{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-swirlai--swirl-search","slug":"swirlai--swirl-search","name":"swirl-search","type":"product","url":"https://swirlaiconnect.com/","page_url":"https://unfragile.ai/swirlai--swirl-search","categories":["rag-knowledge","deployment-infra"],"tags":["ai-search","bigquery","django","federated-query","federated-search","gpt","large-language-models","metasearch","python","rag","relevancy","retrieval-augmented-generation","search","search-engine","unified-search"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-swirlai--swirl-search__cap_0","uri":"capability://search.retrieval.federated.multi.source.query.orchestration.with.parallel.execution","name":"federated multi-source query orchestration with parallel execution","description":"Executes a single user query across 100+ heterogeneous data sources simultaneously using Celery workers and asynchronous task distribution, without copying or indexing data. The Search Orchestrator (swirl/models.py Search class) decomposes queries into source-specific formats, dispatches parallel tasks to Celery workers, and aggregates results as they complete. Uses Django ORM to manage Search objects with state tracking (RUNNING, COMPLETED, FAILED) and WebSocket communication for real-time progress updates to the Galaxy UI.","intents":["Query across enterprise data silos (Salesforce, Jira, Slack, BigQuery, databases) in a single search without ETL","Get results from all sources simultaneously rather than sequential API calls","Maintain data residency and security by querying in-place without data movement","Reduce latency by parallelizing queries across slow and fast sources"],"best_for":["Enterprise teams with fragmented data across 10+ SaaS and on-prem systems","Organizations with strict data residency or compliance requirements (HIPAA, GDPR)","Teams building unified search experiences without data warehouse consolidation"],"limitations":["Latency bounded by slowest source in parallel query set — no timeout-based early termination by default","Requires connector implementation for each data source type; 100+ connectors provided but custom sources need custom code","No built-in query optimization across sources — each source receives full query, may return irrelevant results requiring post-processing","Celery/Redis dependency adds operational complexity; requires message broker setup and worker management"],"requires":["Python 3.9+","Django 3.2+","Celery 5.0+ with Redis or RabbitMQ broker","Network connectivity to all target data sources","Authentication credentials for each source (API keys, OAuth tokens, database credentials)"],"input_types":["natural language query string","structured search parameters (filters, date ranges, field-specific queries)"],"output_types":["unified result set with normalized fields across sources","result metadata (source, relevance score, timestamp)","search execution state and progress tracking"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_1","uri":"capability://tool.use.integration.connector.based.data.source.abstraction.with.format.translation","name":"connector-based data source abstraction with format translation","description":"Provides extensible connector framework (swirl/connectors/connector.py base class) that abstracts 100+ data sources (HTTP APIs, databases, search engines, Microsoft Graph) into a unified interface. Each connector translates SWIRL's normalized query format into source-native syntax (SQL WHERE clauses, Elasticsearch queries, REST API parameters, GraphQL), executes the query, and normalizes results back to SWIRL's unified schema. Supports HTTP connectors for REST/GraphQL APIs, database connectors for SQL/NoSQL, and specialized connectors for Salesforce, Jira, Microsoft 365, Slack, BigQuery, and others.","intents":["Add new data sources without modifying core search engine — implement connector subclass","Query databases, REST APIs, search engines, and SaaS platforms with identical query syntax","Handle source-specific authentication (OAuth, API keys, database credentials) transparently","Normalize heterogeneous result formats (JSON, XML, CSV, database rows) into consistent schema"],"best_for":["Platform teams building extensible search infrastructure for enterprise customers","Developers integrating proprietary or custom data sources into unified search","Organizations needing to support 20+ data sources without maintaining separate query logic per source"],"limitations":["Connector implementation required for each new source type — no automatic discovery or schema inference","Query translation is connector-specific; complex queries may not translate perfectly across all sources","Authentication management is per-connector; no centralized credential vault (requires external integration)","Result normalization assumes common fields (title, body, URL, date); sources with unique schemas require custom mapping"],"requires":["Python 3.9+","Connector base class understanding (swirl/connectors/connector.py)","Source-specific API documentation and authentication credentials","Network access to target data source"],"input_types":["normalized SWIRL query object with query string, filters, pagination parameters","source configuration (endpoint URL, API key, database connection string)"],"output_types":["normalized result objects with standardized fields (title, body, url, date, source, relevance_score)","connector metadata (source name, result count, execution time)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_10","uri":"capability://search.retrieval.galaxy.web.ui.with.search.interface.and.result.visualization","name":"galaxy web ui with search interface and result visualization","description":"Provides Galaxy web-based user interface (Django templates, static files, JavaScript) accessible at port 8000 for searching and visualizing results. Implements real-time search progress tracking via WebSocket, progressive result display as sources complete, and result filtering/sorting. Supports both simple keyword search and advanced search with filters, date ranges, and field-specific queries. Includes result preview, source attribution, and relevance scoring visualization. Built with Django templates and vanilla JavaScript for minimal dependencies.","intents":["Provide end-user search interface for querying federated data sources","Display search results with real-time progress updates","Filter and sort results by relevance, source, date, and other criteria","Show which data sources were queried and their execution time"],"best_for":["End-users searching across enterprise data sources","Teams building search applications with web-based UI","Organizations wanting to provide unified search experience across multiple data sources"],"limitations":["Galaxy UI is basic web interface; no advanced visualization or analytics","Customization requires Django template and JavaScript knowledge","No mobile-optimized interface; primarily desktop-focused","Authentication is basic; requires integration with enterprise SSO for production use"],"requires":["Python 3.9+","Django 3.2+","Modern web browser (Chrome, Firefox, Safari, Edge)","Network access to SWIRL Search API (port 8000)"],"input_types":["search query string","filter parameters (source, date range, field-specific filters)"],"output_types":["HTML web page with search results","JSON API responses (for AJAX requests)","WebSocket messages (for real-time updates)"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_11","uri":"capability://automation.workflow.asynchronous.task.execution.with.celery.worker.pool.and.result.caching","name":"asynchronous task execution with celery worker pool and result caching","description":"Implements asynchronous search execution using Celery task queue (swirl/tasks.py) with configurable worker pool for parallel query execution across sources. Each source query is dispatched as separate Celery task, allowing independent execution and failure handling. Results are cached in Redis (configurable TTL) to avoid redundant queries for identical search parameters. Celery workers can be scaled horizontally to handle increased query load. Supports task monitoring, retry logic, and dead-letter queue for failed tasks.","intents":["Execute queries on slow data sources without blocking user interface","Scale query execution horizontally by adding more Celery workers","Cache search results to avoid redundant queries for identical searches","Monitor and debug query execution through Celery task tracking"],"best_for":["Teams needing to handle high-volume search queries without blocking","Applications with slow data sources (data warehouses, external APIs) requiring asynchronous execution","Organizations wanting to scale search execution horizontally across multiple workers"],"limitations":["Celery adds operational complexity; requires message broker (Redis/RabbitMQ) and worker management","Result caching can return stale results; TTL must be configured appropriately per source","Task monitoring and debugging requires Celery knowledge and tools (Flower, etc.)","Distributed task execution adds latency compared to synchronous execution (~100-500ms per task)"],"requires":["Python 3.9+","Celery 5.0+","Redis 6.0+ or RabbitMQ 3.8+ (message broker)","Celery worker configuration (concurrency, task timeout, retry logic)"],"input_types":["search query and source configuration","Celery task configuration (timeout, retry logic, priority)"],"output_types":["Celery task ID for tracking","search results (cached or fresh)","task execution metadata (status, latency, retry count)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_12","uri":"capability://safety.moderation.source.specific.authentication.and.credential.management","name":"source-specific authentication and credential management","description":"Implements per-source authentication handling (swirl/connectors/) supporting multiple authentication methods: API keys, OAuth 2.0, basic auth, database credentials, and custom authentication schemes. Each connector manages its own authentication logic, allowing sources to use different authentication methods simultaneously. Credentials are stored in Django settings or environment variables (not in code). Supports OAuth token refresh for long-lived sessions. No centralized credential vault; requires external integration for enterprise credential management.","intents":["Authenticate to multiple data sources with different authentication methods (API keys, OAuth, basic auth)","Manage credentials securely without storing in code or version control","Support OAuth token refresh for long-lived sessions","Integrate with enterprise credential management systems"],"best_for":["Teams integrating multiple data sources with different authentication methods","Organizations with security requirements for credential management","Applications needing to support OAuth for SaaS integrations"],"limitations":["No centralized credential vault; requires external integration (HashiCorp Vault, AWS Secrets Manager, etc.)","Credentials stored in environment variables or Django settings; not encrypted at rest by default","OAuth token refresh requires background job management; token expiration handling adds complexity","Per-source authentication means each source needs separate credential configuration"],"requires":["Python 3.9+","Source-specific authentication credentials (API keys, OAuth tokens, database credentials)","Environment variable or Django settings configuration for credentials","Optional: external credential management system (HashiCorp Vault, AWS Secrets Manager)"],"input_types":["authentication credentials (API key, OAuth token, username/password, database connection string)","source configuration with authentication method specification"],"output_types":["authenticated connection to data source","authentication metadata (token expiration, refresh status)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_13","uri":"capability://automation.workflow.admin.interface.for.source.configuration.and.search.management","name":"admin interface for source configuration and search management","description":"Provides Django admin interface for configuring data sources, managing searches, and monitoring system health. Allows admins to add/edit/delete data sources, configure connector parameters, set authentication credentials, and manage search history. Includes admin guide (docs/Admin-Guide.md) for production deployment and troubleshooting. Supports bulk operations for managing multiple sources. Provides search analytics (query volume, source performance, result quality metrics).","intents":["Configure data sources without code changes through admin interface","Monitor search performance and source health","Manage user searches and search history","Troubleshoot search issues and view error logs"],"best_for":["System administrators managing SWIRL Search deployment","Teams needing to add/remove data sources without developer involvement","Organizations wanting to monitor search performance and system health"],"limitations":["Django admin interface is basic; limited customization without code changes","No role-based access control (RBAC) for admin functions; all admins have full access","Search analytics are basic; no advanced reporting or visualization","Admin interface is not optimized for non-technical users"],"requires":["Python 3.9+","Django 3.2+","Admin user account with Django admin access","Network access to SWIRL Search admin interface (port 8000/admin)"],"input_types":["source configuration (name, connector type, endpoint, authentication)","search parameters (query, filters, date range)"],"output_types":["configured data sources","search history and analytics","system health metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_2","uri":"capability://data.processing.analysis.result.normalization.and.relevance.re.ranking.across.heterogeneous.sources","name":"result normalization and relevance re-ranking across heterogeneous sources","description":"Implements result processing pipeline (swirl/processors/) that normalizes results from different sources into unified schema, applies relevance re-ranking algorithms, and deduplicates results. The Mixer component (swirl/mixers/mixer.py) combines results from multiple sources using configurable ranking strategies (BM25, TF-IDF, LLM-based relevance scoring). Processors transform raw connector output into normalized Result objects with standardized fields, handle PII removal (swirl/processors/remove_pii.py), and apply source-specific post-processing. Results are re-ranked based on relevance scores, source credibility, and recency.","intents":["Combine results from Salesforce, Jira, Slack, and internal databases into single ranked list","Deduplicate results when same content appears in multiple sources","Re-rank results by relevance rather than source-specific ranking","Remove sensitive data (PII) from results before returning to users"],"best_for":["Teams building unified search UIs that need consistent result presentation across sources","Organizations with compliance requirements to filter PII from search results","Search applications where relevance ranking must account for source heterogeneity"],"limitations":["Relevance scoring assumes text-based results; non-text sources (images, structured records) require custom scoring logic","PII removal uses pattern matching and regex; may miss domain-specific sensitive data without custom rules","Deduplication is fuzzy-match based on title/URL; exact duplicates across sources may not be detected","Re-ranking adds latency (~100-500ms depending on result set size and ranking algorithm complexity)"],"requires":["Python 3.9+","Processor pipeline configuration (swirl/processors/)","Mixer strategy selection (BM25, TF-IDF, or custom scoring function)","Optional: LLM API key for LLM-based relevance scoring"],"input_types":["raw results from connectors with source-specific fields and formats","relevance scoring configuration (algorithm, weights, thresholds)"],"output_types":["normalized Result objects with standardized fields (title, body, url, date, source, relevance_score)","deduplicated and re-ranked result list","PII-filtered results (if PII removal processor enabled)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_3","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.with.llm.powered.answer.synthesis","name":"retrieval-augmented generation (rag) with llm-powered answer synthesis","description":"Implements RAG pipeline (swirl/processors/rag.py) that uses LLM APIs (OpenAI, Anthropic, Ollama, Azure OpenAI) to synthesize answers from search results without moving data. The RAG processor takes normalized search results, constructs a prompt with result snippets as context, and calls the configured LLM to generate a natural language answer. Supports streaming responses via WebSocket to Galaxy UI for real-time answer generation. Integrates with search result ranking to prioritize high-relevance results in LLM context window.","intents":["Generate natural language answers from search results across multiple sources","Synthesize information from 5-10 top results into a single coherent answer","Stream LLM responses in real-time to user interface","Use LLM to re-rank or filter results based on relevance to user query"],"best_for":["Teams building AI-powered search experiences that generate answers rather than just listing results","Organizations using LLMs to synthesize information from multiple enterprise data sources","Applications needing real-time answer generation with streaming responses"],"limitations":["LLM context window limits number of results that can be included in prompt (typically 4-8 results for GPT-4)","LLM hallucination risk — generated answers may contain information not in source results; requires source attribution","LLM API latency adds 1-5 seconds to search response time depending on model and result set size","Requires external LLM API (OpenAI, Anthropic, Azure) or self-hosted model (Ollama); no local inference by default","Cost per query depends on LLM pricing and token usage; can be significant at scale"],"requires":["Python 3.9+","LLM API key (OpenAI, Anthropic, Azure OpenAI, or Ollama endpoint)","RAG processor configuration (model selection, prompt template, context window size)","Search results from federated query (minimum 1-2 results, optimal 5-10)"],"input_types":["normalized search results with title, body, URL, source, relevance score","user query string","LLM configuration (model name, temperature, max_tokens)"],"output_types":["natural language answer string","streaming response chunks (via WebSocket)","source attribution (which results were used in answer generation)","LLM metadata (model used, tokens consumed, latency)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_4","uri":"capability://data.processing.analysis.query.transformation.and.source.specific.syntax.generation","name":"query transformation and source-specific syntax generation","description":"Implements query processing layer (swirl/search.py, swirl/models.py Query class) that parses user natural language queries and transforms them into source-specific syntax. Each connector implements query_to_native_syntax() method to translate SWIRL's normalized query format (query string, filters, date ranges, field-specific queries) into source-native formats: SQL WHERE clauses for databases, Elasticsearch DSL for search engines, REST API parameters for HTTP APIs, GraphQL queries for GraphQL endpoints, Microsoft Graph query syntax for Microsoft 365. Supports query expansion, synonym replacement, and field mapping per source.","intents":["Write single query that works across SQL databases, Elasticsearch, REST APIs, and GraphQL endpoints","Automatically translate filters and date ranges to source-specific syntax","Map user-friendly field names to source-specific field names (e.g., 'author' → 'created_by' in Jira)","Expand queries with synonyms or related terms per source"],"best_for":["Teams building unified search interfaces that hide source-specific query syntax from users","Developers integrating multiple data sources with different query languages","Applications needing to support complex queries (filters, date ranges, field-specific searches) across heterogeneous sources"],"limitations":["Query translation is lossy — some query features may not translate to all sources (e.g., complex boolean logic may not work in all REST APIs)","Field mapping must be configured per source; no automatic schema discovery","Advanced query features (faceting, aggregations, nested queries) may not translate across all sources","Query optimization is per-source; no cross-source query planning or optimization"],"requires":["Python 3.9+","Query configuration (field mappings, synonym lists, query expansion rules)","Source-specific query syntax knowledge (SQL, Elasticsearch DSL, GraphQL, etc.)"],"input_types":["natural language query string","structured query parameters (filters, date ranges, field-specific queries)","source configuration with field mappings"],"output_types":["source-native query syntax (SQL, Elasticsearch DSL, REST API parameters, GraphQL, etc.)","query metadata (estimated result count, execution plan)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_5","uri":"capability://automation.workflow.real.time.search.progress.tracking.and.websocket.streaming","name":"real-time search progress tracking and websocket streaming","description":"Implements WebSocket-based real-time communication (swirl/views.py, Galaxy UI) that streams search progress and results to clients as they complete. The Search Orchestrator updates Search object state (RUNNING, COMPLETED, FAILED) as Celery workers complete queries on individual sources. WebSocket connection pushes progress updates (source completion, result count, execution time) and result chunks to Galaxy UI in real-time, enabling users to see results from fast sources immediately without waiting for slow sources. Supports both polling (REST API) and streaming (WebSocket) interfaces.","intents":["Show users which sources have completed and which are still executing","Display results from fast sources immediately while waiting for slow sources","Stream result updates to UI without page refresh or polling","Provide real-time feedback on search progress and estimated completion time"],"best_for":["Web applications needing real-time search feedback without polling","Teams building search UIs where user experience depends on immediate result visibility","Applications with slow data sources (data warehouses, external APIs) where progressive result display is important"],"limitations":["WebSocket requires persistent connection; not suitable for stateless HTTP-only environments","Browser compatibility: older browsers may not support WebSocket; requires fallback to polling","Scaling WebSocket connections requires sticky sessions or message broker (Redis) for multi-server deployments","Result streaming adds complexity to client-side UI state management"],"requires":["Python 3.9+","Django 3.2+ with WebSocket support (Django Channels or similar)","Redis or RabbitMQ for message broker (required for multi-server deployments)","Client-side WebSocket support (modern browsers or fallback library)"],"input_types":["search query and configuration","client WebSocket connection"],"output_types":["real-time progress updates (source completion, result count)","result chunks as they complete","search metadata (execution time, source latency)"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_6","uri":"capability://text.generation.language.multi.provider.llm.abstraction.with.streaming.support","name":"multi-provider llm abstraction with streaming support","description":"Provides LLM provider abstraction layer (swirl/processors/rag.py) that supports multiple LLM APIs (OpenAI, Anthropic, Ollama, Azure OpenAI) through unified interface. Each provider implementation handles authentication, request formatting, streaming response parsing, and error handling. Supports streaming responses where LLM output is returned token-by-token via WebSocket, enabling real-time answer generation in Galaxy UI. Allows switching between LLM providers through configuration without code changes.","intents":["Use different LLM providers (OpenAI, Anthropic, Ollama) interchangeably in RAG pipeline","Stream LLM responses token-by-token for real-time answer generation","Switch LLM providers based on cost, latency, or availability without code changes","Support both cloud-hosted (OpenAI, Anthropic, Azure) and self-hosted (Ollama) models"],"best_for":["Teams wanting to avoid vendor lock-in by supporting multiple LLM providers","Organizations using self-hosted models (Ollama) for data privacy","Applications needing to optimize LLM provider selection based on cost or latency"],"limitations":["API differences between providers require provider-specific implementations; not all features available on all providers","Streaming support varies by provider; some providers have higher latency for streaming responses","Model availability and capabilities differ across providers; prompt engineering may need adjustment per provider","Cost and rate limits vary significantly across providers; no built-in cost optimization or rate limiting"],"requires":["Python 3.9+","LLM provider API key (OpenAI, Anthropic, Azure OpenAI) or Ollama endpoint","LLM provider configuration (model name, API endpoint, authentication)"],"input_types":["prompt string with search results as context","LLM configuration (provider, model, temperature, max_tokens)","streaming flag (enable/disable streaming responses)"],"output_types":["LLM response string (non-streaming) or token stream (streaming)","LLM metadata (model used, tokens consumed, latency, cost)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_7","uri":"capability://tool.use.integration.microsoft.365.and.graph.api.integration.with.oauth.authentication","name":"microsoft 365 and graph api integration with oauth authentication","description":"Provides specialized connectors for Microsoft 365 ecosystem (swirl/connectors/) including Microsoft Graph API connector for querying Teams, SharePoint, OneDrive, Outlook, and other Microsoft 365 services. Implements OAuth 2.0 authentication flow for secure credential management without storing passwords. Supports Microsoft Graph query syntax translation, pagination, and result normalization. Includes admin guide (docs/M365-Guide.md) for configuring Microsoft 365 integration in enterprise environments.","intents":["Query Teams messages, SharePoint documents, OneDrive files, and Outlook emails in unified search","Integrate Microsoft 365 data into enterprise search without copying data to external systems","Use OAuth for secure authentication without storing user passwords","Support Microsoft 365 multi-tenant environments with per-tenant configuration"],"best_for":["Microsoft 365 customers wanting to add AI search to their existing Microsoft ecosystem","Enterprise teams needing to search across Microsoft 365 services and other data sources","Organizations with strict data residency requirements for Microsoft 365 data"],"limitations":["Requires Microsoft 365 tenant admin to configure OAuth app and grant permissions","Microsoft Graph API rate limits apply; high-volume searches may hit rate limits","Some Microsoft 365 services have limited search capabilities (e.g., Teams message search is limited)","OAuth token refresh requires background job management; token expiration handling adds complexity"],"requires":["Python 3.9+","Microsoft 365 tenant with admin access","OAuth app registration in Azure AD with appropriate permissions","Microsoft Graph API access (included with Microsoft 365)"],"input_types":["search query string","Microsoft Graph query parameters (filter, select, search)","OAuth credentials (client ID, client secret, tenant ID)"],"output_types":["normalized results from Microsoft 365 services (Teams messages, SharePoint documents, Outlook emails, etc.)","result metadata (source service, last modified date, author)"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_8","uri":"capability://automation.workflow.docker.and.kubernetes.containerized.deployment.with.configuration.management","name":"docker and kubernetes containerized deployment with configuration management","description":"Provides Docker containerization (Dockerfile, docker-compose.yml) and Kubernetes deployment manifests for production deployment. Includes Nginx reverse proxy configuration for load balancing and SSL termination. Supports environment-based configuration management through .env files and Django settings, enabling deployment across development, staging, and production environments without code changes. Includes CI/CD pipeline (GitHub Actions) for automated testing, building, and deployment.","intents":["Deploy SWIRL Search to production using Docker containers","Scale SWIRL Search horizontally using Kubernetes with multiple replicas","Configure SWIRL Search for different environments (dev, staging, prod) without code changes","Automate testing and deployment using CI/CD pipeline"],"best_for":["Teams deploying SWIRL Search to production using containerized infrastructure","Organizations using Kubernetes for container orchestration","DevOps teams needing automated CI/CD pipelines for SWIRL Search deployment"],"limitations":["Requires Docker and Kubernetes knowledge for production deployment","Celery workers and Redis broker must be configured separately; adds operational complexity","Persistent storage (database, Redis) must be managed externally; no built-in persistence","Scaling requires load balancing and session management across multiple instances"],"requires":["Docker 20.10+ or Docker Desktop","Kubernetes 1.20+ (for Kubernetes deployment)","PostgreSQL or MySQL database (for Django ORM)","Redis or RabbitMQ (for Celery message broker)","Nginx (for reverse proxy and load balancing)"],"input_types":["Dockerfile and docker-compose.yml configuration","Environment variables (.env file)","Kubernetes manifests (YAML)"],"output_types":["Docker image (swirlai/swirl-search)","Running container with SWIRL Search service","Kubernetes pods and services"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-swirlai--swirl-search__cap_9","uri":"capability://data.processing.analysis.extensible.processor.pipeline.for.result.transformation.and.filtering","name":"extensible processor pipeline for result transformation and filtering","description":"Implements pluggable processor pipeline architecture (swirl/processors/processor.py base class) where each processor transforms search results independently. Processors are composed in sequence, allowing flexible result transformation workflows: normalization, ranking, PII removal, RAG synthesis, custom filtering. Each processor implements process() method that takes results and returns transformed results. Processors can be enabled/disabled through configuration, and custom processors can be added by subclassing Processor base class.","intents":["Apply multiple transformations to search results (normalization, ranking, filtering) in sequence","Add custom result processing logic without modifying core search engine","Enable/disable processors through configuration for different use cases","Compose processors to build complex result transformation workflows"],"best_for":["Teams needing flexible result processing workflows beyond standard ranking and normalization","Developers building custom search applications with domain-specific result transformations","Organizations with complex compliance or data governance requirements (PII removal, data classification)"],"limitations":["Processor composition adds latency; each processor adds ~10-100ms depending on complexity","Processor ordering matters; incorrect ordering can produce unexpected results","Debugging processor pipelines can be complex; requires understanding of each processor's input/output","Custom processors require Python development; no visual pipeline builder"],"requires":["Python 3.9+","Processor base class understanding (swirl/processors/processor.py)","Configuration of processor pipeline (processor list and order)"],"input_types":["search results from connectors or previous processor","processor configuration (parameters, thresholds)"],"output_types":["transformed results (normalized, ranked, filtered, or synthesized)","processor metadata (execution time, transformations applied)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Django 3.2+","Celery 5.0+ with Redis or RabbitMQ broker","Network connectivity to all target data sources","Authentication credentials for each source (API keys, OAuth tokens, database credentials)","Connector base class understanding (swirl/connectors/connector.py)","Source-specific API documentation and authentication credentials","Network access to target data source","Modern web browser (Chrome, Firefox, Safari, Edge)","Network access to SWIRL Search API (port 8000)"],"failure_modes":["Latency bounded by slowest source in parallel query set — no timeout-based early termination by default","Requires connector implementation for each data source type; 100+ connectors provided but custom sources need custom code","No built-in query optimization across sources — each source receives full query, may return irrelevant results requiring post-processing","Celery/Redis dependency adds operational complexity; requires message broker setup and worker management","Connector implementation required for each new source type — no automatic discovery or schema inference","Query translation is connector-specific; complex queries may not translate perfectly across all sources","Authentication management is per-connector; no centralized credential vault (requires external integration)","Result normalization assumes common fields (title, body, URL, date); sources with unique schemas require custom mapping","Galaxy UI is basic web interface; no advanced visualization or analytics","Customization requires Django template and JavaScript knowledge","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2881363880387457,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:29.528Z","last_commit":"2026-05-03T00:30:02Z"},"community":{"stars":3010,"forks":284,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=swirlai--swirl-search","compare_url":"https://unfragile.ai/compare?artifact=swirlai--swirl-search"}},"signature":"L16mhEMbXj1GuVzANuajHs9RVGy9KgXdo8LEOEEI6T0iFSRP9JCLv2MITiOL0z5M7rlX5fo/OXABK0ZXR5VjCw==","signedAt":"2026-06-22T06:01:36.917Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/swirlai--swirl-search","artifact":"https://unfragile.ai/swirlai--swirl-search","verify":"https://unfragile.ai/api/v1/verify?slug=swirlai--swirl-search","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"}}