Skip to content

ISSUES.md

Latest commit

 

History

History
44 lines (40 loc) · 4.62 KB

ISSUES.md

File metadata and controls

44 lines (40 loc) · 4.62 KB

Issues Checklist

Note

Issue and task tracking now lives in the Beads issue tracker (bd). Use bd list, bd show <issue>, and bd ready for the live source of truth; the checklists below remain as historical context.

Blockers

  • MeshMind client fails without the mgclient module; introduce lazy driver initialization or documented in-memory fallback.

  • Register a default embedding encoder (OpenAI or sentence-transformers) during startup so extraction and hybrid search can run.

  • Update OpenAI integration to match the current SDK (Responses API payload, embeddings API response structure).

  • Replace eager tiktoken imports in meshmind.core.utils and meshmind.pipeline.compress with guarded, optional imports.

  • Align declared Python requirement with supported dependencies (project now pins Python >=3.11,<3.13).

  • Maintain pip/uv package download access (confirmed working on 2025-10-15) so dependency lock regeneration can proceed reliably across sessions.

High Priority

  • Provide configuration documentation and examples for Memgraph, Redis, and OpenAI environment variables.
  • Add automated tests or smoke checks that run without external services (mock OpenAI, stub Memgraph driver).
  • Create real docker-compose services for Memgraph and Redis or remove the placeholder file.
  • Centralize LLM provider usage behind a configurable client wrapper to remove direct openai imports scattered through the codebase.
  • Surface LLM override fields via REST/gRPC payloads and integration tests so service clients can select providers/models like the CLI.
  • Document Neo4j driver requirements and verify connectivity against a live cluster (integration suite now hits the docker-compose Neo4j service).
  • Exercise the new namespace/entity-label filtering against live Memgraph/Neo4j datasets to confirm Cypher predicates behave as expected.
  • Regenerate uv.lock to reflect the updated dependency set (pymgclient, fastapi, uvicorn, extras) so CI tooling stays in sync.

Medium Priority

  • Persist results from consolidation and compression tasks back to the database (currently in-memory only).

  • Refine Memory.importance scoring to reflect actual ranking heuristics instead of a constant.

  • Add vector, regex, and exact-match search helpers to match stated feature set or update documentation to demote them.

  • Harden Celery tasks to initialize dependencies lazily and log failures when the driver is unavailable.

  • Validate consolidation heuristics on larger datasets to measure ranking accuracy and resource usage (synthetic fixtures and benchmark scripts cover scale; rerun with production datasets when available).

  • Document and implement a conflict-resolution/backoff strategy for consolidation when merged metadata conflicts (configurable via MAINTENANCE_MAX_ATTEMPTS and MAINTENANCE_BASE_DELAY_SECONDS).

  • Revisit the compatibility shim once production environments support Pydantic 2.x so the real models can be restored (shim removed; native Pydantic models now required).

  • Replace the gRPC dataclass shim with generated protobuf definitions; follow-up integration tests remain pending until a real gRPC server is provisioned.

  • Implement a production-ready gRPC server (leveraging the generated protobuf modules). Async helpers now live in meshmind.api.grpc_server; integration tests remain blocked on staging infrastructure.

  • Add end-to-end gRPC integration tests (deploy server + grpcurl smoke) once staging infrastructure is provisioned.

  • Push graph-backed retrieval into Memgraph/Neo4j search capabilities once available (current wrappers now filter/paginate server-side but still score vectors in Python).

  • Reconcile tests that depend on Memory.pre_init and outdated OpenAI interfaces with the current implementation.

  • Expose memory_counts via a gRPC endpoint to keep service interfaces aligned.

  • Add linting, formatting, and type-checking tooling to improve code quality.

  • Validate the new Docker Compose stacks (root and meshmind/tests/docker/) on an environment with container support and document host requirements (ports, resources).

Low Priority / Nice to Have

  • Offer alternative storage backends (in-memory driver, SQLite, etc.) for easier local development.
  • Provide an administrative dashboard or CLI commands for listing namespaces, counts, and maintenance statistics (CLI admin subcommands now expose predicates, telemetry, and graph checks).
  • Publish onboarding guides and troubleshooting FAQs for contributors.
  • Explore plugin registration for embeddings and retrieval strategies to reduce manual wiring.