Extract every Chinese string inside backend logger.{info,warning,error,
debug,exception} calls and inside user-facing jsonify({"error|message":
...}) responses across the listed in-scope modules into
locales/{en,zh}.json under nested namespaces (log.<module>.*,
api.{error,message}.<scope>.*). Locale dictionaries stay structurally
identical; the existing flat frontend-facing keys at log.* / api.* are
left untouched. The locale helper (backend/app/utils/locale.py) now
emits a single deduplicated mirofish.locale warning per (locale, key)
pair when a translation is missing instead of silently returning the
raw key, so unknown keys are visible without crashing requests or
background tasks. A repo-root scripts/check_i18n_logs.py verifier
performs an AST-aware source scan for residual Chinese inside the
in-scope logger/jsonify calls and a recursive parity diff between
en.json and zh.json — both modes pass.
Why: backend logs and API errors previously emitted Chinese-only
strings, leaving English-speaking operators with unreadable log
aggregator output and API consumers with locale-mismatched error
messages. The t() helper and per-thread set_locale propagation already
existed; this change makes every backend caller route through them.
Closes#6
Adds a Neo4j service to docker-compose so `docker compose up -d` works
on a clean checkout, and unhardcodes Graphiti's LLM/embedder so the
documented default provider (Qwen via Dashscope) actually works.
- docker-compose: neo4j:5-community service with cypher-shell
healthcheck, named volumes, and `depends_on: service_healthy` on the
app container; in-Docker NEO4J_URI override leaves the host-mode
default untouched.
- Config: new GRAPHITI_LLM_PROVIDER (openai|gemini, default openai) plus
optional EMBEDDING_API_KEY / EMBEDDING_BASE_URL that fall back to the
chat LLM credentials.
- graphiti_adapter: provider switch inside the singleton factory with
lazy per-provider imports; Gemini path is preserved exactly. The
no-op `_GeminiReranker` becomes a provider-agnostic
`_PassthroughReranker`, still injected explicitly so Graphiti does
not fall back to its OpenAI-only default reranker.
- Drop the ignored `reranker=` kwarg from `_GraphNamespace.search` and
the misleading callers in `zep_tools.py` and
`oasis_profile_generator.py`.
- Refresh `.env.example` to mirror the README env section.
Spec, requirements, and design under
`.kiro/specs/graphiti-neo4j-finalize/`.
Closes#1
Replaces the paid, rate-limited Zep Cloud service with Graphiti (graphiti-core
0.11.6) backed by a local Neo4j instance — free, unlimited, and self-hosted.
Key changes:
- Add GraphitiAdapter: drop-in Zep-compatible wrapper around Graphiti with a
persistent event-loop thread to avoid asyncio/Neo4j driver conflicts
- Switch LLM client to native GeminiClient + GeminiEmbedder (text-embedding-004
fails on Gemini compat endpoint; use google-genai SDK directly)
- Add _GeminiReranker passthrough replacing OpenAIRerankerClient (which
hardcodes gpt-4.1-nano and uses logprobs unsupported by Gemini)
- Fix Cypher queries: use s.uuid/t.uuid for edge source/target instead of
r.source_node_uuid (null property in Graphiti's schema)
- Add ontology-based entity type classifier (_classify_entity_type) so nodes
get colored by type in the D3 graph visualization instead of all being Entity
- Apply classifier in ZepEntityReader so filter_defined_entities finds entities
(previously 0 personas loaded because all labels were ['Entity'])
- Add startup recovery: auto-mark graph_building projects as graph_completed
on backend restart if Neo4j already has their data
- Add resume capability to graph build: skip already-processed episodes after
a restart (断点续传)
- Add non-blocking graph data cache with background refresh in graph.py
- Add EMBEDDING_MODEL config (default: gemini-embedding-001 for Gemini users)
- Add CLAUDE.md with project architecture and dev commands
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Updated the `to_text` method in the `PanoramaResult` class to provide complete outputs for current facts, historical facts, and involved entities, improving data visibility.
- Modified the `to_text` method in the `AgentInterview` class to display the full agent bio without truncation.
- Adjusted the `ZepToolsService` class to retrieve all related entity details and facts without limiting the output, ensuring comprehensive data representation.
- Renamed log_section_complete to log_section_content to better reflect its purpose, and added is_subsection parameter for improved logging of subsection content.
- Introduced log_section_full_complete method to log the completion of entire sections, including all subsections, enhancing tracking of report generation status.
- Adjusted maximum tool call limits for sections and chats to optimize performance during report generation.
- Updated system prompts and user prompts in the ReportAgent class to clarify the report's focus on future predictions rather than current analysis.
- Enhanced the Step3Simulation and Step4Report components for improved user experience, including UI updates and better handling of report generation states.
- Updated the AgentInterview class to display the full agent bio, truncating only if it exceeds 1000 characters for better readability.
- Enhanced the Step4Report component to include structured display for tool results, allowing users to toggle between raw and structured views for various tools, improving user experience and clarity.
- Introduced new components for parsing and displaying results from different tools, including InsightForge, PanoramaSearch, InterviewAgents, and QuickSearch, providing a comprehensive view of the data.
- Introduced strict formatting guidelines for chapter content, prohibiting the use of Markdown headers and emphasizing the use of bold text for section titles.
- Implemented a new method to save chapters along with their subsections into a single file, streamlining the report structure.
- Added content cleaning functionality to remove duplicate titles and ensure proper formatting before saving.
- Enhanced the report assembly process to include post-processing for title management and improved readability.
- Updated the "interview_agents" tool in the Report Agent to utilize the OASIS interview API for real-time agent interviews across Twitter and Reddit, providing authentic responses.
- Improved the ZepToolsService to streamline agent selection, question generation, and interview processing, ensuring a more efficient and structured interview workflow.
- Enhanced documentation to reflect the new interview capabilities, including updated usage instructions and important operational notes regarding the OASIS environment.
- Removed deprecated methods related to simulated interviews, focusing on real API interactions for improved accuracy and reliability.
- Introduced a new "interview_agents" tool in the Report Agent to facilitate in-depth interviews with simulation agents, allowing for multi-perspective insights.
- Implemented the InterviewResult and AgentInterview data classes to structure and manage interview data effectively.
- Enhanced ZepToolsService with methods for conducting interviews, including agent selection and question generation based on user requirements.
- Updated documentation to reflect the new interview capabilities and usage instructions for the Report Agent and Zep tools.
- Introduced new core search tools in the Report Agent: InsightForge for deep insights, PanoramaSearch for comprehensive views, and QuickSearch for rapid queries.
- Updated the Report Agent to prioritize tool usage for data retrieval, ensuring all report content is based on simulation results rather than internal knowledge.
- Enhanced the ZepToolsService with methods for InsightForge and PanoramaSearch, allowing for multi-dimensional queries and historical data retrieval.
- Improved documentation to reflect the new functionalities and usage guidelines for the Report Agent and Zep tools.
- Introduced the Report Agent module to facilitate the automatic generation of simulation analysis reports using LangChain and Zep, following the ReACT model.
- Added functionality for report outline planning, segmented content generation, and user interaction through a dialogue interface.
- Implemented new API endpoints for report generation, status checking, and retrieval, enhancing the overall reporting capabilities.
- Updated README.md to include detailed instructions on the new report generation features and API usage.
- Enhanced the project structure to accommodate the new report management functionalities, including report storage and retrieval mechanisms.
Dominik Seemann
Abhishek Yadav
666ghj