add validation for bucket name etd

litellm/rag/ingestion/file_parsers/__init__.py

@@ -0,0 +1,9 @@
+"""
+File parsers for RAG ingestion.
+
+Provides text extraction utilities for various file formats.
+"""
+
+from .pdf_parser import extract_text_from_pdf
+
+__all__ = ["extract_text_from_pdf"]

litellm/rag/ingestion/s3_vectors_ingestion.py

@@ -253,6 +253,20 @@ class S3VectorsRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
         verbose_logger.debug(
             f"Ensuring S3 vector bucket exists: {self.vector_bucket_name}"
         )
+        
+        # Validate bucket name (AWS S3 naming rules)
+        if len(self.vector_bucket_name) < 3:
+            raise ValueError(
+                f"Invalid vector_bucket_name '{self.vector_bucket_name}': "
+                f"AWS S3 bucket names must be at least 3 characters long. "
+                f"Please provide a valid bucket name (e.g., 'my-vector-bucket')."
+            )
+        if not self.vector_bucket_name.replace("-", "").replace(".", "").isalnum():
+            raise ValueError(
+                f"Invalid vector_bucket_name '{self.vector_bucket_name}': "
+                f"AWS S3 bucket names can only contain lowercase letters, numbers, hyphens, and periods. "
+                f"Please provide a valid bucket name (e.g., 'my-vector-bucket')."
+            )
 
         # Try to get bucket info using GetVectorBucket API
         get_url = f"https://s3vectors.{self.aws_region_name}.api.aws/GetVectorBucket"
@@ -455,8 +469,16 @@ class S3VectorsRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
         await self._ensure_config_initialized()
 
         if not embeddings or not chunks:
-            verbose_logger.warning("No embeddings or chunks to store")
-            return self.index_name, None
+            error_msg = (
+                "No text content could be extracted from the file for embedding. "
+                "Possible causes:\n"
+                "  1. PDF files require OCR - add 'ocr' config with a vision model (e.g., 'anthropic/claude-3-5-sonnet-20241022')\n"
+                "  2. Binary files cannot be processed - convert to text first\n"
+                "  3. File is empty or contains no extractable text\n"
+                "For PDFs, either enable OCR or use a PDF extraction library to convert to text before ingestion."
+            )
+            verbose_logger.error(error_msg)
+            raise ValueError(error_msg)
 
         # Prepare vectors for PutVectors API
         vectors = []