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
- Never answer without retrieval.
- Never hallucinate information not present in retrieved chunks.
- Always cite the
document_name when referencing specific facts.
- If retrieval returns zero relevant chunks, explicitly say:
"I don't have that information in the current knowledge base."
- 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:
-
Execute ingestion:
python `skills/scripts/ingest.py` --file <path> --name "<document_name>"
OR
python `skills/scripts/ingest.py` --url "<url>" --name "<document_name>"
-
Capture returned document_id.
-
Poll document status:
python `skills/scripts/manage.py` status --id <document_id>
Repeat until status == ready.
-
Proceed to Retrieval (Case C).
Case B — User Requests Document Management
List documents
python `skills/scripts/manage.py` list
Check document status
python `skills/scripts/manage.py` status --id <document_id>
Delete a document
python `skills/scripts/manage.py` delete --id <document_id>
Return structured results to the user.
Case C — Retrieval (Grounded Question Answering)
Execute:
python `skills/scripts/retrieve.py` \
--query "<user_question>" \
--top-k 6 \
--rerank
Optional flags:
--partition <name>--filter '{"key":"value"}'
Retrieval Output Format
Expected output:
[
{
"text": "...",
"score": 0.87,
"document_name": "Policy Handbook",
"document_id": "doc_abc123"
}
]
Grounded Prompt Construction
After retrieval:
- Extract all chunk
text.
- Concatenate with separators.
- Construct this prompt:
SYSTEM:
You are a helpful assistant.
Answer using ONLY the context provided below.
If the context does not contain the answer, say:
"I don't have that information in the current knowledge base."
CONTEXT:
[chunk 1 text]
---
[chunk 2 text]
---
...
USER QUESTION:
{original user question}
- Generate final answer.
- Cite
document_name when referencing information.
Output Contract
The final response MUST:
- Be grounded only in retrieved chunks
- Cite
document_name for factual claims
- Avoid hallucinations
- Avoid mentioning internal execution steps
- Avoid exposing API keys or raw responses
- Clearly state when information is missing
If no chunks are returned:
I don't have that information in the current knowledge base.
API Reference
Base URL:
https://api.ragie.ai
| Operation | Method | Endpoint |
|---|
| Ingest file | POST | /documents |
| Ingest URL | POST | /documents/url |
| Retrieve chunks | POST | /retrievals |
| List documents | GET | /documents |
| Get document | GET | /documents/{id} |
| Delete document | DELETE | /documents/{id} |
Error Handling
| HTTP Code | Meaning | Action |
|---|
| 404 | Document not found | Verify document_id |
| 422 | Invalid payload | Validate request schema |
| 429 | Rate limited | Retry with backoff |
| 5xx | Server error | Retry or check Ragie status |
If ingestion fails:
- Report failure clearly.
- Do not proceed to retrieval.
If retrieval fails:
- Retry once.
- If still failing, inform user.
Decision Rules Summary
- If user uploads content → ingest → wait until ready → retrieve.
- If user asks question → retrieve immediately.
- If zero chunks → state knowledge gap.
- Always use reranking unless explicitly disabled.
- Never answer without retrieval.
Advanced Usage
- Use metadata
filter to narrow retrieval scope.
- Use partitions to separate tenant data.
- Use
recency_bias only when time relevance matters.
- Adjust
top_k depending on query complexity.
Security
- API keys must be loaded from environment variables.
.env must not be committed.- Do not log sensitive headers.
Summary
This skill provides:
- Deterministic ingestion
- Deterministic retrieval
- Strict grounded answering
- Complete Ragie lifecycle management
- Safe and hallucination-resistant RAG execution
End of Skill.
