fix: Resolve issue with table names in SQL commands

Some SQL commands require lowercase characters in table names unless table name is wrapped in quotes. Renamed all new tables to use lowercase

Fix COG-677

cognee/infrastructure/engine/models/DataPoint.py

@@ -8,6 +8,7 @@ class MetaData(TypedDict):
     index_fields: list[str]
 
 class DataPoint(BaseModel):
+    __tablename__ = "data_point"
     id: UUID = Field(default_factory = uuid4)
     updated_at: Optional[datetime] = datetime.now(timezone.utc)
     _metadata: Optional[MetaData] = {

cognee/modules/chunking/models/DocumentChunk.py

@@ -3,6 +3,7 @@ from cognee.infrastructure.engine import DataPoint
 from cognee.modules.data.processing.document_types import Document
 
 class DocumentChunk(DataPoint):
+    __tablename__ = "document_chunk"
     text: str
     word_count: int
     chunk_index: int

cognee/modules/engine/models/Entity.py

@@ -3,6 +3,7 @@ from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
 from .EntityType import EntityType
 
 class Entity(DataPoint):
+    __tablename__ = "entity"
     name: str
     is_a: EntityType
     description: str

cognee/modules/engine/models/EntityType.py

@@ -2,6 +2,7 @@ from cognee.infrastructure.engine import DataPoint
 from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
 
 class EntityType(DataPoint):
+    __tablename__ = "entity_type"
     name: str
     type: str
     description: str

cognee/tasks/chunks/query_chunks.py

@@ -10,7 +10,7 @@ async def query_chunks(query: str) -> list[dict]:
     """
     vector_engine = get_vector_engine()
 
-    found_chunks = await vector_engine.search("DocumentChunk_text", query, limit = 5)
+    found_chunks = await vector_engine.search("document_chunk_text", query, limit = 5)
 
     chunks = [result.payload for result in found_chunks]
 

cognee/tasks/graph/query_graph_connections.py

@@ -27,8 +27,8 @@ async def query_graph_connections(query: str, exploration_levels = 1) -> list[(s
     else:
         vector_engine = get_vector_engine()
         results = await asyncio.gather(
-            vector_engine.search("Entity_name", query_text = query, limit = 5),
-            vector_engine.search("EntityType_name", query_text = query, limit = 5),
+            vector_engine.search("entity_name", query_text = query, limit = 5),
+            vector_engine.search("entity_type_name", query_text = query, limit = 5),
         )
         results = [*results[0], *results[1]]
         relevant_results = [result for result in results if result.score < 0.5][:5]

cognee/tasks/storage/index_data_points.py

@@ -16,10 +16,10 @@ async def index_data_points(data_points: list[DataPoint]):
         data_point_type = type(data_point)
 
         for field_name in data_point._metadata["index_fields"]:
-            index_name = f"{data_point_type.__name__}.{field_name}"
+            index_name = f"{data_point_type.__tablename__}.{field_name}"
 
             if index_name not in created_indexes:
-                await vector_engine.create_vector_index(data_point_type.__name__, field_name)
+                await vector_engine.create_vector_index(data_point_type.__tablename__, field_name)
                 created_indexes[index_name] = True
 
             if index_name not in index_points:

cognee/tasks/summarization/models.py

@@ -3,6 +3,7 @@ from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
 from cognee.modules.data.processing.document_types import Document
 
 class TextSummary(DataPoint):
+    __tablename__ = "text_summary"
     text: str
     made_from: DocumentChunk
 

cognee/tasks/summarization/query_summaries.py

@@ -10,7 +10,7 @@ async def query_summaries(query: str) -> list:
     """
     vector_engine = get_vector_engine()
 
-    summaries_results = await vector_engine.search("TextSummary_text", query, limit = 5)
+    summaries_results = await vector_engine.search("text_summary_text", query, limit = 5)
 
     summaries = [summary.payload for summary in summaries_results]
 

cognee/tests/test_pgvector.py

@@ -65,7 +65,7 @@ async def main():
     from cognee.infrastructure.databases.vector import get_vector_engine
 
     vector_engine = get_vector_engine()
-    random_node = (await vector_engine.search("Entity_name", "Quantum computer"))[0]
+    random_node = (await vector_engine.search("entity_name", "Quantum computer"))[0]
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(SearchType.INSIGHTS, query_text = random_node_name)