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The CLI uses a state machine pattern to guide users through sequential configuration steps, validating inputs against supported LlamaIndex integrations and outputting structured YAML or JSON configuration files that can be directly consumed by LlamaIndex initialization code.","intents":["I want to quickly scaffold a RAG pipeline without manually writing boilerplate LlamaIndex configuration code","I need to experiment with different vector store and LLM combinations for my retrieval system","I want a guided setup process that explains which configuration options are available for my use case"],"best_for":["developers building RAG applications who want to avoid manual configuration boilerplate","teams prototyping multiple LlamaIndex pipeline variants quickly","non-expert users new to LlamaIndex who need guided configuration"],"limitations":["Limited to LlamaIndex-supported integrations — cannot configure custom or proprietary vector stores outside the LlamaIndex ecosystem","Interactive CLI requires terminal access — not suitable for programmatic/headless configuration generation","No validation of API credentials or connectivity during configuration generation — generated configs may fail at runtime if services are unavailable","Configuration scope limited to common RAG patterns — advanced multi-stage pipelines or custom retrieval logic require manual editing post-generation"],"requires":["Node.js 14+ (npm package registry)","Terminal/CLI environment with TTY support for interactive prompts","Basic understanding of RAG concepts (vector stores, embeddings, LLMs)"],"input_types":["user selections from prompted multiple-choice questions","text input for API keys or custom parameters","optional existing LlamaIndex config file for modification"],"output_types":["YAML configuration file","JSON configuration file","TypeScript/JavaScript code snippet for LlamaIndex initialization"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llamaindex-config-cli__cap_1","uri":"capability://memory.knowledge.multi.provider.vector.store.selection","name":"multi-provider-vector-store-selection","description":"Presents users with a curated list of vector store options (Pinecone, Weaviate, Milvus, Chroma, etc.) compatible with LlamaIndex and generates provider-specific initialization code with correct parameter mappings. 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The CLI maps user-friendly parameter selections (e.g., 'creative' vs 'precise') to provider-specific hyperparameter ranges and generates code that instantiates LlamaIndex LLM wrappers (OpenAI, Anthropic, HuggingFace, etc.) with correct argument signatures.","intents":["I want to quickly switch between different LLM providers without rewriting initialization code","I need to generate correct LLM configuration for my RAG pipeline with sensible defaults for my use case","I want to understand which parameters are available for my chosen LLM provider"],"best_for":["developers building multi-model RAG systems that support provider switching","teams experimenting with different LLM providers (OpenAI vs Anthropic vs open-source)","engineers optimizing LLM costs by comparing provider configurations"],"limitations":["Parameter mappings are approximate — provider-specific tuning (e.g., OpenAI's top_p vs Anthropic's top_k) may require manual adjustment","Does not validate API key validity or model availability at generation time","Limited to LlamaIndex's built-in LLM wrappers — custom LLM providers require manual code after generation","No cost estimation or latency profiling — generated configs may have suboptimal performance characteristics"],"requires":["Node.js 14+","API key for selected LLM provider (OpenAI, Anthropic, etc.)","LlamaIndex 0.8.0+"],"input_types":["user selection of LLM provider (OpenAI, Anthropic, Ollama, HuggingFace, etc.)","model name/version selection","parameter presets (creative, balanced, precise) or custom values","optional system prompt or instructions"],"output_types":["TypeScript/JavaScript code for LLM initialization","YAML configuration with provider-specific parameters","Environment variable template with required API keys"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llamaindex-config-cli__cap_3","uri":"capability://data.processing.analysis.document.loader.integration.selection","name":"document-loader-integration-selection","description":"Guides users through selection of document loaders (PDF, CSV, markdown, web, database) compatible with LlamaIndex and generates initialization code with correct file path or connection parameters. The CLI presents loader options filtered by file type or data source, collects source-specific parameters (file paths, database credentials, web URLs), and outputs LlamaIndex SimpleDirectoryReader or provider-specific loader instantiation code.","intents":["I want to load documents from various sources (PDFs, CSVs, web pages) into my RAG pipeline without writing custom loader code","I need to understand which document loaders are available in LlamaIndex for my data source","I want to generate correct initialization code for my chosen document loader"],"best_for":["developers building RAG systems over heterogeneous data sources","teams ingesting documents from multiple formats (PDF, CSV, markdown, web)","non-technical users who need to load documents without writing custom code"],"limitations":["Limited to LlamaIndex's built-in document loaders — custom data sources require manual loader implementation","Does not validate file paths or data source connectivity at generation time","No built-in document preprocessing (chunking, cleaning) — generated loaders output raw documents requiring separate processing","Large file handling not optimized — no streaming or batch processing configuration"],"requires":["Node.js 14+","Access to data sources (file paths, database credentials, API keys for web sources)","LlamaIndex 0.8.0+"],"input_types":["user selection of document loader type (PDF, CSV, markdown, web, database, etc.)","source-specific parameters (file paths, URLs, database connection strings)","optional metadata or filtering criteria"],"output_types":["TypeScript/JavaScript code for document loader initialization","YAML configuration with loader-specific parameters","Example code for iterating over loaded documents"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llamaindex-config-cli__cap_4","uri":"capability://memory.knowledge.embedding.model.configuration","name":"embedding-model-configuration","description":"Generates embedding model configuration for LlamaIndex with support for multiple providers (OpenAI, HuggingFace, local models, Ollama) and outputs correct initialization code with model selection and parameter tuning. The CLI collects embedding model preferences (provider, model name, dimension), validates compatibility with selected vector store, and generates LlamaIndex ServiceContext or embedding model instantiation code.","intents":["I want to choose an embedding model that works with my vector store and LLM provider","I need to generate correct embedding model initialization code for LlamaIndex","I want to understand the trade-offs between different embedding models (cost, latency, quality)"],"best_for":["developers optimizing RAG pipeline performance by tuning embedding models","teams evaluating embedding model providers (OpenAI vs open-source)","engineers building cost-optimized RAG systems with local embeddings"],"limitations":["Does not validate embedding model compatibility with vector store dimensions — generated code may fail if dimensions mismatch","No built-in performance benchmarking — users must manually test embedding quality","Limited to LlamaIndex's supported embedding providers — custom embedding models require manual integration","No cost estimation for embedding API calls"],"requires":["Node.js 14+","API key for cloud embedding providers (OpenAI, etc.) if selected","LlamaIndex 0.8.0+"],"input_types":["user selection of embedding provider (OpenAI, HuggingFace, Ollama, local)","model name/version selection","optional dimension specification for vector store compatibility"],"output_types":["TypeScript/JavaScript code for embedding model initialization","YAML configuration with embedding model parameters","Environment variable template for API keys"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llamaindex-config-cli__cap_5","uri":"capability://search.retrieval.retrieval.strategy.configuration","name":"retrieval-strategy-configuration","description":"Generates configuration for different retrieval strategies (BM25, semantic search, hybrid, re-ranking) with LlamaIndex-compatible code that sets up query engines and retrieval parameters. The CLI presents retrieval strategy options, collects strategy-specific parameters (similarity threshold, top-k results, re-ranker model), and outputs LlamaIndex QueryEngine or Retriever instantiation code with correct configuration.","intents":["I want to configure different retrieval strategies (semantic vs BM25 vs hybrid) for my RAG pipeline","I need to understand which retrieval strategy is best for my use case","I want to generate correct LlamaIndex QueryEngine code with my chosen retrieval strategy"],"best_for":["developers optimizing RAG retrieval quality by experimenting with different strategies","teams building hybrid search systems combining multiple retrieval methods","engineers tuning retrieval parameters (top-k, similarity threshold) for their domain"],"limitations":["Limited to LlamaIndex's built-in retrieval strategies — custom retrieval logic requires manual implementation","No built-in evaluation metrics for retrieval quality — users must manually assess retrieval performance","Re-ranking configuration limited to LlamaIndex-supported re-rankers — custom re-rankers require manual integration","No guidance on optimal parameter selection for specific domains"],"requires":["Node.js 14+","LlamaIndex 0.8.0+","Optional: API key for re-ranking services (Cohere, etc.)"],"input_types":["user selection of retrieval strategy (semantic, BM25, hybrid, etc.)","strategy-specific parameters (similarity threshold, top-k, re-ranker model)","optional query preprocessing options"],"output_types":["TypeScript/JavaScript code for QueryEngine or Retriever initialization","YAML configuration with retrieval strategy parameters","Example code for executing queries with the configured retriever"],"categories":["search-retrieval","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llamaindex-config-cli__cap_6","uri":"capability://automation.workflow.configuration.file.export.and.validation","name":"configuration-file-export-and-validation","description":"Exports generated RAG pipeline configuration to multiple formats (YAML, JSON, TypeScript) and performs basic validation to ensure configuration consistency (vector store dimensions match embedding model, LLM context windows are sufficient for retrieval context, etc.). The CLI writes configuration files to disk with sensible defaults and generates environment variable templates for API keys and secrets.","intents":["I want to save my RAG configuration to a file that I can version control and share with my team","I need to validate that my configuration is internally consistent before deploying","I want to export my configuration in multiple formats for different use cases"],"best_for":["teams collaborating on RAG pipeline configuration and needing version control","developers building reproducible RAG systems with configuration-as-code","engineers deploying RAG pipelines across multiple environments"],"limitations":["Validation is limited to basic consistency checks — does not validate API credentials or service availability","No built-in configuration migration helpers for upgrading between LlamaIndex versions","File export does not include secrets — requires separate environment variable management","No configuration diffing or merge conflict resolution for collaborative editing"],"requires":["Node.js 14+","Write permissions to output directory","LlamaIndex 0.8.0+"],"input_types":["generated configuration from interactive CLI","optional existing configuration file for modification"],"output_types":["YAML configuration file","JSON configuration file","TypeScript configuration module",".env.example template with required API keys"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llamaindex-config-cli__cap_7","uri":"capability://automation.workflow.interactive.cli.state.machine.navigation","name":"interactive-cli-state-machine-navigation","description":"Implements an interactive CLI state machine that guides users through sequential configuration steps with context-aware question branching based on previous selections. The CLI uses a state machine pattern to manage question flow, validates user inputs before advancing to next state, provides help text and examples for each question, and allows users to navigate back to previous steps to modify selections.","intents":["I want a guided setup process that explains each configuration step and why it matters","I need to modify a previous configuration choice without restarting the entire questionnaire","I want to see examples and help text for each configuration option"],"best_for":["non-technical users new to RAG and LlamaIndex who need guided setup","developers who prefer interactive CLI over manual configuration files","teams onboarding new engineers to RAG pipeline configuration"],"limitations":["Interactive CLI requires terminal with TTY support — not suitable for headless or programmatic configuration","State machine complexity increases with number of configuration options — may become unwieldy for advanced use cases","No built-in configuration templates or presets for common use cases","Navigation history not persisted — restarting CLI loses previous selections"],"requires":["Node.js 14+","Terminal/CLI environment with TTY support","npm or yarn package manager"],"input_types":["user keyboard input (selections, text entry)","terminal control sequences (arrow keys, enter)"],"output_types":["terminal output with formatted questions and help text","configuration files (YAML, JSON, TypeScript)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ (npm package registry)","Terminal/CLI environment with TTY support for interactive prompts","Basic understanding of RAG concepts (vector stores, embeddings, LLMs)","Node.js 14+","API credentials for selected vector store (Pinecone API key, Weaviate URL, etc.)","LlamaIndex 0.8.0+ (for compatibility with generated initialization code)","API key for selected LLM provider (OpenAI, Anthropic, etc.)","LlamaIndex 0.8.0+","Access to data sources (file paths, database credentials, API keys for web sources)","API key for cloud embedding providers (OpenAI, etc.) if selected"],"failure_modes":["Limited to LlamaIndex-supported integrations — cannot configure custom or proprietary vector stores outside the LlamaIndex ecosystem","Interactive CLI requires terminal access — not suitable for programmatic/headless configuration generation","No validation of API credentials or connectivity during configuration generation — generated configs may fail at runtime if services are unavailable","Configuration scope limited to common RAG patterns — advanced multi-stage pipelines or custom retrieval logic require manual editing post-generation","Only supports vector stores with existing LlamaIndex integrations — custom or newly-released vector stores require manual configuration","Does not validate vector store credentials or connectivity — generated code may fail if API keys are invalid or services are down","Abstractions may hide provider-specific tuning options (e.g., Pinecone pod types, Weaviate sharding strategies) that require manual post-generation editing","No built-in migration helpers for switching between vector stores in existing pipelines","Parameter mappings are approximate — provider-specific tuning (e.g., OpenAI's top_p vs Anthropic's top_k) may require manual adjustment","Does not validate API key validity or model availability at generation time","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.04082399653118496,"quality":0.26,"ecosystem":0.55,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.474Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":128,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llamaindex-config-cli","compare_url":"https://unfragile.ai/compare?artifact=llamaindex-config-cli"}},"signature":"3eJ85p89N33MbX8zTcUqzf4qhe/COAANMEsGLou6JPYY6gS12+dNZTazs1pmUZ+XZHxkE9BuLQF7D+31D8K4Cw==","signedAt":"2026-06-15T14:12:26.919Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llamaindex-config-cli","artifact":"https://unfragile.ai/llamaindex-config-cli","verify":"https://unfragile.ai/api/v1/verify?slug=llamaindex-config-cli","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"}}