Merge pull request #4 from chinu0609/delete-last-acessed

Delete last acessed

cognee/infrastructure/engine/models/DataPoint.py

@@ -43,6 +43,9 @@ class DataPoint(BaseModel):
     updated_at: int = Field(
         default_factory=lambda: int(datetime.now(timezone.utc).timestamp() * 1000)
     )
+    last_accessed_at: int = Field(  
+        default_factory=lambda: int(datetime.now(timezone.utc).timestamp() * 1000)  
+    )
     ontology_valid: bool = False
     version: int = 1  # Default version
     topological_rank: Optional[int] = 0

cognee/modules/chunking/models/DocumentChunk.py

@@ -1,5 +1,7 @@
 from typing import List, Union
 
+from pydantic import BaseModel, Field
+from datetime import datetime, timezone  
 from cognee.infrastructure.engine import DataPoint
 from cognee.modules.data.processing.document_types import Document
 from cognee.modules.engine.models import Entity
@@ -22,6 +24,7 @@ class DocumentChunk(DataPoint):
     - cut_type: The type of cut that defined this chunk.
     - is_part_of: The document to which this chunk belongs.
     - contains: A list of entities or events contained within the chunk (default is None).
+    - last_accessed_at: The timestamp of the last time the chunk was accessed.
     - metadata: A dictionary to hold meta information related to the chunk, including index
     fields.
     """
@@ -32,5 +35,4 @@ class DocumentChunk(DataPoint):
     cut_type: str
     is_part_of: Document
     contains: List[Union[Entity, Event]] = None
-
     metadata: dict = {"index_fields": ["text"]}

cognee/modules/engine/models/Entity.py

@@ -1,11 +1,11 @@
 from cognee.infrastructure.engine import DataPoint
 from cognee.modules.engine.models.EntityType import EntityType
 from typing import Optional
-
+from datetime import datetime, timezone  
+from pydantic import BaseModel, Field
 
 class Entity(DataPoint):
     name: str
     is_a: Optional[EntityType] = None
     description: str
-
     metadata: dict = {"index_fields": ["name"]}

cognee/modules/retrieval/chunks_retriever.py

@@ -1,10 +1,11 @@
 from typing import Any, Optional
-
+from cognee.modules.retrieval.utils.access_tracking import update_node_access_timestamps
 from cognee.shared.logging_utils import get_logger
 from cognee.infrastructure.databases.vector import get_vector_engine
 from cognee.modules.retrieval.base_retriever import BaseRetriever
 from cognee.modules.retrieval.exceptions.exceptions import NoDataError
 from cognee.infrastructure.databases.vector.exceptions.exceptions import CollectionNotFoundError
+from datetime import datetime, timezone  
 
 logger = get_logger("ChunksRetriever")
 
@@ -27,21 +28,16 @@ class ChunksRetriever(BaseRetriever):
     ):
         self.top_k = top_k
 
-    async def get_context(self, query: str) -> Any:
+    async def get_context(self, query: str) -> Any:  
         """
         Retrieves document chunks context based on the query.
-
         Searches for document chunks relevant to the specified query using a vector engine.
         Raises a NoDataError if no data is found in the system.
-
         Parameters:
         -----------
-
             - query (str): The query string to search for relevant document chunks.
-
         Returns:
         --------
-
             - Any: A list of document chunk payloads retrieved from the search.
         """
         logger.info(
@@ -53,13 +49,14 @@ class ChunksRetriever(BaseRetriever):
         try:
             found_chunks = await vector_engine.search("DocumentChunk_text", query, limit=self.top_k)
             logger.info(f"Found {len(found_chunks)} chunks from vector search")
+            await update_node_access_timestamps(found_chunks)
+
         except CollectionNotFoundError as error:
             logger.error("DocumentChunk_text collection not found in vector database")
             raise NoDataError("No data found in the system, please add data first.") from error
 
         chunk_payloads = [result.payload for result in found_chunks]
         logger.info(f"Returning {len(chunk_payloads)} chunk payloads")
-        return chunk_payloads
 
     async def get_completion(
         self, query: str, context: Optional[Any] = None, session_id: Optional[str] = None

cognee/modules/retrieval/summaries_retriever.py

@@ -4,6 +4,7 @@ from cognee.shared.logging_utils import get_logger
 from cognee.infrastructure.databases.vector import get_vector_engine
 from cognee.modules.retrieval.base_retriever import BaseRetriever
 from cognee.modules.retrieval.exceptions.exceptions import NoDataError
+from cognee.modules.retrieval.utils.access_tracking import update_node_access_timestamps
 from cognee.infrastructure.databases.vector.exceptions.exceptions import CollectionNotFoundError
 
 logger = get_logger("SummariesRetriever")
@@ -54,6 +55,9 @@ class SummariesRetriever(BaseRetriever):
                 "TextSummary_text", query, limit=self.top_k
             )
             logger.info(f"Found {len(summaries_results)} summaries from vector search")
+            
+            await update_node_access_timestamps(summaries_results)
+        
         except CollectionNotFoundError as error:
             logger.error("TextSummary_text collection not found in vector database")
             raise NoDataError("No data found in the system, please add data first.") from error

cognee/modules/retrieval/utils/access_tracking.py

@@ -0,0 +1,64 @@
+  
+"""Utilities for tracking data access in retrievers."""  
+  
+import json  
+from datetime import datetime, timezone  
+from typing import List, Any  
+  
+from cognee.infrastructure.databases.graph import get_graph_engine  
+from cognee.shared.logging_utils import get_logger  
+  
+logger = get_logger(__name__)  
+  
+  
+async def update_node_access_timestamps(items: List[Any]):  
+    """  
+    Update last_accessed_at for nodes in Kuzu graph database.  
+    Automatically determines node type from the graph database.  
+      
+    Parameters  
+    ----------  
+    items : List[Any]  
+        List of items with payload containing 'id' field (from vector search results)  
+    """  
+    if not items:  
+        return  
+      
+    graph_engine = await get_graph_engine()  
+    timestamp_ms = int(datetime.now(timezone.utc).timestamp() * 1000)  
+      
+    for item in items:  
+        # Extract ID from payload  
+        item_id = item.payload.get("id") if hasattr(item, 'payload') else item.get("id")  
+        if not item_id:  
+            continue  
+              
+        # try:  
+        # Query to get both node type and properties in one call  
+        result = await graph_engine.query(  
+            "MATCH (n:Node {id: $id}) RETURN n.type as node_type, n.properties as props",  
+            {"id": str(item_id)}  
+        )  
+          
+        if result and len(result) > 0 and result[0]:  
+            node_type = result[0][0]  # First column: node_type  
+            props_json = result[0][1]  # Second column: properties  
+              
+            # Parse existing properties JSON  
+            props = json.loads(props_json) if props_json else {}  
+            # Update last_accessed_at with millisecond timestamp  
+            props["last_accessed_at"] = timestamp_ms  
+              
+            # Write back to graph database  
+            await graph_engine.query(  
+                "MATCH (n:Node {id: $id}) SET n.properties = $props",  
+                {"id": str(item_id), "props": json.dumps(props)}  
+            )  
+              
+            logger.debug(f"Updated access timestamp for {node_type} node {item_id}")  
+                  
+        # except Exception as e:  
+        #     logger.error(f"Failed to update timestamp for node {item_id}: {e}")  
+        #     continue  
+      
+    logger.debug(f"Updated access timestamps for {len(items)} nodes")

cognee/tasks/summarization/models.py

@@ -1,5 +1,7 @@
-from typing import Union
 
+from pydantic import BaseModel, Field
+from typing import Union
+from datetime import datetime, timezone  
 from cognee.infrastructure.engine import DataPoint
 from cognee.modules.chunking.models import DocumentChunk
 from cognee.shared.CodeGraphEntities import CodeFile, CodePart
@@ -17,7 +19,6 @@ class TextSummary(DataPoint):
 
     text: str
     made_from: DocumentChunk
-
     metadata: dict = {"index_fields": ["text"]}