docs: add comprehensive How It Works section
- Add architecture diagram - Detail step-by-step process (5 steps) - Include code snippets for each phase - Document session rotation handling - Add error handling documentation - Include collection schema details - Document security notes - Add performance metrics table
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README.md
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README.md
@@ -93,6 +93,225 @@ Edit `config.json` or set environment variables:
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---
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## How It Works
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### Architecture Overview
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```
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┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
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│ OpenClaw Chat │────▶│ Session JSONL │────▶│ Base Watcher │
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│ (You talking) │ │ (/sessions/*.jsonl) │ │ (This daemon) │
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└─────────────────┘ └──────────────────┘ └────────┬────────┘
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│
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▼
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┌────────────────────────────────────────────────────────────────────┐
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│ PROCESSING PIPELINE │
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│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌───────────┐ │
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│ │ Watch File │─▶│ Parse Turn │─▶│ Clean Text │─▶│ Embed │ │
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│ │ (inotify) │ │ (JSON→dict) │ │ (strip md) │ │ (Ollama) │ │
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│ └──────────────┘ └──────────────┘ └──────────────┘ └─────┬─────┘ │
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│ │ │
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│ ┌───────────────────────────────────────────────────────────┘ │
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│ │ │
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│ ▼ │
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│ ┌──────────────┐ ┌──────────────┐ │
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│ │ Store to │─▶│ Qdrant │ │
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│ │ memories_tr │ │ (vector DB) │ │
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│ └──────────────┘ └──────────────┘ │
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└────────────────────────────────────────────────────────────────────┘
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```
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### Step-by-Step Process
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#### Step 1: File Watching
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The watcher monitors OpenClaw session files in real-time:
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```python
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# From realtime_qdrant_watcher.py
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SESSIONS_DIR = Path("/root/.openclaw/agents/main/sessions")
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```
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**What happens:**
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- Uses `inotify` or polling to watch the sessions directory
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- Automatically detects the most recently modified `.jsonl` file
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- Handles session rotation (when OpenClaw starts a new session)
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- Maintains position in file to avoid re-processing old lines
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#### Step 2: Turn Parsing
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Each conversation turn is extracted from the JSONL file:
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```json
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// Example session file entry
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{
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"type": "message",
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"message": {
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"role": "user",
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"content": "Hello, can you help me?",
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"timestamp": "2026-02-27T09:30:00Z"
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}
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}
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```
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**What happens:**
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- Reads new lines appended to the session file
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- Parses JSON to extract role (user/assistant/system)
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- Extracts content text
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- Captures timestamp
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- Generates unique turn ID from content hash + timestamp
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**Code flow:**
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```python
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def parse_turn(line: str) -> Optional[Dict]:
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data = json.loads(line)
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if data.get("type") != "message":
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return None # Skip non-message entries
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return {
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"id": hashlib.md5(f"{content}{timestamp}".encode()).hexdigest()[:16],
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"role": role,
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"content": content,
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"timestamp": timestamp,
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"user_id": os.getenv("USER_ID", "default")
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}
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```
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#### Step 3: Content Cleaning
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Before storage, content is normalized:
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**Strips:**
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- Markdown tables (`| column | column |`)
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- Bold/italic markers (`**text**`, `*text*`)
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- Inline code (`` `code` ``)
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- Code blocks (```code```)
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- Multiple consecutive spaces
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- Leading/trailing whitespace
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**Example:**
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```
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Input: "Check this **important** table: | col1 | col2 |"
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Output: "Check this important table"
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```
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**Why:** Clean text improves embedding quality and searchability.
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#### Step 4: Embedding Generation
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The cleaned content is converted to a vector embedding:
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```python
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def get_embedding(text: str) -> List[float]:
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response = requests.post(
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f"{OLLAMA_URL}/api/embeddings",
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json={"model": EMBEDDING_MODEL, "prompt": text}
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)
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return response.json()["embedding"]
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```
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**What happens:**
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- Sends text to Ollama API (10.0.0.10:11434)
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- Uses `snowflake-arctic-embed2` model
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- Returns 768-dimensional vector
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- Falls back gracefully if Ollama is unavailable
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#### Step 5: Qdrant Storage
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The complete turn data is stored to Qdrant:
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```python
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payload = {
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"user_id": user_id,
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"role": turn["role"],
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"content": cleaned_content[:2000], # Size limit
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"timestamp": turn["timestamp"],
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"session_id": session_id,
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"source": "true-recall-base"
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}
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requests.put(
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f"{QDRANT_URL}/collections/memories_tr/points",
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json={"points": [{"id": turn_id, "vector": embedding, "payload": payload}]}
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)
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```
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**Storage format:**
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| Field | Type | Description |
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|-------|------|-------------|
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| `user_id` | string | User identifier |
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| `role` | string | user/assistant/system |
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| `content` | string | Cleaned text (max 2000 chars) |
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| `timestamp` | string | ISO 8601 timestamp |
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| `session_id` | string | Source session file |
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| `source` | string | "true-recall-base" |
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### Real-Time Performance
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| Metric | Target | Actual |
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|--------|--------|--------|
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| Latency | < 500ms | ~100-200ms |
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| Throughput | > 10 turns/sec | > 50 turns/sec |
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| Embedding time | < 300ms | ~50-100ms |
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| Qdrant write | < 100ms | ~10-50ms |
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### Session Rotation Handling
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When OpenClaw starts a new session:
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1. New `.jsonl` file created in sessions directory
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2. Watcher detects file change via `inotify`
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3. Identifies most recently modified file
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4. Switches to watching new file
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5. Continues from position 0 of new file
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6. Old file remains in `memories_tr` (already captured)
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### Error Handling
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**Qdrant unavailable:**
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- Retries with exponential backoff
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- Logs error, continues watching
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- Next turn attempts storage again
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**Ollama unavailable:**
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- Cannot generate embeddings
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- Logs error, skips turn
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- Continues watching (no data loss in file)
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**File access errors:**
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- Handles permission issues gracefully
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- Retries on temporary failures
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### Collection Schema
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**Qdrant collection: `memories_tr`**
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```python
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{
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"name": "memories_tr",
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"vectors": {
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"size": 768, # snowflake-arctic-embed2 dimension
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"distance": "Cosine" # Similarity metric
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},
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"payload_schema": {
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"user_id": "keyword", # Filterable
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"role": "keyword", # Filterable
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"timestamp": "datetime", # Range filterable
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"content": "text" # Full-text searchable
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}
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}
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```
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### Security Notes
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- **No credential storage** in code
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- All sensitive values via environment variables
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- `USER_ID` isolates memories per user
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- Cleaned content removes PII markers (but review your data)
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- HTTPS recommended for production Qdrant/Ollama
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---
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## Next Step
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Install an **addon** for curation and injection:
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