add document ordering snippets (#10)

- add document ordering snippets (f5dbcaaa85b9df44dd9c58c890e4a1984f3c18ed)

README.md

@@ -156,6 +156,48 @@ END QUESTION
 
 Reported token counts per question are based on the completed prompt, using the `cl100k_base` tokenizer from `tiktoken`.
 
+The order in which documents are loaded in matters - they should be added to the prompt template in the order of the filenames in `data_source_filenames`. Below are code snippets showing how we read the questions and extracted text files from disk.
+
+```
+def load_questions(self) -> list[dict]:
+    """Load LCR questions from HuggingFace dataset"""
+    csv_path = hf_hub_download(
+        repo_id="ArtificialAnalysis/AA-LCR",
+        filename="AA-LCR_Dataset.csv",
+        repo_type="dataset",
+    )
+
+    questions = []
+    with open(csv_path, encoding="utf-8") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            # Parse data_source_filenames as ordered list
+            if "data_source_filenames" in row and isinstance(row["data_source_filenames"], str):
+                row["data_source_filenames"] = row["data_source_filenames"].split(";")
+
+            # Parse answer as list (semicolon-separated criteria)
+            if "answer" in row and isinstance(row["answer"], str):
+                row["answer"] = row["answer"].split(";")
+            questions.append(row)
+
+    return questions
+
+def get_document_set(
+    self, dataset_folder: str, document_category: str, document_set_id: str, data_source_filenames: list[str]
+) -> list[str]:
+    """Get document set for a question in the order specified by data_source_filenames"""
+
+    # Documents are extracted to lcr/lcr/{category}/{set_id}/ from the HuggingFace zip
+    document_set_path = os.path.join(dataset_folder, document_category, document_set_id)
+
+    document_texts = []
+    for filename in data_source_filenames:
+        document_path = os.path.join(document_set_path, filename)
+        with open(document_path, encoding="utf-8") as f:
+            document_texts.append(f.read())
+    return document_texts
+```
+
 ## Scoring Approach
 
 We use an LLM-based equality checker to evaluate responses: