Adds ?format=md|pdf query param to GET /api/report/<id>/download.
PDF is generated from Markdown via fitz.Story (PyMuPDF). Also fixes
create_app() to support dict config and skip JWT auth in TESTING mode.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
graphiti-core 0.28.2 has _extract_entity_attributes required by the
monkey-patch in GraphitiBackend. Override neo4j to resolve conflict
with camel-oasis==0.2.5 which pins neo4j==5.23.0.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
All variables now flow from config.sh through 2-build-deploy.sh to the
container env, so GRAPH_BACKEND=graphiti is honoured at runtime.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Extract parse_azure_url from graphiti_backend into utils/llm_client so all
services (oasis_profile_generator, simulation_config_generator, graphiti_backend,
LLMClient) strip the /chat/completions suffix and api-version before passing
base_url to the OpenAI SDK, fixing 404 errors when the full Azure endpoint URL
is set in LLM_BASE_URL.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The previous fix applied _flatten_attributes too broadly, breaking graphiti's
internal ExtractedEntities validation (lists were converted to strings).
Instead, monkey-patch only _extract_entity_attributes — the exact function
that returns entity attribute dicts to node.attributes before Neo4j write.
Lists of primitives are preserved; only dict-valued attributes are flattened.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Graphiti's attribute extraction LLM sometimes returns values wrapped in
nested dicts ({"value": "CTTI"}) instead of plain strings. Neo4j rejects
these with TypeError: "Property values can only be of primitive types".
Fix: after _AzureGenericClient gets the LLM response, validate it through
the Pydantic response_model and call _flatten_attributes() so every value
reaching Neo4j is a scalar. Also add _flatten_attributes() helper.
UI: exclude 'summary' from the Properties section of the node detail panel
since it already appears in the dedicated Summary section below.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Revert default limits back to 10 edges and 12 entities (10 specific + 2
fallback), matching Zep Cloud's maximum. Expose ONTOLOGY_MAX_ENTITY_TYPES
and ONTOLOGY_MAX_EDGE_TYPES in Config so operators can override without
touching code. Prompts now inject the live values at request time.
Also replace Chinese comments in .env.example with English equivalents.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- ontology_generator: increase entity limit to 20 (18 specific + 2 fallback),
edge types to 20 (12-20 requested); bump max_tokens to 8192
- ontology_generator: fallback type names (Person/Organization) now resolved
via i18n so Catalan gets Persona/Organització
- locales: add ontologyFallback* keys to en/zh/es/ca
- graph_builder: pass entity/edge attributes to non-Zep set_ontology (were discarded)
- graphiti_backend: _make_model builds real Pydantic fields per attribute
- graphiti_backend: _build_extraction_instructions includes per-entity attribute
hints so the LLM knows which fields to extract
- graphiti_backend: add_batch retries up to 3x on "node not found" race condition
with exponential backoff (2s, 4s) before propagating the error
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Dockerfile installs graphiti optional extras so any GRAPH_BACKEND works at runtime
- container-app.bicep adds graphBackend, neo4jUri/User/Password and llmProvider params
- 2-build-deploy.sh validates graph backend config and passes all new vars to Bicep
- config.sh.example documents new variables with inline guidance
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add commented sections for the new pluggable graph backend and Gemini
LLM provider options so users know what variables are available.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When LLM_PROVIDER=gemini, LLMClient automatically uses the Google AI Studio
OpenAI-compatible endpoint instead of requiring manual base_url configuration.
An explicit base_url argument still takes precedence.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace hardcoded English strings (GENERATED ENTITY TYPES, ATTRIBUTES,
EXAMPLES, etc.) and the Uploading message with i18n keys.
Add corresponding translations to all 4 locale files (ca/en/zh/es).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Translate all Chinese comments, docstrings, log messages, error messages,
and LLM prompt text to English across the entire backend codebase.
Locale translation files (locales/*.json) are unchanged.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
gunicorn was declared in pyproject.toml but missing from uv.lock,
so `uv sync --frozen` in the Dockerfile never installed it and the
container crashed with 'no such file or directory' on startup.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Simplifies infrastructure: ACR + Container Apps Environment only.
Log Analytics can be added later if observability is needed.
Update INSTALL.md accordingly.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add locales/es.json with 646 keys (neutral Spanish, based on upstream PR #510)
- Add locales/ca.json with 646 keys (standard Catalan, full translation)
- Add README-ES.md and README-CA.md with complete project documentation
- Update locales/languages.json with ca entry and llmInstruction
- Update README.md language switcher links
Both locales integrate automatically with the existing i18n architecture:
UI labels, progress messages, console logs, and LLM prompt instructions
all respect the user's selected language end-to-end.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Ubuntu
666ghj
ghostubborn