Graphiti's default cross-encoder hard-codes the OpenAI gpt-4.1-nano model
and depends on OpenAI-specific logprobs/logit_bias, so the adapter has
been injecting a no-op _PassthroughReranker just to keep search code
paths working. Search results consumed by SearchResult, InsightForge,
Panorama, and Interview were therefore returned in Graphiti's RRF order
with no real reranking signal.
Add an Ollama-backed CrossEncoderClient that scores passages through a
local chat model via the OpenAI-compatible /v1 surface and wire it into
_get_graphiti() behind a RERANKER_PROVIDER switch (default: ollama).
Construction is side-effect-free and the rank() method never raises:
per-passage parse failures degrade to a deterministic low score, and a
whole-call failure falls back to passthrough order with a single WARNING
log so Flask keeps serving when Ollama is unreachable. Setting
RERANKER_PROVIDER=none preserves the legacy passthrough for CI and slim
containers that cannot pull the model.
Closes#39
Replace the silent placeholder-UUID fallback in
_GraphNamespace.add_batch with logger.exception(...) + raise so
embedder misconfiguration (404 unknown model, connection refused, etc.)
fails the surrounding graph-build Task with a visible error instead of
producing a Task that looks completed while the graph stays empty.
Document the existing-but-undocumented Ollama embedder configuration
in .env.example, CLAUDE.md, README.md, and docker-compose.yml.
mxbai-embed-large is the recommended local model because its 1024-dim
output matches Graphiti's default EMBEDDING_DIM. Adds a curl smoke
test to verify embedder reachability before the first graph build.
No new env var or provider literal: Ollama is reached through the
existing openai-provider branch by setting EMBEDDING_BASE_URL,
EMBEDDING_API_KEY, and EMBEDDING_MODEL.
Closes#18
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>
Dominik Seemann
Abhishek Yadav