Ragie Rag

# Ragie.ai RAG Skill (OpenClaw Optimized)

This skill enables grounded question answering using Ragie.ai as a RAG backend.

Ragie handles:
- Document chunking
- Embedding
- Vector indexing
- Retrieval
- Optional reranking

The agent handles:
- Deciding when to ingest
- Triggering retrieval
- Constructing grounded prompts
- Producing final answers

---

# Core Principles

1. Never answer without retrieval.
2. Never hallucinate information not present in retrieved chunks.
3. Always cite the `document_name` when referencing specific facts.
4. If retrieval returns zero relevant chunks, explicitly say:
   > "I don't have that information in the current knowledge base."
5. Do not expose API keys or raw API payloads in final answers.

---

# Deterministic Workflow

## Case A — User Provides a File or URL

IF the user provides:
- A file
- A document path
- A PDF/URL to ingest

THEN:

1. Execute ingestion:
   ```bash
   python `skills/scripts/ingest.py` --file <path> --name "<document_name>"
   ```
   OR
   ```bash
   python `skills/scripts/ingest.py` --url "<url>" --name "<document_name>"
   ```

2. Capture returned `document_id`.

3. Poll document status:
   ```bash
   python `skills/scripts/manage.py` status --id <document_id>
   ```
   Repeat until status == `ready`.

4. Proceed to Retrieval (Case C).

---

## Case B — User Requests Document Management

### List documents
```bash
python `skills/scripts/manage.py` list
```

### Check document status
```bash
python `skills/scripts/manage.py` status --id <document_id>
```

### Delete a document
```bash
python `skills/scripts/manage.py` delete --id <document_id>
```

Return structured results to the user.

---

## Case C — Retrieval (Grounded Question Answering)

Execute:

```bash
python `skills/scripts/retrieve.py` \
  --query "<user_question>" \
  --top-k 6 \
  --rerank
```

Optional flags:
- `--partition <name>`
- `--filter '{"key":"value"}'`

---

# Retrieval Output Format

Expected output:

```json
[
  {
    "text": ".....