{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-convex-dev-rag","slug":"convex-dev-rag","name":"@convex-dev/rag","type":"repo","url":"https://github.com/get-convex/rag#readme","page_url":"https://unfragile.ai/convex-dev-rag","categories":["rag-knowledge"],"tags":["convex","component","document","embeddings","rag","search","semantic","vector"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-convex-dev-rag__cap_0","uri":"capability://memory.knowledge.semantic.document.embedding.and.vector.storage","name":"semantic document embedding and vector storage","description":"Automatically converts documents into dense vector embeddings using configurable embedding models (OpenAI, Anthropic, or local alternatives) and persists them in Convex's serverless database with metadata indexing. The system handles chunking strategies, batch processing, and incremental updates without requiring external vector databases like Pinecone or Weaviate.","intents":["I need to embed documents and store vectors alongside my application data without managing a separate vector database","I want to automatically generate embeddings for new documents as they're added to my Convex database","I need to update embeddings when documents change without rebuilding the entire index"],"best_for":["teams building Convex-native applications who want RAG without infrastructure overhead","developers prototyping semantic search features without committing to external vector stores","small-to-medium scale applications (thousands to low millions of documents)"],"limitations":["Embedding generation latency depends on chosen model provider (OpenAI ~500ms-2s per document, local models variable)","No built-in approximate nearest neighbor (ANN) indexing — similarity search is linear scan, O(n) complexity unsuitable for >100k documents without pagination/filtering","Metadata filtering during search requires Convex query predicates, not specialized vector DB filtering syntax","Batch embedding operations are not automatically parallelized across Convex functions"],"requires":["Convex account and project setup","API key for embedding provider (OpenAI, Anthropic, or self-hosted model endpoint)","Node.js 16+ for client-side integration","Convex schema definition for document storage"],"input_types":["text documents (strings, markdown, plain text)","structured data with text fields","document metadata (tags, categories, timestamps)"],"output_types":["vector embeddings (float arrays, typically 1536 dimensions for OpenAI)","similarity scores (cosine distance or dot product)","ranked document results with metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-convex-dev-rag__cap_1","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 stored embeddings using cosine distance, dot product, or Euclidean distance metrics. Queries are performed via Convex functions that compute similarity scores between a query embedding and all stored document embeddings, returning ranked results with configurable result limits and filtering predicates applied before or after similarity computation.","intents":["I want to find documents semantically similar to a user query without exact keyword matching","I need to filter documents by metadata before computing similarity to reduce search scope","I want to retrieve top-k most relevant documents ranked by semantic relevance"],"best_for":["applications with <100k documents where linear scan similarity is acceptable","teams wanting semantic search without learning specialized vector DB query languages","use cases combining metadata filtering with semantic relevance (e.g., 'find similar docs from this category')"],"limitations":["Linear O(n) similarity computation scales poorly beyond 50-100k documents; no approximate nearest neighbor acceleration","Distance metric selection is fixed per query; cannot dynamically switch between cosine/dot-product in single query","No built-in query expansion, synonym handling, or semantic reranking","Pagination of large result sets requires manual offset/limit logic; no cursor-based iteration"],"requires":["Convex project with embedded documents and vectors already stored","Query embedding generated from same embedding model used for document embeddings","Convex function to execute similarity search (provided by framework)"],"input_types":["query vector (float array matching document embedding dimensions)","optional metadata filter predicates","result limit parameter (k)"],"output_types":["ranked array of documents with similarity scores","document metadata and original content"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-convex-dev-rag__cap_2","uri":"capability://data.processing.analysis.document.chunking.and.recursive.text.splitting","name":"document chunking and recursive text splitting","description":"Automatically splits long documents into semantically coherent chunks using configurable strategies (character-based, token-based, or recursive with overlap). The framework handles chunk size limits, overlap windows to preserve context, and metadata propagation so each chunk retains references to the original document and its position, enabling retrieval of full context during RAG synthesis.","intents":["I need to break large documents into chunks small enough for embedding models while preserving semantic coherence","I want overlapping chunks so context isn't lost at chunk boundaries","I need to track which original document each chunk came from for citation and context retrieval"],"best_for":["applications processing documents longer than embedding model context windows (>8k tokens)","teams building citation-aware RAG systems that need to trace results back to source documents","use cases requiring configurable chunk sizes for different document types (code vs prose)"],"limitations":["Recursive splitting logic is not language-aware; may split mid-sentence in non-English text or code with unusual formatting","No semantic-aware chunking (e.g., splitting at paragraph/section boundaries); relies on character/token counts","Chunk overlap is fixed per configuration; cannot dynamically adjust based on document structure","No built-in handling of structured documents (tables, code blocks, lists); treats all text uniformly"],"requires":["Document text content as string input","Chunk size configuration (character or token count)","Optional overlap size parameter","Token counter for accurate token-based splitting (if using token strategy)"],"input_types":["plain text documents","markdown or formatted text","document metadata (title, source, etc.)"],"output_types":["array of text chunks with metadata","chunk position/index information","reference to parent document"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-convex-dev-rag__cap_3","uri":"capability://tool.use.integration.embedding.model.provider.abstraction.and.switching","name":"embedding model provider abstraction and switching","description":"Provides a pluggable interface for embedding generation supporting OpenAI, Anthropic, and local/self-hosted models through a unified API. The framework abstracts provider-specific details (API endpoints, authentication, request/response formats) so developers can switch embedding models without changing application code, and handles retries, rate limiting, and error recovery transparently.","intents":["I want to use OpenAI embeddings but have the option to switch to Anthropic or local models without refactoring","I need to handle API rate limits and transient failures when generating embeddings at scale","I want to use a self-hosted embedding model to avoid cloud API costs or latency"],"best_for":["teams evaluating different embedding models and wanting to avoid vendor lock-in","applications with cost sensitivity where local embeddings are preferable","organizations with data residency requirements preventing cloud embedding APIs"],"limitations":["Embedding dimensions and quality vary significantly between models (OpenAI 1536-dim vs Mistral 1024-dim); switching models requires re-embedding all documents","Local model performance depends on hardware; no guidance on GPU requirements or latency expectations","Rate limiting is provider-specific; framework doesn't offer unified rate limit management across providers","No built-in cost tracking or comparison between embedding providers"],"requires":["API key or endpoint URL for chosen embedding provider","Convex environment variables configured for provider credentials","For local models: self-hosted inference server (e.g., Ollama, vLLM) with accessible endpoint"],"input_types":["text to embed (string or array of strings)","provider configuration (model name, endpoint, API key)"],"output_types":["embedding vectors (float arrays)","embedding metadata (model used, dimensions, timestamp)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-convex-dev-rag__cap_4","uri":"capability://planning.reasoning.rag.context.retrieval.and.synthesis.integration","name":"rag context retrieval and synthesis integration","description":"Provides utilities to retrieve relevant documents from semantic search results and format them as context for LLM prompts, handling token budgeting, context window management, and integration with LLM APIs (OpenAI, Anthropic, etc.). The framework manages the retrieval-augmented generation loop: query → embed → search → retrieve → format context → call LLM → return answer.","intents":["I want to automatically retrieve relevant documents and inject them into an LLM prompt for question-answering","I need to manage token budgets so retrieved context doesn't exceed the LLM's context window","I want to build a complete RAG pipeline without manually orchestrating embedding, search, and LLM calls"],"best_for":["teams building question-answering systems over document collections","applications needing grounded LLM responses with source citations","developers wanting a batteries-included RAG implementation without external orchestration frameworks"],"limitations":["No built-in reranking of retrieved documents; relies on raw similarity scores which may not correlate with LLM usefulness","Token budgeting is approximate; no exact token counting for all LLM models, may exceed context window","No support for multi-hop retrieval or iterative refinement; single retrieve-then-generate pattern only","Context formatting is template-based; no adaptive formatting based on document type or query intent"],"requires":["Convex project with embedded documents and search capability","LLM API key (OpenAI, Anthropic, or compatible endpoint)","Query text and optional parameters (result limit, token budget)"],"input_types":["user query (string)","search parameters (k, filters)","LLM configuration (model, temperature, max tokens)"],"output_types":["LLM response with retrieved context","source document references/citations","metadata about retrieval (documents used, similarity scores)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-convex-dev-rag__cap_5","uri":"capability://automation.workflow.incremental.document.indexing.and.update.handling","name":"incremental document indexing and update handling","description":"Automatically detects document changes and re-embeds only modified documents rather than rebuilding the entire index. The system tracks document versions, timestamps, and change hashes to identify which documents need re-embedding, and handles concurrent updates safely within Convex's transactional guarantees without requiring manual index invalidation or rebuild triggers.","intents":["I want to update documents and have their embeddings automatically refresh without manual re-indexing","I need to track which documents have been embedded and avoid redundant embedding operations","I want to handle concurrent document updates safely without corrupting the embedding index"],"best_for":["applications with frequently updated documents (wikis, knowledge bases, content management systems)","teams wanting to avoid expensive full re-indexing operations","systems where document freshness is important and stale embeddings are problematic"],"limitations":["Change detection relies on document version tracking; no automatic detection of semantic changes (e.g., paraphrasing same content)","Incremental updates are per-document; no batch optimization for bulk updates","No built-in scheduling for background re-embedding; updates are synchronous or require manual trigger","Concurrent updates to the same document may cause duplicate embedding operations if not carefully coordinated"],"requires":["Document schema with version/timestamp fields for change tracking","Convex database with update hooks or explicit update function calls","Embedding provider API key for re-embedding operations"],"input_types":["updated document content","document ID and version information","change metadata (what fields changed)"],"output_types":["updated embedding vectors","change confirmation with new version","metadata about re-embedding operation"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-convex-dev-rag__cap_6","uri":"capability://data.processing.analysis.batch.embedding.generation.with.error.handling.and.retries","name":"batch embedding generation with error handling and retries","description":"Processes multiple documents in batches through the embedding API, handling rate limiting, transient failures, and partial failures gracefully. The framework groups documents into optimal batch sizes for the embedding provider, implements exponential backoff retry logic, and tracks which documents succeeded/failed so applications can retry failed embeddings without re-processing successful ones.","intents":["I need to embed thousands of documents efficiently without hitting API rate limits","I want to handle transient API failures without losing progress or re-embedding successful documents","I need visibility into which documents failed to embed so I can retry them selectively"],"best_for":["bulk document import scenarios (initial indexing of large document collections)","teams with cost constraints wanting to batch API calls efficiently","applications requiring robust handling of unreliable network conditions"],"limitations":["Batch size optimization is provider-specific; no automatic tuning based on API response times","Retry logic uses fixed exponential backoff; no adaptive backoff based on rate limit headers","Failed documents are tracked but not automatically re-queued; requires explicit retry logic","No progress tracking or resumption of interrupted batch jobs; full restart required on process failure"],"requires":["Array of documents to embed","Embedding provider API key with sufficient quota","Batch size configuration (typically 10-100 documents per batch)","Timeout and retry configuration"],"input_types":["array of documents (strings or objects with text fields)","batch configuration (size, timeout, max retries)"],"output_types":["array of embeddings with document IDs","error log with failed document IDs and reasons","batch processing metadata (duration, API calls made)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-convex-dev-rag__cap_7","uri":"capability://search.retrieval.metadata.filtering.and.hybrid.search.semantic.keyword","name":"metadata filtering and hybrid search (semantic + keyword)","description":"Combines semantic similarity search with metadata-based filtering and optional keyword matching to refine results. The framework applies metadata predicates (e.g., 'category=finance AND date>2024') before or after similarity computation, and can optionally incorporate keyword/BM25 scoring alongside vector similarity for hybrid ranking that balances semantic relevance with exact term matches.","intents":["I want to search semantically but filter by document properties like category, date, or author","I need hybrid search that combines semantic relevance with keyword matching for better precision","I want to boost results from certain document categories while still ranking by semantic similarity"],"best_for":["applications with rich document metadata (tags, categories, timestamps, authors)","use cases where keyword precision is important alongside semantic relevance (e.g., financial documents)","teams wanting to implement faceted search with semantic ranking"],"limitations":["Metadata filtering is applied via Convex query predicates; complex filters may require custom function logic","Keyword scoring (BM25) is not built-in; requires separate implementation or external service","No automatic weighting between semantic and keyword scores; developers must manually tune combination","Metadata cardinality affects query performance; high-cardinality fields (free text) are inefficient to filter"],"requires":["Documents with structured metadata fields indexed in Convex","Metadata schema definition for filtering predicates","Optional: BM25 implementation or keyword search service"],"input_types":["query vector (for semantic search)","metadata filter predicates (e.g., {category: 'finance', date: {$gte: '2024-01-01'}})","optional: query text for keyword matching"],"output_types":["ranked documents combining semantic and keyword scores","metadata for each result","score breakdown (semantic score + keyword score)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Convex account and project setup","API key for embedding provider (OpenAI, Anthropic, or self-hosted model endpoint)","Node.js 16+ for client-side integration","Convex schema definition for document storage","Convex project with embedded documents and vectors already stored","Query embedding generated from same embedding model used for document embeddings","Convex function to execute similarity search (provided by framework)","Document text content as string input","Chunk size configuration (character or token count)","Optional overlap size parameter"],"failure_modes":["Embedding generation latency depends on chosen model provider (OpenAI ~500ms-2s per document, local models variable)","No built-in approximate nearest neighbor (ANN) indexing — similarity search is linear scan, O(n) complexity unsuitable for >100k documents without pagination/filtering","Metadata filtering during search requires Convex query predicates, not specialized vector DB filtering syntax","Batch embedding operations are not automatically parallelized across Convex functions","Linear O(n) similarity computation scales poorly beyond 50-100k documents; no approximate nearest neighbor acceleration","Distance metric selection is fixed per query; cannot dynamically switch between cosine/dot-product in single query","No built-in query expansion, synonym handling, or semantic reranking","Pagination of large result sets requires manual offset/limit logic; no cursor-based iteration","Recursive splitting logic is not language-aware; may split mid-sentence in non-English text or code with unusual formatting","No semantic-aware chunking (e.g., splitting at paragraph/section boundaries); relies on character/token counts","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.27009631781595494,"quality":0.26,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.474Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":25115,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=convex-dev-rag","compare_url":"https://unfragile.ai/compare?artifact=convex-dev-rag"}},"signature":"BBMIf230HI43c9xc5jEMs3mLqNPrGmJjKs5RJuvqWn7nnlu2CoEMWeW0ov30I5XHJ3z8yvVDNYfaBuHbtfs+Dg==","signedAt":"2026-06-20T23:41:03.595Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/convex-dev-rag","artifact":"https://unfragile.ai/convex-dev-rag","verify":"https://unfragile.ai/api/v1/verify?slug=convex-dev-rag","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"}}