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docs/source/safety_and_evaluation.rst

@@ -21,7 +21,7 @@ Synthetic Dataset Generation
 The evaluation utilizes a synthetic dataset of 1000 queries, carefully structured to test different aspects of the system:
 
 - **Regular Situations (80%)**: Divided equally among three levels of detail:
-  
+
   - Low: 1-2 sentences, brief queries with minimal context
   - Medium: 2-3 sentences with moderate background information
   - High: Detailed paragraph with extensive context
@@ -150,7 +150,7 @@ Performance Metrics
 RAGAS Metrics By Category
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Note: These metrics were obtained using a synthetic dataset specifically generated from services in the Greater Toronto Area (GTA). 
+Note: These metrics were obtained using a synthetic dataset specifically generated from services in the Greater Toronto Area (GTA).
 The RAG system evaluated used specialized prompts that differ marginally from the current system.
 
 .. list-table::
@@ -264,4 +264,4 @@ To enable re-ranking in your API calls, simply set the `rerank` parameter to `tr
         "longitude": -79.3832,
         "radius": 5000,
         "rerank": true
-    }
+    }

eval/collect_rag_outputs.py

@@ -2,7 +2,7 @@
 import asyncio
 import json
 import logging
-from typing import Dict, List, Any
+from typing import Dict, Any
 
 import aiohttp
 from tqdm.asyncio import tqdm_asyncio
@@ -27,7 +27,8 @@ async def fetch_recommendation(
                     "query": query["query"],
                     "answer": result["message"],
                     "context": [
-                        str(service["id"]) for service in (result.get("services", []) or [])
+                        str(service["id"])
+                        for service in (result.get("services", []) or [])
                     ],
                     "ground_truth": query["context"],
                 }
@@ -39,7 +40,9 @@ async def fetch_recommendation(
         return None
 
 
-async def process_samples(samples_file: str, output_file: str, batch_size: int = 5) -> None:
+async def process_samples(
+    samples_file: str, output_file: str, batch_size: int = 5
+) -> None:
     """Process samples in batches and save results."""
     # Load samples
     with open(samples_file, "r") as f:
@@ -90,4 +93,4 @@ def main() -> None:
 
 
 if __name__ == "__main__":
-    main()
+    main()