// Run the RAG-GPT test suite and smoke-test the live app end-to-end. Use /test-pipeline to run all tests, or /test-pipeline <name|path> to run a single test.
| name | test-pipeline |
| description | Run the RAG-GPT test suite and smoke-test the live app end-to-end. Use /test-pipeline to run all tests, or /test-pipeline <name|path> to run a single test. |
Full verification of the RAG-GPT pipeline: unit tests → live app smoke test → sources check.
$ARGUMENTS — optional. A pytest node ID (tests/test_chatbot.py::test_name), a file (tests/test_chatbot.py), or a keyword (-k test_name). If empty, the full suite runs.
All pytest commands must run with RAG-GPT/ as the working directory because pyprojroot.here() resolves paths relative to the working directory.
If $ARGUMENTS is empty, run the full suite:
cd RAG-GPT && python -m pytest tests/ -q --tb=short
If $ARGUMENTS looks like a path or node ID (contains / or ::), run it directly:
cd RAG-GPT && python -m pytest $ARGUMENTS -v --tb=short
If $ARGUMENTS is a plain name (no / or ::), treat it as a keyword filter:
cd RAG-GPT && python -m pytest tests/ -k "$ARGUMENTS" -v --tb=short
Capture exit code. If non-zero, report all failures with the --tb=short output and stop here — do not proceed to the smoke test.
Check whether the two servers are already running:
import socket
def port_open(p):
with socket.socket() as s:
return s.connect_ex(('127.0.0.1', p)) == 0
If port 8000 is not open, start the PDF file server in the background:
cd RAG-GPT && python src/serve.py &
If port 7860 is not open, start the Gradio app in the background and wait up to 20 seconds for it to become reachable:
cd RAG-GPT && python src/raggpt_app.py &
Poll http://127.0.0.1:7860 every 2 seconds until it responds or 20 seconds elapse. If it never becomes reachable, report the startup failure and stop.
Use gradio_client to call the /respond endpoint directly (no browser needed):
import os, sys
sys.path.insert(0, 'RAG-GPT/src')
os.chdir('RAG-GPT')
from gradio_client import Client
client = Client('http://127.0.0.1:7860', verbose=False)
result = client.predict(
chatbot=[],
message="What is CLIP?",
data_type="Preprocessed doc",
temperature=0.0,
api_name="/respond",
)
empty_str, history, retrieved_content = result
Verify the response passes all three checks:
| Check | Pass condition |
|---|---|
| LLM answered | history[-1]['role'] == 'assistant' and len(history[-1]['content']) > 20 |
| Sources returned | retrieved_content is not None and '# Retrieved content' in retrieved_content |
| Not an error message | 'VectorDB does not exist' not in history[-1]['content'] and 'No file was uploaded' not in history[-1]['content'] |
If the "VectorDB does not exist" check fails, instruct the user to run python RAG-GPT/src/upload_data_manually.py to build the vector store, then re-run this skill.
Print a single summary block covering both stages:
══════════════════════════════════════
/test-pipeline results
══════════════════════════════════════
pytest : PASSED (21/21) ✓
or
pytest : FAILED (3 failures — see above)
App smoke test : PASSED ✓
LLM answer : "CLIP (Contrastive Language-Image Pre-training) is…"
Sources : 3 chunks retrieved from CLIP.pdf
or
App smoke test : SKIPPED (pytest failed)
or
App smoke test : FAILED
✗ <which check failed and why>
══════════════════════════════════════
For the sources line, count the number of # Retrieved content headers in retrieved_content and name the source files found in the Source: lines. Keep the LLM answer preview to the first 120 characters.
If any stage failed, end with a concise action item telling the user exactly what to fix.