{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-llmware-ai--llmware","slug":"llmware-ai--llmware","name":"llmware","type":"framework","url":"https://llmware-ai.github.io/llmware/","page_url":"https://unfragile.ai/llmware-ai--llmware","categories":["rag-knowledge"],"tags":["agents","generative-ai-tools","llamacpp","llm","onnx","openvino","parsing","retrieval-augmented-generation","small-specialized-models"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-llmware-ai--llmware__cap_0","uri":"capability://data.processing.analysis.multi.format.document.parsing.with.chunked.indexing","name":"multi-format document parsing with chunked indexing","description":"Converts unstructured documents (PDF, DOCX, TXT, JSON, images) into semantically-indexed text chunks through the Parser class, which applies format-specific extraction logic and stores parsed content via the Library class with configurable chunk sizes and overlap. The parser maintains document structure metadata (page numbers, section hierarchies) enabling source attribution in RAG pipelines.","intents":["I need to ingest a heterogeneous document corpus (PDFs, Word docs, images) into a searchable knowledge base without manual preprocessing","I want to preserve document structure and metadata during parsing so I can cite exact sources in LLM responses","I need to control chunk size and overlap to balance retrieval granularity with context window efficiency"],"best_for":["enterprise teams building document-heavy RAG systems (legal, financial, healthcare)","developers migrating from manual document processing to automated pipelines","organizations requiring source attribution and audit trails in LLM outputs"],"limitations":["OCR quality depends on image resolution; scanned PDFs with poor quality may produce garbled text","Chunk overlap increases storage footprint by 10-30% depending on overlap percentage","No built-in table extraction for complex multi-column layouts; requires custom parser extensions","Parsing latency scales linearly with document size; 500MB+ documents may require streaming approaches"],"requires":["Python 3.9+","PDF parsing dependencies (pypdf or pdfplumber)","Document library storage backend (local filesystem, MongoDB, or Postgres)","For image parsing: Tesseract OCR or cloud vision API"],"input_types":["PDF files","DOCX/Word documents","Plain text files","JSON structured data","Images with text (via OCR)","HTML/web content"],"output_types":["indexed text chunks with metadata","document library records with source references","embedding-ready text segments"],"categories":["data-processing-analysis","document-parsing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-llmware-ai--llmware__cap_1","uri":"capability://data.processing.analysis.vector.embedding.generation.with.multi.backend.support","name":"vector embedding generation with multi-backend support","description":"The EmbeddingHandler class generates dense vector representations for text chunks using configurable embedding models (ONNX, local, or API-based), storing vectors in pluggable vector databases (Milvus, Pinecone, Weaviate, local SQLite). Supports both synchronous batch embedding and asynchronous processing for large-scale document collections.","intents":["I need to embed a large document corpus efficiently using local models to avoid API costs and latency","I want to switch embedding backends (e.g., from local ONNX to cloud API) without rewriting retrieval code","I need to generate embeddings for 100k+ documents with batching and progress tracking"],"best_for":["cost-conscious teams avoiding per-token embedding API charges","organizations with privacy requirements preventing cloud-based embeddings","developers building multi-model RAG systems requiring embedding flexibility"],"limitations":["Local ONNX embeddings are 2-5x slower than GPU-accelerated cloud APIs (Cohere, OpenAI)","Vector database selection is immutable after initial embedding; migration requires re-embedding entire corpus","No built-in vector quantization; full-precision embeddings consume 4KB per vector (1536-dim model)","Batch embedding throughput limited by available GPU VRAM; OOM errors on batches >10k tokens without tuning"],"requires":["Python 3.9+","ONNX Runtime or PyTorch for local embeddings","Vector database client library (milvus-python, pinecone-client, weaviate-client, or sqlite3)","For GPU acceleration: CUDA 11.8+ and compatible GPU","API keys if using cloud embedding providers (OpenAI, Cohere, Hugging Face)"],"input_types":["text chunks (from Parser output)","raw strings","batch lists of documents"],"output_types":["dense vectors (1536-dim or configurable)","vector database records with chunk references","embedding metadata (model name, timestamp, dimensions)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-llmware-ai--llmware__cap_10","uri":"capability://data.processing.analysis.evaluation.and.metrics.tracking.for.rag.quality","name":"evaluation and metrics tracking for rag quality","description":"llmware provides built-in evaluation utilities for measuring RAG quality through metrics like retrieval precision/recall, answer relevance, and source attribution accuracy. The framework logs prompt-response pairs with metadata (model, tokens, latency, sources), enabling post-hoc evaluation and fine-tuning. Supports integration with external evaluation frameworks (RAGAS, DeepEval) for standardized metrics.","intents":["I need to measure retrieval quality (precision, recall) and answer relevance for my RAG system","I want to track prompt-response pairs with sources for evaluation and compliance auditing","I need to identify failure modes and optimize retrieval/prompting based on metrics"],"best_for":["teams iterating on RAG configurations and needing quantitative feedback","regulated industries requiring compliance auditing and answer traceability","developers optimizing retrieval and prompting strategies"],"limitations":["Evaluation metrics are basic; no advanced metrics like BLEU, ROUGE, or semantic similarity","Logging adds overhead (~10-50ms per query); not suitable for ultra-low-latency systems","No automatic evaluation; requires manual metric computation or external framework integration","Evaluation data is not automatically cleaned or deduplicated; requires manual curation","No built-in A/B testing framework; requires external tools for statistical significance testing"],"requires":["Python 3.9+","Logging backend (local filesystem, database, or cloud service)","Optional: external evaluation framework (RAGAS, DeepEval)","Ground truth data for evaluation (gold standard answers, relevant documents)"],"input_types":["prompt-response pairs with metadata","retrieved documents and sources","ground truth annotations"],"output_types":["evaluation metrics (precision, recall, relevance scores)","evaluation reports and dashboards","failure analysis and recommendations"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-llmware-ai--llmware__cap_11","uri":"capability://text.generation.language.gguf.and.onnx.model.loading.for.local.inference","name":"gguf and onnx model loading for local inference","description":"llmware integrates GGUF (Llama.cpp format) and ONNX model loading through the ModelCatalog, enabling local inference of quantized models without cloud APIs. GGUF models are downloaded from llmware's model hub and loaded via llama-cpp-python, supporting CPU and GPU inference. ONNX models enable cross-platform inference with hardware acceleration (CUDA, OpenVINO, CoreML).","intents":["I need to run LLMs locally without cloud APIs for privacy and cost reasons","I want to use quantized models (4-bit, 8-bit) to reduce memory footprint and latency","I need cross-platform inference (CPU, GPU, mobile) with hardware acceleration"],"best_for":["privacy-conscious organizations avoiding cloud-based inference","cost-sensitive teams processing high-volume queries","edge deployments requiring local inference without network connectivity"],"limitations":["GGUF inference is 2-10x slower than cloud APIs (OpenAI, Anthropic) depending on hardware","Quantization (4-bit, 8-bit) reduces accuracy by 5-15% vs full-precision models","GPU memory requirements vary by model size; 7B models need 4GB+, 13B models need 8GB+","GGUF model downloads are large (2-50GB); requires significant disk space and bandwidth","ONNX model optimization is manual; no automatic quantization or pruning"],"requires":["Python 3.9+","llama-cpp-python for GGUF inference","ONNX Runtime for ONNX model inference","For GPU: CUDA 11.8+ and compatible GPU with 4GB+ VRAM","For CPU: 8GB+ RAM and multi-core processor","Sufficient disk space for model files (2-50GB per model)"],"input_types":["model identifiers (e.g., 'llama-2-7b-gguf')","prompts and context","optional: model configuration (temperature, max_tokens)"],"output_types":["text completions","token usage metadata","inference latency metrics"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-llmware-ai--llmware__cap_12","uri":"capability://data.processing.analysis.whispercpp.integration.for.audio.transcription","name":"whispercpp integration for audio transcription","description":"llmware integrates Whisper.cpp for local audio transcription, enabling speech-to-text processing without cloud APIs. Transcribed text is automatically indexed into the document library, enabling RAG over audio content. Supports multiple audio formats (MP3, WAV, FLAC) and language detection.","intents":["I need to transcribe audio files (meetings, interviews, podcasts) and make them searchable via RAG","I want to process audio locally without sending to cloud transcription services","I need to index audio content alongside documents for unified knowledge retrieval"],"best_for":["organizations processing audio content (meetings, customer calls, podcasts)","privacy-sensitive applications avoiding cloud transcription","teams building multimodal RAG systems combining documents and audio"],"limitations":["Transcription quality depends on audio quality; noisy audio produces garbled text","Whisper.cpp is slower than cloud APIs; real-time transcription not feasible","No speaker diarization; cannot distinguish between speakers in multi-speaker audio","Language detection is automatic; may fail on code-mixed or low-resource languages","Audio preprocessing (noise reduction, normalization) is not built-in"],"requires":["Python 3.9+","whisper-cpp or whisper.cpp binary","Audio files in supported formats (MP3, WAV, FLAC, OGG)","Sufficient disk space for audio files and transcripts","For GPU acceleration: CUDA 11.8+ and compatible GPU"],"input_types":["audio files (MP3, WAV, FLAC, OGG)","optional: language specification"],"output_types":["transcribed text","indexed documents in library","optional: speaker timestamps and confidence scores"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-llmware-ai--llmware__cap_2","uri":"capability://search.retrieval.semantic.and.hybrid.retrieval.with.query.expansion","name":"semantic and hybrid retrieval with query expansion","description":"The Query class implements semantic search via vector similarity and hybrid retrieval combining vector and keyword matching against indexed document chunks. Supports query expansion techniques (synonym injection, multi-hop reasoning) to improve recall on ambiguous or complex queries. Retrieval results include relevance scores, source metadata, and chunk context enabling downstream ranking and reranking.","intents":["I need to retrieve the most relevant document chunks for a user query with high precision and recall","I want to combine semantic similarity with keyword matching to handle both conceptual and exact-match queries","I need to expand user queries automatically to capture synonyms and related concepts without manual prompt engineering"],"best_for":["RAG systems requiring high-quality retrieval for downstream LLM prompting","teams building search-heavy applications (customer support, knowledge bases)","developers optimizing retrieval precision without expensive reranking models"],"limitations":["Query expansion adds 50-200ms latency per query; not suitable for sub-100ms SLA requirements","Hybrid retrieval requires tuning alpha parameter (vector vs keyword weight); no automatic tuning provided","Retrieval quality degrades on out-of-domain queries; no domain adaptation without fine-tuning embeddings","Top-k retrieval is fixed at query time; dynamic k-selection based on relevance threshold not supported"],"requires":["Python 3.9+","Indexed document library with embeddings (from prior Parser + EmbeddingHandler steps)","Vector database client configured and populated","Optional: keyword index (BM25 or Elasticsearch) for hybrid retrieval"],"input_types":["natural language queries (strings)","query metadata (user context, filters)","optional: structured query parameters (date ranges, document types)"],"output_types":["ranked list of document chunks with relevance scores","source metadata (document name, page, section)","chunk text and surrounding context"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-llmware-ai--llmware__cap_3","uri":"capability://tool.use.integration.multi.model.orchestration.with.150.model.catalog","name":"multi-model orchestration with 150+ model catalog","description":"The ModelCatalog class provides unified access to 150+ models including proprietary APIs (OpenAI, Anthropic, Cohere), open-source models (Llama, Mistral, Falcon), and llmware's specialized small models (BLING, DRAGON, SLIM). Models are loaded via a factory pattern supporting local inference (GGUF, ONNX), API-based access, and quantized variants. Abstracts model-specific tokenization, context windows, and API authentication.","intents":["I need to compare multiple LLM providers (OpenAI, Anthropic, local Llama) without rewriting prompting code","I want to use small specialized models for specific tasks (classification, extraction, summarization) to reduce costs and latency","I need to switch models at runtime based on task complexity or cost constraints without code changes"],"best_for":["enterprises evaluating multiple LLM providers for cost/performance tradeoffs","teams building cost-optimized RAG systems using small models for retrieval and large models for generation","developers requiring model flexibility for A/B testing or gradual migration strategies"],"limitations":["Model loading time varies: API models instant, local GGUF models 5-30s depending on size and hardware","Context window limits vary by model (2k-200k tokens); no automatic context truncation; exceeding limits causes errors","Small specialized models (SLIM, BLING) are optimized for specific tasks; poor generalization to out-of-domain queries","API rate limits and quota management are user's responsibility; no built-in rate limiting or queue management","Quantized models (4-bit, 8-bit) trade accuracy for speed; quality degradation not quantified per model"],"requires":["Python 3.9+","For local models: GGUF files or ONNX model weights","For API models: API keys (OpenAI, Anthropic, Cohere, etc.)","For GPU inference: CUDA 11.8+ and compatible GPU with 8GB+ VRAM","llama-cpp-python or similar for GGUF inference"],"input_types":["model identifiers (strings like 'gpt-4', 'llama-2-7b', 'slim-extract')","model configuration parameters (temperature, max_tokens, top_p)","prompts and context (text strings)"],"output_types":["model instances with unified inference interface","generated text completions","token usage metadata (input/output tokens, cost estimates)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-llmware-ai--llmware__cap_4","uri":"capability://text.generation.language.prompt.templating.with.source.grounded.generation","name":"prompt templating with source-grounded generation","description":"The Prompt class provides templated prompt construction with automatic source injection from retrieval results, enabling source-grounded generation where LLM outputs cite specific document chunks. Supports prompt variants (few-shot, chain-of-thought, structured output) and integrates with the Model Prompting Pipeline to execute prompts across multiple models. Tracks prompt-response pairs for evaluation and fine-tuning.","intents":["I need to construct prompts that inject retrieved documents as context and ensure LLM responses cite sources","I want to test multiple prompt templates (few-shot, CoT, structured) against the same query without manual rewrites","I need to log prompt-response pairs for evaluation, fine-tuning, and compliance auditing"],"best_for":["RAG systems requiring source attribution and hallucination reduction","teams optimizing prompt engineering through systematic template testing","regulated industries (legal, finance, healthcare) requiring audit trails of LLM reasoning"],"limitations":["Prompt injection attacks possible if user queries are not sanitized; no built-in input validation","Source grounding effectiveness depends on retrieval quality; poor retrieval produces irrelevant sources","Prompt template variables are string-based; no type checking or validation at template definition time","Token counting is approximate; actual token usage may vary by model's tokenizer","No automatic prompt optimization; requires manual tuning or external optimization frameworks"],"requires":["Python 3.9+","Retrieved document chunks with source metadata (from Query.semantic_query())","Loaded model instance (from ModelCatalog.load_model())","Optional: prompt evaluation framework for systematic testing"],"input_types":["prompt templates with variable placeholders","retrieved document chunks","model configuration (temperature, max_tokens)","optional: few-shot examples"],"output_types":["formatted prompts with injected context","LLM completions with source citations","prompt-response logs with metadata (model, tokens, latency)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-llmware-ai--llmware__cap_5","uri":"capability://text.generation.language.specialized.small.model.inference.for.enterprise.tasks","name":"specialized small model inference for enterprise tasks","description":"llmware provides three families of small, task-specific models (BLING, DRAGON, SLIM) optimized for classification, extraction, summarization, and retrieval ranking. These models (typically 1-7B parameters) run locally on CPU/GPU with <100ms latency, reducing costs and latency vs large general-purpose LLMs. Models are quantized (4-bit, 8-bit) and packaged as GGUF files for easy deployment.","intents":["I need to classify documents or extract structured data at scale without paying per-token API costs","I want to rank retrieval results using a specialized model before passing to the main LLM","I need sub-100ms inference latency for real-time applications; large LLMs are too slow"],"best_for":["cost-sensitive teams processing high-volume document classification or extraction","real-time systems requiring <100ms inference latency (chatbots, search ranking)","enterprises with privacy requirements preventing cloud-based inference"],"limitations":["Small models have narrower capabilities; poor generalization to out-of-domain tasks","Accuracy is 5-15% lower than large models (GPT-4) on complex reasoning tasks","Quantization (4-bit, 8-bit) introduces quality degradation; not quantified per model","No fine-tuning support in llmware; requires external frameworks (Hugging Face, Ollama)","Model selection is manual; no automatic model recommendation based on task"],"requires":["Python 3.9+","GGUF model files (downloaded from llmware model hub)","llama-cpp-python for GGUF inference","For GPU: CUDA 11.8+ and 4GB+ VRAM","For CPU: 2GB+ RAM and multi-core processor"],"input_types":["text documents or chunks","classification labels or extraction schemas","ranking queries and candidate documents"],"output_types":["classification labels with confidence scores","extracted structured data (JSON, key-value pairs)","ranking scores for document reranking"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-llmware-ai--llmware__cap_6","uri":"capability://memory.knowledge.document.library.management.with.versioning.and.metadata","name":"document library management with versioning and metadata","description":"The Library class provides persistent document storage with versioning, metadata tracking, and library-level configuration. Libraries organize documents into collections with configurable chunk sizes, embedding models, and vector databases. Supports library snapshots for reproducibility and A/B testing of retrieval configurations. Metadata includes document provenance, ingestion timestamps, and custom tags for filtering.","intents":["I need to organize documents into logical collections with different embedding and chunking strategies","I want to version my document library and compare retrieval results across different configurations","I need to track document provenance and ingestion metadata for compliance and debugging"],"best_for":["enterprises managing multiple document collections with different retrieval requirements","teams iterating on RAG configurations and needing reproducible snapshots","regulated industries requiring document audit trails and versioning"],"limitations":["Library versioning is manual; no automatic version control or diff tracking","Metadata is key-value; no structured schema validation or type checking","Library migration between storage backends (e.g., SQLite to MongoDB) requires manual export/import","No built-in garbage collection; deleted documents remain in vector database unless manually purged","Concurrent writes to same library may cause race conditions; requires external locking"],"requires":["Python 3.9+","Document storage backend (local filesystem, MongoDB, Postgres, etc.)","Vector database configured and accessible","Sufficient disk space for document copies and embeddings"],"input_types":["library configuration (name, chunk size, embedding model)","document files (PDFs, DOCX, TXT, etc.)","metadata tags and custom attributes"],"output_types":["library records with document inventory","library snapshots for versioning","metadata exports for compliance"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-llmware-ai--llmware__cap_7","uri":"capability://planning.reasoning.agent.framework.with.multi.step.reasoning.and.tool.integration","name":"agent framework with multi-step reasoning and tool integration","description":"The Agent framework enables multi-step reasoning workflows combining retrieval, LLM prompting, and external tool calls (APIs, databases, code execution). Agents maintain state across steps, support branching logic and loops, and integrate with the Model Prompting Pipeline for flexible model selection. Supports both agentic loops (ReAct pattern) and DAG-based workflows for deterministic orchestration.","intents":["I need to build multi-step workflows combining document retrieval, LLM reasoning, and external API calls","I want to implement ReAct-style agents that iteratively retrieve documents and call tools based on LLM reasoning","I need deterministic, auditable workflows for compliance-sensitive applications (not pure agentic loops)"],"best_for":["teams building complex RAG applications requiring multi-step reasoning","developers implementing agentic workflows (ReAct, tool-use patterns)","regulated industries requiring deterministic, auditable workflows"],"limitations":["Agentic loops are non-deterministic; same query may produce different results due to LLM sampling","No built-in cost control; agents may make excessive tool calls or API requests without limits","Error handling is manual; no automatic retry logic or fallback strategies","State management is in-memory; no persistence across process restarts without external storage","Tool integration requires custom code; no pre-built connectors for common APIs (Salesforce, Jira, etc.)"],"requires":["Python 3.9+","Loaded model instance (from ModelCatalog)","Configured retrieval pipeline (Library, Query, EmbeddingHandler)","Optional: external tool APIs and authentication credentials","Optional: state persistence backend (Redis, database)"],"input_types":["user queries or tasks","tool definitions (name, description, parameters)","optional: initial context or constraints"],"output_types":["final agent response with reasoning trace","tool call history and results","state snapshots for debugging"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-llmware-ai--llmware__cap_8","uri":"capability://data.processing.analysis.configurable.storage.backends.with.multi.database.support","name":"configurable storage backends with multi-database support","description":"llmware abstracts storage through pluggable backends supporting local filesystem, MongoDB, Postgres, and other databases. The Library class persists document metadata and chunks, while EmbeddingHandler stores vectors in configurable vector databases (Milvus, Pinecone, Weaviate, SQLite). Configuration is centralized in the configs module, enabling environment-based backend selection without code changes.","intents":["I need to deploy RAG systems on different infrastructure (local, cloud, on-premise) without code changes","I want to use existing databases (Postgres, MongoDB) for document storage instead of proprietary solutions","I need to scale document storage and vector search independently using separate backends"],"best_for":["enterprises with existing database infrastructure (Postgres, MongoDB)","teams deploying to multiple environments (dev, staging, production) with different backends","organizations requiring vendor-neutral storage solutions"],"limitations":["Backend selection is immutable after initial setup; migration requires data export/import","Vector database selection affects retrieval performance; no automatic optimization","Cross-backend transactions not supported; consistency is eventual","Configuration is environment-based; no runtime backend switching","Some backends have operational overhead (Milvus requires separate service, Postgres needs tuning)"],"requires":["Python 3.9+","Document storage backend installed and accessible (MongoDB, Postgres, local filesystem, etc.)","Vector database installed and accessible (Milvus, Pinecone, Weaviate, SQLite, etc.)","Network connectivity to remote backends if cloud-based","Appropriate credentials and permissions for database access"],"input_types":["storage backend configuration (connection strings, credentials)","document metadata and chunks","vector embeddings"],"output_types":["persisted documents in configured backend","persisted embeddings in vector database","query results from configured backends"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-llmware-ai--llmware__cap_9","uri":"capability://automation.workflow.batch.processing.and.async.document.ingestion","name":"batch processing and async document ingestion","description":"llmware supports asynchronous document ingestion and batch embedding through the Library.add_files() method with optional async/await patterns. Batch processing enables efficient handling of large document corpora (100k+ documents) with progress tracking, error recovery, and resumable jobs. Integrates with the Parser and EmbeddingHandler for end-to-end batch workflows.","intents":["I need to ingest 100k+ documents efficiently without blocking the application","I want to process documents in batches with progress tracking and error recovery","I need to resume interrupted ingestion jobs without reprocessing completed documents"],"best_for":["teams ingesting large document corpora during initial setup","applications with periodic bulk document updates","systems requiring non-blocking document processing"],"limitations":["Batch processing is not distributed; single-machine throughput limited by CPU/GPU","No built-in job persistence; interrupted jobs require manual resumption","Progress tracking is in-memory; lost on process restart","Error handling is basic; failed documents are skipped without detailed error logs","Memory usage scales with batch size; large batches may cause OOM errors"],"requires":["Python 3.9+","Sufficient disk space for document staging","Sufficient RAM for batch processing (8GB+ recommended for 10k+ document batches)","Optional: async event loop for non-blocking processing"],"input_types":["document file paths or directory","batch size configuration","optional: async processing flag"],"output_types":["ingestion progress reports","error logs for failed documents","indexed documents in Library"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PDF parsing dependencies (pypdf or pdfplumber)","Document library storage backend (local filesystem, MongoDB, or Postgres)","For image parsing: Tesseract OCR or cloud vision API","ONNX Runtime or PyTorch for local embeddings","Vector database client library (milvus-python, pinecone-client, weaviate-client, or sqlite3)","For GPU acceleration: CUDA 11.8+ and compatible GPU","API keys if using cloud embedding providers (OpenAI, Cohere, Hugging Face)","Logging backend (local filesystem, database, or cloud service)","Optional: external evaluation framework (RAGAS, DeepEval)"],"failure_modes":["OCR quality depends on image resolution; scanned PDFs with poor quality may produce garbled text","Chunk overlap increases storage footprint by 10-30% depending on overlap percentage","No built-in table extraction for complex multi-column layouts; requires custom parser extensions","Parsing latency scales linearly with document size; 500MB+ documents may require streaming approaches","Local ONNX embeddings are 2-5x slower than GPU-accelerated cloud APIs (Cohere, OpenAI)","Vector database selection is immutable after initial embedding; migration requires re-embedding entire corpus","No built-in vector quantization; full-precision embeddings consume 4KB per vector (1536-dim model)","Batch embedding throughput limited by available GPU VRAM; OOM errors on batches >10k tokens without tuning","Evaluation metrics are basic; no advanced metrics like BLEU, ROUGE, or semantic similarity","Logging adds overhead (~10-50ms per query); not suitable for ultra-low-latency systems","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7239801787287535,"quality":0.35,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:29.527Z","last_commit":"2026-04-14T18:13:02Z"},"community":{"stars":14870,"forks":2938,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llmware-ai--llmware","compare_url":"https://unfragile.ai/compare?artifact=llmware-ai--llmware"}},"signature":"q9R9axGhfdM5/W3SpA8RinSiYBqP6hjDjhHgiBWSo5YolijR/kQUg4BrmSxuKtcDHYOrZegzFA3vF055Zna6Dg==","signedAt":"2026-06-22T05:14:29.569Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llmware-ai--llmware","artifact":"https://unfragile.ai/llmware-ai--llmware","verify":"https://unfragile.ai/api/v1/verify?slug=llmware-ai--llmware","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"}}