{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-vibe-agent-toolkit-rag-lancedb","slug":"vibe-agent-toolkit-rag-lancedb","name":"@vibe-agent-toolkit/rag-lancedb","type":"repo","url":"https://github.com/jdutton/vibe-agent-toolkit#readme","page_url":"https://unfragile.ai/vibe-agent-toolkit-rag-lancedb","categories":["rag-knowledge"],"tags":["rag","vector-search","lancedb","embeddings"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-vibe-agent-toolkit-rag-lancedb__cap_0","uri":"capability://memory.knowledge.lancedb.backed.vector.storage.and.retrieval","name":"lancedb-backed vector storage and retrieval","description":"Implements persistent vector database storage using LanceDB as the underlying engine, enabling efficient similarity search over embedded documents. The capability abstracts LanceDB's columnar storage format and vector indexing (IVF-PQ by default) behind a standardized RAG interface, allowing agents to store and retrieve semantically similar content without managing database infrastructure directly. Supports batch ingestion of embeddings and configurable distance metrics for similarity computation.","intents":["Store document embeddings in a persistent vector database without managing database infrastructure","Retrieve semantically similar documents from a large corpus using vector similarity search","Build RAG pipelines that can scale to millions of embedded documents with sub-second retrieval latency","Integrate vector search into multi-step agent workflows without vendor lock-in to cloud vector databases"],"best_for":["Teams building local-first or on-premise RAG agents","Developers prototyping multi-agent systems with shared knowledge bases","Organizations requiring vector search without external API dependencies"],"limitations":["LanceDB is optimized for analytical workloads; concurrent write throughput may be limited compared to specialized vector databases like Pinecone or Weaviate","No built-in replication or distributed deployment — single-machine or shared filesystem only","Vector index updates require re-indexing; incremental updates are not optimized","No native support for metadata filtering during vector search (requires post-retrieval filtering)"],"requires":["Node.js 16+ or Python 3.8+","LanceDB library installed (@lancedb/lancedb or equivalent)","Pre-computed embeddings from an embedding model (OpenAI, Hugging Face, or local)","Filesystem access or cloud storage (S3, GCS) for vector database persistence"],"input_types":["embeddings (float arrays, typically 384-1536 dimensions)","document metadata (JSON objects with text, source, timestamp)","query embeddings (float arrays matching stored embedding dimensions)"],"output_types":["ranked document chunks with similarity scores","structured retrieval results (document ID, content, metadata, distance metric)"],"categories":["memory-knowledge","vector-database"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vibe-agent-toolkit-rag-lancedb__cap_1","uri":"capability://data.processing.analysis.embedding.agnostic.document.ingestion.pipeline","name":"embedding-agnostic document ingestion pipeline","description":"Accepts raw documents (text, markdown, code) and orchestrates the embedding generation and storage workflow through a pluggable embedding provider interface. The pipeline abstracts the choice of embedding model (OpenAI, Hugging Face, local models) and handles chunking, metadata extraction, and batch ingestion into LanceDB without coupling agents to a specific embedding service. Supports configurable chunk sizes and overlap for context preservation.","intents":["Ingest a corpus of documents and automatically embed them without writing custom embedding orchestration code","Switch between embedding providers (OpenAI → Hugging Face → local models) without refactoring agent code","Chunk large documents intelligently while preserving semantic context across chunk boundaries","Build reproducible knowledge bases that can be versioned and shared across agent instances"],"best_for":["Developers building knowledge-grounded agents who want to avoid embedding provider lock-in","Teams managing multiple RAG pipelines with different embedding models per use case","Organizations transitioning from one embedding service to another"],"limitations":["Chunking strategy is fixed (sliding window); no semantic-aware chunking (e.g., sentence-level or paragraph-level boundaries)","No built-in deduplication of documents; duplicate ingestion requires external filtering","Embedding generation is synchronous; large corpora (>100k documents) may require external batching infrastructure","No automatic re-embedding when documents are updated; requires manual pipeline re-run"],"requires":["Embedding provider API key (OpenAI, Hugging Face, or local model server)","Document source (file paths, URLs, or in-memory text)","Configured chunk size and overlap parameters"],"input_types":["plain text documents","markdown files","code files","structured metadata (JSON)"],"output_types":["embedded documents stored in LanceDB","ingestion logs with chunk counts and embedding dimensions"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vibe-agent-toolkit-rag-lancedb__cap_2","uri":"capability://search.retrieval.semantic.similarity.search.with.configurable.distance.metrics","name":"semantic similarity search with configurable distance metrics","description":"Executes vector similarity queries against the LanceDB index using configurable distance metrics (cosine, L2, dot product) and returns ranked results with relevance scores. The search capability supports filtering by metadata fields and limiting result sets, enabling agents to retrieve the most contextually relevant documents for a given query embedding. Internally leverages LanceDB's optimized vector search algorithms (IVF-PQ indexing) for sub-linear query latency.","intents":["Query a knowledge base with a user question and retrieve the top-k most relevant documents","Implement multi-stage retrieval (coarse-to-fine) by first searching with a fast metric, then re-ranking with a more expensive metric","Customize similarity metrics per use case (e.g., cosine for semantic similarity, L2 for dense clustering)","Build retrieval-augmented generation pipelines that feed ranked documents into LLM prompts"],"best_for":["Developers building question-answering agents over large document corpora","Teams optimizing retrieval latency and relevance for production RAG systems","Researchers experimenting with different similarity metrics for domain-specific retrieval"],"limitations":["Search latency depends on index size and hardware; no built-in query optimization or caching","Metadata filtering is post-hoc (applied after vector search), not pre-filtered; can be inefficient for sparse metadata","No support for hybrid search (combining keyword and semantic search) — requires external BM25 integration","Distance metric selection is static per index; cannot dynamically switch metrics per query"],"requires":["Query embedding (float array matching stored embedding dimensions)","Populated LanceDB index with stored document embeddings","Optional metadata filters (field names and values)"],"input_types":["query embedding (float array)","metadata filter expressions (JSON or key-value pairs)","result limit (integer, default 10)"],"output_types":["ranked list of documents with similarity scores","document metadata and content","relevance rankings"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vibe-agent-toolkit-rag-lancedb__cap_3","uri":"capability://tool.use.integration.agent.native.rag.interface.abstraction","name":"agent-native rag interface abstraction","description":"Provides a standardized interface for RAG operations (store, retrieve, delete) that integrates seamlessly with the vibe-agent-toolkit's agent execution model. The abstraction allows agents to invoke RAG operations as tool calls within their reasoning loops, treating knowledge retrieval as a first-class agent capability alongside LLM calls and external tool invocations. Implements the toolkit's pluggable interface pattern, enabling agents to swap LanceDB for alternative vector backends without code changes.","intents":["Invoke knowledge retrieval as a tool within an agent's reasoning loop without manual orchestration","Build multi-agent systems where agents share a common knowledge base through the toolkit's interface","Implement dynamic retrieval strategies (e.g., retrieve → reason → retrieve again) within agent workflows","Test agent behavior with different RAG backends (LanceDB, Chroma, Pinecone) by swapping implementations"],"best_for":["Developers building agentic RAG systems using vibe-agent-toolkit","Teams testing different vector database backends without refactoring agent code","Organizations building multi-agent systems with shared knowledge bases"],"limitations":["Abstraction overhead adds ~50-100ms per retrieval call due to interface marshalling","Limited to the operations defined in the toolkit's RAG interface (store, retrieve, delete); custom operations require extending the interface","No built-in observability or logging for retrieval operations; requires external instrumentation","Agent-level error handling for retrieval failures must be implemented by the agent developer"],"requires":["vibe-agent-toolkit installed and configured","Agent implementation using the toolkit's agent base class","LanceDB RAG implementation registered with the toolkit's plugin system"],"input_types":["agent tool call requests (structured JSON with operation type and parameters)","query parameters (embeddings, metadata filters, result limits)"],"output_types":["tool call results (ranked documents, operation status)","structured retrieval responses compatible with agent reasoning"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vibe-agent-toolkit-rag-lancedb__cap_4","uri":"capability://data.processing.analysis.batch.document.deletion.and.index.maintenance","name":"batch document deletion and index maintenance","description":"Supports removal of documents from the vector index by document ID or metadata criteria, with automatic index cleanup and optimization. The capability enables agents to manage knowledge base lifecycle (adding, updating, removing documents) without manual index reconstruction. Implements efficient deletion strategies that avoid full re-indexing when possible, though some operations may require index rebuilding depending on the underlying LanceDB version.","intents":["Remove outdated or irrelevant documents from the knowledge base without rebuilding the entire index","Implement document expiration policies (e.g., remove documents older than 30 days)","Correct ingestion errors by deleting incorrectly embedded documents","Manage knowledge base size and storage costs by removing low-value documents"],"best_for":["Teams managing long-lived knowledge bases with evolving document sets","Applications requiring document lifecycle management (versioning, expiration)","Systems with storage constraints that need periodic cleanup"],"limitations":["Deletion performance depends on index size; deleting large batches may require index rebuilding","No built-in soft-delete or versioning; deletions are permanent","Metadata-based deletion requires scanning the entire index; no indexed metadata filtering","No transaction support; partial failures during batch deletion may leave the index in an inconsistent state"],"requires":["Document IDs or metadata criteria for identifying documents to delete","Write access to the LanceDB index"],"input_types":["document IDs (strings or integers)","metadata filter expressions (JSON or key-value pairs)"],"output_types":["deletion confirmation (count of deleted documents)","index maintenance status"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vibe-agent-toolkit-rag-lancedb__cap_5","uri":"capability://data.processing.analysis.metadata.aware.document.storage.and.retrieval","name":"metadata-aware document storage and retrieval","description":"Stores and retrieves arbitrary metadata alongside document embeddings (e.g., source URL, timestamp, document type, author), enabling agents to filter and contextualize retrieval results. Metadata is stored in LanceDB's columnar format alongside vectors, allowing efficient filtering and ranking based on document attributes. Supports metadata extraction from document headers or custom metadata injection during ingestion.","intents":["Retrieve documents with rich context (source, date, author) to improve LLM reasoning and citation accuracy","Filter retrieval results by document attributes (e.g., only recent documents, specific sources)","Implement retrieval strategies that prioritize documents by metadata (e.g., official documentation over user forums)","Track document provenance and enable citation in agent-generated responses"],"best_for":["Developers building citation-aware RAG systems","Teams managing multi-source knowledge bases with heterogeneous document types","Applications requiring document filtering and ranking beyond semantic similarity"],"limitations":["Metadata filtering is applied post-retrieval (after vector search), not pre-filtered; can be inefficient for sparse metadata","No built-in metadata schema validation; incorrect metadata types may cause retrieval failures","Metadata fields are not indexed; filtering large result sets by metadata is O(n)","No support for nested metadata structures; only flat key-value pairs are efficiently supported"],"requires":["Metadata schema defined for documents (field names and types)","Metadata values provided during document ingestion"],"input_types":["document metadata (JSON objects with string, number, boolean, date fields)","metadata filter expressions (key-value pairs or simple predicates)"],"output_types":["documents with metadata fields included in retrieval results","filtered result sets based on metadata criteria"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ or Python 3.8+","LanceDB library installed (@lancedb/lancedb or equivalent)","Pre-computed embeddings from an embedding model (OpenAI, Hugging Face, or local)","Filesystem access or cloud storage (S3, GCS) for vector database persistence","Embedding provider API key (OpenAI, Hugging Face, or local model server)","Document source (file paths, URLs, or in-memory text)","Configured chunk size and overlap parameters","Query embedding (float array matching stored embedding dimensions)","Populated LanceDB index with stored document embeddings","Optional metadata filters (field names and values)"],"failure_modes":["LanceDB is optimized for analytical workloads; concurrent write throughput may be limited compared to specialized vector databases like Pinecone or Weaviate","No built-in replication or distributed deployment — single-machine or shared filesystem only","Vector index updates require re-indexing; incremental updates are not optimized","No native support for metadata filtering during vector search (requires post-retrieval filtering)","Chunking strategy is fixed (sliding window); no semantic-aware chunking (e.g., sentence-level or paragraph-level boundaries)","No built-in deduplication of documents; duplicate ingestion requires external filtering","Embedding generation is synchronous; large corpora (>100k documents) may require external batching infrastructure","No automatic re-embedding when documents are updated; requires manual pipeline re-run","Search latency depends on index size and hardware; no built-in query optimization or caching","Metadata filtering is post-hoc (applied after vector search), not pre-filtered; can be inefficient for sparse metadata","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.18295610562993928,"quality":0.22,"ecosystem":0.52,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-04-22T08:08:13.653Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":3377,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=vibe-agent-toolkit-rag-lancedb","compare_url":"https://unfragile.ai/compare?artifact=vibe-agent-toolkit-rag-lancedb"}},"signature":"QHqeS+923X7ajud2apnpbuWr6K71h8G1V6JLzJCUVfAlprnAy3X16WvZFMCwad4O93iHIgAxtCAq1pkwJACgBA==","signedAt":"2026-06-17T21:51:01.550Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vibe-agent-toolkit-rag-lancedb","artifact":"https://unfragile.ai/vibe-agent-toolkit-rag-lancedb","verify":"https://unfragile.ai/api/v1/verify?slug=vibe-agent-toolkit-rag-lancedb","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"}}