1 files changed, 78 insertions, 0 deletions
diff --git a/scripts/download_oasst1.py b/scripts/download_oasst1.py
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+#!/usr/bin/env python3
+"""
+Script to download and process OpenAssistant/oasst1 dataset.
+Converts it into a flat list of user turns (ChatTurn) for our pipeline.
+
+Output format per line (JSONL):
+{
+    "original_query": str,
+    "source": "oasst1",
+    "user_id": str,
+    "session_id": str,
+    "turn_id": int
+}
+"""
+
+import json
+import os
+from datasets import load_dataset
+from tqdm import tqdm
+
+def main():
+    output_path = "data/raw_datasets/oasst1_queries.jsonl"
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    
+    print("Downloading OpenAssistant/oasst1 dataset...")
+    # OASST1 is a tree structure. We need to traverse it to reconstruct conversations.
+    # It has 'message_id', 'parent_id', 'user_id', 'text', 'role'
+    ds = load_dataset("OpenAssistant/oasst1", split="train")
+    
+    print(f"Loaded {len(ds)} messages. Reconstructing threads...")
+    
+    # Index by message_id
+    id2msg = {}
+    for row in tqdm(ds, desc="Indexing"):
+        id2msg[row["message_id"]] = row
+        
+    # Find leaf nodes to trace back threads?
+    # Or just find all user messages and trace back to root to establish session context?
+    # For this task: "从 OASST1 里构造统一的 ChatTurn 序列（带 user_id 和 session_id）"
+    # We want valid user turns. 
+    # OASST1 'user_id' is the author ID. 
+    # 'message_tree_id' identifies the conversation tree (session).
+    
+    # We can iterate all messages. If role=='prompter' (user), we treat it as a turn.
+    # We use 'message_tree_id' as session_id.
+    
+    queries = []
+    
+    # Iterate all rows
+    for row in tqdm(ds, desc="Processing"):
+        if row["role"] == "prompter":
+            # This is a user turn
+            user_id = row["user_id"] # Author ID
+            session_id = row["message_tree_id"]
+            text = row["text"]
+            
+            # Simple metadata
+            queries.append({
+                "original_query": text,
+                "source": "oasst1",
+                "user_id": str(user_id),
+                "session_id": str(session_id),
+                "turn_id": 0 # We don't strictly need precise turn_id for Day 1 pipeline right now unless we sort
+            })
+            
+    print(f"Extracted {len(queries)} user queries.")
+    
+    # Save
+    print(f"Saving to {output_path}...")
+    with open(output_path, "w", encoding="utf-8") as f:
+        for q in queries:
+            f.write(json.dumps(q, ensure_ascii=False) + "\n")
+            
+    print("Done.")
+
+if __name__ == "__main__":
+    main()
+