218 lines
7.3 KiB
Markdown
218 lines
7.3 KiB
Markdown
# Avalon Memory Crystal Server (amcs)
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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.
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## What it does
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- **Capture** thoughts with automatic embedding and metadata extraction
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- **Search** thoughts semantically via vector similarity
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- **Organise** thoughts into projects and retrieve full project context
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- **Summarise** and recall memory across topics and time windows
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- **Link** related thoughts and traverse relationships
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## Stack
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- Go — MCP server over Streamable HTTP
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- Postgres + pgvector — storage and vector search
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- LiteLLM — primary hosted AI provider (embeddings + metadata extraction)
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- OpenRouter — default upstream behind LiteLLM
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- Ollama — supported local or self-hosted OpenAI-compatible provider
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## Tools
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| Tool | Purpose |
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|---|---|
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| `capture_thought` | Store a thought with embedding and metadata |
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| `search_thoughts` | Semantic similarity search |
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| `list_thoughts` | Filter thoughts by type, topic, person, date |
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| `thought_stats` | Counts and top topics/people |
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| `get_thought` | Retrieve a thought by ID |
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| `update_thought` | Patch content or metadata |
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| `delete_thought` | Hard delete |
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| `archive_thought` | Soft delete |
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| `create_project` | Register a named project |
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| `list_projects` | List projects with thought counts |
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| `get_project_context` | Recent + semantic context for a project |
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| `set_active_project` | Set session project scope |
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| `get_active_project` | Get current session project |
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| `summarize_thoughts` | LLM prose summary over a filtered set |
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| `recall_context` | Semantic + recency context block for injection |
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| `link_thoughts` | Create a typed relationship between thoughts |
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| `related_thoughts` | Explicit links + semantic neighbours |
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| `backfill_embeddings` | Generate missing embeddings for stored thoughts |
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## Configuration
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Config is YAML-driven. Copy `configs/config.example.yaml` and set:
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- `database.url` — Postgres connection string
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- `auth.mode` — `api_keys` or `oauth_client_credentials`
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- `auth.keys` — API keys for MCP access via `x-brain-key` or `Authorization: Bearer <key>` when `auth.mode=api_keys`
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- `auth.oauth.clients` — client registry when `auth.mode=oauth_client_credentials`
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**OAuth Client Credentials flow** (`auth.mode=oauth_client_credentials`):
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1. Obtain a token — `POST /oauth/token` (public, no auth required):
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```
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POST /oauth/token
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Content-Type: application/x-www-form-urlencoded
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Authorization: Basic base64(client_id:client_secret)
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grant_type=client_credentials
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```
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Returns: `{"access_token": "...", "token_type": "bearer", "expires_in": 3600}`
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2. Use the token on the MCP endpoint:
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```
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Authorization: Bearer <access_token>
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```
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Alternatively, pass `client_id` and `client_secret` as body parameters instead of `Authorization: Basic`. Direct `Authorization: Basic` credential validation on the MCP endpoint is also supported as a fallback (no token required).
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- `ai.litellm.base_url` and `ai.litellm.api_key` — LiteLLM proxy
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- `ai.ollama.base_url` and `ai.ollama.api_key` — Ollama local or remote server
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See `llm/plan.md` for full architecture and implementation plan.
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## Backfill
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Run `backfill_embeddings` after switching embedding models or importing thoughts without vectors.
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```json
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{
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"project": "optional-project-name",
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"limit": 100,
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"include_archived": false,
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"older_than_days": 0,
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"dry_run": false
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}
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```
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- `dry_run: true` — report counts without calling the embedding provider
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- `limit` — max thoughts per call (default 100)
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- Embeddings are generated in parallel (4 workers) and upserted; one failure does not abort the run
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**Automatic backfill** (optional, config-gated):
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```yaml
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backfill:
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enabled: true
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run_on_startup: true # run once on server start
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interval: "15m" # repeat every 15 minutes
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batch_size: 20
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max_per_run: 100
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include_archived: false
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```
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**Search fallback**: when no embeddings exist for the active model in scope, `search_thoughts`, `recall_context`, `get_project_context`, `summarize_thoughts`, and `related_thoughts` automatically fall back to Postgres full-text search so results are never silently empty.
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## Client Setup
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### Claude Code
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```bash
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# API key auth
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claude mcp add --transport http amcs http://localhost:8080/mcp --header "x-brain-key: <key>"
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# Bearer token auth
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claude mcp add --transport http amcs http://localhost:8080/mcp --header "Authorization: Bearer <token>"
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```
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### OpenAI Codex
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Add to `~/.codex/config.toml`:
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```toml
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[[mcp_servers]]
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name = "amcs"
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url = "http://localhost:8080/mcp"
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[mcp_servers.headers]
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x-brain-key = "<key>"
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```
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### OpenCode
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```bash
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# API key auth
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opencode mcp add --name amcs --type remote --url http://localhost:8080/mcp --header "x-brain-key=<key>"
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# Bearer token auth
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opencode mcp add --name amcs --type remote --url http://localhost:8080/mcp --header "Authorization=Bearer <token>"
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```
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Or add directly to `opencode.json` / `~/.config/opencode/config.json`:
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```json
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{
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"mcp": {
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"amcs": {
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"type": "remote",
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"url": "http://localhost:8080/mcp",
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"headers": {
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"x-brain-key": "<key>"
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}
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}
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}
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}
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```
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## Development
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Run the SQL migrations against a local database with:
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`DATABASE_URL=postgres://... make migrate`
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LLM integration instructions are served at `/llm`.
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## Containers
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The repo now includes a `Dockerfile` and Compose files for running the app with Postgres + pgvector.
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1. Set a real LiteLLM key in your shell:
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`export AMCS_LITELLM_API_KEY=your-key`
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2. Start the stack with your runtime:
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`docker compose -f docker-compose.yml -f docker-compose.docker.yml up --build`
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`podman compose -f docker-compose.yml up --build`
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3. Call the service on `http://localhost:8080`
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Notes:
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- The app uses `configs/docker.yaml` inside the container.
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- The local `./configs` directory is mounted into `/app/configs`, so config edits apply without rebuilding the image.
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- `AMCS_LITELLM_BASE_URL` overrides the LiteLLM endpoint, so you can retarget it without editing YAML.
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- `AMCS_OLLAMA_BASE_URL` overrides the Ollama endpoint for local or remote servers.
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- The Compose stack uses a default bridge network named `amcs`.
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- The base Compose file uses `host.containers.internal`, which is Podman-friendly.
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- The Docker override file adds `host-gateway` aliases so Docker can resolve the same host endpoint.
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- Database migrations `001` through `005` run automatically when the Postgres volume is created for the first time.
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- `migrations/006_rls_and_grants.sql` is intentionally skipped during container bootstrap because it contains deployment-specific grants for a role named `amcs_user`.
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## Ollama
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Set `ai.provider: "ollama"` to use a local or self-hosted Ollama server through its OpenAI-compatible API.
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Example:
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```yaml
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ai:
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provider: "ollama"
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embeddings:
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model: "nomic-embed-text"
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dimensions: 768
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metadata:
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model: "llama3.2"
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temperature: 0.1
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ollama:
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base_url: "http://localhost:11434/v1"
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api_key: "ollama"
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request_headers: {}
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```
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Notes:
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- For remote Ollama servers, point `ai.ollama.base_url` at the remote `/v1` endpoint.
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- The client always sends Bearer auth; Ollama ignores it locally, so `api_key: "ollama"` is a safe default.
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- `ai.embeddings.dimensions` must match the embedding model you actually use, or startup will fail the database vector-dimension check.
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