{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_epsilla","slug":"epsilla","name":"Epsilla","type":"product","url":"https://epsilla.com","page_url":"https://unfragile.ai/epsilla","categories":["data-analysis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_epsilla__cap_0","uri":"capability://memory.knowledge.native.vector.embedding.and.storage.with.integrated.embedding.models","name":"native vector embedding and storage with integrated embedding models","description":"Epsilla provides built-in embedding model execution within the vector database itself, eliminating the need for separate embedding pipelines or external embedding services. Rather than requiring developers to call third-party embedding APIs (OpenAI, Cohere) and then insert vectors into a separate database, Epsilla accepts raw text/documents, internally generates embeddings using pre-loaded models, and stores the resulting vectors in optimized columnar format. This reduces operational complexity and network round-trips for embedding generation.","intents":["I want to ingest documents into a vector database without managing a separate embedding service","I need to reduce latency and infrastructure complexity by co-locating embeddings and storage","I want to avoid vendor lock-in to specific embedding model providers"],"best_for":["Researchers and academics prototyping RAG systems quickly","Startup founders building MVP LLM applications with limited DevOps resources","Teams evaluating vector databases without committing to production infrastructure"],"limitations":["Embedding model selection is limited to Epsilla's pre-loaded models; custom fine-tuned embeddings require external generation","Unclear performance characteristics for high-throughput embedding generation (millions of documents/day)","No documented support for streaming or batch embedding with progress tracking"],"requires":["API key or connection credentials to Epsilla cloud or self-hosted instance","Documents in text format (PDF, markdown, plain text support level unclear)","Network connectivity to Epsilla service"],"input_types":["text","documents"],"output_types":["vector embeddings","stored vector records with metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_epsilla__cap_1","uri":"capability://search.retrieval.semantic.similarity.search.with.vector.indexing","name":"semantic similarity search with vector indexing","description":"Epsilla implements approximate nearest neighbor (ANN) search using vector indexing structures (likely HNSW or similar graph-based indices) to enable fast semantic search over stored embeddings. When a query is submitted, it is embedded using the same model as the corpus, and the index is traversed to find the k-nearest neighbors in vector space, returning ranked results by cosine similarity or other distance metrics. This enables semantic search without requiring exact keyword matching.","intents":["I want to find semantically similar documents without keyword matching","I need to retrieve relevant context for RAG systems based on semantic meaning","I want to implement recommendation or similarity-based search without manual feature engineering"],"best_for":["Researchers building semantic search prototypes","LLM application developers implementing RAG retrieval layers","Teams exploring vector-based similarity without production-scale requirements"],"limitations":["Query latency and recall characteristics not publicly documented; unclear performance at scale","No documented support for hybrid search (combining semantic + keyword/BM25 matching)","Index update latency during incremental data ingestion not specified","No built-in support for filtering or metadata-based pre-filtering before ANN search"],"requires":["Epsilla instance with indexed vectors","Query text or pre-computed query embedding","Specification of k (number of results) and optional similarity threshold"],"input_types":["text","vector embeddings"],"output_types":["ranked list of documents with similarity scores","structured JSON with metadata"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_epsilla__cap_2","uri":"capability://data.processing.analysis.multi.modal.document.ingestion.and.indexing","name":"multi-modal document ingestion and indexing","description":"Epsilla accepts various document formats (text, PDF, markdown, potentially images) and automatically parses, chunks, and indexes them into the vector database. The system likely implements document chunking strategies (sliding window, sentence-based, or semantic chunking) to break large documents into manageable segments, embeds each chunk, and stores them with metadata (source, chunk position, page number) for retrieval and citation. This abstracts away the complexity of document preprocessing pipelines.","intents":["I want to upload a PDF or document collection and immediately enable semantic search without writing parsing code","I need to automatically chunk and embed documents while preserving source metadata for citation","I want to index heterogeneous document types (PDFs, markdown, plain text) in a single operation"],"best_for":["Researchers building document-based RAG systems","Non-technical founders prototyping knowledge base search","Teams with limited data engineering resources"],"limitations":["Chunking strategy is not user-configurable; no documented control over chunk size, overlap, or semantic boundaries","PDF parsing quality and handling of complex layouts (tables, multi-column) not documented","No support for image extraction from PDFs or multi-modal embeddings","Metadata extraction from documents (author, date, title) not documented"],"requires":["Documents in supported formats (exact list unclear)","Sufficient storage quota in Epsilla instance","API credentials for document upload endpoint"],"input_types":["PDF","markdown","plain text","documents"],"output_types":["indexed document chunks with embeddings","metadata-enriched vector records"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_epsilla__cap_3","uri":"capability://search.retrieval.metadata.filtering.and.faceted.search","name":"metadata filtering and faceted search","description":"Epsilla stores and indexes metadata alongside vector embeddings, enabling filtered search where results are constrained by metadata predicates (e.g., 'source=research_paper AND date>2023'). The system likely implements metadata indexing (B-tree or hash indices) to support efficient filtering before or alongside ANN search, allowing developers to narrow the search space by document properties, tags, or custom attributes without retrieving all results and filtering client-side.","intents":["I want to search semantically but only within documents from a specific source or time period","I need to implement faceted search where users can filter by document category, author, or custom tags","I want to avoid retrieving irrelevant results by pre-filtering the vector index"],"best_for":["Teams building multi-tenant RAG systems with per-user or per-organization data isolation","Researchers filtering document collections by metadata before semantic search","Applications requiring complex query logic (AND, OR, NOT combinations)"],"limitations":["Metadata filtering syntax and supported operators not documented","No documented support for range queries, regex matching, or complex boolean logic","Unclear whether filtering happens before or after ANN search (impacts performance)","No support for dynamic metadata schema evolution or schema validation"],"requires":["Metadata fields defined and indexed during document ingestion","Query syntax for expressing filter predicates (format unknown)","Epsilla instance with metadata indices enabled"],"input_types":["text query","metadata filter predicates"],"output_types":["filtered ranked list of documents","structured JSON with metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_epsilla__cap_4","uri":"capability://automation.workflow.freemium.cloud.hosting.with.usage.based.scaling","name":"freemium cloud hosting with usage-based scaling","description":"Epsilla offers a freemium cloud service where developers can create vector database instances without upfront payment, paying only for storage and query volume as usage grows. This likely includes a free tier with limited storage (e.g., 1GB) and query quotas, with automatic scaling to paid tiers as thresholds are exceeded. The cloud infrastructure abstracts away database administration, backups, and scaling operations, allowing researchers and startups to experiment without infrastructure overhead.","intents":["I want to prototype a vector database application without paying upfront or managing infrastructure","I need to scale from research to production without migrating to a different database","I want to avoid the operational burden of self-hosting a vector database"],"best_for":["Researchers and academics with limited budgets","Startup founders validating product-market fit before raising capital","Teams exploring vector databases before committing to enterprise solutions"],"limitations":["Free tier quotas and limits not clearly documented","Pricing model for paid tiers (per-GB storage, per-query, or hybrid) not specified","No documented SLA or uptime guarantees for free tier","Unclear data retention policies or account deletion procedures","No documented multi-region or disaster recovery options"],"requires":["Email or OAuth account for registration","Valid payment method for paid tier (if usage exceeds free quota)","Internet connectivity to Epsilla cloud endpoints"],"input_types":[],"output_types":[],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_epsilla__cap_5","uri":"capability://tool.use.integration.rest.api.with.language.agnostic.client.libraries","name":"rest api with language-agnostic client libraries","description":"Epsilla exposes its functionality through a REST API, enabling integration from any programming language or framework without language-specific SDKs. The API likely follows REST conventions (POST for inserts, GET for queries, DELETE for removal) and returns JSON responses, with optional client libraries for popular languages (Python, JavaScript, Go) that wrap the HTTP calls and provide type hints or convenience methods. This enables integration into diverse application stacks without vendor lock-in to a specific language ecosystem.","intents":["I want to integrate Epsilla into a polyglot application stack (Python backend, Node.js frontend, Go microservice)","I need to query Epsilla from a language without an official SDK","I want to avoid dependency bloat by using raw HTTP calls instead of heavy client libraries"],"best_for":["Teams with heterogeneous tech stacks","Developers building API-first applications","Organizations with strict dependency management policies"],"limitations":["REST API documentation and endpoint specifications not publicly available","No documented support for streaming responses or long-polling for real-time updates","Authentication mechanism (API keys, OAuth, JWT) not specified","No documented rate limiting or request throttling policies","Unclear error handling and HTTP status code semantics"],"requires":["HTTP client library (curl, requests, fetch, etc.)","API endpoint URL and authentication credentials","Knowledge of Epsilla API schema and request/response formats"],"input_types":["JSON","HTTP requests"],"output_types":["JSON","HTTP responses"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_epsilla__cap_6","uri":"capability://data.processing.analysis.simplified.data.schema.and.schema.less.document.storage","name":"simplified data schema and schema-less document storage","description":"Epsilla abstracts away complex schema definition by accepting documents with flexible, schema-less metadata. Rather than requiring developers to pre-define column types, constraints, and indices like traditional databases, Epsilla infers or accepts arbitrary JSON metadata alongside vectors, enabling rapid iteration without schema migrations. Documents are stored with their embeddings and metadata as semi-structured records, allowing new fields to be added without altering the database schema.","intents":["I want to ingest documents with heterogeneous metadata without defining a rigid schema upfront","I need to add new metadata fields to documents without database migrations","I want to reduce the learning curve by avoiding complex schema design"],"best_for":["Researchers iterating rapidly on document collections","Startups with evolving data requirements","Teams without dedicated database administrators"],"limitations":["No documented schema validation or type enforcement","Unclear how metadata is indexed and whether all fields are queryable","No documented support for nested or complex metadata structures","Potential performance degradation with highly heterogeneous metadata across documents","No schema versioning or migration tooling"],"requires":["Documents with optional metadata in JSON format","Epsilla instance accepting schema-less inserts"],"input_types":["JSON","documents with metadata"],"output_types":["indexed documents with flexible metadata"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_epsilla__cap_7","uri":"capability://data.processing.analysis.batch.document.upload.and.bulk.indexing","name":"batch document upload and bulk indexing","description":"Epsilla supports bulk ingestion of multiple documents in a single operation, likely accepting a batch endpoint that processes multiple documents concurrently, chunks them, generates embeddings, and indexes them in parallel. This is more efficient than sequential single-document inserts, reducing total ingestion time and network overhead for large document collections. The system likely provides progress tracking or status endpoints to monitor bulk operations.","intents":["I want to ingest a large document collection (thousands of PDFs) efficiently without sequential API calls","I need to monitor the progress of a bulk indexing operation","I want to minimize total ingestion time for initial data loading"],"best_for":["Teams building knowledge bases from existing document archives","Researchers indexing large academic paper collections","Applications with periodic bulk data refreshes"],"limitations":["Batch size limits not documented","No documented support for resuming failed bulk operations","Unclear whether bulk operations are transactional (all-or-nothing) or partial-success","No documented progress tracking or status polling mechanism","Timeout behavior for long-running bulk operations not specified"],"requires":["Multiple documents in supported formats","Batch upload endpoint and API credentials","Sufficient storage quota for all documents"],"input_types":["documents","document collections"],"output_types":["bulk operation status","indexed documents with embeddings"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["API key or connection credentials to Epsilla cloud or self-hosted instance","Documents in text format (PDF, markdown, plain text support level unclear)","Network connectivity to Epsilla service","Epsilla instance with indexed vectors","Query text or pre-computed query embedding","Specification of k (number of results) and optional similarity threshold","Documents in supported formats (exact list unclear)","Sufficient storage quota in Epsilla instance","API credentials for document upload endpoint","Metadata fields defined and indexed during document ingestion"],"failure_modes":["Embedding model selection is limited to Epsilla's pre-loaded models; custom fine-tuned embeddings require external generation","Unclear performance characteristics for high-throughput embedding generation (millions of documents/day)","No documented support for streaming or batch embedding with progress tracking","Query latency and recall characteristics not publicly documented; unclear performance at scale","No documented support for hybrid search (combining semantic + keyword/BM25 matching)","Index update latency during incremental data ingestion not specified","No built-in support for filtering or metadata-based pre-filtering before ANN search","Chunking strategy is not user-configurable; no documented control over chunk size, overlap, or semantic boundaries","PDF parsing quality and handling of complex layouts (tables, multi-column) not documented","No support for image extraction from PDFs or multi-modal embeddings","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.284Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=epsilla","compare_url":"https://unfragile.ai/compare?artifact=epsilla"}},"signature":"FSklQ0jFU7ejbprCUQil8wlb5K2ykrajQiAhw1AZ86yTob67XKs8PPQ9MF8FadZeZBMlIxWzZCI26TgqFBGPAw==","signedAt":"2026-06-17T06:03:46.772Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/epsilla","artifact":"https://unfragile.ai/epsilla","verify":"https://unfragile.ai/api/v1/verify?slug=epsilla","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"}}