# Poimen Memory Service — Agent Integration Guide

## What Is This
Poimen Memory is a knowledge management system that stores, retrieves, and reasons over learned facts. Agents use it to remember solutions, patterns, and skills across sessions. It prevents re-learning the same things and surfaces relevant knowledge when needed.

## Architecture
- **Event Log** (JSONL): Immutable source of truth. All knowledge written here first.
- **pgvector** (Postgres): Vector store for semantic search (768-dim embeddings).
- **OpenSearch**: Lexical index for BM25 keyword search.
- **Hybrid Search**: 60% semantic + 40% lexical via RRF fusion.
- **Three-Tier Retrieval**: Tier 1 (exact signature) → Tier 2 (hybrid search) → Tier 3 (reference docs).

## When to Memorize (What's Worth Storing)

### DO Memorize
- **Bug fixes**: Error message → root cause → solution. Next time the same error appears, instant recall.
- **Configuration patterns**: Port conflicts, env var requirements, service dependencies.
- **Architecture decisions**: Why we chose X over Y, with trade-offs documented.
- **Tool usage patterns**: Commands that work, flags that matter, common gotchas.
- **Deployment procedures**: Steps to deploy, verify, rollback. Infra-specific knowledge.
- **API contracts**: Request/response formats, auth requirements, rate limits.
- **Design patterns**: Patterns that worked well in this codebase, anti-patterns to avoid.
- **Failure signatures**: Specific error outputs mapped to known fixes.

### DO NOT Memorize
- Temporary debugging output or scratch work.
- Information already in official docs (link to docs instead).
- Personal preferences or style opinions (put in CLAUDE.md instead).
- Secrets, tokens, passwords, API keys — NEVER.
- Exact code implementations (too large, changes too often — store the pattern, not the code).
- Trivial facts that any LLM already knows (e.g., "Rust uses cargo for builds").

### Quality Bar
Ask: "If I hit this problem again in 3 months, would this knowledge save me 30+ minutes?" If yes, memorize it. If not, skip it.

## CLI Usage — `mem learn`

### Ingest Markdown Knowledge
```bash
# Ingest a single file
mem learn knowledge/rust-fundamentals.md

# Ingest entire directory
mem learn knowledge/

# Preview chunks without writing (dry run)
mem learn knowledge/ --dry-run

# Custom project namespace
mem learn docs/ --project my-project

# Smaller chunks for fine-grained retrieval
mem learn knowledge/ --chunk-size 500
```

### How Chunking Works
- Splits on `## ` headings — each section becomes a chunk.
- If a section exceeds `--chunk-size`, it hard-splits at the boundary.
- Each chunk gets a SHA256 hash for deduplication.
- Chunks are stored as JSONL events in `log/<project>/learn/latest.jsonl`.

### Writing Good Knowledge Files
```markdown
# Topic Title

## Subtopic A
- Concise bullet points with actionable information.
- Include the WHY, not just the WHAT.
- Code examples with context, not bare snippets.

## Subtopic B
- Each ## section becomes one retrievable chunk.
- Keep sections focused — one concept per section.
- 200-500 chars per section is ideal for retrieval precision.
```

## CLI Usage — Other Commands

### Capture Failures (Auto-Learn from Errors)
```bash
# Record a command execution and its output
mem capture --cmd "cargo build" --exit 1 --output-file /tmp/build-error.log

# Extract failure signature
mem sig --tool cargo --file /tmp/build-error.log
```

### Resolve Lessons (Pair Failures with Fixes)
```bash
# After fixing a bug, derive the lesson
mem resolve --json
```

### Lookup Known Fixes
```bash
# Search memory for known fix
echo "error[E0308]: mismatched types" | mem lookup --tool cargo

# With minimum confidence threshold
mem lookup --tool kubectl --file /tmp/error.log --floor 0.7
```

### Materialize Skills
```bash
# Generate SKILL.md files from memory
mem materialize
```

## HTTP API — Programmatic Access

### Health Check
```bash
curl -s http://localhost:8080/health | jq .
# {"status": "ok", "timestamp": "..."}
```

### Ingest Records
```bash
curl -s -X POST http://localhost:8080/memory/ingest \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "project": "poimen",
    "source": "transcript://session-123",
    "kind": "L1",
    "text": "Port 8080 conflict fixed by changing to 8081 in deployment.yaml",
    "metadata": {"topic": "kubernetes", "session_id": "sess-123"}
  }' | jq .
# Returns: 201 with chunk ID and SHA256
```

### Query Memory (Hybrid Search)
```bash
curl -s -X POST http://localhost:8080/memory/query \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "project": "poimen",
    "query": "how to fix kubernetes port conflict",
    "limit": 5,
    "floor": 0.6
  }' | jq '.results[] | {score, text}'
# Returns: ranked results with semantic + lexical scores
```

### Context Lookup (Three-Tier)
```bash
curl -s -X POST http://localhost:8080/memory/context \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "project": "poimen",
    "tool": "cargo",
    "task": "build",
    "budget": 4096
  }' | jq .
# Returns: tier-1 signature matches, tier-2 search results, tier-3 reference docs
```

### Browse Vault
```bash
curl -s "http://localhost:8080/memory/vault?project=poimen" \
  -H "Authorization: Bearer $TOKEN" | jq '.files[] | .path'
```

### Rebuild from Log
```bash
curl -s -X POST http://localhost:8080/memory/rebuild \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"project": "poimen", "dry_run": true, "verify_parity": true}' | jq .
```

## Authentication
```bash
# Get JWT token from Authentik
TOKEN=$(curl -s -X POST https://authentik.riotpiao.com/application/o/token/ \
  -d "grant_type=client_credentials" \
  -d "client_id=$CLIENT_ID" \
  -d "client_secret=$CLIENT_SECRET" | jq -r .access_token)

# Use token
curl -s -H "Authorization: Bearer $TOKEN" http://localhost:8080/memory/query ...
```

## Rate Limits
- Ingest: 100 requests/hour
- Query: 1000 requests/hour
- Context: 100 requests/hour
- Idempotency: 24-hour TTL (same ingest key → 409 Conflict)

## Agent Workflow — When to Query vs Ingest

### Before Starting a Task
```
1. Extract key terms from the task description
2. Query memory: mem lookup --tool <tool> or POST /memory/query
3. If results found with score > 0.7 → apply known solution
4. If no results → proceed with normal problem-solving
```

### After Completing a Task
```
1. Did I learn something new? (bug fix, config pattern, deployment step)
2. Would this save 30+ minutes if encountered again?
3. If yes → write a markdown note → mem learn note.md
4. If it was a failure→fix pair → mem capture + mem resolve
```

### During Debugging
```
1. Hit an error → mem sig --tool <tool> --file error.log
2. Check if signature matches known fix → mem lookup
3. If known → apply fix
4. If unknown → debug normally, then memorize the solution
```

## Knowledge Levels
- **L0**: Raw evidence (transcripts, logs). Automatically extracted.
- **L1**: Per-query memory (specific facts, fixes, patterns). Most useful for agents.
- **L2**: Project-level synthesis (cross-cutting themes, architecture summaries).
- **R**: Reference documents (Obsidian vault, external docs). Read-only fallback.

## Infrastructure
- **Cluster**: Kubernetes (poimen namespace)
- **Database**: CNPG pgvector (2 replicas)
- **Search**: OpenSearch (2-node HA)
- **Auth**: Authentik (JWT/OIDC)
- **Deployment**: ArgoCD from `forgejo.riotpiao.com/rock/poimen-memory`
- **Obsidian**: REST API at `obsidian-server.poimen.svc` (reference corpus)

## Gotchas
- Event log is immutable — you can't delete knowledge, only supersede it with newer facts.
- Rebuild from log is deterministic — same log → same vector store state.
- OpenSearch is fail-soft — if it's down, semantic search still works via pgvector alone.
- Embedding model is `sentence-transformers/all-MiniLM-L6-v2` (768-dim). Don't mix with other embedding models.
- `mem learn` appends to `latest.jsonl` — running it twice on the same files creates duplicate chunks (use SHA256 to detect).