# Configuration

Zaxy configuration is centralized in `src/zaxy/config.py`. Settings load from process environment variables, `.env`, defaults, and Docker/Kubernetes-style secret files. Direct environment values win over `*_FILE` values. This keeps development simple while allowing production deployments to avoid plaintext secrets in environment dumps.

Projection settings are `PROJECTION_BACKEND`, `EMBEDDED_GRAPH_PATH`, `LATTICEDB_PATH`, `PGGRAPH_DSN`, and the Neo4j settings used only when `PROJECTION_BACKEND=neo4j`. The default backend is `embedded`, which stores the repo-local LadybugDB projection at `.eventloom/projections/embedded.kuzu` and does not start Docker or require a graph endpoint.

Neo4j settings are `NEO4J_URI`, `NEO4J_USER`, `NEO4J_PASSWORD`, `NEO4J_DATABASE`, `NEO4J_CA_CERT`, `NEO4J_TRUST_ALL`, and `NEO4J_AUTO_START`. Development defaults target `bolt://localhost:7687` with password `testpassword`. When `PROJECTION_BACKEND=neo4j` in localhost development, install `zaxy-memory[neo4j]`; then `NEO4J_AUTO_START=true` lets Zaxy start or reuse a named Docker container, `zaxy-neo4j`, when Bolt is not reachable. The local embedded happy path leaves `NEO4J_AUTO_START=false`, `NEO4J_CA_CERT` empty, and `NEO4J_PASSWORD_FILE` empty. Production mode rejects the default password and requires TLS evidence only when Neo4j is the selected backend. Use `bolt+s://` or set `NEO4J_CA_CERT` to a trusted certificate path for production Neo4j.

Eventloom settings are `EVENTLOOM_PATH`, `EVENTLOOM_THREAD`, and `ZAXY_DOMAIN`. The path is the directory containing session JSONL logs. The thread is the default session name when callers do not provide an explicit session. Generated MCP configs derive a domain-prefixed default such as `zaxy-default` so separate projects do not collide on raw `default`. Session identifiers are validated before becoming filenames.

MCP settings include `SERVER_NAME`, `MCP_ADMIN_TOKEN`, `MCP_REMOTE_AUTH_TOKEN`, and `MCP_REMOTE_SESSION_HEADER`. The remote bearer token protects SSE endpoints. The session header scopes remote clients so one client cannot query or replay another client's session by accident. Production mode requires an admin token so replay and invalidation cannot be left open. Public multi-tenant deployments should use OIDC instead by setting `MCP_OIDC_ISSUER`, `MCP_OIDC_AUDIENCE`, `MCP_OIDC_JWKS_URL`, `MCP_OIDC_REQUIRED_SCOPE`, and `MCP_OIDC_SESSION_CLAIM`. Remote MCP/SSE rate limiting is controlled by `MCP_RATE_LIMIT_ENABLED`, `MCP_RATE_LIMIT_REQUESTS`, and `MCP_RATE_LIMIT_WINDOW_SECONDS`. Remote request audit export is controlled by `MCP_AUDIT_ENABLED` and `MCP_AUDIT_PATH`. Automatic MCP tool-call lifecycle events are controlled by `MCP_LIFECYCLE_CAPTURE_ENABLED`; when enabled, Zaxy records redacted `tool.call.completed` metadata for successful and failed tool dispatch. `MCP_TOOL_PROFILE` selects the MCP tool listing profile: `core` (default since 2.1.0) lists only the front-door verb set centered on `memory_checkout`, while `full` lists every tool. The default flipped from `full` in 2.1.0 backed by the internal tool-adoption lane (listing surface 8,165 → 1,344 estimated tokens, an 83.5% reduction); set `MCP_TOOL_PROFILE=full` to restore the previous listing. Profiles change listing, not capability: every tool remains callable by name under either profile, and `zaxy serve --profile core|full` overrides the setting per process. `memory_capabilities` reports the active profile and any tools that are available but unlisted.

Embedding settings include `EMBEDDING_ENABLED`, `EMBEDDING_PROVIDER`, `EMBEDDING_DIMENSION`, `OPENAI_EMBEDDING_MODEL`, `OPENAI_BASE_URL`, and `OPENAI_API_KEY`. The deterministic hash provider is useful for local tests and offline development. The hosted OpenAI-compatible provider is useful when vector similarity quality matters. See embeddings.md.

Embedded vector index scale settings include `VECTOR_ANN_THRESHOLD`, `VECTOR_ANN_MAX_DIMENSION`, `VECTOR_ANN_BYTE_BUDGET_ENGAGEMENT`, `VECTOR_ANN_EFS`, and `VECTOR_QUANTIZATION`. Stored vectors carry the producing provider's version tag (for example `hash@<fingerprint>-dim1536`); search never compares vectors across version tags, `zaxy doctor` reports mixed-version corpora, and `zaxy memory re-embed --session-id <session>` migrates stale-version vectors to the active provider.

ANN engagement is a two-clause rule since 2.2, gated by a dimension ceiling. A (session, version) vector scope whose dimension is at or below `VECTOR_ANN_MAX_DIMENSION` (default `64`) switches from the exact dense matrix to an engine-native (LadybugDB) HNSW index when (a) the scope's vector count reaches `VECTOR_ANN_THRESHOLD` (default `100000`), or (b) the scope's exact float64 matrix (count × dimension × 8 bytes) would exceed the 256 MiB vector index cache byte budget. Scopes above the dimension ceiling always use exact float64 (or explicitly opted-in int8) search regardless of corpus size. The count default came down from `1000000` in 2.2 on gate-G4 evidence: two consecutive vector-scale lane runs at exactly 10^5 vectors (dimension 64) passed every ANN exit criterion — recall@10 of 1.0 on both the strict and tie-aware metrics, ANN p50 at-or-better than the exact matrix in-run, resident index bytes improved, and full `COPY`-based index builds of 92–98s (`docs/research/artifacts/ann-2026-06/ann3-d64-100k-r1.json`/`-r2.json`). The dimension ceiling default is the measured envelope of that evidence — the lane's all-criteria double pass exists only at dimension 64, and the conclusion does not transfer upward: at dimension 1536 with 50k gaussian vectors the lane measured HNSW recall@10 of 0.6 even at `efs` 400 while the exact matrix answered in 22ms p50 (`docs/research/artifacts/ann-2026-06/ann3-d1536-50k-gauss-crossover.json`; a rerun measured 0.6344, `ann4-d1536-50k-gauss-r2.json`), and raising `efs` to 800 recovered recall only to 0.8438 with ANN p50 worse than exact (`ann4-d1536-50k-gauss-efs800.json`) — tuning does not rescue high-dimension scale. Raise `VECTOR_ANN_MAX_DIMENSION` only with lane evidence for your dimension and distribution.

Clause (b) is the dimension-aware memory guard within the ceiling; at the 256 MiB budget the exact matrix crosses it above these row counts:

Dimension | Exact float64 rows that fit the byte budget
--- | ---
64 | 524,288
384 | 87,381
1536 | 21,845

`VECTOR_ANN_BYTE_BUDGET_ENGAGEMENT` (default `true`) controls clause (b) alone. An explicit `VECTOR_ANN_THRESHOLD` remains an absolute count override for clause (a), but within the dimension ceiling the byte clause applies regardless of it — to force the exact float64 matrix above budget, also set `VECTOR_ANN_BYTE_BUDGET_ENGAGEMENT=false` (or opt in to `VECTOR_QUANTIZATION=int8`, which keeps its precedence below the count threshold and stores ~1/8 of the float64 bytes). Exact search above budget is viable — and above the dimension ceiling it is the measured recommendation — because the cache's LRU byte eviction always keeps the newest matrix resident: a single over-budget scope degrades to a cache of one (a 100k-vector corpus at dimension 1536 is a 1.23 GB matrix, 4.58× the budget, and stays resident); the budget bounds multi-scope cache totals, so only workloads alternating across several jointly over-budget scopes rebuild per switch. The HNSW path oversamples candidates from the index and reranks them with exact float64 scores read from the resident entity vectors, so the final ordering is exact while results still report `exact: false` (the candidate set is approximate). Each (session, version) group queries its own shadow table directly — no per-query graph projection or predicate scan. `VECTOR_ANN_EFS` (default `400`) is the HNSW query-time candidate-list size (`efs`), the primary recall knob: higher values trade latency for recall, and the effective value is never below the oversampled candidate count a query requests. The default moved from `200` to `400` in 2.2 on lane evidence: an internal sweep on a realistic embedding distribution (10k vectors, dimension 1536) measured recall@10 of 0.8531 at `efs` 200, 0.9875 at 400, and 1.0 at 800, with roughly 2ms of added p50 per step, so the default now matches the high-dimension recommendation. Set `VECTOR_ANN_EFS=800` for maximum recall when the extra few milliseconds are acceptable. `VECTOR_QUANTIZATION=int8` (default `none`) opts in to int8 matrix storage with per-vector scale factors; quantized search oversamples candidates with integer dot products and reranks them with exact float scores.

Retrieval settings include `RETRIEVAL_PROFILE`, `QUERY_DEFAULT_LIMIT`, `QUERY_SCORING_PROFILE`, `RETENTION_POLICY`, `RETENTION_DECAY_HALF_LIFE_DAYS`, `RETENTION_EXPIRED_WEIGHT`, `CONTEXT_VERBATIM_ENABLED`, `CONTEXT_VERBATIM_SLOTS`, `RERANKER_PROVIDER`, `RERANKER_URL`, `RERANKER_API_KEY`, `OPENAI_RERANK_MODEL`, and `OPENAI_BASE_URL`. `RETRIEVAL_PROFILE` names the retrieval profile: `cognitive` (default since 2.1.0), `local_fast`, `local_sota`, `hosted_sota`, or `custom`. The default flipped from `local_fast` in 2.1.0 backed by the internal forgetting lane (exact cold-start parity, no-recall-loss 1.0, pin/authority exemptions 1.0, ranking lift 1.0 vs 0.0); the cognitive profile composes the same local stack as `local_fast` plus the salience-ranking, cue-blending, and graph-walk flags. Set `RETRIEVAL_PROFILE=local_fast` to restore the previous plain ranking. Leaving the profile unset while explicitly customizing embedding/reranker/scoring knobs still resolves to the `custom` profile, exactly as before the flip. See retrieval.md. `RETENTION_POLICY=none` is the default and preserves current retrieval behavior. `filter_expired` removes expired candidates at query time, while `decay` keeps candidates eligible but downranks stale or expired memory without mutating Eventloom or projected facts. `RERANKER_PROVIDER=lexical` enables deterministic local reranking. `RERANKER_PROVIDER=http` sends fused candidates to a local/self-hosted endpoint. `RERANKER_PROVIDER=openai` uses an OpenAI-compatible chat-completions model and `OPENAI_API_KEY`. `CONTEXT_VERBATIM_ENABLED=true` reserves exact Eventloom source recall during context assembly, and `CONTEXT_VERBATIM_SLOTS` controls how many assembled context slots are reserved for those cited source chunks. `CONTEXT_PACKET_MEMORY_ENABLED=true` adds a bounded proactive lane for recent `llm.packet.projected` memory, and `CONTEXT_PACKET_MEMORY_SLOTS` controls how many assembled context slots are reserved for that packet-derived memory.

Cognitive memory settings include `SALIENCE_HALF_LIFE_DAYS`, `SALIENCE_FLOOR`, and `ENCODING_GATE_ENABLED`. `SALIENCE_HALF_LIFE_DAYS` (default `30.0`, must be greater than zero) is the exponential recency-decay half-life used by the salience reinforcement ledger. `SALIENCE_FLOOR` (default `0.15`) only takes effect under the `RETRIEVAL_PROFILE=cognitive` profile (the default since 2.1.0): memories whose replayed salience falls below the floor leave default checkout ranking, labeled `attenuated` in checkout diagnostics, while staying fully reachable via explicit `memory_query`/`memory_replay`; authority-accepted state and events appended with `"pinned": true` payload metadata are exempt. `ENCODING_GATE_ENABLED` (default `false`) tags each append's payload with a `novel`/`reinforcing`/`redundant` encoding classification computed from verbatim-index and entity-name overlap (no embedding calls); events are always appended and hash-chained regardless, redundant appends additionally emit a weak reinforcement toward the duplicated memory, and the tags are purely observational — re-projecting with the gate off yields identical ranking state. See retrieval.md for the `cognitive` retrieval profile.

Supported secret-file variants are `NEO4J_PASSWORD_FILE`, `MCP_ADMIN_TOKEN_FILE`, `MCP_REMOTE_AUTH_TOKEN_FILE`, `MCP_OIDC_CLIENT_SECRET_FILE`, `OPENAI_API_KEY_FILE`, `RERANKER_API_KEY_FILE`, and `PATHLIGHT_ACCESS_TOKEN_FILE`. Production setup writes these references into `.env`; the settings loader resolves them during initialization. Secret files must not be world-readable.

Validation commands:

    scripts/validate-deployment.sh --root .
    scripts/release-check.sh --root .

The deployment validator checks production mode, selected sidecar TLS posture, remote MCP auth, admin-token configuration, and secret-file permissions. Embedded LadybugDB production deployments do not need Neo4j certificate material unless `PROJECTION_BACKEND=neo4j` is selected. The full release gate also runs tests, package validation, and documentation validation. See deployment.md, security.md, and runbook.md. The short setup path is still documented in README.md.