{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-oramasearch--orama","slug":"oramasearch--orama","name":"orama","type":"framework","url":"https://docs.orama.com/docs/orama-js","page_url":"https://unfragile.ai/oramasearch--orama","categories":["rag-knowledge"],"tags":["algiorithm","data-structures","full-text","javascript","node","search","search-algorithm","search-engine","typescript","typo-tolerance","vector","vector-database","vector-database-embedding","vector-search","vector-search-engine"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-oramasearch--orama__cap_0","uri":"capability://search.retrieval.full.text.search.with.typo.tolerance.and.linguistic.normalization","name":"full-text search with typo tolerance and linguistic normalization","description":"Implements full-text search using a radix tree data structure combined with BM25 ranking algorithm, with built-in support for typo tolerance via Levenshtein distance matching and linguistic normalization through stemming and stop-word removal. The engine tokenizes input text, applies language-specific stemmers (English, Italian, French, Spanish, German, Portuguese, Dutch, Swedish, Norwegian, Danish, Russian, Arabic, Chinese, Japanese), and matches against indexed terms with configurable edit-distance thresholds to handle misspellings without requiring external spell-check services.","intents":["I want to search documents with typo tolerance so users don't need perfect spelling","I need to index and search text in multiple languages with proper stemming","I want to exclude common stop words to improve relevance ranking","I need to search a large corpus efficiently without external search infrastructure"],"best_for":["Documentation sites and knowledge bases needing multilingual search","Browser-based applications requiring offline-first search capabilities","Teams building search without external dependencies like Elasticsearch"],"limitations":["Typo tolerance adds computational overhead — edit distance calculations scale with term length and threshold","Stemming quality varies by language; some languages lack high-quality stemmers in the package","Full-text search performance degrades with very large corpora (100k+ documents) due to in-memory radix tree constraints","No support for phrase queries or proximity search out of the box"],"requires":["JavaScript/TypeScript runtime (Node.js 16+ or modern browser)","Documents must be pre-tokenized or passed as raw text strings","Language-specific stemmer package must be imported separately for non-English languages"],"input_types":["string (raw text)","structured documents with string fields","query strings with optional typo tolerance threshold parameter"],"output_types":["ranked result set with relevance scores","document IDs and matched field metadata","match position and highlighting information (with plugin)"],"categories":["search-retrieval","text-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_1","uri":"capability://search.retrieval.vector.search.with.configurable.embedding.integration","name":"vector search with configurable embedding integration","description":"Implements approximate nearest neighbor (ANN) search using a flat vector index with cosine similarity scoring, supporting integration with external embedding providers (OpenAI, Hugging Face, Ollama) via a pluggable embeddings system. The engine stores dense vectors alongside documents, performs similarity calculations in-memory, and allows custom embedding models through the plugin architecture without requiring changes to core search logic.","intents":["I want to perform semantic search on documents using embeddings from my preferred provider","I need to integrate embeddings from OpenAI, Hugging Face, or local models without vendor lock-in","I want to search by semantic meaning rather than exact keyword matches","I need to combine vector search with full-text search in a single query"],"best_for":["RAG pipelines requiring semantic document retrieval","Applications needing semantic similarity without external vector database","Teams wanting to switch embedding providers without reindexing"],"limitations":["Flat index approach has O(n) query complexity — scales poorly beyond 100k vectors; no HNSW or IVF approximation algorithms","Requires pre-computed embeddings; no built-in embedding generation (must use plugin or external service)","Vector dimension size directly impacts memory usage; no compression or quantization support","No support for approximate nearest neighbor algorithms — all queries perform exhaustive similarity search"],"requires":["Embedding vectors pre-computed or generated via plugin (e.g., @orama/plugin-embeddings)","API key for embedding provider if using cloud embeddings (OpenAI, Hugging Face)","Vectors must match dimensionality across all documents (e.g., all 1536-dim for OpenAI)"],"input_types":["query string (converted to embedding via plugin)","pre-computed embedding vector (Float32Array or number[])","document objects with vector field"],"output_types":["ranked result set with cosine similarity scores","document IDs sorted by relevance","similarity scores as floating-point values (0-1 range)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_10","uri":"capability://memory.knowledge.embeddings.plugin.with.multi.provider.support","name":"embeddings plugin with multi-provider support","description":"Provides a pluggable embeddings abstraction that integrates with external embedding providers (OpenAI, Hugging Face, Ollama, custom endpoints) to automatically generate vector embeddings for documents and queries. The plugin handles API communication, caching of embeddings, batch processing for efficiency, and fallback strategies if embedding generation fails, allowing seamless integration of vector search without vendor lock-in.","intents":["I want to automatically generate embeddings for documents using OpenAI or Hugging Face","I need to switch embedding providers without reindexing my documents","I want to use local embedding models (Ollama) instead of cloud services","I need to cache embeddings to avoid redundant API calls"],"best_for":["RAG applications requiring semantic search","Teams wanting flexibility in embedding provider selection","Applications needing cost optimization through embedding caching"],"limitations":["Embedding generation adds latency to indexing; batch processing helps but doesn't eliminate overhead","API rate limits from embedding providers can throttle indexing speed","Embedding cache is in-memory; no persistent cache across restarts","Switching embedding providers requires reindexing all documents (embeddings are provider-specific)"],"requires":["API key for embedding provider (OpenAI, Hugging Face, etc.)","Network connectivity to embedding service (unless using local Ollama)","Plugin registered before document insertion"],"input_types":["plugin configuration with provider type and API credentials","document text to embed","optional: custom embedding model name"],"output_types":["embedding vectors (Float32Array or number[])","cached embeddings for repeated queries","metadata about embedding generation (model, dimensions)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_11","uri":"capability://automation.workflow.analytics.plugin.with.search.metrics.collection","name":"analytics plugin with search metrics collection","description":"Provides a plugin that automatically tracks search metrics including query frequency, result click-through rates, query latency, and zero-result queries. Collects metrics in-memory or forwards them to external analytics services, enabling monitoring of search quality and user behavior without modifying application code. Metrics can be queried programmatically or exported for analysis.","intents":["I want to track which queries users are searching for","I need to identify zero-result queries to improve my index","I want to monitor search performance and latency","I need to understand search behavior for product improvements"],"best_for":["Applications needing search quality monitoring","Teams optimizing search relevance based on user behavior","Systems requiring search performance observability"],"limitations":["In-memory metrics collection uses memory proportional to unique queries; no automatic cleanup","Metrics are lost on application restart unless persisted separately","No built-in visualization; metrics must be exported to external tools","Tracking user behavior may have privacy implications; requires user consent"],"requires":["Plugin registered before database creation","Optional: external analytics service for metric forwarding"],"input_types":["plugin configuration with metric collection options","search queries and results (captured automatically)"],"output_types":["metrics object with query counts, latency, zero-result queries","exportable metrics data (JSON, CSV)","optional: forwarded metrics to external service"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_12","uri":"capability://text.generation.language.match.highlighting.with.configurable.html.markup","name":"match highlighting with configurable html markup","description":"Provides a plugin that identifies and highlights matched terms in search results by analyzing which terms matched in full-text search and wrapping them with configurable HTML tags (default: `<mark>` elements). The plugin tracks match positions during search, reconstructs the original text with highlights, and supports custom highlight templates for styling matched terms differently based on match type (exact, fuzzy, stemmed).","intents":["I want to highlight matched search terms in results for better UX","I need to show users which parts of the document matched their query","I want to customize highlight styling with CSS classes or HTML attributes","I need to handle different highlight types (exact vs fuzzy matches)"],"best_for":["Search UIs requiring visual feedback on matches","Documentation search interfaces","Applications needing rich result previews"],"limitations":["Highlighting requires storing match position metadata during search; adds memory overhead","Complex highlight templates can be slow for large result sets","No support for context snippets (e.g., showing only matched sentences); highlights applied to full text","Highlighting is post-processing; doesn't affect search ranking or relevance"],"requires":["Plugin registered before search queries","Match position data available from search results"],"input_types":["plugin configuration with highlight template HTML","search results with match metadata","optional: custom CSS classes or attributes"],"output_types":["highlighted text with HTML markup","original text with match positions marked","styled result snippets for display"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_13","uri":"capability://tool.use.integration.secure.proxy.plugin.for.cloud.search.api.integration","name":"secure proxy plugin for cloud search api integration","description":"Provides a plugin that proxies search requests to Orama Cloud infrastructure, allowing applications to use cloud-hosted search indexes while maintaining the same local API. The plugin handles authentication, request forwarding, response transformation, and fallback to local search if cloud is unavailable, enabling hybrid deployments where some searches use cloud infrastructure and others use local indexes.","intents":["I want to use Orama Cloud for large-scale search without changing my application code","I need to proxy search requests securely to cloud infrastructure","I want fallback to local search if cloud is unavailable","I need to scale search to multiple regions using cloud infrastructure"],"best_for":["Applications scaling beyond local search capacity","Teams using Orama Cloud for managed search infrastructure","Hybrid deployments needing local + cloud search"],"limitations":["Cloud proxy adds network latency compared to local search","Requires Orama Cloud account and API credentials","Fallback to local search requires maintaining both local and cloud indexes","Cloud API rate limits may throttle search throughput"],"requires":["Orama Cloud account with API credentials","Network connectivity to Orama Cloud infrastructure","Plugin registered before database creation"],"input_types":["plugin configuration with cloud API endpoint and credentials","search queries (proxied to cloud)"],"output_types":["search results from cloud infrastructure","fallback results from local index if cloud unavailable"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_14","uri":"capability://data.processing.analysis.document.parsing.and.content.extraction.from.multiple.formats","name":"document parsing and content extraction from multiple formats","description":"Provides a plugin that automatically extracts searchable content from various document formats (Markdown, HTML, PDF, JSON) during indexing, handling format-specific parsing, metadata extraction, and content normalization. The plugin supports custom parsers for domain-specific formats and integrates with framework plugins to extract content from documentation source files.","intents":["I want to index Markdown documentation automatically without manual content extraction","I need to extract text from HTML pages for search indexing","I want to parse PDF files and make their content searchable","I need to handle multiple document formats in a single index"],"best_for":["Documentation sites with mixed content formats","Applications indexing external documents","Systems needing flexible content extraction"],"limitations":["PDF parsing requires additional dependencies (pdf-parse, pdfjs); adds bundle size","Complex document structures (nested sections, tables) may not parse correctly","Metadata extraction is format-specific; no unified metadata schema","Large documents may timeout during parsing; no streaming support"],"requires":["Document files in supported formats (Markdown, HTML, PDF, JSON)","Optional: format-specific parsing libraries (pdf-parse for PDFs)"],"input_types":["document content as string or buffer","document format type (markdown, html, pdf, json)","optional: custom parser implementation"],"output_types":["extracted text content","parsed metadata (title, author, date)","structured content (sections, headings)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_15","uri":"capability://text.generation.language.tokenization.with.cjk.language.support","name":"tokenization with cjk language support","description":"Provides language-specific tokenization for full-text indexing, with specialized support for Chinese, Japanese, and Korean (CJK) languages that don't use whitespace-based word boundaries. Implements dictionary-based and statistical tokenization algorithms for CJK, falls back to whitespace tokenization for other languages, and allows custom tokenizers per language for domain-specific needs.","intents":["I want to search Chinese, Japanese, or Korean documents correctly","I need proper word segmentation for CJK languages without external services","I want to support multiple languages in a single search index","I need custom tokenization for domain-specific terminology"],"best_for":["Applications serving CJK-speaking users","Multilingual search systems","Documentation sites with mixed language content"],"limitations":["CJK tokenization quality depends on dictionary completeness; out-of-vocabulary words may tokenize incorrectly","Dictionary-based tokenization is slower than whitespace splitting; adds indexing latency","No support for phonetic search or radical-based search for CJK","Custom tokenizers require language expertise to implement correctly"],"requires":["CJK tokenizer package installed (@orama/tokenizers)","Language-specific tokenizer selected during database creation"],"input_types":["text in CJK or other languages","language code (zh, ja, ko, etc.)","optional: custom tokenizer implementation"],"output_types":["tokenized terms for indexing","language-specific token boundaries","metadata about tokenization method used"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_16","uri":"capability://text.generation.language.stemming.and.linguistic.normalization.for.12.languages","name":"stemming and linguistic normalization for 12+ languages","description":"Provides language-specific stemming algorithms that reduce words to their root forms (e.g., 'running', 'runs', 'ran' → 'run') during indexing and search, improving recall by matching morphological variants. Includes pre-built stemmers for English, Italian, French, Spanish, German, Portuguese, Dutch, Swedish, Norwegian, Danish, Russian, and Arabic, with support for custom stemmers for unsupported languages.","intents":["I want to match word variants (running, runs, ran) with a single search term","I need to improve search recall for morphologically rich languages","I want to reduce index size by normalizing word forms","I need to support multiple languages with proper stemming"],"best_for":["Multilingual search systems","Applications needing high recall for word variants","Documentation sites in non-English languages"],"limitations":["Stemming quality varies by language; some stemmers are more aggressive than others","Over-stemming can reduce precision (e.g., 'universal' and 'universe' stem to same root)","Stemming is language-specific; wrong stemmer selection degrades search quality","Custom stemmers require linguistic expertise to implement correctly"],"requires":["Language-specific stemmer package (@orama/stemmers)","Language code specified during database creation"],"input_types":["text in supported language","language code (en, it, fr, es, de, pt, nl, sv, no, da, ru, ar)"],"output_types":["stemmed terms for indexing","root word forms","metadata about stemmer used"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_17","uri":"capability://text.generation.language.stop.word.filtering.for.20.languages","name":"stop word filtering for 20+ languages","description":"Provides language-specific stop word lists (common words like 'the', 'a', 'and') that are excluded from full-text indexing to reduce index size and improve relevance. Includes pre-built stop word lists for 20+ languages, allowing selective filtering per language without modifying search logic. Stop words are removed during tokenization, reducing index size by 20-30% for typical documents.","intents":["I want to exclude common words from my search index to reduce size","I need language-specific stop word filtering","I want to improve search relevance by ignoring common terms","I need to customize stop word lists for domain-specific needs"],"best_for":["Applications with large text indexes needing size optimization","Multilingual search systems","Systems prioritizing search relevance over recall"],"limitations":["Stop word filtering reduces recall; queries containing only stop words return no results","Stop word lists are language-specific; wrong language selection filters wrong words","Some domains need stop words (e.g., 'the' is important in movie titles); no domain-specific filtering","Removing stop words during indexing is irreversible; can't search for them later"],"requires":["Stop word package (@orama/stopwords)","Language code specified during database creation"],"input_types":["text in supported language","language code (en, it, fr, es, de, pt, nl, sv, no, da, ru, ar, etc.)"],"output_types":["filtered tokens with stop words removed","metadata about stop words filtered"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_2","uri":"capability://search.retrieval.hybrid.search.combining.full.text.and.vector.results","name":"hybrid search combining full-text and vector results","description":"Merges full-text and vector search results using a configurable scoring algorithm that normalizes and weights both ranking signals. The engine executes both search paths in parallel, applies separate relevance scoring (BM25 for full-text, cosine similarity for vectors), normalizes scores to a common scale, and combines them using a weighted formula (configurable via `hybrid_weight` parameter) to produce a single ranked result set.","intents":["I want to search using both keyword matching and semantic meaning in a single query","I need to balance exact matches with semantic relevance for better recall","I want to tune the balance between full-text and vector search importance per use case","I need a single unified search API that handles both search modalities"],"best_for":["RAG systems requiring both keyword and semantic relevance","Documentation search where users may search by exact terms or concepts","Applications needing flexible relevance tuning without separate search endpoints"],"limitations":["Score normalization across different algorithms (BM25 vs cosine) is heuristic-based and may not be optimal for all datasets","Requires both full-text index and vector embeddings — doubles storage overhead vs single-modality search","Tuning the hybrid_weight parameter requires manual experimentation; no automatic optimization","Query latency is sum of both search paths; no early termination or result pruning optimization"],"requires":["Both full-text index and vector index populated with documents","Embedding vectors pre-computed for all documents","Query string or vector representation (or both) provided to search method"],"input_types":["query string (used for both full-text and vector search via embedding)","hybrid search parameters object with `hybrid_weight` (0-1 range)","optional: pre-computed query vector to skip embedding generation"],"output_types":["unified ranked result set with combined relevance scores","document IDs sorted by hybrid score","metadata indicating which search modality contributed to ranking"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_3","uri":"capability://data.processing.analysis.schema.based.document.indexing.with.type.validation","name":"schema-based document indexing with type validation","description":"Provides a TypeScript-first schema system that defines document structure, field types (string, number, boolean, enum), and indexing behavior (searchable, sortable, filterable). The schema is validated at insert time, enforces type safety across the codebase, and enables the engine to optimize indexing strategies per field type — full-text indexing for strings, numeric range indexing for numbers, and facet indexing for categorical fields.","intents":["I want type-safe document indexing with compile-time validation","I need to define which fields are searchable, sortable, or filterable without runtime configuration","I want to prevent invalid documents from being indexed","I need to optimize indexing behavior per field type automatically"],"best_for":["TypeScript projects requiring type safety across search operations","Applications with complex document schemas needing validation","Teams wanting to catch indexing errors at compile time"],"limitations":["Schema is immutable after database creation — adding new fields requires reindexing","No schema migration tools; schema changes require manual data transformation","Type validation adds overhead at insert time; batch inserts may be slower than untyped alternatives","Enum fields limited to predefined values; no dynamic enum generation"],"requires":["TypeScript 4.5+ for full type inference benefits","Schema definition object passed to database creation","Documents must conform to schema structure before insertion"],"input_types":["schema definition object with field types and indexing options","document objects matching schema structure","field metadata (searchable, sortable, filterable flags)"],"output_types":["type-safe document type inferred from schema","validation errors if document doesn't match schema","indexed field metadata for query optimization"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_4","uri":"capability://search.retrieval.faceted.search.and.result.grouping.with.aggregation","name":"faceted search and result grouping with aggregation","description":"Implements faceted navigation by building inverted indexes for categorical fields, allowing queries to return aggregated counts of values per field. The engine tracks document membership in facet categories during indexing, executes facet aggregation queries in parallel with search, and returns both search results and facet metadata (value counts, available options) in a single response for building filter UIs.","intents":["I want to show users available filter options with result counts","I need to group search results by category and show counts per group","I want to build faceted navigation UIs without separate aggregation queries","I need to filter results by multiple facet values simultaneously"],"best_for":["E-commerce and product search interfaces","Documentation sites with category-based filtering","Applications needing rich filtering UIs with result counts"],"limitations":["Facet indexes add memory overhead proportional to unique values per field","Facet aggregation requires scanning all matching documents; no approximate counts","No support for nested facets or hierarchical categories","Facet values must be strings or simple types; complex objects not supported"],"requires":["Fields marked as facetable in schema definition","Facet field values must be categorical (low cardinality preferred)","Query must explicitly request facet aggregation"],"input_types":["search query with facet field names","optional: facet filter values to narrow results","facet aggregation parameters (limit, sort order)"],"output_types":["search results filtered by facet selections","facet metadata object with available values and counts","nested structure: { results: [], facets: { fieldName: { value: count } } }"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_5","uri":"capability://search.retrieval.result.pinning.and.manual.ranking.override","name":"result pinning and manual ranking override","description":"Allows explicit pinning of specific documents to top positions in search results, overriding algorithmic ranking. The engine maintains a pin list per search query, executes the search normally, then reorders results to place pinned documents first while preserving relative ranking of unpinned results. Useful for promoting important documents (e.g., official documentation, sponsored content) regardless of relevance score.","intents":["I want to pin important documents to the top of search results","I need to promote specific results for certain queries without changing the algorithm","I want to feature official documentation above community content","I need to A/B test different result orderings for the same query"],"best_for":["Documentation sites promoting official guides","Search interfaces needing editorial control over results","Applications testing result ranking strategies"],"limitations":["Pinning is query-specific; no global document boost across all queries","Pinned documents still must match the search query; cannot pin unrelated documents","No support for partial pinning or position-specific pins (e.g., pin to position 3)","Pin configuration must be maintained separately from search logic"],"requires":["Document IDs to pin must exist in the index","Pin configuration provided per search query","Documents must match search criteria to be included in results"],"input_types":["search query string","array of document IDs to pin","optional: pin ordering (which pinned doc appears first)"],"output_types":["reordered result set with pinned documents first","metadata indicating which results were pinned","original relevance scores preserved for unpinned results"],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_6","uri":"capability://data.processing.analysis.serialization.and.deserialization.of.search.indexes","name":"serialization and deserialization of search indexes","description":"Enables exporting the entire in-memory search index (including full-text index, vector embeddings, facet data, and metadata) to a binary format that can be persisted to disk or transmitted over the network, and importing it back to reconstruct the index without reindexing. Uses a custom binary serialization format optimized for size and deserialization speed, supporting both Node.js Buffer and browser Blob formats.","intents":["I want to persist my search index to disk and reload it on startup","I need to distribute pre-built search indexes to clients without reindexing","I want to backup and restore search indexes","I need to share search indexes across server instances"],"best_for":["Static site generators needing to ship pre-built indexes with sites","Applications requiring fast startup without reindexing","Distributed systems needing to replicate indexes across nodes"],"limitations":["Serialized index size grows with document count and vector dimensions; no compression built-in","Deserialization is synchronous and blocks the event loop in Node.js for large indexes","No versioning system; serialized indexes from different Orama versions may be incompatible","Binary format is not human-readable; debugging serialization issues is difficult"],"requires":["Fully populated search index in memory","Writable file system (Node.js) or storage API (browser)","Sufficient memory to hold both original and serialized index during export"],"input_types":["populated Orama database instance","optional: compression options (if supported)"],"output_types":["binary buffer (Node.js Buffer or Uint8Array)","Blob (browser environments)","serialized index file on disk"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_7","uri":"capability://tool.use.integration.plugin.system.with.extensible.architecture","name":"plugin system with extensible architecture","description":"Provides a hook-based plugin system allowing third-party code to extend Orama functionality at multiple integration points: before/after indexing, before/after search, custom tokenization, embedding generation, and result post-processing. Plugins are registered at database creation time, receive context objects with access to internal state, and can modify documents, search parameters, or results without forking the core library.","intents":["I want to add custom preprocessing to documents before indexing","I need to integrate with external embedding services without modifying core code","I want to add analytics tracking to search queries","I need to implement custom tokenization for specialized languages"],"best_for":["Teams building domain-specific search applications","Applications requiring custom embedding providers","Projects needing search analytics and monitoring"],"limitations":["Plugin execution order is not guaranteed if multiple plugins hook the same event","Plugins have access to internal state; breaking changes in Orama can break plugins","No plugin dependency management; circular dependencies between plugins not prevented","Plugin errors can crash the entire search operation; no error isolation"],"requires":["Plugin object implementing one or more hook methods","Hooks must match Orama's plugin interface (specific method signatures)","Plugin registered before database creation for most hooks"],"input_types":["plugin configuration object","hook-specific context (document, search params, results)","access to database instance and internal indexes"],"output_types":["modified documents (indexing hooks)","modified search parameters or results (search hooks)","custom data structures (tokenization hooks)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_8","uri":"capability://automation.workflow.framework.integration.plugins.for.static.site.generators","name":"framework integration plugins for static site generators","description":"Provides pre-built plugins for popular static site generators (Docusaurus, VitePress, Nextra, Astro) that automatically extract content from documentation sites, build search indexes during build time, and inject search UI components into the generated site. Each plugin handles framework-specific content parsing, metadata extraction, and asset bundling to produce a ready-to-use search interface.","intents":["I want to add search to my Docusaurus/VitePress/Nextra documentation without custom code","I need to automatically index my documentation during the build process","I want a pre-built search UI that matches my documentation theme","I need to search documentation without external services like Algolia"],"best_for":["Documentation sites using Docusaurus, VitePress, Nextra, or Astro","Teams wanting zero-configuration search for docs","Projects avoiding external search service dependencies"],"limitations":["Each framework requires a separate plugin; no unified plugin for all frameworks","Plugins are tightly coupled to specific framework versions; major version upgrades may break compatibility","Search UI customization requires forking the plugin or wrapping components","Build-time indexing means search indexes must be regenerated on every documentation update"],"requires":["Supported framework installed (Docusaurus 2+, VitePress 1+, Nextra 2+, Astro 3+)","Plugin package installed and configured in framework config","Documentation content in supported formats (Markdown, MDX)"],"input_types":["framework configuration object","plugin options (index fields, search UI styling)","documentation source files (Markdown/MDX)"],"output_types":["pre-built search index bundled with site","search UI components injected into site","search results displayed in framework-native components"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oramasearch--orama__cap_9","uri":"capability://automation.workflow.data.persistence.plugin.with.automatic.index.snapshots","name":"data persistence plugin with automatic index snapshots","description":"Provides a plugin that automatically persists search indexes to disk at configurable intervals or on-demand, enabling recovery of indexes across application restarts without reindexing. 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