Revert "feat: Change Cognee data models to work with Gemini [COG-1352]" (#596)

Reverts topoteretes/cognee#594

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<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **New Features**
- Enhanced AI responses now deliver structured JSON output with clearly
defined sections, improving clarity and consistency.
- Standardized knowledge graph definitions provide a uniform
representation, simplifying integration and interpretation.



<!-- end of auto-generated comment: release notes by coderabbit.ai -->

cognee/infrastructure/llm/gemini/adapter.py

@@ -42,12 +42,81 @@ class GeminiAdapter(LLMInterface):
     ) -> BaseModel:
         try:
             if response_model is str:
+                simplified_prompt = system_prompt
                 response_schema = {"type": "string"}
             else:
-                response_schema = response_model
+                response_schema = {
+                    "type": "object",
+                    "properties": {
+                        "summary": {"type": "string"},
+                        "description": {"type": "string"},
+                        "nodes": {
+                            "type": "array",
+                            "items": {
+                                "type": "object",
+                                "properties": {
+                                    "name": {"type": "string"},
+                                    "type": {"type": "string"},
+                                    "description": {"type": "string"},
+                                    "id": {"type": "string"},
+                                    "label": {"type": "string"},
+                                },
+                                "required": ["name", "type", "description", "id", "label"],
+                            },
+                        },
+                        "edges": {
+                            "type": "array",
+                            "items": {
+                                "type": "object",
+                                "properties": {
+                                    "source_node_id": {"type": "string"},
+                                    "target_node_id": {"type": "string"},
+                                    "relationship_name": {"type": "string"},
+                                },
+                                "required": [
+                                    "source_node_id",
+                                    "target_node_id",
+                                    "relationship_name",
+                                ],
+                            },
+                        },
+                    },
+                    "required": ["summary", "description", "nodes", "edges"],
+                }
+
+                simplified_prompt = f"""
+    {system_prompt}
+
+    IMPORTANT: Your response must be a valid JSON object with these required fields:
+    1. summary: A brief summary
+    2. description: A detailed description
+    3. nodes: Array of nodes with name, type, description, id, and label
+    4. edges: Array of edges with source_node_id, target_node_id, and relationship_name
+
+    Example structure:
+    {{
+      "summary": "Brief summary",
+      "description": "Detailed description",
+      "nodes": [
+        {{
+          "name": "Example Node",
+          "type": "Concept",
+          "description": "Node description",
+          "id": "example-id",
+          "label": "Concept"
+        }}
+      ],
+      "edges": [
+        {{
+          "source_node_id": "source-id",
+          "target_node_id": "target-id",
+          "relationship_name": "relates_to"
+        }}
+      ]
+    }}"""
 
             messages = [
-                {"role": "system", "content": system_prompt},
+                {"role": "system", "content": simplified_prompt},
                 {"role": "user", "content": text_input},
             ]
 
@@ -58,7 +127,7 @@ class GeminiAdapter(LLMInterface):
                     api_key=self.api_key,
                     max_tokens=self.max_tokens,
                     temperature=0.1,
-                    response_format=response_schema,
+                    response_format={"type": "json_object", "schema": response_schema},
                     timeout=100,
                     num_retries=self.MAX_RETRIES,
                 )

cognee/shared/data_models.py

@@ -4,67 +4,36 @@ from enum import Enum, auto
 from typing import Any, Dict, List, Optional, Union
 
 from pydantic import BaseModel, Field
-from cognee.infrastructure.llm.config import get_llm_config
 
-if get_llm_config().llm_provider.lower() == "gemini":
-    """
-    Note: Gemini doesn't allow for an empty dictionary to be a part of the data model
-    so we created new data models to bypass that issue, but other LLMs have slightly worse performance
-    when creating knowledge graphs with these data models compared to the old data models
-    so now there's an if statement here so that the rest of the LLMs can use the old data models.
-    """
 
-    class Node(BaseModel):
-        """Node in a knowledge graph."""
+class Node(BaseModel):
+    """Node in a knowledge graph."""
 
-        id: str
-        name: str
-        type: str
-        description: str
-        label: str
+    id: str
+    name: str
+    type: str
+    description: str
+    properties: Optional[Dict[str, Any]] = Field(
+        None, description="A dictionary of properties associated with the node."
+    )
 
-    class Edge(BaseModel):
-        """Edge in a knowledge graph."""
 
-        source_node_id: str
-        target_node_id: str
-        relationship_name: str
+class Edge(BaseModel):
+    """Edge in a knowledge graph."""
 
-    class KnowledgeGraph(BaseModel):
-        """Knowledge graph."""
+    source_node_id: str
+    target_node_id: str
+    relationship_name: str
+    properties: Optional[Dict[str, Any]] = Field(
+        None, description="A dictionary of properties associated with the edge."
+    )
 
-        summary: str
-        description: str
-        nodes: List[Node] = Field(..., default_factory=list)
-        edges: List[Edge] = Field(..., default_factory=list)
-else:
 
-    class Node(BaseModel):
-        """Node in a knowledge graph."""
+class KnowledgeGraph(BaseModel):
+    """Knowledge graph."""
 
-        id: str
-        name: str
-        type: str
-        description: str
-        properties: Optional[Dict[str, Any]] = Field(
-            None, description="A dictionary of properties associated with the node."
-        )
-
-    class Edge(BaseModel):
-        """Edge in a knowledge graph."""
-
-        source_node_id: str
-        target_node_id: str
-        relationship_name: str
-        properties: Optional[Dict[str, Any]] = Field(
-            None, description="A dictionary of properties associated with the edge."
-        )
-
-    class KnowledgeGraph(BaseModel):
-        """Knowledge graph."""
-
-        nodes: List[Node] = Field(..., default_factory=list)
-        edges: List[Edge] = Field(..., default_factory=list)
+    nodes: List[Node] = Field(..., default_factory=list)
+    edges: List[Edge] = Field(..., default_factory=list)
 
 
 class GraphQLQuery(BaseModel):