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- Added db_factory.py to manage connections to various databases with encryption. - Created models for database credentials in credentials.py using Pydantic. - Introduced API router for connectors with endpoints to register connections, list connections, execute queries, and disconnect. - Implemented support for multiple database types including PostgreSQL, MySQL, MongoDB, Neo4j, Supabase, Databricks, Snowflake, Redis, Pinecone, BigQuery, SQLite, and ClickHouse. - Added validation for credentials and query execution with read-only enforcement.
… logging - Added Neo4j client for asynchronous database interactions. - Updated execution_service to log execution status with graph_node_id. - Refactored graph_service to utilize Neo4j for node and edge management. - Implemented auto-linking of execution results to graph nodes. - Introduced metadata synchronization from PostgreSQL to Neo4j. - Enhanced audit history retrieval for linked nodes. - Updated requirements and docker-compose for Neo4j integration.
Refactor context retrieval and execution services to utilize Neo4j for schema and context management - Updated context retrieval service to build AI context bundles directly from Neo4j, removing reliance on PostgreSQL for context storage. - Modified execution service to eliminate unnecessary PostgreSQL queries for table metadata, simplifying error handling. - Introduced a new connector introspection service to sync live schema from PostgreSQL to Neo4j. - Created a graph knowledge service to manage table schemas, skills, and proposal contexts in Neo4j. - Enhanced skill registry service to mirror skills into the Neo4j knowledge graph. - Deprecated PostgreSQL metadata sync in favor of direct graph interactions, ensuring a cleaner architecture. - Added demo knowledge seeding functionality to bootstrap initial data in Neo4j.
…r schema and context management - Updated context retrieval service to build AI context bundles directly from Neo4j, removing reliance on PostgreSQL for context storage. - Modified execution service to eliminate unnecessary PostgreSQL queries for table metadata, simplifying error handling. - Introduced a new connector introspection service to sync live schema from PostgreSQL to Neo4j. - Created a graph knowledge service to manage table schemas, skills, and proposal contexts in Neo4j. - Enhanced skill registry service to mirror skills into the Neo4j knowledge graph. - Deprecated PostgreSQL metadata sync in favor of direct graph interactions, ensuring a cleaner architecture. - Added demo knowledge seeding functionality to bootstrap initial data in Neo4j.
…validation scripts
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