Covers system recovery strategies post-crash, including fallback mechanisms for Qdrant unavailability and lazy-loading benefits.

Agent Gateway interacts with Redis as part of the backend ecosystem for caching and background jobs. theo server uses Redis as job queue to manage extraction and batch vectorize jobs for document chunks. theo server uses Redis as job queue to manage extraction and batch vectorize jobs for document chunks. The DataLens Platform integrates with Redis 7 for future use with background jobs and caching. DataLens Platform uses Redis as a future component for background jobs and caching. RQ workers depend on Redis for job queue management. The Backend uses Redis as a backing store for the RQ job queue system. Redis functions as the queue backend supporting the RQ worker processing extraction jobs. The Data Discovery system uses Redis for job queue management and caching. The RQ job queue depends on the Redis service for managing asynchronous job scheduling and processing. Redis provides the backend queue for the RQ worker in the extraction pipeline. The SSE progress endpoint integrates with Redis pub/sub for real-time progress streaming.

The Docker deployment includes Redis Rq depends on redis as the message broker for job queueing.

Backend API uses Redis 7 as job queue and caching service. The Platform Backend depends on Redis 7 for future background job management and caching, although it is not yet implemented. The DataLens Platform backend depends on Redis 7 as a caching or queuing technology. Redis 7 server is integrated within the Coolify deployment environment for DataLens Development.

The backend service depends on the Redis 7 service for job queueing with connection configured via environment variable. Backend API depends on Redis 7 service for job queue management. The production deployment on theo uses Redis 7 as job queue infrastructure.

The command-line interface for Redis, used throughout the project to monitor and manage the job queue; recent sessions confirmed its version is compatible and operational, supporting reliable task queuing and progress tracking.

Docker container with no exposed ports, used for Redis cache. Backend service requires a volume for redis_data to persist Redis data. The redis (Docker) service is defined in docker-compose.yml. The redis_data (Docker) service is defined in docker-compose.yml. The redis (Docker) service is defined in docker-compose.coolify.yml. The redis_data (Docker) service is defined in docker-compose.coolify.yml. The redis (Docker) service is defined in docker-compose.yml. The redis_data (Docker) service is defined in docker-compose.yml. The redis (Docker) service is defined in docker-compose.coolify.yml. The redis_data (Docker) service is defined in docker-compose.coolify.yml.

Backend API requires Redis connection string configured via REDIS_URL environment variable. Backend API requires Redis connection string configured via REDIS_URL environment variable.

The User Language Preference capability updates the RegisterRequest schema to include the language field.

Regression & Golden Tests validate Text-to-SQL correctness and prevent regressions in DataLens Agent Mode.

UI listing tables and shared columns, with visual indicators of confirmed and suggested joins for user verification.

Provides contextual information for data analysis, including user identity and environment details.

Qdrant-client uses requests for HTTP communications.

DataLens displays results including natural language answers, semantic matches, generated SQL, confidence scores, and source attribution.

Visual of the findings panel UI, showing detected insights.

Full-page screenshot of analysis results display.

Visualization of Q1 budget analysis results.

Comparison graph for Q2 data analysis.

Metrics visualization for Q3 results.