More cleanup

pdelfin/assemblepipeline.py

@@ -8,6 +8,7 @@ import glob
 import tempfile
 import posixpath
 
+from dataclasses import dataclass
 from pypdf import PdfReader
 from tqdm import tqdm
 from typing import Optional
@@ -30,15 +31,17 @@ class DatabaseManager:
 
     def _initialize_tables(self):
         self.cursor.execute("""
-            CREATE TABLE IF NOT EXISTS index_table (
+            CREATE TABLE IF NOT EXISTS page_results (
-                custom_id TEXT,
                 s3_path TEXT,
+                page_num INTEGER,
                 start_index BIGINT,
-                end_index BIGINT
+                length BIGINT,
+                finish_reason STRING
+                error STRING
             )
         """)
         self.cursor.execute("""
-            CREATE INDEX IF NOT EXISTS idx_custom_id ON index_table(custom_id)
+            CREATE INDEX IF NOT EXISTS idx_path ON index_table(s3_path)
         """)
         self.cursor.execute("""
             CREATE TABLE IF NOT EXISTS pdfs (
@@ -60,15 +63,16 @@ class DatabaseManager:
                 value TEXT
             )
         """)
-        self.cursor.execute("SELECT value FROM metadata WHERE key='round'")
+
-        if self.cursor.fetchone() is None:
-            self.cursor.execute("INSERT INTO metadata (key, value) VALUES ('round', '0')")
         self.conn.commit()
 
-    def get_current_round(self):
+    def get_metadata(self, key: str) -> str:
-        self.cursor.execute("SELECT value FROM metadata WHERE key='round'")
+        self.cursor.execute("SELECT value FROM metadata WHERE key=?", (key,))
         result = self.cursor.fetchone()
-        return int(result[0])
+        return result[0]
+
+    def get_current_round(self):
+        return int(self.get_metadata("round"))
 
     def is_file_processed(self, s3_path, etag):
         self.cursor.execute("SELECT etag FROM processed_files WHERE s3_path = ?", (s3_path,))
@@ -76,6 +80,7 @@ class DatabaseManager:
         return result is not None and result[0] == etag
 
     def add_index_entries(self, index_entries):
+        # TODO MAke it take batchInferenceLines
         if index_entries:
             self.cursor.executemany("""
                 INSERT INTO index_table (custom_id, s3_path, start_index, end_index)
@@ -113,45 +118,6 @@ class DatabaseManager:
     def close(self):
         self.conn.close()
 
-def build_index(s3_path):
-    db_manager = DatabaseManager(s3_path)
-
-    bucket, prefix = parse_s3_path(s3_path)
-
-    # List all .json and .jsonl files under s3_path with their ETags
-    files = expand_s3_glob(s3_path)
-
-    if not files:
-        print("No .json or .jsonl files found in the specified S3 path.")
-        db_manager.close()
-        return
-
-    # Prepare a list of files that need processing
-    files_to_process = [
-        (key, etag) for key, etag in files.items()
-        if not db_manager.is_file_processed(key, etag)
-    ]
-
-    if not files_to_process:
-        print("All files are up to date. No processing needed.")
-        db_manager.close()
-        return
-
-    # Use ProcessPoolExecutor to process files with tqdm progress bar
-    with ProcessPoolExecutor() as executor:
-        futures = [
-            executor.submit(process_file, bucket, key, etag) 
-            for key, etag in files_to_process
-        ]
-        for future in tqdm(as_completed(futures), total=len(futures), desc="Processing files"):
-            s3_path, key, etag, index_entries = future.result()
-            if index_entries:
-                db_manager.add_index_entries(index_entries)
-            # Update the processed_files table
-            db_manager.update_processed_file(key, etag)
-
-    db_manager.close()
-
 def parse_s3_path(s3_path):
     if not s3_path.startswith('s3://'):
         raise ValueError('s3_path must start with s3://')
@@ -159,13 +125,13 @@ def parse_s3_path(s3_path):
     bucket, _, prefix = path.partition('/')
     return bucket, prefix
 
+
 def expand_s3_glob(s3_glob: str) -> dict[str, str]:
     parsed = urlparse(s3_glob)
     bucket_name = parsed.netloc
     prefix = os.path.dirname(parsed.path.lstrip('/')).rstrip('/') + "/"
     pattern = os.path.basename(parsed.path)
 
-    
     paginator = s3.get_paginator('list_objects_v2')
     page_iterator = paginator.paginate(Bucket=bucket_name, Prefix=prefix)
 
@@ -178,37 +144,43 @@ def expand_s3_glob(s3_glob: str) -> dict[str, str]:
 
     return matched_files
 
-def process_file(bucket, key, etag):
+@dataclass(frozen=True)
-    s3 = boto3.client('s3')  # Initialize s3 client in the worker process
+class BatchInferenceLine:
-    s3_path = f's3://{bucket}/{key}'
+    s3_path: str
-    try:
+    page_num: int # 1 indexed!
-        # Get the object
+    start_index: int
-        obj = s3.get_object(Bucket=bucket, Key=key)
+    length: int
-        # Read the content as bytes
+    finish_reason: str
-        content = obj['Body'].read()
+    error: Optional[str]
-        # Process the file as JSONL
+    
-        index_entries = process_jsonl_content(content, s3_path)
+def parse_custom_id(custom_id: str) -> tuple[str, int]:
-        # Return the necessary data to the main process
+    s3_path = custom_id[:custom_id.rindex("-")]
-        return s3_path, key, etag, index_entries
+    page_num = int(custom_id[custom_id.rindex("-") + 1:])
-    except Exception as e:
+
-        print(f"Error processing file {s3_path}: {e}")
+    return s3_path, page_num
-        return s3_path, key, etag, []
+
+def process_jsonl_content(s3_path) -> list[BatchInferenceLine]:
+    content = get_s3_bytes(s3_path).decode("utf-8")
 
-def process_jsonl_content(content, s3_path):
     start_index = 0
     index_entries = []
     lines = content.splitlines(keepends=True)
     for line in lines:
         line_length = len(line)
-        end_index = start_index + line_length
+
         try:
             data = json.loads(line)
-            custom_id = data.get('custom_id')
+            s3_path, page_num = parse_custom_id(data["custom_id"])
-            if custom_id:
+
-                index_entries.append((custom_id, s3_path, start_index, end_index))
+            assert "outputs" in data and len(data["outputs"]) > 0, "No outputs from model detected"
+
+            index_entries.append(BatchInferenceLine(s3_path, page_num, start_index, line_length,
+                                                    finish_reason=data["outputs"][0]["finish_reason"], error=data.get("completion_error", None)))
         except json.JSONDecodeError:
             pass  # Handle JSON decode errors if necessary
-        start_index = end_index
+
+        start_index = start_index + line_length
+
     return index_entries
 
 def get_s3_bytes(s3_path: str, start_index: Optional[int] = None, end_index: Optional[int] = None) -> bytes:
@@ -246,7 +218,7 @@ def get_pdf_num_pages(s3_path: str) -> Optional[int]:
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Manager for running millions of PDFs through a batch inference pipeline')
     parser.add_argument('workspace', help='The S3 path where work will be done e.g., s3://bucket/prefix/)')
-    parser.add_argument('--pdfs', help='Glob path to PDFs (local or s3)', default=None)
+    parser.add_argument('--add_pdfs', help='Glob path to add PDFs (s3) to the workspace', default=None)
     parser.add_argument('--file_size_limit', type=int, default=250, help='Max file size in MB')
     args = parser.parse_args()
 
@@ -258,12 +230,12 @@ if __name__ == '__main__':
     executor = ProcessPoolExecutor()
 
     # If you have new PDFs, add them to the list
-    if args.pdfs:
+    if args.add_pdfs:
-        assert args.pdfs.startswith("s3://"), "PDFs must live on s3"
+        assert args.add_pdfs.startswith("s3://"), "PDFs must live on s3"
         
-        print(f"Querying all PDFs at {args.pdfs}")
+        print(f"Querying all PDFs at {args.add_pdfs}")
         
-        all_pdfs = expand_s3_glob(args.pdfs)
+        all_pdfs = expand_s3_glob(args.add_pdfs)
         print(f"Found {len(all_pdfs)} total pdf paths")
 
         all_pdfs = [pdf for pdf in all_pdfs if not db.pdf_exists(pdf)]
@@ -279,8 +251,21 @@ if __name__ == '__main__':
 
 
     # Now build an index of all the pages that were processed within the workspace so far
-    build_index(f"{args.workspace}/*.jsonl")
+    inference_output_paths = expand_s3_glob(f"{args.workspace}/inference_outputs/*.jsonl")
 
-    # Now, for each pending book, find all pages which still need to be processed
+    inference_output_paths = [
-    # and add them to the next round's batch inference jobs
+        (key, etag) for key, etag in inference_output_paths.items()
+        if not db.is_file_processed(key, etag)
+    ]
+
+    future_to_path = {executor.submit(process_jsonl_content, s3_path): s3_path for s3_path, etag in inference_output_paths}
+
+    for future in tqdm(as_completed(future_to_path), total=len(future_to_path)):
+        s3_path = future_to_path[future]
+        
+        inference_lines = future.result()
+
+        db.add_index_entries(inference_lines)
+
+        db.update_processed_file(s3_path, etag=TODO)