feat(floware): image url base search and tag based document fetch

wavefront/server/modules/knowledge_base_module/knowledge_base_module/controllers/knowledge_base_document_controller.py

@@ -27,8 +27,7 @@
 from flo_cloud.message_queue import MessageQueueManager
 from flo_cloud.cloud_storage import CloudStorageManager
 from pydantic import BaseModel
-from sqlalchemy import Result
-from sqlalchemy import select
+from knowledge_base_module.queries.generate_query import QueryGenerator
 
 kb_document_router = APIRouter()
 
@@ -182,11 +181,23 @@ async def upload_document(
             os.unlink(temp_file_path)
 
 
+def _document_row_to_dict(row: dict) -> dict:
+    """Convert a raw document row to the same format as KnowledgeBaseDocuments.to_dict()."""
+    result = dict(row)
+    for key, value in result.items():
+        if isinstance(value, uuid.UUID):
+            result[key] = str(value)
+        elif isinstance(value, datetime):
+            result[key] = value.isoformat()
+    return result
+
+
 @kb_document_router.get('/v1/knowledge-bases/{kb_id}/documents')
 @inject
 async def get_documents(
     kb_id: uuid.UUID,
     file_type: Optional[str] = Query(None, description='Type of file to filter by'),
+    query_filter: Optional[str] = Query(None, alias='$filter'),
     offset: int = Query(0, ge=0, description='The number of items to skip'),
     limit: int = Query(
         10, ge=1, le=100, description='The maximum number of items to return'
@@ -199,35 +210,28 @@ async def get_documents(
     ] = Depends(Provide[KnowledgeBaseContainer.knowledge_base_documents_repository]),
 ) -> JSONResponse:
     """Get documents for a knowledge base with optional filtering and pagination."""
-    # Validate knowledge base exists
-    existing_document = await knowledge_base_documents_repository.find_one(
-        knowledge_base_id=kb_id
-    )
-    if not existing_document:
-        return JSONResponse(
-            status_code=status.HTTP_200_OK,
-            content=response_formatter.buildSuccessResponse(data={'resources': []}),
+    try:
+        query_generator = QueryGenerator()
+        sql_query, query_params = query_generator.get_documents_list_query(
+            kb_id=str(kb_id),
+            file_type=file_type,
+            filter=query_filter,
+            offset=offset,
+            limit=limit,
         )
-
-    # Fetch documents
-    async with knowledge_base_documents_repository.session() as session:
-        query = select(KnowledgeBaseDocuments).where(
-            KnowledgeBaseDocuments.knowledge_base_id == kb_id
+        rows = await knowledge_base_documents_repository.execute_query(
+            sql_query, query_params
         )
-
-        if file_type:
-            query = query.where(KnowledgeBaseDocuments.file_type == file_type)
-
-        query = query.slice(offset, limit)
-
-        results: Result = await session.execute(query)
-        resources = results.scalars().all()
-        data = [res.to_dict() for res in resources]
-
+        data = [_document_row_to_dict(row) for row in rows]
         return JSONResponse(
             status_code=status.HTTP_200_OK,
             content=response_formatter.buildSuccessResponse(data={'resources': data}),
         )
+    except ValueError as e:
+        return JSONResponse(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            content=response_formatter.buildErrorResponse(str(e)),
+        )
 
 
 @kb_document_router.delete('/v1/knowledge-bases/{kb_id}/documents/{document_id}')

wavefront/server/modules/knowledge_base_module/knowledge_base_module/controllers/rag_retreival_controller.py

@@ -1,4 +1,5 @@
-from typing import List, Optional
+import base64
+from typing import List, Optional, Tuple
 import uuid
 
 from common_module.common_container import CommonContainer
@@ -21,6 +22,7 @@
 )
 from knowledge_base_module.services.kb_rag_retrieve import KBRagResponse
 from knowledge_base_module.services.image_rag_retrieve import ImageRagRetrieve
+from flo_cloud.cloud_storage import CloudStorageManager
 from pydantic import BaseModel, Field
 from datetime import datetime
 from sqlalchemy import Result
@@ -71,9 +73,10 @@ class DocWiseEmbeddingSchema(BaseModel):
 
 
 class ImagePayload(BaseModel):
-    """Payload for Image embedding."""
+    """Payload for Image embedding. Use image_data (base64) or image_url (gs:// or s3://); image_url has priority if both are set."""
 
     image_data: Optional[str] = None
+    image_url: Optional[str] = None
 
 
 class DocumentPayload(BaseModel):
@@ -96,6 +99,112 @@ def convert_uuids_to_str(data):
         return data
 
 
+def _parse_cloud_image_url(url: str) -> Tuple[str, str, str]:
+    """
+    Parse gs:// or s3:// URL into (scheme, bucket, key).
+    Returns (scheme, bucket, key) or raises ValueError.
+    """
+    url = (url or '').strip()
+    if url.startswith('gs://'):
+        rest = url[5:]
+        if '/' not in rest:
+            raise ValueError('Invalid gs:// URL: missing path after bucket')
+        bucket, _, key = rest.partition('/')
+        return ('gs', bucket, key)
+    if url.startswith('s3://'):
+        rest = url[5:]
+        if '/' not in rest:
+            raise ValueError('Invalid s3:// URL: missing path after bucket')
+        bucket, _, key = rest.partition('/')
+        return ('s3', bucket, key)
+    raise ValueError('image_url must be in gs:// or s3:// format')
+
+
+async def _resolve_image_data(
+    payload: ImagePayload,
+    cloud_storage: CloudStorageManager,
+    config: dict,
+    response_formatter: ResponseFormatter,
+) -> Tuple[Optional[str], Optional[JSONResponse]]:
+    """
+    Resolve image payload to a single image_data string (base64) for the inference API.
+    When both are provided, image_url has priority; otherwise uses image_data or fetches from image_url (gs:// or s3://).
+    Returns (image_data, None) on success, or (None, error_json_response) on validation/fetch error.
+    """
+    if payload.image_url:
+        pass
+    elif payload.image_data:
+        return (payload.image_data, None)
+    else:
+        return (
+            None,
+            JSONResponse(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                content=response_formatter.buildErrorResponse(
+                    'Query or Image data should not be empty'
+                ),
+            ),
+        )
+    try:
+        scheme, bucket, key = _parse_cloud_image_url(payload.image_url)
+    except ValueError as e:
+        return (
+            None,
+            JSONResponse(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                content=response_formatter.buildErrorResponse(str(e)),
+            ),
+        )
+    cloud_provider = (
+        (config.get('cloud_config') or {}).get('cloud_provider', '').lower()
+    )
+    if scheme == 'gs' and cloud_provider != 'gcp':
+        return (
+            None,
+            JSONResponse(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                content=response_formatter.buildErrorResponse(
+                    'image_url gs:// is only supported when cloud provider is GCP'
+                ),
+            ),
+        )
+    if scheme == 's3' and cloud_provider != 'aws':
+        return (
+            None,
+            JSONResponse(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                content=response_formatter.buildErrorResponse(
+                    'image_url s3:// is only supported when cloud provider is AWS'
+                ),
+            ),
+        )
+    try:
+        content = cloud_storage.read_file(bucket, key)
+    except Exception as e:
+        return (
+            None,
+            JSONResponse(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                content=response_formatter.buildErrorResponse(
+                    f'Failed to fetch image from storage: {e!s}'
+                ),
+            ),
+        )
+    image_bytes = content.read() if hasattr(content, 'read') else content
+    if not image_bytes:
+        return (
+            None,
+            JSONResponse(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                content=response_formatter.buildErrorResponse(
+                    'Image from URL is empty'
+                ),
+            ),
+        )
+    image_data_b64 = base64.b64encode(image_bytes).decode('utf-8')
+    return (image_data_b64, None)
+
+
 @rag_retrieval_router.post('/v1/knowledge-base/{kb_id}/retrieve')
 @inject
 async def retrieve_query(
@@ -128,6 +237,9 @@ async def retrieve_query(
         Provide[KnowledgeBaseContainer.image_knowledge_base_retrieve]
     ),
     config: dict = Depends(Provide[KnowledgeBaseContainer.config]),
+    cloud_storage: CloudStorageManager = Depends(
+        Provide[KnowledgeBaseContainer.cloud_storage]
+    ),
 ):
     if not query and not payload:
         return JSONResponse(
@@ -156,9 +268,14 @@ async def retrieve_query(
             limit,
         )
     else:
+        image_data, error_response = await _resolve_image_data(
+            payload, cloud_storage, config, response_formatter
+        )
+        if error_response is not None:
+            return error_response
         inference_url = config['model']['inference_service_url']
         retrieved_docs = await image_rag_retrieval.retrieve_images(
-            payload.image_data,
+            image_data,
             inference_url,
             kb_id,
             threshold,

wavefront/server/modules/knowledge_base_module/knowledge_base_module/queries/generate_query.py

@@ -280,6 +280,64 @@ def get_image_embedding_dino(
 
         return sql_query, params
 
+    def get_documents_list_query(
+        self,
+        kb_id: str,
+        file_type: Optional[str] = None,
+        filter: Optional[str] = None,
+        offset: int = 0,
+        limit: int = 10,
+    ) -> Tuple[str, Dict[str, Any]]:
+        """
+        Generate SQL query to list knowledge base documents with optional
+        metadata filter (OData-style $filter) and file_type.
+
+        Returns:
+            Tuple of (SQL query string, query parameters)
+        """
+        params: Dict[str, Any] = {
+            'kb_id': kb_id,
+            'offset': offset,
+            'limit': limit,
+        }
+        conditions = ['knowledge_base_id = :kb_id']
+        if file_type:
+            params['file_type'] = file_type
+            conditions.append('file_type = :file_type')
+
+        metadata_filter_clause = ''
+        if filter:
+            where_clause, filter_params = self.odata_parser.prepare_odata_filter(filter)
+            if where_clause and filter_params:
+                metadata_filter_clause = self.build_metadata_clause(
+                    where_clause,
+                    filter_params,
+                    lambda field: f"(metadata_value ->> '{field}')",
+                )
+                params.update(filter_params)
+                conditions.append(f'({metadata_filter_clause})')
+
+        where_sql = ' AND '.join(conditions)
+        sql_query = f"""
+            SELECT
+                id,
+                knowledge_base_id,
+                file_path,
+                file_name,
+                file_type,
+                file_size,
+                created_at,
+                updated_at,
+                metadata_value
+            FROM
+                {KnowledgeBaseDocuments.__tablename__}
+            WHERE
+                {where_sql}
+            ORDER BY created_at DESC
+            LIMIT :limit OFFSET :offset
+        """
+        return sql_query, params
+
     @staticmethod
     def get_update_tokens_query() -> str:
         """