{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-llm-app","slug":"llm-app","name":"LLM App","type":"framework","url":"https://github.com/pathwaycom/llm-app","page_url":"https://unfragile.ai/llm-app","categories":["data-pipelines"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-llm-app__cap_0","uri":"capability://automation.workflow.real.time.multi.source.document.synchronization.and.ingestion","name":"real-time multi-source document synchronization and ingestion","description":"Pathway LLM App monitors and syncs documents from heterogeneous data sources (file systems, Google Drive, SharePoint, S3) with automatic change detection and incremental updates. The framework uses Pathway's reactive dataflow engine to detect source changes and propagate them through the pipeline without full re-indexing, enabling live document ingestion at scale across millions of documents while maintaining consistency.","intents":["I need to automatically ingest documents from multiple cloud storage providers and keep my search index in sync as files change","I want to build a knowledge base that updates in real-time when new documents are added to our shared drives","I need to monitor multiple data sources simultaneously and process only the documents that have changed since the last run"],"best_for":["Enterprise teams managing documents across multiple cloud platforms","Teams building knowledge bases that need to stay synchronized with live data sources","Developers building real-time search applications over distributed document repositories"],"limitations":["Requires explicit connector implementation for each data source type; not all cloud providers have pre-built connectors","Change detection relies on source API capabilities; some sources may have rate limits on polling","Incremental sync requires maintaining state about previously processed documents, adding storage overhead"],"requires":["Python 3.9+","Pathway framework installed","Authentication credentials for target data sources (Google Drive API key, SharePoint token, S3 credentials, etc.)","Network connectivity to monitored data sources"],"input_types":["file paths (local filesystem)","cloud storage URLs (Google Drive, SharePoint, S3)","API endpoints for data source connectors"],"output_types":["document metadata (path, modification time, content hash)","parsed document content","change event stream (new, modified, deleted documents)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_1","uri":"capability://data.processing.analysis.multi.format.document.parsing.with.metadata.extraction","name":"multi-format document parsing with metadata extraction","description":"Pathway LLM App includes pluggable document parsers that extract text and structured metadata from multiple formats (PDF, DOCX, TXT, HTML, etc.) while preserving document structure and semantic information. The parsing layer integrates with libraries like PyPDF2 and python-docx, handling format-specific quirks and producing normalized output that feeds into the embedding and retrieval pipeline.","intents":["I need to extract text from PDFs, Word documents, and web pages while preserving section structure and metadata","I want to parse documents in bulk and handle format-specific parsing errors gracefully","I need to extract metadata like author, creation date, and document title alongside the content"],"best_for":["Teams building document search systems that need to handle heterogeneous file formats","Enterprise knowledge management systems ingesting documents from multiple sources","Developers building RAG systems that require accurate text extraction from complex document layouts"],"limitations":["PDF parsing quality varies with document complexity; scanned PDFs without OCR produce no text output","Metadata extraction depends on document format compliance; malformed documents may lose metadata","Large documents (>100MB) may cause memory issues during parsing; requires streaming or chunking strategies","Preserving document structure (tables, headers, lists) requires format-specific logic; not all formats supported equally"],"requires":["Python 3.9+","Document parsing libraries (PyPDF2, python-docx, etc.) installed","Sufficient memory for in-memory parsing (configurable chunk size for large documents)","Optional: OCR engine (Tesseract) for scanned PDF support"],"input_types":["PDF files","DOCX/DOC files","TXT files","HTML files","Markdown files"],"output_types":["normalized text content","document metadata (title, author, creation date, page count)","structured sections (chapters, headers, paragraphs)","document embeddings (when integrated with embedding model)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_10","uri":"capability://image.visual.multimodal.rag.with.image.understanding.and.processing","name":"multimodal rag with image understanding and processing","description":"Pathway LLM App includes Multimodal RAG capabilities that process both text and images, enabling RAG systems to retrieve and reason over visual content. The framework integrates vision models (GPT-4V, etc.) to understand image content, extract text via OCR, and generate descriptions that are indexed alongside text chunks. This enables unified search over mixed-media documents.","intents":["I need to index and search documents that contain both text and images","I want to extract text from images (OCR) and use it for retrieval","I need to generate responses that reference both text and visual content from documents"],"best_for":["Teams building RAG systems over documents with mixed text and images (presentations, reports, etc.)","Enterprise applications requiring search over visual content (product catalogs, technical diagrams, etc.)","Developers building multimodal search systems that understand both text and images"],"limitations":["Image processing adds latency; vision model API calls are slower than text-only processing","Image understanding quality varies by model and image complexity; poor images produce low-quality descriptions","OCR accuracy depends on image quality; scanned documents with poor quality produce errors","Multimodal indexing increases storage overhead; image descriptions and embeddings add significant size"],"requires":["Python 3.9+","Pathway framework with multimodal support","Vision model API key (OpenAI GPT-4V, etc.) OR local vision model","Optional: OCR engine (Tesseract, PaddleOCR, etc.) for text extraction from images","Configuration file (app.yaml) with multimodal processing parameters"],"input_types":["documents containing text and images","image files (PNG, JPEG, etc.)","vision model configuration (provider, model, API key)"],"output_types":["extracted text from images (OCR output)","image descriptions (from vision model)","image embeddings (if using multimodal embedding model)","unified index combining text and image content"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_11","uri":"capability://search.retrieval.document.indexing.and.full.text.search.with.keyword.matching","name":"document indexing and full-text search with keyword matching","description":"Pathway LLM App provides document indexing capabilities that create searchable indices over document chunks using both vector embeddings and keyword matching. The framework supports full-text search with inverted indices, enabling fast keyword-based retrieval alongside semantic vector search. Hybrid search combines both approaches to improve retrieval precision and recall.","intents":["I need to create a searchable index over millions of documents for fast retrieval","I want to support both semantic search (vector similarity) and keyword search in the same system","I need to improve retrieval precision by combining vector and keyword matching"],"best_for":["Teams building enterprise search systems over large document collections","Developers optimizing retrieval quality by combining multiple search strategies","Applications requiring both semantic and keyword-based search capabilities"],"limitations":["Maintaining both vector and keyword indices increases storage overhead","Hybrid search requires tuning weights between vector and keyword scores; no automatic optimization","Keyword search is language-dependent; non-English languages may have degraded performance","Index updates must maintain both indices; incremental updates add complexity"],"requires":["Python 3.9+","Pathway framework with indexing support","Vector index implementation (in-memory or external)","Keyword index implementation (inverted index or external search engine)","Configuration file (app.yaml) with indexing strategy and hybrid search weights"],"input_types":["document chunks with metadata","indexing configuration (vector index type, keyword index type, hybrid weights)"],"output_types":["indexed documents (in both vector and keyword indices)","search results from hybrid search (ranked by combined score)","index statistics (document count, index size, etc.)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_12","uri":"capability://planning.reasoning.langgraph.agent.integration.for.multi.step.reasoning","name":"langgraph agent integration for multi-step reasoning","description":"Pathway LLM App integrates with LangGraph to enable multi-step reasoning agents that can decompose complex queries into subtasks, retrieve context iteratively, and make decisions based on intermediate results. Agents can use tools (search, calculation, etc.) and maintain state across multiple reasoning steps. This enables more sophisticated query answering than single-step RAG.","intents":["I need to build agents that can break down complex questions into multiple retrieval and reasoning steps","I want to enable agents to use multiple tools (search, calculation, etc.) to answer questions","I need to maintain state and context across multiple reasoning steps in a conversation"],"best_for":["Teams building sophisticated AI agents that require multi-step reasoning","Developers implementing complex query answering systems with tool use","Enterprise applications requiring conversational agents with memory and reasoning"],"limitations":["Multi-step reasoning adds latency; each step requires LLM calls and retrieval operations","Agent behavior is harder to predict and debug than single-step RAG; requires careful prompt engineering","State management across steps adds complexity; requires careful handling of context and memory","Tool integration requires custom implementation for each tool; no universal tool abstraction"],"requires":["Python 3.9+","Pathway framework with LangGraph integration","LangGraph library installed","LLM API key (OpenAI, Mistral, etc.) for agent reasoning","Tool implementations (search, calculation, etc.)","Configuration file (app.yaml) with agent configuration"],"input_types":["user query (text)","agent configuration (tools, reasoning strategy, max_steps)","optional: conversation history for multi-turn agents"],"output_types":["final agent response (text)","agent reasoning trace (intermediate steps, tool calls, decisions)","tool call results and context used"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_13","uri":"capability://automation.workflow.specialized.pipeline.templates.for.domain.specific.use.cases","name":"specialized pipeline templates for domain-specific use cases","description":"Pathway LLM App provides pre-built pipeline templates for specific use cases including Slides AI Search (searching presentation content), Unstructured to SQL (converting unstructured documents to structured data), and Drive Alert (monitoring cloud storage for changes). These templates are ready-to-deploy examples that can be customized for specific domains, reducing development time for common patterns.","intents":["I need to quickly deploy a RAG system for a specific use case (presentations, SQL generation, etc.) without building from scratch","I want to see example implementations of common RAG patterns to understand best practices","I need to customize a template for my specific domain without understanding the entire framework"],"best_for":["Teams building domain-specific RAG applications and want to start from working examples","Developers learning Pathway LLM App by studying template implementations","Organizations with common use cases (presentation search, document-to-SQL, etc.) that match templates"],"limitations":["Templates are optimized for specific use cases; customization for different domains may require significant changes","Template quality varies; some templates may be less mature or well-documented than others","Templates assume specific data formats and structures; adapting to different formats requires custom code","Template updates may not be synchronized with framework updates; may require manual updates"],"requires":["Python 3.9+","Pathway framework installed","Template-specific dependencies (e.g., presentation parsing libraries for Slides AI Search)","Configuration file (app.yaml) customized for specific use case","Domain-specific data (presentations, documents, etc.)"],"input_types":["domain-specific data (presentations, unstructured documents, cloud storage)","template configuration (app.yaml) customized for specific use case"],"output_types":["domain-specific results (presentation search results, SQL queries, alerts)","template-specific metadata and outputs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_14","uri":"capability://automation.workflow.configuration.driven.pipeline.definition.via.app.yaml","name":"configuration-driven pipeline definition via app.yaml","description":"Pathway LLM App uses declarative configuration files (app.yaml) to define entire RAG pipelines without code changes. Configuration specifies data sources, document parsing, chunking, embedding models, LLM providers, indexing strategy, and retrieval parameters. This enables non-developers to customize pipelines and developers to manage multiple pipeline variants without code duplication.","intents":["I need to customize my RAG pipeline (embedding model, LLM provider, chunk size) without modifying Python code","I want to manage multiple pipeline configurations for different environments (dev, staging, prod) without code duplication","I need to enable non-technical stakeholders to adjust pipeline parameters without developer involvement"],"best_for":["Teams with non-technical stakeholders who need to customize pipelines","Organizations managing multiple pipeline variants for different use cases","Developers seeking to separate configuration from code for maintainability"],"limitations":["Configuration-driven approach limits flexibility; complex customizations still require code changes","Configuration validation is limited; invalid configurations may only be caught at runtime","Configuration schema is framework-specific; requires learning Pathway's configuration format","Large configurations can become unwieldy; no built-in configuration composition or inheritance"],"requires":["Python 3.9+","Pathway framework installed","YAML file (app.yaml) with pipeline configuration","Understanding of Pathway configuration schema"],"input_types":["YAML configuration file (app.yaml)","configuration parameters (data sources, models, parameters)"],"output_types":["configured RAG pipeline","validation errors (if configuration is invalid)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_2","uri":"capability://data.processing.analysis.adaptive.text.chunking.with.semantic.aware.splitting","name":"adaptive text chunking with semantic-aware splitting","description":"Pathway LLM App provides configurable text splitting strategies that divide documents into chunks optimized for embedding and retrieval. The framework supports both fixed-size chunking and semantic-aware splitting that respects document structure (paragraphs, sentences, sections), with configurable overlap to maintain context between chunks. Chunk size and overlap parameters are tunable via the app.yaml configuration system.","intents":["I need to split documents into chunks that fit my embedding model's token limits while preserving semantic coherence","I want to configure chunk size and overlap globally across my entire pipeline without modifying code","I need to ensure that chunks don't split sentences or paragraphs in ways that lose meaning"],"best_for":["Teams building RAG systems that need to optimize chunk size for specific embedding models","Developers tuning retrieval quality by adjusting chunk boundaries and overlap","Enterprise applications requiring consistent chunking strategies across multiple document types"],"limitations":["Semantic-aware splitting requires language-specific tokenizers; non-English languages may have degraded performance","Optimal chunk size varies by embedding model and use case; no automatic tuning mechanism provided","Overlapping chunks increase storage and retrieval latency proportionally to overlap percentage","Very short documents may produce fewer chunks than expected, affecting retrieval coverage"],"requires":["Python 3.9+","Pathway framework with text processing utilities","Tokenizer for target language (NLTK, spaCy, or similar)","Configuration file (app.yaml) with chunking parameters"],"input_types":["parsed document text","document metadata (document ID, source)","chunking configuration (chunk_size, overlap_size, strategy)"],"output_types":["text chunks","chunk metadata (chunk ID, source document ID, position in document)","chunk boundaries (start/end character positions)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_3","uri":"capability://memory.knowledge.vector.and.hybrid.search.indexing.with.configurable.embedding.models","name":"vector and hybrid search indexing with configurable embedding models","description":"Pathway LLM App integrates with embedding models (OpenAI, Mistral, local models) to convert text chunks into vector representations, then indexes these vectors for efficient similarity search. The framework supports both pure vector search and hybrid search (combining vector similarity with keyword matching), with the indexing strategy configurable via app.yaml. Vectors are stored in an in-memory or persistent vector index that supports approximate nearest neighbor queries.","intents":["I need to embed document chunks using a specific embedding model and index them for semantic search","I want to switch between different embedding models (OpenAI, Mistral, local) without changing application code","I need to combine semantic similarity search with keyword matching to improve retrieval precision"],"best_for":["Teams building semantic search systems over large document collections","Developers optimizing retrieval quality by experimenting with different embedding models","Enterprise applications requiring hybrid search to balance recall and precision"],"limitations":["Embedding API calls incur latency and cost; batch embedding can reduce cost but increases latency variance","Vector index size grows linearly with number of chunks; millions of chunks require significant memory or external vector database","Approximate nearest neighbor search trades recall for speed; exact search is slower but guarantees correctness","Embedding model quality varies; poor embeddings degrade retrieval quality regardless of indexing strategy"],"requires":["Python 3.9+","Pathway framework with vector indexing support","Embedding model API key (OpenAI, Mistral) OR local embedding model (sentence-transformers, etc.)","Vector index implementation (in-memory or external: Pinecone, Weaviate, Milvus, etc.)","Configuration file (app.yaml) specifying embedding model and indexing strategy"],"input_types":["text chunks with metadata","embedding model configuration (provider, model name, API key)","indexing strategy configuration (vector, hybrid, keyword-only)"],"output_types":["vector embeddings (float arrays, typically 384-1536 dimensions)","indexed vectors with metadata pointers","search results (ranked by similarity score)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_4","uri":"capability://search.retrieval.context.aware.query.processing.and.retrieval.with.ranking","name":"context-aware query processing and retrieval with ranking","description":"Pathway LLM App processes user queries through a retrieval pipeline that finds relevant document chunks from the indexed corpus. The framework supports query rewriting (reformulating queries for better retrieval), context retrieval (finding top-K similar chunks), and ranking strategies to order results by relevance. Retrieved context is passed to the LLM along with the original query to ground the response in retrieved documents.","intents":["I need to retrieve the most relevant document chunks for a user query and rank them by relevance","I want to rewrite user queries to improve retrieval quality (e.g., expanding abbreviations, adding synonyms)","I need to control how many context chunks are passed to the LLM to balance quality and latency"],"best_for":["Teams building RAG systems that need to retrieve relevant context for LLM responses","Developers tuning retrieval quality by adjusting ranking strategies and context window size","Enterprise applications requiring explainable retrieval (showing which documents were used)"],"limitations":["Query rewriting adds latency; LLM-based rewriting requires additional API calls","Retrieval quality depends on embedding quality and document chunking; poor chunks degrade results","Top-K retrieval may miss relevant documents if K is too small; larger K increases context size and LLM latency","Ranking strategies are heuristic-based; no learning-to-rank mechanism for automatic optimization"],"requires":["Python 3.9+","Pathway framework with retrieval utilities","Indexed vector corpus from previous embedding step","Optional: LLM API key for query rewriting (OpenAI, Mistral, etc.)","Configuration file (app.yaml) with retrieval parameters (top_k, ranking_strategy, etc.)"],"input_types":["user query (text)","query metadata (user ID, session ID, optional)","retrieval configuration (top_k, ranking_strategy, rewrite_enabled)"],"output_types":["retrieved document chunks (ranked by relevance)","relevance scores (similarity scores or ranking scores)","chunk metadata (source document, position, etc.)","rewritten query (if query rewriting enabled)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_5","uri":"capability://text.generation.language.llm.integration.with.multi.provider.support.and.response.generation","name":"llm integration with multi-provider support and response generation","description":"Pathway LLM App provides a unified interface to multiple LLM providers (OpenAI, Mistral, local models via Ollama) for generating responses grounded in retrieved context. The framework handles prompt construction, context injection, and response streaming, with provider selection configurable via app.yaml. Responses are generated by passing the user query and retrieved document chunks to the LLM, enabling RAG-based question answering.","intents":["I need to generate LLM responses grounded in retrieved document context without building custom prompt templates","I want to switch between different LLM providers (OpenAI, Mistral, local) without changing application code","I need to stream LLM responses to users in real-time rather than waiting for complete generation"],"best_for":["Teams building RAG applications that need to generate grounded responses","Developers experimenting with different LLM providers to optimize cost and quality","Enterprise applications requiring on-premises LLM deployment (via Ollama or similar)"],"limitations":["LLM API calls incur latency and cost; response generation is the slowest step in the RAG pipeline","Prompt quality directly impacts response quality; no automatic prompt optimization provided","Context window limits constrain how much retrieved context can be passed to the LLM","Streaming responses require client-side handling; not all LLM providers support streaming equally"],"requires":["Python 3.9+","Pathway framework with LLM integration utilities","LLM provider API key (OpenAI, Mistral) OR local LLM via Ollama","Configuration file (app.yaml) with LLM provider and model selection","Retrieved context from previous retrieval step"],"input_types":["user query (text)","retrieved document context (list of chunks with metadata)","LLM configuration (provider, model, temperature, max_tokens, etc.)"],"output_types":["generated response (text)","response metadata (model used, tokens consumed, generation time)","optional: token usage statistics for cost tracking"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_6","uri":"capability://tool.use.integration.http.rest.api.exposure.with.streaming.response.support","name":"http rest api exposure with streaming response support","description":"Pathway LLM App automatically exposes the RAG pipeline as HTTP REST endpoints that accept queries and return LLM-generated responses with retrieved context. The framework handles request routing, response serialization, and optional streaming of responses to clients. API endpoints are generated from the pipeline configuration without manual endpoint definition, enabling rapid deployment of query interfaces.","intents":["I need to expose my RAG pipeline as a REST API that clients can query without direct Python access","I want to stream responses to clients in real-time rather than waiting for complete generation","I need to handle multiple concurrent queries without blocking"],"best_for":["Teams deploying RAG applications as web services","Developers building client applications (web, mobile) that query RAG pipelines","Enterprise applications requiring HTTP-based access to AI capabilities"],"limitations":["HTTP latency adds overhead; streaming helps but doesn't eliminate network round-trip time","Concurrent request handling depends on Pathway's async capabilities; high concurrency may require load balancing","API authentication/authorization not built-in; requires external API gateway or custom middleware","Response format is fixed; custom response structures require code changes"],"requires":["Python 3.9+","Pathway framework with HTTP server support","HTTP server library (FastAPI, Flask, or similar) integrated with Pathway","Network connectivity for clients to reach the API endpoint","Optional: reverse proxy (nginx) for load balancing and SSL termination"],"input_types":["HTTP POST request with JSON body containing query","optional: query parameters (top_k, model selection, etc.)"],"output_types":["HTTP 200 response with JSON body containing generated response and retrieved context","optional: Server-Sent Events (SSE) stream for streaming responses","HTTP error responses (400, 500, etc.) for invalid requests or server errors"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_7","uri":"capability://automation.workflow.streamlit.ui.generation.for.interactive.query.interface","name":"streamlit ui generation for interactive query interface","description":"Pathway LLM App includes a Streamlit-based user interface that provides an interactive query interface for the RAG pipeline. The UI allows users to submit queries, view generated responses, and inspect retrieved context documents. The Streamlit app is automatically generated from the pipeline configuration, enabling rapid deployment of user-facing interfaces without frontend development.","intents":["I need to provide a user-friendly interface for querying my RAG pipeline without building a custom web application","I want to show users which documents were retrieved and used to generate responses","I need to deploy a demo or prototype quickly without frontend development expertise"],"best_for":["Teams prototyping RAG applications and need quick user-facing interfaces","Non-technical stakeholders who need to interact with RAG pipelines","Developers building internal tools and demos for evaluation"],"limitations":["Streamlit is designed for rapid prototyping, not production-grade web applications; limited customization","Streamlit apps are single-threaded by default; high concurrency requires custom session management","UI styling is limited to Streamlit's built-in components; custom branding requires CSS injection","Streamlit apps require Python runtime; cannot be deployed as static frontend"],"requires":["Python 3.9+","Streamlit library installed","Pathway framework with Streamlit integration","Running Pathway pipeline (backend)","Web browser for accessing the UI"],"input_types":["user text input (query)","optional: UI configuration (title, description, etc.)"],"output_types":["rendered HTML page with Streamlit components","displayed generated response","displayed retrieved context documents","optional: response metadata (model, tokens, etc.)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_8","uri":"capability://search.retrieval.adaptive.rag.with.query.dependent.retrieval.strategy.selection","name":"adaptive rag with query-dependent retrieval strategy selection","description":"Pathway LLM App includes an Adaptive RAG pattern that selects retrieval strategies dynamically based on query characteristics. The framework analyzes incoming queries to determine whether to use vector search, keyword search, or hybrid search, optimizing retrieval for different query types without manual configuration. This pattern improves retrieval quality by matching retrieval strategy to query intent.","intents":["I need to automatically select the best retrieval strategy (vector, keyword, hybrid) based on query characteristics","I want to improve retrieval quality for different types of queries without manual tuning","I need to handle both semantic queries (e.g., 'what is machine learning') and specific queries (e.g., 'find document X') with appropriate strategies"],"best_for":["Teams building RAG systems that need to handle diverse query types","Developers optimizing retrieval quality across different use cases","Enterprise applications requiring robust retrieval across varied query patterns"],"limitations":["Query analysis adds latency; LLM-based analysis requires additional API calls","Strategy selection heuristics are rule-based; no learning mechanism for automatic optimization","Adaptive RAG requires multiple retrieval indices (vector, keyword); increases storage and indexing overhead","Strategy selection quality depends on query analysis accuracy; ambiguous queries may select suboptimal strategies"],"requires":["Python 3.9+","Pathway framework with Adaptive RAG pattern","Multiple retrieval indices (vector index, keyword index, or both)","Optional: LLM for query analysis (OpenAI, Mistral, etc.)","Configuration file (app.yaml) with adaptive RAG parameters"],"input_types":["user query (text)","query metadata (optional: user context, session history)","adaptive RAG configuration (strategy selection rules, thresholds)"],"output_types":["selected retrieval strategy (vector, keyword, hybrid)","retrieved context using selected strategy","strategy metadata (confidence in selection, alternative strategies considered)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-app__cap_9","uri":"capability://memory.knowledge.private.rag.with.local.embedding.and.llm.models","name":"private rag with local embedding and llm models","description":"Pathway LLM App supports Private RAG deployments that use local embedding models (sentence-transformers, etc.) and local LLMs (Ollama, LLaMA, etc.) instead of cloud APIs. This pattern enables RAG applications to run entirely on-premises without sending data to external services, addressing privacy and compliance requirements. Local models are integrated via the same provider abstraction as cloud models, allowing seamless switching.","intents":["I need to build a RAG system that processes sensitive data without sending it to cloud APIs","I want to deploy RAG on-premises for compliance or data sovereignty reasons","I need to reduce API costs by using local models instead of cloud-based APIs"],"best_for":["Enterprise teams with strict data privacy or compliance requirements","Organizations processing sensitive data (healthcare, finance, legal) that cannot use cloud APIs","Teams seeking to reduce operational costs by avoiding per-token API charges"],"limitations":["Local models typically have lower quality than large cloud models; retrieval and response quality may degrade","Local models require significant computational resources (GPU recommended); infrastructure costs may offset API savings","Model updates and improvements require manual redeployment; no automatic model updates like cloud APIs","Debugging and support for local models is more difficult; fewer community resources and examples"],"requires":["Python 3.9+","Pathway framework with local model support","Local embedding model (sentence-transformers, etc.) installed","Local LLM (Ollama, LLaMA, etc.) running locally or on accessible server","Sufficient computational resources (GPU recommended for reasonable latency)","Configuration file (app.yaml) specifying local model paths and endpoints"],"input_types":["document chunks for embedding","queries for processing","local model configuration (model path, endpoint URL, etc.)"],"output_types":["vector embeddings from local embedding model","generated responses from local LLM","no external API calls or data transmission"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Pathway framework installed","Authentication credentials for target data sources (Google Drive API key, SharePoint token, S3 credentials, etc.)","Network connectivity to monitored data sources","Document parsing libraries (PyPDF2, python-docx, etc.) installed","Sufficient memory for in-memory parsing (configurable chunk size for large documents)","Optional: OCR engine (Tesseract) for scanned PDF support","Pathway framework with multimodal support","Vision model API key (OpenAI GPT-4V, etc.) OR local vision model","Optional: OCR engine (Tesseract, PaddleOCR, etc.) for text extraction from images"],"failure_modes":["Requires explicit connector implementation for each data source type; not all cloud providers have pre-built connectors","Change detection relies on source API capabilities; some sources may have rate limits on polling","Incremental sync requires maintaining state about previously processed documents, adding storage overhead","PDF parsing quality varies with document complexity; scanned PDFs without OCR produce no text output","Metadata extraction depends on document format compliance; malformed documents may lose metadata","Large documents (>100MB) may cause memory issues during parsing; requires streaming or chunking strategies","Preserving document structure (tables, headers, lists) requires format-specific logic; not all formats supported equally","Image processing adds latency; vision model API calls are slower than text-only processing","Image understanding quality varies by model and image complexity; poor images produce low-quality descriptions","OCR accuracy depends on image quality; scanned documents with poor quality produce errors","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:20.516Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llm-app","compare_url":"https://unfragile.ai/compare?artifact=llm-app"}},"signature":"p+Dw59JWqFG6fiuOdzL7ZmdIruH5OTIz5oVVh4dniplbi7lXtdyzNI15pCi+L7/LU+JrvA+CEviM4FoUf5WjBg==","signedAt":"2026-06-22T11:15:23.718Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm-app","artifact":"https://unfragile.ai/llm-app","verify":"https://unfragile.ai/api/v1/verify?slug=llm-app","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"}}