{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-meilisearch--meilisearch","slug":"meilisearch--meilisearch","name":"meilisearch","type":"api","url":"https://www.meilisearch.com","page_url":"https://unfragile.ai/meilisearch--meilisearch","categories":["research-search"],"tags":["ai","api","app-search","database","enterprise-search","faceting","full-text-search","fuzzy-search","geosearch","hybrid-search","instantsearch","search","search-as-you-type","search-engine","semantic-search","site-search","typo-tolerance","vector-database","vector-search","vectors"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-meilisearch--meilisearch__cap_0","uri":"capability://search.retrieval.hybrid.keyword.semantic.search.with.weighted.fusion","name":"hybrid keyword-semantic search with weighted fusion","description":"Executes simultaneous full-text and vector similarity searches, then combines results using a configurable semanticRatio parameter that weights keyword relevance against semantic similarity. The milli crate maintains separate inverted indexes (word_docids, word_pair_proximity_docids) for keyword matching and arroy vector stores for embedding-based retrieval, with fusion logic that merges ranked result sets at query time. This dual-index approach enables applications to balance exact-match precision with semantic understanding without requiring separate search infrastructure.","intents":["I need search results that match both exact keywords and semantic meaning without maintaining two separate search systems","I want to tune how much weight exact keyword matches get versus conceptually similar results","I need sub-50ms search latency for both keyword and semantic queries across millions of documents"],"best_for":["E-commerce platforms needing product discovery that handles both SKU/brand searches and intent-based queries","Documentation sites where users search for exact function names and conceptually related topics","Content platforms balancing keyword precision with semantic relevance"],"limitations":["Fusion logic requires both indexes to be populated — keyword-only or semantic-only searches are less optimized","semanticRatio tuning is global per index; cannot vary weighting per query without index reconfiguration","Vector embeddings must be pre-computed externally (OpenAI, HuggingFace, Ollama) — no built-in embedding generation"],"requires":["Meilisearch 1.0+","Vector embeddings pre-computed and included in document payloads","Index configured with both searchableAttributes (for keyword) and vectorizeFrom settings (for semantic)"],"input_types":["JSON documents with text fields and vector embeddings","Search query string with optional filter expressions"],"output_types":["Ranked document list with relevance scores","Facet distributions and hit counts"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_1","uri":"capability://automation.workflow.asynchronous.task.based.document.indexing.with.automatic.batching","name":"asynchronous task-based document indexing with automatic batching","description":"All write operations (document additions, deletions, index creation, settings changes) are enqueued as tasks in the IndexScheduler, which batches and processes them asynchronously in the background. The scheduler implements intelligent batching logic that groups related operations (e.g., multiple document upserts) into single indexing jobs, reducing overhead and improving throughput. Documents flow through a parallel extraction pipeline in the milli crate that tokenizes text via charabia, builds inverted indexes, and creates vector indexes using arroy, with progress tracked via task status endpoints.","intents":["I need to ingest millions of documents without blocking my application's request handling","I want automatic batching of document updates to maximize indexing throughput","I need to track the status of long-running indexing operations and handle failures gracefully"],"best_for":["High-throughput data pipelines ingesting documents from message queues or data lakes","Applications requiring non-blocking document updates with eventual consistency","Teams building search features where indexing latency is decoupled from query latency"],"limitations":["Indexing is asynchronous — newly added documents are not immediately searchable; typical latency is seconds to minutes depending on batch size","Task queue is in-memory by default; no built-in persistence across server restarts without external state store configuration","Batching logic is automatic and not user-configurable per-request; cannot force immediate indexing of a single document"],"requires":["Meilisearch 1.0+","HTTP client capable of polling task status endpoints or using webhooks","LMDB-compatible filesystem for persistent index storage"],"input_types":["JSON, CSV, or NDJSON document batches","Document update/delete operations via REST API"],"output_types":["Task ID for status tracking","Task status object with progress, error details, and completion timestamp"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_10","uri":"capability://tool.use.integration.restful.http.api.with.openapi.specification","name":"restful http api with openapi specification","description":"Exposes all search, indexing, and administrative functionality through a RESTful HTTP API built on actix-web, with complete OpenAPI 3.0 specification for API documentation and client generation. The API follows REST conventions for resource management (indexes, documents, tasks) with standard HTTP methods (GET, POST, PUT, DELETE) and status codes. The OpenAPI spec is automatically validated and published, enabling API-first development and integration with API documentation tools.","intents":["I need to integrate Meilisearch into my application without learning a custom protocol","I want to generate API clients in multiple languages from the OpenAPI specification","I need comprehensive API documentation that stays in sync with the implementation"],"best_for":["Teams building polyglot applications with multiple language stacks","API-first development teams using OpenAPI-based workflows","Organizations requiring comprehensive API documentation and client generation"],"limitations":["HTTP overhead adds latency compared to binary protocols; not suitable for ultra-low-latency applications","OpenAPI spec must be manually maintained in sync with implementation; breaking API changes require spec updates","No built-in authentication beyond API keys; requires external authentication layer for multi-tenant deployments"],"requires":["Meilisearch 1.0+","HTTP client library in target language","API key for authentication (if enabled)"],"input_types":["HTTP requests with JSON payloads","Query parameters for filtering and pagination"],"output_types":["JSON responses with search results, task status, or error details","HTTP status codes indicating success or failure"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_11","uri":"capability://automation.workflow.task.queue.and.webhook.notifications.for.asynchronous.operations","name":"task queue and webhook notifications for asynchronous operations","description":"Implements a task queue system where all write operations are enqueued and processed asynchronously, with webhook support for notifying external systems when tasks complete. The IndexScheduler manages the task queue, persisting task state to LMDB and processing tasks in batches. Applications can poll task status endpoints or subscribe to webhooks to receive completion notifications, enabling event-driven architectures where indexing completion triggers downstream processes (e.g., cache invalidation, analytics updates).","intents":["I need to be notified when document indexing completes so I can invalidate caches or trigger analytics","I want to decouple document ingestion from search availability using asynchronous task processing","I need to track the status of long-running operations like bulk imports or index reindexing"],"best_for":["Event-driven architectures using webhooks for system integration","Data pipelines where indexing completion triggers downstream processes","Applications requiring audit trails of all indexing operations"],"limitations":["Webhook delivery is not guaranteed; failed deliveries are retried with exponential backoff but may eventually be dropped","Task queue is in-memory by default; tasks are lost on server restart unless external persistence is configured","Webhook payload size is limited; very large indexing operations may not fit in webhook payloads"],"requires":["Meilisearch 1.0+","Webhook endpoint URL for receiving notifications","HTTP client capable of receiving POST requests"],"input_types":["Task creation via document indexing, settings changes, or index operations","Webhook subscription configuration"],"output_types":["Task status object with progress and completion details","Webhook POST request to configured endpoint with task completion notification"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_12","uri":"capability://automation.workflow.dump.and.export.functionality.for.backup.and.migration","name":"dump and export functionality for backup and migration","description":"Provides dump and export endpoints that serialize the entire index state (documents, settings, tasks) to a portable format that can be restored on another Meilisearch instance. Dumps include all index metadata, documents, and task history, enabling point-in-time backups and zero-downtime migrations between servers. The dump format is version-aware, allowing upgrades between Meilisearch versions with automatic schema migration.","intents":["I need to back up my search indexes for disaster recovery","I want to migrate my search indexes to a new Meilisearch instance without downtime","I need to export search data for analysis or integration with other systems"],"best_for":["Production deployments requiring backup and disaster recovery","Teams migrating between Meilisearch versions or infrastructure","Organizations needing to export search data for compliance or analytics"],"limitations":["Dumps are large (roughly equal to index size on disk); require significant storage and network bandwidth","Dump creation is synchronous and can block other operations on large indexes","Incremental dumps are not supported; each dump is a full snapshot"],"requires":["Meilisearch 1.0+","Sufficient disk space for dump file (roughly equal to index size)","HTTP client capable of downloading large files"],"input_types":["Dump creation request","Dump file for restoration"],"output_types":["Dump file containing serialized index state","Restoration status with document count and task history"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_13","uri":"capability://search.retrieval.configurable.ranking.rules.and.relevance.tuning","name":"configurable ranking rules and relevance tuning","description":"Allows customization of document ranking through a configurable ranking rules system that applies multiple ranking criteria in sequence (e.g., exact match, word proximity, attribute position, typo count, sort order). Rules are evaluated in order, with earlier rules taking precedence, enabling fine-grained control over relevance without modifying the search algorithm. The ranking system supports both built-in rules and custom sort expressions, allowing applications to tune relevance based on business logic (e.g., boosting bestsellers, deprioritizing out-of-stock items).","intents":["I need to boost exact phrase matches over partial matches in search results","I want to prioritize documents with keywords in the title over those in the body","I need to customize ranking based on business metrics like popularity, recency, or inventory status"],"best_for":["E-commerce platforms tuning product relevance","Content platforms optimizing for user engagement","Search teams iterating on relevance without code changes"],"limitations":["Ranking rules are applied uniformly across all queries; cannot customize per-query without index reconfiguration","Complex ranking logic may require multiple rule evaluations, adding latency to queries","Custom sort expressions are limited to numeric and string comparisons; cannot express complex relevance formulas"],"requires":["Meilisearch 1.0+","Understanding of ranking rule syntax and evaluation order"],"input_types":["Ranking rules configuration","Search query with optional sort expressions"],"output_types":["Ranked document list ordered by ranking rules","Relevance scores per document"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_14","uri":"capability://tool.use.integration.instant.search.ui.integration.with.javascript.sdk","name":"instant search ui integration with javascript sdk","description":"Provides InstantSearch.js library that integrates with Meilisearch to enable rapid development of search-as-you-type interfaces with minimal code. The SDK handles query execution, result rendering, facet management, and pagination, with support for popular UI frameworks (React, Vue, Angular). The library abstracts away HTTP request management and provides reactive components that automatically update as users interact with search filters and input.","intents":["I need to build a search UI quickly without writing custom HTTP request handling","I want to use pre-built UI components for search, facets, and pagination","I need to integrate search into a React/Vue/Angular application with minimal boilerplate"],"best_for":["Frontend developers building search interfaces in JavaScript/TypeScript","Teams using React, Vue, or Angular for UI development","Rapid prototyping of search features without custom backend integration"],"limitations":["InstantSearch.js is JavaScript-only; no support for other languages or native mobile apps","SDK abstracts away low-level control; advanced customization may require direct HTTP API calls","Performance depends on network latency; high-latency connections will result in slower search feedback"],"requires":["Meilisearch 1.0+","Node.js and npm for package management","JavaScript/TypeScript knowledge","React, Vue, or Angular (depending on framework choice)"],"input_types":["Search query strings","Filter selections from UI components"],"output_types":["Rendered search results","Facet distributions","Pagination controls"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_2","uri":"capability://search.retrieval.typo.tolerant.full.text.search.with.configurable.distance.thresholds","name":"typo-tolerant full-text search with configurable distance thresholds","description":"Implements typo tolerance through the charabia tokenization library, which handles misspellings and character variations during both indexing and query processing. The system builds inverted indexes that support fuzzy matching with configurable Levenshtein distance thresholds (typoTolerance setting), allowing queries like 'speling' to match 'spelling'. The tolerance is applied at the token level during query expansion, where the search engine generates candidate tokens within the distance threshold and retrieves documents containing any of those variants.","intents":["I need search to work even when users misspell keywords or product names","I want to control how aggressive typo tolerance is (e.g., stricter for short words, more lenient for long words)","I need to disable typo tolerance for specific fields like SKUs or codes where exact matching is required"],"best_for":["Consumer-facing search interfaces where user input quality is unpredictable","Mobile search where keyboard typos are common","International applications where character encoding variations occur"],"limitations":["Typo tolerance is applied uniformly across all searchable fields; cannot configure per-field sensitivity","Distance thresholds are global per index; cannot adjust tolerance dynamically per query","Very high tolerance thresholds (e.g., distance > 2) can cause performance degradation and false positives on small datasets"],"requires":["Meilisearch 1.0+","typoTolerance setting enabled in index configuration","Searchable attributes defined for the index"],"input_types":["Text query strings with potential misspellings","Index configuration with typoTolerance rules"],"output_types":["Ranked document list including matches with typo corrections","Highlighted matched terms showing which variant was matched"],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_3","uri":"capability://search.retrieval.faceted.search.with.pre.computed.facet.distributions","name":"faceted search with pre-computed facet distributions","description":"Pre-computes facet distributions at indexing time by maintaining facet_id_*_docids databases in LMDB for each faceted attribute, enabling instant facet counts without scanning the entire result set. When a search query is executed, the filter system intersects the result set with pre-computed facet buckets to return accurate counts for each facet value. This approach trades indexing overhead for sub-millisecond facet computation, making it ideal for real-time faceted navigation interfaces.","intents":["I need to display facet counts (e.g., price ranges, categories) alongside search results without slowing down queries","I want users to be able to drill down into facets and see updated counts for remaining results","I need facet counts to be accurate even when filters are applied"],"best_for":["E-commerce search interfaces with category, price, and attribute facets","Content discovery platforms with tag and metadata facets","Analytics dashboards requiring fast facet aggregations"],"limitations":["Facets must be explicitly configured at index creation time; adding new facets requires reindexing","High-cardinality facets (e.g., user IDs with millions of unique values) consume significant memory and slow indexing","Facet counts are computed for the entire result set; cannot efficiently compute facets for a subset of results without filtering"],"requires":["Meilisearch 1.0+","Faceted attributes declared in index settings before indexing documents","LMDB storage with sufficient disk space for facet databases"],"input_types":["JSON documents with faceted attributes","Search query with optional facet filter expressions"],"output_types":["Facet distribution object with counts per facet value","Filtered document list with updated facet counts"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_4","uri":"capability://search.retrieval.geospatial.filtering.and.sorting.with.latitude.longitude.coordinates","name":"geospatial filtering and sorting with latitude/longitude coordinates","description":"Supports location-based search through a special _geo attribute that stores latitude/longitude pairs for each document. The filter system can evaluate geographic distance expressions (e.g., 'distance(lat, lng) < 10km') to filter documents within a radius, and results can be sorted by proximity to a reference point. The implementation uses LMDB-backed storage for coordinate data and applies distance calculations during filter evaluation, enabling location-aware search without requiring a separate geospatial database.","intents":["I need to find documents (e.g., stores, restaurants) within a certain radius of a user's location","I want to sort search results by distance from a reference point","I need to combine geospatial filtering with keyword search and other filters"],"best_for":["Location-based services (maps, local search, store locators)","Delivery and logistics platforms filtering by service area","Real estate and venue discovery applications"],"limitations":["Geospatial queries are limited to distance-based filtering; no support for polygon queries, route optimization, or complex geographic shapes","Distance calculations use Haversine formula (great-circle distance); not suitable for highly accurate geodetic calculations","Sorting by distance requires computing distance for all result documents; can be slow on very large result sets (100k+ documents)"],"requires":["Meilisearch 1.0+","Documents must include _geo attribute with {lat, lng} coordinates","Filter expressions using distance() function syntax"],"input_types":["JSON documents with _geo attribute containing latitude/longitude","Filter expressions with distance() function (e.g., 'distance(lat, lng) < 10000')","Sort expressions with _geoPoint parameter"],"output_types":["Filtered document list within specified radius","Documents sorted by distance from reference point"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_5","uri":"capability://search.retrieval.complex.filter.expressions.with.ast.based.parsing","name":"complex filter expressions with ast-based parsing","description":"Parses complex filter expressions into a FilterCondition abstract syntax tree (AST) using the filter-parser crate, enabling boolean logic (AND, OR, NOT), comparison operators (=, !=, <, >, <=, >=), range queries, and nested conditions. The AST is evaluated during search to determine which documents match the filter criteria, with support for filtering on any indexed attribute. This approach separates filter parsing from evaluation, allowing for query optimization and reuse of parsed filter trees across multiple searches.","intents":["I need to apply complex boolean filters like '(category = \"electronics\" AND price < 1000) OR (category = \"books\" AND price < 50)'","I want to combine multiple filter types (text search, numeric ranges, categorical filters) in a single query","I need to support user-facing filter UI that generates filter expressions programmatically"],"best_for":["E-commerce platforms with complex product filtering","Content platforms with multi-dimensional filtering (date ranges, categories, tags)","Analytics dashboards requiring flexible data filtering"],"limitations":["Filter expressions are evaluated sequentially for each document; very complex filters with many OR conditions can slow down queries","No support for full-text search within filter expressions; filters operate on exact/range matching only","Filter syntax is Meilisearch-specific; applications must generate filter strings programmatically or expose custom filter UI"],"requires":["Meilisearch 1.0+","Attributes to be filtered must be declared as filterable in index settings","Filter expressions following Meilisearch filter syntax"],"input_types":["Filter expression strings (e.g., 'price >= 100 AND price <= 500')","Search query combined with filter expressions"],"output_types":["Filtered document list matching all filter conditions","Document count matching filter criteria"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_6","uri":"capability://data.processing.analysis.parallel.document.extraction.and.indexing.pipeline","name":"parallel document extraction and indexing pipeline","description":"The milli crate implements a parallel extraction architecture that processes documents through multiple stages: tokenization via charabia, field extraction, inverted index construction, and vector index building using arroy. Documents are processed in parallel batches using Rayon thread pool, with each stage operating on independent document chunks to maximize CPU utilization. The pipeline outputs LMDB-backed indexes that are atomically committed, ensuring consistency and enabling zero-downtime index updates.","intents":["I need to index millions of documents quickly without blocking search queries","I want to maximize CPU utilization during indexing by processing documents in parallel","I need indexing to be reliable with atomic commits to prevent partial or corrupted indexes"],"best_for":["High-volume data ingestion pipelines (millions of documents per day)","Batch indexing jobs that can tolerate some latency but need high throughput","Applications with multi-core servers where parallel indexing can be leveraged"],"limitations":["Parallel extraction adds memory overhead proportional to batch size; very large batches (>100k documents) may cause memory pressure","Indexing performance depends on document complexity and field count; documents with many fields or large text content are slower to process","LMDB write performance is single-threaded; parallel extraction benefits plateau after a certain batch size due to I/O bottlenecks"],"requires":["Meilisearch 1.0+","Multi-core CPU for parallel extraction to provide benefit","Sufficient RAM for batch processing (typically 2-4x document size)"],"input_types":["JSON, CSV, or NDJSON document batches","Document schema with field definitions"],"output_types":["LMDB-backed inverted indexes","Vector indexes via arroy","Task completion status with indexing statistics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_7","uri":"capability://automation.workflow.dynamic.index.settings.and.reindexing.without.downtime","name":"dynamic index settings and reindexing without downtime","description":"Allows modification of index settings (searchable attributes, filterable attributes, sortable attributes, ranking rules) through API calls that trigger automatic reindexing via the IndexScheduler. The system builds a new index in the background while the old index remains queryable, then atomically swaps them upon completion. Settings changes are enqueued as tasks with status tracking, enabling applications to update index configuration without downtime or manual index rebuilding.","intents":["I need to add a new searchable field to my index without taking it offline","I want to change ranking rules or sorting configuration based on user feedback","I need to modify filterable attributes to support new filter types in my UI"],"best_for":["Production search systems requiring high availability","Applications iterating on search relevance and needing frequent ranking adjustments","Teams managing search configuration without dedicated DevOps infrastructure"],"limitations":["Reindexing requires re-processing all documents; on very large indexes (>100M documents), reindexing can take hours","Settings changes are not atomic at the document level; queries during reindexing may see inconsistent results if filters reference newly added attributes","Some settings changes (e.g., changing tokenizer) require full reindexing; cannot be applied incrementally"],"requires":["Meilisearch 1.0+","Sufficient disk space for temporary index during reindexing (roughly 2x index size)","HTTP client capable of polling task status"],"input_types":["Index settings object with updated configuration","API request to update specific settings"],"output_types":["Task ID for reindexing operation","Task status with progress and completion timestamp"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_8","uri":"capability://search.retrieval.multi.index.federated.search.with.result.merging","name":"multi-index federated search with result merging","description":"Supports querying multiple indexes simultaneously through federated search endpoints that execute the same query across multiple indexes and merge results using configurable weighting. The system executes searches in parallel across indexes, collects ranked result sets, and applies a merge strategy (e.g., round-robin, weighted scoring) to produce a unified result list. This enables applications to search across logically separate indexes (e.g., products, articles, users) in a single request without client-side result aggregation.","intents":["I need to search across multiple content types (products, articles, reviews) in a single query","I want to merge results from separate indexes with configurable weighting for each index","I need a unified search interface that doesn't require separate queries to each index"],"best_for":["Multi-tenant platforms searching across separate customer indexes","Content platforms with heterogeneous content types (articles, videos, comments)","Enterprise search systems aggregating results from multiple data sources"],"limitations":["Federated search latency is bounded by the slowest index; cannot optimize for low-latency responses if one index is significantly slower","Result merging strategies are limited to simple weighting; no support for complex ranking algorithms that consider cross-index relevance","Facets from multiple indexes cannot be easily merged; facet counts are per-index only"],"requires":["Meilisearch 1.0+","Multiple indexes created and populated","Federated search API endpoint configuration"],"input_types":["Search query string","List of index names to search","Weighting configuration per index"],"output_types":["Merged result list from all indexes","Per-index result counts","Per-index facet distributions"],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meilisearch--meilisearch__cap_9","uri":"capability://search.retrieval.search.as.you.type.with.instant.result.updates","name":"search-as-you-type with instant result updates","description":"Optimizes for real-time search feedback by returning results with minimal latency (sub-50ms target) as users type each character. The system leverages LMDB's memory-mapped I/O and pre-computed indexes to serve results from cache, with query processing optimized for short, incomplete queries. Prefix matching is built into the inverted index structure, enabling efficient retrieval of documents matching partial tokens without scanning the entire index.","intents":["I need to provide instant search feedback as users type without noticeable lag","I want to handle high-frequency search requests (one per keystroke) efficiently","I need to support autocomplete and suggestion features with minimal latency"],"best_for":["Consumer-facing search interfaces (e-commerce, content discovery)","Mobile applications where network latency is variable","Real-time search dashboards and analytics interfaces"],"limitations":["Sub-50ms latency requires sufficient server resources; cannot be achieved on under-provisioned hardware","Prefix matching is less accurate than full-token matching; may return more results than expected for short queries","High-frequency requests (100+ QPS per user) can cause server load spikes; requires rate limiting or caching at the client level"],"requires":["Meilisearch 1.0+","Sufficient server resources (multi-core CPU, adequate RAM for index caching)","Network connectivity with low latency to client"],"input_types":["Partial search query strings (1-3 characters)","Full search queries"],"output_types":["Ranked document list with instant results","Autocomplete suggestions"],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Meilisearch 1.0+","Vector embeddings pre-computed and included in document payloads","Index configured with both searchableAttributes (for keyword) and vectorizeFrom settings (for semantic)","HTTP client capable of polling task status endpoints or using webhooks","LMDB-compatible filesystem for persistent index storage","HTTP client library in target language","API key for authentication (if enabled)","Webhook endpoint URL for receiving notifications","HTTP client capable of receiving POST requests","Sufficient disk space for dump file (roughly equal to index size)"],"failure_modes":["Fusion logic requires both indexes to be populated — keyword-only or semantic-only searches are less optimized","semanticRatio tuning is global per index; cannot vary weighting per query without index reconfiguration","Vector embeddings must be pre-computed externally (OpenAI, HuggingFace, Ollama) — no built-in embedding generation","Indexing is asynchronous — newly added documents are not immediately searchable; typical latency is seconds to minutes depending on batch size","Task queue is in-memory by default; no built-in persistence across server restarts without external state store configuration","Batching logic is automatic and not user-configurable per-request; cannot force immediate indexing of a single document","HTTP overhead adds latency compared to binary protocols; not suitable for ultra-low-latency applications","OpenAPI spec must be manually maintained in sync with implementation; breaking API changes require spec updates","No built-in authentication beyond API keys; requires external authentication layer for multi-tenant deployments","Webhook delivery is not guaranteed; failed deliveries are retried with exponential backoff but may eventually be dropped","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4369719497673833,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:32.037Z","last_commit":"2026-05-02T14:50:39Z"},"community":{"stars":57393,"forks":2533,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=meilisearch--meilisearch","compare_url":"https://unfragile.ai/compare?artifact=meilisearch--meilisearch"}},"signature":"DBGxQn5zeVp3lOBjgOwBz3pqFd7WAxLNoi3f2Yq/TsTZXBh9KlXzaJSVY0ovyTbq87IirdvphnDuBw18ztK+Dg==","signedAt":"2026-06-22T04:32:49.649Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meilisearch--meilisearch","artifact":"https://unfragile.ai/meilisearch--meilisearch","verify":"https://unfragile.ai/api/v1/verify?slug=meilisearch--meilisearch","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"}}