The Anthropic API key is obtained from the Anthropic Console by user registration and key creation.

DataLens validates features by ensuring API endpoints respond to actual requests. The file backend/app/api/analysis.py is part of the API LAYER. The frontend service uses the Backend API endpoint, configured via the PUBLIC_API_URL environment variable, to communicate with the backend. The Backend API is implemented using FastAPI framework. Backend API depends on PostgreSQL 16 service for metadata storage. Backend API depends on Redis 7 service for job queue management. The frontend service uses the Backend API endpoint, configured via the PUBLIC_API_URL environment variable, to communicate with the backend. The Backend API is implemented using FastAPI framework. Backend API depends on PostgreSQL 16 service for metadata storage. Backend API depends on Redis 7 service for job queue management. DataLens agent requires API endpoints to validate features before claiming success.

The User Language Preference capability uses the API auth endpoint for language preference to update and retrieve user language settings. The Multilingual Support (Danish) epic uses the API auth endpoint for language preference to enable user language selection.

The Frontend includes an API client integrating all 11 backend endpoints.

Updated documentation includes new endpoints for discovery, agent, and migration processes. Reflects recent API registration and functionality, ensuring developers can reference current API structure.

The system has 11 API endpoints for various functions, all fully tested with all 60 tests passing, supporting authentication, file management, data extraction, and querying. The Complete E2E Flow Milestone utilizes several API Endpoints as part of the full user journey. KeyError:0 bug in API endpoint causes blocking issues for Phase 1 batch processing API endpoint is likely affected by uvicorn module cache causing KeyError:0 bug The Data Discovery feature provides API endpoints such as /discovery, /tables, and /validate. The Data Discovery feature provides API endpoints such as /discovery, /tables, and /validate. The Discovery backend service uses defined API endpoints for its operation. The Backend Service uses the defined API endpoints including the discovery endpoints. Database session management integrates with API endpoints to provide backend data operations for the agent. API endpoints are governed by authentication constraints to ensure secure access. The DataLens platform backend exposes 11 API endpoints. The Discovery backend service implements the Discovery API endpoints. The DataLens platform backend contains 11 API endpoints.

The Data Discovery system uses the API endpoints /discovery, /tables, and /validate. The backend service discovery.py implements the API endpoints for the Data Discovery system.

The Backend supports API Integration covering all 11 API endpoints including analysis query and history.

ANTHROPIC_API_KEY is required to be set in Coolify environment for OpenClaw to operate successfully without timeout. OpenClaw Gateway requires the Anthropic API key to call Claude and generate responses. OpenClaw Gateway uses the Anthropic API key from environment or config to authenticate calls to the Anthropic API. The Anthropic API key must be set in the elin environment for the OpenClaw Gateway to authenticate API calls to Anthropic. The Anthropic API key is obtained from the Anthropic Console by user registration and key creation. The Anthropic API key was added to the elin environment and resulted in successful OpenClaw Gateway authentication and Claude response. ANTHROPIC_API_KEY is required to be set in Coolify environment for OpenClaw to operate successfully without timeout. OpenClaw Gateway requires the Anthropic API key to call Claude and generate responses. OpenClaw Gateway uses the Anthropic API key from environment or config to authenticate calls to the Anthropic API. The Anthropic API key must be set in the elin environment for the OpenClaw Gateway to authenticate API calls to Anthropic. The Anthropic API key is obtained from the Anthropic Console by user registration and key creation. The Anthropic API key was added to the elin environment and resulted in successful OpenClaw Gateway authentication and Claude response.

API endpoints for discovery and validation are defined; next steps involve final integration. The API LAYER uses QuestionRouter for query routing in analysis endpoints. API LAYER is implemented using FastAPI framework for backend services. API Layer is implemented with FastAPI as the backend web framework. The API Layer contains the backend/app/api/analysis.py file. The backend/app/main.py file starts the FastAPI app that exposes the API Layer. FastAPI framework implements the API Layer.

FindingsGenerator includes findings in the API response for all structured queries. FindingsGenerator results are included in the API response

Updated in backend code to handle findings integration, replacing previous placeholder.

Coolify Integration includes deployment for api.datalens.exerun.com domain.

API interface for user settings, including language preference updates.

The Architecture includes all Python and AI components running on elin. The Architecture includes all Python and AI components running on elin. The Architecture supports and integrates with the DataLens OpenClaw Integration to provide a solid system structure.

The architecture comparison with Vanna 2.0 and WrenAI validates architectural design decisions related to Vanna 2.0. The architecture comparison with Vanna 2.0 and WrenAI validates architectural design decisions related to WrenAI. The architecture comparison document validates design decisions related to Vanna 2.0. The architecture comparison document validates design decisions related to WrenAI. The architecture comparison document validates design decisions. The architecture comparison with Vanna 2.0 and WrenAI validates design decisions made.

The Data Discovery feature architecture uses Qwen3 with large context for table selection and Arctic with smaller context for SQL generation. The Data Discovery feature architecture uses Qwen3 with large context for table selection and Arctic with smaller context for SQL generation. Arctic is integrated within DataLens as the LLM model used for SQL query generation. The Data Discovery System integrates with Arctic LLM inference component for SQL query generation from selected tables. The analysis pipeline uses Arctic to generate SQL queries over the unified schema. The Discovery Service passes unified schemas to Arctic LLM for SQL generation after table consolidation. The Data Discovery Architecture uses Arctic for SQL generation with a limited context window.

Performance constraint identified in the system, restricting effective query size and requiring schema reduction.

Arctic SQL queries are executed against PostgreSQL instead of DuckDB after migration, requiring minimal SQL translation for compatibility. Transient views created during consolidation are used as input schema by Arctic for SQL generation. The Data Discovery System requires the Arctic SQL generation capability to generate SQL queries on consolidated tables. Arctic SQL currently generates SQL queries targeting DuckDB. Post-migration Arctic SQL will generate SQL queries targeting PostgreSQL with minor translation.

A production model deployed on elin, it generates SQL queries using Arctic's architecture, replacing SQLCoder-7B. It handles DuckDB schemas, uses compressed schema representations, and integrates via Ollama API, optimized for efficient and accurate SQL creation for Project 14 data. The Arctic-Text2SQL-R1-7B model is running on Ollama on elin and integrated with the backend. The Arctic-Text2SQL-R1-7B model is running on Ollama on elin and integrated with the backend. The Arctic-Text2SQL-R1-7B model is used via the Backend Service question_router.py to generate SQL. The Arctic-Text2SQL-R1-7B model is utilized by the Backend Service text_to_sql.py for SQL query generation.

Arne Hauge is a verified user with access to Project 14. Arne Hauge is the stakeholder who will use the Data Discovery Feature for real-world analytics and user onboarding. Arne Hauge accesses data stored in the PostgreSQL project_14 schema for budget analysis. Stakeholder Arne Hauge is designated to be onboarded and introduced to the Data Discovery Feature. Arne Hauge is expected to use the Data Discovery system after deployment.

Request model for submitting questions to API.

The Ollama GPU qwen3-coder-next 80B model is used by the async embedding queue to generate GPU embeddings for text chunks. The Nomic-embed-text embedding model is used by the async embedding queue for batch GPU embedding processing. The file extraction process triggers the async embedding queue to generate embeddings asynchronously after extraction completes.