{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-llama-index","slug":"pypi-llama-index","name":"llama-index","type":"framework","url":"https://pypi.org/project/llama-index/","page_url":"https://unfragile.ai/pypi-llama-index","categories":["rag-knowledge"],"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__cap_0","uri":"capability://data.processing.analysis.multi.source.document.ingestion.with.pluggable.readers","name":"multi-source document ingestion with pluggable readers","description":"Ingests structured and unstructured data from 50+ sources (PDFs, web pages, databases, cloud storage) through a unified Reader abstraction pattern. Each reader implements a common interface that converts heterogeneous data formats into a normalized Document/Node representation with metadata preservation. The framework uses a composition pattern where readers can be chained and configured independently, enabling flexible data pipeline construction without modifying core ingestion logic.","intents":["I need to load documents from multiple sources (S3, Google Drive, local files, web URLs) into a single indexing pipeline","I want to extract structured data from PDFs while preserving layout and metadata","I need to ingest data from proprietary databases or APIs with custom transformation logic"],"best_for":["teams building RAG systems with heterogeneous data sources","enterprises migrating unstructured data into LLM-accessible formats","developers prototyping multi-source knowledge bases"],"limitations":["Reader implementations vary in robustness — some cloud readers require explicit credential management and may timeout on large datasets","No built-in deduplication across sources — requires post-ingestion processing to handle duplicate documents","Complex nested document structures (e.g., deeply hierarchical PDFs) may require custom reader implementation"],"requires":["Python 3.9+","Source-specific credentials (AWS keys for S3, Google API keys for Drive, etc.)","llama-index-core>=0.14.19"],"input_types":["PDF files","Markdown/text files","HTML/web pages","JSON/CSV structured data","Database records","Cloud storage objects (S3, GCS, Azure Blob)"],"output_types":["Document objects with metadata","Node objects (chunked text with provenance)","Structured metadata dictionaries"],"categories":["data-processing-analysis","etl"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_1","uri":"capability://data.processing.analysis.intelligent.document.chunking.with.semantic.aware.node.parsing","name":"intelligent document chunking with semantic-aware node parsing","description":"Splits documents into semantically coherent chunks (Nodes) using multiple parsing strategies: recursive character splitting, language-aware parsing (code, markdown), and semantic boundary detection. The NodeParser abstraction allows swapping strategies (SimpleNodeParser, HierarchicalNodeParser, SemanticSplitterNodeParser) based on document type. Preserves document hierarchy, metadata, and relationships between chunks, enabling context-aware retrieval that respects logical document structure rather than arbitrary token boundaries.","intents":["I need to chunk documents while preserving code structure and comments for code-based RAG","I want to maintain document hierarchy (sections, subsections) so retrieval returns contextually complete information","I need to split long documents intelligently without breaking semantic meaning mid-sentence"],"best_for":["RAG systems indexing technical documentation, code repositories, or structured documents","applications requiring hierarchical context preservation (e.g., legal documents, research papers)","teams building domain-specific chunking strategies"],"limitations":["Semantic splitting requires embedding model calls during ingestion, adding latency and cost proportional to document size","Recursive splitting may create overlapping chunks that inflate index size by 20-40%","Language-specific parsers (code, markdown) require explicit configuration — defaults to character-based splitting"],"requires":["Python 3.9+","Embedding model configured (OpenAI, local, or custom) for semantic splitting","llama-index-core>=0.14.19"],"input_types":["Document objects with text content","Metadata dictionaries","Language hints (e.g., 'python', 'markdown')"],"output_types":["Node objects with text, metadata, and relationships","Hierarchical node structures with parent/child references"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_10","uri":"capability://automation.workflow.observability.and.instrumentation.with.event.based.tracing","name":"observability and instrumentation with event-based tracing","description":"Provides comprehensive observability through an event-based instrumentation framework that emits structured events for all framework operations (retrieval, LLM calls, tool execution, workflow steps). Events are captured and can be routed to observability backends (LangSmith, Arize, custom handlers). Includes built-in metrics collection (latency, token usage, cost) and debugging utilities. Supports both synchronous and asynchronous event handling with configurable filtering and sampling.","intents":["I need to trace and debug multi-step RAG/agent workflows to understand where failures occur","I want to monitor LLM costs, latency, and token usage across my application","I need to integrate with observability platforms (LangSmith, Arize) for production monitoring"],"best_for":["teams running production RAG/agent systems requiring observability","developers debugging complex multi-step workflows","applications requiring cost tracking and performance monitoring"],"limitations":["Event emission adds overhead to every operation — can impact latency in latency-sensitive applications","Observability backend integration requires additional configuration and API keys","Event sampling may miss rare failure cases — requires careful sampling strategy tuning"],"requires":["Python 3.9+","llama-index-core>=0.14.19","Observability backend (LangSmith, Arize, custom) for event routing (optional)"],"input_types":["Framework operations (retrieval, LLM calls, etc.)","Event handler configuration","Observability backend credentials (optional)"],"output_types":["Structured event logs","Metrics (latency, token usage, cost)","Debug traces with operation hierarchy"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_11","uri":"capability://data.processing.analysis.fine.tuning.and.model.optimization.with.dataset.generation","name":"fine-tuning and model optimization with dataset generation","description":"Provides utilities for generating fine-tuning datasets from RAG workflows and optimizing models through fine-tuning. Captures query-response pairs from production RAG systems, generates synthetic training data using LLMs, and exports datasets in standard formats (OpenAI, Hugging Face). Supports fine-tuning of embedding models, rerankers, and LLMs. Includes evaluation metrics for assessing fine-tuning impact on retrieval and generation quality.","intents":["I need to generate fine-tuning datasets from my RAG system to improve model performance","I want to fine-tune embedding models or rerankers on domain-specific data","I need to evaluate whether fine-tuning improves my RAG system's quality"],"best_for":["teams optimizing RAG systems through fine-tuning","applications with domain-specific data requiring custom model optimization","developers building feedback loops from production to model improvement"],"limitations":["Synthetic data generation quality depends on base LLM — may introduce biases or hallucinations","Fine-tuning requires significant computational resources and API costs","Evaluation metrics are proxies for actual quality — may not capture all aspects of system performance"],"requires":["Python 3.9+","Production RAG system generating query-response pairs","LLM for synthetic data generation","Fine-tuning infrastructure (OpenAI API, Hugging Face, etc.)","llama-index-core>=0.14.19"],"input_types":["Query-response pairs from production","Document corpus for synthetic data generation","Fine-tuning configuration (model, hyperparameters)"],"output_types":["Fine-tuning datasets (JSONL format)","Evaluation metrics (NDCG, MRR, etc.)","Fine-tuned model artifacts"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_12","uri":"capability://automation.workflow.llamapacks.and.pre.built.templates.for.common.patterns","name":"llamapacks and pre-built templates for common patterns","description":"Provides LlamaPacks — pre-built, composable templates for common RAG and agent patterns (e.g., multi-document QA, code analysis, research assistant). Each pack is a self-contained module with configured components (readers, indexers, query engines, agents) that can be instantiated with minimal configuration. Packs are discoverable through a registry and can be customized by swapping components. Enables rapid prototyping of complex applications without building from scratch.","intents":["I need to quickly prototype a RAG system without configuring all components manually","I want to use pre-built patterns for common use cases (code analysis, document Q&A, research)","I need a starting point that I can customize for my specific domain"],"best_for":["teams prototyping RAG/agent applications quickly","developers new to LlamaIndex wanting reference implementations","applications with common patterns (document QA, code analysis, research)"],"limitations":["Packs are templates — customization requires understanding underlying components","Pack quality varies — some may not be production-ready or well-maintained","Limited to pre-defined patterns — novel use cases require building from scratch"],"requires":["Python 3.9+","llama-index-core>=0.14.19","Specific pack dependencies (varies by pack)"],"input_types":["Pack configuration (model, data source, etc.)","Custom component overrides (optional)"],"output_types":["Instantiated RAG/agent system","Query engine or agent ready for use"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_13","uri":"capability://data.processing.analysis.storage.abstraction.with.pluggable.persistence.backends","name":"storage abstraction with pluggable persistence backends","description":"Abstracts storage of indices, documents, and metadata behind a unified StorageContext interface supporting multiple backends (file system, cloud storage, databases). Enables serialization and deserialization of indices without vendor lock-in. Supports incremental updates, versioning, and backup strategies. Integrates with vector stores, graph stores, and document stores for comprehensive persistence. Handles automatic index rebuilding and cache invalidation.","intents":["I need to persist my indexed data and reload it without re-indexing","I want to switch storage backends (local to cloud) without changing application code","I need to version and backup my indices for disaster recovery"],"best_for":["production RAG systems requiring persistent indices","teams with existing storage infrastructure (S3, databases) wanting integration","applications requiring index versioning and rollback"],"limitations":["Storage backend implementations have varying consistency guarantees — distributed backends may have eventual consistency issues","Index serialization format is framework-specific — cannot be easily migrated to other frameworks","Incremental updates require careful coordination with vector store updates — manual synchronization may be needed"],"requires":["Python 3.9+","Storage backend (file system, S3, database, etc.)","llama-index-core>=0.14.19"],"input_types":["Index objects","Storage configuration (backend, path/credentials)","Serialization options"],"output_types":["Persisted index artifacts","Loaded index objects","Storage metadata (version, timestamp)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_14","uri":"capability://automation.workflow.settings.and.configuration.management.with.environment.based.overrides","name":"settings and configuration management with environment-based overrides","description":"Provides a Settings abstraction for managing framework configuration (LLM models, embedding models, vector stores, chunk sizes, etc.) with environment variable overrides. Supports configuration files (YAML, JSON) and programmatic configuration. Enables easy switching between development and production configurations without code changes. Integrates with dependency injection for component instantiation.","intents":["I need to manage different configurations for development, staging, and production","I want to override configuration via environment variables for containerized deployments","I need to switch between different LLM/embedding models without code changes"],"best_for":["teams deploying RAG systems across multiple environments","applications requiring configuration flexibility without code changes","developers managing complex component dependencies"],"limitations":["Configuration validation is minimal — invalid settings may only fail at runtime","Environment variable overrides are string-based — complex types require custom parsing","No built-in secrets management — requires external tools (Vault, AWS Secrets Manager) for sensitive credentials"],"requires":["Python 3.9+","llama-index-core>=0.14.19"],"input_types":["Configuration files (YAML, JSON)","Environment variables","Programmatic configuration objects"],"output_types":["Settings objects with validated configuration","Component instances (LLM, embeddings, vector store)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_2","uri":"capability://memory.knowledge.multi.index.retrieval.with.pluggable.vector.and.graph.stores","name":"multi-index retrieval with pluggable vector and graph stores","description":"Abstracts vector storage and retrieval behind a unified VectorStore interface, supporting 15+ backends (Pinecone, Weaviate, Milvus, PostgreSQL pgvector, Qdrant, Azure AI Search, etc.). Enables hybrid retrieval combining vector similarity with keyword search, metadata filtering, and graph-based traversal. The Index abstraction (VectorStoreIndex, SummaryIndex, KeywordTableIndex, PropertyGraphIndex) provides different retrieval semantics, allowing developers to choose retrieval strategy based on query characteristics and data structure without changing application code.","intents":["I need to switch between vector stores (e.g., Pinecone to self-hosted Qdrant) without rewriting retrieval logic","I want to combine semantic search with metadata filtering and keyword matching in a single query","I need to index and retrieve over knowledge graphs or structured relationships, not just flat documents"],"best_for":["teams building production RAG systems requiring vector store flexibility","enterprises with existing vector infrastructure (Milvus, Elasticsearch) wanting LLM integration","applications requiring hybrid retrieval (semantic + keyword + graph) for complex queries"],"limitations":["Vector store implementations have varying consistency guarantees — eventual consistency in cloud providers may cause stale retrieval results","Metadata filtering support varies by backend (PostgreSQL pgvector supports rich filtering; Pinecone has limited filter expressions)","Graph-based retrieval (PropertyGraphIndex) requires explicit relationship definition during ingestion — no automatic relationship extraction"],"requires":["Python 3.9+","Vector store instance (cloud-hosted or self-hosted) with network access","Embedding model configured (OpenAI, Hugging Face, local)","llama-index-core>=0.14.19"],"input_types":["Embedding vectors (float arrays)","Document/Node objects with metadata","Query strings or embedding vectors","Metadata filter expressions"],"output_types":["Retrieved Node objects with similarity scores","Ranked result lists with metadata","Graph traversal results with relationship paths"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_3","uri":"capability://planning.reasoning.query.engine.orchestration.with.multi.step.retrieval.and.synthesis","name":"query engine orchestration with multi-step retrieval and synthesis","description":"Orchestrates complex retrieval and LLM synthesis workflows through composable QueryEngine abstractions. Implements patterns like retrieval-augmented generation (retrieve → synthesize), tree-based summarization (hierarchical retrieval), and multi-document synthesis. Uses a Retriever → Response Synthesizer pipeline where retrievers fetch relevant nodes and synthesizers generate LLM responses with citations. Supports advanced patterns like recursive retrieval (refine queries based on intermediate results) and sub-question query engines (decompose complex queries into sub-questions, retrieve for each, then synthesize).","intents":["I need to build a RAG pipeline that retrieves documents and generates cited responses in a single abstraction","I want to handle complex multi-document queries by decomposing them into sub-questions and synthesizing results","I need to implement iterative refinement where initial retrieval results inform follow-up queries"],"best_for":["teams building conversational RAG systems with multi-step reasoning","applications requiring cited responses with source attribution","developers implementing complex query decomposition and synthesis patterns"],"limitations":["Multi-step query engines incur cumulative LLM costs — sub-question decomposition may require 3-5 LLM calls per query","Recursive retrieval can create infinite loops if not bounded — requires explicit max_depth configuration","Response synthesis quality depends heavily on retriever quality — poor retrieval results cannot be recovered by synthesis"],"requires":["Python 3.9+","Configured retriever (VectorStoreIndex, SummaryIndex, etc.)","LLM configured (OpenAI, Anthropic, local via Ollama)","llama-index-core>=0.14.19"],"input_types":["Query strings","Retriever instances","LLM instances","Response synthesizer configuration"],"output_types":["Response strings with optional source citations","Structured responses with metadata","Debug information (retrieval steps, LLM calls)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_4","uri":"capability://planning.reasoning.event.driven.workflow.orchestration.with.stateful.task.composition","name":"event-driven workflow orchestration with stateful task composition","description":"Provides a Workflow abstraction for building stateful, event-driven LLM applications using a step-based execution model. Workflows are defined as directed acyclic graphs (DAGs) of steps, where each step is an async function that processes events and emits new events. The framework handles event routing, state management, and step scheduling automatically. Supports both sequential and parallel execution, conditional branching based on step outputs, and human-in-the-loop checkpoints. Integrates with LLM tool calling for autonomous agent workflows.","intents":["I need to build multi-step LLM agents that maintain state across interactions without manual orchestration","I want to define complex workflows with conditional branching, parallel execution, and error handling","I need to implement human-in-the-loop workflows where certain steps require manual approval"],"best_for":["teams building autonomous AI agents with complex reasoning chains","applications requiring stateful multi-step workflows (e.g., document processing pipelines)","developers implementing conditional logic and branching in LLM applications"],"limitations":["Workflow state is in-memory by default — requires external persistence layer for distributed/fault-tolerant execution","DAG execution model limits dynamic workflow generation (cannot create new steps at runtime based on LLM outputs)","Debugging multi-step workflows is challenging — requires explicit logging and event tracing"],"requires":["Python 3.9+","Async runtime (asyncio)","LLM configured for tool calling (OpenAI, Anthropic, etc.)","llama-index-core>=0.14.19"],"input_types":["Step function definitions (async callables)","Event objects with typed payloads","Workflow configuration (DAG structure, branching logic)"],"output_types":["Final workflow output (typed)","Event logs with execution trace","State snapshots for checkpointing"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_5","uri":"capability://tool.use.integration.multi.agent.orchestration.with.tool.calling.and.memory.management","name":"multi-agent orchestration with tool calling and memory management","description":"Provides an Agent abstraction for building autonomous LLM agents that use tools (function calling) to accomplish tasks. Agents maintain conversation history and can be composed into multi-agent systems where agents delegate tasks to each other. The framework handles tool schema generation, function calling orchestration, and response parsing across multiple LLM providers (OpenAI, Anthropic, Ollama). Supports different agent types (ReActAgent, OpenAIAgent, FunctionCallingAgent) with varying reasoning strategies. Integrates with memory systems for persistent agent state across sessions.","intents":["I need to build an AI agent that can call tools/functions to accomplish complex tasks autonomously","I want to create multi-agent systems where agents collaborate by delegating tasks to each other","I need to maintain agent memory and conversation history across multiple interactions"],"best_for":["teams building autonomous AI agents with tool-use capabilities","applications requiring multi-agent collaboration and task delegation","developers implementing conversational agents with persistent memory"],"limitations":["Tool calling reliability depends on LLM quality — weaker models may fail to call tools correctly or hallucinate tool parameters","Multi-agent coordination lacks built-in conflict resolution — agents may make contradictory decisions without explicit coordination logic","Memory systems are pluggable but not persistent by default — requires external storage (database, vector store) for production use"],"requires":["Python 3.9+","LLM with function calling support (OpenAI, Anthropic, etc.)","Tool definitions with JSON schemas","llama-index-core>=0.14.19"],"input_types":["User queries/instructions","Tool definitions (Python functions with type hints)","Agent configuration (model, tools, memory backend)"],"output_types":["Agent responses (text with tool call results)","Tool call logs with parameters and results","Memory snapshots (conversation history, state)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_6","uri":"capability://tool.use.integration.llm.provider.abstraction.with.unified.interface.across.20.models","name":"llm provider abstraction with unified interface across 20+ models","description":"Abstracts LLM interactions behind a unified LLM interface supporting 20+ providers (OpenAI, Anthropic, Google, AWS Bedrock, Ollama, Hugging Face, etc.). Each provider implementation handles authentication, API communication, message formatting, and response parsing. The framework normalizes different LLM APIs (streaming vs. non-streaming, function calling schemas, token counting) into a consistent interface. Supports both cloud-hosted and self-hosted models, with automatic fallback and retry logic. Integrates with embedding models through a parallel Embeddings abstraction.","intents":["I need to switch between different LLM providers (OpenAI to Anthropic) without changing application code","I want to use local/self-hosted models (Ollama, vLLM) alongside cloud providers in the same application","I need consistent function calling and streaming behavior across different LLM APIs"],"best_for":["teams building LLM applications requiring provider flexibility","enterprises with existing LLM infrastructure (Bedrock, Azure OpenAI) wanting framework integration","developers prototyping with multiple models to compare quality/cost"],"limitations":["LLM provider implementations have varying feature support — some providers lack streaming, function calling, or vision capabilities","Token counting is approximate for non-OpenAI models — actual token usage may differ from estimates","Rate limiting and quota management are provider-specific — framework provides no built-in rate limiter"],"requires":["Python 3.9+","API keys or endpoints for chosen LLM providers","llama-index-core>=0.14.19","Provider-specific SDK (llama-index-llms-openai, llama-index-llms-anthropic, etc.)"],"input_types":["Message lists (system, user, assistant roles)","Tool/function schemas","Generation parameters (temperature, max_tokens, etc.)"],"output_types":["Completion strings","Structured responses (tool calls, JSON)","Streaming token generators","Token usage metadata"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_7","uri":"capability://data.processing.analysis.embedding.model.abstraction.with.multi.provider.support.and.caching","name":"embedding model abstraction with multi-provider support and caching","description":"Abstracts embedding generation behind a unified Embeddings interface supporting 15+ providers (OpenAI, Hugging Face, Ollama, Google, AWS Bedrock, etc.). Handles batch embedding, caching of computed embeddings, and automatic retry logic. Supports both text and multimodal embeddings. The framework normalizes embedding dimensions and similarity metrics across providers. Integrates with vector stores for automatic embedding generation during indexing and retrieval.","intents":["I need to generate embeddings for documents using different embedding models without changing indexing code","I want to cache embeddings to avoid recomputing expensive embedding operations","I need to use local embedding models (Ollama) for privacy-sensitive data while using cloud models elsewhere"],"best_for":["teams building RAG systems requiring embedding model flexibility","applications with privacy constraints requiring local embedding models","developers optimizing embedding costs through caching and batch processing"],"limitations":["Embedding dimensions vary by model (OpenAI: 1536, Cohere: 4096) — vector store must support variable dimensions or require normalization","Caching is in-memory by default — requires external cache (Redis, database) for distributed systems","Batch embedding APIs have different limits per provider — framework provides no automatic batching optimization"],"requires":["Python 3.9+","API keys or endpoints for chosen embedding providers","llama-index-core>=0.14.19","Provider-specific SDK (llama-index-embeddings-openai, etc.)"],"input_types":["Text strings or lists of strings","Embedding model configuration"],"output_types":["Embedding vectors (float arrays)","Batch embedding results","Cache hit/miss metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_8","uri":"capability://memory.knowledge.knowledge.graph.construction.and.property.graph.indexing","name":"knowledge graph construction and property graph indexing","description":"Builds knowledge graphs from documents using LLM-based entity and relationship extraction, storing them in a PropertyGraphIndex. The framework uses LLMs to extract entities, relationships, and properties from text, then constructs a graph representation queryable via graph traversal. Supports multiple graph store backends (Neo4j, TigerGraph, Kuzu, etc.). Enables hybrid retrieval combining semantic search with graph-based relationship traversal. Supports knowledge graph completion and reasoning over extracted relationships.","intents":["I need to extract structured knowledge (entities, relationships) from unstructured documents and query it as a graph","I want to combine semantic search with relationship-based retrieval for more contextual results","I need to build a knowledge base that captures domain-specific relationships and enables reasoning over them"],"best_for":["teams building knowledge-intensive applications (research, biomedical, finance)","applications requiring structured relationship queries alongside semantic search","developers implementing knowledge graph completion and reasoning"],"limitations":["LLM-based entity/relationship extraction is imperfect — hallucinations and missed relationships are common, requiring manual curation","Graph construction requires multiple LLM calls per document — adds significant latency and cost to ingestion","Graph store backends have varying query capabilities — complex traversals may not be supported by all backends"],"requires":["Python 3.9+","LLM configured for entity/relationship extraction","Graph store instance (Neo4j, TigerGraph, Kuzu, etc.)","llama-index-core>=0.14.19"],"input_types":["Document/Node objects with text content","Entity and relationship extraction prompts","Graph store configuration"],"output_types":["Graph nodes (entities with properties)","Graph edges (relationships with metadata)","Query results from graph traversal","Hybrid retrieval results (semantic + graph)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-llama-index__cap_9","uri":"capability://text.generation.language.response.synthesis.with.source.attribution.and.citation.generation","name":"response synthesis with source attribution and citation generation","description":"Generates LLM responses with automatic source attribution and citations using a ResponseSynthesizer abstraction. Implements multiple synthesis strategies: simple concatenation of retrieved context, iterative refinement (generate → retrieve → refine), tree-based summarization (hierarchical synthesis), and compact synthesis (minimize context while maintaining quality). Tracks source provenance throughout synthesis, enabling citation generation with document references and node IDs. Supports custom synthesis prompts and response formatting.","intents":["I need to generate responses that cite their sources with document references and page numbers","I want to synthesize responses from multiple retrieved documents while maintaining source attribution","I need to implement iterative refinement where initial responses inform follow-up retrievals"],"best_for":["RAG systems requiring cited responses for transparency and fact-checking","applications in regulated industries (legal, medical) requiring source attribution","teams building conversational systems with multi-document synthesis"],"limitations":["Citation accuracy depends on LLM quality — models may cite irrelevant sources or hallucinate citations","Iterative refinement increases LLM costs proportionally to refinement iterations","Source attribution is only as good as retrieval quality — poor retrieval results cannot be recovered by synthesis"],"requires":["Python 3.9+","Retrieved nodes with metadata (document name, page number, etc.)","LLM configured for synthesis","llama-index-core>=0.14.19"],"input_types":["Retrieved Node objects with metadata","Query string","Synthesis strategy configuration","Custom synthesis prompts (optional)"],"output_types":["Response strings with inline citations","Structured responses with source metadata","Citation lists with document references"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Source-specific credentials (AWS keys for S3, Google API keys for Drive, etc.)","llama-index-core>=0.14.19","Embedding model configured (OpenAI, local, or custom) for semantic splitting","Observability backend (LangSmith, Arize, custom) for event routing (optional)","Production RAG system generating query-response pairs","LLM for synthetic data generation","Fine-tuning infrastructure (OpenAI API, Hugging Face, etc.)","Specific pack dependencies (varies by pack)","Storage backend (file system, S3, database, etc.)"],"failure_modes":["Reader implementations vary in robustness — some cloud readers require explicit credential management and may timeout on large datasets","No built-in deduplication across sources — requires post-ingestion processing to handle duplicate documents","Complex nested document structures (e.g., deeply hierarchical PDFs) may require custom reader implementation","Semantic splitting requires embedding model calls during ingestion, adding latency and cost proportional to document size","Recursive splitting may create overlapping chunks that inflate index size by 20-40%","Language-specific parsers (code, markdown) require explicit configuration — defaults to character-based splitting","Event emission adds overhead to every operation — can impact latency in latency-sensitive applications","Observability backend integration requires additional configuration and API keys","Event sampling may miss rare failure cases — requires careful sampling strategy tuning","Synthetic data generation quality depends on base LLM — may introduce biases or hallucinations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:11.786Z","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=pypi-llama-index","compare_url":"https://unfragile.ai/compare?artifact=pypi-llama-index"}},"signature":"To0l5fM6vyRgoL4/TRTjv5V8FXUhfv14Y3igOW7t7B+JPa1cqecb5z72i3mncUYA1Zkg4Ey2XU/dwi2gVDKECA==","signedAt":"2026-06-20T02:24:57.870Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-llama-index","artifact":"https://unfragile.ai/pypi-llama-index","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-llama-index","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"}}