feat: Add versioning to the data point model

.github/workflows/profiling.yaml

@@ -57,6 +57,8 @@ jobs:
       run: |
         poetry install --no-interaction --all-extras
         poetry run pip install pyinstrument
+        poetry run pip install parso
+        poetry run pip install jedi
 
 
     # Set environment variables for SHAs

cognee/base_config.py

@@ -10,6 +10,9 @@ class BaseConfig(BaseSettings):
     monitoring_tool: object = MonitoringTool.LANGFUSE
     graphistry_username: Optional[str] = os.getenv("GRAPHISTRY_USERNAME")
     graphistry_password: Optional[str] = os.getenv("GRAPHISTRY_PASSWORD")
+    langfuse_public_key: Optional[str] = os.getenv("LANGFUSE_PUBLIC_KEY")
+    langfuse_secret_key: Optional[str] = os.getenv("LANGFUSE_SECRET_KEY")
+    langfuse_host: Optional[str] = os.getenv("LANGFUSE_HOST")
 
     model_config = SettingsConfigDict(env_file = ".env", extra = "allow")
 

cognee/infrastructure/engine/models/DataPoint.py

@@ -1,26 +1,35 @@
+
+
 from datetime import datetime, timezone
-from typing import Optional
+from typing import Optional, Any, Dict
 from uuid import UUID, uuid4
 
 from pydantic import BaseModel, Field
 from typing_extensions import TypedDict
+import pickle
 
-
+# Define metadata type
 class MetaData(TypedDict):
     index_fields: list[str]
 
+
+# Updated DataPoint model with versioning and new fields
 class DataPoint(BaseModel):
     __tablename__ = "data_point"
-    id: UUID = Field(default_factory = uuid4)
-    updated_at: Optional[datetime] = datetime.now(timezone.utc)
+    id: UUID = Field(default_factory=uuid4)
+    created_at: int = Field(default_factory=lambda: int(datetime.now(timezone.utc).timestamp() * 1000))
+    updated_at: int = Field(default_factory=lambda: int(datetime.now(timezone.utc).timestamp() * 1000))
+    version: int = 1  # Default version
+    type: Optional[str] = "text"  # "text", "file", "image", "video"
     topological_rank: Optional[int] = 0
     _metadata: Optional[MetaData] = {
         "index_fields": [],
         "type": "DataPoint"
     }
 
-    # class Config:
-    #     underscore_attrs_are_private = True
+    # Override the Pydantic configuration
+    class Config:
+        underscore_attrs_are_private = True
 
     @classmethod
     def get_embeddable_data(self, data_point):
@@ -30,16 +39,51 @@ def get_embeddable_data(self, data_point):
 
             if isinstance(attribute, str):
                 return attribute.strip()
-            else:
-                return attribute
+            return attribute
 
     @classmethod
     def get_embeddable_properties(self, data_point):
+        """Retrieve all embeddable properties."""
         if data_point._metadata and len(data_point._metadata["index_fields"]) > 0:
             return [getattr(data_point, field, None) for field in data_point._metadata["index_fields"]]
-
         return []
 
     @classmethod
     def get_embeddable_property_names(self, data_point):
-        return data_point._metadata["index_fields"] or []
+        """Retrieve names of embeddable properties."""
+        return data_point._metadata["index_fields"] or []
+
+    def update_version(self):
+        """Update the version and updated_at timestamp."""
+        self.version += 1
+        self.updated_at = int(datetime.now(timezone.utc).timestamp() * 1000)
+
+ # JSON Serialization
+    def to_json(self) -> str:
+        """Serialize the instance to a JSON string."""
+        return self.json()
+
+    @classmethod
+    def from_json(self, json_str: str):
+        """Deserialize the instance from a JSON string."""
+        return self.model_validate_json(json_str)
+
+    # Pickle Serialization
+    def to_pickle(self) -> bytes:
+        """Serialize the instance to pickle-compatible bytes."""
+        return pickle.dumps(self.dict())
+
+    @classmethod
+    def from_pickle(self, pickled_data: bytes):
+        """Deserialize the instance from pickled bytes."""
+        data = pickle.loads(pickled_data)
+        return self(**data)
+
+    def to_dict(self, **kwargs) -> Dict[str, Any]:
+        """Serialize model to a dictionary."""
+        return self.model_dump(**kwargs)
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "DataPoint":
+        """Deserialize model from a dictionary."""
+        return cls.model_validate(data)

cognee/infrastructure/llm/openai/adapter.py

@@ -6,10 +6,11 @@
 import litellm
 import instructor
 from pydantic import BaseModel
-
+from cognee.shared.data_models import MonitoringTool
 from cognee.exceptions import InvalidValueError
 from cognee.infrastructure.llm.llm_interface import LLMInterface
 from cognee.infrastructure.llm.prompts import read_query_prompt
+from cognee.base_config import get_base_config
 
 class OpenAIAdapter(LLMInterface):
     name = "OpenAI"
@@ -35,6 +36,12 @@ def __init__(
         self.endpoint = endpoint
         self.api_version = api_version
         self.streaming = streaming
+        base_config = get_base_config()
+        if base_config.monitoring_tool == MonitoringTool.LANGFUSE:
+            self.aclient.success_callback = ["langfuse"]
+            self.aclient.failure_callback = ["langfuse"]
+            self.client.success_callback = ["langfuse"]
+            self.client.failure_callback = ["langfuse"]
 
     async def acreate_structured_output(self, text_input: str, system_prompt: str, response_model: Type[BaseModel]) -> BaseModel:
         """Generate a response from a user query."""