Publish Snowflake Doc AI package

actions/snowflake-document-ai/CHANGELOG.md

@@ -5,6 +5,12 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/)
 and this project adheres to [Semantic Versioning](https://semver.org/).
 
+## [0.0.2] - 2025-10-11
+
+### Changed
+- Limited support only to PDF, PPTX, and DOCX files
+- Reduced logging
+
 ## [0.0.1] - 2025-10-01
 
 ### Changed

actions/snowflake-document-ai/README.md

@@ -1,13 +1,13 @@
 # Snowflake Document AI
 
-This action package enables you to upload PDF documents to Snowflake and extract their content using [Snowflake Document AI](https://docs.snowflake.com/en/user-guide/snowflake-cortex/document-ai/overview). It uses one-shot parsing with no training required, making it easy to extract structured data from PDFs and image files.
+This action package enables you to upload PDF, DOCX, or PPTX documents to Snowflake and extract their content using [Snowflake Document AI](https://docs.snowflake.com/en/user-guide/snowflake-cortex/document-ai/overview). It uses one-shot parsing with no training required, making it easy to extract structured data from digital documents.
 
 ## What it does
 
 This action package provides two main capabilities:
 
 1. **List Stage Files**: List all files currently stored in a Snowflake stage
-2. **Parse Document**: Upload PDF or image file files and extract their content using AI-powered parsing
+2. **Parse Document**: Upload PDF, DOCX, or PPTX files and extract their content using AI-powered parsing
 
 The parsing uses Snowflake's `AI_PARSE_DOCUMENT` function which can extract text, tables, and other structured content from PDFs in layout-aware mode.
 
@@ -16,7 +16,7 @@ The parsing uses Snowflake's `AI_PARSE_DOCUMENT` function which can extract text
 Before using this action, you need:
 
 1. **Snowflake Account**: An active Snowflake account with Document AI enabled
-2. **Database, Schema, and Stage**: A Snowflake stage configured to store your PDF files
+2. **Database, Schema, and Stage**: A Snowflake stage configured to store your document files
 3. **Authentication**: One of the following authentication methods:
    - Username and password
    - Key pair authentication
@@ -39,7 +39,7 @@ Lists the most recently modified files in a specific Snowflake stage.
 
 ### 2. Parse Document
 
-Uploads a PDF file from chat and parses its content using Snowflake Document AI.
+Uploads a PDF, DOCX, or PPTX file from chat and parses its content using Snowflake Document AI.
 
 **Parameters:**
 - `filename` (string): The name of the file to upload from chat
@@ -91,9 +91,9 @@ Uploads a PDF file from chat and parses its content using Snowflake Document AI.
 
 Details up to date [here](https://docs.snowflake.com/en/user-guide/snowflake-cortex/parse-document#input-requirements).
 
-- File types: PDF, PPTX, DOCX, JPEG, JPG, PNG, TIFF, TIF, HTML, TXT
+- File types: PDF, PPTX, DOCX (images/HTML/TXT are not supported by this action)
 - Maximum file size: 100 MB
 - Maximum number of pages: 500
 - Stage encryption: Server-side encryption
-- Requires Snowflake Document AI to be enabled and set upin your account
+- Requires Snowflake Document AI to be enabled and set up in your account
 - Processing time depends on document complexity and size - timeouts are possible with larger documents

actions/snowflake-document-ai/actions.py

@@ -70,7 +70,7 @@ def parse_document(
     stage_path: str = ""
 ) -> Response[dict]:
     """
-    Uploads a file (PDF, PPTX, DOCX, JPEG, JPG, PNG, TIFF, TIF, HTML, TXT) from the chat to a specified Snowflake stage and parses it's content using Snowflake Document AI.
+    Uploads a file (PDF, PPTX, DOCX) from the chat to a specified Snowflake stage and parses it's content using Snowflake Document AI.
 
     Args:
         filename: The name of the file to upload from chat
@@ -83,45 +83,46 @@ def parse_document(
         Details of the uploaded file, and the json string of the document ai processing results.
     """
     try:
-        print(f"Starting process_document for file: {filename}")
+        print("Starting document processing")
 
         # Get the file from chat
-        print("Getting file from chat...")
         chat_file = chat.get_file(filename)
         temp_file_path = str(chat_file)
-        print(f"Got file: {temp_file_path}")
 
         # Extract just the original filename without path
         original_filename = os.path.basename(filename)
-        print(f"Original filename: {original_filename}")
 
         # Create a filename with timestamp to ensure uniqueness
         file_base, file_ext = os.path.splitext(original_filename)
+        file_ext_lower = file_ext.lower()
+
+        # Validate supported file types (Snowflake AI_PARSE_DOCUMENT supports .pdf, .docx, .pptx)
+        supported_extensions = {'.pdf', '.docx', '.pptx'}
+        if file_ext_lower not in supported_extensions:
+            unsupported_msg = (
+                f"Unsupported file type '{file_ext}'. Only PDF, DOCX, and PPTX are supported "
+                f"by AI_PARSE_DOCUMENT for this action. See docs: https://docs.snowflake.com/en/user-guide/snowflake-cortex/parse-document"
+            )
+            return Response(error=unsupported_msg)
         timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
         unique_filename = f"{file_base}_{timestamp}{file_ext}"
-        print(f"Using unique filename: {unique_filename}")
 
         # Create a temporary directory to hold our renamed file
-        print("Creating temporary directory...")
         with tempfile.TemporaryDirectory() as temp_dir:
             # Create a path for our correctly named file
             correct_name_path = os.path.join(temp_dir, unique_filename)
-            print(f"Correct name path: {correct_name_path}")
 
             # Copy the temporary file to our new path with the correct name
-            print("Copying file...")
             shutil.copy2(temp_file_path, correct_name_path)
 
-            print("Establishing Snowflake connection...")
+            print("Connecting to Snowflake...")
             with get_snowflake_connection() as conn:
                 cursor = conn.cursor()
-                print("Connected to Snowflake")
 
                 # Access Secret values
                 db_name = database_name.value.upper()
                 schema = schema_name.value.upper()
                 stage = stage_name.value.upper()
-                print(f"Using stage: {db_name}.{schema}.{stage}")
 
                 # Construct fully qualified stage name
                 fully_qualified_stage = f'@"{db_name}"."{schema}"."{stage}"'
@@ -133,27 +134,24 @@ def parse_document(
                     clean_path = stage_path.strip('/')
                     if clean_path:
                         stage_location = f'{fully_qualified_stage}/{clean_path}'
-                print(f"Stage location: {stage_location}")
 
                 # Record the upload time to use for polling
                 upload_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
-                print(f"Upload time: {upload_time}")
 
                 # Execute PUT command with the correctly named file
                 put_command = f"PUT 'file://{correct_name_path}' '{stage_location}' OVERWRITE=TRUE AUTO_COMPRESS=FALSE SOURCE_COMPRESSION=NONE"
-                print(f"[{datetime.datetime.now().strftime('%H:%M:%S.%f')}] Executing PUT command to upload file...")
+                print("Uploading file to stage...")
                 try:
                     cursor.execute(put_command)
-                    print(f"[{datetime.datetime.now().strftime('%H:%M:%S.%f')}] PUT command executed successfully")
+                    print("Upload completed")
                 except Exception as put_error:
-                    print(f"[{datetime.datetime.now().strftime('%H:%M:%S.%f')}] Error executing PUT command: {str(put_error)}")
+                    print("Upload failed")
                     raise
 
                 # Get results of the upload
                 result_rows = cursor.fetchall()
                 status = cursor.sfqid
-                print(f"[{datetime.datetime.now().strftime('%H:%M:%S.%f')}] Upload completed with status ID: {status}")
-                print(f"[{datetime.datetime.now().strftime('%H:%M:%S.%f')}] File upload status: {result_rows}")
+                print("Upload acknowledged by Snowflake")
 
                 # Create file paths for the response
                 if stage_path:
@@ -164,7 +162,6 @@ def parse_document(
 
                 # Create the fully qualified path including the file (like @DB.SCHEMA.STAGE/path/file.ext)
                 fully_qualified_path = f"@{db_name}.{schema}.{stage}/{stage_file_path}"
-                print(f"[{datetime.datetime.now().strftime('%H:%M:%S.%f')}] File uploaded to: {fully_qualified_path}")
 
                 # Prepare the upload result
                 upload_result = {
@@ -177,7 +174,6 @@ def parse_document(
                     "upload_time": upload_time,
                     "query_id": status
                 }
-                print("Upload completed successfully")
 
                 # TODO: handle image (OCR) files differently than others - page splitting does not work for them
 
@@ -187,37 +183,31 @@ def parse_document(
                     TO_FILE('{fully_qualified_path}'),
                     {{'mode': 'LAYOUT' , 'page_split': true}}) AS parsed_document;
                 """
-                print(f"Executing query: {query}")
+                print("Parsing document via AI_PARSE_DOCUMENT...")
                 cursor.execute(query)
 
-                print("Fetching results...")
                 rows = cursor.fetchall()
-                print(f"Got {len(rows) if rows else 0} results")
 
                 processing_result = None
                 if rows and len(rows) > 0:
                     # Convert row to dict
                     columns = [desc[0] for desc in cursor.description]
                     processing_result = dict(zip(columns, rows[0]))
-                    print(f"Available columns: {columns}")
-                    print(f"Found processing result with {len(processing_result)} fields")
-                else:
-                    print("No processing results found")
 
-                print("Query completed")
+                print("Parsing completed")
 
                 # Combine upload result with parsed result
                 combined_result = {
                     "upload": upload_result,
                     "parsed": processing_result
                 }
-                print("Returning combined result")
+                print("Returning result")
 
                 return Response(result=combined_result)
 
     except Exception as e:
         error_msg = f"Error processing document {filename}: {str(e)}"
-        print(f"ERROR: {error_msg}")
+        print("ERROR during document processing")
         import traceback
         print(f"Traceback: {traceback.format_exc()}")
         return Response(error=error_msg)

actions/snowflake-document-ai/package.yaml

@@ -4,10 +4,10 @@ spec-version: v2
 name: Snowflake Document AI
 
 # Required: A description of what's in the action package.
-description: Upload PDF files to Snowflake and read their content with one-shot parse. No training required.
+description: Upload PDF, PPTX, DOCX files to Snowflake and read their content with one-shot parse. No training required.
 
 # Package version number, recommend using semver.org
-version: 0.0.1
+version: 0.0.2
 
 dependencies:
   conda-forge:

bin/publisher/action_packages_whitelist.json

@@ -67,6 +67,7 @@
     "snowflake-cortex-analyst",
     "snowflake-cortex-search",
     "snowflake-data",
+    "snowflake-document-ai",
     "serper",
     "perplexity",
     "robocorp"