{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-local-gpt","slug":"local-gpt","name":"Local GPT","type":"repo","url":"https://github.com/PromtEngineer/localGPT","page_url":"https://unfragile.ai/local-gpt","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-local-gpt__cap_0","uri":"capability://search.retrieval.hybrid.search.retrieval.with.vector.and.bm25","name":"hybrid-search-retrieval-with-vector-and-bm25","description":"Combines vector similarity search with BM25 keyword matching to retrieve relevant document chunks, using late chunking and AI-powered reranking to surface the most contextually relevant results. The system maintains parallel vector and keyword indices, executes both search paths concurrently, and applies a learned reranker to fuse results before passing to the LLM, enabling both semantic and lexical relevance.","intents":["I need to find specific information in documents using both semantic meaning and exact keyword matches","I want search results ranked by relevance rather than just similarity score","I need to retrieve context that balances between conceptual similarity and keyword precision"],"best_for":["enterprises with large document repositories requiring high-precision retrieval","teams building RAG systems where both semantic and keyword relevance matter","organizations needing to minimize hallucinations through better context retrieval"],"limitations":["Reranking adds latency (~100-300ms per query depending on result set size)","Requires maintaining two separate indices (vector + BM25), doubling storage overhead","Late chunking strategy requires larger context windows in the LLM, increasing inference cost"],"requires":["LanceDB vector database (local)","BM25 indexing library (integrated)","LLM with minimum 4K token context window","Embedding model (HuggingFace or Ollama-compatible)"],"input_types":["natural language query","document chunks (pre-processed)"],"output_types":["ranked list of document chunks with relevance scores","structured retrieval context for LLM"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-local-gpt__cap_1","uri":"capability://data.processing.analysis.multi.format.document.ingestion.with.contextual.enrichment","name":"multi-format-document-ingestion-with-contextual-enrichment","description":"Processes documents in multiple formats (PDF, DOCX, TXT, Markdown) through a unified pipeline that extracts text, applies contextual enrichment to preserve document structure and relationships, and batches processing for efficiency. The system uses format-specific parsers, maintains document metadata, and enriches chunks with surrounding context before vectorization to improve retrieval quality.","intents":["I need to upload documents in different formats without manual conversion","I want the system to understand document structure (headings, sections, tables) not just raw text","I need batch processing to handle large document collections efficiently"],"best_for":["organizations with mixed document repositories (PDFs, Word docs, markdown)","teams needing to preserve document hierarchy and context during ingestion","enterprises processing large batches of documents regularly"],"limitations":["PDF parsing quality depends on PDF structure; scanned PDFs require OCR (not built-in)","Contextual enrichment adds ~50-200ms per chunk depending on enrichment strategy","Batch processing requires sufficient memory; large files may need streaming ingestion"],"requires":["Python 3.9+","PDF parsing library (PyPDF2 or similar)","Document processing libraries (python-docx, markdown parser)","Local storage for document cache"],"input_types":["PDF files","DOCX files","TXT files","Markdown files"],"output_types":["normalized text chunks","document metadata","enriched context vectors","indexed document store"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-local-gpt__cap_10","uri":"capability://safety.moderation.privacy.preserving.on.premise.deployment","name":"privacy-preserving-on-premise-deployment","description":"Ensures all data processing (documents, embeddings, chat history, model inference) occurs locally without external API calls or data transmission, using local storage (LanceDB for vectors, SQLite for chat history) and Ollama for model inference. The system is designed for air-gapped or restricted-network environments where data cannot leave the organization.","intents":["I need to process sensitive documents without sending them to cloud services","I want to comply with data residency regulations (GDPR, HIPAA, etc.)","I need to operate in air-gapped or restricted-network environments"],"best_for":["organizations with strict data privacy requirements","regulated industries (healthcare, finance, government)","teams handling sensitive or proprietary documents","deployments in restricted network environments"],"limitations":["Requires significant local hardware investment (GPU for reasonable inference speed)","No access to cloud-based models or services; limited to open-source models","Scaling to multiple users requires distributed deployment (not built-in)","Maintenance burden higher than cloud services (model updates, security patches)"],"requires":["Local hardware with sufficient GPU VRAM (8GB+ recommended)","Ollama installed locally","Local storage for documents and indices","Network isolation or firewall rules to prevent external data transmission"],"input_types":["local document files","local user queries"],"output_types":["local responses","local embeddings","local chat history"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-local-gpt__cap_11","uri":"capability://automation.workflow.extensible.architecture.with.modular.components","name":"extensible-architecture-with-modular-components","description":"Implements a multi-service architecture where document processing, retrieval, generation, and API layers are independently deployable and configurable services orchestrated by a central run_system.py script. Each service has well-defined responsibilities and APIs, allowing developers to swap components (e.g., different embedding models, retrieval strategies) without modifying other services.","intents":["I want to customize specific components without rewriting the entire system","I need to deploy services independently for scaling or maintenance","I want to experiment with different retrieval or generation strategies"],"best_for":["developers building custom RAG systems on top of LocalGPT","teams needing to optimize specific pipeline stages","organizations with complex deployment requirements"],"limitations":["Modular architecture adds complexity; more services to manage and monitor","Inter-service communication adds latency (~50-200ms per service boundary)","Distributed deployment requires orchestration (Docker, Kubernetes) for production","Service versioning and compatibility management becomes critical"],"requires":["Python 3.9+","Understanding of service architecture patterns","Deployment orchestration tools (Docker, optional)","API documentation for service interfaces"],"input_types":["service configuration","component implementations"],"output_types":["modular service instances","inter-service APIs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-local-gpt__cap_2","uri":"capability://tool.use.integration.local.model.orchestration.via.ollama.integration","name":"local-model-orchestration-via-ollama-integration","description":"Manages local LLM and embedding model inference through Ollama, allowing users to run multiple model types (chat, embedding, reranking) on local hardware without external API calls. The system communicates with Ollama via HTTP endpoints (localhost:11434), handles model lifecycle management, and supports dynamic model switching based on query complexity through smart routing.","intents":["I want to run LLMs locally without sending data to cloud APIs","I need to switch between different models (fast vs accurate) based on query complexity","I want to use open-source models (Llama, Mistral, etc.) without vendor lock-in"],"best_for":["organizations with strict data privacy requirements","teams building on-premise AI systems","developers wanting to avoid cloud API costs and latency"],"limitations":["Local inference speed depends on hardware (GPU required for reasonable latency with large models)","Model selection limited to Ollama-compatible models; no native support for proprietary APIs","Requires significant local storage (7B-70B parameter models = 4-40GB disk space)","Inference latency typically 2-10x slower than cloud APIs depending on hardware"],"requires":["Ollama installed and running (http://localhost:11434 accessible)","GPU with VRAM sufficient for target model (8GB+ recommended for 7B models)","Python 3.9+","Network connectivity to local Ollama service"],"input_types":["natural language prompts","document chunks for embedding"],"output_types":["LLM-generated text responses","embedding vectors","reranking scores"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-local-gpt__cap_3","uri":"capability://text.generation.language.session.based.chat.history.with.streaming.responses","name":"session-based-chat-history-with-streaming-responses","description":"Maintains conversation state across multiple turns using SQLite-backed session management, enabling context-aware follow-up questions and multi-turn reasoning. The system streams responses in real-time to the web interface, tracks session metadata, and allows users to manage multiple independent conversation threads without context bleed.","intents":["I want to have multi-turn conversations where the system remembers previous questions","I need real-time response streaming to see results as they're generated","I want to manage multiple independent chat sessions without mixing context"],"best_for":["interactive document analysis workflows requiring multi-turn reasoning","teams building conversational document interfaces","users needing to explore documents through iterative questioning"],"limitations":["SQLite session storage not suitable for distributed deployments (single-machine only)","Streaming adds complexity to error handling and response validation","Session context grows with conversation length; very long sessions may impact retrieval performance","No built-in session persistence across system restarts without manual export"],"requires":["SQLite database (./backend/chat_data.db)","WebSocket or Server-Sent Events support for streaming","Python 3.9+","Web server capable of handling streaming responses"],"input_types":["natural language user messages","session identifiers"],"output_types":["streamed text responses","session metadata","conversation history"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-local-gpt__cap_4","uri":"capability://planning.reasoning.query.decomposition.and.answer.verification","name":"query-decomposition-and-answer-verification","description":"Breaks complex multi-part questions into sub-queries, executes each independently through the RAG pipeline, and verifies answers against source documents before returning to the user. The system uses the LLM to decompose queries, routes each sub-query through retrieval and generation, and applies verification logic to detect hallucinations or unsupported claims.","intents":["I need to ask complex questions that require reasoning across multiple documents","I want the system to verify that answers are actually supported by the documents","I need to understand which documents support each part of the answer"],"best_for":["organizations requiring high-confidence answers with source attribution","teams building RAG systems where hallucination reduction is critical","users analyzing complex documents requiring multi-step reasoning"],"limitations":["Query decomposition adds 1-3 seconds latency per complex question","Verification logic may reject valid inferences not explicitly stated in documents","Decomposition quality depends on LLM capability; weaker models may miss relevant sub-queries","Requires additional LLM calls (decomposition + verification), increasing inference cost"],"requires":["LLM capable of instruction-following (7B+ parameter models recommended)","Document retrieval system (hybrid search)","Verification logic implementation","Python 3.9+"],"input_types":["natural language questions","document context"],"output_types":["decomposed sub-queries","verified answers with source attribution","confidence scores"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-local-gpt__cap_5","uri":"capability://memory.knowledge.semantic.caching.for.repeated.queries","name":"semantic-caching-for-repeated-queries","description":"Caches embeddings and retrieval results for semantically similar queries, avoiding redundant vector search and LLM inference when users ask variations of the same question. The system compares incoming query embeddings against cached queries using similarity thresholds, returns cached results when similarity exceeds the threshold, and updates the cache with new queries.","intents":["I want faster responses when asking similar questions repeatedly","I need to reduce computational cost for common query patterns","I want to avoid redundant retrieval and inference for semantically equivalent questions"],"best_for":["interactive document analysis with repeated question patterns","cost-sensitive deployments where inference is expensive","systems with high query volume and predictable question patterns"],"limitations":["Cache invalidation required when documents are updated; stale cache can return outdated results","Similarity threshold tuning is critical; too low = false positives, too high = cache misses","Cache memory grows with unique queries; requires periodic cleanup for long-running systems","Semantic similarity may miss important nuances between similar-sounding questions"],"requires":["Embedding model for query similarity comparison","In-memory or persistent cache store","Similarity threshold configuration","Cache invalidation mechanism"],"input_types":["natural language queries","query embeddings"],"output_types":["cached retrieval results","cached LLM responses","cache hit/miss indicators"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-local-gpt__cap_6","uri":"capability://automation.workflow.web.interface.with.real.time.progress.tracking","name":"web-interface-with-real-time-progress-tracking","description":"Provides a browser-based UI for document upload, query submission, and result viewing with real-time progress indicators showing document processing, retrieval, and generation stages. The frontend communicates with the backend via REST APIs and WebSockets, displays streaming responses as they arrive, and provides visual feedback on system state and processing stages.","intents":["I want a user-friendly interface to interact with documents without command-line tools","I need to see real-time progress as documents are processed and queries are answered","I want to upload documents and ask questions through a web browser"],"best_for":["non-technical users needing to interact with document analysis","teams building internal tools for document Q&A","organizations wanting to provide self-service document search"],"limitations":["Web interface adds network latency compared to direct API calls","Browser-based file uploads limited by browser memory (typically 2-4GB practical limit)","Real-time progress tracking requires WebSocket support; fallback to polling adds latency","Frontend complexity increases maintenance burden compared to API-only systems"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","Backend REST API server","WebSocket support for streaming","Network connectivity to backend"],"input_types":["file uploads (PDF, DOCX, TXT, Markdown)","text queries via form input"],"output_types":["rendered HTML responses","progress indicators","document metadata display","chat history visualization"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-local-gpt__cap_7","uri":"capability://automation.workflow.index.management.and.document.lifecycle","name":"index-management-and-document-lifecycle","description":"Manages document indices with operations to create, update, delete, and rebuild indices without losing chat history or requiring system restart. The system tracks document metadata, supports incremental indexing for new documents, and provides tools to reindex specific documents or entire collections when needed.","intents":["I need to add new documents to the system without restarting or losing chat history","I want to remove outdated documents from the index","I need to rebuild indices when document content changes"],"best_for":["systems with evolving document collections","teams needing to maintain indices without downtime","organizations with document versioning requirements"],"limitations":["Incremental indexing requires tracking document versions; complex for large collections","Index rebuilds require temporary storage for both old and new indices","Deletion from vector indices may not reclaim storage immediately (depends on database)","No built-in conflict resolution for concurrent document updates"],"requires":["LanceDB vector database","Document metadata tracking system","Sufficient disk space for index operations","Python 3.9+"],"input_types":["document files","document identifiers","index operation commands"],"output_types":["index status reports","operation confirmation","metadata updates"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-local-gpt__cap_8","uri":"capability://tool.use.integration.flexible.model.configuration.with.multiple.backends","name":"flexible-model-configuration-with-multiple-backends","description":"Allows users to configure different LLM and embedding models through YAML configuration files, supporting multiple backends (Ollama, HuggingFace) and enabling easy model swapping without code changes. The system reads configuration at startup, validates model availability, and routes inference requests to the configured backends.","intents":["I want to experiment with different models without changing code","I need to use different models for different tasks (fast embedding vs accurate generation)","I want to switch between open-source and proprietary models easily"],"best_for":["developers experimenting with different model combinations","teams needing to optimize model selection for cost/quality tradeoffs","organizations with model evaluation workflows"],"limitations":["Configuration validation happens at startup; invalid configs cause system startup failure","Model switching requires system restart (no hot-reload)","Configuration complexity increases with more backend options","No built-in model performance benchmarking; users must manually evaluate tradeoffs"],"requires":["YAML configuration files","Model availability (Ollama running or HuggingFace API access)","Python 3.9+","Configuration schema validation"],"input_types":["YAML configuration files","model identifiers"],"output_types":["validated configuration","model availability status","inference routing rules"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-local-gpt__cap_9","uri":"capability://tool.use.integration.restful.api.with.health.monitoring","name":"restful-api-with-health-monitoring","description":"Exposes system functionality through RESTful endpoints for document upload, query submission, session management, and index operations, with comprehensive health monitoring endpoints that report system status, service availability, and performance metrics. The API includes request validation, error handling, and status codes that enable external systems to monitor and orchestrate LocalGPT.","intents":["I want to integrate LocalGPT into my existing application via API","I need to monitor system health and performance programmatically","I want to automate document upload and query workflows"],"best_for":["developers building applications on top of LocalGPT","teams integrating LocalGPT into larger systems","organizations needing programmatic system monitoring"],"limitations":["API rate limiting not built-in; requires external rate limiting layer for production","Authentication/authorization not included; requires external auth system","Health monitoring endpoints may add overhead to system; frequent polling can impact performance","API versioning strategy not specified; breaking changes may affect integrations"],"requires":["HTTP client library","LocalGPT backend running and accessible","Network connectivity to API endpoints","API documentation or schema"],"input_types":["HTTP requests with JSON payloads","file uploads","query parameters"],"output_types":["JSON responses","health status reports","operation confirmations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["LanceDB vector database (local)","BM25 indexing library (integrated)","LLM with minimum 4K token context window","Embedding model (HuggingFace or Ollama-compatible)","Python 3.9+","PDF parsing library (PyPDF2 or similar)","Document processing libraries (python-docx, markdown parser)","Local storage for document cache","Local hardware with sufficient GPU VRAM (8GB+ recommended)","Ollama installed locally"],"failure_modes":["Reranking adds latency (~100-300ms per query depending on result set size)","Requires maintaining two separate indices (vector + BM25), doubling storage overhead","Late chunking strategy requires larger context windows in the LLM, increasing inference cost","PDF parsing quality depends on PDF structure; scanned PDFs require OCR (not built-in)","Contextual enrichment adds ~50-200ms per chunk depending on enrichment strategy","Batch processing requires sufficient memory; large files may need streaming ingestion","Requires significant local hardware investment (GPU for reasonable inference speed)","No access to cloud-based models or services; limited to open-source models","Scaling to multiple users requires distributed deployment (not built-in)","Maintenance burden higher than cloud services (model updates, security patches)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"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":"2026-05-03T14:00:10.321Z","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=local-gpt","compare_url":"https://unfragile.ai/compare?artifact=local-gpt"}},"signature":"30t94uFNkTnKnBwgJLBVJHtAr9dOrdX/lJ1N82YMV7vrkXLFYppfCYj2xmjXY2EKOVj5cLH1gmyQcG1zmTIVBw==","signedAt":"2026-06-20T00:20:10.842Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/local-gpt","artifact":"https://unfragile.ai/local-gpt","verify":"https://unfragile.ai/api/v1/verify?slug=local-gpt","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"}}