering capabilities vary by backend — some stores support rich filtering, others only basic equality","No built-in vector store replication or failover — requires external infrastructure"],"requires":["Python 3.8+","Embedding model API key (OpenAI, Hugging Face) or local model weights","Vector store client library (pinecone-client, weaviate-client, etc.) depending on chosen backend","For local embeddings: sufficient GPU memory or CPU for inference"],"input_types":["Node objects with text content","Raw text strings","Document collections"],"output_types":["Vector embeddings (float arrays)","Indexed vector store with metadata","Retrieval results with similarity scores"],"categories":["memory-knowledge","vector-storage"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_3","uri":"capability://search.retrieval.semantic.search.and.retrieval.with.ranking","name":"semantic search and retrieval with ranking","description":"Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.","intents":["I need to find the most relevant documents for a query without exact keyword matching","I want to combine semantic search with keyword filtering to improve precision","I need to re-rank retrieved results using a cross-encoder or LLM before passing to generation"],"best_for":["RAG system builders optimizing retrieval quality and relevance","teams implementing hybrid search combining dense and sparse retrieval","developers building multi-stage retrieval pipelines with re-ranking"],"limitations":["Retrieval quality depends heavily on embedding model quality — poor embeddings lead to poor results","Re-ranking adds latency (50-200ms per query for LLM-based re-ranking)","Hybrid search requires maintaining both dense and sparse indices, increasing storage overhead","No automatic query expansion or reformulation — requires manual prompt engineering"],"requires":["Python 3.8+","Populated vector store with embeddings","For re-ranking: LLM API access or local cross-encoder model","For hybrid search: BM25 or sparse retrieval index"],"input_types":["Query text (string)","Query embeddings (pre-computed)","Metadata filters (optional)"],"output_types":["Ranked list of Node objects with similarity scores","Retrieval results with metadata and content"],"categories":["search-retrieval","ranking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_4","uri":"capability://planning.reasoning.query.transformation.and.expansion","name":"query transformation and expansion","description":"Automatically transforms user queries to improve retrieval quality through techniques like query expansion (generating multiple query variants), decomposition (breaking complex queries into sub-queries), and rewriting (rephrasing for better embedding alignment). Uses LLM-based transformations with configurable prompts and supports both single-stage and multi-stage query processing pipelines.","intents":["I need to improve retrieval for ambiguous or complex queries by generating multiple search variants","I want to decompose a multi-part question into sub-questions and retrieve answers for each","I need to rephrase user queries to better match my document embeddings"],"best_for":["RAG builders optimizing for complex, multi-part user queries","teams implementing advanced query understanding before retrieval","developers building conversational search systems with query refinement"],"limitations":["Query transformation adds latency (100-500ms per query depending on LLM and strategy)","Expansion can retrieve irrelevant results if generated queries diverge from user intent","Requires careful prompt engineering to avoid hallucinated or nonsensical query variants","No automatic evaluation of transformation quality — manual testing needed per domain"],"requires":["Python 3.8+","LLM API access (OpenAI, Anthropic, etc.) or local LLM","Retrieval system (vector store + embeddings) for executing transformed queries"],"input_types":["User query text","Query context (conversation history, optional)"],"output_types":["Transformed query string","Multiple query variants","Sub-queries with decomposition structure"],"categories":["planning-reasoning","query-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_5","uri":"capability://text.generation.language.context.aware.response.generation.with.source.attribution","name":"context-aware response generation with source attribution","description":"Generates LLM responses grounded in retrieved documents, with automatic source attribution and citation tracking. Supports multiple generation modes (simple context injection, chain-of-thought, multi-step reasoning) and enables custom response synthesis through a pluggable ResponseSynthesizer interface. Tracks which source documents contributed to each response for transparency and fact-checking.","intents":["I need to generate answers grounded in my documents with citations showing where information came from","I want to use retrieved context to improve LLM responses without hallucination","I need to implement multi-step reasoning over retrieved documents before generating final answers"],"best_for":["RAG system builders implementing fact-grounded generation","teams building customer-facing QA systems requiring source attribution","developers implementing multi-step reasoning over document collections"],"limitations":["Citation accuracy depends on LLM's ability to track sources — hallucinated citations are possible","Multi-step reasoning adds latency (multiple LLM calls per query)","Context injection can exceed LLM token limits with large retrieved document sets","No automatic fact-checking or consistency validation across generated responses"],"requires":["Python 3.8+","LLM API access (OpenAI, Anthropic, etc.) or local LLM","Retrieved documents/context from retrieval system"],"input_types":["User query","Retrieved document nodes with content and metadata","Conversation history (optional)"],"output_types":["Generated response text","Source attribution metadata","Citation references to source documents"],"categories":["text-generation-language","response-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_6","uri":"capability://planning.reasoning.agent.based.reasoning.and.tool.orchestration","name":"agent-based reasoning and tool orchestration","description":"Enables LLM agents to reason over multiple steps, decide which tools to use, and execute actions autonomously. Agents can call retrieval tools, external APIs, code execution, and other functions based on LLM reasoning. Supports multiple agent architectures (ReAct, function-calling, custom) with automatic tool binding, error handling, and execution tracing for debugging.","intents":["I need to build an autonomous agent that can decide whether to search documents, call APIs, or perform calculations","I want to implement multi-step reasoning where the agent refines its approach based on intermediate results","I need to enable agents to use retrieval, external tools, and code execution in a unified framework"],"best_for":["developers building autonomous LLM agents with complex reasoning","teams implementing multi-step workflows combining retrieval and external APIs","builders creating interactive agents that refine answers based on tool feedback"],"limitations":["Agent reasoning is non-deterministic — same query may produce different tool sequences","Tool selection errors can cascade (agent calls wrong tool, gets bad data, makes worse decisions)","Execution tracing and debugging can be complex with many tool calls and reasoning steps","Cost scales with number of LLM calls — agents often require 3-10 calls per query vs 1 for simple RAG"],"requires":["Python 3.8+","LLM with function-calling support (OpenAI, Anthropic, etc.) or ReAct-compatible model","Tool implementations (retrieval, APIs, code execution, etc.)","Error handling and retry logic for tool failures"],"input_types":["User query/task","Tool definitions (schema, description, implementation)","Conversation history (optional)"],"output_types":["Final agent response","Execution trace with tool calls and results","Intermediate reasoning steps"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_7","uri":"capability://memory.knowledge.memory.and.conversation.context.management","name":"memory and conversation context management","description":"Manages conversation history and context across multiple turns, with support for different memory types (buffer, summary, hybrid) and automatic context window optimization. Stores conversation state in memory backends (in-memory, persistent storage) and enables selective context retrieval to fit LLM token limits while preserving important information.","intents":["I need to maintain conversation history across multiple turns without exceeding LLM token limits","I want to summarize old conversation turns to preserve context while freeing tokens","I need to retrieve relevant past context based on semantic similarity to current query"],"best_for":["developers building multi-turn conversational AI systems","teams implementing long-running chatbots with memory constraints","builders creating context-aware assistants that reference past interactions"],"limitations":["Summary-based memory loses fine-grained details from earlier turns","Semantic context retrieval adds latency (embedding + retrieval per turn)","No automatic evaluation of memory quality — manual testing needed to ensure important context is preserved","Persistent memory requires external storage backend; in-memory only suitable for single-session use"],"requires":["Python 3.8+","LLM API access for summarization (if using summary memory)","Embedding model and vector store (if using semantic memory retrieval)","Persistent storage backend (database, file system) for multi-session memory"],"input_types":["Conversation messages (user and assistant)","Message metadata (timestamp, role, etc.)"],"output_types":["Conversation history (full or summarized)","Retrieved relevant context","Optimized context for LLM input"],"categories":["memory-knowledge","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_8","uri":"capability://data.processing.analysis.structured.data.extraction.and.schema.based.output","name":"structured data extraction and schema-based output","description":"Extracts structured data from unstructured text using LLM-based extraction with schema validation and type coercion. Supports Pydantic models, JSON schemas, and custom output formats with automatic parsing, error handling, and retry logic. Enables reliable structured output from LLMs without manual parsing or validation code.","intents":["I need to extract structured entities (names, dates, amounts) from documents reliably","I want to validate LLM output against a schema before using it in downstream systems","I need to convert unstructured text into structured data for database insertion or API calls"],"best_for":["teams building data extraction pipelines from documents","developers implementing structured output from LLMs for downstream systems","builders creating knowledge extraction systems with schema validation"],"limitations":["Extraction accuracy depends on schema clarity and LLM capability — ambiguous schemas lead to errors","Complex nested schemas may exceed LLM's ability to generate valid output","Retry logic adds latency and cost if LLM fails to produce valid output","No automatic schema inference — schemas must be manually defined"],"requires":["Python 3.8+","LLM API access (OpenAI, Anthropic, etc.) with structured output support","Pydantic or JSON schema definitions for output validation"],"input_types":["Unstructured text","Document content","Extraction schema/template"],"output_types":["Pydantic model instances","JSON objects matching schema","Validated structured data"],"categories":["data-processing-analysis","structured-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llamaindex__cap_9","uri":"capability://data.processing.analysis.evaluation.and.metrics.for.rag.quality","name":"evaluation and metrics for rag quality","description":"Provides built-in evaluation metrics for RAG systems including retrieval quality (precision, recall, NDCG), generation quality (BLEU, ROUGE, semantic similarity), and end-to-end correctness. Supports both automated metrics and human evaluation workflows, with integration to evaluation datasets and benchmarks for systematic quality assessment.","intents":["I need to measure whether my retrieval system is finding relevant documents","I want to evaluate if my generated answers are factually correct and grounded in sources","I need to benchmark my RAG system against baselines and track quality improvements over time"],"best_for":["RAG system builders optimizing retrieval and generation quality","teams implementing continuous evaluation and monitoring","developers benchmarking RAG approaches before production deployment"],"limitations":["Automated metrics don't always correlate with human judgment — manual evaluation still needed","Evaluation requires ground truth labels (relevant documents, correct answers) which are expensive to create","Metrics are domain-specific — generic metrics may not capture domain-relevant quality","No built-in A/B testing framework — requires external infrastructure for production evaluation"],"requires":["Python 3.8+","Evaluation dataset with ground truth labels","LLM API access for semantic similarity metrics","Optional: human annotators for manual evaluation"],"input_types":["Query-document pairs","Query-answer pairs","Ground truth labels (relevant documents, correct answers)"],"output_types":["Evaluation metrics (precision, recall, NDCG, BLEU, ROUGE, etc.)","Evaluation reports with visualizations","Quality scores and rankings"],"categories":["data-processing-analysis","evaluation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","pypdf, python-docx, or other format-specific libraries depending on document types","For remote sources: network access and appropriate API credentials","tiktoken or similar tokenizer for token-aware splitting","For semantic splitting: embedding model API access (OpenAI, Hugging Face, local)","Vision model API access (OpenAI GPT-4V, Claude, etc.) or local vision model","Table extraction library (e.g., pdf-plumber, camelot)","Code parser for language-specific extraction","LLM API with streaming support (OpenAI, Anthropic, etc.)","Async/await support in application code"],"failure_modes":["Parser accuracy varies by format — complex PDF layouts may lose structural information","Large files (>100MB) require streaming loaders to avoid memory exhaustion","Metadata extraction is best-effort and format-dependent; custom extraction logic often needed","No built-in OCR for scanned PDFs — requires external service integration","Semantic splitting requires embedding calls upfront, adding latency (~100-500ms per document depending on size)","Recursive splitting may produce uneven chunk sizes if document structure is irregular","No automatic detection of optimal chunk size — requires manual tuning per domain/model","Metadata propagation to child nodes is manual; custom logic needed for complex hierarchies","Vision model API calls add significant latency (500ms-2s per image)","Table extraction accuracy varies by complexity — nested tables and merged cells often fail","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"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":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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","compare_url":"https://unfragile.ai/compare?artifact=llamaindex"}},"signature":"EE6ZRYwxlPRRJ6In5xP++vh0NXjFDWwA/meb3utus6luUHE2ZQp3dLgTlk3GDH4GcKJvQ67DnZP8Z3B2Dk9QBw==","signedAt":"2026-06-23T11:20:23.101Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llamaindex","artifact":"https://unfragile.ai/llamaindex","verify":"https://unfragile.ai/api/v1/verify?slug=llamaindex","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"}}