# Avalon Memory Crystal Server  (amcs)

![Avalon Memory Crystal](assets/avelonmemorycrystal.jpg)

A Go MCP server for capturing and retrieving thoughts, memory, and project context. Exposes tools over Streamable HTTP, backed by Postgres with pgvector for semantic search.

## What it does

- **Capture** thoughts with automatic embedding and metadata extraction
- **Search** thoughts semantically via vector similarity
- **Organise** thoughts into projects and retrieve full project context
- **Summarise** and recall memory across topics and time windows
- **Link** related thoughts and traverse relationships

## Stack

- Go — MCP server over Streamable HTTP
- Postgres + pgvector — storage and vector search
- LiteLLM — primary hosted AI provider (embeddings + metadata extraction)
- OpenRouter — default upstream behind LiteLLM
- Ollama — supported local or self-hosted OpenAI-compatible provider

## Tools

| Tool | Purpose |
|---|---|
| `capture_thought` | Store a thought with embedding and metadata |
| `search_thoughts` | Semantic similarity search |
| `list_thoughts` | Filter thoughts by type, topic, person, date |
| `thought_stats` | Counts and top topics/people |
| `get_thought` | Retrieve a thought by ID |
| `update_thought` | Patch content or metadata |
| `delete_thought` | Hard delete |
| `archive_thought` | Soft delete |
| `create_project` | Register a named project |
| `list_projects` | List projects with thought counts |
| `get_project_context` | Recent + semantic context for a project |
| `set_active_project` | Set session project scope |
| `get_active_project` | Get current session project |
| `summarize_thoughts` | LLM prose summary over a filtered set |
| `recall_context` | Semantic + recency context block for injection |
| `link_thoughts` | Create a typed relationship between thoughts |
| `related_thoughts` | Explicit links + semantic neighbours |

## Configuration

Config is YAML-driven. Copy `configs/config.example.yaml` and set:

- `database.url` — Postgres connection string
- `auth.keys` — API keys for MCP endpoint access
- `ai.litellm.base_url` and `ai.litellm.api_key` — LiteLLM proxy
- `ai.ollama.base_url` and `ai.ollama.api_key` — Ollama local or remote server

See `llm/plan.md` for full architecture and implementation plan.

## Development

Run the SQL migrations against a local database with:

`DATABASE_URL=postgres://... make migrate`

## Containers

The repo now includes a `Dockerfile` and Compose files for running the app with Postgres + pgvector.

1. Set a real LiteLLM key in your shell:
   `export OB1_LITELLM_API_KEY=your-key`
2. Start the stack with your runtime:
   `docker compose -f docker-compose.yml -f docker-compose.docker.yml up --build`
   `podman compose -f docker-compose.yml up --build`
3. Call the service on `http://localhost:8080`

Notes:

- The app uses `configs/docker.yaml` inside the container.
- `OB1_LITELLM_BASE_URL` overrides the LiteLLM endpoint, so you can retarget it without editing YAML.
- `OB1_OLLAMA_BASE_URL` overrides the Ollama endpoint for local or remote servers.
- The base Compose file uses `host.containers.internal`, which is Podman-friendly.
- The Docker override file adds `host-gateway` aliases so Docker can resolve the same host endpoint.
- Database migrations `001` through `005` run automatically when the Postgres volume is created for the first time.
- `migrations/006_rls_and_grants.sql` is intentionally skipped during container bootstrap because it contains deployment-specific grants for a role named `amcs_user`.

## Ollama

Set `ai.provider: "ollama"` to use a local or self-hosted Ollama server through its OpenAI-compatible API.

Example:

```yaml
ai:
  provider: "ollama"
  embeddings:
    model: "nomic-embed-text"
    dimensions: 768
  metadata:
    model: "llama3.2"
    temperature: 0.1
  ollama:
    base_url: "http://localhost:11434/v1"
    api_key: "ollama"
    request_headers: {}
```

Notes:

- For remote Ollama servers, point `ai.ollama.base_url` at the remote `/v1` endpoint.
- The client always sends Bearer auth; Ollama ignores it locally, so `api_key: "ollama"` is a safe default.
- `ai.embeddings.dimensions` must match the embedding model you actually use, or startup will fail the database vector-dimension check.