feat: adds input checks for extract graph from data

cognee/tasks/graph/exceptions/__init__.py

@@ -0,0 +1,12 @@
+"""
+Custom exceptions for the Cognee API.
+
+This module defines a set of exceptions for handling various data errors
+"""
+
+from .exceptions import (
+    InvalidDataChunksError,
+    InvalidGraphModelError,
+    InvalidOntologyAdapterError,
+    InvalidChunkGraphInputError
+)

cognee/tasks/graph/exceptions/exceptions.py

@@ -0,0 +1,37 @@
+from cognee.exceptions import (
+    CogneeValidationError,
+    CogneeConfigurationError,
+)
+from fastapi import status
+
+
+class InvalidDataChunksError(CogneeValidationError):
+    def __init__(self, detail: str):
+        super().__init__(
+            message=f"Invalid data_chunks: {detail}",
+            name="InvalidDataChunksError",
+            status_code=status.HTTP_400_BAD_REQUEST,
+        )
+
+class InvalidGraphModelError(CogneeValidationError):
+    def __init__(self, got):
+        super().__init__(
+            message=f"graph_model must be a subclass of BaseModel (got {got}).",
+            name="InvalidGraphModelError",
+            status_code=status.HTTP_400_BAD_REQUEST,
+        )
+
+class InvalidOntologyAdapterError(CogneeConfigurationError):
+    def __init__(self, got):
+        super().__init__(
+            message=f"ontology_adapter lacks required interface (got {got}).",
+            name="InvalidOntologyAdapterError",
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR)
+
+
+class InvalidChunkGraphInputError(CogneeValidationError):
+    def __init__(self, detail: str):
+        super().__init__(
+            message=f"Invalid chunk inputs or LLM Chunkgraphs: {detail}",
+            name="InvalidChunkGraphInputError",
+            status_code=status.HTTP_400_BAD_REQUEST)

cognee/tasks/graph/extract_graph_from_data.py

@@ -12,7 +12,12 @@ from cognee.modules.graph.utils import (
 )
 from cognee.shared.data_models import KnowledgeGraph
 from cognee.infrastructure.llm.LLMGateway import LLMGateway
-
+from cognee.tasks.graph.exceptions import (
+    InvalidGraphModelError,
+    InvalidDataChunksError,
+    InvalidChunkGraphInputError,
+    InvalidOntologyAdapterError,
+)
 
 async def integrate_chunk_graphs(
     data_chunks: list[DocumentChunk],
@@ -21,6 +26,16 @@ async def integrate_chunk_graphs(
     ontology_adapter: OntologyResolver,
 ) -> List[DocumentChunk]:
     """Updates DocumentChunk objects, integrates data points and edges into databases."""
+
+    if not isinstance(data_chunks, list) or not isinstance(chunk_graphs, list):
+        raise InvalidChunkGraphInputError("data_chunks and chunk_graphs must be lists.")
+    if len(data_chunks) != len(chunk_graphs):
+        raise InvalidChunkGraphInputError(f"length mismatch: {len(data_chunks)} chunks vs {len(chunk_graphs)} graphs.")
+    if not isinstance(graph_model, type) or not issubclass(graph_model, BaseModel):
+        raise InvalidGraphModelError(graph_model)
+    if ontology_adapter is None or not hasattr(ontology_adapter, "get_subgraph"):
+        raise InvalidOntologyAdapterError(type(ontology_adapter).__name__ if ontology_adapter else "None")
+
     graph_engine = await get_graph_engine()
 
     if graph_model is not KnowledgeGraph:
@@ -55,6 +70,14 @@ async def extract_graph_from_data(
     """
     Extracts and integrates a knowledge graph from the text content of document chunks using a specified graph model.
     """
+
+    if not isinstance(data_chunks, list) or not data_chunks:
+        raise InvalidDataChunksError("must be a non-empty list of DocumentChunk.")
+    if not all(hasattr(c, "text") for c in data_chunks):
+        raise InvalidDataChunksError("each chunk must have a 'text' attribute")
+    if not isinstance(graph_model, type) or not issubclass(graph_model, BaseModel):
+        raise InvalidGraphModelError(graph_model)
+
     chunk_graphs = await asyncio.gather(
         *[LLMGateway.extract_content_graph(chunk.text, graph_model) for chunk in data_chunks]
     )