- Add `default_free_plan.py` to define the configuration for the Community Free Plan.
- Refactor `quota_stub.py` as a unified entry point, delegating checks to `core/quota_manager`.
- Implement core logic in `quota_manager.py` to support retrieving quotas from the premium module or configuration files.
- Update `tenant_subscription_controller` to return Community Free Plan information.
The knowledge graph retrieval logic in `search.py` was updated to consistently return `DocumentChunk` instances instead of raw dictionaries, improving type safety and alignment with the RAG pipeline's expected data structure. Additionally, debug logging was enhanced in `draft_run_service.py` to log the full `retrieve_chunks_result` before extracting page content, aiding troubleshooting.
The pdfplumber parser now uses a global lock to prevent concurrent access issues during PDF image rendering. Additionally, added a warning log to trace knowledge retrieval results for debugging purposes. The syntax fix in knowledge node's match case ensures correct pattern matching behavior.
BREAKING CHANGE: The pdfplumber parser now requires LOCK_KEY_pdfplumber to be defined in sys.modules for thread safety.
Closes#841
- Replace version-based optimistic locking and retry loop with apoc.atomic.add/insert for concurrent safety
- Merge duplicate accesses within a batch before updating (access_count_delta)
- Simplify _calculate_update to only compute on new timestamps instead of full history rebuild
- Remove max_retries instance variable (kept as param for backward compat)
- Trim verbose docstrings and inline comments
- Increase max_retries from 3 to 5 for concurrent conflict recovery
- Add randomized exponential backoff between retries to reduce contention
- Merge duplicate node accesses in batch operations to avoid self-conflicts
- Support access_times parameter for merged batch access counting
- Add Community node label support in atomic update content field map
- Strengthen anti-hallucination rules in extract_triplet prompt to
enforce verbatim-only alias extraction, removing suggestive examples
- Add _extract_deduped_entity_aliases to sync historical aliases from
Neo4j two-stage dedup into PgSQL end_user_info
- Remove unused _fetch_neo4j_user_aliases; reuse injected connector
instead of instantiating new Neo4jConnector
- Simplify _would_merge_cross_role and reuse clean_cross_role_aliases
in _normalize_special_entity_names
- Reuse _USER_PLACEHOLDER_NAMES from dedup module to avoid duplication
- Extract fetch_neo4j_assistant_aliases() into deduped_and_disamb.py as
single source of truth, replacing inline Cypher in write_tools and
extraction_orchestrator
- Normalize USER_PLACEHOLDER_NAMES to lowercase and apply .lower() on
all comparisons to prevent case-variant names leaking into aliases
- Extract user aliases from raw dialog statements instead of post-dedup
entities to bypass merge pollution
- Add alias cross-cleaning step in _normalize_special_entity_names to
strip AI assistant aliases from user entities before dedup
- Call clean_cross_role_aliases after second-layer dedup to handle
historical dirty data merged from Neo4j
- Fix syntax error in prompt_utils.py (ontology_types variable assignment)
- Trigger opening statement on new conversation in run/run_stream
- Fix /opening endpoint to support workflow app type
- Fix features field missing in workflow config release snapshot
- Knowledge node returns citations alongside chunks
- Aggregate citations from all knowledge nodes in result builder
- Filter citations based on features.citation.enabled switch
- Fix WorkflowConfigCreate circular import in app_schema
- Add speaker context to triplet extraction prompt to distinguish alias ownership
- Add explicit examples and rules in extract_triplet.jinja2 for user vs AI alias attribution
- Introduce cross-role merge protection in dedup (accurate, fuzzy, and LLM stages)
- Normalize special entity names (用户/AI助手) before deduplication
- Add clean_cross_role_aliases() to sanitize aliases before Neo4j write
- Refactor _update_end_user_other_name to merge aliases from PgSQL instead of Neo4j
- Filter AI assistant aliases from user alias extraction in orchestrator
