{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-llama-index-core","slug":"pypi-llama-index-core","name":"llama-index-core","type":"framework","url":"https://pypi.org/project/llama-index-core/","page_url":"https://unfragile.ai/pypi-llama-index-core","categories":["frameworks-sdks"],"tags":["LLM","NLP","RAG","data","devtools","index","retrieval"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-llama-index-core__cap_0","uri":"capability://data.processing.analysis.multi.source.document.ingestion.with.pluggable.readers","name":"multi-source document ingestion with pluggable readers","description":"Ingests documents from diverse sources (files, web, cloud APIs) through a modular reader architecture that abstracts source-specific logic. Each reader implements a common interface that normalizes heterogeneous data formats (PDF, markdown, HTML, JSON, databases) into a unified Document object with metadata preservation. The framework uses a registry pattern to discover and instantiate readers, enabling extensibility without core framework changes.","intents":["I need to load documents from multiple sources (local files, S3, web URLs, databases) into a single indexing pipeline","I want to extract structured metadata from documents during ingestion to preserve context for retrieval","I need to support custom document types without modifying the core framework"],"best_for":["teams building RAG systems with heterogeneous data sources","developers needing to ingest proprietary or custom document formats","enterprises integrating multiple data connectors (Notion, Google Drive, Salesforce, etc.)"],"limitations":["Reader implementations vary in robustness — some community readers lack error handling for edge cases","Large file ingestion (>100MB) may require streaming implementations not available for all readers","Metadata extraction quality depends on document structure; unstructured text loses contextual information"],"requires":["Python 3.9+","llama-index-core>=0.14.19","source-specific dependencies (e.g., boto3 for S3, google-cloud-storage for GCS)","appropriate API credentials or file system access"],"input_types":["local files (PDF, DOCX, TXT, MD, JSON, CSV)","web URLs (HTML, markdown)","cloud storage (S3, GCS, Azure Blob)","databases (SQL, NoSQL)","APIs (Notion, Google Drive, Slack, etc.)"],"output_types":["Document objects with content and metadata","structured Node objects ready for indexing"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_1","uri":"capability://data.processing.analysis.hierarchical.document.chunking.with.semantic.awareness","name":"hierarchical document chunking with semantic awareness","description":"Splits documents into chunks using multiple strategies (fixed-size, recursive, semantic) that preserve document structure and relationships. The NodeParser abstraction allows pluggable chunking logic; implementations include SimpleNodeParser (basic splitting), HierarchicalNodeParser (preserves heading hierarchy), and SemanticSplitter (uses embeddings to find natural boundaries). Chunk metadata includes parent-child relationships, document source, and custom attributes for context-aware retrieval.","intents":["I need to chunk documents while preserving hierarchical structure (sections, subsections) for better context in retrieval","I want to split documents semantically based on meaning rather than fixed token counts","I need to maintain relationships between chunks so I can retrieve full context when needed"],"best_for":["RAG systems processing long documents (research papers, books, technical documentation)","applications requiring hierarchical context (legal documents, specifications with nested sections)","teams optimizing for retrieval quality over raw chunk count"],"limitations":["SemanticSplitter requires embedding model calls for every document, adding 10-50ms per chunk depending on model","Hierarchical parsing assumes well-structured documents with clear heading markers; unstructured text falls back to simple splitting","Chunk size optimization is heuristic-based; optimal sizes vary by use case and embedding model"],"requires":["Python 3.9+","llama-index-core>=0.14.19","embedding model for SemanticSplitter (OpenAI, local, or custom)","document must be parsed into Document objects first"],"input_types":["Document objects with text content and metadata"],"output_types":["Node objects with chunk text, metadata, relationships, and embedding references"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_10","uri":"capability://text.generation.language.fine.tuning.system.for.model.adaptation","name":"fine-tuning system for model adaptation","description":"Provides utilities for fine-tuning LLMs on domain-specific data generated from RAG systems. The framework can generate synthetic training data from retrieval results, format it for fine-tuning APIs (OpenAI, Anthropic), and manage fine-tuning jobs. Fine-tuned models can be used as drop-in replacements in RAG pipelines, improving performance on domain-specific tasks without retraining from scratch. The system tracks fine-tuning experiments and enables comparison of base vs fine-tuned model performance.","intents":["I want to fine-tune an LLM on domain-specific data to improve RAG performance","I need to generate training data from my RAG system's retrieval results","I want to experiment with different fine-tuning approaches and compare results","I need to use fine-tuned models as drop-in replacements in my RAG pipeline"],"best_for":["teams with domain-specific RAG systems wanting to improve model performance","applications requiring specialized knowledge or writing style","organizations with budget for fine-tuning costs"],"limitations":["Fine-tuning requires significant training data (hundreds to thousands of examples); small datasets may overfit","Fine-tuning costs are substantial (OpenAI: $0.03-0.06 per 1K tokens); ROI depends on performance improvement","Fine-tuning is asynchronous; jobs can take hours to complete","Fine-tuned models are provider-specific; switching providers requires re-fine-tuning","No built-in evaluation framework; assessing fine-tuning impact requires custom benchmarks"],"requires":["Python 3.9+","llama-index-core>=0.14.19","LLM provider supporting fine-tuning (OpenAI, Anthropic, etc.)","training data (generated from RAG or provided manually)","API credentials and budget for fine-tuning"],"input_types":["training examples (query-response pairs from RAG)","fine-tuning parameters (learning rate, epochs, etc.)"],"output_types":["fine-tuned model ID","fine-tuning job status and metrics"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_11","uri":"capability://text.generation.language.structured.output.generation.with.schema.validation","name":"structured output generation with schema validation","description":"Enables LLMs to generate structured outputs (JSON, Pydantic models, dataclasses) with schema validation. The framework uses provider-specific structured output APIs (OpenAI JSON mode, Anthropic structured output) or LLM-based parsing with validation fallback. Output schemas are defined as Pydantic models or JSON schemas; the framework automatically formats prompts to guide LLM generation and validates outputs against schemas. Failed validations trigger retries with corrected prompts.","intents":["I need LLMs to generate structured JSON or Python objects, not just text","I want to validate LLM outputs against a schema and retry on validation failure","I need to extract structured data (entities, relationships, classifications) from documents","I want to use LLM outputs directly in downstream code without manual parsing"],"best_for":["applications requiring structured data extraction from LLM outputs","systems using LLM outputs in downstream code (APIs, databases, etc.)","teams needing reliable structured generation with validation"],"limitations":["Provider-specific structured output APIs have different capabilities and limitations","LLM-based parsing with validation fallback adds latency and cost for retries","Complex schemas may confuse LLMs; generation quality degrades with schema complexity","Validation failures don't guarantee successful retry; some schemas are inherently difficult for LLMs"],"requires":["Python 3.9+","llama-index-core>=0.14.19","LLM integration supporting structured output (OpenAI, Anthropic, etc.)","Pydantic or JSON schema definition"],"input_types":["prompt text","output schema (Pydantic model or JSON schema)"],"output_types":["structured output (Pydantic model instance, dict, or dataclass)","validation errors if schema violated"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_12","uri":"capability://tool.use.integration.mcp.model.context.protocol.integration.for.tool.standardization","name":"mcp (model context protocol) integration for tool standardization","description":"Integrates with the Model Context Protocol (MCP) standard for tool definition and execution, enabling standardized tool calling across applications. MCP servers expose tools through a standard interface; the framework discovers and registers MCP tools for use in agents and workflows. This enables reuse of tools across different LLM applications and providers without reimplementation. MCP integration handles authentication, request/response serialization, and error handling transparently.","intents":["I want to use standardized tools (MCP servers) across multiple LLM applications","I need to integrate with external MCP servers for specialized functionality","I want to expose my tools as MCP servers for use by other applications","I need to avoid reimplementing tools for different LLM frameworks"],"best_for":["teams building multiple LLM applications needing tool reuse","organizations standardizing on MCP for tool integration","applications requiring integration with external MCP servers"],"limitations":["MCP ecosystem is nascent; limited availability of production-ready MCP servers","MCP server discovery and registration requires manual configuration","Error handling and authentication vary across MCP servers; no standardized error codes","Performance depends on MCP server implementation; no built-in optimization or caching"],"requires":["Python 3.9+","llama-index-core>=0.14.19","MCP server (local or remote)","MCP client library"],"input_types":["MCP server configuration (URL, credentials)","tool invocation requests"],"output_types":["tool execution results","errors from MCP server"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_13","uri":"capability://memory.knowledge.context.window.management.with.automatic.summarization","name":"context window management with automatic summarization","description":"Manages LLM context windows by tracking token usage and automatically summarizing or truncating context when approaching limits. The framework estimates token counts for prompts, retrieved context, and conversation history using provider-specific tokenizers. When context approaches the model's limit, it applies strategies: summarization (condense context with LLM), truncation (remove oldest messages), or hierarchical retrieval (fetch higher-level summaries). This enables long conversations and large document sets without hitting context limits.","intents":["I need to handle long conversations without hitting LLM context limits","I want to include large amounts of retrieved context without token overflow","I need to automatically summarize context to fit within model limits","I want to estimate token usage and costs before making LLM calls"],"best_for":["applications with long conversations or large document sets","systems needing cost optimization through context management","teams building RAG systems with variable context sizes"],"limitations":["Token counting is approximate; actual token usage may differ from estimates","Automatic summarization loses information; summarized context may miss relevant details","Context truncation (removing old messages) breaks conversation continuity","Hierarchical retrieval requires pre-computed summaries; not all indices support this"],"requires":["Python 3.9+","llama-index-core>=0.14.19","LLM integration with token counting support"],"input_types":["prompt text","conversation history","retrieved context"],"output_types":["token count estimates","summarized or truncated context","cost estimates"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_14","uri":"capability://data.processing.analysis.dataset.and.benchmark.utilities.for.evaluation","name":"dataset and benchmark utilities for evaluation","description":"Provides LlamaDatasets and evaluation utilities for benchmarking RAG systems. Datasets include pre-built question-answer pairs for common domains (finance, medical, legal). The framework supports custom dataset creation from documents, automatic evaluation metrics (BLEU, ROUGE, semantic similarity), and comparison of different RAG configurations. Evaluation results are tracked and can be exported for analysis. This enables systematic optimization of RAG pipelines.","intents":["I need to evaluate my RAG system's performance on standard benchmarks","I want to create custom evaluation datasets from my documents","I need to compare different RAG configurations (indices, retrievers, LLMs) systematically","I want to track evaluation metrics over time to measure improvement"],"best_for":["teams optimizing RAG systems for production","researchers benchmarking RAG approaches","applications requiring quantitative evaluation of retrieval quality"],"limitations":["Pre-built datasets are limited to common domains; custom datasets require manual creation","Automatic evaluation metrics (BLEU, ROUGE) don't correlate well with human judgment for RAG","Evaluation is computationally expensive; benchmarking large datasets requires significant time","No built-in statistical significance testing; results may vary due to randomness"],"requires":["Python 3.9+","llama-index-core>=0.14.19","evaluation dataset (pre-built or custom)","LLM integration for evaluation"],"input_types":["RAG system (index, query engine, etc.)","evaluation dataset (questions, expected answers)"],"output_types":["evaluation metrics (BLEU, ROUGE, similarity scores)","comparison results across configurations","evaluation reports"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_2","uri":"capability://memory.knowledge.multi.index.data.structure.with.query.engine.abstraction","name":"multi-index data structure with query engine abstraction","description":"Provides multiple index types (VectorStoreIndex, SummaryIndex, TreeIndex, PropertyGraphIndex, KeywordTableIndex) that organize ingested nodes for different retrieval patterns. Each index implements a common Index interface with a query_engine() method that returns a QueryEngine for executing retrieval. Indices are backed by pluggable storage (vector stores, graph databases, in-memory) and support hybrid retrieval combining multiple strategies. The framework handles index construction, persistence, and updates transparently.","intents":["I need to index documents for semantic similarity search using embeddings","I want to support keyword-based retrieval alongside semantic search for hybrid results","I need to index structured relationships between entities for graph-based traversal and reasoning","I want to build a summary index for quick overview retrieval before detailed search"],"best_for":["teams building production RAG systems requiring multiple retrieval strategies","applications with structured data requiring graph-based reasoning (knowledge graphs, entity relationships)","systems needing to optimize for both recall (semantic) and precision (keyword/metadata filtering)"],"limitations":["VectorStoreIndex requires external vector database (Pinecone, Weaviate, Milvus, etc.) for production; in-memory storage unsuitable for >100k nodes","PropertyGraphIndex requires graph database backend (Neo4j, Nebula, etc.) adding operational complexity","Index updates are not real-time; rebuilding large indices (>1M nodes) takes minutes to hours","Hybrid retrieval combining multiple indices adds latency; no built-in optimization for query routing"],"requires":["Python 3.9+","llama-index-core>=0.14.19","embedding model for VectorStoreIndex (OpenAI, local, or custom)","vector store client (optional; in-memory storage available for prototyping)","graph database client for PropertyGraphIndex (optional)"],"input_types":["Node objects from document parsing/chunking"],"output_types":["Index objects with query_engine() method","QueryEngine instances for executing retrieval","retrieved nodes with scores/rankings"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_3","uri":"capability://search.retrieval.query.engine.with.multi.stage.retrieval.and.reranking","name":"query engine with multi-stage retrieval and reranking","description":"Executes queries against indices through a multi-stage pipeline: retrieval (fetch candidate nodes), reranking (score/filter candidates), synthesis (generate response from top nodes). QueryEngine implementations (RetrieverQueryEngine, RouterQueryEngine, SubQuestionQueryEngine) support different retrieval patterns. Rerankers (Cohere, LLM-based, similarity-based) re-score retrieved nodes to improve relevance. The synthesis stage uses an LLM to generate grounded responses from retrieved context, with configurable prompts and response modes (compact, tree_summarize, accumulate).","intents":["I need to retrieve relevant documents and generate a grounded response in a single query","I want to rerank retrieved results to improve relevance before generating responses","I need to decompose complex queries into sub-questions and synthesize answers from multiple retrievals","I want to route queries to different indices based on query type (semantic vs keyword vs graph)"],"best_for":["RAG applications requiring end-to-end query-to-response pipelines","systems needing multi-stage retrieval with reranking for improved accuracy","applications with complex queries requiring decomposition and multi-hop reasoning"],"limitations":["Multi-stage pipelines add latency: retrieval (50-500ms) + reranking (100-1000ms) + synthesis (500-3000ms) depending on model and result count","Reranking requires additional API calls (Cohere, LLM-based) adding cost; similarity-based reranking is free but less effective","SubQuestionQueryEngine requires LLM to decompose queries; decomposition quality varies and can hallucinate irrelevant sub-questions","Response synthesis depends on retrieved context quality; garbage in = garbage out"],"requires":["Python 3.9+","llama-index-core>=0.14.19","LLM integration (OpenAI, Anthropic, Ollama, etc.)","Index with query_engine() method","optional: reranker (Cohere API key or LLM for LLM-based reranking)"],"input_types":["query string (natural language)","optional: query parameters (filters, metadata constraints)"],"output_types":["Response object with generated text and source nodes","optional: structured response (JSON, code) depending on response mode"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_4","uri":"capability://tool.use.integration.llm.provider.abstraction.with.unified.interface","name":"llm provider abstraction with unified interface","description":"Abstracts LLM interactions behind a common LLM interface supporting 20+ providers (OpenAI, Anthropic, Google, AWS Bedrock, Ollama, Azure, etc.). Each provider implements complete_message() and stream_message() methods accepting ContentBlock messages (text, image, tool calls). The framework handles provider-specific details: API authentication, request formatting, response parsing, streaming, and error handling. Tool calling is standardized across providers through a schema-based function registry that maps to native provider APIs (OpenAI functions, Anthropic tools, etc.).","intents":["I want to switch between LLM providers (OpenAI to Anthropic to local Ollama) without changing application code","I need to use tool calling with multiple LLM providers that have different function calling APIs","I want to stream responses from LLMs for real-time user feedback","I need to handle provider-specific features (vision, extended context, cost optimization) transparently"],"best_for":["teams building LLM applications requiring multi-provider flexibility","developers optimizing for cost by switching providers based on query complexity","applications using tool calling across multiple LLM providers"],"limitations":["Provider-specific features (vision, extended context, structured output) require conditional code; abstraction doesn't hide all differences","Tool calling schemas must be manually defined; no automatic schema generation from Python functions","Streaming responses require provider-specific handling; some providers have latency overhead for streaming","Rate limiting and quota management are provider-specific; framework provides no unified rate limiting"],"requires":["Python 3.9+","llama-index-core>=0.14.19","provider-specific SDK (openai, anthropic, google-generativeai, boto3, ollama, etc.)","API credentials for chosen provider(s)"],"input_types":["ContentBlock messages (text, image, tool calls)","optional: tool schemas for function calling"],"output_types":["ContentBlock responses (text, tool calls)","streaming iterators for real-time responses"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_5","uri":"capability://memory.knowledge.embedding.model.integration.with.vector.store.abstraction","name":"embedding model integration with vector store abstraction","description":"Abstracts embedding generation and storage behind pluggable Embedding and VectorStore interfaces. Embedding implementations support 15+ providers (OpenAI, Cohere, HuggingFace, local models via Ollama). VectorStore implementations support 10+ backends (Pinecone, Weaviate, Milvus, Qdrant, PostgreSQL, Azure AI Search, etc.). The framework handles embedding generation during indexing, storage in vector databases, and similarity search during retrieval. Batch embedding operations optimize API calls; caching prevents redundant embeddings for identical text.","intents":["I need to generate embeddings for documents and store them in a vector database for semantic search","I want to switch embedding models or vector stores without reindexing","I need to perform similarity search across millions of embeddings efficiently","I want to use local embedding models to avoid API costs and latency"],"best_for":["RAG systems requiring semantic similarity search at scale","teams optimizing for cost by using local embedding models","applications needing to switch vector stores for operational reasons (cost, latency, compliance)"],"limitations":["Embedding generation is synchronous; large-scale indexing (>1M documents) requires external batch processing","Vector store performance varies dramatically by backend; Pinecone optimized for latency, Milvus for cost, PostgreSQL for simplicity","Embedding model switching requires re-indexing all documents; no built-in migration tools","Similarity search quality depends on embedding model; no automatic evaluation or model selection"],"requires":["Python 3.9+","llama-index-core>=0.14.19","embedding model (OpenAI, Cohere, HuggingFace, or local via Ollama)","vector store client (Pinecone, Weaviate, Milvus, Qdrant, PostgreSQL, etc.)","API credentials for embedding provider and vector store"],"input_types":["text strings (document chunks, queries)"],"output_types":["embedding vectors (1536-dim for OpenAI, variable for others)","similarity scores from vector store search"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_6","uri":"capability://automation.workflow.event.driven.workflow.orchestration.with.state.management","name":"event-driven workflow orchestration with state management","description":"Provides a Workflow abstraction for building event-driven, stateful LLM applications using a step-based execution model. Workflows are defined as classes with step methods decorated with @step; each step is an async function that processes input and emits events triggering downstream steps. The framework manages event routing, step scheduling, and state persistence across step executions. Workflows support branching (conditional step execution), loops (iterative processing), and error handling with retry logic. State is managed through a unified context object passed between steps.","intents":["I need to build multi-step LLM applications with conditional logic and state management","I want to orchestrate complex workflows with branching, loops, and error handling without writing orchestration code","I need to persist workflow state across step executions for fault tolerance and resumability","I want to compose reusable workflow steps that can be combined into larger applications"],"best_for":["teams building complex LLM agents with multi-step reasoning","applications requiring fault tolerance and resumable workflows","developers building reusable workflow components for team use"],"limitations":["Async-only execution model requires understanding of Python async/await; synchronous code must be wrapped","State management is in-memory by default; distributed workflows require external state store (Redis, database)","Debugging async workflows is harder than synchronous code; error traces can be difficult to follow","Workflow definition is code-based; no visual workflow builder or YAML configuration support"],"requires":["Python 3.9+","llama-index-core>=0.14.19","async runtime (asyncio)","optional: external state store (Redis, database) for distributed workflows"],"input_types":["workflow input parameters (any serializable type)","event payloads (any type)"],"output_types":["workflow output (any serializable type)","event stream for monitoring"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_7","uri":"capability://planning.reasoning.agent.system.with.tool.calling.and.reasoning","name":"agent system with tool calling and reasoning","description":"Provides Agent abstraction for building autonomous LLM agents that use tools to accomplish goals. Agents implement a reasoning loop: observe (read state/context), think (LLM generates reasoning + tool calls), act (execute tools), and repeat until goal achieved or max iterations reached. Tool calling is standardized through a schema-based function registry that maps to LLM provider APIs. The framework supports multiple agent types: ReActAgent (reasoning + acting), OpenAIAgent (native OpenAI function calling), and custom agents. Memory management tracks conversation history and tool execution results. Multi-agent orchestration enables agent-to-agent communication and delegation.","intents":["I need to build autonomous agents that use tools to accomplish complex tasks","I want agents to reason about which tools to use and when to use them","I need to manage agent memory and conversation history across multiple interactions","I want to orchestrate multiple agents that can delegate tasks to each other"],"best_for":["teams building autonomous AI agents for customer support, research, or task automation","applications requiring tool-augmented reasoning (web search, API calls, database queries)","systems needing multi-agent collaboration and delegation"],"limitations":["Agent reasoning quality depends on LLM capability; weaker models hallucinate tool calls or get stuck in loops","Tool calling requires manual schema definition; no automatic schema generation from Python functions","Agent loops are not guaranteed to terminate; max_iterations is crude safeguard against infinite loops","Memory management is simplistic (conversation history); no automatic summarization or forgetting for long conversations","Multi-agent orchestration requires manual delegation logic; no automatic task routing or load balancing"],"requires":["Python 3.9+","llama-index-core>=0.14.19","LLM integration (OpenAI, Anthropic, etc.)","tool definitions (functions with schemas)","optional: memory backend (Redis, database) for persistent agent state"],"input_types":["user query or task description","tool definitions (functions with schemas)","optional: context or constraints"],"output_types":["agent response (text or structured output)","tool execution trace (for debugging)","conversation history"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_8","uri":"capability://memory.knowledge.property.graph.indexing.with.entity.extraction.and.relationship.reasoning","name":"property graph indexing with entity extraction and relationship reasoning","description":"Builds knowledge graphs from documents by extracting entities and relationships using LLMs, then storing them in a graph database (Neo4j, Nebula, Kuzu). The PropertyGraphIndex uses an LLM to extract structured triples (subject, predicate, object) from document chunks, deduplicates entities across chunks, and builds a connected graph. Query execution traverses the graph to find relevant entities and relationships, then retrieves associated document chunks. The framework supports graph-based reasoning: multi-hop traversal, relationship filtering, and entity-centric retrieval.","intents":["I need to extract structured knowledge (entities and relationships) from unstructured documents","I want to query documents by entity relationships (e.g., 'find all people who worked at Company X')","I need to perform multi-hop reasoning across relationships (e.g., 'find companies that invested in startups founded by people from Company X')","I want to build a knowledge graph that can be queried and reasoned over"],"best_for":["applications with highly interconnected data (knowledge graphs, entity networks, relationship reasoning)","systems requiring multi-hop reasoning across relationships","teams building semantic search on structured knowledge"],"limitations":["Entity extraction quality depends on LLM; hallucinations create spurious entities and relationships","Graph database setup and maintenance adds operational complexity (Neo4j, Nebula require separate infrastructure)","Entity deduplication is heuristic-based; similar entities may not be merged, creating duplicate nodes","Graph traversal can be expensive for large graphs; no built-in query optimization or caching","Relationship extraction is limited to what LLM can infer; implicit relationships are missed"],"requires":["Python 3.9+","llama-index-core>=0.14.19","LLM integration for entity/relationship extraction","graph database (Neo4j, Nebula, Kuzu, etc.) with client library","graph database credentials and connection details"],"input_types":["Document objects or Node chunks"],"output_types":["PropertyGraphIndex with query_engine() method","retrieved nodes with entity/relationship context","graph traversal results"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index-core__cap_9","uri":"capability://automation.workflow.observability.and.instrumentation.framework","name":"observability and instrumentation framework","description":"Provides instrumentation hooks throughout the framework (LLM calls, embeddings, retrievals, agent steps) that emit structured events for monitoring and debugging. Events are captured through a pluggable event handler system supporting multiple backends (console, file, cloud services like Datadog, New Relic). The framework tracks latency, token usage, cost, and errors for each operation. Integration with observability platforms enables real-time monitoring, tracing, and alerting. 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